WO2023231280A1 - Product for evaluating recurrence risk of lung cancer patient - Google Patents

Abstract

Disclosed in the present invention is the use of a reagent for detecting markers in preparation of a product for evaluating the recurrence risk of lung cancer patients. A first aspect of the present application provides the use of the reagent for detecting markers in preparation of the product for evaluating the recurrence risk of lung cancer patients. Different models concerning the recurrence risk are established for lung cancer patients receiving adjuvant chemotherapy and not receiving adjuvant chemotherapy, different adjuvant treatment means for the lung cancer patients are distinguished, the recurrence risk of the patients is predicted by means of two sets of markers, and thus a proper adjuvant treatment means can be selected therefrom. For patients having a recurrence risk of receiving adjuvant chemotherapy higher than a recurrence risk of not receiving adjuvant chemotherapy, other treatment schemes in addition to adjuvant chemotherapy are preferentially; whereas adjuvant chemotherapy is preferred for patients having a higher recurrence risk of not receiving adjuvant chemotherapy.

Description

Products to evaluate the risk of recurrence in lung cancer patients Technical field

This application relates to the field of molecular diagnostic technology, and in particular to products for evaluating the recurrence risk of lung cancer patients.

Background technique

Lung cancer is a malignant tumor with the highest morbidity and mortality in the world. It is difficult to cure lung cancer. Some types are still prone to recurrence after radical surgical resection, which is also an important cause of death in lung cancer patients. The recurrence of lung cancer includes different types such as local recurrence, regional recurrence and distant recurrence. Therefore, postoperative adjuvant therapy has become an important means to reduce the risk of recurrence and metastasis and improve the prognosis. Currently, the clinical gold standard for monitoring the recurrence of lung cancer is imaging examinations, including ultrasound, chest Only tumors can be identified, which often misses the best time window for treatment. Therefore, it is necessary to provide markers that can accurately assess the risk of recurrence.

Contents of the invention

This application aims to solve at least one of the technical problems existing in the prior art. To this end, this application proposes the application of reagents for detecting markers in the preparation of recurrence risk assessment products for lung cancer patients.

The first aspect of this application provides the application of reagents for detecting markers in the preparation of recurrence risk assessment products for lung cancer patients. The markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, At least N of RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, and N is optionally a positive integer from 1 to 43.

According to the application of the embodiments of this application, it has at least the following beneficial effects:

This application establishes a model of recurrence risk for lung cancer patients who receive adjuvant chemotherapy and who do not receive adjuvant chemotherapy. To this end, different adjuvant treatments for lung cancer patients are distinguished, and different markers are used to predict the patient's risk of recurrence, so that the patient's recurrence risk can be predicted. Choose appropriate auxiliary treatments. For those who have a higher risk of recurrence after receiving adjuvant chemotherapy than those who do not receive adjuvant chemotherapy, treatment options other than adjuvant chemotherapy are preferred; while for those who have a higher risk of recurrence without receiving adjuvant chemotherapy, adjuvant chemotherapy is preferred. Two tests can be chosen by doctors and patients, thus serving as a basis for doctors to formulate more accurate treatment plans. For patients who are undergoing adjuvant chemotherapy after surgery or who have not yet received adjuvant chemotherapy, recurrence risk assessment products using the above detection reagents can improve and optimize existing treatment plans for patients.

Among them, AGFG2 (ArfGAP With FG Repeats 2) is a protein 2 gene containing Arf GAP domain and FG repeats. This gene is a member of the HIV-1Rev binding protein (HRB) family and encodes an Arf-GAP zinc finger domain. , several phe-gly(FG) motifs, and four proteins with NPF motifs.

CCND2 (Cyclin D2) is the cyclin D2 gene. The encoded protein belongs to the highly conserved cyclin family. It can form a complex with CDK4 or CDK6 and function as a regulatory subunit of the complex. Its activity is cell cycle G1 Required for /S conversion. In addition, it can interact with the tumor suppressor protein Rb and participate in its phosphorylation.

CDC42BPA (CDC42Binding Protein Kinase Alpha) is a Cdc42-binding protein kinase alpha gene. The protein encoded by this gene is a member of the serine/threonine protein kinase family. This kinase contains multiple functional domains, and its kinase domain is highly similar to myotonic dystrophin protein kinase (DMPK).

CFH (Complement Factor H) is complement factor H. This gene is a member of the complement activation regulator (RCA) gene cluster and encodes a protein with 20 short consensus repeats (SCR) domains. It plays a role in the regulation of complement activation. It plays a vital role.

DHCR7 (7-Dehydrocholesterol Reductase) is the 7-dehydrocholesterol reductase gene. The enzyme encoded by this gene can remove the C (7-8) double bond in the B ring of sterol and catalyze the conversion of 7-dehydrocholesterol into cholesterol. This gene is located in the endoplasmic membrane and outer membrane.

ECI2 (Enoyl-CoA Delta Isomerase 2) is the Enoyl-COA delta isomerase 2 gene. This gene encodes a member of the hydratase/isomerase superfamily. The encoded protein is a key mitochondrial enzyme involved in the β-transformation of unsaturated fatty acids. Oxidation.

GPX2 (Glutathione Peroxidase 2) is the glutathione peroxidase 2 gene. The protein encoded by this gene belongs to the glutathione peroxidase family. Its members catalyze organic hydrogen peroxide and hydrogen peroxide through glutathione. reduction, thus protecting cells from oxidative damage.

KIAA0355 (also known as GARRE1, Granule Associated Rac And RHOG Effector 1) is a granule-associated Rac and RHOG effector 1 gene that enables CCR4-NOT complex binding activity and small GTPase binding activity, and is involved in Rac protein signal transduction.

The KIAA1109 gene is located on the long arm of chromosome 4 and encodes a protein related to spermatocyte and adipocyte differentiation. The protein's C-terminus also resembles a C. elegans protein that plays a role in lipid storage. It has been hypothesized that in mammals this protein may play a role in regulating epithelial growth and differentiation and tumor development.

MAOB (Monoamine Oxidase B) is the monoamine oxidase B gene. The protein encoded by this gene belongs to the flavin monoamine oxidase family. This enzyme is located in the outer mitochondrial membrane, catalyzes the oxidative deamination of biogenic and heterologous amines, and plays an important role in the metabolism of neuroactive and vasoactive amines in the central nervous system and peripheral tissues.

PGGT1B (Protein Geranylgeranyltransferase Type I Subunit Beta) is the protein flavanyl thrombin type I subunit β. The enzyme consists of a 48kD α subunit and a 43kD β subunit. It transfers the flavanyl acyl group to A cysteine residue of a candidate protein containing a C-terminal CAAX motif.

PIK3CG (Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Gamma) is the catalytic subunit γ of phosphatidylinositol-4,5-bisphosphate 3-kinase. This catalytic subunit and other class I catalytic subunits (p110α, p110β and p110δ) combine with the p85 regulatory subunit to form PI3K, which phosphorylates inositol lipids and participates in immune responses.

RHEB (Ras Homolog, MTORC1Binding) is the RAS homolog MTORC1 binding gene. This gene is a member of the GTPase superfamily and encodes a lipid-anchored cell membrane protein with five repeat sequences of the RAS-related GTP binding region. Essential in regulating growth and cell cycle progression.

SNRPB (Small Nuclear Ribonucleoprotein Polypeptides B And B1) is the small nuclear ribonucleoprotein polypeptides B and B1 genes. The protein encoded by this gene is one of several common nuclear proteins in small nuclear ribonucleoprotein particles (SNRNP) and is involved in precursor mRNA. splicing, and plays a role in SNRNP structure.

UTP20 (UTP20Small Subunit Processome Component) is a component of the UTP20 small subunit process. It is a component of the U3 small nucleolar RNA (snoRNA) protein complex and participates in 18S rRNA processing.

ZKSCAN7 (Zinc Finger With KRAB And SCAN Domains 7) is a zinc finger gene with KRAB and scan domain 7. It is predicted to initiate DNA-binding transcription factor activity, RNA polymerase II specificity and RNA polymerase II cis-regulatory region sequence specificity DNA binding activity, involved in the transcriptional regulation of RNA polymerase II, active in the nucleus.

ADAM8 (ADAM Metallopeptidase Domain 8) is the ADAM metallopeptidase domain 8 gene, encoding a member of the ADAM (disintegrin and metalloprotease domain) family. Members of this family are membrane-anchored proteins that are structurally related to snake venom disintegrins and are involved in a variety of biological processes involving cell-cell and cell-matrix interactions.

ANGPT2 (Angiopoietin 2) is an angiogenesis 2 gene and belongs to the angiopoietin family of growth factors. The protein encoded by this gene is an antagonist of angiopoietin 1, which affects angiogenesis during embryogenesis and tumorigenesis, destroys the vascular remodeling ability of angiopoietin 1, and may induce endothelial cell apoptosis.

CAMP (Cathelicidin Antimicrobial Peptide) is the Cathelicidin antimicrobial peptide gene, which encodes a member of the antimicrobial peptide family. This protein plays an important role in innate immune defense against viruses and also has functions in cell chemotaxis, immune mediator induction, and inflammatory response regulation.

CPE (Carboxypeptidase E) is the carboxypeptidase E gene, which encodes a member of the metallocarboxypeptidase M14 family. The encoded protein breaks down the C-terminal amino acid residue and is involved in the biosynthesis of peptide hormones and neurotransmitters (including insulin). At the same time, this protein can also function independently of peptidase activity as a neurotrophic factor that promotes neuronal survival.

CYP1A1 (Cytochrome P450 Family 1 Subfamily A Member 1) is a gene of Cytochrome P450 Family 1 Subfamily A Member 1. This protein is located in the endoplasmic reticulum. The expression of this protein is induced by some polycyclic aromatic hydrocarbons (PAHs), but endogenous Substrate unknown. In addition, it can metabolize some polycyclic aromatic hydrocarbons into carcinogenic intermediates.

DDR2 (Discoidin Domain Receptor Tyrosine Kinase 2) is a discoidin domain receptor tyrosine kinase 2 gene. The encoded protein is a collagen-induced receptor that is expressed in a variety of cell types and can activate cell adhesion, Signal transduction pathways for proliferation and extracellular matrix remodeling, and may be involved in wound repair, regulating tumor growth and invasion.

DOPEY2 (also known as DOP1B, DOP1 Leucine Zipper Like Protein B) is the DOP1 leucine zipper-like protein B gene. This gene is involved in cognition and is located in the early endosomal membrane. It may play a role in regulating the membrane transport of cargo proteins.

E2F3 (E2F Transcription Factor 3) is the E2F transcription factor 3 gene. This gene encodes a member of a small family of transcription factors. The encoded protein recognizes specific sequence motifs in DNA and directly interacts with the retinoblastoma protein (pRB). To regulate the expression of genes involved in the cell cycle.

EP300 (E1A Binding Protein P300) is the E1A binding protein P300 gene, which encodes the adenovirus E1A-related cellular P300 transcriptional coactivator protein. It acts as a histone acetyltransferase that regulates transcription through chromatin remodeling and has important roles in cell proliferation and differentiation.

EPHA2 (EPH Receptor A2) is the EPH receptor A2 gene, which belongs to the ephrin receptor subfamily of the protein tyrosine kinase family, and EPH and EPH-related receptors are related to mediating developmental events, especially in the nervous system.

FANCL (FA Complementation Group L) is the FA complementation group L gene, which encodes ubiquitin ligase and is a member of the Fanconi Anemia Complementation Group (FANC). This protein mediates monoubiquitination of FANCD2 and FANCI.

GABPA (GA Binding Protein Transcription Factor Subunit Alpha) is the GA binding protein transcription factor subunit α gene. This gene encodes one of the three GA binding protein transcription factor subunits and functions as a DNA binding subunit. This subunit may be involved in the activation of cytochrome oxidase expression and nuclear control of mitochondrial function.

KRT17 (Keratin 17) is the keratin 17 gene, which encodes type I intermediate filament chain keratin 17 and is expressed in nail beds, hair follicles, sebaceous glands and other epidermal attachments.

MIS18A (MIS18 Kinetochore Protein A) is the MIS18 kinetochore protein A gene. The encoded protein enables the same protein binding activity and is localized in the cytoplasm and nucleoplasm. It is predicted to participate in CENP-A-containing chromatin assembly and chromosome segregation, and play a role in In the upstream or internal regulation of DNA methylation.

NDRG1 (N-Myc Downstream Regulated 1) is an N-Myc downstream regulated 1 gene. The encoded protein is a cytoplasmic protein that participates in stress response, hormone response, cell growth and differentiation, and is responsible for p53-mediated caspase activation and apoptosis. Required.

NOTCH1 (Notch Receptor 1) is the Notch receptor 1 gene. The encoded preprotein is hydrolyzed in the trans-Golgi network to generate two polypeptide chains. These polypeptide chains heterodimerize to form mature cell surface receptors. This receptor plays a role in the development of many cell and tissue types.

NXT2 (Nuclear Transport Factor 2 Like Export Factor 2) is a nuclear transport factor 2-like export factor 2 gene. The encoded protein contains a nuclear transport factor 2 (NTF2) domain, which regulates macromolecules, ions and molecules between the cytoplasm and the nucleus. Plays an important role in the transport of small molecules. The protein may also play a role in mRNA nuclear export.

OXCT1 (3-Oxoacid CoA-Transferase 1) is the 3-oxoacetate CoA-Transferase 1 gene. The encoded protein is a homodimeric mitochondrial matrix enzyme that catalyzes the reversible transfer of coenzyme A from succinyl-CoA to acetoacetate. , plays a central role in extrahepatic ketone body catabolism.

PBRM1 (Polybromo 1) encodes a subunit of the ATP-dependent chromatin remodeling complex. This encoded protein is a component of a complex required for ligand-dependent activation of transcription by nuclear hormone receptors. Mutations at this site are associated with primary clear cell renal cancer.

PITRM1 (Pitrilysin Metallopeptidase 1) is the pituitarysin metallopeptidase 1 gene. The protein encoded by it is an ATP-dependent metalloprotease that can degrade cleaved mitochondrial transport peptides. It can not only bind zinc but also degrade amyloid βA4 protein.

RNF43 (Ring Finger Protein 43) is the ring finger protein 43 gene. The protein encoded by this gene is a ring-type E3 ubiquitin ligase. This protein has a negative regulatory effect on Wnt signaling. The expression of this gene leads to the ubiquitination of the frizz receptor. Increased, altered subcellular distribution, resulting in reduced surface levels of these receptors.

SCGB2A2 (Secretoglobin Family 2A Member 2) is the Secretoglobin family 2A member 2 gene. This gene is predicted to participate in the androgen receptor signaling pathway, is located in the extracellular region, and is active in the extracellular space.

SLC39A6 (Solute Carrier Family 39 Member 6) is a solute carrier family 39 member 6 gene, and its encoded protein belongs to a protein subfamily showing structural characteristics of zinc transporters.

STC1 (Stanniocalcin 1) encodes a secreted homodimeric glycoprotein that is expressed in a variety of tissues and may have autocrine or paracrine functions. This protein may play a role in the regulation of kidney and intestinal phosphate transport, cellular metabolism, or cellular/phosphate homeostasis.

STC2 (Stanniocalcin 2) encodes a secreted homodimeric glycoprotein that may play a role in regulating calcium and phosphorus transport in the kidney and intestine, cellular metabolism, or cellular calcium and phosphorus homeostasis. Overexpression in mice results in prenatal and postnatal growth restriction, reduced bone and skeletal muscle growth, and organ hypertrophy.

TSPYL5 (TSPY Like 5) is a TSPY-like 5 gene, which is predicted to activate chromatin-binding activity and histone-binding activity. Involved in a variety of processes, including cell response to gamma radiation, positive regulation of protein kinase B signaling, positive regulation of protein ubiquitination, etc.

TUBG1 (Tubulin Gamma 1) is the tubulin γ1 gene, which encodes a member of the tubulin superfamily. The encoded protein is located at the centrosome and binds to microtubules at the centrosome as a protein called γ-tubulin. Part of the ring complex. This protein mediates microtubule nucleation and is required for microtubule formation and cell cycle progression.

Recurrence refers to the appearance of new lesions around the original lesions, or in the lymph nodes near the original lesions, or in the bones, brain, adrenal glands, liver, etc., after lung cancer is cured by surgery, etc. The risk of recurrence refers to the probability that a patient will have at least one of the above-mentioned recurrence conditions after cure. For example, a high risk of recurrence means that the probability of at least one of the above-mentioned recurrence conditions is higher.

In some embodiments of the present application, in the presence of adjuvant chemotherapy, markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, and ZKSCAN7 at least N ₁ ;

When not receiving adjuvant chemotherapy, markers include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, At least N ₂ of RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1;

Among them, N ₁ is optionally selected from a positive integer from 1 to 16, and N ₂ is optionally selected from a positive integer from 1 to 27.

With adjuvant chemotherapy, it means that lung cancer patients are undergoing adjuvant chemotherapy or planning to receive adjuvant chemotherapy, while without adjuvant chemotherapy means that lung cancer patients have never received adjuvant chemotherapy or plan not to receive adjuvant chemotherapy. In some specific embodiments, lung cancer patients refer to lung cancer patients who have undergone surgery, such as 1min, 2min, 5min, 30min, 1h, 2h, 3h, 5h, 12h, 24h, 2d, 3d, 5d, 7d, 14d after surgery , 30 days and other different time intervals for lung cancer patients. The specific regimen of adjuvant chemotherapy can be a regimen including cisplatin (P), specifically a double-drug regimen including cisplatin, such as TP (paclitaxel + cisplatin), GP (gemcitabine + cisplatin), DP (docetaxel) + cisplatin), AP (pemetrexed + cisplatin), LP (paclitaxel liposome + cisplatin), etc. It is understandable that for some patients who cannot tolerate cisplatin, carboplatin can be used instead.

In some embodiments of the present application, in the presence of adjuvant chemotherapy, markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, and ZKSCAN7 At least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen one, at least fourteen, at least fifteen and all sixteen.

In some embodiments of the present application, when there is adjuvant chemotherapy, the markers include at least one, at least two, at least three of AGFG2, PGGT1B, ZKSCAN7, KIAA0355, SNRPB, MAOB, UTP20, DHCR7, CFH, GPX2, Elevated levels of at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten indicate a higher risk of recurrence.

In some embodiments of the present application, when there is adjuvant chemotherapy, the markers include at least one, at least two, at least three, at least four, at least five of CDC42BPA, ECI2, CCND2, RHEB, KIAA1109, and PIK3CG. A decrease in levels of at least six indicates a higher risk of recurrence.

In some embodiments of the present application, when there is adjuvant chemotherapy, the markers include at least one, at least two, at least three of AGFG2, PGGT1B, ZKSCAN7, KIAA0355, SNRPB, MAOB, UTP20, DHCR7, CFH, GPX2, Elevated levels of at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, and at least one, at least of CDC42BPA, ECI2, CCND2, RHEB, KIAA1109, PIK3CG A decrease in two, at least three, at least four, at least five, or at least six levels indicates a higher risk of relapse.

In some embodiments of the present application, in the absence of adjuvant chemotherapy, markers include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, At least two, at least three, at least four, at least five, at least six, at least seven, at least eight of NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1 , at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least Nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four, at least twenty-five, at least twenty-six and all twenty-seven .

In some embodiments of the present application, in the absence of adjuvant chemotherapy, the markers include PBRM1, GABPA, DDR2, SCGB2A2, CAMP, ADAM8, RNF43, PITRM1, STC2, TUBG1, E2F3, ANGPT2, NXT2, NDRG1, CYP1A1, DOPEY2, At least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least ten of SLC39A, OXCT1, KRT17 Elevated levels of two, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen indicate a higher risk of recurrence .

In some embodiments of the present application, in the absence of adjuvant chemotherapy, the markers include at least two, at least three, at least four, and at least five of CPE, TSPYL5, STCl, FANCL, EPHA2, EP300, NOTCH1, and MIS18A A decrease in levels of at least six, at least seven, or at least eight indicates a higher risk of recurrence.

In some embodiments of the present application, in the absence of adjuvant chemotherapy, the markers include PBRM1, GABPA, DDR2, SCGB2A2, CAMP, ADAM8, RNF43, PITRM1, STC2, TUBG1, E2F3, ANGPT2, NXT2, NDRG1, CYP1A1, DOPEY2, At least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least ten of SLC39A, OXCT1, KRT17 Elevated levels of two, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, and CPE, TSPYL5, STC1, Decreased levels of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight of FANCL, EPHA2, EP300, NOTCH1, MIS18A indicate a higher risk of relapse .

In some embodiments of the present application, the reagent detects the mRNA expression level of the marker.

In some embodiments of the present application, the reagent detects the protein expression level of the marker.

In some embodiments of the present application, the lung cancer is selected from at least one of small cell lung cancer and non-small cell lung cancer.

In some embodiments of the present application, the non-small cell lung cancer is selected from at least one of squamous cell carcinoma, adenocarcinoma, and large cell carcinoma.

In some embodiments of the present application, the sample is selected from at least one of blood, tissue, feces, and urine.

The second aspect of this application provides a recurrence risk assessment product for lung cancer patients. The product includes a reagent for detecting markers. The markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, and PGGT1B. , PIK3CG, RHEB, SNRPB, UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1 , at least N of RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, and N is optionally a positive integer from 1 to 43.

In some embodiments of the present application, the markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, At least one of CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1 Two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, At least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty Three, at least twenty-four, at least twenty-five, at least twenty-six, at least twenty-seven, at least twenty-eight, at least twenty-nine, at least thirty, at least thirty-one, At least thirty-two, at least thirty-three, at least thirty-four, at least thirty-five, at least thirty-six, at least thirty-seven, at least thirty-eight, at least thirty-nine, at least three Ten, at least forty-one, at least forty-two, and all forty-three.

In some embodiments of the present application, the marker includes at least N ₁ of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7, N ₁ is optionally selected from a positive integer from 1 to 16.

In some embodiments of the present application, the markers include at least two of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7, at least Three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen , at least fifteen and all sixteen.

In some embodiments of the present application, markers include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, At least N ₂ of PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, and N ₂ is optionally selected from a positive integer from 1 to 27.

In some embodiments of the present application, markers include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, At least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least of PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1 Ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least two Ten, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four, at least twenty-five, at least twenty-six and all twenty-seven.

In some embodiments of the present application, the markers include at least one, at least two, at least three, at least four, at least five of AGFG2, PGGT1B, ZKSCAN7, KIAA0355, SNRPB, MAOB, UTP20, DHCR7, CFH, and GPX2 at least one, at least six, at least seven, at least eight, at least nine, at least ten, and/or at least one, at least two, at least three, at least CDC42BPA, ECI2, CCND2, RHEB, KIAA1109, PIK3CG Four, at least five, at least six.

In some embodiments of the present application, markers include PBRM1, GABPA, DDR2, SCGB2A2, CAMP, ADAM8, RNF43, PITRM1, STC2, TUBG1, E2F3, ANGPT2, NXT2, NDRG1, CYP1A1, DOPEY2, SLC39A, OXCT1, KRT17 At least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, and/or CPE, TSPYL5, STC1, FANCL, EPHA2, EP300, NOTCH1, MIS18A At least two, at least three, at least four, at least five, at least six, at least seven, at least eight of them.

In some embodiments of the present application, the reagent detects the mRNA expression level of the marker.

In some embodiments of the present application, the lung cancer is selected from at least one of small cell lung cancer and non-small cell lung cancer.

In some embodiments of the present application, the non-small cell lung cancer is selected from at least one of squamous cell carcinoma, adenocarcinoma, and large cell carcinoma.

The third aspect of this application provides markers for recurrence risk assessment in lung cancer patients, including AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB , UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6 , at least N of STC1, STC2, TSPYL5 and TUBG1, N is optionally a positive integer from 1 to 43;

In some embodiments of the present application, in the presence of adjuvant chemotherapy, markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, and ZKSCAN7 At least N ₁ , N ₁ is optionally a positive integer from 1 to 16; for example, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least Nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen and all sixteen.

In some embodiments of the present application, in the absence of adjuvant chemotherapy, markers include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, At least N 2 of NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, and _{N 2 is} optionally a positive integer from 1 to 27; for example, at least two or at least three of them , at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least Fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty Four, at least twenty-five, at least twenty-six and all twenty-seven.

In some embodiments of the present application, the markers include at least one, at least two, at least three, at least four, at least five of AGFG2, PGGT1B, ZKSCAN7, KIAA0355, SNRPB, MAOB, UTP20, DHCR7, CFH, and GPX2 at least one, at least six, at least seven, at least eight, at least nine, at least ten, and/or at least one, at least two, at least three, at least CDC42BPA, ECI2, CCND2, RHEB, KIAA1109, PIK3CG Four, at least five, at least six.

In some embodiments of the present application, markers include PBRM1, GABPA, DDR2, SCGB2A2, CAMP, ADAM8, RNF43, PITRM1, STC2, TUBG1, E2F3, ANGPT2, NXT2, NDRG1, CYP1A1, DOPEY2, SLC39A, OXCT1, KRT17 At least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, and/or CPE, TSPYL5, STC1, FANCL, EPHA2, EP300, NOTCH1, MIS18A At least two, at least three, at least four, at least five, at least six, at least seven, at least eight of them.

A fourth aspect of the present application provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. The computer-executable instructions are used to cause the computer to perform the following operations:

Step 1: Obtain information on the expression levels of markers in lung cancer patient samples, including AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 , ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1 , at least N of STC2, TSPYL5 and TUBG1, N is optionally a positive integer from 1 to 43;

Step 2: Expression levels are mathematically correlated to obtain a score; the score is used to indicate the risk of recurrence in lung cancer patients.

In some embodiments of the present application, the markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, At least one of CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1 Two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, At least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty Three, at least twenty-four, at least twenty-five, at least twenty-six, at least twenty-seven, at least twenty-eight, at least twenty-nine, at least thirty, at least thirty-one, At least thirty-two, at least thirty-three, at least thirty-four, at least thirty-five, at least thirty-six, at least thirty-seven, at least thirty-eight, at least thirty-nine, at least three Ten, at least forty-one, at least forty-two, and all forty-three.

In some embodiments of the present application, the marker includes at least N ₁ of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7, N ₁ is optionally selected from a positive integer from 1 to 16.

In some embodiments of the present application, the markers include at least two of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7, at least Three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen , at least fifteen and all sixteen.

In some embodiments of the present application, the markers include at least one, at least two, at least three, at least four, at least five of AGFG2, PGGT1B, ZKSCAN7, KIAA0355, SNRPB, MAOB, UTP20, DHCR7, CFH, and GPX2 at least one, at least six, at least seven, at least eight, at least nine, at least ten, and/or at least one, at least two, at least three, at least CDC42BPA, ECI2, CCND2, RHEB, KIAA1109, PIK3CG Four, at least five, at least six.

In some embodiments of the present application, markers include PBRM1, GABPA, DDR2, SCGB2A2, CAMP, ADAM8, RNF43, PITRM1, STC2, TUBG1, E2F3, ANGPT2, NXT2, NDRG1, CYP1A1, DOPEY2, SLC39A, OXCT1, KRT17 At least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, and/or CPE, TSPYL5, STC1, FANCL, EPHA2, EP300, NOTCH1, MIS18A At least two, at least three, at least four, at least five, at least six, at least seven, at least eight of them.

In some embodiments of the present application, markers include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, At least N ₂ of PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, and N ₂ is optionally selected from a positive integer from 1 to 27.

In some embodiments of the present application, markers include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, At least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least of PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1 Ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least two Ten, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four, at least twenty-five, at least twenty-six and all twenty-seven.

In some embodiments of the present application, mathematically correlating expression levels in step 2 to obtain a score includes correlating ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A , NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1 were mathematically correlated to obtain a recurrence score, AGFG2, CCND2, CDC42BPA, The expression levels of at least one of CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7 were mathematically correlated to obtain another recurrence score, and lung cancer patients were compared based on the two recurrence scores. Difference in risk of recurrence with and without adjuvant chemotherapy. It is understandable that whether to take adjuvant chemotherapy can be determined based on the difference between the two. Specifically, assuming that adjuvant chemotherapy is used, in this case, the recurrence score is obtained by correlating a specific set of markers (one or more), and the recurrence risk ratio reflected is based on the risk ratio without adjuvant chemotherapy. When the recurrence score obtained by correlating a specific set of markers (one or more) reflects a greater risk of recurrence, the latter is obviously used, that is, other adjuvant therapies are considered; and when the opposite is the case, adjuvant chemotherapy is used In this case, the risk of recurrence is lower, so adjuvant chemotherapy is obviously used.

The obtained recurrence risk score is used to guide personalized precision medicine plans with the clinical goal of reducing the risk of recurrence. Those with a higher risk of recurrence who use adjuvant chemotherapy should choose other adjuvant treatment options besides adjuvant chemotherapy; those who do not receive adjuvant chemotherapy have a higher risk of recurrence. Adjuvant chemotherapy should be the first choice for patients with high risk. The two parts of testing content can be selected by doctors and patients, or they can be tested at the same time for mutual verification, providing a more accurate basis for formulating treatment plans. For patients who are undergoing adjuvant chemotherapy after surgery or who have not yet received adjuvant chemotherapy, assessment of recurrence risk provides a tool to improve and optimize existing treatment plans.

In some embodiments of the present application, the reagent detects the mRNA expression level of the marker.

a _i为标志物的表达水平，b _i为标志物的设定权重，n为标志物的个数，n≤N。 In some embodiments of the present application,

a _i is the expression level of the marker, b _i is the set weight of the marker, n is the number of markers, n≤N.

a _i为标志物的表达水平，b _i为标志物的设定权重，n为标志物的个数，n≤N ₁，标志物选自AGFG2、CCND2、CDC42BPA、CFH、DHCR7、ECI2、GPX2、KIAA0355、KIAA1109、MAOB、PGGT1B、PIK3CG、RHEB、SNRPB、UTP20和ZKSCAN7。 In some embodiments of the present application,

a _i is the expression level of the marker, b _i is the set weight of the marker, n is the number of markers, n≤N ₁ , and the markers are selected from AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7.

a _i为标志物的表达水平，b _i为标志物的设定权重，n为标志物的个数，n≤N ₂，标志物选自ADAM8、ANGPT2、CAMP、CPE、CYP1A1、DDR2、DOPEY2、E2F3、EP300、EPHA2、FANCL、GABPA、KRT17、MIS18A、NDRG1、NOTCH1、NXT2、OXCT1、PBRM1、PITRM1、RNF43、SCGB2A2、SLC39A6、STC1、STC2、TSPYL5和TUBG1。 In some embodiments of the present application,

a _i is the expression level of the marker, b _i is the set weight of the marker, n is the number of markers, n≤N ₂ , and the markers are selected from ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1.

In some embodiments of the present application, when the score is higher than a set value, it indicates that the lung cancer patient has a higher risk of recurrence. Among them, the set value can at least be a set threshold based on a specific scoring formula that can effectively distinguish between patients with high recurrence risk and low recurrence risk. For example, there is a significant difference in the risk of recurrence between two groups of patients who are higher than the set value and lower than the set value. When it is higher than the set value, the patient has a higher risk of recurrence; when it is lower than the set value, the patient has a higher risk of recurrence. At a certain value, the patient's risk of recurrence is low. The above set value can be understood as a cutoff value, that is, the boundary value between positive and negative. In this plan, it is the boundary value between high or low recurrence risk. It can be understood that in some specific implementations, there is no general set value/cut-off value, so it is necessary to determine the set value/cut-off value in advance through pre-experiments and other methods during implementation. The set value/cutoff value can be determined by conventional technical means in the field, for example, by comparing the accuracy of pre-experiment samples (such as detection rate, precision rate, false positive rate, false positive rate, etc.) under different set value/cutoff value conditions. Negative rate, sensitivity, specificity, etc.), adjust the cutoff value upward or downward according to the detection effect until the best or best detection effect is achieved. It is understood that pre-experiment samples can be obtained by extracting and testing specific samples, or samples that meet the standards can be obtained from relevant databases.

In some embodiments of the present application, when the score of the marker is higher than a set value, such as 0.1 times, 0.2 times, 0.3 times, 0.4 times, 0.5 times, 1 times, 1.5 times, 2 times, 2.5 times, 3 times, More than 4 times, 4.5 times, 5 times, 6 times, 7 times, 8 times, 9 times, and 10 times indicate a higher risk of recurrence.

It is understandable that since there are two sets of markers that characterize the risk of recurrence under different circumstances, the corresponding score can also be normalized to the two set values and then consider the absolute value of the two scores, or directly consider the score and Based on the relative size of the respective setting values, the choice of chemotherapy or adjuvant treatment other than chemotherapy is determined based on the size comparison results.

In some embodiments of the present application, score=0.5931×AGFG2+0.5562×PGGT1B+0.4632×ZKSCAN7+0.3192×KIAA0355+0.2759×SNRPB+0.2276×MAOB+0.2107×UTP20+0.145×DHCR7+0.1276×CFH+0.0613× GPX2 -0.212×CDC42BPA-0.2196×ECI2-0.2914×CCND2-0.3073×RHEB-0.405×KIAA1109-0.4517×PIK3CG. The abbreviation of the marker in the formula indicates the normalized expression level of the corresponding marker.

In some embodiments of the present application, score=0.4322×PBRM1+0.3548×GABPA+0.3147×DDR2+0.3003×SCGB2A2+0.275×CAMP+0.2562×ADAM8+0.2284×RNF43+0.2239×PITRM1+0.222×STC2+0.2097×TUBG1 +0.2044×E2F3+0.2039×ANGPT2+0.2011×NXT2+0.1644×NDRG1+0.1589×CYP1A1+0.1533×DOPEY2+0.1409×SLC39A6+0.1201×OXCT1+0.0815×KRT17-0.0657×CPE-0.06 65×TSPYL5-0.157×STC1-0.1615 ×FANCL-0.1786×EPHA2-0.2235×EP300-0.2285×NOTCH1-0.2628×MIS18A. The abbreviation of the marker in the formula represents the normalized expression level of the corresponding marker.

In some embodiments of the present application, the normalized expression level is the expression level after sample normalization and gene normalization.

A fifth aspect of the present application provides a device, which includes a processor and a memory. The memory stores a computer program that can be run on the processor. The processor implements the following operations when running the computer program:

Step 1: Obtain information on the expression levels of markers in lung cancer patient samples, including AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 , ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1 , at least N of STC2, TSPYL5 and TUBG1, N is optionally a positive integer from 1 to 43;

Step 2: Expression levels are mathematically correlated to obtain a score; the score is used to indicate the risk of recurrence in lung cancer patients.

In some embodiments of the present application, the markers in step 1 include at least N of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7 ₁ , N ₁ is optionally selected from a positive integer from 1 to 16.

In some embodiments of the present application, markers in step 1 include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1 , PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, at least N ₂ , and N ₂ is optionally selected from a positive integer from 1 to 27.

In some embodiments of the present application, mathematically correlating expression levels in step 2 to obtain a score includes correlating ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A , NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1 were mathematically correlated to obtain a recurrence score, AGFG2, CCND2, CDC42BPA, The expression levels of at least one of CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7 were mathematically correlated to obtain another recurrence score, and lung cancer patients were compared based on the two recurrence scores. Difference in risk of recurrence with and without adjuvant chemotherapy. It is understandable that whether to take adjuvant chemotherapy can be determined based on the difference between the two.

a _i为标志物的表达水平，b _i为标志物的设定权重，n为标志物的个数，n≤N。 In some embodiments of the present application,

a _i is the expression level of the marker, b _i is the set weight of the marker, n is the number of markers, n≤N.

In some embodiments of the present application, score=0.5931×AGFG2+0.5562×PGGT1B+0.4632×ZKSCAN7+0.3192×KIAA0355+0.2759×SNRPB+0.2276×MAOB+0.2107×UTP20+0.145×DHCR7+0.1276×CFH+0.0613× GPX2 -0.212×CDC42BPA-0.2196×ECI2-0.2914×CCND2-0.3073×RHEB-0.405×KIAA1109-0.4517×PIK3CG. The abbreviation of the marker in the formula indicates the expression level of the corresponding marker.

In some embodiments of the present application, score=0.4322×PBRM1+0.3548×GABPA+0.3147×DDR2+0.3003×SCGB2A2+0.275×CAMP+0.2562×ADAM8+0.2284×RNF43+0.2239×PITRM1+0.222×STC2+0.2097×TUBG1 +0.2044×E2F3+0.2039×ANGPT2+0.2011×NXT2+0.1644×NDRG1+0.1589×CYP1A1+0.1533×DOPEY2+0.1409×SLC39A6+0.1201×OXCT1+0.0815×KRT17-0.0657×CPE-0.06 65×TSPYL5-0.157×STC1-0.1615 ×FANCL-0.1786×EPHA2-0.2235×EP300-0.2285×NOTCH1-0.2628×MIS18A. The abbreviation of the marker in the formula indicates the expression level of the corresponding marker.

Among them, the memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer executable programs, such as the process of assessing the recurrence risk of lung cancer patients described in the embodiment of this application. . The processor implements the evaluation of recurrence risk of lung cancer patients by running non-transient software programs and instructions stored in the memory.

The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required for at least one function; the storage data area may store the computer program for executing the above. In addition, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device.

In some embodiments of the present application, the memory may optionally include memory located remotely relative to the processor, and these remote memories may be connected to the processor through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.

The non-transitory software programs and instructions required to implement the above evaluation are stored in the memory, and when executed by one or more processors, perform the above evaluation.

The device implementation described above is only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

It can be understood that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit . Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). It will be understood that computer storage media includes volatile and nonvolatile, removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. and non-removable media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disk (DVD) or other optical disk storage, magnetic cassettes, tapes, disk storage or other magnetic storage devices, or may Any other medium used to store the desired information and that can be accessed by a computer.

Furthermore, it will be appreciated that communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

The sixth aspect of the present application provides a recurrence risk assessment device for lung cancer patients. The recurrence risk assessment device for lung cancer patients includes:

The acquisition module is used to obtain information on the expression levels of markers in samples from lung cancer patients. The markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB , UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6 , at least N of STC1, STC2, TSPYL5 and TUBG1, N is optionally a positive integer from 1 to 43;

A scoring module that mathematically correlates expression levels to obtain a score that is used to indicate the risk of recurrence in lung cancer patients.

In some embodiments of the present application, the scoring module mathematically correlates expression levels to obtain scores including ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, The expression levels of at least one of NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1 were mathematically correlated to obtain a recurrence score, and AGFG2, CCND2, CDC42BPA, CFH The expression levels of at least one of , DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7 were mathematically correlated to obtain another recurrence score, and lung cancer patients were compared according to the two recurrence scores. Difference in risk of recurrence with and without adjuvant chemotherapy. It is understandable that whether to take adjuvant chemotherapy can be determined based on the difference between the two.

It should be noted that in the embodiment of the present application, it is not limited to the model of all 43 markers, or the model of all 16 markers, or the model of all 27 markers, from all 43, all 16 or Selecting several markers from all 17 markers can also build other models to evaluate the risk of recurrence.

Compared with existing imaging methods for clinically diagnosing lung cancer recurrence, the markers or combinations used in the embodiments of the present application have no time lag when evaluating the risk of lung cancer recurrence, and the risk of recurrence can be assessed immediately after lung cancer surgery. And it can separately evaluate whether to take adjuvant chemotherapy, providing a basis for doctors and patients to choose treatment options. On the other hand, for patients who have not been evaluated for recurrence risk after surgery but have already been treated, the risk of recurrence can also be evaluated without waiting for recurrence. Diagnosis by imaging has been established.

Linear models were constructed based on one or more of the above two sets of gene sets as markers to calculate the recurrence scores of the two lung cancers with or without adjuvant chemotherapy. Individualized treatment plans can be implemented for patients based on two recurrence scores: those with high recurrence scores from adjuvant chemotherapy should be given the first choice of other treatment options without adjuvant chemotherapy; those with high recurrence scores without adjuvant chemotherapy should be given first choice with adjuvant chemotherapy. It can be understood that two lung cancer recurrence scores should be compared with each other based on the same or close standards. For example, the two can be standardized and transformed in a set way and then compared in absolute size, or the two can be directly compared. The relative size of the scores to their corresponding thresholds, and so on.

Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.

Description of the drawings

Figure 1 is a Venn diagram of four groups of genes when NCHEMO data is clustered in the embodiment of this application.

Figure 2 is the ROC curve corresponding to the maximum, median and minimum AUC values of the NCHEMO model cross-validation repeated 20 times in the embodiment of this application.

Figure 3 is the ROC curve of the NCHEMO model verified in all samples in the embodiment of the present application.

Figure 4 is the ROC curve corresponding to the maximum, median and minimum AUC values of CHEMO's model cross-validation repeated 20 times in the embodiment of this application.

Figure 5 is the ROC curve of the CHEMO model verified in all samples in the embodiment of the present application.

Figure 6 is the ROC curve of the single markers of the 19 positive weight genes in the NCHEMO model in the embodiment of the present application.

Figure 7 is the ROC curve of the single markers of the 8 negatively weighted genes in the NCHEMO model in the embodiment of the present application.

Figure 8 is the ROC curve of the single markers of the 10 positive weight genes in the CHEMO model in the embodiment of the present application.

Figure 9 is the ROC curve of the single markers of the six negatively weighted genes in the CHEMO model in the embodiment of the present application.

Figure 10 is a histogram of the expression levels of 19 positive weight genes in different populations in the NCHEMO model in the embodiment of the present application.

Figure 11 is a histogram of the expression levels of eight negatively weighted genes in different populations in the NCHEMO model in the embodiment of the present application.

Figure 12 is a histogram of the expression levels of 10 positive weight genes in different populations in the CHEMO model in the embodiment of the present application.

Figure 13 is a histogram of the expression levels of six negatively weighted genes in different populations in the CHEMO model in the embodiment of the present application.

Detailed ways

The concept of the present application and the technical effects produced will be clearly and completely described below in conjunction with the embodiments to fully understand the purpose, features and effects of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, other embodiments obtained by those skilled in the art without exerting creative efforts are all The scope of protection of this application.

The embodiments of the present application are described in detail below. The described embodiments are exemplary and are only used to explain the present application and cannot be understood as limiting the present application.

In the description of this application, several means one or more, plural means two or more, greater than, less than, exceeding, etc. are understood to exclude the original number, and above, below, within, etc. are understood to include the original number. If there is a description of first and second, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the order of indicated technical features. relation.

In the description of this application, reference to the description of the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" is intended to be in conjunction with the description of the embodiment. or examples describe specific features, structures, materials, or characteristics that are included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Example 1

In this embodiment, lung cancer markers are screened out using mRNA gene expression data, in which the definitions of the adjuvant chemotherapy population and the no-adjuvant chemotherapy population are as follows:

Adjuvant chemotherapy population (CHEMO): refers to post-operative lung cancer patients who are undergoing adjuvant chemotherapy (but not including those who have started adjuvant chemotherapy before surgery, that is, neoadjuvant chemotherapy) or who plan to receive adjuvant chemotherapy, represented by CHEMO (Adjuvant Chemotherapy);

No adjuvant chemotherapy population (NCHEMO): refers to post-operative lung cancer patients who have never received adjuvant chemotherapy or plan not to receive adjuvant chemotherapy, represented by NCHEMO (None Adjuvant Chemotherapy).

1. Data set preparation

1. A total of 46 lung cancer gene expression data sets were selected from the U.S. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, including full transcriptome data of 6597 lung cancer tissue sections. After excluding gene transcripts with extremely low expression (the number of samples with non-zero expression does not exceed 10), miRNA and lncRNA were eliminated, and the common genes of these data sets were selected, resulting in a gene number of 8366.

2. Perform data standardization on each data set, and perform it on samples and genes in two steps:

(1) Standardize each sample: Calculate the median expression level of all genes in each sample, and then subtract the median expression level of all genes in the sample from the original expression level of each gene in the sample. , obtain the once normalized expression level of each gene in the sample, and remove the difference in sample mRNA input amount through this normalization method;

(2) Standardize each gene: Based on the standardized gene expression data in step (1), further calculate the median of its expression in all samples for each gene, and then divide each gene into Subtract the median expression level of the gene in all samples from the primary normalized expression level of the gene in the sample to obtain the secondary normalized expression level of the gene in each sample.

The three standardized data sets were assembled into a comprehensive data set and assembled into a comprehensive gene expression matrix, with rows representing 6597 samples and columns representing 8400 variables. Among them, columns 1-34 include sample and patient information, including basic information such as patient age and gender, lung cancer pathological classification, mutation status of driver genes such as ALK, EGFR, KRAS, and TP53, as well as recurrence-free survival (RFS), overall survival ( OS) indicators and long-term clinical prognosis information, etc.

2. Marker screening and modeling

Samples with non-empty adjuvant chemotherapy markers and non-empty recurrence markers were extracted from the gene expression matrix, and a total of 666 cases were obtained, respectively from the data set CONSORTIUM (308 cases), GSE31210 (219 cases), GSE37745 (96 cases), and GSE20875 (22 cases). Example), GSE1037 (21 cases). Among them, 129 cases were classified as CHEMO after adjuvant chemotherapy, and a total of 537 cases were classified as NCHEMO without adjuvant chemotherapy. The recurrence rate of the CHEMO group was 73.64% (95 people), and the recurrence rate of the NCHEMO group was only half of the former, 36.69% (197 people), t- The test p value was 8.48E-15, showing that the difference in recurrence rate between the two groups of patients was statistically significant. In addition, it should be noted that the adjuvant chemotherapy regimens received by the 129 patients mentioned above are not exactly the same. According to statistics, these adjuvant chemotherapy regimens cover a variety of chemotherapy regimens including a variety of different drugs, including Cisplatin, carboplatin, or cisplatin and carboplatin combined with docetaxel, gemcitabine, paclitaxel, pemetrexed, or vinorelbine respectively, and each of the patients received at least one of these chemotherapy regimens. Therefore, This can reflect the recurrence risk of overall adjuvant chemotherapy.

The two groups of people were subjected to a comparative analysis of basic information such as age, gender and clinical pathology such as pathological stage, grade, TNM grade, etc. in the relapsed and non-recurred populations. The results are shown in Table 1. The M grade was because of the sample. Omit if not enough. In the two groups, there were no statistically significant differences in age, gender proportion, and grade between the non-recurrence and recurrence groups. Although there was a difference in age between the recurrence and non-recurrence groups in the NCHEMO group (p=0.04), the difference was less than two years. . In addition, for NCHEMO's Nx and CHEMO's Tx, there is no statistically significant difference between the non-recurrence and recurrence groups, but for NCHEMO's Tx, the average value of the recurrence group is 0.03 higher than that of the non-recurrence group, which is statistically significant; for CHEMO's Nx, the recurrence The average value of the group is 0.51 higher than that of the non-recurrence group, which is also statistically significant. Finally, comparing the average survival times of OS and RFS respectively, in the NCHEMO group, the OS survival time of the non-recurrence group was 24.96 months longer than the recurrence group (n=340), and the RFS survival time was 42.55 months longer (n=202); In the CHEMO group, the OS survival time of the non-recurrence group was 33.29 months longer (n=34) and the RFS survival time was 51.04 months longer (n=11) than the recurrence group. This shows that recurrence is a fatal factor in the decline of OS and RFS in lung cancer patients. The OS and RFS of non-recurring people (regardless of chemotherapy or not) are 2-4 years longer on average than those of relapsed people. Longitudinal comparison found that for the non-recurrence group, the average OS survival time of CHEMO was 10.03 months longer than that of NCHEMO, while the average RFS survival time was 11.38 months longer; for the recurrence group, the average OS survival time of CHEMO was only 1.7 months longer than NCHEMO. The average RFS survival period was 1.89 months longer. This shows that the clinical benefit of chemotherapy for people who do not relapse is reflected in extending OS and RFS by almost one year, but for those who relapse, it is only less than two months.

Table 1. Results of group test variables

Based on the above results, lung cancer recurrence risk assessment models can be established based on whether adjuvant chemotherapy is performed or not, so that the risk of recurrence of lung cancer patients with different treatment options (whether adjuvant chemotherapy or not) can be evaluated after surgery, so that appropriate treatment methods can be selected to prolong the patient's life. improve survival and reduce the risk of recurrence.

Use the relevant data of the NCHEMO and CHEMO groups in the above data sets to build a model, use each data set as training data, and use the recurrence index (Recurrence) as the target variable to build the model:

1. Identify genes associated with recurrence risk. Use t-test (t-test) to find statistically significant (p<0.05) genes that can distinguish different groups of target variables (Recurrence=0, no recurrence vs Recurrence=1, recurrence), and initially obtain differentially expressed genes.

2. Group genes into up-regulated or down-regulated groups. Differentially expressed genes are divided into two groups. In the t-test results, positive t numbers represent genes whose expression is down-regulated in patient tissues; negative t numbers represent genes whose expression is up-regulated in patient tissues. Hierarchical correlation coefficient analysis was performed on the two groups of genes respectively.

3. Hierarchical correlation coefficient analysis. For the gene groups whose expression is up-regulated or down-regulated, hierarchical correlation coefficient clustering is performed respectively. The purpose is that within a given correlation coefficient level interval, the genes in each cluster need to be roughly related to each other in pairs, and the genes in each cluster are selected. The gene with the highest average correlation with other genes in the cluster is regarded as the representative, and the genes that meet the criteria of all clusters constitute the candidate genes of the marker model. Since correlation coefficient clustering needs to handle positive correlation and negative correlation separately, the following four combinations appear:

pp: Positive correlation clustering of down-regulated gene groups;

pn: Negative correlation clustering of down-regulated gene groups;

np: Positive correlation clustering of up-regulated gene groups;

nn: Negative correlation clustering of upregulated genes.

The four candidate genomes were merged to obtain the final model candidate genome. Figure 1 is a Venn diagram of the four groups of genes in NCHEMO.

4. Iterative linear regression analysis to determine the genome. For gene groups whose expression is up-regulated or down-regulated, in the hierarchical correlation coefficient analysis, the number of genes (s) used as model parameters is pre-given and iterative linear regression analysis is performed. Recycle different s values to find the optimal number of model parameters, which is determined by the maximum value of the corresponding R square value (rsq) to obtain the optimal model.

5. Pre-select the genes on the cancer-related gene mutation map, a total of 741, and repeat step 4 to obtain the optimal model;

6. Combine the genes from 4 and 5, repeat 4 again, and get the final model.

After the modeling is completed, perform cross-validation: divide the data set equally according to the population of the target variable, half of which is the training set, and the other half is the validation set, calculate ROC and AUC, and repeat this N (= 20) times. And calculate the statistical characteristics of AUC, such as minimum value, maximum value, and median value.

The final recurrence risk model corresponding to NCHEMO without adjuvant chemotherapy is a model composed of 27 markers: ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A , NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1.

The parameters of each gene in this model are shown in Table 2 below:

Table 2. Related parameters of each gene in the 27-marker linear regression model

Therefore, the formula score for calculating the risk of lung cancer recurrence without adjuvant chemotherapy=0.4322×PBRM1+0.3548×GABPA+0.3147×DDR2+0.3003×SCGB2A2+0.275×CAMP+0.2562×ADAM8+0.2284×RNF43+0.2239×PITRM1+0.222×STC2+ 0.2097×TUBG1+0.2044×E2F3+0.2039×ANGPT2+0.2011×NXT2+0.1644×NDRG1+0.1589×CYP1A1+0.1533×DOPEY2+0.1409×SLC39A6+0.1201×OXCT1+0.0815×KRT17-0.0 657×CPE-0.0665×TSPYL5-0.157× STC1-0.1615×FANCL-0.1786×EPHA2-0.2235×EP300-0.2285×NOTCH1-0.2628×MIS18A. The abbreviation of the marker in the formula represents the normalized value of the expression level of the corresponding marker.

The ROC curve of the model cross-validation repeated 20 times based on the above 27 markers is shown in Figure 2. The maximum AUC value is 0.84, the minimum value is 0.71, and the median value is 0.76, indicating that the model has good classification significance. It can well separate groups with different recurrence risks among lung cancer patients without adjuvant chemotherapy.

According to the established 27-gene linear regression model, a ROC curve was drawn for the above-mentioned NCHEMO entire sample to evaluate the model's ability to assess the risk of patient recurrence. The results are shown in Figure 3. The AUC is 0.832. The optimal decision point on the ROC curve (shown as the dotted line) corresponds to a specificity (1-false positive rate) of 74% and a sensitivity of 78%.

The recurrence risk model corresponding to CHEMO in the final adjuvant chemotherapy situation is a model composed of 16 markers: AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7.

The parameters of each gene in this model are shown in Table 3 below:

Table 3. Related parameters of each gene in the 16-marker linear regression model

Therefore, the formula score for calculating the risk of lung cancer recurrence under adjuvant chemotherapy=0.5931×AGFG2+0.5562×PGGT1B+0.4632×ZKSCAN7+0.3192×KIAA0355+0.2759×SNRPB+0.2276×MAOB+0.2107×UTP20+0.145×DHCR7+0.1276×CFH+0.06 13 ×GPX2-0.212×CDC42BPA-0.2196×ECI2-0.2914×CCND2-0.3073×RHEB-0.405×KIAA1109-0.4517×PIK3CG. The abbreviation of the marker in the formula represents the normalized value of the expression level of the corresponding marker.

The ROC curve of the model cross-validation repeated 20 times based on the above 16 markers is shown in Figure 4. The maximum value of the AUC is 0.97, the minimum value is 0.65, and the median value is 0.9, indicating that the model has good classification significance. It can well separate groups with different recurrence risks among lung cancer patients under adjuvant chemotherapy.

Based on the established 16-gene linear regression model, a ROC curve was drawn for all the above-mentioned CHEMO samples to evaluate the model's ability to assess the risk of patient recurrence. The results are shown in Figure 5. The AUC is 0.976. The optimal decision point on the ROC curve (shown as the dotted line) corresponds to a specificity (1-false positive rate) of 94% and a sensitivity of 92%.

For the evaluation model for NCHEMO constructed above, the ROC curves using a single gene as a marker in a combination of 27 genes are shown in Figures 6 to 7, and the histograms of single gene expression are shown in Figures 10 to 11; For the CHEMO evaluation model, the ROC curves using a single gene as a marker in a combination of 16 genes are shown in Figures 8 to 9, and the histograms of single gene expression are shown in Figures 12 to 13.

For multiple genes in the CHEMO model and NCHEMO model, randomly select K (2, 3...N ₁ -1/N ₂ -1) genes among the markers, reconstruct the model according to the aforementioned method and conduct cross-validation. The minimum, median, and maximum AUC of model cross-validation are shown in Table 4. The minimum, median, and maximum AUC of the sub-model cross-validation of the CHEMO model are shown in Table 5.

Table 4. NCHEMO sub-model cross-validation results

Table 5. CHEMO sub-model cross-validation results

It can be seen from the data in the above table that the model rebuilt by selecting a subset of 2 or more genes from the above 27 gene sets or 16 gene sets also has good diagnostic value. Overall, the diagnostic value As the number of genes increases, multiple genes can be selected from the 27 gene sets or 16 gene sets screened above as indicators for evaluating the recurrence risk of lung cancer patients, and the closer to the total number of genes, the The diagnostic accuracy may be higher.

Example 2

This embodiment provides a kit for assessing the recurrence risk of lung cancer patients. The kit includes reagents capable of quantitatively detecting the mRNA levels of the following 43 genes: AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1. The reagent includes reverse transcriptase, primers, Taq enzyme, fluorescent dyes, etc.

Example 3

This embodiment provides a device for assessing the recurrence risk of lung cancer patients. The device includes a processor and a memory, and a computer program that can be run by the processor is stored on the memory. The method of using this device to assess the recurrence risk of lung cancer patients is as follows:

1. Select postoperative tissue section samples from lung cancer patients to extract mRNA.

2. Send the extracted mRNA to the detection device to obtain the following 24+8 genes: ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, KRT17, MIS18A, NDRG1, NOTCH1, Information on the quantitative expression levels of NXT2, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and CDC42BPA, ECI2, GPX2, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 a _i .

分别对前24个基因和后8个基因的表达水平代入计算出复发风险分数；其中b _i的设定权重值为实施例1中前24个基因和后8个基因构建的对应模型的权重或按照实施例1中的方法重新建模设置权重，并设置各自的门槛值把受试者的复发风险分数分成不同风险人群。比较两个模型评估出的复发风险，若NCHEMO模型相对于对应的门槛值更高，则选择辅助化疗；如果CHEMO模型相对于对应的门槛值更高，则选择其它的辅助治疗方式。 3. According to the scoring formula

Substitute the expression levels of the first 24 genes and the last 8 genes to calculate the recurrence risk score respectively; where the set weight value of _bi is the weight of the corresponding model constructed by the first 24 genes and the last 8 genes in Example 1 or Re-model and set weights according to the method in Example 1, and set respective thresholds to divide the subjects' recurrence risk scores into different risk groups. Comparing the risk of recurrence estimated by the two models, if the NCHEMO model is higher than the corresponding threshold, adjuvant chemotherapy is selected; if the CHEMO model is higher than the corresponding threshold, other adjuvant treatments are selected.

Example 4

This embodiment provides a device for assessing the recurrence risk of lung cancer patients, which uses a centrifugal microfluidic chip for liquid biopsy for detection. The centrifugal microfluidic chip is equipped with at least 25+10 detection slots, and the liquid is passed through the centrifugal microfluidic chip. The test is carried out by biopsy. The dropped blood sample is centrifuged through binding, washing, elution and other processes and enters the detection tank to react with the reagents in it. Information on the quantitative expression level of the markers is obtained through fluorescence. The markers include the following 25 +10 genes: ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, E2F3, EP300, EPHA2, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2 , TSPYL5, TUBG1 and CDC42BPA, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, ZKSCAN7.

The present application has been described in detail above with reference to the embodiments. However, the present application is not limited to the above-mentioned embodiments. Various changes can be made within the knowledge scope of those of ordinary skill in the art without departing from the purpose of the present application. In addition, the embodiments of the present application and the features in the embodiments may be combined with each other without conflict.

Claims (10)

The application of reagents for detecting markers in the preparation of recurrence risk assessment products for lung cancer patients is characterized by: The markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2 , at least N of E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, N is optionally selected from Positive integer from 1 to 43; Preferably, when there is adjuvant chemotherapy, the marker includes at least N ₁ of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7 without adjuvant chemotherapy, the markers include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1 , PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, at least N ₂ ; wherein, N ₁ is optionally selected from a positive integer from 1 to 16, and N ₂ is optionally selected from a positive integer from 1 to 27. The application according to claim 1, wherein the reagent detects the expression level of the marker; Preferably, the expression level is the mRNA level of the marker. The application according to claim 1, wherein the lung cancer is selected from at least one of small cell lung cancer and non-small cell lung cancer; Preferably, the non-small cell lung cancer is selected from at least one of squamous cell carcinoma, adenocarcinoma, and large cell carcinoma. The application according to any one of claims 1 to 3, characterized in that the sample is selected from at least one of tissue, blood, feces and urine. Recurrence risk assessment product for lung cancer patients, characterized by including reagents for detecting markers, and the markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB , UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6 , at least N of STC1, STC2, TSPYL5 and TUBG1, N is optionally a positive integer from 1 to 43; Preferably, the marker includes at least N ₁ of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7, and N ₁ is optional. Positive integer from 1 to 16; Preferably, the markers include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, At least N ₂ of SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, and N ₂ is optionally selected from a positive integer from 1 to 27; Preferably, the reagent detects the expression level of the marker; Preferably, the expression level is the mRNA level of the marker. Preferably, the lung cancer is selected from at least one of small cell lung cancer and non-small cell lung cancer; Preferably, the non-small cell lung cancer is selected from at least one of squamous cell carcinoma, adenocarcinoma, and large cell carcinoma. Markers for recurrence risk assessment of lung cancer patients, characterized in that the markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, At least N of STC2, TSPYL5 and TUBG1, N is optionally a positive integer from 1 to 43; Preferably, when there is adjuvant chemotherapy, the marker includes at least N ₁ of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7 without adjuvant chemotherapy, the markers include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1 , PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, at least N ₂ ; wherein, N ₁ is optionally selected from a positive integer from 1 to 16, and N ₂ is optionally selected from a positive integer from 1 to 27. Computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to cause the computer to perform the following operations: Step 1: Obtain information on the expression levels of markers in samples from lung cancer patients, including AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB , UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6 , at least N of STC1, STC2, TSPYL5 and TUBG1, N is optionally a positive integer from 1 to 43; Step 2: Perform a mathematical correlation on the expression levels to obtain a score; the score is used to indicate the recurrence risk of lung cancer patients; Preferably, when there is adjuvant chemotherapy, the marker includes at least N ₁ of AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB, UTP20 and ZKSCAN7 N ₁ is optionally selected as a positive integer from 1 to 16; Preferably, in the absence of adjuvant chemotherapy, the markers include ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, At least N 2 of PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, and _{N 2 is} optionally selected from a positive integer from 1 to 27. The computer-readable storage medium according to claim 7, characterized in that:

a _i is the expression level of the marker, b _i is the set weight of the marker, and n is the number of markers; Preferably, when the score is higher than the set value, it indicates that the lung cancer patient has a higher risk of recurrence. The device is characterized in that it includes a processor and a memory, the memory stores a computer program that can be run on the processor, and the processor implements the following operations when running the computer program: Step 1: Obtain information on the expression levels of markers in samples from lung cancer patients, including AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B, PIK3CG, RHEB, SNRPB , UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1, RNF43, SCGB2A2, SLC39A6 , at least N of STC1, STC2, TSPYL5 and TUBG1, N is optionally a positive integer from 1 to 43; Step 2: The expression levels are mathematically correlated to obtain a score; the score is used to indicate the risk of recurrence in lung cancer patients. The recurrence risk assessment device for lung cancer patients is characterized by including: Acquisition module, the acquisition module is used to obtain information on the expression levels of markers in samples from lung cancer patients, the markers include AGFG2, CCND2, CDC42BPA, CFH, DHCR7, ECI2, GPX2, KIAA0355, KIAA1109, MAOB, PGGT1B , PIK3CG, RHEB, SNRPB, UTP20, ZKSCAN7, ADAM8, ANGPT2, CAMP, CPE, CYP1A1, DDR2, DOPEY2, E2F3, EP300, EPHA2, FANCL, GABPA, KRT17, MIS18A, NDRG1, NOTCH1, NXT2, OXCT1, PBRM1, PITRM1 , at least N of RNF43, SCGB2A2, SLC39A6, STC1, STC2, TSPYL5 and TUBG1, N is optionally a positive integer from 1 to 43; and a scoring module for mathematically correlating the expression levels to obtain a score that is used to indicate the risk of recurrence in lung cancer patients.

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