CN116179703B - Molecular marker for melanoma prognosis and application thereof - Google Patents

Abstract

The invention provides a biomarker related to melanoma prognosis and application thereof. The biomarkers can be used for diagnosis and/or prediction of the risk of melanoma prognosis. The invention also provides a kit suitable for prognosis diagnosis and/or prediction of melanoma.

Description

Molecular marker for melanoma prognosis and application thereof

Technical Field

The invention belongs to the field of biomedicine, and in particular relates to a molecular marker for melanoma prognosis and application thereof.

Background

Melanoma is a skin cancer developed from pigment cells, which has been widely studied, particularly in terms of immune response of tumors, and used as a model for the development of immunotherapy. This is due in part to the high mutational burden observed in melanoma, which has increased immunogenicity and immune cell infiltration compared to other types of cancer. Although in melanoma, immune checkpoint blockade has met with great success, most patients do not benefit from long-term relief, and a significant proportion of patients suffer from long-term immune-related side effects. Therefore, a deep understanding of the biological mechanisms of tumor genesis is critical for the development of novel melanoma therapies. With the development of tumors, it accumulates more and more genetic and epigenetic changes, and although these epigenetic factors cannot directly influence gene sequences, they can regulate the transcription efficiency of specific genes in cells, contribute to the improvement of the immunogenicity of tumor cells and infiltration of immune cells, and play an important role in the formation and diffusion of melanoma.

Most cells in humans have cilia, an organelle protruding from the surface of vertebrate cells, which is the junction of various signal pathways, especially the sonic hedgehog (SHH) and the Width (WNT) signal pathways. In a paper published at NC, at 11.1.2021, authors used the zebra fish melanoma model to find that a unique "interface" where tumors were in contact with adjacent tissues consisted of specific tumor cells and microenvironment cells that up-regulated a common set of ciliated genes, ciliated proteins were enriched only where tumors were in contact with the microenvironment. Expression of the cilia gene is regulated by ETS family transcription factors, which generally act to inhibit extrainterfacial cilia genes. Further studies have shown that a large number of solid cancers show that primary cilia loss is consistent with reduced ciliary body gene expression. However, the mechanism by which cancer cells lose cilia and the significance of cilia loss to tumorigenesis remains a mystery. Among melanomas, benign nevi have cilia, while malignant melanomas mostly have no primary cilia. In 7 2018, a study report published CANCER CELL shows that these excellent sensory tentacles on the cell surface probably play a key role in melanoma development, and that cilia loss enhances pro-tumor WNT/β -catenin signaling, and that cells degenerate and progress to malignant forms of melanoma when cilia are inhibited from growing in benign pigment cells. Due to the very close link between cilia and the occurrence of cancer, scientists have found that blocking cilia growth in drug resistant cancer cell lines or restoring sensitivity of cancer cells to therapy.

Therefore, there is a need to find biomarkers based on the expression values of cilia-related genes that can identify prognosis and predictability of populations with high risk of recurrence or high progression, and further to investigate whether therapies targeting cilia structures can be used as cancer therapies.

Disclosure of Invention

The invention aims to provide a biomarker related to melanoma prognosis and application thereof. The biomarkers include TMEM67, TMEM107, SLC9A3R1, PKHD1L1, DZIP1, which can be used for diagnosis and/or prognosis of melanoma prognosis risk. The invention also provides a kit suitable for prognosis diagnosis and/or prediction of melanoma.

In a first aspect of the invention there is provided the use of a molecular marker in the manufacture of a detection system for the detection or prognosis of melanoma, or prognosis of the suitability for immunotherapy; the molecular marker is selected from TMEM67, TMEM107, SLC9A3R1, PKHD1L1, DZIP1 or a combination thereof.

In one or more embodiments, the detecting or prognosticating comprises: according to the expression of the molecular marker:

(a) Analyzing the susceptibility of melanoma patients to immunotherapy; preferably further comprising: formulating a treatment/medication regimen; preferably, the immunotherapy is an immune checkpoint inhibitor therapy; preferably, the immunotherapy is anti-PD-1/anti-CTLA-4 immunotherapy;

(b) Analyzing major pathological remission, and/or survival of melanoma patients; or (b)

(C) A risk analysis or scoring of melanoma progression is performed in melanoma patients.

In one or more embodiments, the molecular marker is a gene associated with melanoma.

In one or more embodiments, the melanoma-associated gene is a gene associated with cilia growth, and/or cilia structure.

In one or more embodiments, the immunotherapy comprises: anti-PD-1/anti-CTLA-4 immune checkpoint inhibitor treatment.

In one or more embodiments, expression of the gene includes expression at the DNA (gDNA or cDNA) level, expression at the transcription level.

In one or more embodiments, the molecular markers include transcripts of gene level TMEM67, TMEM107, SLC9A3R1, PKHD L1 or DZIP1, gDNA, cDNA, DNA/RNA hybrids, or fragments thereof; the fragments are specific enough to distinguish TMEM67, TMEM107, SLC9A3R1, PKHD1L1 or DZIP1 genes from other molecules.

In one or more embodiments, the molecular markers include a protein level of TMEM67 protein, TMEM107 protein, SLC9A3R1 protein, PKHD L1 protein, DZIP1 protein, or a fragment thereof; the fragments are specific enough to distinguish TMEM67 protein, TMEM107 protein, SLC9A3R1 protein, PKHD L1 protein, or DZIP protein from other molecules.

In one or more embodiments, the immune checkpoint inhibitor comprises an anti-PD-1 antibody.

In one or more embodiments, the immune checkpoint inhibitor comprises an anti-CTLA-4 antibody.

In one or more embodiments, the detection system includes (but is not limited to): a detection reagent, kit or detection device; preferably, the detection reagent includes (but is not limited to): PCR detection reagent and sequencing reagent; more preferably, the detection reagent comprises: a primer for specifically amplifying the molecular marker gene and a probe for specifically recognizing the molecular marker gene; preferably, the kit comprises the detection reagent; preferably, the detection device includes (but is not limited to): a gene sequencing instrument, a chip, a probe set (module), a primer probe set (module) or an electrophoresis device.

In one or more embodiments, the combination of genes comprises: SLC9A3R1 and DZIP1, a gene selected from TMEM67, TMEM107 or PKHD L1.

In one or more embodiments, the combination of genes is selected from the group consisting of:

TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and DZIP1;

TMEM67, TMEM107, SLC9A3R1 and DZIP1; or (b)

SLC9A3R1, PKHD1L1 and DZIP1.

In one or more embodiments, the method of detecting comprises:

(a) For the sample to be tested, the expression of TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and/or DZIP1 is determined for risk analysis or scoring using any risk scoring formula selected from the group consisting of:

Risk score 1= (Coef _TMEM67 x TMEM67 expression) + (Coef _TMEM107 x TMEM107 expression) + (Coef _SLC9A3R1 x SLC9A3R1 expression) + (Coef _PKHD1L1 x PKHD1L1 expression) + (Coef _DZIP1 x DZIP1 expression);

Risk score 2= (Coef _TMEM67 x TMEM67 expression) + (Coef _TMEM107 x TMEM107 expression) + (Coef _SLC9A3R1 x SLC9A3R1 expression) + (Coef _DZIP1 x DZIP1 expression);

Risk score 3= (Coef _SLC9A3R1 ×slc9A3R1 expression) + (Coef _PKHD1L1 × PKHD1L1 expression) + (Coef _DZIP1 × DZIP1 expression);

(b) Determining a threshold value (cut-off value);

(c) Comparing the result of (a) with the threshold value of (b) to obtain a detection result.

In one or more embodiments, in (a), the risk factor of the gene is:

coef _TMEM67: 0 to 1, preferably 0.01.+ -. 0.005 (more preferably 0.01.+ -. 0.002);

coef _TMEM107: 0 to 1, preferably 0.031.+ -. 0.015 (more preferably 0.031.+ -. 0.01);

Coef _SLC9A3R1: -1 to 0, preferably-0.024±0.01 (more preferably-0.024±0.005);

Coef _PKHD1L1: -1 to 0, preferably-0.172±0.07 (more preferably-0.172±0.03);

coef _DZIP1: 0 to 1, preferably 0.012.+ -. 0.008 (more preferably-0.024.+ -. 0.004).

In one or more embodiments, in the 0 to 1 or-1 to 0, the Coef includes 0, 1 or-1.

In one or more embodiments, in (c), if the score value is greater than or equal to the threshold value, the score value is evaluated as: poor prognosis, high risk of recurrence, or insensitivity to immune checkpoint inhibitor treatment; if the score value is less than the threshold value, the evaluation is as follows: good prognosis, low risk of recurrence, or sensitivity to immune checkpoint inhibitor treatment; preferably, the risk score 1 has a threshold value of 0.093; the threshold for risk score 2 is 0; the risk score 3 has a threshold value of 0.

In a second aspect of the present invention, there is provided a kit or test device for predicting prognosis of melanoma or suitability for immunotherapy, comprising a test reagent for predicting prognosis of melanoma or suitability for immunotherapy, comprising: a detection reagent for a molecular marker selected from TMEM67, TMEM107, SLC9A3R1, PKHD1L1, DZIP1, or a combination thereof.

In one or more embodiments, the detection reagent includes (but is not limited to): PCR detection reagent and sequencing reagent; more preferably, the detection reagent comprises: and a primer for specifically amplifying the molecular marker gene and a probe for specifically recognizing the molecular marker gene.

In one or more embodiments, the detection device further includes (but is not limited to): a gene sequencing instrument, a chip, a probe set (module), a primer probe set (module) or an electrophoresis device.

In one or more embodiments, the molecular marker is a gene associated with melanoma.

In one or more embodiments, the melanoma-associated gene is a gene associated with cilia growth, and/or cilia structure.

In a third aspect of the invention, there is provided a system for melanoma prognosis, or suitability for immunotherapy, comprising a detection unit and a data analysis unit;

The detection unit includes: a detection reagent for measuring the expression level of a molecular marker, or a kit or a detection device containing the detection reagent; the molecular marker is selected from TMEM67, TMEM107, SLC9A3R1, PKHD1L1, DZIP1 or a combination thereof;

the data analysis unit includes: and the processing unit is used for analyzing and processing the detection result of the detection unit to obtain a melanoma prognosis or immunotherapy applicability result.

In one or more embodiments, the molecular marker is a gene associated with melanoma.

In one or more embodiments, the melanoma-associated gene is a gene associated with cilia growth, and/or cilia structure.

In one or more embodiments, the combination of genes comprises: SLC9A3R1 and DZIP1, a gene selected from TMEM67, TMEM107 or PKHD L1.

In one or more embodiments, the detection results include: diagnostic results or risk assessment/scoring (e.g., grading) results.

In one or more embodiments, the kit further comprises instructions for use, describing the following detection methods:

(a) For the sample to be tested, the expression of TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and/or DZIP1 is determined for risk analysis or scoring using any risk scoring formula selected from the group consisting of:

Risk score 1= (Coef _TMEM67 x TMEM67 expression) + (Coef _TMEM107 x TMEM107 expression) + (Coef _SLC9A3R1 x SLC9A3R1 expression) + (Coef _PKHD1L1 x PKHD1L1 expression) + (Coef _DZIP1 x DZIP1 expression);

Risk score 2= (Coef _TMEM67 x TMEM67 expression) + (Coef _TMEM107 x TMEM107 expression) + (Coef _SLC9A3R1 x SLC9A3R1 expression) + (Coef _DZIP1 x DZIP1 expression);

Risk score 3= (Coef _SLC9A3R1 ×slc9A3R1 expression) + (Coef _PKHD1L1 × PKHD1L1 expression) + (Coef _DZIP1 × DZIP1 expression);

(b) Determining a threshold value (cut-off value);

(c) Comparing the result of (a) with the threshold value of (b) to obtain a detection result.

In one or more embodiments, the "risk score formula" and/or the "threshold" are included or integrated in the data analysis unit.

In a fourth aspect of the invention, there is provided a method of predicting prognosis, or suitability for immunotherapy, of melanoma, characterised in that the method comprises: detecting the molecular marker by using a detection system for specifically detecting the molecular marker; the molecular marker is selected from TMEM67, TMEM107, SLC9A3R1, PKHD1L1, DZIP1 or a combination thereof; the detection system comprises a detection reagent, a kit or a detection device.

In one or more embodiments, the molecular marker is a gene associated with melanoma.

In one or more embodiments, the melanoma-associated gene is a gene associated with cilia growth, and/or cilia structure.

In one or more embodiments, the combination of genes comprises: SLC9A3R1 and DZIP1, a gene selected from TMEM67, TMEM107 or PKHD L1.

In one or more embodiments, the method includes: according to the expression of the molecular marker; (a) Analyzing the sensitivity of a melanoma patient to immunotherapy; preferably further comprising: formulating a treatment/medication regimen; preferably, the immunotherapy is an anti-PD-1/anti-CTLA-4 immune checkpoint inhibitor therapy; (b) Analyzing major pathological remission, and/or survival of melanoma patients; or, (c) performing a risk analysis or scoring of melanoma progression in the melanoma patient.

In one or more embodiments, the method includes:

(a) For the test sample, the expression of TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and/or DZIP1 is determined for risk analysis or scoring using a risk scoring formula selected from the group consisting of:

Risk score 1= (Coef _TMEM67 x TMEM67 expression) + (Coef _TMEM107 x TMEM107 expression) + (Coef _SLC9A3R1 x SLC9A3R1 expression) + (Coef _PKHD1L1 x PKHD1L1 expression) + (Coef _DZIP1 x DZIP1 expression);

Risk score 2= (Coef _TMEM67 x TMEM67 expression) + (Coef _TMEM107 x TMEM107 expression) + (Coef _SLC9A3R1 x SLC9A3R1 expression) + (Coef _DZIP1 x DZIP1 expression);

Risk score 3= (Coef _SLC9A3R1 ×slc9A3R1 expression) + (Coef _PKHD1L1 × PKHD1L1 expression) + (Coef _DZIP1 × DZIP1 expression);

(b) Determining a threshold value (cut-off value);

(c) Comparing the result of (a) with the threshold value of (b) to obtain a detection result.

In one or more embodiments, in (c), if the score value is greater than or equal to the threshold value, the score value is evaluated as: poor prognosis, high risk of recurrence, or insensitivity to immune checkpoint inhibitor treatment; if the score value is less than the threshold value, the evaluation is as follows: good prognosis, low risk of recurrence, or sensitivity to immune checkpoint inhibitor treatment; preferably, the risk score 1 has a threshold value of 0.093; the threshold for risk score 2 is 0; the risk score 3 has a threshold value of 0.

In one or more embodiments, a method of making a prognosis of melanoma or a prognosis of immunotherapy suitability comprises: an auxiliary disease analysis method that does not have the direct purpose of obtaining a diagnosis result of a disease, but provides only auxiliary analysis/evaluation/scoring.

Other aspects of the invention will be apparent to those skilled in the art in view of the disclosure herein.

Drawings

FIG. 1, LASSO regression analysis chart.

FIG. 2, model genes TMEM67, TMEM107, SLC9A3R1, PKHD1L1, DZIP1 and their risk factors (Coef).

FIG. 3, kaplan-Meier survival analysis of patients in different risk groups in training set.

FIG. 4, time dependent ROC analysis graph (left) and response frequency to immunotherapy (right) for patients with different risk scores in training set; wherein AUC represents area under the curve and numerals 1, 2, 4 represent ROC curves of subjects receiving immunotherapy for 1 year, 2 years, 4 years, respectively.

FIG. 5, kaplan-Meier survival analysis of different risk groups of patients in the test set.

FIG. 6, time dependent ROC analysis graph (left) and response frequency to immunotherapy (right) for patients with different risk scores in the test set; wherein AUC represents area under the curve and numerals 1, 2, 4 represent ROC curves of subjects receiving immunotherapy for 1 year, 2 years, 4 years, respectively.

FIG. 7, 5 Gene models correlation with individual characteristics (mutant subtype, pathological stage M stage, mutant load, neoantigen load, neopolypeptide load) in pre-treatment samples of Bms 038.

FIG. 8, 5 Gene models correlation of individual characteristics (mutant subtype, pathological stage M stage, mutant load, neoantigen load, neopolypeptide load) in samples after Bms038 treatment.

FIGS. 9, 5 correlation of gene models with individual characteristics (disease stage, sex, braf mutation, NF1 mutation, NRAS mutation, reactivity, purity, ploidy) in Hugo16 dataset.

FIG. 10, 5 correlation of gene models with individual characteristics (disease stage, sex, pre-Braf mutation, post-Braf mutation, synonymous mutation, non-synonymous mutation, indels, SNPs, mutations) in Van dataset. Wherein the mutation is the sum of synonymous mutation and non-synonymous mutation.

Fig. 11, 5 validation results of gene models in Bms038 dataset Pre-treatment samples (Bms 038_pre, fig. 11A-B) and post-treatment samples (Bms 038_on, fig. 11C-D).

FIG. 12A-B, validation results of the 5 gene model in Hugo16 dataset.

FIGS. 13A-B, validation of the 5 gene model in Van dataset.

FIGS. 14A-B, validation of the 5 gene model in Puch datasets.

FIG. 15, immune characteristics of different populations in training set; dark color indicates a low risk group and light color indicates a high risk group.

FIG. 16, immune characteristics of different populations in the test set; dark color indicates a low risk group and light color indicates a high risk group.

Fig. 17, bms, 038, sets of data for immune characteristics of different populations; dark color indicates a low risk group and light color indicates a high risk group.

FIG. 18, hugo dataset of immune characteristics for different populations; dark color indicates a low risk group and light color indicates a high risk group.

Fig. 19, puch dataset of immune characteristics of different populations; dark color indicates a low risk group and light color indicates a high risk group.

Fig. 20, 5, results of validation of the gene model in tumor tissue samples of patients considered to have melanoma by 82 out-patient diagnoses.

Fig. 21A-B, validation results of 4 gene models in Bms038 dataset (pre-treatment sample).

Fig. 22A-B, validation results of 4 gene models in Bms038 dataset (post-treatment sample).

FIGS. 23A-B, validation results of 4 gene models in Hugo16 dataset.

FIGS. 24A-B, validation of 4 gene models in Puch datasets.

FIGS. 25A-B, validation results of the 4 gene model in the van dataset.

26A-B, 4 results of validation of the gene model in tumor tissue samples of 82 patients diagnosed with clinical diagnosis considered to be afflicted with melanoma.

Results of validation of the gene models in Bms038,038 dataset (pre-treatment sample) of fig. 27, 3.

Fig. 28, 3 validation results of gene models in Bms038 dataset (post-treatment sample).

Fig. 29, 3 results of validation of gene models in Hugo16 dataset.

FIG. 30, 3 results of validation of gene models in Puch datasets.

FIG. 31, 3 results of validation of gene models in van datasets.

Fig. 32, 3 gene models were validated in tumor tissue samples of 82 patients diagnosed with melanoma.

Detailed Description

Aiming at the defect of lack of melanoma prognosis, especially lack of biomarker of melanoma related to cilia in the prior art, the inventor provides a biomarker related to melanoma prognosis and application thereof through intensive research. The biomarkers include TMEM67, TMEM107, SLC9A3R1, PKHD1L1, DZIP1, which can be used for diagnosis and/or prognosis of melanoma prognosis risk. The invention also provides a kit suitable for prognosis diagnosis and/or prediction of melanoma. The invention provides a new scheme for diagnosing and treating melanoma (especially cilia-related melanoma) clinically.

Terminology

As used herein, "molecular marker (marker)" refers to a biomolecule or fragment of a biomolecule, the change and/or detection of which may be associated with a particular physical condition or state. The terms "marker", "molecular marker" or "biomarker" are used interchangeably throughout the disclosure. In the present invention, the "molecular marker" means "cancer (tumor) marker" unless otherwise specified. In the present invention, unless otherwise indicated, the cancer is melanoma. In some specific embodiments, the "melanoma-associated gene" refers to a gene associated with cilia growth, and/or cilia structure.

As used herein, the term "detecting" includes "assessing," determining, "" analyzing, "" predicting, "" evaluating; the term "evaluation" or "assessment" also includes "scoring".

As used herein, the "patient," "subject," or "individual" may refer to an organism, and in certain aspects, the subject may be a human. The subject providing the sample may include a population at risk of a potential disease or a population diagnosed with a disease. The disease in the present invention refers to melanoma.

As used herein, the term "sample" is used interchangeably with "sample" and includes a substance obtained from an individual or isolated tissue, cell or body fluid that is suitable for tumor marker detection.

As used herein, "prognosis" refers to the prediction of the consequences that may be caused by a wound or disease (e.g., a tumor), and includes both recent and distant indications, including but not limited to ORR, DCR, OS, PFS. Where "PFS" refers to "Progression Free Survival", progression free survival, generally refers to the time that the patient has not progressed or is resistant to the disease after treatment; "OS" refers to "Overall Survival" and total survival refers to the total time to survival of all patients in the study. Typically clinically expressed as median OS, median PFS, i.e., time to survival/progression free survival achieved by 50% of patients.

As used herein, the "ROC curve" refers to a subject's working characteristic curve (Receiver Operating Characteristic curve). In certain embodiments of the invention, the ROC curve refers to a ROC curve between a true positive rate and a false positive rate.

As used herein, the "expression level" may refer to the concentration or amount of the gene/protein of the marker/indicator of the invention in a sample.

As used herein, the terms "high expression," "high expression level," and the like are interchangeable and shall mean at least a 5%, 10% or 20%, preferably at least 30% or 50%, more preferably at least 80% or 100% or more significant improvement as compared to a "control" or "threshold" in the sense of use. For example, the presence of at least one gene-multiplexed Student's T-test whose expression strength exceeds a threshold may be examined to determine significance.

As used herein, the terms "low expression", "low expression level", etc. are interchangeable and shall mean a reduction of at least 5%, 10% or 20%, preferably at least 30% or 50%, more preferably at least 80% or 100% or more significantly compared to a "control" or "threshold" in the sense of application. For example, the presence of at least one gene-multiplexed Student's T-test whose expression intensity is below a threshold value may be examined to determine significance.

As used herein, the setting of a "control" or "threshold" for gene or protein expression is readily set by one of skill in the art based on the teachings of the present invention. Selection of an appropriate "control" or "threshold" is a routine part of the design of an experiment, for example, the expression level of the corresponding gene/protein may first be analyzed statistically based on a sample of a subject (patient) whose prognosis/therapeutic efficacy is clear, and the obtained expression value is referred to as "control" or "threshold".

As used herein, the term "kit" may refer to a system of materials or reagents for performing the methods disclosed herein.

As used herein, definition of the stage of cancer may be performed with definition criteria already in the art.

As used herein, "/" may mean "and", or may also be denoted "or".

Biomarkers of the invention

In the invention, through deep analysis of samples of clinical patients with melanoma, modules related to melanoma progression are identified, and further screening is performed to obtain core genes: TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and/or DZIP. The TMEMs 67, 107 and DZIP are closely related to the occurrence and development of melanoma, and play an important role in the process of cilia formation, and the DZIP is an important component of the growth and development of some important organs. The SLC9A3R1 encodes a sodium/hydrogen exchange regulatory cofactor that interacts with a variety of proteins, such as cystic fibrosis transmembrane conductance regulator and G-protein coupled receptor, which are important factors for vital signaling. Their combined use as molecular markers is of greater interest for prognostic evaluation of disease.

Based on the new findings of the present invention, a set of markers of diagnostic significance for melanoma are revealed: TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and/or DZIP1 genes. Markers, kits and methods for prognosis of melanoma and evaluation of therapeutic efficacy of therapy are also disclosed.

The sequence position of TMEM67 gene can be referred to Chromosome 8:93,754,844-93,819,234GRCH38:CM000670.2, the sequence information can be referred to https:// www.ncbi.nlm.nih.gov/gene/91147#reference-sequences, and the invention also covers the sequence variants thereof in organisms.

The sequence position of TMEM107 gene can be referred to Chromosome 17:8,172,457-8,176,399GRCH38:CM000679.2, the sequence information can be referred to https:// www.ncbi.nlm.nih.gov/gene/84314#reference-sequences, and the invention can also cover the sequence variant in organisms.

The sequence position of the SLC9A3R1 gene can be referred to Chromosome 17:74,748,628-74,769,353GRCh38:CM000679.2, the sequence information can be referred to https:// www.ncbi.nlm.nih.gov/gene/9368# reference-sequences, and the invention also covers sequence variants thereof in organisms.

The sequence position of PKHD L1 gene can be referred to Chromosome 8:109,362,461-109,537,207GRCH38:CM000670.2, the sequence information can be referred to https:// www.ncbi.nlm.nih.gov/gene/93035#reference-sequences, and the invention also covers the sequence variants thereof in organisms.

The sequence position of DZIP gene can be referred to Chromosome 13:95,578,202-95,644,706GRCH38:CM000675.2, the sequence information can be referred to https:// www.ncbi.nlm.nih.gov/gene/22873#reference-sequences, and the invention also covers the sequence variants thereof in organisms.

The molecular markers disclosed in the invention can be used as judgment marks (markers) for evaluating the development of melanoma and the curative effect of the therapy. Thus, can be used to understand what disease state an individual is in, to evaluate or predict the risk of a prognostic disease, and to formulate a treatment/dosing regimen.

As one mode, the method for predicting melanoma using the molecular marker comprises: (1) Detecting expression levels of TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and/or DZIP1 in a sample of melanoma patients; (2) based on the expression level obtained in (1): when TMEM67, TMEM107, and/or DZIP1 were highly expressed, the melanoma patients were suggested to have poor prognosis and short survival; when SLC9A3R1 and/or PKHD L1 are highly expressed, this patient with melanoma is indicated to have a good prognosis and a long survival.

In some embodiments, the expression levels of TMEM67, TMEM107, SLC9A3R1, PKHD1L1, and DZIP1 genes are substituted into the risk scoring formula at the time of detection, and a risk score value (risk value) can be obtained. Comparing the risk value with a preset threshold value, a predicted outcome of the disease prognosis risk can be obtained. The higher the risk number, the higher the risk of disease prognosis. As used in the present invention, when the risk value is higher than a preset threshold, it means that the disease prognosis risk is high, belonging to a high risk group; and when the risk value is lower than a preset threshold value, the disease prognosis risk is low, and the disease prognosis risk belongs to a low risk group.

As used herein, the "risk score" (RiskScore) is calculated as: riskscore=gene expression level ₁×Coef₁ +gene expression level ₂×Coef₂ + & gene expression level _n×Coef_n (Coef: regression coefficient of gene in multifactor Cox regression analysis, n: total number of genes related to prognosis).

The level of expression of the molecular markers of the present invention can be determined according to established standard procedures (references) well known in the art. The assay may be performed at the RNA level or cDNA levels may be detected after reverse transcription of the RNA, for example by real-time fluorescent quantitative PCR techniques. At the protein level, ELISA is for example performed by immunohistochemical techniques.

As an alternative, depending on the protein of the molecular marker, detection may be achieved using antibodies that specifically bind to the protein, which may be performed by immunohistochemical staining of clinical samples. Common immunohistochemical methods include, but are not limited to, immunofluorescence, immunoenzyme-labeling, immunocolloidal gold, and the like. Immunofluorescence method uses the principle of antigen-antibody specific binding, firstly, the known antibody is marked with fluorescein, and the fluorescein is used as probe to check the correspondent antigen in cell or tissue, and then the cell or tissue is observed under the fluorescence microscope; when the fluorescein in the antigen-antibody complex is excited to emit light, the fluorescein emits fluorescence with a certain wavelength, so that the positioning of a certain antigen in the tissue can be determined, and further quantitative analysis can be performed. In the immune enzyme labeling method, an enzyme-labeled antibody acts on tissues or cells, then enzyme substrates are added to generate colored insoluble products or particles with certain electron density, and various antigen components on the surfaces and in the cells are subjected to localization research through a light mirror or an electron microscope. In the immune colloidal gold method, colloidal gold (gold hydrosol) which is a special metal particle is used as a marker, and the colloidal gold can rapidly and stably adsorb protein without obvious influence on the biological activity of the protein; the colloidal gold is used for marking primary antibody, secondary antibody or other molecules capable of specifically combining with immunoglobulin, etc. as probes, and can be used for qualitatively, positionally and quantitatively researching antigens in tissues or cells.

Alternatively, primers can be designed to specifically amplify the molecular markers based on their sequence for detection. Polymerase Chain Reaction (PCR) technology is a technique well known to those skilled in the art, the basic principle of which is a method of enzymatic synthesis of specific DNA fragments in vitro. The method of the present invention can be performed using conventional PCR techniques. For one molecular marker, the arrangement of one or more pairs of primers is possible, and the arrangement of multiple pairs of primers can obtain multiple sets of amplification products, which may be more beneficial for the confirmation of the results.

As an alternative, suitable probes can be designed based on the sequence of the molecular marker, immobilized on a microarray (microarray) or a gene chip. The gene chip generally comprises a solid carrier and oligonucleotide probes orderly fixed on the solid carrier, wherein the oligonucleotide probes consist of continuous nucleotides. In order to enhance the intensity of the detection signal and improve the accuracy of the detection result, the hybridization related site is preferably located in the middle of the probe. The solid phase carrier can be made of various common materials in the field of gene chips, such as but not limited to nylon membranes, glass slides or silicon wafers modified by active groups (such as aldehyde groups, amino groups, isothiocyanates and the like), unmodified glass slides, plastic sheets and the like. The probe may also comprise a stretch of amino-modified 1-30 poly-polydT (poly dT) at its 5' end. The gene chip comprises probes for at least one molecular marker of the invention; more preferably, the gene chip comprises probes for two or more than two molecular markers; most preferably, probes for all of the molecular markers of the invention are contained on one or more gene chips. For a molecular marker, the arrangement of one or more probes is possible, and the arrangement of a plurality of probes may be more advantageous for the confirmation of the result.

As an alternative, a method of binding the probe by the primer may be utilized, thereby making the qualitative and quantitative detection more sensitive and rapid. For example, taqman real-time fluorescent PCR detection techniques may be employed: in PCR amplification, a pair of primers is added, and a specific fluorescein-labeled Taqman probe is added, wherein the probe is an oligonucleotide, and a reporter fluorescent group and a quenching fluorescent group are respectively labeled at two ends of the oligonucleotide. When the probe is complete, the fluorescent signal emitted by the reporter group is absorbed by the quencher group; during PCR amplification, the 5 '. Fwdarw.3' exonuclease activity of Taq enzyme is used for carrying out enzyme digestion degradation on a probe, so that a report fluorescent group and a quenching fluorescent group are separated, fluorescein is dissociated in a reaction system, and emits fluorescence under specific light excitation, and along with the increase of the cycle times, the amplified target gene fragment grows exponentially, and a Ct (cycle threshold, ct) value is obtained by detecting the corresponding fluorescence signal intensity which changes along with the amplification in real time. The Ct value, i.e. the number of amplification cycles passed when the fluorescence signal of the amplified product reaches a set threshold in the PCR amplification process, has a linear relationship with the logarithm of the initial copy number of the template, and the more the template DNA amount, the fewer the cycle number when the fluorescence reaches the threshold, i.e. the smaller the Ct value, thereby realizing quantitative and qualitative analysis of the initial template.

Methods for amplifying specific fragments of genes by PCR are well known in the art and are not particularly limited in the present invention. The amplification product may be labeled by amplification using a primer having a labeling group at the 5' end, or by incorporation of a single nucleotide having a labeling group during amplification, or by adding detection probes that specifically bind to the amplified products at the time of hybridization, including but not limited to: digoxin molecules (DIG), biotin molecules (Bio), fluorescein and its derivative biomolecules (FITC, etc.), other fluorescent molecules (e.g., cy3, cy5, etc.), alkaline Phosphatase (AP), horseradish peroxidase (HRP), etc.

The invention also provides a kit for detection that may include a system for storing, transporting, or delivering reaction reagents or devices (e.g., primers, probes, etc. in appropriate containers) and/or cooperating materials (e.g., buffers, written instructions to perform an assessment, etc.) from one location to another. For example, the kit may include one or more housings (e.g., cassettes) containing the relevant reagents and/or cooperating materials. These contents may be delivered to the intended recipient simultaneously or separately.

In addition, various reagents required for DNA extraction, PCR, hybridization, color development, etc., may be included in the kit, including but not limited to: extract, amplification solution, hybridization solution, enzyme, control solution, color development solution, washing solution, antibody, etc.

In addition, the kit can also comprise instructions for use, chip image analysis software and the like.

The invention also provides a system for assessing the development of melanoma and the efficacy of therapy, comprising a detection unit and a data analysis unit; the detection unit includes: a detection reagent for determining the expression level of the molecular marker, or a reagent or device of a kit or detection device containing the detection reagent; the data analysis unit includes: and the processing unit is used for analyzing and processing the detection result of the detection unit to obtain the detection or prognosis result of the melanoma. The detection reagent includes (but is not limited to): an antibody specifically binding to a protein encoded by the molecular marker, a primer specifically amplifying the molecular marker gene, a probe specifically recognizing the molecular marker gene, and the like. Devices specific for detection may include, but are not limited to: immunohistochemical devices (e.g., ELISA detection kit/module/device), gene sequencing instruments, chips, probe sets (modules), primer probe sets (modules), or electrophoresis devices, and the like. The detection result comprises: diagnostic results, or risk assessment/scoring (e.g., grading) results.

As an alternative, the data analysis unit that can be used for analyzing the detection result of the detection unit includes: a calculation or scoring unit; preferably, the unit is provided with a detection result of the prognosis risk of melanoma, wherein the prediction value calculated by the risk scoring formula for the TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and DZIP1 genes is compared with a preset threshold value.

As an alternative, the system further comprises: a unit for detecting a characteristic of the individual; preferably, the features include: mutant subtype, pathological stage M stage, mutant load, neoantigen load, new polypeptide load, sex, braf mutation (before Braf mutation, after Braf mutation), NF1 mutation, NRAS mutation, reactivity, purity, ploidy, total number of non-synonymous mutations, synonymous mutation, non-synonymous mutation, indels, SNPs, mutations, etc.

The invention has the following beneficial effects:

1) The invention finds the markers related to melanoma prognosis through bioinformatics for the first time: TMEM67, TMEM107, SLC9A3R1, PKHD1L1, DZIP1; the risk of melanoma prognosis can be predicted by the expression of the gene;

2) The invention firstly provides a prognosis mode for predicting melanoma patients, which is formed by taking 5 gene combinations, 4 gene combinations and 3 gene combinations as molecular markers.

The invention will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. The experimental procedures, which are not specifically noted in the examples below, are generally carried out according to conventional conditions such as those described in J.Sam Brookfield et al, molecular cloning guidelines, third edition, scientific Press, or according to the manufacturer's recommendations.

Experimental method

1. Data collection and arrangement

The inventors have collected a total of 6 sets of RNA high throughput sequencing (RNA-seq) and partial exome sequencing (Whole Exome Sequencing, WES) data from literature searches that received anti-PD-1 or anti-CTLA-4 antibody immunotherapy. The total of 121 samples in the Liu19 dataset and 91 samples in gide dataset are combined to form 212 samples, 152 samples are randomly selected from the 212 samples to be used as training sets, and the remaining 60 samples are used as test sets. An additional 4 sets of data were used as independent validation sets (table 1).

TABLE 1 dataset of the invention

2. Gene set enrichment analysis

In the tumor microenvironment, tumor tissues are infiltrated by various cells, and immune cells infiltrating the tumor can deeply influence the progress of the tumor. In order to accurately assess the composition of immune cells in the tumor microenvironment, the inventors have been able to quantify tumor-infiltrating immune cells from RNA sequencing data by a number of methods. Gene set enrichment (GSVA) analysis is a well-established method, the main idea being to concentrate information in the gene expression profile into one pathway or feature. Advantages of this approach include reduced noise and dimension reduction, and higher biological interpretation capabilities compared to single-gene analysis. The inventors utilized two gene sets of gene set 29_Immu (derived from: DOI:10.1016/j. Ccell. 2021.04.014) and danaher (derived from: DOI:10.1186/s 40425-017-0215-8) as background, combined with R package (R language kit) GSVA to convert gene expression profile into enrichment scores of samples in individual immune characteristics.

3. LASSO regression

LASSO regression is an example of regularization of a regression algorithm. Regularization is a method that solves the over-fitting problem by adding additional parameters, thereby reducing the parameters of the model, limiting complexity. LASSO regression is an L1 penalty model, and the inventor only needs to add an L1 norm to a least squares cost function, so that the regularization strength of the model is enhanced and the weight of the model is reduced by increasing the value of the super parameter alpha. By adjusting the strength of regularization, certain weights can be made zero, which makes the LASSO method a very powerful dimension reduction technique. The invention uses the R package glmnet to optimize the features and construct the corresponding COX proportional risk model.

4. Survival analysis

The survival analysis is a statistical analysis method for researching the distribution rule of survival time and the relation between the survival time and related factors. Survival analysis involves time-dependent indicators of healing, death, or growth and development of organs associated with the disease. The Kaplan-Meier survival curve is used for estimating survival rate and median survival time according to survival time distribution and displaying in a survival curve mode, so that survival characteristics are analyzed. The Log-rank test investigates the differences between groups by comparing the survival curves, typically the survival rates and their standard errors, between two or more groups. The COX risk ratio model was used to analyze the effect of two or more variables on survival. According to the invention, a Kaplan-Meier (KM) method is utilized to estimate survival rate, a survival curve is prepared, whether the survival curves among multiple groups have obvious differences is analyzed according to Log-rank test, and finally a COX risk proportion model is used for researching the influence of a certain factor on survival. The above methods were all based on R-package survminer and survivinal.

5. Software package

The glmnet, survminer, survivin, GSVA, tidyverse, timeROC, ggplot2, caret, stats, MCPcounter, clusterProfiler, limma, and enrichplot packages referred to in the examples below are all prior art and are derived from https:// cran. R-project. Org or http:// www.bioconductor.org/, and are run in R software after loading.

Example 1 screening candidate cilia characteristics

The inventors screened 112 cilia-related pathways from the GO database (http:// geneonyl log /) as background pathways, and then performed GSVA analysis on 121 samples of liu19 to convert the gene expression profile into ES scores between the samples and immune characteristics. Survival analysis was performed based on ES analysis, screening 10 ciliated pathways associated with prognosis (PFS and OS). Finally, 13 prognostic (PFS and OS) -related features were screened from 161 genes in these 10 pathways as cilia-selected features (table 2).

TABLE 213 selected cilia characteristics

Example 2 construction of risk models by LASSO

The 152 samples were used as a training set in which LASSO (the Least Absolute SHRINKAGE AND Selection Operator) regression was used to screen the optimal gene set (FIG. 1). The 9 best gene sets (CDH 23, CEP290, DZIP1, PCDHB15, PKHD1L1, RIPOR2, SLC9A3R1, TMEM107, TMEM 67) were obtained among the 13 candidate cilia features. The 9 genes and COX regression were combined to construct a risk prognosis model. The risk model consisting of the five genes shown in Table 3 (TMEM 67, TMEM107, SLC9A3R1, PKHD L1, DZIP 1) and their risk coefficients was finally obtained by stepwise regression, cycling 500 times (FIG. 2).

TABLE 3 model genes and their risk factors

And obtaining a risk scoring formula according to regression coefficients of COX and the optimized 5 genes:

Risk score = (0.01×tmem 67) + (0.031×tmem 107) + (-0.024×slc9A3R 1) + (-0.172× PKHD1L 1) + (0.012× DZIP 1).

The threshold was determined from survivin_ cutpoint in the R-language kit (according to the arithmetic logic of the R-package survivin_ cutpoint function, the best cut-in point with survival data was calculated as the threshold using the maximum selection rank statistical method, the final threshold was determined to be 0.093), the patients in the training set were divided into a high risk group (gtoreq 0.093, rated as poor prognosis, high risk of recurrence and insensitive to immunotherapy) and a low risk group (rated as good prognosis, low risk of recurrence and sensitive to immunotherapy) (< 0.093). Kaplan-Meier survival analysis showed that the prognosis for the high risk group patients was worse than the low risk group (fig. 3), the difference was extremely pronounced, p <0.0001.

For patients with different risk scores in the training set, the time-dependent ROC curves showed area under the curve (AUC) values of 0.703, 0.683 and 0.909 (fig. 4) for 1 year, 2 years and 4 years, respectively, indicating that the risk scores can predict patient survival with higher accuracy.

In addition, when analyzing the different immune effects in the training set, it was found that the response rate (responsive and stable) was significantly higher in the low risk group than in the non-responsive group. Further described models can be used to predict the response of a patient to anti-PD-1/anti-CTLA-4 immunotherapy.

Likewise, the prognosis for the low risk group was also observed significantly better than for the high risk group (fig. 5), the difference was extremely significant, p <0.0001.

Time-dependent ROC curves showed area under the curve (AUC) values of 0.708, 0.683 and 0.791 for 1 year, 2 years and 4 years, respectively, for different risk scoring patients in the test set (fig. 6).

In addition, when different immune effects in the test set were analyzed, it was found that the non-response rate in the high risk group was approximately 2 times (50% vs 24%) that in the low risk group. Further described models can be used to predict the response of a patient to anti-PD-1/anti-CTLA-4 immunotherapy.

Example 3 function of TMEM67, TMEM107, SLC9A3R1, PKHD1L1, DZIP1 Gene

The 5 genes in the model are all related to cilia composition and their signal transmission, details are shown in Table 4. Of these, TMEM67, TMEM107 and DZIP1 play an important role in the cilia formation process, and DZIP1 is also an important component of the growth and development of some important organs. SLC9A3R1 encodes a sodium/hydrogen exchange regulatory cofactor, which interacts with a variety of proteins, such as cystic fibrosis transmembrane conductance regulator and G protein-coupled receptor, which are important factors for vital signaling.

TABLE 4 functional information of genes

Example 4 independence of the model

To verify model independence, the inventors analyzed the relationship between the model and individual characteristics such as pathological stage, sex, tumor mutation load, neoantigen load, tumor purity, ploidy, and melanoma important gene mutation (NF 1/KRAS/BRAF).

The data set and information thereof are shown in Table 1.

The 5-gene model according to example 2 above distinguishes data into high risk groups (. Gtoreq.0.093, assessed as poor prognosis, high risk of recurrence) and low risk groups (. < 0.093, assessed as good prognosis, low risk of recurrence), respectively. Correlation of clinical profile factors between high risk and low risk groups was analyzed by chi-square test. Among these, clinical characterization factors include pathological stage, sex, tumor mutation load, neoantigen load, tumor purity, ploidy, and melanoma important gene mutation (NF 1/KRAS/BRAF).

The results showed that none of the individual features were significantly relevant to the model among Bms pre-treatment sample dataset 038 (fig. 7), bms post-treatment sample dataset 038 (fig. 8), hugo16 dataset (fig. 9), van dataset (fig. 10).

The results indicate that the 5-gene model is independent, independent of the factors described above, and can be used independently for prognosis or prediction of low risk populations effective for immunotherapy (immune checkpoint inhibitors, including anti-PD-1 antibody/anti-CTLA-4 antibody immunotherapy in particular).

Examples 5, robustness of 5 Gene models and suitability for immunotherapy

Independent validation set data in the dataset of table 1 was used as a validation sample, analyzed with the risk scoring formula described above, to verify model robustness and immunotherapy (anti-PD-1/anti-CTLA-4 immunotherapy) suitability:

Risk score = (0.01×tmem 67) + (0.031×tmem 107) + (-0.024×slc9A3R 1) +(-0.172× PKHD1L 1) + (0.012× DZIP 1);

Wherein, if the grading value is more than or equal to 0.093, the prognosis is poor and the recurrence is high; if the score value is less than 0.093, the prognosis is good and the recurrence risk is low. Kaplan-Meier survival analysis (Log-Rank) and fischer accurate test (FISHER TEST) were performed in combination with risk scoring results to evaluate the model for immunotherapeutic applicability.

The inventors validated the Pre-treatment samples (Bms 038_pre) and the post-treatment samples (Bms 038_on) of the Bms038 set of data respectively, and found that the prognosis and response rate of the model-screened low risk group was significantly higher than that of the high risk group, both in the Pre-treatment samples and in the post-treatment samples (fig. 11, pre-treatment: log_rank p= 0.031,Fisher test p =0.143, post-treatment: log_rank p <0.001,Fisher test p =0.02). Similar results were also observed in the Hugo16 dataset (fig. 12, log_rank p= 0.004,Fisher test p =0.033), the Van dataset (fig. 13, log_rank p= 0.017,Fisher test p =0.408) and the Puch dataset (fig. 14, log_rank p= 0.038,Fisher test p =0.087).

Further, the sensitivity and specificity of the 5-gene model of the present invention in a plurality of data sets were analyzed so as to be positive for the immune therapy response, and the results are shown in tables 5 and 6. The results show that the model of the invention shows good specificity and sensitivity in a plurality of data sets.

TABLE 5 sensitivity of the inventive 5 Gene model

TABLE 6 specificity of the 5 Gene models of the invention

These results all show that the model has strong robustness and immune therapy applicability, and can be seen in multiple sets of melanoma related data to screen out low risk populations with significantly good prognosis, so that the model is robust after actual disease prognosis analysis.

Example 6 immunofeature analysis of data

Bioinformatic analysis of immune characteristics on the data set of table 1, found that the low risk group population was mainly enriched on immune activation related pathways, showing immune activation status; whereas the high risk group population is mainly enriched in such immunosuppression-related pathways as EMT and exhibits immunosuppression (fig. 15, 16, 17, 18, 19).

The bioinformatics analysis results again confirm the applicability of the 5-gene model to immunotherapy.

Example 7 clinical application of markers

With informed consent, tumor tissue samples were collected from 82 patients clinically diagnosed with melanoma. These patient samples were scored in the same manner as in the 5-gene model and analysis described in example 2.

TABLE 7 prediction of the 5 Gene models of the invention on clinical patients

The results of model predictions according to the present invention are shown in table 7, with 42 subjects out of 82 having detection results above the threshold, and the population of patients expected to be high risk patients and also the population not responding to immunotherapy. The 40 subjects tested below the threshold, and the population of patients was expected to be low risk patients, and also a population effective for immunotherapy.

Thus, the prognosis and response rate of patients in the high and low risk groups receiving anti-PD-L1 mab treatment are evaluated based on the prediction results. The results showed that the overall survival of the low risk group patients was significantly higher two years after receiving anti-PD-L1 mab treatment than the high risk group (fig. 20), and the response rate of the low risk group was also significantly higher than the high risk group (fig. 20). The model prediction result of the invention is accurate.

Example 8 predictive models and predictive results for different Gene combinations

Next, the inventors examined whether or not a prediction model whose prediction result is accurate as in the model in embodiment 2 can be established. 4 gene prediction models and 3 gene prediction models shown in Table 8 were constructed, kaplan-Meier survival analysis and Fisher exact test were performed by using the data sets in Table 1 in combination with the risk scoring results, and the prediction accuracy of the models was explored.

TABLE 8 predictive models for different Gene combinations

The prediction results of the 4-gene model showed that the prognosis of the low risk group population was significantly better than that of the high risk population (fig. 21A p =0.041, fig. 22 ap=0.0066, fig. 23A p =0.027, fig. 24A p =0.031, fig. 25A p =0.038, fig. 26 Ap < 0.0001), whether in the respective dataset of table 1 or in 82 clinical samples. Also, the response rate of low risk populations against PD-1/CTLA-4 immunotherapy will be significantly higher than that of high risk groups. The rate of disease progression is approximately twice that of the low risk group in the high risk group (fig. 21B, 22B, 23B, 24B, 25B, 26B).

The prediction results of the 3 gene model showed that the prognosis of the low risk group population was significantly better than that of the high risk group population (fig. 27A p =0.0015, fig. 28 ap=0.00033, fig. 29A p =0.0076, fig. 30A p =0.0029, fig. 31A p =0.0048, fig. 32A p =0.0017), both in the respective dataset of table 1 and in 82 clinical samples. Also, the response rate of low risk populations against PD-1/CTLA-4 immunotherapy will be significantly higher than that of high risk groups. The rate of disease progression is approximately twice that of the low risk group in the high risk group (fig. 27B, 28B, 29B, 30B, 31B, 32B).

The results show that the 4 gene combination (DZIP 1, SLC9A3R1, TMEM107 and TMEM 67) 3 gene combination (DZIP 1, PKHD1L1 and SLC9A3R 1) has significance and can be used as a biomarker related to melanoma prognosis and immunotherapy.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims. All documents referred to in this disclosure are incorporated by reference herein as if each was individually incorporated by reference.

Claims (16)

1. Use of a molecular marker for the preparation of a detection system for prognosis of melanoma; the molecular marker consists of TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and DZIP1, or consists of TMEM67, TMEM107, SLC9A3R1 and DZIP1, or consists of SLC9A3R1, PKHD L1 and DZIP 1; the detection system includes: detection reagents, kits or detection devices.

2. The use of claim 1, wherein the prognosis comprises a prognosis of immunotherapy suitability, the immunotherapy being an immune checkpoint inhibitor therapy, the immune checkpoint inhibitor therapy being an anti-PD-1/anti-CTLA-4 immunotherapy.

3. The use of claim 1, wherein the prognosis comprises: according to the expression of the molecular marker:

(a) Analyzing the susceptibility of melanoma patients to immunotherapy;

(b) Analyzing major pathological remission, and/or survival of melanoma patients; or (b)

(C) A risk analysis or scoring of melanoma progression is performed in melanoma patients.

4. The use of claim 3, wherein said analyzing the susceptibility of a melanoma patient to immunotherapy further comprises: a treatment/medication regimen is formulated.

5. The use according to any one of claims 1 to 4, wherein the detection reagent comprises: PCR detection reagent and sequencing reagent.

6. The use of claim 5, wherein the detection reagent comprises: and a primer for specifically amplifying the molecular marker gene and a probe for specifically recognizing the molecular marker gene.

7. The use of claim 1, wherein said detection reagent is included in said kit.

8. The use of claim 7, wherein the detection means comprises: a gene sequencing instrument, a chip, a probe set, a primer probe set or an electrophoresis device.

9. The use of claim 1, wherein the analysis method comprises:

(a) For the sample to be tested, the expression of TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and/or DZIP1 is determined for risk analysis or scoring using any risk scoring formula selected from the group consisting of:

risk score 1= (Coef _TMEM67 x TMEM67 expression) + (Coef _TMEM107 x TMEM107 expression) + (Coef _SLC9A3R1 x SLC9A3R1 expression) + (Coef _PKHD1L1 x PKHD1L1 expression) + (Coef _DZIP1 x DZIP1 expression);

Risk score 2= (Coef _TMEM67 x TMEM67 expression) + (Coef _TMEM107 x TMEM107 expression) + (Coef _SLC9A3R1 x SLC9A3R1 expression) + (Coef _DZIP1 x DZIP1 expression);

Risk score 3= (Coef _SLC9A3R1 ×slc9A3R1 expression) + (Coef _PKHD1L1 × PKHD1L1 expression) + (Coef _DZIP1 × DZIP1 expression);

(b) Determining a threshold value (cut-off value);

(c) Comparing the result of (a) with the threshold value of (b) to obtain a detection result.

10. The use of claim 9 wherein in (a), coef _TMEM67 is 0 to 1; the Coef _TMEM107 is 0 to 1; the Coef _SLC9A3R1 is-1 to 0; the Coef _PKHD1L1 is-1 to 0; and the Coef _DZIP1 is 0 to 1.

11. The use of claim 10, wherein in (a), said Coef _TMEM67 is 0.01±0.005; the Coef _TMEM107 is 0.031 + -0.015; the Coef _SLC9A3R1 is-0.024+/-0.01; the Coef _PKHD1L1 is-0.172+/-0.07; the Coef _DZIP1 is 0.012+ -0.008.

12. The use of claim 11 wherein in (a) said Coef _TMEM67 is 0.01±0.002; the Coef _TMEM107 is 0.031 + -0.01; the Coef _SLC9A3R1 is-0.024+/-0.005; the Coef _PKHD1L1 is-0.172+/-0.03; the Coef _DZIP1 is-0.024 + -0.004.

13. The use of claim 9, wherein in (c) if the score value is greater than or equal to the threshold value, the score value is: poor prognosis, high risk of recurrence, or insensitivity to immune checkpoint inhibitor treatment; if the score value is less than the threshold value, the evaluation is as follows: good prognosis, low risk of recurrence, or sensitivity to immune checkpoint inhibitor treatment.

14. A system for melanoma prognosis comprising a detection unit and a data analysis unit;

The detection unit includes: a detection reagent for measuring the expression level of a molecular marker, or a kit or a detection device containing the detection reagent; the molecular marker consists of TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and DZIP1, or consists of TMEM67, TMEM107, SLC9A3R1 and DZIP1, or consists of SLC9A3R1, PKHD L1 and DZIP 1;

The data analysis unit includes: the processing unit is used for analyzing and processing the detection result of the detection unit to obtain a melanoma prognosis result; the analysis of the data analysis unit includes:

(a) For the sample to be tested, the expression of TMEM67, TMEM107, SLC9A3R1, PKHD1L1 and/or DZIP1 is determined for risk analysis or scoring using any risk scoring formula selected from the group consisting of:

risk score 1= (Coef _TMEM67 x TMEM67 expression) + (Coef _TMEM107 x TMEM107 expression) + (Coef _SLC9A3R1 x SLC9A3R1 expression) + (Coef _PKHD1L1 x PKHD1L1 expression) + (Coef _DZIP1 x DZIP1 expression);

Risk score 2= (Coef _TMEM67 x TMEM67 expression) + (Coef _TMEM107 x TMEM107 expression) + (Coef _SLC9A3R1 x SLC9A3R1 expression) + (Coef _DZIP1 x DZIP1 expression);

Risk score 3= (Coef _SLC9A3R1 ×slc9A3R1 expression) + (Coef _PKHD1L1 × PKHD1L1 expression) + (Coef _DZIP1 × DZIP1 expression);

(b) Determining a threshold value (cut-off value);

(c) Comparing the result of (a) with the threshold value of (b) to obtain a detection result;

(a) Wherein, the Coef _TMEM67 is 0 to 1; the Coef _TMEM107 is 0 to 1; the Coef _SLC9A3R1 is-1 to 0; the Coef _PKHD1L1 is-1 to 0; and the Coef _DZIP1 is 0 to 1.

15. The system for melanoma prognosis of claim 14, wherein the prognosis comprises a prognosis of immunotherapy suitability, the immunotherapy being an immune checkpoint inhibitor therapy, the immune checkpoint inhibitor therapy being an anti-PD-1/anti-CTLA-4 immunotherapy.

16. The system for melanoma prognosis as claimed in claim 14, wherein in (c), if the score value is greater than or equal to the threshold value, it is evaluated as: poor prognosis, high risk of recurrence, or insensitivity to immune checkpoint inhibitor treatment; if the score value is less than the threshold value, the evaluation is as follows: good prognosis, low risk of recurrence, or sensitivity to immune checkpoint inhibitor treatment.