WO2025169993A1 - Method for predicting prognosis of colorectal cancer patient - Google Patents

Abstract

The present disclosure includes a method for predicting the prognosis of a colorectal cancer patient, the method including the measurement of the expression levels of genes of one or more gene groups selected from gene groups (a) to (e): (a) MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16, (b) DEFA6, DEFA5, and SPINK1, (c) BST2, BATF, RAMP1, and TPM2, (d) MAGEA6, MAGEA3, MAGEA12, and CSAG1, and (e) IGF2, NELL2, and RBMAS1 in a cancer sample from a patient; and a kit for predicting the prognosis of a colorectal cancer patient, the kit including a reagent for measuring the expression levels of genes of one or more gene groups selected from the gene groups (a) to (e); etc.

Description

Method for predicting prognosis of colorectal cancer patients

This application claims priority to Japanese Patent Application No. 2024-017258, the entire contents of which are incorporated herein by reference.
The present disclosure includes methods and kits for predicting the prognosis of a colorectal cancer patient.

Colorectal cancer (CRC) is the third most common cancer in humans and the second most deadly. Most deaths from CRC are due to metastasis to major organs such as the liver and lungs. The mRNA expression of numerous CRC tissue samples has been analyzed and classified, as exemplified by the Consensus Molecular Subtyping (CMS) classification. Most of these analyses use fresh frozen CRC tissue samples or paraffin-embedded blocks. These samples are composed of "cancer epithelium" and "stroma," the latter of which includes blood cells, vascular endothelial cells, inflammatory cells, immune cells, and fibroblasts. This complicates the data, and phenotypic changes in the cancer epithelium itself also affect gene expression. Patient-derived xenografts (PDXs) have also been used to search for "epithelial cell-intrinsic subtypes" (CRC epithelial cell-intrinsic subtypes; CRIS) of CRC. This is because stromal cells derived from human tumors are replaced with mouse stromal cells, leaving only the epithelium of human origin. Furthermore, to avoid heterogeneity within individual tumors, analysis of multiple regions of the same tumor has also been attempted. However, to date, the prognosis prediction of colorectal cancer patients using gene expression analysis has not been fully established.

The present disclosure aims to provide a method and kit for predicting the prognosis of colorectal cancer patients.

In one aspect, the present disclosure provides a method for predicting the prognosis of a colorectal cancer patient, comprising: determining a set of genes (a) to (e) in a cancer sample from the patient:
(a) MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16,
(b) DEFA6, DEFA5, and SPINK1,
(c) BST2, BATF, RAMP1, and TPM2,
(d) MAGEA6, MAGEA3, MAGEA12, and CSAG1, and (e) IGF2, NELL2, and RBMS1.
The present invention provides a method for measuring the expression level of one or more genes selected from the group consisting of:

In one aspect, the present disclosure provides a kit for predicting the prognosis of a colorectal cancer patient, the kit comprising: a group of genes (a) to (e):
(a) MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16,
(b) DEFA6, DEFA5, and SPINK1,
(c) BST2, BATF, RAMP1, and TPM2,
(d) MAGEA6, MAGEA3, MAGEA12, and CSAG1, and (e) IGF2, NELL2, and RBMS1.
The present invention provides a kit comprising a reagent for measuring the expression level of one or more genes selected from the group consisting of:

The present disclosure provides a method and kit for predicting the prognosis of colorectal cancer patients.

Flowchart for the discovery of colorectal cancer stem cell (CRC-SC)-specific expression signatures. A, A panel of patient-derived CRC-SCs consisting of 20 previous lines {Yamamoto, 2020} and 37 additional lines. B, Genomic DNA was analyzed for 409 or 50 cancer-associated gene mutation hotspots. C, Five normal colorectal epithelial stem cell (NCE-SC) lines (3N, 50N, 59N, 70N, and 73N) were used as controls. D, Transcriptome analysis was performed using microarray analysis. E and F, Low- and high-expression genes were determined by fold change in expression levels in CRC-SC lines relative to that of control NCE-SC lines. G and H, Search and identification of additional genes within each lead gene subgroup for inclusion in each lead gene signature. Expression levels of the additional genes correlated with each other and with CRC patient survival in three public databases. I-L, Finally, the individual standardized signatures were summed, with positive (+) representing a good prognosis and negative (-) representing a poor prognosis, to form the colorectal cancer comprehensive signature (GCS). M, Preclinical evaluation of the GCS score was performed by transplanting CRC-SC spheroids into the colon of immunodeficient mice. Logarithmic plot showing the ranking of the mean mRNA expression level (Mean TPM) of each gene in three NCE-SC lines. Circles in the figure represent the following gene groups: NCE-SC markers (light grey), lucky 5 gene group (white), CRC-SC markers (dark grey), and lead genes (CXCL14, BST2, DEFA6, IGF2, MAGEA6). Numbers in parentheses indicate the ranking of mRNA expression levels in NCE-SC spheroid cells. ND, below detection limit. Single-cell RNA expression analysis of the 70N-NCE-SC line. The cell cycle depicted in each region of the single cell population shown in the left panel was analyzed using Metascape expression analysis {Zhou et al., 2019}. The nine panels on the right show the expression of marker genes in that cell population (dark grey): MKI67 and PCNA; cell proliferation markers; CD24, CD44, and BMI1; intestinal epithelial stem cell markers; EPCAM; epithelial cell marker; MUC12, PIGR, and PLA2G2A; lucky 5 gene. Enrichment analysis of cell type signatures using the top 100 genes highly expressed in NCE-SCs under spheroid culture conditions compared to organoid culture conditions. Gene expression in CRC-SC lines compared to NCE-SC lines. (Top left) Flowchart of this study. Transcriptional activity of NCE-SC and CRC-SC spheroids was measured as mRNA expression levels using microarrays. (Top right) Expression levels of CRC-SC relative to NCE-SC. Gene expression levels are shown as circles in 1og2 scale. Dark gray, white, and light gray circles indicate top, bottom, and lead genes, respectively. Numbers in parentheses indicate the rank order of highly (+) or lowly (-) expressed genes in CRC-SC spheroid lines relative to the reference NCE-SC spheroid line. (Bottom) Heatmap showing the mRNA expression levels of the lucky 5 genes and other candidate genes in 57 CRC-SC spheroid lines (1T to 6T) and 5 NCE-SC spheroid lines (3N to 37N; 3N-d in the leftmost column is a cell line obtained by in vitro differentiation of 3N by removing the medium containing Wnt ligands). The boxes in the top row indicate the subgroups with the highest relative expression values, as shown in Table 2. Violin plot showing the microarray probe signal values for 36 genes (horizontal axis) in 57 CRC-SC spheroid lines relative to the mRNA expression level of NCE-SC lines. Lines enclosed by rounded rectangles represent a subgroup highly expressing the lucky 5 genes. Lines enclosed by ellipses represent a subgroup represented by the lead genes DEFA6, BST2, MAGEA6, and IGF2. The horizontal wavy line at 0 indicates the average probe signal value for NCE-SC lines. Overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) for colorectal cancer patients (all stages) in the TCGA-CRC database (validation cohort 1). The graph on the right shows the hazard ratio (HR) and its 95% confidence interval (CI). OS or recurrence-free survival (RFS) of colorectal cancer patients (all stages) in the GSE39582 database (validation cohort 2). The graph shows the survival time of patients with high (H) or low (L) expression of the lucky 5 genes. The graph on the right shows the HR and its 95% CI. OS, DSS, PFS, or RFS for colorectal cancer patients (all stages) in TCGA-CRC (top) or GSE39582 (bottom). Survival time for patients with high or low lucky 5 signatures is shown. The graph on the right shows HR and its 95% CI. Heatmap showing mRNA expression levels in 57 CRC-SC spheroid lines (55T to 83T) and 5 NCE-SC lines (3N to 73N; 3N-d in the leftmost column is a cell line differentiated in vitro from 3N by removing the medium containing Wnt ligands). The boxes in the top row represent colorectal cancer subgroups and indicate the five lead genes with the highest expression in those CRC-SC spheroids (see "Keys" below the figure). OS, DSS, and PFS for colorectal cancer patients (all stages) in the TCGA-CRC database. The graph shows the survival time for patients with high or low levels of the four lead gene signature. The graph on the right shows the HR and 95% CI for the five-signature, including the lead gene candidate CXCL14. OS or RFS of colorectal cancer patients (all stages) in the GSE39582 database. This shows the survival time of patients with high or low levels of the four lead gene signature. The graph on the right shows the HR and 95% CI for the five-signature, including the lead gene candidate CXCL14. Flowchart for calculating the colorectal cancer comprehensive signature (GCS). a) The five signatures were classified into two categories: long-term survival signatures and short-term survival signatures. b and c) The mean Z-scores of the long-term and short-term survival signatures were treated as positive (+) and negative (-) scores, respectively. d) The GCS score was calculated by summing the signature scores. e) A retrospective observational study of the GCS score was performed using the TCGA-CRC-DSS, TCGA-CRC-PFS, or GSE39582-RFS datasets as validation cohorts. f) Finally, in a prospective study using a mouse model, CRC-SC lines were divided into Q1-Q4 and orthotopically transplanted into the cecal mucosa of NSG mice. 26 weeks later, the mice were examined for primary tumors and metastatic lesions in the liver and lung. Kaplan-Meier plots (left) and HR comparisons (right) showing comparisons of DSS or PFS in colorectal cancer patients from the TCGA-CRC database divided by GCS quartiles (Q1-Q4, from lowest to highest GCS score). Ptrend and P values were calculated using the log-rank trend test and log-rank test for trend, respectively. HRs (95% CI) were obtained using the Mantel-Haenszel test. The sample sizes for DSS were Q1; 0-25%, n = 137; Q2; 25-50%, n = 136; Q3; 50-75%, n = 136; and Q4; 75-100%, n = 136. The sample sizes for PFS were Q1; 0-25%, n = 141; Q2; 25-50%, n = 141; Q3; 50-75%, n = 141; and Q4; 75-100%, n = 141. Kaplan-Meier plot (left) and HR comparison (right) showing the comparison of RFS for colorectal cancer patients in the GSE39582 database divided by GCS quartiles (Q1-Q4, from lowest to highest GCS score). Sample sizes were Q1; 0-25%, n = 129; Q2; 25-50%, n = 130; Q3; 50-75%, n = 130; and Q4; 75-100%, n = 130. A radar chart showing the contribution of five signatures to the GCS score. The results are for two colorectal cancer patients (D5-6920 and AG-4021, or CIT478 and CIT295) from TCGA-CRC or GSE39582, whose GCS scores were in the top 25% (Q4) (left) and bottom 25% (Q1) (right) of the total patient population. The numbers from -4 to 4 indicate the average Z-score of each signature used in the GCS. The good-prognosis signatures Lucky 5 and DEFA6 are shown on the bottom, while the poor-prognosis signatures IGF2, BST2, and MAGEA6 are shown on the top. Statistics of GSCs (top) and histograms showing the distribution of GSCs (bottom) in CRC-SC spheroids, TCGA-CRC, or GSE39582. Top left: Kaplan-Meier plot comparing GCS score quartiles of CRC-SC lines orthotopically transplanted into NSG mice. (Number of mice: Q1 = 14, Q2 = 15, Q3 = 14, Q4 = 14.) Top center: Kaplan-Meier plot comparing GCS scores of Q4 and Q1. Ptrend and P values were calculated using the log-rank test for trend and the log-rank test, respectively. Bottom left: Cancer mortality rates in mice orthotopically transplanted with CRC-SC lines. Bottom center: Number of cancer deaths in mice orthotopically transplanted. Right: Liver and lung metastases resulting from orthotopic transplantation of two lines from the lower GCS score group (Q1), 8T and 13T, into mice. These tumor-bearing mice died within 26 weeks after transplantation. Top right: Macroscopic findings of resected livers. Right center and bottom right: Histopathological observations of the liver and lungs by H&E staining. White scale bar is 10 mm. The black scale bar is 400 μm. The arrowhead indicates a metastatic lesion. Test to determine whether NSG mice orthotopically transplanted with CRC-SC lines with GCS scores of Q4 to Q1 died 6 months later. Column L indicates cell lines with high or low lucky 5 signatures, and column P indicates cell lines that resulted in lethal or viable mouse deaths. Asterisks indicate cell lines that showed mucinous histopathological findings. Flowchart of patient subtype determination using maximum signature score (MSS). a, The maximum of the four lead gene signature (LGS) scores was defined as MSS. b, Each patient was divided into lead gene subgroups based on MSS. c-e, Each subgroup was divided into high or low MSS subpopulations. Patients with high scores were used as the subtype prototype, and patients with low scores in each subgroup were combined into one as the remainder. A retrospective observational study was conducted using the TCGA-CRC-PFS or GSE39582-RFS dataset as a validation cohort to compare each LGS subtype with the remaining patients. Heatmaps showing the subtype, stage, and LGS score of CRC patients in CRC-SC (top), TCGA-CRC (middle), and GSE39582 (bottom). Kaplan-Meier plot of PFS or RFS by subtype for TCGA-CRC or GSE39582 colorectal cancer patients (all stages) and HR between each LGS subtype and the rest of the patients. Comparison of GCS and ColoGuidePro in RFS for colorectal cancer patients (all stages). Shown are RFS for patients with Q1 to Q4 (left), the highest and lowest scores (Q1 and Q4) (center), or a good prognosis (80%) and a poor prognosis (20%) (right). Note that GCS and ColoGuidePro rank Q1 to Q4 in opposite ways. Comparison of GCS and ColoGuidePro in terms of RFS in stage II colorectal cancer patients. RFS is shown for patients with Q1-Q4 (left), the highest and lowest scores (Q1 and Q4) (center), or a good prognosis (80%) and a poor prognosis (20%) (right). Comparison of GCS and ColoGuidePro in terms of RFS in stage III colorectal cancer patients. RFS is shown for patients with Q1-Q4 (left), the highest and lowest scores (Q1 and Q4) (center), or a good prognosis (80%) and a poor prognosis (20%) (right).

Unless otherwise specified, terms used herein have the meanings commonly understood by those skilled in the art of organic chemistry, medicine, pharmacology, molecular biology, microbiology, etc. Definitions of some terms used herein are provided below, but these definitions take precedence over common understandings in this specification.

In one aspect, the present disclosure relates to a method for predicting the prognosis of a colorectal cancer patient. The prognosis prediction method of the present disclosure comprises measuring the expression levels of genes in one or more gene groups selected from gene groups (a) to (e) shown in Table 1. The expression of genes in each gene group (a) to (e) in colorectal cancer cells is correlated with each other, and each gene group forms a signature. These gene signatures may exhibit stronger statistical significance as a prognostic predictor for colorectal cancer patients than a single gene included in that gene group. Genbank accession numbers for representative mRNA sequences and amino acid sequences encoded by each gene are shown in Table 1. The sequences of each gene in individual colorectal cancer patients are not limited to these sequences and may include natural mutations.

The expression level of a gene can be measured by measuring the expression of the mRNA or protein encoded by that gene. The expression of mRNA or protein may be measured by any method. Measurement methods include polymerase chain reaction (PCR), RNA sequencing (RNA-Seq), microarrays, and immunological methods such as immunostaining, ELISA, Western blot, and dot blot. In this specification, the term PCR is used to include any method that utilizes PCR, such as reverse transcription PCR (RT-PCR), quantitative PCR (qPCR), and real-time PCR. Those skilled in the art can appropriately process cancer samples according to the measurement method, and can appropriately prepare or select reagents such as primers, probes, or antibodies suitable for measurement based on the mRNA sequence or amino acid sequence encoded by each gene. In one embodiment, the expression level of a gene is measured by measuring the mRNA expression level.

The primers or probes may comprise or consist of a sequence complementary to a portion of the mRNA sequence encoded by each gene. The primers or probes may be, for example, 10 to 50 nucleotides, 15 to 35 nucleotides, or 20 to 30 nucleotides. In one embodiment, the primers or probes comprise or consist of a sequence of 10 to 50 nucleotides, 15 to 35 nucleotides, or 20 to 30 nucleotides complementary to a portion of the mRNA sequence encoded by each gene. In one embodiment, the primers or probes comprise or consist of a sequence of 10 to 50 nucleotides, 15 to 35 nucleotides, or 20 to 30 nucleotides complementary to a portion of any sequence selected from SEQ ID NOs: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, and 37.

In one embodiment, the gene expression levels are measured by PCR using primers specific to each gene. PCR reactions for each gene may be performed in separate containers, or reactions for multiple genes may be performed in a single container. Those skilled in the art can appropriately determine suitable PCR conditions.

The large intestine is divided into the colon and the rectum, and colorectal cancer includes colon cancer and rectal cancer. Colorectal cancer is divided into stages 0, I, II, III, and IV depending on the degree of progression. Colorectal cancer in this disclosure includes colorectal cancer of any stage. In one embodiment, the colorectal cancer patient is a patient at stage I, II, III, or IV. In one embodiment, the colorectal cancer patient is a patient at stage II, III, or IV. In one embodiment, the colorectal cancer patient is a patient at stage III or IV.

A cancer sample may be any sample that can be used to examine gene expression levels in a patient's colon cancer tissue. The cancer sample may be, for example, a surgical sample, a biopsy sample, or a cancer spheroid. In one embodiment, the surgical sample is a sample obtained at the time of resection of the primary lesion. As used herein, cancer spheroid refers to a cell mass containing tumor-initiating cells, and tumor-initiating cells refer to cells that form tumors when transplanted into an immunodeficient animal. Cancer spheroids are created from a patient's cancer cells or tissue. Cancer spheroids may be created by any method, for example, by the method described in WO 2019/111998.

In one embodiment, the method of the present disclosure calculates a Z-score (Z) for each gene from the expression level of each gene in a patient using formula (I):
(where x is the expression level of the gene in the patient, and μ and σ are the mean and standard deviation of the expression levels of the gene in multiple colorectal cancer patients, respectively)
To seek by
determining a gene expression signature score for each group of genes; and comparing the gene expression signature score to a cutoff value.

μ and σ are the mean and standard deviation of the expression levels of the gene in question in multiple colorectal cancer patients, and are predetermined values for each gene. The values of μ and σ can be determined, for example, from the gene expression levels of multiple colorectal cancer patients measured using the same method as that used to measure the patient's gene expression levels. Alternatively, the values of μ and σ can be determined based on gene expression data of multiple colorectal cancer patients registered in a database. Available databases include TCGA-CRC (The Cancer Genome Atlas) (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) and GSE39582 in the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/geo/). The values of μ and σ can also be determined based on gene expression data of cancer spheroids from multiple colorectal cancer patients. The number of colorectal cancer patients can be, for example, but is not limited to, 50 to 1000, 50 to 500, 50 to 200, or 50 to 100. Those skilled in the art can appropriately determine the required number of cases, and can also correct the values of μ and σ when the number of cases increases (e.g., each time the number of cases doubles). The gene expression levels used to calculate the Z-score may be values that reflect the expression level of each gene, and may be measured values, relative values, or logarithms of these, or may be appropriately corrected or standardized values.

The gene expression signature score is a value indicating the expression level of genes in each gene group, and is the sum of the Z scores of the genes in that gene group, or the average Z score of the genes in that gene group. For example, the gene expression signature score of gene group (a) (Lucky 5 signature) can be determined by adding up the Z scores of the five genes MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16, or by dividing the sum of the Z scores of these genes by 5, which is the number of genes in the gene group. In one embodiment, the gene expression signature score is the average Z score of the genes in each gene group.

The gene expression signature score is compared with a cutoff value. The cutoff value is the gene expression signature score value that can statistically significantly divide multiple colorectal cancer patients with known prognosis into a good prognosis group and a poor prognosis group. The cutoff value can be, for example, the value that minimizes the P value when multiple colorectal cancer patients are divided into a good prognosis group and a poor prognosis group in a Kaplan-Meier survival curve. The statistical significance between the good prognosis group and the poor prognosis group can be determined, for example, by the logrank test.

The gene expression signature score may be compared with multiple cutoff values. For example, for multiple colorectal cancer patients whose prognosis is known, the prognosis of each patient may be ranked into three or more levels, and two or more gene expression signature scores that can separate each rank group with a statistically significant difference may be used as cutoff values. Statistical significance can be determined, for example, by the logrank test. Alternatively, the cutoff values may be multiple values that can separate multiple colorectal cancer patients at a certain ratio in order of their gene expression signature scores, and may be, for example, the quartiles of the gene expression signature scores of multiple colorectal cancer patients. By comparing with multiple cutoff values, colorectal cancer patients can be stratified according to prognosis.

The comparison of the gene expression signature score with the cutoff value may or may not involve statistical analysis. Statistical analysis methods include one-way ANOVA, two-way ANOVA, Dunnett's test, and Tukey's test.

The gene expression signature score for gene group (a) is compared with the cutoff value for that gene group. If the gene expression signature score for gene group (a) is higher than the cutoff value, the patient is predicted to have a good prognosis.

The gene expression signature score of gene group (b) is compared with the cutoff value for that gene group. If the gene expression signature score of gene group (b) is higher than the cutoff value, the patient is predicted to have a good prognosis.

The gene expression signature score of gene group (c) is compared with the cutoff value for that gene group. If the gene expression signature score of gene group (c) is higher than the cutoff value, the patient is predicted to have a poor prognosis.

The gene expression signature score of gene group (d) is compared with the cutoff value for that gene group. If the gene expression signature score of gene group (d) is higher than the cutoff value, the patient is predicted to have a poor prognosis.

The gene expression signature score for gene group (e) is compared with the cutoff value for that gene group. If the gene expression signature score for gene group (e) is higher than the cutoff value, the patient is predicted to have a poor prognosis.

In one embodiment, the method of the present disclosure includes measuring the expression levels of genes in two gene groups selected from gene groups (a) to (e). In one embodiment, the method of the present disclosure includes measuring the expression levels of genes in three gene groups selected from gene groups (a) to (e). In one embodiment, the method of the present disclosure includes measuring the expression levels of genes in four gene groups selected from gene groups (a) to (e). In one embodiment, the method of the present disclosure includes measuring the expression levels of genes in five gene groups selected from gene groups (a) to (e).

In one embodiment, the method of the present disclosure comprises:
Measuring the expression levels of genes in gene groups (a) to (e);
The colon cancer comprehensive signature (GCS) score was calculated using formula (II):
(In the formula,
is a coefficient indicating the polarity of prognosis, where the coefficient of gene group (a) to (b) is 1, the coefficient of gene group (c) to (e) is -1,
is the average Z score of the genes in each gene group)
and comparing the GCS score with a cutoff value.

The GCS score is a comprehensive signature that integrates five types of signatures from gene groups (a) to (e).
is a coefficient indicating the polarity of the prognosis of each gene group (i.e., each gene signature). The coefficient of gene groups (a) to (b) correlated with a good prognosis is 1, and the coefficient of gene groups (c) to (e) correlated with a poor prognosis is -1.
is the average Z score of the genes in each gene group, and is calculated by adding up the Z scores of the genes in each gene group and dividing by the number of genes. In other words, the GCS score is calculated for gene groups (a) to (e) by multiplying the average Z score of the genes in each gene group by a coefficient indicating the polarity of prognosis and then adding these values together.

The GCS score is compared with a cutoff value. The cutoff value is a GCS score value that can statistically significantly divide multiple colorectal cancer patients with known prognosis into good prognosis and poor prognosis groups. The cutoff value can be, for example, the value that minimizes the P value when multiple colorectal cancer patients are divided into good prognosis and poor prognosis groups using the Kaplan-Meier survival curve. The statistical significance between the good prognosis and poor prognosis groups can be determined, for example, using the logrank test. If the colorectal cancer composite score is higher than the cutoff value, the patient is predicted to have a good prognosis.

The GCS score may be compared with multiple cutoff values. For example, for multiple colorectal cancer patients whose prognosis is known, the prognosis of each patient may be ranked into three or more levels, and two or more colorectal cancer comprehensive scores that can separate each ranked group with a statistically significant difference may be used as cutoff values. Statistical significance can be determined, for example, by the log-rank test. Alternatively, the cutoff values may be multiple values that can separate multiple colorectal cancer patients at a certain ratio in order of GCS score, and may be, for example, the quartiles of the colorectal cancer comprehensive scores of multiple colorectal cancer patients. By comparing with multiple cutoff values, colorectal cancer patients can be stratified according to prognosis.

Comparison of GCS scores with cutoff values may or may not involve statistical analysis. Statistical analysis methods include one-way ANOVA, two-way ANOVA, Dunnett's test, and Tukey's test.

For colorectal cancer, treatment guidelines stipulate treatment strategies for each stage. For example, stage II cancer is generally not a candidate for chemotherapy, but if the method disclosed herein predicts a poor prognosis, chemotherapy can be administered. Furthermore, while chemotherapy is generally prescribed for stage III cancer, if the method disclosed herein predicts a good prognosis, chemotherapy can be avoided or a drug with fewer side effects can be used, and if a poor prognosis is predicted, a drug with stronger effects can be used. In other words, the prognosis prediction method disclosed herein contributes to optimizing treatment methods.

In this way, colorectal cancer patients whose prognosis has been predicted by the method of the present disclosure can be treated with a therapeutic agent or therapy selected based on the prediction results. Therefore, in one aspect, the present disclosure provides a method for treating colorectal cancer patients, which comprises predicting the prognosis of a colorectal cancer patient using the prognosis prediction method of the present disclosure, and administering to the patient a therapeutic agent or therapy selected based on the prediction results. The therapeutic agent may be an anticancer drug such as 5-fluorouracil (5-FU), tegafur, tegafur-uracil (UFT), doxifluridine (5'-DFUR), carmofur, tegafur-gimeracil-oteracil potassium (S-1), capecitabine (Cape), regorafenib, trifluridine-tipiracil hydrochloride (FTD/TPI), mitomycin C, irinotecan (IRI), oxaliplatin (OX), or vascular endothelial growth factor (VGF). Examples of molecular targeted drugs include VEGF inhibitors (e.g., bevacizumab, aflibercept), vascular endothelial growth factor receptor (VEGFR) inhibitors (e.g., ramucirumab), FGFR inhibitors (e.g., erdafitinib, rogaratinib, AZD4547, infigratinib, fisogatinib, LY2874455, futibatinib, ASP5878, derazantinib), and EGFR inhibitors (e.g., cetuximab, panitumumab, erlotinib). Treatment options include chemotherapy such as FOLFOX therapy (intravenous 5-FU + levofolinate + OX), FOLFIRI therapy (5-FU + levofolinate + IRI), FOLFOXIRI therapy (5-FU + levofolinate + OX + IRI), CapeOX therapy (Cape + OX), SOX therapy (S-1 + OX), and IRIS therapy (S-1 + IRI), as well as endoscopic resection, surgical resection, and radiation therapy.

In one aspect, the present disclosure provides a kit for predicting the prognosis of a colorectal cancer patient, the kit comprising: a group of genes (a) to (e):
(a) MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16,
(b) DEFA6, DEFA5, and SPINK1,
(c) BST2, BATF, RAMP1, and TPM2,
(d) MAGEA6, MAGEA3, MAGEA12, and CSAG1, and (e) IGF2, NELL2, and RBMS1.
The present invention relates to a kit comprising a reagent for measuring the expression level of one or more genes in a group selected from the group consisting of:

The reagents can be prepared or selected based on the mRNA sequence or amino acid sequence encoded by each gene, as described in relation to the prognosis prediction method of the present disclosure. In one embodiment, the reagents are primers or probes. In one embodiment, the reagents are PCR primers. In addition to the reagents, the kit may also include measurement containers, buffer solutions, instructions for use, etc.

Exemplary embodiments of the present disclosure are described below.
[1]
A method for predicting the prognosis of a colorectal cancer patient, comprising:
(a) MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16,
(b) DEFA6, DEFA5, and SPINK1,
(c) BST2, BATF, RAMP1, and TPM2,
(d) MAGEA6, MAGEA3, MAGEA12, and CSAG1, and (e) IGF2, NELL2, and SPINK1.
and measuring the expression levels of one or more genes selected from the group consisting of:
[2]
The Z-score (Z) of each gene was calculated using the formula (I):
(where x is the expression level of the gene in the patient, and μ and σ are the mean and standard deviation of the expression levels of the gene in multiple colorectal cancer patients, respectively)
To seek by
2. The method of claim 1, further comprising determining a gene expression signature score for each gene group, wherein the gene expression signature score for each gene group is the sum of the Z-scores of the genes in each gene group or the average of the Z-scores of the genes in each gene group; and comparing the gene expression signature score to a cutoff value.
[3]
3. The method according to 2, wherein the gene expression signature score of each gene group is the average value of the Z scores of the genes in each gene group.
[4]
4. The method according to any one of 2 and 3, comprising comparing the gene expression signature score of the gene group (a) with a cutoff value, wherein if the gene expression signature score is higher than the cutoff value, the patient is predicted to have a good prognosis.
[5]
5. The method according to any one of 2 to 4, further comprising comparing a gene expression signature score of the gene group (b) with a cutoff value, wherein the patient is predicted to have a good prognosis if the gene expression signature score is higher than the cutoff value.
[6]
6. The method according to any one of 2 to 5, further comprising comparing a gene expression signature score of the gene group (c) with a cutoff value, wherein the patient is predicted to have a poor prognosis if the gene expression signature score is higher than the cutoff value.
[7]
7. The method according to any one of 2 to 6, further comprising comparing a gene expression signature score of the gene group (d) with a cutoff value, wherein the patient is predicted to have a poor prognosis if the gene expression signature score is higher than the cutoff value.
[8]
8. The method according to any one of 2 to 7, further comprising comparing a gene expression signature score of the gene group (e) with a cutoff value, wherein the patient is predicted to have a poor prognosis if the gene expression signature score is higher than the cutoff value.
[9]
9. The method according to any one of 1 to 8 above, wherein the one or more gene groups are gene groups (a) to (e).
[10]
The one or more gene groups are gene groups (a) to (e), and
The colon cancer comprehensive signature (GCS) score was calculated using formula (II):
(In the formula,
is a coefficient indicating the polarity of prognosis, where the coefficient of gene group (a) to (b) is 1, the coefficient of gene group (c) to (e) is -1,
is the average Z score of the genes in each gene group)
and comparing the GCS score to a cutoff value.
[11]
11. The method according to any one of 1 to 10, wherein the cancer sample is a surgical sample or a biopsy sample.
[12]
11. The method according to any one of 1 to 10, wherein the cancer sample is a cancer spheroid.

[13]
A kit for predicting the prognosis of a colorectal cancer patient, comprising:
(a) MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16,
(b) DEFA6, DEFA5, and SPINK1,
(c) BST2, BATF, RAMP1, and TPM2,
(d) MAGEA6, MAGEA3, MAGEA12, and CSAG1, and (e) IGF2, NELL2, and RBMS1.
A kit comprising a reagent for measuring the expression level of one or more genes in a group selected from the group consisting of:
[14]
14. The kit according to 13 above, wherein the one or more gene groups are gene groups (a) to (e).
[15]
15. The kit according to 13 or 14 above, wherein the reagent is a primer or a probe.

1. A method for treating a patient with colorectal cancer, comprising:
13. A method comprising: predicting the prognosis of a colorectal cancer patient by the method according to any one of 1 to 12; and administering to the patient a therapeutic agent or treatment selected based on the result of the prediction.

The present invention will be further explained below using examples, but the present invention is not limited to the following examples in any way.

1. Materials and Methods Human Specimens A total of 216 tumor specimens were collected from 208 patients with colorectal cancer who underwent primary tumor resection at Kyoto University Hospital (KUHP) between October 2014 and August 2018. Their diagnoses were confirmed as well-, moderately, or poorly differentiated colorectal cancer, mucinous colorectal cancer, or papillary colorectal cancer through histopathological examination by a pathologist at KUHP. The research protocol for human specimens was approved by the Medical Ethics Committee of Kyoto University, Kyoto, Japan (approval numbers R0915 and R0857, from 2015 to 2024), and patients provided written informed consent for the use of their specimens and data analysis.

Isolation and culture of patient-derived CRC-SCs as spheroids was previously described {Miyoshi et al., 2018}. Briefly, two tumor tissue fragments (approximately 1 cm each) were collected from the protruding portion of the colon cancer lesion, and one normal mucosal tissue fragment (approximately 1 cm) was collected from the surrounding normal colon tissue. The tissue fragments were transported to the laboratory in ice-cold wash medium [DMEM/F12 with HEPES and L-glutamine (Nacalai tesque, Kyoto, Japan) containing 100 units/ml penicillin (Nacalai tesque), 0.1 mg/ml streptomycin (Nacalai tesque), and 10% fetal bovine serum (Sigma)]. Tissues were processed within 24 hours after surgery. Colon cancer tissue fragments and normal colon mucosa samples were separately minced with scissors on 60-mm Petri dishes. Each specimen was digested in 2 ml of collagenase solution (washing medium supplemented with 0.2% collagenase type I (Thermo Fisher Scientific, Waltham, MA, USA) and 50 μg/ml gentamicin (Thermo Fisher)) at 37°C for 40–60 min, and the cells were separated by pipetting. CRC and NCE cells were then filtered once through a 100 μm cell strainer (Corning Inc., Corning, NY) and collected by centrifugation. The isolated cells were then suspended in Matrigel (Corning) and placed in the center of each well of a 12-well cell culture plate (TPP, Trasadingen, Switzerland) (30 μl per well). After Matrigel gelation at 37°C, cells were cultured in Advanced DMEM/F12 (Thermo Fisher Scientific) supplemented with penicillin (100 units/ml, Nacalai tesque), streptomycin (0.1 mg/ml, Nacalai tesque), L-glutamine (2 mM), Y27632 (10 μM), SB431542 (1 μM), EGF (50 ng/ml, Peprotech), and basic FGF (100 ng/ml, Peprotech) in 5% CO2 at 37°C. NCE-SC cells were cultured in eL-WRN medium as previously described {Miyoshi et al., 2018}.

RNA sequencing analysis. RNA sequencing (RNA-seq) analysis of the NCE-SC spheroid line (GSE189212) was performed by Macrogen (Seoul, Korea) using a previously reported method {Yamamoto, Miyoshi et al. 2020, Kitano, Yamamoto et al. 2022}. Briefly, synthesized libraries were sequenced from both ends (2 × 100 bases) using an Illumina HighSeq 2000 sequencer at a depth of approximately 40 million reads per sample. Sequence reads were mapped to the hg19 reference genome using the HISAT2 program {Kim, Langmead et al. 2015, Kim, Paggi et al. 2019}, and transcripts were integrated using the StringTie program {Pertea, Pertea et al. 2015}. Read counts were determined using the featureCounts program {Liao, Smyth et al. 2014}.

Database analysis of human colorectal cancer datasets. We used RNA-seq and clinical data from TCGA-CRC {TCGA Network, 2012} (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) using FPKM (fragments per kilobase of exon per million reads mapped) based on the Human Protein Atlas {Uhlen et al., 2017} version 21.0 and Ensembl version 103.38 (https://www.proteinatlas.org/about/download). We also used RNA-seq and clinical data from TCGA-CRC normalized using RSEM (RNA-Seq by Expectation Maximization) from cBioportal {Cerami et al., 2012} (https://www.cbioportal.org/datasets). We collected microarray and clinical data for GSE39582 {Marisa et al. 2013} from the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/geo/).

Logarithmic transformation of RNA-seq data. In this study, we initially performed all statistical calculations using FPKM data values, and then finally double-checked the results using RSEM data. To calculate FPKM data, raw RNA-seq expression levels were logarithmically scaled (log2) using a previously reported method. Because Log2(0) cannot be defined, FPKM was added to the logarithmic scale before being transformed: Log2(FPKM+0.1) {Reinhold et al., 2019}. However, because some genes in the RSEM output still had negative values even after adding 0.1, we added 1 to the output values and then transformed them into logarithmic scales, converting all values to positive values: Log2(RSEM+1), as previously reported {Jones et al., 2019}. We confirmed that similar results were obtained for GCS in TCGA-CRC colorectal cancer patients using both Log2(FPKM+0.1) and Log2(RSEM+1) (data not shown).

Microarray Analysis: NCE-SC or CRC-SC were cultured as described above. After aspirating the medium, lysis buffer (TaKaRa Bio, Kusatsu, Japan) was added directly to each well of the spheroid culture. Total RNA from NCE-SC and CRC-SC spheroid lines was prepared using the NucleoSpin RNA II kit (TaKaRa Bio) according to the manufacturer's protocol. Microarray analysis was performed by Macrogen Inc. (Seoul, Republic of Korea). Briefly, RNA labeling and hybridization were performed using the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technology, V 6.5, 2010), which exhibits a high dynamic range. 100 ng of total RNA from each specimen was linearly amplified and labeled with Cy3-dCTP. Labeled cRNA was purified using the RNAeasy Mini Kit (Qiagen). The concentration and specific activity of the labeled cRNA preparation (pmol Cy3/μg cRNA) were measured using a NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA). Each labeled cRNA sample (600 ng) was fragmented with 5 μl 10x blocking agent and 1 μl 25x fragmentation buffer, then heated at 60°C for 30 min. Finally, 25 μl of 2x GE hybridization buffer was added to dilute the labeled cRNA. 40 μl of hybridization solution was poured into a gasket slide and loaded onto Agilent SurePrint G3 Human GE 8x 60K, V3 Microarrays (Agilent). Slides were incubated at 65°C for 17 hours in an Agilent hybridization oven and then washed at room temperature using the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technology, v6.5, 2010). Hybridized array slides were immediately scanned using an Agilent Microarray Scanner D (Agilent Technologies, Inc.). Raw data were extracted using Agilent Feature Extraction software (v11.0.1.1). To create a raw data text file containing expression data for each gene probe on the array slide, raw data for the same gene were automatically aggregated using the Agilent feature extraction protocol. gProcessedSignal values were log-transformed and normalized using the quantile method. Statistical significance of expression data was determined using the false discovery rate (q < 0.1) and fold change (FC > 2). Data analysis was performed using R3.0.2 (www.r-project.org), GraphPad Prism software (Dotmatics, San Diego, USA), and Microsoft Excel software (Microsoft, Tokyo, Japan).

Lead genes and their associated genes: We identified genes with extremely variable mRNA expression (SD > 6.5) in CRC-SC lines. Among these genes, we selected five lead genes encoded on different chromosomes and independently regulated; their expression levels were not correlated with each other (Spearman correlation coefficient |r| < 0.5). We classified CRC-SC lines into six subgroups based on lead gene expression, including five lead gene subgroups and an unclassified subgroup. We then identified 12 lead gene-associated genes whose expression correlated with the levels of specific lead gene mRNA species. We also investigated the survival time of colorectal cancer patients.

Gene expression signature scores Gene expression signature scores were calculated as previously reported {Jackstadt et al., 2019}. First, the Z-score Zij for the jth specific gene in the ith patient in the dataset was calculated using the following formula:
Here, Xij (uppercase letters) indicates the logarithmic value of the mRNA expression level after log2 transformation for each patient's tumor. The GSE39582 data was measured using microarrays and was originally displayed as log2 values, whereas the TCGA-CRC data used in this study was RNA-seq data, so values were displayed without logarithmic transformation. Therefore, for the TCGA-CRC RNA-seq data, a constant was added while log2 transformation was performed using the following formula:
Here, xij (lowercase) denotes the mRNA level of the jth gene in the ith patient data (mRNA amount measured by RNA-seq), and the constant α was set to 0.1 for FPKM (data normalized to correct for differences in gene length and the number of fragments), and 1 for RSEM. Adding a constant during log2 transformation is performed because log2 transformation can result in negative values. This routine method ensures that all transformed values are positive, and is recommended as a standard procedure by cBioportal (https://docs.cbioportal.org/5.1-data-loading/data-loading/file-formats/z-score-normalization-script). The constant value was adjusted due to differences in normalization methods between FPKM and RSEM.
μj and σj represent the mean and standard deviation of the logarithmic expression level of the jth gene in each dataset, respectively. μj and σj were calculated using the following formulas:
Here, N denotes the number of patients in the dataset.
The cutoff score was chosen to result in the smallest statistical P value.

The gene expression signature score was the average Z score calculated using the following formula:
That is, the lucky 5 gene expression signature score was calculated by adding the Z scores of the five jth genes, and that of the lead gene was calculated by adding the Z scores of the lead gene and the jth gene whose expression is correlated within that subgroup, and then dividing by the number of genes (m) (i.e., 5 for the lucky 5, 3-4 for the lead gene signature).
To investigate the overall survival (OS), disease-specific survival (DSS), progression-free survival (PFS), and recurrence-free survival (RFS) of these signatures, colorectal cancer patients in the TCGA-CRC and GSE39582 databases were divided into high and low signature score groups. The cutoff score was determined to minimize the statistical P value.

General Colorectal Cancer Score (GCS) score Individual mean Z scores were multiplied by positive or negative prognostic coefficients and added to obtain the GCS score.
where:
is a coefficient indicating the polarity of the prognosis (good prognosis is +1, poor prognosis is -1),
is the mean Z-score for each signature, i.e., Z-score divided by the number of genes in the signature group, such as 5 for the lucky 5 subgroup, 3 for the DEFA6, CXCL14, and IGF2 subgroups, and 4 for the BST2 and MAGEA6 subgroups.
For quartile analysis, CRC specimens were divided into four equal subgroups according to GCS score, and analyzed using the Kaplan-Meier method followed by the log-rank test. The 95% CI and HR for the poorest quartile group were calculated using the Mantel-Haenszel method in the GraphPad Prism software package.

Gene enrichment analysis. False discovery rate (FDR; q) and mean (A) values obtained by comparing the NCE-SC spheroid line (GSE189212) and NCE-SC organoid line (GSE125472) were calculated using TCC-GUI {Su et al., 2019}. Differentially expressed genes (DEGs) were filtered for q < 0.1 and A > 1. Enrichment analysis using cell type signatures {Subramanian et al., 2005} was performed in Metascape {Zhou et al., 2019}. Gene set enrichment analysis (GSEA) using hallmark gene sets was previously described {Subramanian et al., 2005}.

Hotspot Mutation Analysis. Matrigel-enclosed spheroids were suspended in cell recovery solution (Corning) and collected in a 1.5 mL tube. Matrigel was dissolved at 4°C for 30 minutes with rotation and mixing. Spheroids were centrifuged at 4°C for 5 minutes and washed twice with PBS. Genomic DNA from the spheroids was purified using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. Hotspot mutations in 50 or 409 cancer-related genes in CRC-SC lines were detected by Macrogen Inc. (Seoul, Republic of Korea). A list of sequenced genes and mutations was available on the manufacturer's website (https://tools.thermofisher.com/content/sfs/brochures/CO25560_Ion_AmpliSeq_Comprehensive_Cancer_Panel_Gene_List_final9062012.pdf). The dataset was analyzed using Integrative Genomics Viewer software (Broad Institute).

k-means analysis. Among the genes highly expressed in CRC-SC lines compared to NCE-SC lines, the 100 most differentially expressed genes were subjected to gene cluster analysis using Integrated Differential Expression and Pathway analysis v0.951 (iDEP.951) {Ge, Son et al. 2018}.

Single-cell RNA-seq (scRNA-seq) library preparation, data processing, and quality check. scRNA-seq library preparation was performed by Rhelixa Inc. (Tokyo, Japan) as previously described {Joanito, Wirapati et al. 2022}. Briefly, fresh 70N-NCE-SC were cultured in single suspension and loaded into a Chromium system (10X Genomics) at 5,000 cells per well. Barcoded sequencing libraries were generated using the Chromium Single Cell 3' v3.1 reagent Kit Dual (10X Genomics). The libraries were sequenced on an Illumina NovaSeq 6000 until full saturation was achieved. Immediately after detection, sequence information from the sequence reads was matched to the hg38 reference genome and further processed using CellRanger v6.1.2 (10X Genomics) to generate an expression matrix based on unique molecular identifier (UMI) values (representing the number of single RNA molecules detected) for each gene in each cell (droplet). Droplets with fewer than 2500 detected gene expressions, more than 6000 detected genes, or more than 15% mitochondrial genome were excluded as droplets containing no target genes, multiplets, or low-quality/dead cells, respectively. UMI values were normalized using the LogNormalize function in Seurat 4.0 {Hao, Hao et al. 2021} to result in a total of 10,000 UMIs for all genes in each cell. Normalized values were then log-transformed after adding a pseudocount of 1.

Dimensionality Reduction Analysis Using Unsupervised Clustering As previously described {Wang, Dang et al. 2021}, we used Seurat 4.0 {Hao, Hao et al. 2021} on the normalized gene-cell matrix to identify highly variable genes for unsupervised cell clustering. To identify highly variable genes, we used the MeanVarPlot method in the Seurat package to calculate the mean-variance relationship between normalized expression counts per cell. A set of 2,000 highly variable genes was selected. An elbow plot was created using the PCelbowPlot function in Seurat, and the number of significant principal components was determined based on this. Different resolution parameters were then tested for unsupervised clustering to determine the optimal number of clusters. In this study, the first 10 principal components and highly variable genes identified by Seurat were used for unsupervised clustering at a resolution of 0.5, resulting in a total of seven cell populations. To visualize these, we further reduced the dimensionality using the UMAP method with the Seurat function RunUMAP, which showed that the 70N-NCE-SC cell population was roughly a single population (Fig. 2B, left). The principal components used to calculate the embedding were the same as those used for clustering. The plot of single cells with respect to the marker gene group (Fig. 2B, right) was colored by the Seurat function FindMarkers.

Lead gene subtype assignment for 57 CRC-SC strains. The subtypes of the 57 CRC-SC strains were determined by the most highly expressed lead gene, as calculated in Table 2. That is, the highest value among the five lead gene candidates was used as the determinant of the subtype, as shown in the top box in Figure 5A. CRC-SC strains showing a lead gene fold change of less than 70-fold were assigned to the unclassified "Others" category.

Isolation of the lucky 5 genes We compared mRNA expression data of CRC-SC spheroid lines to NCE-SC spheroid lines as a discovery cohort, and evaluated the relationship between the lowest expressed mRNAs in CRC-SC and patient survival when compared with NCE-SC spheroids as a validation cohort.
Screening in the discovery cohort: The set of 19 candidate markers for favorable CRC outcome was detected by the microarray probes with the lowest ranked fold change in the transcriptome of CRC-SC lines compared with that of NCE-SC lines.
The first evaluation criterion for the validation cohort was that the log-rank P value for the comparison of patient survival between high and low expression levels of 19 candidate genes in the TCGA-CRC-OS (n = 597), TCGA-CRC-DSS (n = 545), and TCGA-CRC-PFS (n = 564) datasets was 0.05 or less. Eleven genes met the criterion: CLCA4, ZG16, AQP8, CA2, CEACAM7, PIGR, SLC26A3, MUC12, NXPE4, SLC4A4, and PLA2G2A.
The second evaluation criterion for the validation cohort was that the log-rank P value for the comparison of patient survival between high and low expression levels of 11 candidate genes in the GSE39582-OS (n = 556) and GSE39582-RFS (n = 564) datasets was 0.05 or less. Five genes, ZG16, PIGR, MUC12, SLC4A4, and PLA2G2A, met this criterion.
Expression thresholds for candidate genes were set using maximally selected logrank statistics (MSRS) {Hothorn, Lausen et al. 2003}.

Patient-derived spheroid orthotopic transplantation (PDSOX) model. Ten-week-old NSG mice were anesthetized using an isoflurane anesthesia system (NARCOBI-E, Natsume Seisakusho) on a heat mat (KN-475, Natsume Seisakusho). Abdominal hair was shaved with clippers (ER807PP-A, Panasonic). The shaved abdomen was disinfected with 80% ethanol (#699749, Marusan Sangyo) and povidone-iodine (#46026800, Kenei Pharmaceutical). The abdomen was then covered with a small animal drape (Cleyera). An abdominal incision was made slightly left of the center with sterilized scissors (Daiyu Medical Industries). The end of the cecum was extracted from the abdominal cavity using ring forceps (Daiyu Medical Industries) and placed on the drape. At this point, the mouse's body was positioned so that its head was facing forward and its tail was facing backward. The tip of the cecum was held in place with the index finger of the left hand, and the smooth muscle layer of the cecum was incised with a 26-gauge needle (TERUMO) in the right hand, creating a space between the mucosa and the smooth muscle. The space was then further widened with an 18-gauge needle. Under a long-focus microscope (M651-1, Leica Microsystems), a cluster of spheroids (approximately 10,000 CRC-SC cells) was transplanted into the smooth muscle pocket using tweezers (11252-30 Dumont #5, Fine Science Tools). The cecum was carefully returned to the abdominal cavity, and the incised abdominal wall and epidermis were sutured. The PDSOX mice were observed for 26 weeks after transplantation. Moribund PDSOX mice were euthanized for pain relief and considered to have died from cancer. Subsequently, they were analyzed macroscopically and pathologically. Survival time was determined as the average survival time of two PDSOX mice. All animal experiments were conducted in accordance with protocols approved by the Kyoto University Animal Care and Use Committee.

Statistical analysis was performed using GraphPad Prism software (Dotmatics, San Diego, USA) or Microsoft Excel software (Microsoft, Tokyo, Japan). Data were analyzed using Kaplan-Meier survival curves, the log-rank test, the Mantel-Haenszel test, the Spearman correlation test, the Mann-Whitney U test, the Kruskal-Wallis test, the Welch t test, and the Benjamini-Hochberg method.

2. Results Gene Expression Analysis of Normal Colon Epithelial Stem Cells (NCE-SCs) Human NCE-SCs have been cultured using several methods. We established NCE-SCs from over 160 colorectal cancer patients using serum-containing medium. We first measured mRNA expression levels in five NCE-SC spheroid cell lines using microarray hybridization-based mRNA quantification and RNA sequencing (data not shown). The resulting data revealed a unique pattern: the complexity and levels of mRNA expression were essentially identical across different patients, with only gender differences observed for genes located on the sex chromosomes. This contrasts with the colorectal cancer stem cell (CRC-SC) spheroid line population, which showed a broader distribution of mRNA levels.

Meanwhile, analysis using the Metascape program revealed that our NCE-SC spheroid lines conspicuously expressed a higher proportion of mRNA species expressed in the fetal digestive system compared to cells cultured in organoids {Michels et al., 2019} (Figure 2C). Furthermore, Gene Set Enrichment Analysis (GSEA) results revealed significant differences in several gene sets (data not shown). These results were more similar to those of NCE-SC organoids cultured in "refined medium" than in the "original medium" described in the literature {Sato, T. et al. 2011}. To be sure, we analyzed the cell type composition of the NCE-SC we established and used using single-cell RNA (scRNA) analysis, confirming that they were essentially composed of a single undifferentiated cell type (Figure 2B).

High mRNA expression levels in colorectal cancer stem cells correlate with favorable prognosis; the lucky pentad (5) signature. We previously reported approximately 160 stem cell (also known as tumor-initiating cell) lines cultured as spheroids from primary colorectal cancer tumors. From these CRC-SC cell lines and recent additions, we selected 57 lines, relatively enriched for clinically relevant stage III and IV tumors (79%) (epithelial stem cells, not tumor stroma), for further characterization of intrinsic markers of CRC-SC. The male/female ratio was 34:23. They represented a wide variety of driver gene mutations and stages (data not shown). These colorectal cancers harbored mutations in expected driver genes, such as APC, TP53, and RAS, as well as mutations in SMAD4 and FBXW7. The histopathological findings of the primary tumors were mostly well- to moderately- or poorly differentiated adenocarcinomas, including some myxoid types, two cases likely derived from serrated adenomas with BRAF V600E mutations, and one case with microsatellite DNA instability (MSI-H). The low frequency of MSI-H cases is due to the fact that 75% of them were in stage I/II disease, and none of the 216 cases were in stage III/IV disease.

We hypothesized that some of the genes expressed in the CRC-SC transcriptome would provide data that influence prognosis even when measured using the patient's entire cancer tissue. To test this hypothesis, we devised a new strategy using CRC-SC as a prognostic biomarker discovery cohort (Figure 3A). Specifically, we compared the mRNA expression data of CRC-SC with that of the above-mentioned NCE-SC (Figure 3A). We then focused on the 20 gene probes ranked at the bottom and top of the resulting list of differentially expressed genes, i.e., the groups of genes whose expression was significantly decreased and increased compared to NCE-SC (Table 2).

For the first set of analyses, we first examined the genes ranked lowest in expression in CRC-SC as a whole compared with NCE-SC to identify a subgroup of cell lines. Specifically, we found that 19 genes were expressed at a low level in most CRC-SC spheroid lines compared with NCE-SC, but their expression was increased to levels similar to those in NCE-SC in a subset of CRC-SC lines (Fig. 3A and Table 2). From these 19 genes, we initially selected 11 as candidate markers for favorable prognosis based on the TCGA-CRC database (n = approximately 600), and further narrowed it down to five genes that also showed favorable prognosis in the GSE39582 database (n = approximately 600). These genes were MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16 (Fig. 2A, Fig. 3B).

As expected, high expression of these five genes was associated with favorable prognosis in both TCGA-CRC and GSE39582 patients, and each gene was associated with statistically significant differences in not only OS data but also PFS/RFS/DSS data (Figures 4A and 4B). Therefore, we named these five genes the "lucky pentad (5)."

To assess the clinical significance of these five gene expressions collectively, these standardized expression levels were normalized by Z-score and integrated using Z-score transformation. The resulting statistical significance of the difference in prognosis between the high and low Lucky 5 signature score expression groups showed low HRs and P values for both TCGA-CRC and GSE39582. TCGA-CRC-OS/DSS/PFS were HR 0.48/0.48/0.6 (P = 0.002/0.01/0.01), and GSE39582-OS/RFS were HR 0.46/0.39 (P = 0.003/0.0007) (Figure 4C).

Furthermore, these genes are highly expressed in specific cell types that make up the normal colonic mucosa: MUC12 and PIGR are expressed in colonocytes, PLA2G2A and ZG16 are expressed in goblet cells, and SLC4A4 is expressed in CA1-positive colonocytes.

Furthermore, of these five genes, MUC12, SLC4A4, and ZG16 were expressed at even higher levels when NCE-SCs were induced to differentiate in a cell culture system by depriving them of Wnt ligands (Fig. 3A (bottom), leftmost row 3N-d). The expression levels of PIGR and PLA2G2A were comparable in both NCE-SCs and their differentiated cells. Therefore, NCE-SCs cultured as spheroids in our medium appear to be partially differentiated toward fetal colon and small intestine, as suggested by the Metascape analysis data above (Fig. 2C). On the other hand, in our CRC-SC spheroids, the EMT (epithelial-mesenchymal transition) signature genes were expressed at levels comparable to those of NCE-SC stem cell markers (Fig. 3B).

A novel prognostic signature candidate represented by high, non-intersecting mRNA expression of DEFA6, CXCL14, BST2, MAGEA6, and IGF2 in human colorectal cancer stem cell spheroid cell lines. In a second analysis, we searched for genes that were expressed at higher levels in independent, non-intersecting subsets of CRC-SC spheroids than in NCE-SC. We found five lead gene candidates representing novel molecular subgroups (Table 2 and Figure 5A). These gene candidates are DEFA6, CXCL14, BST2, MAGEA6, and IGF2. These lead gene candidates are only moderately expressed in NCE-SC, and largely do not intersect with each other or with the lucky 5-gene signature expression group (Figure 2A, Figure 5A).

All five subtypes represented by these lead genes were found in left-sided and right-sided colon cancers as well as rectal cancers, but primary tumors expressing BST2 were statistically significantly more common in colon cancers (P = 0.0001), and those expressing IGF2 were more common in rectal cancers (P = 0.002).

To evaluate the clinical significance of these lead gene candidates in the five molecular subtypes of CRC-SC spheroid lines, we analyzed the TCGA-CRC and GSE39582 databases to examine their correlation with patient prognosis and survival, similar to the Lucky 5 analysis described above. Specifically, we first performed Kaplan-Meier analysis of the five lead gene candidates to examine the difference in prognosis between high- and low-expressing patients in the public database (data not shown). Next, we selected genes (excluding the lead gene) that were highly expressed within each subgroup compared with NCE-SC from the list of the top 20 genes (Table 2), and selected genes whose expression correlated with each other and with patient prognosis. Finally, we calculated Z-scores for these gene groups (including the lead gene) as subgroups to examine whether they formed statistically stronger prognostic signatures compared with the data for the lead gene alone (Figures 5B and 5C). These candidate gene groups are: (i) DEFA6, DEFA5, and SPINK1; (ii) CXCL14, MME, and MSX1; (iii) BST2, BATF, RAMP1, and TPM2; (iv) MAGEA6, MAGEA3, MAGEA12, and CSAG1; and (v) IGF2, NELL2, and RBMS1. In a preliminary study, we used the mRNA expression levels from the TCGA-CRC database without logarithmic transformation and calculated a gene expression signature score by summing the Z scores of the genes in each gene group. A similar correlation with patient prognosis was observed (data not shown).

To statistically examine the method used here, we used the k-means method, a representative non-hierarchical statistical method, to classify the data into five subgroups. The five lead gene candidates mentioned above, namely, DEFA6, CXCL14, BST2, MAGEA6, and IGF2, were found in each independent cluster, a result consistent with the classification described above (data not shown). We further set the number of clusters in the k-means method to 10, but the five lead gene candidates still belonged to independent clusters, and the lead genes in the additional clusters were found to be genes unrelated to non-coding RNAs or patient survival (data not shown). These results suggest that our method essentially left no stone unturned in identifying genes specific to CRC-SC that predict outcome.

Below we explain the five molecular subtypes represented by individual lead genes.

Lead Gene Signature (LGS)
For the DEFA6, BST2, and MAGEA6 signatures, the differences in prognosis (OS/DSS/PFS/RFS) between patients with high and low expression of TCGA-CRC and GSE39582 showed statistically significant differences with small P values (<0.05). For the CXCL14 signature, patients with high expression in TCGA-CRC-OS tumors had a favorable prognosis, but PFS was reversed to a poor prognosis. Similarly, GSE39582-OS and RFS also reversed between favorable and poor prognosis. Because these statistical results do not meet the criteria for a prognostic predictor, we have decided to exclude CXCL14 and its signature from our prognostic biomarkers. The questionable prognostic value of CXCL14 has been discussed in a recent review {Westrich 2020}. IGF2 and its signature significantly correlated with poor prognosis in both TCGA-CRC and GSE39582. Although the statistical power was weaker in GSE39582-OS (P = 0.1), the trend toward poor prognosis was consistent, and IGF2 was retained as the lead gene signature. Similarly, the MAGEA6 signature showed a P = 0.07 in the GSE39582-OS dataset, but was consistent across all other data sets, and was therefore retained as the lead gene signature. In summary, the DEFA6, BST2, MAGEA6, and IGF2 signatures were ultimately adopted as the lead gene signature for prognosis prediction, while CXCL14 and its signature were excluded.

Colon Cancer Comprehensive Signature (GCS): A Novel Prognostic Parameter Integrating Lucky 5, DEFA6, BST2, MAGEA6, and IGF2 Signatures. As described above, we identified five molecular subgroups and their signatures that correlate with patient prognosis. Two of these, Lucky 5 and DEFA6, are associated with a favorable prognosis, while the remaining three, BST2, MAGEA6, and IGF2, are associated with a poor prognosis. Our established CRC stem cell spheroid lines belonging to these five molecular subgroups included some typical lines, but also borderline lines and lines that did not fit into any subgroup (Figure 5A). Furthermore, some CRC-SC spheroid lines expressing Lucky 5 also expressed high levels of BST2 and IGF2, two of the four lead genes mentioned above, which are associated with poor prognosis (Figure 3A (bottom), see the subgroups in the top row). However, predicting patient prognosis is always important in any clinical setting.

To this end, we devised a new mRNA expression signature that integrates the above five signatures according to the diagram in Figure 6A. This signature is the sum of the individual standardized signatures, with + indicating a good prognosis and - indicating a poor prognosis, and is called the General Colorectal Cancer Signature (GCS). The colorectal cancer stem cell spheroid lines whose total mRNA we analyzed as our discovery cohort were a set that included additional stage III and IV lines in consideration of clinical importance. However, since the number of lines was limited to 57, we first analyzed the correlation with overall survival (OS) using the TCGA-CRC cancer tissue mRNA database of nearly 600 cases as a validation cohort.

Statistical analysis of patients by quartile revealed a stratification of patients with a gradual decline in TCGA-CRC-DSS scores (Q4-Q1, Ptrend = 0.001, Figure 6B), with a significant difference observed between the highest quartile (Q4) and the lowest quartile (Q1) (P = 0.004, HR, 0.39; 95% CI, 0.20-0.74; Figure 6B).

These results suggest that the GCS can be a very useful prognostic signature. In other words, by examining which quartile an individual patient's GCS value falls in, a unified prognosis prediction may be possible, and relatively mild chemotherapy may be sufficient for patients with high GCS values, while more intensive chemotherapy may be recommended for patients with low GCS values, if possible.

We also analyzed OS/RFS using mRNA data from GSE39582, which contains roughly the same number of colorectal cancer patients as TCGA-CRC-DSS and TCGA-CRC-PFS. Although the statistical power, such as P values, was weaker due to the smaller number of cases compared to the TCGA-CRC-OS and TCGA-CRC-PFS data, we observed trends similar to those observed in the TCGA-CRC-PFS data (Figure 6C).

To intuitively visualize the GCS features of each patient's tumor, we used a radar chart as an example in Figure 6D. Tumors from patients in TCGA-CRC Q4 (highest) showed high signature scores for Lucky 5 and DEFA6, while tumors from patients in Q1 (lowest) showed significantly higher values for the MAGEA6 signature. Interestingly, patients in groups showing similar GSE39582 values had features quite similar to those of TCGA-CRC (Figure 6D).

We examined the distribution of GCS scores in the discovery cohort (n = 57) and the TCGA-CRC and GSE39582 datasets. Unsurprisingly, the mean GCS score (μ) was close to 0 in all three datasets. Meanwhile, the median GCS score was positive (0.24 and 0.20) in the two databases, while negative (-0.45) in the discovery cohort's CRC stem cells (Figure 6E, top). This is likely due to the intentionally high proportion of stage III and IV cases in the discovery cohort. Meanwhile, the GCS score distributions in TCGA-CRC (n = 597) and GSE39582 (n = 566), which have larger case numbers, were close to normal, demonstrating the ease of interpretation of the GCS and its adaptability to clinical situations involving larger numbers of patients (Figure 6E, bottom).

We developed a new xenograft model system, called patient-derived spheroid orthotopic xenografts (PDSOX), as an animal model for predicting the prognosis and outcome of postoperative colorectal cancer patients. Briefly, approximately 10,000 limited CRC-SC cells were transplanted under the cecal epithelium of immunodeficient mice. CRC-SC lines from the above-mentioned GCS score quartiles Q1 to Q4 were then tested. These xenografts formed primary tumors in the cecal mucosa. Four of the 14 CRC-SC lines derived from the poor-prognosis Q1 quartile developed multiple metastatic lesions in the liver and lungs over the 26-week observation period (Figure 6F, right). Meanwhile, mice transplanted with the favorable-prognosis Q4 quartile CRC-SC line survived throughout the observation period (Figure 6F, upper left), and consistent with this, no metastatic lesions were detected in the liver or lungs at autopsy. Two CRC-SC lines from each of the Q2 and Q3 quartiles died within 26 weeks after transplantation (Figure 6F, lower left). Furthermore, many of the Q4 CRC-SC lines had high levels of the Lucky 5 signature, a signature associated with a favorable prognosis (Figure 6G). Although the possibility remains that the tumor microenvironment may have an additional influence on these results, these results strongly suggest that the GCS score may play an important role in the outcome of colorectal cancer patients, reflecting the unique growth and metastatic characteristics of individual CRC-SCs.

Four typical lead gene subtypes account for approximately 40% of the patient population. Up to this point, we have focused on developing diagnostic tools applicable to all individual patients. However, when we limited our analysis to patients with high lead gene signature (LGS) scores, it became clear that highly typical molecular subtypes exist. To this end, we analyzed the same two public databases using a different statistical analysis method to identify such unique molecular subtypes. Specifically, as shown in Figure 7A, we artificially assigned each individual patient to the LGS subgroup with the highest score out of four LGS scores. This resulted in a strict classification of all patients into four distinct subgroups (this method has been described in a recent paper). Each of these four subgroups was then divided into high- and low-expressing subgroups using the MSRS method. Only the high-expressing subgroup was defined as the typical subtype, while the low-expressing subgroup was combined with patients from the low-expressing subgroups of the other subgroups in the database (Figure 7B). The four typical subtypes thus constructed accounted for approximately 40% of TCGA-CRC and GSE39582 patients, respectively, with the remaining 60% being relatively poorly characterized subgroups (Figure 7B).

Based on these results, we examined progression-free survival (PFS) among typical subtypes in the TCGA-CRC data and found a stratification of DEFA6, BST2, MAGEA6, and IGF2, which showed progressively decreased survival (logrank test P = 0.02). As expected, the DEFA6 subtype tended to have a longer PFS (HR, 0.60; 95% CI, 0.35-1.0), while the BST2, MAGEA6, and IGF2 subtypes tended to have a shorter PFS (Figure 7C, left). These results suggest that these subtypes may form distinctive signatures. That is, patients in the DEFA6 subtype may be able to receive relatively mild chemotherapy, while more intensive chemotherapy may be recommended for patients with the BST2, MAGEA6, or IGF2 subtypes. Similarly, when RFS was analyzed for the mRNA data of GSE39582, a trend very similar to that of the TCGA-CRC-PFS data was observed (logrank test P = 0.003) (Figure 7C, right).

Comparison of GCS with existing colon cancer prognosis prediction methods Prognosis prediction by GCS was compared with an existing colon cancer prognosis prediction method, ColoGuidePro (Inven2, Oslo, Norway) {Sveen et al. 2012}. We combined the RFS data from GSE14333 {Jorissen et al. 2009}, GSE17536 {Smith et al. 2009}, GSE17537 {Smith et al. 2009}, GSE33113 {de Sousa E Melo et al. 2011}, GSE37892 {Laibe et al. 2012}, GSE38832 {Tripathi et al. 2014}, and GSE39582 to show the RFS of patients with Q1–Q4 (left), the highest and lowest scores (Q1 and Q4) (center), or a favorable (80%) and poor (20%) prognosis (right) in a cohort of patients with colorectal cancer of all stages (Figure 8A), stage II (Figure 8B), or stage III (Figure 8C). GCS showed a much stronger statistical significance than ColoGuidePro.

These results indicate that each gene signature and the GCS can be very useful signatures for predicting the prognosis of colorectal cancer patients. In particular, the GCS is a value that reflects the integrated analysis of each gene signature, and can be an extremely practical prognostic prediction tool. As a result, the GCS score is expected to facilitate understanding of the condition of colorectal cancer patients and the physicians involved in their treatment, and to be useful in determining the most appropriate treatment plan.

References
Cerami, E., et al. (2012). "The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data." Cancer Discovery 2(5): 401-404.

Ge, SX., et al. (2018). "iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data." BMC Bioinformatics 19(534): 1-24.

Hao., Y., et al. (2021). "Integrated analysis of multimodal single-cell data." Cell 184:3573-3587.

Hothorn., Y., et al. (2003). "On the exact distribution of maximally selected rank statistics." Comput. Stat. Data Anal. 43:121-137.

Jackstadt., I., et al. (2019). "Epithelial Notch signaling rewires the tumor microenvironment of colorectal cancer to drive poor-prognosis subtypes and metastasis." Cancer Cell 36: 319-336.

Joanito., et al. (2022). "Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the molecular consensus classification of colorectal cancer." Nat. Genet. 54(7): 963-975.

Jones, W., et al. (2019). "Deleterious effects of formalin-fixation and delays to fixation on RNA and miRNA-seq profiles." Sci. Reports 9: 6980.

Jorissen, RN et al. (2009) "Metastasis-associated gene expression changes predict poor outcomes in patients with Dukes stage B and C colorectal cancer." Clin Cancer Res 15, 7642-7651.

Kim, D., et al. (2015). "HISAT: A fast spliced aligner with low memory requirements." Nat. Methods 12: 357-360.

Kim, D., et al. (2019). "Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype." Nat Biotechnol 37: 907-915.

Kitano, S., et al. (2022). "A novel parameter for cancer chemosensitivity to FGFR inhibitors." Cancer Sci. 113(11): 4005-4010

Laibe, S. et al. (2012). "A seven-gene signature aggregates a subgroup of stage II colon cancers with stage III." OMICS 16, 560-565.

Liao, Y., et al. (2014). "featureCounts: an efficient general purpose program for assigning sequence reads to genomic features." Bioinformatics 30(7): 923-930.

Loboda, A., et al. (2011). "EMT is the dominant program in human colon cancer." BMC Medical Genomics 4(9): 1-10.

Marisa, L., et al. (2013). "Gene expression classification of colon cancer into molecular subtypes: characterization, validation and prognostic value." PLoS Med 10: e1001453.

Miyoshi, H., et al. (2018). "An improved method for culturing patient-derived colorectal cancer spheroids." Oncotarget 9(31): 21950-21964.

The Cancer Genome Atlas Research Network (2012). "Comprehensive molecular characterization of human colon and rectal cancer." Nature 489(7407): 330-337.

Pertea, M., et al. (2015). "StringTie enables improved reconstruction of a transcriptome from RNA-seq reads." Nat. Biotechnol. 33: 290-295.

Reinhold, W. C., et al. (2019). "RNA sequencing of the NCI-60: integration into CellMiner and CellMiner CDB." Cancer Res. 79(13): 3514-3524.

Smith, JJ et al. (2009). "Experimentally derived metastasis gene expression profile predicts recurrence and eath in patients with colon cancer." Gastroenterology 138, 958-968.

de Sousa E Melo, F. et al. (2011). "Methylation of cancer-stem-cell-associated Wnt target genes predicts poor prognosis in colorectal cancer patients." Cell Stem Cell 9, 476-485.

Su, W., et al. (2019). "TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data." BMC Research Notes 12(133).

Subramanian, A., et al. (2005). "Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles." PNAS 102: 15545-15550.

Sveen, A. et al. (2012). "ColoGuidePro: A prognostic 7-gene expression siganture for stage III colorectal cancer patients." Clin. Cancer Res. 11.

Tripathi, MK et al. (2014). "Nuclear factor of activated T-cell activity is associated with metastatic capacity in colon cancer." Cancer Res 74, 6947-6957.

Uhlen, M., et al. (2017). "A pathology atlas of the human cancer transcriptome." Science 357(6352): eaan2507.

Yamamoto, T., et al. (2020). "Chemosensitivity of Patient-Derived Cancer Stem Cells Identifies Colorectal Cancer Patients with Potential Benefit from FGFR Inhibitor Therapy." Cancers 12(8): 2010.

Wang, R., et al. (2021). "Single-cell dissection of intratumoral heterogeneity and lineage diversity in metastatic gastric adenocarcinoma." Nat. Med. 27(1): 141-151.

Zhou, Y., et al. (2019). "Metascape provides a biologist-oriented resource for the analysis of systems-level datasets." Nat. Comm. 10(1523).

Claims (14)

A method for predicting the prognosis of a colorectal cancer patient, comprising:
(a) MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16,
(b) DEFA6, DEFA5, and SPINK1,
(c) BST2, BATF, RAMP1, and TPM2,
(d) MAGEA6, MAGEA3, MAGEA12, and CSAG1, and (e) IGF2, NELL2, and RBMS1.
and measuring the expression levels of one or more genes selected from the group consisting of: The Z-score (Z) of each gene was calculated using the formula (I):
(where x is the expression level of the gene in the patient, and μ and σ are the mean and standard deviation of the expression levels of the gene in multiple colorectal cancer patients, respectively)
To seek by
2. The method of claim 1, further comprising: determining a gene expression signature score for each gene group, wherein the gene expression signature score for each gene group is the sum of the Z-scores of the genes in each gene group or the average of the Z-scores of the genes in each gene group; and comparing the gene expression signature score to a cutoff value. The method of claim 2, wherein the gene expression signature score for each gene group is the average Z-score of the genes in each gene group. The method of claim 2, further comprising comparing the gene expression signature score of the gene group (a) with a cutoff value, and predicting that the patient will have a good prognosis if the gene expression signature score is higher than the cutoff value. The method of claim 2, further comprising comparing the gene expression signature score of gene group (b) with a cutoff value, wherein the patient is predicted to have a good prognosis if the gene expression signature score is higher than the cutoff value. The method of claim 2, further comprising comparing the gene expression signature score of gene group (c) with a cutoff value, wherein the patient is predicted to have a poor prognosis if the gene expression signature score is higher than the cutoff value. The method of claim 2, further comprising comparing the gene expression signature score of gene group (d) with a cutoff value, wherein the patient is predicted to have a poor prognosis if the gene expression signature score is higher than the cutoff value. The method of claim 2, further comprising comparing the gene expression signature score of the gene group (e) with a cutoff value, wherein the patient is predicted to have a poor prognosis if the gene expression signature score is higher than the cutoff value. The method of claim 1, wherein the one or more gene groups are gene groups (a) to (e). The one or more gene groups are gene groups (a) to (e), and
The colon cancer comprehensive signature (GCS) score was calculated using formula (II):
(In the formula,
is a coefficient indicating the polarity of prognosis, where the coefficient of gene group (a) to (b) is 1, the coefficient of gene group (c) to (e) is -1,
is the average Z score of the genes in each gene group)
and comparing the GCS score to a cutoff value. The method of any one of claims 1 to 10, wherein the cancer sample is a surgical sample or a biopsy sample. The method of any one of claims 1 to 10, wherein the cancer sample is a cancer spheroid. A kit for predicting the prognosis of a colorectal cancer patient, comprising:
(a) MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16,
(b) DEFA6, DEFA5, and SPINK1,
(c) BST2, BATF, RAMP1, and TPM2,
(d) MAGEA6, MAGEA3, MAGEA12, and CSAG1, and (e) IGF2, NELL2, and RBMS1.
A kit comprising a reagent for measuring the expression level of one or more genes in a group selected from the group consisting of: The kit described in claim 13, wherein the one or more gene groups are gene groups (a) to (e).