II-III stage colorectal cancer molecular typing method, system, device and medium

Abstract

The invention discloses a II-III stage colorectal cancer molecular typing method, system, device and medium, and relates to the technical field of biology, transcriptome expression analysis is performed on an RNA-seq sample of a II-III stage colorectal cancer patient, transcriptome expression profile data is generated according to a coding gene, a non-coding gene and a pseudogene, and the transcriptome expression profile data is analyzed according to the coding gene, the non-coding gene and the pseudogene. Carrying out hypervariant gene screening and survival analysis on the basis of the transcriptome expression profile data to obtain characteristic genes, executing clustering analysis, synthesizing analysis results of ten clustering algorithms to recognize molecular subtypes of the colorectal cancer in the stages II-III, and providing specific gene expression characteristics and enriched biological functions of each molecular subtype; through big data analysis of gene expression profiles and clinical data of colorectal cancer patients, a II-III stage colorectal cancer molecular typing evaluation model and a detection method based on next-generation sequencing and a PCR platform are established, the II-III stage colorectal cancer patients are divided into a CS1 type, a CS2 type and a CS3 type, and help is provided for improving the clinical treatment efficiency of the colorectal cancer.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and more particularly, to a molecular typing method, system, device and medium for stage II-III colorectal cancer. Background Art

[0002] Colorectal cancer is the third most common cancer globally, with increasing incidence and mortality rates worldwide. Colorectal cancer exhibits high heterogeneity, and even among patients with the same pathological type or disease stage, there are still significant differences in treatment response and survival prognosis. Therefore, in-depth understanding of the molecular typing of CRC is of crucial significance for optimizing clinical treatment plans and improving prognosis. In recent years, with the rapid development of high-throughput omics technologies, multiple research teams have attempted to apply these technologies to the molecular typing of colorectal cancer. However, a practical framework with broad clinical guidance value has not been formed yet;

[0003] In 2015, the CRC Typing Consortium revealed a set of core consensus samples (accounting for 78%) by integrating six independent classification systems and identified four significantly interconnected CRC consensus molecular subtypes (CMS), while 22% of the cases had no common features. The CMS1 subtype (14%) showed high mutation enrichment and a low incidence of chromosomal instability (CIN), including most MSI, CIMP-H cases, and BRAF mutation cases, and was associated with increased gene expression related to diffuse immune cell infiltration, showing immune escape characteristics. The three subtypes of CMS2 to CMS4 were mainly characterized by CIN. In the CMS3 subtype (13%), less CIN was observed, more CIMP (mainly CIMP-L), and nearly 30% of the cases were hypermutated, rich in KRAS mutations, and had significant changes in the metabolic profile. The two subtypes of CMS2 and CMS4 both showed characteristics such as a high frequency of CIN, a high TP53 mutation rate, and a low CIMP rate;

[0004] The design of the CMS typing is based on the overall patient population of stages I-IV of colorectal cancer and is used for the classification, prognosis assessment, and precision treatment of colorectal cancer patients in general. However, the CMS typing currently performs poorly in predicting the prognosis of stage II-III patients, and there is no significant prognostic difference among the four typings. Stage II-III patients are the largest group among colorectal cancer patients and rely on chemotherapy as the main treatment method. Therefore, it is necessary to construct a new molecular typing system for stage II-III colorectal cancer patients to better predict the prognosis of stage II-III colorectal cancer patients and evaluate the sensitivity of patients to treatment methods such as chemotherapy. Summary of the Invention

[0005] To address the deficiencies of the prior art, this application analyzed the clinical and transcriptomic data of patients with stage II-III colorectal cancer, and developed a molecular typing method, system, and device for stage II-III colorectal cancer based on transcriptomic features. Molecular typing was performed for three molecular subtypes of colorectal cancer, including CS1 type (Wnt-dominated, immune desert, poorly differentiated), CS2 type (mesenchymal, TGF-β signaling, activated and inhibitory immune cell infiltration), and CS3 type (proliferative signal, metabolically active, well-differentiated), and molecular features that were distinct in clinical prognosis and biological characteristics were provided. The molecular typing method precisely molecularly types colorectal cancer by analyzing the transcriptomic data of colorectal cancer patients, and further reveals the biological differences between different types, which can provide survival prognosis evaluation, personalized treatment strategies, and new drug development guidance for colorectal cancer patients of different molecular subtypes, and provides a molecular typing method, system, device, and medium for stage II-III colorectal cancer.

[0006] The present invention provides a molecular typing method for stage II-III colorectal cancer, comprising the following steps:

[0007] Step 1: Perform transcriptomic expression analysis on RNA-seq samples of stage II-III colorectal cancer patients, and generate transcriptomic expression profile data according to coding genes, non-coding genes, and pseudogenes;

[0008] Step 2: Based on the transcriptomic expression profile data, perform high-variable gene screening and survival analysis to obtain characteristic genes and perform clustering analysis, and identify the molecular subtypes of stage II-III colorectal cancer by comprehensively analyzing the results of ten clustering algorithms;

[0009] Step 3: Provide the specific gene expression characteristics and enriched biological functions of each molecular subtype.

[0010] Preferably, the transcriptomic expression analysis uses RNA-seq technology, and the clustering analysis uses ten molecular typing algorithms built into the MOVICS package.

[0011] Preferably, the molecular subtype includes any one of CS1 type, CS2 type, and CS3 type;

[0012] When the specific expression characteristics obtained by clustering analysis are Wnt-dominated, immune desert, and poorly differentiated characteristics, it is identified as CS1 type;

[0013] When the specific expression characteristics obtained by clustering analysis are mesenchymal, TGF-β signaling, activated and inhibitory immune cell infiltration expression characteristics, it is identified as CS2 type;

[0014] When the specific expression characteristics obtained by clustering analysis are reproductive signal, metabolically active, and well-differentiated expression characteristics, it is identified as CS3 type.

[0015] The present invention also provides a molecular typing system for stage II-III colorectal cancer. The above-mentioned molecular typing method for stage II-III colorectal cancer includes a transcriptomics analysis module for performing transcriptome expression analysis on RNA-seq samples of colorectal cancer patients to obtain transcriptome expression profile data, and the transcriptome expression analysis uses RNA-seq technology;

[0016] A molecular subtype typing module for performing clustering analysis based on the transcriptome expression profile data, analyzing the specific transcriptome expression characteristics obtained from the clustering analysis, and identifying the molecular subtypes of colorectal cancer. The clustering analysis uses ten molecular typing algorithms built into the MOVICS package;

[0017] A typing output module for outputting the molecular typing results and providing the specific gene expression characteristics of each molecular subtype.

[0018] The present invention also provides a computer-readable storage medium containing a computer program, which, when run by a processor, executes the colorectal cancer molecular typing method according to any one of claims 1 to 3;

[0019] It also includes a terminal comprising a memory and a processor, wherein the memory contains a computer program, and is characterized in that the computer program, when run by the processor, executes the colorectal cancer molecular typing method according to any one of claims 1 to 3.

[0020] The present invention also provides an application device for the molecular typing method of stage II-III colorectal cancer, which is applicable to the molecular typing method of stage II-III colorectal cancer according to any one of claims 1 to 3, including a kit, and the kit includes reagents required for any one or more of CS1-CS3 types for detecting samples;

[0021] It includes reagents required for RNA-seq technology and / or reagents required for protein extraction.

[0022] Advantageous effects: For the molecular typing system for stage II-III colorectal cancer and its in vitro diagnostic products and applications, the present invention analyzes the gene expression profiles and clinical data big data of colorectal cancer patients, establishes a molecular typing evaluation model for stage II-III colorectal cancer and a detection method based on the next-generation sequencing and PCR platforms, and classifies stage II-III colorectal cancer patients into CS1 type, CS2 type and CS3 type, which helps to improve the clinical treatment efficiency of colorectal cancer;

[0023] The molecular typing system for stage II-III colorectal cancer, its in vitro diagnostic products and applications. According to the expression levels of genes in the gene cluster of the present invention in colorectal cancer samples, a classification system for molecular typing of colorectal cancer is established to provide more targeted personalized treatment for colorectal cancer patients belonging to different types. Combining molecular typing can provide more targeted individualized treatment for colorectal cancer patients. Combining the molecular typing system can judge the prognosis of colorectal cancer patients, screen out the advantageous populations for different treatment regimens, and provide potential treatment approaches. For the CS2 type, it can be considered to reduce the proportion of chemotherapy, reduce the occurrence of adverse reactions and the economic burden of treatment; for CS1 type patients, the proportion of chemotherapy can be increased in order to achieve the greatest clinical benefit. For stage II-III colorectal cancer patients, molecular diagnosis based on the expression profile can help identify the populations that can benefit from a treatment regimen, improve the treatment efficiency, and avoid ineffective treatment.

[0024] The molecular typing system for stage II-III colorectal cancer, its in vitro diagnostic products and applications. Compared with the current methods for colorectal cancer risk assessment and molecular typing, the advantages of the present invention focus on the prognosis assessment and chemotherapy benefit of stage II-III colorectal cancer patients, distinguishing between patients with better prognosis and those with worse prognosis in stage II-III colorectal cancer. Another advantage is that it provides a classifier that is convenient for determining the type. Through a convenient diagnostic kit, the type to which a patient belongs can be determined stably and reliably. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Showing the construction and evaluation of molecular typing for stage II-III colorectal cancer (A) The distribution trends of the Cluster prediction index and Gap-statistics index with the change of the number of types k, (B) The heat map obtained by clustering the Xinhua types CS1-CS3. The yellower the color, the greater the similarity between samples. (C) The Sihouette plot of CS1-3 types. The horizontal axis represents the Sihouette index of the samples. The larger the Sihouette, the stronger the correlation within the cluster. (D) The pie chart shows the proportion of CS1-3 types in the total samples of the Xinhua cohort. (E-G) The distribution differences of CS1-3 types in terms of cancer stage (stage), lymph node metastasis (N) and recurrence (Recurrence) proportions. (H-I) The survival curves show the prognostic differences of CS1-3 types in overall survival (OS) and progression-free survival (DFS);

[0026] Figure 2Showing the biological functional significance of molecular typing of stage II-III colorectal cancer (A) The heatmap shows the KEGG pathways enriched by the CS1-3 types using the GSEA method. The redder the color, the stronger the enrichment degree of the pathway. The bluer the color, the weaker it is. (B) The heatmap shows the difference in the activation degree of the hallmark pathways of the CS1-3 types. The more orange the color, the stronger the activation degree of the pathway. The bluer the color, the weaker it is. (C) The heatmap shows the difference in the activation degree of the immune-related pathways and characteristics of the CS1-3 types. The more orange the color, the stronger the activation degree of the pathway. The bluer the color, the weaker it is. The pathway activation score is calculated by the GSVA method. (D) The heatmap shows the infiltration abundance of 22 immune cells in the CS1-3 type samples. The more orange the color, the higher the cell infiltration abundance, and vice versa;

[0027] Figure 3 Showing the construction of the molecular typing classifier for stage II-III colorectal cancer (A) The bar chart shows the number of times the feature genes appear in 100 cycles of training during the feature screening process of the classifier. The red dashed line represents 80 times. (B) The violin plot shows the difference between the accuracy obtained from training the 207-gene classifier and the accuracy of the final model;

[0028] Figure 4 Showing the validation of the molecular typing classifier for stage II-III colorectal cancer in an external cohort (A) Validating the typing through the classifier on the external cohort TCGA dataset. The heatmap shows the difference in the activation degree of the hallmark pathways of the CS1-3 types in the TCGA dataset. The more orange the color, the stronger the activation degree of the pathway. The bluer the color, the weaker it is. (B-D) Survival curve analysis shows the prognostic differences of the CS1-CS3 types in the TCGA cohort, the GSE39582 dataset, and the Sweden U-CAN cohort. The survival analysis uses the log-rank test. The CS1 type is the red line, the CS2 type is the blue line, and the CS3 type is the yellow line. Detailed implementation method

[0029] As Figure 1 shown: A molecular typing method for stage II-III colorectal cancer, comprising the following steps:

[0030] Step 1: Perform transcriptome expression analysis on the RNA-seq samples of stage II-III colorectal cancer patients to generate transcriptome expression profile data according to coding genes, non-coding genes, and pseudogenes; it should be noted that on the basis of generating transcriptome expression profile data according to coding genes, non-coding genes, and pseudogenes, it is also necessary to follow reference genes, and the reference genes include three of ACTB, GAPDH, GUSB, TFRC, MRPL19, PSMC4, and SF3A1;

[0031] More preferably, the reference genes include ACTB, GAPDH, and GUSB;

[0032] Step 2: Based on the transcriptome expression profile data, perform high-variable gene screening and survival analysis to obtain characteristic genes and perform clustering analysis, and identify the molecular subtypes of stage II-III colorectal cancer by integrating the analysis results of ten clustering algorithms;

[0033] Step 3: Provide the specific gene expression characteristics and enriched biological functions of each molecular subtype.

[0034] As an optional embodiment: The transcriptome expression analysis uses RNA-seq technology, and the clustering analysis uses ten molecular typing algorithms built into the MOVICS package.

[0035] As an optional embodiment: The molecular subtypes include any one of CS1 type, CS2 type, and CS3 type; It should be noted that the CS1 type is Wnt-dominated, immune desert, poorly differentiated, the CS2 type is interstitial, TGF-β signaling, activated and inhibitory immune cell infiltration, and the CS3 type is proliferative signal, metabolically active, well-differentiated;

[0036] When the specific expression characteristics obtained by clustering analysis are Wnt-dominated, immune desert, and poorly differentiated characteristics, it is identified as CS1 type;

[0037] When the specific expression characteristics obtained by clustering analysis are interstitial, TGF-β signaling, activated and inhibitory immune cell infiltration expression characteristics, it is identified as CS2 type;

[0038] When the specific expression characteristics obtained by clustering analysis are reproductive signal, metabolically active, and well-differentiated expression characteristics, it is identified as CS3 type.

[0039] The present invention also proposes a molecular typing system for stage II-III colorectal cancer, which is applicable to the above-mentioned molecular typing method for stage II-III colorectal cancer, including a transcriptomics analysis module for performing transcriptome expression analysis on RNA-seq samples of colorectal cancer patients to obtain transcriptome expression profile data, and the transcriptome expression analysis uses RNA-seq technology;

[0040] A molecular subtype typing module for performing clustering analysis based on the transcriptome expression profile data, analyzing the specific transcriptome expression characteristics obtained by the clustering analysis, and identifying the molecular subtypes of colorectal cancer, and the clustering analysis uses ten molecular typing algorithms built into the MOVICS package;

[0041] A typing output module for outputting the molecular typing results and providing the specific gene expression characteristics of each molecular subtype.

[0042] The present invention also provides a computer-readable storage medium containing a computer program which, when run by a processor, executes the colorectal cancer molecular typing method according to any one of claims 1 to 3;

[0043] The present invention also provides a terminal comprising a memory and a processor, wherein the memory contains a computer program, and is characterized in that the computer program, when run by the processor, executes the colorectal cancer molecular typing method according to any one of claims 1 to 3.

[0044] The present invention also provides an apparatus for applying the colorectal cancer molecular typing method for stage II-III colorectal cancer, which is applicable to the colorectal cancer molecular typing method according to any one of claims 1 to 3, and includes a kit, and the kit includes reagents required for detecting any one or more of CS1-CS3 types of test samples;

[0045] It includes reagents required for RNA-seq technology and / or reagents required for protein extraction. It should be noted that the reagents are reagents for detecting the amount of RNA transcripts, especially mRNA, of the gene; or, they are reagents for detecting the amount of cDNA complementary to mRNA, and the reagents are one or a combination of two of primers and probes;

[0046] Preferably, the reagents are reagents for detecting the amount of polypeptides encoded by the gene, and the reagents are antibodies, antibody fragments or affinity proteins, a product for molecular typing of colorectal cancer;

[0047] Preferably, the product is used for the evaluation of colorectal cancer molecular typing;

[0048] Preferably, the product is an in vitro diagnostic product, preferably a diagnostic kit, and the product is a next-generation sequencing kit, a real-time fluorescence quantitative PCR detection kit, a gene chip, a protein chip, an ELISA diagnostic kit or an immunohistochemistry (IHC) kit or a combination thereof, which contains primers for the 64 genes shown in Table 2:1-64, and optionally contains one or more selected from the following: total RNA extraction reagent, reverse transcription reagent and next-generation sequencing reagent. The product is a real-time fluorescence quantitative PCR detection kit, which contains primers for the 64 genes shown in Table 2:1-64. The real-time fluorescence quantitative PCR detection kit also contains TaqMan probes, and optionally contains one or more selected from the following: total RNA extraction reagent, reverse transcription reagent and reagents for TaqMan RT-PCR. The real-time fluorescence quantitative PCR detection kit contains TaqMan probes for the 64 genes shown in Table 2:1-64 and the 6 genes shown in Table 3:1-6. The real-time fluorescence quantitative PCR detection kit also contains one or more selected from the following: total RNA extraction reagent, reverse transcription reagent and reagents for SYBR Green RT-PCR;

[0049] Preferably, the molecular typing of stage II-III colorectal cancer can be divided into three types;

[0050] It should be noted that the uses of the kit include any one or more of the following in vitro diagnostic products:

[0051] 1. Preparation of molecular typing products for stage I-III colorectal cancer;

[0052] 2. Preparation of prognostic assessment products for stage I-III colorectal cancer patients;

[0053] 3. Preparation of clinical feature assessment products for stage I-III colorectal cancer patients;

[0054] 4. Preparation of biological behavior assessment products for stage I-III colorectal cancer;

[0055] 5. Preparation of companion diagnostic products for stage I-III colorectal cancer;

[0056] It should also be noted that the above in vitro diagnostic products, including genes related to molecular typing construction, are characterized in that:

[0057] One or more of the following protein-coding genes: PLXNB3, COMP, PPP2R2C, RBP2, L1CAM, MCEMP1, HBB, HERC3, HBA2, F5, CRISPLD1, PTPN20, SFRP2, RIMBP2, ISL1, MYBPC1, DEFA5, C8orf88, CDH2, CELF3, PLPP4, HBA1, NFE2, INSM1, NPR3, ERMN, COL10A1, KLK6, AL049839.2, AC004593.2, TNMD, KLK11, ANKRD18A, TENM3, PRKAA2, VIP, FAM83A, IL10, TNFRSF19, APOBEC3A, SCG2, THBS4, GSTA1, LRRC46, GAP43, CA9, TIFAB, MMP1, ENPP3, ZNF727, CDK15, THBS2, KCNQ3, SPTBN5, SPP1, MFAP5, B4GALNT4, PRELP, ERFE, RASSF9, PCDHB2, RGS6, Z84488.1, COL24A1, PIGR, TACSTD2, DIRAS3, NEB, ST6GALNAC3, PCDHB16, ZNF334, MAK, SPOCK1, PDZD4, HOXD10, WBP2NL, RND2, FNDC1, MSI1, CES1, RLN2, CCDC110, SHC4, UCHL1, HOXD11, BMP6, CYP1B1, INHBB, TNFSF8, BCL2L10, FGF7, SUSD2, ZNF667, NGFR, ELAPOR2, TYRP1, MEDAG, IGFBPL1, PROM2, NOVA1, HSD11B1, ST6GALNAC5, NLGN4X, TRPC4, NOS2, ESM1, ANOS1, COL8A1, MYH7B, RHCG, FN1, PRR36, DIPK1B, NOX4, FAP, SRPK3, ADHFE1, PPP1R1C, RGS4, PPP1R1A, ASXL3, GFPT2, CXCL6, F10, POSTN, SLFN13, SERPINE1, ALDH1A1, ADAM12, PKP1, COL1A1, CDH5, RNF183, VLDLR, WFDC3, GPR82, PARP15, SCG5, CTHRC1, ELOVL4, CIART, H3C4, NTN1, CARMIL2, KLRD1, FCGR2B, CARD11, COL1A2, SULF1, SLCO5A1, LGALS2, COL3A1, STC1, ST18, HTRA3, ARNT2, INHBA, ADAMTS4, TNC, DNAH5, TIMP3, COL15A1, SEMA3D, ADAM32, ALPK3, PLPPR4, COL12A1, IDO1, NDST2, DSEL, VCAN, GPR135, COL5A1, ANTXR1, TNFRSF11B, ZNF354C, ADAMTS12, LPL, CDKN2A, HSPB8, ANKRD36C, CLU, GXYLT2, TMEM17, DTNA, JCAD, PTPRR, STOX1, ANKRD6, OSMR, CCN1, COL5A2, SPARC, FBN1, TTLL7, SPAG4, APC2, ANGPT2, COL18A1, BX664615.2, B3GNT10, KCNH2, MCC, SLC2A3, S1PR1, ITGA5 and EFHD1.

[0058] One or more of the following non-coding genes: AP001029.2, LINC02432, VAC14-AS1, AC104534.1, ZNF687-AS1, LINC02532, FLJ16779, CLMAT3, AL035701.1, CHRM3-AS2, LINC01871, LINC00242, AC020659.1, HAR1A, AL683813.1, LINC02009, AL133243.3, FZD10-AS1, AC004847.1, CEBPB-AS1, CERNA2, SLCO4A1-AS1, LINC00460, LINC00501, AC124248.1, AC121761.2, PROX1-AS1, CR381653.1, LINC02846, AP005136.3, ERICH6-AS1, and AL162458.1;

[0059] One or more of the following pseudogenes: BMS1P10, AP001992.1, CROCCP3, MST1L, AC024940.1, AC004923.1, MTATP8P2, UBE2MP1, MT1L, TEKT4P2, AC009093.8, MTND4P24, MTCO1P2, SDAD1P1, PIGCP1, MTND4P35, MTCO1P40, MTND6P22, MTND6P4, MTCO1P22, FCGR2C, AC093809.1, MTCYBP16, MTCO1P53, HSPD1P4, AC139769.1, MTND5P11, MTCO2P22, MTCO2P2, RPL17P36, CBX3P9, MTCO2P12, MTCO3P22, MTATP6P2, MTCYBP35, MTCO1P12, MTND4P12, and MUC20P1;

[0060] Genes related to the 64-gene classifier for molecular typing of stage II-III colorectal cancer patients: NTSR1, CD300C, SLAMF7, POSTN, SERPINE1, DMRT2, CLDN9, MMP11, PLAC8L1, AQP5, PPP1R1A, SULF1, BEST3, EPHB6, ARHGEF4, TERT, CCDC114, HEATR4, TIFAB, CD163, MMP17, NGFR, KCNMB1, MAP6, IGFBP6, ACSBG1, C15orf48, PRRT2, THBS4, RBPMS2, CCBE1, EPB41L4A, PNCK, SLIT3, SALL4, C9orf163, SYCP3, DNAH3, LRP4, SMC1B, CYP2C19, FHDC1, SLC14A1, PCDHGB7, DSG1, ASAP2, COLQ, CRYBB3, COMP, PLXNB3, FBXL14, FUT3, CILP, COL1A2, GAS1, COL8A1, IL22RA2, HCN3, GPNMB, ISM1, MSR1, SPP1, FRMD3 and AMPH.

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] Example 1:

[0063] Screening of gene clusters related to the molecular typing system of stage II-III colorectal cancer:

[0064] On the Xinhua cohort II-III stage colorectal cancer RNA-seq dataset, for three types of genes, namely coding genes, non-coding genes, and pseudogenes, the top 20% genes with the largest absolute median difference were selected for each type. Univariate cox regression analysis was further performed on these top 20% highly variable genes. According to the criterion of p < 0.05, the genes with survival significance finally obtained were used as characteristic genes. Through the MOVICS program tool (see Lu, Meng et al, Bioinformatics. 2021 Apr 1; 36(22-23): 5539-5541), ten molecular subtyping algorithms built into the application package were used to cluster the characteristic genes of the three categories. By synthesizing the results of the ten methods, a subtyping system for Xinhua cohort II-III stage colorectal cancer was determined. Taking the observation of case death or tumor recurrence within 5 years in colorectal cancer cases as the observation event, Kaplan-Meier survival curve and log-rank test were used for survival analysis;

[0065] Results: A total of 207 coding genes, 32 non-coding genes, and 38 pseudogenes were screened as characteristic genes (see Table 1) and included in the subsequent clustering. By synthesizing the results of the ten clustering methods and according to two clustering evaluation indicators, namely Cluster Prediction Index and Gap Statistics, a three-subtype system (CS1, CS2, and CS3) was finally constructed. There were 146 cases (30.1%) in CS1 type, 112 cases (23.1%) in CS2 type, and 227 cases (46.8%) in CS3 type. These three subtypes were significantly correlated with the TNM stage (p = 0.0072, Chi-square test), lymph node metastasis (p = 0.0161, Fisher's exact test), recurrence rate (p = 8.66e-04, Chi-square test), and chemotherapy rate (p = 0.004, Chi-square test) of II-III stage patients. Importantly, the three-subtype system was significantly correlated with the overall survival (p = 0.002, log-rank test) and progression-free survival (p < 0.001, log-rank test) of colorectal cancer patients. Patients in CS2 type showed the worst prognosis in OS and DFS, while patients in CS3 type showed the best prognosis in both OS and DFS. Patients in CS1 type had a poor prognosis, especially more obvious in DFS. (Appendix Figure 1 )

[0066] Example 2:

[0067] Functional characteristics for evaluating the molecular subtyping of II-III stage colorectal cancer:

[0068] Using Gene Set Enrichment Analysis (GSEA) and equal enrichment analysis methods (see Subramanian, Tamayo, et al. (2005), PNAS 102, 15545 - 15550), the biological functions and pathways enriched in CS1 - CS3 types were evaluated, and by the Gene Set Variation Analysis (GSVA) method (see S, Castelo R et al. (2013), BMC Bioinformatics. 1471 - 2105 - 14 - 7), the activity scores of 50 classical cancer - related pathways in the Hallmark gene set were performed at the single - sample level to compare the differences in the activation of cancer - related signaling pathways among CS1 - CS3 types. In addition, the GSVA method was used to evaluate the enrichment levels of immune - related pathways and immune cell characteristics among CS1 - CS3 types;

[0069] Experimental results:

[0070] According to the standard test data obtained in Example 1, using the colorectal cancer molecular typing system described above, the characteristic genes in Table 1 were clustered using the MOVICS package. Combining the results of ten clustering methods, 485 cases were divided into CS1, CS2, and CS3 types;

[0071] The gene enrichment analysis was performed using the GSEA method and the pathway activity score was performed using the GSVA method. The results showed that CS1 type had the highest WNT pathway activity and the lowest immune activation level. In addition, CS1 type had moderate levels of cell cycle, mitosis, and proliferation - related pathway activities such as Myc; in CS2 type, mesenchymal - related signals such as epithelial - mesenchymal transition (EMT) and TGF - β were significantly activated, and pathways related to tumor invasion and metastasis such as hypoxia, angiogenesis, and cell adhesion showed that CS2 type had a higher malignant phenotype and invasion potential. In addition, a high activation of immune - and inflammation - related pathways such as interferon response, inflammatory response, and TNF - α was also observed in CS2 type, revealing the complex microenvironment and cell - cell interactions in CS2 type; CS3 type was specifically enriched in pathways such as fatty acid metabolism, bile acid metabolism, xenobiotic metabolism, and protein secretion, revealing its high metabolic capacity. In addition, CS3 type also showed activation in proliferation pathways such as Myc and PI3K and DNA damage repair - related pathways. Interestingly, there was a cross - activation phenomenon in CS1 and CS3 types in cell cycle and mitosis - related pathways (attached Figure 2 );

[0072] CS3 has a moderate degree of immune pathway enrichment. CS1 has the weakest activation of immune pathways, while CS2 shows the strongest enrichment in pathways such as Cytolytic activity, APC co-stimulation, and type II interferon response. At the same time, CS2 also shows significant enrichment in inhibitory pathways such as Check-point and APC co-inhibition. Further analysis of the abundance of immune cell infiltration in each subtype showed that there was significant infiltration of activated immune cells such as Natural killer cell, Activated B cell, and Activated dendritic cell in CS2, and there was also infiltration of inhibitory immune cells such as regulatory T cells and myeloid-derived suppressor cells (MDSC), which explained the coexistence of immune activation and inhibition in CS2. In contrast, CS1 presented an immune desert state with little infiltration of Activated CD8 T cell, Activated CD4 T cell, and Activated B cell, etc. CS3 showed moderate-intensity immune cell infiltration, and most immune cells had a certain degree of infiltration (attached Figure 2 );

[0073] Example 3:

[0074] For constructing a portable classifier for molecular typing of stage II-III colorectal cancer:

[0075] According to the molecular typing system of stage II-III colorectal cancer constructed in Example 1, according to logFC, the top 300 differentially expressed genes (p.adjust < 0.05) up-regulated or down-regulated were screened in CS1-CS3 respectively. After removing redundant genes, a total of 1297 genes were obtained. The 485 samples in the Xinhua cohort were randomly split into a training set and a test set at a ratio of 0.8:0.2. Based on 5-fold cross-validation, the optimal hyperparameter C was selected. On the training set, 1297 feature genes were incorporated into the support vector machine recursive feature elimination (SVM-RFE) model to calculate the importance of feature genes. In the SVM-RFE model, the last 10 features with the lowest importance were eliminated each time. After 109 iterations, 1090 feature genes with relatively low importance were eliminated until 207 important features were retained. The above was taken as one cycle;

[0076] Set random numbers, randomly split again to obtain a training set and a test set, and perform a new cycle for 100 times. Count the frequency of each feature gene appearing in the final 207-gene set during the 100 cycles. Screening high-frequency feature genes according to the standard that the frequency is greater than 80 to obtain stable and efficient predictive genes. Based on the high-frequency feature genes, a molecular typing classifier was constructed using the support vector machine algorithm;

[0077] Experimental results:

[0078] Using the scikit-learn library (v0.21.2) in Python (version 3), we applied a linear kernel support vector machine (SVM) to construct a subtype classifier. The hyperparameters C and the model were optimized through 5-fold cross-validation. We adopted the Monte Carlo cross-validation (MCCV) method for unbiased performance estimation and model optimization. To maximize the number of training and test samples, the dataset was first randomly divided into a training set (80%) and a test set (20%). The training set and the test set were randomly split 100 times, and the test performance (accuracy) and 207 most important features were recorded for each iteration;

[0079] In each iteration, we performed feature screening through the support vector machine-recursive feature elimination (SVM-RFE) algorithm. 10 unimportant genes were eliminated in each iteration, starting from 1297 genes, and iterated 109 times until 207 genes remained. Subsequently, the model containing 207 most important features was applied to the test data to estimate its accuracy;

[0080] Finally, the average accuracy of 100 MCCV iterations was reported, and genes that appeared more than 80 times in 100 iterations were selected for the final model construction (Figure 4E). The 64 key genes screened out constructed the final model (see Table 2), defined as CS-Classfier. We found that the average accuracy of the model was improved from 0.912 of the 207-gene model to 0.969 of CS-Classfier (Appended Figure 3 );

[0081] Example 4:

[0082] To evaluate the performance of molecular typing of stage II-III colorectal cancer in an external cohort:

[0083] The 64-gene support vector machine classifier constructed according to Example 3 was used to verify the functional characteristics and prognostic prediction performance of CS1-CS3 types in an external cohort. The external cohort included the TCGA cohort, the GSE39582 dataset, and the Swedish U-CAN cohort;

[0084] Experimental results:

[0085] The constructed support vector machine classifier could effectively divide the external cohort into three subtypes. The functional characteristics of the three subtypes were consistent with those in the Xinhua cohort in the external cohort. And in terms of recurrence-free survival, in the external cohort, the prognosis of CS3 type was shown to be good, while that of CS1 and CS2 types was poor (such as Figure 4 );

[0086] In a specific embodiment, the 64-gene classifier of the present invention can be used to determine the molecular subtypes of patients with stage II-III colorectal cancer, and the molecular subtypes of colorectal cancer patients can be divided into CS1, CS2, and CS3, respectively;

[0087] Those skilled in the art should understand that the molecular subtype classifier of the present invention is not limited to the combinations listed above. In view of the content disclosed in the present invention, those skilled in the art should be able to combine the genes related to the molecular subtype system of stage II-III colorectal cancer of the present invention with the reference genes, so as to obtain gene groups containing different combinations of genes, and these gene groups are also within the protection scope of the present invention;

[0088] Table 1: Molecular typing and clustering genes for stage II-III colorectal cancer: Gene type Gene name; Coding gene PLXNB3; Coding gene COMP; Coding gene PPP2R2C; Coding gene RBP2; Coding gene L1CAM; Coding gene MCEMP1; Coding gene HBB; Coding gene HERC3; Coding gene HBA2; Coding gene F5; Coding gene CRISPLD1; Coding gene PTPN20; Coding gene SFRP2; Coding gene RIMBP2; Coding gene ISL1; Coding gene MYBPC1; Coding gene DEFA5; Coding gene C8orf88; Coding gene CDH2; Coding gene CELF3; Coding gene PLPP4; Coding gene HBA1; Coding gene NFE2; Coding gene INSM1; Coding gene NPR3; Coding gene ERMN; Coding gene COL10A1; Coding gene KLK6; Coding gene AL049839.2; Coding gene AC004593.2; Coding gene TNMD; Coding gene KLK11; Coding gene ANKRD18A; Coding gene TENM3; Coding gene PRKAA2; Coding gene VIP; Coding gene FAM83A; Coding gene IL10; Coding gene TNFRSF19; Coding gene APOBEC3A; Coding gene SCG2; Coding gene THBS4; Coding gene GSTA1; Coding gene LRRC46; Coding gene GAP43; Coding gene CA9; Coding gene TIFAB; Coding gene MMP1; Coding gene ENPP3; Coding gene ZNF727; Coding gene CDK15; Coding gene THBS2; Coding gene KCNQ3; Coding gene SPTBN5; Coding gene SPP1; Coding gene MFAP5; Coding gene B4GALNT4; Coding gene PRELP; Coding gene ERFE; Coding gene RASSF9; Coding gene PCDHB2; Coding gene RGS6; Coding gene Z84488.1; Coding gene COL24A1; Coding gene PIGR; Coding gene TACSTD2; Coding gene DIRAS3; Coding gene NEB; Coding gene ST6GALNAC3; Coding gene PCDHB16; Coding gene ZNF334; Coding gene MAK; Coding gene SPOCK1; Coding gene PDZD4; Coding gene HOXD10; Coding gene WBP2NL; Coding gene RND2; Coding gene FNDC1; Coding gene MSI1; Coding gene CES1; Coding gene RLN2; Coding gene CCDC110; Coding gene SHC4; Coding gene UCHL1; Coding gene HOXD11; Coding gene BMP6; Coding gene CYP1B1; Coding gene INHBB; Coding gene TNFSF8; Coding gene BCL2L10; Coding gene FGF7; Coding gene SUSD2; Coding gene ZNF667;Coding gene NGFR; Coding gene ELAPOR2; Coding gene TYRP1; Coding gene MEDAG; Coding gene IGFBPL1; Coding gene PROM2; Coding gene NOVA1; Coding gene HSD11B1; Coding gene ST6GALNAC5; Coding gene NLGN4X; Coding gene TRPC4; Coding gene NOS2; Coding gene ESM1; Coding gene ANOS1; Coding gene COL8A1; Coding gene MYH7B; Coding gene RHCG; Coding gene FN1; Coding gene PRR36; Coding gene DIPK1B; Coding gene NOX4; Coding gene FAP; Coding gene SRPK3; Coding gene ADHFE1; Coding gene PPP1R1C; Coding gene RGS4; Coding gene PPP1R1A; Coding gene ASXL3; Coding gene GFPT2; Coding gene CXCL6; Coding gene F10; Coding gene POSTN; Coding gene SLFN13; Coding gene SERPINE1; Coding gene ALDH1A1; Coding gene ADAM12; Coding gene PKP1; Coding gene COL1A1; Coding gene CDH5; Coding gene RNF183; Coding gene VLDLR; Coding gene WFDC3; Coding gene GPR82; Coding gene PARP15; Coding gene SCG5; Coding gene CTHRC1; Coding gene ELOVL4; Coding gene CIART; Coding gene H3C4; Coding gene NTN1; Coding gene CARMIL2; Coding gene KLRD1; Coding gene FCGR2B; Coding gene CARD11; Coding gene COL1A2; Coding gene SULF1; Coding gene SLCO5A1; Coding gene LGALS2; Coding gene COL3A1; Coding gene STC1; Coding gene ST18; Coding gene HTRA3; Coding gene ARNT2; Coding gene INHBA; Coding gene ADAMTS4; Coding gene TNC; Coding gene DNAH5; Coding gene TIMP3; Coding gene COL15A1; Coding gene SEMA3D; Coding gene ADAM32; Coding gene ALPK3; Coding gene PLPPR4; Coding gene COL12A1; Coding gene IDO1; Coding gene NDST2; Coding gene DSEL; Coding gene VCAN; Coding gene GPR135; Coding gene COL5A1; Coding gene ANTXR1; Coding gene TNFRSF11B; Coding gene ZNF354C; Coding gene ADAMTS12; Coding gene LPL; Coding gene CDKN2A; Coding gene HSPB8; Coding gene ANKRD36C; Coding gene CLU; Coding gene GXYLT2; Coding gene TMEM17; Coding gene DTNA; Coding gene JCAD; Coding gene PTPRR; Coding gene STOX1; Coding gene ANKRD6;Coding gene OSMR; Coding gene CCN1; Coding gene COL5A2; Coding gene SPARC; Coding gene FBN1; Coding gene TTLL7; Coding gene SPAG4; Coding gene APC2; Coding gene ANGPT2; Coding gene COL18A1; Coding gene BX664615.2; Coding gene B3GNT10; Coding gene KCNH2; Coding gene MCC; Coding gene SLC2A3; Coding gene S1PR1; Coding gene ITGA5; Coding gene EFHD1; Non-coding gene AP001029.2; Non-coding gene LINC02432; Non-coding gene VAC14-AS1; Non-coding gene AC104534.1; Non-coding gene ZNF687-AS1; Non-coding gene LINC02532; Non-coding gene FLJ16779; Non-coding gene CLMAT3; Non-coding gene AL035701.1; Non-coding gene CHRM3-AS2; Non-coding gene LINC01871; Non-coding gene LINC00242; Non-coding gene AC020659.1; Non-coding gene HAR1A; Non-coding gene AL683813.1; Non-coding gene LINC02009; Non-coding gene AL133243.3; Non-coding gene FZD10-AS1; Non-coding gene AC004847.1; Non-coding gene CEBPB-AS1; Non-coding gene CERNA2; Non-coding gene SLCO4A1-AS1; Non-coding gene LINC00460; Non-coding gene LINC00501; Non-coding gene AC124248.1; Non-coding gene AC121761.2; Non-coding gene PROX1-AS1; Non-coding gene CR381653.1; Non-coding gene LINC02846; Non-coding gene AP005136.3; Non-coding gene ERICH6-AS1; Non-coding gene AL162458.1; Pseudogene BMS1P10; Pseudogene AP001992.1; Pseudogene CROCCP3; Pseudogene MST1L; Pseudogene AC024940.1; Pseudogene AC004923.1; Pseudogene MTATP8P2; Pseudogene UBE2MP1; Pseudogene MT1L; Pseudogene TEKT4P2; Pseudogene AC009093.8; Pseudogene MTND4P24; Pseudogene MTCO1P2; Pseudogene SDAD1P1; Pseudogene PIGCP1; Pseudogene MTND4P35; Pseudogene MTCO1P40; Pseudogene MTND6P22; Pseudogene MTND6P4; Pseudogene MTCO1P22; Pseudogene FCGR2C; Pseudogene AC093809.1; Pseudogene MTCYBP16; Pseudogene MTCO1P53; Pseudogene HSPD1P4; Pseudogene AC139769.1; Pseudogene MTND5P11; Pseudogene MTCO2P22;Pseudogene MTCO2P2; Pseudogene RPL17P36; Pseudogene CBX3P9; Pseudogene MTCO2P12; Pseudogene MTCO3P22; Pseudogene MTATP6P2; Pseudogene MTCYBP35; Pseudogene MTCO1P12; Pseudogene MTND4P12; Pseudogene MUC20P1;

[0089] Table 2: Genes of the molecular typing classifier for stage II-III colorectal cancer, gene type gene name: classifier gene NTSR1; classifier gene CD300C; classifier gene SLAMF7; classifier gene POSTN; classifier gene SERPINE1; classifier gene DMRT2; classifier gene CLDN9; classifier gene MMP11; classifier gene PLAC8L1; classifier gene AQP5; classifier gene PPP1R1A; classifier gene SULF1; classifier gene BEST3; classifier gene EPHB6; classifier gene ARHGEF4; classifier gene TERT; classifier gene CCDC114; classifier gene HEATR4; classifier gene TIFAB; classifier gene CD163; classifier gene MMP17; classifier gene NGFR; classifier gene KCNMB1; classifier gene MAP6; classifier gene IGFBP6; classifier gene ACSBG1; classifier gene C15orf48; classifier gene PRRT2; classifier gene THBS4; classifier gene RBPMS2; classifier gene CCBE1; classifier gene EPB41L4A; classifier gene PNCK; classifier gene SLIT3; classifier gene SALL4; classifier gene C9orf163; classifier gene SYCP3; classifier gene DNAH3; classifier gene LRP4; classifier gene SMC1B; classifier gene CYP2C19; classifier gene FHDC1; classifier gene SLC14A1; classifier gene PCDHGB7; classifier gene DSG1; classifier gene ASAP2; classifier gene COLQ; classifier gene CRYBB3; classifier gene COMP; classifier gene PLXNB3; classifier gene FBXL14; classifier gene FUT3; classifier gene CILP; classifier gene COL1A2; classifier gene GAS1; classifier gene COL8A1; classifier gene IL22RA2; classifier gene HCN3; classifier gene GPNMB; classifier gene ISM1; classifier gene MSR1; classifier gene SPP1; classifier gene FRMD3; classifier gene AMPH;

[0090] Table 3: Reference genes, gene type, gene name: reference gene ACTB; reference gene GAPDH; reference gene GUSB; reference gene TFRC; reference gene MRPL19; reference gene PSMC4; reference gene SF3A1.

[0091] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as within the protection scope of this template.

Claims

1. A molecular typing method for stage II-III colorectal cancer, characterized in that, It includes the following steps: Step 1: Perform transcriptome expression analysis on the RNA-seq samples of stage II-III colorectal cancer patients, and generate transcriptome expression profile data according to coding genes, non-coding genes, and pseudogenes; Step 2: Based on the transcriptome expression profile data, perform highly variable gene screening and survival analysis to obtain characteristic genes and perform clustering analysis, and comprehensively analyze the results of ten clustering algorithms to identify the molecular subtypes of stage II-III colorectal cancer; Step 3: Provide the specific gene expression characteristics and enriched biological functions of each molecular subtype.

2. The molecular typing method for stage II-III colorectal cancer according to claim 1, wherein The transcriptome expression analysis uses RNA-seq technology, and the clustering analysis uses ten molecular typing algorithms built into the MOVICS package.

3. The molecular typing method for stage II-III colorectal cancer according to claim 2, characterized in that, The molecular subtypes include any one of CS1 type, CS2 type, and CS3 type; When the specific expression characteristics obtained by clustering analysis are Wnt-dominated, immune desert, and low differentiation characteristics, it is identified as CS1 type; When the specific expression characteristics obtained by clustering analysis are interstitial, TGF-β signal, activated and inhibitory immune cell infiltration expression characteristics, it is identified as CS2 type; When the specific expression characteristics obtained by clustering analysis are reproductive signal, metabolically active, and high differentiation expression characteristics, it is identified as CS3 type.

4. A molecular typing system for stage II-III colorectal cancer, applicable to a molecular typing method for stage II-III colorectal cancer as described in claims 1 to 3, characterized in that, It includes a transcriptomics analysis module for performing transcriptome expression analysis on the RNA-seq samples of colorectal cancer patients to obtain transcriptome expression profile data, and the transcriptome expression analysis uses RNA-seq technology; A molecular subtype typing module for performing clustering analysis based on the transcriptome expression profile data, analyzing the specific transcriptome expression characteristics obtained by the clustering analysis, and identifying the molecular subtypes of colorectal cancer. The clustering analysis uses ten molecular typing algorithms built into the MOVICS package; A typing output module for outputting the molecular typing results and providing the specific gene expression characteristics of each molecular subtype.

5. A computer-readable storage medium, comprising a computer program, characterized in that, When the computer program is run by a processor, it executes the colorectal cancer molecular typing method according to any one of claims 1 to 3; It also includes a terminal, which includes a memory and a processor. The memory contains a computer program, and is characterized in that when the computer program is run by the processor, it executes the colorectal cancer molecular typing method according to any one of claims 1 to 3.

6. An apparatus for applying a molecular typing method for stage II - III colorectal cancer, which is applicable to the molecular typing method for colorectal cancer according to any one of claims 1 to 3, characterized in that, It includes a kit, and the kit includes reagents required for detecting any one or more of CS1-CS3 types of samples; It includes reagents required for RNA-seq technology and / or reagents required for protein extraction.

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