Combination of glucose metabolism molecular markers for colorectal cancer risk prediction and its application

Abstract

The present invention provides a prognostic model of genes related to sugar metabolism in colorectal cancer, and a construction method and application thereof. The model is correlated with disease characteristics such as TNM analysis, classical gene mutations, and immune infiltration, providing a new direction for prognostic judgment and treatment target selection.

Description

Technical Field

[0001] The present invention belongs to the technical field of tumor markers and biomedical detection, and specifically relates to a prognostic model of genes related to sugar metabolism of colorectal cancer, and a construction method and application thereof. Background Art

[0002] Colorectal cancer is the second most common cancer in women and the third most common cancer in men worldwide. Approximately 10% of cancer deaths are caused by colorectal cancer, which is the second leading cause of cancer death and one of the global public health challenges. Currently, the treatment and prognosis of colorectal cancer patients are based on the TNM staging system, but there is still a lack of reliable and accurate biomarkers for screening and prognostic judgment. Recently, it has been considered that clinically informative gene analysis is an important step in developing effective treatment strategies using molecular stratification. Therefore, it is crucial to develop more accurate models to predict cancer prognosis and treatment outcomes. Alterations in glucose metabolism during cancer occurrence, progression, and treatment resistance are widely considered to be hallmarks of the disease. Emerging evidence suggests that targeting glucose metabolism, intermittent fasting, and caloric restriction can enhance the effects of targeted therapy and immunotherapy. Disturbances in glucose metabolism have great clinical significance in colorectal cancer progression and immunotherapy sensitivity. Better understanding of the reprogramming profile of glucose metabolism-related genes and identifying new prognostic risk models for colorectal cancer while exploring the connection between tumor immunity and disorders of glucose metabolism may provide new ideas for treatment options for colorectal cancer patients.

[0003] At present, colorectal cancer is mainly predicted and assisted in diagnosis and treatment decisions based on the AJCC-UICC tumor node metastasis (TNM) staging system in clinical practice. With the increasing research on the tumor microenvironment and carcinogenesis mechanism of colorectal cancer and the clarification of key molecules in recent years, a large number of new diagnostic models have emerged, such as markers such as peripheral blood DNA methylation and lncRNA methylation; some transcriptome markers such as lncRNA, microRNA and circRNA; multi-factor risk analysis using whole genome data modeling, immune-related gene prognostic models, energy metabolism-related gene prognostic models, etc.

[0004] It can be found from clinical data that most stage II CRC patients receive surgical treatment without adjuvant chemotherapy, based on the treatment strategy adopted according to TNM staging. However, nearly 15% of these patients will experience tumor recurrence and die from disease progression. A considerable number of patients who were assessed as low-risk still later experienced recurrence and progression, indicating that these patients did not respond the same way. A large number of new diagnostic models that emerged later remained more at the stage of database construction and verification, and there were few related experimental verifications combined with the key mechanisms of tumor occurrence and development, which could not provide a clear theoretical basis. At the same time, the focus was relatively single, and it was impossible to fully apply or accurately diagnose and prognose. Summary of the invention

[0005] Reprogramming sugar metabolism is a hallmark of the occurrence and development of human cancer. This application aims to explore the comprehensive overview and specific mechanisms of sugar metabolism-related genes related to the prognosis of colorectal cancer, and at the same time determine a new prognostic risk model for colorectal cancer. Combined with relevant analyses such as mutations and immune infiltration, this application provides a new prognostic judgment model and treatment options for colorectal cancer patients.

[0006] In order to improve the above technical problems, on the one hand, the present invention provides a method for constructing a prognostic risk model of genes related to sugar metabolism in colorectal cancer, which comprises the following steps:

[0007] (1) Collect transcriptome data and clinical information of colorectal cancer patients from The Cancer Genome Atlas (TCGA) public database;

[0008] (2) Cox univariate regression analysis was performed and specific glucose metabolism-related genes (GRGs) were identified;

[0009] (3) Cox multivariate regression analysis was performed, and then the risk score was outputted, and the risk score was calculated according to the mRNA expression level of the aforementioned GRG according to the equation “risk score = regression coefficient × mRNA expression of the corresponding gene”;

[0010] (4) Use risk scores to divide patients into different risk clusters.

[0011] In one embodiment of the present invention, the aforementioned method further comprises step (5), verifying the accuracy of the model through ROC curve and survival curve.

[0012] On the other hand, the present invention provides a colorectal cancer glucose metabolism-related gene prognosis risk model obtained by the aforementioned construction method.

[0013] In one embodiment of the present invention, the specific sugar metabolism-related genes in the aforementioned model are a combination of the following 19 genes: ANKZF1, CHST1, CLDN9, FKBP4, GLCE, GPC1, IDUA, NOL3, P4HA1, PMM2, PPP2CB, RBCK1, SPAG4, STC2, ALDH1A3, ENO3, G6PC2, NDC1 and SLC2A3.

[0014] In one embodiment of the present invention, the risk score in the aforementioned model = (0.260479*ANKZF1 gene expression level) + (0.183322*CHST1 gene expression level) + (0.082092*CLDN9 gene expression level) + (0.422546*FKBP4 gene expression level) - (0.05152*GLCE gene expression level) + (0.058003*GPC1 gene expression level) + (0.033317*IDUA gene expression level) + (0.456214*NOL3 gene expression level) + (0.547072*P4HA1 gene expression level) )-(0.31426*PMM2 gene expression level)-(0.324*PPP2CB gene expression level)+(0.198214*RBCK1 gene expression level)+(0.065966*SPAG4 gene expression level)+(0.129602*STC2 gene expression level)+(0.231511*ALDH1A3 gene expression level)+(0.465562*ENO3 gene expression level)-(0.92638*G6PC2 gene expression level)-(0.07933*NDC1 gene expression level)+(0.093052*SLC2A3 gene expression level).

[0015] In one embodiment of the present invention, the median of the aforementioned risk score is used as a threshold to divide colorectal cancer patients into a high-risk group and a low-risk group.

[0016] On the other hand, the present invention provides the use of the aforementioned model in the preparation of a product for diagnosing or assisting in diagnosing the overall survival rate of patients with colorectal cancer.

[0017] In another aspect, the present invention provides use of the aforementioned model in preparing a kit.

[0018] In one embodiment of the present invention, the present invention provides use of the aforementioned model in a kit for predicting the prognosis of colorectal cancer.

[0019] In one embodiment of the present invention, the aforementioned colorectal cancer prognosis includes risk assessment and immunotherapy efficacy assessment.

[0020] On the other hand, the present application provides the use of a reagent for detecting the expression level of GRG in the preparation of a kit for predicting the prognosis risk of colorectal cancer, wherein the GRG is a combination of the following 19 genes: ANKZF1, CHST1, CLDN9, FKBP4, GLCE, GPC1, IDUA, NOL3, P4HA1, PMM2, PPP2CB, RBCK1, SPAG4, STC2, ALDH1A3, ENO3, G6PC2, NDC1 and SLC2A3.

[0021] In one embodiment of the present invention, the reagent is a primer or a probe that specifically binds to the gene.

[0022] In one embodiment of the present invention, the kit includes instructions for calculating a GRG risk score, wherein the GRG risk score = (0.260479*ANKZF1 gene expression level) + (0.183322*CHST1 gene expression level) + (0.082092*CLDN9 gene expression level) + (0.422546*FKBP4 gene expression level) - (0.05152*GLCE gene expression level) + (0.058003*GPC1 gene expression level) + (0.033317*IDUA gene expression level) + (0.456214*NOL3 gene expression level) + (0.547072*P 4HA1 gene expression level)-(0.31426*PMM2 gene expression level)-(0.324*PPP2CB gene expression level)+(0.198214*RBCK1 gene expression level)+(0.065966*SPAG4 gene expression level)+(0.129602*STC2 gene expression level)+(0.231511*ALDH1A3 gene expression level)+(0.465562*ENO3 gene expression level)-(0.92638*G6PC2 gene expression level)-(0.07933*NDC1 gene expression level)+(0.093052*SLC2A3 gene expression level).

[0023] Beneficial Effects

[0024] This application describes for the first time a comprehensive overview of genes related to sugar metabolism in colorectal cancer, and uses the database to establish and verify the effectiveness of the prognostic model. At the same time, the model is correlated with disease characteristics such as TNM analysis, classic gene mutations, and immune infiltration, providing a new direction for prognosis judgment and treatment target selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Shown is a prognostic model developed based on CRC carbohydrate metabolism-related genes (GRGs). Figure 1 (AB) 19 GRGs were identified by LASSOCox regression model analysis; (C) Colorectal cancer risk score distribution and survival status based on the prognostic model; (D) ROC curve of the prognostic model constructed based on 19 GRGs; (EF) Survival curves of high-risk and low-risk groups.

[0026] Figure 2 The relationship between prognostic factors and GRG risk score is shown. Figure 2(AD) show the relationship between CRC stage, tumor size, distant metastasis and lymph node metastasis and GRG risk score; (E) shows the correlation between clinical pathological indicators such as tumor size, distant metastasis, lymph node metastasis and GRG risk score and clinical pathology; (F) shows the univariate Cox analysis of prognostic factors; (G) shows the multivariate Cox analysis of prognostic factors.

[0027] Figure 3 The efficiency of validating the prognostic model using the dataset is shown. Figure 3 (A) shows the distribution of colorectal cancer risk scores and survival status based on the prognostic model in the validation dataset; (B) shows the survival curves of high-risk and low-risk groups; (C) PCA distribution of whole transcriptome data of GRG high-risk and low-risk groups; (D) ROC curve of the validation dataset based on the prognostic model; (EF) Univariate and multivariate Cox analysis of prognostic factors; (G) Relationship between CRC stage, tumor size, distant metastasis and lymph node metastasis and GRG risk score.

[0028] Figure 4 Shown is a Normgram prediction model constructed using clinical pathological indicators and GRG risk score. Figure 4 (A) is the nomogram for constructing survival prediction; (B) is the calibration curve showing the accuracy of the nomogram; (C) is the ROC curve based on the nomogram prediction model; (DE) univariate and multivariate Cox analysis of prognostic factors and nomogram scores. DETAILED DESCRIPTION

[0029] The technical scheme of the present invention will be further described in detail below in conjunction with specific embodiments. It should be understood that the following embodiments are only exemplary descriptions and explanations of the present invention and should not be construed as limiting the scope of protection of the present invention. All technologies implemented based on the above content of the present invention are included in the scope that the present invention is intended to protect.

[0030] definition

[0031] As used herein, a carbohydrate metabolism-related gene (GRG) refers to a nucleic acid encoding all or part of a carbohydrate metabolism-related protein or a variant thereof.

[0032] Unless otherwise specified, the raw materials and reagents used in the following examples are commercially available or can be prepared by known methods.

[0033] Example 1: Establishment and verification of LASSO regression model for CRC glucose metabolism-related characteristic genes

[0034] 1. Data download

[0035] Transcriptome data and clinical information were obtained from The Cancer Genome Atlas (TCGA-COAD dataset) and Gene Expression Omnibus (GEO, GSE39582 dataset).

[0036] 2. LASSOCox analysis

[0037] The mRNA expression of 297 genes related to CRC glucose metabolism (Glycometabolism-related gene, GRG, obtained by downloading and collating the "glycometabolism" related gene list from the Reactome database) was used as the initial data (from TCGA-COAD), and the differences in glucose metabolism-related genes between CRC patients (n=476) and normal people (n=41) were analyzed. 152 differential genes were found. Using LASSO regression and glmnet software packages, the 152 differential genes were used to construct a risk model based on univariate and multivariate Cox regression analysis, from which 19 GRG genes were identified for calculating risk scores. The equation for calculating the risk score is "Risk score = regression coefficient × mRNA expression of the corresponding gene". The median of the GRG risk score is used to divide cases into Risk H and Risk L The cutoff value for the category.

[0038] The genes finally determined for constructing the risk scoring model included the following 19 genes: ANKZF1, CHST1, CLDN9, FKBP4, GLCE, GPC1, IDUA, NOL3, P4HA1, PMM2, PPP2CB, RBCK1, SPAG4, STC2, ALDH1A3, ENO3, G6PC2, NDC1, and SLC2A3. Risk score = (0.260479*ANKZF1 gene expression level) + (0.183322*CHST1 gene expression level) + (0.082092*CLDN9 gene expression level) + (0.422546*FKBP4 gene expression level) + (-0.05152*GLCE gene expression level) + (0.058003*GPC1 gene expression level) + (0.033317*IDUA gene expression level) + (0.456214*NOL3 gene expression level) + (0.547072*P4HA1 gene expression level) - (0.3142 6*PMM2 gene expression level)-(0.324*PPP2CB gene expression level)+(0.198214*RBCK1 gene expression level)+(0.065966*SPAG4 gene expression level)+(0.129602*STC2 gene expression level)+(0.231511*ALDH1A3 gene expression level)+(0.465562*ENO3 gene expression level)-(0.92638*G6PC2 gene expression level)-(0.07933*NDC1 gene expression level)+(0.093052*SLC2A3 gene expression level).

[0039] The survivalROC package was used to analyze the survival results using the KM method and ROC curve. The R software was used to perform principal component analysis (PCA), plot and visualize the survival curve, cluster heatmap (pheatmap) and operating characteristic (ROC curve).

[0040] The LASSOCox regression model is Figure 1 As shown in AB, according to this model, Risk H The mortality rate of the group was higher than that of the Risk L Group (such as Figure 1 C). Among them, the AUCs of the GRG risk scores obtained according to the ROC curve for one, two, and three years were 0.760, 0.766, and 0.769, respectively (as shown in Figure 1 D). In addition, the survival curve shows that Risk H Patients had significantly worse outcomes, with shorter overall survival (OS) and lower progression-free survival (PFS) (eg Figure 1 EF).

[0041] 3. GRG risk score as an independent predictor of CRC patients

[0042] The GRG risk score level increased significantly and progressively with the increase of CRC stage, tumor size, lymph node metastasis, and distant metastasis (e.g. Figure 2 AD). At the same time, there is a significant correlation between the risk score and the clinical pathological stage, and it is better than some clinical pathological indicators (such as Figure 2 In univariate and multivariate Cox analyses, the GRG risk score was still not associated with OS (as shown in Figure 5E). Figure 2 FG).

[0043] The GSE39582 dataset was used to verify the efficiency of the prognostic model (e.g. Figure 3 Risk H The mortality rate of the group was higher than that of the Risk L The higher the group, the shorter the OS (such as Figure 3 According to the ROC curve, the AUC of GRG score reached 0.704 (one year), 0.695 (two years) and 0.685 (three years) (as shown in Figure AB). Figure 3 D). Multivariate and univariate Cox regression analyses also showed that GRG risk score was an independent predictor (as shown in Figure 3 F).

[0044] Example 2: Establishment and verification of Normgram regression model for CRC glucose metabolism-related characteristic genes

[0045] Normgram prediction models were constructed using clinical parameters and GRG risk scores to evaluate the predictive accuracy of GRG (e.g. Figure 4 A). The nomogram showed highly consistent predictions based on the calibration curve (e.g. Figure 4 The AUCs of the nomogram and GRG characteristic risk score were 0.843 and 0.833, respectively, which were higher than those of clinical pathological parameters (such as Figure 4 In addition, univariate and multivariate Cox regression analysis identified the nomogram score as an independent risk factor for CRC prognosis (as shown in Figure 2A). Figure 4 DE).

[0046] The above is an explanation of the embodiments of the present invention. However, the present invention is not limited to the above embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A prognostic risk model for carbohydrate metabolism-related genes in colorectal cancer, wherein the specific carbohydrate metabolism-related genes in the model are a combination of the following 19 genes: ANKZF1, CHST1, CLDN9, FKBP4, GLCE, GPC1, IDUA, NOL3, P4HA1, PMM2, PPP2CB, RBCK1, SPAG4, STC2, ALDH1A3, ENO3, G6PC2, NDC1 and SLC2A3, and the risk score in the model = (0.260479*ANKZF1 gene expression level) + (0.183322*CHST1 gene expression level) + (0.082092*CLDN9 gene expression level) + (0.422546*FKBP4 gene expression level) - (0.05152*GLCE gene expression level) + (0.058003*GPC1 (0.033317*IDUA gene expression level) + (0.456214*NOL3 gene expression level) + (0.547072*P4HA1 gene expression level) - (0.31426*PMM2 gene expression level) - (0.324*PPP2CB gene expression level) + (0.198214*RBCK1 gene expression level) + (0.065966*SPAG4 gene expression level) + (0.129602*STC2 gene expression level) + (0.231511*ALDH1A3 gene expression level) + (0.465562*ENO3 gene expression level) - (0.92638*G6PC2 gene expression level) - (0.07933*NDC1 gene expression level) + (0.093052*SLC2A3 gene expression level).

2. Use of the model according to claim 1 in the preparation of a product for diagnosing or assisting diagnosis of the overall survival rate of patients with colorectal cancer.

3. Use of the model according to claim 1 in preparing a kit.

4. The use according to claim 3, wherein the kit is a kit for predicting the prognosis of colorectal cancer.

5. According to the use according to claim 3 or 4, the colorectal cancer prognosis includes risk assessment and immunotherapy efficacy assessment.

6. Use of a reagent for detecting GRG expression in the preparation of a kit for predicting the prognosis risk of colorectal cancer, wherein the GRG is a combination of the following 19 genes: ANKZF1, CHST1, CLDN9, FKBP4, GLCE, GPC1, IDUA, NOL3, P4HA1, PMM2, PPP2CB, RBCK1, SPAG4, STC2, ALDH1A3, ENO3, G6PC2, NDC1 and SLC2A3, and the reagent is a primer or probe that specifically binds to the gene. The kit includes instructions for calculating the GRG risk score, and the GRG risk score = (0.260479*ANKZF1 gene expression level) + (0.183322*CHST1 gene expression level) + (0.082092*CLDN9 gene expression level) + (0.422546*FKBP4 gene expression level) - (0.05152*GLC E gene expression level)+(0.058003*GPC1 gene expression level)+(0.033317*IDUA gene expression level)+(0.456214*NOL3 gene expression level)+(0.547072*P4HA1 gene expression level)-(0.31426*PMM2 gene expression level)-(0.324*PPP2CB gene expression level)+(0.198214*RBCK1 gene expression level) )+(0.065966*SPAG4 gene expression level)+(0.129602*STC2 gene expression level)+(0.231511*ALDH1A3 gene expression level)+(0.465562*ENO3 gene expression level)-(0.92638*G6PC2 gene expression level)-(0.07933*NDC1 gene expression level)+(0.093052*SLC2A3 gene expression level).

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