Construction and Validation of a m7G-Related Gene-Based Prognostic Model for Gastric Cancer

Abstract

Background: Gastric cancer (GC) is one of the most common malignant tumors of the digestive system. Chinese cases of GC account for about 40% of the global rate, with approximately 1.66 million people succumbing to the disease each year. Despite the progress made in the treatment of GC, most patients are diagnosed at an advanced stage due to the lack of obvious clinical symptoms in the early stages of GC, and their prognosis is still very poor. The m7G modification is one of the most common forms of base modification in post-transcriptional regulation, and it is widely distributed in the 5' cap region of tRNA, rRNA, and eukaryotic mRNA.

Methods: RNA sequencing data of GC were downloaded from The Cancer Genome Atlas. The differentially expressed m7G-related genes in normal and tumour tissues were determined, and the expression and prognostic value of m7G-related genes were systematically analysed. We then built models using the selected m7G-related genes with the help of machine learning methods.The model was then validated for prognostic value by combining the receiver operating characteristic curve (ROC) and forest plots. The model was then validated on an external dataset. Finally, quantitative real-time PCR (qPCR) was performed to detect gene expression levels in clinical gastric cancer and paraneoplastic tissue.

Results: The model is able to determine the prognosis of GC samples quantitatively and accurately. The ROC analysis of model has an AUC of 0.761 and 0.714 for the 3-year overall survival (OS) in the training and validation sets, respectively. We determined a correlation between risk scores and immune cell infiltration and concluded that immune cell infiltration affects the prognosis of GC patients. NUDT10, METTL1, NUDT4, GEMIN5, EIF4E1B, and DCPS were identified as prognostic hub genes and potential therapeutic agents were identified based on these genes.

Conclusion: The m7G-related gene-based prognostic model showed good prognostic discrimination. Understanding how m7G modification affect the infiltration of the tumor microenvironment (TME) cells will enable us to better understand the TME's anti-tumor immune response, and hopefully guide more effective immunotherapy methods.

Keywords: N7-methyladenosine; bioinformatics; gastric cancer; overall survival; prognostic model.

Figure 1

Flow diagram.

Figure 2

(A) Heatmap of the differential m7G-related genes expression in the two groups. (B) The relationship between m7G -related DEGs. ns, p > 0.05; *p < 0.05; **P <0.01; ***p < 0.001.

Figure 3

(A) Univariate Cox regression analysis. (B) Processes of SVM model fitting.

Figure 4

(A) The distribution and median value of the risk scores in the derivation cohort. (B) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the derivation cohort. (C) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the derivation cohort. (D) PCA plot of the derivation cohort.

Figure 5

(A) Results of qRT-PCR analysis. *p < 0.05; **P <0.01; ***p < 0.001 (B) The distribution and median value of the risk scores in the derivation cohort. (C) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the validation cohort. (D) AUC of time-dependent ROC curves verified the prognostic performance of the risk score in the validation cohort.

Figure 6

The scores of 16 immune cells (A) and 13 immune-related functions (B) are displayed in boxplots. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ns, P > 0.05.

Figure 7

The relationship between genes and immune cells. The color and size of the datapoint represent the direction and significance (P-value) of the correlation.