Data mining combines bioinformatics discover immunoinfiltration-related gene SERPINE1 as a biomarker for diagnosis and prognosis of stomach adenocarcinoma

Abstract

Stomach adenocarcinoma (STAD) is a type of cancer which often at itsadvanced stage apon diagnosis and mortality in clinical practice. Several factors influencethe prognosis of STAD, including the expression and regulation of immune cells in the tumor microenvironment. We here investigated the biomarkers related to the diagnosis and prognosis of gastric cancer, hoping to provide insights for the diagnosis and treatment of gastric cancer in the future. STAD and normal patient RNA sequencing data sets were accessed from the cancer genome atlas (TCGA database). Differential genes were determined and obtained by using the R package DESeq2. The stromal, immune, and ESTIMATE scores are calculated by the ESTIMATE algorithm, followed by the modular genes screening using the R package WGCNA. Subsequently, the intersection between the modular gene and the differential gene was taken and the STRING database was used for PPI network module analysis. The R packages clusterProfiler, enrichplot, and ggplot2 were used for GO and KEGG enrichment analysis. Cox regression analysis was used to screen survival-related genes, and finally, the R package Venn Diagram was used to take the intersection and obtain 7 hub genes. The time-dependent ROC curve and Kaplan-Meier survival curve were used to find the SERPINE1 gene, which plays a critical role in prognosis. Finally, the expression pattern, clinical characteristics, and regulatory mechanism of SERPINE1 were analyzed in STAD. We revealed that the expression of SERPINE1 was significantly increased in the samples from STAD compared with normal samples. Cox regression, time-dependent ROC, and Kaplan-Meier survival analyses demonstrated that SERPINE1 was significantly related to the adverse prognosis of STAD patients. The expression of SERPINE1 increased with the progression of T, N, and M classification of the tumor. In addition, the results of immune infiltration analysis indicated that the immune cells' expression were higher in high SERPINE1 expression group than that in low SERPINE1 expression group, including CD4⁺ T cells, B cells, CD8⁺ T cells, macrophages, neutrophils and other immune cells. SERPINE1 was closely related to immune cells in the STAD immune microenvironment and had a synergistic effect with the immune checkpoints PD1 and PD-L1. In conclusion, we proved that SERPINE1 is a promising prognostic and diagnostic biomarker for STAD and a potential target for immunotherapy.

Figure 1

Identification of module genes associated with both clustering and immunity in the WGCNA. (A) Volcano plot of differential analysis. (B) Analysis of network topology for soft powers. (C) Differential genes dendrogram and trait heatmap. (D) Dendrogram and genes module colors. (E) Heatmap between module eigengenes and ESTIMATE results. (F) Scatter plot of module eigengenes in the tan module. The figures were created by R4.1.2 (https://www.r-project.org/).

Figure 2

Venn plots, Protein–protein interaction network, enrichment analysis of GO and KEGG for DEGs and Univariate Cox regression analysis. (A) Venn plot of the intersection of differential genes and tan modular genes. (B) 54 intersection genes interaction network constructed with the nodes with interaction confidence value > 0.4. (C) GO enrichment analysis of 54 intersection genes with p < 0.05. (D) KEGG enrichment analysis of 54 intersection genes with p < 0.05. Permission has been obtained from Kanehisa laboratories for using KEGG pathway database. (E) 1504 DEGs single Univariate Cox regression analysis, listing the top significant factors with p < 0.01. (F) The Venn plot of the intersection of the intersection gene was obtained from the A diagram, and the intersection gene was obtained from the E diagram.

Figure 3

Survival and Prognostic Analysis graphs and heat map for correlation analysis of hub genes. (A–G) Kaplan–Meier survival curves of 7 hub genes. The horizontal axis indicates the overall survival time in months, and the vertical axis represents the survival rate. P value < 0.05 was considered statistically significant. (H–N) Time-dependent ROC curves of 7 hub genes. Time-dependent ROC curves illustrate the prognostic value of the7 hub genes. (O) Heat map of correlation between 7 hub genes and immune cell. The heatmap was created by using “pheatmap” package in R4.1.2 (https://www.r-project.org/).

Figure 4

Expression and prognosis of SERPINE1 in pan-cancer. (A) Expression of SERPINE1 in pan-cancer. (B) Prognosis of SERPINE1 in pan-cancer. The p values are labeled using asterisks (*, p < 0.05; **, p < 0.01; ***, p < 0.001; -, not significant, p > 0.05).

Figure 5

The expression of SERPINE1 in tumor and normal samples and its correlation with clinical indicators. (A) Differences in the expression of SERPINE1 in tumor tissues and normal tissues. (B) Gender. (C) Age. (D–G) The correlation between the expression of SERPINE1 and clinicopathological staging. Wilcoxon rank sum or Kruskal–Wallis rank sum test served as the statistical significance test.

Figure 6

Comparison of Immune Cell Proportion and Immune Score between High and Low Expression of SERPINE1. (A) Bar chart of the proportion of 21 immune cells in STAD tumor samples. (B) The correlation between the expression of SERPINE1 and the stromal score. (C) The correlation between the expression of SERPINE1 and the immune score. (D) The correlation between the expression of SERPINE1 and the estimate score. (E) The correlation between the expression of SERPINE1 and the tumor purity. (F) The correlation between the expression of SERPINE1 and the proportion of immune cells. The P values are labeled using asterisks (^*p < 0.05, ^**p < 0.01, ^***p < 0.001, ns, not significant, p > 0.05).

Figure 7

Expression and immunoinfiltration analysis of SERPINE1 in STAD. (A) The correlation between the expression of SERPINE1 and the expression of immune cells. (B) Correlation of SERPINE1 expression with immune infiltration level in STAD.

Figure 8

Correlation analysis between SERPINE1 and immune checkpoints. (A) The relationship between the high and low expression of SERPINE1 and the expression of PDCD1. (B) The relationship between the high and low expression of SERPINE1 and the expression of CD274. (C) The relationship between the high and low expression of SERPINE1 and the expression of PDCD1LG2. (D) The correlation between the expression of SERPINE1 and the level of PDCD1. (E) The correlation between the expression of SERPINE1 and the level of CD274. (F) The correlation between the expression of SERPINE1 and the level of PDCD1LG2. (G) High expression of SERPINE1 and high and low expression of PDCD1 in STAD patients stratified by Kaplan–Meier survival analysis. (H) High expression of SERPINE1 and high and low expression of CD274 in STAD patients stratified by Kaplan–Meier survival analysis. (I) High expression of SERPINE1 and high and low expression of PDCD1LG2 in STAD patients stratified by Kaplan–Meier survival analysis. p < 0.05 was considered statistically significant.

Figure 9

Functional enrichment analysis of SERPINE1 related genes in STAD. (A) GO:BP. (B) GO:CC. (C) GO:MF. (D) KEGG. The terms in red were the mainly enriched GO terms and KEGG pathway. Permission has been obtained from Kanehisa laboratories for using KEGG pathway database.