胃癌关键标志物和免疫细胞浸润特征的生物信息学分析。

Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer.

机构信息

School of Pharmacy, Tianjin University of Traditional Chinese Medicine, Tianjin, China.

School of Pharmacy, Jiangxi University of Traditional Chinese Medicine, Nanchang, China.

出版信息

Front Immunol. 2023 Jun 9;14:1202529. doi: 10.3389/fimmu.2023.1202529. eCollection 2023.

DOI:10.3389/fimmu.2023.1202529

PMID:37359529

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10288199/

Abstract

BACKGROUND

Gastric cancer (GC) is the fifth most common cancer and the second leading cause of cancer-related deaths worldwide. Due to the lack of specific markers, the early diagnosis of gastric cancer is very low, and most patients with gastric cancer are diagnosed at advanced stages. The aim of this study was to identify key biomarkers of GC and to elucidate GC-associated immune cell infiltration and related pathways.

METHODS

Gene microarray data associated with GC were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were analyzed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia, Gene Set Enrichment Analysis (GSEA) and Protein-Protein Interaction (PPI) networks. Weighted gene coexpression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to identify pivotal genes for GC and to assess the diagnostic accuracy of GC hub markers using the subjects' working characteristic curves. In addition, the infiltration levels of 28 immune cells in GC and their interrelationship with hub markers were analyzed using ssGSEA. And further validated by RT-qPCR.

RESULTS

A total of 133 DEGs were identified. The biological functions and signaling pathways closely associated with GC were inflammatory and immune processes. Nine expression modules were obtained by WGCNA, with the pink module having the highest correlation with GC; 13 crossover genes were obtained by combining DEGs. Subsequently, the LASSO algorithm and validation set verification analysis were used to finally identify three hub genes as potential biomarkers of GC. In the immune cell infiltration analysis, infiltration of activated CD4 T cell, macrophages, regulatory T cells and plasmacytoid dendritic cells was more significant in GC. The validation part demonstrated that three hub genes were expressed at lower levels in the gastric cancer cells.

CONCLUSION

The use of WGCNA combined with the LASSO algorithm to identify hub biomarkers closely related to GC can help to elucidate the molecular mechanism of GC development and is important for finding new immunotherapeutic targets and disease prevention.

摘要

背景

胃癌（GC）是全球第五大常见癌症，也是癌症相关死亡的第二大主要原因。由于缺乏特异性标志物，胃癌的早期诊断率非常低，大多数胃癌患者在晚期才被诊断出来。本研究旨在鉴定 GC 的关键生物标志物，并阐明与 GC 相关的免疫细胞浸润和相关途径。

方法

从基因表达综合数据库（GEO）中下载与 GC 相关的基因微阵列数据。使用基因本体论（GO）、京都基因与基因组百科全书、基因集富集分析（GSEA）和蛋白质-蛋白质相互作用（PPI）网络分析差异表达基因（DEGs）。使用加权基因共表达网络分析（WGCNA）和最小绝对收缩和选择算子（LASSO）算法鉴定 GC 的关键基因，并使用受试者工作特征曲线评估 GC 枢纽标记物的诊断准确性。此外，使用 ssGSEA 分析 GC 中 28 种免疫细胞的浸润水平及其与枢纽标记物的相互关系，并通过 RT-qPCR 进一步验证。

结果

共鉴定出 133 个 DEGs。与 GC 密切相关的生物学功能和信号通路是炎症和免疫过程。通过 WGCNA 获得 9 个表达模块，其中粉色模块与 GC 的相关性最高；通过结合 DEGs 获得 13 个交叉基因。随后，使用 LASSO 算法和验证集验证分析最终确定了三个枢纽基因作为 GC 的潜在生物标志物。在免疫细胞浸润分析中，GC 中活化 CD4 T 细胞、巨噬细胞、调节性 T 细胞和浆细胞样树突状细胞的浸润更为显著。验证部分表明，三种枢纽基因在胃癌细胞中的表达水平较低。