Department of Biology, Faculty of science, Ferdowsi University of Mashhad, Mashhad, Iran.
Department of Biochemistry, Faculty of Medicine, Birjand University of medical sciences, Birjand, Iran.
Asian Pac J Cancer Prev. 2022 Feb 1;23(2):683-694. doi: 10.31557/APJCP.2022.23.2.683.
Gastric cancer (GC) is a complex disorder with an inadequate response to treatment. Although many efforts have been made to clarify the development of GC, the exact etiology and molecular mechanisms of this malignancy remain unclear. This study was designed to identify and characterize essential associated genes with GC to construct a prognostic model.
In this Insilco study, the gene expression microarray dataset GSE122401 was downloaded from the Gene Expression Omnibus (GEO). The raw data were processed and quantile-normalized with the edgeR package of R ver.3.5.3. The module-trait relationship and hub-genes associated with GC were analyzed with Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Cluepedia and Enrichr Database. Finally, hub-genes were screened and validated by GEPIA online database.
According to the WGCNA results, the blue module was found to be strongly correlated with the GC (r=0.91, p-value=1e-57). DEGs analysis was performed by edgeR package of R and indicated a total of 47 genes as hub-genes. Verifying the hub-genes expression using GEPIA online database showed a significantly increased level of ACAN gene expression in primary cancer cell line compared to metastatic cell line. On the other hand, the expression of MDFI and CHST1 genes in primary cell lines were lower compared to metastatic cancer cell lines.
This study provides a framework of the co-expression gene modules ACAN, MDFI, and CHST1 as hub-genes. These hub-genes might offer candidate biomarkers to targeted therapy against GC. Further experiment validation and animal models are needed to reveal the exact mechanism of the above-mentioned genes in the pathogenesis and prognoses of GC.
胃癌（GC）是一种复杂的疾病，治疗反应不佳。尽管已经做了很多努力来阐明 GC 的发展，但这种恶性肿瘤的确切病因和分子机制仍不清楚。本研究旨在鉴定和描述与 GC 相关的关键基因，以构建一个预后模型。
在这项 Insilco 研究中，从基因表达综合数据库（GEO）下载了基因表达微阵列数据集 GSE122401。使用 R ver.3.5.3 中的 edgeR 包对原始数据进行处理和分位数归一化。通过加权基因共表达网络分析（WGCNA）分析与 GC 相关的模块-性状关系和枢纽基因。使用 Cluepedia 和 Enrichr 数据库进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析。最后，通过 GEPIA 在线数据库筛选和验证枢纽基因。
根据 WGCNA 结果，发现蓝色模块与 GC 呈强相关（r=0.91，p 值=1e-57）。通过 R 中的 edgeR 包进行差异表达基因分析，共鉴定出 47 个基因作为枢纽基因。使用 GEPIA 在线数据库验证枢纽基因的表达，显示在原发性癌细胞系中 ACAN 基因的表达水平明显高于转移性细胞系。另一方面，在原发性细胞系中 MDFI 和 CHST1 基因的表达水平低于转移性癌细胞系。
本研究提供了一个由 ACAN、MDFI 和 CHST1 等枢纽基因组成的共表达基因模块框架。这些枢纽基因可能为针对 GC 的靶向治疗提供候选生物标志物。需要进一步的实验验证和动物模型来揭示上述基因在 GC 发病机制和预后中的确切机制。
