Department of Biology, Faculty of Sciences, University of Sistan and Balouchestan, Zahedan, Iran.
Department of Clinical Biochemistry, School of Medicine, Birjand University of Medical Sciences, Birjand, Iran; Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran.
Biomed Pharmacother. 2022 Jan;145:112477. doi: 10.1016/j.biopha.2021.112477. Epub 2021 Dec 2.
Gastric cancer (GC) is the world's second-leading cause of cancer-related mortality, continuing to make it a serious healthcare concern. Even though the prevalence of GC reduces, the prognosis for GC patients remains poor in terms of a lack of reliable biomarkers to diagnose early GC and predict chemosensitivity and recurrence.
We integrated the gene expression patterns of gastric cancers from four RNAseq datasets (GSE113255, GSE142000, GSE118897, and GSE130823) from Gene Expression Omnibus (GEO) database to recognize differentially expressed genes (DEGs) between normal and GC samples. A gene co-expression network was built using weighted co-expression network analysis (WGCNA). Furthermore, RT-qPCR was performed to validate the in silico results.
The red modules in GSE113255, Turquoise in GSE142000, Brown in GSE118897, and the green-yellow module in GSE130823 datasets were found to be highly correlated with the anatomical site of GC. ITGAX, CCL14, ADHFE1, and HOXB13) as the hub gene are differentially expressed in tumor and non-tumor gastric tissues in this study. RT-qPCR demonstrated a high level of the expression of this gene.
The expression levels of ITGAX, CCL14, ADHFE1, and HOXB13 in GC tumor tissues are considerably greater than in adjacent normal tissues. Systems biology approaches identified that these genes could be possible GC marker genes, providing ideas for other experimental studies in the future.
胃癌（GC）是全球第二大癌症相关死亡原因，仍然是一个严重的医疗保健问题。尽管 GC 的发病率有所下降，但由于缺乏可靠的生物标志物来早期诊断 GC 并预测化疗敏感性和复发，GC 患者的预后仍然很差。
我们整合了来自 GEO 数据库的四个 RNAseq 数据集（GSE113255、GSE142000、GSE118897 和 GSE130823）中胃癌的基因表达模式，以识别正常和 GC 样本之间的差异表达基因（DEGs）。使用加权共表达网络分析（WGCNA）构建基因共表达网络。此外，还进行了 RT-qPCR 以验证计算结果。
在 GSE113255 中红色模块、GSE142000 中绿松石色模块、GSE118897 中棕色模块和 GSE130823 中的绿黄色模块与 GC 的解剖部位高度相关。在本研究中，ITGAX、CCL14、ADHFE1 和 HOXB13 作为枢纽基因在肿瘤和非肿瘤胃组织中差异表达。RT-qPCR 表明该基因的表达水平较高。
GC 肿瘤组织中 ITGAX、CCL14、ADHFE1 和 HOXB13 的表达水平明显高于相邻正常组织。系统生物学方法表明，这些基因可能是潜在的 GC 标记基因，为未来的其他实验研究提供了思路。
