Sadat Kalaki Niloufar, Ahmadzadeh Mozhgan, Najafi Mohammad, Mobasheri Meysam, Ajdarkosh Hossein, Karbalaie Niya Mohammad Hadi
Department of Cellular and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran.
International Institute of New Sciences (IINS), Tehran, Iran.
Biochem Biophys Rep. 2024 Jan 12;37:101633. doi: 10.1016/j.bbrep.2023.101633. eCollection 2024 Mar.
Colorectal cancer (CRC), is the third most prevalent cancer across the globe, and is often detected at advanced stage. Late diagnosis of CRC, leave the chemotherapy and radiotherapy as the main options for the possible treatment of the disease which are associated with severe side effects. In the present study, we seek to explore CRC gene expression data using a systems biology framework to identify potential biomarkers and therapeutic targets for earlier diagnosis and treatment of the disease.
The expression data was retrieved from the gene expression omnibus (GEO). Differential gene expression analysis was conducted using R/Bioconductor package. The PPI network was reconstructed by the STRING. Cystoscope and Gephi software packages were used for visualization and centrality analysis of the PPI network. Clustering analysis of the PPI network was carried out using k-mean algorithm. Gene-set enrichment based on Gene Ontology (GO) and KEGG pathway databases was carried out to identify the biological functions and pathways associated with gene groups. Prognostic value of the selected identified hub genes was examined by survival analysis, using GEPIA.
A total of 848 differentially expressed genes were identified. Centrality analysis of the PPI network resulted in identification of 99 hubs genes. Clustering analysis dissected the PPI network into seven interactive modules. While several DEGs and the central genes in each module have already reported to contribute to CRC progression, survival analysis confirmed high expression of central genes, CCNA2, CD44, and ACAN contribute to poor prognosis of CRC patients. In addition, high expression of TUBA8, AMPD3, TRPC1, ARHGAP6, JPH3, DYRK1A and ACTA1 was found to associate with decreased survival rate.
Our results identified several genes with high centrality in PPI network that contribute to progression of CRC. The fact that several of the identified genes have already been reported to be relevant to diagnosis and treatment of CRC, other highlighted genes with limited literature information may hold potential to be explored in the context of CRC biomarker and drug target discovery.
结直肠癌（CRC）是全球第三大常见癌症，且常于晚期被发现。CRC的晚期诊断使得化疗和放疗成为该疾病可能的主要治疗选择，但这些治疗伴有严重的副作用。在本研究中，我们试图使用系统生物学框架探索CRC基因表达数据，以识别潜在的生物标志物和治疗靶点，用于该疾病的早期诊断和治疗。
从基因表达综合数据库（GEO）检索表达数据。使用R/Bioconductor软件包进行差异基因表达分析。通过STRING重建蛋白质-蛋白质相互作用（PPI）网络。使用Cystoscope和Gephi软件包对PPI网络进行可视化和中心性分析。使用k均值算法对PPI网络进行聚类分析。基于基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路数据库进行基因集富集分析，以识别与基因组相关的生物学功能和通路。使用GEPIA通过生存分析检查所选鉴定的枢纽基因的预后价值。
共鉴定出848个差异表达基因。对PPI网络的中心性分析导致鉴定出99个枢纽基因。聚类分析将PPI网络分解为七个相互作用模块。虽然每个模块中的几个差异表达基因和中心基因已被报道与CRC进展有关，但生存分析证实中心基因CCNA2、CD44和ACAN的高表达导致CRC患者预后不良。此外，发现TUBA8、AMPD3、TRPC1、ARHGAP6、JPH3、DYRK1A和ACTA1的高表达与生存率降低相关。
我们的结果鉴定出PPI网络中几个具有高中心性的基因，这些基因有助于CRC的进展。事实上，已报道其中几个鉴定出的基因与CRC的诊断和治疗相关，其他文献信息有限但突出的基因可能在CRC生物标志物和药物靶点发现方面具有探索潜力。
