Khan Jiyauddin, Ghosh Priyanjana, Bajpai Urmi, Dwivedi Kountay, Saluja Daman
Dr B R Ambedkar Center for Biomedical Research, University of Delhi, Delhi, 110007, India.
Department of Biomedical Sciences, Acharya Narendra Dev College University of Delhi, University of Delhi, New Delhi, 110019, India.
Discov Oncol. 2024 Dec 23;15(1):832. doi: 10.1007/s12672-024-01712-8.
Cancer, a leading cause of death worldwide, is projected to increase by 76.6% in new cases and 89.7% in mortality by 2050 (WHO 2022). Among various types, lung cancer is the most prevalent with high morbidity, while breast, colorectal, and pancreatic cancers also show high mortality rates. Cancer progression often involves disruption in cell cycle regulation and signaling pathways, with mutations in genes like TP53, EGFR, and K-RAS playing significant roles. In this study, we analyzed gene expression datasets to identify common molecular signatures across breast, colorectal, lung and pancreatic cancers. Our focus was on genes related to cell cycle regulation and p53 signaling pathway, intending to discover potential biomarkers for improved diagnosis and treatment strategies. The study analyzed GEO datasets; GSE45827, GSE9348, GSE30219, and GSE62165 for breast, colorectal, lung, and pancreatic cancers respectively. Differentially expressed genes (DEGs) were identified using GEO2R, and functional annotation and pathway analysis were performed using WebGestalt. Common cell cycle and p53 signaling genes were acquired from MSigDB using GSEA. A protein-protein interaction network was constructed using STRING and Cytoscape, identifying top hub genes. Validation of hub genes at mRNA and protein levels was done via GEPIA2 and Human Protein Atlas. Survival analysis was conducted using TCGA data by GEPIA2 and LASSO, and drug sensitivity was analyzed with the GSCA drug bank database, highlighting potential therapeutic targets. The study identified 411 common DEGs among these four cancers. Pathway and functional enrichment revealed key biological processes and pathways like p53 signaling, and cell cycle. The intersection of these DEGs with genes involved in cell cycle and p53 signaling, identified 23 common genes that were used for constructing a PPI network. The top 10 hub genes were validated both for mRNA and protein expression, revealing they are significantly overexpressed in all studied cancers. Prognostic relevance showed that MCM4, MCM6, CCNA2, CDC20, and CHEK1 are associated with survival. Additionally, drug sensitivity analysis highlighted key gene-drug interactions, suggesting potential targets for therapeutic intervention.
癌症是全球主要死因之一，预计到2050年新发病例将增加76.6%，死亡率将增加89.7%（世界卫生组织，2022年）。在各种癌症类型中，肺癌最为常见，发病率很高，而乳腺癌、结直肠癌和胰腺癌的死亡率也很高。癌症进展通常涉及细胞周期调控和信号通路的破坏，TP53、EGFR和K-RAS等基因的突变起着重要作用。在本研究中，我们分析了基因表达数据集，以确定乳腺癌、结直肠癌、肺癌和胰腺癌共有的分子特征。我们关注的是与细胞周期调控和p53信号通路相关的基因，旨在发现潜在的生物标志物，以改进诊断和治疗策略。该研究分别分析了GEO数据集；乳腺癌的GSE45827、结直肠癌的GSE9348、肺癌的GSE30219和胰腺癌的GSE62165。使用GEO2R鉴定差异表达基因（DEG），并使用WebGestalt进行功能注释和通路分析。使用GSEA从MSigDB中获取常见的细胞周期和p53信号基因。使用STRING和Cytoscape构建蛋白质-蛋白质相互作用网络，确定顶级中心基因。通过GEPIA2和人类蛋白质图谱对中心基因在mRNA和蛋白质水平上进行验证。使用GEPIA2和LASSO通过TCGA数据进行生存分析，并使用GSCA药物库数据库分析药物敏感性，突出潜在的治疗靶点。该研究在这四种癌症中鉴定出411个常见的DEG。通路和功能富集揭示了关键的生物学过程和通路，如p53信号通路和细胞周期。这些DEG与参与细胞周期和p53信号通路的基因的交集，确定了23个用于构建PPI网络的常见基因。前10个中心基因在mRNA和蛋白质表达水平上均得到验证，表明它们在所有研究的癌症中均显著过表达。预后相关性表明，MCM4、MCM6、CCNA2、CDC20和CHEK1与生存相关。此外，药物敏感性分析突出了关键的基因-药物相互作用，提示了治疗干预的潜在靶点。
