School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.
Division of Molecular Genetics & Biochemistry, National Institute of Cancer Prevention & Research (ICMR-NICPR), Noida, India.
Biotechnol Genet Eng Rev. 2023 Oct;39(2):1273-1296. doi: 10.1080/02648725.2023.2174688. Epub 2023 Feb 21.
Ovarian cancer (OC) is second most common form of gynaecological cancer world wide . In this study, we collected and analyzed three ovarian cancer microarray raw datasets from Gene Expression Omnibus, NCBI, and identified a total of 1806 significant DEGs (Differentially expressed genes). The functional analysis of the DEGs showed that the 885 upregulated DEGs were mostly enriched in protein-binding activity, while the downregulated 796 genes were mostly enriched in retinal dehydrogenase activity and GABA receptor binding. We then constructed a protein-protein interaction network of the DEGs DEGs in ovarian cancer datasetsand analyzed the network to find cluster subnets, using molecular complex detection (MCODE). Common genes among top hub gene list, bottleneck gene list and maximum clique centrality (MCC) gene lists were identified as key driver genes, After analyzing the network. The following genes, STK12 (Serine threonine protein kinase), UBE2C (Ubiquitin-conjugating enzyme E2 C), CENPA (Centromere protein A), CCNB1 (Cyclin B1), POLD1 (polymerase delta 1) and KIF11 (Kinesin Family Member 11) were finally identified as driver genes. Higher expression of the key driver genes, STK12, UBE2C, CENPA, CCNB1, POLD1 and KIF11, was associated with lower overall survival (OS) among ovarian cancer patients. Therefore, the identified driver genes could be important diagnostic and prognostic biomarkers for predicting ovarian cancer progression and understanding the mechanism of tumour formation and recurrence.
卵巢癌 (OC) 是全球第二常见的妇科癌症。在这项研究中，我们从基因表达综合数据库（GEO）、NCBI 收集并分析了三个卵巢癌微阵列原始数据集，共鉴定出 1806 个显著差异表达基因（DEGs）。DEGs 的功能分析表明，885 个上调的 DEGs 主要富集在蛋白结合活性，而下调的 796 个基因主要富集在视网膜脱氢酶活性和 GABA 受体结合。然后，我们构建了卵巢癌数据集 DEGs 的蛋白质-蛋白质相互作用网络，并使用分子复合物检测（MCODE）分析网络以找到聚类子网。在网络分析中，我们确定了拓扑基因列表、瓶颈基因列表和最大团中心性（MCC）基因列表中的常见基因作为关键驱动基因。在分析网络后，我们确定了以下基因作为驱动基因：STK12（丝氨酸/苏氨酸蛋白激酶）、UBE2C（泛素结合酶 E2 C）、CENPA（着丝粒蛋白 A）、CCNB1（细胞周期蛋白 B1）、POLD1（聚合酶 delta 1）和 KIF11（驱动蛋白家族成员 11）。卵巢癌患者中关键驱动基因 STK12、UBE2C、CENPA、CCNB1、POLD1 和 KIF11 的高表达与整体生存率（OS）降低相关。因此，鉴定出的驱动基因可能是预测卵巢癌进展和理解肿瘤形成和复发机制的重要诊断和预后生物标志物。
