Yao Qigu, Chen Wenyi, Gao Feiqiong, Wu Yuchen, Zhou Lingling, Xu Haoying, Yu Jong, Zhu Xinli, Wang Lan, Li Lanjuan, Cao Hongcui
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou 310003, China.
Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou 310003, China.
Biomedicines. 2023 Mar 10;11(3):847. doi: 10.3390/biomedicines11030847.
The noninvasive diagnosis of cholangiocarcinoma (CCA) is insufficiently accurate. Therefore, the discovery of new prognostic markers is vital for the understanding of the CCA mechanism and related treatment. The information on CCA patients in The Cancer Genome Atlas database was used for weighted gene co-expression network analysis. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to analyze the modules of interest. By using receiver operating characteristic (ROC) analysis to analyze the Human Protein Atlas (HPA), the featured genes were subsequently verified. In addition, clinical samples and GSE119336 cohort data were also collected for the validation of these hub genes. Using WGCNA, we identified 61 hub genes that regulated the progression and prognosis of CCA. Eight hub genes (VSNL1, TH, PCP4, IGDCC3, RAD51AP2, MUC2, BUB1, and BUB1B) were identified which exhibited significant interactions with the tumorigenic mechanism and prognosis of CCA. In addition, GO and KEGG clarified that the blue and magenta modules were involved with chromosome segregation, mitotic and oocyte meiosis, the cell cycle, and sister chromatid segregation. Four hub genes (VSNL1, PCP4, BUB1, and BUB1B) were also verified as featured genes of progression and prognosis by the GSE119336 cohort data and five human tissue samples.
胆管癌(CCA)的无创诊断准确性不足。因此,发现新的预后标志物对于理解CCA机制及相关治疗至关重要。利用癌症基因组图谱数据库中CCA患者的信息进行加权基因共表达网络分析。应用基因本体论(GO)分析和京都基因与基因组百科全书(KEGG)通路分析来分析感兴趣的模块。通过使用受试者工作特征(ROC)分析来分析人类蛋白质图谱(HPA),随后对特征基因进行验证。此外,还收集了临床样本和GSE119336队列数据用于验证这些核心基因。使用WGCNA,我们鉴定出61个调控CCA进展和预后的核心基因。确定了八个核心基因(VSNL1、TH、PCP4、IGDCC3、RAD51AP2、MUC2、BUB1和BUB1B),它们与CCA的致瘤机制和预后表现出显著相互作用。此外,GO和KEGG阐明蓝色和品红色模块与染色体分离、有丝分裂和卵母细胞减数分裂、细胞周期以及姐妹染色单体分离有关。四个核心基因(VSNL1、PCP4、BUB1和BUB1B)也通过GSE119336队列数据和五个人体组织样本被验证为进展和预后的特征基因。