A 9‐gene prognostic signature for kidney renal clear cell carcinoma overall survival based on co‐expression and regression analyses. (25th September 2022)

• Record Type: Journal Article
• Authors: Zhu, Wenwen; Ding, Mengyu; Chang, Jian; Liao, Hui; Xiao, Geqiong; Wang, Qiong
• Is Part Of: Chemical biology & drug design. Volume 101:Number 2(2023)
• Page Start: 422
• Page End: 437
• Publication Date: 2022-09-25
• Subjects: biomarker selection -- KIRC -- PPI network construction -- WGCNA; Drugs -- Design -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; 615.19005
• DOI: 10.1111/cbdd.14141
• Languages: English
• ISSNs: 1747-0277

Abstract

Abstract: This research attempted to screen potential signatures associated with KIRC progression and overall survival by weighted gene co‐expression network analysis (WGCNA) and Cox regression. The KIRC‐associated mRNA expression and clinical data were accessed from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened by differential analysis. A co‐expression network was constructed by "WGCNA". Based on WGCNA module, GO and KEGG analyses were performed. Protein–protein interaction (PPI) network was constructed. Prognostic signatures were screened by Lasso‐Cox regression. Prognostic model was evaluated by Receiver Operating Characteristic (ROC) and Kaplan‐Meier (K‐M) curves. Multivariate Cox and nomogram were introduced to examine whether risk score could be an independent marker. qRT‐PCR was introduced to determine expression of 9 hub genes in KIRC clinical tumor tissues and adjacent tissues, respectively. Genes in the green module were highly associated with clinical status, and green module genes were significantly enriched in mitotic nuclear division, cell cycle, and p 53 signaling pathway. Twenty‐six candidates were subsequently screened out from the green module. Next, a 9‐gene prognostic model (DLGAP5, NUF2, TOP2A, RRM2, HJURP, PLK1, AURKB, KIF18A, CCNB2) was constructed. The predicting ability of the model was optimal. Some cancer‐related signaling pathways were differently activated between two risk score groups. Additionally, under‐expression of some signature genes (AURKB, CCNB2, PLK1, RRM2, TOP2A) was associated with better survival rate for KIRC patients. Meanwhile, all 9 hub genes were substantially overexpressed in KIRC patients. A KIRC prognostic signature was screened in this study, contributing valuable findings to KIRC biomarker development. Abstract : Through weighted gene co‐expression network analysis and protein–protein interaction network analysis, nine hub genes that are significantly related to progression and prognosis of kidney renal clear cell carcinoma were screened out. The screened nine prognostic biomarkers were validated by survival analysis and risk assessment.