Identification of key prognostic genes in ovarian cancer using WGCNA and LASSO analysis

Published: 16 May 2022| Version 2 | DOI: 10.17632/mtjvvmmmcw.2
Zhong Yu


This study aims to identify new prognostic genes of OC through bioinformatics analysis.Based on two datasets from the NCBI GEO public database, we constructed two WGCNA networks. We then selected and intersected two key modules and 177 key genes were identified. The GO, KEGG and PPI network were determined based on the screened genes.According to the LASSO analysis and KM plotter, three hub genes were associated with the overall survival. GEPIA and IHC staining showed that CCDC80 expression level was lower while FBXO16 expression level was higher in OC tissues than in normal ovarian tissues. The relationship between hub genes and clinical characteristics was evaluated and the prognostic significance was validated in 60 clinical patients with OC. CIBERSORT shows that both CCDC80 and FBXO16 were significantly correlated with the immune cell content. Drug sensitivity analysis shows that CCDC80 affected the IC50 value of Gefitinib, and FBXO16 affected the IC50 value of Cisplatin and Docetaxel. CCDC80 and FBXO16 were identified as two robust prognostic factors in OC that could be explored further with respect to the diagnosis and treatment of OC patients.



Shengjing Hospital of China Medical University


Photography, Textbook, Chart Analysis, Fig