Identification and validation of a 9-gene signature for the prognosis of ovarian cancer by integrated bioinformatical analysis

Abstract

Background: Ovarian cancer (OC) is the most lethal malignancy among gynecological cancers worldwide.It is urgent to identify effective biomarkers for the prognosis and diagnosis of OC.Method: We analyzed 4 OC Gene Expression Omnibus (GEO) data sets to detect differentially expressed genes (DEGs). To explore potential correlations between the gene sets and clinical features, we conducted weighted gene co-expression network analysis (WGCNA). Hub genes were identified from the key modules by univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses and risk scores were calculated based on the expressions of the hub genes. Univariate and multivariate Cox regression analyses were conducted to determine the values of the diagnoses for OC patients. We also determined the predictive value of the long non-coding RNA (lncRNA) score in response to immunotherapy and chemotherapeutic drugs.Results: DEGs were analyzed between the OC and normal ovarian tissues and prognostic modules were identified by a WGCNA. Nine hub genes chose from the prognostic modules were determined the prognostic values in OC. The risk scores were calculated based on the expression of hub genes, and patients with high-risk scores had poor survival. Univariate and multivariate Cox regression analyses showed that the risk score was an independent prognostic factor for OC. Additionally, the levels of hub genes were also found to be related to immune cell infiltration in OC microenvironments. An immunotherapy cohort showed that high-risk scores enhanced the response to anti-programmed death-ligand 1 (PD-L1) immunotherapy and was remarkably correlated with the inflamed immune phenotype, and had significant therapeutic advantages and clinical benefits. Further, patients with high-risk scores were more sensitive to midostaurin. Conclusions:We identified the risk score including protein phosphatase, Mg2+/Mn2+ dependent 1K (PPM1K), protein phosphatase 1 catalytic subunit alpha (PPP1CA), exostosin glycosyltransferase 1 (EXT1), RAB GTPase activating protein 1 like (RABGAP1L), mitotic arrest deficient 2 like 1 (MAD2L1), xeroderma pigmentosum complementation group C (XPC), Egl-9 family hypoxia inducible factor 3 (EGLN3), cyclin D1 binding protein 1 (CCNDBP1), and zinc finger protein 25 (ZNF25), and validated their prognostic and predicted values for OC.

Glutamine metabolism prognostic index predicts tumour microenvironment characteristics and therapeutic efficacy in ovarian cancer

“…The proportional hazards (PH) assumption was verified with the Schoenfeld test. Additionally, the 3‐ and 5‐year ROC values of the GMPI were compared with those of other published gene signatures for OC, including a prognosis‐related genes signature reported by Chen et al 24 . (“Chen signature”), a prognostic model for platinum‐treated OC reported by Sabatier et al 25 .…”

“…Additionally, the 3‐ and 5‐year ROC values of the GMPI were compared with those of other published gene signatures for OC, including a prognosis‐related genes signature reported by Chen et al. 24 (“Chen signature”), a prognostic model for platinum‐treated OC reported by Sabatier et al. 25 (“Sabatier signature”), a lactate metabolism‐related signature reported by Xiang et al.…”

“…The proportional hazards (PH) assumption was verified with the Schoenfeld test. Additionally, the 3-and 5-year ROC values of the GMPI were compared with those of other published gene signatures for OC, including a prognosisrelated genes signature reported by Chen et al 24 ("Chen signature"), a prognostic model for platinum-treated OC reported by Sabatier et al 25 ("Sabatier signature"), a lactate metabolism-related signature reported by Xiang et al 26 ("Xiang signature"), a hematogenous and lymphatic metastasis signature reported by Yue et al 27 ("Yue signature"), and an omentum metastasis-related prognostic signature reported by Zhang et al 28 ("Zhang signature"). Finally, the precision and accuracy of nomogram was evaluated with ROC curves, the Concordance index (C index) and restricted mean survival (RMS) time curves.…”