Open AccessConstruction of a robust prognostic model for adult adrenocortical carcinoma: Results from bioinformatics and real‐world data
Author(s) -
Tian Xi,
Xu WenHao,
Anwaier Aihetaimujiang,
Wang HongKai,
Wan FangNing,
Cao DaLong,
Luo WenJie,
Shi GuoHai,
Qu YuanYuan,
Zhang HaiLiang,
Ye DingWei
Publication year - 2021
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.16323
Subject(s) - adrenocortical carcinoma , medicine , cohort , oncology , database , computer science
This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large‐scale multiomics analysis and real‐world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis‐related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI’s GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN ( P = .039), FIBRONECTIN ( P < .001), TFRC ( P < .001), TSC1 ( P < .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival ( P < .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki‐67 protein in FUSCC cohort ( P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort ( P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort ( P = .0898, HR = 2.318). This research constructed IPRPs model for predicting adult ACC patients’ prognosis using proteomic data, gene expression data and real‐world data and this prognostic model showed stronger predictive value than other biomarkers (Ki‐67, Beta‐catenin, etc) in multi‐cohorts.