Abstract
Although papillary renal cell carcinoma (PRCC) accounts for 10%–15% of renal cell carcinoma (RCC), no predictive molecular biomarker is currently applicable to guiding disease stage of PRCC patients. The mRNASeq data of PRCC and adjacent normal tissue in The Cancer Genome Atlas was analyzed to identify 1148 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 11 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = 0.45) by Pearson's correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on nuclear division, cell cycle phase, and spindle (all P < 1e-10). All 40 hub genes in blue module can distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) PRCC (P < 0.01). A good molecular biomarker for pathological stage of RCC must be a prognostic gene in clinical practice. Survival analysis was performed to reversely validate if hub genes were associated with pathological stage. Survival analysis unveiled that all hub genes were associated with patient prognosis (P < 0.01). The validation cohort GSE2748 verified that 30 hub genes can differentiate localized from non-localized PRCC (P < 0.01), and 18 hub genes are prognosis-associated (P < 0.01).ROC curve indicated that the 17 hub genes exhibited excellent diagnostic efficiency for localized and non-localized PRCC (AUC > 0.7). These hub genes may serve as a biomarker and help to distinguish different pathological stages for PRCC patients.
Highlights
Kidney malignant tumor is a heterogeneous disease of which epithelial renal cell carcinoma (RCC) constitutes the vast majority [1]
We identified 17 candidate biomarkers for papillary renal cell carcinoma (PRCC) by applying Weighted gene co-expression network analysis (WGCNA), a systems biology method, and other analysis methods on mRNASeq data and clinical information of PRCC patients in The Cancer Genome Atlas (TCGA) for the first time
We found that the 17 biomarkers can distinguish between localized and non-localized PRCC, which was verified by a microarray-based validation cohort GSE2748
Summary
Kidney malignant tumor is a heterogeneous disease of which epithelial renal cell carcinoma (RCC) constitutes the vast majority [1]. RCC can be divided into multiple histological subtypes, encompassing clear cell, papillary, chromophobe, collecting duct, and unclassified subtypes [2]. No effective therapeutic approach is available for patients with advanced PRCC [4]. Many biomarkers for renal clear cell carcinoma have been discovered, including VHL, VEGF, CAIX and HIF1a/2a mutations, some of which could predict therapeutic effect and clinical prognosis [5]. PRCC’s molecular biomarkers for predicting curative www.impactjournals.com/oncotarget effect and prognosis have rarely been reported [6]. It is necessary to identify novel molecular biomarkers that can predict disease stage and clinical outcome of PRCC patients, which could help understand its pathogenesis and provide personalized treatment
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