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Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis.

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.

SUBMITTER: He Z

PROVIDER: S-EPMC5438617 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis.

He Zhongshi Z Sun Min M Ke Yuan Y Lin Rongjie R Xiao Youde Y Zhou Shuliang S Zhao Hong H Wang Yan Y Zhou Fuxiang F Zhou Yunfeng Y

Oncotarget 20170401 17

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 betwe ...[more]

PMID: 28427189

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Project description:Heterotopic ossification (HO) is the formation of abnormal mature lamellar bone in extra-skeletal sites, including soft tissues and joints, which result in high rates of disability. The understanding of the mechanism of HO is insufficient. The aim of this study was to explore biomarkers and pathological processes in HO+ samples. The gene expression profile GSE94683 was downloaded from the Gene Expression Omnibus database. Sixteen samples from nine HO- and seven HO+ subjects were analyzed. After data preprocessing, 3,529 genes were obtained for weighted gene co-expression network analysis. Highly correlated genes were divided into 13 modules. Finally, the cyan and purple modules were selected for further study. Gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment indicated that the cyan module was enriched in a variety of components, including protein binding, membrane, nucleoplasm, cytosol, poly(A) RNA binding, biosynthesis of antibiotics, carbon metabolism, endocytosis, citrate cycle, and metabolic pathways. In addition, the purple module was enriched in cytosol, mitochondrion, protein binding, structural constituent of ribosome, rRNA processing, oxidative phosphorylation, ribosome, and non-alcoholic fatty liver disease. Finally, 10 hub genes in the cyan module [actin related protein 3 (ACTR3), ADP ribosylation factor 4 (ARF4), progesterone receptor membrane component 1 (PGRMC1), ribosomal protein S23 (RPS23), mannose-6-phosphate receptor (M6PR), WD repeat domain 12 (WDR12), synaptosome associated protein 23 (SNAP23), actin related protein 2 (ACTR2), siah E3 ubiquitin protein ligase 1 (SIAH1), and glomulin (GLMN)] and 2 hub genes in the purple module [proteasome 20S subunit alpha 3 (PSMA3) and ribosomal protein S27 like (RPS27L)] were identified. Hub genes were validated through quantitative real-time polymerase chain reaction. In summary, 12 hub genes were identified in two modules that were associated with HO. These hub genes could provide new biomarkers, therapeutic ideas, and targets in HO.

Project description:Background The genes and genetic mechanisms underlying the occurrence and progression of papillary thyroid carcinoma (PTC) are still unknown. This study aimed to find candidate genes related to the pathogenesis and progression of PTC. Methods RNA sequencing (RNA-seq) data of PTC and normal tissues were downloaded from The Cancer Genome Atlas (TCGA) database with clinical stage data to form a test, validation, and clinical-stage data matrix. We used the test data set to analyze differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) to find those gene clusters highly correlated with PTC. We then verified the expression of genes in the interested modules using the validation matrix. The quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the reliability of the expression of selected genes. Five key genes (GDF15, LCN2, KCNN4, SH3BGRL3, and MMP2) were used to analyze the connection between gene expression and the American Joint Committee on Cancer (AJCC) stage. The upregulated and downregulated DEGs, along with the modules of interest, were subjected to Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Results We used WGCNA to find two modules of interest, the yellow module, which was positively associated with PTC, and the blue module, which was negatively correlated with PTC. Four genes (GDF15, LCN2, KCNN4, and SH3BGRL3) from the yellow module were determined to be highly expressed in PTC in the test data matrix and were verified in both the validation data matrix and quantitative real-time PCR, which indicated that these four genes were highly correlated with the occurrence of the PTC. Furthermore, these four genes also had a significantly higher expression in the advanced levels of pathological T, N, and AJCC stage, meaning that they were correlated with the progression of PTC. Genes in the yellow module and upregulated DEGs were significantly enriched in three vital signaling pathways, including focal adhesion, extracellular matrix (ECM)-receptor interaction, and the PI3K-Akt signaling pathway. Conclusions Four candidate genes (GDF15, LCN2, KCNN4, and SH3BGRL3) may be potential biomarkers for the PTC’s pathogenesis and may be useful for predicting the disease stage.

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Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis.

Publications

Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis.

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