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

He, Zhong; Sun, Min; Yuan, Ke; Lin, Rongjie; Xiao, Youde; Zhou, Shaosheng; Zhao, Hong; Zhou, Fuxiang; Zhou, Youwen

doi:10.18632/oncotarget.15842

Cited by 31 publications

(28 citation statements)

References 37 publications

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“…Entry criteria do not include MET amplification or mutation, although tumor response by MET abnormality and expression level is being assessed. Furthermore, recent studies have looked at identifying biomarkers associated with the pathologic stage of PRCC (21). This is important as it has been shown that outcomes for PRCC, as well as being inferior to ccRCC, can be stratified into risk groups according to the International Metastatic Renal Cell Carcinoma Database Consortium prognostic model; the definition of additional biomarkers will only serve to strengthen such prognostic models (22).…”

Section: Discussionmentioning

confidence: 99%

First-in-Human Phase I Study of the Selective MET Inhibitor, Savolitinib, in Patients with Advanced Solid Tumors: Safety, Pharmacokinetics, and Antitumor Activity

Gan

Millward

Ye³

et al. 2019

Clinical Cancer Research

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Purpose: Aberrant activation of MET (hepatocyte growth factor receptor) signaling is implicated in the tumorigenesis of human cancers. This phase I study assessed the safety, tolerability, and MTD of the potent and selective MET inhibitor, savolitinib (AZD6094, HMPL-504, volitinib). Patients and Methods: This open-label, multicenter doseescalation and-expansion study evaluated oral savolitinib for patients with locally advanced or metastatic solid tumors. A 3 þ 3 design assessed repeated daily (QD) and twice daily (BID) dosing schedules. The dose-expansion phase included 12 patients. Primary objectives were to evaluate the safety, tolerability, MTD, and dose-limiting toxicities (DLT) of savolitinib. Secondary and exploratory objectives included pharmacokinetics, biomarker research, and antitumor activity. Results: Overall, 48 patients were enrolled. Four patients had DLTs following QD savolitinib (600 mg N ¼ 1, 800 mg N ¼ 1, and 1,000 mg N ¼ 2); the MTD was 800 mg QD and not reached for BID dosing. The recommended phase II dose (RP2D) was 600 mg QD. The most frequent adverse events were nausea (30 patients, 63%), vomiting (20 patients, 42%), fatigue (20 patients, 42%), and peripheral edema (15 patients, 31%). At 600 mg QD, C max was 2,414.8 ng/mL, AUC was 17053.9 hÁng/mL, and there was no apparent drug accumulation. Three patients with papillary renal cell carcinoma (PRCC) and MET aberrations had partial responses with durations from 39 to 147 weeks. Conclusions: The tolerability profile of savolitinib was acceptable and the RP2D was established as 600 mg QD. Preliminary antitumor activity was demonstrated supporting further study in patients with PRCC.

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Section: Discussionmentioning

confidence: 99%

First-in-Human Phase I Study of the Selective MET Inhibitor, Savolitinib, in Patients with Advanced Solid Tumors: Safety, Pharmacokinetics, and Antitumor Activity

Gan

Millward

Ye³

et al. 2019

Clinical Cancer Research

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“…Weighted gene coexpression network analysis (WGCNA) has been widely used to explore potential biomarkers of various diseases (Giulietti, Occhipinti, Principato, & Piva, ; Li et al, ; Wan, Tang, Han, & Wang, ; Yin, Cai, Zhu, & Xu, ). Similar studies selected only one interested phenotype for analysis, such as histologic grade (Clarke et al, ), pathological stage (He et al, ), metastasis (Tian, Guan, & Li, ), and so forth but very few studies had been done to analyze multiple phenotypes as we did, which could reduce the systemic bias that result from selecting a particular phenotype. In our study, the yellow modules were not only related to histologic grade ( r = 0.37; p = 1e−18), but also significantly correlated with pathological stage ( r = 0.32; p = 2e−14).…”

Section: Discussionmentioning

confidence: 99%

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Wang

Liang

Wang

et al. 2018

Journal Cellular Physiology

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Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA‐sequencing (RNA‐seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single‐samples gene‐set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein–protein interaction (PPI) network analysis. After verification of TCGA’s ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.

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“…Renal cell carcinoma is one of the most common malignant carcinomas in the world and has a high incidence and mortality rate . In a previous study, we identified biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co‐expression network analysis . CcRCC is the most common type of renal cancer, and there is an urgent need to explore the mechanism of the disease.…”

Section: Discussionmentioning

confidence: 99%

“…28 In a previous study, we identified biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene coexpression network analysis. 29 CcRCC is the most common type of renal cancer, and there is an urgent need to explore the mechanism of the disease. LncRNA play important roles in tumor progression and may be biomarkers for clinical diagnosis and prognosis according to recent studies.…”

Section: Discussionmentioning

confidence: 99%

Integrated analysis of long noncoding RNA associated‐competing endogenous RNA as prognostic biomarkers in clear cell renal carcinoma

et al. 2018

Self Cite

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Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant carcinomas and its molecular mechanisms remain unclear. Long noncoding RNA (lncRNA) could bind sites of miRNA which affect the expression of mRNA according to the competing endogenous (ceRNA) theory. The aim of the present study was to construct a ceRNA network and to identify key lncRNA to predict survival prognosis. We identified differentially expressed mRNA, lncRNA and miRNA between tumor tissues and normal tissues from The Cancer Genome Atlas database. Then, using bioinformatics tools, we explored the connection of 89 lncRNA, 10 miRNA and 22 mRNA, and we constructed the ceRNA network. Furthermore, we analyzed the functions and pathways of 22 differentially expressed mRNA. Then, univariate and multivariate Cox regression analyses of these 89 lncRNA and overall survival were explored. Nine lncRNA were finally screened out in the training group. The patients were divided into high‐risk and low‐risk groups according to the 9 lncRNA and low‐risk scores having better clinical overall survival (P < .01). Furthermore, the receiver operating characteristic curve demonstrates the predicted role of the 9 lncRNA. The 9‐lncRNA signature was successfully proved in the testing group and the entire group. Finally, multivariate Cox regression analysis and stratification analysis further proved that the 9‐lncRNA signature was an independent factor to predict survival. In summary, the present study provides a deeper understanding of the lncRNA‐related ceRNA network in ccRCC and suggests that the 9‐lncRNA signature could serve as an independent biomarker to predict survival in ccRCC patients.

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

Cited by 31 publications

References 37 publications

First-in-Human Phase I Study of the Selective MET Inhibitor, Savolitinib, in Patients with Advanced Solid Tumors: Safety, Pharmacokinetics, and Antitumor Activity

First-in-Human Phase I Study of the Selective MET Inhibitor, Savolitinib, in Patients with Advanced Solid Tumors: Safety, Pharmacokinetics, and Antitumor Activity

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Integrated analysis of long noncoding RNA associated‐competing endogenous RNA as prognostic biomarkers in clear cell renal carcinoma

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