Bioinformatics analysis reveals biomarkers with cancer stem cell characteristics in kidney renal clear cell carcinoma

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Abstract

Background

Kidney renal clear cell carcinoma (KIRC) is a renal cortical tumor. KIRC is the most common subtype of kidney cancer, accounting for 70%–80% of kidney cancer. Early identification of the risk of KIRC patients can facilitate more accurate clinical treatment, but there is a lack of effective prognostic markers. We aimed to identify new prognostic biomarkers for KIRC on the basis of the cancer stem cell (CSC) theory.

Methods

RNA-sequencing (RNA-seq) data and related clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis (WGCNA) was used to identify significant modules and hub genes, and predictive hub genes were used to construct prognostic characteristics.

Results

The messenger RNA expression-based stemness index (mRNAsi) in tumor tissues of patients in the TCGA database is higher than that of the corresponding normal tissues. In addition, some clinical features and results are highly correlated with mRNAsi. WGCNA found that the green module is the most prominent module associated with mRNAsi; the genes in the green module are mainly concentration in Notch binding, endothelial cell development, Notch signaling pathway, and Rap 1 signaling pathway. A protein-protein interaction (PPI) network showed that the top 10 central genes were significantly associated with the transcriptional level. Moreover, the 10 hub genes were up-regulated in KIRC. Regarding survival analysis, the nomogram of the prognostic markers of the seven pivotal genes showed a higher predictive value. The classical receiver operating characteristic (ROC) curve analysis showed that risk score biomarkers had the highest accuracy and specificity with an area under the curve (AUC) value of 0.701.

Conclusions

mRNAsi-related genes may be good prognostic biomarkers for KIRC.

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Most cited references 37

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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Renal cell carcinoma

James Hsieh, Mark Purdue, Sabina Signoretti … (2017)

Renal cell carcinoma (RCC) denotes cancer originated from the renal epithelium and accounts for >90% of cancers in the kidney. The disease encompasses >10 histological and molecular subtypes, of which clear cell RCC (ccRCC) is most common and accounts for most cancer-related deaths. Although somatic VHL mutations have been described for some time, more-recent cancer genomic studies have identified mutations in epigenetic regulatory genes and demonstrated marked intra-tumour heterogeneity, which could have prognostic, predictive and therapeutic relevance. Localized RCC can be successfully managed with surgery, whereas metastatic RCC is refractory to conventional chemotherapy. However, over the past decade, marked advances in the treatment of metastatic RCC have been made, with targeted agents including sorafenib, sunitinib, bevacizumab, pazopanib and axitinib, which inhibit vascular endothelial growth factor (VEGF) and its receptor (VEGFR), and everolimus and temsirolimus, which inhibit mechanistic target of rapamycin complex 1 (mTORC1), being approved. Since 2015, agents with additional targets aside from VEGFR have been approved, such as cabozantinib and lenvatinib; immunotherapies, such as nivolumab, have also been added to the armamentarium for metastatic RCC. Here, we provide an overview of the biology of RCC, with a focus on ccRCC, as well as updates to complement the current clinical guidelines and an outline of potential future directions for RCC research and therapy.

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Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures

Yasin Senbabaoglu, Ron Gejman, Andrew G. Winer … (2016)

Background Tumor-infiltrating immune cells have been linked to prognosis and response to immunotherapy; however, the levels of distinct immune cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery genes, remain poorly characterized. Here, we employ a gene expression-based computational method to profile the infiltration levels of 24 immune cell populations in 19 cancer types. Results We compare cancer types using an immune infiltration score and a T cell infiltration score and find that clear cell renal cell carcinoma (ccRCC) is among the highest for both scores. Using immune infiltration profiles as well as transcriptomic and proteomic datasets, we characterize three groups of ccRCC tumors: T cell enriched, heterogeneously infiltrated, and non-infiltrated. We observe that the immunogenicity of ccRCC tumors cannot be explained by mutation load or neo-antigen load, but is highly correlated with MHC class I antigen presenting machinery expression (APM). We explore the prognostic value of distinct T cell subsets and show in two cohorts that Th17 cells and CD8+ T/Treg ratio are associated with improved survival, whereas Th2 cells and Tregs are associated with negative outcomes. Investigation of the association of immune infiltration patterns with the subclonal architecture of tumors shows that both APM and T cell levels are negatively associated with subclone number. Conclusions Our analysis sheds light on the immune infiltration patterns of 19 human cancers and unravels mRNA signatures with prognostic utility and immunotherapeutic biomarker potential in ccRCC. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1092-z) contains supplementary material, which is available to authorized users.

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Author and article information

Journal

Journal ID (nlm-ta): Transl Androl Urol

Journal ID (iso-abbrev): Transl Androl Urol

Journal ID (publisher-id): TAU

Title: Translational Andrology and Urology

Publisher: AME Publishing Company

ISSN (Print): 2223-4683

ISSN (Electronic): 2223-4691

Publication date (Print and electronic): August 2021

Publication date (Print): August 2021

Volume: 10

Issue: 8

Pages: 3501-3514

Affiliations

[1 ]deptCollege of Life Sciences and Technology , Beijing University of Chemical Technology , Beijing, China;

[2 ]deptDepartment of Nephrology , Shenzhen Traditional Chinese Medicine Hospital , Shenzhen, China

Author notes

Contributions: (I) Conception and design: Z Zhang; (II) Administrative support: L Xu; C Yu; (III) Provision of study materials or patients: G Xiong; (IV) Collection and assembly of data: Z Zhang; X Xiong; (V) Data analysis and interpretation: Z Zhang; X Xiong; R Zhang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Changyuan Yu; Lida Xu. College of Life Sciences and Technology, Beijing University of Chemical Technology, Beijing, China. Email: yucy@ 123456mail.buct.edu.cn ; xuld@ 123456mail.buct.edu.cn .

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ORCID: Zan Zhang, 0000-0001-5081-2697; Rufeng Zhang, 0000-0001-7379-6597.

Article

Publisher ID: tau-10-08-3501

DOI: 10.21037/tau-21-647

PMC ID: 8421844

PubMed ID: 34532274

SO-VID: 047e37e3-0ea7-4861-9d68-6715a79edda1

License:

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

Bioinformatics analysis reveals biomarkers with cancer stem cell characteristics in kidney renal clear cell carcinoma

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Core Readings in Statistical Mediation Analysis

Most cited references 37

WGCNA: an R package for weighted correlation network analysis

Renal cell carcinoma

Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures

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