Identification of genes that correlate clear cell renal cell carcinoma and obesity and exhibit potential prognostic value
TL;DR: In this paper, several bioinformatics tools were used to identify the key genes in clear cell RCC related to obesity, and the five genes differentially expressed in ccRCC and obesity are related to disease progression and prognosis, and therefore could provide prognostic value for patients with cc RCC.
read more
Abstract: Background Renal cell carcinoma (RCC) is a common urologic malignancy. Although the relationship between clear cell RCC (ccRCC) and obesity has been well-established by several large-scale retrospective studies, the molecular mechanisms and genetic characteristics behind this correlation remains unclear. In the current study, several bioinformatics tools were used to identify the key genes in ccRCC related to obesity. Methods Microarray data comparing ccRCC with normal renal tissues in patients with and without obesity were downloaded from the GEO database for screening of differentially expressed genes (DEGs). The DEGs were verified with expression level and survival analysis using several online bioinformatics tools. Results In the current study, the differential expression of five genes correlated with both ccRCC and obesity; IGHA1 and IGKC as oncogenes, and MAOA, MUC20 and TRPM3 as tumor suppressor genes. These genes were verified by comparing the relationship between the expression levels and survival outcomes from open-source data in The Cancer Genome Atlas (TCGA) dataset. Conclusions In conclusion, the five genes differentially expressed in ccRCC and obesity are related to disease progression and prognosis, and therefore could provide prognostic value for patients with ccRCC.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Renal Cell Cancer and Obesity
TL;DR: This review focuses on the impact of obesity on the risk of renal cancers development, their aggressiveness and patients’ survival.
Molecular differences in renal cell carcinoma between males and females
TL;DR: Current evidence suggests meaningful genomic differences between male and female RCC, highlighting the need for sex- specific RCC research and personalized sex-specific treatment approaches.
6
Heterogeneous miRNA-mRNA Regulatory Networks of Visceral and Subcutaneous Adipose Tissue in the Relationship Between Obesity and Renal Clear Cell Carcinoma.
TL;DR: In this paper, different miRNA-mRNA networks of obesity-related clear cell renal cell carcinoma (ccRCC) in VAT and SAT using datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA).
Identification of testicular cancer immune infiltrates and novel immune cell subtypes
Zhiguo Zhu,Xujun Xuan,Xinkun Wang,Miaomiao Wang,Chunyang Meng,Zhonghai Li +5 more
TL;DR: Genes relating to the WNT signalling pathway, TGF‐β signaling pathway, antigen processing and presentation, and NK cell‐mediated cytotoxicity were associated with TGCT.
1
References
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Freddie Bray,Jacques Ferlay,Isabelle Soerjomataram,Rebecca L. Siegel,Lindsey A. Torre,Ahmedin Jemal +5 more
TL;DR: A status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions.
80.7K
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses.
TL;DR: GEPIA (Gene Expression Profiling Interactive Analysis) fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources.
8.9K
KEGG: new perspectives on genomes, pathways, diseases and drugs
TL;DR: The content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases, and the newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined.
8K
Body Fatness and Cancer — Viewpoint of the IARC Working Group
Béatrice Lauby-Secretan,Chiara Scoccianti,Dana Loomis,Yann Grosse,Franca Bianchini,Kurt Straif +5 more
TL;DR: The International Agency for Research on Cancer convened a workshop on the relationship between body fatness and cancer, from which an IARC handbook on the topic will appear.