{"title":"Identification and expression of prognostic-related genes in kidney renal clear cell carcinoma and their possible regulatory mechanisms.","authors":"Qian Liu, Jun Ding","doi":"10.21037/tau-24-299","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Many factors affect the prognosis of kidney renal clear cell carcinoma (KIRC). Early diagnosis can significantly improve the prognosis of KIRC patients. Therefore, a method needs to be developed to diagnose KIRC early, predict patient prognosis, and improve personalized treatments. The objective of this study is to utilize bioinformatics tools and public database resources to identify differentially expressed genes (DEGs) between renal cancer tissues and adjacent normal tissues, and to further screen for prognostic-related genes (PRGs) of KIRC.</p><p><strong>Methods: </strong>KIRC was studied using R language and FunRich software and several databases, including the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), the University of Alabama at Birmingham cancer data analysis Portal (UALCAN), and Tumor Immune Estimation Resource (TIMER) databases. Moreover, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the expression of multiple genes in KIRC and adjacent normal tissues.</p><p><strong>Results: </strong>There were substantial differences in immune cell infiltration between the KIRC and adjacent normal tissues in the GSE40435 and GSE46699 datasets. In addition, we screened multiple PRGs of KIRC by combining the GEO and TCGA data. The UALCAN database verified that some representative PRGs were differently expressed depending on the lymph node metastasis status, grade, and stage of KIRC. The qRT-PCR results confirmed the expression of the PRGs in KIRC and adjacent normal tissues. Through the GO and KEGG analyses, interaction analysis, and TIMER database, we found that the prognosis of KIRC was closely related to immune microenvironment and vascular endothelial growth factor (VEGF)/VEGF receptor (VEGFR) signaling.</p><p><strong>Conclusions: </strong>Our findings could contribute to the prognosis prediction of KIRC, the selection of personalized treatments, and the early diagnosis of KIRC.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11399047/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/tau-24-299","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/26 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Many factors affect the prognosis of kidney renal clear cell carcinoma (KIRC). Early diagnosis can significantly improve the prognosis of KIRC patients. Therefore, a method needs to be developed to diagnose KIRC early, predict patient prognosis, and improve personalized treatments. The objective of this study is to utilize bioinformatics tools and public database resources to identify differentially expressed genes (DEGs) between renal cancer tissues and adjacent normal tissues, and to further screen for prognostic-related genes (PRGs) of KIRC.
Methods: KIRC was studied using R language and FunRich software and several databases, including the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), the University of Alabama at Birmingham cancer data analysis Portal (UALCAN), and Tumor Immune Estimation Resource (TIMER) databases. Moreover, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the expression of multiple genes in KIRC and adjacent normal tissues.
Results: There were substantial differences in immune cell infiltration between the KIRC and adjacent normal tissues in the GSE40435 and GSE46699 datasets. In addition, we screened multiple PRGs of KIRC by combining the GEO and TCGA data. The UALCAN database verified that some representative PRGs were differently expressed depending on the lymph node metastasis status, grade, and stage of KIRC. The qRT-PCR results confirmed the expression of the PRGs in KIRC and adjacent normal tissues. Through the GO and KEGG analyses, interaction analysis, and TIMER database, we found that the prognosis of KIRC was closely related to immune microenvironment and vascular endothelial growth factor (VEGF)/VEGF receptor (VEGFR) signaling.
Conclusions: Our findings could contribute to the prognosis prediction of KIRC, the selection of personalized treatments, and the early diagnosis of KIRC.
期刊介绍：
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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