Bioinformatics identification of prognostic genes and potential interaction analysis in renal cell carcinoma

Renal cell carcinoma (RCC) is one of the ten most prevalent cancers in the world and its incidence has been rising over the past decade. However, effective biomarkers to predict the prognosis of patients remains absent, and the exact molecular mechanism of the disease remains unclear. Therefore, the...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Translational cancer research 2023-04, Vol.12 (4), p.774-783
Hauptverfasser: Yuan, Yimin, Wang, Jingzi, Huang, Liqu, Guo, Yunfei
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 783
container_issue 4
container_start_page 774
container_title Translational cancer research
container_volume 12
creator Yuan, Yimin
Wang, Jingzi
Huang, Liqu
Guo, Yunfei
description Renal cell carcinoma (RCC) is one of the ten most prevalent cancers in the world and its incidence has been rising over the past decade. However, effective biomarkers to predict the prognosis of patients remains absent, and the exact molecular mechanism of the disease remains unclear. Therefore, the identification of key genes and their biological pathways are of great significance to identify the differential expressed genes associated with the prognosis for patients with RCC, and to further explore their potential protein-protein interactions (PPIs) in tumorigenesis. The gene expression microarray data for GSE15641 and GSE40435 were extracted from the Gene Expression Omnibus (GEO) database, including 150 primary tumors and their matched adjacent non-tumor tissues. Afterwards, gene expression for fold changes (FCs) and P value for tumor and non-tumor tissues were analyzed using online tool GEO2R. Gene expression with logFCs of greater than two combined with P value of lower than 0.01 were considered as candidate targets for treatment of RCC. The survival analysis of candidate genes was performed by online software OncoLnc. The PPI network was implemented with Search Tool for the Retrieval of Interacting Genes (STRING). In total, there were 625 differentially expressed genes (DEGs) in GSE15641, including 415 increased and 210 decreased genes. A total of 343 DEGs were identified in the GSE40435 with 101 upregulated and 242 downregulated genes, the 20 genes with highest FC in high or low expression in each database were summarized. Five candidate genes were overlapped genes in the two GEO datasets. However, aldolase, fructose-bisphosphate B (ALDOB) was found to be the only gene affecting the prognosis. A number of critical genes were identified behind the mechanism, of which they interacted with ALDOB. Among them, phosphofructokinase, platelet ( ), phosphofructokinase, muscle ( ), pyruvate kinase L/R ( ), and fructose-bisphosphatase 1 ( ) showed a better prognosis, whereas only glyceraldehyde-3-phosphate dehydrogenase ( ) rendered a bleak outcome. Five genes were found to be overlappingly expressed in the top 20 greatest FC in two human GEO datasets. This is of great value in the treatment and prognosis of RCC.
doi_str_mv 10.21037/tcr-22-2242
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10174992</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2813886749</sourcerecordid><originalsourceid>FETCH-LOGICAL-c342t-bd9af282eea7bb597b99454d8a933889b4212e4a1114153a9d430b33349539093</originalsourceid><addsrcrecordid>eNpVkc1LxDAQxYMoKqs3z5KjB6v5apucRBe_QPCi4C1M03SNtMmadIX9783u6qIQJhnmx8sbHkInlFwwSnh9OZpYMJaPYDvokDGqikoSvrt-y6Kqq7cDdJzSByGEUSoFqfbRAa-pJFVZHqL-xgXnuxAHGJ1J2LXWj65zJrfB49DheQwzH1Ke4pn1NmHwLZ6HccVBj50fbQSzpsFDv0wuq3gcbW6wsX0uEI3zYYAjtNdBn-zxzz1Br3e3L9OH4un5_nF6_VQYLthYNK2CjklmLdRNU6q6UUqUopWgOJdSNYJRZgVQSgUtOahWcNJwzoUquSKKT9DVRne-aAbbmmw1Qq_n0Q0QlzqA0_8n3r3rWfjSlNBaKMWywtmPQgyfC5tGPbi0Wga8DYukmaTZSZXhjJ5vUBNDStF2238o0euQdA5JM6ZXIWX89K-3LfwbCf8GWRuPnw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2813886749</pqid></control><display><type>article</type><title>Bioinformatics identification of prognostic genes and potential interaction analysis in renal cell carcinoma</title><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><source>PubMed Central Open Access</source><creator>Yuan, Yimin ; Wang, Jingzi ; Huang, Liqu ; Guo, Yunfei</creator><creatorcontrib>Yuan, Yimin ; Wang, Jingzi ; Huang, Liqu ; Guo, Yunfei</creatorcontrib><description>Renal cell carcinoma (RCC) is one of the ten most prevalent cancers in the world and its incidence has been rising over the past decade. However, effective biomarkers to predict the prognosis of patients remains absent, and the exact molecular mechanism of the disease remains unclear. Therefore, the identification of key genes and their biological pathways are of great significance to identify the differential expressed genes associated with the prognosis for patients with RCC, and to further explore their potential protein-protein interactions (PPIs) in tumorigenesis. The gene expression microarray data for GSE15641 and GSE40435 were extracted from the Gene Expression Omnibus (GEO) database, including 150 primary tumors and their matched adjacent non-tumor tissues. Afterwards, gene expression for fold changes (FCs) and P value for tumor and non-tumor tissues were analyzed using online tool GEO2R. Gene expression with logFCs of greater than two combined with P value of lower than 0.01 were considered as candidate targets for treatment of RCC. The survival analysis of candidate genes was performed by online software OncoLnc. The PPI network was implemented with Search Tool for the Retrieval of Interacting Genes (STRING). In total, there were 625 differentially expressed genes (DEGs) in GSE15641, including 415 increased and 210 decreased genes. A total of 343 DEGs were identified in the GSE40435 with 101 upregulated and 242 downregulated genes, the 20 genes with highest FC in high or low expression in each database were summarized. Five candidate genes were overlapped genes in the two GEO datasets. However, aldolase, fructose-bisphosphate B (ALDOB) was found to be the only gene affecting the prognosis. A number of critical genes were identified behind the mechanism, of which they interacted with ALDOB. Among them, phosphofructokinase, platelet ( ), phosphofructokinase, muscle ( ), pyruvate kinase L/R ( ), and fructose-bisphosphatase 1 ( ) showed a better prognosis, whereas only glyceraldehyde-3-phosphate dehydrogenase ( ) rendered a bleak outcome. Five genes were found to be overlappingly expressed in the top 20 greatest FC in two human GEO datasets. This is of great value in the treatment and prognosis of RCC.</description><identifier>ISSN: 2218-676X</identifier><identifier>EISSN: 2219-6803</identifier><identifier>DOI: 10.21037/tcr-22-2242</identifier><identifier>PMID: 37180655</identifier><language>eng</language><publisher>China: AME Publishing Company</publisher><subject>Original</subject><ispartof>Translational cancer research, 2023-04, Vol.12 (4), p.774-783</ispartof><rights>2023 Translational Cancer Research. All rights reserved.</rights><rights>2023 Translational Cancer Research. All rights reserved. 2023 Translational Cancer Research.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174992/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174992/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37180655$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yuan, Yimin</creatorcontrib><creatorcontrib>Wang, Jingzi</creatorcontrib><creatorcontrib>Huang, Liqu</creatorcontrib><creatorcontrib>Guo, Yunfei</creatorcontrib><title>Bioinformatics identification of prognostic genes and potential interaction analysis in renal cell carcinoma</title><title>Translational cancer research</title><addtitle>Transl Cancer Res</addtitle><description>Renal cell carcinoma (RCC) is one of the ten most prevalent cancers in the world and its incidence has been rising over the past decade. However, effective biomarkers to predict the prognosis of patients remains absent, and the exact molecular mechanism of the disease remains unclear. Therefore, the identification of key genes and their biological pathways are of great significance to identify the differential expressed genes associated with the prognosis for patients with RCC, and to further explore their potential protein-protein interactions (PPIs) in tumorigenesis. The gene expression microarray data for GSE15641 and GSE40435 were extracted from the Gene Expression Omnibus (GEO) database, including 150 primary tumors and their matched adjacent non-tumor tissues. Afterwards, gene expression for fold changes (FCs) and P value for tumor and non-tumor tissues were analyzed using online tool GEO2R. Gene expression with logFCs of greater than two combined with P value of lower than 0.01 were considered as candidate targets for treatment of RCC. The survival analysis of candidate genes was performed by online software OncoLnc. The PPI network was implemented with Search Tool for the Retrieval of Interacting Genes (STRING). In total, there were 625 differentially expressed genes (DEGs) in GSE15641, including 415 increased and 210 decreased genes. A total of 343 DEGs were identified in the GSE40435 with 101 upregulated and 242 downregulated genes, the 20 genes with highest FC in high or low expression in each database were summarized. Five candidate genes were overlapped genes in the two GEO datasets. However, aldolase, fructose-bisphosphate B (ALDOB) was found to be the only gene affecting the prognosis. A number of critical genes were identified behind the mechanism, of which they interacted with ALDOB. Among them, phosphofructokinase, platelet ( ), phosphofructokinase, muscle ( ), pyruvate kinase L/R ( ), and fructose-bisphosphatase 1 ( ) showed a better prognosis, whereas only glyceraldehyde-3-phosphate dehydrogenase ( ) rendered a bleak outcome. Five genes were found to be overlappingly expressed in the top 20 greatest FC in two human GEO datasets. This is of great value in the treatment and prognosis of RCC.</description><subject>Original</subject><issn>2218-676X</issn><issn>2219-6803</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpVkc1LxDAQxYMoKqs3z5KjB6v5apucRBe_QPCi4C1M03SNtMmadIX9783u6qIQJhnmx8sbHkInlFwwSnh9OZpYMJaPYDvokDGqikoSvrt-y6Kqq7cDdJzSByGEUSoFqfbRAa-pJFVZHqL-xgXnuxAHGJ1J2LXWj65zJrfB49DheQwzH1Ke4pn1NmHwLZ6HccVBj50fbQSzpsFDv0wuq3gcbW6wsX0uEI3zYYAjtNdBn-zxzz1Br3e3L9OH4un5_nF6_VQYLthYNK2CjklmLdRNU6q6UUqUopWgOJdSNYJRZgVQSgUtOahWcNJwzoUquSKKT9DVRne-aAbbmmw1Qq_n0Q0QlzqA0_8n3r3rWfjSlNBaKMWywtmPQgyfC5tGPbi0Wga8DYukmaTZSZXhjJ5vUBNDStF2238o0euQdA5JM6ZXIWX89K-3LfwbCf8GWRuPnw</recordid><startdate>20230428</startdate><enddate>20230428</enddate><creator>Yuan, Yimin</creator><creator>Wang, Jingzi</creator><creator>Huang, Liqu</creator><creator>Guo, Yunfei</creator><general>AME Publishing Company</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20230428</creationdate><title>Bioinformatics identification of prognostic genes and potential interaction analysis in renal cell carcinoma</title><author>Yuan, Yimin ; Wang, Jingzi ; Huang, Liqu ; Guo, Yunfei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c342t-bd9af282eea7bb597b99454d8a933889b4212e4a1114153a9d430b33349539093</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Original</topic><toplevel>online_resources</toplevel><creatorcontrib>Yuan, Yimin</creatorcontrib><creatorcontrib>Wang, Jingzi</creatorcontrib><creatorcontrib>Huang, Liqu</creatorcontrib><creatorcontrib>Guo, Yunfei</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Translational cancer research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yuan, Yimin</au><au>Wang, Jingzi</au><au>Huang, Liqu</au><au>Guo, Yunfei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bioinformatics identification of prognostic genes and potential interaction analysis in renal cell carcinoma</atitle><jtitle>Translational cancer research</jtitle><addtitle>Transl Cancer Res</addtitle><date>2023-04-28</date><risdate>2023</risdate><volume>12</volume><issue>4</issue><spage>774</spage><epage>783</epage><pages>774-783</pages><issn>2218-676X</issn><eissn>2219-6803</eissn><abstract>Renal cell carcinoma (RCC) is one of the ten most prevalent cancers in the world and its incidence has been rising over the past decade. However, effective biomarkers to predict the prognosis of patients remains absent, and the exact molecular mechanism of the disease remains unclear. Therefore, the identification of key genes and their biological pathways are of great significance to identify the differential expressed genes associated with the prognosis for patients with RCC, and to further explore their potential protein-protein interactions (PPIs) in tumorigenesis. The gene expression microarray data for GSE15641 and GSE40435 were extracted from the Gene Expression Omnibus (GEO) database, including 150 primary tumors and their matched adjacent non-tumor tissues. Afterwards, gene expression for fold changes (FCs) and P value for tumor and non-tumor tissues were analyzed using online tool GEO2R. Gene expression with logFCs of greater than two combined with P value of lower than 0.01 were considered as candidate targets for treatment of RCC. The survival analysis of candidate genes was performed by online software OncoLnc. The PPI network was implemented with Search Tool for the Retrieval of Interacting Genes (STRING). In total, there were 625 differentially expressed genes (DEGs) in GSE15641, including 415 increased and 210 decreased genes. A total of 343 DEGs were identified in the GSE40435 with 101 upregulated and 242 downregulated genes, the 20 genes with highest FC in high or low expression in each database were summarized. Five candidate genes were overlapped genes in the two GEO datasets. However, aldolase, fructose-bisphosphate B (ALDOB) was found to be the only gene affecting the prognosis. A number of critical genes were identified behind the mechanism, of which they interacted with ALDOB. Among them, phosphofructokinase, platelet ( ), phosphofructokinase, muscle ( ), pyruvate kinase L/R ( ), and fructose-bisphosphatase 1 ( ) showed a better prognosis, whereas only glyceraldehyde-3-phosphate dehydrogenase ( ) rendered a bleak outcome. Five genes were found to be overlappingly expressed in the top 20 greatest FC in two human GEO datasets. This is of great value in the treatment and prognosis of RCC.</abstract><cop>China</cop><pub>AME Publishing Company</pub><pmid>37180655</pmid><doi>10.21037/tcr-22-2242</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2218-676X
ispartof Translational cancer research, 2023-04, Vol.12 (4), p.774-783
issn 2218-676X
2219-6803
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10174992
source EZB-FREE-00999 freely available EZB journals; PubMed Central; Alma/SFX Local Collection; PubMed Central Open Access
subjects Original
title Bioinformatics identification of prognostic genes and potential interaction analysis in renal cell carcinoma
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T18%3A12%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Bioinformatics%20identification%20of%20prognostic%20genes%20and%20potential%20interaction%20analysis%20in%20renal%20cell%20carcinoma&rft.jtitle=Translational%20cancer%20research&rft.au=Yuan,%20Yimin&rft.date=2023-04-28&rft.volume=12&rft.issue=4&rft.spage=774&rft.epage=783&rft.pages=774-783&rft.issn=2218-676X&rft.eissn=2219-6803&rft_id=info:doi/10.21037/tcr-22-2242&rft_dat=%3Cproquest_pubme%3E2813886749%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2813886749&rft_id=info:pmid/37180655&rfr_iscdi=true