Exploration of the shared pathways and common biomarkers in cervical and ovarian cancer using integrated bioinformatics analysis.

IF 2.8 4区 医学 Q3 ENDOCRINOLOGY & METABOLISM
Fang Liu, Min Wang, Tian Zhu, Cong Xu, Guangming Wang
{"title":"Exploration of the shared pathways and common biomarkers in cervical and ovarian cancer using integrated bioinformatics analysis.","authors":"Fang Liu, Min Wang, Tian Zhu, Cong Xu, Guangming Wang","doi":"10.1007/s12672-024-01725-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Searching for potential biomarkers and therapeutic targets for early diagnosis of gynecological tumors to improve patient survival.</p><p><strong>Methods: </strong>Microarray datasets of cervical cancer (CC) and ovarian cancer (OC) were downloaded from the Gene Expression Omnibus (GEO) database, then, differential gene expression between cancerous and normal tissues in the datasets was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to screen for co-expression modules associated with CC and OC. The screened shared genes were then further analyzed for functional pathway enrichment. Next, the least absolute shrinkage and selection operator (LASSO) with tenfold cross validation is used to further screened for common diagnostic biomarkers for the two diseases, and further validation is performed using two independent GEO datasets. Finally, the CIBERSORT algorithm was used to estimate the immune infiltration levels of CC and OC, and the correlation between immune cell infiltration and common biomarkers was explored.</p><p><strong>Results: </strong>After crossing the common DEGs detected by \"Limma\" R package with the common module genes identified by WGCNA, 44 shared genes were obtained. Functional enrichment indicates that these shared genes are mainly related to DNA synthesis pathways. Lasso regression analysis revealed that EFNA1, TYMS, and WISP2 were co-diagnostic markers for CC and OC, and then based on their expression levels and diagnostic efficacy, EFNA1 was selected as the best co-marker for CC and OC. Immune infiltration analysis shows that the immune environment has a significant impact on the occurrence and development of CC and OC, and the expression of EFNA1 is related to changes in immune cells. Gene-drug interaction analyses identified 27 common drug compounds that interact with candidate genes.</p><p><strong>Conclusion: </strong>This study adopted bioinformatics methods to investigate the common pathways and identify diagnostic markers between CC and OC, suggesting that DNA synthesis and immune environment are closely related to the occurrence and development of CC and OC. EFNA1 may be a potential diagnostic indicator and therapeutic target for patients with CC and OC.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"15 1","pages":"826"},"PeriodicalIF":2.8000,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discover. Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12672-024-01725-3","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0

Abstract

Objective: Searching for potential biomarkers and therapeutic targets for early diagnosis of gynecological tumors to improve patient survival.

Methods: Microarray datasets of cervical cancer (CC) and ovarian cancer (OC) were downloaded from the Gene Expression Omnibus (GEO) database, then, differential gene expression between cancerous and normal tissues in the datasets was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to screen for co-expression modules associated with CC and OC. The screened shared genes were then further analyzed for functional pathway enrichment. Next, the least absolute shrinkage and selection operator (LASSO) with tenfold cross validation is used to further screened for common diagnostic biomarkers for the two diseases, and further validation is performed using two independent GEO datasets. Finally, the CIBERSORT algorithm was used to estimate the immune infiltration levels of CC and OC, and the correlation between immune cell infiltration and common biomarkers was explored.

Results: After crossing the common DEGs detected by "Limma" R package with the common module genes identified by WGCNA, 44 shared genes were obtained. Functional enrichment indicates that these shared genes are mainly related to DNA synthesis pathways. Lasso regression analysis revealed that EFNA1, TYMS, and WISP2 were co-diagnostic markers for CC and OC, and then based on their expression levels and diagnostic efficacy, EFNA1 was selected as the best co-marker for CC and OC. Immune infiltration analysis shows that the immune environment has a significant impact on the occurrence and development of CC and OC, and the expression of EFNA1 is related to changes in immune cells. Gene-drug interaction analyses identified 27 common drug compounds that interact with candidate genes.

Conclusion: This study adopted bioinformatics methods to investigate the common pathways and identify diagnostic markers between CC and OC, suggesting that DNA synthesis and immune environment are closely related to the occurrence and development of CC and OC. EFNA1 may be a potential diagnostic indicator and therapeutic target for patients with CC and OC.

求助全文
约1分钟内获得全文 求助全文
来源期刊
Discover. Oncology
Discover. Oncology Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
2.40
自引率
9.10%
发文量
122
审稿时长
5 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信