A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer
BackgroundOvarian cancer (OC) is a highly lethal and aggressive gynecologic cancer, with an overall survival rate that has shown little improvement over the decades. Robust models are urgently needed to distinguish high-risk cases and predict reliable treatment options for OC. Although anoikis-relat...
| الحاوية / القاعدة: | Frontiers in Endocrinology |
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| المؤلفون الرئيسيون: | , |
| التنسيق: | مقال |
| اللغة: | الإنجليزية |
| منشور في: |
Frontiers Media S.A.
2023-06-01
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1193622/full |
| _version_ | 1850317658460782592 |
|---|---|
| author | Yiqi Duan Xiao Xu |
| author_facet | Yiqi Duan Xiao Xu |
| author_sort | Yiqi Duan |
| collection | DOAJ |
| container_title | Frontiers in Endocrinology |
| description | BackgroundOvarian cancer (OC) is a highly lethal and aggressive gynecologic cancer, with an overall survival rate that has shown little improvement over the decades. Robust models are urgently needed to distinguish high-risk cases and predict reliable treatment options for OC. Although anoikis-related genes (ARGs) have been reported to contribute to tumor growth and metastasis, their prognostic value in OC remains unknown. The purpose of this study was to construct an ARG pair (ARGP)-based prognostic signature for patients with OC and elucidate the potential mechanism underlying the involvement of ARGs in OC progression.MethodsThe RNA-sequencing and clinical information data of OC patients were obtained from The Center Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A novel algorithm based on pairwise comparison was utilized to select ARGPs, followed by the Least Absolute Shrinkage and Selection Operator Cox analysis to construct a prognostic signature. The predictive ability of the model was validated using an external dataset, a receiver operating characteristic curve, and stratification analysis. The immune microenvironment and the proportion of immune cells were analyzed in high- and low-risk OC cases using seven algorithms. Gene set enrichment analysis and weighted gene co-expression network analysis were performed to investigate the potential mechanisms of ARGs in OC occurrence and prognosis.ResultsThe 19-ARGP signature was identified as an important prognostic predictor for 1-, 2-, and 3-year overall survival of patients with OC. Gene function enrichment analysis showed that the high-risk group was characterized by the infiltration of immunosuppressive cells and the enrichment of adherence-related signaling pathway, suggesting that ARGs were involved in OC progression by mediating immune escape and tumor metastasis.ConclusionWe constructed a reliable ARGP prognostic signature of OC, and our findings suggested that ARGs exerted a vital interplay in OC immune microenvironment and therapeutic response. These insights provided valuable information regarding the molecular mechanisms underlying this disease and potential targeted therapies. |
| format | Article |
| id | doaj-art-e7c2b4096d0e433094ea4aa230349f4e |
| institution | Directory of Open Access Journals |
| issn | 1664-2392 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| spelling | doaj-art-e7c2b4096d0e433094ea4aa230349f4e2025-08-19T23:23:55ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-06-011410.3389/fendo.2023.11936221193622A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancerYiqi Duan0Xiao Xu1Department of Pharmacy, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Obstetrics and Gynecology, Zhongshan Hospital, Fudan University, Shanghai, ChinaBackgroundOvarian cancer (OC) is a highly lethal and aggressive gynecologic cancer, with an overall survival rate that has shown little improvement over the decades. Robust models are urgently needed to distinguish high-risk cases and predict reliable treatment options for OC. Although anoikis-related genes (ARGs) have been reported to contribute to tumor growth and metastasis, their prognostic value in OC remains unknown. The purpose of this study was to construct an ARG pair (ARGP)-based prognostic signature for patients with OC and elucidate the potential mechanism underlying the involvement of ARGs in OC progression.MethodsThe RNA-sequencing and clinical information data of OC patients were obtained from The Center Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A novel algorithm based on pairwise comparison was utilized to select ARGPs, followed by the Least Absolute Shrinkage and Selection Operator Cox analysis to construct a prognostic signature. The predictive ability of the model was validated using an external dataset, a receiver operating characteristic curve, and stratification analysis. The immune microenvironment and the proportion of immune cells were analyzed in high- and low-risk OC cases using seven algorithms. Gene set enrichment analysis and weighted gene co-expression network analysis were performed to investigate the potential mechanisms of ARGs in OC occurrence and prognosis.ResultsThe 19-ARGP signature was identified as an important prognostic predictor for 1-, 2-, and 3-year overall survival of patients with OC. Gene function enrichment analysis showed that the high-risk group was characterized by the infiltration of immunosuppressive cells and the enrichment of adherence-related signaling pathway, suggesting that ARGs were involved in OC progression by mediating immune escape and tumor metastasis.ConclusionWe constructed a reliable ARGP prognostic signature of OC, and our findings suggested that ARGs exerted a vital interplay in OC immune microenvironment and therapeutic response. These insights provided valuable information regarding the molecular mechanisms underlying this disease and potential targeted therapies.https://www.frontiersin.org/articles/10.3389/fendo.2023.1193622/fullanoikis-related gene pairsovarian cancerprognostic signatureimmunoinfiltrationtherapeutic response |
| spellingShingle | Yiqi Duan Xiao Xu A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer anoikis-related gene pairs ovarian cancer prognostic signature immunoinfiltration therapeutic response |
| title | A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer |
| title_full | A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer |
| title_fullStr | A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer |
| title_full_unstemmed | A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer |
| title_short | A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer |
| title_sort | signature based on anoikis related genes for the evaluation of prognosis immunoinfiltration mutation and therapeutic response in ovarian cancer |
| topic | anoikis-related gene pairs ovarian cancer prognostic signature immunoinfiltration therapeutic response |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1193622/full |
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