Gene co-expression network analysis revealed novel biomarkers for ovarian cancer
Ovarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the deve...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.971845/full |
| _version_ | 1798030987538464768 |
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| author | Ceyda Kasavi |
| author_facet | Ceyda Kasavi |
| author_sort | Ceyda Kasavi |
| collection | DOAJ |
| description | Ovarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the development of diagnostic and treatment strategies. In the present study, a differential co-expression network analysis was performed via meta-analysis of three transcriptome datasets of serous ovarian adenocarcinoma to identify novel candidate biomarker signatures, i.e. genes and miRNAs. We identified 439 common differentially expressed genes (DEGs), and reconstructed differential co-expression networks using common DEGs and considering two conditions, i.e. healthy ovarian surface epithelia samples and serous ovarian adenocarcinoma epithelia samples. The modular analyses of the constructed networks indicated a co-expressed gene module consisting of 17 genes. A total of 11 biomarker candidates were determined through receiver operating characteristic (ROC) curves of gene expression of module genes, and miRNAs targeting these genes were identified. As a result, six genes (CDT1, CNIH4, CRLS1, LIMCH1, POC1A, and SNX13), and two miRNAs (mir-147a, and mir-103a-3p) were suggested as novel candidate prognostic biomarkers for ovarian cancer. Further experimental and clinical validation of the proposed biomarkers could help future development of potential diagnostic and therapeutic innovations in ovarian cancer. |
| first_indexed | 2024-04-11T19:50:02Z |
| format | Article |
| id | doaj.art-28d6a1d74bdf4a048742abdff8a1f2f3 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-11T19:50:02Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-28d6a1d74bdf4a048742abdff8a1f2f32022-12-22T04:06:20ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-10-011310.3389/fgene.2022.971845971845Gene co-expression network analysis revealed novel biomarkers for ovarian cancerCeyda KasaviOvarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the development of diagnostic and treatment strategies. In the present study, a differential co-expression network analysis was performed via meta-analysis of three transcriptome datasets of serous ovarian adenocarcinoma to identify novel candidate biomarker signatures, i.e. genes and miRNAs. We identified 439 common differentially expressed genes (DEGs), and reconstructed differential co-expression networks using common DEGs and considering two conditions, i.e. healthy ovarian surface epithelia samples and serous ovarian adenocarcinoma epithelia samples. The modular analyses of the constructed networks indicated a co-expressed gene module consisting of 17 genes. A total of 11 biomarker candidates were determined through receiver operating characteristic (ROC) curves of gene expression of module genes, and miRNAs targeting these genes were identified. As a result, six genes (CDT1, CNIH4, CRLS1, LIMCH1, POC1A, and SNX13), and two miRNAs (mir-147a, and mir-103a-3p) were suggested as novel candidate prognostic biomarkers for ovarian cancer. Further experimental and clinical validation of the proposed biomarkers could help future development of potential diagnostic and therapeutic innovations in ovarian cancer.https://www.frontiersin.org/articles/10.3389/fgene.2022.971845/fullovarian cancertranscriptome profilingdifferential gene co-expression networkprognostic gene modulebiomarkers |
| spellingShingle | Ceyda Kasavi Gene co-expression network analysis revealed novel biomarkers for ovarian cancer Frontiers in Genetics ovarian cancer transcriptome profiling differential gene co-expression network prognostic gene module biomarkers |
| title | Gene co-expression network analysis revealed novel biomarkers for ovarian cancer |
| title_full | Gene co-expression network analysis revealed novel biomarkers for ovarian cancer |
| title_fullStr | Gene co-expression network analysis revealed novel biomarkers for ovarian cancer |
| title_full_unstemmed | Gene co-expression network analysis revealed novel biomarkers for ovarian cancer |
| title_short | Gene co-expression network analysis revealed novel biomarkers for ovarian cancer |
| title_sort | gene co expression network analysis revealed novel biomarkers for ovarian cancer |
| topic | ovarian cancer transcriptome profiling differential gene co-expression network prognostic gene module biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.971845/full |
| work_keys_str_mv | AT ceydakasavi genecoexpressionnetworkanalysisrevealednovelbiomarkersforovariancancer |