Metabolic reprogramming and prognostic insights in molecular landscapes driven by glycolysis in ovarian cancer
Abstract Ovarian cancer (OC) is a highly fatal gynecological malignancy primarily attributable to late-stage detection and restricted treatment options. Aberrant glycolysis, exemplified by the Warburg effect, facilitates tumor development, immunological evasion, and alteration of the microenvironmen...
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Nature Portfolio
2025-07-01
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| author | Mingwei Wang Qiaohui Ying Yuncan Xing Shuchang Dai Jue Wang Zhong Liu |
| author_facet | Mingwei Wang Qiaohui Ying Yuncan Xing Shuchang Dai Jue Wang Zhong Liu |
| author_sort | Mingwei Wang |
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| description | Abstract Ovarian cancer (OC) is a highly fatal gynecological malignancy primarily attributable to late-stage detection and restricted treatment options. Aberrant glycolysis, exemplified by the Warburg effect, facilitates tumor development, immunological evasion, and alteration of the microenvironment. Identifying glycolysis-related biomarkers could provide novel insights into prognosis and potential therapeutic targets for OC.The transcriptomic and clinical information of OC patients were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differentially expressed glycolysis-related genes (GRGs) were identified and analyzed for their prognostic significance. Consensus clustering was employed to identify glycolysis subtypes, followed by pathway enrichment and immune infiltration analyses. A ten-gene GRG signature was developed with LASSO-Cox regression and verified in various cohorts. Single-cell RNA sequence and drug susceptibility analysis were performed to explore tumor microenvironment heterogeneity and potential therapeutic agents.A total of 457 differentially expressed GRGs were discovered, of which 30 were substantially linked with OC prognosis. Three molecular subtypes were characterized, with cluster C exhibiting the worst prognosis and activation of tumor-associated pathways. A prognostic model comprising ten genes (LMCD1, L1CAM, MYCN, GALT, IDO1, RPL18, XBP1, LPAR3, RUNX3, PLCG1) was developed and validated, demonstrating robust predictive efficacy across various cohorts. Immune analysis revealed substantial disparities in immune infiltration among risk groups, whereas single-cell analysis identified several critical genes essential for metabolism, proliferation, and interactions within the tumor microenvironment.This work highlights the prognostic and therapeutic significance of GRGs in OC. The ten-gene GRG signature serves as a reliable framework for risk assessment and the formulation of individualized treatment regimens. Nonetheless, further experimental validation and extensive clinical research are necessary to enable the application of these findings in clinical practice. These results highlight the potential of targeting glycolytic pathways as a promising approach to improve the management and treatment outcomes of OC. |
| format | Article |
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| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-db12be7f63db4e5c8aa0008bee2875762025-08-20T04:02:46ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-12350-7Metabolic reprogramming and prognostic insights in molecular landscapes driven by glycolysis in ovarian cancerMingwei Wang0Qiaohui Ying1Yuncan Xing2Shuchang Dai3Jue Wang4Zhong Liu5Institute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical CollegeInstitute of Oral Basic Research, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong UniversityNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeInstitute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical CollegeInstitute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical CollegeInstitute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical CollegeAbstract Ovarian cancer (OC) is a highly fatal gynecological malignancy primarily attributable to late-stage detection and restricted treatment options. Aberrant glycolysis, exemplified by the Warburg effect, facilitates tumor development, immunological evasion, and alteration of the microenvironment. Identifying glycolysis-related biomarkers could provide novel insights into prognosis and potential therapeutic targets for OC.The transcriptomic and clinical information of OC patients were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differentially expressed glycolysis-related genes (GRGs) were identified and analyzed for their prognostic significance. Consensus clustering was employed to identify glycolysis subtypes, followed by pathway enrichment and immune infiltration analyses. A ten-gene GRG signature was developed with LASSO-Cox regression and verified in various cohorts. Single-cell RNA sequence and drug susceptibility analysis were performed to explore tumor microenvironment heterogeneity and potential therapeutic agents.A total of 457 differentially expressed GRGs were discovered, of which 30 were substantially linked with OC prognosis. Three molecular subtypes were characterized, with cluster C exhibiting the worst prognosis and activation of tumor-associated pathways. A prognostic model comprising ten genes (LMCD1, L1CAM, MYCN, GALT, IDO1, RPL18, XBP1, LPAR3, RUNX3, PLCG1) was developed and validated, demonstrating robust predictive efficacy across various cohorts. Immune analysis revealed substantial disparities in immune infiltration among risk groups, whereas single-cell analysis identified several critical genes essential for metabolism, proliferation, and interactions within the tumor microenvironment.This work highlights the prognostic and therapeutic significance of GRGs in OC. The ten-gene GRG signature serves as a reliable framework for risk assessment and the formulation of individualized treatment regimens. Nonetheless, further experimental validation and extensive clinical research are necessary to enable the application of these findings in clinical practice. These results highlight the potential of targeting glycolytic pathways as a promising approach to improve the management and treatment outcomes of OC.https://doi.org/10.1038/s41598-025-12350-7Ovarian cancerGlycolysis-related genesPrognostic modelTumor microenvironmentWarburg effect |
| spellingShingle | Mingwei Wang Qiaohui Ying Yuncan Xing Shuchang Dai Jue Wang Zhong Liu Metabolic reprogramming and prognostic insights in molecular landscapes driven by glycolysis in ovarian cancer Scientific Reports Ovarian cancer Glycolysis-related genes Prognostic model Tumor microenvironment Warburg effect |
| title | Metabolic reprogramming and prognostic insights in molecular landscapes driven by glycolysis in ovarian cancer |
| title_full | Metabolic reprogramming and prognostic insights in molecular landscapes driven by glycolysis in ovarian cancer |
| title_fullStr | Metabolic reprogramming and prognostic insights in molecular landscapes driven by glycolysis in ovarian cancer |
| title_full_unstemmed | Metabolic reprogramming and prognostic insights in molecular landscapes driven by glycolysis in ovarian cancer |
| title_short | Metabolic reprogramming and prognostic insights in molecular landscapes driven by glycolysis in ovarian cancer |
| title_sort | metabolic reprogramming and prognostic insights in molecular landscapes driven by glycolysis in ovarian cancer |
| topic | Ovarian cancer Glycolysis-related genes Prognostic model Tumor microenvironment Warburg effect |
| url | https://doi.org/10.1038/s41598-025-12350-7 |
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