Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics
Abstract Background Ovarian cancer (OC) is a formidable gynecological tumor marked with the highest mortality rate. The lack of effective biomarkers and treatment drugs places a substantial proportion of patients with OC at significant risk of mortality, primarily due to metastasis. Glycolysis metab...
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2025-05-01
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| Online Access: | https://doi.org/10.1186/s12905-025-03750-y |
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| author | Lele Ling Bingrong Li Boliang Ke Yinjie Hu Kaiyong Zhang Siwen Li Te Liu Peng Liu Bimeng Zhang |
| author_facet | Lele Ling Bingrong Li Boliang Ke Yinjie Hu Kaiyong Zhang Siwen Li Te Liu Peng Liu Bimeng Zhang |
| author_sort | Lele Ling |
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| description | Abstract Background Ovarian cancer (OC) is a formidable gynecological tumor marked with the highest mortality rate. The lack of effective biomarkers and treatment drugs places a substantial proportion of patients with OC at significant risk of mortality, primarily due to metastasis. Glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism are closely intertwined with the occurrence and progression of OC. Thus, it is of utmost significance to identify potent prognostic biomarkers and delve into the exploration of novel therapeutic drugs and targets, in pursuit of advancing the treatment of OC. Methods Single-cell RNA sequencing (scRNA-seq) data related to OC were analyzed using AUCell scores to identify subpopulations at the single-cell level. The “AddModuleScore” function of the “Seurat” package was adopted to score and select marker genes from four gene sets: glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism. A prognostic model for metabolism-related genes (MRGs) was constructed and validated using OC-related marker genes selected from bulk RNAseq data. The MRG-based prognostic model was further utilized for functional analysis of the model gene set, pan-cancer analysis of genomic variations, spatial transcriptomics analysis, as well as GO and KEGG enrichment analysis. CIBERSORT and ESTIMATE algorithms were utilized for assessing the immune microenvironment of TCGA-ovarian serous cystadenocarcinoma (OV) samples. Furthermore, the Tracking Tumor Immunophenotype (TIP) database was employed to examine the anti-cancer immune response in patients with OC. To gain a more in-depth understanding of the process, the frequency of somatic mutations and different types of mutated genes were visualized through the somatic mutation profile of the TCGA database. Moreover, the benefits of immune checkpoint inhibitor (ICI) therapy in individuals with OC were predicted in the TIDE database. In addition, the CMap database was used to predict small-molecule drugs for the treatment of OC. Furthermore, immunohistochemistry, RT-qPCR, CCK-8, Transwell assay, and in vivo tumor xenograft experiments were conducted to validate the prognostic ability of the MRG Triggering Receptor Expressed on Myeloid Cells-1 (TREM1) in OC. Results Monocytes were selected using AUCell scoring, and two subpopulations of monocytes, marked by the expression of C1QC+ tumor-associated macrophages (TAMs) and FCN1+ resident tissue macrophages (RTMs), were identified as marker genes for OC. Subsequently, a prognostic model consisting of 12 MRGs was constructed and validated. Genomic exploration of the prognostic model unveiled an array of biological functions linked with metabolism. Furthermore, copy number variation (CNV), mRNA expression, single nucleotide variation (SNV), and methylation were significantly different across diverse tumors. Analysis of the TIP database demonstrated that the low-risk group, as determined by the MRG-based prognostic model, exhibited significantly higher anti-cancer immune activity relative to the high-risk group. Furthermore, predictions from the TIDE database revealed that individuals in the high-risk group were more prone to immune evasion when treated with ICIs. The resulting data identified candesartan and PD-123319 as potential therapeutic drugs for OC, possibly acting on the target ATGR2. In vitro and in vivo experiments elucidated that the targeted downregulation of TREM1 effectively inhibited the proliferation and migration of OC cells. Conclusion The MRG-based prognostic model constructed through the combined analysis of glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism is potentially effective as a prognostic biomarker. Furthermore, candesartan and PD-123319 may be potential therapeutic drugs for OC, possibly acting on the target ATGR2. Graphical Abstract |
| format | Article |
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| institution | Kabale University |
| issn | 1472-6874 |
| language | English |
| publishDate | 2025-05-01 |
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| series | BMC Women's Health |
| spelling | doaj-art-18eddd66421b445ca74af46ea6793e702025-08-20T03:48:02ZengBMCBMC Women's Health1472-68742025-05-0125112810.1186/s12905-025-03750-yMetabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomicsLele Ling0Bingrong Li1Boliang Ke2Yinjie Hu3Kaiyong Zhang4Siwen Li5Te Liu6Peng Liu7Bimeng Zhang8Department of Acupuncture, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Acupuncture, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineSchool of Health Science and Engineering, University of Shanghai for Science and TechnologyDepartment of Acupuncture, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Acupuncture, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Acupuncture, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineShanghai Geriatric Institute of Chinese Medicine, Shanghai University of Traditional Chinese MedicineDepartment of Acupuncture, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Acupuncture, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineAbstract Background Ovarian cancer (OC) is a formidable gynecological tumor marked with the highest mortality rate. The lack of effective biomarkers and treatment drugs places a substantial proportion of patients with OC at significant risk of mortality, primarily due to metastasis. Glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism are closely intertwined with the occurrence and progression of OC. Thus, it is of utmost significance to identify potent prognostic biomarkers and delve into the exploration of novel therapeutic drugs and targets, in pursuit of advancing the treatment of OC. Methods Single-cell RNA sequencing (scRNA-seq) data related to OC were analyzed using AUCell scores to identify subpopulations at the single-cell level. The “AddModuleScore” function of the “Seurat” package was adopted to score and select marker genes from four gene sets: glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism. A prognostic model for metabolism-related genes (MRGs) was constructed and validated using OC-related marker genes selected from bulk RNAseq data. The MRG-based prognostic model was further utilized for functional analysis of the model gene set, pan-cancer analysis of genomic variations, spatial transcriptomics analysis, as well as GO and KEGG enrichment analysis. CIBERSORT and ESTIMATE algorithms were utilized for assessing the immune microenvironment of TCGA-ovarian serous cystadenocarcinoma (OV) samples. Furthermore, the Tracking Tumor Immunophenotype (TIP) database was employed to examine the anti-cancer immune response in patients with OC. To gain a more in-depth understanding of the process, the frequency of somatic mutations and different types of mutated genes were visualized through the somatic mutation profile of the TCGA database. Moreover, the benefits of immune checkpoint inhibitor (ICI) therapy in individuals with OC were predicted in the TIDE database. In addition, the CMap database was used to predict small-molecule drugs for the treatment of OC. Furthermore, immunohistochemistry, RT-qPCR, CCK-8, Transwell assay, and in vivo tumor xenograft experiments were conducted to validate the prognostic ability of the MRG Triggering Receptor Expressed on Myeloid Cells-1 (TREM1) in OC. Results Monocytes were selected using AUCell scoring, and two subpopulations of monocytes, marked by the expression of C1QC+ tumor-associated macrophages (TAMs) and FCN1+ resident tissue macrophages (RTMs), were identified as marker genes for OC. Subsequently, a prognostic model consisting of 12 MRGs was constructed and validated. Genomic exploration of the prognostic model unveiled an array of biological functions linked with metabolism. Furthermore, copy number variation (CNV), mRNA expression, single nucleotide variation (SNV), and methylation were significantly different across diverse tumors. Analysis of the TIP database demonstrated that the low-risk group, as determined by the MRG-based prognostic model, exhibited significantly higher anti-cancer immune activity relative to the high-risk group. Furthermore, predictions from the TIDE database revealed that individuals in the high-risk group were more prone to immune evasion when treated with ICIs. The resulting data identified candesartan and PD-123319 as potential therapeutic drugs for OC, possibly acting on the target ATGR2. In vitro and in vivo experiments elucidated that the targeted downregulation of TREM1 effectively inhibited the proliferation and migration of OC cells. Conclusion The MRG-based prognostic model constructed through the combined analysis of glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism is potentially effective as a prognostic biomarker. Furthermore, candesartan and PD-123319 may be potential therapeutic drugs for OC, possibly acting on the target ATGR2. Graphical Abstracthttps://doi.org/10.1186/s12905-025-03750-yMetabolismOvarian cancerBiomarkersSingle-cell sequencingBulk RNA sequencing |
| spellingShingle | Lele Ling Bingrong Li Boliang Ke Yinjie Hu Kaiyong Zhang Siwen Li Te Liu Peng Liu Bimeng Zhang Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics BMC Women's Health Metabolism Ovarian cancer Biomarkers Single-cell sequencing Bulk RNA sequencing |
| title | Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics |
| title_full | Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics |
| title_fullStr | Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics |
| title_full_unstemmed | Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics |
| title_short | Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics |
| title_sort | metabolism associated marker gene based predictive model for prognosis targeted therapy and immune landscape in ovarian cancer an integrative analysis of single cell and bulk rna sequencing with spatial transcriptomics |
| topic | Metabolism Ovarian cancer Biomarkers Single-cell sequencing Bulk RNA sequencing |
| url | https://doi.org/10.1186/s12905-025-03750-y |
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