Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer
Abstract Background Personalized medicine remains an unmet need in ovarian cancer due to its heterogeneous nature and complex immune microenvironments, which has gained increasing attention in the era of immunotherapy. A key obstacle is the lack of reliable biomarkers to identify patients who would...
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BMC
2024-09-01
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| Series: | Journal of Ovarian Research |
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| Online Access: | https://doi.org/10.1186/s13048-024-01518-w |
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| author | Shuo-Fu Chen Liang-Yun Wang Yi-Sian Lin Cho-Yi Chen |
| author_facet | Shuo-Fu Chen Liang-Yun Wang Yi-Sian Lin Cho-Yi Chen |
| author_sort | Shuo-Fu Chen |
| collection | DOAJ |
| description | Abstract Background Personalized medicine remains an unmet need in ovarian cancer due to its heterogeneous nature and complex immune microenvironments, which has gained increasing attention in the era of immunotherapy. A key obstacle is the lack of reliable biomarkers to identify patients who would benefit significantly from the therapy. While conventional clinicopathological factors have exhibited limited efficacy as prognostic indicators in ovarian cancer, multi-omics profiling presents a promising avenue for comprehending the interplay between the tumor and immune components. Here we aimed to leverage the individual proteomic and transcriptomic profiles of ovarian cancer patients to develop an effective protein-based signature capable of prognostication and distinguishing responses to immunotherapy. Methods The workflow was demonstrated based on the Reverse Phase Protein Array (RPPA) and RNA-sequencing profiles of ovarian cancer patients from The Cancer Genome Atlas (TCGA). The algorithm began by clustering patients using immune-related gene sets, which allowed us to identify immune-related proteins of interest. Next, a multi-stage process involving LASSO and Cox regression was employed to distill a prognostic signature encompassing five immune-related proteins. Based on the signature, we subsequently calculated the risk score for each patient and evaluated its prognostic performance by comparing this model with conventional clinicopathological characteristics. Results We developed and validated a protein-based prognostic signature in a cohort of 377 ovarian cancer patients. The risk signature outperformed conventional clinicopathological factors, such as age, grade, stage, microsatellite instability (MSI), and homologous recombination deficiency (HRD) status, in terms of prognoses. Patients in the high-risk group had significantly unfavorable overall survival (p < 0.001). Moreover, our signature effectively stratified patients into subgroups with distinct immune landscapes. The high-risk group exhibited higher levels of CD8 T-cell infiltration and a potentially greater proportion of immunotherapy responders. The co-activation of the TGF-β pathway and cancer-associated fibroblasts could impair the ability of cytotoxic T cells to eliminate cancer cells, leading to poor outcomes in the high-risk group. Conclusions The protein-based signature not only aids in evaluating the prognosis but also provides valuable insights into the tumor immune microenvironments in ovarian cancer. Together our findings highlight the importance of a thorough understanding of the immunosuppressive tumor microenvironment in ovarian cancer to guide the development of more effective immunotherapies. |
| format | Article |
| id | doaj-art-dcec9745b253488197c0a913f1f8b1e2 |
| institution | Kabale University |
| issn | 1757-2215 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Ovarian Research |
| spelling | doaj-art-dcec9745b253488197c0a913f1f8b1e22025-02-02T12:36:57ZengBMCJournal of Ovarian Research1757-22152024-09-0117111810.1186/s13048-024-01518-wNovel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancerShuo-Fu Chen0Liang-Yun Wang1Yi-Sian Lin2Cho-Yi Chen3Department of Heavy Particles & Radiation Oncology, Taipei Veterans General HospitalInstitute of Biomedical Informatics, National Yang Ming Chiao Tung UniversityProgram in Genetics and Genomics, Baylor College of MedicineInstitute of Biomedical Informatics, National Yang Ming Chiao Tung UniversityAbstract Background Personalized medicine remains an unmet need in ovarian cancer due to its heterogeneous nature and complex immune microenvironments, which has gained increasing attention in the era of immunotherapy. A key obstacle is the lack of reliable biomarkers to identify patients who would benefit significantly from the therapy. While conventional clinicopathological factors have exhibited limited efficacy as prognostic indicators in ovarian cancer, multi-omics profiling presents a promising avenue for comprehending the interplay between the tumor and immune components. Here we aimed to leverage the individual proteomic and transcriptomic profiles of ovarian cancer patients to develop an effective protein-based signature capable of prognostication and distinguishing responses to immunotherapy. Methods The workflow was demonstrated based on the Reverse Phase Protein Array (RPPA) and RNA-sequencing profiles of ovarian cancer patients from The Cancer Genome Atlas (TCGA). The algorithm began by clustering patients using immune-related gene sets, which allowed us to identify immune-related proteins of interest. Next, a multi-stage process involving LASSO and Cox regression was employed to distill a prognostic signature encompassing five immune-related proteins. Based on the signature, we subsequently calculated the risk score for each patient and evaluated its prognostic performance by comparing this model with conventional clinicopathological characteristics. Results We developed and validated a protein-based prognostic signature in a cohort of 377 ovarian cancer patients. The risk signature outperformed conventional clinicopathological factors, such as age, grade, stage, microsatellite instability (MSI), and homologous recombination deficiency (HRD) status, in terms of prognoses. Patients in the high-risk group had significantly unfavorable overall survival (p < 0.001). Moreover, our signature effectively stratified patients into subgroups with distinct immune landscapes. The high-risk group exhibited higher levels of CD8 T-cell infiltration and a potentially greater proportion of immunotherapy responders. The co-activation of the TGF-β pathway and cancer-associated fibroblasts could impair the ability of cytotoxic T cells to eliminate cancer cells, leading to poor outcomes in the high-risk group. Conclusions The protein-based signature not only aids in evaluating the prognosis but also provides valuable insights into the tumor immune microenvironments in ovarian cancer. Together our findings highlight the importance of a thorough understanding of the immunosuppressive tumor microenvironment in ovarian cancer to guide the development of more effective immunotherapies.https://doi.org/10.1186/s13048-024-01518-wOvarian cancerImmunotherapyProteomicsPrognosisTumor immune microenvironment |
| spellingShingle | Shuo-Fu Chen Liang-Yun Wang Yi-Sian Lin Cho-Yi Chen Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer Journal of Ovarian Research Ovarian cancer Immunotherapy Proteomics Prognosis Tumor immune microenvironment |
| title | Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer |
| title_full | Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer |
| title_fullStr | Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer |
| title_full_unstemmed | Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer |
| title_short | Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer |
| title_sort | novel protein based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer |
| topic | Ovarian cancer Immunotherapy Proteomics Prognosis Tumor immune microenvironment |
| url | https://doi.org/10.1186/s13048-024-01518-w |
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