Proteomic Studies That Predict Patients' Responses to High-Grade Serous Ovarian Cancer Treatments: A Systematic Review.
메타분석
1/5 보강
The survival rates of high-grade serous ovarian cancer have not improved in the last three decades, despite extensive research into the molecular determinants of chemoresistance that could inform pers
- 연구 설계 systematic review
APA
Scanlan J, Mittal P, et al. (2026). Proteomic Studies That Predict Patients' Responses to High-Grade Serous Ovarian Cancer Treatments: A Systematic Review.. Journal of proteome research, 25(2), 1126-1138. https://doi.org/10.1021/acs.jproteome.5c01103
MLA
Scanlan J, et al.. "Proteomic Studies That Predict Patients' Responses to High-Grade Serous Ovarian Cancer Treatments: A Systematic Review.." Journal of proteome research, vol. 25, no. 2, 2026, pp. 1126-1138.
PMID
41605417 ↗
Abstract 한글 요약
The survival rates of high-grade serous ovarian cancer have not improved in the last three decades, despite extensive research into the molecular determinants of chemoresistance that could inform personalized therapies. This systematic review synthesizes proteomic studies that have used varied sample types, including cell lines, serum, plasma, and ascites, to propose molecular markers of response to treatment regimens consisting of platinum-based chemotherapeutics, taxanes, doxorubicin, and combinations thereof. Gene ontology analyses of differentially expressed proteins across all studies highlight key biological functions, such as heat shock response, cell adhesion, and cell migration. Frequently implicated protein families include keratins, annexins, thioredoxin-related proteins, and SERPINs. We evaluate methodological rigor, orthogonal validation attempts, and adherence to MIAPE data reporting standards to contextualize current knowledge and promote reproducibility in future studies. Collectively, this review underscores proteomics as a promising tool for the prediction of chemotherapy response in high-grade serous ovarian cancer, while emphasizing the need for prospective, standardized approaches that align with data reporting guidelines.