Integrative multi-omics and machine learning approach reveals tumor microenvironment-associated prognostic biomarkers in ovarian cancer DOI Open Access
Wenzhi Jiao, Shasha Yang, Yue Li

et al.

Translational Cancer Research, Journal Year: 2024, Volume and Issue: 13(11), P. 6182 - 6200

Published: Nov. 1, 2024

Ovarian cancer (OC) is a globally prevalent malignancy with significant morbidity and mortality, yet its heterogeneity poses challenges in treatment prognosis. Recognizing the crucial role of tumor microenvironment (TME) OC progression, this study leverages integrative multi-omics machine learning to uncover TME-associated prognostic biomarkers, paving way for more personalized therapeutic interventions. Employing rigorous approach, analyzed single-cell RNA sequencing (scRNA-seq) data from normal tissue samples, including high-grade serous (HGSOC) Gene Expression Omnibus (GEO: GSE184880) The Cancer Genome Atlas (TCGA) cohort, utilizing Seurat package annotate 700 TME-related genes. A model was developed using least absolute shrinkage selection operator (LASSO) regression independently validated against similarly composed HGSOC datasets. Comprehensive gene expression immune cell infiltration analyses were conducted, employing advanced algorithms like xCell delineate landscape HGSOC. Our investigation unveiled distinctive patterns profiles within TME Notably, prevalence exhausted CD8+ T cells high-risk patient samples emerged as critical finding, underscoring dualistic nature response OC. model, incorporating markers, exhibited robust predictive accuracy outcomes, showing correlations immunotherapy responses drug sensitivities. This presents groundbreaking exploration TME, offering vital insights into molecular intricacies. By systematically deciphering signatures, research illuminates potential these biomarkers refining prognosis guiding strategies. findings underscore necessity medicine treatment, potentially enhancing survival rates quality life. marks stride understanding combatting complexities

Language: Английский

Nanotechnology for boosting ovarian cancer immunotherapy DOI Creative Commons
Prabhjot Kaur, Santosh Kumar Singh, Manoj K. Mishra

et al.

Journal of Ovarian Research, Journal Year: 2024, Volume and Issue: 17(1)

Published: Oct. 14, 2024

Ovarian cancer, often referred to as the "silent killer," is notoriously difficult detect in its early stages, leading a poor prognosis for many patients. Diagnosis delayed until cancer has advanced, primarily due ambiguous and frequently occurring clinical symptoms. leads more deaths than any other of female reproductive system. The main reasons high mortality rates include diagnosis resistance treatment. As result, there an urgent need improved diagnostic treatment options ovarian cancer. standard treatments typically involve debulking surgery along with platinum-based chemotherapies. Among patients advanced-stage who initially respond current therapies, 50-75% experience recurrence. Recently, immunotherapy-based approaches enhance body's immune response combat tumor growth have shown promise. Immune checkpoint inhibitors promising results treating types tumors. However, only few these been effective because tumor's environment suppresses system creates barriers This hampers effectiveness existing immunotherapies. Nonetheless, advanced immunotherapy techniques delivery systems based on nanotechnology hold promise overcoming challenges.

Language: Английский

Citations

7
Advanced Therapeutic Approaches for Metastatic Ovarian Cancer DOI Open Access

Soohyun Choe,

Mir Jeon, Hyunho Yoon

et al.

Cancers, Journal Year: 2025, Volume and Issue: 17(5), P. 788 - 788

Published: Feb. 25, 2025

Ovarian cancer is the fifth leading cause of cancer-related death among women, which one most common gynecological cancers worldwide. Although several cytoreductive surgeries and chemotherapies have been attempted to address ovarian cancer, disease still shows poor prognosis survival rates due prevalent metastasis. Peritoneal metastasis recognized as primary route metastatic progression in cancer. It causes severe symptoms patients, but it generally difficult detect at an early stage. Current anti-cancer therapy insufficient completely treat its high recurrence resistance. Therefore, developing strategies for treating requires a deeper understanding tumor microenvironment (TME) identification effective therapeutic targets through precision oncology. Given that various signaling pathways, including TGF-β, NF-κB, PI3K/AKT/mTOR influence progression, their activity significance can vary depending on type. In these pathways are particularly important, they not only drive also impact TME, contributes potential. The TME plays critical role driving features altered immunologic interactions. Recent advances focused targeting distinct improve treatment outcomes. Deciphering complex interaction between immune populations contributing provides opportunity enhance efficacy. Hereby, this review highlights mechanisms immunological interactions understand improved immunotherapy against

Language: Английский

Citations

0
Activity of NAD(P)H-Oxidoreductases in Ovarian Cancer DOI Creative Commons
Maria V. Fedorova,

V I Voznesensky,

Elena A. Sosnova

et al.

Biomedicines, Journal Year: 2024, Volume and Issue: 12(5), P. 1052 - 1052

Published: May 10, 2024

Reactive oxygen species (ROS) play an important and controversial role in carcinogenesis. Microsomal redox chains containing NADH- NADPH-dependent oxidoreductases are among the main sites of intracellular ROS synthesis, but their oxidative balance has not been fully studied. Here, we studied activity cytochrome b5 reductase (CYB5R) P450 (CYPOR) ovarian cancer tissues cells isolated from peritoneal fluid, along with antioxidant capacity fluid. We used developed a chemiluminescence assay based on stimulation NADH NADPH, which reflects CYB5R CYPOR, respectively. The CYPOR was significantly higher moderately poorly differentiated adenocarcinomas compared well-differentiated cystadenomas. For chemotherapy-resistant tumors, tissue lower to non-resistant tumors. In increased this series, benign tumors < adenocarcinomas, so excess observed for adenocarcinomas. fluid were characterized by direct moderate correlation These results indicate significant NAD(P)H potential biochemistry. parameters useful diagnostics prognostics. can be analyze other cells.

Language: Английский

Citations

1
Integrative multi-omics and machine learning approach reveals tumor microenvironment-associated prognostic biomarkers in ovarian cancer DOI Open Access
Wenzhi Jiao, Shasha Yang, Yue Li

et al.

Translational Cancer Research, Journal Year: 2024, Volume and Issue: 13(11), P. 6182 - 6200

Published: Nov. 1, 2024

Ovarian cancer (OC) is a globally prevalent malignancy with significant morbidity and mortality, yet its heterogeneity poses challenges in treatment prognosis. Recognizing the crucial role of tumor microenvironment (TME) OC progression, this study leverages integrative multi-omics machine learning to uncover TME-associated prognostic biomarkers, paving way for more personalized therapeutic interventions. Employing rigorous approach, analyzed single-cell RNA sequencing (scRNA-seq) data from normal tissue samples, including high-grade serous (HGSOC) Gene Expression Omnibus (GEO: GSE184880) The Cancer Genome Atlas (TCGA) cohort, utilizing Seurat package annotate 700 TME-related genes. A model was developed using least absolute shrinkage selection operator (LASSO) regression independently validated against similarly composed HGSOC datasets. Comprehensive gene expression immune cell infiltration analyses were conducted, employing advanced algorithms like xCell delineate landscape HGSOC. Our investigation unveiled distinctive patterns profiles within TME Notably, prevalence exhausted CD8+ T cells high-risk patient samples emerged as critical finding, underscoring dualistic nature response OC. model, incorporating markers, exhibited robust predictive accuracy outcomes, showing correlations immunotherapy responses drug sensitivities. This presents groundbreaking exploration TME, offering vital insights into molecular intricacies. By systematically deciphering signatures, research illuminates potential these biomarkers refining prognosis guiding strategies. findings underscore necessity medicine treatment, potentially enhancing survival rates quality life. marks stride understanding combatting complexities

Language: Английский

Citations

0