Survival expectations in melanoma patients: a molecular prognostic model associated with aging

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 28, 2025

Language: Английский

---

Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma

BMC Cancer, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 9, 2025

The assessment of immunotherapy plays a pivotal role in the clinical management skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise advancing cancer diagnosis treatment strategies. GNNs models were developed to predict response pinpoint key pathways. Utilizing genes from these pathways, multi-omics methods employed refine construction gene signature, termed responseScore, aimed at enhancing precision predictions. Subsequently, responseScore was explored perspectives prognosis, genetic variation, pathway enrichment, tumor microenvironment. Concurrently, association among 13 contributing factors such as response, microenvironment investigated. Among genes, PSMB6 subjected an in-depth analysis its biological effect through experimental approaches like transfection co-culture. In finalized model utilizing GNNs, it has revealed AUC 0.854 within training dataset 0.824 testing set, pinpointing pathways R-HSA-70,268. indicator named excelled predictive accuracy regarding patient prognosis. Investigations into disclosed profound between enhancement immune cell infiltration anti-tumor immunity. A negative correlation observed expression with elevated correlating poor ELISA detection after co-cultivation experiments significant reductions levels cytokines IL-6 IL-1β specimens PCDH-PSMB6 group. prediction this research effectively indicate prognosis for patients Additionally, provides insights characteristics immunity Thirteen identified study show potential markers or therapeutic targets. Notably, emerges target melanoma, where exhibits inhibitory on

Language: Английский

---

Unveiling the Anti-Aging Potential of Marine Natural Bioproducts

Marine Drugs, Journal Year: 2025, Volume and Issue: 23(4), P. 165 - 165

Published: April 11, 2025

Aging is a natural process resulting in the progressive impairment of multiple functions human body, leading to decline cellular functionality and development aging-related diseases. External stress factors, such as ultraviolet (UV) radiation, pollution, toxin exposure, increase oxidative stress, damage repair mechanisms, speed up aging processes. With rise world’s population, there are enlarged demands for use sustainable products food, nutrient supplements cosmetics that can slow down prolong healthy life longevity. Algae, including both macroalgae microalgae, have been recognised source valuable proteins, amino acids, fatty vitamins, minerals useful consumption medical applications. increasing nutraceutical pharmaceutical bioproducts from environmentally friendly resources, biotechnological industry, over recent decades, has had provide new, advanced solutions using modern high-throughput omics technologies. The application proteomics area discoveries with anti-aging properties become more popular wide industry New profiling provides better understanding changes occurring protein peptide content, their structure, function interactions, well regulatory processes molecular pathways. Mass spectrometry-based used range applications identification, characterisation, quantification proteins within proteome sub-proteome. chemical facilitated identification approach included synthesis probes target fishing, allowing interest. This review focuses on marine macro- microalgal compounds novel approaches, providing experimental evidence involvement should facilitate innovative approaches

Language: Английский

---

Machine learning-based identification of an immunotherapy-related signature to enhance outcomes and immunotherapy responses in melanoma

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Sept. 17, 2024

Background Immunotherapy has revolutionized skin cutaneous melanoma treatment, but response variability due to tumor heterogeneity necessitates robust biomarkers for predicting immunotherapy response. Methods We used weighted gene co-expression network analysis (WGCNA), consensus clustering, and 10 machine learning algorithms develop the immunotherapy-related model (ITRGM) signature. Multi-omics analyses included bulk single-cell RNA sequencing of patients, mouse sequencing, pathology sections patients. Results identified 66 prognostic genes (CITPGs) using WGCNA differentially expressed (DEGs) from two cohorts. The CITPG-high group showed better prognosis enriched immune activities. DEGs between CITPG-low groups in TCGA-SKCM cohort were analyzed three additional cohorts univariate Cox regression, resulting 44 genes. Using 101 algorithm combinations, we constructed ITRGM signature based on seven outperformed 37 published signatures across training cohort, testing cohorts, a meta-cohort. It effectively stratified patients into high-risk or low-risk group, with high levels genes, correlated increased characteristics such as mutation burden cell infiltration, indicating immune-hot tumors prognosis. ITRGM’s relationship microenvironment was further validated our experiments GBP5, an important gene, CD8 IHC analysis. also predicted eight including urothelial carcinoma stomach adenocarcinoma, broad applicability. Conclusions is stable predictor stratifying ‘immune-hot’ ‘immune-cold’ tumors, enhancing immunotherapy.

Language: Английский

---

Advanced machine learning unveils CD8 + T cell genetic markers enhancing prognosis and immunotherapy efficacy in breast cancer

BMC Cancer, Journal Year: 2024, Volume and Issue: 24(1)

Published: Oct. 1, 2024

Language: Английский

---