Role of arachidonic acid metabolism in osteosarcoma prognosis by integrating WGCNA and bioinformatics analysis

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

Published: March 12, 2025

Abstract Background Osteosarcoma is a rare tumor with poor clinical outcomes. New therapeutic targets are urgently needed. Previous research indicates that genes abnormally expressed in osteosarcoma significantly involved the arachidonic acid (AA) metabolic pathway. However, role of metabolism-related (AAMRGs) prognosis remains unknown. Methods samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were classified into high-score low-score groups based on AAMRGs scores obtained through ssGSEA analysis. intersecting identified weighted gene co-expression network analysis (WGCNA), DEGs (osteosarcoma vs. normal) DE-AAMRGs (high- low-score). An AA metabolism predictive model five established by Cox regression LASSO algorithm. Model performance was evaluated using Kaplan-Meier survival receiver operating characteristic (ROC) curve In vitro experiments related biomarkers validated. Results Our study constructed an prognostic signature (CD36, CLDN11, STOM, EPYC, PANX3). K-M indicated patients low-risk group showed superior overall to high-risk ( p <0.05). ROC curves all AUC values exceeded 0.76. By ESTIMATE algorithms, we discovered had lower immune score, stromal estimate score. Correlation strongest positive correlation between STOM natural killer cells, highest negative association PANX3 central memory CD8 T cells. for prognosis. Conclusion suggested high level might serve as biomarker offers potential explanation cyclooxygenase inhibitors cancer. PANX3, STOM) screened construct risk value, providing new reference treatment osteosarcoma.

Language: Английский

WGCNA-ML-MR integration: uncovering immune-related genes in prostate cancer

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: April 7, 2025

Prostate cancer is one of the most common tumors in men, with its incidence and mortality rates continuing to rise year by year. Prostate-specific antigen (PSA) commonly used screening indicator, but lack specificity leads overdiagnosis overtreatment. Therefore, identifying new biomarkers related prostate crucial for early diagnosis treatment cancer. This study utilized datasets from Gene Expression Omnibus (GEO) screen differentially expressed genes (DEGs) employed Weighted Co-expression Network Analysis (WGCNA) identify driver highly associated within modules. The intersection was taken, Kyoto Encyclopedia Genes Genomes (KEGG) Ontology (GO) enrichment analyses were performed. Furthermore, a machine learning algorithm core construct diagnostic model, which then validated an external validation dataset. correlation between immune cell infiltration analyzed, Mendelian randomization (MR) analysis conducted closely identified six biomarkers: SLC14A1, ARHGEF38, NEFH, MSMB, KRT23, KRT15. MR demonstrated that MSMB may be important protective factor In q-PCR experiments on tumor tissues adjacent non-cancerous patients, it found that: compared tissues, expression level ARHGEF38 significantly increased, while levels KRT15 decreased. To further validate these findings at protein level, we Western blot analysis, corroborated results, demonstrating consistent patterns all biomarkers. IHC results confirmed markedly reduced. Our reveals are potential cancer, among play role

Language: Английский