Revealing propionate metabolism-related genes in glioblastoma and investigating their underlying mechanisms.

BACKGROUND

Propionate metabolism may affect tumor growth and aggressiveness, but the role of propionate metabolism-related genes (PMRGs) in glioblastoma (GBM) remains poorly understood.

METHODS

Differentially expressed PMRGs (DE-PMRGs) were identified by comparing differentially expressed genes (DEGs) between GBM and normal tissues using TCGA-GBM, GSE42669, GSE162631 datasets. Functional enrichment analysis of DE-PMRGs was performed, followed by univariate Cox regression and least absolute shrinkage with selection operator (LASSO) analysis to identify potential prognostic biomarkers. In addition, prognostic models were developed and validated using independent cohorts. Genomic enrichment analysis (GSEA) was used to assess immune-related pathways in different risk subgroups. Finally, biomarker expression was confirmed using quantitative reverse transcription polymerase chain reaction (qRT-PCR).

RESULTS

Differential expression analysis identified a total of 180 DE-PMRGs, which were strongly associated with drug response and insulin signaling pathways. Six biomarkers (SARDH, ACHE, ADSL, PNPLA3, MAPK1 and SREBF2) were identified to be associated with prognosis. The accuracy of the prognostic model was confirmed using the GSE42669 dataset, with risk score and MGMT promoter status identified as independent prognostic factors. GSEA showed enrichment of immune response activation and cell cycle regulatory pathways. qRT-PCR validation showed up-regulation of PNPLA3 and SARDH, and down-regulation of ADSL, in tumor tissues.

CONCLUSIONS

This study identified six PMRGs (SARDH, ACHE, ADSL, PNPLA3, MAPK1 and SREBF2) as potential prognostic biomarkers for glioblastoma. These biomarkers reveal the role of propionate metabolism in the progression of glioblastoma and may serve as important indicators of patient prognosis and treatment strategies.