Integrated bioinformatics approach for the identification and validation of novel biomarkers in ACC progression and prognosis.

Adrenocortical carcinoma (ACC) is a rare and extremely lethal endocrine malignancy emerging from adrenal cortex, characterized by a poor prognosis. This study, performed integrated bioinformatics to elucidate the underlying molecular mechanisms and identify novel biomarkers, validating them as therapeutic targets for ACC prognosis. The RNA-seq data across five gene expression profiles identified 79 DEGs through a comparative analysis of normal and ACC specimens. Functional enrichment and pathway analyses using the DAVID database revealed the most significant GO terms and enriched KEGG pathways. PPI network was constructed utilizing the STRING database, followed by module analysis in Cytoscape. Finally, 10 hub genes were identified including TAGLN, LUM, PDGFRA, FBLN5, MMP2, LAMA2, DCN, IGF1, FBLN1 and CXCL12 as potential biomarkers. Subsequent survival analysis confirmed that TAGLN, LUM, LAMA2, FBLN5 and FBLN1 are significantly associated with poor patient survivability. Furthermore, TFs-DEGs and miRNAs-DEGs network analyses, identified 10 transcriptional and post-translational regulators. Finally, gene-disease and gene-drug association, highlighted correlated diseases and their promising inhibitors. In conclusion, the identified novel biomarkers and associated pathways, provides a comprehensive insight into the molecular mechanisms, prognosis, and potential clinical applications for the diagnosis and therapeutic interventions of ACC.