Abstract
Adrenocortical carcinoma (ACC) is a rare epithelial tumor originating from adrenal cortical cells, notable for its high degree of malignancy and poor prognosis. Owing to heterogeneity, patient outcomes vary significantly. Current biomarkers for ACC risk stratification have notable limitations. However, with the advancement of multi-omics sequencing technology, we can utilize multi-omics data to explore the heterogeneity of ACC, thereby identifying novel biomarkers. In this study, we establish multicenter transcriptomics and ATAC-seq data from the TCGA and GEO databases to perform weighted gene coexpression network analysis (WGCNA) clustering and conduct comprehensive analyses of various ACC samples. These findings are integrated with univariate Cox regression, receiver operating characteristic (ROC) curve analysis, and survival analysis to identify potential biomarkers. We establish FSCN1 as an independent risk factor associated with poor ACC prognosis. ATAC-seq data demonstrate higher chromatin accessibility of FSCN1 in ACC patients with progressive disease. Immunohistochemical analysis confirms the expression of FSCN1 at the protein level, while functional cell assays reveal its role in promoting tumor invasion and proliferation. Functional enrichment analyses highlight the biological characteristics of FSCN1, and estimation of TME-infiltrating cells suggests that FSCN1 expression contributes to poor prognosis by inhibiting CD8 (+) T-cell infiltration within the ACC microenvironment. Finally, multi-omics analyses elucidate the role of FSCN1 at the mutation level. Taken together, our findings highlight FSCN1 as a promising novel biomarker and potential therapeutic target, underscoring its value in guiding the strategic management of ACC.