Sarcoma accounts for less than 1% of human malignancies but is a highly aggressive and heterogeneous group of tumors originating from mesenchymal tissues. Current treatments yield unsatisfactory results due to unclear progression mechanisms, highlighting the need for novel biomarkers for treatment efficacy and prognosis prediction. FSCN1 is an actin-bundling protein that localizes to the core actin bundles within microvillar projections and filopodial extensions in migrating cells. This study aims to investigate the role of FSCN1 in pan-cancer, with a particular focus on sarcoma, by conducting comprehensive bioinformatics analysis. We analyzed FSCN1’s pan-cancer expression, epigenetic status, and genetic alterations. FSCN1 expression in tumor and normal tissues was assessed using multiple databases. Its association with pathological stages was analyzed, and prognostic and diagnostic values were evaluated via TCGA data and ROC curves. Genetic alterations, DNA methylation, and mRNA modifications were studied using relevant tools. The FSCN1- lncRNA-miRNA regulatory network was constructed in sarcoma, and co-expressed gene functional analysis was also carried out. Our findings demonstrate that FSCN1 is frequently upregulated across a broad spectrum of human cancers. Elevated FSCN1 expression correlates with unfavorable overall survival in multiple cancer types and exhibits robust diagnostic performance in pan-cancer cohorts, with particular prominence in sarcoma. The FSCN1 promoter region displays hypomethylation in sarcoma, and its transcript levels are positively associated with mRNA modification signatures. We constructed a competing endogenous RNA (ceRNA) network encompassing FSCN1, lncRNAs, and miRNAs, and identified critical miRNA-FSCN1 interactions. Functional enrichment analysis of FSCN1 co-expressed genes uncovered key biological pathways. Immunohistochemical validation confirmed FSCN1 protein overexpression in selected sarcoma tissue specimens. FSCN1 is a potential biomarker for the diagnosis, prognosis, and prediction of treatment response in sarcoma, and is involved in sarcoma progression through multiple mechanisms, providing insights for targeted sarcoma therapies.
Xu et al. (Fri,) studied this question.