Identifying effective biomarkers in osteosarcoma (OS) is important for predicting prognosis. We investigated the prognostic value of ferroptosis-related genes (FRGs) in OS. Transcriptome and clinical data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. FRGs were obtained from the ferroptosis database. Univariate COX regression and LASSO regression screening were performed and an FRG-based prognostic model was constructed, which was validated using the Gene Expression Omnibus cohort. The predictive power of the model was assessed via a subgroup analysis. A nomogram was constructed using clinical markers with independent prognostic significance and risk score results. The CIBERSORT algorithm was used to detect the correlation between prognostic genes and 22 tumor-infiltrating lymphocytes. The expression of prognostic genes in erastin-treated OS cell lines was verified via real-time PCR. Six prognostic FRGs (ACSL5, ATF4, CBS, CDO1, SCD, and SLC3A2) were obtained and used to construct the risk prognosis model. Subjects were divided into high- and low-risk groups. Prognosis was worse in the high-risk group, and the model had satisfactory prediction performance for patients younger than 18 years, males, females, and those with non-metastatic disease. Univariate COX regression analysis showed that metastasis and risk score were independent risk factors for patients with OS. Nomogram was built on independent prognostic factors with superior predictive power and patient benefit. There was a significant correlation between prognostic genes and tumor immunity. Six prognostic genes were differentially expressed in ferroptosis inducer-treated OS cell lines. The identified prognostic genes can regulate tumor growth and progression by affecting the tumor microenvironment.
Youfu Zhang,Xuyang Wang,Huaxiang Wang,Yi Jiang,Zhidan Xu,Laibang Luo
Background Elevated Small Nuclear Ribonucleoprotein Polypeptide A ( SNRPA ) can enhance tumor cell growth and proliferation in various cancers. However, rarely studies focus on the comprehensive analysis of SNRPA in hepatocellular carcinoma (HCC). Methods TCGA and GEO databases were used to analyze the mRNA expression of SNRPA in HCC. Protein expression of SNAPA was validated using immunohistochemistry. Stably transfected HCC cells were used to investigate the role of SNRPA in the progression of HCC. The functional enrichment analysis was utilized for the biological function prediction. The CIBERSORT and ssGSEA algorithms were used to evaluate the composition of the tumor microenvironment and immunocyte infiltration ratio. Results The SNRPA expression was upregulated in HCC and positively correlated with tumor stage and grade. SNRPA overexpression were independent risk factors for poor overall survival (OS) and recurrence-free survival (RFS). In patients with early-stage disease, low alpha-fetoprotein expression, and better differentiation, SNRPA still exhibited the excellent prognostic value. Knockdown of SNRPA inhibited the proliferation and migration while promoting the apoptosis of HCC cells. Higher methylation of the CpG site cg16596691 correlated with longer OS in HCC patients. Genes co-expressed with SNRPA were overexpressed in HCC and correlated with shorter OS. The GO and KEGG enrichment analysis showed that SNRPA expression was related to mRNA splicing, spliceosome signaling. GSEA demonstrated that the main enrichment pathway of SNRPA-related differential genes was spliceosome signaling, cell cycle signaling, P53 signaling pathway, T cell receptor signaling pathway, natural killer cell-mediated signaling. CIBERSORT and ssGSEA algorithm revealed that SNRPA was mainly associated with the higher proportion of CD8+T cells, T cells follicular helper, T cells regulatory, Macrophages M0, and the lower proportion of T cells CD4 memory resting, NK cells resting, Monocytes, and Mast cells resting. Conclusion Elevated SNRPA enhances tumor cell proliferation and correlated with poor prognosis and immune infiltrates in patients with HCC.
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