Pan-Cancer Analysis Revealed as a New Biomarker for Prognosis and Immunotherapy.

BACKGROUND

Serine/arginine-rich splicing factor 9 () is one of the members of gene family and related to the tumorigenesis and the progression of tumor. However, whether has a crucial role across pan-cancer is still unknown.

METHODS

In this study, we used public databases, such as The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and Genotype-Tissue Expression (GTEx), to analyze expression level among tumor and normal cells. Survival analysis, K-M plotter, and PrognoScan were used to analyze the prognosis value of SRSF9, regarding to overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Moreover, we performed the correlation between and clinical characteristics (including the outcome of prognosis), as well as molecular events of tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint gene, tumor microenvironment (TME), immune infiltrating cells, mismatch repair (MMR) genes, m6A genes, DNA methyltransferases, and neoantigen with bioinformatics methods and TISIDB, TIMER, and Sangerbox websites.

RESULTS

In general, expression was upregulated in most cancers, such as BLCA, CHOL, and UCEC, which was associated with short survival and severe progression. In COAD, STAD, and UCEC, SRSF9 expression was positively related to both TMB and MSI. In BRCA, BLCA, ESCA, GBM, HNSC, LUSC, LUAD, OV, PRAD, TGCT, THCA, and UCEC, both immune score and stomal score showed a negative relationship with SRSF9 expression. Immune score showed a positive relationship with SRSF9 expression in LGG. SRSF9 expression had a significant and positive correlation with six types of immune infiltration cells in LGG, KIRC, LIHC, PCPG, PRAD, SKCM, THCA, and THYM, except in LUSC. In LIHC, SRSF9 was highly significant correlated with most immune checkpoint genes. For neoantigens, correlation between SRSF9 and the quantity of neoantigens was significantly positive in some cancer types. SRSF9 was also correlated with MMR genes, m6A genes, and DNA methyltransferases. In the 33 cancer types, gene set enrichment analysis (GSEA) demonstrated that SRSF9 was correlated with multiple functions and signaling pathways.

CONCLUSION

These findings demonstrated that may be a new biomarker for the prognosis and immunotherapy in various cancers. As a result, it will be beneficial to provide new therapies for cancer patients, thereby improving the treatment and prognosis of cancer patients.