Tumor microenvironment and immune function analysis and chemotherapy prediction of SDHB in PPGL (pheochromocytoma/ paraganglioma)

Abstract

Abstract Background SDHB mutations are risk factors for PPGL metastasis and poor prognosis. This study aimed to identify the SDHB gene signature and mechanisms in PPGL, and investigate its association with immunotherapy response. Method PPGL transcriptome, clinical, and single nucleotide mutation data were obtained from TCGA database. Univariate, LASSO, and multivariate Cox regression analysis was applied to construct the prognostic signature. Survival analysis, ROC curve, Cox regression analysis, and nomoplot were utilized to evaluate accuracy of the model. GO and KEGG enrichment of differentially expressed genes between risk groups were used to explore potential action mechanisms. Prognostic lncRNA co-expressed with risk signature genes were also identified. The CIBERSORT, ssGSEA, GSVA, and ESTIMATE algorithms were employed to assess the association between risk score and variations of tumor microenvironment, immune cell infiltration, immune checkpoints, and immune responses. The maftools and pRRophetic packages were enrolled to predict tumor mutation burden and drug sensitivity. Result A signature of SDHB genes were identified immune checkpoint and alternative splicing, which showed great value of mechanisms for PPGL. Functional enrichment implied the variation of immune pathways and metallopeptidase activity between expression groups. High- expression group exhibited higher immune score, but lower tumor purity. Finally, we screened sensitive drugs for different risk groups. Conclusion The novel prognostic signature of cuproptosis genes could help risk stratification, immunotherapy response prediction, and individualized treatment strategy-making for glioma patients.