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Construction and validation of a pyroptosis-related gene signature associated with the tumor microenvironment in uveal melanoma

ABSTRACT: The present study aimed to construct a pyroptosis-related gene signature in uveal melanoma (UM) patients. Patients from The Cancer Genome Atlas (TCGA) served as the training cohort, whereas patients (GSE22138) from Gene Expression Omnibus (GEO) served as the validation cohort. Using the Kaplan–Meier (KM) method, univariate analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression, A five pyroptosis-related gene signature was constructed in the training cohort. Patients were divided into high- and low-risk groups. Survival analysis showed that patients in the high-risk group had a shorter survival time. Risk and survival analysis, time-independent receiver operating characteristic (ROC) curve analysis and principal component analysis (PCA) validated that the prognostic signature had greater predictive value in both cohorts. Multivariate analysis proved that the risk score was an independent prognostic factor. Functional analysis showed that the expressed genes in the high-risk group were most abundant in immunological repose-related and tumor-related signaling pathways. Single-sample gene-set enrichment analysis (ssGSEA) revealed that the different risk groups were associated with the tumor microenvironment. Moreover, the predictive signature could help patients be better matched to immunotherapy and targeted treatments. In conclusion, the pyroptosis-related gene signature associated with the tumor microenvironment maybe a reliable tool for predicting the prognosis of UM patients.

SUBMITTER: Zhang F

PROVIDER: S-EPMC8803850 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Necroptosis has been identified recently as a newly recognized programmed cell death that has an impact on tumor progression and prognosis, although the necroptosis-related gene (NRGs) potential prognostic value in skin cutaneous melanoma (SKCM) has not been identified. The aim of this study was to construct a prognostic model of SKCM through NRGs in order to help SKCM patients obtain precise clinical treatment strategies. Methods: RNA sequencing data collected from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed and prognostic NRGs in SKCM. Depending on 10 NRGs via the univariate Cox regression analysis usage and LASSO algorithm, the prognostic risk model had been built. It was further validated by the Gene Expression Omnibus (GEO) database. The prognostic model performance had been assessed using receiver operating characteristic (ROC) curves. We evaluated the predictive power of the prognostic model for tumor microenvironment (TME) and immunotherapy response. Results: We constructed a prognostic model based on 10 NRGs (FASLG, TLR3, ZBP1, TNFRSF1B, USP22, PLK1, GATA3, EGFR, TARDBP, and TNFRSF21) and classified patients into two high- and low-risk groups based on risk scores. The risk score was considered a predictive factor in the two risk groups regarding the Cox regression analysis. A predictive nomogram had been built for providing a more beneficial prognostic indicator for the clinic. Functional enrichment analysis showed significant enrichment of immune-related signaling pathways, a higher degree of immune cell infiltration in the low-risk group than in the high-risk group, a negative correlation between risk scores and most immune checkpoint inhibitors (ICIs), anticancer immunity steps, and a more sensitive response to immunotherapy in the low-risk group. Conclusions: This risk score signature could be applied to assess the prognosis and classify low- and high-risk SKCM patients and help make the immunotherapeutic strategy decision.

Project description:BackgroundThe incidence of malignant melanoma accounts for only approximately 5% of skin malignant tumors, however, it accounts for 75% of its mortality. Long-chain non-coding RNA (lncRNA) has a wide range of functional activities. Disorders of lncRNAs may lead to the occurrence and development of melanoma, and may also be related to immunotherapy.MethodsThe transcriptomic data of primary and metastatic melanoma patients and 331 immune-related genes were downloaded from skin cutaneous melanoma (SKCM) in the The Cancer Genome Atlas (TCGA) database. On this basis, 460 immunologically relevant lncRNAs were identified by constructing a co-expression network of immunogenic genes and lncRNAs in primary and metastatic melanoma patients. Prognostic genes were screened using univariate Cox regression analysis. ROC analysis was performed to evaluate the robustness of the prognostic signature.ResultsUnivariate correlation analysis showed that only 3 of the 23 immune-related lncRNAs were at high risk and the rest were at low risk. Signatures of 7 immune-related lncRNAs were identified by multivariate correlation analysis. The clinical correlation analysis showed that the 7 immune-related lncRNAs were associated with the clinical stage of primary and metastatic melanoma. Principal component analysis (PCA) showed that only 7 immune-related lncRNA signals divided tumor patients into high-risk and low-risk groups, while the low-risk group was enriched in the immune system process M13664 and immune response M19817 sets. PPI interaction network analysis showed that 11 G protein-coupled receptors and 6 corresponding ligands in the 2 gene sets affected the tumor microenvironment and were negatively related to the risk of the 7 immune-related lncRNAs. The tumor microenvironment immune cell infiltration analysis also supported the finding that anti-tumor immunity in the low-risk group was stronger than in the high-risk group.ConclusionsThese results indicate that characteristics of the 7 immune-related lncRNAs have prognostic value for melanoma patients and can be used as potential immunotherapy targets.

Project description:Background: Hepatocellular carcinoma (HCC) is a typical inflammatory-related malignant tumor with complex immune tolerance microenvironment and poor prognosis. In this study, we aimed to construct a novel immune-related gene signature for the prognosis of HCC patients, exploring tumor microenvironment (TME) cell infiltration characterization and potential mechanisms. Methods: A total of 364 HCC samples with follow-up information in the TCGA-LIHC dataset were analyzed for the training of the prognostic signature. The Least Absolute Shrinkage and Selector Operation (LASSO) regression based on the IRGs was conducted to identify the prognostic genes and establish an immune risk signature. The immune cell infiltration in TME was estimated via the CIBERSORT method. Gene Set Variation Analysis (GSVA) was conducted to compare the biological pathways involved in the low-risk and high-risk groups. Furthermore, paraffin sections of HCC tissue microarrays containing 77 patients from Fudan University Shanghai Cancer Center were used for IHC staining. The clinical characteristics of the 77 HCC patients were collected and summarized for survival analysis validation via the Kaplan-Meier (KM) method. Results: Three-gene signature with close immune correlation (Risk score = EPO * 0.02838 + BIRC5 * 0.02477 + SPP1 * 0.0002044) was constructed eventually and proven to be an effective prognostic factor for HCC patients. The patients were divided into a high-risk and a low-risk group according to the optimal cutoff, and the survival analysis revealed that HCC samples with high-risk immuno-score had significantly poorer outcomes than the low-risk group (p < 0.0001). The results of CIBERSORT suggested that the immune cell activation was relatively higher in the low-risk group with better prognosis. Besides, GSVA analysis showed multiple signaling differences between the high- and low-risk group, indicating that the three-gene prognostic model can affect the prognosis of patients by affecting immune-related mechanisms. Tissue microarray (TMA) results further confirmed that the expression of three genes in HCC tissues was closely related to the prognosis of patients, respectively. Conclusion: In this study, we constructed and validated a robust three-gene signature with close immune correlation in HCC, which presented a reliable performance in the prediction of HCC patients' survival.

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Construction and validation of a pyroptosis-related gene signature associated with the tumor microenvironment in uveal melanoma

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