Dataset Information

A CD8⁺ T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma.

ABSTRACT:

Background

Skin cutaneous melanoma (SKCM) is the most frequently encountered tumor of the skin. Immunotherapy has opened a new horizon in melanoma treatment. We aimed to construct a CD8⁺ T cell-associated immune gene prognostic model (CDIGPM) for SKCM and unravel the immunologic features and the benefits of immunotherapy in CDIGPM-defined SKCM groups.

Method

Single-cell SKCM transcriptomes were utilized in conjunction with immune genes for the screening of CD8⁺ T cell-associated immune genes (CDIGs) for succeeding assessment. Thereafter, through protein-protein interaction (PPI) networks analysis, univariate COX analysis, and multivariate Cox analysis, six genes (MX1, RSAD2, IRF2, GBP2, IFITM1, and OAS2) were identified to construct a CDIGPM. We detected cell proliferation of SKCM cells transfected with IRF2 siRNA. Then, we analyzed the immunologic features and the benefits of immunotherapy in CDIGPM-defined groups.

Results

The overall survival (OS) was much better in low-CDIGPM group versus high CDIGPM group in TCGA dataset and GSE65904 dataset. On the whole, the results unfolded that a low CDIGPM showed relevance to immune response-correlated pathways, high expressions of CTLA4 and PD-L1, a high infiltration rate of CD8⁺ T cells, and more benefits from immunotherapy.

Conclusion

CDIGPM is an good model to predict the prognosis, the potential immune escape from immunotherapy for SKCM, and define immunologic and molecular features.

SUBMITTER: Sun S

PROVIDER: S-EPMC9633226 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

A CD8<sup>+</sup> T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma.

Sun Shanwen S Zhi Zhengke Z Su Yang Y Sun Jingxian J Li Qianjun Q

Frontiers in immunology 20221020

<h4>Background</h4>Skin cutaneous melanoma (SKCM) is the most frequently encountered tumor of the skin. Immunotherapy has opened a new horizon in melanoma treatment. We aimed to construct a CD8<sup>+</sup> T cell-associated immune gene prognostic model (CDIGPM) for SKCM and unravel the immunologic features and the benefits of immunotherapy in CDIGPM-defined SKCM groups.<h4>Method</h4>Single-cell SKCM transcriptomes were utilized in conjunction with immune genes for the screening of CD8<sup>+</su ...[more]

PMID: 36341357

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Project description:BackgroundCD8+ T cells work as a key effector of adaptive immunity and are closely associated with immune response for killing tumor cells. It is crucial to understand the role of tumor-infiltrating CD8+ T cells in uveal melanoma (UM) to predict the prognosis and response to immunotherapy.Materials and methodsSingle-cell transcriptomes of UM with immune-related genes were combined to screen the CD8+ T-cell-associated immune-related genes (CDIRGs) for subsequent analysis. Next, a prognostic gene signature referred to tumor-infiltrating CD8+ T cells was constructed and validated in several UM bulk RNA sequencing datasets. The risk score of UM patients was calculated and classified into high- or low-risk subgroup. The prognostic value of risk score was estimated by using multivariate Cox analysis and Kaplan-Meier survival analysis. Moreover, the potential ability of gene signature for predicting immunotherapy response was further explored.ResultsIn total, 202 CDIRGs were screened out from the single-cell RNA sequencing of GSE139829. Next, a gene signature containing three CDIRGs (IFNGR1, ANXA6, and TANK) was identified, which was considered as an independent prognostic indicator to robustly predict overall survival (OS) and metastasis-free survival (MFS) of UM. In addition, the UM patients were classified into high- and low-risk subgroups with different clinical characteristics, distinct CD8+ T-cell immune infiltration, and immunotherapy response. Gene set enrichment analysis (GSEA) showed that immune pathways such as allograft rejection, inflammatory response, interferon alpha and gamma response, and antigen processing and presentation were all positively activated in low-risk phenotype.ConclusionOur work gives an inspiration to explain the limited response for the current immune checkpoint inhibitors to UM. Besides, we constructed a novel gene signature to predict prognosis and immunotherapy responses, which may be regarded as a promising therapeutic target.

Project description:As the second common diagnosed cancer among gynecological tumors, endometrial cancer (EC) has heterogeneous pathogenesis and clinical manifestations. Therefore, prognosis prediction that considers gene expression value and clinical characteristics, is helpful to patients with EC. We downloaded RNA expression and clinical data from the TCGA database. We achieved 4 DEPRGs and constructed the PRG panel by univariate, lasso and multivariate Cox analysis. Based on the median value of the risk score, patients were divided into two groups. The Kaplan-Meier curve suggested that the patients with lower risk scores had better clinical outcomes of EC. AUC of ROC curves suggested the panel can be used as an independent predictor. Future analysis indicated the positive correlations between risk score and clinical characteristics. What's more, we performed GO and KEGG functional analysis and immune environment exploration to get an understanding of the potential molecular mechanism and immunotherapeutic target. To future validate the panel, we found that the relapse-free and overall survival probability of 4 prognostic DEPRGs between high-expression group and low-expression group were different through the Kaplan-Meier plotter in UCEC. In addition, GEPIA database and RT-PCR experiment indicated GPX4 and GSDMD were highly expressed in UCEC compared to normal endometrial tissue, and TIRAP and ELANE were downregulated. This study identified a PRG panel to predict the prognosis immune microenvironment in human EC. Then, Kaplan-Meier analysis and AUC below the ROC curves was used to validate the panel. In addition, Chi-square was used to show the clinical significance. GO, KEGG and GSEA were used to show the functional differences. Different immune-related databases were used to analyze the immune characteristics. The Kaplan-Meier plotter website was used to assess the effect of genes on survival. GEPIA and RT-PCR were used to analyze the expression level. In summary, we identified 4 prognosis-associated pyroptosis-related genes (ELANE, GPX4, GSDMD, and TIRAP). The panel can also predict prognosis prediction and immune microenvironment in human endometrial cancer.

Project description:Ferroptosis is a newly discovered form of non-apoptotic cell death that relies on iron-mediated oxidative damage, playing a crucial role in the progression and therapy resistance of melanoma. Hence, the potential value of ferroptosis-related genes (FRGs) as a prognostic model and therapeutic target in melanoma requires further investigation. In this study, the relationship between FRGs and melanoma was revealed by analyzing the mRNA expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO). A 6-FRGs signature was constructed by Univariate, multivariate, and lasso Cox regression analyses in the TCGA cohort. The GEO database was used to validate the efficacy of the signature. The protein and mRNA expression level of the signature genes were examined in real-world melanoma tissues via immunohistochemical and quantificational real-time polymerase chain reaction (qRT-PCR). Functional enrichment analysis and immune-related analysis were conducted to identify the potential biological functions and pathways of the signature. Ten putative small molecule drugs were predicted by Connectivity Map (CMAP). As a result, a 6-FRGs signature was constructed to stratify melanoma patients into two risk groups. Compared with the low-risk group, patients in the high-risk group had a worse prognosis and a lower ImmuneScore. Immune-related pathways were enriched in the low-risk group. Immune Function and immune cell infiltration of the low-risk group were significantly higher than that of the high-risk group. The differential expression of these six FRGs in melanoma and adjacent normal tissues was confirmed. Moreover, higher expression of immune checkpoint molecules and a greater sensitivity to immunotherapy were observed in the low-risk group. Some small molecular drugs in the CMAP database hold the potential to treat melanoma. Overall, we identified a novel FRGs signature for prognostic prediction in melanoma. Based on the signature-related immune infiltration landscape found in our study, targeting the FRGs might be a therapeutic alternative for melanoma.

Dataset Information

A CD8⁺ T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma.

Background

Method

Results

Conclusion

Publications

A CD8<sup>+</sup> T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

A CD8+ T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma.

Background

Method

Results

Conclusion

Publications

A CD8<sup>+</sup> T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

A CD8⁺ T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma.