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The Landscape of the Tumor Microenvironment in Skin Cutaneous Melanoma Reveals a Prognostic and Immunotherapeutically Relevant Gene Signature

ABSTRACT: The tumorigenesis of skin cutaneous melanoma (SKCM) remains unclear. The tumor microenvironment (TME) is well known to play a vital role in the onset and progression of SKCM. However, the dynamic mechanisms of immune regulation are insufficient. We conducted a comprehensive analysis of immune cell infiltration in the TME. Based on the differentially expressed genes (DEGs) in clusters grouped by immune infiltration status, a set of hub genes related to the clinical prognosis of SKCM and tumor immune infiltration was explored. Methods: We analyzed immune cell infiltration in two independent cohorts and assessed the relationship between the internal pattern of immune cell infiltration and SKCM characteristics, including clinicopathological features, potential biological pathways, and gene mutations. Genes related to the infiltration pattern of TME immune cells were determined. Furthermore, the unsupervised clustering method (k-means) was used to divide samples into three different categories according to TME, which were defined as TME cluster-A, -B, and -C. DEGs among three groups of samples were analyzed as signature genes. We further distinguished common DEGs between three groups of samples according to whether differences were significant and divided DEGs into the Signature gene-A group with significant differences and the Signature gene-B group with insignificant differences. The Signature gene-A gene set mainly had exon skipping in SKCM, while the Signature gene-B gene set had no obvious alternative splicing form. Subsequently, we analyzed genetic variations of the two signatures and constructed a competing endogenous RNA (ceRNA) regulatory network. LASSO Cox regression was used to determine the immune infiltration signature and risk score of SKCM. Finally, we obtained 13 hub genes and calculated the risk score based on the coefficient of each gene to explore the impact of the high- and low-risk scores on biologically related functions and prognosis of SKCM patients further. The correlation between the risk score and clinicopathological characteristics of SKCM patients indicated that a low-risk score was associated with TME cluster-A classification (p < 0.001) and metastatic SKCM (p < 0.001). Thirteen hub genes also showed different prognostic effects in pan-cancer. The results of univariate and multivariate Cox analyses revealed that risk score could be used as an independent risk factor for predicting the prognosis of SKCM patients. The nomogram that integrated clinicopathological characteristics and immune characteristics to predict survival probability was based on multivariate Cox regression. Finally, 13 hub genes that showed different prognostic effects in pan-cancers were obtained. According to immunohistochemistry staining results, Ube2L6, SRPX2, and IFIT2 were expressed at higher levels, while CLEC4E, END3, and KIR2DL4 were expressed at lower levels in 25 melanoma specimens. Conclusion: We performed a comprehensive assessment of the immune-associated TME. To elucidate the potential development of immune-genomic features in SKCM, we constructed an unprecedented set of immune characteristic genes (EDN3, CLEC4E, SRPX2, KIR2DL4, UBE2L6, and IFIT2) related to the immune landscape of TME. These genes are related to different prognoses and drug responses of SKCM. The immune gene signature constructed can be used as a robust prognostic biomarker of SKCM and a predictor of an immunotherapy effect.

SUBMITTER: Zhou S

PROVIDER: S-EPMC8517264 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Invasive ductal carcinoma (IDC) is a clinically and molecularly distinct disease. Tumor microenvironment (TME) immune phenotypes play crucial roles in predicting clinical outcomes and therapeutic efficacy. Method: In this study, we depict the immune landscape of IDC by using transcriptome profiling and clinical characteristics retrieved from The Cancer Genome Atlas (TCGA) data portal. Immune cell infiltration was evaluated via single-sample gene set enrichment (ssGSEA) analysis and systematically correlated with genomic characteristics and clinicopathological features of IDC patients. Furthermore, an immune signature was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. A random forest algorithm was applied to identify the most important somatic gene mutations associated with the constructed immune signature. A nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed by multivariate Cox regression. Results: The IDC were clustered into low immune infiltration, intermediate immune infiltration, and high immune infiltration by the immune landscape. The high infiltration group had a favorable survival probability compared with that of the low infiltration group. The low-risk score subtype identified by the immune signature was characterized by T cell-mediated immune activation. Additionally, activation of the interferon-? response, interferon-? response, and TNF-? signaling via the NF?B pathway was observed in the low-risk score subtype, which indicated T cell activation and may be responsible for significantly favorable outcomes in IDC patients. A random forest algorithm identified the most important somatic gene mutations associated with the constructed immune signature. Furthermore, a nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed, revealing that the immune signature was an independent prognostic biomarker. Finally, the relationship of VEGFA, PD1, PDL-1, and CTLA-4 expression with the immune infiltration landscape and the immune signature was analyzed to interpret the responses of IDC patients to immunotherapy. Conclusion: Taken together, we performed a comprehensive evaluation of the immune landscape of IDC and constructed an immune signature related to the immune landscape. This analysis of TME immune infiltration landscape has shed light on how IDC respond to immunotherapy and may guide the development of novel drug combination strategies.

Project description:BackgroundPyroptosis is closely related to the programmed death of cancer cells as well as the tumor immune microenvironment (TIME) via the host-tumor crosstalk. However, the role of pyroptosis-related genes as prognosis and TIME-related biomarkers in skin cutaneous melanoma (SKCM) patients remains unknown.MethodsWe evaluated the expression profiles, copy number variations, and somatic mutations (CNVs) of 27 genes obtained from MSigDB database regulating pyroptosis among TCGA-SKCM patients. Thereafter, we conducted single-sample gene set enrichment analysis (ssGSEA) for evaluating pyroptosis-associated expression patterns among cases and for exploring the associations with clinicopathological factors and prognostic outcome. In addition, a prognostic pyroptosis-related signature (PPRS) model was constructed by performing Cox regression, weighted gene coexpression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) analysis to score SKCM patients. On the other hand, we plotted the ROC and survival curves for model evaluation and verified the robustness of the model through external test sets (GSE22153, GSE54467, and GSE65904). Meanwhile, we examined the relations of clinical characteristics, oncogene mutations, biological processes (BPs), tumor stemness, immune infiltration degrees, immune checkpoints (ICs), and treatment response with PPRS via multiple methods, including immunophenoscore (IPS) analysis, gene set variation analysis (GSVA), ESTIMATE, and CIBERSORT. Finally, we constructed a nomogram incorporating PPRS and clinical characteristics to improve risk evaluation of SKCM.ResultsMany pyroptosis-regulated genes showed abnormal expression within SKCM. TP53, TP63, IL1B, IL18, IRF2, CASP5, CHMP4C, CHMP7, CASP1, and GSDME were detected with somatic mutations, among which, a majority displayed CNVs at high frequencies. Pyroptosis-associated profiles established based on pyroptosis-regulated genes showed markedly negative relation to low stage and superior prognostic outcome. Blue module was found to be highly positively correlated with pyroptosis. Later, this study established PPRS based on the expression of 8 PAGs (namely, GBP2, HPDL, FCGR2A, IFITM1, HAPLN3, CCL8, TRIM34, and GRIPAP1), which was highly associated with OS, oncogene mutations, tumor stemness, immune infiltration degrees, IC levels, treatment responses, and multiple biological processes (including cell cycle and immunoinflammatory response) in training and test set samples.ConclusionsBased on our observations, analyzing modification patterns associated with pyroptosis among diverse cancer samples via PPRS is important, which can provide more insights into TIME infiltration features and facilitate immunotherapeutic development as well as prognosis prediction.

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.

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The Landscape of the Tumor Microenvironment in Skin Cutaneous Melanoma Reveals a Prognostic and Immunotherapeutically Relevant Gene Signature

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