Identification of Immune Function-Related Subtypes in Cutaneous Melanoma.
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ABSTRACT:
SUBMITTER: Liu L
PROVIDER: S-EPMC8467264 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Immune checkpoint inhibition (ICI) improves outcomes in melanoma patients, but associated T-cell activation frequently leads to immune-related cutaneous adverse events (cutAEs). To dynamically identify T-cell subtypes and immune signatures associated with cutAEs, a pilot study was performed in stage III-IV melanoma patients using blood samples for flow cytometry and cytokine analysis. Blood samples were taken from patients before initiation of ICI (naive), at the onset of a cutAE, and after 6 months of ICI treatment. Overall, 30 patients were treated either with anti-PD1 monotherapy or with anti-PD-1/anti-CTLA-4 combination therapy. Flow cytometry analysis of PBMCs showed that ICI induced an overall shift from a Th2 towards a Th1 profile. Twelve patients (40%) developed cutAEs, which were associated with increased Th22 cells and Th17 cells, supported by a tendency to have elevated Th17/Th22-associated cytokines such as IL-17A, IL-22 and IL-23 levels in the plasma. Cytokine signatures specific for urticaria and T-cell-mediated cutAEs were identified in the plasma of patients by a bead-based assay. IL-10 was elevated in non-responders and, interestingly, during cutAEs. In conclusion, we identified distinct immune signatures based on the Th17/Th22 pathway in cutAEs, both in PBMCs and plasma. In addition, our finding of upregulated IL-10 during cutAEs supports the notion of treating these patients early and adequately to avoid implications for the overall outcome.
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Project description:Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein-protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify "real" hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM.
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Project description:Skin cutaneous melanoma (SKCM) is one of the most malignant and aggressive forms of cancer. Investigating the mechanisms of carcinogenesis further could lead to the discovery of prognostic biomarkers that could be used to guide cancer treatment. In this study, we conducted integrative bioinformatics analyses of TCGA database, STRING, cBioPortal, TRRUST, The Human Protein Atlas, and DGIdb to determine which hub genes contributed to tumor progression and the cancer-associated immunology of SKCM. The results show that immune-related 873 differential genes grouped SKCM samples into subtypes. The initial results showed that the optimal number of clusters was two subgroups. Further analysis showed that there were significant differences in survival rate and immune infiltration level between the two subgroups. Subsequently, obtaining the different genes between groups, construct PPI to screen 6 hub genes (HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DRA, HLA-DRB1, HLA-DRB5). In total, 6 MHC class II molecules were significantly related to overall survival. We then analyzed the expression of these genes along with their mutation landscapes, transcription factor regulation, and drug regulatory networks. In summary, our study identified 6 MHC class II molecules (HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DRA, HLA-DRB1, HLA-DRB5) as important biomarkers in the occurrence and progression of SKCM tumors. Their expression levels are closely related to prognosis and immune infiltration and can help us better understand the tumorigenesis of SKCM.
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Project description:BackgroundSkin cutaneous melanoma (SKCM) is one of the most malignant and aggressive cancers, causing about 72% of deaths in skin carcinoma. Although extensive study has explored the mechanism of recurrence and metastasis, the tumorigenesis of cutaneous melanoma remains unclear. Exploring the tumorigenesis mechanism may help identify prognostic biomarkers that could serve to guide cancer therapy.MethodIntegrative bioinformatics analyses, including GEO database, TCGA database, DAVID, STRING, Metascape, GEPIA, cBioPortal, TRRUST, TIMER, TISIDB and DGIdb, were performed to unveil the hub genes participating in tumor progression and cancer-associated immunology of SKCM. Furthermore, immunohistochemistry (IHC) staining was performed to validate differential expression levels of hub genes between SKCM tissue and normal tissues from the First Affiliated Hospital of Soochow University cohort.ResultsA total of 308 differentially expressed genes (DEGs) and 12 hub genes were found significantly differentially expressed between SKCM and normal skin tissues. Functional annotation indicated that inflammatory response, immune response was closely associated with SKCM tumorigenesis. KEGG pathways in hub genes include IL-10 signaling and chemokine receptors bind chemokine signaling. Five chemokines members (CXCL9, CXCL10, CXCL13, CCL4, CCL5) were associated with better overall survival and pathological stages. IHC results suggested that significantly elevated CXCL9, CXCL10, CXCL13, CCL4 and CCL5 proteins expressed in the SKCM than in the normal tissues. Moreover, our findings suggested that IRF7, RELA, NFKB1, IRF3 and IRF1 are key transcription factors for CCL4, CCL5, CXCL10. In addition, the expressions of CXCL9, CXCL10, CXCL13, CCL4 and CCL5 were positively correlated with infiltration of six immune cells (B cell, CD8+T cells, CD4+T cells, macrophages, neutrophils, dendritic cells) and 28 types of TILs. Among them, high levels of B cells, CD8+T cells, neutrophils and dendritic cells were significantly related to longer SKCM survival time.ConclusionIn summary, this study mainly identified five chemokine members (CXCL9, CXCL10, CXCL13, CCL4, CCL5) associated with SKCM tumorigenesis, progression, prognosis and immune infiltrations, which might help us evaluate several immune-related targets for cutaneous melanoma therapy.
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Project description:Because immune checkpoint inhibitors (ICIs) are effective for a subset of melanoma patients, identification of melanoma subtypes responsive to ICIs is crucial. We performed clustering analyses to identify immune subtypes of melanoma based on the enrichment levels of 28 immune cells using transcriptome datasets for six melanoma cohorts, including four cohorts not treated with ICIs and two cohorts treated with ICIs. We identified three immune subtypes (Im-H, Im-M, and Im-L), reproducible in these cohorts. Im-H displayed strong immune signatures, low stemness and proliferation potential, genomic stability, high immunotherapy response rate, and favorable prognosis. Im-L showed weak immune signatures, high stemness and proliferation potential, genomic instability, low immunotherapy response rate, and unfavorable prognosis. The pathways highly enriched in Im-H included immune, MAPK, apoptosis, calcium, VEGF, cell adhesion molecules, focal adhesion, gap junction, and PPAR. The pathways highly enriched in Im-L included Hippo, cell cycle, and ErbB. Copy number alterations correlated inversely with immune signatures in melanoma, while tumor mutation burden showed no significant correlation. The molecular features correlated with favorable immunotherapy response included immune-promoting signatures and pathways of PPAR, MAPK, VEGF, calcium, and glycolysis/gluconeogenesis. Our data recapture the immunological heterogeneity in melanoma and provide clinical implications for the immunotherapy of melanoma.
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Project description:PurposeFatty acid metabolism (FAM) affects the immune phenotype in a metabolically dynamic tumor microenvironment (TME), but the use of FAM-related genes (FAMGs) to predict the prognosis and immunotherapy response of cutaneous melanoma (CM) patients has not been investigated. In this study, we aimed to construct FAM molecular subtypes and identify key prognostic biomarkers in CM.MethodsWe used a CM dataset in The Cancer Genome Atlas (TCGA) to construct FAM molecular subtypes. We performed Kaplan-Meier (K-M) analysis, gene set enrichment analysis (GSEA), and TME analysis to assess differences in the prognosis and immune phenotype between subtypes. We used weighted gene co-expression network analysis (WGCNA) to identify key biomarkers that regulate tumor metabolism and immunity between the subtypes. We compared overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) between CM patients with high or low biomarker expression. We applied univariable and multivariable Cox analyses to verify the independent prognostic value of the FAM biomarkers. We used GSEA and TME analysis to investigate the immune-related regulation mechanism of the FAM subtype biomarker. We evaluated the immune checkpoint inhibition (ICI) response and chemotherapy sensitivity between CM patients with high or low biomarker expression. We performed real-time fluorescent quantitative PCR (qRT-PCR) and semi-quantitative analysis of the immunohistochemical (IHC) data from the Human Protein Atlas to evaluate the mRNA and protein expression levels of the FAM biomarkers in CM.ResultsWe identified 2 FAM molecular subtypes (cluster 1 and cluster 2). K-M analysis showed that cluster 2 had better OS and PFS than cluster 1 did. GSEA showed that, compared with cluster 1, cluster 2 had significantly upregulated immune response pathways. The TME analysis indicated that immune cell subpopulations and immune functions were highly enriched in cluster 2 as compared with cluster 1. WGCNA identified 6 hub genes (ACSL5, ALOX5AP, CD1D, CD74, IL4I1, and TBXAS1) as FAM biomarkers. CM patients with high expression levels of the six biomarkers had better OS, PFS, and DSS than those with low expression levels of the biomarkers. The Cox regression analyses verified that the 6 FAM biomarkers can be independent prognostic factors for CM patients. The single-gene GSEA showed that the high expression levels of the 6 genes were mainly enriched in T-cell antigen presentation, the PD-1 signaling pathway, and tumor escape. The TME analysis confirmed that the FAM subtype biomarkers were not only related to immune infiltration but also highly correlated with immune checkpoints such as PD-1, PD-L1, and CTLA-4. TIDE scores confirmed that patients with high expression levels of the 6 biomarkers had worse immunotherapy responses. The 6 genes conveyed significant sensitivity to some chemotherapy drugs. qRT-PCR and IHC analyses verified the expression levels of the 6 biomarkers in CM cells.ConclusionOur FAM subtypes verify that different FAM reprogramming affects the function and phenotype of infiltrating immune cells in the CM TME. The FAM molecular subtype biomarkers can be independent predictors of prognosis and immunotherapy response in CM patients.
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Project description:Yutao Wang, China Medical University, ChinaThe tumor microenvironment (TME) has been shown to impact the prognosis of tumors in patients including cutaneous melanoma (CM); however, not all components of TME are important. Given the aforementioned situation, the functional immune cell contents correlated with CM patient prognosis are needed to optimize present predictive models and reflect the overall situation of TME. We developed a novel risk score named core tumor-infiltrating immune cell score (cTICscore), which showed certain advantages over existing biomarkers or TME-related signatures in predicting the prognosis of CM patients. Furthermore, we explored a new gene signature named cTILscore-related module gene score (cTMGs), based on four identified TME-associated genes (GCH1, GZMA, PSMB8, and PLAAT4) showing a close correlation with the cTICscore, which was generated by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis to facilitate clinical application. Patients with low cTMGs had significantly better overall survival (OS, P = 0.002,< 0.001, = 0.002, and = 0.03, respectively) in the training and validating CM datasets. In addition, the area under the curve values used to predict the immune response in four CM cohorts were 0.723, 0.723, 0.754, and 0.792, respectively, and that in one gastric cohort was 0.764. Therefore, the four-gene signature, based on cTICscore, might improve prognostic information, serving as a predictive tool for CM patients receiving immunotherapy.cutaneous melanoma, tumor microenvironment, prognosis, immunotherapy, cTICscore.
| S-EPMC9441634 | biostudies-literature
Project description:Skin cutaneous melanoma (SKCM) is one of the most aggressive and life-threatening cancers with high incidence rate, metastasis rate and mortality. Early detection and stratification of risk assessment are essential to treat SKCM and to improve survival rate. The aim of this study is to construct an immune-related lncRNAs (immlncRNAs) prognosis risk model to identify immune biomarkers for early diagnosis, prognosis assessment and target immunotherapy of SKCM. For this purpose, we identified 46 immlncRNAs significantly correlated with SKCM prognosis to construct the prognostic risk model and patients were stratified into the high- and low-risk subgroups according to the developed model. The predictive efficiency of this model has been proved by K-M survival analysis and receiver operating characteristic curve. Moreover, CIBERSORT algorithms confirmed that there were differences in immune cell infiltration between the high- and low-risk groups. Functional enrichment analysis further indicated that immlncRNAs were related to a variety of immune response process signaling pathways, suggesting that relevant immlncRNAs could play an important role in the immune regulation of SKCM. Finally, subgroup analysis and multiple Cox regression analysis further proved the stability of the model. In summary, we successfully constructed a 46 immlncRNA-related prognostic risk score model with excellent predictive efficacy and provided more possibilities to investigate the immune regulation mechanisms and to develop immunotherapy of SKCM.
| S-EPMC9037265 | biostudies-literature
Project description:Despite the remarkable efficacy of PD-1-associated immune checkpoint inhibitors in treating cutaneous melanoma (CM), the inconsistency in the expression of PD-1 and its ligand PD-L1, and resulting variability in the effectiveness of immunotherapy, present significant challenges for clinical application. Therefore, further research is necessary to identify tumor-related biomarkers that can predict the prognosis of immunotherapy. Tertiary lymphoid structures (TLSs) have been recognized as a crucial factor in predicting the response of immune checkpoint inhibitors in solid tumors, including CM. However, the study of TLSs in CM is not yet comprehensive. Gene expression profiles have been shown to correlate with CM risk stratification and patient outcomes. In this study, we identified TLS-related genes that can be used for prognostic purposes and developed a corresponding risk model. The impact of TLS-related genes on clinicopathological characteristics, immune infiltration and drug susceptibility was also explored. Our biological function enrichment analysis provided preliminary evidence of related signaling pathways. Our findings provide a new perspective on risk stratification and individualized precision therapy for CM.
| S-EPMC10754817 | biostudies-literature
Project description:BackgroundCutaneous melanoma is a common but aggressive tumor. Ferroptosis is a recently discovered cell death with important roles in tumor biology. Nevertheless, the prognostic power of ferroptosis-linked genes remained unclear in cutaneous melanoma.MethodsCutaneous melanoma patients of TCGA (The Cancer Genome Atlas) were taken as the training cohort while GSE65904 and GSE22153 as the validation cohorts. Multifactor Cox regression model was used to build a prognostic model, and the performance of the model was assessed. Functional enrichment and immune infiltration analysis were used to clarify the mechanisms.ResultsA five ferroptosis-linked gene predictive model was developed. ALOX5 and GCH1 were illustrated as independent predictive factors. Functional assessment showed enriched immune-linked cascades. Immune infiltrating analysis exhibited the distinct immune microenvironment.ConclusionHerein, a novel ferroptosis-related gene prognostic model was built in cutaneous melanoma. This model could be used for prognostic prediction, and maybe helpful for the targeted and immunotherapies.
| S-EPMC8384470 | biostudies-literature