Identification of an Immune-Related Prognostic Signature Associated With Immune Infiltration in Melanoma

Liu, Nian; Liu, Zijian; Liu, Xinxin; Duan, Xiaoru; Huang, Yongjun; Jin, Zhao-Hui; Niu, Yi; Zhang, Liling; Chen, Hongxiang

doi:10.3389/fgene.2020.01002

Cited by 13 publications

(14 citation statements)

References 35 publications

(42 reference statements)

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“…Liu et al. identified 10 DE IRGs between primary and metastatic melanoma, and investigated the immune infiltration and tumor mutation burden in different risk groups ( 22 ).…”

Section: Discussionmentioning

confidence: 99%

Development of an Immune-Related Gene Signature for Prognosis in Melanoma

et al. 2021

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Melanoma remains a potentially deadly malignant tumor. The incidence of melanoma continues to rise. Immunotherapy has become a new treatment method and is widely used in a variety of tumors. Original melanoma data were downloaded from TCGA. ssGSEA was performed to classify them. GSVA software and the "hclust" package were used to analyze the data. The ESTIMATE algorithm screened DEGs. The edgeR package and Venn diagram identified valid immune-related genes. Univariate, LASSO and multivariate analyses were used to explore the hub genes. The "rms" package established the nomogram and calibrated the curve. Immune infiltration data were obtained from the TIMER database. Compared with that of samples in the high immune cell infiltration cluster, we found that the tumor purity of samples in the low immune cell infiltration cluster was higher. The immune score, ESTIMATE score and stromal score in the low immune cell infiltration cluster were lower. In the high immune cell infiltration cluster, the immune components were more abundant, while the tumor purity was lower. The expression levels of TIGIT, PDCD1, LAG3, HAVCR2, CTLA4 and the HLA family were also higher in the high immune cell infiltration cluster. Survival analysis showed that patients in the high immune cell infiltration cluster had shorter OS than patients in the low immune cell infiltration cluster. IGHV1-18, CXCL11, LTF, and HLA-DQB1 were identified as immune cell infiltration-related DEGs. The prognosis of melanoma was significantly negatively correlated with the infiltration of CD4+ T cells, CD8+ T cells, dendritic cells, neutrophils and macrophages. In this study, we identified immune-related melanoma core genes and relevant immune cell subtypes, which may be used in targeted therapy and immunotherapy of melanoma.

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“…Liu et al. identified 10 DE IRGs between primary and metastatic melanoma, and investigated the immune infiltration and tumor mutation burden in different risk groups ( 22 ).…”

Section: Discussionmentioning

confidence: 99%

Development of an Immune-Related Gene Signature for Prognosis in Melanoma

et al. 2021

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“…Numerous studies have demonstrated that enhanced local immune activation contributes to a good prognosis in different kind of tumors (25,26). In cutaneous melanoma, though several studies have reported that patients with high immune cell infiltration showed better prognosis (27)(28)(29), some types of immune cells are associated with worse prognosis, such as CD20-positive tumor-infiltrating lymphocytes, neutrophil granulocytes and mast cells (30,31). In our study, based on the immunogenomic profiling of 29 immune signatures, we found that Immunity_L group was associated with better prognosis, which might be the infiltrated immune cells are non-tumorspecific and do not show the anti-tumor effect.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical Clustering of Cutaneous Melanoma Based on Immunogenomic Profiling

Xie

et al. 2020

Front. Oncol.

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Cutaneous melanoma is an aggressive malignancy with high heterogeneity. Several studies have been performed to identify cutaneous melanoma subtypes based on genomic profiling. However, few classifications based on assessments of immune-associated genes have limited clinical implications for cutaneous melanoma. Using 470 cutaneous melanoma samples from The Cancer Genome Atlas (TCGA), we calculated the enrichment levels of 29 immune-associated gene sets in each sample and hierarchically clustered them into Immunity High (Immunity_H, n=323, 68.7%), Immunity Medium (Immunity_M, n=135, 28.7%), and Immunity Low (Immunity_L, n=12, 2.6%) based on the ssGSEA score. The ESTIMATE algorithm was used to calculate stromal scores (range: -1,800.51–1,901.99), immune scores (range: -1,476.28–3,780.33), estimate scores (range: -2,618.28–5,098.14) and tumor purity (range: 0.216–0.976) and they were significantly correlated with immune subtypes (Kruskal–Wallis test, P < 0.001). The Immunity_H group tended to have higher expression levels of HLA and immune checkpoint genes (Kruskal–Wallis test, P < 0.05). The Immunity_H group had the highest level of naïve B cells, resting dendritic cells, M1 macrophages, resting NK cells, plasma cells, CD4 memory activated T cells, CD8 T cells, follicular helper T cells and regulatory T cells, and the Immunity_L group had better overall survival. The GO terms identified in the Immunity_H group were mainly immune related. In conclusion, immune signature-associated cutaneous melanoma subtypes play a role in cutaneous melanoma prognosis stratification. The construction of immune signature-associated cutaneous melanoma subtypes predicted possible patient outcomes and provided possible immunotherapy candidates.

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“…However, the specific mechanism of how these genes influence T cell function remains elusive, it would be very interesting to further investigate into each of them in the following study. In this work, we found the combination of these six genes had a novel and good predictivity on melanoma patients, which performed better than the existing signature genes (Supplementary Figures 4, 5) (43,44,(49)(50)(51). Although the six gene signature in our work had a similar performance with the signature from Tian' s study for melanoma patients' survival, our signature had a better capability in predicting patients' response to immunotherapy (Figures 7H, K, Supplementary Figures 7A-C).…”

Section: Discussionmentioning

confidence: 74%

“…We further compared the predictiblibity of this signature with other previously developed immune-related signatures. Eleven studies were screened out after literature searching (42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52), but five signatures were not included for further analyses for at least one of the following reasons: lack of formula to calculate the risk score or immune-related score; lack of validation in extra datasets; lack of RNA expression data of some signature genes in validated datasets in the current work (45)(46)(47)(48)52). As reflected by the AUC values at 1-year, 3-year and 5-years in Supplementary Figures 4A-D and 5A-C, the signatures developed in our work and Tian's work had relatively better performance in predicting outcome of melanoma patients.…”

Section: A Representative Risk Score For Patient Survival Is Constructed Based On Six Signature Genesmentioning

confidence: 99%

Development and Validation of a CD8+ T Cell Infiltration-Related Signature for Melanoma Patients

Yuan

Zhu

Lan³

et al. 2021

Front. Immunol.

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AimImmunotherapy shows efficacy in only a subset of melanoma patients. Here, we intended to construct a risk score model to predict melanoma patients’ sensitivity to immunotherapy.MethodsIntegration analyses were performed on melanoma patients from high-dimensional public datasets. The CD8+ T cell infiltration related genes (TIRGs) were selected via TIMER and CIBERSORT algorithm. LASSO Cox regression was performed to screen for the crucial TIRGs. Single sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to evaluate the immune activity. The prognostic value of the risk score was determined by univariate and multivariate Cox regression analysis.Results184 candidate TIRGs were identified in melanoma patients. Based on the candidate TIRGs, melanoma patients were classified into three clusters which were characterized by different immune activity. Six signature genes were further screened out of 184 TIRGs and a representative risk score for patient survival was constructed based on these six signature genes. The risk score served as an indicator for the level of CD8+ T cell infiltration and acted as an independent prognostic factor for the survival of melanoma patients. By using the risk score, we achieved a good predicting result for the response of cancer patients to immunotherapy. Moreover, pan-cancer analysis revealed the risk score could be used in a wide range of non-hematologic tumors.ConclusionsOur results showed the potential of using signature gene-based risk score as an indicator to predict melanoma patients’ sensitivity to immunotherapy.

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Identification of an Immune-Related Prognostic Signature Associated With Immune Infiltration in Melanoma

Cited by 13 publications

References 35 publications

Development of an Immune-Related Gene Signature for Prognosis in Melanoma

Development of an Immune-Related Gene Signature for Prognosis in Melanoma

Hierarchical Clustering of Cutaneous Melanoma Based on Immunogenomic Profiling

Development and Validation of a CD8+ T Cell Infiltration-Related Signature for Melanoma Patients

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