Correlation of tumor‐infiltrating immune cells of melanoma with overall survival by immunogenomic analysis

Huang, Lili; Chen, Hong; Xu, Yu; Chen, Jianhua; Liu, Zhuqing; Xu, Qing

doi:10.1002/cam4.3466

Cited by 23 publications

(20 citation statements)

References 51 publications

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“…Different types of tumor-infiltrating immune cells can upregulate or downregulate melanoma immunity in the microenvironment. [ 54 ] By comparing the correlations of immune cells in the high- and low-risk groups, we found that immune cell infiltration was lower in the high-risk group than in the low-risk group. Immune cells are a major component of the tumor microenvironment and are closely related to tumor cell proliferation, treatment response, and prognosis.…”

Section: Discussionmentioning

confidence: 99%

Immune-related lncRNA pairs as novel signature to predict prognosis and immune landscape in melanoma patients

Wei

Zheng

et al. 2022

Medicine

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To investigate immune-related long non-coding RNA (irlncRNA) signatures for predicting survival and the immune landscape in melanoma patients. We retrieved gene expression files from The Cancer Genome Atlas and the Genotype-Tissue Expression database and extracted all the long non-coding RNAs from the original data. Then, we selected immune-related long non-coding RNAs (irlncRNAs) using co-expression networks and screened differentially expressed irlncRNAs (DEirlncRNAs) to form pairs. We also performed univariate analysis and Least absolute shrinkage and selection operator (LASSO) penalized regression analysis to identify prognostic DEirlncRNA pairs, constructed receiver operating characteristic curves, compared the areas under the curves, and calculated the optimal cut-off point to divide patients into high-risk and low-risk groups. Finally, we performed multivariate Cox regression analysis, Kaplan–Meier (K–M) survival analysis, clinical correlation analysis, and investigated correlations with tumor-infiltrating immune cells, chemotherapeutic effectiveness, and immunogene biomarkers. A total of 297 DEirlncRNAs were identified, of which 16 DEirlncRNA pairs were associated with prognosis in melanoma. After grouping patients by the optimal cut-off value, we could better distinguish melanoma patients with different survival outcomes, clinical characteristics, tumor immune status changes, chemotherapeutic drug sensitivity, and specific immunogene biomarkers. The DEirlncRNA pairs showed potential as novel biomarkers to predict the prognosis of melanoma patients. Furthermore, these DEirlncRNA pairs could be used to evaluate treatment efficacy in the future.

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Section: Discussionmentioning

confidence: 99%

Immune-related lncRNA pairs as novel signature to predict prognosis and immune landscape in melanoma patients

Wei

Zheng

et al. 2022

Medicine

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“…For instance, Luo et al displayed uveal melanoma patients' prognosis had close interactions with the immunodominant tumor environment [ 50 ]. Various immune cells contributes to the tumor progression of melanoma including adaptive immune CD8+ T-lymphocytes, macrophages, and so on [ 51 ]. Then we found that risk score was positively involved in the level of infiltration by various immune cells (CD4+ T cells, dendritic cells and so on).…”

Section: Discussionmentioning

confidence: 99%

Effects of RNA methylation N6-methyladenosine regulators on malignant progression and prognosis of melanoma

Liu

Zhou

et al. 2021

Cancer Cell Int

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Background Melanoma is an extremely aggressive type of skin cancer and experiencing a expeditiously rising mortality in a current year. Exploring new potential prognostic biomarkers and therapeutic targets of melanoma are urgently needed. The ambition of this research was to identify genetic markers and assess prognostic performance of N6-methyladenosine (m6A) regulators in melanoma. Methods Gene expression data and corresponding clinical informations of melanoma patients as well as sequence data of normal controls are collected from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. Quantitative real-time PCR (qRT-PCR) analysis was carried out to detect the RNA expression of IGF2BP3 in A375 cell line, melanoma tissues, and normal tissues. Western blot, cell proliferation, and migration assays were performed to assess the ability of IGF2BP3 in A375 cell line. Results Differently expressed m6A regulators between tumor samples and normal samples were analyzed. A three-gene prognostic signature including IGF2BP3, RBM15B, and METTL16 was constructed, and the risk score of this signature was identified to be an independent prognostic indicator for melanoma. In addition, IGF2BP3 was verified to promote melanoma cell proliferation and migration in vitro and associate with lymph node metastasis in clinical samples. Moreover, risk score and the expression of IGF2BP3 were positively associated with the infiltrating immune cells and these hub genes made excellent potential drug targets in melanoma. Conclusion We identified the genetic changes in m6A regulatory genes and constructed a three-gene risk signature with distinct prognostic value in melanoma. This research provided new insights into the epigenetic understanding of m6A regulators and novel therapeutic strategies in melanoma.

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“…Several computational methods, alternative ways to immunohistochemistry and flow cytometry [ 15 ], have been recently developed to derive tumor immune infiltrates using gene expression profiles of bulk tumors. In this study, we use the latest popular deconvolution method [ 16 – 20 ], CIBERSORTx [ 21 ], to investigate the immune patterns of tumors and analyze the relationship between immune composition and clinical features of osteosarcoma patients.…”

Section: Introductionmentioning

confidence: 99%

Immune classification of osteosarcoma

Shahriyari

2021

MBE

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Tumor immune microenvironment has been shown to be important in predicting the tumor progression and the outcome of treatments. This work aims to identify different immune patterns in osteosarcoma and their clinical characteristics. We use the latest and best performing deconvolution method, CIBERSORTx, to obtain the relative abundance of 22 immune cells. Then we cluster patients based on their estimated immune abundance and study the characteristics of these clusters, along with the relationship between immune infiltration and outcome of patients. We find that abundance of CD8 T cells, NK cells and M1 Macrophages have a positive association with prognosis, while abundance of γδ T cells, Mast cells, M0 Macrophages and Dendritic cells have a negative association with prognosis. Accordingly, the cluster with the lowest proportion of CD8 T cells, M1 Macrophages and highest proportion of M0 Macrophages has the worst outcome among clusters. By grouping patients with similar immune patterns, we are also able to suggest treatments that are specific to the tumor microenvironment.

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Correlation of tumor‐infiltrating immune cells of melanoma with overall survival by immunogenomic analysis

Cited by 23 publications

References 51 publications

Immune-related lncRNA pairs as novel signature to predict prognosis and immune landscape in melanoma patients

Immune-related lncRNA pairs as novel signature to predict prognosis and immune landscape in melanoma patients

Effects of RNA methylation N6-methyladenosine regulators on malignant progression and prognosis of melanoma

Immune classification of osteosarcoma

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