ArticleFrontiers in genetics2022
Significance of immunogenic cell death-related genes in prognosis prediction and immune microenvironment landscape of patients with cutaneous melanoma.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Targeting RIPK1 to modulate cell death and tumour microenvironment in cancer therapy.Journal of enzyme inhibition and medicinal chemistry · 2025Review
- A model based on immunogenic cell death-related genes predicts prognosis and response to immunotherapy in kidney renal clear cell carcinoma.Translational cancer research · 2024Article
- Implications of different cell death patterns for prognosis and immunity in lung adenocarcinoma.NPJ precision oncology · 2023Article
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- GEO data mining identifies potential immune-related genes in hypertrophic scar and verities in a rabbit model.Heliyon · 2023Article
- Genomic analysis of immunogenic cell death-related subtypes for predicting prognosis and immunotherapy outcomes in glioblastoma multiforme.Open medicine (Warsaw, Poland) · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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Abstract
Cutaneous melanoma (CM) is one of the most life-threatening tumors. Although targeted therapies and immune checkpoint inhibitors have significantly improved patient outcomes over the past decades, they still have their efficacy limitations. Immunogenic cell death (ICD) induces regulated cell death through immunogenic signal secretion and exposure. Accumulated evidence suggests that the ICD process is an effective target for the treatment of a variety of tumor types, including CM. However, the research on ICD in CM is far from complete, and its clinical value has not been widely concerned. By analyzing the Cancer Genome Atlas (TCGA) database, we constructed a new risk model based on 4 ICD-related genes and validated its ability to predict the prognosis of CM patients. In addition, we comprehensively analyzed the tumor microenvironment (TME) of CM patients and showed a significant immunosuppressive TME in the high-risk group compared with the low-risk group. By Immunophenoscore (IPS), we further explored the correlation between the model and immunotherapy response. The data of Genomics of Drug Sensitivity in Cancer (GDSC) database were further extracted to analyze drug sensitivity and evaluate its correlation with the established risk model. In the end, differential expressed genes (DEGs) were analyzed by Gene Set Variation Analysis (GSVA) to preliminarily explore the possible signaling pathways related to the prognosis of ICD and CM. The results of this study provide new perspectives and insights for individualized and accurate treatment strategies for CM patients.
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