Expression of Autoimmunity-Related Genes in Melanoma

Background Skin cutaneous melanoma (SKCM) is the most aggressive skin cancer, accounting for more than 75% mortality rate of skin-related cancers. As a newly identified programmed cell death, pyroptosis has been found to be closely associated with tumor progression. Nevertheless, the prognostic significance of pyroptosis in SKCM remains elusive. Methods A total of 469 SKCM samples and 812 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Firstly, differentially expressed pyroptosis-related genes (PRGs) between normal samples and SKCM samples were identified. Secondly, we established a prognostic model based on univariate Cox and LASSO Cox regression analyses, which was validated in the test cohort from GSE65904. Thirdly, a nomogram was used to predict the survival probability of SKCM patients. The R package “pRRophetic” was utilized to identify the drug sensitivity between the low- and high-risk groups. Tumor immune infiltration was evaluated using “immuneeconv” R package. Finally, the function of GSDMD and SB525334 was explored in A375 and A2058 cells. Results Based on univariate Cox and LASSO regression analyses, we established a prognostic model with identified eight PRGs (AIM2, CASP3, GSDMA, GSDMC, GSDMD, IL18, NLRP3, and NOD2), which was validated in the test cohort. SKCM patients were divided into low- and high-risk groups based on the median of risk score. Kaplan–Meier survival analysis showed that high-risk patients had shorter overall survival than low-risk patients. Additionally, time-dependent ROC curves validated the accuracy of the risk model in predicting the prognosis of SKCM. More importantly, 4 small molecular compounds (SB525334, SR8278, Gemcitabine, AT13387) were identified, which might be potential drugs for patients in different risk groups. Finally, overexpression of GSDMD and SB525334 treatment inhibit the proliferation, migration, and invasion of SKCM cells. Conclusion In this study, we constructed a prognostic model based on PRGs and identified GSDMD as a potential therapeutic target, which provide new insights into SKCM treatment.

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Novel Macrophage Subtype Genes for Predicting Prognosis and Immunotherapy Response in Melanoma Based on Single-Cell and Bulk RNA Sequencing Analysis

Zhu,

Luo,

Zhang

et al. 2024

Preprint

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Melanoma is a malignancy of aggressive behaviour and related with poor prognosis. Immunotherapy for metastatic melanoma shows great promise. However, the development of rapid resistance remains a largely insurmountable challenge. In this study, we aim to identify macrophage marker genes and construct an immune risk model, hoping to provide clinical significance in prognosis and immunotherapy response prediction. A total of 16,291 cell samples from 48 melanoma tissues in GSE120575 were enrolled. Firstly, 1,662 macrophages were identified with marker gene annotation. Subsequently, we acquired 6 macrophage subtypes in TCGA-SKCM dataset based on the expression characteristics of 724 gene that differentially expressed between macrophages and other immune cells. Since macrophage subtypes A and B had most distinguishing differences of immune- and tumour-related pathway enrichment, prognosis and immune microenvironment features that 102 immunity- and prognosis-related genes were further identified from them. Ultimately, we developed a risk signature of 21 immune genes through multivariate Cox regression, dividing patients into high- and low-risk groups. Explicitly, low-risk patients had a longer survival than high-risk patients, and similar results were also found in GSE65904 and GSE59455. Moreover, low-risk patients were found to have more favourable anti-tumour immune environment including more immune, stromal components, less tumour components, and higher infiltration of immune effector cells like activated memory CD4 + T cells, CD8 + T cells, M1 macrophages, plasma cells. Also, low-risk groups with higher gene expression of PD-1, PD-L1, CTLA4 were associated with better responses to immune checkpoint inhibitors (ICIs). Additionally, the differential expression of gene and protein levers of the 21 genes in normal and melanoma tissues demonstrated their importance in cellular physiology and disease mechanisms. To conclude, we developed an immune risk signature which could distinguish patients with different clinical prognoses and contribute to predicting the response to immunotherapy in melanoma.

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Expression of Autoimmunity-Related Genes in Melanoma

Cited by 3 publications

References 67 publications

Pyroptosis-associated genes and tumor immune response in endometrial cancer

Pyroptosis-associated genes and tumor immune response in endometrial cancer

System analysis based on the pyroptosis-related genes identifes GSDMD as a novel therapy target for skin cutaneous melanoma

Novel Macrophage Subtype Genes for Predicting Prognosis and Immunotherapy Response in Melanoma Based on Single-Cell and Bulk RNA Sequencing Analysis

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