Medical School of Chinese PLA, Beijing 100853, China.
Department of Plastic and Reconstructive Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China.
Math Biosci Eng. 2022 Jan;19(1):688-706. doi: 10.3934/mbe.2022031. Epub 2021 Nov 19.
Skin cutaneous melanoma (SKCM) is one of the most malignant skin cancers and remains a health concern worldwide. Pyroptosis is a newly recognized form of programmed cell death and plays a vital role in cancer progression. We aim to construct a prognostic model for SKCM patients based on pyroptosis-related genes (PRGs). SKCM patients from The Cancer Genome Atlas (TCGA) were divided into training and validation cohorts. We used GSE65904 downloaded from GEO database as an external validation cohort. We performed Cox regression and the least absolute shrinkage and selection operator (LASSO) regression to identify prognostic genes and built a risk score. Patients were divided into high- and low-risk groups based on the risk score. Differently expressed genes (DEGs), immune cell infiltration and immune-related pathways activation were compared between the two groups. We established a model containing 4 PRGs, i.e., GSDMA, GSDMC, AIM2 and NOD2. The overall survival (OS) time was significantly different between the 2 groups. The risk score was an independent predictor for prognosis in both the uni- and multi-variable Cox regressions. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses showed that DEGs were enriched in immune-related pathways. Most types of immune cells were highly expressed in the low risk group. All immune pathways were significantly up-regulated in the low-risk group. In addition, low-risk patients had a better response to immune checkpoint inhibitors. Our novel pyroptosis-related gene signature could predict the prognosis of SKCM patients and their response to immune checkpoint inhibitors.
皮肤皮肤黑色素瘤(SKCM)是最恶性的皮肤癌之一,仍然是全球关注的健康问题。细胞焦亡是一种新发现的程序性细胞死亡形式,在癌症进展中起着至关重要的作用。我们旨在基于细胞焦亡相关基因(PRGs)构建 SKCM 患者的预后模型。来自癌症基因组图谱(TCGA)的 SKCM 患者被分为训练和验证队列。我们使用从 GEO 数据库下载的 GSE65904 作为外部验证队列。我们进行了 Cox 回归和最小绝对值收缩和选择算子(LASSO)回归,以识别预后基因并构建风险评分。根据风险评分将患者分为高风险组和低风险组。比较两组之间的差异表达基因(DEGs)、免疫细胞浸润和免疫相关途径激活。我们建立了一个包含 4 个 PRGs 的模型,即 GSDMA、GSDMC、AIM2 和 NOD2。两组之间的总生存期(OS)时间有显著差异。风险评分在单变量和多变量 Cox 回归中都是预后的独立预测因子。基因本体论(GO)和京都基因与基因组百科全书(KEGG)分析表明,DEGs 富集在免疫相关途径中。在低风险组中,大多数类型的免疫细胞高度表达。在低风险组中,所有免疫途径均显著上调。此外,低风险患者对免疫检查点抑制剂的反应更好。我们的新的细胞焦亡相关基因特征可以预测 SKCM 患者的预后及其对免疫检查点抑制剂的反应。