Scholarlink

<p>Necroptosis is a novel programmed cell death that affects the tumor heterogeneity, microenvironment, and prognosis, which is not well elucidated in skin cutaneous melanoma (SKCM). The SKCM-TCGA, GSE65904, and GSE215120 datasets were downloaded from the TCGA and GEO databases, respectively. The necroptosis-related genes were identified by weighted co-expression network analysis (WGCNA) and single-cell sequencing analysis. COX and LASSO regression was used to construct the prognostic model. Survival analysis, immune infiltration analysis, and tumor mutation analysis between the high-necroptosis score (NCPTS) and low-NCPTS groups were performed. Finally, real-time PCR experiment was carried out to verify the results. A prognostic model based on 9 necroptosis-related genes (TUFM, CD53, CLEC2D, KLRC1, STAT4, IFI35, XCL1, TAPBP, and SOD2) was constructed to predict the prognosis of SKCM patients. The patients in high-NCPTS group had a poor prognosis. The expression of immune checkpoint-related gene and drug sensitivity were higher than those in the low-NCPTS groups, indicating susceptible for immunotherapy. Real-time PCR showed that TUFM expression was significantly higher in A375 cells than control (<i>P</i> &lt; 0.05). Besides, TUFM expression was also validated by TISCH and HPA database. The prognostic model might provide guidance for the prognosis and immunotherapy for SKCM patients, contributing to a better understanding of necroptosis in SKCM.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A novel necroptosis related prognostic signature for skin cutaneous melanoma based on transcriptome and single cell sequencing analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

查看全文
  • Xin Fan,
  • Lingyi Lu,
  • Sihan Wang,
  • Qiongyan Zhou,
  • Bingjiang Lin

摘要

Necroptosis is a novel programmed cell death that affects the tumor heterogeneity, microenvironment, and prognosis, which is not well elucidated in skin cutaneous melanoma (SKCM). The SKCM-TCGA, GSE65904, and GSE215120 datasets were downloaded from the TCGA and GEO databases, respectively. The necroptosis-related genes were identified by weighted co-expression network analysis (WGCNA) and single-cell sequencing analysis. COX and LASSO regression was used to construct the prognostic model. Survival analysis, immune infiltration analysis, and tumor mutation analysis between the high-necroptosis score (NCPTS) and low-NCPTS groups were performed. Finally, real-time PCR experiment was carried out to verify the results. A prognostic model based on 9 necroptosis-related genes (TUFM, CD53, CLEC2D, KLRC1, STAT4, IFI35, XCL1, TAPBP, and SOD2) was constructed to predict the prognosis of SKCM patients. The patients in high-NCPTS group had a poor prognosis. The expression of immune checkpoint-related gene and drug sensitivity were higher than those in the low-NCPTS groups, indicating susceptible for immunotherapy. Real-time PCR showed that TUFM expression was significantly higher in A375 cells than control (P < 0.05). Besides, TUFM expression was also validated by TISCH and HPA database. The prognostic model might provide guidance for the prognosis and immunotherapy for SKCM patients, contributing to a better understanding of necroptosis in SKCM.