Scholarlink

Background <p>Despite advancements in multimodal therapy, glioma remains a lethal brain tumor with limited prognostic biomarkers. Programmed cell death (PCD) pathways and long non-coding RNAs (lncRNAs) are emerging as therapeutic targets, but their combined prognostic potential in glioma remains underexplored.</p> Methods <p>RNA sequencing data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts were analyzed to identify PCD-related lncRNAs. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression and multivariate Cox proportional hazards regression analyses were applied to construct a prognostic signature, with external validation performed in an independent cohort. Tumor immune microenvironment characterization was subsequently conducted through the Tumor IMmune Estimation Resource (TIMER) database using the inverse fold product algorithm, which quantified six tumor-infiltrating immune cells (TIIC) subtypes across 10,897 pan-cancer samples. Immune checkpoint differential expression between risk strata was assessed via single-sample gene set enrichment analysis (ssGSEA). Drug sensitivity prediction was performed using the Cancer Genome Project (GCP) databases.</p> Results <p>A 4-lncRNA signature effectively stratified patients into high-risk and low-risk groups. The prognosis of the high-risk group is worse. In multivariate analysis, a risk score can independently predict the prognosis and has a robust prediction efficiency in the CGGA external verification set. The infiltration of immunosuppressive cells, such as M2 macrophages, increased the expression of immune checkpoints, such as PD-L1, increased, and the infiltration of CD8 + T cells decreased in high-risk patients. Computational screening identified elesclomol as a potential therapeutic agent showing subtype-specific efficacy.</p> Conclusions <p>This study highlights the prognostic significance of lncRNAs associated with PCD in glioma and provides computational evidence for their potential as therapeutic targets. While the results suggest novel avenues for treatment development, they must be interpreted cautiously due to the lack of experimental validation. Future studies should aim to validate these results through controlled clinical trials and explore underlying molecular mechanisms.</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 computational analysis of programmed cell death-associated LncRNA signatures in glioma and drug prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月18日

查看全文
  • Xingyu Liu,
  • Dongliang Luo,
  • Peng Zhang,
  • Di Tian,
  • Wei Liu,
  • Liyan Jia

摘要

Background

Despite advancements in multimodal therapy, glioma remains a lethal brain tumor with limited prognostic biomarkers. Programmed cell death (PCD) pathways and long non-coding RNAs (lncRNAs) are emerging as therapeutic targets, but their combined prognostic potential in glioma remains underexplored.

Methods

RNA sequencing data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts were analyzed to identify PCD-related lncRNAs. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression and multivariate Cox proportional hazards regression analyses were applied to construct a prognostic signature, with external validation performed in an independent cohort. Tumor immune microenvironment characterization was subsequently conducted through the Tumor IMmune Estimation Resource (TIMER) database using the inverse fold product algorithm, which quantified six tumor-infiltrating immune cells (TIIC) subtypes across 10,897 pan-cancer samples. Immune checkpoint differential expression between risk strata was assessed via single-sample gene set enrichment analysis (ssGSEA). Drug sensitivity prediction was performed using the Cancer Genome Project (GCP) databases.

Results

A 4-lncRNA signature effectively stratified patients into high-risk and low-risk groups. The prognosis of the high-risk group is worse. In multivariate analysis, a risk score can independently predict the prognosis and has a robust prediction efficiency in the CGGA external verification set. The infiltration of immunosuppressive cells, such as M2 macrophages, increased the expression of immune checkpoints, such as PD-L1, increased, and the infiltration of CD8 + T cells decreased in high-risk patients. Computational screening identified elesclomol as a potential therapeutic agent showing subtype-specific efficacy.

Conclusions

This study highlights the prognostic significance of lncRNAs associated with PCD in glioma and provides computational evidence for their potential as therapeutic targets. While the results suggest novel avenues for treatment development, they must be interpreted cautiously due to the lack of experimental validation. Future studies should aim to validate these results through controlled clinical trials and explore underlying molecular mechanisms.