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Objectives <p>Lipid metabolism reprogramming was a key adaptive mechanism for rapid proliferation of tumor cells. It affected tumor progression by altering the expression of lipid metabolism related genes (LMRGs). The aim of this study was to identify novel LMRGs-related prognostic biomarkers and therapeutic targets for glioblastoma multiforme (GBM).</p> Methods <p>The clinical and transcriptomic data of GBM patients were extracted from the TCGA database. the LMRGs were obtained from the MSigDB database, and screened for prognostic related genes. The immune cell infiltration status was evaluated using ESTIMATE, QUANTISEQ, CIBERSORT, MCPcounter algorithm and ssGSEA. The relevant signaling pathways were screened by the GO, KEGG, GSEA and GSVA analyses. The hub genes with prognostic value were screened through LASSO-Cox regression analysis. The distribution of the hub genes in GBM cells was determined by the TISCH2 database. Use DepMap database to demonstrate functional dependence of hub genes. Finally, validation was performed using the GEO dataset.</p> Results <p>We divided GBM patients into two clusters, with cluster 2 (C2) had a worse prognosis than cluster 1 (C1) ( <i>P</i> = 0.0269 ), and C2 had higher abundance of M2 macrophages, NK cells and dendritic cells (DCs). 40 prognostic LMRGs were mainly enriched in lipid metabolism pathways such as steroid and phospholipid through GO/KEGG analysis. GSVA showed significant enrichment of phospholipid and steroid metabolism in C1. Four hub genes, <i>INPP5F</i> (hazard ratio &gt; 1), <i>PTEN</i>, <i>MTMR2</i> and <i>IDI1</i>, were identified through LASSO-Cox regression. Single-cell sequencing data showed a high proportion of oligodendrocytes in INPP5F-positive cells, and DepMap validation showed peak gene effects of <i>INPP5F</i> and <i>IDI1</i> &lt; 0, <i>MTMR2</i> and <i>PTEN</i>&gt;0.</p> Conclusion <p>We have identified four prognostic LMRGs: <i>INPP5F</i> ( risk gene ), <i>PTEN</i>, <i>MTMR2</i> and <i>IDI1</i>, which provide new insights into the mechanisms of GBM progression, as well as new prognostic biomarkers and immunotherapy targets.</p>

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Exploration of prognosis and immune infiltration characteristics in glioblastoma multiforme based on lipid metabolism related genes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月5日

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  • Peng-Cheng Li,
  • Hua-Xuan Chen,
  • De-Bo Yun,
  • Qian-Yi Huang

摘要

Objectives

Lipid metabolism reprogramming was a key adaptive mechanism for rapid proliferation of tumor cells. It affected tumor progression by altering the expression of lipid metabolism related genes (LMRGs). The aim of this study was to identify novel LMRGs-related prognostic biomarkers and therapeutic targets for glioblastoma multiforme (GBM).

Methods

The clinical and transcriptomic data of GBM patients were extracted from the TCGA database. the LMRGs were obtained from the MSigDB database, and screened for prognostic related genes. The immune cell infiltration status was evaluated using ESTIMATE, QUANTISEQ, CIBERSORT, MCPcounter algorithm and ssGSEA. The relevant signaling pathways were screened by the GO, KEGG, GSEA and GSVA analyses. The hub genes with prognostic value were screened through LASSO-Cox regression analysis. The distribution of the hub genes in GBM cells was determined by the TISCH2 database. Use DepMap database to demonstrate functional dependence of hub genes. Finally, validation was performed using the GEO dataset.

Results

We divided GBM patients into two clusters, with cluster 2 (C2) had a worse prognosis than cluster 1 (C1) ( P = 0.0269 ), and C2 had higher abundance of M2 macrophages, NK cells and dendritic cells (DCs). 40 prognostic LMRGs were mainly enriched in lipid metabolism pathways such as steroid and phospholipid through GO/KEGG analysis. GSVA showed significant enrichment of phospholipid and steroid metabolism in C1. Four hub genes, INPP5F (hazard ratio > 1), PTEN, MTMR2 and IDI1, were identified through LASSO-Cox regression. Single-cell sequencing data showed a high proportion of oligodendrocytes in INPP5F-positive cells, and DepMap validation showed peak gene effects of INPP5F and IDI1 < 0, MTMR2 and PTEN>0.

Conclusion

We have identified four prognostic LMRGs: INPP5F ( risk gene ), PTEN, MTMR2 and IDI1, which provide new insights into the mechanisms of GBM progression, as well as new prognostic biomarkers and immunotherapy targets.