Decoding the role of lipid metabolism in <scp>NSCLC</scp> : From macrophage subtype identification to prognostic model development

Lipid metabolism plays a pivotal role in shaping the tumor microenvironment, particularly by influencing macrophage function. This study aimed to identify lipid-associated macrophage (LAM) marker genes involved in the onset and progression of non-small cell lung cancer (NSCLC) through integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) analyses. Mutation and RNA-seq data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed to explore the relationship between lipid metabolism pathways and NSCLC progression. scRNA-seq analysis revealed macrophage subtypes closely associated with lipid metabolism, with three key marker genes-S100A10, HLA-DMB, and CTSL-identified as predictive factors for patient prognosis. A prognostic risk scoring model was constructed and validated using survival analysis and ROC curves, demonstrating high accuracy in stratifying NSCLC patients by risk. Further in vivo experiments using subcutaneous tumor xenografts and lung metastasis models showed that S100A10 and CTSL promoted tumor growth and metastasis, while HLA-DMB inhibited these processes. Immune infiltration analysis highlighted the immunological relevance of the identified marker genes, providing insights into their functional roles. This study underscores the critical influence of LAMs in NSCLC progression and highlights a robust prognostic model that offers potential therapeutic targets for improving patient outcomes.

Authors

Institutions

Publication Details

Journal
The FASEB Journal
Published
2025-04-25
DOI
https://doi.org/10.1096/fj.202500124
Citations
2
Primary Topic
Ferroptosis and cancer prognosis
Type
article
Field-Weighted Citation Impact
3.04
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Decoding the role of lipid metabolism in NSCLC : From macrophage subtype identification to prognostic model development

Chao Song, Jinhua Peng, Zhihao Huang, Shan Zhang et al.
2 citations
The FASEB Journal
Ferroptosis and cancer prognosis
3.04
article

Decoding the role of lipid metabolism in NSCLC : From macrophage subtype identification to prognostic model development

Chao Song, Jinhua Peng, Zhihao Huang, Shan Zhang, Jiakun Wang, Xianglai Chen, Hongcheng Lu, Kai Qi, Fan Li
article en
2 citations

Abstract

Lipid metabolism plays a pivotal role in shaping the tumor microenvironment, particularly by influencing macrophage function. This study aimed to identify lipid-associated macrophage (LAM) marker genes involved in the onset and progression of non-small cell lung cancer (NSCLC) through integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) analyses. Mutation and RNA-seq data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed to explore the relationship between lipid metabolism pathways and NSCLC progression. scRNA-seq analysis revealed macrophage subtypes closely associated with lipid metabolism, with three key marker genes-S100A10, HLA-DMB, and CTSL-identified as predictive factors for patient prognosis. A prognostic risk scoring model was constructed and validated using survival analysis and ROC curves, demonstrating high accuracy in stratifying NSCLC patients by risk. Further in vivo experiments using subcutaneous tumor xenografts and lung metastasis models showed that S100A10 and CTSL promoted tumor growth and metastasis, while HLA-DMB inhibited these processes. Immune infiltration analysis highlighted the immunological relevance of the identified marker genes, providing insights into their functional roles. This study underscores the critical influence of LAMs in NSCLC progression and highlights a robust prognostic model that offers potential therapeutic targets for improving patient outcomes.

The FASEB JournalVol. 39(8)
Nanchang University (CN), Second Affiliated Hospital of Nanchang University (CN)
Good health and well-being
Openalex Percentile: Top 10%
Ferroptosis and cancer prognosis
3.04
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.