Prognostic value and immunotherapy analysis of immune cell-related genes in laryngeal cancer
Background Laryngeal cancer (LC) is a prevalent head and neck carcinoma. Extensive research has established a link between immune cells in the tumor microenvironment (TME) and cancer progression, as well as responses to immunotherapy. This study aims to develop a prognostic model based on immune cel...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-04-01
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| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/19239.pdf |
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| Summary: | Background Laryngeal cancer (LC) is a prevalent head and neck carcinoma. Extensive research has established a link between immune cells in the tumor microenvironment (TME) and cancer progression, as well as responses to immunotherapy. This study aims to develop a prognostic model based on immune cell-related genes and examine the TME in LC. Methods RNA-seq data for LC were sourced from The Cancer Genome Atlas (TCGA), and GSE27020 and GSE51985 datasets were retrieved from the Gene Expression Omnibus (GEO) database. Key genes were identified through the intersection of differentially expressed genes (DEGs) between normal and LC samples and module genes derived from weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. The prognostic risk model was constructed using univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Gene Set Variation Analysis (GSVA) was subsequently performed for hallmark and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses in high- and low-risk groups. Immune infiltration analysis between risk groups was conducted via Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and single sample gene set enrichment analysis (ssGSEA). Finally, the relationship between the risk model and immunotherapy response was explored. Results A total of 124 key genes were identified through the overlap analysis, predominantly enriched in GO terms such as defense response to viruses and regulation of response to biotic stimuli, as well as KEGG pathways related to phagosome and Epstein-Barr virus infection. Machine learning indicated that the optimal prognostic model was constructed from two biomarkers, RENBP and OLR1. GSVA revealed that in the high-risk group, epithelial-mesenchymal transition and ECM-receptor interaction were the most significantly enriched pathways, while autoimmune thyroid disease, ribosome, and oxidative phosphorylation predominated in the low-risk group. Additionally, the stromal score was significantly higher in the high-risk group, while CD8+ T cells, cytolytic activity, inflammation promotion, and T cell co-stimulation were elevated in the low-risk group. Tumor Immune Dysfunction and Exclusion (TIDE) analysis showed higher TIDE and exclusion scores in the high-risk group, whereas the CD8 score was higher in the low-risk group. Finally, CD274 (PD-L1) expression was significantly elevated in the low-risk group. Conclusions This study identified two key prognostic biomarkers, RENBP and OLR1, and characterized TME differences across risk groups, offering novel insights into the diagnosis and treatment of LC. |
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| ISSN: | 2167-8359 |