Identification and validation of eight lysosomes-related genes signatures and correlation with immune cell infiltration in lung adenocarcinoma

Lung cancer is the leading cause of cancer-related death. Lysosomes are key degradative compartments that maintain protein homeostasis. In current study, we aimed to construct a lysosomes-related genes signature to predict the overall survival (OS) of patients with Lung Adenocarcinoma (LUAD). Differ...

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Bibliographic Details
Published in:Cancer cell international 2023-12, Vol.23 (1), p.322-322, Article 322
Main Authors: Song, Dingli, Zhao, Lili, Zhao, Guang, Hao, Qian, Wu, Jie, Ren, Hong, Zhang, Boxiang
Format: Article
Language:English
Subjects:
Adenocarcinoma
Algorithms
Angiogenesis
Atrophy
Biomarkers
Gene expression
Gene set enrichment analysis
Genes
Genomes
Genomics
Homeostasis
Immune infiltration
Immunotherapy
Infiltration
Lung adenocarcinoma
Lung cancer
Lymphatic system
Lysosomes
Medical prognosis
Metastases
Metastasis
Mutation
Myc protein
Patients
Regression analysis
Risk groups
Signal transduction
Signature
TOR protein
Tumor microenvironment
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Summary:Lung cancer is the leading cause of cancer-related death. Lysosomes are key degradative compartments that maintain protein homeostasis. In current study, we aimed to construct a lysosomes-related genes signature to predict the overall survival (OS) of patients with Lung Adenocarcinoma (LUAD). Differentially expressed lysosomes-related genes (DELYs) were analyzed using The Cancer Genome Atlas (TCGA-LUAD cohort) database. The prognostic risk signature was identified by Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression and multivariate Cox analysis. The predictive performance of the signature was assessed by Kaplan-Meier curves and Time-dependent receiver operating characteristic (ROC) curves. Gene set variant analysis (GSVA) was performed to explore the potential molecular biological function and signaling pathways. ESTIMATE and single sample gene set enrichment analysis (ssGSEA) were applied to estimate the difference of tumor microenvironment (TME) between the different risk subtypes. An eight prognostic genes (ACAP3, ATP8B3, BTK, CAV2, CDK5R1, GRIA1, PCSK9, and PLA2G3) signature was identified and divided patients into high-risk and low-risk groups. The prognostic signature was an independent prognostic factor for OS (HR > 1, p 
ISSN:1475-2867
1475-2867
DOI:10.1186/s12935-023-03149-5