Exploring exosome data to identify prognostic gene signatures for lung adenocarcinoma.
Jialin LiXin-Liang GaoSuyan TianMingbo TangWei LiuPublished in: Future oncology (London, England) (2021)
Background: Exosomes are involved in tumorigenesis, growth and metastasis. However, the prognostic value of exosome-related genes in lung adenocarcinoma (LUAD) remains unclear. Methods: Clinical and transcriptome data from The Cancer Genome Atlas LUAD cohort were used to construct a model based on exosome-related genes, which was validated with LUAD data from the Gene Expression Omnibus (GEO). Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were used to explore underlying mechanisms; the single-sample gene set enrichment analysis score was used to determine immune functions. Results: A 19-exosome-related gene signature for overall survival in LUAD was predictive in both The Cancer Genome Atlas and GEO LUAD cohorts. Immune-related and extracellular matrix-related pathways were enriched in differentially expressed genes. Immune states differed between high- and low-risk groups. Conclusion: The novel signature can be used to predict outcomes in LUAD.
Keyphrases
- genome wide
- dna methylation
- genome wide identification
- gene expression
- copy number
- extracellular matrix
- electronic health record
- papillary thyroid
- single cell
- big data
- stem cells
- genome wide analysis
- mesenchymal stem cells
- squamous cell
- transcription factor
- bone marrow
- adipose tissue
- skeletal muscle
- lymph node metastasis
- rna seq
- weight loss
- artificial intelligence
- drug induced
- deep learning