ArticlePeerJ2019
Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed, 79 citations in OpenAlex.
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- Unraveling key genes and mitochondrial-related mechanisms of atherosclerosis severity: explorations based on interpretable machine learning.BMC cardiovascular disorders · 2026Article
- Clinical risk prediction in lung adenocarcinoma using MAL gene and tumor microenvironment features.Discover oncology · 2026Article
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- Elucidating immunological characteristics of the adenoma-carcinoma sequence in colorectal cancer patients in South Korea using a bioinformatics approach.Scientific reports · 2024Article
- The emerging roles of lysine-specific demethylase 4A in cancer: Implications in tumorigenesis and therapeutic opportunities.Genes & diseases · 2024Review
- A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma.The clinical respiratory journal · 2023Article
- Identification and validation of tumor microenvironment-related signature for predicting prognosis and immunotherapy response in patients with lung adenocarcinoma.Scientific reports · 2023Article
- Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation.Breast cancer (Tokyo, Japan) · 2023Article
- Construction of anIranian journal of biotechnology · 2023Article
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- 5-mRNA-based prognostic signature of survival in lung adenocarcinoma.World journal of clinical oncology · 2023Article
- Identification and characterization of a novel molecular classification incorporating oxidative stress and metabolism-related genes for stomach adenocarcinoma in the framework of predictive, preventive, and personalized medicine.Frontiers in endocrinology · 2023Article
- A new immune signature for survival prediction and immune checkpoint molecules in non-small cell lung cancer.Frontiers in oncology · 2023Article
- The identification of hub biomarkers and pathways in lung cancer and prognostic evaluation.Translational cancer research · 2022Article
- Prognostic value and underlying mechanism of autophagy-related genes in bladder cancer.Scientific reports · 2022Article
- ADAM metallopeptidase domain 12 overexpression correlates with prognosis and immune cell infiltration in clear cell renal cell carcinoma.Bioengineered · 2022Article
- Predicting Differences in Treatment Response and Survival Time of Lung Adenocarcinoma Patients Based on a Prognostic Risk Model of Glycolysis-Related Genes.Frontiers in genetics · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient's outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients.
methodsThe expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model.
resultsA total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (
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