ArticleBioengineered2021
Exploration of the prognostic signature reflecting tumor microenvironment of lung adenocarcinoma based on immunologically relevant genes.
Article in Bioengineered, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The impact of BTK knockdown on lung adenocarcinoma growth and immune response.Cancer science · 2025Article
- Overexpression of PIK3CG in Cancer Cells Promotes Lung Cancer Cell Migration and Metastasis Through Enhanced MMPs Expression and Neutrophil Recruitment and Activation.Biochemical genetics · 2025Article
- Robust Cancer Biomarker Identification From Matched Transcriptomic Data Via Bootstrapped Regularized Conditional Logistic Regression.Cancer informatics · 2025Article
- Role of vascular endothelial growth factor D in lung adenocarcinoma immunotherapy response.American journal of translational research · 2024Article
- Molecular profiling and prognostic biomarkers in chinese non-small cell lung cancer cohort.Diagnostic pathology · 2023Article
- LncRNA ARAP1-AS1 contributes to lung adenocarcinoma development by targeting miR-8068 to upregulate CEACAM5.Cancer biomarkers : section A of Disease markers · 2023Article
- Kruppel like factor 16 promotes lung adenocarcinoma progression by upregulating lamin B2.Bioengineered · 2022Article
- miR-873 and miR-105-2 May Affect the Tumour Microenvironment and are Potential Biomarkers for Lung Adenocarcinoma.International journal of general medicine · 2022Article
- Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients.Frontiers in molecular biosciences · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung adenocarcinoma (LUAD) represents the major histological type of lung cancer with high mortality globally. Due to the heterogeneous nature, the same treatment strategy to various patients may result in different therapeutic responses. Hence, we aimed to elaborate an effective signature for predicting patient survival outcomes. The TCGA-LUAD cohort from the TCGA portal was used as a training dataset. The GSE26939 and GSE68465 cohorts from the GEO database were taken as validation datasets. All immunologically relevant genes were extracted from the ImmPort. The ESTIMATE algorithm was employed to explore LUAD microenvironment in the training dataset. Further, the DEGs were picked out based on the immune-associated genes reflecting different statuses in the immune context of TME. Univariate/multivariate Cox regression was performed to determine six prognosis- specific genes (PIK3CG, BTK, VEGFD, INHA, INSL4, and PTPRC) and established a risk predictive signature. The time-dependent ROC indicated that AUC values were all greater than 0.70 at 1-, 3-, and 5- year intervals. Corresponding RiskScore of each LUAD patient was calculated from the signature, and they were stratified into the high- and low-risk groups by the median value of RiskScore. K-M curves and Log-rank test demonstrated significant survival differences between the two groups (
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