Details

Autor(en) / Beteiligte

• Wu, Wei
• Jia, Liye
• Zhang, Yanan
• Zhao, Juanjuan
• Dong, Yunyun
• Qiang, Yan

Titel

Exploration of the prognostic signature reflecting tumor microenvironment of lung adenocarcinoma based on immunologically relevant genes

Ist Teil von

• Bioengineered, 2021-01, Vol.12 (1), p.7417-7431

Ort / Verlag

United States: Taylor & Francis

Erscheinungsjahr

2021

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• 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 (P < 0.05). Similar results were exhibited in the validation datasets. The RiskScore was incredibly relevant to clinicopathological factors like gender, AJCC stage, and T stage. Also, it can mirror the distribution state of 15 kinds of TIICs and have some predictive value for the sensitivity of therapeutic drugs.