Research Paper Volume 14, Issue 11 pp 4786—4818

Identification of prognostic candidate signatures by systematically revealing transcriptome characteristics in lung adenocarcinoma with differing tumor microenvironment immune phenotypes

Qiang Chen1, *, , Jiakang Ma2, *, , Xiaoyi Wang1, *, , Xiangqing Zhu3, &, ,

  • 1 Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, China
  • 2 Department of Medical Oncology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
  • 3 Basic Medical Laboratory, The 920th Hospital of Joint Logistics Support Force of PLA, Kunming, China
* Equal contribution

Received: May 24, 2021       Accepted: May 24, 2022       Published: June 7, 2022
How to Cite

Copyright: © 2022 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Accumulated evidence shows that tumor microenvironment plays crucial roles in predicting clinical outcomes of lung adenocarcinoma (LUAD). The current study aimed to identify some potentially prognostic signatures by systematically revealing the transcriptome characteristics in LUADs with differing immune phenotypes. LUAD gene expression data were retrieved from the public TCGA and GEO databases, and the transcriptome characteristics were systematically revealed using a comprehensive bioinformatics method including single-sample gene set enrichment analysis, differentially expressed gene (DEG) analysis, protein and protein interaction (PPI) network construction, competitive endogenous RNA (ceRNA) network construction, weighted gene coexpression network analysis and prognostic model establishment. Finally, 1169 key DEGs associated with LUAD immune phenotype, including 88 immune DEGs, were excavated. Five essential and eight immune essential DEGs were separately identified by constructing two PPI networks based on the above DEGs. Totals of 1085 key DElncRNAs and 45 key DEmiRNAs were excavated and one ceRNA network consisting of 26 DEmRNAs, 3 DEmiRNAs and 57 DElncRNAs were established. The most significant gene coexpression module (cor=0.63 and p=3e-55) associated with LUAD immune phenotypes and three genes (FGR, BTK, SPI1) related to the immune cell infiltration were identified. Three robust prognostic signatures including a 9-lncRNA, an 8-lncRNA and an 8-mRNA were established. The areas under the curves of 5-year correlated with overall survival rate were separately 0.7319, 0.7228 and 0.713 in the receiver operating characteristic curve. The findings provide novel insights into the immunological mechanism in LUAD biology and in predicting the prognosis of LUAD patients.


LC: lung cancer; NSCLC: non-small cell lung cancer; LUAD: lung adenocarcinoma; TME: tumor microenvironment; TCGA: the cancer genome atlas; ssGSEA: single-sample gene set enrichment analysis; TIICs: tumor-infiltrating immune cells; DEGs: differentially expressed genes; PPI: protein and protein interaction; ceRNA: competitive endogenous RNA; AUC: areas under the curve; UK: united kingdom; TNM: tumor node metastasis; EGFR: epidermal growth factor receptor; TP53: tumor protein P53; TMM: trimmed mean of M-values; FC: fold change; MCODE: molecular complex detection; OS: overall survival; ROC: receiver operating characteristic; SVM: support vector machine; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; BP: biological process; DO: disease ontology; KM: Kaplan-Meier; LR: log-rank; HR: hazard rate; CI: confidence interval; WGCNA: weighted gene coexpression network analysis; TOM: topological overlap measure; ME: module eigengene; MM: module membership; GS: gene significance; MS: module significance.