Research Paper Volume 15, Issue 21 pp 12330—12368

A robust six-gene prognostic signature based on two prognostic subtypes constructed by chromatin regulators is correlated with immunological features and therapeutic response in lung adenocarcinoma

Qiang Chen1, *, , Hongbo Zhao2, *, , Jing Hu3,4, ,

  • 1 Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, China
  • 2 Department of Laboratory Animal Science, Kunming Medical University, Kunming, China
  • 3 Department of Medical Oncology, First People’s Hospital of Yunnan Province, Kunming, China
  • 4 Department of Medical Oncology, Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
* Equal contribution

Received: February 20, 2023       Accepted: October 2, 2023       Published: November 7, 2023
How to Cite

Copyright: © 2023 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Accumulating evidence has demonstrated that chromatin regulators (CRs) regulate immune cell infiltration and are correlated with prognoses of patients in some cancers. However, the immunological and prognostic roles of CRs in lung adenocarcinoma (LUAD) are still unclear. Here, we systematically revealed the correlations of CRs with immunological features and the survival in LUAD patients based on a cohort of gene expression datasets from the public TCGA and GEO databases and real RNA-seq data by an integrative analysis using a comprehensive bioinformatics method. Totals of 160 differentially expressed CRs (DECRs) were identified between LUAD and normal lung tissues, and two molecular prognostic subtypes (MPSs) were constructed and evaluated based on 27 prognostic DECRs using five independent datasets (p =0.016, <0.0001, =0.008, =0.00038 and =0.00055, respectively). Six differentially expressed genes (DEGs) (CENPK, ANGPTL4, CCL20, CPS1, GJB3, TPSB2) between two MPSs had the most important prognostic feature and a six-gene prognostic model was established. LUAD patients in the low-risk subgroup showed a higher overall survival (OS) rate than those in the high-risk subgroup in nine independent datasets (p <0.0001, =0.021, =0.016, =0.0099, <0.0001, =0.0045, <0.0001, =0.0038 and =0.00013, respectively). Six-gene prognostic signature had the highest concordance index of 0.673 compared with 19 reported prognostic signatures. The risk score was significantly correlated with immunological features and activities of oncogenic signaling pathways. LUAD patients in the low-risk subgroup benefited more from immunotherapy and were less sensitive to conventional chemotherapy agents. This study provides novel insights into the prognostic and immunological roles of CRs in LUAD.


AIC: Akaike information criterion; ANGPTL4: angiopoietin like 4; AUC: area under curve; C1: cluster 1; C2: cluster 2; CCL20: C-C motif chemokine ligand 20; CENPK: centromere protein K; cfDNA: circulating free DNA; CI: confidence interval; CIBERSORT: cell-type identification by estimating relative subsets of RNA transcript; CPS1: carbamoyl-phosphate synthase 1; CR: chromatin regulator; CTC: circulating tumor cells; DECR: differentially expressed chromatin regulator; DEG: differentially expressed gene; DEGA: differentially expressed gene analysis; GEO: gene expression omnibus; GJB3: gap junction protein beta 3; GSEA: gene set enrichment analysis; GSVA: gene set variation analysis; HDI: human development index; HR: hazard rate; IC50: 50% inhibiting concentration; ICB: immune checkpoint blockade; ICG: immune checkpoint gene; KEGG: Kyoto encyclopedia of genes and genomes; KM: Kaplan-Meier; LASSO: least absolute shrinkage and selection operator; LC: lung cancer; LUAD: lung adenocarcinoma; MCRA: multivariate Cox regression analysis; NSCLC: non-small cell lung cancer; OS: overall survival; OSP: oncogenic signaling pathway; PAM: partitioning around medoid; ROC: receiver operating characteristic; ssGSEA: single sample gene set enrichment analysis; TCGA: the cancer genome atlas; TIDE: tumor immune dysfunction and exclusion; TIME: tumor immune microenvironment; TIMER: tumor immune estimation resource; TME: tumor microenvironment; TNM: tumor node metastasis; TPSB2: tryptase beta 2; UCRA: univariate Cox regression analysis.