Defining lung adenocarcinoma subtypes with glucocorticoid-related genes and constructing a prognostic index for immunotherapy guidance

Abstract

Background: Several studies have shown that glucocorticoid-related genes (GCGs) play a crucial role in cancer. However, the mechanism of GCGs in lung adenocarcinoma (LUAD) is not fully understood. This study aimed to identify distinct subtypes of LUAD by integrating GCGs and to develop prognostic models for precise prognosis prediction and immunotherapy guidance.

Methods: In this study, sample data of LUAD were collected from The Cancer Genome Atlas (TCGA) database, and unsupervised clustering was used to identify LUAD subtypes with different GCGs characteristics. Survival-related genes were screened by differential expression analysis and protein-protein interaction (PPI) network analysis. After that, the least absolute shrinkage and selection operator (LASSO) combined with Cox regression analysis was used to establish the prognosis model. Differences in the immune microenvironment of different risk groups were analyzed, and Tumor Immune Dysfunction and Exclusion (TIDE) was used to predict the response of patients to immunotherapy. Finally, the CellMiner database was used to predict potential drugs.

Results: Two subtypes of LUAD were identified, namely cluster 1 (high survival rate) and cluster 2 (low survival rate). A prognostic model was constructed based on 9 characteristic genes, including CLCA1, CYP17A1, GRIA2, IGFBP1, IGF2BP1, NTSR1, RPE65, VGF, and WNT16, and the prognosis of LUAD patients was positively predicted. There were differences in the immune microenvironment of different risk LUAD patients, and high-risk LUAD patients may benefit less from immunotherapy. BGB-283 was a candidate for LUAD targeting VGF.

Conclusions: Our study elucidates the impact of GCGs on LUAD prognosis and immune responses, offering insights for prognostic forecasting and immunotherapeutic strategies for LUAD patients.

Keywords: Glucocorticoid-related genes (GCGs); immunotherapy; lung adenocarcinoma (LUAD); prognostic models; subtypes.

Figure 1

Identification of LUAD subtypes and evaluation of the typing ability of GCGs. (A) LUAD clustering results based on GCG expression; (B) survival analysis of the two subtypes after clustering. GCGs, glucocorticoids-related genes; LUAD, lung adenocarcinoma.

Figure 2

PPI network of 159 DEGs in clusters 1 and 2. DEGs, differentially expressed genes; PPI, protein-protein interaction.

Figure 3

Construction of the prognostic model for LUAD. (A) Coefficient distribution chart from ten-fold cross-validation for the LASSO model, with λ as the tuning parameter; (B) LASSO coefficient curve; (C) forest plot from the multivariate Cox regression analysis. *, P<0.05; **, P<0.01. AIC, Akaike information criterion; CI, confidence interval; LASSO, least absolute shrinkage and selection operator; LUAD, lung adenocarcinoma.

Figure 4

Evaluation and validation of LUAD prognostic model. (A) Risk score distribution, (B) survival conditions, (C) characteristic gene expression, (D) survival curves, and (E) ROC curves for the high- and low-risk groups in the TCGA training cohort. (F) Risk score distribution, (G) survival conditions, (H) characteristic gene expression, (I) survival curves, and (J) ROC curves for the high- and low-risk groups in the GSE50081 validation cohort. AUC, area under the curve; LUAD, lung adenocarcinoma; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas.

Figure 5

The independent prediction of the prognostic model. (A) Forest plot from univariate Cox regression analysis with combined clinical features and risk scores. (B) Forest plot from multivariate Cox regression analysis with integrated clinical features and risk scores. (C) Nomogram created based on clinical features and risk scores. (D) Calibration plots for 1-, 2-, and 3-year predictions by the nomogram. (E) ROC curves for the assessment of clinical features and the risk score. (F) DCA curves for the nomograms at 1, 2, and 3 years. AUC, area under the curve; CI, confidence interval; DCA, decision curve analysis; ROC, receiver operating characteristic.

Figure 6

Examination of the underlying biological functions in LUAD patients in different risk groups. (A) GO enrichment plot for DEGs between the high- and low-risk groups. (B) KEGG enrichment plot for DEGs between the high- and low-risk groups. BP, biological process; cAMP, cyclic adenosine monophosphate; CC, cellular component; DEGs, differentially expressed genes; GO, Gene Ontology; LUAD, lung adenocarcinoma; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 7

Immune profiling in LUAD patients with varying risk values. (A) Infiltration levels of immune cells in both risk groups. (B) Expression levels of immune cell functions in both risk groups. (C) Expression of HLA-related genes in both risk groups. (D) Expression of immune checkpoints in the high- and low-risk groups. (E) TIDE scores between the high- and low-risk groups. ***, P<0.001. aDCs, activated dendritic cells; APC, antigen-presenting cell; CCR, C-C chemokine receptor; iDCs, immature dendritic cells; MHC, major histocompatibility complex; NK, natural killer cells; IFN, interferon; HLA, human leukocyte antigen; LUAD, lung adenocarcinoma; pDCs, plasmacytoid dendritic cells; ssGSEA, single sample gene set enrichment analysis; TIDE, Tumor Immune Dysfunction and Exclusion; TIL, tumor-infiltrating lymphocytes.

Figure 8

Mutation profiling in high- and low-risk LUAD patient groups. (A,B) A statistical overview of mutation types and their distribution in the (A) high-risk and (B) low-risk groups. (C,D) Top 20 mutation types and their prevalence in the (C) high-risk and (D) low-risk groups. LUAD, lung adenocarcinoma.

Figure 9

Prediction of potential drugs targeting the gene VGF. (A) Sensitivity analysis of the top 9 drugs targeting the gene VGF. (B) IC50 values of the 9 drugs in high and low expression groups of VGF. ns, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001. VGF, nerve growth factor inducible; IC50, inhibitory concentration.