The hypoxia-associated genes in immune infiltration and treatment options of lung adenocarcinoma

Abstract

Background: Lung adenocarcinoma (LUAD) is a common lung cancer with a poor prognosis under standard chemotherapy. Hypoxia is a crucial factor in the development of solid tumors, and hypoxia-related genes (HRGs) are closely associated with the proliferation of LUAD cells.

Methods: In this study, LUAD HRGs were screened, and bioinformatics analysis and experimental validation were conducted. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to gather LUAD RNA-seq data and accompanying clinical information. LUAD subtypes were identified by unsupervised cluster analysis, and immune infiltration analysis of subtypes was conducted by GSVA and ssGSEA. Cox regression and LASSO regression analyses were used to obtain prognosis-related HRGs. Prognostic analysis was used to evaluate HRGs. Differences in enrichment pathways and immunotherapy were observed between risk groups based on GSEA and the TIDE method. Finally, RT-PCR and in vitro experiments were used to confirm prognosis-related HRG expression in LUAD cells.

Results: Two hypoxia-associated subtypes of LUAD were distinguished, demonstrating significant differences in prognostic analysis and immunological characteristics between subtypes. A prognostic model based on six HRGs (HK1, PDK3, PFKL, SLC2A1, STC1, and XPNPEP1) was developed for LUAD. HK1, SLC2A1, STC1, and XPNPEP1 were found to be risk factors for LUAD. PDK3 and PFKL were protective factors in LUAD patients.

Conclusion: This study demonstrates the effect of hypoxia-associated genes on immune infiltration in LUAD and provides options for immunotherapy and therapeutic strategies in LUAD.

Keywords: Hypoxia; Immunotherapy; Lung adenocarcinoma; Prognostic; Unsupervised clustering.

Figure 1. Research workflow.

Figure 2. Identification of Hypoxic subtypes of LUAD cohorts.

(A) Consensus clustering of the TCGA-LUAD cohort using hypoxia-associated genes (K = 2). (B) The Kaplan–Meier survival curves for the hypoxic subtypes Cluster 1 and Cluster 2 in the TCGA-LUAD cohort. (C) T-SNE diagrams of the hypoxic subpopulation of TCGA-LUAD. (D) Sankey diagrams of hypoxic subgroups and clinical characteristics of patients based on the TCGA-LUAD cohort.

Figure 3. Immunological characteristics and clinical features of the hypoxic subtype.

(A) Analysis of GSVA based on hypoxic subtypes. (B) ESTIMATE score (1), immune score (2), stromal score (3), TIDE score (4), TMB score (5) between hypoxic subtypes. (C) ssGSEA analysis to assess differences in the expression of 28 immune cells between hypoxic subtypes. (D) Heat map of clinical features and immune cell expression based on combined ssGSEA analysis of hypoxic subtypes. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001.

Figure 4. LASSO regression prognostic model in the training set and multivariate COX model regression analysis and subgroup analysis for six-HRG signature.

(A) LASSO regression model to find the best optimal λ. (B) The process of changing LASSO coefficients of HRGs. (C) Forest plots of the Multivariate COX model regression analysis for six-HRG signature. (D) Subgroup analysis for six-HRG signature. * p-value < 0.05, ** p-value < 0.01, *** p-value ¡ 0.001.

Figure 5. Survival prediction capability of the six-HRG signature in the training and validation datasets.

(A) The Kaplan–Meier survival curves of high- and low-risk groups in training dataset GSE72094. (B) The AUC of time-dependent ROC curves in GSE72094 dataset. (C) DCA of the diagnostic nomogram in the training datasets. (D) The calibration curve of 3-year survival in the training datasets. The Kaplan–Meier survival curves, AUC of time-dependent ROC curves, DCA, and the calibration curve of 3-year survival in validation dataset GSE31210 dataset (E, F, G, H), GSE30219 dataset (I, J, K, L), and TCGA-LUAD dataset (M, N, O, P).

Figure 6. Information of the risk scores in the training dataset GSE72094 and waterfall plot of genes in risk groups in TCGA-LUAD.

(A) Expression heatmap of the six-HRGs, a dot plot of risk scores and survival status of LUAD patients in the training dataset GSE72094. (B) Waterfall plot of top 10 genes in high-risk group in TCGA-LUAD. (C) Waterfall plot of top 10 genes in the low-risk group in TCGA-LUAD.

Figure 7. Estimate of tumor-infiltrating immune cells and ICI analysis, GSEA analysis and Evaluation of candidate targeted drugs.

(A) Estimates of tumor-infiltrating immune cells were calculated separately for the risk groups. Pearson’s correlation analysis between risk score and TMB score (B), stromal score (C), immune score (D), and microenvironment score (E), respectively, in the LUAD-TCGA cohort. (F) (1) Immune checkpoint inhibitors analysis of high-risk and low-risk groups. IC50 analysis of candidate small molecule drug Docetaxel (2), Erlotinib (3), Gefitinib (4), Paclitaxel (5), and Vinorelbine (6) in two risk groups in the LUAD-TCGA cohort. (G) KEGG pathway analyses between the high- and low-risk groups; ridge plot and Gene-Concept Network based on the GSEA analysis. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001.

Figure 8. The Kaplan–Meier survival analysis and experiments on single six-HRGs.

The Kaplan–Meier survival analysis and experiments on single six-HRGs, namely STC1 (A), SLC2A1 (B), XPNPEP1 (C), PFKL (D), HK1 (E), and PDK3 (F), in GSE72094 dataset to explore the mechanism of each specific gene. (G) Predictive gene expression level validation-PCR. (H) SLC2A1 and XPNPEP1 knockdown efficiency after cell transfection. (I) Effect of knockdown of SLC2A1 on A549 cell proliferation. (J) Effect of knockdown of XPNPEP1 on A549 cell proliferation. (K) Effect of knockdown of SLC2A1 and on A549 cell apoptosis. (L) Effect of knockdown of XPNPEP1 and on A549 cell apoptosis. (M) Effects of knock-down of SLC2A1 and XPNPEP1 on the migration abilities of A549 cells. (N) Effects of knockdown of SLC2A1 and XPNPEP1 on the invasion abilities of A549 cells. A T-test was used to compare the results. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001.

Figure 9. “Scissor” algorithm to identify subpopulations of TAMs associated with the LUAD hypoxic subtype phenotype in single cells.

(A) UMAP diagram showing the distribution of TSPO, CD163 expression in TAMs of scRNA-seq of LUAD. (B) Scissor for visualization of UMAP of selected cells. The red and blue dots are LUAD hypoxic Cluster 1 and LUAD hypoxic Cluster 2. (C) Selected enrichment bars associated with hypoxia.