Role of cuproptosis-related gene in lung adenocarcinoma

Abstract

Backgrounds: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, which is the leading cause of cancer death. Dysregulation of cell proliferation and death plays a crucial role in the development of LUAD. As of recently, the role of a new form of cell death, cuproptosis, and it has attracted more and more attention. As of yet, it is not clear whether cuproptosis is involved in the progression of LUAD.

Methods: An integrated set of bioinformatics tools was utilized to analyze the expression and prognostic significance of cuproptosis-related genes. Meanwhile, a robust risk signature was developed using machine learning based on prognostic cuproptosis-related genes and explored the value of prognostic cuproptosis-related signature for clinical applications, functional enrichment and immune landscape. Lastly, the dysregulation of the cuproptosis-related genes in LUAD was validated by in vitro experiment.

Results: In this study, first, cuproptosis-related genes were found to be differentially expressed in LUAD patients of public databases, and nine of them had prognostic value. Next, a cuproptosis-related model with five features (DLTA, MTF1, GLS, PDHB and PDHA1) was constructed to separate the patients into high- and low-risk groups based on median risk score. Internal validation set and external validation set were used for model validation and evaluation. What's more, Enrichment analysis of differential genes and the WGCNA identified that cuproptosis-related signatures affected tumor prognosis by influencing tumor immunity. Small molecule compounds were predicted based on differential expressed genes to improve poor prognosis in the high-risk group and a nomogram was constructed to further advance clinical applications. In closing, our data showed that FDX1 affected the prognosis of lung cancer by altering the expression of cuproptosis-related signature.

Conclusion: A new cuproptosis-related signature for survival prediction was constructed and validated by machine learning algorithm and in vitro experiments to reflect tumor immune infiltration in LUAD patients. The purpose of this article was to provide a potential diagnostic and therapeutic strategy for LUAD.

Keywords: cuproptosis; immune infiltration; immune microenvironment; lung adenocarcinoma; prognostic signature.

Figure 1

The expression of cuproptosis-related genes in LUAD patients and their prognostic value. Box plots (A) and heatmap (B) of cuproptosis-related genes in LUAD when compared to normal tissues (with green and red signifying low and high expression levels, respectively). (C) Spearman correlation and prognostic values of cuproptosis-related genes in LUAD patients. Red represents HR>1 whereas green represents HR<1. The larger the circle, the smaller the log-rank p. (D) Correlation analysis between cuproptosis-related genes in LUAD patients. (E) Protein-protein interactions among the cuproptosis-associated genes. The bigger the circle is, the most important gene it might be. ^* P<0.05, ^** P<0.01, ^*** P<0.001.

Figure 2

Standardized mortality of 9 cuproptosis-related genes in the TCGA cohort. The standardized mortality of CDKN2A (A), DLAT (B), MTF1 (C), PDHB (D), GLS (E), FDX1 (F), LIAS (G), LIPT1 (H), PDHA1 (I) in TCGA cohort. Blue dots correspond to events, and black dots indicate censor.

Figure 3

Characteristics of Cuproptosis-related Cluster in TCGA-LUAD cohort. (A) Delta area curve of consensus clustering indicated the relative change in area under the cumulative distribution function (CDF) curve from k = 2 to 10. (B) The intragroup correlations were the highest and the inter-group correlations were low when k = 2. (C) Cluster diagram for consensus clustering analysis (k = 2) of cuproptosis-related genes in 442 LUAD samples in TCGA. (D) Kaplan-Meier curve showed survival probability of cluster1 and cluster2. (E) The heatmap showed the relationship between clinical features and the expression of cuproptosis-related genes in two clusters. (F) The expression of 9 cuproptosis-related genes in two clusters. (G) The volcano plot showed the different expression of genes between the two clusters. (H) PCA analysis for the two clusters. The most significant GO enrichment (I) and multiple pathways by GSEA enrichment analysis (J) in two clusters.

Figure 4

Identification of cuproptosis-related signature via LASSO-stepwise algorithms. (A, B) LASSO analysis with minimal lambda value. (C) Five genes were screen out by stepwise Cox algorithm. (D) Coefficients of 5 cuproptosis-related genes finally obtained in stepwise Cox regression. (E) The time-dependent ROC curve for Lasso-stepwise signature. (F) The ROC curve for LASSO-stepwise signature. (G) MRNA expression values in paired samples in TCGA. (H) Protein expressions of 5 differentially expressed cuproptosis-related signature in the tumor and normal tissues from the Human Protein Atlas platform.

Figure 5

Evaluation and validation of prognostic signature. The risk-score, survival time, survival status and gene expression of the training set (A), testing set (B), and external set GSE31210 (C) and GSE30219 (D). Kaplan-Meier analysis demonstrated the prognostic significance of the risk model in TCGA training set (E), testing set (F) and GSE31210 (G), GSE30219 cohort (H).

Figure 6

Survival analysis after stratification of clinical characteristics and distribution of clinical characteristics after risk stratification. (A-C) Kaplan–Meier curves and the log-rank test showed that the overall survival of the high-risk set was worse than that of the low-risk set in age and gender subgroups of patients. (D-H) The distribution of riskscore in clusters, status, age, gender, as well as pathological stage.

Figure 7

Identification of cuproptosis-related signature via WGCNA. (A) The correlation between soft threshold and scale free topology model fit signed R2. (B) The correlation between soft threshold and mean connectivity. (C) Clustering of module feature vectors. (D) The correlations between modules and clinical traits were calculated. (E) The high correlation between GS and MM in the brown module in riskscore subgroups. (F) Genes in the brown module were associated with survival status in TCGA. (G) GO enrichment analysis after screening out Hub gene from brown module. (H) KEGG enrichnment analysis after screening out Hub gene from brown module.

Figure 8

Identification of differentially expressed genes (DEGs) and potential signaling pathways in different isoforms. The heatmap (A) and volcano plot (B) of the differential gene expression between high and low expressed cuproptosis-related signature in LUAD. GO enrichment (C) and GSEA (D) analysis of the differential expressed genes. (E) GSVA enrichment analysis of the differential genes.

Figure 9

Immune infiltration analysis of signature. (A) The correlation analysis of PD-L1/CTL4 expression and riskscore distribution in LUAD. (B) The percentage abundance of tumor-infiltrating immune cells showed the immune infiltration analysis between high risk-score and low risk-score in LUAD patients. (C) The infiltrating levels of immune cells in high risk-score and low risk-score groups in LUAD patients. (D) Xcell algorithms detected immune cell expression between the high-risk and low-risk subtypes. (E-H) Comparison of ESTIMATE, stromal, and immune scores between the cluster 1 and cluster 2. (I) MCP-counter algorithm calculated the immune infiltrating cell score for each subgroups. (J) Comparison of immune checkpoints between high-risk and low-risk subgroups. (K) The HLA genes between high-risk and low-risk subtypes. ^* P<0.05, ^** P<0.01, ^*** P<0.001, ^**** P<0.0001.

Figure 10

Clinical application of cuproptosis-related signature. (A) The potential drug for LUAD treatment. (B) The 2D structure of mitoxantrone. (C) The 3D structure of mitoxantrone. (D) The nomogram of the riskscore and clinical parameters (age, gender, T and pathological stage) of TCGA. (E) The calibration curves displayed the accuracy of the nomogram in the 1-, 3-, and 5-years. (F) Kaplan-Meier curve in multiple Cox regression analysis. (G) The time-ROC curve in multiple Cox regression analysis. (H) DCA curves to assess the ability of age, gender, risk score, T stage, and their combination to predict overall survival of LUAD patients in TCGA-LUAD cohort. ^* P<0.05, ^*** P<0.001, ^**** P<0.0001.

Figure 11

Validation of vitro experiment and molecular docking. (A) Validation of mRNA expression in prognostic cuproptosis-related signature by qRT-PCR. ^* P<0.05, ^** P<0.01. (B) The molecular docking between FDX1 and mitoxantrone. (C) The IHC results showed that FDX1 protein is highly expressed in tumor tissues when compared with normal tissues. (D) Representative immunohistochemical microarray of FDX1. (E) Kaplan–Meier curves showed that the overall survival of the low-risk set was worse than that of the high-risk set in Nantong cohort. (F, G) Kaplan–Meier curves showed that the overall survival of the low-risk set was worse than that of the high-risk set in LUAD (F) and lung squamous cell (LUSC) subgroups (G) of patients. (H) The nomogram of the H-score and clinical parameters (age, gender, smoking and T, N, M stage) of Nantong cohort. ns, not significant.