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. 2022 Aug 16:9:942402.
doi: 10.3389/fmolb.2022.942402. eCollection 2022.

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Ti-Wei Miao  1   2 , De-Qing Yang  3 , Li-Juan Gao  4 , Jie Yin  5 , Qi Zhu  1 , Jie Liu  1 , Yan-Qiu He  2 , Xin Chen  1
Affiliations

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Ti-Wei Miao et al. Front Mol Biosci. .

Abstract

Background: An imbalance in the redox homeostasis has been reported in multiple cancers and is associated with a poor prognosis of disease. However, the prognostic value of redox-related genes in non-small-cell lung cancer (NSCLC) remains unclear. Methods: RNA sequencing data, DNA methylation data, mutation, and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Redox-related differentially expressed genes (DEGs) were used to construct the prognostic signature using least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan-Meier survival curve and receiver operator characteristic (ROC) curve analyses were applied to validate the accuracy of the gene signature. Nomogram and calibration plots of the nomogram were constructed to predict prognosis. Pathway analysis was performed using gene set enrichment analysis. The correlations of risk score with tumor stage, immune infiltration, DNA methylation, tumor mutation burden (TMB), and chemotherapy sensitivity were evaluated. The prognostic signature was validated using GSE31210, GSE26939, and GSE68465 datasets. Real-time polymerase chain reaction (PCR) was used to validate dysregulated genes in NSCLC. Results: A prognostic signature was constructed using the LASSO regression analysis and was represented as a risk score. The high-risk group was significantly correlated with worse overall survival (OS) (p < 0.001). The area under the ROC curve (AUC) at the 5-year stage was 0.657. The risk score was precisely correlated with the tumor stage and was an independent prognostic factor for NSCLC. The constructed nomogram accurately predicted the OS of patients after 1-, 3-, and 5-year periods. DNA replication, cell cycle, and ECM receptor interaction were the main pathways enriched in the high-risk group. In addition, the high-risk score was correlated with higher TMB, lower methylation levels, increased infiltrating macrophages, activated memory CD4+ T cells, and a higher sensitivity to chemotherapy. The signature was validated in GSE31210, GSE26939, and GSE68465 datasets. Real-time PCR validated dysregulated mRNA expression levels in NSCLC. Conclusions: A prognostic redox-related gene signature was successfully established in NSCLC, with potential applications in the clinical setting.

Keywords: NSCLC; cancer prognosis; gene signature; immune infiltration; redox homeostasis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Schematic flowchart representing the procedure followed for establishing the gene signature, and the main findings of the study. Numbers within parenthesis indicate the size of the sample obtained. NSCLC, Non-small cell lung cancer; PCR, polymerase chain reaction; LASSO, least absolute shrinkage, and selection operator; ROC: receiver operator characteristic; TCGA, The Cancer Genome Atlas.
FIGURE 2
FIGURE 2
Construction of gene signature. Gene signature constructed using (A) univariate Cox regression analysis and (B–D) LASSO regression analysis in NSCLC. (E) Kaplan–Meier survival analysis. (F) ROC curve analysis. (G) risk score distribution. (H) survival status of the prognostic gene signature. LASSO, least absolute shrinkage and selection operator; NSCLC, non-small cell lung cancer; ROC, receiver operating characteristic; AUC, area under the ROC curve.
FIGURE 3
FIGURE 3
Validation of gene signature. (A) Kaplan–Meier survival analysis and (B) ROC curve analysis in GSE31210. Kaplan–Meier survival analysis in (C) GSE26939 and (D) GSE68465, respectively. ROC, receiver operating characteristic. AUC, an area under the ROC curve.
FIGURE 4
FIGURE 4
Graphs showing the correlation of risk score with clinical characteristics. (A) UICC stages. (B) T stages. (C) N stages. (D) M stages. (E) genders. (F) ages. UICC, Union for International Cancer Control. ns, no significance. *p < 0.05; **p < 0.01; ***p < 0.001.
FIGURE 5
FIGURE 5
Identification of independent prognostic factors and construction of nomogram. Validation of the risk score as an independent prognostic factor using the univariate Cox analysis in (A) TCGA cohorts and (C) GSE31210 cohorts, and multivariate Cox analysis in (B) TCGA cohorts and (D) GSE31210 cohorts (E) Nomogram to predict OS at the end of 1-, 3-, and 5-year periods (F) Calibration plots of the nomogram. TCGA, The Cancer Genome Atlas; OS, overall survival. **p < 0.01; ***p < 0.001.
FIGURE 6
FIGURE 6
The primary Kyoto Encyclopedia of Genes and Genomes pathways enriched in the (A) high-risk group and (B) low-risk group.
FIGURE 7
FIGURE 7
The methylation levels of prognostic genes in the two risk groups (A) CAV1, (B) CDC25C, (C) CHEK2, (D) COL1A1, (E) CYP24A1, (F) GPR37, (G) GRIA1, (H) S100P, (I) SPP1, and (J) SLC7A5.
FIGURE 8
FIGURE 8
The tumor mutation analysis. Comparison of gene mutation frequencies in the high-risk group of (A) LUAD and (C) LUSC. Comparison of mutation frequencies in the low-risk group (B) LUAD and (D) LUSC (E) Boxplot illustrated that the TMB value was significantly higher in the high-risk group in NSCLC (F) Scatter plot of correlations between the TMB value and the risk score in NSCLC (G) Kaplan–Meier survival curve between the high-risk score + high TMB group and the low-risk score + low TMB group (H) ROC curve analysis. LUAD, lung adenocarcinoma. LUSC, lung squamous cell carcinoma; NSCLC, Non-small cell lung cancer; TMB, tumor mutation burden; ROC, receiver operating characteristic. AUC, an area under the ROC curve. ***p < 0.001.
FIGURE 9
FIGURE 9
The correlation of risk score with immune infiltration (A) The different immune-infiltration profiles between the two risk groups. A positive correlation with the risk score was seen for (B) resting NK cells, (C) activated memory CD4+ T cells, (D) M0 macrophages, and (E) activated mast cells. Whereas (F) resting memory CD4+ T cells, (G) naïve B cells, (H) memory B cells, (I) resting dendritic cells, (J) resting mast cells, and (K) monocytes were negatively correlated with the risk score. *p < 0.05; **p < 0.01; ***p < 0.001.
FIGURE 10
FIGURE 10
The IC50 of chemotherapy drugs including (A) bortezomib, (B) dasatinib, (C) docetaxel, (D) midostaurin, (E) paclitaxel, (F) parthenolide, (G) pazopanib, (H) shikonin, and (I) thapsigargin. IC50, half inhibitory concentration. ***p < 0.001.
FIGURE 11
FIGURE 11
Real-time PCR validation. The levels of (A) CYP24A1, (B) CDC25C, (C) CHEK2, (D) KLK6, (E) S100P, (F) COL1A1, (G) GPR37, (H) SLC7A5, (I) SCN1A, (J) CAV1, (K) GRIA1, and (L) SPP1 mRNA expression in NSCLC. Data are presented as median (interquartile range). NSCLC, non-small cell lung cancer, PCR, polymerase chain reaction. *p < 0.05; **p < 0.01; ***p < 0.001.
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