# The Risk Model Based on the Three Oxidative Stress-Related Genes Evaluates the Prognosis of LAC Patients

Authors: Qiang Guo; Xiao-Li Liu; Hua-Song Liu; Xiang-Yu Luo; Ye Yuan; Yan-Mei Ji; Tao Liu; Jia-Long Guo; Jun Zhang
Journal: Oxidative Medicine and Cellular Longevity
 (2022)

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## Abstract

Background. Oxidative stress plays a role in carcinogenesis. This study explores the roles of oxidative stress-related genes (OSRGs) in lung adenocarcinoma (LAC). Besides, we construct a risk score model of OSRGs that evaluates the prognosis of LAC patients. Methods. OSRGs were downloaded from the Gene Set Enrichment Analysis (GSEA) website. The expression levels of OSRGs were confirmed in LAC tissues of the TCGA database. GO and KEGG analyses were used to evaluate the roles and mechanisms of oxidative stress-related differentially expressed genes (DEGs). Survival, ROC, Cox analysis, and AIC method were used to screen the prognostic DEGs in LAC patients. Subsequently, we constructed a risk score model of OSRGs and a nomogram. Further, this work investigated the values of the risk score model in LAC progression and the relationship between the risk score model and immune infiltration. Results. We discovered 163 oxidative stress-related DEGs in LAC, involving cellular response to oxidative stress and reactive oxygen species. Besides, the areas under the curve of CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 were 0.970, 0.984, 0.984, 0.945, 0.984, 0.771, and 0.959, respectively. This indicates that these OSRGs have diagnosis values of LAC and are significantly related to the overall survival of LAC patients. ERO1A, CDC25C, and ITGB4 overexpressions were independent risk factors for the poor prognosis of LAC patients and were associated with risk scores in the risk model. High-risk score levels affected the poor prognosis of LAC patients. Notably, a high-risk score may be implicated in LAC progression via cell cycle, DNA replication, mismatch repair, and other mechanisms. Further, ERO1A, CDC25C, and ITGB4 expression levels were related to the immune infiltrating cells of LAC, including mast cells, NK cells, and CD8 T cells. Conclusion. In summary, ERO1A, CDC25C, and ITGB4 of OSRGs are associated with poor prognosis of LAC patients. We confirmed that the risk model based on the ERO1A, CDC25C, and ITGB4 is expected to assess the prognosis of LAC patients.


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## Body

## 1. Introduction

Oxidative stress is a state of imbalance between the oxidation and antioxidant effects in the human body. Increasing the neutrophil infiltration and oxidative intermediates by oxidative stress contributes to disease occurrence. Current studies indicate that oxidative stress regulates cancer progression [1–3]. For instance, interleukin-8 (IL-8) is a bridge between inflammation and oxidative stress-induced death of cancer cells. IL-8 overexpression promotes the proliferation of prostate cancer cells and inhibits cell apoptosis. IL-8 and mTOR reduce cellular oxidative stress by suppressing GSK-3β expression and protecting prostate cancer cells [3]. Excessive reactive oxygen species (ROS) production triggers oxidative stress, potentially causing cancer. Overexpression of miR-526b/miR-655 promotes the invasive capacity of breast cancer (BC) cells. miR-526b and miR-655 regulate the TXNRD1 expression to cause oxidative stress in BC [4].Oxidative stress plays a significant role in cancer progression [5–8]. Twist-related protein 2 (TWIST2) modulates tumorigenesis, tumor progression, and epithelial-mesenchymal transformation (EMT). TWIST2 is substantially downregulated in lung cancer tissues and cells. TWIST2 overexpression causes apoptosis, promotes the expression of E-cadherin protein, and inhibits the expression of N-cadherin, vimentin, and slug proteins. Besides, TWIST2 causes oxidative stress in lung cancer cells and inhibits lung cancer progression by modulating the FGF21-mediated AMPK/mTOR signaling pathway [5]. The nuclear factor, erythroid-derived 2 (Nrf2), is a hub transcription factor for cell adaptation and defense against oxidative stress. Oxidative stress reduces Nrf2 SUMOylation and promotes LAK cell invasion and migration. SUMOylation of Nrf2 increases its antioxidant capacity and reduces the level of ROS in LAC cells. Decreased SUMOylation of Nrf2 and increased ROS stimulate the JNK/c-Jun signaling axis to enhance cell migration and cell adhesion, as well as promote LAC cell invasion [7]. At present, risk score models are utilized to evaluate the prognosis of cancer patients [9–11]. Herein, the oxidative stress-related genes (OSRGs) were downloaded from the official website of Gene Set Enrichment Analysis (GSEA). The expression levels of OSRGs were identified in LAC tissues of The Cancer Genome Atlas (TCGA) database. Thereafter, we investigated oxidative stress-related differentially expressed gene (DEG) mechanisms. The contributing DEGs to poor prognosis in patients with LAC were screened using the Kaplan-Meier (K-M) survival analysis, receiver operating characteristic (ROC) analysis, and Cox analysis AIC method. Subsequently, we constructed the risk score model and nomogram of LAC patients and then identified the roles of the risk score model in the progression and prognosis of LAC patients.


## 2. Materials and Methods

### 2.1. Acquisition of OSRGs

The OSRGs were searched on the online GSEA website (http://www.gsea-msigdb.org/gsea/index.jsp) [12]. The input keywords included oxidative stress and the 32 gene sets related to oxidative stress. All 32 gene sets were extracted, and the remaining genes, after eliminating the duplicate genes, were defined as OSRGs.


### 2.2. Oxidative Stress-Related the DEGs in LAC

The gene expression data of 594 LAC patients with FPKM type were downloaded from the official website of TCGA (https://portal.gdc.cancer.gov/) database. Of these, 59 were normal lung samples, whereas 535 were LAC samples. The expression data of OSRGs in 594 samples were retrieved. The expression of OSRGs in LAC tissues was identified by the limma package. The inclusion criteria were logFC=1 and falsediscoveryrateFDR<0.05, which were defined as the oxidative stress-related DEGs.


### 2.3. Biological Functions in the Oxidative Stress-Related DEGs

Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis were used to analyze the roles and mechanisms of multiple genes [13, 14]. The biological process (BP), cell composition (CC), and molecular function (MF) of the oxidative stress-related DEGs were explored through GO annotation. The screening standard was adjusted to P<0.05. The signaling mechanisms involved in the oxidative stress-related DEGs were analyzed using the KEGG signaling pathway, and the screening standard was adjusted to P<0.05.


### 2.4. Protein-Protein Interaction (PPI) Network between the Oxidative Stress-Related DEGs

The online STRING (version: 11.5) website (https://string-db.org/) was used to observe the interaction between multiple genes [14]. Therefore, the oxidative stress-related DEGs were entered into the STRING database to display the PPI network between the oxidative stress-related DEGs. The screening criteria of the PPI network is the combined score>0.4. The visualization of the PPI network of the oxidative stress-related DEGs was further enhanced by the Cytoscape (version: 3.8.2) software. The oxidative stress-related DEGs were enriched and analyzed using the MCODE method.


### 2.5. The Prognostic Values of the Oxidative Stress-Related DEGs

The prognostic data and clinicopathological features of 522 patients with LAC were downloaded from the official website of the TCGA database. After excluding 522 patients with incomplete prognostic information of LAC, the oxidative stress-related DEGs of 535 patients with LAC were matched with the prognostic information of LAC patients. By grouping the median values of the oxidative stress-related DEGs, the roles of the DEGs in the overall survival (OS) of patients with LAC were investigated by K-M survival analysis. The screening standard was set atP<0.001.


### 2.6. Construction of the Prognostic Nomogram of the Oxidative Stress-Related DEGs

ROC analysis was used to evaluate the diagnostic values of gene expression levels in cancer tissues. The diagnostic values were better when the area under the curve (AUC) was closer to 1 [11, 15]. In LAC, the diagnostic values of oxidative stress-related DEGs (CCNA2, ERO1A, CDK1, PLK1, CDC25C, ITGB4, and GJB2) were investigated through the ROC analysis. Further, we constructed a nomogram of the oxidative stress-related DEGs with prognostic and diagnostic values.


### 2.7. Risk Score Model of the Oxidative Stress-Related DEGs

Univariate Cox regression analysis was performed to evaluate the relationship between the oxidative stress-related DEGs (CCNA2, ERO1A, CDK1, PLK1, CDC25C, ITGB4, and GJB2) and the prognosis of LAC patients. The screening standard was P<0.05. Multivariate Cox regression analysis and AIC criteria were performed to screen the oxidative stress-related DEGs that independently influence the prognosis of patients with LAC. Subsequently, we constructed a risk score model [16, 17].


### 2.8. Verification of the Roles of the Risk Score Model and Construction of the Risk Model-Related Nomogram

Correlation analysis was performed to investigate the relationship between the expression levels of risk model genes (ERO1A, CDC25C, and ITGB4) and the risk score model. The expression levels of ERO1A, CDC25C, and ITGB4, and their relationship with the clinicopathological characteristics of patients with LAC in the high- and low-risk groups were explored and observed by scatter diagram and heat map. K-M survival and Cox regression analyses were performed to evaluate the relationship between the risk model and the OS of patients with LAC. The risk model-related nomogram was constructed based on multivariate COX regression analysis results.


### 2.9. Signaling Mechanisms Involved in the Risk Score Model

The GSEA (version: 4.1.0) software platform was used to analyze the BP, MF, CC, and signaling pathways of the DEGs. The gene expression data of 535 LAC patients in the TCGA database were grouped via the risk score and recorded as the high- and low-risk groups. The impact of the high- and low-risk groups on each gene set on the GSEA platform was explored to understand the signaling pathways involved in the risk score model. The running process was performed 1000-fold [18, 19]. Nominal (NOM) P was the screening standard for GSEA analysis.


### 2.10. Relationship between Risk Score Genes and Immune Cell Infiltration

ssGSEA analysis method was used to calculate the immune cell infiltration levels in the tissues with LAC. Spearman correlation analysis was used to explore the correlation between the expression levels of oxidative stress-related DEGs (ERO1A, CDC25C, and ITGB4) and the immune cell infiltration levels. Thereafter, the expression levels of LAC immune infiltrating cells in the high- and low-expression groups of ERO1A, CDC25C, and ITGB4 were analyzed by the median expression values of ERO1A, CDC25C, and ITGB4.


### 2.11. Identification of Risk Score Model Gene Expression in LAC Tissues

In April 2022, we extracted the cancer tissues and adjacent normal tissues from 8 patients who underwent surgical treatment in our hospital and were diagnosed with LAC. All patients signed the informed consent. The study was reviewed and approved by the ethics committee of Taihe Hospital. The expression levels ofERO1A, CDC25C, and ITGB4 in 8 LAC tissues and paired normal tissues were examined based on the standard PCR assays [19]. The primer sequences included as follows: ERO1A 5′-ATGACATCAGCCAGTGTGGA-3′ (forward); 5′-CATGCTTGGTCCACTGAAGA-3′ (reverse); CDC25C 5′-TGGTCACCTGGATTCTTC-3′ (forward); 5′-ACCATTCGGAGTGCTACA-3′ (reverse); and ITGB4 5′-TTCAATGTCGTCTCCTCCAC-3′ (forward); 5′-CAATAGGTCGGTTGTCATCG-3′ (reverse).


### 2.12. Statistical Analysis

The oxidative stress-related DEGs in LAC were analyzed by limma package ort-test. Survival and ROC analyses were performed to analyze the LAC prognosis and diagnostic value of the oxidative stress-related DEGs, as well as the roles of the risk model in the prognosis of LAC patients. Correlation analysis was conducted to explore the relationship between the expression of ERO1A, CDC25C, and ITGB4 and LAC immune infiltration. P<0.05 was considered statistically significant.


## 2.1. Acquisition of OSRGs

The OSRGs were searched on the online GSEA website (http://www.gsea-msigdb.org/gsea/index.jsp) [12]. The input keywords included oxidative stress and the 32 gene sets related to oxidative stress. All 32 gene sets were extracted, and the remaining genes, after eliminating the duplicate genes, were defined as OSRGs.


## 2.2. Oxidative Stress-Related the DEGs in LAC

The gene expression data of 594 LAC patients with FPKM type were downloaded from the official website of TCGA (https://portal.gdc.cancer.gov/) database. Of these, 59 were normal lung samples, whereas 535 were LAC samples. The expression data of OSRGs in 594 samples were retrieved. The expression of OSRGs in LAC tissues was identified by the limma package. The inclusion criteria were logFC=1 and falsediscoveryrateFDR<0.05, which were defined as the oxidative stress-related DEGs.


## 2.3. Biological Functions in the Oxidative Stress-Related DEGs

Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis were used to analyze the roles and mechanisms of multiple genes [13, 14]. The biological process (BP), cell composition (CC), and molecular function (MF) of the oxidative stress-related DEGs were explored through GO annotation. The screening standard was adjusted to P<0.05. The signaling mechanisms involved in the oxidative stress-related DEGs were analyzed using the KEGG signaling pathway, and the screening standard was adjusted to P<0.05.


## 2.4. Protein-Protein Interaction (PPI) Network between the Oxidative Stress-Related DEGs

The online STRING (version: 11.5) website (https://string-db.org/) was used to observe the interaction between multiple genes [14]. Therefore, the oxidative stress-related DEGs were entered into the STRING database to display the PPI network between the oxidative stress-related DEGs. The screening criteria of the PPI network is the combined score>0.4. The visualization of the PPI network of the oxidative stress-related DEGs was further enhanced by the Cytoscape (version: 3.8.2) software. The oxidative stress-related DEGs were enriched and analyzed using the MCODE method.


## 2.5. The Prognostic Values of the Oxidative Stress-Related DEGs

The prognostic data and clinicopathological features of 522 patients with LAC were downloaded from the official website of the TCGA database. After excluding 522 patients with incomplete prognostic information of LAC, the oxidative stress-related DEGs of 535 patients with LAC were matched with the prognostic information of LAC patients. By grouping the median values of the oxidative stress-related DEGs, the roles of the DEGs in the overall survival (OS) of patients with LAC were investigated by K-M survival analysis. The screening standard was set atP<0.001.


## 2.6. Construction of the Prognostic Nomogram of the Oxidative Stress-Related DEGs

ROC analysis was used to evaluate the diagnostic values of gene expression levels in cancer tissues. The diagnostic values were better when the area under the curve (AUC) was closer to 1 [11, 15]. In LAC, the diagnostic values of oxidative stress-related DEGs (CCNA2, ERO1A, CDK1, PLK1, CDC25C, ITGB4, and GJB2) were investigated through the ROC analysis. Further, we constructed a nomogram of the oxidative stress-related DEGs with prognostic and diagnostic values.


## 2.7. Risk Score Model of the Oxidative Stress-Related DEGs

Univariate Cox regression analysis was performed to evaluate the relationship between the oxidative stress-related DEGs (CCNA2, ERO1A, CDK1, PLK1, CDC25C, ITGB4, and GJB2) and the prognosis of LAC patients. The screening standard was P<0.05. Multivariate Cox regression analysis and AIC criteria were performed to screen the oxidative stress-related DEGs that independently influence the prognosis of patients with LAC. Subsequently, we constructed a risk score model [16, 17].


## 2.8. Verification of the Roles of the Risk Score Model and Construction of the Risk Model-Related Nomogram

Correlation analysis was performed to investigate the relationship between the expression levels of risk model genes (ERO1A, CDC25C, and ITGB4) and the risk score model. The expression levels of ERO1A, CDC25C, and ITGB4, and their relationship with the clinicopathological characteristics of patients with LAC in the high- and low-risk groups were explored and observed by scatter diagram and heat map. K-M survival and Cox regression analyses were performed to evaluate the relationship between the risk model and the OS of patients with LAC. The risk model-related nomogram was constructed based on multivariate COX regression analysis results.


## 2.9. Signaling Mechanisms Involved in the Risk Score Model

The GSEA (version: 4.1.0) software platform was used to analyze the BP, MF, CC, and signaling pathways of the DEGs. The gene expression data of 535 LAC patients in the TCGA database were grouped via the risk score and recorded as the high- and low-risk groups. The impact of the high- and low-risk groups on each gene set on the GSEA platform was explored to understand the signaling pathways involved in the risk score model. The running process was performed 1000-fold [18, 19]. Nominal (NOM) P was the screening standard for GSEA analysis.


## 2.10. Relationship between Risk Score Genes and Immune Cell Infiltration

ssGSEA analysis method was used to calculate the immune cell infiltration levels in the tissues with LAC. Spearman correlation analysis was used to explore the correlation between the expression levels of oxidative stress-related DEGs (ERO1A, CDC25C, and ITGB4) and the immune cell infiltration levels. Thereafter, the expression levels of LAC immune infiltrating cells in the high- and low-expression groups of ERO1A, CDC25C, and ITGB4 were analyzed by the median expression values of ERO1A, CDC25C, and ITGB4.


## 2.11. Identification of Risk Score Model Gene Expression in LAC Tissues

In April 2022, we extracted the cancer tissues and adjacent normal tissues from 8 patients who underwent surgical treatment in our hospital and were diagnosed with LAC. All patients signed the informed consent. The study was reviewed and approved by the ethics committee of Taihe Hospital. The expression levels ofERO1A, CDC25C, and ITGB4 in 8 LAC tissues and paired normal tissues were examined based on the standard PCR assays [19]. The primer sequences included as follows: ERO1A 5′-ATGACATCAGCCAGTGTGGA-3′ (forward); 5′-CATGCTTGGTCCACTGAAGA-3′ (reverse); CDC25C 5′-TGGTCACCTGGATTCTTC-3′ (forward); 5′-ACCATTCGGAGTGCTACA-3′ (reverse); and ITGB4 5′-TTCAATGTCGTCTCCTCCAC-3′ (forward); 5′-CAATAGGTCGGTTGTCATCG-3′ (reverse).


## 2.12. Statistical Analysis

The oxidative stress-related DEGs in LAC were analyzed by limma package ort-test. Survival and ROC analyses were performed to analyze the LAC prognosis and diagnostic value of the oxidative stress-related DEGs, as well as the roles of the risk model in the prognosis of LAC patients. Correlation analysis was conducted to explore the relationship between the expression of ERO1A, CDC25C, and ITGB4 and LAC immune infiltration. P<0.05 was considered statistically significant.


## 3. Results

### 3.1. Oxidative Stress-Related DEGs

A total of 32 gene sets related to oxidative stress were searched on the GSEA platform. These 32 gene sets comprised 784 OSRGs. The OSRGs in normal lung and LAC tissues were corrected and extracted from the TCGA database. Differential expression analysis showed 163 DEGs in LAC tissues compared to normal lung tissues (Table1). Among them, 104 genes were overexpressed, whereas 59 were downregulated. The scatter plot displayed 8 overexpressed and 8 downregulated genes (Figure 1).Table 1
The oxidative stress-related DEGs in LAC tissues.
GenelogFCGenelogFCGenelogFCGenelogFCGPX26.013780623SUMO42.257715062PCNA1.251080564NFIX-1.302326118S100A75.41728031FBXO322.253504422PPIF1.228327064JUND-1.316092598MMP115.370660455ERO1A2.169220374P4HB1.224045072SNCA-1.343581323GJB24.664130361MMP92.153880308CBX41.194091529TLR4-1.375114638PTPRN4.431031909CDH22.13556165GCLM1.175006466AQP1-1.395667559UBE2C4.33254131MT1H2.048791978UTP251.172874466HMOX1-1.474996851WNT164.221055426MARCKSL12.0461911ERBB21.170033481SESN1-1.488992986XDH4.189103258SLC4A112.023752334SPINT21.147192812ERBB4-1.508245365MMP34.042213517PDK11.897233653CALU1.146319227SELENOP-1.513089809MYBL23.97196672ITGB41.769385266RGS141.130235535FOS-1.547672977CDC203.923133178TXNRD11.713452233NME21.120700065KRT1-1.611799687PYCR13.849785485FANCD21.694266871GSR1.118622392BMP2-1.62052111CDC25C3.822823814WNT11.652280572NUDT11.110791055KLF2-1.630982183MELK3.79228364ABCB111.648035243OAT1.108313278RCAN1-1.645841006CDKN2A3.660292428IGFBP21.643496508TCF31.106493729FBLN5-1.650383453PLK13.355128242E2F11.629703342IER31.104634685ALOX5-1.670847714SLC7A113.206827355GDF151.594567934CDK41.1019358MSRB3-1.695874161NOX53.181844416FUT81.582037385PARP11.097260888EGR1-1.730851757NQO13.122327878CYP2E11.575457932MGST11.083753103CHRNA4-1.759665302HGFAC3.084341171NOX11.572211162NR2F61.077443474CAT-1.761079975COL1A13.04475487E2F31.541174573DHFR1.064060918CYP1A1-1.771485237CCNA23.037368198UCN1.536180525NOL31.017279978HYAL1-1.793702837CDH32.906398001PRDX41.524830048HBB-4.072278882CRYAB-1.799207942GPR372.892829344SRXN11.523512148HBA2-3.971614753LRRK2-1.837429368SLC7A52.832579597TPO1.497068338ETS1-1.009611374EDN1-1.837496875TRPA12.725732428NET11.492518868JUNB-1.029141797CA3-1.923666594EZH22.71140365NOX41.469389165ETV5-1.03192463SLC1A1-2.043700892SGK22.696077295CBX81.467297358ITGAL-1.045741495NR4A3-2.088467157CDK12.607317686G6PD1.459631279VIM-1.048813585KCNA5-2.103363458CBX22.603102932MET1.424801169CYGB-1.072716336GPX3-2.12554072LPO2.537856476FMO11.418278305MYLK-1.073347764DUOX1-2.428487405E2F22.515712683TRPM21.383090886SIRPA-1.104069804HBEGF-2.431447934CDC25A2.506554418PRKAA21.373646285SELENBP1-1.12795438EPAS1-2.587591866HMGA12.412891462GPR37L11.36067259CDKN2B-1.146620037IL6-2.664089275FOXO62.408776331IPCEF11.347748923CYBB-1.155101406CD36-2.768036549CHEK12.394961963TAT1.339357309HYAL2-1.155726104AGRP-2.793810482GCLC2.381107058GPX81.337343767NCF1-1.157427085RETN-2.813480794MCM42.359143052MAP2K61.332194326UTRN-1.180127364MGAT3-2.885893674ECT22.328988223MMP141.302518357BTK-1.217464546SCGB1A1-3.072434982EFNA42.310996951TRAP11.281380934PPARGC1B-1.249251755ANGPTL7-3.157773456HYOU11.280070739BNIP31.270862371HBA1-4.201724914Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.Figure 1
The DEGs associated with oxidative stress visualized with statistical significance: (a) overexpressed genes; (b) downregulated genes. Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes;∗∗∗P<0.001.
(a)(b)


### 3.2. Functions, Mechanisms, and PPI Network of Oxidative Stress-Related the DEGs

GO annotation revealed that the oxidative stress-related DEGs contributed to the cellular response to oxidative stress, reactive oxygen species, toxic substance, antibiotics, hydrogen peroxide, metallic process, hydrogen peroxide, cellular oxidative detoxification, etc. (Figures2(a)–2(c) and Table S1). KEGG analysis revealed that the oxidative stress-related DEGs are involved in the cell cycle, cellular sensitivity, endocrine resistance, FOXO signaling pathway, non-small-cell lung cancer, TNF signaling pathway, ferroptosis, transcriptional misregulation in cancer, HIF-1, IL-17, p53, and among other signaling pathways (Figure 2(d) and Table 2). Figure 3(a) shows the PPI network between the oxidative stress-related DEGs and the enriched PPI networks through enrichment analysis (Figures 3(b)–3(d)).Figure 2
Functions and mechanisms of oxidative stress-related the DEGs using GO and KEGG analysis: (a) biological process; (b) cell composition; (c) molecular function; (d) signaling pathways. Note: DEGs: differentially expressed genes; BP: biological process; CC: cell composition; MF: molecular function; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes.
(a)(b)(c)(d)Table 2
The mechanisms of the oxidative stress-related DEGs.
IDDescriptionAdjustPCounthsa04110Cell cycle1.76724E-0715hsa04218Cellular senescence1.43121E-0514hsa05219Bladder cancer1.43121E-058hsa05144Malaria5.35207E-058hsa05166Human T-cell leukemia virus 1 infection6.57929E-0515hsa05418Fluid shear stress and atherosclerosis6.57929E-0512hsa00480Glutathione metabolism8.24298E-058hsa01522Endocrine resistance8.24298E-0510hsa05225Hepatocellular carcinoma0.00031494412hsa04068FoxO signaling pathway0.00087354210hsa05223Non-small-cell lung cancer0.0026457647hsa04933AGE-RAGE signaling pathway in diabetic complications0.0031888448hsa05161Hepatitis B0.0039796510hsa04668TNF signaling pathway0.0059605858hsa04216Ferroptosis0.0063751265hsa05215Prostate cancer0.0107431627hsa05202Transcriptional misregulation in cancer0.01074316210hsa04380Osteoclast differentiation0.0107431628hsa05218Melanoma0.0107431626hsa04918Thyroid hormone synthesis0.0123311076hsa05206MicroRNAs in cancer0.01233110713hsa05212Pancreatic cancer0.0123311076hsa05169Epstein-Barr virus infection0.01269401310hsa04066HIF-1 signaling pathway0.0151247367hsa05224Breast cancer0.0197240318hsa05226Gastric cancer0.020637178hsa05143African trypanosomiasis0.0214180194hsa00590Arachidonic acid metabolism0.0236284585hsa04934Cushing syndrome0.0236284588hsa05022Pathways of neurodegeneration-multiple diseases0.02399357516hsa04657IL-17 signaling pathway0.0258676896hsa05230Central carbon metabolism in cancer0.0334963615hsa05205Proteoglycans in cancer0.0341537219hsa04115p53 signaling pathway0.0376820515hsa05214Glioma0.0410223665hsa05220Chronic myeloid leukemia0.0421608265hsa05140Leishmaniasis0.0433249115hsa05167Kaposi sarcoma-associated herpesvirus infection0.0657242918hsa00130Ubiquinone and other terpenoid-quinone biosynthesis0.0697836692hsa05222Small-cell lung cancer0.0827342285hsa05323Rheumatoid arthritis0.0842487145Note: DEGs: differentially expressed genes.Figure 3
PPI network of the oxidative stress-related DEGs. Note: PPI: protein-protein interaction; DEGs: differentially expressed genes.
(a)(b)(c)(d)


### 3.3. Construction of the Prognostic Nomogram of Oxidative Stress-Related DEGs

K-M survival analysis showed that the expression levels ofBTK, CAT, CCNA2, CDC25C, CDH3, ERO1A, CDK1, PLK1, ITGB4, GJB2, CHEK1, CYBB, ECT2, FANCD2, FBLN5, GPR37, GPX3, GPX8, HMGA1, ITGAL, KCNA5, LRRK2, MCM4, MELK, MMP3, MMP14, MYBL2, NFIX, NOX4, NOX5, NUDT1, OAT, PRKAA2, PTPRN, RGS14, SELENBP1, SELENOP, SLC1A1, TRPA1, UBE2C, and XDH significantly correlated with the poor prognosis of LAC patients (Table 3). Based on the significance criterion of the P<0.001, the overexpression levels of CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 significantly correlated with the poor prognosis of patients with LAC (Figure 4).Table 3
The expression levels of oxidative stress-related DEGs are significantly correlated with the poor prognosis of LAC patients.
GenePGenePGenePBTK1.493e-03GPX31.072e-02NOX52.935e-02CAT1.529e-02GPX85.265e-03NUDT12.177e-02CCNA29.186e-05HMGA13.920e-03OAT3.028e-02CDC25C3.027e-04ITGAL2.305e-02PLK13.684e-04CDH34.037e-02ITGB48.341e-04PRKAA24.034e-02CDK11.854e-04KCNA58.226e-03PTPRN3.107e-02CHEK13.871e-03LRRK24.389e-02RGS146.938e-03CYBB3.658e-02MCM46.422e-03SELENBP12.610e-02ECT24.329e-03MELK3.742e-02SELENOP2.561e-02ERO1A3.599e-04MMP31.822e-02SLC1A13.487e-02FANCD22.336e-02MMP143.558e-02TPRA14.763e-03FBLN54.130e-02MYBL23.134e-02UBE2C4.573e-02GJB21.853e-04NFIX1.384e-02XDH4.819e-02GPR379.159e-03NOX41.578e-02Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.Figure 4
7 oxidative stress-related DEGs assess the overall survival of LAC: (a)CDC25C; (b) GJB2; (c) ITGB4; (d) PLK1; (e) CCNA2; (f) ERO1A; (g) CDK1. Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.
(a)(b)(c)(d)(e)(f)(g)ROC analysis demonstrated that the expression levels ofCCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 have diagnosis values of LAC (Figure 5). The AUCs of CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 were 0.97, 0.984, 0.984, 0.945, 0.984, 0.771, and 0.959, respectively, indicating that OSRGs CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 have diagnosis values of LAC. Based on K-M survival and ROC analyses, we constructed a nomogram of OSRGs CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 (Figure 6).Figure 5
7 oxidative stress-related DEGs have diagnosis values of LAC: (a)CDC25C; (b) GJB2; (c) ITGB4; (d) PLK1; (e) CCNA2; (f) ERO1A; (g) CDK1. Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.
(a)(b)(c)(d)(e)(f)(g)Figure 6
Prognostic nomogram of 7 oxidative stress-related DEGs in LAC. LAC: lung adenocarcinoma; DEGs: differentially expressed genes.


### 3.4. Construction of Risk Score Model

Univariate Cox regression analysis was used to explore the relationship between the expression levels ofCCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 and the OS of patients with LAC. Consequently, the overexpression of CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 was the risk factors for poor prognosis in patients with LAC (Figure 7(a)). Based on multivariate Cox regression analysis and the AIC method, ERO1A, CDC25C, and ITGB4 were independent risk factors affecting the poor prognosis of patients with LAC (Table 4 and Figure 7(b)). The risk score model was constructed based on ERO1A, CDC25C, and ITGB4. Correlation analysis revealed that the expression levels of ERO1A, CDC25C, and ITGB4 significantly correlated with the risk score (Figure S1A-C). Grouping by high- and low-risk showed significant differences between the two groups in ERO1A, CDC25C, and ITGB4 (Figure S1D-F).Figure 7
Construction of risk model based on the 3 oxidative stress-related DEGs: (a) prognostic DEGs are shown in overall survival using COX analysis; (b) the relationship between 3 oxidative stress-related DEGs and prognosis in LAC; (c, d) the relationship between the risk score and prognosis in patients with LAC is visualized. Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.
(a)(b)(c)(d)Table 4
Independent prognostic factors of oxidative stress-related DEGs.
GeneHR95% CIPERO1A1.3633600011.124731516-1.6526170620.001592209CDC25C1.4592144081.138311378-1.8705836830.002860696ITGB41.169329491.039123642-1.3158505890.009400705Note: DEGs: differentially expressed genes; HR: hazard ratio; CI: confidence interval.


### 3.5. Risk Score as a Factor for Poor Prognosis in Patients with LAC

The expression levels ofERO1A, CDC25C, and ITGB4 were significantly upregulated in LAC tissues from our hospital with significant statistical significance (Figure S2). Figures 7(c) and 7(d) show the relationship between risk score and OS of patients with LAC, and LAC with high-risk scores had a poor prognosis. Univariate Cox regression analysis showed that clinical stage, T stage, lymph node metastasis, and risk score affect the poor prognosis of patients with LAC (Figure 8(a)). Besides, multivariate Cox regression analysis revealed that age, clinical stage, and risk score contribute to the poor prognosis of patients with LAC (Figure 8(b)). Figure 8(c) shows that the high- and low-risk groups are associated with the survival status, clinical stage, T stage, and lymph node metastasis in patients with LAC. To evaluate the prognosis of patients with LAC, a risk score prognostic nomogram was constructed based on multivariate Cox analysis results (Figure 9).Figure 8
A risk score model based on the 3 oxidative stress-related DEGs is significantly associated with the prognosis in LAC patients. (a, b) COX analysis shows that risk score affects the poor prognosis of patients with LAC. (c) Risk score is associated with the survival status, clinical stage, T stage, and lymph node metastasis in patients with LAC. Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.
(a)(b)(c)Figure 9
Construction of the risk score prognostic nomogram.


### 3.6. Signaling Mechanisms in a High-Risk Score Group

GSEA results showed that the high-risk score is involved in cell cycle, splice some, DNA replication, mismatch repair, homologous recombination, proteasome, nucleoside precision repair, p53 signaling pathway, base precision repair, oocyte meiosis, regulation of actin cytoskeleton, pathways in cancer, and among other mechanisms (FigureS3 and Table 5).Table 5
Signaling mechanisms are involved in the high-risk score group.
NameSizeESNESNOMPCell cycle1240.669150772.18713140Spliceosome1260.642069762.0832240DNA replication360.8377182.06721430Mismatch repair230.800367652.0622280Pathogenic Escherichia coli infection550.57397812.0566690Homologous recombination280.76816762.03616790P53 signaling pathway680.495796921.89502310Pyrimidine metabolism970.51734131.92805150.001923077Nucleotide excision repair440.63240021.9951880.001980198Base excision repair330.62852621.86935170.002Proteasome440.79883792.01418540.002040816Oocyte meiosis1120.46848531.82510960.004140787Pentose phosphate pathway270.574220361.75239180.005825243Glycolysis gluconeogenesis610.500601351.76041590.01010101Ubiquitin mediated proteolysis1330.42878241.65898280.018907564Bladder cancer420.397203451.53458890.02296451Pancreatic cancer700.414408531.5985840.024948025Small-cell lung cancer840.410049651.57955160.02631579Galactose metabolism250.509816051.63085890.027985075Renal cell carcinoma700.379611881.5181210.028077753Regulation of actin cytoskeleton2120.376228631.58100930.028688524Drug metabolism other enzymes510.44899141.56821740.029850746Pathways in cancer3250.312332841.42148690.042105265Progesterone mediated oocyte maturation850.39168161.51446130.04375Note: ES: enrichment score; NES: normalized enrichment score; NOM: nominal.


### 3.7. The Risk Score Model-Related DEGs Correlate with Immune Infiltrating Cells

Spearman correlation analysis demonstrated that the expression level ofCDC25C correlated with the levels of Th2 cells, mast cells, iDC, eosinophils, DC, NK cells, Tfh, Tgd, NK cd56dim cells, CD8 T cells, macrophages, pDC, Tcm, Th17 cells, T helper cells, aDC, neutrophils, Tem, NK cd56bright cells, B cells, and Treg (Figure 10 and Table 6). ERO1A expression level correlated with Th2 cells, mast cells, eosinophils, Tfh, CD8 T cells, NK cd56dim cells, aDC, iDC, NK cells, NK cd56bright cells, Tgd, DC, pDC, neutrophils, and Treg (Figure S4 and Table 6). ITGB4 expression level correlated with the NK cells, T helper cells, neutrophils, B cells, NK cd56bright cells, TFH, NK cd56dim cells, iDC, and mast cells (Figure S5 and Table 6).Figure 10
The expression level ofCDC25C correlates with the levels of immune infiltrating cells: (a) Th2 cells; (b) mast cells; (c) eosinophils; (d) NK cells; (e) iDC; (f) Tgd; (g) TFH; (h) DC; (i) NK CD56dim cells.
(a)(b)(c)(d)(e)(f)(g)(h)(i)Table 6
The expression levels of oxidative stress-related DEGs are correlated with the levels of immune infiltrating cells in LAC.
Immune cellsCDC25C (r)PERO1A (r)PITGB4 (r)PaDC0.1210.0050.174<0.0010.0610.159B cells-0.0950.027-0.0810.061-0.156<0.001CD8 T cells-0.195<0.001-0.210<0.0010.0780.070Cytotoxic cells-0.0080.847-0.0150.737-0.0540.217DC-0.277<0.001-0.1300.0030.0740.088Eosinophils-0.347<0.001-0.236<0.0010.0710.102iDC-0.352<0.001-0.164<0.0010.0910.035Macrophages-0.173<0.0010.0340.4310.0490.257Mast cells-0.517<0.001-0.354<0.0010.0870.044Neutrophils-0.1150.0080.0960.0270.162<0.001NK CD56bright cells-0.1010.019-0.1390.0010.1280.003NK CD56dim cells0.211<0.0010.178<0.0010.0970.026NK cells-0.238<0.001-0.163<0.0010.255<0.001pDC-0.152<0.001-0.1100.0110.0100.823T cells-0.0700.105-0.0220.610-0.0850.050T helper cells0.1350.0020.0840.053-0.196<0.001Tcm-0.151<0.001-0.0490.258-0.0200.638Tem-0.1070.013-0.0070.870-0.0250.566TFH-0.236<0.001-0.222<0.001-0.1040.017Tgd0.220<0.0010.1370.001-0.0510.235Th1 cells-0.0400.3530.0760.077-0.0020.954Th17 cells-0.143<0.001-0.0630.1440.0800.066Th2 cells0.771<0.0010.464<0.001-0.0730.092TReg0.0910.0360.0910.0360.0820.057Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.Grouping by the median values of oxidative stress-related DEGs (CDC25C, ERO1A, and ITGB4) showed abnormal and statistically significant expression of mast cells, iDC, eosinophils, DC, NK cd56dim cells, NK cells, Tfh, Tgd, Th2 cells, macrophages, CD8 T cells, pDC, T helper cells, Th17 cells, Tcm, neutrophils, and Tem in the high- and low-expression groups of CDC25C (Figure 11 and Table 7). The expression of mast cells, iDC, eosinophils, CD8 T cells, NK cells, Tfh, Th2 cells, NK cd56bright cells, Tgd, aDC, T helper cells, NK cd56dim cells, DC, neutrophils, and B cells in the high- and low-expression groups of ERO1A was abnormal and statistically significant (Figure S6 and Table 7). The expression of NK cells, T helper cells, NK cd56bright cells, NK cd56dim cells, B cells, and neutrophils in the high- and low-expression groups of ITGB4 was abnormal and statistically significant (Figure S7 and Table 7).Figure 11
Abnormal expression of immune cells in the high- and low-expression groups ofCDC25C: (a) Th2 cells; (b) Tgd; (c) eosinophils; (d) TFH; (e) NK CD56dim cells; (f) mast cells; (g) NK cells; (h) DC; (i) iDC.
(a)(b)(c)(d)(e)(f)(g)(h)(i)Table 7
The levels of immune infiltrating cells are differentially expressed in the groups of oxidative stress-related DEGs.
Immune cellsCDC25C (P)ERO1A (P)ITGB4 (P)aDC0.0650.0030.35B cells0.0690.0380.009CD8 T cells0.00100.089Cytotoxic cells0.9610.8710.372DC00.0080.678Eosinophils000.815iDC000.254Macrophages0.0010.7190.681Mast cells000.549Neutrophils0.040.020.047NK CD56bright cells0.1390.0010.001NK CD56dim cells00.0070.003NK cells000pDC0.0010.0790.801T cells0.2960.7320.112T helper cells0.0060.0050Tcm0.0280.8930.299Tem0.0440.9020.959TFH000.205Tgd00.0020.449Th1 cells0.4520.0520.974Th17 cells0.0090.5030.052Th2 cells000.926TReg0.0510.2690.065Note: DEGs: differentially expressed genes.


## 3.1. Oxidative Stress-Related DEGs

A total of 32 gene sets related to oxidative stress were searched on the GSEA platform. These 32 gene sets comprised 784 OSRGs. The OSRGs in normal lung and LAC tissues were corrected and extracted from the TCGA database. Differential expression analysis showed 163 DEGs in LAC tissues compared to normal lung tissues (Table1). Among them, 104 genes were overexpressed, whereas 59 were downregulated. The scatter plot displayed 8 overexpressed and 8 downregulated genes (Figure 1).Table 1
The oxidative stress-related DEGs in LAC tissues.
GenelogFCGenelogFCGenelogFCGenelogFCGPX26.013780623SUMO42.257715062PCNA1.251080564NFIX-1.302326118S100A75.41728031FBXO322.253504422PPIF1.228327064JUND-1.316092598MMP115.370660455ERO1A2.169220374P4HB1.224045072SNCA-1.343581323GJB24.664130361MMP92.153880308CBX41.194091529TLR4-1.375114638PTPRN4.431031909CDH22.13556165GCLM1.175006466AQP1-1.395667559UBE2C4.33254131MT1H2.048791978UTP251.172874466HMOX1-1.474996851WNT164.221055426MARCKSL12.0461911ERBB21.170033481SESN1-1.488992986XDH4.189103258SLC4A112.023752334SPINT21.147192812ERBB4-1.508245365MMP34.042213517PDK11.897233653CALU1.146319227SELENOP-1.513089809MYBL23.97196672ITGB41.769385266RGS141.130235535FOS-1.547672977CDC203.923133178TXNRD11.713452233NME21.120700065KRT1-1.611799687PYCR13.849785485FANCD21.694266871GSR1.118622392BMP2-1.62052111CDC25C3.822823814WNT11.652280572NUDT11.110791055KLF2-1.630982183MELK3.79228364ABCB111.648035243OAT1.108313278RCAN1-1.645841006CDKN2A3.660292428IGFBP21.643496508TCF31.106493729FBLN5-1.650383453PLK13.355128242E2F11.629703342IER31.104634685ALOX5-1.670847714SLC7A113.206827355GDF151.594567934CDK41.1019358MSRB3-1.695874161NOX53.181844416FUT81.582037385PARP11.097260888EGR1-1.730851757NQO13.122327878CYP2E11.575457932MGST11.083753103CHRNA4-1.759665302HGFAC3.084341171NOX11.572211162NR2F61.077443474CAT-1.761079975COL1A13.04475487E2F31.541174573DHFR1.064060918CYP1A1-1.771485237CCNA23.037368198UCN1.536180525NOL31.017279978HYAL1-1.793702837CDH32.906398001PRDX41.524830048HBB-4.072278882CRYAB-1.799207942GPR372.892829344SRXN11.523512148HBA2-3.971614753LRRK2-1.837429368SLC7A52.832579597TPO1.497068338ETS1-1.009611374EDN1-1.837496875TRPA12.725732428NET11.492518868JUNB-1.029141797CA3-1.923666594EZH22.71140365NOX41.469389165ETV5-1.03192463SLC1A1-2.043700892SGK22.696077295CBX81.467297358ITGAL-1.045741495NR4A3-2.088467157CDK12.607317686G6PD1.459631279VIM-1.048813585KCNA5-2.103363458CBX22.603102932MET1.424801169CYGB-1.072716336GPX3-2.12554072LPO2.537856476FMO11.418278305MYLK-1.073347764DUOX1-2.428487405E2F22.515712683TRPM21.383090886SIRPA-1.104069804HBEGF-2.431447934CDC25A2.506554418PRKAA21.373646285SELENBP1-1.12795438EPAS1-2.587591866HMGA12.412891462GPR37L11.36067259CDKN2B-1.146620037IL6-2.664089275FOXO62.408776331IPCEF11.347748923CYBB-1.155101406CD36-2.768036549CHEK12.394961963TAT1.339357309HYAL2-1.155726104AGRP-2.793810482GCLC2.381107058GPX81.337343767NCF1-1.157427085RETN-2.813480794MCM42.359143052MAP2K61.332194326UTRN-1.180127364MGAT3-2.885893674ECT22.328988223MMP141.302518357BTK-1.217464546SCGB1A1-3.072434982EFNA42.310996951TRAP11.281380934PPARGC1B-1.249251755ANGPTL7-3.157773456HYOU11.280070739BNIP31.270862371HBA1-4.201724914Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.Figure 1
The DEGs associated with oxidative stress visualized with statistical significance: (a) overexpressed genes; (b) downregulated genes. Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes;∗∗∗P<0.001.
(a)(b)


## 3.2. Functions, Mechanisms, and PPI Network of Oxidative Stress-Related the DEGs

GO annotation revealed that the oxidative stress-related DEGs contributed to the cellular response to oxidative stress, reactive oxygen species, toxic substance, antibiotics, hydrogen peroxide, metallic process, hydrogen peroxide, cellular oxidative detoxification, etc. (Figures2(a)–2(c) and Table S1). KEGG analysis revealed that the oxidative stress-related DEGs are involved in the cell cycle, cellular sensitivity, endocrine resistance, FOXO signaling pathway, non-small-cell lung cancer, TNF signaling pathway, ferroptosis, transcriptional misregulation in cancer, HIF-1, IL-17, p53, and among other signaling pathways (Figure 2(d) and Table 2). Figure 3(a) shows the PPI network between the oxidative stress-related DEGs and the enriched PPI networks through enrichment analysis (Figures 3(b)–3(d)).Figure 2
Functions and mechanisms of oxidative stress-related the DEGs using GO and KEGG analysis: (a) biological process; (b) cell composition; (c) molecular function; (d) signaling pathways. Note: DEGs: differentially expressed genes; BP: biological process; CC: cell composition; MF: molecular function; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes.
(a)(b)(c)(d)Table 2
The mechanisms of the oxidative stress-related DEGs.
IDDescriptionAdjustPCounthsa04110Cell cycle1.76724E-0715hsa04218Cellular senescence1.43121E-0514hsa05219Bladder cancer1.43121E-058hsa05144Malaria5.35207E-058hsa05166Human T-cell leukemia virus 1 infection6.57929E-0515hsa05418Fluid shear stress and atherosclerosis6.57929E-0512hsa00480Glutathione metabolism8.24298E-058hsa01522Endocrine resistance8.24298E-0510hsa05225Hepatocellular carcinoma0.00031494412hsa04068FoxO signaling pathway0.00087354210hsa05223Non-small-cell lung cancer0.0026457647hsa04933AGE-RAGE signaling pathway in diabetic complications0.0031888448hsa05161Hepatitis B0.0039796510hsa04668TNF signaling pathway0.0059605858hsa04216Ferroptosis0.0063751265hsa05215Prostate cancer0.0107431627hsa05202Transcriptional misregulation in cancer0.01074316210hsa04380Osteoclast differentiation0.0107431628hsa05218Melanoma0.0107431626hsa04918Thyroid hormone synthesis0.0123311076hsa05206MicroRNAs in cancer0.01233110713hsa05212Pancreatic cancer0.0123311076hsa05169Epstein-Barr virus infection0.01269401310hsa04066HIF-1 signaling pathway0.0151247367hsa05224Breast cancer0.0197240318hsa05226Gastric cancer0.020637178hsa05143African trypanosomiasis0.0214180194hsa00590Arachidonic acid metabolism0.0236284585hsa04934Cushing syndrome0.0236284588hsa05022Pathways of neurodegeneration-multiple diseases0.02399357516hsa04657IL-17 signaling pathway0.0258676896hsa05230Central carbon metabolism in cancer0.0334963615hsa05205Proteoglycans in cancer0.0341537219hsa04115p53 signaling pathway0.0376820515hsa05214Glioma0.0410223665hsa05220Chronic myeloid leukemia0.0421608265hsa05140Leishmaniasis0.0433249115hsa05167Kaposi sarcoma-associated herpesvirus infection0.0657242918hsa00130Ubiquinone and other terpenoid-quinone biosynthesis0.0697836692hsa05222Small-cell lung cancer0.0827342285hsa05323Rheumatoid arthritis0.0842487145Note: DEGs: differentially expressed genes.Figure 3
PPI network of the oxidative stress-related DEGs. Note: PPI: protein-protein interaction; DEGs: differentially expressed genes.
(a)(b)(c)(d)


## 3.3. Construction of the Prognostic Nomogram of Oxidative Stress-Related DEGs

K-M survival analysis showed that the expression levels ofBTK, CAT, CCNA2, CDC25C, CDH3, ERO1A, CDK1, PLK1, ITGB4, GJB2, CHEK1, CYBB, ECT2, FANCD2, FBLN5, GPR37, GPX3, GPX8, HMGA1, ITGAL, KCNA5, LRRK2, MCM4, MELK, MMP3, MMP14, MYBL2, NFIX, NOX4, NOX5, NUDT1, OAT, PRKAA2, PTPRN, RGS14, SELENBP1, SELENOP, SLC1A1, TRPA1, UBE2C, and XDH significantly correlated with the poor prognosis of LAC patients (Table 3). Based on the significance criterion of the P<0.001, the overexpression levels of CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 significantly correlated with the poor prognosis of patients with LAC (Figure 4).Table 3
The expression levels of oxidative stress-related DEGs are significantly correlated with the poor prognosis of LAC patients.
GenePGenePGenePBTK1.493e-03GPX31.072e-02NOX52.935e-02CAT1.529e-02GPX85.265e-03NUDT12.177e-02CCNA29.186e-05HMGA13.920e-03OAT3.028e-02CDC25C3.027e-04ITGAL2.305e-02PLK13.684e-04CDH34.037e-02ITGB48.341e-04PRKAA24.034e-02CDK11.854e-04KCNA58.226e-03PTPRN3.107e-02CHEK13.871e-03LRRK24.389e-02RGS146.938e-03CYBB3.658e-02MCM46.422e-03SELENBP12.610e-02ECT24.329e-03MELK3.742e-02SELENOP2.561e-02ERO1A3.599e-04MMP31.822e-02SLC1A13.487e-02FANCD22.336e-02MMP143.558e-02TPRA14.763e-03FBLN54.130e-02MYBL23.134e-02UBE2C4.573e-02GJB21.853e-04NFIX1.384e-02XDH4.819e-02GPR379.159e-03NOX41.578e-02Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.Figure 4
7 oxidative stress-related DEGs assess the overall survival of LAC: (a)CDC25C; (b) GJB2; (c) ITGB4; (d) PLK1; (e) CCNA2; (f) ERO1A; (g) CDK1. Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.
(a)(b)(c)(d)(e)(f)(g)ROC analysis demonstrated that the expression levels ofCCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 have diagnosis values of LAC (Figure 5). The AUCs of CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 were 0.97, 0.984, 0.984, 0.945, 0.984, 0.771, and 0.959, respectively, indicating that OSRGs CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 have diagnosis values of LAC. Based on K-M survival and ROC analyses, we constructed a nomogram of OSRGs CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 (Figure 6).Figure 5
7 oxidative stress-related DEGs have diagnosis values of LAC: (a)CDC25C; (b) GJB2; (c) ITGB4; (d) PLK1; (e) CCNA2; (f) ERO1A; (g) CDK1. Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.
(a)(b)(c)(d)(e)(f)(g)Figure 6
Prognostic nomogram of 7 oxidative stress-related DEGs in LAC. LAC: lung adenocarcinoma; DEGs: differentially expressed genes.


## 3.4. Construction of Risk Score Model

Univariate Cox regression analysis was used to explore the relationship between the expression levels ofCCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 and the OS of patients with LAC. Consequently, the overexpression of CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 was the risk factors for poor prognosis in patients with LAC (Figure 7(a)). Based on multivariate Cox regression analysis and the AIC method, ERO1A, CDC25C, and ITGB4 were independent risk factors affecting the poor prognosis of patients with LAC (Table 4 and Figure 7(b)). The risk score model was constructed based on ERO1A, CDC25C, and ITGB4. Correlation analysis revealed that the expression levels of ERO1A, CDC25C, and ITGB4 significantly correlated with the risk score (Figure S1A-C). Grouping by high- and low-risk showed significant differences between the two groups in ERO1A, CDC25C, and ITGB4 (Figure S1D-F).Figure 7
Construction of risk model based on the 3 oxidative stress-related DEGs: (a) prognostic DEGs are shown in overall survival using COX analysis; (b) the relationship between 3 oxidative stress-related DEGs and prognosis in LAC; (c, d) the relationship between the risk score and prognosis in patients with LAC is visualized. Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.
(a)(b)(c)(d)Table 4
Independent prognostic factors of oxidative stress-related DEGs.
GeneHR95% CIPERO1A1.3633600011.124731516-1.6526170620.001592209CDC25C1.4592144081.138311378-1.8705836830.002860696ITGB41.169329491.039123642-1.3158505890.009400705Note: DEGs: differentially expressed genes; HR: hazard ratio; CI: confidence interval.


## 3.5. Risk Score as a Factor for Poor Prognosis in Patients with LAC

The expression levels ofERO1A, CDC25C, and ITGB4 were significantly upregulated in LAC tissues from our hospital with significant statistical significance (Figure S2). Figures 7(c) and 7(d) show the relationship between risk score and OS of patients with LAC, and LAC with high-risk scores had a poor prognosis. Univariate Cox regression analysis showed that clinical stage, T stage, lymph node metastasis, and risk score affect the poor prognosis of patients with LAC (Figure 8(a)). Besides, multivariate Cox regression analysis revealed that age, clinical stage, and risk score contribute to the poor prognosis of patients with LAC (Figure 8(b)). Figure 8(c) shows that the high- and low-risk groups are associated with the survival status, clinical stage, T stage, and lymph node metastasis in patients with LAC. To evaluate the prognosis of patients with LAC, a risk score prognostic nomogram was constructed based on multivariate Cox analysis results (Figure 9).Figure 8
A risk score model based on the 3 oxidative stress-related DEGs is significantly associated with the prognosis in LAC patients. (a, b) COX analysis shows that risk score affects the poor prognosis of patients with LAC. (c) Risk score is associated with the survival status, clinical stage, T stage, and lymph node metastasis in patients with LAC. Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.
(a)(b)(c)Figure 9
Construction of the risk score prognostic nomogram.


## 3.6. Signaling Mechanisms in a High-Risk Score Group

GSEA results showed that the high-risk score is involved in cell cycle, splice some, DNA replication, mismatch repair, homologous recombination, proteasome, nucleoside precision repair, p53 signaling pathway, base precision repair, oocyte meiosis, regulation of actin cytoskeleton, pathways in cancer, and among other mechanisms (FigureS3 and Table 5).Table 5
Signaling mechanisms are involved in the high-risk score group.
NameSizeESNESNOMPCell cycle1240.669150772.18713140Spliceosome1260.642069762.0832240DNA replication360.8377182.06721430Mismatch repair230.800367652.0622280Pathogenic Escherichia coli infection550.57397812.0566690Homologous recombination280.76816762.03616790P53 signaling pathway680.495796921.89502310Pyrimidine metabolism970.51734131.92805150.001923077Nucleotide excision repair440.63240021.9951880.001980198Base excision repair330.62852621.86935170.002Proteasome440.79883792.01418540.002040816Oocyte meiosis1120.46848531.82510960.004140787Pentose phosphate pathway270.574220361.75239180.005825243Glycolysis gluconeogenesis610.500601351.76041590.01010101Ubiquitin mediated proteolysis1330.42878241.65898280.018907564Bladder cancer420.397203451.53458890.02296451Pancreatic cancer700.414408531.5985840.024948025Small-cell lung cancer840.410049651.57955160.02631579Galactose metabolism250.509816051.63085890.027985075Renal cell carcinoma700.379611881.5181210.028077753Regulation of actin cytoskeleton2120.376228631.58100930.028688524Drug metabolism other enzymes510.44899141.56821740.029850746Pathways in cancer3250.312332841.42148690.042105265Progesterone mediated oocyte maturation850.39168161.51446130.04375Note: ES: enrichment score; NES: normalized enrichment score; NOM: nominal.


## 3.7. The Risk Score Model-Related DEGs Correlate with Immune Infiltrating Cells

Spearman correlation analysis demonstrated that the expression level ofCDC25C correlated with the levels of Th2 cells, mast cells, iDC, eosinophils, DC, NK cells, Tfh, Tgd, NK cd56dim cells, CD8 T cells, macrophages, pDC, Tcm, Th17 cells, T helper cells, aDC, neutrophils, Tem, NK cd56bright cells, B cells, and Treg (Figure 10 and Table 6). ERO1A expression level correlated with Th2 cells, mast cells, eosinophils, Tfh, CD8 T cells, NK cd56dim cells, aDC, iDC, NK cells, NK cd56bright cells, Tgd, DC, pDC, neutrophils, and Treg (Figure S4 and Table 6). ITGB4 expression level correlated with the NK cells, T helper cells, neutrophils, B cells, NK cd56bright cells, TFH, NK cd56dim cells, iDC, and mast cells (Figure S5 and Table 6).Figure 10
The expression level ofCDC25C correlates with the levels of immune infiltrating cells: (a) Th2 cells; (b) mast cells; (c) eosinophils; (d) NK cells; (e) iDC; (f) Tgd; (g) TFH; (h) DC; (i) NK CD56dim cells.
(a)(b)(c)(d)(e)(f)(g)(h)(i)Table 6
The expression levels of oxidative stress-related DEGs are correlated with the levels of immune infiltrating cells in LAC.
Immune cellsCDC25C (r)PERO1A (r)PITGB4 (r)PaDC0.1210.0050.174<0.0010.0610.159B cells-0.0950.027-0.0810.061-0.156<0.001CD8 T cells-0.195<0.001-0.210<0.0010.0780.070Cytotoxic cells-0.0080.847-0.0150.737-0.0540.217DC-0.277<0.001-0.1300.0030.0740.088Eosinophils-0.347<0.001-0.236<0.0010.0710.102iDC-0.352<0.001-0.164<0.0010.0910.035Macrophages-0.173<0.0010.0340.4310.0490.257Mast cells-0.517<0.001-0.354<0.0010.0870.044Neutrophils-0.1150.0080.0960.0270.162<0.001NK CD56bright cells-0.1010.019-0.1390.0010.1280.003NK CD56dim cells0.211<0.0010.178<0.0010.0970.026NK cells-0.238<0.001-0.163<0.0010.255<0.001pDC-0.152<0.001-0.1100.0110.0100.823T cells-0.0700.105-0.0220.610-0.0850.050T helper cells0.1350.0020.0840.053-0.196<0.001Tcm-0.151<0.001-0.0490.258-0.0200.638Tem-0.1070.013-0.0070.870-0.0250.566TFH-0.236<0.001-0.222<0.001-0.1040.017Tgd0.220<0.0010.1370.001-0.0510.235Th1 cells-0.0400.3530.0760.077-0.0020.954Th17 cells-0.143<0.001-0.0630.1440.0800.066Th2 cells0.771<0.0010.464<0.001-0.0730.092TReg0.0910.0360.0910.0360.0820.057Note: LAC: lung adenocarcinoma; DEGs: differentially expressed genes.Grouping by the median values of oxidative stress-related DEGs (CDC25C, ERO1A, and ITGB4) showed abnormal and statistically significant expression of mast cells, iDC, eosinophils, DC, NK cd56dim cells, NK cells, Tfh, Tgd, Th2 cells, macrophages, CD8 T cells, pDC, T helper cells, Th17 cells, Tcm, neutrophils, and Tem in the high- and low-expression groups of CDC25C (Figure 11 and Table 7). The expression of mast cells, iDC, eosinophils, CD8 T cells, NK cells, Tfh, Th2 cells, NK cd56bright cells, Tgd, aDC, T helper cells, NK cd56dim cells, DC, neutrophils, and B cells in the high- and low-expression groups of ERO1A was abnormal and statistically significant (Figure S6 and Table 7). The expression of NK cells, T helper cells, NK cd56bright cells, NK cd56dim cells, B cells, and neutrophils in the high- and low-expression groups of ITGB4 was abnormal and statistically significant (Figure S7 and Table 7).Figure 11
Abnormal expression of immune cells in the high- and low-expression groups ofCDC25C: (a) Th2 cells; (b) Tgd; (c) eosinophils; (d) TFH; (e) NK CD56dim cells; (f) mast cells; (g) NK cells; (h) DC; (i) iDC.
(a)(b)(c)(d)(e)(f)(g)(h)(i)Table 7
The levels of immune infiltrating cells are differentially expressed in the groups of oxidative stress-related DEGs.
Immune cellsCDC25C (P)ERO1A (P)ITGB4 (P)aDC0.0650.0030.35B cells0.0690.0380.009CD8 T cells0.00100.089Cytotoxic cells0.9610.8710.372DC00.0080.678Eosinophils000.815iDC000.254Macrophages0.0010.7190.681Mast cells000.549Neutrophils0.040.020.047NK CD56bright cells0.1390.0010.001NK CD56dim cells00.0070.003NK cells000pDC0.0010.0790.801T cells0.2960.7320.112T helper cells0.0060.0050Tcm0.0280.8930.299Tem0.0440.9020.959TFH000.205Tgd00.0020.449Th1 cells0.4520.0520.974Th17 cells0.0090.5030.052Th2 cells000.926TReg0.0510.2690.065Note: DEGs: differentially expressed genes.


## 4. Discussion

Lung adenocarcinoma has a high incidence and mortality rates [7, 9, 15, 20]. At present, the prognosis of LAC patients is significantly poor. Therefore, new biomarkers are required to predict this and provide novel treatment targets. An oxidative stress response is involved in the progression of LAC [5–8]. Long-chain noncoding RNA (lncRNA) nuclear LUCAT1 (NLUCAT1) is strongly upregulated during hypoxia in vitro and is associated with hypoxia markers and poor prognosis in LAC. NLUCAT1 downregulation inhibits the proliferation and invasion of LAC cells and increases oxidative stress and sensitivity to cisplatin [8]. Several OSRGs were abnormally expressed in LAC tissues in this study. The oxidative stress-related DEGs regulate the cellular response to oxidative stress, reactive oxygen species, toxic substances, antibiotics, hydrogen peroxide, reactive oxygen species, metabolic process, hydrogen peroxide, cellular oxidant detoxification, etc. This confirms that our oxidative stress-related DEGs are related to oxidative stress.The expression levels ofCCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 could influence the cancer progression [21–26]. For instance, ERO1A, also known as ERO1L, promotes IL6R secretion by targeting disulfide bond formation. IL-6R binds to IL-6, resulting in the activation of the NF-κB signaling pathway. NF-κB, in turn, binds to the promoter of MUC16, causing its overexpression. ERO1L may trigger CA125 secretion via the IL-6 signaling pathway, form a positive feedback loop, and promote lung cancer development [23]. Through survival, ROC, and Cox analyses, we found that CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 significantly correlated with overexpression levels and poor prognosis of patients with LAC and exhibited diagnosis values of LAC. Bioinformatics analysis and PCR identification showed overexpressed oxidative stress-related DEGs ERO1A, CDC25C, and ITGB4 in LAC tissues and were independent risk factors for poor prognosis in patients with LAC. The risk model based on ERO1A, CDC25C, and ITGB4 is an independent risk factor for poor prognosis in patients with LAC. In the risk model-related nomogram, the risk score demonstrated the greatest impact on the prognosis of LAC patients. This indicates that our risk score model evaluates the prognosis of LAC patients.Cell cycle, homologous recombination, and p53 signaling pathway are associated with cancer progression [27–31]. Cyclin B1 (CCNB1) is an important gene in mitosis and is upregulated in LAC tissues. CCNB1 overexpression contributes to the advanced tumor stage and short OS. A negative correlation has been discovered between miR-139-5p and CCNB1 expression levels. Through negative CCNB1 regulation, miR-139-5p inhibits cell proliferation and migration [27]. lncRNA CASC2 is downregulated in LAC. Its overexpression inhibits the proliferation of LAC cells and improves apoptosis. It also directly inhibits miR-21 expression and upregulates p53 protein expression to mediate cell proliferation and apoptosis in LAC [31]. GSEA results showed that the high-risk score is implicated in cell cycle, DNA replication, homologous recombination, p53 signaling pathway, and other mechanisms in cancer progression. Our risk model based on the ERO1A, CDC25C, and ITGB4 is closely related to the signaling mechanisms of cancer progression, preliminarily confirming that our risk model is closely associated with LAC progression.In recent years, immunotherapy has been a crucial treatment option for patients with LAC [32–35]. Additionally, immunotherapy improves the clinical stage in patients with advanced cancer, hence providing them with an opportunity for surgery. Of note, the immune microenvironment is an important component in immunotherapy. For instance, PD-1 and PD-L1 blockers have been approved as standard therapy for non-small-cell lung cancer. In contrast with chemotherapy or radiotherapy, PD-1/PD-L1 blocking therapy improves the remission rate. It prolongs the survival time, with fewer side effects in patients with advanced non-small-cell lung cancer treated with a single drug or combined therapy [32, 33]. NK cells act on targeted tumor cells, contributing to antitumor immunity. In non-small-cell lung cancer, there was an increase in the expression of immune checkpoint receptor PD-1 on the surface of NK cells. In contrast with peripheral NK cells, the role of NK cells in tumor is poor, and this dysfunction is associated with the expression level of PD-1. PD-1 blocking therapy reverses the PD-L1-mediated inhibition of PD-1 NK cells [35]. We explored the relationship between the OSRGs ERO1A, CDC25C, and ITGB4 expression levels and the immune microenvironment. As a result, the expression levels of ERO1A, CDC25C, and ITGB4 significantly correlated with the levels of NK cells, mast cells, Tfh, NK cd56dim cells, iDC, neutrophils, and NK cd56bright cells. Nonetheless, additional future studies are necessary to confirm the roles of the OSRGs ERO1A, CDC25C, and ITGB4 in the LAC immune microenvironment.Our study applies bioinformatics analysis to investigate the roles of the OSRGs in the progression of LAC. The strengths of this study include large sample size, long follow-up time, and comprehensive prognostic data in the TCGA database. Besides, we provide novel candidate markers for LAC treatment and a risk model that evaluates the prognosis of LAC patients. Through PCR detection,ERO1A, CDC25C, and ITGB4 expressions were significantly upregulated in the tissues from our hospital. Nevertheless, large amounts of tissues and patient prognostic data are necessary to verify the risk score model. Therefore, future studies should collect additional clinical tissue samples to detect the expression levels of CDC25C, ERO1A, and ITGB4 and investigate their roles in the prognosis of LAC. Moreover, other research should explore the roles and mechanisms of CDC25C, ERO1A, and ITGB4 in the immunity and progression of LAC at the cellular level.


## 5. Conclusion

In conclusion,CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 of OSRGs have diagnosis values of LAC and are associated with the prognosis of patients with LAC. ERO1A, CDC25C, and ITGB4 overexpressions are independent risk factors for poor prognosis in patients with LAC. A high-risk score is an independent factor affecting the poor prognosis of LAC patients. ERO1A, CDC25C, and ITGB4 expressions of risk score model genes significantly correlate with the levels of mast cells, IDC, NK cells, and CD8 T cells of LAC immune infiltrating cells. Therefore, the risk score model based on the ERO1A, CDC25C, and ITGB4 is expected to predict the prognosis of patients with LAC.



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*Source: 4022896-2022-06-23.xml*