According to the global cancer statistics analysis in 2020, the incidence of lung cancer ranks second only to breast cancer in the world, accounting for about 18% of all cancer deaths, and being the leading cause of cancer death in the world (1). The causes of lung cancer are very complex, including history of exposure to smoking and secondhand smoke, air pollution, history of pulmonary diseases, family history of cancer, occupational exposure to silica and asbestos, poor diet, mental and psychological factors (2–4). The early symptoms of lung cancer are not obvious. Generally, there are corresponding clinical symptoms in the middle and late stage, such as chest pain, hemoptysis, etc. According to relevant studies, 75% of lung cancer patients have been diagnosed at stage III or IV, at which time they have lost the opportunity for surgery, and the treatment means are relatively limited. Conventional radiotherapy and chemotherapy have no obvious effect, and the survival and prognosis are very poor (5). The overall 5-year survival rate for patients with lung cancer is 19%, which drops to 5% if distant metastasis is present at the time of diagnosis, and approximately 57% for patients in the localized stage (6). The diagnosis and treatment of lung cancer are still the focus of current research.

According to pathological types, lung cancer can be divided into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). NSCLC accounts for about 85% of all cases diagnosed with lung cancer, which mainly includes lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LSCC), and large cell lung cancer (LCLC) (7). LUAD is the most common pathological type of lung cancer, accounting for approximately 50-70% of surgically resected lung cancers (8) and almost 50% of all lung cancers (9). Precision medicine for disease requires accurate prognostic prediction, such as the risk of future recurrence after the initial treatment and responsiveness to different treatments (10, 11). At present, TNM staging is still the main basis for the treatment of LUAD and has been used clinically for many years as a prognostic predictor of LUAD (12). However, the reproducibility and discrimination ability of TNM staging for prognosis prediction are still not satisfied, and the prognosis is also different among LUAD patients with the same pathological type and stage. At the same time, although the emerging diagnosis and treatment technologies such as gene testing, targeted therapy and immunotherapy have been applied in the clinical diagnosis and treatment of lung cancer, the overall survival rate of lung cancer has only slightly improved compared with other malignant tumors (6, 13). Therefore, there are individual differences in LUAD, and prognosis prediction needs individual predictors.

Tumors often have oxidative stress (OXS), which is an imbalance between oxidation and anti-oxidation in the body that causes aberrant oxidative signal regulation and macromolecular oxidative damage (14). Cellular OXS is caused by ROS accumulation (15). OXS is the principal cause of cell damage, targeting intracellular macromolecules and promoting and suppressing tumor growth (14, 16–18). Tumor cell redox homeostasis control may improve tumor therapy. OXS regulates tumor cell fate in various ways that depend on tumor type and etiology. Future study will focus on controlling OXS’s anti-tumor and tumor-promoting effects. We can evaluate OXS heterogeneity in cancers and find new therapeutic targets using bioinformatics and other big data analysis methods.

With the emergence of public biomedical databases such as TCGA (The Cancer Genome Atlas) database, the use of bioinformatics to mine disease gene data has become an important direction of disease research (19). TCGA aims to focus on acquired changes of cancer genes. Up to now, a total of 33 types of cancers have been included in TCGA database (19). Clinical sample information and sequencing data (including transcriptome data, epigenetic data, genomic mutation data, etc.) of more than 20,000 patients can be accessed openly, which has become an important database for cancer research (19, 20). The gene expression data and clinical information of LUAD patients needed in this study were obtained from public databases. In this study, we obtained transcriptome and corresponding clinical data from TCGA, Genecards, and GEO databases. Firstly, Univariate Cox regression analysis was performed and oxidative stress-related genes (ORGs) affecting overall survival of LUAD were selected. Random Survival Forest and Least absolute shrinkage and selection operator (LASSO) analyses were used to screen and construct the OS-signature. We carried out efficacy evaluation for the OS-signature of LUAD using Kaplan-Meier and receiver operating characteristic (ROC) curves and the LUAD-cohort from GEO was used to validate the OS-signature. In addition, we evaluated the somatic mutation, genomic mutation, immunological characteristics, and sensitivity to chemotherapy for OS-signature. Finally, Quantitative Real-time PCR assays were used to detect the expression of the three genes establishing the OS-signature in LUAD cell lines.

Lung cancer is a malignant tumor originating from the bronchial epithelium. According to histopathological classification, lung cancer is divided into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). NSCLC is the main pathological type of lung cancer, and lung adenocarcinoma (LUAD) accounts for the vast majority of NSCLC. Lung cancer ranks second only to breast cancer in incidence and is the most important cause of cancer-related deaths. Late diagnosis, poor sensitivity to chemoradiotherapy, acquired resistance to targeted therapy and other related factors can lead to poor prognosis of patients with lung cancer (47, 48). At present, histopathological diagnosis and tumor staging system are still the main basis for predicting the prognosis and survival of lung cancer patients. However, traditional methods cannot accurately assess the prognosis of patients with LUAD. In addition, Computed Tomography (CT) and serum tumor markers such as carcinoembryonic antigen (CEA) are often used to determine the prognosis of lung cancer. However, traditional methods are limited by cumulative radiation damage, low sensitivity and specificity (49, 50). Therefore, clinicians need an accurate prognostic prediction model to help optimize the treatment strategy of LUAD patients. Bioinformatics is one of the emerging fields of biological research. It uses mathematics, statistics and computer technology to process and analyze biological data. In our study, extracting data from public database, we identified eight prognostic genes with HR>1 (MRPL44, CYCS, G3BP1, GFM1, SOD1, TXN, OSGIN2, and CRP) and two prognostic genes with HR<1 (CAT and XBP1). And the OS-signature could be used for prognosis and treatment prediction in LUAD.

Because of the functional correlation between genes in a cell, diseases are rarely the result of abnormalities in a single gene, but rather result from abnormalities in a complex intracellular gene network (51–53). Like most diseases, the occurrence and development of LUAD is a complex process involving multiple genes and multiple pathogenic mechanisms, involving the activation of proto-oncogenes and the inactivation or mutation of tumor suppressor genes (54, 55). Therefore, the application of network for gene interaction in LAC research can simplify and visualize complex and high-throughput data. Compared with the focus on local gene function in single gene and single molecule biological research methods, network analysis focuses more on the integrity and systematization of biological processes (51). In the OS-signature, there were three ORGs (CAT, CYCS, and MRPL44), forming a network to predict the prognosis of LUAD. It is more reliable to explore the occurrence and development of LUAD from the perspective of multiple genes.

For early and mid-stage NSCLC that cannot be completely resected by surgery, and for some locally evolved or metastatic NSCLC that is advanced or advanced (stage IIIA-IV), comprehensive systemic and local combination therapy can be used, including surgical resection, chemotherapy, radiotherapy, targeted therapy and immunotherapy. At present, the 3rd generation chemotherapy drugs, including Docetaxel, Vinorelbine, Gemcitabine and Paclitaxel, have been widely used in clinical practice, combining platinum drugs to develop personalized treatment plans for patients. Radiotherapy is an effective means of local treatment of lung cancer, which plays a positive role in slowing down the clinical symptoms, prolonging the survival time and improving the quality of life of patients with advanced lung cancer. These treatment methods have been widely studied and applied at home and abroad. Genomic studies have shown that adenocarcinoma and squamous cell carcinoma have significantly different gene mutation types, and tyrosine kinase inhibitor (TKI) can be used to inhibit the catalytic phosphorylation of the corresponding kinases in the treatment of NSCLC patients with significant clinical benefits. Genomic studies have shown that adenocarcinoma and squamous cell carcinoma have significantly different gene mutation types, and tyrosine kinase inhibitor (TKI) can be used to inhibit the catalytic phosphorylation of the corresponding kinases in the treatment of NSCLC patients with significant clinical benefits (56, 57). A variety of effective and well-tolerated TKIs targets, including EGFR, ALK, ROSI, HER2, etc., have emerged continuously, and promoted significant progress in cancer treatment. For example, EGFR driver gene mutations have a high incidence in various subtypes of NSCLC. The most common EGFR mutations include exon19 deletion (delE746-750, etc.) or exon 21 arginine substitution leucine (L858R) mutation. EGFR inhibitors such as Gefitinib, Erlotinib, Afatinib, or Osimertinib play an important role in the treatment of NSCLC patients (58). However, some studies have shown that the proportion of NSCLC patients carrying EGFR mutations is about 30-40%, and there are still a large number of patients who cannot benefit directly from targeted therapy (59). With the development of Crizotinib and next-generation ALK-TKIs, considerable progress has been made in the treatment of patients with ALK recombinant NSCLC (60). Crizotinib, a first-generation ALK inhibitor originally approved for patients with ALK-positive NSCLC, was found to have a median progression-free survival of 8-10 months in treated patients (61). Subsequent randomized controlled trials compared Crizotinib with chemotherapy in patients undergoing treatment with a significant improvement in progression-free survival. Subsequently, second-generation ALK inhibitors Ceritinib, Alectinib and Brigatinib were developed to overcome Crizotinib resistance in patients (62). So far, other treatments, including third-generation ALK inhibitors Lorlatinib, Entrectinib and Ensartinib, have shown better results (60). For the above mentioned chemotherapeutic drugs and small molecule targeted therapy drugs, the Genomics of Drug Sensitivity in Cancer (GDSC) database was created. The immediate goal is to identify potential therapeutic biomarkers that may predict drug response (chemotherapeutic drugs, small molecule targeted drugs, and other drugs), while the ultimate goal is to improve the current status of cancer treatment based on biomarkers (44, 63). It has been shown that changes in the tumor genome directly affect the therapeutic effect of the tumor (64). With the emergence of novel compounds, the screening of predictive biomarkers in their early development process will have a profound impact on the entire process of new cancer drug development, including its design, development cost and final outcome (64). Based on the clinical and basic research background, researchers present the results of large-scale drug screening in human cancer cell lines in GDSC, a database that combines detailed genomic profiles and gene expression analysis to systematically provide biomarker identification patterns for drug sensitivity science for a variety of cancer drugs. In our study, we compared the IC50 levels of Osimertinib_1919, Sapitinib_1549, Acetalax_1804, Ibrutinib_1799, Erlotinib_1168, Gefitinib_1010, AZD3759_1915, Afatinib_1032, and Lapatinib_1558 between subgroups with high OS-score or low OS-score based on GDSC database. We found that LUAD patients with high OS-scores may be more sensitive to these nine chemotherapeutics drugs. Our study will provide reference for the treatment of LUAD.

Recent studies have shown that tumor microenvironment (TME) plays an important role in the development and treatment of tumors (65). TME refers to the microenvironment surrounding the occurrence, growth and metastasis of tumor cells, including not only the tumor cells themselves, but also the immune cells, inflammatory cells, fibroblasts, various signaling molecules, extracellular matrix and blood vessels (66). To fully understand and overcome the complexity of TME is helpful for clinicians to provide more feasible and precise individualized treatment plan for tumor treatment. The rapid development of single-cell sequencing, second-generation sequencing and other technologies has gradually deepened researchers’ understanding of the relationship between T cells and other immune cell populations and immunotherapy. Tumor-associated immune cells play an important role in tumor spread, recurrence, metastasis and influencing immunotherapy treatment (67). They can be used as biomarkers to predict the efficacy of immunotherapy drugs or predict the prognosis of patients (67). Increased levels of tumor-infiltrating lymphocytes (TILs), such as CD4+T cells and CD8+T cells, are associated with immunotherapy response and longer survival (68). Immune checkpoint inhibition activates existing TILs, which recognize and eliminate abnormal and tumor cells, and TILs play a key role in immunotherapy response. Studies have shown that increased T-cell infiltration and increased IFN-γ-related mRNA expression can increase ICIs (immune checkpoint inhibitors) benefit and significantly improve patient prognosis in a variety of tumor types (69, 70). In advanced NSCLC patients, increased expression of CD8+ TILs detected by IHC or CD8A mRNA transcripts was associated with prolonged PFS treatment with PD-L1 inhibitors, especially in combination with PD-L1 mRNA and protein expression, suggesting that integrated biomarkers may provide higher predictive value (71). Another study using multiple quantitative immunofluorescences to detect TIL in paraffin tumor specimens found that ICI treatment resulted in lasting clinical benefits and longer OS in NSCLC patients with increased CD3+ T-cell infiltration (72). In addition, studies have found that tumor-associated macrophages (TAMs) secrete interleukin-10 (interleukin-10), Il-10, Transforming growth factor-B (TGFb) and other immunosuppressive cytokines play a variety of tumor-promoting effects, which increase the density of TAM and inhibit other related immune cells (73).

The limitations this study remain. The OS-signature we constructed and validated by retrospectively using the public database hence, more prospective studies are needed for clinical practicability. We selected the three genes in the OS-signature to detect their expression in cell lines. Biological experiments in this study are lacking, and more wet experiments are needed to explore the function of related genes.

In conclusion, immunotherapy by regulating the immune microenvironment may become a promising new strategy for cancer treatment. The precise regulation of immune gene expression is the key to generate strong immunity and intervene the development of cancer. In our study, we found that patients with high OS-scores had lower immunomodulators levels except CD276, TNFSF9, and HMGB1. From a general view, the level of infiltrating immune cell populations decreased as the OS-scores increased. It is necessary to further study the tumor microenvironment (TME) of lung cancer.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

WW conceived, designed, and supervised the study. HP performed data analysis and drafted the manuscript. XL performed PCR experiments. YL and CW assisted with the analysis. All authors contributed to the article and approved the submitted version.