A Bioinformatics-Based Analysis of an Cuproptosis and Ferroptosis-Related Gene Signature Predicts the Prognosis of Patients with lung adenocarcinoma

A Bioinformatics-Based Analysis of an Cuproptosis and Ferroptosis-Related Gene Signature Predicts the Prognosis of Patients with lung adenocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Bioinformatics-Based Analysis of an Cuproptosis and Ferroptosis-Related Gene Signature Predicts the Prognosis of Patients with lung adenocarcinoma Xizhi Liu, Shanzhi Gu, Xinhan Zhao, Yujiao Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3192529/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cuproptosis and ferroptosis acts important defense for the organism by preventing tumor cells migration and preventing their growth. In this study, cuproptosis and ferroptosis-related genes were used to construct a prognostic model for lung adenocarcinoma (LUAD) patients. Methods TCGA database was used to acquire RNA sequencing data and clinical information for LUAD samples. The Cox and LASSO regression analysis were performed to construct the prognostic genes signature. In addition, GSEA, GO, KEGG were performed to investigate the potential molecular mechanism. Moreover, we analyzed the relationship between our identified signature and immune cell infiltration, tumor microenvironment, immunotherapy response, drug sensitivity analysis. Results Three prognosis related genes were selected (SRXN1, GLS2, SLC2A1). Finally, in vitro experiments we performed qRT-PCR, western blot, scratch test, colony-formation, lipid ROS analysis to validate the expression and function of SRXN1 gene. Conclusion Combined with clinicopathological characteristics, the risk model was validated as a new independent prognostic factor for LUAD. lung adenocarcinoma ferroptosis cuproptosis gene signature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Lung cancer has become the leading cause of death in human malignant tumors (Sung et al. 2021 ). The incidence of non-small cell lung cancer (NSCLC) accounts for about 85% of the total incidence of lung cancer, because of its special biological behavior, it is prone to brain metastasis, the five-year survival rate of NSCLC is only 15% in 2021(Yang et al. 2022 ; Tfayli et al. 2021 ). Among which lung adenocarcinoma is the most common subtype (Thai et al. 2021 ). Several studies have build prognostic models to predict the OS of patients with lung cancer, but the accuracy is still limited ( Liang et al. 2015 ; Zheng et al. 2023). Therefore, to improve the prognosis of lung cancer patients, early diagnosis is essential, which makes the comprehensive understanding of the diagnostic biomarkers quite important. Ferroptosis is a non-apoptotic cell death mode triggered by oxidative disturbance in the intracellular microenvironment, regulated by glutathione peroxidase 4 (GPX4), and inhibited by iron chelating agents and lipophilic antioxidants (Dixon et al. 2012). Ferroptosis is a novel programmed cell death characterized by causing cell death. Compared to normal cells, cancer cells are more iron-dependent, more iron was consumed and iron depletion occurred (Zheng et al. 2020; Torti et al. 2020) . Tsvetkov P demonstrated an unexpected mechanism by which cell death is triggered by targeting Cu to mitochondria: cuproptosis (Tsvetkov et al. 2022). Copper is an essential cofactor for all organisms, and yet it becomes toxic if concentrations exceed a threshold maintained by evolutionarily conserved homeostatic mechanisms. Copper chelators have been considered as anticancer agents(Zheng et al. 2022 ). It is copper-triggered and mediated by protein lipoylation mainly in mitochondria. Cuproptosis occurs through the direct binding of copper to the lipoylated components of the tricarboxylic acid (TCA) cycle. When respiring, the lipoylated TCA enzymes increase and results in an abnormal aggregation of lipoacylated proteins and the loss of Fe-S cluster-containing proteins, which leads to inevitably acute proteotoxic stress and ultimately cell death (Zheng et al. 2022 ). The research provided a further in-depth look at the role of copper and mitochondria homeostasis, demonstrating a potentially critical role of cuproptosis in cell biology (Tang et al. 2022 ) . However, few studies focused on the relationship between the process of cuproptosis and ferroptosis related genes of LUAD. An accurate prognosis predictive model is required to evaluate patients prognosis, account for individual differences, and guide individualized treatment to benefit survival. Our study was to identify ferroptosis and cuproptosis related genes that have prognostic value in LUAD. We utilized TCGA database for LUAD to create a gene signature and explain its ability to predict OS. Materials and Methods Data Retrieval and Identification of Genes. We downloaded LUAD patients transcriptomic and clinical data from the TCGA database. Expressions of 19 cuproptosis-related genes (CRGs) (Table S1 ) were obtained from the literature review. And ferroptosis-related genes (FRGs) were derived from FerrDb website ( http://www.zhounan.org/ferrdb/ ) (Zhou et al. 2020). Differential expression analysis of differentially expressed genes (DEGs) was performed for the TCGA cohort and different subgroups using the R software “limma” package with | logFC | > 1.0 and FDR < 0.05 as thresholds. Functional Enrichment Analysis According to the risk scores, all samples were categorized into high-risk and low-risk groups. The “clusterProfiler” package in R was used to analyze enrichment analyses of GO, FDR < 0.05, and KEGG, FDR < 0.05, between the two groups. The “GSVA” R package was used to perform GSVA enrichment analysis. In addition, gene set enrichment analysis (GSEA) between the different groups was conducted by the clusterProfiler R package to discover the potential molecular mechanisms. Immune microenvironment, immune check-point and immune therapy response analysis. To estimate the connection between the gene signature and immune microenvironment of LUAD samples, a gene expression matrix-based ESTIMATE algorithm was utilized to determine the infiltration levels of stromal cells and immune cells in tumors. The immune and stromal scores reflected the infiltration levels of immune cells and stromal cells, respectively. While the ESTIMATE score was a stroma-immune composite score. Tumor-infiltrating immune cell datasets was obtained from TIMER2.0 ( http://timer.cistrome.org ) database. We applied TIMER, CIBERSORT, QUANTIseq, MCP-counter, xCELL, and EPIC algorithms to compare immune cell abundance between high-risk and low-risk groups immunotherapy response in patients with malignant tumors, tumor immune dysfunction and exclusion (TIDE) score ( http://tide.dfci.harvard.edu/ ) was calculated. Prediction of the effect of chemotherapy and target therapy. The “pRRophetic” R package was performed to predict the drug sensitivity of commonly used chemotherapeutic drugs. Wilcoxon signed-rank test was used to determine the difference between the groups. quantitative real-time PCR Total RNA of bladder cancer cells was extracted with TRIzol. 2µl of extracted RNA was used for RNA quantification and was reverse transcribed using a Reverse Transcription Reaction Kit. Then cDNA was amplified using specific primers. Primer sequences are listed at suplementary table (Table S2 ) Protein extraction and Western blotting Tissues or cells were collected and lysed in RIPA buffer mixed with protease inhibitors and incubated on ice for 15 min. The lysates were centrifuged at 13000 rpm for 15 min at 4°C, and the supernatant was collected. The denatured proteins were added to the chamber for electrophoresis conducted for the proper length of time, followed by transfer onto PVDF membranes. The membranes were blocked in 3% BSA for 1 h at room temperature. Antibodies against SRXN1 and GAPDH were used. The next day, the primary antibody was washed away with TBST solution, and the secondary antibody was added for 1 h at room temperature. Finally, the immune complexes were detected via enhanced chemiluminescence. Quantification of the bands was carried out with ImageJ software. Colony formation assay Cells in the logarithmic growth phase were seeded into each well of a 6cm plate (500 cells/well) and cultured with DMEM medium supplemented with 10% FBS in an incubator at 37°C with 5% CO 2 . Two weeks after seeding when colony formation became visible, the medium was discarded and the colonies were washed three times with PBS, then fixed with 4% paraformaldehyde for 15 min and stained with crystal violet for 15 min. Then, the staining solution was slowly washed away with running water. After the plate was air dried, the number of colonies was determined. Lipid Peroxidation Assay Fresh media containing 0.1X BODIPY 581/591 C11 dye was added to each plate. The cells were rinsed with PBS and trypsinized to obtain a cell suspension after incubation for 30 minutes in a humidified incubator (at 37° C, 5% CO2). Lipid peroxidation levels were analyzed by flow cytometry using NovoExpress and analyzed with FlowJo software. Statistical Analysis. R was used to conduct all statistical analyses (v 4.2.2) and p-values < 0.05 were considered statistically significant. Results Acquisition of genes Based on the PPI network analysis of the correlation, we discovered 135 FRGs and CRGs (Table S3 ). When compared to normal adjacent tissues, we found 34 DEGs in the TCGA cohorts (Fig. 1 A, B). 9 of 34 ARGs were linked with survival and statistically distinct, according to univariate Cox regression analysis (Fig. 1 C). Meanwhile, network plots showed the relationship between the expression levels of the these genes more clearly (Fig. 1 D). Since LUAD patients frequently lost or gained chromosomal regions, we downloaded CNV data from the TCGA database to further explore the alteration of these genes on chromosomes and the location of each gene on chromosomes (Fig. 1 E; Fig. S1 ). Consistent clustering in LUAD To comprehend the function of genes in LUAD better, we used the ‘Consensus Cluster Plus’ R program to perform consensus clustering based on 9 prognosis-related genes. The cohort could be effectively divided into two subtypes (Fig.e 2A). A substantial difference in prognosis among the two subtypes was revealed by the overall survival analysis (Fig. 2 B). Its accuracy was examined by PCA (Fig. 2 C). We also explore these genes differences between different genotypes (Fig. 2 D). A boxplot was utilized to demonstrate the considerable variation in immune cell infiltration levels among the two groupings (Fig. 2 E). Heatmaps of genes expression and corresponding clinicopathological features of the two subtypes indicated that higher expression of genes in cluster A might be associated with a worse prognosis in LUAD patients (Fig. 2 F). We used the GSVA software to concentrate on the differential enrichment between cluster A and cluster B given the obvious disparities among cluster A and B in addition to examining the overall distribution of the genes in clusters (Fig. 2 G). Functional analysis GO enrichment analyses were performed for these differential genes, and these DEGs were associated with a variety of items, including “nuclear division” and “chromosome segregation” in the BP class, “collagen − containing extracellular matrix” and “spindle” in the CC class, MF results show that these genes are associated with “signaling receptor activator activity” and “receptor ligand activity” (Fig. 3 A,B). KEGG pathway analyses indicated that DEGs prone to be enriched in cell cycle (Fig. 3 C,D). Finally, to investigate the potential molecular mechanisms of the signature, GSEA was conducted. In the cluster A, there were significant enrichments in cell cycle, DNA replication and P53 signaling pathway (Fig. 3 E). systemic lupus erythematosus and linolenic acid metabolism were remarkably enriched in the cluster B (Fig. 3 F). The Development and Validation Prognostic Signature First, we obtained 9 OS-related genes from univariate Cox regression (Fig. 1 C). LASSO regression were performed to reduce multicollinearity, which resulted in the selection of 5 genes (Fig. 4 A,B). At last, a subsequent multivariate Cox regression analysis highlights 3 genes (SRXN1, GLS2, SLC2A1) for prognosis based on the lowest AIC(Table S4 ). The risk score was calculated using the formula: Risk score = SRXN1 × 1.06 - GLS2 × 0.50 + SLC2A1×0.21. LUAD patients were randomly grouped as test and train group at the ratio of 1:1, which were further classified into two groups by median risk score. The risk score distribution, survival status and survival time expression results of the patients in the whole, train and test group were evaluated by this model formula. The results showed that the OS was much lower in the high-risk group (Fig. 4 C-E). Risk plots display specific survival results for each patient in the three cohort, showing a steady rise in mortality with increasing risk scores (Figure S2 A-C). Risk scores were significantly different in the two clusters (Fig. 4 F), with cluster A having a higher risk score. The risk score was identified as an independent predictive factor for LUAD by Cox regression analyses (Fig. 5 A). We created a nomogram using the clinical factors and risk score to further study the predictive power of the model, which was able to predict the OS of 1-, 3- and 5-year (Fig. 5 B). The ROC curves to assess the predictive performance of 1-, 3- and 5-year OS of this prognostic model in whole, train and test cohort, respectively (Fig. 5 C-E). In addition, we utilized calibration plot exhibited good agreement between the nomogram and the model (Fig. 5 F-H). We also explore the Cumulative hazard of the high and low risk group in nomogram (FigS. 3 A-C). Immune cell infiltration, tumor microenvironment, mutation landscape, immunotherapy response, and drug sensitivity of LUAD patients. The development of LUAD and the effectiveness of immunotherapy are both significantly influenced by the immune microenvironment. To achieve this, we looked more closely at the tumor microenvironment (TME) of LUAD patients. First, the risk scores for the LUAD samples were ranked from low to high to display the proportion of various immune cells (Fig. 6 A-C), the infiltration of CD8 + T cells, Macrophage M0 was greater in the high-risk group. The 3 genes utilized to build the risk score were strongly connected with numerous immune cells (Fig. 6 D, Figure S4 ). We were able to determine the stromal score and immunological score of the high-risk and low-risk groups using the ESTIMATE score of the expression profile (Fig. 7 A). Additionally, we discovered that practically mostly immune checkpoints, including CD274, IDO1, HAVCR2, CD70, TMIGD2, TNFSF4, PDCD1LG2, CD276, TNFSF9, LAG3 and PDCD1 displayed greater activity in the high-risk group by comparing to the low risk groups (Fig. 7 B). TIDE algorithm was established for predicting the immune checkpoint inhibitor (ICI) responders of the two subtypes of patients and further to predict whether immunotherapy could benefit LUAD patients. The results showed that the low risk group responded better than the high risk group (Fig. 7 C). In view of the importance of target therapeutic and chemotherapy agents to LUAD, we selected Cisplatin, Docetaxel, Gemcitabine, Paclitaxel and Savolitinib for further study, comparing the drug sensitivity between the two subtypes of patients. Our data showed the low-risk group was more sensitive to these drugs than that in high-risk group (Figs. 7 D-H). The expression level and function of genes in vitro. To further verify the expression of these screened genes in lung adenocarcinoma cells, RNA of A549, PC9 were extracted. Compared with normal lung epithelial cells (BEAS-2B), the expression level of SRXN1 and SLC2A1 was significantly higher while GLS2 was lower in lung adenocarcinoma cells (A549/PC9) in qPCR analysis (Fig. 8 A). The coefficient of SRXN1is higher than other two genes (Table S4 ), so we choose SRXN1 to do the further study. We constructed SRXN1 knockdown cells in A549 cells (Fig. 8 B). SRXN1 knockdown led to the decrease the ability of migration (Fig. 8 C) and colon formation ability (Fig. 8 D). We have found that SRXN1 knock-down increased the intracellular lipid ROS (Fig. 8 E). As the iron overload is a major character of ferroptosis, the levels of cytosolic iron contents were measured in A549 cells and representative images showed iron levels in cytoplasm identified by FerroOrange probes obviously increased in SRXN1 knockdown cells (Fig. 8 F). Discussion Although the number of new cancer cases is high, with the rapid development of chemotherapy or radiotherapy, surgery, targeted therapy, and immunotherapy, the annual number of deaths for many cancers has dropped significantly, but the 5-year survival rate of lung cancer is less than 20% (Zeng et al. 2018 ). In our study, the combination analysis of Cox and LASSO regression was applied to establish a prognosis signature. The signature showed good predictive performance, patients in the low-risk group having a better survival. Our studies have identified three prognosis-associated genes: SRXN1, GLS2, SLC2A1. They are potential biomarkers for LUAD, and potential therapeutic targets. SRXN1 (sulfiredoxin 1) was an antioxidant protein (Li et al. 2019 ). It catalyses the reduction of hyperoxidized peroxiredoxins to the reduced form, to thereby restoring their peroxidase activity (Mishra et al. 2015 ; Li et al. 2018; Kim et al. 2016 ). As a cancer-promoting factor, it has been reported in many literatures. CircABCA13 stabilizes SRXN1 to facilitate esophageal squamous cell carcinoma development through acting as a miR-4429 sponge (Jun et al. 2023). Moreover, several lines of evidence indicate that SRXN1 can stimulates hepatocellular carcinoma tumorigenesis and metastasis through modulating ROS/p65/BTG2 signalling (Lv et al. 2020 ). SRXN1 can aberrant activation of the Wnt/β-Catenin signaling pathway which is promoted cervical cancer metastasis ( Lan et al. 2017). Some studies found that inhibition of SRXN1 suppressed viability and enhanced apoptosis in human lung carcinoma cell line (Jia et al. 2022). So SRXN1 may play an important role in lung carcinoma. SLC2A1(Solute carrier family 2 member 1) is known as glucose transporter 1 (GLUT1) (Cao et al. 2021 ). SLC2A1 plays a crucial role in the process of cell glycometabolism, whether in cancer or normal cells (Masoud et al. 2015). SLC2A1 is highly expressed in many kinds of cancer, and the overexpression of SLC2A1 can promote the growth and metastasis of cancers, such as liver, lung, endometrial, oral, breast, and gastric cancers (Pereira et al. 2013; Avanzato et al. 2018 ; Sun et al. 2016 ) (Berlth et al. 2015 ; Goldman et al. 2006 ; Smolle et al. 2020). GLS2 is a mitochondrial phosphate activated glutaminase. Glutamine metabolism is a widely known target for slowing cancer development, while the p53-inducible gene GLS2 was linked to a unique metabolic role in suppressing tumor growth ( Suzuki et al. 2010). Previous studies shows that GLS2 expression was decreased in human hepatocellular carcinoma due to hypermethylation, negatively regulating the PI3K/AKT pathway, GLS2 plays a role to suppress of hepatocellular carcinoma (Liu et al. 2014). Similar to previous reports, GLS2 was a favorable factors in lung adenocarcinoma (Tao et al. 2022 ). KEGG and GO enrichment analyses indicated that DEGs prone to be enriched in Cell cycle, nuclear division. GSEA result demonstrate that there were significant enrichments in cell cycle and P53 signaling pathway. ICB treatment can bring a lot of clinical benefits, but only one-third of patients respond to treatment (Sharma et al. 2017 ). To predict ICB response, TIDE has developed a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. validated that an accurate gene signature to model tumor immune escape could serve as a reliable surrogate biomarker to predict ICB response (Jiang et al. 2018 ). Our tumor immune dysfunction and exclusion analysis demonstrated that the low-risk group will get a better survival through immunotherapy than the high-risk group. In the present study, we developed a prognostic model based on prognosis genes and effectively categorized LUAD patients into low and high-risk groups. In addition, immune cell infiltration analysis and functional enrichment analysis demonstrated the correlation between the LUAD model and the immunosuppressive microenvironment. This work will lay a preliminary foundation for further experimental verification. Conclusions We developed a prognostic signature that has been shown to be independent, reliable, and may provide some new perceptions into future studies investigating the mechanisms between cuproptosis, ferroptosis-associated genes and LUAD. Declarations Acknowledgements Not applicable. Authors’ contributions YJZ and XHZ were in charge of designing the research. XZL were responsible for data analysis and manuscript writing. XZL and SZG mainly revised the article. All the authors gave final approval of the published version, and agreed to be responsible for all aspects of the work. Funding None. Availability of data and materials The datasets generated and/or analysed during the current study are available in the TCGA repository [https://portal.gdc.cancer.gov/repository]. Ethics approval and consent to participate The TCGA database is an open database and the information is freely available. 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Cell 168:707–723. https://doi.org/10.1016/j.cell.2017.01.017. Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X et al (2018) Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 24:1550–8. https://doi.org/10.1038/s41591-018-0136-1. Additional Declarations No competing interests reported. Supplementary Files TableS1.txt TableS2.txt TableS3.txt TableS4.txt FigS1.pdf Figure S1. CNVs of 9 genes in TCGA. FigS2.tif FigureS2. (A-C) Heat map and risk plots were used to illustrate the survival status of each sample in whole, the train and test group. FigS3.tif FigureS3. (A-C) Cumulative hazard of the high-risk and low-risk group in whole, the train and test group. FigS4.tif FigureS4. The correlation between immune cells and risk score . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3192529","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":220715414,"identity":"6b1c6668-1021-43aa-99f4-5534ad17dcda","order_by":0,"name":"Xizhi Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xizhi","middleName":"","lastName":"Liu","suffix":""},{"id":220715415,"identity":"0f970737-6f63-412c-a581-49e0fabf6739","order_by":1,"name":"Shanzhi Gu","email":"","orcid":"","institution":"Medical School of Xi’an Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shanzhi","middleName":"","lastName":"Gu","suffix":""},{"id":220715417,"identity":"12bc9578-14aa-442f-8738-fca7a827037b","order_by":2,"name":"Xinhan Zhao","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinhan","middleName":"","lastName":"Zhao","suffix":""},{"id":220715418,"identity":"521aaeeb-4e59-49d3-af3e-cfd246360add","order_by":3,"name":"Yujiao Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYHCChAMgAsKsqJFjY28/QIKWB2eOGfPxnEkgzioQwfiwjTlxnoSDAV6lBjcSHh74UVGXxz+7/QJDAhtbepsEUP+Pim34tCQc7DnDVixx50wBQwKPTG6bdOMBxp4zt/FqOcDbxpPYcCMngSFBgi23TeZAAjNjG34tB/+2SSTOB2sxYE5nkwCShLQc5m0zSNxwI/0AQ0ICcwJBLZJnHiQcljmTkLjxRg4ovI8ZtgED+SA+v/Adz0n++KaiLnHejfQHjD//1cjLt7cffPCjArcWhQM8CVAmj/kPmOgBnOqBQL6BHSbP/gCfwlEwCkbBKBjBAADq1mNB3zmmiQAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yujiao","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-07-21 15:44:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3192529/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3192529/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":40608413,"identity":"5061a89e-a3e6-4a0b-8c4f-35e42f65c970","added_by":"auto","created_at":"2023-07-26 14:39:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":620481,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of genes in LUAD. (A) Heatmap of 34 genes differentially expressed in normal tissues and tumors. (B) Volcano plot of differentially expressed genes. (C) Forest map of prognostic genes extracted by uni-COX regression. (D) The network diagram showed the correlations between the top 9 genes. (E) Localization of 9 genes in chromosomal regions.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/8fc2f8628eea752273947e17.png"},{"id":40608404,"identity":"aa678c86-93a7-4414-81d3-64d75151aabf","added_by":"auto","created_at":"2023-07-26 14:39:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":722003,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroups of LUAD associated with 34 genes. (A) The consensus matrix was obtained by applying consensus clustering when k = 2, the slope of the CDF curve is the lowest. (B) Overall survival of two subtypes (p \u0026lt; 0.001). (C) PCA distinguished two subtypes based on the expression of genes. (D) Genes differences between different genotypes. (E) Immune infiltration patterns of two subtype groups were obtained using ssGSEA. (F) Heatmap of the expression of genes and corresponding clinicopathological features of two subtypes. (G) GSVA analysis focused on the differential enrichment of KEGG pathways between clusters A and B.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/94b53a80f5eb668e49599481.png"},{"id":40608405,"identity":"0b07c06e-7791-4a08-846f-ce0783a5c499","added_by":"auto","created_at":"2023-07-26 14:39:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":889540,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis. (A)(B) The circle graph and bar plot showed top GO signaling pathways involved BP, MF, and CC biological processes. (C)(D) The circle graph and bar plot showed top KEGG signaling pathways. (E)(F) GSEA analysis between clusters A and B.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/653fad68fd2593f2c8689344.png"},{"id":40608408,"identity":"1c210cae-74e4-480a-95a2-729c8eb34ff8","added_by":"auto","created_at":"2023-07-26 14:39:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1069817,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of prognostic features. (A)(B) The LASSO regression was performed with the minimum criteria. An optimal log λ value is indicated by the vertical black line in the plot. (C-E) The KM curves showed a different prognosis in the subtype risk group. (F) Risk score in the A and B clusters.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/15126a81a5fc82e89862b668.png"},{"id":40609804,"identity":"c7317951-41c8-4282-9ab7-38d23b0ce632","added_by":"auto","created_at":"2023-07-26 14:55:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1831793,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic value of risk scores in LUAD patients. (A) Multivariate Cox analyses of clinical features. (B) Nomogram of risk groupings and clinical characteristics. (C-E) 1-, 3-, and 5-year ROC curves in whole, train and test cohort. (F-H) Calibration curves tested for agreement between actual and predicted outcomes at 1, 3and5 years.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/6a1c007022c2c8a39e5fb7b3.png"},{"id":40608419,"identity":"0c67259b-8b37-4507-a6f2-078b94bf9633","added_by":"auto","created_at":"2023-07-26 14:39:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":961551,"visible":true,"origin":"","legend":"\u003cp\u003eThe immune microenvironment of LUAD samples. (A)(B) Relative proportions of infiltrating immune cells for different risk subgroups. (C) Association of each various immune cells. (D) Correlation between immune cells and three hub genes.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/f29b5ad41b3ac3991c3807cb.png"},{"id":40608414,"identity":"c520349c-1a3d-4b66-bdb6-24d941e43081","added_by":"auto","created_at":"2023-07-26 14:39:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":983267,"visible":true,"origin":"","legend":"\u003cp\u003eThe immune landscape analysis of genes in LUAD patients. (A) Estimate score of the expression profile in the high-risk group and low-risk group. (B) The differential expression of immune checkpoint between high-risk and low-risk group. (C) TIDE score between the high-risk and low-risk groups. (D-H) The distribution of drug sensitivity showed a significant difference of patients in the high and low-risk groups among Cisplatin, Docetaxel, Gemcitabine, Paclitaxel and Savolitinib.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/cfd17ad397195aa9c72be133.png"},{"id":40608416,"identity":"65b9520f-2f7d-497a-89d6-901f92aad8aa","added_by":"auto","created_at":"2023-07-26 14:39:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":357050,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression level of three genes in vitro. (A)Expression of SRXN1, GLS2 and SLC2A1 in normal lung epithelial cells (BEAS-2B) and lung adenocarcinoma cells (A549/PC9), detected by RT-qPCR. (B) Construct SRXN1 knockdown A549 cells. (C)(D) The scratch test and colon ability of MET knockdown PANC-1 cells. (E) probe was used to assess lipid ROS by flow cytometry. (F) The expression of fluorescent images of intracellular iron level in A549 cells.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/d5490896ce93957836bbc8d9.png"},{"id":42967703,"identity":"82b6561a-c8b6-4542-8333-577d24072048","added_by":"auto","created_at":"2023-09-12 01:52:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3395860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/8f2d5dd4-09b3-4d22-99b2-a679b275e6b9.pdf"},{"id":40609510,"identity":"13c5b86f-63d2-41ff-8aec-16a4c365baad","added_by":"auto","created_at":"2023-07-26 14:47:05","extension":"txt","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":124,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.txt","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/e2165cbe7babe40d1856da33.txt"},{"id":40609511,"identity":"d011608c-d4eb-4df7-9044-6b16169000bf","added_by":"auto","created_at":"2023-07-26 14:47:05","extension":"txt","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":269,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.txt","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/d2f9e33f5c0afe6cc0a4984a.txt"},{"id":40609513,"identity":"e2c84c50-9a14-48a7-adeb-421e2995b04d","added_by":"auto","created_at":"2023-07-26 14:47:05","extension":"txt","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":887,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.txt","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/9acf8b49e7d16c62eacd944e.txt"},{"id":40609512,"identity":"085eec3a-a588-44ec-b42c-aed0972087f8","added_by":"auto","created_at":"2023-07-26 14:47:05","extension":"txt","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":84,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.txt","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/db67f1335bd7a2b59c87fcbc.txt"},{"id":40608410,"identity":"2ddba0f6-6baa-4ad3-a484-861666284e38","added_by":"auto","created_at":"2023-07-26 14:39:05","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":5665,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1\u003c/strong\u003e. CNVs of 9 genes in TCGA.\u003c/p\u003e","description":"","filename":"FigS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/fc7f672f0887a6cbd639ad7d.pdf"},{"id":40608418,"identity":"f4be60f4-0482-4d6e-8864-99b31f96a23d","added_by":"auto","created_at":"2023-07-26 14:39:05","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6779432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigureS2\u003c/strong\u003e. (A-C) Heat map and risk plots were used to illustrate the survival status of each sample in whole, the train and test group.\u003c/p\u003e","description":"","filename":"FigS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/98a95154f37474c82df66181.tif"},{"id":40608415,"identity":"48eb599a-e922-4d47-83ea-55f883d77ae6","added_by":"auto","created_at":"2023-07-26 14:39:05","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":440272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigureS3\u003c/strong\u003e. (A-C) Cumulative hazard of the high-risk and low-risk group in whole, the train and test group.\u003c/p\u003e","description":"","filename":"FigS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/e83600f5aaac7b6779767fe4.tif"},{"id":40608417,"identity":"454803c1-c446-42b7-92cc-6369080799bd","added_by":"auto","created_at":"2023-07-26 14:39:05","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":2181028,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigureS4\u003c/strong\u003e. The correlation between immune cells and risk score .\u003c/p\u003e","description":"","filename":"FigS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-3192529/v1/9c40e1ee1e1ea9639b7b396b.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Bioinformatics-Based Analysis of an Cuproptosis and Ferroptosis-Related Gene Signature Predicts the Prognosis of Patients with lung adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer has become the leading cause of death in human malignant tumors (Sung et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The incidence of non-small cell lung cancer (NSCLC) accounts for about 85% of the total incidence of lung cancer, because of its special biological behavior, it is prone to brain metastasis, the five-year survival rate of NSCLC is only 15% in 2021(Yang et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tfayli et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among which lung adenocarcinoma is the most common subtype (Thai et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Several studies have build prognostic models to predict the OS of patients with lung cancer, but the accuracy is still limited ( Liang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zheng et al. 2023). Therefore, to improve the prognosis of lung cancer patients, early diagnosis is essential, which makes the comprehensive understanding of the diagnostic biomarkers quite important.\u003c/p\u003e \u003cp\u003eFerroptosis is a non-apoptotic cell death mode triggered by oxidative disturbance in the intracellular microenvironment, regulated by glutathione peroxidase 4 (GPX4), and inhibited by iron chelating agents and lipophilic antioxidants (Dixon et al. 2012). Ferroptosis is a novel programmed cell death characterized by causing cell death. Compared to normal cells, cancer cells are more iron-dependent, more iron was consumed and iron depletion occurred (Zheng et al. 2020; Torti et al. 2020) .\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTsvetkov P demonstrated an unexpected mechanism by which cell death is triggered by targeting Cu to mitochondria: cuproptosis (Tsvetkov et al. 2022). Copper is an essential cofactor for all organisms, and yet it becomes toxic if concentrations exceed a threshold maintained by evolutionarily conserved homeostatic mechanisms. Copper chelators have been considered as anticancer agents(Zheng et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is copper-triggered and mediated by protein lipoylation mainly in mitochondria. Cuproptosis occurs through the direct binding of copper to the lipoylated components of the tricarboxylic acid (TCA) cycle. When respiring, the lipoylated TCA enzymes increase and results in an abnormal aggregation of lipoacylated proteins and the loss of Fe-S cluster-containing proteins, which leads to inevitably acute proteotoxic stress and ultimately cell death (Zheng et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The research provided a further in-depth look at the role of copper and mitochondria homeostasis, demonstrating a potentially critical role of cuproptosis in cell biology (Tang et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) .\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHowever, few studies focused on the relationship between the process of cuproptosis and ferroptosis related genes of LUAD. An accurate prognosis predictive model is required to evaluate patients prognosis, account for individual differences, and guide individualized treatment to benefit survival. Our study was to identify ferroptosis and cuproptosis related genes that have prognostic value in LUAD. We utilized TCGA database for LUAD to create a gene signature and explain its ability to predict OS.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eData Retrieval and Identification of Genes.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe downloaded LUAD patients transcriptomic and clinical data from the TCGA database. Expressions of 19 cuproptosis-related genes (CRGs) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) were obtained from the literature review. And ferroptosis-related genes (FRGs) were derived from FerrDb website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.zhounan.org/ferrdb/\u003c/span\u003e\u003cspan address=\"http://www.zhounan.org/ferrdb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Zhou et al. 2020). Differential expression analysis of differentially expressed genes (DEGs) was performed for the TCGA cohort and different subgroups using the R software \u0026ldquo;limma\u0026rdquo; package with | logFC | \u0026gt; 1.0 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as thresholds.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eAccording to the risk scores, all samples were categorized into high-risk and low-risk groups. The \u0026ldquo;clusterProfiler\u0026rdquo; package in R was used to analyze enrichment analyses of GO, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and KEGG, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, between the two groups. The \u0026ldquo;GSVA\u0026rdquo; R package was used to perform GSVA enrichment analysis. In addition, gene set enrichment analysis (GSEA) between the different groups was conducted by the clusterProfiler R package to discover the potential molecular mechanisms.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmune microenvironment, immune check-point and immune therapy response analysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo estimate the connection between the gene signature and immune microenvironment of LUAD samples, a gene expression matrix-based ESTIMATE algorithm was utilized to determine the infiltration levels of stromal cells and\u003c/p\u003e \u003cp\u003eimmune cells in tumors. The immune and stromal scores reflected the infiltration levels of immune cells and stromal cells, respectively. While the ESTIMATE score was a stroma-immune composite score. Tumor-infiltrating immune cell datasets was obtained from TIMER2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. We applied TIMER,\u003c/p\u003e \u003cp\u003eCIBERSORT, QUANTIseq, MCP-counter, xCELL, and EPIC algorithms to compare\u003c/p\u003e \u003cp\u003eimmune cell abundance between high-risk and low-risk groups immunotherapy response in patients with malignant tumors, tumor immune dysfunction and exclusion (TIDE) score (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was calculated.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrediction of the effect of chemotherapy and target therapy.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe \u0026ldquo;pRRophetic\u0026rdquo; R package was performed to predict the drug sensitivity of commonly used chemotherapeutic drugs. Wilcoxon signed-rank test was used to determine the difference between the groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003equantitative real-time PCR\u003c/h2\u003e \u003cp\u003eTotal RNA of bladder cancer cells was extracted with TRIzol. 2\u0026micro;l of extracted RNA was used for RNA quantification and was reverse transcribed using a Reverse Transcription Reaction Kit. Then cDNA was amplified using specific primers. Primer sequences are listed at suplementary table (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eProtein extraction and Western blotting\u003c/h2\u003e \u003cp\u003eTissues or cells were collected and lysed in RIPA buffer mixed with protease inhibitors and incubated on ice for 15 min. The lysates were centrifuged at 13000 rpm for 15 min at 4\u0026deg;C, and the supernatant was collected. The denatured proteins were added to the chamber for electrophoresis conducted for the proper length of time, followed by transfer onto PVDF membranes. The membranes were blocked in 3% BSA for 1 h at room temperature. Antibodies against SRXN1 and GAPDH were\u003c/p\u003e \u003cp\u003eused. The next day, the primary antibody was washed away with TBST\u003c/p\u003e \u003cp\u003esolution, and the secondary antibody was added for 1 h at room temperature. Finally, the immune complexes were detected via enhanced chemiluminescence. Quantification of the bands was carried out with ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eColony formation assay\u003c/h2\u003e \u003cp\u003eCells in the logarithmic growth phase were seeded into each well of a 6cm plate (500 cells/well) and cultured with DMEM medium supplemented with 10% FBS in an incubator at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e. Two weeks after seeding when colony formation became visible, the medium was discarded and the colonies were washed three times with PBS, then fixed with 4% paraformaldehyde for 15 min and stained with crystal violet for 15 min. Then, the staining solution was slowly washed away with running water. After the plate was air dried, the number of colonies was determined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eLipid Peroxidation Assay\u003c/h2\u003e \u003cp\u003eFresh media containing 0.1X BODIPY 581/591 C11 dye was added to each plate. The cells were rinsed with PBS and trypsinized to obtain a cell suspension after incubation for 30 minutes in a humidified incubator (at 37\u0026deg; C, 5% CO2). Lipid peroxidation levels were analyzed by flow cytometry using NovoExpress and analyzed with FlowJo software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis.\u003c/h2\u003e \u003cp\u003eR was used to conduct all statistical analyses (v 4.2.2) and p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of genes\u003c/h2\u003e \u003cp\u003eBased on the PPI network analysis of the correlation, we discovered 135 FRGs and CRGs (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). When compared to normal adjacent tissues, we found 34 DEGs in the TCGA cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). 9 of 34 ARGs were linked with survival and statistically distinct, according to univariate Cox regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Meanwhile, network plots showed the relationship between the expression levels of the these genes more clearly (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Since LUAD patients frequently lost or gained chromosomal regions, we downloaded CNV data from the TCGA database to further explore the alteration of these genes on chromosomes and the location of each gene on chromosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConsistent clustering in LUAD\u003c/h2\u003e \u003cp\u003eTo comprehend the function of genes in LUAD better, we used the \u0026lsquo;Consensus Cluster Plus\u0026rsquo; R program to perform consensus clustering based on 9 prognosis-related genes. The cohort could be effectively divided into two subtypes (Fig.e 2A). A substantial difference in prognosis among the two subtypes was revealed by the overall survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Its accuracy was examined by PCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). We also explore these genes differences between different genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). A boxplot was utilized to demonstrate the considerable variation in immune cell infiltration levels among the two groupings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHeatmaps of genes expression and corresponding clinicopathological features of the two subtypes indicated that higher expression of genes in cluster A might be associated with a worse prognosis in LUAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). We used the GSVA software to concentrate on the differential enrichment between cluster A and cluster B given the obvious disparities among cluster A and B in addition to examining the overall distribution of the genes in clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis\u003c/h2\u003e \u003cp\u003eGO enrichment analyses were performed for these differential genes, and these DEGs were associated with a variety of items, including \u0026ldquo;nuclear division\u0026rdquo; and \u0026ldquo;chromosome segregation\u0026rdquo; in the BP class, \u0026ldquo;collagen\u0026thinsp;\u0026minus;\u0026thinsp;containing extracellular matrix\u0026rdquo; and \u0026ldquo;spindle\u0026rdquo; in the CC class, MF results show that these genes are associated with \u0026ldquo;signaling receptor activator activity\u0026rdquo; and \u0026ldquo;receptor ligand activity\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA,B). KEGG pathway analyses indicated that DEGs prone to be enriched in cell cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC,D). Finally, to investigate the potential molecular mechanisms of the signature, GSEA was conducted. In the cluster A, there were significant enrichments in cell cycle, DNA replication and P53 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). systemic lupus erythematosus and linolenic acid metabolism were remarkably enriched in the cluster B (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe Development and Validation Prognostic Signature\u003c/h2\u003e \u003cp\u003eFirst, we obtained 9 OS-related genes from univariate Cox regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). LASSO regression were performed to reduce multicollinearity, which resulted in the selection of 5 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA,B). At last, a subsequent multivariate Cox regression analysis highlights 3 genes (SRXN1, GLS2, SLC2A1) for prognosis based on the lowest AIC(Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). The risk score was calculated using the formula: Risk score\u0026thinsp;=\u0026thinsp;SRXN1 \u0026times; 1.06 - GLS2 \u0026times; 0.50\u0026thinsp;+\u0026thinsp;SLC2A1\u0026times;0.21.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLUAD patients were randomly grouped as test and train group at the ratio of 1:1, which were further classified into two groups by median risk score. The risk score distribution, survival status and survival time expression results of the patients in the whole, train and test group were evaluated by this model formula. The results showed that the OS was much lower in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-E).\u003c/p\u003e \u003cp\u003eRisk plots display specific survival results for each patient in the three cohort, showing a steady rise in mortality with increasing risk scores (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-C). Risk scores were significantly different in the two clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eF), with cluster A having a higher risk score. The risk score was identified as an independent predictive factor for LUAD by Cox regression analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). We created a nomogram using the clinical factors and risk score to further study the predictive power of the model, which was able to predict the OS of 1-, 3- and 5-year (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The ROC curves to assess the predictive performance of 1-, 3- and 5-year OS of this prognostic model in whole, train and test cohort, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-E). In addition, we utilized calibration plot exhibited good agreement between the nomogram and the model (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-H). We also explore the Cumulative hazard of the high and low risk group in nomogram (FigS. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eImmune cell infiltration, tumor microenvironment, mutation landscape, immunotherapy response, and drug sensitivity of LUAD patients.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe development of LUAD and the effectiveness of immunotherapy are both significantly influenced by the immune microenvironment. To achieve this, we looked more closely at the tumor microenvironment (TME) of LUAD patients. First, the risk scores for the LUAD samples were ranked from low to high to display the proportion of various immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C), the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells, Macrophage M0 was greater in the high-risk group. The 3 genes utilized to build the risk score were strongly connected with numerous immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe were able to determine the stromal score and immunological score of the high-risk and low-risk groups using the ESTIMATE score of the expression profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Additionally, we discovered that practically mostly immune checkpoints, including CD274, IDO1, HAVCR2, CD70, TMIGD2, TNFSF4, PDCD1LG2, CD276, TNFSF9, LAG3 and PDCD1 displayed greater activity in the high-risk group by comparing to the low risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). TIDE algorithm was established for predicting the immune checkpoint inhibitor (ICI) responders of the two subtypes of patients and further to predict whether immunotherapy could benefit LUAD patients. The results showed that the low risk group responded better than the high risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn view of the importance of target therapeutic and chemotherapy agents to LUAD, we selected Cisplatin, Docetaxel, Gemcitabine, Paclitaxel and Savolitinib for further study, comparing the drug sensitivity between the two subtypes of patients. Our data showed the low-risk group was more sensitive to these drugs than that in high-risk group (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-H).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe expression level and function of genes in vitro.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further verify the expression of these screened genes in lung adenocarcinoma cells, RNA of A549, PC9 were extracted. Compared with normal lung epithelial cells (BEAS-2B), the expression level of SRXN1 and SLC2A1 was significantly higher while GLS2 was lower in lung adenocarcinoma cells (A549/PC9) in qPCR analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). The coefficient of SRXN1is higher than other two genes (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e), so we choose SRXN1 to do the further study. We constructed SRXN1 knockdown cells in A549 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). SRXN1 knockdown led to the decrease the ability of migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eC) and colon formation ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). We have found that SRXN1 knock-down increased the intracellular lipid ROS (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). As the iron overload is a major character of ferroptosis, the levels of cytosolic iron contents were measured in A549 cells and representative images showed iron levels in cytoplasm identified by FerroOrange probes obviously increased in SRXN1 knockdown cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough the number of new cancer cases is high, with the rapid development of chemotherapy or radiotherapy, surgery, targeted therapy, and immunotherapy, the annual number of deaths for many cancers has dropped significantly, but the 5-year survival rate of lung cancer is less than 20% (Zeng et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In our study, the combination analysis of Cox and LASSO regression was applied to establish a prognosis signature. The signature showed good predictive performance, patients in the low-risk group having a better survival. Our studies have identified three prognosis-associated genes: SRXN1, GLS2, SLC2A1. They are potential biomarkers for LUAD, and potential therapeutic targets.\u003c/p\u003e \u003cp\u003eSRXN1 (sulfiredoxin 1) was an antioxidant protein (Li et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It catalyses the reduction of hyperoxidized peroxiredoxins to the reduced form, to thereby restoring their peroxidase activity (Mishra et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al. 2018; Kim et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As a cancer-promoting factor, it has been reported in many literatures. CircABCA13 stabilizes SRXN1 to facilitate esophageal squamous cell carcinoma development through acting as a miR-4429 sponge (Jun et al. 2023). Moreover, several lines of evidence indicate that SRXN1 can stimulates hepatocellular carcinoma tumorigenesis and metastasis through modulating ROS/p65/BTG2 signalling (Lv et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). SRXN1 can aberrant activation of the Wnt/β-Catenin signaling pathway which is promoted cervical cancer metastasis ( Lan et al. 2017). Some studies found that inhibition of SRXN1 suppressed viability and enhanced apoptosis in human lung carcinoma cell line (Jia et al. 2022). So SRXN1 may play an important role in lung carcinoma.\u003c/p\u003e \u003cp\u003eSLC2A1(Solute carrier family 2 member 1) is known as glucose transporter 1 (GLUT1) (Cao et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). SLC2A1 plays a crucial role in the process of cell glycometabolism, whether in cancer or normal cells (Masoud et al. 2015). SLC2A1 is highly expressed in many kinds of cancer, and the overexpression of SLC2A1 can promote the growth and metastasis of cancers, such as liver, lung, endometrial, oral, breast, and gastric cancers (Pereira et al. 2013; Avanzato et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (Berlth et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Goldman et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Smolle et al. 2020).\u003c/p\u003e \u003cp\u003eGLS2 is a mitochondrial phosphate activated glutaminase. Glutamine metabolism is a widely known target for slowing cancer development, while the p53-inducible gene GLS2 was linked to a unique metabolic role in suppressing tumor growth ( Suzuki et al. 2010).\u003c/p\u003e \u003cp\u003ePrevious studies shows that GLS2 expression was decreased in human hepatocellular carcinoma due to hypermethylation, negatively regulating the PI3K/AKT pathway, GLS2 plays a role to suppress of hepatocellular carcinoma (Liu et al. 2014). Similar to previous reports, GLS2 was a favorable factors in lung adenocarcinoma (Tao et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKEGG and GO enrichment analyses indicated that DEGs prone to be enriched in Cell cycle, nuclear division. GSEA result demonstrate that there were significant enrichments in cell cycle and P53 signaling pathway. ICB treatment can bring a lot of clinical benefits, but only one-third of patients respond to treatment (Sharma et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo predict ICB response, TIDE has developed a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. validated that an accurate gene signature to model tumor immune escape could serve as a reliable surrogate biomarker to predict ICB response (Jiang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our tumor immune dysfunction and exclusion analysis demonstrated that the low-risk group will get a better survival through immunotherapy than the high-risk group.\u003c/p\u003e \u003cp\u003eIn the present study, we developed a prognostic model based on prognosis genes and effectively categorized LUAD patients into low and high-risk groups. In addition, immune cell infiltration analysis and functional enrichment analysis demonstrated the correlation between the LUAD model and the immunosuppressive microenvironment. This work will lay a preliminary foundation for further experimental verification.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe developed a prognostic signature that has been shown to be independent, reliable, and may provide some new perceptions into future studies investigating the mechanisms between cuproptosis, ferroptosis-associated genes and LUAD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003eYJZ and XHZ were in charge of designing the research. XZL were responsible for data analysis and manuscript writing. XZL and SZG mainly revised the article. All the authors gave final approval of the published version, and agreed to be responsible for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analysed during the current study are available in the TCGA repository [https://portal.gdc.cancer.gov/repository].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eThe TCGA database is an open database and the information is freely available. Ethics statement is not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209\u0026ndash;249. https://doi.org/10.3322/caac.21660.\u003c/li\u003e\n\u003cli\u003eYang H, Guo Q, Wu J, et al (2022) Deciphering the Effects and Mechanisms of Yi-Fei-San-Jie-pill on Non-Small Cell Lung Cancer With Integrating Network Target Analysis and Experimental Validation. 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Nat Med 24:1550\u0026ndash;8. https://doi.org/10.1038/s41591-018-0136-1.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lung adenocarcinoma, ferroptosis, cuproptosis, gene signature","lastPublishedDoi":"10.21203/rs.3.rs-3192529/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3192529/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCuproptosis and ferroptosis acts important defense for the organism by preventing tumor cells migration and preventing their growth. In this study, cuproptosis and ferroptosis-related genes were used to construct a prognostic model for lung adenocarcinoma (LUAD) patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTCGA database was used to acquire RNA sequencing data and clinical information for LUAD samples. The Cox and LASSO regression analysis were performed to construct the prognostic genes signature. In addition, GSEA, GO, KEGG were performed to investigate the potential molecular mechanism. Moreover, we analyzed the relationship between our identified signature and immune cell infiltration, tumor microenvironment, immunotherapy response, drug sensitivity analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThree prognosis related genes were selected (SRXN1, GLS2, SLC2A1). Finally, in vitro experiments we performed qRT-PCR, western blot, scratch test, colony-formation, lipid ROS analysis to validate the expression and function of SRXN1 gene.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCombined with clinicopathological characteristics, the risk model was validated as a new independent prognostic factor for LUAD.\u003c/p\u003e","manuscriptTitle":"A Bioinformatics-Based Analysis of an Cuproptosis and Ferroptosis-Related Gene Signature Predicts the Prognosis of Patients with lung adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-26 14:39:00","doi":"10.21203/rs.3.rs-3192529/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8053f4e7-a0c8-4e5f-9064-53fb3008a147","owner":[],"postedDate":"July 26th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-09-12T01:44:25+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-26 14:39:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3192529","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3192529","identity":"rs-3192529","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}