Expression profile, prognostic values, and immune infiltration of IRX family members in lung adenocarcinoma 

Expression profile, prognostic values, and immune infiltration of IRX family members in 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 Expression profile, prognostic values, and immune infiltration of IRX family members in lung adenocarcinoma Feng Wang, Xinglu Zhang, Wan Li, Lefei Zhou, Dongbing Li, Dongliang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2769505/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 The iroquois homologous homeobox (IRX) gene family may be involved in the development of a variety of tumors. However, comprehensive analysis of IRX family members in lung adenocarcinoma (LUAD) has rarely been reported. Methods From the Cancer Genome Atlas (TCGA), LUAD samples were extracted. The roles of IRXs were comprehensively analyzed using Kaplan–Meier Plotter, cBioPortal, and R software (version 3.6.3). Results The expression of IRX1/2/3/6 was significantly lower in LUAD compared to normal lung tissue, while the expression of IRX4 was significantly higher in LUAD compared to normal lung tissue. The expression of IRX was associated with T stage, number_pack_years_smoked, N stage, gender, primary treatment outcome, and smokers. In LUAD, IRX2 downregulation was an independent factor that contributes to poor prognosis. Expression of multiple IRX genes showed some diagnostic biomarker values for LUAD. IRX genes were key players mediating the development and progression of LUAD through multiple pathways, including ras signaling pathway, glycosphingolipid biosynthesis-ganglio series, inositol phosphate metabolism, metabolic pathways, and pertussis. There was a significant association between immune infiltration and IRX genes. Conclusions The IRX family may represent novel prognostic biomarkers, as well as immunotherapeutic targets for LUAD. IRX bioinformatics analysis lung adenocarcinoma expression prognosis immune infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Lung cancer remains the leading cause of cancer-related deaths worldwide, accounting for nearly 20% of cancer deaths ( 1 ). Histologically, lung cancer is divided into two major forms: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) ( 2 ). In NSCLC, lung adenocarcinoma (LUAD) is an important clinical type ( 3 ). Despite significant advances in diagnostic methods and clinical treatment of LUAD, it remains a killer of human health with unfavorable outcomes ( 4 ). In addition to chemotherapy, radiotherapy, and targeted therapy, immune checkpoint inhibitors (ICIs) therapy is also used in the treatment of NSLCL ( 5 ). Immune checkpoint proteins such as PD-1/PD-L1 and CTLA-4 are important targets for the treatment of NSCLC ( 6 ). However, only a limited percentage of NSCLC patients respond well to ICI. Therefore, it is necessary to find more meaningful immune-related biomarkers as target drugs and prognostic predictors for patients with LUAD. The iroquois homologous homeobox (IRX) genes encode cardiac transcription factors ( 7 ). The IRX family consists of six members, including IRX1, IRX2, IRX3, IRX4, IRX5, and IRX6 ( 7 ). IRX1 acts as an epigenetically regulated tumor suppressor in different types of cancer ( 8 , 9 ). IRX2 inhibits cell motility and chemokine expression in breast cancer cells ( 10 ). Promotion of IRX3 signaling or inhibition of IRX5 signaling may be a pathway for differentiation therapy in nephroblastoma ( 11 ). Epigenetic inactivation of IRX4 accelerates the growth of human pancreatic cancer cells ( 12 ). IRX5 plays an important role in VSMC G1/S phase cell cycle checkpoint control and apoptosis ( 13 ). It is not completely understood how IRX in LUAD correlates with immune infiltration and its prognostic value. In this study, we investigated the expression, prognosis, and biological functions of IRX family members in LUAD and the correlation between IRX and immune infiltration through a comprehensive analysis of multiple bioinformatics databases. Our study aims to provide useful information for LUAD prognosis and treating. Methods cBioPortal analysis of IRX genes in LUAD cBioPortal is an intuitive Web interface for genetic variation analysis of LUAD (http://www.cbioportal.org/), including amplification, mutation and copy number variation. The cancer type was lung adenocarcinoma. There were 11 selected studies, including lung adenocarcinoma (Broad, Cell 2012), lung adenocarcinoma (CPTAC, Cell 2020), lung adenocarcinoma (MSKCC, 2020), lung adenocarcinoma (MSKCC, 2021), lung adenocarcinoma (MSKCC, Science 2015), lung adenocarcinoma (NPJ Precision Oncology, MSK 2021), lung adenocarcinoma (OncoSG, Nat Genet 2020), lung adenocarcinoma (TCGA, Firehose Legacy), lung adenocarcinoma (TCGA, Nature 2014), lung adenocarcinoma (TCGA, PanCancer Atlas), and lung adenocarcinoma (TSP, Nature 2008). Molecular profile: mutations, structural variants, and copy number alterations; (4) There were 3394 samples in total. The genes were IRX1 [ENSG00000170549], IRX2 [ENSG00000170561], IRX3 [ENSG00000177508], IRX4 [ENSG00000113430], IRX5[ENSG00000176842], and IRX6[ENSG00000159387]. Expression of IRX genes in LUAD RNAseq data were obtained from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Project (GTEx) by Toil process (14). Software was R (version 3.6.3) (statistical analysis and visualization) and R package was mainly ggplot2 [version 3.3.3] (for visualization) were used to analyze the differential expression of IRXs (15). The correlation between each two IRXs was assessed using the Pearson correlation coefficient (15, 16). Statistical analysis and graphs were done with R software (version 3.6.3) (16). P-values below 0.05 were considered as significant correlations (16). Clinical significance of IRX gene expression in TCGA-LUAD R (version 3.6.3) was used to analyze the association of IRX gene expression with clinical features in TCGA-LUAD. The survminer package [version 0.4.9] (for visualization) and survival package [version 3.2-10] were used to analyze the association of IRX gene expression with prognosis in LUAD. Cox regression was used to analyze the effect of variables including IRX1, IRX2, IRX3, IRX4, IRX5, and IRX6 on the survival of LUAD patients. Supplementary data was prognostic data from the reference (17). The expression of IRX genes in LUAD of different clinical stages were obtained from the GEPIA 2 website (http://gepia2.cancer-pku.cn/#index) (18). The pROC package [version 1.17.0.1] (for analysis) and ggplot2 package [version 3.3.3] were used to do ROC curve analysis (15). Molecules were IRX1, IRX2, IRX3, IRX4, IRX5, and IRX6. Clinical variables were tumor and normal. Disease was LUAD. UCSC XENA (https://xenabrowser.net/datapages/) RNAseq data were uniformly processed by the Toil process (14) in TPM format for TCGA and GTEx. Analysis of genes associated with IRX genes R (version 3.6.3) and stat package [version 3.6.3] were used to analyze the correlation analysis for genes associated with IRX genes (15). Molecules were IRX1, IRX2, IRX3, IRX4, IRX5, and IRX6. R (version 3.6.3), ggplot2 package [version 3.3.3], and clusterProfiler package [version 3.14.3] were used to do functional enrichment analysis of genes associated with IRX genes. Correlation between the expression of IRX genes in LUAD and immune cells R (version 3.6.3) and GSVA package [version 1.34.0] were used (19). SsGSEA (built-in algorithm in the GSVA package) was used for immuno-infiltration. Molecules were IRX1, IRX2, IRX3, IRX4, IRX5, and IRX6. Immune cells included 24 cells. The markers for 24 immune cells were obtained from the reference (20). Statistical analysis R (v.3.6.3) was used to perform all statistical analyses. For analyzing the relationship between clinical characteristics and IRX genes, Wilcoxon rank-sum tests, chi-square tests, and Fisher exact tests were used. Statistical significance was defined as a P value less than 0.05. Results IRX gene alterations and mRNA expression in LUAD An analysis of IRX gene alterations in LUAD patients was conducted using the cBioPortal online tool. A range of 1% to 8% of the IRX genes in LUAD were altered ( Fig. 1 ). In Fig. 2 , structural variations, mutations, and CNAs (copy number alterations) were presented from 7 studies. We analyzed IRX gene expression in 535 LUAD tumor tissue samples and 59 normal lung tissue samples ( Fig. 3) . IRX1 expression was significantly lower in LUAD tumor samples than it was in normal lung tissues (1.276±1.447 vs. 3.378±0.639, P<0.001), IRX2 expression was significantly lower in LUAD tumor samples than it was in normal lung tissues (3.801±2.083 vs. 5.823±0.558, P<0.001), IRX3 expression was significantly lower in LUAD tumor samples than it was in normal lung tissues (5.230±1.447 vs. 5.631±1.006, P =0.024), IRX4 expression was significantly higher in LUAD tumor samples than it was in normal lung tissues (0.202±0.696 vs. 0.029±0.097, P<0.001), there was no significant difference in IRX5 expression between LUAD tumor samples and normal lung tissue (3.687±1.475 vs. 3.640±0.792, P=0.335), IRX6 expression was significantly higher in LUAD tumor samples than it was in normal lung tissues (1.436±1.442 vs. 2.363±0.883, P<0.001). Pearson correlation analysis was used to examine the correlation between IRX genes. As shown in Fig. 4 , significant negative correlations were observed between IRX4 and IRX5, significant negative correlations between IRX4 and IRX6, and significant positive correlations between other IRX genes. Relationship between IRX gene expression and clinical characteristics of TCGA-LUAD patients We downloaded data from the TCGA database on 435 LUAD tumor samples in order to obtain clinical characteristics and gene expression data ( Table S1 ). T stage (P=0.023) and number_pack_years_smoked (P=0.02) were associated with IRX1 expression. N stage (P=0.021), gender (P=0.034), and number_pack_years_smoked (P=0.014) were associated with IRX2 expression. Primary therapy outcome (P=0.041) was associated with IRX3 expression. Primary therapy outcome (P=0.012) was associated with IRX5 expression. T stage (P=0.047), primary therapy outcome (P=0.024), and gender (P=0.022), and smoker (P<0.001) were associated with IRX6 expression. However, clinical characteristics were not associated with IRX4 expression. Association of the IRX gene with prognosis and staging in LUAD Three genes (IRX2, IRX3, and IRX5) were found to show significant correlation with the OS of LUAD ( Fig. 5 ). Four genes (IRX2, IRX3, IRX5, and IRX6) were found to be significantly correlated with the PFI of LUAD ( Fig. 5 ). Three genes (IRX2, IRX3, and IRX5) were found to be significantly correlated with the DSS of LUAD ( Fig. 5 ). As shown in Fig. 6 , the expression of IRX1 (P=0.00407), IRX2 (P=0.0229), and IRX4 (P=0.0237) was significantly correlated with staging. This suggests that the IRX genes were associated with the development of LUAD. Univariate and multivariate Cox regression analyses Univariate Cox regression analysis ( Table 1 ) showed that IRX2 (HR: 0.665; 95%CI: 0.497-0.890, P=0.006), IRX3 (HR: 0.682; 95%CI: 0.511-0.912, P=0.010), and IRX5 (HR: 0.500; 95%CI: 0.372-0.672, P<0.001) were correlated with OS in LUAD patients. Multivariate Cox regression analysis ( Table 1 ) showed that IRX2 (HR: 0.730; 95%CI: 0.538-0.990, P=0.043) and IRX5 (HR: 0.479; 95%CI: 0.329-0.699, P<0.001) were independently related to OS in LUAD patients. Table 1 A multivariate and univariate cox regression analysis using IRX genes and other clinical features as predictors and overall survival (OS) of LUAD patients as outcomes. Characteristics Total (N) Univariate analysis Multivariate analysis HR (95% CI) P value HR (95% CI) P value IRX1 (Low vs. High) 526 0.839 (0.629-1.119) 0.233 IRX2 (Low vs. High) 526 0.665 (0.497-0.890) 0.006 0.730 (0.538-0.990) 0.043 IRX3 (Low vs. High) 526 0.682 (0.511-0.912) 0.01 1.180 (0.806-1.726) 0.395 IRX5 (Low vs. High) 526 0.500 (0.372-0.672) <0.001 0.479 (0.329-0.699) <0.001 IRX6 (Low vs. High) 526 0.828 (0.620-1.105) 0.2 Diagnostic value of IRX gene expression in LUAD As shown in Fig. 7 , in predicting tumor and normal outcomes, the variables had some accuracy in predicting (IRX1, AUC=0.880, CI=0.853-0.907; IRX2, AUC=0.792, CI=0.755-0.829; IRX6, AUC=0.752, CI=0.709-0.795), and the variables had low accuracy in predicting (IRX3, AUC=0.589, CI=0.522-0.657; IRX4, AUC=0.622, CI=0.561-0.683; IRX5, AUC=0.538, CI=0.478-0.598). The function of genes associated with IRX genes A single gene co-expression heat map displayed the top 10 positively associated genes for each IRX gene ( Fig. 8 ). Genes most positively associated with IRX1 include NFIX, LRRC36, C5orf38, IRX2, ACOXL, AC012651.1, CACNA2D2, CIRBP, SFTPD, and DUOX1. Genes most positively associated with IRX2 include C5orf38, SFTA3, SUSD2, ADGRF5, FAM189A2, ABCA3, ACOXL, FAM184A, SCNN1B, and C16orf89. Genes most positively associated with IRX3 include IRX5, SELENBP1, C16orf89, SLC22A31, EPHX1, SCNN1B, TMEM125, SFTA3, CACNA2D2, and ST6GALNAC4. Genes most positively associated with IRX5 include IRX3, SFTA3, NKX2-1, C16orf89, SCNN1B, PPP1R13B, SELENBP1, ST3GAL5, SLC22A31, and NAPSA. Genes most positively associated with IRX6 include MFSD2A, HLF, PTRH1, CAPN3, RTN4RL1, KCNJ15, INSYN1, SFTPA1, NRGN, and SYNDIG1L. GO and KEGG enrichment analyses were conducted on 500 genes including the IRX pathway and related genes ( Table S2 ). The top five biological process, including surfactant homeostasis, cellular protein metabolic process, regulation of ion transmembrane transport, phospholipid metabolic process, and multicellular organismal water homeostasis; the top five cell components, including lamellar body, plasma membrane, alveolar lamellar body, intracellular membrane-bounded organelle, and integral component of plasma membrane, and the top five molecular functions, including flavin adenine dinucleotide binding, syntaxin-1 binding, ion channel binding, oxidoreductase activity, acting on the CH-CH group of donors, and voltage-gated potassium channel activity involved in cardiac muscle cell action potential repolarization, are shown in Fig. 9 and Table S3 . The 8 pathways with significant differences, including dopaminergic synapse, ras signaling pathway, glycosphingolipid biosynthesis - ganglio series, amphetamine addiction, inositol phosphate metabolism, metabolic pathways, pertussis, and valine, leucine and isoleucine degradation, were shown in Fig. 10 and Table S3 . Correlation of IRX gene expression and immune cells in LUAD Immune cells in LUAD are significantly correlated with gene expression of the IRX pathway ( Fig.11 ). Some tumor infiltrating immune cells (TIICs), including aDC, B cells, CD8 T cells, Cytotoxic cells, DC, Eosinophils, iDC, Macrophages, Mast cells, Neutrophils, NK CD56bright cells, NK cells, pDC, T cells, Tcm, TFH, Th1 cells, and Th17 cells, showed positive correlation with IRX1 gene expression, while others, including Tgd and Th2 cells, showed negative correlation with IRX1 gene expression. Some TIICs, including CD8 T cells, DC, Eosinophils, iDC, Macrophages, Mast cells, NK cells, pDC, T cells, Tcm, TFH, and Th17 cells, showed positive correlation with IRX2 gene expression, while others, including NK CD56dim cells, Tgd, and Th2 cells, showed negative correlation with IRX2 gene expression. Some TIICs, including CD8 T cells, Eosinophils, Mast cells, NK CD56bright cells, TFH, and Th17 cells, showed positive correlation with IRX3 gene expression, while others, including aDC, Macrophages, Neutrophils, NK CD56dim cells, T cells, T helper cells, Tgd, Th1 cells, Th2 cells, and TReg, showed negative correlation with IRX3 gene expression. Some TIICs, including NK cells, Tgd, and Th2 cells, showed positive correlation with IRX4 gene expression, while others, including Eosinophils, mast cells, T cells, Tcm, and Tem, and TFH, showed negative correlation with IRX4 gene expression. Some TIICs, including Eosinophils, Mast cells, TFH, and Th17 cells, showed positive correlation with IRX5 gene expression, while others, including aDC, B cells, Cytotoxic cells, Macrophages, Neutrophils, NK CD56dim cells, T cells, T helper cells, Tgd, Th1 cells, Th2 cells, and TReg, showed negative correlation with IRX5 gene expression. Some TIICs, including aDC, DC, Eosinophils, iDC, Macrophages, Mast cells, Neutrophils, NK cells, pDC, TFH, Th1 cells, and Th17 cells, showed positive correlation with IRX6 gene expression, while others, including T helper cells, Tgd, and Th2 cells, showed negative correlation with IRX6 gene expression. Discussion LUAD is a malignant disease that is highly complex and heterogeneous in terms of development, progression and response to treatment. The full prognostic significance of patients with LUAD has not been demonstrated by clinical biomarkers to date. To determine the long-term prognosis and therapeutic targets of LUAD lesions, new prognostic biomarkers need to be investigated. IRX1 acts as an epigenetically regulated tumor suppressor in the pathogenesis of lung cancer ( 21 ). Low IRX2 mRNA expression in breast cancer correlates with high tumor grade, positive lymph node status, negative hormone receptor status, and the underlying type of primary breast tumor ( 10 ). IRX4 expression was significantly lower in tumorigenic SCC-9 cells than in non-tumorigenic human OKF6-TERT1R cells ( 22 ). IRX5 overexpressing cells are more likely to form metastatic tumors in nude mice ( 23 ). In this study, the expression of IRX1/2/3/6 was significantly lower in LUAD compared to normal lung tissue, while the expression of IRX4 was significantly higher in LUAD compared to normal lung tissue. T stage and number_pack_years_smoked were associated with IRX1 expression. N stage, gender, and number_pack_years_smoked were associated with IRX2 expression. Primary therapy outcome was associated with IRX3 expression. T stage, primary therapy outcome, gender, and smoker were associated with IRX6 expression. In patients with LUAD, IRX2 (HR: 2.062; 95%CI: 1.015–4.192, P = 0.045) and IRX5 (HR: 0.388; 95%CI: 0.195–0.773, P = 0.007) correlated independently with OS. These confirm that downregulation of IRX2 was an independent factor in the poorer prognosis of LUAD. MiR-646 promotes invasive ductal carcinoma (IDC) tumorigenesis through regulation of the TET1/IRX1/HIST2H2BE axis ( 24 ). Down-regulated microRNA-150 upregulates IRX1 to inhibit proliferation, migration, and invasion, but promotes apoptosis in gastric cancer cells ( 25 ). Protein arginine methyltransferase 5-mediated epigenetic silencing of IRX1 contributes to the tumorigenicity and metastasis of gastric cancer ( 26 ). IRX2 is a target of the FGF8/MAP kinase cascade and is involved in cerebellar formation ( 27 ). 1,25-Dihydroxyvitamin D3 signaling-induced reduction of IRX4 inhibits NANOG-mediated cancer stem cell-like properties and gefitinib resistance in NSCLC cells ( 28 ). IRX4 of 5p15 confers susceptibility to prostate cancer by inhibiting prostate cancer growth through interaction with vitamin D receptors ( 29 ). IRX5 promotes colorectal cancer metastasis by inhibiting the RHOA-ROCK1-LIMK1 axis, which is associated with poor prognosis ( 30 ). IRX5 promotes G1/S phase transition of vascular smooth muscle cells through CDK2-dependent activation ( 13 ). IRX5 is regulated by 1,25-dihydroxyvitamin D3 in human prostate cancer and regulates apoptosis and cell cycle in LNCaP prostate cancer cells ( 31 ). In this study, IRX genes were found to be associated with pathways, including dopaminergic synapse, ras signaling pathway, glycosphingolipid biosynthesis - ganglio series, amphetamine addiction, inositol phosphate metabolism, metabolic pathways, pertussis, and valine, leucine and isoleucine degradation. It is complex how the immune system interacts with cancer ( 32 ). In order to predict clinical outcomes and develop immunotherapies, it is important to assess immune infiltration systematically ( 33 ). Immunomodulatory cells inhibit anti-tumor responses, recognize and eliminate mutated tumor cells, and thus inhibit tumor growth ( 34 ). There was a positive correlation between IRX1/2/3/4/5/6 expression and some TIICs, as well as a negative correlation between some TIICs in this study. There was a significant relationship between IRX1/2/3/4/5/6 and immune infiltration. An integrated approach was used to identify potential biomarkers and alterations of IRX family members in LUAD by integrating information about expression levels, mutations, and immune responses. It may help improve clinical decision-making to better understand the complex impact of IRX family members on LUAD based on these results. Its limitations are that no in vitro or in vivo studies have been carried out to validate the role of IRX family members in LUAD. The role of IRX family members in LUAD should be explored further. Conclusions Clinical features are associated with the expression of IRX family genes. There is an independent relationship between IRX2 downregulation and poorer prognosis in LUAD. Expression of multiple IRX genes showed some diagnostic biomarker values for OC. Immune infiltration and multiple pathways, including dopaminergic synapse, ras signaling pathway, glycosphingolipid biosynthesis-ganglio series, amphetamine addiction, inositol phosphate metabolism, metabolic pathways, pertussis, and valine, leucine and isoleucine degradation, mediated the development and progression of LUAD through IRX genes. There is potential that IRX family members can be used to predict prognosis and treatment response in LUAD. Abbreviations SCLC Small cell lung cancer NSCLC Non-small cell lung cancer LUAD Lung adenocarcinoma ICIs Immune checkpoint inhibitors IRX Iroquois homologous homeobox TCGA The Cancer Genome Atlas GTEx Genotype-Tissue Expression Project CNAs Copy number alterations IDC Invasive ductal carcinoma TIICs Tumor infiltrating immune cells Declarations Acknowledgements The datasets generated in this study are available from TCGA that provide free resources. Author Contributions FW and XLZ designed the research study. WL and LFZ performed the research. DBL and DLW analyzed the data. FW, XLZ, WL, and LFZ wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the Beijing Municipal Science & Technology Commission (No. Z18110000171818). Availability of data and materials The data analyzed during the current study are available in the TCGA database with the accession number TCGA-LUAD. The data used to support the findings of this study are included within the article. Ethics approval and consent to participate Not applicable (TCGA is an open database with ethical permission obtained for patients who participated. The data can be downloaded for free for research and publication. Open-source data is used in our research, so there are no ethical concerns). Consent for publication Not applicable. Competing Interests DBL and DLW are employees of ChosenMed Technology. The remaining authors declare that they have no conflicts of interest. References Hirsch FR, Scagliotti GV, Mulshine JL, Kwon R, Curran WJ, Jr., Wu YL, et al. Lung cancer: current therapies and new targeted treatments. Lancet. 2017;389(10066):299-311. Meng Y, Sun J, Zhang G, Yu T, Piao H. Clinical Prognostic Value of the PLOD Gene Family in Lung Adenocarcinoma. Frontiers in molecular biosciences. 2022;8:770729-. Li Y, Gu J, Xu F, Zhu Q, Chen Y, Ge D, et al. Molecular characterization, biological function, tumor microenvironment association and clinical significance of m6A regulators in lung adenocarcinoma. Brief Bioinform. 2021;22(4). Jacobsen MM, Silverstein SC, Quinn M, Waterston LB, Thomas CA, Benneyan JC, et al. Timeliness of access to lung cancer diagnosis and treatment: A scoping literature review. Lung Cancer. 2017;112:156-64. Zhou F, Qiao M, Zhou C. The cutting-edge progress of immune-checkpoint blockade in lung cancer. Cellular & Molecular Immunology. 2021;18(2):279-93. Perets R, Bar J, Rasco DW, Ahn MJ, Yoh K, Kim DW, et al. Safety and efficacy of quavonlimab, a novel anti-CTLA-4 antibody (MK-1308), in combination with pembrolizumab in first-line advanced non-small-cell lung cancer. Annals of Oncology. 2021;32(3):395-403. Hu W, Xin Y, Zhang L, Hu J, Sun Y, Zhao Y. Iroquois Homeodomain transcription factors in ventricular conduction system and arrhythmia. International Journal of Medical Sciences. 2018;15(8):808-15. Guo X, Liu W, Pan Y, Ni P, Ji J, Guo L, et al. Homeobox gene IRX1 is a tumor suppressor gene in gastric carcinoma. Oncogene. 2010;29(27):3908-20. Jung IH, Jung DE, Chung YY, Kim KS, Park SW. Iroquois Homeobox 1 Acts as a True Tumor Suppressor in Multiple Organs by Regulating Cell Cycle Progression. Neoplasia. 2019;21(10):1003-14. Werner S, Stamm H, Pandjaitan M, Kemming D, Brors B, Pantel K, et al. Iroquois homeobox 2 suppresses cellular motility and chemokine expression in breast cancer cells. BMC Cancer. 2015;15:896. Holmquist Mengelbier L, Lindell-Munther S, Yasui H, Jansson C, Esfandyari J, Karlsson J, et al. The Iroquois homeobox proteins IRX3 and IRX5 have distinct roles in Wilms tumour development and human nephrogenesis. Journal of Pathology. 2019;247(1):86-98. Chakma K, Gu Z, Abudurexiti Y, Hata T, Motoi F, Unno M, et al. Epigenetic inactivation of IRX4 is responsible for acceleration of cell growth in human pancreatic cancer. Cancer Science. 2020;111(12):4594-604. Liu D, Pattabiraman V, Bacanamwo M, Anderson LM. Iroquois homeobox transcription factor (Irx5) promotes G1/S-phase transition in vascular smooth muscle cells by CDK2-dependent activation. American Journal of Physiology Cell Physiology. 2016;311(2):C179-89. Vivian J, Rao AA, Nothaft FA, Ketchum C, Armstrong J, Novak A, et al. Toil enables reproducible, open source, big biomedical data analyses. Nature Biotechnology. 2017;35(4):314-6. Chen B, Gao C, Wang H, Sun J, Han Z. Molecular Analysis of Prognosis and Immune Infiltration of Ovarian Cancer Based on Homeobox D Genes. Computational and Mathematical Methods in Medicine. 2022;2022:3268386. Li Y, Liu Z, Zhang Y. Expression and prognostic impact of FZDs in pancreatic adenocarcinoma. BMC Gastroenterology. 2021;21(1):79. Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173(2):400-16.e11. Li M, Wang X, Liu J, Mao X, Li D, Wang Z, et al. Identification of Core Prognosis-Related Candidate Genes in Chinese Gastric Cancer Population Based on Integrated Bioinformatics. BioMed research international. 2020;2020:8859826-. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39(4):782-95. Küster MM, Schneider MA, Richter AM, Richtmann S, Winter H, Kriegsmann M, et al. Epigenetic Inactivation of the Tumor Suppressor IRX1 Occurs Frequently in Lung Adenocarcinoma and Its Silencing Is Associated with Impaired Prognosis. Cancers. 2020;12(12). Marcinkiewicz KM, Gudas LJ. Altered epigenetic regulation of homeobox genes in human oral squamous cell carcinoma cells. Experimental Cell Research. 2014;320(1):128-43. Zhu Q, Wu Y, Yang M, Wang Z, Zhang H, Jiang X, et al. IRX5 promotes colorectal cancer metastasis by negatively regulating the core components of the RHOA pathway. Molecular Carcinogenesis. 2019;58(11):2065-76. He B, Chen J, Song W, Bai Y. miR-646/TET1 mediated demethylation of IRX1 promoter upregulates HIST2H2BE and promotes the progression of invasive ductal carcinoma. Genomics. 2021;113(3):1469-81. Wu D, Li Z, Zhao S, Yang B, Liu Z. Downregulated microRNA-150 upregulates IRX1 to depress proliferation, migration, and invasion, but boost apoptosis of gastric cancer cells. IUBMB Life. 2020;72(3):476-91. Liu X, Zhang J, Liu L, Jiang Y, Ji J, Yan R, et al. Protein arginine methyltransferase 5-mediated epigenetic silencing of IRX1 contributes to tumorigenicity and metastasis of gastric cancer. Biochim Biophys Acta Mol Basis Dis. 2018;1864(9 Pt B):2835-44. Matsumoto K, Nishihara S, Kamimura M, Shiraishi T, Otoguro T, Uehara M, et al. The prepattern transcription factor Irx2, a target of the FGF8/MAP kinase cascade, is involved in cerebellum formation. Nature Neuroscience. 2004;7(6):605-12. Jia Z, Zhang Y, Yan A, Wang M, Han Q, Wang K, et al. 1,25-dihydroxyvitamin D3 signaling-induced decreases in IRX4 inhibits NANOG-mediated cancer stem-like properties and gefitinib resistance in NSCLC cells. Cell Death & Disease. 2020;11(8):670. Nguyen HH, Takata R, Akamatsu S, Shigemizu D, Tsunoda T, Furihata M, et al. IRX4 at 5p15 suppresses prostate cancer growth through the interaction with vitamin D receptor, conferring prostate cancer susceptibility. Human Molecular Genetics. 2012;21(9):2076-85. Lv Y, Lin W. Comprehensive analysis of the expression, prognosis, and immune infiltrates for CHDs in human lung cancer. Discover Oncology. 2022;13(1):29-. Myrthue A, Rademacher BL, Pittsenbarger J, Kutyba-Brooks B, Gantner M, Qian DZ, et al. The iroquois homeobox gene 5 is regulated by 1,25-dihydroxyvitamin D3 in human prostate cancer and regulates apoptosis and the cell cycle in LNCaP prostate cancer cells. Clinical Cancer Research. 2008;14(11):3562-70. Symmans WF. Interpreting the Complex Landscape of Immune-Tumor Interface. Clinical Cancer Research. 2021;27(20):5446-8. Nebot-Bral L, Brandao D, Verlingue L, Rouleau E, Caron O, Despras E, et al. Hypermutated tumours in the era of immunotherapy: The paradigm of personalised medicine. European Journal of Cancer. 2017;84:290-303. Wang H, Liu Y, Zhu X, Chen C, Fu Z, Wang M, et al. Multistage Cooperative Nanodrug Combined with PD-L1 for Enhancing Antitumor Chemoimmunotherapy. Adv Healthc Mater. 2021;10(21):e2101199. Additional Declarations Competing interest reported. DBL and DLW are employees of ChosenMed Technology. The remaining authors declare that they have no conflicts of interest. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-2769505","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":190900206,"identity":"71a0ca7c-d5dc-47c1-8c4a-557495d00dd0","order_by":0,"name":"Feng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYHACwwcJBjZyBmC2gQVh9TwMDMYGHyrSjA0YmEFaJIjSYiY548zhxA1gLQxEaLFnP7xBmrctLX07e//RDT8KJBj427sT8NvCk1ZgzNtmk7uz5zDbzR6gwyTOnN1AwGE5BslAW3I33Ehmu8ED1GIgkUtAC/8bg8O8bYfTDYBabv4hSotEjmEj0PsJIC23ibPlxrNiBmAgG244c9jstoyBBA9Bv7D3J2//AYxKeYPjjc9uvvljI8ff3otfC6a1pCkfBaNgFIyCUYAVAADuz0dQEEX70gAAAABJRU5ErkJggg==","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Wang","suffix":""},{"id":190900207,"identity":"6913c4b0-608d-405e-b0dc-67ac5dd664c2","order_by":1,"name":"Xinglu Zhang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinglu","middleName":"","lastName":"Zhang","suffix":""},{"id":190900208,"identity":"e6bb2d81-4fef-4fcf-890c-b1eec9a94b97","order_by":2,"name":"Wan Li","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wan","middleName":"","lastName":"Li","suffix":""},{"id":190900209,"identity":"b32b9127-2975-4196-b258-03a8db6e67ba","order_by":3,"name":"Lefei Zhou","email":"","orcid":"","institution":"Ordos Central Hospital, Ordos Clinical Medical College of Inner Mongolia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lefei","middleName":"","lastName":"Zhou","suffix":""},{"id":190900210,"identity":"b5a02e3f-c5c6-4358-8c9a-92d93ad9bb76","order_by":4,"name":"Dongbing Li","email":"","orcid":"","institution":"ChosenMed Technology (Zhejiang) Co., Ltd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongbing","middleName":"","lastName":"Li","suffix":""},{"id":190900211,"identity":"90914919-af1a-4ec2-a38d-67489c749bdf","order_by":5,"name":"Dongliang Wang","email":"","orcid":"","institution":"ChosenMed Technology (Zhejiang) Co., Ltd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongliang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-04-03 04:14:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2769505/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2769505/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":35730895,"identity":"9ab7c80d-dc6a-435d-9605-9cd2f35dd77d","added_by":"auto","created_at":"2023-04-13 19:33:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67965,"visible":true,"origin":"","legend":"\u003cp\u003emRNA expression of IRX genes in LUAD in cBioPortal (RNA Seq V2 RSEM).\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/cc3be6745b8c8b95ecdbe4dd.png"},{"id":35730498,"identity":"fe3fe2c7-6f2a-43a1-a1a7-decb5805fc1e","added_by":"auto","created_at":"2023-04-13 19:25:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29238,"visible":true,"origin":"","legend":"\u003cp\u003eAmount of IRX genes found in cancer types listed in cBioPortal for LUAD cases.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/efbd7d4ab8afc12fa49bcec9.png"},{"id":35729943,"identity":"d43b4e5b-4e9a-4522-baa6-c8160a1ca94f","added_by":"auto","created_at":"2023-04-13 19:17:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11172,"visible":true,"origin":"","legend":"\u003cp\u003eThe mRNA levels of IRX genes between LUAD tissue and unpaired normal lung tissue in TCGA.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/eecf61502f84a128cad3b8d6.png"},{"id":35730892,"identity":"6518cf77-a7ee-4ab4-b681-4213a6f8cd88","added_by":"auto","created_at":"2023-04-13 19:33:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14036,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between every two genes of IRX genes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/172332b5ca5061534139a8e9.png"},{"id":35730501,"identity":"d0787c8f-e205-4801-a501-ad52ef3a86ef","added_by":"auto","created_at":"2023-04-13 19:25:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":148033,"visible":true,"origin":"","legend":"\u003cp\u003eKM plots web tool for survival analysis. OS of (A) IRX1, (D) IRX2, (G) IRX3, (J) IRX5, and (M) IRX6; PFI of (B) IRX1, (E) IRX2, (H) IRX3, (K) IRX5, and (N) IRX6; DSS of (C) IRX1, (F) IRX2, (I) IRX3, (L) IRX5, and (O) IRX6. OS, overall survival; PFI, progression free interval; DSS, disease specific survival.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/ec696625c2f7dcfbd24996bc.png"},{"id":35730506,"identity":"27e40b90-5df5-4c66-a742-0b5b7628e66a","added_by":"auto","created_at":"2023-04-13 19:25:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":83696,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of IRX genes with stage. The relationship between (A) IRX1, (B) IRX2, (C) IRX3, (D) IRX4, (E) IRX5, and (F) IRX6, and stage.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/6ce1d6e23b912e717567f6eb.png"},{"id":35731486,"identity":"d1de0d7e-bd08-4563-9b5b-06011ff4f68e","added_by":"auto","created_at":"2023-04-13 19:41:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":29195,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of IRX genes in LUAD and normal lung tissues. The area under the ROC curve is between 0.5 and 1. The closer the AUC is to 1, the better the diagnosis. the AUC is between 0.5 and 0.7 with low accuracy, the AUC is between 0.7 and 0.9 with some accuracy, and the AUC is above 0.9 with high accuracy.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/84de538af36c976df6529493.png"},{"id":35731747,"identity":"9466a194-25de-446c-99b4-a2ad7c5fb540","added_by":"auto","created_at":"2023-04-13 19:49:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":93869,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap plot of top 10 correlated genes to IRX genes. (A) IRX1, (B) IRX2, (C) IRX3, (D) IRX5, and (E) IRX6.\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/06d98ef2af1cd0e2545f9c9a.png"},{"id":35730896,"identity":"1b58d3a5-1d13-4404-8ceb-cbd288b2b59c","added_by":"auto","created_at":"2023-04-13 19:33:31","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":27771,"visible":true,"origin":"","legend":"\u003cp\u003eGO analysis of genes associated with IRX genes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/cd67dcfea523746293c66519.png"},{"id":35729955,"identity":"561c9b70-af8f-4988-a6a1-bfcce6fb772c","added_by":"auto","created_at":"2023-04-13 19:17:31","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":27191,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG analysis of genes associated with IRX genes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/8adf7e9be03d57b84a24c721.png"},{"id":35729956,"identity":"6b44e7b2-96a6-4457-be06-e74222ff4e9c","added_by":"auto","created_at":"2023-04-13 19:17:31","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":86049,"visible":true,"origin":"","legend":"\u003cp\u003eAn analysis of the correlation between each IRX gene and the 24 TIICs of LUAD (lollipop plot). (A) IRX1, (B) IRX2, (C) IRX3, (D) IRX4, (D) IRX5, and (F) IRX6.\u003c/p\u003e","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/c99c31a016bb46663cf1c743.png"},{"id":39535429,"identity":"07e646dc-ad82-4669-b140-b1250ac08e0f","added_by":"auto","created_at":"2023-07-04 16:44:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2438883,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/feab8226-8e6b-4f46-acf4-a0affa375309.pdf"},{"id":35730499,"identity":"c19032db-227a-4d02-a3e4-8016134e6c48","added_by":"auto","created_at":"2023-04-13 19:25:31","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23061,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/f5e433aff45c99ab81a802db.xlsx"},{"id":35729945,"identity":"e0bd62cf-3fa8-459e-8b22-4d888c29d1b2","added_by":"auto","created_at":"2023-04-13 19:17:31","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14847,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/34dea16d95532ddbc3529d20.xlsx"},{"id":35729951,"identity":"c9da8693-444f-482e-96b1-28cfdde634e8","added_by":"auto","created_at":"2023-04-13 19:17:31","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":45310,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2769505/v1/d07ec01946485abaa2b608ea.xlsx"}],"financialInterests":"Competing interest reported. DBL and DLW are employees of ChosenMed Technology. The remaining authors declare that they have no conflicts of interest.","formattedTitle":"Expression profile, prognostic values, and immune infiltration of IRX family members in lung adenocarcinoma ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related deaths worldwide, accounting for nearly 20% of cancer deaths (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Histologically, lung cancer is divided into two major forms: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In NSCLC, lung adenocarcinoma (LUAD) is an important clinical type (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Despite significant advances in diagnostic methods and clinical treatment of LUAD, it remains a killer of human health with unfavorable outcomes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In addition to chemotherapy, radiotherapy, and targeted therapy, immune checkpoint inhibitors (ICIs) therapy is also used in the treatment of NSLCL (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Immune checkpoint proteins such as PD-1/PD-L1 and CTLA-4 are important targets for the treatment of NSCLC (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, only a limited percentage of NSCLC patients respond well to ICI. Therefore, it is necessary to find more meaningful immune-related biomarkers as target drugs and prognostic predictors for patients with LUAD.\u003c/p\u003e \u003cp\u003eThe iroquois homologous homeobox (IRX) genes encode cardiac transcription factors (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The IRX family consists of six members, including IRX1, IRX2, IRX3, IRX4, IRX5, and IRX6 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). IRX1 acts as an epigenetically regulated tumor suppressor in different types of cancer (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). IRX2 inhibits cell motility and chemokine expression in breast cancer cells (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Promotion of IRX3 signaling or inhibition of IRX5 signaling may be a pathway for differentiation therapy in nephroblastoma (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Epigenetic inactivation of IRX4 accelerates the growth of human pancreatic cancer cells (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). IRX5 plays an important role in VSMC G1/S phase cell cycle checkpoint control and apoptosis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). It is not completely understood how IRX in LUAD correlates with immune infiltration and its prognostic value.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the expression, prognosis, and biological functions of IRX family members in LUAD and the correlation between IRX and immune infiltration through a comprehensive analysis of multiple bioinformatics databases. Our study aims to provide useful information for LUAD prognosis and treating.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ecBioPortal analysis of IRX genes in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ecBioPortal is an intuitive Web interface for genetic variation analysis of LUAD (http://www.cbioportal.org/), including amplification, mutation and copy number variation. The cancer type was lung adenocarcinoma. There were 11 selected studies, including lung adenocarcinoma (Broad, Cell 2012), lung adenocarcinoma (CPTAC, Cell 2020), lung adenocarcinoma (MSKCC, 2020), lung adenocarcinoma (MSKCC, 2021), lung adenocarcinoma (MSKCC, Science 2015), lung adenocarcinoma (NPJ Precision Oncology, MSK 2021), lung adenocarcinoma (OncoSG, Nat Genet 2020), lung adenocarcinoma (TCGA, Firehose Legacy), lung adenocarcinoma (TCGA, Nature 2014), lung adenocarcinoma (TCGA, PanCancer Atlas), and lung adenocarcinoma (TSP, Nature 2008). Molecular profile: mutations, structural variants, and copy number alterations; (4) There were 3394 samples in total. The genes were IRX1 [ENSG00000170549], IRX2 [ENSG00000170561], IRX3 [ENSG00000177508], IRX4 [ENSG00000113430], IRX5[ENSG00000176842], and IRX6[ENSG00000159387]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpression of IRX genes in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNAseq data were obtained from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Project (GTEx) by Toil process\u0026nbsp;(14). Software was R (version 3.6.3) (statistical analysis and visualization) and R package was mainly ggplot2 [version 3.3.3] (for visualization) were used to analyze the differential expression of IRXs\u0026nbsp;(15).\u003c/p\u003e\n\u003cp\u003eThe correlation between each two IRXs was assessed using the Pearson correlation coefficient\u0026nbsp;(15, 16). Statistical analysis and graphs were done with R software (version 3.6.3)\u0026nbsp;(16). P-values below 0.05 were considered as significant correlations\u0026nbsp;(16).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical significance of IRX gene expression in TCGA-LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR (version 3.6.3) was used to analyze the association of IRX gene expression with clinical features in TCGA-LUAD.\u003c/p\u003e\n\u003cp\u003eThe survminer package [version 0.4.9] (for visualization) and survival package [version 3.2-10] were used to analyze the association of IRX gene expression with prognosis in LUAD. Cox regression was used to analyze the effect of variables including IRX1, IRX2, IRX3, IRX4, IRX5, and IRX6 on the survival of LUAD patients. Supplementary data was prognostic data from the reference\u0026nbsp;(17).\u0026nbsp;The expression of IRX genes in LUAD of different clinical stages were obtained from the GEPIA 2 website (http://gepia2.cancer-pku.cn/#index)\u0026nbsp;(18).\u003c/p\u003e\n\u003cp\u003eThe pROC package [version 1.17.0.1] (for analysis) and ggplot2 package [version 3.3.3] were used to do ROC curve analysis (15). Molecules were IRX1, IRX2, IRX3, IRX4, IRX5, and IRX6. Clinical variables were tumor and normal. Disease was LUAD. UCSC XENA (https://xenabrowser.net/datapages/) RNAseq data were uniformly processed by the Toil process (14) in TPM format for TCGA and GTEx. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of genes associated with IRX genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR (version 3.6.3) and stat package [version 3.6.3] were used to analyze the correlation analysis for genes associated with IRX genes (15). Molecules were IRX1, IRX2, IRX3, IRX4, IRX5, and IRX6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eR (version 3.6.3), ggplot2 package [version 3.3.3], and clusterProfiler package [version 3.14.3] were used to do functional enrichment analysis of genes associated with IRX genes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between the expression of IRX genes in LUAD and immune cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR (version 3.6.3) and GSVA package [version 1.34.0] were used (19). SsGSEA (built-in algorithm in the GSVA package) was used for immuno-infiltration. Molecules were IRX1, IRX2, IRX3, IRX4, IRX5, and IRX6. Immune cells included 24 cells. The markers for 24 immune cells were obtained from the reference (20).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR (v.3.6.3) was used to perform all statistical analyses. For analyzing the relationship between clinical characteristics and IRX genes, Wilcoxon rank-sum tests, chi-square tests, and Fisher exact tests were used. Statistical significance was defined as a P value less than 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIRX gene alterations and mRNA expression in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn analysis of IRX gene alterations in LUAD patients was conducted using the cBioPortal online tool. A range of 1% to 8% of the IRX genes in LUAD were altered (\u003cstrong\u003eFig. 1\u003c/strong\u003e). In \u003cstrong\u003eFig. 2\u003c/strong\u003e, structural variations, mutations, and CNAs (copy number alterations) were presented from 7 studies.\u003c/p\u003e\n\u003cp\u003eWe analyzed IRX gene expression in 535 LUAD tumor tissue samples and 59 normal lung tissue samples (\u003cstrong\u003eFig. 3)\u003c/strong\u003e. IRX1 expression was significantly lower in LUAD tumor samples than it was in normal lung tissues (1.276\u0026plusmn;1.447 vs. 3.378\u0026plusmn;0.639, P\u0026lt;0.001), IRX2 expression was significantly lower in LUAD tumor samples than it was in normal lung tissues (3.801\u0026plusmn;2.083 vs. 5.823\u0026plusmn;0.558, P\u0026lt;0.001), IRX3 expression was significantly lower in LUAD tumor samples than it was in normal lung tissues (5.230\u0026plusmn;1.447 vs. 5.631\u0026plusmn;1.006, P =0.024), IRX4 expression was significantly higher in LUAD tumor samples than it was in normal lung tissues (0.202\u0026plusmn;0.696 vs. 0.029\u0026plusmn;0.097, P\u0026lt;0.001), there was no significant difference in IRX5 expression between LUAD tumor samples and normal lung tissue (3.687\u0026plusmn;1.475 vs. 3.640\u0026plusmn;0.792, P=0.335), IRX6 expression was significantly higher in LUAD tumor samples than it was in normal lung tissues (1.436\u0026plusmn;1.442 vs. 2.363\u0026plusmn;0.883, P\u0026lt;0.001). Pearson correlation analysis was used to examine the correlation between IRX genes. As shown in \u003cstrong\u003eFig. 4\u003c/strong\u003e, significant negative correlations were observed between IRX4 and IRX5, significant negative correlations between IRX4 and IRX6, and significant positive correlations between other IRX genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between IRX gene expression and clinical characteristics of TCGA-LUAD patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe downloaded data from the TCGA database on 435 LUAD tumor samples in order to obtain clinical characteristics and gene expression data (\u003cstrong\u003eTable S1\u003c/strong\u003e). T stage (P=0.023) and number_pack_years_smoked (P=0.02) were associated with IRX1 expression. N stage (P=0.021), gender (P=0.034), and number_pack_years_smoked (P=0.014) were associated with IRX2 expression. Primary therapy outcome (P=0.041) was associated with IRX3 expression. Primary therapy outcome (P=0.012) was associated with IRX5 expression. T stage (P=0.047), primary therapy outcome (P=0.024), and gender (P=0.022), and smoker (P\u0026lt;0.001) were associated with IRX6 expression. However, clinical characteristics were not associated with IRX4 expression.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of the IRX gene with prognosis and staging in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree genes (IRX2, IRX3, and IRX5) were found to show significant correlation with the OS of LUAD (\u003cstrong\u003eFig. 5\u003c/strong\u003e). Four genes (IRX2, IRX3, IRX5, and IRX6) were found to be significantly correlated with the PFI of LUAD (\u003cstrong\u003eFig. 5\u003c/strong\u003e). Three genes (IRX2, IRX3, and IRX5) were found to be significantly correlated with the DSS of LUAD (\u003cstrong\u003eFig. 5\u003c/strong\u003e). As shown in \u003cstrong\u003eFig. 6\u003c/strong\u003e, the expression of IRX1 (P=0.00407), IRX2 (P=0.0229), and IRX4 (P=0.0237) was significantly correlated with staging. This suggests that the IRX genes were associated with the development of LUAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate and multivariate Cox regression analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate Cox regression analysis (\u003cstrong\u003eTable 1\u003c/strong\u003e) showed that IRX2 (HR: 0.665; 95%CI: 0.497-0.890, P=0.006), IRX3 (HR: 0.682; 95%CI: 0.511-0.912, P=0.010), and IRX5 (HR: 0.500; 95%CI: 0.372-0.672, P\u0026lt;0.001) were correlated with OS in LUAD patients. Multivariate Cox regression analysis (\u003cstrong\u003eTable 1\u003c/strong\u003e) showed that IRX2 (HR: 0.730; 95%CI: 0.538-0.990, P=0.043) and IRX5 (HR: 0.479; 95%CI: 0.329-0.699, P\u0026lt;0.001) were independently related to OS in LUAD patients. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e A multivariate and univariate cox regression analysis using IRX genes and other clinical features as predictors and overall survival (OS) of LUAD patients as outcomes.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"24.006908462867013%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"11.744386873920552%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"32.12435233160622%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"32.12435233160622%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIRX1 (Low vs. High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.839 (0.629-1.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIRX2 (Low vs. High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.665 (0.497-0.890)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.730 (0.538-0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIRX3 (Low vs. High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.682 (0.511-0.912)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.180 (0.806-1.726)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIRX5 (Low vs. High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.500 (0.372-0.672)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.479 (0.329-0.699)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIRX6 (Low vs. High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.828 (0.620-1.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic value of IRX gene expression in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eFig. 7\u003c/strong\u003e, in predicting tumor and normal outcomes, the variables had some accuracy in predicting (IRX1, AUC=0.880, CI=0.853-0.907; IRX2, AUC=0.792, CI=0.755-0.829; IRX6, AUC=0.752, CI=0.709-0.795), and the variables had low accuracy in predicting (IRX3, AUC=0.589, CI=0.522-0.657; IRX4, AUC=0.622, CI=0.561-0.683; IRX5, AUC=0.538, CI=0.478-0.598).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe function of genes associated with IRX genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA single gene co-expression heat map displayed the top 10 positively associated genes for each IRX gene (\u003cstrong\u003eFig. 8\u003c/strong\u003e). Genes most positively associated with IRX1 include NFIX, LRRC36, C5orf38, IRX2, ACOXL, AC012651.1, CACNA2D2, CIRBP, SFTPD, and DUOX1. Genes most positively associated with IRX2 include C5orf38, SFTA3, SUSD2, ADGRF5, FAM189A2, ABCA3, ACOXL, FAM184A, SCNN1B, and C16orf89. Genes most positively associated with IRX3 include IRX5, SELENBP1, C16orf89, SLC22A31, EPHX1, SCNN1B, TMEM125, SFTA3, CACNA2D2, and ST6GALNAC4. Genes most positively associated with IRX5 include IRX3, SFTA3, NKX2-1, C16orf89, SCNN1B, PPP1R13B, SELENBP1, ST3GAL5, SLC22A31, and NAPSA. Genes most positively associated with IRX6 include MFSD2A, HLF, PTRH1, CAPN3, RTN4RL1, KCNJ15, INSYN1, SFTPA1, NRGN, and SYNDIG1L.\u003c/p\u003e\n\u003cp\u003eGO and KEGG enrichment analyses were conducted on 500 genes including the IRX pathway and related genes (\u003cstrong\u003eTable S2\u003c/strong\u003e). The top five biological process, including surfactant homeostasis, cellular protein metabolic process, regulation of ion transmembrane transport, phospholipid metabolic process, and multicellular organismal water homeostasis; the top five cell components, including lamellar body, plasma membrane, alveolar lamellar body, intracellular membrane-bounded organelle, and integral component of plasma membrane, and the top five molecular functions, including flavin adenine dinucleotide binding, syntaxin-1 binding, ion channel binding, oxidoreductase activity, acting on the CH-CH group of donors, and voltage-gated potassium channel activity involved in cardiac muscle cell action potential repolarization, are shown in \u003cstrong\u003eFig. 9\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Table S3\u003c/strong\u003e. The 8 pathways with significant differences, including dopaminergic synapse, ras signaling pathway, glycosphingolipid biosynthesis - ganglio series, amphetamine addiction, inositol phosphate metabolism, metabolic pathways, pertussis, and valine, leucine and isoleucine degradation, were shown in \u003cstrong\u003eFig. 10\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Table S3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation of IRX gene expression and immune cells in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune cells in LUAD are significantly correlated with gene expression of the IRX pathway (\u003cstrong\u003eFig.11\u003c/strong\u003e). Some tumor infiltrating immune cells (TIICs), including aDC, B cells, CD8 T cells, Cytotoxic cells, DC, Eosinophils, iDC, Macrophages, Mast cells, Neutrophils, NK CD56bright cells, NK cells, pDC, T cells, Tcm, TFH, Th1 cells, and Th17 cells, showed positive correlation with IRX1 gene expression, while others, including Tgd and Th2 cells, showed negative correlation with IRX1 gene expression. Some TIICs, including CD8 T cells, DC, Eosinophils, iDC, Macrophages, Mast cells, NK cells, pDC, T cells, Tcm, TFH, and Th17 cells, showed positive correlation with IRX2 gene expression, while others, including NK CD56dim cells, Tgd, and Th2 cells, showed negative correlation with IRX2 gene expression. Some TIICs, including CD8 T cells, Eosinophils, Mast cells, NK CD56bright cells, TFH, and Th17 cells, showed positive correlation with IRX3 gene expression, while others, including aDC, Macrophages, Neutrophils, NK CD56dim cells, T cells, T helper cells, Tgd, Th1 cells, Th2 cells, and TReg, showed negative correlation with IRX3 gene expression. Some TIICs, including NK cells, Tgd, and Th2 cells, showed positive correlation with IRX4 gene expression, while others, including Eosinophils, mast cells, T cells, Tcm, and Tem, and TFH, showed negative correlation with IRX4 gene expression. Some TIICs, including Eosinophils, Mast cells, TFH, and Th17 cells, showed positive correlation with IRX5 gene expression, while others, including aDC, B cells, Cytotoxic cells, Macrophages, Neutrophils, NK CD56dim cells, T cells, T helper cells, Tgd, Th1 cells, Th2 cells, and TReg, showed negative correlation with IRX5 gene expression. Some TIICs, including aDC, DC, Eosinophils, iDC, Macrophages, Mast cells, Neutrophils, NK cells, pDC, TFH, Th1 cells, and Th17 cells, showed positive correlation with IRX6 gene expression, while others, including T helper cells, Tgd, and Th2 cells, showed negative correlation with IRX6 gene expression.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLUAD is a malignant disease that is highly complex and heterogeneous in terms of development, progression and response to treatment. The full prognostic significance of patients with LUAD has not been demonstrated by clinical biomarkers to date. To determine the long-term prognosis and therapeutic targets of LUAD lesions, new prognostic biomarkers need to be investigated.\u003c/p\u003e \u003cp\u003eIRX1 acts as an epigenetically regulated tumor suppressor in the pathogenesis of lung cancer (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Low IRX2 mRNA expression in breast cancer correlates with high tumor grade, positive lymph node status, negative hormone receptor status, and the underlying type of primary breast tumor (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). IRX4 expression was significantly lower in tumorigenic SCC-9 cells than in non-tumorigenic human OKF6-TERT1R cells (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). IRX5 overexpressing cells are more likely to form metastatic tumors in nude mice (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In this study, the expression of IRX1/2/3/6 was significantly lower in LUAD compared to normal lung tissue, while the expression of IRX4 was significantly higher in LUAD compared to normal lung tissue. T stage and number_pack_years_smoked were associated with IRX1 expression. N stage, gender, and number_pack_years_smoked were associated with IRX2 expression. Primary therapy outcome was associated with IRX3 expression. T stage, primary therapy outcome, gender, and smoker were associated with IRX6 expression. In patients with LUAD, IRX2 (HR: 2.062; 95%CI: 1.015\u0026ndash;4.192, P\u0026thinsp;=\u0026thinsp;0.045) and IRX5 (HR: 0.388; 95%CI: 0.195\u0026ndash;0.773, P\u0026thinsp;=\u0026thinsp;0.007) correlated independently with OS. These confirm that downregulation of IRX2 was an independent factor in the poorer prognosis of LUAD.\u003c/p\u003e \u003cp\u003eMiR-646 promotes invasive ductal carcinoma (IDC) tumorigenesis through regulation of the TET1/IRX1/HIST2H2BE axis (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Down-regulated microRNA-150 upregulates IRX1 to inhibit proliferation, migration, and invasion, but promotes apoptosis in gastric cancer cells (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Protein arginine methyltransferase 5-mediated epigenetic silencing of IRX1 contributes to the tumorigenicity and metastasis of gastric cancer (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). IRX2 is a target of the FGF8/MAP kinase cascade and is involved in cerebellar formation (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). 1,25-Dihydroxyvitamin D3 signaling-induced reduction of IRX4 inhibits NANOG-mediated cancer stem cell-like properties and gefitinib resistance in NSCLC cells (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). IRX4 of 5p15 confers susceptibility to prostate cancer by inhibiting prostate cancer growth through interaction with vitamin D receptors (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). IRX5 promotes colorectal cancer metastasis by inhibiting the RHOA-ROCK1-LIMK1 axis, which is associated with poor prognosis (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). IRX5 promotes G1/S phase transition of vascular smooth muscle cells through CDK2-dependent activation (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). IRX5 is regulated by 1,25-dihydroxyvitamin D3 in human prostate cancer and regulates apoptosis and cell cycle in LNCaP prostate cancer cells (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In this study, IRX genes were found to be associated with pathways, including dopaminergic synapse, ras signaling pathway, glycosphingolipid biosynthesis - ganglio series, amphetamine addiction, inositol phosphate metabolism, metabolic pathways, pertussis, and valine, leucine and isoleucine degradation.\u003c/p\u003e \u003cp\u003eIt is complex how the immune system interacts with cancer (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In order to predict clinical outcomes and develop immunotherapies, it is important to assess immune infiltration systematically (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Immunomodulatory cells inhibit anti-tumor responses, recognize and eliminate mutated tumor cells, and thus inhibit tumor growth (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). There was a positive correlation between IRX1/2/3/4/5/6 expression and some TIICs, as well as a negative correlation between some TIICs in this study. There was a significant relationship between IRX1/2/3/4/5/6 and immune infiltration.\u003c/p\u003e \u003cp\u003eAn integrated approach was used to identify potential biomarkers and alterations of IRX family members in LUAD by integrating information about expression levels, mutations, and immune responses. It may help improve clinical decision-making to better understand the complex impact of IRX family members on LUAD based on these results. Its limitations are that no in vitro or in vivo studies have been carried out to validate the role of IRX family members in LUAD. The role of IRX family members in LUAD should be explored further.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eClinical features are associated with the expression of IRX family genes. There is an independent relationship between IRX2 downregulation and poorer prognosis in LUAD. Expression of multiple IRX genes showed some diagnostic biomarker values for OC. Immune infiltration and multiple pathways, including dopaminergic synapse, ras signaling pathway, glycosphingolipid biosynthesis-ganglio series, amphetamine addiction, inositol phosphate metabolism, metabolic pathways, pertussis, and valine, leucine and isoleucine degradation, mediated the development and progression of LUAD through IRX genes. There is potential that IRX family members can be used to predict prognosis and treatment response in LUAD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSCLC \u0026nbsp; \u0026nbsp; Small cell lung cancer\u003c/p\u003e\n\u003cp\u003eNSCLC \u0026nbsp; \u0026nbsp;Non-small cell lung cancer\u003c/p\u003e\n\u003cp\u003eLUAD \u0026nbsp; \u0026nbsp; \u0026nbsp;Lung adenocarcinoma\u003c/p\u003e\n\u003cp\u003eICIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Immune checkpoint inhibitors\u003c/p\u003e\n\u003cp\u003eIRX \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Iroquois homologous homeobox\u003c/p\u003e\n\u003cp\u003eTCGA \u0026nbsp; \u0026nbsp; \u0026nbsp;The Cancer Genome Atlas\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGTEx \u0026nbsp; \u0026nbsp; \u0026nbsp; Genotype-Tissue Expression Project\u003c/p\u003e\n\u003cp\u003eCNAs \u0026nbsp; \u0026nbsp; \u0026nbsp; Copy number alterations\u003c/p\u003e\n\u003cp\u003eIDC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Invasive ductal carcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTIICs \u0026nbsp; \u0026nbsp; \u0026nbsp; Tumor infiltrating immune cells\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated in this study are available from TCGA that provide free resources.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eFW and XLZ\u0026nbsp;designed the research study. WL and LFZ performed the research. DBL and DLW analyzed the data. FW, XLZ, WL, and LFZ wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Beijing Municipal Science \u0026amp; Technology Commission (No. Z18110000171818).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed during the current study are available in the TCGA database with the accession number\u0026nbsp;TCGA-LUAD. The data used to support the findings of this study are included within the article.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable (TCGA is an open database with ethical permission obtained for patients who participated. The data can be downloaded for free for research and publication. Open-source data is used in our research, so there are no ethical concerns).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eDBL and DLW are employees of ChosenMed Technology. The remaining authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHirsch FR, Scagliotti GV, Mulshine JL, Kwon R, Curran WJ, Jr., Wu YL, et al. Lung cancer: current therapies and new targeted treatments. Lancet. 2017;389(10066):299-311.\u003c/li\u003e\n\u003cli\u003eMeng Y, Sun J, Zhang G, Yu T, Piao H. Clinical Prognostic Value of the PLOD Gene Family in Lung Adenocarcinoma. Frontiers in molecular biosciences. 2022;8:770729-.\u003c/li\u003e\n\u003cli\u003eLi Y, Gu J, Xu F, Zhu Q, Chen Y, Ge D, et al. Molecular characterization, biological function, tumor microenvironment association and clinical significance of m6A regulators in lung adenocarcinoma. Brief Bioinform. 2021;22(4).\u003c/li\u003e\n\u003cli\u003eJacobsen MM, Silverstein SC, Quinn M, Waterston LB, Thomas CA, Benneyan JC, et al. Timeliness of access to lung cancer diagnosis and treatment: A scoping literature review. Lung Cancer. 2017;112:156-64.\u003c/li\u003e\n\u003cli\u003eZhou F, Qiao M, Zhou C. The cutting-edge progress of immune-checkpoint blockade in lung cancer. Cellular \u0026amp; Molecular Immunology. 2021;18(2):279-93.\u003c/li\u003e\n\u003cli\u003ePerets R, Bar J, Rasco DW, Ahn MJ, Yoh K, Kim DW, et al. Safety and efficacy of quavonlimab, a novel anti-CTLA-4 antibody (MK-1308), in combination with pembrolizumab in first-line advanced non-small-cell lung cancer. Annals of Oncology. 2021;32(3):395-403.\u003c/li\u003e\n\u003cli\u003eHu W, Xin Y, Zhang L, Hu J, Sun Y, Zhao Y. Iroquois Homeodomain transcription factors in ventricular conduction system and arrhythmia. International Journal of Medical Sciences. 2018;15(8):808-15.\u003c/li\u003e\n\u003cli\u003eGuo X, Liu W, Pan Y, Ni P, Ji J, Guo L, et al. Homeobox gene IRX1 is a tumor suppressor gene in gastric carcinoma. Oncogene. 2010;29(27):3908-20.\u003c/li\u003e\n\u003cli\u003eJung IH, Jung DE, Chung YY, Kim KS, Park SW. Iroquois Homeobox 1 Acts as a True Tumor Suppressor in Multiple Organs by Regulating Cell Cycle Progression. Neoplasia. 2019;21(10):1003-14.\u003c/li\u003e\n\u003cli\u003eWerner S, Stamm H, Pandjaitan M, Kemming D, Brors B, Pantel K, et al. Iroquois homeobox 2 suppresses cellular motility and chemokine expression in breast cancer cells. BMC Cancer. 2015;15:896.\u003c/li\u003e\n\u003cli\u003eHolmquist Mengelbier L, Lindell-Munther S, Yasui H, Jansson C, Esfandyari J, Karlsson J, et al. The Iroquois homeobox proteins IRX3 and IRX5 have distinct roles in Wilms tumour development and human nephrogenesis. Journal of Pathology. 2019;247(1):86-98.\u003c/li\u003e\n\u003cli\u003eChakma K, Gu Z, Abudurexiti Y, Hata T, Motoi F, Unno M, et al. Epigenetic inactivation of IRX4 is responsible for acceleration of cell growth in human pancreatic cancer. Cancer Science. 2020;111(12):4594-604.\u003c/li\u003e\n\u003cli\u003eLiu D, Pattabiraman V, Bacanamwo M, Anderson LM. Iroquois homeobox transcription factor (Irx5) promotes G1/S-phase transition in vascular smooth muscle cells by CDK2-dependent activation. American Journal of Physiology Cell Physiology. 2016;311(2):C179-89.\u003c/li\u003e\n\u003cli\u003eVivian J, Rao AA, Nothaft FA, Ketchum C, Armstrong J, Novak A, et al. Toil enables reproducible, open source, big biomedical data analyses. Nature Biotechnology. 2017;35(4):314-6.\u003c/li\u003e\n\u003cli\u003eChen B, Gao C, Wang H, Sun J, Han Z. Molecular Analysis of Prognosis and Immune Infiltration of Ovarian Cancer Based on Homeobox D Genes. Computational and Mathematical Methods in Medicine. 2022;2022:3268386.\u003c/li\u003e\n\u003cli\u003eLi Y, Liu Z, Zhang Y. Expression and prognostic impact of FZDs in pancreatic adenocarcinoma. BMC Gastroenterology. 2021;21(1):79.\u003c/li\u003e\n\u003cli\u003eLiu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173(2):400-16.e11.\u003c/li\u003e\n\u003cli\u003eLi M, Wang X, Liu J, Mao X, Li D, Wang Z, et al. Identification of Core Prognosis-Related Candidate Genes in Chinese Gastric Cancer Population Based on Integrated Bioinformatics. BioMed research international. 2020;2020:8859826-.\u003c/li\u003e\n\u003cli\u003eH\u0026auml;nzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.\u003c/li\u003e\n\u003cli\u003eBindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39(4):782-95.\u003c/li\u003e\n\u003cli\u003eK\u0026uuml;ster MM, Schneider MA, Richter AM, Richtmann S, Winter H, Kriegsmann M, et al. Epigenetic Inactivation of the Tumor Suppressor IRX1 Occurs Frequently in Lung Adenocarcinoma and Its Silencing Is Associated with Impaired Prognosis. Cancers. 2020;12(12).\u003c/li\u003e\n\u003cli\u003eMarcinkiewicz KM, Gudas LJ. Altered epigenetic regulation of homeobox genes in human oral squamous cell carcinoma cells. Experimental Cell Research. 2014;320(1):128-43.\u003c/li\u003e\n\u003cli\u003eZhu Q, Wu Y, Yang M, Wang Z, Zhang H, Jiang X, et al. IRX5 promotes colorectal cancer metastasis by negatively regulating the core components of the RHOA pathway. Molecular Carcinogenesis. 2019;58(11):2065-76.\u003c/li\u003e\n\u003cli\u003eHe B, Chen J, Song W, Bai Y. miR-646/TET1 mediated demethylation of IRX1 promoter upregulates HIST2H2BE and promotes the progression of invasive ductal carcinoma. Genomics. 2021;113(3):1469-81.\u003c/li\u003e\n\u003cli\u003eWu D, Li Z, Zhao S, Yang B, Liu Z. Downregulated microRNA-150 upregulates IRX1 to depress proliferation, migration, and invasion, but boost apoptosis of gastric cancer cells. IUBMB Life. 2020;72(3):476-91.\u003c/li\u003e\n\u003cli\u003eLiu X, Zhang J, Liu L, Jiang Y, Ji J, Yan R, et al. Protein arginine methyltransferase 5-mediated epigenetic silencing of IRX1 contributes to tumorigenicity and metastasis of gastric cancer. Biochim Biophys Acta Mol Basis Dis. 2018;1864(9 Pt B):2835-44.\u003c/li\u003e\n\u003cli\u003eMatsumoto K, Nishihara S, Kamimura M, Shiraishi T, Otoguro T, Uehara M, et al. The prepattern transcription factor Irx2, a target of the FGF8/MAP kinase cascade, is involved in cerebellum formation. Nature Neuroscience. 2004;7(6):605-12.\u003c/li\u003e\n\u003cli\u003eJia Z, Zhang Y, Yan A, Wang M, Han Q, Wang K, et al. 1,25-dihydroxyvitamin D3 signaling-induced decreases in IRX4 inhibits NANOG-mediated cancer stem-like properties and gefitinib resistance in NSCLC cells. Cell Death \u0026amp; Disease. 2020;11(8):670.\u003c/li\u003e\n\u003cli\u003eNguyen HH, Takata R, Akamatsu S, Shigemizu D, Tsunoda T, Furihata M, et al. IRX4 at 5p15 suppresses prostate cancer growth through the interaction with vitamin D receptor, conferring prostate cancer susceptibility. Human Molecular Genetics. 2012;21(9):2076-85.\u003c/li\u003e\n\u003cli\u003eLv Y, Lin W. Comprehensive analysis of the expression, prognosis, and immune infiltrates for CHDs in human lung cancer. Discover Oncology. 2022;13(1):29-.\u003c/li\u003e\n\u003cli\u003eMyrthue A, Rademacher BL, Pittsenbarger J, Kutyba-Brooks B, Gantner M, Qian DZ, et al. The iroquois homeobox gene 5 is regulated by 1,25-dihydroxyvitamin D3 in human prostate cancer and regulates apoptosis and the cell cycle in LNCaP prostate cancer cells. Clinical Cancer Research. 2008;14(11):3562-70.\u003c/li\u003e\n\u003cli\u003eSymmans WF. Interpreting the Complex Landscape of Immune-Tumor Interface. Clinical Cancer Research. 2021;27(20):5446-8.\u003c/li\u003e\n\u003cli\u003eNebot-Bral L, Brandao D, Verlingue L, Rouleau E, Caron O, Despras E, et al. Hypermutated tumours in the era of immunotherapy: The paradigm of personalised medicine. European Journal of Cancer. 2017;84:290-303.\u003c/li\u003e\n\u003cli\u003eWang H, Liu Y, Zhu X, Chen C, Fu Z, Wang M, et al. Multistage Cooperative Nanodrug Combined with PD-L1 for Enhancing Antitumor Chemoimmunotherapy. Adv Healthc Mater. 2021;10(21):e2101199.\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":"IRX, bioinformatics analysis, lung adenocarcinoma, expression, prognosis, immune infiltration","lastPublishedDoi":"10.21203/rs.3.rs-2769505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2769505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe iroquois homologous homeobox (IRX) gene family may be involved in the development of a variety of tumors. However, comprehensive analysis of IRX family members in lung adenocarcinoma (LUAD) has rarely been reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the Cancer Genome Atlas (TCGA), LUAD samples were extracted. The roles of IRXs were comprehensively analyzed using Kaplan–Meier Plotter, cBioPortal, and R software (version 3.6.3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe expression of IRX1/2/3/6 was significantly lower in LUAD compared to normal lung tissue, while the expression of IRX4 was significantly higher in LUAD compared to normal lung tissue. The expression of IRX was associated with T stage, number_pack_years_smoked, N stage, gender, primary treatment outcome, and smokers. In LUAD, IRX2 downregulation was an independent factor that contributes to poor prognosis. Expression of multiple IRX genes showed some diagnostic biomarker values for LUAD. IRX genes were key players mediating the development and progression of LUAD through multiple pathways, including ras signaling pathway, glycosphingolipid biosynthesis-ganglio series, inositol phosphate metabolism, metabolic pathways, and pertussis. There was a significant association between immune infiltration and IRX genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe IRX family may represent novel prognostic biomarkers, as well as immunotherapeutic targets for LUAD.\u003c/p\u003e","manuscriptTitle":"Expression profile, prognostic values, and immune infiltration of IRX family members in lung adenocarcinoma ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-13 19:17:26","doi":"10.21203/rs.3.rs-2769505/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":"f101e440-89b3-4767-9181-cf3e8fe319be","owner":[],"postedDate":"April 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-07-04T16:44:26+00:00","versionOfRecord":[],"versionCreatedAt":"2023-04-13 19:17:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2769505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2769505","identity":"rs-2769505","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}