Exploring the role and mechanism of rheumatoid arthritis-related pathway in squamous lung cancer based on Mendelian randomization analysis

Exploring the role and mechanism of rheumatoid arthritis-related pathway in squamous lung cancer based on Mendelian randomization analysis | 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 Exploring the role and mechanism of rheumatoid arthritis-related pathway in squamous lung cancer based on Mendelian randomization analysis Jiaxing Dai, Hong Huang, Huanghui Zhang, Bing Yang, Dongxin Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4143609/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 Objective： Rheumatoid arthritis (RA) is a chronic, autoimmune inflammatory joint disease. Squamous cell carcinoma of the lung (LUSC) is a malignant tumor of non-small cell lung cancer. Studies have shown a complex relationship between rheumatoid arthritis and lung cancer. This study aimed to investigate the role and mechanism of rheumatoid arthritis-related pathways in lung squamous carcinoma using bioinformatics methods combined with Mendelian randomization analysis. Methods: Download RA gene expression profile data set GSE1919 from GEO database, screen differential genes using GEO 2 R tool, and download lung gene expression profile data from TCGA database, and screen differential genes by Rstudio software. GO / KEGG functional enrichment analysis yielded RA signaling pathway genes. After downloading RA signaling pathway SNP data as exposure factor and lung SNP data for squamous cell carcinoma as outcome factor, we used two-sample Mendelian randomization analysis to determine the causal relationship between RA signaling pathway and lung squamous cell carcinoma. In addition, gene-drug regulatory network, ceRNA regulatory network, immune cell infiltration analysis and validation group difference analysis were constructed. Results: We found 188 genes codifferentially expressed between RA and LUSC. Results of GO / KEGG functional enrichment analysis showed that these differential genes were mainly enriched in the rheumatoid arthritis signaling pathway. The results of Mendelian randomization analysis showed that enhanced activity of RA signaling pathway was associated with a reduced risk of lung squamous carcinoma. Conclusion: The study found that the enhanced activity of rheumatoid arthritis signaling pathway may be associated with the reduced risk of breast cancer, which provides new ideas and vision for studying the mechanism and treatment of lung squamous cell carcinoma. Mendelian randomization rheumatoid arthritis lung squamous cell carcinoma pathway genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Rheumatoid arthritis (RA) is a chronic, progressive autoimmune disease, which usually presents with joint swelling, pain, stiffness and dysfunction, and seriously affects patients' quality of life [1]. Lung squamous cell carcinoma (LUSC) is a common subtype of non-small cell lung cancer (NSCLC), which accounts for about 25% of the global lung cancer incidence, originates from the epithelial cells of the lungs and is named after its squamous cell appearance [2]. Several studies have suggested that there is a relationship between rheumatoid arthritis and cancer, and that the incidence of certain types of cancer may be slightly increased in people with rheumatoid arthritis [3]. Therefore, by investigating the roles and mechanisms of rheumatoid arthritis signaling-related signaling pathways in squamous lung cancer, it may be possible to unearth the potential pathogenic mechanisms of squamous lung cancer. As an emerging biostatistical method, Mendelian randomization analysis (MRA) is a method to infer whether there is a causal link between exposure and disease by exploiting the property that the effects of genetic variation on phenotype and exposure are random [4]. In recent years, bioinformatics technology combined with Mendelian randomization analysis is becoming a new research hotspot, which can be combined with gene expression data and genetic variation data to explore the mechanism of disease occurrence and development from different perspectives. In the present study, bioinformatics methods were used to screen the common differentially expressed genes associated with LUSC in the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets for RA. After mining the signaling pathways enriched by the genes, the causal relationship between the pathways and lung squamous carcinoma was then judged with the help of Mendelian randomization analysis, which can more effectively explore the role and mechanism of rheumatoid arthritis pathways in lung squamous carcinoma. As a result, it was found that 11 rheumatoid arthritis signaling pathway genes, such as MMP1, MMP3, and TNFSF11, may be closely related to the occurrence and development of LUSC, and these genes are expected to be new biological markers and potential molecular targets for LUSC. In addition, targeted drugs can be predicted through these findings, opening up new directions for molecular mechanism studies and drug development in LUSC(Figure 1). 1. Materials and methods 1. Materials and methods 1.1 Data acquisition The GEO database (https://www.ncbi.nlm.nih.gov/geo/) was searched to obtain rheumatoid arthritis (RA) microarray data (GSE1919), which included five rheumatoid arthritis samples and five normal samples; and the TCGAbiolinnks package of the Rstudio software was used to download gene expression profile data and clinical information of 547 samples of lung squamous carcinoma (LUSC) from the TCGA database (https://www.ncbi.nlm.nih.gov/geo/). TCGAbiolinnks" package of Rstudio software was used to download the gene expression profiles and clinical information of 547 samples of lung squamous carcinoma (LUSC) from the TCGA database (https://portal.gdc.cancer.gov/), which included gene expression data of 496 squamous carcinoma samples and 51 paraneoplastic tissue samples. expression data of 496 cases of lung squamous carcinoma tissues and 51 cases of paraneoplastic tissues. 1.2 Screening of differentially expressed genes Using GEO2R, an official analysis tool of GEO, the normal group was compared with the rheumatoid arthritis group by setting the threshold values of p-value1 as the screening conditions to screen the differentially expressed genes. After the gene expression profile data of lung squamous carcinoma were downloaded, the gene expression profile data were normalized and normalized by DEseq2 and limma packages in Rstudio software, and the differential genes were screened with the same p-value1 as the screening conditions, and the volcano diagrams of the differential genes in the rheumatoid arthritis and lung squamous carcinoma groups were drawn using the ggplot2 package, as shown in the figure. volcano plots of differential genes, as shown in Figure 2A-B. Finally, we took the intersection of rheumatoid arthritis and lung squamous carcinoma differential genes using the online Wayne diagram website (http://www.bioinformatics.com.cn/static/others/jvenn/example.html), as shown in Figure 2C. 1.3 Functional enrichment analysis Annotation of co-expressed differential genes using Gene Ontology (GO) analysis is a commonly used method in bioinformatics analysis. This analysis(Figure 3) covers annotations in terms of Biological Process (BP), Cell Component (CC) and Molecular Function (MF). In addition, the Kyoto Encyclopedia of Gene and Genomes (KEGG) database was used to perform pathway enrichment analyses to identify important signaling pathways enriched by co-expression of differential genes. These analyses we accomplished by using the ClusterProfiler, enrichplot, and org.Hs.eg.db packages in Rstudio software. For GO/KEGG functional enrichment analysis, p-value values and adjp-value values equal to 0.05 were used as thresholds. Finally, the corresponding functional enrichment results were plotted using the ggplot2, circlize, RColorBrewer, dplyr, and ComplexHeatmap packages in the Rstudio software, as shown in Figure 4. 1.4 Mendelian randomization analysis In this study, a two-sample MR design was used to explore the causal link between the rheumatoid arthritis signaling pathway as exposure data and squamous lung cancer as outcome data.Genetic data for the RA signaling pathway were obtained from pooled data from independent European populations in the IEUopenGWAS (https://gwas.mrcieu.ac.uk/) database. This dataset contains 80,799 samples, of which 19,234 are CD patients and 61,565 are controls. A total of 9,739,304 SNP (single nucleotide polymorphism) single nucleotide polymorphisms were included in this dataset. The data related to squamous lung cancer were also derived from pooled data from independent European populations in the IEUopenGWAS database. This dataset contains 62,467 samples, including 7704 patients with squamous lung cancer and 54,763 controls. A total of 10,341,529 SNPs were included in this dataset.The SNP screening condition for the RA signaling pathway was to set a p-value threshold of 5×10-8 for screening, and a total of 49 SNPs were screened as instrumental variables (IVs), and 49 SNPs were included for subsequent analyses after exclusion of chain imbalance and deletion of alleles. SNPs for subsequent analysis. MR analysis was performed using the TwoSampleMR package in Rstudio software, including MR Egger, Weighted median, Inverse variance weighted, Simple mode and Weighted mode, to determine the causal relationship between RA and LUSC, as shown in Fig. 5. 1.5 Gene-Drug Networks DGIdb Database (Drug Gene Interaction Database) is a database for drug-gene interaction information, which provides detailed information about drug-gene interactions. According to the KEGG pathway enrichment results, 11 pathway genes in the rheumatoid arthritis signaling pathway were selected and entered into the DGIdb database (https://dgidb.genome.wustl.edu/) and then the antitumor drug files corresponding to the genes were downloaded, and the software perl was used to obtain the gene-drug regulatory network files, which were eventually used to Cytoscape3.10.1 software was used to draw the gene-drug regulatory network map, as shown in Figure 6. 1.6 ceRNA network construction The miRNA related data were obtained by using miRanda database, miRDB database, miRWalk database, TargetScan database, and predicted miRNAs bound by 11 pathway genes by perl software, and lncRNA data were obtained by spongeScan database, and miRNA binding was constructed by perl software. lncRNA, and finally construct ceRNA regulatory network map by Cytoscape3.10.1 software, as shown in Figure 7. 1.7 Immune infiltration analysis Through the "CIBERSORT" package in Rstudio software, 11 rheumatoid arthritis signaling pathway genes were selected for immune infiltration analysis, and the percentage of the expression of these 11 genes in each immune cell was analyzed, as shown in Figure 8. 1.8 Validation analysis Based on the gene expression values of the 11 rheumatoid arthritis signaling pathway genes screened in lung squamous carcinoma in the TCGA database, and the lung squamous carcinoma data in TCGA were divided into lung squamous carcinoma group and normal group for T-test, the box-and-line diagram was plotted by using the Rstudio software, as shown in Figure 9. 2. Results 2.1. results of differential gene analysis Rheumatoid arthritis microarray data (GSE1919) were screened by the tool GEO2R in the GEO database, and a total of 389 differential genes for rheumatoid arthritis were obtained by screening differential genes with the screening criteria of p-value1. With the same screening criteria, 7200 differential genes were obtained by screening the expression data profiles of lung squamous carcinoma in the TCGA database. Subsequently, 188 rheumatoid arthritis and lung squamous coexpressed genes were obtained by taking coexpressed genes. 2.2 Results of functional enrichment analysis For the 188 rheumatoid arthritis and lung squamous co-expressed differential genes, we performed GO/KEGG functional enrichment analysis using Rstudio software.The GO annotation results included three parts: biological process, molecular function and cellular component. The results showed that the co-expressed differential genes were mainly enriched in biological process (BP) for positive regulation of cell adhesion, positive regulation of cell activation, response to corticosteroids, etc.; in cellular component (CC) for outer plasma membrane, collagen-containing extracellular matrix, and ruffled membrane, etc.; and in molecular function (MF) for chemokine activity, chemokine receptor binding, and cytokine receptor activity, etc. In the KEGG functional enrichment results, we found that the differential genes were mainly involved in the rheumatoid arthritis signaling pathway, etc., which mainly included 11 co-expressed genes, MMP1, MMP3, TNFSF11, IL23A, HLA-DMA, HLA-DOA, ITGAL, ITGB2, HLA-DMB, CXCL6, CD86. 2.3 Results of Mendelian randomization analysis In the IEUopenGWAS database, there were a total of 59 instrumental variables related to squamous lung cancer. After excluding continuous imbalance and deleting incompatible alleles, 49 instrumental variables related to lung squamous carcinoma were finally selected for analysis. Five MR analysis methods were performed in the study using the TwoSampleMR package in Rstudio software. The results showed that MR Egger (pvalue=0.7908), Weighted median (pvalue=0.1123), Inverse variance weighted (pvalue=0.0363), Simple mode (pvalue=0.7599) and Weighted mode (pvalue=0.4893). Based on a p-value of less than 0.05 for the inverse variance weighted method, we concluded a positive result and suggested a causal relationship between rheumatoid arthritis pathway and squamous lung cancer. The P values for heterogeneity of Mendelian randomization results for rheumatoid arthritis and squamous lung cancer calculated by the inverse variance weighting method and the MR Egger method were 0.0258 and 0.0195, respectively, indicating heterogeneity. Horizontal multiplicity test using Egger intercept method had a P value of 0.1299 indicating that the pathways by which the instrumental variables affected the outcome were not significant except for exposure. Results of sensitivity analysis by leave-one-out method indicated stability. 2.4 Gene-drug network The 11 rheumatoid arthritis pathway genes obtained were entered into https://dgidb.genome.wustl.edu/得到药物文件, and the gene-drug regulatory network file was obtained by perl software. The target drugs corresponding to genes MMP1, MMP3, TNFSF11, IL23A, ITGB2, IL23A, ITGB2, and CD86 were obtained respectively, as shown in Table 1. Table 1: Gene-Drug Table: Possible targeted drugs for the genes MMP 1, MMP 3, TNFSF11, IL 23 A, ITGB 2, IL 23 A, ITGB 2, and CD86 gene targeted drug MMP1 DOXYCYCLINE CALCIUM、APRATASTAT、DOXYCYCLINE、DOXYCYCLINE HYCLATE、CIPEMASTAT、MARIMASTAT、PRINOMASTAT、LEUPROLIDE ACETATE、SIROLIMUS、COLLAGENASE CLOSTRIDIUM HISTOLYTICUM、MEDROXYPROGESTERONE ACETATE、LAMIVUDINE、LEFLUNOMIDE、HYDROCORTISONE、PENTOSAN POLYSULFATE SODIUM、TRIAMCINOLONE、RIBAVIRIN MMP3 MARIMASTAT、PRINOMASTAT、BERKELEYAMIDE C、BERKELEYDIONE、LISINOPRIL、BERKELEYAMIDE B、BERKELEYACETAL A、PRAVASTATIN、CHLORTHALIDONE、BERKELEYTRIONE、BERKELEYACETAL B、BERKELEYACETAL C IL23A BRIAKINUMAB、TILDRAKIZUMAB、USTEKINUMAB、GUSELKUMAB、BRAZIKUMAB、RISANKIZUMAB ITGAL LIFITEGRAST、EFALIZUMAB、ROVELIZUMAB、ODULIMOMAB、CYCLOSPORINE、CASEARINOL、CYCLOPHOSPHAMIDE、STAUROSPORINE、THROMBIN、CASEARINONE A、MYCOPHENOLATE、 MOFETIL、EPOETIN ALFA、CASEARINONE B、BUSULFAN、SIROLIMUS、ETOPOSIDE、FLUOROURACIL ITGB2 MLNM-2201、ROVELIZUMAB、EFALIZUMAB、LIFITEGRAST、ERLIZUMAB、AME-133V、SODIUM CHLORIDE、MYCOPHENOLATE MOFETIL、PREDNISONE、THALIDOMIDE、ANTIBIOTIC、METHYLPREDNISOLONE、INDOMETHACIN、CYCLOSPORINE、CYCLOPHOSPHAMIDE、PENTOXIFYLLINE、BUTEIN、COLCHICINE、ALCOHOL CD86 ABATACEPT、BELATACEPT、PAMIDRONIC ACID、ROXITHROMYCIN、DEXAMETHASONE、TERFENADINE、INDOMETHACIN TNFSF11 DENOSUMAB、LENALIDOMIDE、ANASTROZOLE、LETROZOLE 2.5 ceRNA network construction By using miRanda database, miRDB database, miRWalk database, TargetScan database to get miRNA related data, by perl to predict the pathway gene binding miRNA, by spongeScan database to get lncRNA data, by perl to construct the miRNA-binding lncRNAs to construct ceRNA networks. Among them, there were 58 mRNAs bound to 11 rheumatoid arthritis pathways, and a total of 124 lncRNAs bound to miRNAs. Finally, Cytoscape software was utilized to map out the ceRNA regulatory network. 2.6 Immune infiltration analysis To further confirm the correlation between the expression of the 11 genes and the immune microenvironment, we analyzed the proportion of tumor-infiltrating immune subpopulations using the CIBERSORT algorithm and constructed immune cell maps for 22 LUSC samples, immune cells such as Neutrophils, Eosinophils, Mast cells activated, Mast cells resting, etc. The graph shows that there is a positive correlation between the pathway gene CXCL6 and immune cells Mast cells resting, and the correlation between the two is the largest by the width of the line, as shown in Figure 8. 2.7 Validation analysis We went through 11 important significant genes (MMP1, MMP3, TNFSF11, IL23A, HLA-DMA, HLA-DOA, ITGAL, ITGB2, HLA-DMB, CXCL6, CD86). Then, the LUSC clinical sample data were categorized into two groups according to the TCGA dataset: the normal group and the lung squamous carcinoma group. We observed the expression of these 11 genes in the two groups. The results showed that the expression of these 11 genes was significantly different in both the normal group and the lung squamous carcinoma carcinoma. 3. Discussion Lung cancer is the leading cause of cancer-related deaths worldwide, and squamous cell carcinoma is one of the major histologic subtypes of lung cancer.Detecting lung cancer at an early stage is crucial for successful treatment and improved survival.However, there are still no satisfactory biomarkers for the early detection of lung cancer and squamous cell carcinoma is the second most common histologic subtype of lung cancer after adenocarcinoma of the lung, which accounts for approximately 30% of all non-small-cell lung cancers, and the onset of the disease is mostly associated with smoking It is closely related to smoking, and due to the challenging diagnosis, many patients with squamous lung cancer are diagnosed in the middle to late stages [5][6]. We may be able to reveal the potential pathogenesis of squamous lung cancer through the rheumatoid arthritis-related signaling pathway, and the KEGG enrichment results showed that the common differential genes between rheumatoid arthritis and squamous lung cancer were mainly enriched in the signaling pathway of rheumatoid arthritis and other signaling pathways. Through Mendelian randomization analysis, we found a positive causal relationship between rheumatoid arthritis signaling pathway and lung squamous carcinoma. Accordingly, we focused on the mechanism and role of rheumatoid arthritis signaling pathway in lung squamous carcinoma, which includes 11 pathway genes, such as MMP1 (matrix metalloproteinase 1), TNFSF11 (tumor necrosis factor superfamily member 11), IL23A (interleukin 23), HLA-DMA (major histocompatibility antigen complex IIDMα), ITGB2 ( int-egrin β2 subunit), CXCL6 (CXC motif chemokine ligand 6), and others. MMP1 (Matrix metalloproteinase 1) and MMP3 (Matrix metalloproteinase 3) belong to the isoforms of matrix metalloproteinases (MMPs), and more than 25 different members of MMPs have been identified to date [7]. Several studies have shown that MMP1 plays an important role in cancer progression, and abnormally high expression of MMP1 is strongly associated with poor prognosis in many cancers [8] [9]. Previous studies have shown that ETV4 (ETS variant 4) can activate the expression of MMP genes, and MMPs are key mediators of cancer progression [10,11,12]. It has been demonstrated that MMP1 is the most critical downstream target of ETV4, and knockdown of MMP1 significantly inhibited ETV4-induced cell proliferation and migration, and MMP1-positive expression was associated with poor prognosis in NSCLC (non-small cell lung cancer) patients, suggesting the importance of MMP1 in the tumorigenic activity of ETV4 [12].TNFSF11 (RANKL) belongs to the TNF cytokine superfamily, a type II transmembrane protein with an extracellular structural domain at the carboxyl terminus [13] [14]. This extracellular structural domain is cleaved by enzymes such as matrix metalloproteinases and released into the extracellular milieu as soluble TNFSF11 (RANKL).Expression of TNFSF11 is commonly found in the cells of lung cancer patients and correlates with a poor prognosis.Activation of the RANKL/RANK pathway regulates the expansion of stem cell-like cells in lung cancer through a mechanism that is dependent on mitochondrial respiration [15]. HLA-DM is a heterodimeric molecule important for normal antigen presentation, which is encoded by the adjacent sites HLA-DMA and HLA-DMB located between the DP and DQ subregions.In normal antigen-presenting cells, DM is present in the intracellular lumen compartment but not on the cell surface; it either facilitates the binding of antigenic peptides to classical class II molecules [16]. It has been shown that enrichment of HLA-DMA in cancer RNAseq expression profiles involves activation of antigen processing and presentation pathways [17]. As a peptide processor, HLA-DMA catalyzes peptide conversion on classical MHCII proteins and protects empty MHCII-like molecules from functional inactivation upon efficient presentation of protein antigens, HLA-DMA and is involved in cancer progression and drug resistance [18-20].IL-23 is part of the broader IL-6/IL-12 superfamily, sharing a receptor subunit with IL-12, IL- 12 1 sharing a receptor subunit. This binds to the new IL-23R subunit, completing the IL-23 receptor [21][22]. In cancer, IL-23 is involved in all three stages of immune editing [23-25], is elevated in the cancer microenvironment [26-28], and correlates with poor prognosis [29],it also promotes tumor growth and progression. It was found that IL-23 expression in NSCLC can be dynamically regulated by chemotherapeutic agents and epigenetic treatments, and can induce proliferation of NSCLC cell lines [30]. Integrin 2 (ITGB2), also known as CD18, is one of the subunits of integrins and was first observed on the surface of leukocytes. Several studies have shown that ITGB2 functions to promote leukocyte adhesion and extravasation [31,32]. ITGB2 was found to promote oral squamous cell carcinoma proliferation indirectly by regulating cancer-associated fibroblasts [33] and to mediate yap-induced breast cancer cell invasion across the endothelium [34]. These findings suggest that ITGB2 plays an important role in tumor development. Studies have demonstrated that overexpression of ITGB2 can increase its expression level In H1792 cells, ITGB2 can reduce the expression levels of N - cadherin, Vimentin, Slug, Snail and Twist, suggesting that ITGB2 can affect the growth of NSCLC cells by inhibiting the EMT pathway [35]. 4. Conclusion In this study, we linked the rheumatoid arthritis-related pathway to squamous lung cancer to explore its role and mechanism in squamous lung cancer. Specifically, gene expression data, Mendelian randomization, and other bioinformatics analyses were used in this study to validate the possibility that the rheumatoid arthritis-related pathway plays an important role in lung squamous carcinoma. This study combined different bioinformatics analysis methods on lung squamous carcinoma samples, including gene expression analysis, functional enrichment analysis, pathway analysis, two-sample Mendelian randomization analysis, etc., and comprehensively explored the role of rheumatoid arthritis-related pathways in lung squamous carcinoma from different levels. Revealing the role of rheumatoid arthritis signaling pathway in lung squamous carcinoma: This study reveals that increased activity of rheumatoid arthritis pathway pathway may affect the reduced risk of lung squamous carcinoma development through comprehensive analysis. The results of this study may provide a new theoretical basis for the treatment of squamous lung cancer, i.e., the use of rheumatoid arthritis-related pathways to regulate or inhibit the occurrence and development of squamous lung cancer. In conclusion, the present study provides new ideas and methods for us to better understand the mechanism of lung squamous carcinoma occurrence and development, or provides a new theoretical basis for the treatment of this disease. Declarations 1. Ethics approval and consent to participate The is not applicable to our study. 2. Consent for publication All the authors of this paper have given their consent for publication. 3. Availability of data and materials The datasets analysed during the current study are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov) databases. The datasets analysed during the current study are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov) databases. 4. Competing interests The authors declare that they have no potential conflict of interests. 5. Funding Guizhou Provincial Higher Education Traditional Chinese and Medicine Ethnic Medicine Cancer Prevention and Treatment Medical Transformation Engineering Research Center（No. Qian Jiaoji [2023]037）; Talent Base of Traditional Chinese Medicine Tumor Inheritance and Scientific and Technological Innovation in Guizhou Province (Guizhou Renjianzhiefa [2018] No. 3); Talent Team of Traditional Chinese Medicine Tumor Inheritance and Scientific and Technological Innovation in Guizhou Province (Guizhou Science and Technology Cooperative Talent [2020] No. 5013); Study on Mechanisms of Bone Cancer Pain Intervention by Bufo Spirit Cream through Regulation of Research on the mechanism of JNK pathway intervention in bone cancer pain by regulating the "intestinal flora" of toad spirit cream (Guizhou Science and Technology Cooperation Academic New Seedling ([2023]-03)). Authors' contributions Jiaxing Dai completed the first draft and processed the data; Hong Huang and Huanghui Zhang are responsible for the modification of the picture drawing; Bing Yang were responsible for the language proofreading of the article; Dongxin Tang is responsible for the overall framework of the article as well as the ideas. References Smolen JS, Aletaha D, Barton A, Burmester GR, et al. Rheumatoid arthritis. Nat Rev Dis Primers. 2018 Feb 8;4:18001. Zappa C, Mousa SA. Non-small cell lung cancer: current treatment and future advances. Transl Lung Cancer Res. 2016 Jun;5(3):288-300. Raaschou P, Simard JF, Holmqvist M, et al. Rheumatoid arthritis, anti-tumour necrosis factor therapy, and risk of malignant melanoma: nationwide population based prospective cohort study from Sweden. BMJ. 2013 Apr 8;346:f1939. Evans DM, Davey Smith G. Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality. Annu Rev Genomics Hum Genet. 2015;16:327-50. Li B, Chang J, Chu Y, et al. Membrane proteomic analysis comparing squamous cell lung cancer tissue and tumour-adjacent normal tissue. Cancer Lett. 2012 Jun 1;319(1):118-24. Lau SCM, Pan Y, Velcheti V, et al. Squamous cell lung cancer: Current landscape and future therapeutic options. Cancer Cell. 2022 Nov 14;40(11):1279-1293. Liu M, Hu Y, Zhang MF, et al. MMP1 promotes tumor growth and metastasis in esophageal squamous cell carcinoma. Cancer Lett. 2016 Jul 10;377(1):97-104. Ozden F, Saygin C, Uzunaslan D, et al. Expression of MMP-1, MMP-9 and TIMP-2 in prostate carcinoma and their influence on prognosis and survival. J Cancer Res Clin Oncol. 2013 Aug;139(8):1373-82. Cai QW, Li J, Li XQ, et al. Expression of STAT3, MMP-1 and TIMP-1 in gastric cancer and correlation with pathological features. Mol Med Rep. 2012 Jun;5(6):1438-42. Dumortier M, Ladam F, Damour I, et al. ETV4 transcription factor and MMP13 metalloprotease are interplaying actors of breast tumorigenesis. Breast Cancer Res. 2018 Jul 11;20(1):73. Kim E, Kim D, Lee JS, et al.Capicua suppresses hepatocellular carcinoma progression by controlling the ETV4-MMP1 axis. Hepatology. 2018 Jun;67(6):2287-2301. Fung TM, Ng KY, Tong M, et al. Neuropilin-2 promotes tumourigenicity and metastasis in oesophageal squamous cell carcinoma through ERK-MAPK-ETV4-MMP-E-cadherin deregulation. J Pathol. 2016 Jul;239(3):309-19. Nakashima T, Hayashi M, Takayanagi H. New insights into osteoclastogenic signaling mechanisms. Trends Endocrinol Metab. 2012 Nov;23(11):582-90. Okamoto K, Nakashima T, Shinohara M, et al.Osteoimmunology: The Conceptual Framework Unifying the Immune and Skeletal Systems. Physiol Rev. 2017 Oct 1;97(4):1295-1349. Ono T, Hayashi M, Sasaki F, et al. RANKL biology: bone metabolism, the immune system, and beyond. Inflamm Regen. 2020 Feb 7;40:2. Moxley G, Han J. HLA DMA and DMB show no association with rheumatoid arthritis in US Caucasians. Eur J Immunogenet. 2001 Oct;28(5):539-43. Hang X, Li D, Wang J, Wang G. Prognostic significance of microsatellite instability‑associated pathways and genes in gastric cancer. Int J Mol Med. 2018 Jul;42(1):149-160. Alvaro-Benito M, Morrison E, Wieczorek M, et al. Human leukocyte Antigen-DM polymorphisms in autoimmune diseases. Open Biol. 2016 Aug;6(8):160165. Vogt AB, Arndt SO, Hämmerling GJ, et al. Quality control of MHC class II associated peptides by HLA-DM/H2-M. Semin Immunol. 1999 Dec;11(6):391-403. Kobayashi S, Yamada-Okabe H, Suzuki M, et al.LGR5-positive colon cancer stem cells interconvert with drug-resistant LGR5-negative cells and are capable of tumor reconstitution. Stem Cells. 2012 Dec;30(12):2631-44. Oppmann B, Lesley R, Blom B, et al. Novel p19 protein engages IL-12p40 to form a cytokine, IL-23, with biological activities similar as well as distinct from IL-12. Immunity. 2000 Nov;13(5):715-25. Parham C, Chirica M, Timans J, et al. A receptor for the heterodimeric cytokine IL-23 is composed of IL-12Rbeta1 and a novel cytokine receptor subunit, IL-23R. J Immunol. 2002 Jun 1;168(11):5699-708. Parham C, Chirica M, Timans J, et al. A receptor for the heterodimeric cytokine IL-23 is composed of IL-12Rbeta1 and a novel cytokine receptor subunit, IL-23R. J Immunol. 2002 Jun 1;168(11):5699-708. Langowski JL, Zhang X, Wu L, et al. IL-23 promotes tumour incidence and growth. Nature. 2006 Jul 27;442(7101):461-5. Langowski JL, Kastelein RA, Oft M. Swords into plowshares: IL-23 repurposes tumor immune surveillance. Trends Immunol. 2007 May;28(5):207-12. doi: 10.1016/j.it.2007.03.006. Epub 2007 Mar 28. PMID: 17395538. Fukuda M, Ehara M, Suzuki S, et al. IL-23 promotes growth and proliferation in human squamous cell carcinoma of the oral cavity. Int J Oncol. 2010 Jun;36(6):1355-65. Lan F, Zhang L, Wu J, et al. IL-23/IL-23R: potential mediator of intestinal tumor progression from adenomatous polyps to colorectal carcinoma. Int J Colorectal Dis. 2011 Dec;26(12):1511-8. Malvicini M, Rizzo M, Alaniz L, et al. A novel synergistic combination of cyclophosphamide and gene transfer of interleukin-12 eradicates colorectal carcinoma in mice. Clin Cancer Res. 2009 Dec 1;15(23):7256-65. Nguyen GH, Schetter AJ, Chou DB, et al. Inflammatory and microRNA gene expression as prognostic classifier of Barrett's-associated esophageal adenocarcinoma. Clin Cancer Res. 2010 Dec 1;16(23):5824-34. Baird AM, Leonard J, Naicker KM, et al. IL-23 is pro-proliferative, epigenetically regulated and modulated by chemotherapy in non-small cell lung cancer. Lung Cancer. 2013 Jan;79(1):83-90. Lee, S.H.; Corry, D.B. Homing alone? CD18 in infectious and allergic disease. Trends Mol. Med. 2004, 10, 258–262. Tan, S.M. The leucocyte beta2 (CD18) integrins: The structure, functional regulation and signalling properties. Biosci. Rep. 2012,32, 241–269. Zhang, X.; Dong, Y.; Zhao, M.; et al. ITGB2-mediated metabolic switch in CAFs promotes OSCC proliferation by oxidation of NADH in mitochondrial oxidative phosphorylation system. Theranostics 2020,10, 12044–12059. Liu, H.; Dai, X.; Cao, X.; et al. PRDM4 mediates YAP-induced cell invasion by activating leukocyte-specific integrin beta2 expression. EMBO Rep. 2018, 19, e45180. Zu L, He J, Zhou N, et al. The Profile and Clinical Significance of ITGB2 Expression in Non-Small-Cell Lung Cancer. J Clin Med. 2022 Oct 29;11(21):6421. Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4143609","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282917522,"identity":"5056c15d-2399-4ad0-94e0-c536936e9f4f","order_by":0,"name":"Jiaxing Dai","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiaxing","middleName":"","lastName":"Dai","suffix":""},{"id":282917523,"identity":"7dca04b4-2ace-4031-86f2-e988295a8001","order_by":1,"name":"Hong Huang","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Huang","suffix":""},{"id":282917526,"identity":"a4dc1dcc-b53d-496b-835d-4a0ce619e7a8","order_by":2,"name":"Huanghui Zhang","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Huanghui","middleName":"","lastName":"Zhang","suffix":""},{"id":282917527,"identity":"23eeaf72-0756-4926-aa8b-e8b35dd4b725","order_by":3,"name":"Bing Yang","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Yang","suffix":""},{"id":282917530,"identity":"1b3b2ff6-d89a-4f6b-9bd0-5eed1d1188fb","order_by":4,"name":"Dongxin Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYLACHhDBzMD4gMGAWNVQLcwGJGphYGCTIMpN9uxnD794U3HHbsNx3mOVPwruyDOwHz66Aa8tPHlplnPOPEue2cyXdpvH4JlhA09a2g38DssxM+ZtO5zMz8xjdpvB4DBjgwSPGX4t/G8gWtiAWgp/GBy2J6xFIsf4MVCLHcgWBh6Dw4mEtdx4Y8Y458zhBMlmHmNpoJbkNkJ+Ye/PMf7wpuKwvcH5M4Yff/w5bNvPfvgYXi0M0OhIbIBzCSgHAeYPQMKeCIWjYBSMglEwUgEAHKVFxIBKPZIAAAAASUVORK5CYII=","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Dongxin","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-03-21 12:48:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4143609/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4143609/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53627622,"identity":"7ea4fef4-2619-4ce8-bd75-84919b075556","added_by":"auto","created_at":"2024-03-28 09:13:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":432698,"visible":true,"origin":"","legend":"\u003cp\u003eThe process of this paper studies\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143609/v1/83e99126acff831d60da0397.jpg"},{"id":53627626,"identity":"ad250806-34ed-4db4-a585-afc5705b5dbc","added_by":"auto","created_at":"2024-03-28 09:13:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":396379,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis and intersection genes between RA and LUSC:\u003c/p\u003e\n\u003cp\u003eA for differential analysis of rheumatoid arthritis; B for differential analysis of lung squamous cell carcinoma; C Venn diagram of common differences in rheumatoid arthritis and lung scales\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143609/v1/e287ac17a29c2ff42c6db0a4.jpg"},{"id":53627623,"identity":"7270ce8d-8282-4bb2-b6fc-75028c446e44","added_by":"auto","created_at":"2024-03-28 09:13:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":493970,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology (GO) pathway enrichment map:\u003c/p\u003e\n\u003cp\u003eA represents the bar chart of GO pathway enrichment; B represents the bubble chart of GO pathway enrichment; C represents the loop chart of GO pathway enrichment\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143609/v1/e0f1b6a28a0ca66a73ba250a.jpg"},{"id":53627629,"identity":"48a7815e-cb2d-462a-82e6-ecd9424267a8","added_by":"auto","created_at":"2024-03-28 09:13:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":293178,"visible":true,"origin":"","legend":"\u003cp\u003ePathway enrichment diagram of differential genes Kyoto Encyclopedia of Gene and Genomes (KEGG):\u003c/p\u003e\n\u003cp\u003eA represents the bar chart of KEGG pathway enrichment; B shows the pathway enrichment bubble diagram\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143609/v1/bd7e540c43131e261592345b.jpg"},{"id":53628381,"identity":"5f4b1b11-3f21-4de4-8f85-c5369c58712f","added_by":"auto","created_at":"2024-03-28 09:21:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":339801,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization analysis of lung squamous cell carcinoma:\u003c/p\u003e\n\u003cp\u003eA is the leave-one sensitivity analysis indicating which or more SNP has disproportionate impact on the overall MR results; B is the scatter plot indicating whether the effect of SNP affects SNP on LUSC risk; C is the forest plot red line indicating the overall estimate using different MR methods (such as inverse variance weighting method)); D is the funnel plot indicating the effect estimate of each SNP and its accuracy\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143609/v1/82b0e7ba5ae7342a5220a790.jpg"},{"id":53627627,"identity":"3887cefd-2278-4f60-bd41-6e36c6f2245d","added_by":"auto","created_at":"2024-03-28 09:13:52","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":407630,"visible":true,"origin":"","legend":"\u003cp\u003eGene-drug regulatory network diagram:\u003c/p\u003e\n\u003cp\u003eGreen circles indicate genes, pink ellipses indicate drugs, and connecting lines indicate a relationship between a specific gene and a drug\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143609/v1/11aab66d1bb522ab5d3424de.jpg"},{"id":53627630,"identity":"9930a412-7150-43e8-b35c-b3fc9415bcca","added_by":"auto","created_at":"2024-03-28 09:13:52","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":442371,"visible":true,"origin":"","legend":"\u003cp\u003eceRNA regulatory network analysis:\u003c/p\u003e\n\u003cp\u003eYellow circles represent pathway genes, pink nodes represent lncRNA regulating gene expression, and green nodes represent human mRNA\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143609/v1/4feb98d628a9182d0dc01e9a.jpg"},{"id":53627635,"identity":"fa36beba-371c-47ee-b80d-dc89c7a8e4a6","added_by":"auto","created_at":"2024-03-28 09:13:53","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":551458,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of immune cell infiltration :\u003c/p\u003e\n\u003cp\u003eboth horizontal and vertical coordinates represent the name of immune cells, the depth of color represents the intensity of correlation, blue represents negative correlation, red represents positive correlation, lines represent the correlation between specific cells and specific genes or proteins, red represents positive correlation, green represents negative correlation, and width represents the absolute value of correlation coefficient\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143609/v1/471b2ca46ea113d8953f68de.jpg"},{"id":53627624,"identity":"e3cdab4a-0040-4cfc-be24-8aadd5fa0f5c","added_by":"auto","created_at":"2024-03-28 09:13:52","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":960108,"visible":true,"origin":"","legend":"\u003cp\u003eValidation group difference analysis:\u003c/p\u003e\n\u003cp\u003ethe ordinate indicates gene expression, the horizontal coordinate indicates the gene name, the blue represents the normal group, the red represents the tumor sample, which indicates that the control and experimental groups, three stars represent p \u0026lt;0.001, two stars represent p \u0026lt;0.01, and one star represents p \u0026lt;0.05\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4143609/v1/ae6c4d78bda6fa713f323092.jpg"},{"id":55265574,"identity":"86eb4858-fcaa-41a4-95dc-4a49ca511226","added_by":"auto","created_at":"2024-04-25 02:08:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1348013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4143609/v1/096a3b64-3220-4979-85ce-8984e3aba545.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the role and mechanism of rheumatoid arthritis-related pathway in squamous lung cancer based on Mendelian randomization analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic, progressive autoimmune disease, which usually presents with joint swelling, pain, stiffness and dysfunction, and seriously affects patients\u0026apos; quality of life [1]. Lung squamous cell carcinoma (LUSC) is a common subtype of non-small cell lung cancer (NSCLC), which accounts for about 25% of the global lung cancer incidence, originates from the epithelial cells of the lungs and is named after its squamous cell appearance [2]. Several studies have suggested that there is a relationship between rheumatoid arthritis and cancer, and that the incidence of certain types of cancer may be slightly increased in people with rheumatoid arthritis [3]. Therefore, by investigating the roles and mechanisms of rheumatoid arthritis signaling-related signaling pathways in squamous lung cancer, it may be possible to unearth the potential pathogenic mechanisms of squamous lung cancer. As an emerging biostatistical method, Mendelian randomization analysis (MRA) is a method to infer whether there is a causal link between exposure and disease by exploiting the property that the effects of genetic variation on phenotype and exposure are random [4]. In recent years, bioinformatics technology combined with Mendelian randomization analysis is becoming a new research hotspot, which can be combined with gene expression data and genetic variation data to explore the mechanism of disease occurrence and development from different perspectives. In the present study, bioinformatics methods were used to screen the common differentially expressed genes associated with LUSC in the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets for RA. After mining the signaling pathways enriched by the genes, the causal relationship between the pathways and lung squamous carcinoma was then judged with the help of Mendelian randomization analysis, which can more effectively explore the role and mechanism of rheumatoid arthritis pathways in lung squamous carcinoma. As a result, it was found that 11 rheumatoid arthritis signaling pathway genes, such as MMP1, MMP3, and TNFSF11, may be closely related to the occurrence and development of LUSC, and these genes are expected to be new biological markers and potential molecular targets for LUSC. In addition, targeted drugs can be predicted through these findings, opening up new directions for molecular mechanism studies and drug development in LUSC(Figure 1).\u003c/p\u003e"},{"header":"1. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e1. Materials and methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.1 Data acquisition\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe GEO database (https://www.ncbi.nlm.nih.gov/geo/) was searched to obtain rheumatoid arthritis (RA) microarray data (GSE1919), which included five rheumatoid arthritis samples and five normal samples; and the TCGAbiolinnks package of the Rstudio software was used to download gene expression profile data and clinical information of 547 samples of lung squamous carcinoma (LUSC) from the TCGA database (https://www.ncbi.nlm.nih.gov/geo/). TCGAbiolinnks\u0026quot; package of Rstudio software was used to download the gene expression profiles and clinical information of 547 samples of lung squamous carcinoma (LUSC) from the TCGA database (https://portal.gdc.cancer.gov/), which included gene expression data of 496 squamous carcinoma samples and 51 paraneoplastic tissue samples. expression data of 496 cases of lung squamous carcinoma tissues and 51 cases of paraneoplastic tissues.\u003c/p\u003e\n\u003cp\u003e1.2 Screening of differentially expressed genes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing GEO2R, an official analysis tool of GEO, the normal group was compared with the rheumatoid arthritis group by setting the threshold values of p-value\u0026lt;0.05 and logFC\u0026gt;1 as the screening conditions to screen the differentially expressed genes. After the gene expression profile data of lung squamous carcinoma were downloaded, the gene expression profile data were normalized and normalized by DEseq2 and limma packages in Rstudio software, and the differential genes were screened with the same p-value\u0026lt;0.05,logFC\u0026gt;1 as the screening conditions, and the volcano diagrams of the differential genes in the rheumatoid arthritis and lung squamous carcinoma groups were drawn using the ggplot2 package, as shown in the figure. volcano plots of differential genes, as shown in Figure 2A-B. Finally, we took the intersection of rheumatoid arthritis and lung squamous carcinoma differential genes using the online Wayne diagram website \u0026nbsp; \u0026nbsp;(http://www.bioinformatics.com.cn/static/others/jvenn/example.html), \u0026nbsp;as shown in Figure 2C.\u003c/p\u003e\n\u003cp\u003e1.3 Functional enrichment analysis\u003c/p\u003e\n\u003cp\u003eAnnotation of co-expressed differential genes using Gene Ontology (GO) analysis is a commonly used method in bioinformatics analysis. This analysis(Figure 3) covers annotations in terms of Biological Process (BP), Cell Component (CC) and Molecular Function (MF). In addition, the Kyoto Encyclopedia of Gene and Genomes (KEGG) database was used to perform pathway enrichment analyses to identify important signaling pathways enriched by co-expression of differential genes. These analyses we accomplished by using the ClusterProfiler, enrichplot, and org.Hs.eg.db packages in Rstudio software. For GO/KEGG functional enrichment analysis, p-value values and adjp-value values equal to 0.05 were used as thresholds. Finally, the corresponding functional enrichment results were plotted using the ggplot2, circlize, RColorBrewer, dplyr, and ComplexHeatmap packages in the Rstudio software, as shown in Figure 4.\u003c/p\u003e\n\u003cp\u003e1.4 Mendelian randomization analysis\u003c/p\u003e\n\u003cp\u003eIn this study, a two-sample MR design was used to explore the causal link between the rheumatoid arthritis signaling pathway as exposure data and squamous lung cancer as outcome data.Genetic data for the RA signaling pathway were obtained from pooled data from independent European populations in the IEUopenGWAS (https://gwas.mrcieu.ac.uk/) database. This dataset contains 80,799 samples, of which 19,234 are CD patients and 61,565 are controls. A total of 9,739,304 SNP (single nucleotide polymorphism) single nucleotide polymorphisms were included in this dataset. The data related to squamous lung cancer were also derived from pooled data from independent European populations in the IEUopenGWAS database. This dataset contains 62,467 samples, including 7704 patients with squamous lung cancer and 54,763 controls. A total of 10,341,529 SNPs were included in this dataset.The SNP screening condition for the RA signaling pathway was to set a p-value threshold of 5\u0026times;10-8 for screening, and a total of 49 SNPs were screened as instrumental variables (IVs), and 49 SNPs were included for subsequent analyses after exclusion of chain imbalance and deletion of alleles. SNPs for subsequent analysis. MR analysis was performed using the TwoSampleMR package in Rstudio software, including MR Egger, Weighted median, Inverse variance weighted, Simple mode and Weighted mode, to determine the causal relationship between RA and LUSC, as shown in Fig. 5.\u003c/p\u003e\n\u003cp\u003e1.5 Gene-Drug Networks\u003c/p\u003e\n\u003cp\u003eDGIdb Database (Drug Gene Interaction Database) is a database for drug-gene interaction information, which provides detailed information about drug-gene interactions. According to the KEGG pathway enrichment results, 11 pathway genes in the rheumatoid arthritis signaling pathway were selected and entered into the DGIdb database (https://dgidb.genome.wustl.edu/) and then the antitumor drug files corresponding to the genes were downloaded, and the software perl was used to obtain the gene-drug regulatory network files, which were eventually used to Cytoscape3.10.1 software was used to draw the gene-drug regulatory network map, as shown in Figure 6.\u003c/p\u003e\n\u003cp\u003e1.6 ceRNA network construction\u003c/p\u003e\n\u003cp\u003eThe miRNA related data were obtained by using miRanda database, miRDB database, miRWalk database, TargetScan database, and predicted miRNAs bound by 11 pathway genes by perl software, and lncRNA data were obtained by spongeScan database, and miRNA binding was constructed by perl software. lncRNA, and finally construct ceRNA regulatory network map by Cytoscape3.10.1 software, as shown in Figure 7.\u003c/p\u003e\n\u003cp\u003e1.7 Immune infiltration analysis\u003c/p\u003e\n\u003cp\u003eThrough the \u0026quot;CIBERSORT\u0026quot; package in Rstudio software, 11 rheumatoid arthritis signaling pathway genes were selected for immune infiltration analysis, and the percentage of the expression of these 11 genes in each immune cell was analyzed, as shown in Figure 8.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1.8 Validation analysis\u003c/p\u003e\n\u003cp\u003eBased on the gene expression values of the 11 rheumatoid arthritis signaling pathway genes screened in lung squamous carcinoma in the TCGA database, and the lung squamous carcinoma data in TCGA were divided into lung squamous carcinoma group and normal group for T-test, the box-and-line diagram was plotted by using the Rstudio software, as shown in Figure 9.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003e2.1. results of differential gene analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRheumatoid arthritis microarray data (GSE1919) were screened by the tool GEO2R in the GEO database, and a total of 389 differential genes for rheumatoid arthritis were obtained by screening differential genes with the screening criteria of p-value\u0026lt;0.05,logFC\u0026gt;1. With the same screening criteria, 7200 differential genes were obtained by screening the expression data profiles of lung squamous carcinoma in the TCGA database. Subsequently, 188 rheumatoid arthritis and lung squamous coexpressed genes were obtained by taking coexpressed genes.\u003c/p\u003e\n\u003cp\u003e2.2 Results of functional enrichment analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the 188 rheumatoid arthritis and lung squamous co-expressed differential genes, we performed GO/KEGG functional enrichment analysis using Rstudio software.The GO annotation results included three parts: biological process, molecular function and cellular component. The results showed that the co-expressed differential genes were mainly enriched in biological process (BP) for positive regulation of cell adhesion, positive regulation of cell activation, response to corticosteroids, etc.; in cellular component (CC) for outer plasma membrane, collagen-containing extracellular matrix, and ruffled membrane, etc.; and in molecular function (MF) for chemokine activity, chemokine receptor binding, and cytokine receptor activity, etc. In the KEGG functional enrichment results, we found that the differential genes were mainly involved in the rheumatoid arthritis signaling pathway, etc., which mainly included 11 co-expressed genes, MMP1, MMP3, TNFSF11, IL23A, HLA-DMA, HLA-DOA, ITGAL, ITGB2, HLA-DMB, CXCL6, CD86.\u003c/p\u003e\n\u003cp\u003e2.3 Results of Mendelian randomization analysis\u003c/p\u003e\n\u003cp\u003eIn the IEUopenGWAS database, there were a total of 59 instrumental variables related to squamous lung cancer. After excluding continuous imbalance and deleting incompatible alleles, 49 instrumental variables related to lung squamous carcinoma were finally selected for analysis. Five MR analysis methods were performed in the study using the TwoSampleMR package in Rstudio software. The results showed that MR Egger (pvalue=0.7908), Weighted median (pvalue=0.1123), Inverse variance weighted (pvalue=0.0363), Simple mode (pvalue=0.7599) and Weighted mode (pvalue=0.4893). Based on a p-value of less than 0.05 for the inverse variance weighted method, we concluded a positive result and suggested a causal relationship between rheumatoid arthritis pathway and squamous lung cancer. The P values for heterogeneity of Mendelian randomization results for rheumatoid arthritis and squamous lung cancer calculated by the inverse variance weighting method and the MR Egger method were 0.0258 and 0.0195, respectively, indicating heterogeneity. Horizontal multiplicity test using Egger intercept method had a P value of 0.1299 indicating that the pathways by which the instrumental variables affected the outcome were not significant except for exposure. Results of sensitivity analysis by leave-one-out method indicated stability.\u003c/p\u003e\n\u003cp\u003e2.4 Gene-drug network\u003c/p\u003e\n\u003cp\u003eThe 11 rheumatoid arthritis pathway genes obtained were entered into https://dgidb.genome.wustl.edu/得到药物文件, and the gene-drug regulatory network file was obtained by perl software. The target drugs corresponding to genes MMP1, MMP3, TNFSF11, IL23A, ITGB2, IL23A, ITGB2, and CD86 were obtained respectively, as shown in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1: Gene-Drug Table: Possible targeted drugs for the genes MMP 1, MMP 3, TNFSF11, IL 23 A, ITGB 2, IL 23 A, ITGB 2, and CD86\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"557\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003egene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003etargeted drug\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMP1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDOXYCYCLINE CALCIUM、APRATASTAT、DOXYCYCLINE、DOXYCYCLINE HYCLATE、CIPEMASTAT、MARIMASTAT、PRINOMASTAT、LEUPROLIDE ACETATE、SIROLIMUS、COLLAGENASE CLOSTRIDIUM HISTOLYTICUM、MEDROXYPROGESTERONE ACETATE、LAMIVUDINE、LEFLUNOMIDE、HYDROCORTISONE、PENTOSAN POLYSULFATE SODIUM、TRIAMCINOLONE、RIBAVIRIN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.827338129496402%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMP3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.1726618705036%\" valign=\"top\"\u003e\n \u003cp\u003eMARIMASTAT、PRINOMASTAT、BERKELEYAMIDE C、BERKELEYDIONE、LISINOPRIL、BERKELEYAMIDE B、BERKELEYACETAL A、PRAVASTATIN、CHLORTHALIDONE、BERKELEYTRIONE、BERKELEYACETAL B、BERKELEYACETAL C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL23A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBRIAKINUMAB、TILDRAKIZUMAB、USTEKINUMAB、GUSELKUMAB、BRAZIKUMAB、RISANKIZUMAB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eITGAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLIFITEGRAST、EFALIZUMAB、ROVELIZUMAB、ODULIMOMAB、CYCLOSPORINE、CASEARINOL、CYCLOPHOSPHAMIDE、STAUROSPORINE、THROMBIN、CASEARINONE A、MYCOPHENOLATE、\u0026nbsp;MOFETIL、EPOETIN ALFA、CASEARINONE B、BUSULFAN、SIROLIMUS、ETOPOSIDE、FLUOROURACIL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eITGB2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMLNM-2201、ROVELIZUMAB、EFALIZUMAB、LIFITEGRAST、ERLIZUMAB、AME-133V、SODIUM CHLORIDE、MYCOPHENOLATE MOFETIL、PREDNISONE、THALIDOMIDE、ANTIBIOTIC、METHYLPREDNISOLONE、INDOMETHACIN、CYCLOSPORINE、CYCLOPHOSPHAMIDE、PENTOXIFYLLINE、BUTEIN、COLCHICINE、ALCOHOL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eABATACEPT、BELATACEPT、PAMIDRONIC ACID、ROXITHROMYCIN、DEXAMETHASONE、TERFENADINE、INDOMETHACIN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNFSF11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDENOSUMAB、LENALIDOMIDE、ANASTROZOLE、LETROZOLE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e2.5 ceRNA network construction By using miRanda database, miRDB database, miRWalk database, TargetScan database to get miRNA related data, by perl to predict the pathway gene binding miRNA, by spongeScan database to get lncRNA data, by perl to construct the miRNA-binding lncRNAs to construct ceRNA networks. Among them, there were 58 mRNAs bound to 11 rheumatoid arthritis pathways, and a total of 124 lncRNAs bound to miRNAs. Finally, Cytoscape software was utilized to map out the ceRNA regulatory network.\u003c/p\u003e\n\u003cp\u003e2.6 Immune infiltration analysis To further confirm the correlation between the expression of the 11 genes and the immune microenvironment, we analyzed the proportion of tumor-infiltrating immune subpopulations using the CIBERSORT algorithm and constructed immune cell maps for 22 LUSC samples, immune cells such as Neutrophils, Eosinophils, Mast cells activated, Mast cells resting, etc. The graph shows that there is a positive correlation between the pathway gene CXCL6 and immune cells Mast cells resting, and the correlation between the two is the largest by the width of the line, as shown in Figure 8.\u003c/p\u003e\n\u003cp\u003e2.7 Validation analysis We went through 11 important significant genes (MMP1, MMP3, TNFSF11, IL23A, HLA-DMA, HLA-DOA, ITGAL, ITGB2, HLA-DMB, CXCL6, CD86). Then, the LUSC clinical sample data were categorized into two groups according to the TCGA dataset: the normal group and the lung squamous carcinoma group. We observed the expression of these 11 genes in the two groups. The results showed that the expression of these 11 genes was significantly different in both the normal group and the lung squamous carcinoma carcinoma.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eLung cancer is the leading cause of cancer-related deaths worldwide, and squamous cell carcinoma is one of the major histologic subtypes of lung cancer.Detecting lung cancer at an early stage is crucial for successful treatment and improved survival.However, there are still no satisfactory biomarkers for the early detection of lung cancer and squamous cell carcinoma is the second most common histologic subtype of lung cancer after adenocarcinoma of the lung, which accounts for approximately 30% of all non-small-cell lung cancers, and the onset of the disease is mostly associated with smoking It is closely related to smoking, and due to the challenging diagnosis, many patients with squamous lung cancer are diagnosed in the middle to late stages [5][6]. We may be able to reveal the potential pathogenesis of squamous lung cancer through the rheumatoid arthritis-related signaling pathway, and the KEGG enrichment results showed that the common differential genes between rheumatoid arthritis and squamous lung cancer were mainly enriched in the signaling pathway of rheumatoid arthritis and other signaling pathways. Through Mendelian randomization analysis, we found a positive causal relationship between rheumatoid arthritis signaling pathway and lung squamous carcinoma. Accordingly, we focused on the mechanism and role of rheumatoid arthritis signaling pathway in lung squamous carcinoma, which includes 11 pathway genes, such as MMP1 (matrix metalloproteinase 1), TNFSF11 (tumor necrosis factor superfamily member 11), IL23A (interleukin 23), HLA-DMA (major histocompatibility antigen complex IIDM\u0026alpha;), ITGB2 ( int-egrin \u0026beta;2 subunit), CXCL6 (CXC motif chemokine ligand 6), and others.\u003c/p\u003e\n\u003cp\u003eMMP1 (Matrix metalloproteinase 1) and MMP3 (Matrix metalloproteinase 3) belong to the isoforms of matrix metalloproteinases (MMPs), and more than 25 different members of MMPs have been identified to date [7]. Several studies have shown that MMP1 plays an important role in cancer progression, and abnormally high expression of MMP1 is strongly associated with poor prognosis in many cancers [8] [9]. Previous studies have shown that ETV4 (ETS variant 4) can activate the expression of MMP genes, and MMPs are key mediators of cancer progression [10,11,12]. It has been demonstrated that MMP1 is the most critical downstream target of ETV4, and knockdown of MMP1 significantly inhibited ETV4-induced cell proliferation and migration, and MMP1-positive expression was associated with poor prognosis in NSCLC (non-small cell lung cancer) patients, suggesting the importance of MMP1 in the tumorigenic activity of ETV4 [12].TNFSF11 (RANKL) belongs to the TNF cytokine superfamily, a type II transmembrane protein with an extracellular structural domain at the carboxyl terminus [13] [14]. This extracellular structural domain is cleaved by enzymes such as matrix metalloproteinases and released into the extracellular milieu as soluble TNFSF11 (RANKL).Expression of TNFSF11 is commonly found in the cells of lung cancer patients and correlates with a poor prognosis.Activation of the RANKL/RANK pathway regulates the expansion of stem cell-like cells in lung cancer through a mechanism that is dependent on mitochondrial respiration [15]. HLA-DM is a heterodimeric molecule important for normal antigen presentation, which is encoded by the adjacent sites HLA-DMA and HLA-DMB located between the DP and DQ subregions.In normal antigen-presenting cells, DM is present in the intracellular lumen compartment but not on the cell surface; it either facilitates the binding of antigenic peptides to classical class II molecules [16]. It has been shown that enrichment of HLA-DMA in cancer RNAseq expression profiles involves activation of antigen processing and presentation pathways [17]. As a peptide processor, HLA-DMA catalyzes peptide conversion on classical MHCII proteins and protects empty MHCII-like molecules from functional inactivation upon efficient presentation of protein antigens, HLA-DMA and is involved in cancer progression and drug resistance [18-20].IL-23 is part of the broader IL-6/IL-12 superfamily, sharing a receptor subunit with IL-12, IL- 12 1 sharing a receptor subunit. This binds to the new IL-23R subunit, completing the IL-23 receptor [21][22]. In cancer, IL-23 is involved in all three stages of immune editing [23-25], is elevated in the cancer microenvironment [26-28], and correlates with poor prognosis [29],it also promotes tumor growth and progression. It was found that IL-23 expression in NSCLC can be dynamically regulated by chemotherapeutic agents and epigenetic treatments, and can induce proliferation of NSCLC cell lines [30]. Integrin 2 (ITGB2), also known as CD18, is one of the subunits of integrins and was first observed on the surface of leukocytes. Several studies have shown that ITGB2 functions to promote leukocyte adhesion and extravasation [31,32]. ITGB2 was found to promote oral squamous cell carcinoma proliferation indirectly by regulating cancer-associated fibroblasts [33] and to mediate yap-induced breast cancer cell invasion across the endothelium [34]. These findings suggest that ITGB2 plays an important role in tumor development. Studies have demonstrated that overexpression of ITGB2 can increase its expression level In H1792 cells, ITGB2 can reduce the expression levels of N - cadherin, Vimentin, Slug, Snail and Twist, suggesting that ITGB2 can affect the growth of NSCLC cells by inhibiting the EMT pathway [35].\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn this study, we linked the rheumatoid arthritis-related pathway to squamous lung cancer to explore its role and mechanism in squamous lung cancer. Specifically, gene expression data, Mendelian randomization, and other bioinformatics analyses were used in this study to validate the possibility that the rheumatoid arthritis-related pathway plays an important role in lung squamous carcinoma. This study combined different bioinformatics analysis methods on lung squamous carcinoma samples, including gene expression analysis, functional enrichment analysis, pathway analysis, two-sample Mendelian randomization analysis, etc., and comprehensively explored the role of rheumatoid arthritis-related pathways in lung squamous carcinoma from different levels. Revealing the role of rheumatoid arthritis signaling pathway in lung squamous carcinoma: This study reveals that increased activity of rheumatoid arthritis pathway pathway may affect the reduced risk of lung squamous carcinoma development through comprehensive analysis. The results of this study may provide a new theoretical basis for the treatment of squamous lung cancer, i.e., the use of rheumatoid arthritis-related pathways to regulate or inhibit the occurrence and development of squamous lung cancer. In conclusion, the present study provides new ideas and methods for us to better understand the mechanism of lung squamous carcinoma occurrence and development, or provides a new theoretical basis for the treatment of this disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e1. Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe is not applicable to our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Consent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors of this paper have given their consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets \u0026nbsp;analysed during the current study are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov) databases.\u003c/p\u003e\n\u003cp\u003eThe datasets \u0026nbsp;analysed during the current study are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov) databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no potential conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuizhou Provincial Higher Education Traditional Chinese and Medicine Ethnic Medicine Cancer Prevention and Treatment Medical Transformation Engineering Research Center（No. Qian Jiaoji [2023]037）; Talent Base of Traditional Chinese Medicine Tumor Inheritance and Scientific and Technological Innovation in Guizhou Province (Guizhou Renjianzhiefa [2018] No. 3); Talent Team of Traditional Chinese Medicine Tumor Inheritance and Scientific and Technological Innovation in Guizhou Province (Guizhou Science and Technology Cooperative Talent [2020] No. 5013); Study on Mechanisms of Bone Cancer Pain Intervention by Bufo Spirit Cream through Regulation of Research on the mechanism of JNK pathway intervention in bone cancer pain by regulating the \u0026quot;intestinal flora\u0026quot; of toad spirit cream (Guizhou Science and Technology Cooperation Academic New Seedling ([2023]-03)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJiaxing Dai completed the first draft and processed the data; Hong Huang and Huanghui Zhang are responsible for the modification of the picture drawing; Bing Yang were responsible for the language proofreading of the article; Dongxin Tang is responsible for the overall framework of the article as well as the ideas.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSmolen JS, Aletaha D, Barton A, Burmester GR, et al. Rheumatoid arthritis. Nat Rev Dis Primers. 2018 Feb 8;4:18001. \u003c/li\u003e\n\u003cli\u003eZappa C, Mousa SA. Non-small cell lung cancer: current treatment and future advances. Transl Lung Cancer Res. 2016 Jun;5(3):288-300. \u003c/li\u003e\n\u003cli\u003eRaaschou P, Simard JF, Holmqvist M, et al. Rheumatoid arthritis, anti-tumour necrosis factor therapy, and risk of malignant melanoma: nationwide population based prospective cohort study from Sweden. BMJ. 2013 Apr 8;346:f1939. \u003c/li\u003e\n\u003cli\u003eEvans DM, Davey Smith G. Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality. Annu Rev Genomics Hum Genet. 2015;16:327-50. \u003c/li\u003e\n\u003cli\u003eLi B, Chang J, Chu Y, et al. Membrane proteomic analysis comparing squamous cell lung cancer tissue and tumour-adjacent normal tissue. Cancer Lett. 2012 Jun 1;319(1):118-24. \u003c/li\u003e\n\u003cli\u003eLau SCM, Pan Y, Velcheti V, et al. Squamous cell lung cancer: Current landscape and future therapeutic options. Cancer Cell. 2022 Nov 14;40(11):1279-1293. \u003c/li\u003e\n\u003cli\u003eLiu M, Hu Y, Zhang MF, et al. MMP1 promotes tumor growth and metastasis in esophageal squamous cell carcinoma. Cancer Lett. 2016 Jul 10;377(1):97-104. \u003c/li\u003e\n\u003cli\u003eOzden F, Saygin C, Uzunaslan D, et al. Expression of MMP-1, MMP-9 and TIMP-2 in prostate carcinoma and their influence on prognosis and survival. J Cancer Res Clin Oncol. 2013 Aug;139(8):1373-82.\u003c/li\u003e\n\u003cli\u003eCai QW, Li J, Li XQ, et al. Expression of STAT3, MMP-1 and TIMP-1 in gastric cancer and correlation with pathological features. Mol Med Rep. 2012 Jun;5(6):1438-42. \u003c/li\u003e\n\u003cli\u003eDumortier M, Ladam F, Damour I, et al. ETV4 transcription factor and MMP13 metalloprotease are interplaying actors of breast tumorigenesis. Breast Cancer Res. 2018 Jul 11;20(1):73.\u003c/li\u003e\n\u003cli\u003eKim E, Kim D, Lee JS, et al.Capicua suppresses hepatocellular carcinoma progression by controlling the ETV4-MMP1 axis. Hepatology. 2018 Jun;67(6):2287-2301.\u003c/li\u003e\n\u003cli\u003eFung TM, Ng KY, Tong M, et al. Neuropilin-2 promotes tumourigenicity and metastasis in oesophageal squamous cell carcinoma through ERK-MAPK-ETV4-MMP-E-cadherin deregulation. J Pathol. 2016 Jul;239(3):309-19. \u003c/li\u003e\n\u003cli\u003eNakashima T, Hayashi M, Takayanagi H. New insights into osteoclastogenic signaling mechanisms. Trends Endocrinol Metab. 2012 Nov;23(11):582-90. \u003c/li\u003e\n\u003cli\u003eOkamoto K, Nakashima T, Shinohara M, et al.Osteoimmunology: The Conceptual Framework Unifying the Immune and Skeletal Systems. Physiol Rev. 2017 Oct 1;97(4):1295-1349. \u003c/li\u003e\n\u003cli\u003eOno T, Hayashi M, Sasaki F, et al. RANKL biology: bone metabolism, the immune system, and beyond. Inflamm Regen. 2020 Feb 7;40:2. \u003c/li\u003e\n\u003cli\u003eMoxley G, Han J. HLA DMA and DMB show no association with rheumatoid arthritis in US Caucasians. Eur J Immunogenet. 2001 Oct;28(5):539-43. \u003c/li\u003e\n\u003cli\u003eHang X, Li D, Wang J, Wang G. Prognostic significance of microsatellite instability‑associated pathways and genes in gastric cancer. Int J Mol Med. 2018 Jul;42(1):149-160. \u003c/li\u003e\n\u003cli\u003eAlvaro-Benito M, Morrison E, Wieczorek M, et al. Human leukocyte Antigen-DM polymorphisms in autoimmune diseases. Open Biol. 2016 Aug;6(8):160165. \u003c/li\u003e\n\u003cli\u003eVogt AB, Arndt SO, H\u0026auml;mmerling GJ, et al. Quality control of MHC class II associated peptides by HLA-DM/H2-M. Semin Immunol. 1999 Dec;11(6):391-403. \u003c/li\u003e\n\u003cli\u003eKobayashi S, Yamada-Okabe H, Suzuki M, et al.LGR5-positive colon cancer stem cells interconvert with drug-resistant LGR5-negative cells and are capable of tumor reconstitution. Stem Cells. 2012 Dec;30(12):2631-44. \u003c/li\u003e\n\u003cli\u003eOppmann B, Lesley R, Blom B, et al. Novel p19 protein engages IL-12p40 to form a cytokine, IL-23, with biological activities similar as well as distinct from IL-12. Immunity. 2000 Nov;13(5):715-25. \u003c/li\u003e\n\u003cli\u003eParham C, Chirica M, Timans J, et al. A receptor for the heterodimeric cytokine IL-23 is composed of IL-12Rbeta1 and a novel cytokine receptor subunit, IL-23R. J Immunol. 2002 Jun 1;168(11):5699-708.\u003c/li\u003e\n\u003cli\u003eParham C, Chirica M, Timans J, et al. A receptor for the heterodimeric cytokine IL-23 is composed of IL-12Rbeta1 and a novel cytokine receptor subunit, IL-23R. J Immunol. 2002 Jun 1;168(11):5699-708. \u003c/li\u003e\n\u003cli\u003eLangowski JL, Zhang X, Wu L, et al. IL-23 promotes tumour incidence and growth. Nature. 2006 Jul 27;442(7101):461-5. \u003c/li\u003e\n\u003cli\u003eLangowski JL, Kastelein RA, Oft M. Swords into plowshares: IL-23 repurposes tumor immune surveillance. Trends Immunol. 2007 May;28(5):207-12. doi: 10.1016/j.it.2007.03.006. Epub 2007 Mar 28. PMID: 17395538.\u003c/li\u003e\n\u003cli\u003eFukuda M, Ehara M, Suzuki S, et al. IL-23 promotes growth and proliferation in human squamous cell carcinoma of the oral cavity. Int J Oncol. 2010 Jun;36(6):1355-65. \u003c/li\u003e\n\u003cli\u003eLan F, Zhang L, Wu J, et al. IL-23/IL-23R: potential mediator of intestinal tumor progression from adenomatous polyps to colorectal carcinoma. Int J Colorectal Dis. 2011 Dec;26(12):1511-8. \u003c/li\u003e\n\u003cli\u003eMalvicini M, Rizzo M, Alaniz L, et al. A novel synergistic combination of cyclophosphamide and gene transfer of interleukin-12 eradicates colorectal carcinoma in mice. Clin Cancer Res. 2009 Dec 1;15(23):7256-65. \u003c/li\u003e\n\u003cli\u003eNguyen GH, Schetter AJ, Chou DB, et al. Inflammatory and microRNA gene expression as prognostic classifier of Barrett\u0026apos;s-associated esophageal adenocarcinoma. Clin Cancer Res. 2010 Dec 1;16(23):5824-34. \u003c/li\u003e\n\u003cli\u003eBaird AM, Leonard J, Naicker KM, et al. IL-23 is pro-proliferative, epigenetically regulated and modulated by chemotherapy in non-small cell lung cancer. Lung Cancer. 2013 Jan;79(1):83-90. \u003c/li\u003e\n\u003cli\u003eLee, S.H.; Corry, D.B. Homing alone? CD18 in infectious and allergic disease. Trends Mol. Med. 2004, 10, 258\u0026ndash;262. \u003c/li\u003e\n\u003cli\u003eTan, S.M. The leucocyte beta2 (CD18) integrins: The structure, functional regulation and signalling properties. Biosci. Rep. 2012,32, 241\u0026ndash;269. \u003c/li\u003e\n\u003cli\u003eZhang, X.; Dong, Y.; Zhao, M.; et al. ITGB2-mediated metabolic switch in CAFs promotes OSCC proliferation by oxidation of NADH in mitochondrial oxidative phosphorylation system. Theranostics 2020,10, 12044\u0026ndash;12059. \u003c/li\u003e\n\u003cli\u003eLiu, H.; Dai, X.; Cao, X.; et al. PRDM4 mediates YAP-induced cell invasion by activating leukocyte-specific integrin beta2 expression. EMBO Rep. 2018, 19, e45180. \u003c/li\u003e\n\u003cli\u003eZu L, He J, Zhou N, et al. The Profile and Clinical Significance of ITGB2 Expression in Non-Small-Cell Lung Cancer. J Clin Med. 2022 Oct 29;11(21):6421.\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":"Mendelian randomization, rheumatoid arthritis, lung squamous cell carcinoma, pathway genes","lastPublishedDoi":"10.21203/rs.3.rs-4143609/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4143609/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective：\u003c/strong\u003eRheumatoid arthritis (RA) is a chronic, autoimmune inflammatory joint disease. Squamous cell carcinoma of the lung (LUSC) is a malignant tumor of non-small cell lung cancer. Studies have shown a complex relationship between rheumatoid arthritis and lung cancer. This study aimed to investigate the role and mechanism of rheumatoid arthritis-related pathways in lung squamous carcinoma using bioinformatics methods combined with Mendelian randomization analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Download RA gene expression profile data set GSE1919 from GEO database, screen differential genes using GEO 2 R tool, and download lung gene expression profile data from TCGA database, and screen differential genes by Rstudio software. GO / KEGG functional enrichment analysis yielded RA signaling pathway genes. After downloading RA signaling pathway SNP data as exposure factor and lung SNP data for squamous cell carcinoma as outcome factor, we used two-sample Mendelian randomization analysis to determine the causal relationship between RA signaling pathway and lung squamous cell carcinoma. In addition, gene-drug regulatory network, ceRNA regulatory network, immune cell infiltration analysis and validation group difference analysis were constructed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe found 188 genes codifferentially expressed between RA and LUSC. Results of GO / KEGG functional enrichment analysis showed that these differential genes were mainly enriched in the rheumatoid arthritis signaling pathway. The results of Mendelian randomization analysis showed that enhanced activity of RA signaling pathway was associated with a reduced risk of lung squamous carcinoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe study found that the enhanced activity of rheumatoid arthritis signaling pathway may be associated with the reduced risk of breast cancer, which provides new ideas and vision for studying the mechanism and treatment of lung squamous cell carcinoma.\u003c/p\u003e","manuscriptTitle":"Exploring the role and mechanism of rheumatoid arthritis-related pathway in squamous lung cancer based on Mendelian randomization analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 09:13:47","doi":"10.21203/rs.3.rs-4143609/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":"d551b2f8-3c08-4814-aae3-dba3516699d4","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-23T16:35:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 09:13:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4143609","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4143609","identity":"rs-4143609","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}