Exploring the molecular mechanisms and shared gene signatures between dermatomyositis and gastric cancer

Exploring the molecular mechanisms and shared gene signatures between dermatomyositis and gastric cancer | 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 molecular mechanisms and shared gene signatures between dermatomyositis and gastric cancer Yixin Ding, Chuanyu Leng, Shufei Wang, Kongjia Wang, Weiwei Qi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3847315/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: Several studies have reported a clinical association between gastric cancer(GC) and dermatomyositis (DM), but the molecular features and underlying mechanisms between the two diseases have not been investigated. Methods： We obtained the strongly associated genes of DM and GC and the clinical characteristics from the Gene Expression Omnibus (GEO), The Comparative Toxicogenomics Database (CTD), GeneCards, and DisGeNET databases. We next screened hub genes, constructed co-expression and interaction networks, transcription factor-gene-miRNA regulatory networks, and performed enrichment analysis of cell signaling pathways and candidate drugs prediction. Finally, a single-gene immune infiltration assay was performed on the hub genes. Results： Our study revealed commonalities at the genetic level between DM and GC. A deep dive into the 8 hub genes revealed the role in immune response, especially cytokines, which were involved in the co-development of the two diseases. The obtained hub genes have the potential to be biomarkers as well as therapeutic targets for DM patients with a potential predisposition to GC tumorigenesis. DM GC gene signatures immune response cytokines Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction With the aging of the population and the increase of high-risk groups, the incidence and the number of deaths of GC (GC) continue to rise. In 2020, there were approximately 1 million new cases of GC and about 770,000 deaths. GC often remains hidden until its advanced stages, leading to limited treatment options. Early diagnosis and detection are crucial to improving the survival rate of GC. Few studies have explored the relationship between other specific diseases and the risk of developing GC, while most guidelines identify Helicobacter pylori infection, alcohol consumption, and high-salt food intake as risk factors for GC. Autoimmune diseases can disrupt the body's immune response and may accelerate the development of gastritis. And it can eventually progress to precancerous GC. One such autoimmune disease is DM (DM), an idiopathic inflammatory muscle disease that typically presents with muscle weakness and rash. In the United States, DM has a higher incidence among middle-aged women and affects approximately 5–10 per million people [ 1 ]. It is worth noting that research has demonstrated a correlation between DM and a heightened occurrence of malignancies. A meta-analysis involving 4538 patients revealed that the risk of malignant tumors in patients with DM is approximately 4.66 times higher than that of the normal population [ 2 ]. Further investigations have supported the link between DM and cancer. In a retrospective study by Hill et al., approximately 30% of patients with DM were diagnosed with malignant tumors, and more than 60% of these diagnoses occurred after the diagnosis of DM. The incidence of malignant tumors was significantly higher in patients with DM compared to the general population [ 3 ]. Other studies have also reported an increased incidence of common malignancies, including hematological, lung, gastric, breast, ovarian, and prostate cancers, among patients diagnosed with DM [ 2 ]. A meta-analysis involving 52 observational studies found a significant association between nine autoimmune diseases and an increased risk of GC. The study showed that nine autoimmune diseases were significantly associated with an increased risk of GC (DM, Pernicious anemia, Addison's disease, herpetiform dermatitis, IgG4 related diseases, primary biliary cirrhosis, Type 1 diabetes, systemic lupus erythematosus and Graves disease), with DM being the most strongly associated with GC [ 4 ]. These findings underscore the urgent need to further explore the causal relationship and molecular genetic mechanisms linking DM to GC. Method Data Download and Processing The bulk RNA sequencing data of DM patients were obtained from the GSE1551 and GSE103236 datasets available in the Gene Expression Omnibus (GEO) database. Differential analysis of RNA-seq data was conducted using the DESeq2 and limma packages to identify differentially expressed genes (DEGs) in DM. DEGs were selected using a cutoff criteria of adjusted p-value 1. Additionally, the top 500 genes most associated with DM were identified by querying the The Comparative Toxicogenomics Database (CTD), GeneCards, and DisGeNET databases based on their respective "Inference Score," "Score_gda," and "Relevance score" parameters. The resulting genes from these online databases were combined with the DEGs from GEO to form the set of DM-related genes. The same process was applied to obtain GC-related genes, and subsequently, the overlapping genes between DM and GC were further analyzed. Protein-Protein Interaction (PPI) Analysis, Hub Genes Selection, and Co-expression Network Construction The strength of interaction relationships, direct binding relationships, and upstream and downstream regulatory pathways between proteins encoded by co-related genes were explored using the STRING online databae. Interactions with a combined score of over 0.4 were considered statistically significant. The Molecular Complex Detection (MCODE) plugin in Cytoscape (v3.8.2) was used to visualize the PPI network data obtained from STRING analysis. The hub genes were selected using the cytoHubba plugin in Cytoscape software (v3.8.2) with seven standard algorithms. The hub genes obtained from these algorithms were further filtered using the Upset algorithm to select those satisfying all the criteria. The GeneMANIA database was utilized to construct the co-expression network of the hub genes, and the correlation between the genes within the hub gene set was analyzed. The results were visualized using the circlize package (version v0.4.1). 3.Functional Enrichment Analysis of Co-associated Gene Ontologies and Pathways Gene Ontology (GO) database was used to categorize gene functions and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was employed to explore the complex interrelationships between genes and metabolites. GO and KEGG analyses were performed on the common related genes between DM and GC, as well as on the hub genes. 4.Construction of Transcription Factor-Gene-miRNA Regulatory Network The Encyclopedia of DMA Elements (ENCODE) database, available on the Networkanalyst platform, was used to construct a transcription factor-gene regulatory network and select the transcription factor with the strongest regulatory relationship based on the "Degree" score. Additionally, the RegNetwork database ( http://www.regnetworkweb.org ), also available on the Networkanalyst platform, was used to construct the gene-miRNA regulatory network. 5.Candidate Drug Prediction The Drug Signature Database (DSigDB) containing 22527 gene. We access the DSigDB database from Enrichr database and explored potential drug molecules that have a significant relationship with hub genes. Candidate drugs were ranked based on their adjusted p-value, and those with an adjusted p-value < 0.01 were considered statistically significant. 6.Hub Genes Single Gene Immune Infiltration Analysis The CIBERSORT program, which utilizes linear support vector regression, is employed to deconstruct the expression matrix of several subtypes of human immune cells for the purpose of immune-immersion analysis. The calculation of immune cell proportions was performed for datasets GSE1551 and GSE103236. Additionally, the relationship between immune cells and hub genes, as well as the association of each immune cell, were examined. Results Acquisition of Genes and Co-associated Genes Related to DM and GC The overall flow chart of our study was shown in Fig. 1 . The CTD, GeneCards, and DisGeNET databases yielded a combined total of 10,176, 1,413, and 235 genes associated with DM, respectively. Differential analysis of RNA-seq data from the GSE1551 dataset resulted in the identification of 261 differentially expressed genes in DM. Similarly, 38,824, 820, 15,117, and 343 GC-related genes were obtained from the four databases. 1,161 DM-related genes, 1,363 GC-related genes, and 658 common related genes were identified after correlation screening and integration (Fig. 2 A). The PPI Network Display of Common Related Genes and the Acquisition of 8 Key Hub Genes We apply 7 algorithms to select 8 hub genes based on their central roles in the network. The identified hub genes are interleukin-1β (IL1B), interferon gamma (IFNG), CD4, signal transducer and activator of transcription 3 (STAT3), interleukin-10 (IL10), CD8A, C-X-C motif chemokine ligand 8 (CXCL8), and toll like receptor 4 (TLR4) (Fig. 2 B). The co-expression and correlation network of these eight hub genes were constructed and displayed (Fig. 2 C-F). We investigated hub gene expression in DM and GC, respectively. All eight hub genes were substantially overexpressed in GC, while only CD4 and CD8A were significantly overexpressed in DM (Fig. 3 A, B). The receiver operating characteristic (ROC) curves and area under curve (AUC) values indicated that STAT3, CD4, TLR4, CXCL8, and IFNG had a high diagnostic value for GC (AUC > 0.7), whereas CD4, STAT4, IL10, and CD8A had a high diagnostic value for DM (AUC > 0.7) (Fig. 3 C, D). Commonly Related Genes Play an Important Role in Immune Pathways and Inflammatory Pathways 658 co-related genes were included in the GO and KEGG analysis. The results showed that the co-related genes were mainly enriched in extracellular components such as extracellular matrix and other biological processes such as cell adhesion, cytokine activity and receptor-ligand binding (Fig. 4 A-C). The KEGG analysis showed that the common related genes were enriched in signaling pathways such as "cytokine-cytokine receptor interaction, Th17 cell differentiation, and the AGE-RAGE signaling pathway" (Fig. 4 D). In addition, 8 hub genes was involved in immune-related processes and pathways, including "monocyte differentiation, lymphocyte differentiation, adaptive immune response, and interleukin-6 production" (Fig. 4 E-H). Construction of Transcription Factor-Gene-miRNA Regulatory Network and Candidate Drug Prediction Strong connections were found between STAT3, IFNG, and IL10 and transcription factors among the eight hub genes. The constructed transcription factor-gene regulatory network is displayed (Fig. 5 A). Moreover, all the 8 hub genes showed correlations with corresponding miRNAs, with STAT3, CXCL8, and TLR4 exhibiting particularly strong relationships with miRNAs (Fig. 5 B). Using the hub genes for screening in the DSigDB database, the top ten drug molecules with the most predictive significance were identified. Sodium sulfate, retinol, thalidomide, pyrophosphatase (dUTP), and histamine are drug molecules that interact with most hub genes (Table 1 ). Table 1 The top 10 drug molecules with the most predictive significance associated with hub genes from DSigDB database . Term Overlap P -value Adjusted p -value Combined score Genes Sodium sulfate 7/65 2.19e-17 3.18e-14 92289.63761 IL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4 Retinol 7/135 4.34e-15 3.15e-12 35925.7132 IL10;CD4;CXCL8;IFNG;CD8A;IL1B;TLR4 Thalidomide 6/67 3.13e-14 4.64e-12 30480.4935 IL10;CD4;IFNG;CD8A;IL1B;STAT3 Dutp 7/179 3.25e-14 4.64e-12 25053.05748 IL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4 Histamine 7/180 3.38e-14 4.64e-12 24875.21126 IL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4 Bilirubin 7/182 3.65e-14 4.64e-12 24526.15551 IL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4 Maltotriose 7/182 3.65e-14 4.64e-12 24526.15551 IL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4 Melatonin 7/184 3.95e-14 4.64e-12 24185.6856 IL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4 Glycoprotein 7/189 4.78e-14 4.64e-12 23370.12008 IL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4 Hub Genes Immune Infiltration Analysis We determined the percentage of immune cell infiltration in DM and GC samples and compared them to controls. It was discovered that infiltration of M1 and M2 macrophages was greater in DM than in controls, while regulatory T cells (Treg) and Natural Killer cells (NK cells) was lower in DM (Fig. 6 A, C). While in GC, M0, M1 macrophage and activated Dendritic cells (DCs) infiltration was greater than in the control group, whereas plasma cell infiltration was lower (Fig. 6 B). Analysis of hub gene correlations with immune cell infiltration revealed that CD4 was strongly correlated with a greater number of immune cells in DM, including positive correlations with M1, M2 and negative e relations with memory B cells, M0, and activated memory CD4 + T cells (Fig. 6 C). IL1B correlated positively with activated mast cell nuclear plasma cells and negatively with M2 and dormant mast cells in GC (Fig. 6 D). We obtained correlations between immune cells in DM and GC samples, respectively (Fig. 6 G, H). Discussion Autoimmune diseases are known to result from an abnormal response of the immune system against the body's own tissues. Some autoimmune diseases have been linked to promoting dysplasia of gastric epithelium and increasing the risk of GC. For example, autoimmune gastritis is characterized by abnormal immune response leading to a reduction in gastric parietal cells and hyperplasia of chromaffin cells.And autoimmune gastritis has been associated with GC [ 7 ]. The diagnosis of DM can sometimes be challenging due to the absence of specific clinical signs, such as muscle weakness and rash. The relationship between DM and malignant tumors has been a subject of study for a long time. Early studies proposed that DM may have a paraneoplastic nature, given the short time interval between the onset of the two conditions and the parallel trend of their disease courses [ 8 ]. Although subsequent research has found that DM patients with malignancies have a worse prognosis, the underlying mechanism requires further exploration [ 9 ]. The inflammatory pathological features of DM suggest that it may produce more specific autoantigens in the body. Specifically, the researchers found that the expression level of myositis-specific autoantigen (MSA) was significantly higher in the regenerating myoblasts of myositis tissue than in normal muscle cells. More importantly, myositis-specific antigen levels are increased in cancers closely associated with myositis, such as lung cancer [ 10 ]. This suggests that the occurrence of malignant tumors leads to an increased level of myositis-specific antigens, activating immune cells in the body and generating anti-tumor immune responses. Due to the existence of specific immune response mechanisms targeting myositis specific antigens in the body, patients will develop myositis after muscle injury. Several diagnostic markers with clinical significance have been identified for DM, including various antinuclear antibodies associated with an increased incidence of malignant tumors. For instance, anti-transcription intermediate factor 1 (TIF1) antibodies, targeting tumor suppressor, have shown a high negative predictive value for cancer-associated DM [ 12 , 13 ]. Patients with positive anti-TIF1-γ antibodies have a 9.4-fold higher risk of malignancy compared to negative patients [ 14 ]. Positive anti- nuclear matrix protein (NXP)-2 antibodies have also been associated with an increased incidence of malignancies in DM patients, with the proportion of males being particularly high [ 15 , 16 ]. Additionally, the incidence of malignancy in patients with anti-SAE antibody-positive DM is approximately 14–57%, although there is no specific advantage for any particular malignancy [ 17 – 19 ]. Existing data show that the pathological manifestations of DM in the skin are hyperkeratosis, dermal edema and epidermal atrophy. The pathological manifestations of DM in the skin are composed of CD4 + lymphocytes and perivascular infiltrate [ 20 ]. In muscle tissue, DM primarily exhibits perimyocyte and perivascular inflammatory infiltrates. It is also accompanied by an increase in major histocompatibility complex (MHC) I class expression [ 21 , 22 ]. The immune-related pathogenesis of DM remains uncertain, but studies suggest that it may be associated with complement activation and type I interferon activity. The activated complement system can lead to inflammation by dissolving endomysial capillaries [ 23 ]. The tumor microenvironment, which includes components such as the extracellular matrix, macrophages, lymphocytes, neutrophils, and endothelial cells, plays a tumor suppressor role in the early development of GC. However, under the influence of certain factors, immune tolerance occurs in the tumor microenvironment, promoting the progression of GC. Unlike other solid tumors, GC is not very responsive to immunotherapy. It is necessary to reverse the immune tolerance within the tumor microenvironment to improve the efficacy of immunotherapy in GC patients. Macrophages infiltrating the tumor microenvironment can be divided into two subgroups: M1 and M2. The M1 subgroup exhibits tumor-suppressing properties, while the M2 subgroup has the opposite effect. GC immune tolerance is mediated by the differentiation of macrophages into the M2 subgroup [ 24 ]. Cancer-associated fibroblasts (CAFs) secrete numerous cytokines and chemokines in the tumor microenvironment, exerting regulatory effects on immune cells such as T cells and macrophages [ 25 ]. Cytokines act as regulators within the GC immune-tolerant tumor microenvironment, inducing various biological processes in immune cells, fibroblasts, and endothelial cells. For example, interleukin 15 (IL-15) and serum interleukin 8 (sIL-8) promote immune escape in GC cells by upregulating PD-L1 expression on the surface of T cells [ 26 , 27 ]. Tumor-associated macrophages (TAMs) inhibit NK cell killing of GC cells by secreting TGF-β1 and CHI3L1 [ 27 , 28 ]. In our study, we identified a total of 8 hub genes through differential gene analysis and analysis of their interaction strengths. We analyzed their immune infiltration and found that the high expression group of hub genes exhibited increased infiltration of DCs, iDCs, macrophages, neutrophils, Th1 cells, and Th2 cells. The GO and KEGG analyses of the co-related genes and hub genes yielded similar results, with a high enrichment of immune response-related pathways, cellular components, and functions. These included MHC, cytokines, signaling receptors (ligands), and the activity and differentiation of specific immune cells. These findings suggest that inflammatory responses and immune responses play significant roles in the occurrence and development of DM and GC. Cytokines, which include interleukins, interferons, the tumor necrosis factor superfamily, colony-stimulating factors, and chemokines, play a crucial role in the occurrence and development of DM and GC. The 8 hub genes identified in our study are closely associated with cytokines and mediate the progression of both diseases through other signaling pathways. For instance, TLR4 is a member of the Toll-like receptor (TLR) family, and its expression is stimulated by antigen-presenting cells (APCs), resulting in the production of various cytokines, chemokines, and their receptors via two pathways [ 29 ]. IL1B and IL10 belong to the interleukin cytokine family. Polymorphism in the IL1 gene cluster promotes interleukin-1-β production, which may exacerbate gastric mucosal damage and increase the risk of GC [ 30 ]. CXCL8 is a member of the CXC chemokine family and serves as a major mediator of inflammatory responses. Additionally, this protein is secreted by tumor cells to promote tumor migration, invasion, angiogenesis, and metastasis [ 31 – 33 ]. IFNG encodes type II interferon IFN-γ. Sánchez-Zauco et al. found that the levels of IL-1β, IL-10 and interferon-γ (IFN-γ) in the circulating blood of patients with GC were significantly higher than those of normal people [ 34 ]. The CD4 gene encodes the CD4 membrane glycoprotein of T lymphocytes, and the CD4 antigen, together with the T cell receptor on T lymphocytes, acts as a co-receptor to recognize antigens displayed by antigen-presenting cells in the context of class II MHC molecules, ultimately resulting in lymphatic Factor production and activation of T helper cells. The protein encoded by STAT3 is a member of the STAT protein family that responds to cytokines and growth factors. The CD4 gene encodes the CD4 membrane glycoprotein of T lymphocytes, and the CD4 antigen, together with the T cell receptor on T lymphocytes, acts as a co-receptor to recognize antigens displayed by antigen-presenting cells in the context of class II MHC molecules, ultimately resulting in lymphatic Factor production and activation of T helper cells. STAT3 is a constituent of the STAT protein family, which exhibits responsiveness to cytokines and growth factors. The activation and interplay of STAT3 and nuclear factor kappa-B (NF-кB) are of paramount importance in facilitating communication in TME. STAT3 helps tumor cells resist apoptosis caused by tumor surveillance and regulates angiogenesis during tumor development[ 35 ]. In GC-related studies, STAT3 was found to drive enhancer of zeste homolog 2 (EZH2) transcriptional activation, suggesting a poor prognosis in GC patients [ 36 ]. Activated STAT3 mediates autoimmune diseases by inducing differentiation of Th17 cells [ 37 ]. We found that phosphorylated STAT3 by receptor-associated kinases, pSTAT3, is significantly elevated in muscle tissue from patients with DM. Receiver characteristic curves suggest that it is a good diagnostic for DM [ 38 ]. Non-coding RNAs include miRNAs, lncRNAs and circle RNAsdo not have coding functions but play important roles in gene expression and protein function regulation. Linc-DGCR6-1 belongs to the category of lncRNAs. Linc-DGCR6-1 have been found to be capable of targeting the USP18 protein and regulating the signalling pathway of type 1 IFN. And the type 1 IFN signalling pathway is closely related to tissue injury in DM [ 39 ]. The antisense lncRNA, AL136018.1, was found to be overexpressed in the muscle tissue of DM patients. AL136018.1 could increase the transcription level of Cathepsin G (CTSG) gene, which contributed to the excessive infiltration of CD4 + T cells in DM tissues and perivascular [ 40 , 41 ]. Previous studies have found that some specific non-coding RNAs can promote the differentiation of TAMs into the M2 subgroup and mediate GC immune tolerance [ 42 ]. In addition, non-coding RNA can induce drug resistance in GC cells [ 43 ]. Platinum is an extremely important drug in the chemotherapy regimen of GC. Studies have found that miR-21 can enhance the resistance of GC cells to cisplatin [ 44 ]. The regulatory network between miR-21-5p and STAT3, IL1B and TLR4 was found in the competitive endogenous RNA (ceRNA) network we established. However, the specific molecular mechanism of DM still needs comprehensive and in-depth exploration, especially the regulatory role played by non-coding RNAs other than lncRNA. Given the close association between DM and malignant tumours, especially GC, it is necessary to explore the commonality at the genetic level between the two. This also provides reference value for our clinical diagnosis and treatment, such as for patients with DM, imaging and laboratory examination of the site of common malignant tumours can be targeted. For the symptoms of DM after diagnosis of GC, it is necessary to carefully identify whether it is paraneoplastic myositis or drug-associated myositis caused by anti-tumour therapy. And we should adopt targeted treatment for myositis symptoms caused by different etiological factors[ 45 ]. There are still limitations in our study. For example, whether different key hub genes are suggestive of disease extent at the expression level, and whether the key hub genes have molecular mechanisms that promote the progression of the two diseases, etc. Additional investigation is required to delve into the molecular pathways and ascertain possible biomarkers and therapeutic targets. Despite the limitations of the study, the findings have significant implications for public health prevention and control of cancer. Identifying key hub genes can potentially aid in the early detection of GC in DM patients and help in improving overall patient outcomes and reducing medical costs. Conclusion Our study uncovered shared genetic characteristics between DM and GC. The eight core genes examined potentially contribute to the development of both conditions, specifically by participating in immunological responses related with cytokines. Overall, the study provides a foundation for future research in this field and offers new perspectives for understanding the co-development of DM and GC. Abbreviations GEO Gene Expression Omnibu GC Gastric Cancer DM Dermatomyositis CTD The Comparative Toxicogenomics Database DEGs Differentially Expressed Genes PPI Protein-Protein Interaction MCODE Molecular Complex Detection GO Gene Ontology KEGG Kyoto Encyclopedia Of Genes And Genomes ENCODE Encyclopedia Of DNA Elements DSigDB Drug Signature Database IL1B Interleukin-1Β IFNG Interferon Gamma STAT3 Signal Transducer And Activator Of Transcription 3 IL10 Interleukin-10 CXCL8 C-X-C Motif Chemokine Ligand 8 TLR4 Toll Like Receptor 4 ROC Receiver Operating Characteristic AUC Area Under Curve CC Cell Component BP Biological Process MF Molecular Function NK cells Natural Killer Cell Treg Regulatory T Cells DCs Dendritic Cells MSA Myositis-Specific Autoantigen TIF1 Transcription Intermediate Factor 1 NXP Nuclear Matrix Protein MHC Major Histocompatibility Complex sIL-8 Serum Interleukin 8 TAMs Tumor-Associated Macrophages IFN-γ Interferon-Γ NF-кB Nuclear Factor Kappa-B EZH2 Enhancer Of Zeste Homolog 2 CTSG Cathepsin G ceRNA Competitive Endogenous RNA Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and material The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding Our study was supported by the following funds: Beijing Xisike Clinical Oncology Research Foundation (Grant No. Y-BMS2019-038); Science and Technology Development Plan Project of Shandong Province(Grant No. 202003030451); The Youth Scientific Research Fund of the Affiliated Hospital of Qingdao University(Grant No. QDFYQN202101007) and Beijing Science and Technology Innovation Medical Development Foundation（Grant No. KC2021-JX-0186-145）. Authors' contributions We would like to express gratitude to all authors for their contributions: YD provided the idea and data analysis of the article; CL expanded and constructed the molecular function network; KW ensured the correctness of the logic of the article; WQ ensured the originality of the topic selection and fund support. 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The induction of innate and adaptive immunity by biodegradable poly(γ-glutamic acid) nanoparticles via a TLR4 and MyD88 signaling pathway. Biomaterials 2011; 32: 5206–5212. El-Omar EM, Carrington M, Chow WH, McColl KE, Bream JH, Young HA, Herrera J, Lissowska J, Yuan CC, Rothman N, Lanyon G, Martin M, Fraumeni JF, Jr. and Rabkin CS. Interleukin-1 polymorphisms associated with increased risk of GC. Nature 2000; 404: 398–402. Boppana NB, Devarajan A, Gopal K, Barathan M, Bakar SA, Shankar EM, Ebrahim AS and Farooq SM. Blockade of CXCR2 signalling: a potential therapeutic target for preventing neutrophil-mediated inflammatory diseases. Exp Biol Med (Maywood) 2014; 239: 509–518. Xu L, Ashkenazi A and Chaudhuri A. Duffy antigen/receptor for chemokines (DARC) attenuates angiogenesis by causing senescence in endothelial cells. Angiogenesis 2007; 10: 307–318. Veltri RW, Miller MC, Zhao G, Ng A, Marley GM, Wright GL, Jr., Vessella RL and Ralph D. Interleukin-8 serum levels in patients with benign prostatic hyperplasia and prostate cancer. Urology 1999; 53: 139–147. Sánchez-Zauco N, Torres J, Gómez A, Camorlinga-Ponce M, Muñoz-Pérez L, Herrera-Goepfert R, Medrano-Guzmán R, Giono-Cerezo S and Maldonado-Bernal C. Circulating blood levels of IL-6, IFN-γ, and IL-10 as potential diagnostic biomarkers in GC: a controlled study. BMC Cancer 2017; 17: 384. Fan Y, Mao R and Yang J. NF-κB and STAT3 signaling pathways collaboratively link inflammation to cancer. Protein Cell 2013; 4: 176–185. Pan YM, Wang CG, Zhu M, Xing R, Cui JT, Li WM, Yu DD, Wang SB, Zhu W, Ye YJ, Wu Y, Wang S and Lu YY. STAT3 signaling drives EZH2 transcriptional activation and mediates poor prognosis in GC. Mol Cancer 2016; 15: 79. Damasceno LEA, Prado DS, Veras FP, Fonseca MM, Toller-Kawahisa JE, Rosa MH, Públio GA, Martins TV, Ramalho FS, Waisman A, Cunha FQ, Cunha TM and Alves-Filho JC. PKM2 promotes Th17 cell differentiation and autoimmune inflammation by fine-tuning STAT3 activation. J Exp Med 2020; 217: Li D, Jia W, Zhou L, Hao Y, Wang K, Yang B, Yang J, Luo D and Fu Z. Increased Expression of the p-STAT3/IL-17 Signaling pathway in patients with DM. Mod Rheumatol 2022; Salajegheh M, Kong SW, Pinkus JL, Walsh RJ, Liao A, Nazareno R, Amato AA, Krastins B, Morehouse C, Higgs BW, Jallal B, Yao Y, Sarracino DA, Parker KC and Greenberg SA. Interferon-stimulated gene 15 (ISG15) conjugates proteins in DM muscle with perifascicular atrophy. Ann Neurol 2010; 67: 53–63. Liang Y and Peng Y. Gene body methylation facilitates the transcription of CTSG via antisense lncRNA AL136018.1 in dermatomyositic myoideum. Cell Biol Int 2021; 45: 456–462. Gao S, Zhu H, Yang H, Zhang H, Li Q and Luo H. The role and mechanism of cathepsin G in DM. Biomed Pharmacother 2017; 94: 697–704. Yang B, Su K, Sha G, Bai Q, Sun G, Chen H, Xie H and Jiang X. LINC00665 interacts with BACH1 to activate Wnt1 and mediates the M2 polarization of tumor-associated macrophages in GC. Mol Immunol 2022; 146: 1–8. Cui HY, Rong JS, Chen J, Guo J, Zhu JQ, Ruan M, Zuo RR, Zhang SS, Qi JM and Zhang BH. Exosomal microRNA-588 from M2 polarized macrophages contributes to cisplatin resistance of GC cells. World J Gastroenterol 2021; 27: 6079–6092. Zheng P, Chen L, Yuan X, Luo Q, Liu Y, Xie G, Ma Y and Shen L. Exosomal transfer of tumor-associated macrophage-derived miR-21 confers cisplatin resistance in GC cells. J Exp Clin Cancer Res 2017; 36: 53. Shibata C, Kato J, Toda N, Imai M, Fukumura Y, Arai J, Kurokawa K, Kondo M, Takagi K, Kojima K, Ohki T, Seki M, Yoshida M, Suzuki A and Tagawa K. Paraneoplastic DM appearing after nivolumab therapy for GC: a case report. J Med Case Rep 2019; 13: 168. Additional Declarations No competing interests reported. Supplementary Files additionalfile1datasheetDM.csv additionalfile2datasheetGC.csv 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-3847315","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268852694,"identity":"efdd1507-364c-45e9-91c3-fec8ef9044d4","order_by":0,"name":"Yixin Ding","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Yixin","middleName":"","lastName":"Ding","suffix":""},{"id":268852695,"identity":"52688bfb-e938-4d62-a150-1e1f1da1ae3c","order_by":1,"name":"Chuanyu Leng","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Chuanyu","middleName":"","lastName":"Leng","suffix":""},{"id":268852696,"identity":"2442f65e-2c77-41a3-922f-f8c143ae9a69","order_by":2,"name":"Shufei Wang","email":"","orcid":"","institution":"Shandong First Medical University \u0026 Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shufei","middleName":"","lastName":"Wang","suffix":""},{"id":268852697,"identity":"c2fdbeac-38d4-4552-822d-300e76253efc","order_by":3,"name":"Kongjia Wang","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Kongjia","middleName":"","lastName":"Wang","suffix":""},{"id":268852698,"identity":"65fb33b6-37fb-4d9e-961b-7190fc8e9ec9","order_by":4,"name":"Weiwei Qi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYJACCYYKBsYGMIN4LWdI1sLYRooWvhs5hjd/zrOW3XCA+eBtHga7PIJaJG/kGFtIbks33nCALdmahyG5mKAWg9u52yQMtx1O3HCAx0yah+FAYgNRWhLngLTwfyNBy8EGsC1sxGmRvP/+s2XDsXTjmYfZjC3nGCQT1sJ35ljizR811rJ9x5sf3nhTYUdYC8MBMMkMRkB3ElSPrGUUjIJRMApGAS4AAPL8P8DUmOnHAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Qi","suffix":""}],"badges":[],"createdAt":"2024-01-09 05:29:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3847315/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3847315/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50105181,"identity":"48169672-ec43-4b88-9f73-9b1cbd6d04eb","added_by":"auto","created_at":"2024-01-24 15:39:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":384934,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe overall flowchart of this study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FIG1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3847315/v1/2b617bffb12d41991b14595a.jpg"},{"id":50106104,"identity":"4cfd1791-af14-4448-bc38-ed8e7738fddc","added_by":"auto","created_at":"2024-01-24 15:55:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1045630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelection and interaction network of common related genes and hub genes.\u003c/strong\u003e (A) Venn diagram of 658 common related genes related to DM and GC. (B) Upset map was used to evaluate seven MCODE algorithms (MCC, MNC, Degree, Closeness, Radiality, Stress, and EPC) to select hub genes. The co-expression (C-D) and correlation network (E) of these 8 hub genes. ns, p≥0.05; *p\u0026lt; 0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"FIG2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3847315/v1/2c5ce33471b7577936e80843.jpg"},{"id":50105182,"identity":"bc5d031b-4eef-4537-8674-e954775be6f3","added_by":"auto","created_at":"2024-01-24 15:39:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2432256,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression and predicted ability of 8 hub genes in DM and GC. \u003c/strong\u003e(A) The expression level of 8 hub genes in DM and normal tissues. (B) The expression level of 8 hub genes in GC and normal tissues. (C) The ROC with AUC value of 8 hub genes in DM. (D) The ROC with AUC value of 8 hub genes in GC. ns, p≥0.05; *p\u0026lt; 0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"FIG3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3847315/v1/1bad452f2835b860974ac638.jpg"},{"id":50105185,"identity":"6821c2b1-fa1c-47fe-ab71-b70495a6afe3","added_by":"auto","created_at":"2024-01-24 15:39:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1486537,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO and KEGG analyses of 658 common related genes and 8 hub genes, respectively.\u003c/strong\u003e Cell component (CC), Biological process (BP), and Molecular function (MF) of 658 common related genes (A-C) and 8 hub genes (E-G). \u0026nbsp;analysis of 658 common related genes (D) and 8 hub genes (H).\u003c/p\u003e","description":"","filename":"FIG4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3847315/v1/b63610dd00b2640c33eef989.jpg"},{"id":50105688,"identity":"34b1dfc6-49f5-49a7-bdc8-1996385cf05d","added_by":"auto","created_at":"2024-01-24 15:47:58","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":767237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscription Factor-Gene-miRNA Regulatory Network of hub genes. \u003c/strong\u003e(A) The constructed transcription factor-gene regulatory network of hub genes. (B) The constructed transcription gene-miRNA regulatory network.\u003c/p\u003e","description":"","filename":"FIG5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3847315/v1/aed1ab375418bb069170dffe.jpg"},{"id":50105187,"identity":"4fdd8f4e-e671-43bb-ac5a-e51317858024","added_by":"auto","created_at":"2024-01-24 15:39:58","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2443330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune Infiltration Analysis. \u003c/strong\u003eComparative proportional stacked diagrams of the distribution of immune cells in DM (A), GC (B), and the control group. (C) Compared immune cell infiltration in DM versus control group. (D) Compared immune cell infiltration in GC versus control group. Correlation heatmap between 8 hub genes and immune cells in DM (E) and GC (F). Correlation analysis between immune cells in DM (G) and GC (H). ns, p≥0.05; *p\u0026lt; 0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"FIG6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3847315/v1/c4a57c7d1f16472feb5afca1.jpg"},{"id":53239210,"identity":"5384a0d8-000c-4bfe-bdfc-1826dd5dd758","added_by":"auto","created_at":"2024-03-22 09:35:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1217761,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3847315/v1/20385ab2-4024-4006-9dc7-d70eee008dcb.pdf"},{"id":50105686,"identity":"5a27fc00-2040-4f74-b3eb-ad1863400278","added_by":"auto","created_at":"2024-01-24 15:47:58","extension":"csv","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1106,"visible":true,"origin":"","legend":"","description":"","filename":"additionalfile1datasheetDM.csv","url":"https://assets-eu.researchsquare.com/files/rs-3847315/v1/ded5943ceb2fb897d0048da3.csv"},{"id":50105189,"identity":"f0f77f82-cf21-441c-afcc-f66d23e10f6a","added_by":"auto","created_at":"2024-01-24 15:39:59","extension":"csv","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":14958024,"visible":true,"origin":"","legend":"","description":"","filename":"additionalfile2datasheetGC.csv","url":"https://assets-eu.researchsquare.com/files/rs-3847315/v1/efa3de042caef70728d4b74d.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the molecular mechanisms and shared gene signatures between dermatomyositis and gastric cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the aging of the population and the increase of high-risk groups, the incidence and the number of deaths of GC (GC) continue to rise. In 2020, there were approximately 1\u0026nbsp;million new cases of GC and about 770,000 deaths. GC often remains hidden until its advanced stages, leading to limited treatment options. Early diagnosis and detection are crucial to improving the survival rate of GC. Few studies have explored the relationship between other specific diseases and the risk of developing GC, while most guidelines identify Helicobacter pylori infection, alcohol consumption, and high-salt food intake as risk factors for GC.\u003c/p\u003e \u003cp\u003eAutoimmune diseases can disrupt the body's immune response and may accelerate the development of gastritis. And it can eventually progress to precancerous GC. One such autoimmune disease is DM (DM), an idiopathic inflammatory muscle disease that typically presents with muscle weakness and rash. In the United States, DM has a higher incidence among middle-aged women and affects approximately 5\u0026ndash;10 per million people [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is worth noting that research has demonstrated a correlation between DM and a heightened occurrence of malignancies. A meta-analysis involving 4538 patients revealed that the risk of malignant tumors in patients with DM is approximately 4.66 times higher than that of the normal population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurther investigations have supported the link between DM and cancer. In a retrospective study by Hill et al., approximately 30% of patients with DM were diagnosed with malignant tumors, and more than 60% of these diagnoses occurred after the diagnosis of DM. The incidence of malignant tumors was significantly higher in patients with DM compared to the general population [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Other studies have also reported an increased incidence of common malignancies, including hematological, lung, gastric, breast, ovarian, and prostate cancers, among patients diagnosed with DM [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA meta-analysis involving 52 observational studies found a significant association between nine autoimmune diseases and an increased risk of GC. The study showed that nine autoimmune diseases were significantly associated with an increased risk of GC (DM, Pernicious anemia, Addison's disease, herpetiform dermatitis, IgG4 related diseases, primary biliary cirrhosis, Type 1 diabetes, systemic lupus erythematosus and Graves disease), with DM being the most strongly associated with GC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These findings underscore the urgent need to further explore the causal relationship and molecular genetic mechanisms linking DM to GC.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Download and Processing\u003c/h2\u003e \u003cp\u003eThe bulk RNA sequencing data of DM patients were obtained from the GSE1551 and GSE103236 datasets available in the Gene Expression Omnibus (GEO) database. Differential analysis of RNA-seq data was conducted using the DESeq2 and limma packages to identify differentially expressed genes (DEGs) in DM. DEGs were selected using a cutoff criteria of adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2(Fold Change)| \u0026gt; 1. Additionally, the top 500 genes most associated with DM were identified by querying the The Comparative Toxicogenomics Database (CTD), GeneCards, and DisGeNET databases based on their respective \"Inference Score,\" \"Score_gda,\" and \"Relevance score\" parameters. The resulting genes from these online databases were combined with the DEGs from GEO to form the set of DM-related genes. The same process was applied to obtain GC-related genes, and subsequently, the overlapping genes between DM and GC were further analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eProtein-Protein Interaction (PPI) Analysis, Hub Genes Selection, and Co-expression Network Construction\u003c/h2\u003e \u003cp\u003eThe strength of interaction relationships, direct binding relationships, and upstream and downstream regulatory pathways between proteins encoded by co-related genes were explored using the STRING online databae. Interactions with a combined score of over 0.4 were considered statistically significant. The Molecular Complex Detection (MCODE) plugin in Cytoscape (v3.8.2) was used to visualize the PPI network data obtained from STRING analysis. The hub genes were selected using the cytoHubba plugin in Cytoscape software (v3.8.2) with seven standard algorithms. The hub genes obtained from these algorithms were further filtered using the Upset algorithm to select those satisfying all the criteria. The GeneMANIA database was utilized to construct the co-expression network of the hub genes, and the correlation between the genes within the hub gene set was analyzed. The results were visualized using the circlize package (version v0.4.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.Functional Enrichment Analysis of Co-associated Gene Ontologies and Pathways\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) database was used to categorize gene functions and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was employed to explore the complex interrelationships between genes and metabolites. GO and KEGG analyses were performed on the common related genes between DM and GC, as well as on the hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.Construction of Transcription Factor-Gene-miRNA Regulatory Network\u003c/h2\u003e \u003cp\u003eThe Encyclopedia of DMA Elements (ENCODE) database, available on the Networkanalyst platform, was used to construct a transcription factor-gene regulatory network and select the transcription factor with the strongest regulatory relationship based on the \"Degree\" score. Additionally, the RegNetwork database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.regnetworkweb.org\u003c/span\u003e\u003cspan address=\"http://www.regnetworkweb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), also available on the Networkanalyst platform, was used to construct the gene-miRNA regulatory network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e5.Candidate Drug Prediction\u003c/h2\u003e \u003cp\u003eThe Drug Signature Database (DSigDB) containing 22527 gene. We access the DSigDB database from Enrichr database and explored potential drug molecules that have a significant relationship with hub genes. Candidate drugs were ranked based on their adjusted p-value, and those with an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e6.Hub Genes Single Gene Immune Infiltration Analysis\u003c/h2\u003e \u003cp\u003eThe CIBERSORT program, which utilizes linear support vector regression, is employed to deconstruct the expression matrix of several subtypes of human immune cells for the purpose of immune-immersion analysis. The calculation of immune cell proportions was performed for datasets GSE1551 and GSE103236. Additionally, the relationship between immune cells and hub genes, as well as the association of each immune cell, were examined.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of Genes and Co-associated Genes Related to DM and GC\u003c/h2\u003e \u003cp\u003eThe overall flow chart of our study was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The CTD, GeneCards, and DisGeNET databases yielded a combined total of 10,176, 1,413, and 235 genes associated with DM, respectively. Differential analysis of RNA-seq data from the GSE1551 dataset resulted in the identification of 261 differentially expressed genes in DM. Similarly, 38,824, 820, 15,117, and 343 GC-related genes were obtained from the four databases. 1,161 DM-related genes, 1,363 GC-related genes, and 658 common related genes were identified after correlation screening and integration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe PPI Network Display of Common Related Genes and the Acquisition of 8 Key Hub Genes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe apply 7 algorithms to select 8 hub genes based on their central roles in the network. The identified hub genes are interleukin-1β (IL1B), interferon gamma (IFNG), CD4, signal transducer and activator of transcription 3 (STAT3), interleukin-10 (IL10), CD8A, C-X-C motif chemokine ligand 8 (CXCL8), and toll like receptor 4 (TLR4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The co-expression and correlation network of these eight hub genes were constructed and displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe investigated hub gene expression in DM and GC, respectively. All eight hub genes were substantially overexpressed in GC, while only CD4 and CD8A were significantly overexpressed in DM (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). The receiver operating characteristic (ROC) curves and area under curve (AUC) values indicated that STAT3, CD4, TLR4, CXCL8, and IFNG had a high diagnostic value for GC (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7), whereas CD4, STAT4, IL10, and CD8A had a high diagnostic value for DM (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCommonly Related Genes Play an Important Role in Immune Pathways and Inflammatory Pathways\u003c/h2\u003e \u003cp\u003e658 co-related genes were included in the GO and KEGG analysis. The results showed that the co-related genes were mainly enriched in extracellular components such as extracellular matrix and other biological processes such as cell adhesion, cytokine activity and receptor-ligand binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C).\u003c/p\u003e \u003cp\u003eThe KEGG analysis showed that the common related genes were enriched in signaling pathways such as \"cytokine-cytokine receptor interaction, Th17 cell differentiation, and the AGE-RAGE signaling pathway\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In addition, 8 hub genes was involved in immune-related processes and pathways, including \"monocyte differentiation, lymphocyte differentiation, adaptive immune response, and interleukin-6 production\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Transcription Factor-Gene-miRNA Regulatory Network and Candidate Drug Prediction\u003c/h2\u003e \u003cp\u003eStrong connections were found between STAT3, IFNG, and IL10 and transcription factors among the eight hub genes. The constructed transcription factor-gene regulatory network is displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Moreover, all the 8 hub genes showed correlations with corresponding miRNAs, with STAT3, CXCL8, and TLR4 exhibiting particularly strong relationships with miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Using the hub genes for screening in the DSigDB database, the top ten drug molecules with the most predictive significance were identified. Sodium sulfate, retinol, thalidomide, pyrophosphatase (dUTP), and histamine are drug molecules that interact with most hub genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eThe top 10 drug molecules with the most predictive significance associated with hub genes from DSigDB database\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCombined score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSodium sulfate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19e-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.18e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92289.63761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRetinol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.34e-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35925.7132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL10;CD4;CXCL8;IFNG;CD8A;IL1B;TLR4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThalidomide\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6/67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.13e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.64e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30480.4935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL10;CD4;IFNG;CD8A;IL1B;STAT3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDutp\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.25e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.64e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25053.05748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistamine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.38e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.64e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24875.21126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilirubin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.64e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24526.15551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaltotriose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.64e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24526.15551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMelatonin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.95e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.64e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24185.6856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlycoprotein\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.78e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.64e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23370.12008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL10;CD4;IFNG;CD8A;IL1B;STAT3;TLR4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHub Genes Immune Infiltration Analysis\u003c/h2\u003e \u003cp\u003eWe determined the percentage of immune cell infiltration in DM and GC samples and compared them to controls. It was discovered that infiltration of M1 and M2 macrophages was greater in DM than in controls, while regulatory T cells (Treg) and Natural Killer cells (NK cells) was lower in DM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, C). While in GC, M0, M1 macrophage and activated Dendritic cells (DCs) infiltration was greater than in the control group, whereas plasma cell infiltration was lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Analysis of hub gene correlations with immune cell infiltration revealed that CD4 was strongly correlated with a greater number of immune cells in DM, including positive correlations with M1, M2 and negative e relations with memory B cells, M0, and activated memory CD4\u003csup\u003e+\u003c/sup\u003e T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). IL1B correlated positively with activated mast cell nuclear plasma cells and negatively with M2 and dormant mast cells in GC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). We obtained correlations between immune cells in DM and GC samples, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG, H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAutoimmune diseases are known to result from an abnormal response of the immune system against the body's own tissues. Some autoimmune diseases have been linked to promoting dysplasia of gastric epithelium and increasing the risk of GC. For example, autoimmune gastritis is characterized by abnormal immune response leading to a reduction in gastric parietal cells and hyperplasia of chromaffin cells.And autoimmune gastritis has been associated with GC [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The diagnosis of DM can sometimes be challenging due to the absence of specific clinical signs, such as muscle weakness and rash. The relationship between DM and malignant tumors has been a subject of study for a long time. Early studies proposed that DM may have a paraneoplastic nature, given the short time interval between the onset of the two conditions and the parallel trend of their disease courses [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although subsequent research has found that DM patients with malignancies have a worse prognosis, the underlying mechanism requires further exploration [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe inflammatory pathological features of DM suggest that it may produce more specific autoantigens in the body. Specifically, the researchers found that the expression level of myositis-specific autoantigen (MSA) was significantly higher in the regenerating myoblasts of myositis tissue than in normal muscle cells. More importantly, myositis-specific antigen levels are increased in cancers closely associated with myositis, such as lung cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This suggests that the occurrence of malignant tumors leads to an increased level of myositis-specific antigens, activating immune cells in the body and generating anti-tumor immune responses. Due to the existence of specific immune response mechanisms targeting myositis specific antigens in the body, patients will develop myositis after muscle injury.\u003c/p\u003e \u003cp\u003eSeveral diagnostic markers with clinical significance have been identified for DM, including various antinuclear antibodies associated with an increased incidence of malignant tumors. For instance, anti-transcription intermediate factor 1 (TIF1) antibodies, targeting tumor suppressor, have shown a high negative predictive value for cancer-associated DM [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Patients with positive anti-TIF1-γ antibodies have a 9.4-fold higher risk of malignancy compared to negative patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Positive anti- nuclear matrix protein (NXP)-2 antibodies have also been associated with an increased incidence of malignancies in DM patients, with the proportion of males being particularly high [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, the incidence of malignancy in patients with anti-SAE antibody-positive DM is approximately 14\u0026ndash;57%, although there is no specific advantage for any particular malignancy [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExisting data show that the pathological manifestations of DM in the skin are hyperkeratosis, dermal edema and epidermal atrophy. The pathological manifestations of DM in the skin are composed of CD4\u0026thinsp;+\u0026thinsp;lymphocytes and perivascular infiltrate [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In muscle tissue, DM primarily exhibits perimyocyte and perivascular inflammatory infiltrates. It is also accompanied by an increase in major histocompatibility complex (MHC) I class expression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The immune-related pathogenesis of DM remains uncertain, but studies suggest that it may be associated with complement activation and type I interferon activity. The activated complement system can lead to inflammation by dissolving endomysial capillaries [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The tumor microenvironment, which includes components such as the extracellular matrix, macrophages, lymphocytes, neutrophils, and endothelial cells, plays a tumor suppressor role in the early development of GC. However, under the influence of certain factors, immune tolerance occurs in the tumor microenvironment, promoting the progression of GC. Unlike other solid tumors, GC is not very responsive to immunotherapy. It is necessary to reverse the immune tolerance within the tumor microenvironment to improve the efficacy of immunotherapy in GC patients. Macrophages infiltrating the tumor microenvironment can be divided into two subgroups: M1 and M2. The M1 subgroup exhibits tumor-suppressing properties, while the M2 subgroup has the opposite effect. GC immune tolerance is mediated by the differentiation of macrophages into the M2 subgroup [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Cancer-associated fibroblasts (CAFs) secrete numerous cytokines and chemokines in the tumor microenvironment, exerting regulatory effects on immune cells such as T cells and macrophages [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Cytokines act as regulators within the GC immune-tolerant tumor microenvironment, inducing various biological processes in immune cells, fibroblasts, and endothelial cells. For example, interleukin 15 (IL-15) and serum interleukin 8 (sIL-8) promote immune escape in GC cells by upregulating PD-L1 expression on the surface of T cells [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Tumor-associated macrophages (TAMs) inhibit NK cell killing of GC cells by secreting TGF-β1 and CHI3L1 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, we identified a total of 8 hub genes through differential gene analysis and analysis of their interaction strengths. We analyzed their immune infiltration and found that the high expression group of hub genes exhibited increased infiltration of DCs, iDCs, macrophages, neutrophils, Th1 cells, and Th2 cells. The GO and KEGG analyses of the co-related genes and hub genes yielded similar results, with a high enrichment of immune response-related pathways, cellular components, and functions. These included MHC, cytokines, signaling receptors (ligands), and the activity and differentiation of specific immune cells. These findings suggest that inflammatory responses and immune responses play significant roles in the occurrence and development of DM and GC.\u003c/p\u003e \u003cp\u003eCytokines, which include interleukins, interferons, the tumor necrosis factor superfamily, colony-stimulating factors, and chemokines, play a crucial role in the occurrence and development of DM and GC. The 8 hub genes identified in our study are closely associated with cytokines and mediate the progression of both diseases through other signaling pathways. For instance, TLR4 is a member of the Toll-like receptor (TLR) family, and its expression is stimulated by antigen-presenting cells (APCs), resulting in the production of various cytokines, chemokines, and their receptors via two pathways [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. IL1B and IL10 belong to the interleukin cytokine family. Polymorphism in the IL1 gene cluster promotes interleukin-1-β production, which may exacerbate gastric mucosal damage and increase the risk of GC [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. CXCL8 is a member of the CXC chemokine family and serves as a major mediator of inflammatory responses. Additionally, this protein is secreted by tumor cells to promote tumor migration, invasion, angiogenesis, and metastasis [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. IFNG encodes type II interferon IFN-γ. S\u0026aacute;nchez-Zauco et al. found that the levels of IL-1β, IL-10 and interferon-γ (IFN-γ) in the circulating blood of patients with GC were significantly higher than those of normal people [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The CD4 gene encodes the CD4 membrane glycoprotein of T lymphocytes, and the CD4 antigen, together with the T cell receptor on T lymphocytes, acts as a co-receptor to recognize antigens displayed by antigen-presenting cells in the context of class II MHC molecules, ultimately resulting in lymphatic Factor production and activation of T helper cells. The protein encoded by STAT3 is a member of the STAT protein family that responds to cytokines and growth factors. The CD4 gene encodes the CD4 membrane glycoprotein of T lymphocytes, and the CD4 antigen, together with the T cell receptor on T lymphocytes, acts as a co-receptor to recognize antigens displayed by antigen-presenting cells in the context of class II MHC molecules, ultimately resulting in lymphatic Factor production and activation of T helper cells. STAT3 is a constituent of the STAT protein family, which exhibits responsiveness to cytokines and growth factors. The activation and interplay of STAT3 and nuclear factor kappa-B (NF-кB) are of paramount importance in facilitating communication in TME. STAT3 helps tumor cells resist apoptosis caused by tumor surveillance and regulates angiogenesis during tumor development[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In GC-related studies, STAT3 was found to drive enhancer of zeste homolog 2 (EZH2) transcriptional activation, suggesting a poor prognosis in GC patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Activated STAT3 mediates autoimmune diseases by inducing differentiation of Th17 cells [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. We found that phosphorylated STAT3 by receptor-associated kinases, pSTAT3, is significantly elevated in muscle tissue from patients with DM. Receiver characteristic curves suggest that it is a good diagnostic for DM [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNon-coding RNAs include miRNAs, lncRNAs and circle RNAsdo not have coding functions but play important roles in gene expression and protein function regulation. Linc-DGCR6-1 belongs to the category of lncRNAs. Linc-DGCR6-1 have been found to be capable of targeting the USP18 protein and regulating the signalling pathway of type 1 IFN. And the type 1 IFN signalling pathway is closely related to tissue injury in DM [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The antisense lncRNA, AL136018.1, was found to be overexpressed in the muscle tissue of DM patients. AL136018.1 could increase the transcription level of Cathepsin G (CTSG) gene, which contributed to the excessive infiltration of CD4\u0026thinsp;+\u0026thinsp;T cells in DM tissues and perivascular [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Previous studies have found that some specific non-coding RNAs can promote the differentiation of TAMs into the M2 subgroup and mediate GC immune tolerance [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In addition, non-coding RNA can induce drug resistance in GC cells [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Platinum is an extremely important drug in the chemotherapy regimen of GC. Studies have found that miR-21 can enhance the resistance of GC cells to cisplatin [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The regulatory network between miR-21-5p and STAT3, IL1B and TLR4 was found in the competitive endogenous RNA (ceRNA) network we established. However, the specific molecular mechanism of DM still needs comprehensive and in-depth exploration, especially the regulatory role played by non-coding RNAs other than lncRNA.\u003c/p\u003e \u003cp\u003eGiven the close association between DM and malignant tumours, especially GC, it is necessary to explore the commonality at the genetic level between the two. This also provides reference value for our clinical diagnosis and treatment, such as for patients with DM, imaging and laboratory examination of the site of common malignant tumours can be targeted. For the symptoms of DM after diagnosis of GC, it is necessary to carefully identify whether it is paraneoplastic myositis or drug-associated myositis caused by anti-tumour therapy. And we should adopt targeted treatment for myositis symptoms caused by different etiological factors[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. There are still limitations in our study. For example, whether different key hub genes are suggestive of disease extent at the expression level, and whether the key hub genes have molecular mechanisms that promote the progression of the two diseases, etc. Additional investigation is required to delve into the molecular pathways and ascertain possible biomarkers and therapeutic targets.\u003c/p\u003e \u003cp\u003eDespite the limitations of the study, the findings have significant implications for public health prevention and control of cancer. Identifying key hub genes can potentially aid in the early detection of GC in DM patients and help in improving overall patient outcomes and reducing medical costs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study uncovered shared genetic characteristics between DM and GC. The eight core genes examined potentially contribute to the development of both conditions, specifically by participating in immunological responses related with cytokines. Overall, the study provides a foundation for future research in this field and offers new perspectives for understanding the co-development of DM and GC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eGene Expression Omnibu\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eGastric Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eDermatomyositis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eCTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eThe Comparative Toxicogenomics Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eProtein-Protein Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eMCODE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eMolecular Complex Detection\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eKyoto Encyclopedia Of Genes And Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eENCODE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eEncyclopedia Of DNA Elements\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eDSigDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eDrug Signature Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eIL1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eInterleukin-1\u0026Beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eIFNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eInterferon Gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eSTAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eSignal Transducer And Activator Of Transcription 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eIL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eInterleukin-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eCXCL8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eC-X-C Motif Chemokine Ligand 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eTLR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eToll Like Receptor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eArea Under Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eCell Component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eBiological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eMolecular Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eNK cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eNatural Killer Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eTreg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eRegulatory T Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eDCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eDendritic Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eMSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eMyositis-Specific Autoantigen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eTIF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eTranscription Intermediate Factor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eNXP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eNuclear Matrix Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eMHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eMajor Histocompatibility Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003esIL-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eSerum Interleukin 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eTAMs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eTumor-Associated Macrophages\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eIFN-\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eInterferon-\u0026Gamma;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eNF-кB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eNuclear Factor Kappa-B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eEZH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eEnhancer Of Zeste Homolog 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eCTSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eCathepsin G\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.806509945750452%\" valign=\"top\"\u003e\n \u003cp\u003eceRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"81.19349005424955%\" valign=\"top\"\u003e\n \u003cp\u003eCompetitive Endogenous RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study was supported by the following funds: Beijing Xisike Clinical Oncology Research Foundation (Grant No. Y-BMS2019-038); Science and Technology Development Plan Project of Shandong Province(Grant No. 202003030451); The Youth Scientific Research Fund of the Affiliated Hospital of Qingdao University(Grant No. QDFYQN202101007) and Beijing Science and Technology Innovation Medical Development Foundation（Grant No. KC2021-JX-0186-145）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express gratitude to all authors for their contributions: YD provided the idea and data analysis of the article; CL expanded and constructed the molecular function network; KW ensured the correctness of the logic of the article; WQ ensured the originality of the topic selection and fund support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKamiyama H, Niwa K, Ishiyama S, Takahashi M, Kojima Y, Goto M, Tomiki Y, Higashihara Y and Sakamoto K. 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J Med Case Rep 2019; 13: 168.\u003c/span\u003e\u003c/li\u003e\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":"DM, GC, gene signatures, immune response, cytokines","lastPublishedDoi":"10.21203/rs.3.rs-3847315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3847315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eSeveral studies have reported a clinical association between gastric cancer(GC) and dermatomyositis (DM), but the molecular features and underlying mechanisms between the two diseases have not been investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods：\u003c/strong\u003eWe obtained the strongly associated genes of DM and GC and the clinical characteristics from the Gene Expression Omnibus (GEO), The Comparative Toxicogenomics Database (CTD), GeneCards, and DisGeNET databases. We next screened hub genes, constructed co-expression and interaction networks, transcription factor-gene-miRNA regulatory networks, and performed enrichment analysis of cell signaling pathways and candidate drugs prediction. Finally, a single-gene immune infiltration assay was performed on the hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults：\u003c/strong\u003eOur study revealed commonalities at the genetic level between DM and GC. A deep dive into the 8 hub genes revealed the role in immune response, especially cytokines, which were involved in the co-development of the two diseases. The obtained hub genes have the potential to be biomarkers as well as therapeutic targets for DM patients with a potential predisposition to GC tumorigenesis.\u003c/p\u003e","manuscriptTitle":"Exploring the molecular mechanisms and shared gene signatures between dermatomyositis and gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-24 15:39:53","doi":"10.21203/rs.3.rs-3847315/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":"2ca1a351-d4f4-48b9-8be2-997e978ae954","owner":[],"postedDate":"January 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-22T09:27:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-24 15:39:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3847315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3847315","identity":"rs-3847315","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}