Identification of pyroptosis-related biomarkers in venous thromboembolism

Identification of pyroptosis-related biomarkers in venous thromboembolism | 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 Identification of pyroptosis-related biomarkers in venous thromboembolism Shengbin Han, Jingzhe Xu, Lianlin Wang, Jiarong Wang, Chenchen Yu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5313033/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract OBJECTIVE The study was designed with the aim of excavating diagnostic biomarkers of venous thromboembolism (VTE). METHODS The GSE19151 and GSE48000 datasets were subjected into this study. The pyroptosis-related genes (PRGs) were sourced from literature 1 . Differential expression analysis and WGCNA were applied to identify differential genes related with Pyroptosis (DE-PRGs) in VTE. The possible functions of DE-PRGs were defined by means of enrichment analysis. The biomarkers related with pyroptosis in VTE were determined by plotting receiver operating characteristic (ROC) curve. The gene set enrichment analysis (GSEA) was employed to analyze the correlation between biomarkers and pathways. Finally, the quantificational Reverse Transcription-Polymerase Chain Reaction (qRT-PCR) was proceeded to verify the expression level of the biomarkers in VTE. RESULTS A number of 52 DE-PRGs were identified by feat of differential expression analysis and WGCNA. A number of five biomarkers (RPL31, RPL34, RPL9, RPS27L and HINT1) were further screened by ROC curves. GSEA pointed to the linkage of five biomarkers to the ribosome proteins and oxidative phosphorylation signaling transduction, which may cause cell pyrodeath and trigger VTE through mitochondrial pathways. qRT-PCR manifested the expression levels of RPL31, RPL9 and HINT1 were all observably higher in VTE samples than in normal samples. CONCLUSION A number of five biomarkers, RPL31, RPL34, RPL9, RPS27L and HINT1, were identified as pyroptosis-related biomarkers in VTE, which provided a basis for understanding VTE pathogenesis and new insights into VTE diagnosis and treatment. thrombotic inflammation pyroptosis venous thromboembolism biomarker quantificational reverse transcription-polymerase chain reaction (qRT-PCR) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction VTE is a condition caused by abnormal coagulation and detachment of thrombus in deep veins, including pulmonary embolism (PE) and deep venous thrombosis (DVT). The fatality rate of VTE is clinically the most important cause of sudden death in patients 2 , exceeding that of myocardial infarction and stroke. VTE may occur in community population but is more common in hospitalized patients, resulting in significantly longer hospitalizations and higher morbidity and mortality, which increases family and socioeconomic burden 3 . To a large extent, this is because of the difficulty in accurate prediction and diagnosis of VTE 4 , followed by treatment. At present, all tests for VTE (including blood and imaging examinations) are indirect rather than specific. The timely and accurate diagnosis becomes complex due to the clinical silent nature of VTE in most cases, as well as non-specific symptoms and signs. Therefore, the development of biomarkers that can accurately predict the occurrence of VTE is of great significance for the initial management of VTE. Our study is designed with the aim of excavating diagnostic biomarkers of VTE. Inflammation and immunity are involved in the process of thrombosis 5 . Vascular endothelial cells can be activated by several cytokines and express various adhesion molecules leading to white blood cell adhesion and platelet aggregation, ultimately promoting the formation of thrombus 6 . Researchers have been searching for VTE biomarkers related to inflammation and immunity in order to better understand the pathophysiology of VTE 7 . Pyrotosis is a new type of programmed cell death discovered in recent years, triggering of a strong inflammatory response and thrombotic inflammation related to innate immunity 8 . Its characteristic is the quick formation of membrane pores, leading to cell swelling and membrane rupture followed by the release of large amounts of pro-inflammatory mediators such as interleukin-1 and interleukin-18 9 . Pyroptosis plays a crucial role in the pathogenesis of various cardiovascular diseases 10 . In research on myocardial infarction and ischemia-reperfusion injury, inhibiting the process of pyroptosis through drug or gene intervention can improve these diseases 11 . Limited research suggests that pyroptosis may play an important role in the formation of venous thrombosis 12 . However, the specific molecular mechanisms of pyroptosis and VTE have not been elucidated. The aim of this study is to identify biomarkers related to pyroptosis in VTE, explore their role in VTE, and provide new clues for the diagnosis and treatment of VTE. 2 Materials and methods 2.1 Data collection The sequencing data of dataset GSE19151 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19151 ) and dataset GSE48000 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48000 ) were stemmed from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). At the same time, microarray data of GSE19151 contained 70 VTE samples as well as 63 control samples, which was quoted as a training cohort. GSE48000 dataset contained 107 VTE samples and 25 control samples, which was acted as a validation cohort. The GSE19151 dataset was sequenced on GPL571, the GSE48000 dataset was sequenced on GPL10558, which all derived from human body. The sample type of GSE19151 dataset was blood, while the sample type of GSE48000 dataset was whole blood. A sum of 52 pyroptosis-related genes (PRGs) were sourced from literature. 2.2 Single sample Gene Set Enrichment Analysis (ssGSEA) Single sample Gene Set Enrichment Analysis (ssGSEA) was generally regarded as an unsupervised method. Based on background set PRGs 13 , the ssGSEA analysis was utilized for calculating the Pyroptosis Score of the samples in GSE19151 training set through R package ‘GSVA’ (version 1.46.0) 14 , and the score was used as a phenotypic trait to screen out the highest correlation genes modules with them. 2.3 Weight Gene Co-Expression Network Analysis (WGCNA) WGCNA was put into use to synthesize a network by means of R package ‘WGCNA’ (version 1.71) 15 based on all genes from blood samples in GSE19151 training set. In the first place, clustering analysis was utilized for eliminating outliers of samples. Next, the most appropriate soft threshold power was chosen to construct network under the condition of the scale-free index (R 2 ) reaches 0.85 and the average connectivity was close to 0. Then, a systematic intergene cluster tree was drawn according to the coefficient of dissimilarity between genes. Besides, the minimum gene numbers of per module were set to 100, and modules were merged when the threshold was 0.3. VTE and Pyroptosis Score were taken as phenotypic traits to find the highest correlation coefficient gene modules related to VTE and Pyroptosis Score, and adj.p.val < 0.05 was deemed statistically significant. Last but not least, gene significance (GS), module membership (MM) and pyroptosis Score between key modules and VTE were computed. The genes obtained from key modules were used as hub genes obtained by WGCNA analysis. 2.4 Identification of VTE Differential Expressed Genes In GSE19151 training set, the differential genes between VTE and control groups were filtrated through R package ‘limma’ (version 3.52.4) 16 . The thresholds for filtrating differential genes were stipulated on condition that |log 2 FC| ≥ 1 and adj.p.val < 0.05. The results were visualized via volcano and heat maps through the ‘ggplot2’ in R (version 3.3.6) 17 . The intersected genes of differential expressed genes and hub genes were defined as differential expressed pyroptosis-related genes (DE-PRGs). 2.5 Function analyses of DE-PRGs In order to understand the biological functions involved in key differential module genes and the enriched signaling pathways, GO annotation analysis and KEGG pathway enrichment analysis were executed by means of R package ‘clusterProfiler’ (version 4.4.4) on DE-PRGs in dataset GSE19151, and visualization was performed using ‘ggplot2’ package in R (version 3.3.6). Adj.p.val < 0.05 for false discovery rate was deemed statistically significant. 2.6 Protein-Protein Interaction Network (PPI Network) The PPI network was created for showing gene interaction at the protein level. The STRING database ( https://cn.string-db.org/ ) is commonly utilized to look for known proteins as well as predict relationships between proteins. In this study, a PPI network of DE-PRGs was synthesized with the aid of STRING database. Subsequently, based on the six algorithms Maximal Clique Centrality (MCC), Closeness, Maximum Neighborhood Component (MNC), Degree, Radiality and Edge Percolated Component (EPC), the TOP20 genes of each algorithm were selected rely on the cytoHubba plug-in of Cytoscape, and then the intersected genes of the TOP20 genes selected by each algorithm was verified, and these intersected genes were named as candidate genes. The PPI network was demonstrated by feat of Cytoscape (version 3.9.1). 2.7 Identification of biomarkers associated with pyroptosis in VTE The least absolute shrinkage and selection operator (LASSO) algorithm as well as support vector machine recursive feature elimination (SVM-RFE) model was utilized for shortlisting feature genes from the candidate genes. The LASSO model was applied through R package ‘glmnet’ (version 4.1-4) and the SVM-RFE model was established by R package ‘e1071’ (version 1.7–11) 18 . The feature genes with the least error were taken for the analysis results of LASSO model and SVM-RFE mode. Then, candidate biomarkers for VET were obtained via overlapping feature genes originated in the two algorithms. Furthermore, the biomarkers were screened from candidate biomarkers with ROC > 0.7 as the criterion. 2.8 Gene set enrichment analysis (GSEA) Gene Set Enrichment Analysis (GSEA) was carried out to further probe into the related biological pathways of biomarkers through ‘clusterProfiler’ package in R (version 4.4.4). First, the correlation between the biomarkers and other genes in GSE19151 training set was calculated. All genes, which was ranked from high to bottom on the basis of their correlations, were deemed as the test set to be analysed. Subsequently, the C2: KEGG signaling pathway set acquired from MSigDB database was regarded as a background set to recognize these sorted genes enrichment in the background set ( https://ngdc.cncb.ac.cn/databasecommons/database/id/1077 ). 2.9 The validation of quantificational reverse transcription PCR (qRT-PCR) The quantificational Reverse Transcription-Polymerase Chain Reaction (qRT-PCR) was performed on five biomarkers in this study for the sake of proving the reliability of the above results. In sum of five pairs of VET and normal blood samples were collected from the First Affiliated Hospital of Kunming Medical University. All participants were given informed consent. This study got the approval of the the First Affiliated Hospital of Kunming Medical University ethics committee (approval number: 2023L198). Firstly, the total RNA was extracted from the ten samples with the TRIzol reagent (Ambion, USA) in accordance with the manufacturer's protocol. The concentrations of RNA were next detected using the NanoPhotometer N50. Ulteriorly, the cDNA synthesis was reverse-transcribed by virtue of the SureScript-First strand-cDNA-synthesis-kit (Servicebio, China). Additionally, the qPCR assay was accomplished via CFX Connect Thermal Cycler (Bio-Rad, USA). The GAPDH was identified as an internal reference gene for qRT-PCR. The relative transcript levels of mRNAs were assessed through the 2 −ΔΔCT method. The details of all primers were attached in Supplementary Table 1. 2.10 Statistical analysis Bioinformatics analysis in this study was implemented through R software (version 4.2.3). Usually, a p value of less than 0.05 was considered to be statistically significant. 3 Results 3.1 Totally 619 hub genes were ascertained The correlations between Pyroptosis Score and PRGs modules were calculated by ssGSEA, the results showed that pyroptosis score in VET group was significantly higher than another group ( p < 0.05), suggesting pyroptosis might have a significant influence on occurrence and development of VET. Soft threshold power was verified as 11 on condition that R 2 arrived 0.85 and mean connectivity was endless closing to 0. The hierarchical clustering tree analysis showed that filtrated genes were classified into distinct co-expression blocks, and a total of 10 modules were verified. After WGCNA network was generated, the relationships between modules and traits were shown on heatmap (Fig. 1 ). The results visualized that in the column of VTE group, the MEgreenyellow module had strong correlations with VTE progression, and there are 941 genes in the MEgreenyellow module, these genes could accelerate or inhibit the occurrence and progress of VTE. Pyroptosis Score block displayed the MEyellow module had strong correlations with Pyroptosis, and there are 1739 genes in the MEyellow module. Then, module membership (MM)- gene significance (GS) showed that a number of 155 genes in the MEgreenyellow block and 464 genes in the MEyellow block were filtrated, and these 619 genes served as hub genes in the WGCNA analysis. 3.2 There were a number of 91 DEGs were acquired The distinction of gene expression between VTE samples and normal samples was analyzed, the volcanic map (Fig. 2) showed that there are 91 genes were identified as DEGs, the expression levels of 85 DEGs were apparently increased, and the expression levels of 6 DEGs were noteworthy decreased. 3.3 The function of 52 DE-PRGs might be related to ribosome A number of 52 DE-PRGs were filtrated by feat of intersection of differential expressed genes and hub genes. Subsequently, the biological functions DE-PRGs were analyzed. GO analysis showed that these DE-PRGs were enriched to 155 entries, including 42 Cellular Components (CC) such as ribosome, mitochondrial respiratory chain complex Ⅳ and cytochrome complex and the like, 25 Molecular Functions (MF) such as structural constituent of ribosome and enzyme inhibitor activity and so on, and 88 Biological Process (BP) including oxidative phosphorylation and synthesis coupled electron transport and ribosome biogenesis and the like. Besides, DE-PRGs were enriched into 15 functional pathways, including non-alcoholic fatty liver disease, ribosome, oxidative phosphorylation and so on. 3.4 PPI Network of DE-PRGs In order to explore the interaction of DE-PRGs at the protein level, a PPI network of DE-PRGs was constructed, which contained 46 nodes and 227 edges. The PPI network demonstrated the interaction of 46 key difference-building genes at the protein level (Fig. 3 ). The candidate 18 genes obtained by intersection of the TOP20 genes screened via 6 algorithms respectively were RPS24, RPL31, RPL34, RPS17, RPL39, RSL24D1, RPL9, RPL17, RPS3A, RPL7, RPL35, RPS27L, RPL23, RPL17-C18orf32, COX7C, HINT1, SNRPD2 and UQCRQ. 3.5 A number of five biomarkers associated with pyroptosis in VTE were identified In order to identify the biomarkers associated with pyroptosis in VTE, LASSO logistic regression algorithm were used to select 11 feature genes (Fig. 4A, B), a sum of 14 feature genes were verified by the SVM-RFE algorithm (Fig. 4C). Then, feature genes, which were severally acquired from the LASSO and SVM-RFE models, were intersected, and 10 candidate biomarkers (RPL31, RPL34, RPS17, RPL9, RPL17, RPS27L, RPL17-C18orf32, HINT1, SNRPD2 and UQCRQ) were subsequently screened for follow-up analysis (Fig. 4D). The diagnostic value of these 10 candidate biomarkers was diagnosed and further screened by ROC analysis, the results showed that five biomarkers (RPL31, RPL34, RPL9, RPS27L and HINT1) were screened under the condition of ROC > 0.7. The AUC for five biomarkers were greater than 0.8 (Fig. 4E). Moreover, the expression of five biomarkers in the training and validation cohort were shown in Fig. 4F, 4G, indicating that the expression levels of these five biomarkers were significantly increased in VTE group. 3.6 Five biomarkers were closely linked to the pathways that may cause pyroptosis Gene set enrichment analysis (GSEA) was put into use to further clarify the underlying pathways influenced to pyroptosis in VTE. The results revealed that ribosome and oxidative phosphorylation signaling pathways were highly enriched in these five biomarkers. The results revealed that these five biomarkers may cause pyrodeath by affecting the mitochondrial pathway in VTE. 3.7 The relative transcript levels of five biomarkers were all observably increased in VTE The qRT-PCR findings revealed that the expression levels of RPL31, RPL9 and HINT1 were all observably higher in VTE samples than in normal samples (Fig. 5 ). Importantly, their expression trends were consistent with the expression trend of the GSE19151 and GSE48000 datasets, indicating that the results of bioinformatics analysis were reliable. 4 Discussion Venous thromboembolism (VTE) includes pulmonary embolism (PE) and deep venous thrombosis (DVT). PE and DVT are different manifestations of the same disease at different times and spaces, so it is necessary to conduct joint research. DVT can cause limb swelling due to acute venous obstruction or leads to venous gangrene in some circumstances. In the later stage, after the damage of the venous valve at the site of thrombus deposition, the original function of the venous valve to prevent venous reflux is lost, which often leads to repeated swelling of the limbs, skin nutritional disorders, pigmentation and even ulceration, resulting in impaired mobility in patients, known as post thrombotic syndrome (PTS). After venous thrombus falls off to the pulmonary artery and its branches, PE occurs: PE that affects the main trunk of the pulmonary artery or large area of lung can induce sudden death in patients. PE referring small area can also cause damage to the lung tissue supplied by the affected pulmonary artery. In the later stage of PE, it may also lead to chronic pulmonary hypertension, cardiac dysfunction, and further endanger the patient's cardiovascular system. Due to the clinical silent nature of VTE in most patients, as well as non-specific symptoms and signs, the total rate of missed diagnosis and misdiagnosis is almost 30%. Although D-dimer detection, Duplex and chest enhanced CT have brought technological innovations to the diagnosis of VTE, there are still a large number of VTE cases that have not been diagnosed in a timely and accurate manner. In fact, VTE is the most lethal but controllable disease to be prevented and treated. Clinically and preclinically, unlike tumors and other specific immune diseases, lack of specific biological markers has always been the biggest influencing factor in the diagnosis and prevention of VTE. Therefore, it is compelling to explore specific biomarkers related to VTE. Existing signs and studies indicate that VTE is more like a comprehensive syndrome than an independent disease. Pyrotosis is a new type of programmed cell death discovered in recent years, triggering of a strong inflammatory response and thrombotic inflammation related to innate immunity. Cookson and Brennan first proposed the concept of cell pyroptosis, but it was not until 2020 that the molecular mechanism of pyroptosis was preliminarily elucidated. At present, the vast majority of research has only stayed at the stage of molecular biology. Research and application history of pyroptosis in clinical practice is almost scarce, the relationship between pyroptosis and VTE is unclear. The aim of this study is to identify biomarkers related to pyroptosis in VTE, explore their role in VTE, and provide new clues for the diagnosis and treatment of VTE. In the present study, we filtrated 52 DE-PRGs and analyzed their biological functions. Finally, DE-PRGs were enriched into 15 functional pathways, including COVID-19 infection, non-alcoholic fatty liver disease, ribosome, oxidative phosphorylation and so on. From the constructed PPI network, we can find that the five decisive pyroptosis markers of VTE (RPL31, RPL34, RPL9, RPS27L, and HINT1) are closely related to each other. Gene set enrichment analysis (GSEA) also revealed that ribosome and oxidative phosphorylation signaling pathways were highly enriched in these five biomarkers. The results revealed that these five biomarkers may cause pyrodeath by affecting the mitochondrial pathway in VTE. Structurally, these biomarkers are associated with biosynthesis (ribosomal proteins RPL31, RPL34, RPL9 and RPS27L) and signal transduction (HINT1). Through literature search and investigation, we found that these biomarkers were discovered relatively late in the past, and related studies were almost isolated. Moreover, other related studies mostly focused on tumor immunity. Therefore, through bioinformatics analysis, we have systematically identified for the first time five biomarkers related to pyroptosis in VTE. Ribosomal proteins are components of ribosomes involved in protein translation and ribosome assembly 19 , which are required for the growth and survival of all types of cells 20 .Meanwhile, ribosomal proteins have other functions involved in DNA repairing, cell development regulation and cell differentiation. Some ribosomal protein genes are highly expressed in tumor tissues such as gastric cancer, colorectal cancer, esophageal cancer, and liver cancer. In the eukaryotic cells, the ribosome is divided into the 40S and 60S subunits, RPL31, RPL34, and RPL9 are one of the 60S subunit members. In detail, Ribosomal Protein L31 (RPL31) regulates a variety of physiological and pathological processes in the cytoplasm 21 . RPL31 has been shown to be involved in ribosome self-assembly, protein synthesis, cell proliferation, DNA repair, and tumorigenesis. RPL34 contains a zinc finger motif. In addition to functioning as a ribosomal protein, RPL34 has been reported to play an important role in other cellular processes 22 . RPL9 was suggested to be related to the growth of colorectal carcinoma 23 . Recent report suggested a relationship between RPL9 and inflammation 24 . RPS27L is an evolutionarily conserved 84-amino acid ribosomal protein in ribosome 40S small subunit, differs from its family member RPS27 by only three amino acids (R5K, L12P, K17R) at the N-terminus 25 . Xiong’s study demonstrated that neddylation stabilizes RPS27L and RPS27 to confer the survival of cancer cells 26 . Histidine triad nucleotide binding protein 1 (HINT1) is a highly conserved protein that is commonly found in mammalian tissues during evolution. Mainly distributed in the nucleus and cytoskeleton, it participates in intracellular and extracellular signal transduction by binding to protein kinase C. HINT1 is known to be involved in numerous protein-protein interactions 27 , contributing to biochemical processes ranging from nociception to mast cell activation 28 , 29 . However, the expression, function and prognostic correlation of aforementioned five biomarkers in VTE have not been revealed before. The comprehensive application of multiple statistical methods is another important guarantee for the identification of these biomarkers. LASSO regression is a contraction estimation method, whose basic idea is to minimize the sum of squared residuals under the constraint that the absolute sum of regression coefficients is less than a constant, thereby generating some regression coefficients that are strictly equal to 0, and further obtaining an interpretable model. Support Vector Machines (SVM) are a binary classification model whose learning strategy is to maximize the interval to solve the optimization of convex quadratic programming. However, the receiver operating characteristic curve (ROC) is the most core indicator for evaluating the performance discrimination of medical diagnostic experiments and predictive models. For the ROC curve, an important feature is its area under the curve (AUC), and the closer the area is to 1, the stronger the recognition ability. The AUC of all 5 biomarkers in this group is between 0.8 and 0.9, indicating excellent recognition ability. Despite the lack of large-scale clinical data aggregation, our qRT-PCR pilot study preliminarily revealed that the expression levels of RPL31, RPL9 and HINT1 were all observably higher in VTE samples than in normal samples. Importantly, their expression trends were consistent with the expression trend of the GSE19151 and GSE48000 datasets, indicating that the results of bioinformatics analysis were reliable. In summary, based on the important impact of pyroptosis and associated thrombotic inflammation on VTE, we attempted to understand and explain this comprehensive disease from the perspective of pyroptosis, and for the first time identified five biomarkers related to pyroptosis in VTE. Meanwhile, we also realize that the application of biomarkers requires more animal and clinical data support. However, as our VTE-model in animal establishes and clinical experiment progresses, we will continue to excavate the roles of these genes. Declarations Conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The present study was supported by the Foundation of Applied Basic Research Program of Yunnan Province (Kunming, China; grant nos. 2019FE001(-216)). Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. Ethics approval Ethical approval for this study was obtained from the Medical Ethics Committee of First Affiliated Hospital of Kunming Medical University. Guarantor Grantor：Shengbin Han Department of Vascular Surgery, the First Affiliated Hospital, Kunming Medical University, #295 Xichang Road, Kunming, Yunnan 650032, China. Email: [email protected] Contributorship Shengbin Han: research design, original draft writing & Project administration Jingzhe Xu: Methodology & translation Lianlin Wang: data collection Jiarong Wang: data collection Chenchen Yu: data collection Hongxi Guan: Conceptualization Shun Ding: data collection and statistical analysis Acknowledgements We thank all the doctors, nurses, and students who participated in this study during these years. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5313033","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369542092,"identity":"191eeea8-d774-4b08-8ebe-ac5db6b930b2","order_by":0,"name":"Shengbin Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYBACAyBmZmywYWA4QKKWNNK1HCZBizn72cOfC3ecT+y7kWPA8HMHEVose/LSpGeeuZ04E6iFsfcMMQ47kGPGzNt2O3EDUAszYxsxWs6/Mf7M23aOFC1AldK8bQdI0vLGTJr3TLLxzDPPCg72EuewHKDDdtjJ9h1P3vjgJzFaYMCxQSDD4AAJGhgY7Bn4jz8gSccoGAWjYBSMHAAAw0E+2SkF+x4AAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shengbin","middleName":"","lastName":"Han","suffix":""},{"id":369542094,"identity":"c6246ac5-034d-4a1c-b9d6-dc0b0ea66364","order_by":1,"name":"Jingzhe Xu","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingzhe","middleName":"","lastName":"Xu","suffix":""},{"id":369542097,"identity":"a1c45c51-d869-4063-8065-427e61738131","order_by":2,"name":"Lianlin Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lianlin","middleName":"","lastName":"Wang","suffix":""},{"id":369542103,"identity":"35b4fb21-f686-4886-9629-cc5192fac12b","order_by":3,"name":"Jiarong Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiarong","middleName":"","lastName":"Wang","suffix":""},{"id":369542107,"identity":"623ec9dd-b9ac-4774-a0d8-6b85d8043693","order_by":4,"name":"Chenchen Yu","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chenchen","middleName":"","lastName":"Yu","suffix":""},{"id":369542109,"identity":"37ccc7f3-fc0e-4b49-934a-2e436c56b830","order_by":5,"name":"Hongxi Guan","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongxi","middleName":"","lastName":"Guan","suffix":""},{"id":369542114,"identity":"35955f5b-afca-4d9a-a614-da750d6b651c","order_by":6,"name":"Shun Ding","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shun","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2024-10-22 15:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5313033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5313033/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67464923,"identity":"94ace8a8-ef67-41d1-87a9-1e10d1605c6d","added_by":"auto","created_at":"2024-10-25 10:36:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModule trait\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5313033/v1/fb8e6f902af1c3d350fcb38a.png"},{"id":67466026,"identity":"6efa2e12-6d65-44d4-be4d-6aa193b8fe3b","added_by":"auto","created_at":"2024-10-25 10:44:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSE19151 DEGs volcano\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5313033/v1/1a036c96e2b4aab709161db1.png"},{"id":67466027,"identity":"aeebfe15-4f21-4785-a145-e53072588184","added_by":"auto","created_at":"2024-10-25 10:44:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":260364,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network of DE-PRGs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5313033/v1/3d11e2aa8df963a8587016d7.png"},{"id":67464926,"identity":"6737e655-0280-4348-86ef-94926201d8b7","added_by":"auto","created_at":"2024-10-25 10:36:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":729138,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5313033/v1/39ff73e3b768de8c9eb6be71.png"},{"id":67464929,"identity":"086a8894-a19d-4e69-a7b6-98eb7e1ab97c","added_by":"auto","created_at":"2024-10-25 10:36:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eexpression levels of five biomarkers\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5313033/v1/7e4d62423b420f5fd09c9a74.png"},{"id":72420855,"identity":"8bc3f7c9-5b94-4e27-ba7d-d0958fe9ca31","added_by":"auto","created_at":"2024-12-27 00:31:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1673086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5313033/v1/65da4ee5-783c-4ed6-aba3-6ace4f19ce8a.pdf"},{"id":67464927,"identity":"f46aeacf-54ef-4e0b-8ba9-4e773f05b348","added_by":"auto","created_at":"2024-10-25 10:36:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15770,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5313033/v1/c2937c101958e89c80d07ca6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of pyroptosis-related biomarkers in venous thromboembolism","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eVTE is a condition caused by abnormal coagulation and detachment of thrombus in deep veins, including pulmonary embolism (PE) and deep venous thrombosis (DVT). The fatality rate of VTE is clinically the most important cause of sudden death in patients\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e, exceeding that of myocardial infarction and stroke. VTE may occur in community population but is more common in hospitalized patients, resulting in significantly longer hospitalizations and higher morbidity and mortality, which increases family and socioeconomic burden\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. To a large extent, this is because of the difficulty in accurate prediction and diagnosis of VTE\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e, followed by treatment. At present, all tests for VTE (including blood and imaging examinations) are indirect rather than specific. The timely and accurate diagnosis becomes complex due to the clinical silent nature of VTE in most cases, as well as non-specific symptoms and signs. Therefore, the development of biomarkers that can accurately predict the occurrence of VTE is of great significance for the initial management of VTE. Our study is designed with the aim of excavating diagnostic biomarkers of VTE.\u003c/p\u003e \u003cp\u003eInflammation and immunity are involved in the process of thrombosis\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Vascular endothelial cells can be activated by several cytokines and express various adhesion molecules leading to white blood cell adhesion and platelet aggregation, ultimately promoting the formation of thrombus\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Researchers have been searching for VTE biomarkers related to inflammation and immunity in order to better understand the pathophysiology of VTE\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Pyrotosis is a new type of programmed cell death discovered in recent years, triggering of a strong inflammatory response and thrombotic inflammation related to innate immunity\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Its characteristic is the quick formation of membrane pores, leading to cell swelling and membrane rupture followed by the release of large amounts of pro-inflammatory mediators such as interleukin-1 and interleukin-18\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e. Pyroptosis plays a crucial role in the pathogenesis of various cardiovascular diseases\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. In research on myocardial infarction and ischemia-reperfusion injury, inhibiting the process of pyroptosis through drug or gene intervention can improve these diseases\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Limited research suggests that pyroptosis may play an important role in the formation of venous thrombosis\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. However, the specific molecular mechanisms of pyroptosis and VTE have not been elucidated. The aim of this study is to identify biomarkers related to pyroptosis in VTE, explore their role in VTE, and provide new clues for the diagnosis and treatment of VTE.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data collection\u003c/h2\u003e \u003cp\u003eThe sequencing data of dataset GSE19151\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19151\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19151\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and dataset GSE48000 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48000\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48000\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were stemmed from the Gene Expression Omnibus (GEO) database\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). At the same time, microarray data of GSE19151 contained 70 VTE samples as well as 63 control samples, which was quoted as a training cohort. GSE48000 dataset contained 107 VTE samples and 25 control samples, which was acted as a validation cohort. The GSE19151 dataset was sequenced on GPL571, the GSE48000 dataset was sequenced on GPL10558, which all derived from human body. The sample type of GSE19151 dataset was blood, while the sample type of GSE48000 dataset was whole blood. A sum of 52 pyroptosis-related genes (PRGs) were sourced from literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Single sample Gene Set Enrichment Analysis (ssGSEA)\u003c/h2\u003e \u003cp\u003eSingle sample Gene Set Enrichment Analysis (ssGSEA) was generally regarded as an unsupervised method. Based on background set PRGs\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e, the ssGSEA analysis was utilized for calculating the Pyroptosis Score of the samples in GSE19151 training set through R package \u0026lsquo;GSVA\u0026rsquo; (version 1.46.0)\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e, and the score was used as a phenotypic trait to screen out the highest correlation genes modules with them.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Weight Gene Co-Expression Network Analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eWGCNA was put into use to synthesize a network by means of R package \u0026lsquo;WGCNA\u0026rsquo; (version 1.71)\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e based on all genes from blood samples in GSE19151 training set. In the first place, clustering analysis was utilized for eliminating outliers of samples. Next, the most appropriate soft threshold power was chosen to construct network under the condition of the scale-free index (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) reaches 0.85 and the average connectivity was close to 0. Then, a systematic intergene cluster tree was drawn according to the coefficient of dissimilarity between genes. Besides, the minimum gene numbers of per module were set to 100, and modules were merged when the threshold was 0.3. VTE and Pyroptosis Score were taken as phenotypic traits to find the highest correlation coefficient gene modules related to VTE and Pyroptosis Score, and adj.p.val\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistically significant. Last but not least, gene significance (GS), module membership (MM) and pyroptosis Score between key modules and VTE were computed. The genes obtained from key modules were used as hub genes obtained by WGCNA analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Identification of VTE Differential Expressed Genes\u003c/h2\u003e \u003cp\u003eIn GSE19151 training set, the differential genes between VTE and control groups were filtrated through R package \u0026lsquo;limma\u0026rsquo; (version 3.52.4)\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. The thresholds for filtrating differential genes were stipulated on condition that |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge; 1 and adj.p.val\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The results were visualized via volcano and heat maps through the \u0026lsquo;ggplot2\u0026rsquo; in R (version 3.3.6)\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. The intersected genes of differential expressed genes and hub genes were defined as differential expressed pyroptosis-related genes (DE-PRGs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Function analyses of DE-PRGs\u003c/h2\u003e \u003cp\u003eIn order to understand the biological functions involved in key differential module genes and the enriched signaling pathways, GO annotation analysis and KEGG pathway enrichment analysis were executed by means of R package \u0026lsquo;clusterProfiler\u0026rsquo; (version 4.4.4) on DE-PRGs in dataset GSE19151, and visualization was performed using \u0026lsquo;ggplot2\u0026rsquo; package in R (version 3.3.6). Adj.p.val\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for false discovery rate was deemed statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Protein-Protein Interaction Network (PPI Network)\u003c/h2\u003e \u003cp\u003eThe PPI network was created for showing gene interaction at the protein level. The STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is commonly utilized to look for known proteins as well as predict relationships between proteins. In this study, a PPI network of DE-PRGs was synthesized with the aid of STRING database. Subsequently, based on the six algorithms Maximal Clique Centrality (MCC), Closeness, Maximum Neighborhood Component (MNC), Degree, Radiality and Edge Percolated Component (EPC), the TOP20 genes of each algorithm were selected rely on the cytoHubba plug-in of Cytoscape, and then the intersected genes of the TOP20 genes selected by each algorithm was verified, and these intersected genes were named as candidate genes. The PPI network was demonstrated by feat of Cytoscape (version 3.9.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Identification of biomarkers associated with pyroptosis in VTE\u003c/h2\u003e \u003cp\u003eThe least absolute shrinkage and selection operator (LASSO) algorithm as well as support vector machine recursive feature elimination (SVM-RFE) model was utilized for shortlisting feature genes from the candidate genes. The LASSO model was applied through R package \u0026lsquo;glmnet\u0026rsquo; (version 4.1-4) and the SVM-RFE model was established by R package \u0026lsquo;e1071\u0026rsquo; (version 1.7\u0026ndash;11)\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. The feature genes with the least error were taken for the analysis results of LASSO model and SVM-RFE mode. Then, candidate biomarkers for VET were obtained via overlapping feature genes originated in the two algorithms. Furthermore, the biomarkers were screened from candidate biomarkers with ROC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 as the criterion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Gene set enrichment analysis (GSEA)\u003c/h2\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA) was carried out to further probe into the related biological pathways of biomarkers through \u0026lsquo;clusterProfiler\u0026rsquo; package in R (version 4.4.4). First, the correlation between the biomarkers and other genes in GSE19151 training set was calculated. All genes, which was ranked from high to bottom on the basis of their correlations, were deemed as the test set to be analysed. Subsequently, the C2: KEGG signaling pathway set acquired from MSigDB database was regarded as a background set to recognize these sorted genes enrichment in the background set (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/databasecommons/database/id/1077\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/databasecommons/database/id/1077\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.9 The validation of quantificational reverse transcription PCR (qRT-PCR)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe quantificational Reverse Transcription-Polymerase Chain Reaction (qRT-PCR) was performed on five biomarkers in this study for the sake of proving the reliability of the above results. In sum of five pairs of VET and normal blood samples were collected from the First Affiliated Hospital of Kunming Medical University. All participants were given informed consent. This study got the approval of the the First Affiliated Hospital of Kunming Medical University ethics committee (approval number: 2023L198). Firstly, the total RNA was extracted from the ten samples with the TRIzol reagent (Ambion, USA) in accordance with the manufacturer's protocol. The concentrations of RNA were next detected using the NanoPhotometer N50. Ulteriorly, the cDNA synthesis was reverse-transcribed by virtue of the SureScript-First strand-cDNA-synthesis-kit (Servicebio, China). Additionally, the qPCR assay was accomplished via CFX Connect Thermal Cycler (Bio-Rad, USA). The GAPDH was identified as an internal reference gene for qRT-PCR. The relative transcript levels of mRNAs were assessed through the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method. The details of all primers were attached in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e \u003cp\u003eBioinformatics analysis in this study was implemented through R software (version 4.2.3). Usually, a p value of less than 0.05 was considered to be statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Totally 619 hub genes were ascertained\u003c/h2\u003e \u003cp\u003eThe correlations between Pyroptosis Score and PRGs modules were calculated by ssGSEA, the results showed that pyroptosis score in VET group was significantly higher than another group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting pyroptosis might have a significant influence on occurrence and development of VET. Soft threshold power was verified as 11 on condition that R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e arrived 0.85 and mean connectivity was endless closing to 0. The hierarchical clustering tree analysis showed that filtrated genes were classified into distinct co-expression blocks, and a total of 10 modules were verified. After WGCNA network was generated, the relationships between modules and traits were shown on heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results visualized that in the column of VTE group, the MEgreenyellow module had strong correlations with VTE progression, and there are 941 genes in the MEgreenyellow module, these genes could accelerate or inhibit the occurrence and progress of VTE. Pyroptosis Score block displayed the MEyellow module had strong correlations with Pyroptosis, and there are 1739 genes in the MEyellow module. Then, module membership (MM)- gene significance (GS) showed that a number of 155 genes in the MEgreenyellow block and 464 genes in the MEyellow block were filtrated, and these 619 genes served as hub genes in the WGCNA analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 There were a number of 91 DEGs were acquired\u003c/h2\u003e \u003cp\u003eThe distinction of gene expression between VTE samples and normal samples was analyzed, the volcanic map (Fig.\u0026nbsp;2) showed that there are 91 genes were identified as DEGs, the expression levels of 85 DEGs were apparently increased, and the expression levels of 6 DEGs were noteworthy decreased.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The function of 52 DE-PRGs might be related to ribosome\u003c/h2\u003e \u003cp\u003eA number of 52 DE-PRGs were filtrated by feat of intersection of differential expressed genes and hub genes. Subsequently, the biological functions DE-PRGs were analyzed. GO analysis showed that these DE-PRGs were enriched to 155 entries, including 42 Cellular Components (CC) such as ribosome, mitochondrial respiratory chain complex Ⅳ and cytochrome complex and the like, 25 Molecular Functions (MF) such as structural constituent of ribosome and enzyme inhibitor activity and so on, and 88 Biological Process (BP) including oxidative phosphorylation and synthesis coupled electron transport and ribosome biogenesis and the like. Besides, DE-PRGs were enriched into 15 functional pathways, including non-alcoholic fatty liver disease, ribosome, oxidative phosphorylation and so on.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 PPI Network of DE-PRGs\u003c/h2\u003e \u003cp\u003eIn order to explore the interaction of DE-PRGs at the protein level, a PPI network of DE-PRGs was constructed, which contained 46 nodes and 227 edges. The PPI network demonstrated the interaction of 46 key difference-building genes at the protein level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The candidate 18 genes obtained by intersection of the TOP20 genes screened via 6 algorithms respectively were RPS24, RPL31, RPL34, RPS17, RPL39, RSL24D1, RPL9, RPL17, RPS3A, RPL7, RPL35, RPS27L, RPL23, RPL17-C18orf32, COX7C, HINT1, SNRPD2 and UQCRQ.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 A number of five biomarkers associated with pyroptosis in VTE were identified\u003c/h2\u003e \u003cp\u003eIn order to identify the biomarkers associated with pyroptosis in VTE, LASSO logistic regression algorithm were used to select 11 feature genes (Fig.\u0026nbsp;4A, B), a sum of 14 feature genes were verified by the SVM-RFE algorithm (Fig.\u0026nbsp;4C). Then, feature genes, which were severally acquired from the LASSO and SVM-RFE models, were intersected, and 10 candidate biomarkers (RPL31, RPL34, RPS17, RPL9, RPL17, RPS27L, RPL17-C18orf32, HINT1, SNRPD2 and UQCRQ) were subsequently screened for follow-up analysis (Fig.\u0026nbsp;4D). The diagnostic value of these 10 candidate biomarkers was diagnosed and further screened by ROC analysis, the results showed that five biomarkers (RPL31, RPL34, RPL9, RPS27L and HINT1) were screened under the condition of ROC\u0026thinsp;\u0026gt;\u0026thinsp;0.7. The AUC for five biomarkers were greater than 0.8 (Fig.\u0026nbsp;4E). Moreover, the expression of five biomarkers in the training and validation cohort were shown in Fig.\u0026nbsp;4F, 4G, indicating that the expression levels of these five biomarkers were significantly increased in VTE group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Five biomarkers were closely linked to the pathways that may cause pyroptosis\u003c/h2\u003e \u003cp\u003eGene set enrichment analysis (GSEA) was put into use to further clarify the underlying pathways influenced to pyroptosis in VTE. The results revealed that ribosome and oxidative phosphorylation signaling pathways were highly enriched in these five biomarkers. The results revealed that these five biomarkers may cause pyrodeath by affecting the mitochondrial pathway in VTE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 The relative transcript levels of five biomarkers were all observably increased in VTE\u003c/h2\u003e \u003cp\u003eThe qRT-PCR findings revealed that the expression levels of RPL31, RPL9 and HINT1 were all observably higher in VTE samples than in normal samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Importantly, their expression trends were consistent with the expression trend of the GSE19151 and GSE48000 datasets, indicating that the results of bioinformatics analysis were reliable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eVenous thromboembolism (VTE) includes pulmonary embolism (PE) and deep venous thrombosis (DVT). PE and DVT are different manifestations of the same disease at different times and spaces, so it is necessary to conduct joint research. DVT can cause limb swelling due to acute venous obstruction or leads to venous gangrene in some circumstances. In the later stage, after the damage of the venous valve at the site of thrombus deposition, the original function of the venous valve to prevent venous reflux is lost, which often leads to repeated swelling of the limbs, skin nutritional disorders, pigmentation and even ulceration, resulting in impaired mobility in patients, known as post thrombotic syndrome (PTS). After venous thrombus falls off to the pulmonary artery and its branches, PE occurs: PE that affects the main trunk of the pulmonary artery or large area of lung can induce sudden death in patients. PE referring small area can also cause damage to the lung tissue supplied by the affected pulmonary artery. In the later stage of PE, it may also lead to chronic pulmonary hypertension, cardiac dysfunction, and further endanger the patient's cardiovascular system. Due to the clinical silent nature of VTE in most patients, as well as non-specific symptoms and signs, the total rate of missed diagnosis and misdiagnosis is almost 30%. Although D-dimer detection, Duplex and chest enhanced CT have brought technological innovations to the diagnosis of VTE, there are still a large number of VTE cases that have not been diagnosed in a timely and accurate manner. In fact, VTE is the most lethal but controllable disease to be prevented and treated. Clinically and preclinically, unlike tumors and other specific immune diseases, lack of specific biological markers has always been the biggest influencing factor in the diagnosis and prevention of VTE. Therefore, it is compelling to explore specific biomarkers related to VTE. Existing signs and studies indicate that VTE is more like a comprehensive syndrome than an independent disease. Pyrotosis is a new type of programmed cell death discovered in recent years, triggering of a strong inflammatory response and thrombotic inflammation related to innate immunity. Cookson and Brennan first proposed the concept of cell pyroptosis, but it was not until 2020 that the molecular mechanism of pyroptosis was preliminarily elucidated. At present, the vast majority of research has only stayed at the stage of molecular biology. Research and application history of pyroptosis in clinical practice is almost scarce, the relationship between pyroptosis and VTE is unclear. The aim of this study is to identify biomarkers related to pyroptosis in VTE, explore their role in VTE, and provide new clues for the diagnosis and treatment of VTE.\u003c/p\u003e \u003cp\u003eIn the present study, we filtrated 52 DE-PRGs and analyzed their biological functions. Finally, DE-PRGs were enriched into 15 functional pathways, including COVID-19 infection, non-alcoholic fatty liver disease, ribosome, oxidative phosphorylation and so on. From the constructed PPI network, we can find that the five decisive pyroptosis markers of VTE (RPL31, RPL34, RPL9, RPS27L, and HINT1) are closely related to each other. Gene set enrichment analysis (GSEA) also revealed that ribosome and oxidative phosphorylation signaling pathways were highly enriched in these five biomarkers. The results revealed that these five biomarkers may cause pyrodeath by affecting the mitochondrial pathway in VTE. Structurally, these biomarkers are associated with biosynthesis (ribosomal proteins RPL31, RPL34, RPL9 and RPS27L) and signal transduction (HINT1). Through literature search and investigation, we found that these biomarkers were discovered relatively late in the past, and related studies were almost isolated. Moreover, other related studies mostly focused on tumor immunity.\u003c/p\u003e \u003cp\u003eTherefore, through bioinformatics analysis, we have systematically identified for the first time five biomarkers related to pyroptosis in VTE. Ribosomal proteins are components of ribosomes involved in protein translation and ribosome assembly\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e, which are required for the growth and survival of all types of cells\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.Meanwhile, ribosomal proteins have other functions involved in DNA repairing, cell development regulation and cell differentiation. Some ribosomal protein genes are highly expressed in tumor tissues such as gastric cancer, colorectal cancer, esophageal cancer, and liver cancer. In the eukaryotic cells, the ribosome is divided into the 40S and 60S subunits, RPL31, RPL34, and RPL9 are one of the 60S subunit members. In detail, Ribosomal Protein L31 (RPL31) regulates a variety of physiological and pathological processes in the cytoplasm\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. RPL31 has been shown to be involved in ribosome self-assembly, protein synthesis, cell proliferation, DNA repair, and tumorigenesis. RPL34 contains a zinc finger motif. In addition to functioning as a ribosomal protein, RPL34 has been reported to play an important role in other cellular processes\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. RPL9 was suggested to be related to the growth of colorectal carcinoma\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Recent report suggested a relationship between RPL9 and inflammation\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. RPS27L is an evolutionarily conserved 84-amino acid ribosomal protein in ribosome 40S small subunit, differs from its family member RPS27 by only three amino acids (R5K, L12P, K17R) at the N-terminus\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Xiong\u0026rsquo;s study demonstrated that neddylation stabilizes RPS27L and RPS27 to confer the survival of cancer cells\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Histidine triad nucleotide binding protein 1 (HINT1) is a highly conserved protein that is commonly found in mammalian tissues during evolution. Mainly distributed in the nucleus and cytoskeleton, it participates in intracellular and extracellular signal transduction by binding to protein kinase C. HINT1 is known to be involved in numerous protein-protein interactions\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e, contributing to biochemical processes ranging from nociception to mast cell activation\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. However, the expression, function and prognostic correlation of aforementioned five biomarkers in VTE have not been revealed before.\u003c/p\u003e \u003cp\u003eThe comprehensive application of multiple statistical methods is another important guarantee for the identification of these biomarkers. LASSO regression is a contraction estimation method, whose basic idea is to minimize the sum of squared residuals under the constraint that the absolute sum of regression coefficients is less than a constant, thereby generating some regression coefficients that are strictly equal to 0, and further obtaining an interpretable model. Support Vector Machines (SVM) are a binary classification model whose learning strategy is to maximize the interval to solve the optimization of convex quadratic programming. However, the receiver operating characteristic curve (ROC) is the most core indicator for evaluating the performance discrimination of medical diagnostic experiments and predictive models. For the ROC curve, an important feature is its area under the curve (AUC), and the closer the area is to 1, the stronger the recognition ability. The AUC of all 5 biomarkers in this group is between 0.8 and 0.9, indicating excellent recognition ability. Despite the lack of large-scale clinical data aggregation, our qRT-PCR pilot study preliminarily revealed that the expression levels of RPL31, RPL9 and HINT1 were all observably higher in VTE samples than in normal samples. Importantly, their expression trends were consistent with the expression trend of the GSE19151 and GSE48000 datasets, indicating that the results of bioinformatics analysis were reliable.\u003c/p\u003e \u003cp\u003eIn summary, based on the important impact of pyroptosis and associated thrombotic inflammation on VTE, we attempted to understand and explain this comprehensive disease from the perspective of pyroptosis, and for the first time identified five biomarkers related to pyroptosis in VTE. Meanwhile, we also realize that the application of biomarkers requires more animal and clinical data support. However, as our VTE-model in animal establishes and clinical experiment progresses, we will continue to excavate the roles of these genes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The present study was supported by the Foundation of Applied Basic Research Program of Yunnan Province (Kunming, China; grant nos. 2019FE001(-216)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Medical Ethics Committee of First Affiliated Hospital of Kunming Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuarantor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGrantor：Shengbin Han\u003c/p\u003e\n\u003cp\u003eDepartment of Vascular Surgery, the First Affiliated Hospital, Kunming Medical University, #295 Xichang Road, Kunming, Yunnan 650032, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributorship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShengbin Han: research design, original draft writing \u0026amp; Project administration\u003c/p\u003e\n\u003cp\u003eJingzhe Xu: Methodology\u0026nbsp;\u0026amp; translation\u003c/p\u003e\n\u003cp\u003eLianlin Wang: data collection\u003c/p\u003e\n\u003cp\u003eJiarong Wang: data collection\u003c/p\u003e\n\u003cp\u003eChenchen Yu: data collection\u003c/p\u003e\n\u003cp\u003eHongxi Guan: Conceptualization\u003c/p\u003e\n\u003cp\u003eShun Ding: data collection and statistical analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the doctors, nurses, and students who participated in this study during these years.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen H, Luo H, Wang J, et al. Identification of a pyroptosis-related prognostic signature in breast cancer. BMC Cancer. 2022;22(1):429.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Nisio M, van Es N, B\u0026uuml;ller HR. Deep vein thrombosis and pulmonary embolism. Lancet. 2016;388:3060\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWendelboe AM, Raskob GE. Global burden of thrombosis: epidemiologic aspects. Circ Res. 2016;118:1340\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLutsey PL, Zakai NA. Epidemiology and prevention of venous thromboembolism. Nat Rev Cardiol 2022;1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColling ME, Tourdot BE, Kanthi Y. Inflammation, infection and venous thromboembolism. CircRes. 2021; 2017\u0026ndash;2036.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakirci EM, Topcu S, Kalkan K, et al. 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WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng Q, Chen X, Wu H, et al. Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis. J Transl Med. 2021;19(1):18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan Z, Li F, Chen Q, et al. Integrated bulk and single-cell RNA-sequencing reveals SPOCK2 as a novel biomarker gene in the development of congenital pulmonary airway malformation. Respir Res. 2023;24(1):127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu X, Qin K, Iroegbu CD, et al. Genetic analysis of potential biomarkers and therapeutic targets in ferroptosis from coronary artery disease. J CELL MOL MED. 2022;26(8):2177\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFatica A, Tollervey D. Making ribosomes. Curr Opin Cell Biol. 2002;14:313\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts E, Sethi A, Montoya J, et al. Molecular signatures of ribosomal evolution. Proc Natl Acad Sci USA. 2008;105:13953\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu F, Shenglin YL. Ribosomal protein L31 (RPL31) inhibits the proliferation and migration of gastric cancer cells. Heliyon. 2023;9:e13076.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng Wei L, Ding Z, Wei, et al. Ribosomal protein L34 promotes the proliferation, invasion and metastasis of pancreatic cancer cells. Oncotarget. 2016;51(7):85259\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaik IH, Jo G-H, Seo D, et al. Knockdown of RPL9 expression inhibits colorectal carcinoma growth via the inactivation of Id-1/NF-κB signaling axis. Int J Oncol. 2016;49:1953\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe M, Toyomura T, Wake H, et al. Identification of ribosomal protein L9 as a novel regulator of proinflammatory damage-associated molecular pattern molecules. Mol Biol Rep. 2022;49:2831\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong X, Zhao Y, He H, et al. Ribosomal protein S27-like and S27 interplay with p53-MDM2 axis as a target, a substrate and a regulator. Oncogene. 2011;30:1798\u0026ndash;811.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiufang Xiong D, Cui Y, Bi, et al. Neddylation modification of ribosomal protein RPS27L or RPS27 by MDM2 or NEDP1 regulates cancer cell survival. FASEB J. 2020;34:13419\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjit SK, Ramineni S, Edris W, et al. RGSZ1 interacts with protein kinase C interacting protein PKCI-1 and modulates mu opioid receptor signaling. Cell Signal. 2007;19(4):723\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGenovese G, Ghosh P, Li H, et al. The tumor suppressor HINT1 regulates MITF and β-catenin transcriptional activity in melanoma cells. CellCycle. 2012;11(11):2206\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxwell Dillenburg J, Smith CR, Wagner. The Many Faces of Histidine Triad Nucleotide Binding Protein 1(HINT1). ACS Pharmacol Transl Sci. 2023;6:1310\u0026ndash;22.\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":"thrombotic inflammation, pyroptosis, venous thromboembolism, biomarker, quantificational reverse transcription-polymerase chain reaction (qRT-PCR)","lastPublishedDoi":"10.21203/rs.3.rs-5313033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5313033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eOBJECTIVE\u003c/h2\u003e \u003cp\u003eThe study was designed with the aim of excavating diagnostic biomarkers of venous thromboembolism (VTE).\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eThe GSE19151 and GSE48000 datasets were subjected into this study. The pyroptosis-related genes (PRGs) were sourced from literature\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Differential expression analysis and WGCNA were applied to identify differential genes related with Pyroptosis (DE-PRGs) in VTE. The possible functions of DE-PRGs were defined by means of enrichment analysis. The biomarkers related with pyroptosis in VTE were determined by plotting receiver operating characteristic (ROC) curve. The gene set enrichment analysis (GSEA) was employed to analyze the correlation between biomarkers and pathways. Finally, the quantificational Reverse Transcription-Polymerase Chain Reaction (qRT-PCR) was proceeded to verify the expression level of the biomarkers in VTE.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eA number of 52 DE-PRGs were identified by feat of differential expression analysis and WGCNA. A number of five biomarkers (RPL31, RPL34, RPL9, RPS27L and HINT1) were further screened by ROC curves. GSEA pointed to the linkage of five biomarkers to the ribosome proteins and oxidative phosphorylation signaling transduction, which may cause cell pyrodeath and trigger VTE through mitochondrial pathways. qRT-PCR manifested the expression levels of RPL31, RPL9 and HINT1 were all observably higher in VTE samples than in normal samples.\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003eA number of five biomarkers, RPL31, RPL34, RPL9, RPS27L and HINT1, were identified as pyroptosis-related biomarkers in VTE, which provided a basis for understanding VTE pathogenesis and new insights into VTE diagnosis and treatment.\u003c/p\u003e","manuscriptTitle":"Identification of pyroptosis-related biomarkers in venous thromboembolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-25 10:36:01","doi":"10.21203/rs.3.rs-5313033/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":"3cea8755-e5f2-4ee5-ac2b-657c242e3d57","owner":[],"postedDate":"October 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-27T00:23:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-25 10:36:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5313033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5313033","identity":"rs-5313033","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}