Identification of apoptosis-related biomarkers of apoptosis in pulpitis based on biological informatics

Identification of apoptosis-related biomarkers of apoptosis in pulpitis based on biological informatics | 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 apoptosis-related biomarkers of apoptosis in pulpitis based on biological informatics Xiaoshan Huang, 霞 李 This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3903309/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Pulpitis (PPS) is a dental disease caused by the destruction of dental hard tissue around the dental pulp. Studies have confirmed that apoptosis has a role in the production of PPS. Hence, it was vital to screen apoptosis related biomarkers for PPS. Methods To identify differentially expressed genes (DEGs) in GSE77459, we conducted a differential expression analysis (normal versus PPS). Then, apoptosisrelated differential expression genes (AR-DEGs) were got via overlapping DEGs and apoptosis related genes (ARGs). The five algorithms of cytoHubba in protein-protein interaction (PPI) network and receiver operating characteristic (ROC) were applied to screen apoptosis related biomarkers. Subsequently, we further conducted gene functional enrichment and immune microenvironment analyses for these biomarkers. We finally verified the expression in clinical tissue samples by RT-qPCR. Results A sum of 4,089 DEGs were obtained between PPS and normal groups. Soon afterwards, 19AR-DEGs were screened by the intersection of DEGs and ARGs. Moreover, we got 5 apoptosis related biomarkers via five machine learning algorithms, including TNFSF10 , BIRC3 , IL1A , NFKBIA and CASP10 .We found that these three biomarkers participated immune-related processes ‘immunoglobulin complex’. In additional, we discovered that TNFSF10 was correlated with Neutrophil and MAIT in immune microenvironment of PPS. In agreement with the results of the public database data analysis, the expression of TNFSF10 , BIRC3 , IL1A , NFKBIA and CASP10 was markedly over-expressed in clinical PPS samples versus normal samples. Conclusion Overall, we obtained five apoptosis related biomarkers ( TNFSF10 , BIRC3 , IL1A , NFKBIA and CASP10 ) associated with PPS, which laid a theoretical foundation for the treatment of PPS. Pulpitis Apoptosis Biomarkers Bioinformatics Immune cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Pulpitis (PPS) is an inflammatory disease of the dental pulp, it is associated with microbial infection of the root canal system and the host response, which is characterized by spontaneous or provoked pain [ 1 , 2 ]. Without proper treatment, PPS may lead to pulp necrosis, periapical inflammation, and more severe condition [ 3 ]. The clinical diagnosis and treatment of PPS is limited to determine the degree of pulp inflammation [ 4 , 5 ]. However, histopathological examination showed a weak correlation between clinical features and endodontic status [ 6 ]. Therefore, a new noninvasive method for dental pulp diagnosis is urgently needed. Studies have shown that the pathophysiology of caries-induced PPS included increased inflammatory cytokine production, oxidative stress, damaged mitochondria, apoptosis, and cell death [ 7 , 8 ]. Numerous studies have shown that inflammation is one of the key processes in mitochondrial dynamics. Dysregulated homeostasis in pulp tissue is caused by an imbalance in mitochondrial dynamics, which ultimately results in cellular oxidative stress and apoptosis[ 7 , 9 , 10 ]. Apoptosis is one of the forms of cell death that can be activated during inflammation, but only limited studies have focused on the occurrence of apoptosis in PPS [ 10 ]. H S Wang et al. demonstrated that the apoptotic markers were expressed in the inflamed dental pulp, indicating that activated autophagy and increased apoptosis played a key role in PPS [ 11 ]. Francine Benetti et al. pointed that pulp inflammation was significantly associated with apoptosis through the rat experiment [ 12 ]. Similarly, T T Wu et al. further indicated that this mechanism is related to the reactive oxygen pathway by cellular experiments [ 13 ]. However, the role of apoptosis and the apoptosis related genes (ARGs) in PPS is still undefined. Therefore, this study focused on gene expression in the pulp tissue of PPS patientsformpublic database, screening apoptosis-related biomarkers of PPS to provide novel therapeutic targets and strategies for PPS patients, and further illustrating the mechanism of ARGs in PPS. 2. Materials and Methods 2.1 Data acquisition Three datasets of PPS totaling GSE77459 and GSE92681, which included clinical characteristics and gene expression profiles from dental pulp tissue, were compiled from GEO database. GSE77459dataset, which considered as training set, consisted of 6 normal and 6 PPS samples. GSE92681 dataset, which included 5 normal and 7 PPS samples, was selected for external validation. A sum of 87 apoptosis related genes (ARGs) were obtained from MsigDB database[ 14 ]( Table S1 ). 2.2 Analysis of differential genes The ‘limma’ package [ 15 ] was executed to obtain the differentially expressed genes (DEGs) between normal and PPS groups. The threshold was established as p.value 0.5. Volcano plot and heat map were applied to show DEGs via ‘ggVolcano’ [ 16 ]and ‘ComplexHeatmap’ [ 17 ] packages, respectively. 2.3 Gene enrichment analysis The apoptosisrelated differential expression genes (AR-DEGs) were got via overlapping DEGs and ARGs. GO and KEGG enrichment analysis of AR-DEGs was conducted via ‘clusterProfiler’ package [ 18 ]. P.adjust value < 0.05 was considered meaningful. 2.4 PPI network The STRING website (interaction score = 0.4) was applied to build the protein-protein interaction network, which was used to investigate the interactions between AR-DEGs. The top 10 genes from each of five algorithms (MCC, MNC, EPC, DMNC, Degree) in the Cytoscape plug-in cytoHubba were intersected to obtain candidate genes. 2.5 ROC analysis Using the ‘pROC’ tool, a ROC curve was created to assess the diagnostic utility of candidate genes[ 19 ].At the same time, the GSE92681 was regarded as an external verification set for the diagnostic value.Then, genes with strong diagnostic value (AUC > 0.7) in the training set and external verification setwere identified as biomarkers. 2.6 Immune infiltration Analysis The ImmuCellAI algorithm was applied to calculate the relative abundance of 24 immune cells infiltrated in EP microenvironment through ImmuCellAI ( http://bioinfo.life.hust.edu.cn/web/ImmuCellAI ). These immune cells primarily consisted of 6 additional immune cell types (B cells, natural killer cells, monocytes, macrophages, neutrophils, and dendritic cells) and 18 T cell sub-types. Subsequently, The difference of immune infiltrating cells between PPS group and normal group was compared by Wilcoxon test. The spearman correlation analysis were performed between differential immune cells. Meanwhile, correlation analysis was performed between biomarkers and differential immune cells by ‘corrplot’ package. In additional, 2.7 GSEA Analysis Single-gene GSEA was conducted to explore the potential KEGG pathways associated with biomarkers through ‘clusterProfiler’ package in GSE77459 dataset [ 20 ]. The significant enrichment pathway activity score of each sample was calculated using ssGSEA algorithm in the ‘GSVA’ package, depending on the correlation of biomarkers expression. According to the score, the correlation of significant enrichment and key gene expression were estimated.P.adjust value < 0.05 was considered meaningful. The ‘c5.go.v2022.1.Hs.symbols.gmt’ and ‘c2.cp.kegg.v7.4.1.symbols.gmt’ of MsigDB database were used as the background set. In additional, GeneMANIA was yielded to predict the genes related to the function of biomarkers and the genes involved. 2.8 Construction of regulatory network and ‘drug-gene’ networks The NetworkAnalyst database was applied to predict TF linked to biomarkers. The transcript regulatory network was constructed through ‘ggalluvial’ package [ 21 ]. The TargetScan database was applied to predict the miRNAs linked to biomarkers. Meantime, the lncRNAs associated with miRNAs were predicted via miRnet database.Through DGIDB database, the targeting medications were found in order to investigate prospective therapeutic treatments for biomarkers in PPS. Moreover, Cytoscape software was applied to optimize the results of ‘lncRNA-miRNA-mRNA’ and ‘drug-gene’ networks [ 22 ]. 2.9 Evaluation of biomarker expression RNA extracts from clinical PPS and normal tissue samples were taken for RT-qPCR analysis to further assess biomarker expression levels. TRIzol (Ambion, Austin, USA) was applied to extract total RNA from the samples, which was then reverse transcribed to cDNA using the First-strand-cDNA-synthesis-kit (Servicebio, Wuhan, China). The biomarker's expression relative to the internal reference GAPDH was determined using the 2 −ΔΔCq method [ 23 ].PCR primer sequences are presented in Table S2 . 3. Results 3.1 Identification and function of AR-DEGs in PPS As shown in Fig. 1 A-B, we obtained 4,089 DEGs between normal and PPS groups, including 1,979 down-regulated and 2,110 up-regulated genes. Then, 19 AR-DEGs in PPS that overlapped DEGs and ARGs were obtained (Fig. 1 C).To further probe the function of these AR-DEGs in PPS, functional enrichment analysis was conducted. GO results indicated that these AR-DEGs were principally involved in the molecular function of ‘positive regulation of I-kappaB kinase/NF-kappaB signaling’ and ‘extrinsic apoptotic signaling pathway’ (Fig. 1 D). Additionally, the KEGG analysis implied that these AR-DEGs were mainly enriched in the ‘apoptosis’ and ‘TNF signaling pathway’ (Fig. 1 E). 3.2Screening apoptosis related biomarkers in PPS In order to explore the interaction between these AR-DEGs, PPI network was constructed(Fig. 2 A). We discovered that TNF acted as the hub of the network and interacted with a wide variety of proteins.Ultimately, 7 candidate genes were obtained via the intersection of five algorithms, including FASLG , CFLAR , TNFSF10 , BIRC3 , IL1A , NFKBIA and CASP10 (Fig. 2 B). Then, the AUC value of TNFSF10 , BIRC3 , IL1A , NFKBIA and CASP10 were greater than 0.7, indicating that the these genes had strong diagnostic value for PPS (Fig. 2 C). We also observed the same results in datasets from external dataset (GSE92681) (Fig. 2 D). Conclusively, TNFSF10 , BIRC3 , IL1A , NFKBIA and CASP10 were identified as apoptosis related biomarkers in PPS. 3.3 Analysis of the role of biomarkers in PPS immune microenvironment Since the pathophysiology of PPS and the immune microenvironment were related, we examined the immune microenvironment. The expression abundance of 24 types of immune cells was analyzed (Fig. 3 A). Notably, there were 11 immune cell abundances that differed significantly in PPS (Fig. 3 B). In the PPS group, there were significantly higher DC, neutrophil, CD4 T cell, Tr1, iTreg and Tfh than in normal group, whereas the expression of CD8T cell, gammadeltaT cell, MAIT, central memory, effector memory in PPS group was significantly lower than normal group. Then, we analyzed the correlation between these differential immune cells, finding that Tr1 was positively associated with Tfh(cor = 0.996), while Neutrophil was negatively associated with Gamma delta T cell (cor=-0.867) (Fig. 3 C). In addition, we discovered that NFKBIA and TNFSF10 were positively correlated with Neutrophil (r = 0.888), while TNFSF10 was negatively correlated with MAIT (r=-0.896)(Fig. 3 D). These results suggested that these biomarkers might played an important role in the immune microenvironment of PPS. 3.4 Apoptotic signaling pathways were enriched in five biomarkers To further study the potential roles of TNFSF10 , BIRC3 , IL1A , NFKBIA and CASP10 in PPS, we performed single-gene GSEA on biomarkers. The GO results showed that these biomarkers synchronously were participated in ‘immunoglobulin complex’, ‘antigen binding’ and ‘B Cell receptor signaling pathway’(Fig. 4 A-E). In addition, Functional similarity analysis also suggested that these biomarkers were related to ‘regulation of extrinsic apoptotic signaling pathway’, ‘regulation of I-kappaB kinase/NF-kappaB signaling’ and ‘negative regulation of apoptotic signaling pathway’(Fig. 4 F). 3.5 Analysis of regulatory network and drug in PPS The ‘mRNA-TF’ network was build to investigate the regulatory mechanisms of TNFSF10 , BIRC3 , IL1A , and NFKBIA (Fig. 5 A). We found that these four biomarkers were regulated by both NFKB1 and RELA. Meanwhile, ‘5 mRNAs-24 miRNAs-328 lncRNAs’ network was constructed, in which hsa-miR-140-3p affected the expression of CASP10 , and hsa-miR-98-5p regulated the expression of TNFSF10 (Fig. 5 B). The drugs that targeted CASP 10 , BIRC3 , IL1A , and NFKBIA were predicted in the DGIDB database. The relationship between biomarkers and drugs was shown in Fig. 5 C. Drugs targeting NFKBIA was CHEMBL401565, DEMETHYLWEDELOLACTONE and PEPEROMIN E etc. Drugs targeting IL1A was RILONACEPT and OLANZAPINE. And drugs targeting BIRC3 was LCL-161 and BESTATIN METHYL ESTER. Drugs targeting CASP10 was EMRICASAN. 3.6 The expression of biomarkers in PPS At the transcription level, we observed higher expression of TNFSF10 , BIRC3 , IL1A , NFKBIA and CASP10 in PPS group compared to the normal group (Fig. 6 A). The verification set also showed a consistent trend (GSE92681) (Fig. 6 B).We finally verified the expression in clinical tissue samples by RT-qPCR. In agreement with the results of the public database data analysis, the expression of TNFSF10 , BIRC3 , IL1A , NFKBIA and CASP10 was markedly over-expressed in clinical PPS samples versus normal samples (Fig. 6 C). 4. Discussion Apoptosis has been shown to be significantly associated with PPS, which might be related to the reactive oxygen species pathway [ 11 , 13 ]. However, the specific action pathway and the related mechanism are still unclear. In this study, we first obtained the 19 AR-DEGs, founding they were significantly associated with apoptosis, NF-kappaB signaling pathway, TNF signaling pathway, and etc. Researches pointed out that NF-kappaB signaling pathway participated in the production of pro-inflammatory cytokines in dental pulp cells [ 24 , 25 ]. And inhibiting the NF-kappaB and β-catenin/Wnt signaling pathways could enhanced odonto/osteogenic differentiation of inflammatory dental pulp stem cells [ 26 ]. In addition, 19 AR-DEGs were annotated to TNF signaling pathway (a key mechanism involved in apoptosis), which further proved that the occurrence of PPS was closely related to apoptosis. Immediately after, we obtained five biomarkers of PPS by PPI and ROC analyses, namely TNFSF10 , BIRC3 , IL1A , NFKBIA , and CASP10. and they were involved in regulation of apoptotic signaling pathway, NF-kappaB signaling pathway, and multiple immune-related signaling pathways, such as B cell receptor signaling pathway, immune receptor activity, and etc.. Established researches pointed that tumor necrosis factor (ligand) super family member 10 ( TNFSF10 ) could induce apoptotic cell death in cancer by binding to its functional death receptors (TNFRSF10A/TRAIL-R1 and TNFRSF10B/TRAIL-R2) to activate the extrinsic apoptosis pathway [ 27 , 28 ]. It was worth noting that TRAIL could activate the transcription factor nuclear factor-kappaB (NF-kappaB)[ 29 , 30 ], and further participating in the production of pro-inflammatory cytokines in dental pulp cells [ 24 ]. In addition, Bridget Charbonneau et al. found that Interleukin-1α ( IL1A ) and TNFSF10 were co-expressed and both able to activate NF-kappaB, and further induced transcription of many proinflammatory genes, suggetsingthese biomarkers may be an important mediator in carcinogenesis [ 31 ]. Baculoviral IAP repeat containing 3 ( BIRC3 ) belong to the family of inhibitor of apoptosis proteins (IAPs)[ 32 ], which was implicated in multiple signaling pathways, such as cell death, immunity, inflammation, the cell cycle, and cell migration[ 33 , 34 ]. Many studies have shown that BIRC3 was highly expressed in many diseases and cancer tissues, and the high expression of BIRC3 was significantly associated with the poor prognosis of the disease [ 35 – 37 ]. In our study, the expression of BIRC3 was also significantly increased in PPS. Moreover, similar to TNFSF10 , BIRC3 play pivotal roles in regulation of NF-kappaB signaling and apoptosis [ 38 , 39 ]. Besides, nuclear factor-kappaB inhibitor alpha ( NFKBIA ) has the great potency to suppress NF-kappaB, that critically function as regulators in cell growth, cell apoptosis and immune inflammatory responses [ 40 ]. In a word, all these results indicate that TNFSF10 , BIRC3 , IL1A , and NFKBIA were involved in or affected cell apoptosis through the NF-kappaB signaling pathway, and further involved in the pathological process of PPS. To further investigate the regulatory mechanism of the biomarkers, we predicted their upstream TFs and constructed the 5 mRNAs-24 miRNAs-328 lncRNAs regulatory network. It was worth noting that the four biomarkers were regulated by both NFKB1 and RELA at the same time. Although there were no reports that NFKB1 and RELAL could regulate biomarkers (except TNFSF10), their co-expression were found in several diseases [ 41 , 42 ]. Ziliang Zeng et al. suggested that the interaction between TGFB1 and TNFSF10 could up-regulate the inflammatory response and cell senescence [ 43 ]. Vladimir V Yurovsky et al. pointed that TGFB1 and TNFSF10 were involved in apoptosis signaling [ 44 ]. Besides, Katherine T Best et al. also suggested that the NFKB1 was involved in both NF-kappaB and MAPK signaling cascades, and the NFKB1 was associated with the expression of macrophage-associated genes and general inflammation[ 45 ]. Noticeably, the RELA was involved in the canonical pathway of NF-kappaB (RELA/p50) [ 46 ]. These results further justify our previous conclusion that the biomarkers of PPS were involved in or affected cell apoptosis in PPS through the NF-kappaB signaling pathway. In addition, we explored the changes of immune microenvironment in PPS, the correlation results pointed out that the biomarkers were positively correlated with DC, neutrophil, CD4 T cell, Tr1, iTreg and Tfh, and were negatively correlated with CD8 T cell, gamma delta T cell, MAIT, central memory cell, effector memory cell. Among them, NFKBIA and TNFSF10 were significantly positively correlated with Neutrophil, while TNFSF10 was significantly negatively correlated with MAIT. J Wang et al. suggested that neutrophils and M0 macrophages might be the most important immune cells in the progression of PPS[ 47 ]. Interestingly, Justin T Schwartz et al. showed that the involvement of BIRC3 and IL1A in neutrophils apoptosis seems to be associated with NF-kappaB signaling [ 48 ]. In addition, the relevant studies have also pointed out DC could contribute to the immune response of human dental pulp by producing and secreting TNF-α, IL-1, and etc.[ 47 , 49 ], which was in agreement with our findings. These evidences imply an important role for these apoptosis-related biomarkers in the regulation of the PPS immune microenvironment. Finally, we predicted the targeted drug of the biomarkers, the results show that drugs targeting NFKBIA included CHEMBL401565, DEMETHYLWEDELOLACTONE, PEPEROMINE, and etc. Reportedly, RILONACEPT was a recombinant IL-1 antagonist, which was used in the therapy of autoinflammatory conditions [ 50 , 51 ]. Chieko Tsutsui et al. showed that the PEPEROMINE could regard as an anti-inflammatory agent that inhibit the NF-kappaB signaling pathway [ 52 ]. Moreover, OLANZAPINE could modulate hepatic oxidative stress and inflammation [ 53 ]. Ying Zhu et al. showed that the OLANZAPINE induced autophagy through suppression of NF-kappaB activation [ 54 ]. These targeted drugs had limited clinical use and has yet to be linked to cases of clinically in PPS, and our results provide theoretical support for the clinical application of these drugs. Conclusion In conclusion, this study screened five apoptosis-related biomarkers of PPS, namely TNFSF10 , BIRC3 , IL1A , NFKBIA , and CASP10 . We conclude that these biomarkers take part in cell apoptosis through the NF-kappaB signaling pathway, and further involved in the pathological process of PPS through functional enrichment and molecular regulation analyses. Nonetheless, our conjecture about the regulatory mechanism of biomarkers is unconfirmed, the mechanism of NFKB1 and RELAL regulatory biomarkers will be further verified by cell experiments. Abbreviations DEGs Differentially expressed genes GEO Gene expression omnibus GO Gene ontology KEGG Kyoto encyclopedia of Genes and Genomes PPI Protein - protein interaction R-DEGs Immune - related differentially expressed genes BP Biological process MF Molecular function CC Cellular component IncRNA Long non - coding RNA TFs Transcription factors TNFSF10 Tumor necrosis factor superfamily member 10 BIRC3Baculoviral IAP repeat containing 3 IL1AInterleukin 1 alpha NFKBIA NF - kappa B inhibitor alpha CASP10 Caspase 10 Declarations Acknowledgements Not applicable Ethics approval and consent to participate An Ethics Committee at XinDu Hospital of Traditional Chinese Medicine in Chengdu City, Sichuan Province, China, approved this study. Consent for publication Not applicable. Availability of data and materials The GSE77459 and GSE92681datasets generated and analysed during the current study are available in GEO DataSets repository, https://www.ncbi.nlm.nih.gov/gds. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors’contributions Xiaoshan Huang and Xia Li contributed equally to this work and conceived the idea. All authors read and approved the final manuscript. References Lei F, Zhang H, Xie X. Comprehensive analysis of an lncRNA-miRNA-mRNA competing endogenous RNA network in pulpitis. PeerJ. 2019;7:e7135. Rôças IN, Lima KC, Assunção IV, Gomes PN, Bracks IV, Siqueira JF. Jr. Advanced Caries Microbiota in Teeth with Irreversible Pulpitis. J Endod. 2015;41(9):1450–5. Sturm AC, Schmidlen T, Scheinfeldt L, Hovick S, McElroy JP, Toland AE et al. Early Outcome Data Assessing Utility of a Post-Test Genomic Counseling Framework for the Scalable Delivery of Precision Health. J Pers Med. 2018;8(3). Chen M, Zeng J, Yang Y, Wu B. Diagnostic biomarker candidates for pulpitis revealed by bioinformatics analysis of merged microarray gene expression datasets. BMC Oral Health. 2020;20(1):279. 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Klein AL, Imazio M, Cremer P, Brucato A, Abbate A, Fang F, et al. Phase 3 Trial of Interleukin-1 Trap Rilonacept in Recurrent Pericarditis. N Engl J Med. 2021;384(1):31–41. Tsutsui C, Yamada Y, Ando M, Toyama D, Wu JL, Wang L, et al. Peperomins as anti-inflammatory agents that inhibit the NF-kappaB signaling pathway. Bioorg Med Chem Lett. 2009;19(15):4084–7. Todorović N, Tomanović N, Gass P, Filipović D. Olanzapine modulation of hepatic oxidative stress and inflammation in socially isolated rats. Eur J Pharm Sci. 2016;81:94–102. Zhu Y, Zhao YF, Liu RS, Xiong YJ, Shen X, Wang Y, et al. Olanzapine induced autophagy through suppression of NF-κB activation in human glioma cells. CNS Neurosci Ther. 2019;25(9):911–21. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 22 Feb, 2024 Editor assigned by journal 22 Feb, 2024 Editor invited by journal 16 Feb, 2024 Submission checks completed at journal 16 Feb, 2024 First submitted to journal 27 Jan, 2024 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-3903309","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273165058,"identity":"869bdd01-f733-48cd-a022-282ca2d3fada","order_by":0,"name":"Xiaoshan Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACfvaG9A8fDCTsgAwitUj2HHjGOKPAJhnIIFKLwYzEZ8w8H9IYN9xIIFaLRHLawxkGh5klZz7eeIOhxiaaoBZznmfpBh8MDvPxS6cVWzAcS8ttIKTFsj0nQRJsy+wcMwnGhsOEtRgcyP8gzWNwmHHDzTPEajmRkAbUAvI+D5FagGGbbDjDABTIQL8kEOMXYAwmPvjwBxSVhzfe+FBjQ1gLiiMlEkhRDtFCqo5RMApGwSgYGQAAWchEk9Wj0oEAAAAASUVORK5CYII=","orcid":"","institution":"XinDu Hospital of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Xiaoshan","middleName":"","lastName":"Huang","suffix":""},{"id":273165059,"identity":"7e4f3155-47be-4a6e-bfcd-72510f1c0322","order_by":1,"name":"霞 李","email":"","orcid":"","institution":"Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"霞","middleName":"","lastName":"李","suffix":""}],"badges":[],"createdAt":"2024-01-27 14:49:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3903309/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3903309/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51309610,"identity":"700ac70c-3f96-4852-99d1-9a7a4761169b","added_by":"auto","created_at":"2024-02-19 10:24:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":671308,"visible":true,"origin":"","legend":"\u003cp\u003eScreening and functional enrichment of AR-DEGs. \u003cstrong\u003eA\u003c/strong\u003e Volcano maps of DEGs in the GSE77459 dataset. The image highlights genes up-regulated by top10. \u003cstrong\u003eB \u003c/strong\u003eHeatmap of DEGs (top10 upregulated DEGs and downregulated DEGs). \u003cstrong\u003eC\u003c/strong\u003e Intersection venn plot of DEGs and ARGs. \u003cstrong\u003eD \u003c/strong\u003eGO enrichment of AR-DEGs, including BP, MF, and CC. \u003cstrong\u003eE\u003c/strong\u003e KEGG enrichment pathway involving AR-DEGs.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3903309/v1/4bc440fac00868351bc14b32.png"},{"id":51309612,"identity":"a149577e-c0a6-4784-9f76-939e228a1394","added_by":"auto","created_at":"2024-02-19 10:24:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":448548,"visible":true,"origin":"","legend":"\u003cp\u003eScreening and validation of biomakers. \u003cstrong\u003eA\u003c/strong\u003e Construction of a PPI interworking network between AR-DEGs. \u003cstrong\u003eB\u003c/strong\u003e Five different algorithms to obtain candidate genes. \u003cstrong\u003eC-D \u003c/strong\u003eROC curves of candidate genes in the GSE77459 and GSE92681 datasets. \u003cstrong\u003eC \u003c/strong\u003eROC curves in GSE77459 dataset. \u003cstrong\u003eD\u003c/strong\u003e ROC curves in GSE92681 dataset.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3903309/v1/48895eb9c8a466c33a6e0bca.png"},{"id":51309615,"identity":"52c1140e-76b3-4239-acb0-43e212be834b","added_by":"auto","created_at":"2024-02-19 10:24:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":229529,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the immune microenvironment in PPS. \u003cstrong\u003eA\u003c/strong\u003e The abundance of immune cell infiltration between samples in the GSE77459 dataset. \u003cstrong\u003eB\u003c/strong\u003e Comparison of differences in immune cell infiltration between PPS and normal groups in the GSE77459 dataset. \u003cstrong\u003eC\u003c/strong\u003e Spearman's correlation between differential immune cells. \u003cstrong\u003eD\u003c/strong\u003eSpearman's correlation between differential immune cells and biomarkers.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3903309/v1/101c4a715a791d2f82166837.png"},{"id":51309618,"identity":"f0a2df7e-aea5-4727-a74e-932c7c6b7dad","added_by":"auto","created_at":"2024-02-19 10:24:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1255063,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional exploration of biomarkers. \u003cstrong\u003eA-E\u003c/strong\u003e GSEA enrichment analysis of biomarkers. \u003cstrong\u003eF\u003c/strong\u003e Interaction regulation network construction of biomarkers.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3903309/v1/3f856a29f7e9c8e552bd71cf.png"},{"id":51309619,"identity":"0bfd7863-4f67-4fb6-8ae7-469be1d2ac22","added_by":"auto","created_at":"2024-02-19 10:24:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1485796,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular regulation and drug prediction. \u003cstrong\u003eA \u003c/strong\u003eMulberry diagram of biomarker-TFs regulation. \u003cstrong\u003eB\u003c/strong\u003e Biomarker-miRNA-lncRNA networks were built. \u003cstrong\u003eC\u003c/strong\u003e Construction of biomarker-drug networks.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3903309/v1/5529377c43c69b18ece73684.png"},{"id":51309966,"identity":"aeb8b611-863b-4c4c-89fe-746183daa3aa","added_by":"auto","created_at":"2024-02-19 10:32:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":188270,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of biomarker expression. \u003cstrong\u003eA\u003c/strong\u003e The examination of biomarker expression in the GSE77459 dataset. \u003cstrong\u003eB\u003c/strong\u003e Validation of biomarker expression in the GSE92681 dataset. \u003cstrong\u003eC \u003c/strong\u003eBiomarker expression in PPS and normal tissue clinical samples. *P\u0026lt;0.05; **P\u0026lt; 0.01; ***P\u0026lt;0.001; ****P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3903309/v1/d9daba05d02406b001d62143.png"},{"id":51310126,"identity":"69356a72-c5be-4937-bc24-b8c6a686c4ae","added_by":"auto","created_at":"2024-02-19 10:40:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2703970,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3903309/v1/0751e496-0415-4b19-9e0f-9fb605d19dc4.pdf"},{"id":51309611,"identity":"f4f9e484-3980-4045-98f5-59444c527c30","added_by":"auto","created_at":"2024-02-19 10:24:36","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10446,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3903309/v1/c3f69869b810483f3a503660.xlsx"},{"id":51309614,"identity":"c7b1239b-5b70-4bf3-a2f2-0615c0eac883","added_by":"auto","created_at":"2024-02-19 10:24:36","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10911,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3903309/v1/52d8171012b412e12989336b.xlsx"},{"id":51309616,"identity":"cf9fbc6c-9d2d-4075-9ec9-fc2da5fb4237","added_by":"auto","created_at":"2024-02-19 10:24:36","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15233,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3903309/v1/403e759b1c7661fcdc85025e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of apoptosis-related biomarkers of apoptosis in pulpitis based on biological informatics","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePulpitis (PPS) is an inflammatory disease of the dental pulp, it is associated with microbial infection of the root canal system and the host response, which is characterized by spontaneous or provoked pain [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Without proper treatment, PPS may lead to pulp necrosis, periapical inflammation, and more severe condition [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The clinical diagnosis and treatment of PPS is limited to determine the degree of pulp inflammation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, histopathological examination showed a weak correlation between clinical features and endodontic status [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, a new noninvasive method for dental pulp diagnosis is urgently needed.\u003c/p\u003e \u003cp\u003eStudies have shown that the pathophysiology of caries-induced PPS included increased inflammatory cytokine production, oxidative stress, damaged mitochondria, apoptosis, and cell death [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Numerous studies have shown that inflammation is one of the key processes in mitochondrial dynamics. Dysregulated homeostasis in pulp tissue is caused by an imbalance in mitochondrial dynamics, which ultimately results in cellular oxidative stress and apoptosis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Apoptosis is one of the forms of cell death that can be activated during inflammation, but only limited studies have focused on the occurrence of apoptosis in PPS [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. \u003cem\u003eH S Wang\u003c/em\u003e et al. demonstrated that the apoptotic markers were expressed in the inflamed dental pulp, indicating that activated autophagy and increased apoptosis played a key role in PPS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. \u003cem\u003eFrancine Benetti\u003c/em\u003e et al. pointed that pulp inflammation was significantly associated with apoptosis through the rat experiment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, \u003cem\u003eT T Wu\u003c/em\u003e et al. further indicated that this mechanism is related to the reactive oxygen pathway by cellular experiments [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the role of apoptosis and the apoptosis related genes (ARGs) in PPS is still undefined.\u003c/p\u003e \u003cp\u003eTherefore, this study focused on gene expression in the pulp tissue of PPS patientsformpublic database, screening apoptosis-related biomarkers of PPS to provide novel therapeutic targets and strategies for PPS patients, and further illustrating the mechanism of ARGs in PPS.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data acquisition\u003c/h2\u003e \u003cp\u003eThree datasets of PPS totaling GSE77459 and GSE92681, which included clinical characteristics and gene expression profiles from dental pulp tissue, were compiled from GEO database. GSE77459dataset, which considered as training set, consisted of 6 normal and 6 PPS samples. GSE92681 dataset, which included 5 normal and 7 PPS samples, was selected for external validation. A sum of 87 apoptosis related genes (ARGs) were obtained from MsigDB database[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e](\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analysis of differential genes\u003c/h2\u003e \u003cp\u003eThe \u0026lsquo;limma\u0026rsquo; package [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] was executed to obtain the differentially expressed genes (DEGs) between normal and PPS groups. The threshold was established as p.value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026amp;|log2FoldChange|\u0026gt;0.5. Volcano plot and heat map were applied to show DEGs via \u0026lsquo;ggVolcano\u0026rsquo; [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]and \u0026lsquo;ComplexHeatmap\u0026rsquo; [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] packages, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Gene enrichment analysis\u003c/h2\u003e \u003cp\u003eThe apoptosisrelated differential expression genes (AR-DEGs) were got via overlapping DEGs and ARGs. GO and KEGG enrichment analysis of AR-DEGs was conducted via \u0026lsquo;clusterProfiler\u0026rsquo; package [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. P.adjust value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered meaningful.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 PPI network\u003c/h2\u003e \u003cp\u003eThe STRING website (interaction score\u0026thinsp;=\u0026thinsp;0.4) was applied to build the protein-protein interaction network, which was used to investigate the interactions between AR-DEGs. The top 10 genes from each of five algorithms (MCC, MNC, EPC, DMNC, Degree) in the Cytoscape plug-in cytoHubba were intersected to obtain candidate genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 ROC analysis\u003c/h2\u003e \u003cp\u003eUsing the \u0026lsquo;pROC\u0026rsquo; tool, a ROC curve was created to assess the diagnostic utility of candidate genes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].At the same time, the GSE92681 was regarded as an external verification set for the diagnostic value.Then, genes with strong diagnostic value (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7) in the training set and external verification setwere identified as biomarkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Immune infiltration Analysis\u003c/h2\u003e \u003cp\u003eThe ImmuCellAI algorithm was applied to calculate the relative abundance of 24 immune cells infiltrated in EP microenvironment through ImmuCellAI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/web/ImmuCellAI\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/web/ImmuCellAI\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These immune cells primarily consisted of 6 additional immune cell types (B cells, natural killer cells, monocytes, macrophages, neutrophils, and dendritic cells) and 18 T cell sub-types. Subsequently, The difference of immune infiltrating cells between PPS group and normal group was compared by Wilcoxon test. The spearman correlation analysis were performed between differential immune cells. Meanwhile, correlation analysis was performed between biomarkers and differential immune cells by \u0026lsquo;corrplot\u0026rsquo; package. In additional,\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 GSEA Analysis\u003c/h2\u003e \u003cp\u003eSingle-gene GSEA was conducted to explore the potential KEGG pathways associated with biomarkers through \u0026lsquo;clusterProfiler\u0026rsquo; package in GSE77459 dataset [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The significant enrichment pathway activity score of each sample was calculated using ssGSEA algorithm in the \u0026lsquo;GSVA\u0026rsquo; package, depending on the correlation of biomarkers expression. According to the score, the correlation of significant enrichment and key gene expression were estimated.P.adjust value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered meaningful. The \u0026lsquo;c5.go.v2022.1.Hs.symbols.gmt\u0026rsquo; and \u0026lsquo;c2.cp.kegg.v7.4.1.symbols.gmt\u0026rsquo; of MsigDB database were used as the background set. In additional, GeneMANIA was yielded to predict the genes related to the function of biomarkers and the genes involved.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.8 Construction of regulatory network and ‘drug-gene’ networks\u003c/h3\u003e\n\u003cp\u003eThe NetworkAnalyst database was applied to predict TF linked to biomarkers. The transcript regulatory network was constructed through \u0026lsquo;ggalluvial\u0026rsquo; package [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The TargetScan database was applied to predict the miRNAs linked to biomarkers. Meantime, the lncRNAs associated with miRNAs were predicted via miRnet database.Through DGIDB database, the targeting medications were found in order to investigate prospective therapeutic treatments for biomarkers in PPS. Moreover, Cytoscape software was applied to optimize the results of \u0026lsquo;lncRNA-miRNA-mRNA\u0026rsquo; and \u0026lsquo;drug-gene\u0026rsquo; networks [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Evaluation of biomarker expression\u003c/h2\u003e \u003cp\u003eRNA extracts from clinical PPS and normal tissue samples were taken for RT-qPCR analysis to further assess biomarker expression levels. TRIzol (Ambion, Austin, USA) was applied to extract total RNA from the samples, which was then reverse transcribed to cDNA using the First-strand-cDNA-synthesis-kit (Servicebio, Wuhan, China). The biomarker's expression relative to the internal reference GAPDH was determined using the 2\u003csup\u003e\u0026minus;ΔΔCq\u003c/sup\u003e method [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].PCR primer sequences are presented in \u003cb\u003eTable S2\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification and function of AR-DEGs in PPS\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B, we obtained 4,089 DEGs between normal and PPS groups, including 1,979 down-regulated and 2,110 up-regulated genes. Then, 19 AR-DEGs in PPS that overlapped DEGs and ARGs were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).To further probe the function of these AR-DEGs in PPS, functional enrichment analysis was conducted. GO results indicated that these AR-DEGs were principally involved in the molecular function of \u0026lsquo;positive regulation of I-kappaB kinase/NF-kappaB signaling\u0026rsquo; and \u0026lsquo;extrinsic apoptotic signaling pathway\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Additionally, the KEGG analysis implied that these AR-DEGs were mainly enriched in the \u0026lsquo;apoptosis\u0026rsquo; and \u0026lsquo;TNF signaling pathway\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2Screening apoptosis related biomarkers in PPS\u003c/h2\u003e \u003cp\u003eIn order to explore the interaction between these AR-DEGs, PPI network was constructed(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We discovered that TNF acted as the hub of the network and interacted with a wide variety of proteins.Ultimately, 7 candidate genes were obtained via the intersection of five algorithms, including \u003cem\u003eFASLG\u003c/em\u003e,\u003cem\u003eCFLAR\u003c/em\u003e,\u003cem\u003eTNFSF10\u003c/em\u003e,\u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e,\u003cem\u003eNFKBIA\u003c/em\u003eand \u003cem\u003eCASP10\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Then, the AUC value of \u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003eand \u003cem\u003eCASP10\u003c/em\u003e were greater than 0.7, indicating that the these genes had strong diagnostic value for PPS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). We also observed the same results in datasets from external dataset (GSE92681) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Conclusively, \u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003eand \u003cem\u003eCASP10\u003c/em\u003ewere identified as apoptosis related biomarkers in PPS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of the role of biomarkers in PPS immune microenvironment\u003c/h2\u003e \u003cp\u003eSince the pathophysiology of PPS and the immune microenvironment were related, we examined the immune microenvironment. The expression abundance of 24 types of immune cells was analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Notably, there were 11 immune cell abundances that differed significantly in PPS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In the PPS group, there were significantly higher DC, neutrophil, CD4 T cell, Tr1, iTreg and Tfh than in normal group, whereas the expression of CD8T cell, gammadeltaT cell, MAIT, central memory, effector memory in PPS group was significantly lower than normal group. Then, we analyzed the correlation between these differential immune cells, finding that Tr1 was positively associated with Tfh(cor\u0026thinsp;=\u0026thinsp;0.996), while Neutrophil was negatively associated with Gamma delta T cell (cor=-0.867) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In addition, we discovered that \u003cem\u003eNFKBIA\u003c/em\u003eand\u003cem\u003eTNFSF10\u003c/em\u003e were positively correlated with Neutrophil (r\u0026thinsp;=\u0026thinsp;0.888), while \u003cem\u003eTNFSF10\u003c/em\u003e was negatively correlated with MAIT (r=-0.896)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). These results suggested that these biomarkers might played an important role in the immune microenvironment of PPS.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Apoptotic signaling pathways were enriched in five biomarkers\u003c/h2\u003e \u003cp\u003eTo further study the potential roles of \u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003eand \u003cem\u003eCASP10\u003c/em\u003e in PPS, we performed single-gene GSEA on biomarkers. The GO results showed that these biomarkers synchronously were participated in \u0026lsquo;immunoglobulin complex\u0026rsquo;, \u0026lsquo;antigen binding\u0026rsquo; and \u0026lsquo;B Cell receptor signaling pathway\u0026rsquo;(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-E). In addition, Functional similarity analysis also suggested that these biomarkers were related to \u0026lsquo;regulation of extrinsic apoptotic signaling pathway\u0026rsquo;, \u0026lsquo;regulation of I-kappaB kinase/NF-kappaB signaling\u0026rsquo; and \u0026lsquo;negative regulation of apoptotic signaling pathway\u0026rsquo;(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Analysis of regulatory network and drug in PPS\u003c/h2\u003e \u003cp\u003eThe \u0026lsquo;mRNA-TF\u0026rsquo; network was build to investigate the regulatory mechanisms of \u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, and \u003cem\u003eNFKBIA\u003c/em\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). We found that these four biomarkers were regulated by both NFKB1 and RELA. Meanwhile, \u0026lsquo;5 mRNAs-24 miRNAs-328 lncRNAs\u0026rsquo; network was constructed, in which hsa-miR-140-3p affected the expression of \u003cem\u003eCASP10\u003c/em\u003e, and hsa-miR-98-5p regulated the expression of \u003cem\u003eTNFSF10\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The drugs that targeted CASP\u003cem\u003e10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, and \u003cem\u003eNFKBIA\u003c/em\u003e were predicted in the DGIDB database. The relationship between biomarkers and drugs was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC. Drugs targeting \u003cem\u003eNFKBIA\u003c/em\u003e was CHEMBL401565, DEMETHYLWEDELOLACTONE and PEPEROMIN E etc. Drugs targeting \u003cem\u003eIL1A\u003c/em\u003e was RILONACEPT and OLANZAPINE. And drugs targeting \u003cem\u003eBIRC3\u003c/em\u003e was LCL-161 and BESTATIN METHYL ESTER. Drugs targeting CASP10 was EMRICASAN.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 The expression of biomarkers in PPS\u003c/h2\u003e \u003cp\u003eAt the transcription level, we observed higher expression of \u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eCASP10\u003c/em\u003e in PPS group compared to the normal group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The verification set also showed a consistent trend (GSE92681) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).We finally verified the expression in clinical tissue samples by RT-qPCR. In agreement with the results of the public database data analysis, the expression of \u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003eand \u003cem\u003eCASP10\u003c/em\u003e was markedly over-expressed in clinical PPS samples versus normal samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e "},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003cp\u003eApoptosis has been shown to be significantly associated with PPS, which might be related to the reactive oxygen species pathway [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the specific action pathway and the related mechanism are still unclear. In this study, we first obtained the 19 AR-DEGs, founding they were significantly associated with apoptosis, NF-kappaB signaling pathway, TNF signaling pathway, and etc. Researches pointed out that NF-kappaB signaling pathway participated in the production of pro-inflammatory cytokines in dental pulp cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. And inhibiting the NF-kappaB and β-catenin/Wnt signaling pathways could enhanced odonto/osteogenic differentiation of inflammatory dental pulp stem cells [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition, 19 AR-DEGs were annotated to TNF signaling pathway (a key mechanism involved in apoptosis), which further proved that the occurrence of PPS was closely related to apoptosis.\u003c/p\u003e \u003cp\u003eImmediately after, we obtained five biomarkers of PPS by PPI and ROC analyses, namely \u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, and \u003cem\u003eCASP10.\u003c/em\u003e and they were involved in regulation of apoptotic signaling pathway, NF-kappaB signaling pathway, and multiple immune-related signaling pathways, such as B cell receptor signaling pathway, immune receptor activity, and etc.. Established researches pointed that tumor necrosis factor (ligand) super family member 10 (\u003cem\u003eTNFSF10\u003c/em\u003e) could induce apoptotic cell death in cancer by binding to its functional death receptors (TNFRSF10A/TRAIL-R1 and TNFRSF10B/TRAIL-R2) to activate the extrinsic apoptosis pathway [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It was worth noting that \u003cem\u003eTRAIL\u003c/em\u003ecould activate the transcription factor nuclear factor-kappaB (NF-kappaB)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and further participating in the production of pro-inflammatory cytokines in dental pulp cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In addition, \u003cem\u003eBridget Charbonneau\u003c/em\u003e et al. found that Interleukin-1α (\u003cem\u003eIL1A\u003c/em\u003e) and \u003cem\u003eTNFSF10\u003c/em\u003e were co-expressed and both able to activate NF-kappaB, and further induced transcription of many proinflammatory genes, suggetsingthese biomarkers may be an important mediator in carcinogenesis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Baculoviral IAP repeat containing 3 (\u003cem\u003eBIRC3\u003c/em\u003e) belong to the family of inhibitor of apoptosis proteins (IAPs)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which was implicated in multiple signaling pathways, such as cell death, immunity, inflammation, the cell cycle, and cell migration[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Many studies have shown that \u003cem\u003eBIRC3\u003c/em\u003e was highly expressed in many diseases and cancer tissues, and the high expression of \u003cem\u003eBIRC3\u003c/em\u003e was significantly associated with the poor prognosis of the disease [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In our study, the expression of \u003cem\u003eBIRC3\u003c/em\u003e was also significantly increased in PPS. Moreover, similar to \u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e play pivotal roles in regulation of NF-kappaB signaling and apoptosis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Besides, nuclear factor-kappaB inhibitor alpha (\u003cem\u003eNFKBIA\u003c/em\u003e) has the great potency to suppress NF-kappaB, that critically function as regulators in cell growth, cell apoptosis and immune inflammatory responses [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In a word, all these results indicate that \u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, and \u003cem\u003eNFKBIA\u003c/em\u003e were involved in or affected cell apoptosis through the NF-kappaB signaling pathway, and further involved in the pathological process of PPS.\u003c/p\u003e \u003cp\u003eTo further investigate the regulatory mechanism of the biomarkers, we predicted their upstream TFs and constructed the 5 mRNAs-24 miRNAs-328 lncRNAs regulatory network. It was worth noting that the four biomarkers were regulated by both \u003cem\u003eNFKB1\u003c/em\u003e and \u003cem\u003eRELA\u003c/em\u003e at the same time. Although there were no reports that \u003cem\u003eNFKB1\u003c/em\u003e and \u003cem\u003eRELAL\u003c/em\u003e could regulate biomarkers (except TNFSF10), their co-expression were found in several diseases [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. \u003cem\u003eZiliang Zeng\u003c/em\u003e et al. suggested that the interaction between \u003cem\u003eTGFB1\u003c/em\u003e and \u003cem\u003eTNFSF10\u003c/em\u003ecould up-regulate the inflammatory response and cell senescence [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003cem\u003eVladimir V Yurovsky\u003c/em\u003e et al. pointed that \u003cem\u003eTGFB1\u003c/em\u003e and \u003cem\u003eTNFSF10\u003c/em\u003ewere involved in apoptosis signaling [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Besides, \u003cem\u003eKatherine T Best\u003c/em\u003e et al. also suggested that the\u003cem\u003eNFKB1\u003c/em\u003e was involved in both NF-kappaB and MAPK signaling cascades, and the \u003cem\u003eNFKB1\u003c/em\u003ewas associated with the expression of macrophage-associated genes and general inflammation[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Noticeably, the \u003cem\u003eRELA\u003c/em\u003e was involved in the canonical pathway of NF-kappaB (RELA/p50) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. These results further justify our previous conclusion that the biomarkers of PPS were involved in or affected cell apoptosis in PPS through the NF-kappaB signaling pathway.\u003c/p\u003e \u003cp\u003eIn addition, we explored the changes of immune microenvironment in PPS, the correlation results pointed out that the biomarkers were positively correlated with DC, neutrophil, CD4 T cell, Tr1, iTreg and Tfh, and were negatively correlated with CD8 T cell, gamma delta T cell, MAIT, central memory cell, effector memory cell. Among them, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eTNFSF10\u003c/em\u003e were significantly positively correlated with Neutrophil, while \u003cem\u003eTNFSF10\u003c/em\u003e was significantly negatively correlated with MAIT. \u003cem\u003eJ Wang\u003c/em\u003e et al. suggested that neutrophils and M0 macrophages might be the most important immune cells in the progression of PPS[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Interestingly, \u003cem\u003eJustin T Schwartz\u003c/em\u003e et al. showed that the involvement of \u003cem\u003eBIRC3\u003c/em\u003e and \u003cem\u003eIL1A\u003c/em\u003e in neutrophils apoptosis seems to be associated with NF-kappaB signaling [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In addition, the relevant studies have also pointed out DC could contribute to the immune response of human dental pulp by producing and secreting TNF-α, IL-1, and etc.[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], which was in agreement with our findings. These evidences imply an important role for these apoptosis-related biomarkers in the regulation of the PPS immune microenvironment.\u003c/p\u003e \u003cp\u003eFinally, we predicted the targeted drug of the biomarkers, the results show that drugs targeting NFKBIA included CHEMBL401565, DEMETHYLWEDELOLACTONE, PEPEROMINE, and etc. Reportedly, RILONACEPT was a recombinant IL-1 antagonist, which was used in the therapy of autoinflammatory conditions [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. \u003cem\u003eChieko Tsutsui\u003c/em\u003e et al. showed that the PEPEROMINE could regard as an anti-inflammatory agent that inhibit the NF-kappaB signaling pathway [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Moreover, OLANZAPINE could modulate hepatic oxidative stress and inflammation [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. \u003cem\u003eYing Zhu\u003c/em\u003e et al. showed that the OLANZAPINE induced autophagy through suppression of NF-kappaB activation [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These targeted drugs had limited clinical use and has yet to be linked to cases of clinically in PPS, and our results provide theoretical support for the clinical application of these drugs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study screened five apoptosis-related biomarkers of PPS, namely\u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, and \u003cem\u003eCASP10\u003c/em\u003e. We conclude that these biomarkers take part in cell apoptosis through the NF-kappaB signaling pathway, and further involved in the pathological process of PPS through functional enrichment and molecular regulation analyses. Nonetheless, our conjecture about the regulatory mechanism of biomarkers is unconfirmed, the mechanism of \u003cem\u003eNFKB1\u003c/em\u003e and \u003cem\u003eRELAL\u003c/em\u003e regulatory biomarkers will be further verified by cell experiments.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDEGs Differentially expressed genes\u003c/p\u003e \u003cp\u003eGEO Gene expression omnibus\u003c/p\u003e \u003cp\u003eGO Gene ontology\u003c/p\u003e \u003cp\u003eKEGG Kyoto encyclopedia of Genes and Genomes\u003c/p\u003e \u003cp\u003ePPI Protein - protein interaction\u003c/p\u003e \u003cp\u003eR-DEGs Immune - related differentially expressed genes\u003c/p\u003e \u003cp\u003eBP Biological process\u003c/p\u003e \u003cp\u003eMF Molecular function\u003c/p\u003e \u003cp\u003eCC Cellular component\u003c/p\u003e \u003cp\u003eIncRNA Long non - coding RNA\u003c/p\u003e \u003cp\u003eTFs Transcription factors\u003c/p\u003e \u003cp\u003eTNFSF10 Tumor necrosis factor superfamily member 10\u003c/p\u003e \u003cp\u003eBIRC3Baculoviral IAP repeat containing 3\u003c/p\u003e \u003cp\u003eIL1AInterleukin 1 alpha\u003c/p\u003e \u003cp\u003eNFKBIA NF - kappa B inhibitor alpha\u003c/p\u003e \u003cp\u003eCASP10 Caspase 10\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn Ethics Committee at XinDu Hospital of Traditional Chinese Medicine in Chengdu City, Sichuan Province, China, approved this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GSE77459 and GSE92681datasets generated and analysed during the current study are available in GEO DataSets repository, https://www.ncbi.nlm.nih.gov/gds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo;contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaoshan Huang and Xia Li contributed equally to this work and conceived the idea. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLei F, Zhang H, Xie X. Comprehensive analysis of an lncRNA-miRNA-mRNA competing endogenous RNA network in pulpitis. PeerJ. 2019;7:e7135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026ocirc;\u0026ccedil;as IN, Lima KC, Assun\u0026ccedil;\u0026atilde;o IV, Gomes PN, Bracks IV, Siqueira JF. Jr. Advanced Caries Microbiota in Teeth with Irreversible Pulpitis. J Endod. 2015;41(9):1450\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSturm AC, Schmidlen T, Scheinfeldt L, Hovick S, McElroy JP, Toland AE et al. Early Outcome Data Assessing Utility of a Post-Test Genomic Counseling Framework for the Scalable Delivery of Precision Health. J Pers Med. 2018;8(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen M, Zeng J, Yang Y, Wu B. 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CNS Neurosci Ther. 2019;25(9):911\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulpitis, Apoptosis, Biomarkers, Bioinformatics, Immune cells","lastPublishedDoi":"10.21203/rs.3.rs-3903309/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3903309/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePulpitis (PPS) is a dental disease caused by the destruction of dental hard tissue around the dental pulp. Studies have confirmed that apoptosis has a role in the production of PPS. Hence, it was vital to screen apoptosis related biomarkers for PPS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo identify differentially expressed genes (DEGs) in GSE77459, we conducted a differential expression analysis (normal \u003cem\u003eversus\u003c/em\u003e PPS). Then, apoptosisrelated differential expression genes (AR-DEGs) were got via overlapping DEGs and apoptosis related genes (ARGs). The five algorithms of cytoHubba in protein-protein interaction (PPI) network and receiver operating characteristic (ROC) were applied to screen apoptosis related biomarkers. Subsequently, we further conducted gene functional enrichment and immune microenvironment analyses for these biomarkers. We finally verified the expression in clinical tissue samples by RT-qPCR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA sum of 4,089 DEGs were obtained between PPS and normal groups. Soon afterwards, 19AR-DEGs were screened by the intersection of DEGs and ARGs. Moreover, we got 5 apoptosis related biomarkers via five machine learning algorithms, including \u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eCASP10\u003c/em\u003e.We found that these three biomarkers participated immune-related processes \u0026lsquo;immunoglobulin complex\u0026rsquo;. In additional, we discovered that\u003cem\u003eTNFSF10\u003c/em\u003e was correlated with Neutrophil and MAIT in immune microenvironment of PPS. In agreement with the results of the public database data analysis, the expression of \u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eCASP10\u003c/em\u003e was markedly over-expressed in clinical PPS samples versus normal samples.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOverall, we obtained five apoptosis related biomarkers (\u003cem\u003eTNFSF10\u003c/em\u003e, \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eIL1A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003eand \u003cem\u003eCASP10\u003c/em\u003e) associated with PPS, which laid a theoretical foundation for the treatment of PPS.\u003c/p\u003e","manuscriptTitle":"Identification of apoptosis-related biomarkers of apoptosis in pulpitis based on biological informatics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-19 10:24:31","doi":"10.21203/rs.3.rs-3903309/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2024-02-22T15:21:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-22T15:13:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-16T07:50:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-16T07:42:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2024-01-27T14:36:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cea5aba6-af74-40f9-a44a-38a6ec5d0fe3","owner":[],"postedDate":"February 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-02-19T10:24:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-19 10:24:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3903309","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3903309","identity":"rs-3903309","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}