Integration of machine learning and risk modeling to analyze and validate the role of senescent genes in lupus nephritis with monkeypox virus

Integration of machine learning and risk modeling to analyze and validate the role of senescent genes in lupus nephritis with monkeypox virus | 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 Article Integration of machine learning and risk modeling to analyze and validate the role of senescent genes in lupus nephritis with monkeypox virus Yaojun Wang, Qiang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5276430/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Objective Monkeypox virus is now spreading rapidly around the world, but the mechanism of interaction between it and lupus nephritis is not yet clear.The purpose of this study is to explore the role and mechanism of cell aging in lupus nephritis combined with monkeypox virus infection. Method The data comes from GEO and GeneCards.Through Limma and WGCNA analysis, differential expression genes (DEGs) and module genes were identified, and KEGG and GO enrichment analysis was carried out.In addition, a protein-protein interaction (PPI) network was constructed and LASSO regression was used to screen genes related to aging. The diagnostic effectiveness was evaluated by Norma diagram and ROC curve, and verified on GSE99967.Immune infiltration and gene set enrichment analysis (GSEA) Were also included in the study.In the end, miRNet was used to construct a miRNA-mRNA-TF network and screen targeted drugs through DGIdb. Results 5707 DEGs were identified in the lupus nephritis data set and 737 in the monkeypox data. The two have a total of 113 genes, which are related to immune cell function and aging.The three central genes screened showed an AUC of 0.71 to 0.88 in the column chart, indicating good diagnostic potential.Immune infiltration analysis showed immune cell disorders and related pathway activation.The miRNA-mRNA-TF network covers 516 miRNAs and 15 transcription factors, and enrichment analysis shows that it plays an important role in aging and inflammation.Potential Target Drugs Screened Include Guttiferone K And Silicon Phthalocyanine 4. Conclusion This study confirmed the key genes (STAT1, ORC1, GTF2B) related to cell aging and immunity, and developed a line chart for the diagnosis of monkeypox virus infection combined with lupus nephritis, and at the same time screened drug candidates with targeted potential. Health sciences/Diseases/Infectious diseases/Viral infection Health sciences/Diseases/Kidney diseases/Lupus nephritis Biological sciences/Genetics/Genetic markers Health sciences/Nephrology/Kidney diseases Health sciences/Biomarkers/Diagnostic markers Health sciences/Biomarkers/Predictive markers Health sciences/Nephrology Health sciences/Pathogenesis Health sciences/Risk factors Senescence lupus nephritis monkeypox biomarkers machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Lupus nephritis (LN) represents a severe complication associated with systemic lupus erythematosus (SLE). The condition originates from the accumulation of immune complexes or autoantibodies within the glomerular basement membrane, subsequently leading to the recruitment of inflammatory cells [1] . The complex pathogenesis of LN encompasses various factors, including genetic predisposition, environmental influences, and hormonal changes, as well as a range of inflammatory pathways and cellular interactions [2] . Recent advancements in research have highlighted the roles of innate immune cells, including neutrophils, monocytes, and dendritic cells, while the introduction of novel biomarkers is transforming the utility of kidney biopsy in clinical settings [3, 4] . In August 2024, the World Health Organization (WHO) designated the outbreak of monkeypox in Africa as a public health emergency [5] . By August 2, the total cases of infection had escalated to 99,176, with 208 reported fatalities globally [6] . Monkeypox occurs due to a variant of the monkeypox virus, primarily transmitted through close contact, with common symptoms being rashes, high fevers, and swollen lymph nodes. Strategies for prevention and control encompass educational initiatives, therapeutic options such as tecovirimat, and the modified cowpox vaccine known as JYNNEOS [7, 8] . Recent findings indicate a connection between MPXV infection and dysfunction of NK cells as well as immune evasion, highlighting the importance of the immune response in resisting infections caused by poxviruses [9–11] . Cellular senescence arises from the gradual shortening of telomeres, oxidative stress, and ongoing inflammation, ultimately resulting in an irreversible state of growth arrest [12, 13] . Cells that have undergone senescence continue to be metabolically active and demonstrate both morphological and physiological alterations, such as increased β-galactosidase activity and the pro-inflammatory, pro-fibrotic senescence-associated secretory phenotype (SASP) [14] . While cellular senescence contributes positively to tissue repair, it can also have detrimental consequences in chronic diseases and various cancers [13] . Research indicates that senescence is significantly involved in the progression of lupus nephritis, correlating with markers of renal dysfunction like p16 INK4a and β-galactosidase-positive cells [15] ; nevertheless, the mechanisms behind cellular senescence in the context of monkeypox virus infection remain largely unexplored. While no definitive link exists between lupus nephritis and monkeypox, both conditions are characterized by an inflammatory response and immune cell infiltration. In patients with active lupus nephritis (LN), the ratio of T follicular helper cells (TFH) to regulatory T cells (Treg) is elevated, accompanied by an infiltration of TFH1 cells in the kidneys affected by LN [16] . Additionally, natural killer (NK) cells and dendritic cells are instrumental in combating the monkeypox virus [11] . Consequently, identifying immune genes associated with senescence in patients suffering from lupus nephritis and monkeypox is crucial for early diagnosis and treatment planning. In this research, we accessed datasets for lupus nephritis and monkeypox from the Gene Expression Omnibus (GEO) database and senescence gene sets from GeneCard. We then screened DEGs using Limma, identified significant modular genes through weighted co-expression network analysis (WGCNA), and conducted functional enrichment analysis. Furthermore, we constructed a PPI network utilizing LASSO and performed immune cell infiltration analysis to pinpoint candidate genes. Finally, the critical immune-related diagnostic genes associated with lupus nephritis and monkeypox were validated through nomogram and receiver operating characteristic (ROC) curve analyses, establishing a practical framework for detecting the immune markers relevant to these two conditions. 2 Methods 2.1 Data Acquisition and Processing We sourced 5,830 senescence-related genes (SRGs) from the GeneCards website ( https://www.genecards.org/ ). Concurrently, we accessed two datasets via the Gene Expression Omnibus (GEO) database: GSE112943, which includes kidney biopsy samples from 14 lupus nephritis (LN) patients alongside 7 healthy controls, and GSE99967, comprising kidney biopsy samples from 42 LN patients with 17 healthy controls. Additionally, we utilized GSE36854, which features two HL cell lines infected with the monkeypox virus and their corresponding blank controls. GSE112943 and GSE36854 served as the training dataset, whereas GSE99967 functioned as the validation dataset. We employed the "limma" package in R to identify DEGs across these three datasets, applying screening criteria of p-value 1.5. The workflow of the study is summarized in Fig. 1 , with DEGs visualized using the Sangerbox platform. 2.2 WGCNA and identification of clinically significant modules In this study, we explored gene-phenotype associations by constructing gene co-expression networks utilizing the "WGCNA" package in R software [17] . The analysis was aimed at assessing the relationship between modular feature genes and lupus nephritis (LN). We extracted genes that had a median absolute deviation (MAD) falling within the top 75% of the analyzed genes via "WGCNA" and evaluated them using the same package. To ensure no abnormal samples or genes were included, we employed the goodSamplesGenes algorithm. Furthermore, we created a clustering dendrogram with the hclust function to identify any outlier samples, ultimately excluding two samples with a height exceeding 70. Following the establishment of a soft threshold of 24 and a minimum of 30 genes per module, we derived modules consisting of co-expressed genes and conducted Pearson correlation analysis to elucidate the association of these modules with disease phenotypes. 2.3 Functional enrichment analysis and PPI network analysis To investigate the biological roles of the intersecting genes, we initially pinpointed the overlap among the DEGs associated with lymph nodes (LN), the essential module genes, and the DEGs related to monkeypox. We utilized the "clusterProfile" package [18] within R software to conduct Gene Ontology (GO) [19] and Kyoto Encyclopedia of Genes and Genomes (KEGG) [20] analyses, ensuring a significance level of p < 0.05. Furthermore, we carried out an analysis of PPIs for the intersecting genes through the use of the GeneMANIA too [21] 。 2.4 Identifying Candidate Hub Genes by Machine Learning We utilized the LASSO regression machine learning algorithm to pinpoint differentially expressed senescent-related genes (DE-SRGs). Initially, we intersected the collection of senescent genes with the DEGs that are prevalent in both lymph nodes (LN) and monkeypox. Based on these intersecting genes, we executed a LASSO analysis. LASSO, a machine learning methodology, marries variable selection with regularization to enhance predictive accuracy [22] . The analysis was conducted using the "glmnet" package in R software, enabling us to derive potential pivotal genes for diagnostic purposes. 2.5 Nomogram construction and Receiver Operating Characteristic assessment To assess the significance of candidate genes in diagnosing lymph nodes (LN) in conjunction with monkeypox virus infection, we initially analyzed the differential expression of these candidate genes within the validation cohort. Subsequently, we utilized the "rms" package in R to create a column-line graph featuring "Points" (the scores attributed to the candidate genes) and "Total Points" (the cumulative scores of all genes), which proved to be a key instrument for predicting LN combined with monkeypox. Further, we assessed the prognostic value of both the candidate genes and the generated column plot using subject operating characteristics (ROC) analysis on the validation dataset, yielding results presented as the area under the curve (AUC) along with a 95% confidence interval (CI). An AUC exceeding 0.7 was deemed indicative of strong diagnostic efficacy. 2.6 Immune Infiltration Analysis and GSEA Pathway Analysis We employed the CIBERSORT algorithm to determine the proportions of immune cells in patients from the lymph node (LN) and control groups. This algorithm facilitates the analysis and quantification of 22 distinct immune cell subpopulations in clinical samples via gene expression profiling [23] . Additionally, we illustrated the correlations among immune cells and the signaling pathways relevant to the candidate genes utilizing heatmap analysis and GSEA [24] . 2.7 Construction of miRNA-mRNA-TF network Screening for tiny RNAs (miRNAs) and transcription factors (TFs) associated with intersecting genes based on the miRNet2/0 online database ( https://www.mirnet.ca/ ) [25] . 2.8 Prediction of targeted drugs To predict drug responsiveness to key autophagy-related genes, this study was based on the DGldb online platform ( https://dgidb.org/ , accessed October 15, 2024), which is an open-source search engine focusing on drug-gene interactions and information on druggable genomes, aiming at obtaining targeted drugs against target genes [26] . 2.9 Statistical analysis We utilized R software (version 4.1.2) along with GraphPad Prism 9 to conduct our data analysis. For datasets that encompassed multiple groups, we applied analysis of variance (ANOVA) to evaluate statistical significance, subsequently comparing differences between two groups with either Student's t-test or the Wilcoxon rank sum test. p-values below 0.05 were considered statistically significant. 3. Results 3.1 Identification of DEGs in the LN The findings indicated that we identified a total of 5707 DEGs within the GSE36854 dataset, all meeting the criteria of p-values 1.5. Volcano plot (Fig. 2 A) and heat maps (Fig. 2 B) illustrate the differential expression patterns of these DEGs. Likewise, the GSE112943 dataset revealed a total of 734 DEGs, also adhering to the same criteria (p-values 1.5). Figures 3 A and 3 B display the differential expression patterns of DEGs associated with monkeypox. 3.2 WGCNA and key module identification A scale-free co-expression network was constructed using Weighted Gene Co-expression Network Analysis (WGCNA) to pinpoint the modules most strongly associated with lymph nodes (LN). Following the analysis, we opted for a "soft" threshold β of 24 (Fig. 3 C, D), generating clustering dendrograms for both LNs and controls. By setting a module merging threshold at 0.25 and a minimum module size of 50, we identified a total of 19 distinct colored gene co-expression modules (Fig. 3 E-G). Clinical correlation analysis revealed that the antiquewhite4 module exhibited the highest association with LN (r = 0.84, p-value < 0.001) (Fig. 3 H), prompting us to select this module, which contains 4107 genes, for further in-depth analysis (Supplementary table). We computed the correlation between module eigenvectors and gene expression, resulting in module eigenvalues (MM) that displayed a significant positive correlation (correlation coefficient = 0.81, p-value < 0.001) (Fig. 3 I). These findings indicate that the genes within the antiquewhite4 module are closely related to LN. 3.3 Enrichment analysis of co-expressed genes of LN combined with monkeypox virus infection To explore the biological roles of lymph nodes (LN) in conjunction with monkeypox virus infection, we analyzed LN differentially DEGs, along with key modular genes and monkeypox DEGs, leading to the identification of 113 shared genes (Fig. 4 A, Supplementary table). The KEGG enrichment analysis indicated that these genes were primarily implicated in pathways such as "Cellular senescence," "Human T-cell leukemia virus infection," and "Ferroptosis" (Fig. 4 B). Additionally, these genes were associated with biological processes (BPs) like "lymph vessel development," "regulation of lymphangiogenesis," and "mRNA metabolic process." In terms of cellular components (CC), they were enriched in locations including "cytoplasm," "nucleoplasm," and "cytoplasmic fraction." Regarding molecular function (MF), the predominant enrichment was in "protein N-terminal binding" (Fig. 4 C-E). Ultimately, we constructed a PPI network highlighting the cross-gene interactions, with the CDK7 node showing the highest frequency of connections, as illustrated in Fig. 4 F. 3.4 Identify DE-SRGs through machine learning To examine the involvement of senescence genes in various diseases, we identified 47 senescence co-expressed genes related to disease (Fig. 5 A, Supplementary table). Subsequently, we utilized the R package glmnet to conduct regression analysis employing the lasso-cox method. Additionally, a 3-fold cross-validation was implemented to determine the optimal model. We assigned a Lambda value of 0.00521316702464377, resulting in a model formula for the 11 gene constructs expressed as RiskScore = 0.0814342818246804WDR82–0.0489265899752115NFIX + 0.230987868325894STAT1 + 0.0576775996331519PANX1–0.0192816848884446PTGS1 + 0.0465632183810925TLR4 + 0.0245669295263691ZCCHC9 + 0.0255499371067976WDR36 + 0.142435132705123ORC2 + 0.106186688914399GTF2B − 0.874693126720345*ZFP36. Through LASSO regression analysis, we identified 11 DE-SRGs closely linked to disease (Fig. 5 B-C). The ROC curve analysis revealed that the gene models derived from this LASSO regression exhibited exceptional predictive accuracy (AUC = 1, 95% CI: 1–1) (Fig. 5 D). Furthermore, we created prognostic heat maps for the 11 genes based on risk scores and clinical attributes, indicating that, with the exception of PANX1, PTGS1, and ZFP36, the other genes provided greater diagnostic precision in identifying LN(Fig. 5 E). 3.5 Diagnostic value assessment To verify the validity of the 11 DE-SRGs screened, we analyzed them in the validation set GSE99967. The results showed that only the expression levels of STAT1, ORC2, and GTF2B were statistically different (Fig. 6 A). Further, we constructed a Nomogram containing these three key diagnostic genes (Fig. 6 B) and plotted ROC curves to evaluate their diagnostic efficacy. The AUC values for STAT1, ORC2, and GTF2B were 0.88 (95% CI: 0.79–0.97), 0.85 (95% CI: 0.74–0.96), and 0.71 (95% CI: 0.55–0.87), respectively (Fig. 6 C-E). These results suggest that STAT1, ORC2 and GTF2B can be used as potential biomarkers for the diagnosis of LN complicated with monkeypox, and the Nomogram constructed by them also shows good diagnostic value. 3.6 Relationship between diagnostic models and LN immune cell infiltration The research revealed a critical role for the diagnostic genes linked to monkeypox virus infection in modulating LN pathogenesis, with a significant involvement in immune-related pathways. To delve deeper into the immune dynamics in LN, an analysis of immune infiltration between the LN and control groups was undertaken. As depicted in Fig. 7 A and supplementary table, there were notable differences in the proportion of 22 immune cell types between the two groups. The study observed a lower abundance of CD8 naive T cells, cytotoxic T cells, nTregs, monocytes, and neutrophils in the LN group compared to the control, whereas the levels of Tr1 cells, Th17 cells, NKT cells, MAIT cells, dendritic cells (DCs), natural killer (NK) cells, and CD4 + T cells were significantly increased (Fig. 7 C). A correlation analysis uncovered that Th1 cells were positively correlated with Th17 and NKT cells, while CD8 + T cells had a negative correlation with γδ T cells. Cytotoxic T cells displayed a negative relationship with NKT cells (Fig. 7 B). The GSEA on the genes STAT1, ORC2, and GTF2B from patients with LN pinpointed their enrichment in immune pathways, such as the T cell receptor signaling, B cell receptor signaling, and Toll-like receptor signaling pathways (Fig. 7 D - G). In summary, monkeypox virus appears to contribute to LN pathogenesis through its impact on immune cell infiltration, suggesting that targeted regulation of immune cell populations could represent a novel therapeutic strategy for LN management. 3.7 miRNA-mRNA-TF regulatory network To investigate the regulatory mechanisms of key genes, we constructed a miRNA-mRNA-TF network covering 516 miRNAs and 15 transcription factors (TFs) using an online platform (Fig. 8 A). Subsequently, functional enrichment analysis of these mRNAs and TFs showed that they play an important role in the Aging, Cell Cycle, and Inflammation pathways, which is consistent with our analysis of the function of key genes (Fig. 8 B). 3.8 Predicting targeted drugs for key genes To search for potential therapeutic agents targeting aging-related genes in LN-MPOX, we screened 10 targeted drugs, including GUTTIFERONE K and SILICON PHTHALOCYANINE 4, using the DGIdb (v.5.0.7) online platform (Table 1). Based on the interaction score, GUTTIFERONE K and SILICON PHTHALOCYANINE 4 were identified as drug candidates with high targeting potential. Table 1 gene drug interaction score STAT1 GUTTIFERONE K 8.751279 STAT1 PICOPLATIN 8.751279 STAT1 IPRIFLAVONE 0.729273 STAT1 GARCINOL 2.18782 STAT1 CHEMBL:CHEMBL85826 0.972364 STAT1 CISPLATIN 0.060354 GTF2B SILICON PHTHALOCYANINE 4 8.751279 GTF2B CODRITUZUMAB 6.563459 GTF2B THERAPEUTIC STEROID HORMONE 3.281729 GTF2B VITAMIN D 3.029289 Discussion Lupus nephritis (LN), as the most severe form of systemic lupus erythematosus (SLE), often surfaces within the first five years following diagnosis and often presents as the initial symptom, according to literature [27] .The intricate pathogenesis of LN involves genetic, environmental, and hormonal factors that intertwine various inflammatory processes and cell interactions [2] . Recently, MPX has garnered attention, but its replication mechanisms within host cells and genomic characteristics remain under-explored. New findings suggest that MPXV infection can disrupt NK cell function, lead to lymphopenia, and facilitate immune evasion, emphasizing the significance of the immune response in combating poxviruses [10] . To understand and combat its implications, we obtained datasets GSE36854 of monkeypox-infected HeLa cells and GSE112943, focusing on LN patients, from the GEO database. We cross-screened 113 genes with significant differential expression and employed bioinformatics and machine learning approaches to develop a predictive model. Our analysis identified three crucial DE-SRGs, namely STAT1, ORC2, and GTF2B, for further investigation, given their potential diagnostic value in LN in the context of MPX. Cellular senescence, a response to telomere erosion, oxidative stress, and persistent inflammation, leads to a permanent growth arrest through the accumulation of cell cycle inhibitors, such as p16 INK4a [28] . Senescent cells exhibit distinct characteristics, including increased β-galactosidase activity and the secretion of the pro-inflammatory and profibrotic Senescence-Associated Secretory Proteins (SASP) [29] . In renal senescence and kidney diseases, like LN, p16 INK4a- or β-galactosidase-positive cells are associated with tissue damage and renal dysfunction [30, 31] . However, the role of cellular senescence in monkeypox virus infection is unknown. In this study, we analyzed the co-expressed genes of LN combined with monkeypox virus infection, and functional enrichment and immune infiltration analyses revealed that these genes were involved in "Cellular senescence" "Human T - cell leukemia virus l infection" and "Ferroptosis"; BP was enriched in "lymph vessel development"; CC was enriched in cytoplasm; MF is mainly enriched in "protein N-terminal binding". The analysis of immune cell infiltration showed that the level of Tr1 and other cells was high, and Th1 was positively correlated with Th17 and NKT. GSEA analysis showed that STAT1, ORC2, and GTF2B genes were mainly enriched in immune-related pathways. In conclusion, DE-SRGs may affect the proportion of immune cells by regulating cellular senescence, which in turn affects the development of the disease. Signal transducer and activator of transcription 1 (STAT1) is a key transcription factor that is important in immune-inflammatory response and closely associated with lupus nephritis (LN) and monkeypox virus infection [32–34] . Studies have shown that STAT1 is a core gene in the progression of LN. iFN-γ activates the JAK/STAT1 pathway to promote CXCL10 expression, and inhibition of this pathway reduces lupus kidney injury [33] . In addition, miR-574-5p activates STAT1 signaling by triggering the immune-inflammatory response [34] . Single-cell sequencing has shown that STAT1 is highly active as an interferon-stimulated gene in some cells of LN patients [32]. STAT1 is also important in studies of monkeypox virus infection, and STAT1 knockout mice are sensitive to a variety of infections and are used in disease models such as respiratory syncytial virus [35] and influenza [36] . In monkeypox virus infection, STAT1 deletion causes up to 100% mortality in mice [37] , and recombinant monkeypox virus vaccine is safe and effective in STAT1 knockout mouse models [38] . In the present study, we confirmed that STAT1 is mainly enriched in immune-related signaling pathways in LN patients. In conclusion, STAT1 may play an important role in the pathogenesis of LN and monkeypox virus infection by regulating immune responses. Origin recognition complex 2 (ORC2) is a key protein for the initiation of DNA replication [39] .Phosphorylation of ORC2 dissociates the replication initiation complex from chromatin and the replication start [40] .Deletion of ORC2 leads to defective DNA replication [41] , whereas co-expression of ORC1 and ORC2 prevents apoptosis and facilitates the assembly of ORC on chromatin [42] . This suggests that ORC2 may influence disease progression by regulating DNA replication and the cell cycle. Although the role of ORC2 in LN and monkeypox virus infection is unknown, it has been shown that ORC2 is associated with other diseases. For example, bioinformatics and immunohistochemical analyses have shown that ORC2 can serve as a diagnostic prognostic gene for Barrett's esophagus and esophageal adenocarcinoma [43] . In addition, overexpression of ORC2 in a mouse model of myocardial infarction improved cardiac function and reduced fibrosis and immune cell infiltration [44] . KEGG enrichment analysis in this study showed that LN-related genes were mainly involved in "Cellular senescence", "Human T-cell leukemia virus infection" and "Fibrosis". "GSEA analysis further showed that ORC2 was mainly enriched in signaling pathways such as Toll-like receptor, cell cycle and T-lymphocyte receptor. These results are consistent with the known functions of ORC2 in DNA replication and cell cycle. General transcription factor IIB (GTF2B) is important in the regulation of gene expression, especially in the initiation of RNA polymerase II transcription to assist in the recognition of promoters and complex formation.The function of GTF2B in LN and monkeypox virus infections has been studied in limited detail, but it is known to be associated with other diseases, e.g., Elsby L M et al. showed that it is involved in hepatocellular carcinoma proliferation by inhibiting hepatitis B virus X protein transcriptional activation [45] and Cai F et al. found that GTF2B binds to AIP and promotes AIP expression to inhibit GHPA tumor development [46] . In addition, Zhou W et al. showed that it regulates apoptosis and invasion in invasive trophoblast cells affecting trophoblast dysfunction in preeclampsia [47] . Multi-omics studies have shown its involvement in the development of diseases such as heart failure [48] , osteogenic differentiation of MSCs [49] , and depression [50] . In summary, GSEA analysis in this study showed that ORC2 was mainly enriched in signaling pathways such as Toll-like receptor, adhesion junction, Rig I-like receptor and T lymphocyte receptor, which was consistent with the known function of GTF2B, reflecting its transcriptional signaling regulation in disease development. In conclusion, our study constructed and validated a cellular senescence-related diagnostic model based on STAT1, ORC2, and GTF2B. These genes show potential as diagnostic candidates in LN combined with monkeypox infection. In addition, our study emphasized the immune dysfunction in LN combined with monkeypox infection. We also constructed a column-line diagram for the diagnosis of LN combined with monkeypox to aid in clinical decision-making. Overall, our findings may provide new insights into the pathogenesis and diagnosis of LN-combined monkeypox. Further validation studies are needed to confirm the clinical relevance of these genes in LN-combined monkeypox. Declarations Data availability statement Data is provided within the manuscript or supplementary information files Ethics Statement This study does not involve human and animal experiments and does not require ethical approval. Author contribution Q L :Conceptualization. YJ W: Conceptualization, data curation, formal analysis, methodology, software, validation, writing original drafts. Funds This study was not funded in any way Conflict of interest This research was conducted in the absence of commercial or financial relationships, which may be understood as potential conflicts of interest. References Hoi A, Igel T, Mok C C, et al. Systemic lupus erythematosus[J]. Lancet, 2024,403(10441):2326-2338. Roveta A, Parodi E L, Brezzi B, et al. Lupus Nephritis from Pathogenesis to New Therapies: An Update[J]. International journal of molecular sciences, 2024,25(16):8981. Tsai C Y, Li K J, Shen C Y, et al. 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Lee K Y, Bang S W, Yoon S W, et al. Phosphorylation of ORC2 protein dissociates origin recognition complex from chromatin and replication origins[J]. J Biol Chem, 2012,287(15):11891-11898. Prasanth S G, Prasanth K V, Siddiqui K, et al. Human Orc2 localizes to centrosomes, centromeres and heterochromatin during chromosome inheritance[J]. EMBO J, 2004,23(13):2651-2663. Saha T, Ghosh S, Vassilev A, et al. Ubiquitylation, phosphorylation and Orc2 modulate the subcellular location of Orc1 and prevent it from inducing apoptosis[J]. J Cell Sci, 2006,119(Pt 7):1371-1382. Nangraj A S, Selvaraj G, Kaliamurthi S, et al. Integrated PPI- and WGCNA-Retrieval of Hub Gene Signatures Shared Between Barrett's Esophagus and Esophageal Adenocarcinoma[J]. Front Pharmacol, 2020,11:881. Xiao S, Liang R, Lucero E, et al. STEMIN and YAP5SA synthetic modified mRNAs regenerate and repair infarcted mouse hearts[J]. J Cardiovasc Aging, 2022,2(3). Elsby L M, O'Donnell A J, Green L M, et al. 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The protective effects of curcumin on depression: Genes, transcription factors, and microRNAs involved[J]. J Affect Disord, 2022,319:526-537. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable.xlsx Cite Share Download PDF Status: Published Journal Publication published 19 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Feb, 2025 Reviews received at journal 23 Jan, 2025 Reviewers agreed at journal 13 Jan, 2025 Reviews received at journal 03 Jan, 2025 Reviewers agreed at journal 24 Dec, 2024 Reviewers invited by journal 19 Dec, 2024 Editor assigned by journal 13 Dec, 2024 Editor invited by journal 30 Oct, 2024 Submission checks completed at journal 29 Oct, 2024 First submitted to journal 16 Oct, 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. 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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-5276430","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":374751543,"identity":"3d4b4da4-c42c-444c-a190-a332f714e3e5","order_by":0,"name":"Yaojun Wang","email":"","orcid":"","institution":"Hebei University","correspondingAuthor":false,"prefix":"","firstName":"Yaojun","middleName":"","lastName":"Wang","suffix":""},{"id":374751544,"identity":"eb2f7bac-0468-4bbd-ae33-bb09989c977a","order_by":1,"name":"Qiang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACNvb+h49/VNjw8BOthY/nDLMxw5k0OckGYrXISfiwSTO2HTI2OEC0wyR4DxsXsB1I3Hw8eQPDj4ptRGiR7kt8PIPnTuK2M88KGHvO3CZCi8wBYwMeiWeJ227kGDAzthGjRSLBTILH4HDi5hnEa8kxk+ZJOGxsIEG0Fp5jyYYzDqTJSQD9cpAov8i3Nx988PEfMCrbkzc++FFBhBYkkEB81CC0kKpjFIyCUTAKRggAAAaCPvC4twvYAAAAAElFTkSuQmCC","orcid":"","institution":"Air Force Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-10-16 13:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5276430/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5276430/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-97791-w","type":"published","date":"2025-04-19T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68524326,"identity":"f15e528e-6678-40f2-8048-bed354222104","added_by":"auto","created_at":"2024-11-08 08:14:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":441228,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the analysis.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5276430/v1/cf3eec7e43ba415807936911.png"},{"id":68523182,"identity":"8b6867ad-f0a6-41f1-9b12-b48cec334ae3","added_by":"auto","created_at":"2024-11-08 08:06:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":548576,"visible":true,"origin":"","legend":"\u003cp\u003eThe differentially expressed genes (DEGs) between the monkeypox and the control . (A) The red and green colours indicate significantly upregulated and downregulated genes, respectively, in the monkeypox . (B) The heatmap presents the top 20 significantly expressed genes in both s.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5276430/v1/6426f82aa2a30449d28df91f.png"},{"id":68523185,"identity":"dbf65261-7ac1-4a11-91ab-a7d6ddf89255","added_by":"auto","created_at":"2024-11-08 08:06:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2057965,"visible":true,"origin":"","legend":"\u003cp\u003eThe DEGs identified in lupus nephritis (LN) using Limma and WGCNA. (A) The volcano plot represents the distribution of DEGs. (B) The heatmap shows the top 20 upregulated and downregulated DEGs in the LN dataset, represented in red and blue, respectively. (C, D) The soft threshold of β=24 was selected based on scale independence and average connectivity. (E) A hierarchical clustering dendrogram of LN and control samples. (F) An adjacency heatmap of characteristic genes. (G) Co-expression modules represented in different colours below the gene tree. (H) A correlation heatmap depicting the association between module genes and LN, with the antiquewhite4 module displaying the highest correlation with LN. The upper left triangle uses colour to denote the correlation coefficient, while the lower right triangle uses colour to indicate the p-value. (I) A graph showing the correlation of module members and gene significance for antiquewhite4 module genes.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5276430/v1/5c10967bfe2ab6dee2ccbc6f.png"},{"id":68525828,"identity":"f2e02d02-e76a-4ee4-925a-2c57da99bbbb","added_by":"auto","created_at":"2024-11-08 08:30:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1777465,"visible":true,"origin":"","legend":"\u003cp\u003eThe functional enrichment analysis of the intersecting genes related to lupus nephritis (LN) and monkeypox virus infection. (A) The intersection of DEGs identified by Limma and monkeypox DEGs includes 113 genes, as represented in a Venn diagram. (B) Results from the KEGG pathway analysis, where different colours denote distinct significant pathways and their associated genes. (C-E) GO analysis covering biological processes, cellular components, and molecular functions, with the vertical axis denoting GO terms and the horizontal axis representing the proportion of genes involved in respective GO processes. The size of the circles corresponds to the number of genes, and the shade of colour indicates the p-value. (F) Protein-protein interaction analysis of the 113 intersecting genes, with black circles representing gene nodes and the frequency of connecting lines denoting the degree of interaction.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5276430/v1/22648ca4d2cde0d2da669c6d.png"},{"id":68523184,"identity":"0cb9ce9c-d26a-4e97-8e59-99ec75ac9635","added_by":"auto","created_at":"2024-11-08 08:06:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":732010,"visible":true,"origin":"","legend":"\u003cp\u003eThe application of machine learning in identifying key diagnostic genes from the senescence gene set and the intersecting genes. (A) A Venn diagram shows the intersection of the senescence gene set with the intersecting genes. (B-C) Key genes identified by the LASSO model, totalling 11 genes deemed suitable for diagnosis. (D) The ROC curve from the LASSO model is presented. (E) A heatmap depicting high and low-risk prognoses for the 11 genes in the training cohort.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5276430/v1/abe2846b6e9553a04737a7c2.png"},{"id":68524833,"identity":"351d8f16-137d-4ba6-8392-0b4bc1c5a63b","added_by":"auto","created_at":"2024-11-08 08:22:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":742822,"visible":true,"origin":"","legend":"\u003cp\u003eThe construction of a nomogram and the assessment of diagnostic value. (A) A box plot in the validation set illustrates the differences in expression of the 11 key genes between LN and the control . (B) A nomogram is employed to diagnose senescence-related genes in LN with monkeypox infection. (C-E) ROC curve validation results for candidate genes STAT1, ORC2, and GTF2B in the validation set.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5276430/v1/cebcc7134b09131c4f5644c6.png"},{"id":68524327,"identity":"298e5f74-a37f-4c34-8fe3-4fec6f0ce1fa","added_by":"auto","created_at":"2024-11-08 08:14:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2419987,"visible":true,"origin":"","legend":"\u003cp\u003eThe immune infiltration analysis between LN and the control . (A) A stacked bar chart shows the proportions of 22 immune cell types across all samples. (B) A correlation heatmap of the 22 immune cell types, with red indicating positive correlations and blue indicating negative correlations, denoted by *p \u0026lt; 0.05; p \u0026lt; 0.01; *p \u0026lt; 0.001. (C) A box plot comparing the proportions of immune cells between LN and the control . (D - G) Analysis of GSEA pathways for STAT1, ORC2, and GTF2B in the validation cohort is presented.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-5276430/v1/52d91f83617ca0faecc9bd6c.png"},{"id":68523189,"identity":"ab773661-724c-4229-a50d-c3ceeb330403","added_by":"auto","created_at":"2024-11-08 08:06:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":23724575,"visible":true,"origin":"","legend":"\u003cp\u003eRegulatory mechanism and functional enrichment analysis of three key genes. (A) The miRNA-mRNA-TF regulatory network was constructed, where yellow circles represent mRNAs, blue squares represent miRNAs, and green circles represent transcription factors (TFs). (B) Pathway analysis of key genes. The length of the rectangles reflects the number of enriched genes, while the color indicates the P-value; the redder the color, the higher the significance of the pathway.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-5276430/v1/7d8efb714645e6bf5a7c7f38.png"},{"id":81050815,"identity":"69a18db3-ea1c-426c-947a-7b0653ce6819","added_by":"auto","created_at":"2025-04-21 16:05:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27988012,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5276430/v1/128fdfa1-0709-401e-9a4f-292dbc649f89.pdf"},{"id":68523188,"identity":"3df2f0ae-d37c-41cb-91c1-9d883c8039b2","added_by":"auto","created_at":"2024-11-08 08:06:06","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":1180142,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5276430/v1/08fe5a69d1661aab8ab3122c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integration of machine learning and risk modeling to analyze and validate the role of senescent genes in lupus nephritis with monkeypox virus ","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLupus nephritis (LN) represents a severe complication associated with systemic lupus erythematosus (SLE). The condition originates from the accumulation of immune complexes or autoantibodies within the glomerular basement membrane, subsequently leading to the recruitment of inflammatory cells \u003csup\u003e[1]\u003c/sup\u003e. The complex pathogenesis of LN encompasses various factors, including genetic predisposition, environmental influences, and hormonal changes, as well as a range of inflammatory pathways and cellular interactions \u003csup\u003e[2]\u003c/sup\u003e. Recent advancements in research have highlighted the roles of innate immune cells, including neutrophils, monocytes, and dendritic cells, while the introduction of novel biomarkers is transforming the utility of kidney biopsy in clinical settings \u003csup\u003e[3, 4]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn August 2024, the World Health Organization (WHO) designated the outbreak of monkeypox in Africa as a public health emergency \u003csup\u003e[5]\u003c/sup\u003e. By August 2, the total cases of infection had escalated to 99,176, with 208 reported fatalities globally \u003csup\u003e[6]\u003c/sup\u003e. Monkeypox occurs due to a variant of the monkeypox virus, primarily transmitted through close contact, with common symptoms being rashes, high fevers, and swollen lymph nodes. Strategies for prevention and control encompass educational initiatives, therapeutic options such as tecovirimat, and the modified cowpox vaccine known as JYNNEOS \u003csup\u003e[7, 8]\u003c/sup\u003e. Recent findings indicate a connection between MPXV infection and dysfunction of NK cells as well as immune evasion, highlighting the importance of the immune response in resisting infections caused by poxviruses\u003csup\u003e[9\u0026ndash;11]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCellular senescence arises from the gradual shortening of telomeres, oxidative stress, and ongoing inflammation, ultimately resulting in an irreversible state of growth arrest \u003csup\u003e[12, 13]\u003c/sup\u003e. Cells that have undergone senescence continue to be metabolically active and demonstrate both morphological and physiological alterations, such as increased β-galactosidase activity and the pro-inflammatory, pro-fibrotic senescence-associated secretory phenotype (SASP) \u003csup\u003e[14]\u003c/sup\u003e. While cellular senescence contributes positively to tissue repair, it can also have detrimental consequences in chronic diseases and various cancers \u003csup\u003e[13]\u003c/sup\u003e. Research indicates that senescence is significantly involved in the progression of lupus nephritis, correlating with markers of renal dysfunction like p16 INK4a and β-galactosidase-positive cells \u003csup\u003e[15]\u003c/sup\u003e; nevertheless, the mechanisms behind cellular senescence in the context of monkeypox virus infection remain largely unexplored.\u003c/p\u003e \u003cp\u003eWhile no definitive link exists between lupus nephritis and monkeypox, both conditions are characterized by an inflammatory response and immune cell infiltration. In patients with active lupus nephritis (LN), the ratio of T follicular helper cells (TFH) to regulatory T cells (Treg) is elevated, accompanied by an infiltration of TFH1 cells in the kidneys affected by LN \u003csup\u003e[16]\u003c/sup\u003e. Additionally, natural killer (NK) cells and dendritic cells are instrumental in combating the monkeypox virus \u003csup\u003e[11]\u003c/sup\u003e. Consequently, identifying immune genes associated with senescence in patients suffering from lupus nephritis and monkeypox is crucial for early diagnosis and treatment planning. In this research, we accessed datasets for lupus nephritis and monkeypox from the Gene Expression Omnibus (GEO) database and senescence gene sets from GeneCard. We then screened DEGs using Limma, identified significant modular genes through weighted co-expression network analysis (WGCNA), and conducted functional enrichment analysis. Furthermore, we constructed a PPI network utilizing LASSO and performed immune cell infiltration analysis to pinpoint candidate genes. Finally, the critical immune-related diagnostic genes associated with lupus nephritis and monkeypox were validated through nomogram and receiver operating characteristic (ROC) curve analyses, establishing a practical framework for detecting the immune markers relevant to these two conditions.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Acquisition and Processing\u003c/h2\u003e \u003cp\u003eWe sourced 5,830 senescence-related genes (SRGs) from the GeneCards website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Concurrently, we accessed two datasets via the Gene Expression Omnibus (GEO) database: GSE112943, which includes kidney biopsy samples from 14 lupus nephritis (LN) patients alongside 7 healthy controls, and GSE99967, comprising kidney biopsy samples from 42 LN patients with 17 healthy controls. Additionally, we utilized GSE36854, which features two HL cell lines infected with the monkeypox virus and their corresponding blank controls. GSE112943 and GSE36854 served as the training dataset, whereas GSE99967 functioned as the validation dataset. We employed the \"limma\" package in R to identify DEGs across these three datasets, applying screening criteria of p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026gt; 1.5. The workflow of the study is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with DEGs visualized using the Sangerbox platform.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 WGCNA and identification of clinically significant modules\u003c/h2\u003e \u003cp\u003eIn this study, we explored gene-phenotype associations by constructing gene co-expression networks utilizing the \"WGCNA\" package in R software \u003csup\u003e[17]\u003c/sup\u003e. The analysis was aimed at assessing the relationship between modular feature genes and lupus nephritis (LN). We extracted genes that had a median absolute deviation (MAD) falling within the top 75% of the analyzed genes via \"WGCNA\" and evaluated them using the same package. To ensure no abnormal samples or genes were included, we employed the goodSamplesGenes algorithm. Furthermore, we created a clustering dendrogram with the hclust function to identify any outlier samples, ultimately excluding two samples with a height exceeding 70. Following the establishment of a soft threshold of 24 and a minimum of 30 genes per module, we derived modules consisting of co-expressed genes and conducted Pearson correlation analysis to elucidate the association of these modules with disease phenotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Functional enrichment analysis and PPI network analysis\u003c/h2\u003e \u003cp\u003eTo investigate the biological roles of the intersecting genes, we initially pinpointed the overlap among the DEGs associated with lymph nodes (LN), the essential module genes, and the DEGs related to monkeypox. We utilized the \"clusterProfile\" package \u003csup\u003e[18]\u003c/sup\u003e within R software to conduct Gene Ontology (GO) \u003csup\u003e[19]\u003c/sup\u003e and Kyoto Encyclopedia of Genes and Genomes (KEGG) \u003csup\u003e[20]\u003c/sup\u003e analyses, ensuring a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Furthermore, we carried out an analysis of PPIs for the intersecting genes through the use of the GeneMANIA too \u003csup\u003e[21]\u003c/sup\u003e。\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Identifying Candidate Hub Genes by Machine Learning\u003c/h2\u003e \u003cp\u003eWe utilized the LASSO regression machine learning algorithm to pinpoint differentially expressed senescent-related genes (DE-SRGs). Initially, we intersected the collection of senescent genes with the DEGs that are prevalent in both lymph nodes (LN) and monkeypox. Based on these intersecting genes, we executed a LASSO analysis. LASSO, a machine learning methodology, marries variable selection with regularization to enhance predictive accuracy \u003csup\u003e[22]\u003c/sup\u003e. The analysis was conducted using the \"glmnet\" package in R software, enabling us to derive potential pivotal genes for diagnostic purposes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Nomogram construction and Receiver Operating Characteristic assessment\u003c/h2\u003e \u003cp\u003eTo assess the significance of candidate genes in diagnosing lymph nodes (LN) in conjunction with monkeypox virus infection, we initially analyzed the differential expression of these candidate genes within the validation cohort. Subsequently, we utilized the \"rms\" package in R to create a column-line graph featuring \"Points\" (the scores attributed to the candidate genes) and \"Total Points\" (the cumulative scores of all genes), which proved to be a key instrument for predicting LN combined with monkeypox. Further, we assessed the prognostic value of both the candidate genes and the generated column plot using subject operating characteristics (ROC) analysis on the validation dataset, yielding results presented as the area under the curve (AUC) along with a 95% confidence interval (CI). An AUC exceeding 0.7 was deemed indicative of strong diagnostic efficacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Immune Infiltration Analysis and GSEA Pathway Analysis\u003c/h2\u003e \u003cp\u003eWe employed the CIBERSORT algorithm to determine the proportions of immune cells in patients from the lymph node (LN) and control groups. This algorithm facilitates the analysis and quantification of 22 distinct immune cell subpopulations in clinical samples via gene expression profiling \u003csup\u003e[23]\u003c/sup\u003e. Additionally, we illustrated the correlations among immune cells and the signaling pathways relevant to the candidate genes utilizing heatmap analysis and GSEA \u003csup\u003e[24]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Construction of miRNA-mRNA-TF network\u003c/h2\u003e \u003cp\u003eScreening for tiny RNAs (miRNAs) and transcription factors (TFs) associated with intersecting genes based on the miRNet2/0 online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirnet.ca/\u003c/span\u003e\u003cspan address=\"https://www.mirnet.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e[25]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Prediction of targeted drugs\u003c/h2\u003e \u003cp\u003eTo predict drug responsiveness to key autophagy-related genes, this study was based on the DGldb online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dgidb.org/\u003c/span\u003e\u003cspan address=\"https://dgidb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed October 15, 2024), which is an open-source search engine focusing on drug-gene interactions and information on druggable genomes, aiming at obtaining targeted drugs against target genes \u003csup\u003e[26]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe utilized R software (version 4.1.2) along with GraphPad Prism 9 to conduct our data analysis. For datasets that encompassed multiple groups, we applied analysis of variance (ANOVA) to evaluate statistical significance, subsequently comparing differences between two groups with either Student's t-test or the Wilcoxon rank sum test. p-values below 0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of DEGs in the LN\u003c/h2\u003e \u003cp\u003eThe findings indicated that we identified a total of 5707 DEGs within the GSE36854 dataset, all meeting the criteria of p-values \u0026lt; 0.05 and |log2FC| \u0026gt; 1.5. Volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and heat maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) illustrate the differential expression patterns of these DEGs. Likewise, the GSE112943 dataset revealed a total of 734 DEGs, also adhering to the same criteria (p-values \u0026lt; 0.05 and |log2FC| \u0026gt; 1.5). Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB display the differential expression patterns of DEGs associated with monkeypox.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 WGCNA and key module identification\u003c/h2\u003e \u003cp\u003eA scale-free co-expression network was constructed using Weighted Gene Co-expression Network Analysis (WGCNA) to pinpoint the modules most strongly associated with lymph nodes (LN). Following the analysis, we opted for a \"soft\" threshold β of 24 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D), generating clustering dendrograms for both LNs and controls. By setting a module merging threshold at 0.25 and a minimum module size of 50, we identified a total of 19 distinct colored gene co-expression modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-G). Clinical correlation analysis revealed that the antiquewhite4 module exhibited the highest association with LN (r = 0.84, p-value \u0026lt; 0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH), prompting us to select this module, which contains 4107 genes, for further in-depth analysis (Supplementary table). We computed the correlation between module eigenvectors and gene expression, resulting in module eigenvalues (MM) that displayed a significant positive correlation (correlation coefficient = 0.81, p-value \u0026lt; 0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). These findings indicate that the genes within the antiquewhite4 module are closely related to LN.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Enrichment analysis of co-expressed genes of LN combined with monkeypox virus infection\u003c/h2\u003e \u003cp\u003eTo explore the biological roles of lymph nodes (LN) in conjunction with monkeypox virus infection, we analyzed LN differentially DEGs, along with key modular genes and monkeypox DEGs, leading to the identification of 113 shared genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Supplementary table). The KEGG enrichment analysis indicated that these genes were primarily implicated in pathways such as \"Cellular senescence,\" \"Human T-cell leukemia virus infection,\" and \"Ferroptosis\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Additionally, these genes were associated with biological processes (BPs) like \"lymph vessel development,\" \"regulation of lymphangiogenesis,\" and \"mRNA metabolic process.\" In terms of cellular components (CC), they were enriched in locations including \"cytoplasm,\" \"nucleoplasm,\" and \"cytoplasmic fraction.\" Regarding molecular function (MF), the predominant enrichment was in \"protein N-terminal binding\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-E). Ultimately, we constructed a PPI network highlighting the cross-gene interactions, with the CDK7 node showing the highest frequency of connections, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Identify DE-SRGs through machine learning\u003c/h2\u003e \u003cp\u003eTo examine the involvement of senescence genes in various diseases, we identified 47 senescence co-expressed genes related to disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Supplementary table). Subsequently, we utilized the R package glmnet to conduct regression analysis employing the lasso-cox method. Additionally, a 3-fold cross-validation was implemented to determine the optimal model. We assigned a Lambda value of 0.00521316702464377, resulting in a model formula for the 11 gene constructs expressed as RiskScore = 0.0814342818246804WDR82–0.0489265899752115NFIX + 0.230987868325894STAT1 + 0.0576775996331519PANX1–0.0192816848884446PTGS1 + 0.0465632183810925TLR4 + 0.0245669295263691ZCCHC9 + 0.0255499371067976WDR36 + 0.142435132705123ORC2 + 0.106186688914399GTF2B − 0.874693126720345*ZFP36. Through LASSO regression analysis, we identified 11 DE-SRGs closely linked to disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-C). The ROC curve analysis revealed that the gene models derived from this LASSO regression exhibited exceptional predictive accuracy (AUC = 1, 95% CI: 1–1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Furthermore, we created prognostic heat maps for the 11 genes based on risk scores and clinical attributes, indicating that, with the exception of PANX1, PTGS1, and ZFP36, the other genes provided greater diagnostic precision in identifying LN(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Diagnostic value assessment\u003c/h2\u003e \u003cp\u003eTo verify the validity of the 11 DE-SRGs screened, we analyzed them in the validation set GSE99967. The results showed that only the expression levels of STAT1, ORC2, and GTF2B were statistically different (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Further, we constructed a Nomogram containing these three key diagnostic genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) and plotted ROC curves to evaluate their diagnostic efficacy. The AUC values for STAT1, ORC2, and GTF2B were 0.88 (95% CI: 0.79–0.97), 0.85 (95% CI: 0.74–0.96), and 0.71 (95% CI: 0.55–0.87), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-E). These results suggest that STAT1, ORC2 and GTF2B can be used as potential biomarkers for the diagnosis of LN complicated with monkeypox, and the Nomogram constructed by them also shows good diagnostic value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Relationship between diagnostic models and LN immune cell infiltration\u003c/h2\u003e \u003cp\u003eThe research revealed a critical role for the diagnostic genes linked to monkeypox virus infection in modulating LN pathogenesis, with a significant involvement in immune-related pathways. To delve deeper into the immune dynamics in LN, an analysis of immune infiltration between the LN and control groups was undertaken. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and supplementary table, there were notable differences in the proportion of 22 immune cell types between the two groups. The study observed a lower abundance of CD8 naive T cells, cytotoxic T cells, nTregs, monocytes, and neutrophils in the LN group compared to the control, whereas the levels of Tr1 cells, Th17 cells, NKT cells, MAIT cells, dendritic cells (DCs), natural killer (NK) cells, and CD4 + T cells were significantly increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). A correlation analysis uncovered that Th1 cells were positively correlated with Th17 and NKT cells, while CD8 + T cells had a negative correlation with γδ T cells. Cytotoxic T cells displayed a negative relationship with NKT cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe GSEA on the genes STAT1, ORC2, and GTF2B from patients with LN pinpointed their enrichment in immune pathways, such as the T cell receptor signaling, B cell receptor signaling, and Toll-like receptor signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD - G). In summary, monkeypox virus appears to contribute to LN pathogenesis through its impact on immune cell infiltration, suggesting that targeted regulation of immune cell populations could represent a novel therapeutic strategy for LN management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.7 miRNA-mRNA-TF regulatory network\u003c/h2\u003e \u003cp\u003eTo investigate the regulatory mechanisms of key genes, we constructed a miRNA-mRNA-TF network covering 516 miRNAs and 15 transcription factors (TFs) using an online platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Subsequently, functional enrichment analysis of these mRNAs and TFs showed that they play an important role in the Aging, Cell Cycle, and Inflammation pathways, which is consistent with our analysis of the function of key genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Predicting targeted drugs for key genes\u003c/h2\u003e \u003cp\u003eTo search for potential therapeutic agents targeting aging-related genes in LN-MPOX, we screened 10 targeted drugs, including GUTTIFERONE K and SILICON PHTHALOCYANINE 4, using the DGIdb (v.5.0.7) online platform (Table\u0026nbsp;1). Based on the interaction score, GUTTIFERONE K and SILICON PHTHALOCYANINE 4 were identified as drug candidates with high targeting potential.\u003c/p\u003e \u003c/div\u003e\u003cp\u003eTable 1\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003egene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.2245%;\"\u003e\n \u003cp\u003edrug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003einteraction score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.2245%;\"\u003e\n \u003cp\u003eGUTTIFERONE K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e8.751279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.2245%;\"\u003e\n \u003cp\u003ePICOPLATIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e8.751279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.2245%;\"\u003e\n \u003cp\u003eIPRIFLAVONE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e0.729273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.2245%;\"\u003e\n \u003cp\u003eGARCINOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2.18782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.2245%;\"\u003e\n \u003cp\u003eCHEMBL:CHEMBL85826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e0.972364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.2245%;\"\u003e\n \u003cp\u003eCISPLATIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e0.060354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003eGTF2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.2245%;\"\u003e\n \u003cp\u003eSILICON PHTHALOCYANINE 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e8.751279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003eGTF2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.2245%;\"\u003e\n \u003cp\u003eCODRITUZUMAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e6.563459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003eGTF2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.2245%;\"\u003e\n \u003cp\u003eTHERAPEUTIC STEROID HORMONE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e3.281729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003eGTF2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.2245%;\"\u003e\n \u003cp\u003eVITAMIN D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e3.029289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eLupus nephritis (LN), as the most severe form of systemic lupus erythematosus (SLE), often surfaces within the first five years following diagnosis and often presents as the initial symptom, according to literature \u003csup\u003e[27]\u003c/sup\u003e.The intricate pathogenesis of LN involves genetic, environmental, and hormonal factors that intertwine various inflammatory processes and cell interactions \u003csup\u003e[2]\u003c/sup\u003e. Recently, MPX has garnered attention, but its replication mechanisms within host cells and genomic characteristics remain under-explored. New findings suggest that MPXV infection can disrupt NK cell function, lead to lymphopenia, and facilitate immune evasion, emphasizing the significance of the immune response in combating poxviruses \u003csup\u003e[10]\u003c/sup\u003e. To understand and combat its implications, we obtained datasets GSE36854 of monkeypox-infected HeLa cells and GSE112943, focusing on LN patients, from the GEO database. We cross-screened 113 genes with significant differential expression and employed bioinformatics and machine learning approaches to develop a predictive model. Our analysis identified three crucial DE-SRGs, namely STAT1, ORC2, and GTF2B, for further investigation, given their potential diagnostic value in LN in the context of MPX.\u003c/p\u003e\u003cp\u003eCellular senescence, a response to telomere erosion, oxidative stress, and persistent inflammation, leads to a permanent growth arrest through the accumulation of cell cycle inhibitors, such as p16 INK4a \u003csup\u003e[28]\u003c/sup\u003e. Senescent cells exhibit distinct characteristics, including increased β-galactosidase activity and the secretion of the pro-inflammatory and profibrotic Senescence-Associated Secretory Proteins (SASP) \u003csup\u003e[29]\u003c/sup\u003e. In renal senescence and kidney diseases, like LN, p16 INK4a- or β-galactosidase-positive cells are associated with tissue damage and renal dysfunction \u003csup\u003e[30, 31]\u003c/sup\u003e. However, the role of cellular senescence in monkeypox virus infection is unknown. In this study, we analyzed the co-expressed genes of LN combined with monkeypox virus infection, and functional enrichment and immune infiltration analyses revealed that these genes were involved in \"Cellular senescence\" \"Human T - cell leukemia virus l infection\" and \"Ferroptosis\"; BP was enriched in \"lymph vessel development\"; CC was enriched in cytoplasm; MF is mainly enriched in \"protein N-terminal binding\". The analysis of immune cell infiltration showed that the level of Tr1 and other cells was high, and Th1 was positively correlated with Th17 and NKT. GSEA analysis showed that STAT1, ORC2, and GTF2B genes were mainly enriched in immune-related pathways. In conclusion, DE-SRGs may affect the proportion of immune cells by regulating cellular senescence, which in turn affects the development of the disease.\u003c/p\u003e\u003cp\u003eSignal transducer and activator of transcription 1 (STAT1) is a key transcription factor that is important in immune-inflammatory response and closely associated with lupus nephritis (LN) and monkeypox virus infection \u003csup\u003e[32–34]\u003c/sup\u003e. Studies have shown that STAT1 is a core gene in the progression of LN. iFN-γ activates the JAK/STAT1 pathway to promote CXCL10 expression, and inhibition of this pathway reduces lupus kidney injury \u003csup\u003e[33]\u003c/sup\u003e. In addition, miR-574-5p activates STAT1 signaling by triggering the immune-inflammatory response \u003csup\u003e[34]\u003c/sup\u003e. Single-cell sequencing has shown that STAT1 is highly active as an interferon-stimulated gene in some cells of LN patients [32]. STAT1 is also important in studies of monkeypox virus infection, and STAT1 knockout mice are sensitive to a variety of infections and are used in disease models such as respiratory syncytial virus \u003csup\u003e[35]\u003c/sup\u003e and influenza \u003csup\u003e[36]\u003c/sup\u003e. In monkeypox virus infection, STAT1 deletion causes up to 100% mortality in mice \u003csup\u003e[37]\u003c/sup\u003e, and recombinant monkeypox virus vaccine is safe and effective in STAT1 knockout mouse models \u003csup\u003e[38]\u003c/sup\u003e. In the present study, we confirmed that STAT1 is mainly enriched in immune-related signaling pathways in LN patients. In conclusion, STAT1 may play an important role in the pathogenesis of LN and monkeypox virus infection by regulating immune responses.\u003c/p\u003e\u003cp\u003eOrigin recognition complex 2 (ORC2) is a key protein for the initiation of DNA replication \u003csup\u003e[39]\u003c/sup\u003e.Phosphorylation of ORC2 dissociates the replication initiation complex from chromatin and the replication start \u003csup\u003e[40]\u003c/sup\u003e.Deletion of ORC2 leads to defective DNA replication \u003csup\u003e[41]\u003c/sup\u003e, whereas co-expression of ORC1 and ORC2 prevents apoptosis and facilitates the assembly of ORC on chromatin \u003csup\u003e[42]\u003c/sup\u003e. This suggests that ORC2 may influence disease progression by regulating DNA replication and the cell cycle. Although the role of ORC2 in LN and monkeypox virus infection is unknown, it has been shown that ORC2 is associated with other diseases. For example, bioinformatics and immunohistochemical analyses have shown that ORC2 can serve as a diagnostic prognostic gene for Barrett's esophagus and esophageal adenocarcinoma \u003csup\u003e[43]\u003c/sup\u003e. In addition, overexpression of ORC2 in a mouse model of myocardial infarction improved cardiac function and reduced fibrosis and immune cell infiltration \u003csup\u003e[44]\u003c/sup\u003e. KEGG enrichment analysis in this study showed that LN-related genes were mainly involved in \"Cellular senescence\", \"Human T-cell leukemia virus infection\" and \"Fibrosis\". \"GSEA analysis further showed that ORC2 was mainly enriched in signaling pathways such as Toll-like receptor, cell cycle and T-lymphocyte receptor. These results are consistent with the known functions of ORC2 in DNA replication and cell cycle.\u003c/p\u003e\u003cp\u003eGeneral transcription factor IIB (GTF2B) is important in the regulation of gene expression, especially in the initiation of RNA polymerase II transcription to assist in the recognition of promoters and complex formation.The function of GTF2B in LN and monkeypox virus infections has been studied in limited detail, but it is known to be associated with other diseases, e.g., Elsby L M et al. showed that it is involved in hepatocellular carcinoma proliferation by inhibiting hepatitis B virus X protein transcriptional activation \u003csup\u003e[45]\u003c/sup\u003e and Cai F et al. found that GTF2B binds to AIP and promotes AIP expression to inhibit GHPA tumor development \u003csup\u003e[46]\u003c/sup\u003e. In addition, Zhou W et al. showed that it regulates apoptosis and invasion in invasive trophoblast cells affecting trophoblast dysfunction in preeclampsia \u003csup\u003e[47]\u003c/sup\u003e. Multi-omics studies have shown its involvement in the development of diseases such as heart failure \u003csup\u003e[48]\u003c/sup\u003e, osteogenic differentiation of MSCs \u003csup\u003e[49]\u003c/sup\u003e, and depression \u003csup\u003e[50]\u003c/sup\u003e. In summary, GSEA analysis in this study showed that ORC2 was mainly enriched in signaling pathways such as Toll-like receptor, adhesion junction, Rig I-like receptor and T lymphocyte receptor, which was consistent with the known function of GTF2B, reflecting its transcriptional signaling regulation in disease development.\u003c/p\u003e\u003cp\u003eIn conclusion, our study constructed and validated a cellular senescence-related diagnostic model based on STAT1, ORC2, and GTF2B. These genes show potential as diagnostic candidates in LN combined with monkeypox infection. In addition, our study emphasized the immune dysfunction in LN combined with monkeypox infection. We also constructed a column-line diagram for the diagnosis of LN combined with monkeypox to aid in clinical decision-making. Overall, our findings may provide new insights into the pathogenesis and diagnosis of LN-combined monkeypox. Further validation studies are needed to confirm the clinical relevance of these genes in LN-combined monkeypox.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not involve human and animal experiments and does not require ethical approval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ L\u003c/strong\u003e:Conceptualization.\u003cstrong\u003eYJ W:\u0026nbsp;\u003c/strong\u003eConceptualization, data curation, formal analysis, methodology, software, validation, writing original drafts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not funded in any way\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted in the absence of commercial or financial relationships, which may be understood as potential conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHoi A, Igel T, Mok C C, et al. 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J Affect Disord, 2022,319:526-537.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Senescence;lupus nephritis, monkeypox, biomarkers, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-5276430/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5276430/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eMonkeypox virus is now spreading rapidly around the world, but the mechanism of interaction between it and lupus nephritis is not yet clear.The purpose of this study is to explore the role and mechanism of cell aging in lupus nephritis combined with monkeypox virus infection.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe data comes from GEO and GeneCards.Through Limma and WGCNA analysis, differential expression genes (DEGs) and module genes were identified, and KEGG and GO enrichment analysis was carried out.In addition, a protein-protein interaction (PPI) network was constructed and LASSO regression was used to screen genes related to aging. The diagnostic effectiveness was evaluated by Norma diagram and ROC curve, and verified on GSE99967.Immune infiltration and gene set enrichment analysis (GSEA) Were also included in the study.In the end, miRNet was used to construct a miRNA-mRNA-TF network and screen targeted drugs through DGIdb.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e5707 DEGs were identified in the lupus nephritis data set and 737 in the monkeypox data. The two have a total of 113 genes, which are related to immune cell function and aging.The three central genes screened showed an AUC of 0.71 to 0.88 in the column chart, indicating good diagnostic potential.Immune infiltration analysis showed immune cell disorders and related pathway activation.The miRNA-mRNA-TF network covers 516 miRNAs and 15 transcription factors, and enrichment analysis shows that it plays an important role in aging and inflammation.Potential Target Drugs Screened Include Guttiferone K And Silicon Phthalocyanine 4.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study confirmed the key genes (STAT1, ORC1, GTF2B) related to cell aging and immunity, and developed a line chart for the diagnosis of monkeypox virus infection combined with lupus nephritis, and at the same time screened drug candidates with targeted potential.\u003c/p\u003e","manuscriptTitle":"Integration of machine learning and risk modeling to analyze and validate the role of senescent genes in lupus nephritis with monkeypox virus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-08 08:06:01","doi":"10.21203/rs.3.rs-5276430/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-04T07:05:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-23T23:37:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261318485565956223979697730755311591664","date":"2025-01-13T08:53:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-03T19:47:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72778421222342468790989308717501989531","date":"2024-12-24T18:18:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-19T17:48:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-13T17:25:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-30T09:46:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-29T15:31:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-16T13:46:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"742a650c-9539-4de8-9edc-36b05ebeb440","owner":[],"postedDate":"November 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39888078,"name":"Health sciences/Diseases/Infectious diseases/Viral infection"},{"id":39888079,"name":"Health sciences/Diseases/Kidney diseases/Lupus nephritis"},{"id":39888080,"name":"Biological sciences/Genetics/Genetic markers"},{"id":39888081,"name":"Health sciences/Nephrology/Kidney diseases"},{"id":39888082,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":39888083,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":39888084,"name":"Health sciences/Nephrology"},{"id":39888085,"name":"Health sciences/Pathogenesis"},{"id":39888086,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-04-21T15:59:34+00:00","versionOfRecord":{"articleIdentity":"rs-5276430","link":"https://doi.org/10.1038/s41598-025-97791-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-19 15:57:15","publishedOnDateReadable":"April 19th, 2025"},"versionCreatedAt":"2024-11-08 08:06:01","video":"","vorDoi":"10.1038/s41598-025-97791-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-97791-w","workflowStages":[]},"version":"v1","identity":"rs-5276430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5276430","identity":"rs-5276430","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}