Blood transcriptomics identifies FEZ1 as a novel non-invasive diagnostic biomarker for inflammatory bowel disease

Blood transcriptomics identifies FEZ1 as a novel non-invasive diagnostic biomarker for inflammatory bowel disease | 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 Blood transcriptomics identifies FEZ1 as a novel non-invasive diagnostic biomarker for inflammatory bowel disease Arman Mokaram Doust Delkhah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5255906/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jan, 2025 Read the published version in Computers in Biology and Medicine → Version 1 posted You are reading this latest preprint version Abstract In comparison with conventional diagnostic methods, blood biomarkers are an easily accessible source for diagnosing inflammatory bowel disease (IBD). To identify such a biomarker, an integrated transcriptomics approach was employed to detect RNAs exhibiting diagnostic efficacy. For this purpose, four gene expression datasets obtained from blood specimens were integrated (GSE119600, GSE94648, GSE86434, and GSE71730). After determining differentially expressed genes in Crohn’s disease and ulcerative colitis, DEGs in IBD were defined as genes with a consistent direction of alteration in both disorders. Mapping the PPI network for these genes revealed TNF as the central hub gene. Subsequently, weighted gene-expression network analysis (WGCNA) was carried out to determine IBD-specific modules. Considering the degree metrics, module membership, and gene significance, PRF1 was the only gene discerned as a hub gene in a module that was prominently enriched in IBD. Genes with converging results from differential expression analysis and WGCNA were subjected to the random forest decision tree-based and LASSO regression methods. Following the identification of FEZ1 and NLRC5 as genes highlighted by both analyses, ROC analysis was applied to assess their diagnostic potential. Although both genes demonstrated acceptable diagnostic efficacy in the integrated data, only FEZ1 was considered as a potential biomarker based on the replication of results in validation datasets (GSE119600, GSE94648, GSE86434, and GSE71730). While autophagy is currently the most convincing explanation for the involvement of FEZ1 in IBD, further investigations are required to elucidate its immunological role. Biological sciences/Genetics/Gene expression Biological sciences/Genetics Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Data integration IBD Biomarkers Blood Crohn’s disease Ulcerative colitis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Crohn’s disease (CD) and ulcerative colitis (UC), the most prevalent inflammatory bowel diseases (IBD), stem from mucosal inflammation of the gastrointestinal tract caused by an immune response [ 1 ]. The global burden of IBD is anticipated to escalate in the forthcoming decades [ 2 ]. Therefore, establishing non-invasive diagnostic methods is of significant value. Emerging investigations have underscored the importance of blood specimens, particularly peripheral blood mononuclear cells (PBMCs), as a valuable source for biomarker discovery [ 3 ]. Considering the difficulty of gathering large sample sizes in the study of autoimmune disorders, the present study aimed to identify potential biomarkers from a large sample pool by integrating transcriptomic data from blood samples. Circulating adaptive and innate immune cells are involved in the pathogenesis of CD and UC [ 4 ]. The ratio of blood T cells is elevated in patients with IBD [ 5 ], and they play a central role in the immune-mediated damage in the disorder [ 6 ]. CD4 + T cells, accounting for the majority of lymphocytes in the intestine, are responsible for the defense against pathogens, which are discriminated from normal gut flora. However, they exert an overamplified response to the pathogens in IBD. Along with autocrine activation, they can also trigger innate immune responses via inflammatory mediators [ 6 , 7 ]. B cells are substantially involved in the protective role of the mucosal layer of the gastrointestinal tract [ 8 ]. The predominance of IgA-producing plasma cells in the gastrointestinal mucosal layers of healthy individuals shifts toward IgG-producing plasma cells in patients with UC. Furthermore, autoreactive B cell clones responding to integrin αvβ6 self-antigen have been detected in the mucosal layer. Dysregulation of B cells is also reflected in peripheral blood and can be used as a prognostic indicator of disease severity [ 9 ]. Similarly, alterations in B cell differentiation have been observed in both the gastrointestinal tract and peripheral blood of CD patients, and these alterations have also been suggested as markers of treatment efficacy [ 10 ]. In spite of similarities in the pathogenesis of CD and UC, there are prominent distinctions. For instance, CD lesions can be found in all parts of the gastrointestinal tract, while UC mainly involves the rectum and colon [ 11 ]. Moreover, in CD, plasma cells that secrete IgG2 constitute the majority of plasma cell populations, whereas plasma cells that express IgG1 are the most prevalent in UC [ 5 ]. Additionally, association studies have revealed that CD is characterized by dysregulation in bacterial intracellular recognition and clearance, whereas in UC, inflammation arises from abnormalities in the mucosal barrier and the regulation of the human leukocyte antigen (HLA) system [ 8 ]. Diagnosis of IBD relies on a combination of assessments rather than a single test [ 12 ]. Compared with many conventional tests, the evaluation of blood biomarkers benefits from being non-invasive, cost-effective, and easily accessible, which are key characteristics of an ideal biomarker [ 13 ]. Herein, by conducting transcriptomic analysis of blood samples following the integration of four datasets (GSE119600, GSE94648, GSE86434, and GSE71730), differentially expressed genes (DEGs) were determined. Subsequently, DEGs were compared with the results of weighted gene co-expression network analysis (WGCNA), and the overlay was subjected to random forest (RF) and least absolute shrinkage and selection operator (LASSO) regression. FEZ1 and NLRC5 were the most significant genes detected by both RF and LASSO machine learning methods. Notably, the LASSO regression prominently highlighted these genes. Receiver operating characteristic (ROC) analysis indicated that these genes have acceptable diagnostic efficacy (> 0.7) in the integrated data as well as in the individual analysis of the datasets. Examination of the area under the curve (AUC) of ROC analysis in validation sets (GSE169568, GSE112057, GSE100833, and GSE3365) further emphasized the diagnostic potential of FEZ1, while that of NLRC5 was merely acceptable in GSE169568. Results Identification of DEGs in IBD The distribution of samples in the principal component analysis (PCA) graph following batch-effect correction reflected the proper integration of the datasets, as there was no obvious clustering of samples with respect to the implemented study (Figs. 1 a-c). Comparing 189 CD patients with 103 healthy controls, 652 DEGs, including 479 upregulated and 173 downregulated genes, were identified (Fig. 1 d). Meanwhile, 420 DEGs, including 327 upregulated and 93 downregulated genes, were detected in 155 patients with UC compared to 103 healthy individuals (Fig. 1 e). There were 284 upregulated and 73 downregulated genes exhibiting identical alterations in both CD and UC, subsequently defined as DEGs in IBD. Interestingly, no gene was oppositely regulated between these disorders based on the applied thresholds (Fig. 1 f). Functional enrichment of DEGs in IBD The Gene Ontology (GO) analysis reflects the functional relevance of DEGs in three distinct categories, namely biological processes (BP), cellular components (CC), and molecular functions (MF). In the BP category, positive regulation of cytokine production (GO:0001819) and regulation of innate immune response (GO:0045088) were the most significant terms. Moreover, the remaining terms mainly reflected the involvement of different aspects of innate and adaptive immune responses. The CC classification results indicated that a substantial number of DEGs contributed significantly to vesicle and granule structures, as well as membrane-associated components. Among these, secretory granule membrane (GO:0030667) and specific granule (GO:0042581) were the most prominent terms. In the MF category, immune receptor activity (GO:0140375) and carbohydrate binding (GO:0030246) were the most significantly enriched terms. Meanwhile, from a holistic perspective, the remaining terms were representative of immune system functioning and pathogen recognition (Fig. 1 g). Among the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, tuberculosis and the NOD-like receptor signaling pathway emerged as the most prominent pathways. The majority of significant pathways fell into subcategories related to the immune system or infectious diseases (bacterial, parasitic, or viral) (Fig. 1 g). PPI and miRNA-mRNA networks A network with 277 nodes was created from the 357 DEGs in IBD. The 26 highly connected nodes, with interactions ranging from 27 to 87, were identified as hub genes and located in the middle of the network. The tumor necrosis factor (TNF) gene interacted with 87 nodes and was the most prominent hub gene in the protein-protein interaction (PPI) network (Fig. 2 a). Exploration of regulatory miRNAs targeting hub genes of the PPI network revealed that the majority of these miRNAs belong to the miR-548, miR-181, miR-92, and miR-513 families (Fig. 2 b). Identification of IBD-specific modules by WGCNA Sample clustering of the integrated dataset demonstrated no obvious outliers or strong batch effects (Fig. 3 a). Blockwise module construction in WGCNA grouped 16,512 genes into 23 distinct modules, using a soft thresholding power of 7 as the optimal value (Figs. 3 b-c). A p-value threshold of < 0.001 was set to consider a gene module significant. Modules 2, 10, 11, and 16 were identified as exhibiting positive correlations, whereas modules 6, 12, and 17 demonstrated negative correlations. The KEGG pathway enrichment of modules showed that module 2 was enriched in osteoclast differentiation, along with immune system functioning. Modules 10 and 11 substantially reflected viral and bacterial infectious disorders. Meanwhile, module 16 was enriched in the cell cycle. On the other hand, among the modules that exhibited a negative correlation, module 6 was involved in the ribosome, while module 12 demonstrated a marked involvement in pathways related to IBD, such as graft-versus-host disease (GVHD), differentiation of Th1, Th2, and Th17, and inflammatory bowel disease (Fig. 3 d). Therefore, module 12 was further investigated to detect key genes. A PPI network consisting of 113 nodes was created using 165 genes from this module. The top 11 genes, based on degree metrics with interactions ranging from 39 to 65, were identified as highly connected nodes and positioned at the center of the network (Fig. 4 a). Pathway enrichment analysis of the highly connected nodes in the PPI network of this module indicated that they were primarily involved in immune system pathways, with a focus on T cell functioning (Fig. 4 b). Since hub genes must also meet the criteria of having a module membership > 0.8 and a gene significance > 0.2, only PRF1 was identified as a hub gene in this module (Fig. 4 c). Identification of potential biomarkers by machine learning methods The converging results of the differential expression analysis and WGCNA were candidates for biomarker discovery (Figs. 5 a-b). Among the genes underscored by both RF and LASSO methods, genes FEZ1 and NLRC5 were commonly identified and were the most prominent according to the LASSO regression (Figs. 5 c-h). The area under the ROC curve for FEZ1 and NLRC5 was evaluated as 0.7539 and 0.7544, respectively (Fig. 5 i). The expression levels of FEZ1 and NLRC5 were observed to be significantly downregulated and upregulated, respectively, in patients with IBD when compared with healthy controls (Fig. 5 j). Examination of the selected biomarkers in external datasets To assess whether the selected biomarkers can distinguish patients with IBD from healthy subjects, ROC analysis was conducted using the external datasets GSE169568, GSE112057, GSE100833, and GSE3365. The area under the ROC curve for FEZ1 was computed as 0.762, 0.9245, 0.7794, and 0.684 in GSE169568, GSE112057, GSE100833, and GSE3365, respectively. Meanwhile, the area under the ROC curve for NLRC5 was computed as 0.7676, 0.6436, and 0.6875 in GSE169568, GSE112057, and GSE100833, respectively. Expression data of NLRC5 was not incorporated in GSE3365. In summary, except for GSE94648 and GSE3365, the AUC of FEZ1 surpassed the threshold of 0.7 in the remaining analyzed datasets; thus, it was considered a potential biomarker of IBD. Although FEZ1 failed to exhibit diagnostic potential in GSE94648 and GSE3365, a downward expression trend in IBD patients was detected in both datasets, with statistical significance observed in GSE3365 but not in GSE94648. On the other hand, NLRC5 demonstrated acceptable diagnostic efficacy in only half of the datasets (GSE119600, GSE94648, GSE71730, and GSE169568), which was insufficient for it to be considered a biomarker (Fig. 6 ). Discussion Considering the anticipated exacerbation of the global prevalence of IBD, the imperative for the discovery of non-invasive biomarkers and new therapeutic targets has intensified. To this end, a meta-analysis of blood specimens was employed to discover potential biomarkers of IBD. Datasets GSE119600, GSE94648, GSE86434, and GSE717304 were merged since they had a higher number of genes among the retrieved datasets, allowing for a more comprehensive analysis of the transcript variations present in the samples. Differential expression analysis of the integrated data, following batch-effect correction, led to the identification of 357 genes whose expression alterations had an identical direction both in CD and UC, which were subsequently defined as DEGs in IBD. By reflecting the pathways activated following the immune response, functional enrichment analysis implied the biological relevance of the DEGs in the pathogenesis of IBD. It is worth noting that the CC results of GO analysis markedly underscored granules and vesicles. The terms including 'specific granules,' 'secretory granules,' 'tertiary granules,' and 'ficolin-1-rich granules' explicitly refer to neutrophil function. The spectrum of terms reflects various aspects of their ability to recognize pathogens and release immune mediators, such as lactoferrin, lysozyme, and elastase, in response to pathogen invasion. Recent investigations have unveiled the significance of neutrophils in the pathogenesis of IBD. Multiple susceptibility genes associated with IBD are involved in orchestrating neutrophil responses against pathogens. Furthermore, genetic defects that disrupt neutrophil functions lead to the formation of IBD-like intestinal conditions manifested by inflammation and changes in gut microbiota [ 14 ]. Lactoferrin, which is primarily secreted by mature neutrophils and highlighted by the specific granule (GO:0042581), represents intestinal inflammation and has been suggested as a biomarker in stool specimens [ 15 ]. Considering lactoferrin's anti-inflammatory, immunomodulatory, and barrier-protective properties, its administration has been suggested as a therapeutic strategy [ 16 ]. The results from KEGG pathway analysis also highlighted the enrichment of neutrophil extracellular trap (NET) formation. Neutrophils, as pioneer immune cells involved in orchestrating immune responses against pathogens, release NETs and play a dual role in gut inflammation. On the one hand, they release proteases such as elastase and matrix metalloproteases (MMP), which directly damage the epithelial barrier in the gut, leading to amplification of inflammation. On the other hand, NETs contribute to the clearance of damage-associated molecular patterns (DAMPs) in the peripheral blood, resulting in an attenuated inflammatory response. Multiple investigations have shown that NETs exhibit heightened abundance in the inflamed mucosal tissue, fecal matter, and peripheral blood of IBD patients, particularly during periods of active disease [ 17 ]. To conclude, the results from the functional analysis, in line with recent advancements in the field, underscored the contribution of neutrophils to orchestrating immune responses in IBD. Of the 357 DEGs in IBD, a PPI network encompassing known interactions of 277 nodes was extracted using a confidence threshold of 0.4. By interacting with 87 nodes, TNF was the most prominent hub gene in the PPI network of IBD patients’ blood. The results of the present meta-analysis, in contrast to previous studies [ 18 ], demonstrated a significant downregulation of the TNF gene. Separate differential expression analysis on each retrieved dataset indicated significant downregulation of TNF in the GSE119600, GSE71730, GSE169568, and GSE112057 datasets, while the alterations of this gene were not statistically significant in the remaining datasets (Supplementary Fig. S1 ). This discrepancy may be attributed to a variety of factors, such as potential biases in gene expression resulting either from small sample sizes or unaccounted pretreatment conditions. Nevertheless, TNF is a central cytokine involved in orchestrating gut inflammation in IBD and has long been considered a major therapeutic target [ 19 ]. Although inhibition of TNF-α has been widely reported to be a beneficial treatment, recent paradoxical reports of its drawbacks make its systemic inhibition challenging [ 20 , 21 ]. Recent cohort studies have revealed that IBD patients receiving anti-TNF therapy have an elevated risk of developing psoriasis, rheumatoid arthritis (RA), and hidradenitis suppurativa [ 22 ]. Given that the TNF level is increased in the intestine of patients with IBD and correlated with the severity of the disease, recent investigations have suggested localized inhibition of TNF in the intestine rather than systemic treatment to avoid the adverse effects of systemic administration [ 23 ]. The exploration of regulatory miRNAs targeting hub genes of the PPI network underscored four families of miRNA, namely, miR-548, miR-181, miR-92, and miR-513 families. Among these, the four members of the miR-181 family, including miR-181a-5p, -181b-5p, -181c-5p, and − 181d-5p, as well as miR-150-5p, were found to target TNF. In both mice and humans, the miR-181 family demonstrates an abnormal expression pattern following intestinal inflammation. By conducting qPCR, a significant downregulation was reported for the levels of miR-181a and miR-181b in the colitis-induced mouse model, whereas in UC patients, the downregulation was observed only for MIR-181A. The therapeutic potential of miR-181 was reflected by the observation that miR-181 is required for the restoration of the intestinal epithelial barrier, which was attributed to its regulatory effect on the Wnt signaling pathway. According to the miRNA-mRNA network in this study, it can also be concluded that the beneficial effect of miR-181 could partially stem from its inhibitory effect on TNF, which is abundant in the inflamed intestinal mucosal layer [ 24 ]. A study on serum levels of miRNAs in patients with UC detected miR-150 as the only downregulated miRNA, which was reported by qPCR [ 25 ]. In addition to targeting TNF-a, miR-150-5p is involved in the immunopathology network of diseases by targeting IL-6 and IL-1b. Furthermore, by targeting c-Myb, it has been reported to be positively correlated with the induction of apoptosis in intestinal epithelial cells. Although there is inconsistency in the previous reports about whether miR-150-5p is upregulated or downregulated in the colonic specimens, its significant dysregulation in IBD has been documented by multiple studies [ 26 ]. There have also been reports of an association of the three remaining families with IBD. Among the members of the miR-92 family, qPCR analysis demonstrated that miR-92a has an elevated expression level in the blood of UC patients compared to healthy individuals. Additionally, ROC analysis revealed an AUC above 0.9 for this miRNA, reflecting its potential as a biomarker [ 27 ]. In the miR-513 family, miR-513a-3p was detected as a target of circRNA_102610, an upregulated circRNA in PBMCs of patients with CD [ 28 ], according to the miRanda and TargetScan databases [ 29 ], which reflect its potential involvement in the pathogenesis of IBD. According to the extant corpus of literature, there is an absence of reports regarding the IBD-associated dysregulation of the miR-548 family members identified in this investigation [ 26 , 30 , 31 ]. This particular family of microRNAs was highlighted by the miRNA-mRNA regulatory network in this study due to its regulatory effect on CD163 and KLRD1. Examination of the expression of CD163, a marker of monocytes and macrophages, in biopsies obtained from the colon by qPCR showed its upregulation in treatment-naive patients compared to healthy controls [ 32 ]. The association of KLRD1, which is primarily expressed in NK cells [ 33 ], with IBD has yet to be directly reported. Seven modules were singled out from the results of WGCNA, meeting the criterion of p-value < 0.01. Among these, modules 10 and 11 were substantially involved in viral and bacterial infections. Previous studies have reported an elevated susceptibility for developing Epstein-Barr virus (EBV) [ 34 ], influenza [ 35 ], Salmonella [ 36 ], and tuberculosis [ 37 ] in patients with IBD, reflecting shared potential pathogenic mechanisms. Reports, however, demonstrated no increased risk of hepatitis C virus (HCV) [ 38 ] or Covid-19 [ 39 ] in IBD patients. Module 12, which exhibited enrichment and a negative correlation with IBD, was further analyzed to identify the key genes within this module. While 11 genes, namely CD8A, IFNG, PRF1, KLRD1, CD247, CD2, EOMES, GZMA, IL2RB, GNLY, and KLRB1, were detected as highly connected nodes in the PPI network of this module, only PRF1 was selected as the hub regarding module membership and gene significance with IBD. Of the 11 genes identified as hub genes in the PPI network of module 12 in this study, 5 genes, including CD8A, CD2, IL2RB, PRF1, and GNLY, were previously reported as hub genes in the shared PPI network of UC and non-alcoholic fatty liver disease (NASH) [ 40 ]. In a study on gene expression patterns of circulating CD4 + T cells of patients with CD, PRF1 was identified as a hub gene in a gene module particularly activated in the penetrating subtype of CD, a severe form of the disease [ 41 ]. Moreover, differential methylation analysis detected altered methylation of the PRF1 in the blood of CD patients compared to healthy individuals [ 42 ]. Of the 357 DEGs in IBD, 298 genes with consistent correlation and expression direction were analyzed using RF and LASSO machine learning methods. FEZ1 and NLRC5 were identified by both analyses and demonstrated acceptable diagnostic efficacy in integrated data. However, the diagnostic potential of NLRC5 failed to meet the AUC > 0.7 threshold in two of the three validation datasets that included data on the gene. Meanwhile, FEZ1 met the threshold in six of the eight analyzed datasets. FEZ1, a kinesin adaptor with multiple biological functions, has been widely studied in the context of its role in neurons [ 43 ]. For instance, FEZ1 is a well-established susceptibility gene for schizophrenia (SCZ) [ 44 ], and its downregulation in the peripheral blood of patients with the disease has been reported by qPCR analysis [ 45 ]. Given that the results of the present study highlighted FEZ1 as a key gene in IBD, and in light of its contribution to brain abnormalities, including attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASD), and SCZ [ 46 ], it can be hypothesized that FEZ1 serves as a pathogenic link in the gut-brain axis. The correlation between IBD and the risk of developing SCZ remains controversial, as some studies have reported an increased risk of SCZ in patients with IBD, whereas others found no significant correlation [ 47 ]. However, a population-based cohort study observed an elevated incidence rate of IBD in SCZ patients [ 48 ]. Similar to the downregulation of FEZ1 observed in this study, FEZ1 has also been documented as a downregulated gene in the blood of patients SCZ [ 45 , 49 ] and bipolar disorder (BD) [ 50 ]. To conclude, although the pathogenic role of FEZ1 in IBD has yet to be elucidated, its identification as an IBD-specific gene coupled with the consistent records of its downregulation patterns in SCZ and BD suggests that FEZ1 is involved in the gut-brain axis. Emerging investigations have indicated that the function of FEZ1 is not limited to neurons. FEZ1 is involved in the pathways of innate immunity by modulating the translocation of heat shock protein 8 (HSPA8) to the nucleus and the expression of interferon-stimulated genes [ 43 ]. Furthermore, through direct interactions with NBR1, SMC3, IMMT, RIF1, and RAB3GAP2, FEZ1 is involved in autophagy networks implicated in CD [ 51 ]. Dysregulated autophagy is acknowledged as a pathogenic mechanism in various inflammatory autoimmune disorders, including RA [ 52 ], psoriasis [ 53 ], multiple sclerosis [ 54 ], and IBD [ 55 ]. Particularly in IBD, autophagy contributes to the clearance of pathogens, release of granules, inflammasome functioning, expression of pro-inflammatory cytokines, and endoplasmic reticulum stress [ 56 ]. FEZ1 was shown to be upregulated in the blood of CD risk allele carriers (rs13361189) compared to non-carriers, suggesting a potential role for FEZ1 in the pathophysiology associated with this genetic variant [ 57 ]. Previous studies have revealed significant blood alterations of FEZ1 in patients diagnosed with other autoimmune disorders, such as systemic lupus erythematosus (SLE) and type 1 diabetes (T1D). In a study on circulating T cells of patients with SLE, the analysis of RNA-seq data by both generalized linear model (GLM) and support vector machine (SVM) methods identified FEZ1 as a prominent gene, with an AUC exceeding 0.9 in ROC analysis [ 58 ]. Microarray analysis of PBMCs of patients with T1D revealed that FEZ1 is downregulated, with a log fold change (logFC) of -1.13 and an adjusted p-value of below 0.001 [ 59 ]. NLRC5 is a unique member of the NLRC subfamily of the NOD-like receptor (NLR) family, characterized by a caspase recruitment domain (CARD) without an acidic domain [ 60 ]. NLRC5 regulates the transcription of genes in T cells and contributes to the T cell response, primarily through complexes that lead to PANoptosis, involving mediators of pyroptosis, apoptosis, and necroptosis. This process occurs following heme exposure during inflammatory conditions [ 61 ]. Given that intestinal bleeding increases the heme levels in the mucosal layer [ 62 ], it can be assumed that the NLRC5-mediated response in T cells could be further amplified [ 61 ], underscoring the role of NLRC5 in IBD pathogenesis. In a study on intestinal mucosa, differential methylation analysis identified NLRC5 as a hypomethylated gene in patients with IBD compared to healthy controls [ 63 ]. In summary, this study aimed to identify blood RNA markers with diagnostic potential in patients with IBD. FEZ1 exhibited acceptable diagnostic capability through the integrated transcriptomics approach applied. However, the findings of this study are based on computational analyses, and their clinical applicability has yet to be established. This study was limited by the lack of comprehensive biological information from patients, which hindered the accurate matching of case and control groups. Confounding factors, such as smoking history, gender, or age, may have implicitly influenced the results. Nevertheless, according to the phenotypic data retrieved by the GEOquery package, an initial phase of filtration aimed at minimizing disparities between groups was executed to ensure that blood constituted the sample origin and that no prior treatments were documented. Conclusion By employing an integrated transcriptomic analysis on blood samples obtained from patients with IBD, this study aimed to unveil potential non-invasive diagnostic markers of the disease. TNF, the central hub gene of the PPI network of DEGs in IBD, has long been considered a therapeutic target. However, its downregulation in the blood suggests that TNF inhibition might be substantially beneficial when the inhibition is confined to the gastrointestinal tract rather than systemically. FEZ1 demonstrated promising potential as a blood biomarker of IBD, as both RF and LASSO machine learning methods highlighted it, and it passed the acceptable AUC threshold in ROC analyses in most datasets. Based on the current understanding of FEZ1 functionalities, the contribution of FEZ1 in autophagy is the most probable explanation for its dysregulation. Furthermore, considering the involvement of this gene in brain abnormalities, it can be hypothesized that FEZ1 may serve as a pathogenic link in the gut-brain axis. From a holistic perspective, several key genes identified, including PRF1 and NLRC5, reflect T cell functioning, underscoring their role in IBD pathogenesis. While the findings of this study fundamentally relied on bioinformatic analysis, prospective experimental studies are needed to assess the reproducibility of these results. Methods Data collection and preparation The Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ) database [ 64 ] was explored to retrieve the blood transcriptome of IBD. To this end, a simultaneous search was conducted for Crohn’s disease, ulcerative colitis, or IBD, along with blood or PBMC. A total of eight datasets were extracted from GEO, published between 2005 and 2021 (Table 1 ). Table 1 Gene expression data from the blood of patients with IBD GEO accession Source Year Control Crohn’s disease Ulcerative colitis Platform Number of genes Reference Integrated data GSE119600 Blood 2019 47 95 93 GPL10588 31320 [ 68 ] GSE94648 Blood 2017 22 50 25 GPL19109 22880 [ 69 ] GSE86434 Blood 2016 24 22 22 GPL10558 31171 [ 70 ] GSE71730 PBMC 2015 10 22 15 GPL570 22880 [ 71 ] Validation sets GSE169568 Blood 2021 30 52 58 GPL10588 11646 [ 72 ] GSE112057 Blood 2018 12 59 15 GPL11154 11923 [ 73 ] GSE100833 Blood 2017 49 - 589 GPL13158 20277 [ 74 ] GSE3365 PBMC 2005 42 59 26 GPL96 13237 [ 75 ] According to the sample information retrieved using the GEOquery [ 65 ] package, an initial stage of filtration was conducted to ensure that blood was the sample source, the disorders were IBD, and no prior treatment had been reported. In GSE119600, patients with either primary biliary cholangitis or primary sclerosing cholangitis were excluded from the samples. In GSE86434, only samples obtained from whole blood were included, and specific lineages of leucocytes, including naïve CD4 T cells, CD4 + T cells, CD8 + T cells, and CD14 + monocytes, were excluded. In GSE169568, symptomatic controls were discarded. In GSE112057, samples with systemic juvenile idiopathic arthritis (JIA), oligoarticular JIA, or polyarticular JIA were removed. In GSE100833, samples that were derived from sources other than blood were excluded. Subsequently, outlier detection was conducted using PCA and hierarchical clustering of samples by average linkage to enhance the significance of the findings. GSM2302588 and GSM3056505 were detected as outliers in GSE86434 and GSE112057, respectively, and were excluded from these datasets. Meanwhile, no obvious outliers were discerned in the remaining datasets. Based on the number of genes available in datasets, GSE119600, GSE94648, GSE86434, and GSE71730 were selected and subsequently merged for further analyses. The SVA [ 66 ] package was used to remove batch effects from the integrated dataset. Then, the distribution of samples within the PCA graph prior to and after batch-effect correction was illustrated using the scatterplot3d [ 67 ] package. Ultimately, the integrated dataset encompassed 447 samples, including 189 individuals diagnosed with CD, 155 individuals diagnosed with UC, and 103 healthy subjects, with data on 16,512 genes for each sample. Differential expression analysis Considering the thresholds of |logFC| > 0.2 and adjusted p-value < 0.05, DEGs were separately determined in CD and UC using the limma [ 76 ] package. Subsequently, upregulated and downregulated genes in both disorders were identified as DEGs in IBD for further analyses. The overlay of DEGs between CD and UC was displayed using the ggVennDiagram [ 77 ] package. Gene Ontology and KEGG pathway analyses To determine the biological relevance of the DEGs in IBD, the GO and KEGG pathway analyses were conducted using the ClusterProfiler [ 78 ] package. Terms that met the criterion of having an adjusted p-value less than 0.05 were considered significant, and the top 10 terms in each GO category were presented in the order of enrichment and significance. Construction of PPI and miRNA-mRNA networks The STRING [ 79 ] plugin of the Cytoscape v3.10.2 [ 80 ] app was used to extract the PPI network of DEGs in IBD. The confidence threshold was set at 0.4, and subsequently, discrete and partially connected nodes were excluded from the resulting network. The network was analyzed to determine the hub genes, defined as the top decile of nodes based on their number of interactions. Nodes with fewer than six interactions were excluded to improve visualization efficiency. The hub genes in the PPI network of IBD were used as input in miRDB [ 81 ] to identify regulatory miRNAs, with the target score set at 80. Then, miRNAs targeting more than one hub gene, along with their target genes, established the final miRNA-mRNA network. Weighted Gene Co-expression Network Analysis (WGCNA) The WGCNA [ 82 ] package was used to detect modules significantly correlated with IBD and to further filter DEGs. To this end, after selecting the optimal soft threshold (power), which has a scale independence exceeding 0.8 and a mean connectivity near zero, blockwise construction of modules was carried out. Then, the modules with a p-value less than 0.001 were considered significant. Pathway analysis of these modules was conducted using the Enrichr [ 83 ] online tool. Since module 12 was significantly correlated with IBD and enriched for the disease, a PPI network was constructed for this module. After removing partially connected and discrete nodes, the network was analyzed, and hub genes ranking in the top 10 percent according to degree metrics were identified. Finally, KEGG pathway analysis was conducted, and significant pathways were illustrated using the circlize [ 84 ] package. Identification of potential biomarkers of IBD by machine learning methods DEGs that demonstrated a consistent correlation with IBD through WGCNA were further analyzed using machine learning techniques. The decision-tree-based RF and LASSO regression methods were implemented using the randomForest [ 85 ] and glmnet [ 86 ] packages to identify potential biomarkers. The top 10 genes selected by each method were compared to identify candidate biomarkers common to both. These candidate biomarkers were subsequently evaluated through ROC analysis using the pROC [ 87 ] package. Assessment of the diagnostic efficacy of selected biomarkers in the validation datasets ROC analysis was employed to assess the diagnostic efficacy of the candidate genes. The AUC was evaluated in each dataset that was part of the integrated data framework individually, as well as in external datasets, including GSE169568, GSE112057, GSE100833, and GSE3365. Declarations Data availability The datasets analyzed during the current study are available in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) with the accession numbers GSE119600, GSE94648, GSE86434, GSE71730, GSE169568, GSE112057, GSE100833, and GSE3365. Acknowledgements The author appreciates the contributions made by the authors of the GSE119600, GSE94648, GSE86434, GSE71730, GSE169568, GSE112057, GSE100833, and GSE3365. Author Contributions A.M.D.D. designed the research, acquired the data, conducted the analyses, interpreted the results, and prepared the manuscript. 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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-5255906","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":370395142,"identity":"248ef130-5c8a-4c62-a776-4a5421f8b86e","order_by":0,"name":"Arman Mokaram Doust Delkhah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDADCTBZAcTMzA2kaDkD0sJIihbGNjCJXwv/7NOJnwsYtslJtp99+OnmvNpo/naglh8V23Abfi53s/QMhtvG0jzpxtK5247nzjjM2MDYc+Y2bmvO8G6Q5mG4nTiPIY0BqOVYbgNQCzNjG24t8md4N/8Ga+F/xvw7d86x3PmEtBic4d0GtmW2RBqbdG5DTe4GQloMgVqseQxuG0vOeMZmnXPsQO5GoJaD+PwiB3TYbZ6K23IS59OYb+fU1OXOO3/44IMfFXi8D3EenHUYTB4goB4F1JGieBSMglEwCkYIAABhvFdM1S74IwAAAABJRU5ErkJggg==","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":true,"prefix":"","firstName":"Arman","middleName":"Mokaram Doust","lastName":"Delkhah","suffix":""}],"badges":[],"createdAt":"2024-10-13 15:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5255906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5255906/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.compbiomed.2025.109742","type":"published","date":"2025-02-01T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67630453,"identity":"6573818e-9a17-4fcb-ad54-30dbaa62fd5f","added_by":"auto","created_at":"2024-10-28 08:39:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1302838,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of DEGs in IBD and their biological relevance. (\u003cstrong\u003ea\u003c/strong\u003e) The distribution of samples in the PCA graph prior to batch-effect correction demonstrated their clustering based on the implemented study. (\u003cstrong\u003eb-c\u003c/strong\u003e) The distribution of samples in the PCA graph after batch-effect removal, based on the conducted study and disease state, respectively. (d) Volcano plot of DEGs in CD. (\u003cstrong\u003ee\u003c/strong\u003e) Volcano plot of DEGs in UC. (\u003cstrong\u003ef\u003c/strong\u003e) The Venn diagram revealed genes exhibiting either upregulation or downregulation in both CD and UC, defined as DEGs in IBD. (\u003cstrong\u003eg\u003c/strong\u003e) GO and KEGG pathway enrichment analysis of DEGs in IBD.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5255906/v1/2854d88c53e69aa6f6cffac4.png"},{"id":67629385,"identity":"f7db8d71-019c-454f-beba-17e8b9401ea6","added_by":"auto","created_at":"2024-10-28 08:31:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3752483,"visible":true,"origin":"","legend":"\u003cp\u003ePPI and miRNA-mRNA networks of DEGs in IBD. (\u003cstrong\u003ea\u003c/strong\u003e) The PPI network was mapped using the confidence threshold of 0.4. Highly connected nodes, ranked in the top 10 percent based on the number of interactions, are located in the center of the plot. The size of the nodes represents the number of their interactions, which decreases counterclockwise in each circle. (\u003cstrong\u003eb\u003c/strong\u003e) Identification of regulatory miRNAs targeting highly connected nodes in the PPI network. Upregulated and downregulated genes were represented by red and blue colors, respectively, in the networks.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5255906/v1/4d07c37717c0d5ecbb18f240.png"},{"id":67629380,"identity":"f8e8c751-ecc5-45d0-ad5c-e24888ca5c6e","added_by":"auto","created_at":"2024-10-28 08:31:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2091540,"visible":true,"origin":"","legend":"\u003cp\u003eDetection of IBD-specific modules by WGCNA. (\u003cstrong\u003ea\u003c/strong\u003e) Sample clustering of integrated data was performed using the average linkage. (\u003cstrong\u003eb\u003c/strong\u003e) The soft threshold of 7 was selected as the optimal power according to the scale independence and mean connectivity. (\u003cstrong\u003ec\u003c/strong\u003e) Heat map of module correlations with IBD. The statistical significance of correlations from the Student’s t-test has been represented by asterisks (*P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001). (\u003cstrong\u003ed\u003c/strong\u003e) The pie chart illustrates the proportion of each module of co-expressed genes. Positively correlated modules are in red, while negatively correlated modules are in blue. Highly enriched pathways are reported below each gene module.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5255906/v1/850bef83ccc0de1433b77479.png"},{"id":67629382,"identity":"eac47e07-306f-4ac5-be48-fb88b649711e","added_by":"auto","created_at":"2024-10-28 08:31:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4506591,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of hub genes in module 12. (\u003cstrong\u003ea\u003c/strong\u003e) PPI network of module 12. Highly connected nodes, ranked in the top 10 percent based on the number of interactions in the module, are located at the center of the PPI network. Genes with blue labels were significantly downregulated with logFC below 0.2 in both CD and UC, while genes with black labels were not among DEGs in IBD. (\u003cstrong\u003eb\u003c/strong\u003e) KEGG pathway analysis of highly connected nodes. The pathways in blue belong to the subcategory of immune system, and infectious disorders are represented in yellow. (\u003cstrong\u003ec\u003c/strong\u003e) Considering the absolute module membership and gene significance thresholds of 0.8 and 0.2, respectively, PRF1 is the only gene in this module that was selected as a hub.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5255906/v1/fe0cdc816c0e623e9152a569.png"},{"id":67629383,"identity":"c238aea6-e8e1-4cbc-bdef-892001df9869","added_by":"auto","created_at":"2024-10-28 08:31:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":869339,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of biomarkers by machine learning models. (\u003cstrong\u003ea\u003c/strong\u003e) A total of 266 genes were upregulated in both CD and UC, belonging to selected positively correlated modules. (\u003cstrong\u003eb\u003c/strong\u003e) A total of 32 genes were downregulated in both CD and UC, belonging to selected negatively correlated modules. These genes were subsequently subjected to RF and LASSO machine learning analysis. (\u003cstrong\u003ec\u003c/strong\u003e) The impact of the number of decision trees on the error rate of RF. (\u003cstrong\u003ed\u003c/strong\u003e) The top 10 genes identified by RF, based on mean decrease in accuracy and mean decrease in Gini. (\u003cstrong\u003ee\u003c/strong\u003e) Profiles of LASSO coefficients for the genes. (\u003cstrong\u003ef\u003c/strong\u003e) A plot of binomial deviance against the logarithm of the regularization parameter (λ) in LASSO regression. (\u003cstrong\u003eg\u003c/strong\u003e) Bar plot of the top 10 genes selected using LASSO regression. (\u003cstrong\u003eh\u003c/strong\u003e) Venn diagram of the top 10 gene features identified by RF and LASSO. FEZ1 and NLRC5 were consistently identified by both methods. (\u003cstrong\u003ei\u003c/strong\u003e) Assessment of the AUC of ROC analysis for FEZ1 and NLRC5 in the integrated data. (\u003cstrong\u003ej\u003c/strong\u003e) The expression levels of FEZ1 and NLRC5 in patients with IBD and controls.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5255906/v1/3188ff1062dba79a463c244d.png"},{"id":67629387,"identity":"2535e086-8e69-4f01-84c9-da32eefa1ec0","added_by":"auto","created_at":"2024-10-28 08:31:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1824937,"visible":true,"origin":"","legend":"\u003cp\u003eIndependent evaluations of the area under the ROC curve and differential expression analysis for FEZ1 and NLRC5 in (\u003cstrong\u003ea\u003c/strong\u003e) GSE119600, (\u003cstrong\u003eb\u003c/strong\u003e) GSE94648, (\u003cstrong\u003ec\u003c/strong\u003e) GSE86434, (\u003cstrong\u003ed\u003c/strong\u003e) GSE71730, (\u003cstrong\u003ee\u003c/strong\u003e) GSE169568, (\u003cstrong\u003ef\u003c/strong\u003e) GSE112057, (\u003cstrong\u003eg\u003c/strong\u003e) GSE100833, and (\u003cstrong\u003eh\u003c/strong\u003e) GSE3365.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5255906/v1/14d8b0f4006728b2c4c6f50f.png"},{"id":75364133,"identity":"3ed0ba82-fa56-40b2-bb7f-98e8d6d743ad","added_by":"auto","created_at":"2025-02-03 18:36:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15462364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5255906/v1/af4d532b-e432-46ee-bf36-178b17b719ef.pdf"},{"id":67630454,"identity":"16af6da5-9325-45f5-af1c-b21b335d9334","added_by":"auto","created_at":"2024-10-28 08:39:52","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":112780,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5255906/v1/acfe78f64b845c335d3f2f6b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blood transcriptomics identifies FEZ1 as a novel non-invasive diagnostic biomarker for inflammatory bowel disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCrohn\u0026rsquo;s disease (CD) and ulcerative colitis (UC), the most prevalent inflammatory bowel diseases (IBD), stem from mucosal inflammation of the gastrointestinal tract caused by an immune response [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The global burden of IBD is anticipated to escalate in the forthcoming decades [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, establishing non-invasive diagnostic methods is of significant value. Emerging investigations have underscored the importance of blood specimens, particularly peripheral blood mononuclear cells (PBMCs), as a valuable source for biomarker discovery [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Considering the difficulty of gathering large sample sizes in the study of autoimmune disorders, the present study aimed to identify potential biomarkers from a large sample pool by integrating transcriptomic data from blood samples.\u003c/p\u003e \u003cp\u003eCirculating adaptive and innate immune cells are involved in the pathogenesis of CD and UC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The ratio of blood T cells is elevated in patients with IBD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and they play a central role in the immune-mediated damage in the disorder [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CD4\u0026thinsp;+\u0026thinsp;T cells, accounting for the majority of lymphocytes in the intestine, are responsible for the defense against pathogens, which are discriminated from normal gut flora. However, they exert an overamplified response to the pathogens in IBD. Along with autocrine activation, they can also trigger innate immune responses via inflammatory mediators [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. B cells are substantially involved in the protective role of the mucosal layer of the gastrointestinal tract [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The predominance of IgA-producing plasma cells in the gastrointestinal mucosal layers of healthy individuals shifts toward IgG-producing plasma cells in patients with UC. Furthermore, autoreactive B cell clones responding to integrin αvβ6 self-antigen have been detected in the mucosal layer. Dysregulation of B cells is also reflected in peripheral blood and can be used as a prognostic indicator of disease severity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, alterations in B cell differentiation have been observed in both the gastrointestinal tract and peripheral blood of CD patients, and these alterations have also been suggested as markers of treatment efficacy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn spite of similarities in the pathogenesis of CD and UC, there are prominent distinctions. For instance, CD lesions can be found in all parts of the gastrointestinal tract, while UC mainly involves the rectum and colon [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, in CD, plasma cells that secrete IgG2 constitute the majority of plasma cell populations, whereas plasma cells that express IgG1 are the most prevalent in UC [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, association studies have revealed that CD is characterized by dysregulation in bacterial intracellular recognition and clearance, whereas in UC, inflammation arises from abnormalities in the mucosal barrier and the regulation of the human leukocyte antigen (HLA) system [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiagnosis of IBD relies on a combination of assessments rather than a single test [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Compared with many conventional tests, the evaluation of blood biomarkers benefits from being non-invasive, cost-effective, and easily accessible, which are key characteristics of an ideal biomarker [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Herein, by conducting transcriptomic analysis of blood samples following the integration of four datasets (GSE119600, GSE94648, GSE86434, and GSE71730), differentially expressed genes (DEGs) were determined. Subsequently, DEGs were compared with the results of weighted gene co-expression network analysis (WGCNA), and the overlay was subjected to random forest (RF) and least absolute shrinkage and selection operator (LASSO) regression. FEZ1 and NLRC5 were the most significant genes detected by both RF and LASSO machine learning methods. Notably, the LASSO regression prominently highlighted these genes. Receiver operating characteristic (ROC) analysis indicated that these genes have acceptable diagnostic efficacy (\u0026gt;\u0026thinsp;0.7) in the integrated data as well as in the individual analysis of the datasets. Examination of the area under the curve (AUC) of ROC analysis in validation sets (GSE169568, GSE112057, GSE100833, and GSE3365) further emphasized the diagnostic potential of FEZ1, while that of NLRC5 was merely acceptable in GSE169568.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs in IBD\u003c/h2\u003e \u003cp\u003eThe distribution of samples in the principal component analysis (PCA) graph following batch-effect correction reflected the proper integration of the datasets, as there was no obvious clustering of samples with respect to the implemented study (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-c). Comparing 189 CD patients with 103 healthy controls, 652 DEGs, including 479 upregulated and 173 downregulated genes, were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Meanwhile, 420 DEGs, including 327 upregulated and 93 downregulated genes, were detected in 155 patients with UC compared to 103 healthy individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). There were 284 upregulated and 73 downregulated genes exhibiting identical alterations in both CD and UC, subsequently defined as DEGs in IBD. Interestingly, no gene was oppositely regulated between these disorders based on the applied thresholds (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional enrichment of DEGs in IBD\u003c/h3\u003e\n\u003cp\u003eThe Gene Ontology (GO) analysis reflects the functional relevance of DEGs in three distinct categories, namely biological processes (BP), cellular components (CC), and molecular functions (MF). In the BP category, positive regulation of cytokine production (GO:0001819) and regulation of innate immune response (GO:0045088) were the most significant terms. Moreover, the remaining terms mainly reflected the involvement of different aspects of innate and adaptive immune responses. The CC classification results indicated that a substantial number of DEGs contributed significantly to vesicle and granule structures, as well as membrane-associated components. Among these, secretory granule membrane (GO:0030667) and specific granule (GO:0042581) were the most prominent terms. In the MF category, immune receptor activity (GO:0140375) and carbohydrate binding (GO:0030246) were the most significantly enriched terms. Meanwhile, from a holistic perspective, the remaining terms were representative of immune system functioning and pathogen recognition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003eAmong the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, tuberculosis and the NOD-like receptor signaling pathway emerged as the most prominent pathways. The majority of significant pathways fell into subcategories related to the immune system or infectious diseases (bacterial, parasitic, or viral) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg).\u003c/p\u003e\n\u003ch3\u003ePPI and miRNA-mRNA networks\u003c/h3\u003e\n\u003cp\u003eA network with 277 nodes was created from the 357 DEGs in IBD. The 26 highly connected nodes, with interactions ranging from 27 to 87, were identified as hub genes and located in the middle of the network. The tumor necrosis factor (TNF) gene interacted with 87 nodes and was the most prominent hub gene in the protein-protein interaction (PPI) network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Exploration of regulatory miRNAs targeting hub genes of the PPI network revealed that the majority of these miRNAs belong to the miR-548, miR-181, miR-92, and miR-513 families (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eIdentification of IBD-specific modules by WGCNA\u003c/h3\u003e\n\u003cp\u003eSample clustering of the integrated dataset demonstrated no obvious outliers or strong batch effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Blockwise module construction in WGCNA grouped 16,512 genes into 23 distinct modules, using a soft thresholding power of 7 as the optimal value (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c). A p-value threshold of \u0026lt;\u0026thinsp;0.001 was set to consider a gene module significant. Modules 2, 10, 11, and 16 were identified as exhibiting positive correlations, whereas modules 6, 12, and 17 demonstrated negative correlations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe KEGG pathway enrichment of modules showed that module 2 was enriched in osteoclast differentiation, along with immune system functioning. Modules 10 and 11 substantially reflected viral and bacterial infectious disorders. Meanwhile, module 16 was enriched in the cell cycle. On the other hand, among the modules that exhibited a negative correlation, module 6 was involved in the ribosome, while module 12 demonstrated a marked involvement in pathways related to IBD, such as graft-versus-host disease (GVHD), differentiation of Th1, Th2, and Th17, and inflammatory bowel disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Therefore, module 12 was further investigated to detect key genes.\u003c/p\u003e \u003cp\u003eA PPI network consisting of 113 nodes was created using 165 genes from this module. The top 11 genes, based on degree metrics with interactions ranging from 39 to 65, were identified as highly connected nodes and positioned at the center of the network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Pathway enrichment analysis of the highly connected nodes in the PPI network of this module indicated that they were primarily involved in immune system pathways, with a focus on T cell functioning (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Since hub genes must also meet the criteria of having a module membership\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and a gene significance\u0026thinsp;\u0026gt;\u0026thinsp;0.2, only PRF1 was identified as a hub gene in this module (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eIdentification of potential biomarkers by machine learning methods\u003c/h3\u003e\n\u003cp\u003eThe converging results of the differential expression analysis and WGCNA were candidates for biomarker discovery (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b). Among the genes underscored by both RF and LASSO methods, genes FEZ1 and NLRC5 were commonly identified and were the most prominent according to the LASSO regression (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-h). The area under the ROC curve for FEZ1 and NLRC5 was evaluated as 0.7539 and 0.7544, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ei). The expression levels of FEZ1 and NLRC5 were observed to be significantly downregulated and upregulated, respectively, in patients with IBD when compared with healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ej).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExamination of the selected biomarkers in external datasets\u003c/h2\u003e \u003cp\u003eTo assess whether the selected biomarkers can distinguish patients with IBD from healthy subjects, ROC analysis was conducted using the external datasets GSE169568, GSE112057, GSE100833, and GSE3365. The area under the ROC curve for FEZ1 was computed as 0.762, 0.9245, 0.7794, and 0.684 in GSE169568, GSE112057, GSE100833, and GSE3365, respectively. Meanwhile, the area under the ROC curve for NLRC5 was computed as 0.7676, 0.6436, and 0.6875 in GSE169568, GSE112057, and GSE100833, respectively. Expression data of NLRC5 was not incorporated in GSE3365.\u003c/p\u003e \u003cp\u003eIn summary, except for GSE94648 and GSE3365, the AUC of FEZ1 surpassed the threshold of 0.7 in the remaining analyzed datasets; thus, it was considered a potential biomarker of IBD. Although FEZ1 failed to exhibit diagnostic potential in GSE94648 and GSE3365, a downward expression trend in IBD patients was detected in both datasets, with statistical significance observed in GSE3365 but not in GSE94648. On the other hand, NLRC5 demonstrated acceptable diagnostic efficacy in only half of the datasets (GSE119600, GSE94648, GSE71730, and GSE169568), which was insufficient for it to be considered a biomarker (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eConsidering the anticipated exacerbation of the global prevalence of IBD, the imperative for the discovery of non-invasive biomarkers and new therapeutic targets has intensified. To this end, a meta-analysis of blood specimens was employed to discover potential biomarkers of IBD. Datasets GSE119600, GSE94648, GSE86434, and GSE717304 were merged since they had a higher number of genes among the retrieved datasets, allowing for a more comprehensive analysis of the transcript variations present in the samples. Differential expression analysis of the integrated data, following batch-effect correction, led to the identification of 357 genes whose expression alterations had an identical direction both in CD and UC, which were subsequently defined as DEGs in IBD.\u003c/p\u003e \u003cp\u003eBy reflecting the pathways activated following the immune response, functional enrichment analysis implied the biological relevance of the DEGs in the pathogenesis of IBD. It is worth noting that the CC results of GO analysis markedly underscored granules and vesicles. The terms including 'specific granules,' 'secretory granules,' 'tertiary granules,' and 'ficolin-1-rich granules' explicitly refer to neutrophil function. The spectrum of terms reflects various aspects of their ability to recognize pathogens and release immune mediators, such as lactoferrin, lysozyme, and elastase, in response to pathogen invasion. Recent investigations have unveiled the significance of neutrophils in the pathogenesis of IBD. Multiple susceptibility genes associated with IBD are involved in orchestrating neutrophil responses against pathogens. Furthermore, genetic defects that disrupt neutrophil functions lead to the formation of IBD-like intestinal conditions manifested by inflammation and changes in gut microbiota [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Lactoferrin, which is primarily secreted by mature neutrophils and highlighted by the specific granule (GO:0042581), represents intestinal inflammation and has been suggested as a biomarker in stool specimens [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Considering lactoferrin's anti-inflammatory, immunomodulatory, and barrier-protective properties, its administration has been suggested as a therapeutic strategy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results from KEGG pathway analysis also highlighted the enrichment of neutrophil extracellular trap (NET) formation. Neutrophils, as pioneer immune cells involved in orchestrating immune responses against pathogens, release NETs and play a dual role in gut inflammation. On the one hand, they release proteases such as elastase and matrix metalloproteases (MMP), which directly damage the epithelial barrier in the gut, leading to amplification of inflammation. On the other hand, NETs contribute to the clearance of damage-associated molecular patterns (DAMPs) in the peripheral blood, resulting in an attenuated inflammatory response. Multiple investigations have shown that NETs exhibit heightened abundance in the inflamed mucosal tissue, fecal matter, and peripheral blood of IBD patients, particularly during periods of active disease [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To conclude, the results from the functional analysis, in line with recent advancements in the field, underscored the contribution of neutrophils to orchestrating immune responses in IBD.\u003c/p\u003e \u003cp\u003eOf the 357 DEGs in IBD, a PPI network encompassing known interactions of 277 nodes was extracted using a confidence threshold of 0.4. By interacting with 87 nodes, TNF was the most prominent hub gene in the PPI network of IBD patients\u0026rsquo; blood. The results of the present meta-analysis, in contrast to previous studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], demonstrated a significant downregulation of the TNF gene. Separate differential expression analysis on each retrieved dataset indicated significant downregulation of TNF in the GSE119600, GSE71730, GSE169568, and GSE112057 datasets, while the alterations of this gene were not statistically significant in the remaining datasets (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This discrepancy may be attributed to a variety of factors, such as potential biases in gene expression resulting either from small sample sizes or unaccounted pretreatment conditions. Nevertheless, TNF is a central cytokine involved in orchestrating gut inflammation in IBD and has long been considered a major therapeutic target [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Although inhibition of TNF-α has been widely reported to be a beneficial treatment, recent paradoxical reports of its drawbacks make its systemic inhibition challenging [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Recent cohort studies have revealed that IBD patients receiving anti-TNF therapy have an elevated risk of developing psoriasis, rheumatoid arthritis (RA), and hidradenitis suppurativa [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Given that the TNF level is increased in the intestine of patients with IBD and correlated with the severity of the disease, recent investigations have suggested localized inhibition of TNF in the intestine rather than systemic treatment to avoid the adverse effects of systemic administration [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe exploration of regulatory miRNAs targeting hub genes of the PPI network underscored four families of miRNA, namely, miR-548, miR-181, miR-92, and miR-513 families. Among these, the four members of the miR-181 family, including miR-181a-5p, -181b-5p, -181c-5p, and \u0026minus;\u0026thinsp;181d-5p, as well as miR-150-5p, were found to target TNF. In both mice and humans, the miR-181 family demonstrates an abnormal expression pattern following intestinal inflammation. By conducting qPCR, a significant downregulation was reported for the levels of miR-181a and miR-181b in the colitis-induced mouse model, whereas in UC patients, the downregulation was observed only for MIR-181A. The therapeutic potential of miR-181 was reflected by the observation that miR-181 is required for the restoration of the intestinal epithelial barrier, which was attributed to its regulatory effect on the Wnt signaling pathway. According to the miRNA-mRNA network in this study, it can also be concluded that the beneficial effect of miR-181 could partially stem from its inhibitory effect on TNF, which is abundant in the inflamed intestinal mucosal layer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A study on serum levels of miRNAs in patients with UC detected miR-150 as the only downregulated miRNA, which was reported by qPCR [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In addition to targeting TNF-a, miR-150-5p is involved in the immunopathology network of diseases by targeting IL-6 and IL-1b. Furthermore, by targeting c-Myb, it has been reported to be positively correlated with the induction of apoptosis in intestinal epithelial cells. Although there is inconsistency in the previous reports about whether miR-150-5p is upregulated or downregulated in the colonic specimens, its significant dysregulation in IBD has been documented by multiple studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere have also been reports of an association of the three remaining families with IBD. Among the members of the miR-92 family, qPCR analysis demonstrated that miR-92a has an elevated expression level in the blood of UC patients compared to healthy individuals. Additionally, ROC analysis revealed an AUC above 0.9 for this miRNA, reflecting its potential as a biomarker [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the miR-513 family, miR-513a-3p was detected as a target of circRNA_102610, an upregulated circRNA in PBMCs of patients with CD [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], according to the miRanda and TargetScan databases [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], which reflect its potential involvement in the pathogenesis of IBD.\u003c/p\u003e \u003cp\u003eAccording to the extant corpus of literature, there is an absence of reports regarding the IBD-associated dysregulation of the miR-548 family members identified in this investigation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This particular family of microRNAs was highlighted by the miRNA-mRNA regulatory network in this study due to its regulatory effect on CD163 and KLRD1. Examination of the expression of CD163, a marker of monocytes and macrophages, in biopsies obtained from the colon by qPCR showed its upregulation in treatment-naive patients compared to healthy controls [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The association of KLRD1, which is primarily expressed in NK cells [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], with IBD has yet to be directly reported.\u003c/p\u003e \u003cp\u003eSeven modules were singled out from the results of WGCNA, meeting the criterion of p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Among these, modules 10 and 11 were substantially involved in viral and bacterial infections. Previous studies have reported an elevated susceptibility for developing Epstein-Barr virus (EBV) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], influenza [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], Salmonella [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and tuberculosis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] in patients with IBD, reflecting shared potential pathogenic mechanisms. Reports, however, demonstrated no increased risk of hepatitis C virus (HCV) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] or Covid-19 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] in IBD patients.\u003c/p\u003e \u003cp\u003eModule 12, which exhibited enrichment and a negative correlation with IBD, was further analyzed to identify the key genes within this module. While 11 genes, namely CD8A, IFNG, PRF1, KLRD1, CD247, CD2, EOMES, GZMA, IL2RB, GNLY, and KLRB1, were detected as highly connected nodes in the PPI network of this module, only PRF1 was selected as the hub regarding module membership and gene significance with IBD. Of the 11 genes identified as hub genes in the PPI network of module 12 in this study, 5 genes, including CD8A, CD2, IL2RB, PRF1, and GNLY, were previously reported as hub genes in the shared PPI network of UC and non-alcoholic fatty liver disease (NASH) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In a study on gene expression patterns of circulating CD4\u0026thinsp;+\u0026thinsp;T cells of patients with CD, PRF1 was identified as a hub gene in a gene module particularly activated in the penetrating subtype of CD, a severe form of the disease [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Moreover, differential methylation analysis detected altered methylation of the PRF1 in the blood of CD patients compared to healthy individuals [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOf the 357 DEGs in IBD, 298 genes with consistent correlation and expression direction were analyzed using RF and LASSO machine learning methods. FEZ1 and NLRC5 were identified by both analyses and demonstrated acceptable diagnostic efficacy in integrated data. However, the diagnostic potential of NLRC5 failed to meet the AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 threshold in two of the three validation datasets that included data on the gene. Meanwhile, FEZ1 met the threshold in six of the eight analyzed datasets.\u003c/p\u003e \u003cp\u003eFEZ1, a kinesin adaptor with multiple biological functions, has been widely studied in the context of its role in neurons [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. For instance, FEZ1 is a well-established susceptibility gene for schizophrenia (SCZ) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and its downregulation in the peripheral blood of patients with the disease has been reported by qPCR analysis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Given that the results of the present study highlighted FEZ1 as a key gene in IBD, and in light of its contribution to brain abnormalities, including attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASD), and SCZ [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], it can be hypothesized that FEZ1 serves as a pathogenic link in the gut-brain axis. The correlation between IBD and the risk of developing SCZ remains controversial, as some studies have reported an increased risk of SCZ in patients with IBD, whereas others found no significant correlation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. However, a population-based cohort study observed an elevated incidence rate of IBD in SCZ patients [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Similar to the downregulation of FEZ1 observed in this study, FEZ1 has also been documented as a downregulated gene in the blood of patients SCZ [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and bipolar disorder (BD) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. To conclude, although the pathogenic role of FEZ1 in IBD has yet to be elucidated, its identification as an IBD-specific gene coupled with the consistent records of its downregulation patterns in SCZ and BD suggests that FEZ1 is involved in the gut-brain axis.\u003c/p\u003e \u003cp\u003eEmerging investigations have indicated that the function of FEZ1 is not limited to neurons. FEZ1 is involved in the pathways of innate immunity by modulating the translocation of heat shock protein 8 (HSPA8) to the nucleus and the expression of interferon-stimulated genes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Furthermore, through direct interactions with NBR1, SMC3, IMMT, RIF1, and RAB3GAP2, FEZ1 is involved in autophagy networks implicated in CD [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Dysregulated autophagy is acknowledged as a pathogenic mechanism in various inflammatory autoimmune disorders, including RA [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], psoriasis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], multiple sclerosis [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and IBD [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Particularly in IBD, autophagy contributes to the clearance of pathogens, release of granules, inflammasome functioning, expression of pro-inflammatory cytokines, and endoplasmic reticulum stress [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. FEZ1 was shown to be upregulated in the blood of CD risk allele carriers (rs13361189) compared to non-carriers, suggesting a potential role for FEZ1 in the pathophysiology associated with this genetic variant [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have revealed significant blood alterations of FEZ1 in patients diagnosed with other autoimmune disorders, such as systemic lupus erythematosus (SLE) and type 1 diabetes (T1D). In a study on circulating T cells of patients with SLE, the analysis of RNA-seq data by both generalized linear model (GLM) and support vector machine (SVM) methods identified FEZ1 as a prominent gene, with an AUC exceeding 0.9 in ROC analysis [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Microarray analysis of PBMCs of patients with T1D revealed that FEZ1 is downregulated, with a log fold change (logFC) of -1.13 and an adjusted p-value of below 0.001 [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNLRC5 is a unique member of the NLRC subfamily of the NOD-like receptor (NLR) family, characterized by a caspase recruitment domain (CARD) without an acidic domain [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. NLRC5 regulates the transcription of genes in T cells and contributes to the T cell response, primarily through complexes that lead to PANoptosis, involving mediators of pyroptosis, apoptosis, and necroptosis. This process occurs following heme exposure during inflammatory conditions [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Given that intestinal bleeding increases the heme levels in the mucosal layer [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], it can be assumed that the NLRC5-mediated response in T cells could be further amplified [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], underscoring the role of NLRC5 in IBD pathogenesis. In a study on intestinal mucosa, differential methylation analysis identified NLRC5 as a hypomethylated gene in patients with IBD compared to healthy controls [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, this study aimed to identify blood RNA markers with diagnostic potential in patients with IBD. FEZ1 exhibited acceptable diagnostic capability through the integrated transcriptomics approach applied. However, the findings of this study are based on computational analyses, and their clinical applicability has yet to be established. This study was limited by the lack of comprehensive biological information from patients, which hindered the accurate matching of case and control groups. Confounding factors, such as smoking history, gender, or age, may have implicitly influenced the results. Nevertheless, according to the phenotypic data retrieved by the GEOquery package, an initial phase of filtration aimed at minimizing disparities between groups was executed to ensure that blood constituted the sample origin and that no prior treatments were documented.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBy employing an integrated transcriptomic analysis on blood samples obtained from patients with IBD, this study aimed to unveil potential non-invasive diagnostic markers of the disease. TNF, the central hub gene of the PPI network of DEGs in IBD, has long been considered a therapeutic target. However, its downregulation in the blood suggests that TNF inhibition might be substantially beneficial when the inhibition is confined to the gastrointestinal tract rather than systemically. FEZ1 demonstrated promising potential as a blood biomarker of IBD, as both RF and LASSO machine learning methods highlighted it, and it passed the acceptable AUC threshold in ROC analyses in most datasets. Based on the current understanding of FEZ1 functionalities, the contribution of FEZ1 in autophagy is the most probable explanation for its dysregulation. Furthermore, considering the involvement of this gene in brain abnormalities, it can be hypothesized that FEZ1 may serve as a pathogenic link in the gut-brain axis. From a holistic perspective, several key genes identified, including PRF1 and NLRC5, reflect T cell functioning, underscoring their role in IBD pathogenesis. While the findings of this study fundamentally relied on bioinformatic analysis, prospective experimental studies are needed to assess the reproducibility of these results.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eData collection and preparation\u003c/h2\u003e\n\u003cp\u003eThe Gene Expression Omnibus (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e) database [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e] was explored to retrieve the blood transcriptome of IBD. To this end, a simultaneous search was conducted for Crohn\u0026rsquo;s disease, ulcerative colitis, or IBD, along with blood or PBMC. A total of eight datasets were extracted from GEO, published between 2005 and 2021 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGene expression data from the blood of patients with IBD\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGEO accession\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrohn\u0026rsquo;s disease\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUlcerative colitis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlatform\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eIntegrated data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE119600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPL10588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE94648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPL19109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE86434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPL10558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE71730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePBMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPL570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eValidation sets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE169568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPL10588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE112057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPL11154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE100833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPL13158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE3365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePBMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPL96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003cp\u003eAccording to the sample information retrieved using the GEOquery [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e] package, an initial stage of filtration was conducted to ensure that blood was the sample source, the disorders were IBD, and no prior treatment had been reported. In GSE119600, patients with either primary biliary cholangitis or primary sclerosing cholangitis were excluded from the samples. In GSE86434, only samples obtained from whole blood were included, and specific lineages of leucocytes, including na\u0026iuml;ve CD4 T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and CD14\u0026thinsp;+\u0026thinsp;monocytes, were excluded. In GSE169568, symptomatic controls were discarded. In GSE112057, samples with systemic juvenile idiopathic arthritis (JIA), oligoarticular JIA, or polyarticular JIA were removed. In GSE100833, samples that were derived from sources other than blood were excluded.\u003c/p\u003e\n\u003cp\u003eSubsequently, outlier detection was conducted using PCA and hierarchical clustering of samples by average linkage to enhance the significance of the findings. GSM2302588 and GSM3056505 were detected as outliers in GSE86434 and GSE112057, respectively, and were excluded from these datasets. Meanwhile, no obvious outliers were discerned in the remaining datasets.\u003c/p\u003e\n\u003cp\u003eBased on the number of genes available in datasets, GSE119600, GSE94648, GSE86434, and GSE71730 were selected and subsequently merged for further analyses. The SVA [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e] package was used to remove batch effects from the integrated dataset. Then, the distribution of samples within the PCA graph prior to and after batch-effect correction was illustrated using the scatterplot3d [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e] package. Ultimately, the integrated dataset encompassed 447 samples, including 189 individuals diagnosed with CD, 155 individuals diagnosed with UC, and 103 healthy subjects, with data on 16,512 genes for each sample.\u003c/p\u003e\n\u003ch2\u003eDifferential expression analysis\u003c/h2\u003e\n\u003cp\u003eConsidering the thresholds of |logFC| \u0026gt; 0.2 and adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, DEGs were separately determined in CD and UC using the limma [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e] package. Subsequently, upregulated and downregulated genes in both disorders were identified as DEGs in IBD for further analyses. The overlay of DEGs between CD and UC was displayed using the ggVennDiagram [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e] package.\u003c/p\u003e\n\u003ch2\u003eGene Ontology and KEGG pathway analyses\u003c/h2\u003e\n\u003cp\u003eTo determine the biological relevance of the DEGs in IBD, the GO and KEGG pathway analyses were conducted using the ClusterProfiler [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e] package. Terms that met the criterion of having an adjusted p-value less than 0.05 were considered significant, and the top 10 terms in each GO category were presented in the order of enrichment and significance.\u003c/p\u003e\n\u003ch2\u003eConstruction of PPI and miRNA-mRNA networks\u003c/h2\u003e\n\u003cp\u003eThe STRING [\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e] plugin of the Cytoscape v3.10.2 [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e] app was used to extract the PPI network of DEGs in IBD. The confidence threshold was set at 0.4, and subsequently, discrete and partially connected nodes were excluded from the resulting network. The network was analyzed to determine the hub genes, defined as the top decile of nodes based on their number of interactions. Nodes with fewer than six interactions were excluded to improve visualization efficiency.\u003c/p\u003e\n\u003cp\u003eThe hub genes in the PPI network of IBD were used as input in miRDB [\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e] to identify regulatory miRNAs, with the target score set at 80. Then, miRNAs targeting more than one hub gene, along with their target genes, established the final miRNA-mRNA network.\u003c/p\u003e\n\u003ch2\u003eWeighted Gene Co-expression Network Analysis (WGCNA)\u003c/h2\u003e\n\u003cp\u003eThe WGCNA [\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e] package was used to detect modules significantly correlated with IBD and to further filter DEGs. To this end, after selecting the optimal soft threshold (power), which has a scale independence exceeding 0.8 and a mean connectivity near zero, blockwise construction of modules was carried out. Then, the modules with a p-value less than 0.001 were considered significant. Pathway analysis of these modules was conducted using the Enrichr [\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e] online tool.\u003c/p\u003e\n\u003cp\u003eSince module 12 was significantly correlated with IBD and enriched for the disease, a PPI network was constructed for this module. After removing partially connected and discrete nodes, the network was analyzed, and hub genes ranking in the top 10 percent according to degree metrics were identified. Finally, KEGG pathway analysis was conducted, and significant pathways were illustrated using the circlize [\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e] package.\u003c/p\u003e\n\u003ch2\u003eIdentification of potential biomarkers of IBD by machine learning methods\u003c/h2\u003e\n\u003cp\u003eDEGs that demonstrated a consistent correlation with IBD through WGCNA were further analyzed using machine learning techniques. The decision-tree-based RF and LASSO regression methods were implemented using the randomForest [\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e] and glmnet [\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e] packages to identify potential biomarkers. The top 10 genes selected by each method were compared to identify candidate biomarkers common to both. These candidate biomarkers were subsequently evaluated through ROC analysis using the pROC [\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e] package.\u003c/p\u003e\n\u003ch2\u003eAssessment of the diagnostic efficacy of selected biomarkers in the validation datasets\u003c/h2\u003e\n\u003cp\u003eROC analysis was employed to assess the diagnostic efficacy of the candidate genes. The AUC was evaluated in each dataset that was part of the integrated data framework individually, as well as in external datasets, including GSE169568, GSE112057, GSE100833, and GSE3365.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) with the accession numbers GSE119600, GSE94648, GSE86434, GSE71730, GSE169568, GSE112057, GSE100833, and GSE3365.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author appreciates the contributions made by the authors of the\u0026nbsp;GSE119600, GSE94648, GSE86434, GSE71730, GSE169568, GSE112057, GSE100833, and\u0026nbsp;GSE3365.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.M.D.D. designed the research, acquired the data, conducted the analyses, interpreted the results, and prepared the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ede Mattos, B. 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To identify such a biomarker, an integrated transcriptomics approach was employed to detect RNAs exhibiting diagnostic efficacy. For this purpose, four gene expression datasets obtained from blood specimens were integrated (GSE119600, GSE94648, GSE86434, and GSE71730). After determining differentially expressed genes in Crohn\u0026rsquo;s disease and ulcerative colitis, DEGs in IBD were defined as genes with a consistent direction of alteration in both disorders. Mapping the PPI network for these genes revealed TNF as the central hub gene. Subsequently, weighted gene-expression network analysis (WGCNA) was carried out to determine IBD-specific modules. Considering the degree metrics, module membership, and gene significance, PRF1 was the only gene discerned as a hub gene in a module that was prominently enriched in IBD. Genes with converging results from differential expression analysis and WGCNA were subjected to the random forest decision tree-based and LASSO regression methods. Following the identification of FEZ1 and NLRC5 as genes highlighted by both analyses, ROC analysis was applied to assess their diagnostic potential. Although both genes demonstrated acceptable diagnostic efficacy in the integrated data, only FEZ1 was considered as a potential biomarker based on the replication of results in validation datasets (GSE119600, GSE94648, GSE86434, and GSE71730). While autophagy is currently the most convincing explanation for the involvement of FEZ1 in IBD, further investigations are required to elucidate its immunological role.\u003c/p\u003e","manuscriptTitle":"Blood transcriptomics identifies FEZ1 as a novel non-invasive diagnostic biomarker for inflammatory bowel disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-28 08:31:46","doi":"10.21203/rs.3.rs-5255906/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61b682db-d572-4baf-b73a-56d8ca257fe9","owner":[],"postedDate":"October 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39424065,"name":"Biological sciences/Genetics/Gene expression"},{"id":39424066,"name":"Biological sciences/Genetics"},{"id":39424067,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":39424068,"name":"Biological sciences/Computational biology and bioinformatics/Data integration"}],"tags":[],"updatedAt":"2025-02-03T18:35:55+00:00","versionOfRecord":{"articleIdentity":"rs-5255906","link":"https://doi.org/10.1016/j.compbiomed.2025.109742","journal":{"identity":"computers-in-biology-and-medicine","isVorOnly":true,"title":"Computers in Biology and Medicine"},"publishedOn":"2025-02-01 00:00:00","publishedOnDateReadable":"February 1st, 2025"},"versionCreatedAt":"2024-10-28 08:31:46","video":"","vorDoi":"10.1016/j.compbiomed.2025.109742","vorDoiUrl":"https://doi.org/10.1016/j.compbiomed.2025.109742","workflowStages":[]},"version":"v1","identity":"rs-5255906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5255906","identity":"rs-5255906","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}