Bioinformatics Analysis Reveals shared molecular pathways for Relationship between Ulcerative Colitis and Primary Sclerosing Cholangitis

doi:10.21203/rs.3.rs-5045747/v1

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Research Article

Bioinformatics Analysis Reveals shared molecular pathways for Relationship between Ulcerative Colitis and Primary Sclerosing Cholangitis

https://doi.org/10.21203/rs.3.rs-5045747/v1

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Background

Inflammatory bowel disease (IBD) is a group of chronic inflammatory disorders, including ulcerative colitis (UC) and Crohn’s disease, affecting the gastrointestinal tract and is associated with high morbidity and mortality. Accumulating evidence indicates that IBD not only impacts the gastrointestinal tract but also affects multiple extra-intestinal organs, which may manifest prior to the diagnosis of IBD. Among these extra-intestinal manifestations associated with IBD, primary sclerosing cholangitis (PSC) stands out as a prominent example. PSC is recognized as a progressive cholestatic disorder, characterized by the narrowing of bile ducts, eventual development of liver cirrhosis, end-stage liver disease, and the potential emergence of cholangiocarcinoma.

Aim of the study:

In this study, we aimed to identify the molecular contributors in UC-induced PSC by detecting the essential regulatory genes that are differentially expressed in both diseases.

Materials and Methods

We conducted a comprehensive bioinformatics analysis to detect UC and PSC-associated genes. The common differentially expressed genes (DEGs) and common single nucleotide polymorphisms (SNPs) were detected using the GEO and DisGeNET databases, respectively. Then, the top module and hub genes within the protein-protein interaction network were identified. Furthermore, the co-expression network of the top module was constructed using the HIPPIE database. Additionally, the gene regulatory network was constructed based on miRNAs and circRNAs. At last, we searched the DGIdb database for possible interacting drugs with UC-PSC top module genes.

Results

Our microarray dataset analysis identified 56 common DEGs between UC and PSC with significant enrichment in genes involved in mRNA processes such as mRNA splicing process, and RNA bindings. We also identified 291 common DEGs from the DisGeNET database, demonstrating only one common DEG, called PTPN2, with microarray dataset. In this study we also identified 122 common SNPs between UC and PSC. Additionally, the top cluster of PPI network analysis was consisted of PABPC1, SNRPA1, NOP56, NHP2L1, and HNRNPA2B1 genes. We constructed a ceRNA network involving 4 specific mRNAs, 94 miRNAs, and 200 selected circRNAs.

Conclusion

In conclusion, the present study provides novel potential candidate genes that may be involved in the molecular association between ulcerative colitis and primary sclerosing cholangitis, resulting in the development of diagnostic tools and therapeutic targets to prevent the progression of PSC from UC.

Inflammatory bowel disease

Ulcerative colitis

Primary Sclerosing cholangitis

Bioinformatics

Inflammatory bowel disease (IBD) is a public health issue which is associated with high morbidity and mortality. It has been reported that the global prevalence and burden of IBD increased over recent decades; The prevalence rate of IBD elevates from 79.5 to 84.3 per 100 000 population during 1990–2017 period (1). IBD is a chronic idiopathic disorder of the gastrointestinal (GI) tract, characterized by excess inflammatory responses in GI tract. Crohn’s disease (CD) and ulcerative colitis (UC) are two major categories of IBD (2). Accumulating evidence indicated that IBD not only affects the GI tract, but it also involves various extra-intestinal organs which may present before IBD diagnosis (3). Extra-intestinal manifestations (EIMs) can occur in up to 50% IBD cases (4), which 24% of those may manifest before the onset of intestinal features (3).

Primary sclerosing cholangitis (PSC) is one of the most IBD-associated EIMs, which is known as a progressive cholestatic disease. Triggering the inflammation and fibrosis of intrahepatic and extra-hepatic biliary tree contribute to the pathogenesis of PSC, which may eventually lead to strictures of bile ducts, liver cirrhosis, end-stage liver disease, and cholangiocarcinoma (5–7). The prevalence of PSC is about 2–8% and 3.5% among patients with UC and CD, respectively. In contrast, the presence of IBD, most frequently UC, has been reported in approximately 70% of patients with PSC (7, 8). A strong association between PSC and UC was found which may be resulted from interrelated underlying mechanisms. For instance, antibodies against cytoplasmic and nuclear antigens of neutrophils with a characteristic perinuclear staining pattern (p-ANCA) have been commonly found in both patients with UC and PSC (9). High-throughput DNA sequencing technologies and gene chip experiments have enabled high potential investigation fields to screen and compare differentially expressed genes (DEGs) in pathologic and normal tissue cells (10). Recently, growing body of large-scale genome-wide association studies (GWAS) have identified a strong role of genetic components in the pathogenesis of both PSC and IBD (11, 12). Furthermore, several studies have found the genome-wide genetic relationship between PSC and IBD, particularly UC (13, 14). However, the exact underlying mechanisms of the pathogenesis of UC-induced PSC have remained poorly understood.

Due to the strong evidence concerning UC and PSC association and limited data regarding the genomic relationship between them, we aimed to identify the various aspects of possible molecular correlations between these two conditions. In this way, we detected the common genes significantly differentiated (DEGs) and common SNPs in UC and PSC, using GEO and DisGeNET datasets, respectively. Then, functional enrichment analysis and protein-protein network construction were performed and top module was determined. Finally, long non coding RNAs (lncRNAs), competitive endogenous RNA (ceRNA) network, and co-expression network related to the genes of top module were assessed. Our study offers a comprehensive insight into the function of commonly expressed differentially expressed genes in inducing PSC following UC. Our analyses have the potential to pave the way for the development of novel pharmaceuticals and treatments that can more effectively target omics, as well as improve preventative care measures.

Common DEGs between UC and PSC were detected by analyzing two microarray datasets of GEO database. Following, functional enrichment analysis was performed for these common DEGs to determine whether these set of genes are significantly enriched for a particular biological process, molecular function, or cellular component. Also, we analyzed the protein-protein interaction (PPI) network to identify the top cluster proteins with strongest associations. Subsequently, we evaluated miRNAs, circRNAs, lncRNA, transcription factors, co-expression network, and drug-gene interaction which were previously found to be associated with the top clusters' genes. Then, ceRNA network was constructed. Additionally, common SNPs between UC and PSC were detected (Fig. 1).

Identification of common SNPs between UC and PSC (DisGeNET)

To detect common single nucleotide polymorphisms (SNPs), the UC and PSC were searched in DisGeNET database (https://www.disgenet.org) which is one of the largest available bioinformatics database and provides the genetic data about human disorders (15). The related DisGeNET IDs for UC and PSC were extracted as shown in Table 1. Moreover, the present study involved the isolation of UC and PSC-linked genes separately, followed by the identification of common genes associated with both conditions via pairwise analysis using the DisGeNET database. To identify SNPs associated with the risk of UC and PSC, the study employed the disgenet2r package, an analysis tool that allows the identification of relevant SNPs (15, 16). The common SNPs between UC and PSC were then selected using a DisGeNET cut-off score of > 0.5, while SNPs lacking specific gene names were excluded from further analysis. The study adopted two approaches: first, it determined the overlap between UC and PSC-associated SNPs and the genes obtained from UC and PSC pairwise analysis, and secondly, it investigated the correlation between UC-associated SNPs and PSC-associated SNPs.

Table 1

The list of selected disorders and their DisGeNET codes in the present study
No.	Disease name	Disease Abbreviation	DisGeNET IDs
1	Primary Sclerosing Cholangitis	PSC	C0008313 C0566602
2	Ulcerative colitis	UC	C0009324 C2919828 C3873482 C3843756 C0941057 C1260710 C3873482

Microarray mining for UC and PSC

To assess the common gene expression profile between UC and PSC, we used the data from Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo) which fulfilled the following inclusion criteria: (1) containing UC and/or PSC blood samples, and (2) minimum sample size of 10 in each group. The exclusion criteria were duplicated research, non-human studies, and studies with incomplete data. As a result, only GSE119600 and GSE944648 met our criteria. The GSE119600 (platforms: GPL10558 Illumina HumanHT-12 V4.0 expression beadchip) consisted of 45 PSC, 93 UC, and 47 healthy samples. Further, the GSE94648 (platforms: [HG-U133_Plus_2] Affymetrix Human Genome U133 plus 2.0 Array [CDF: Brainarray HGU133Plus2_Hs_ENTREZG_v18]) consisted of 25 UC and 22 healthy samples.

We pre-processed the CEL files of Affymetrix microarrays by utilizing the Affy package (http://bioconductor.org/packages/release/bioc/html/affy.html; version 1.74.0) and the Robust Multi-array Average (RMA) method (17) in R software (version 4.4.2; http://www.r-project.org/). The limma package (version 3.52.2) (18) in R software was used to screen DEGs between UC and PSC. The cut-off criteria for the identification of DEGs was a |log2 Fold change (FC)| value of ≥ 1 and adjusted P-value of < 0.05. Then, Venn diagram was drawn by jvenn tool (http://jvenn.toulouse.inra.fr/app/example.html), and the intersection between the datasets was shown.

Table 2

Characteristic of the studied microarray dataset
No.	GSE no.	GPL/Platform	Samples (n)			Sample type	Update (year)	Country	Total
			UC (n)	PSC (n)	HC (n)
1	GSE119600	GPL10558 Illumina HumanHT-12 V4.0 expression beadchip	44	45	47	Blood	2019	Poland	146
2	GSE94648	[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array [CDF: Brainarray HGU133Plus2_Hs_ENTREZG_v18]	25	-	22	Blood	2017	Spain	47
UC: Ulcerative colitis, PSC: Primary Sclerosing Cholangitis, HC: Healthy Control

Enrichment analysis

To evaluate DEGs’ potential functions and related pathways, gene ontology (GO) enrichment analysis including biological processes (BP), molecular functions (MF), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed, using Enrichr (https://maayanlab.cloud/Enrichr/) database. The threshold was a p-value < 0.05.

PPI network analysis, top cluster, and hub genes identification

STRING database (https://string-db.org) was used to construct PPI network and results were visualized using Cytoscape software (https://cytoscape.org; version 3.9.1). Further analysis to identify the top cluster among the network was performed by MCODE plugin based on the following parameters: MCODE scores > 5, degree cut-off = 2, node score cut-off = 0.2, max depth = 100, and k-score = 2. In this study, hub genes were defined as the nodes with a degree > 5, and were extracted.

MicroRNAs, and circRNAs Regulating Genes, and ceRNA network

To determine the miRNAs involved in regulating the expression of the top cluster genes, we utilize the miRTarBase database (https://mirtarbase.cuhk.edu.cn). Next, the circRNAs that target the identified miRNAs were determined. We downloaded the data of the circBank database (http://www.circbank.cn) and used SQL Server to detect certain circRNAs. Then, the top circRNAs were selected based on the total score (Tot Score). Subsequently, we constructed ceRNA network between mRNAs, miRNAs, and circRNAs by using Cytoscape software 3.9.1 (https://cytoscape.org) (19). We also used the cytoHubba package to determine the top ceRNA network, hub nodes, miRNAs, and circRNAs based on the degree method (20).

LncRNAs

The lncRNAs associated with regulating the expression of the top module genes were identified using the miRNet database, a miRNA-centric network visual analytics platform (https://www.mirnet.ca/). We extracted the experimentally validated miRNAs from the database miRTarBase, identified the lncRNAs associated with these miRNAs, and conducted a meticulous analysis of their overlap using the Genomics web tool. (https://bioinformatics.psb.ugent.be/webtools/Venn/).

Transcription factor

To detect the transcription factors (TFs) regulating the top cluster genes, we employed hTFtarget database (http://bioinfo.life.hust.edu.cn/hTFtarget#). Then, we identified the overlapped TFs between these genes using the Genomics web tool (https://bioinformatics.psb.ugent.be/webtools/Venn/).

Co-expression network

The data of the HIPPIE database (http://cbdm.uni-mainz.de/hippie/ ) was employed to retrieve the co-expression networks of the top cluster genes in UC and PSC. The HIPPIE resource serves as a comprehensive solution for both creating and analyzing protein-protein interaction networks pertinent to a particular research inquiry (21). The co-expression network was considered in colon and liver tissues. The HIPPIE confidence score was considered 0.01, and analysis was performed at a high confidence level (> 0.73). Also, we used the Cytoscape software 3.9.1 to construct the network.

DGIDB

In present study, the interactions between drugs and genes of top cluster were assessed by using Drug Gene Interaction Database (DGIdb) (https://www.dgidb.org/), which provides data on drug-gene interactions from publications, databases, and other web-based sources.

Identification of Common Risk SNPs between UC and PSC

Utilizing the DisGeNET database, we identified 1533 and 452 genes associated with UC and PSC, respectively (Supplementary File1). Upon further analysis, it was found that a total of 291 genes were common to both UC and PSC. In addition, the study identified 475 SNPs associated with UC and 311 SNPs associated with PSC, both having a DisGeNET score over 0.5. Furthermore, 151 common SNPs were identified which were belonged to common genes. (Supplementary File1).

Determination of common DEGs between UC and PSC

The microarray data were extracted from the GEO database to detect the DEGs in the two selected diseases. Based on the inclusion/exclusion criteria, GSE119600 and GSE169568 were selected, consisting of gene expression profiles in UC, PSC, and normal tissue. According to adjusted P-value < 0.05 and log |FC| >1 as the cut-off criterion, 4189 UC-related DEGs were detected from expression profile datasets of GSE119600 and GSE169568, and 1384 PSC-related DEGs were obtained from expression profile datasets of GSE119600, (Supplementary File1). There were 157 common DEGs identified among two UC GSE datasets (Fig. 2A) which by integrated bioinformatics analysis, 56 common DEGs were significantly differentiated in both UC and PSC patients (Fig. 2B and Supplementary File1). Additionally, pairwise analysis was conducted to identify the overlapped genes between the DisGeNET database and microarray datasets. The analysis revealed 11 genes that were common to both the DisGeNET database (Fig. 2C) and microarray datasets for UC, and 22 genes that were common for PSC (Fig. 2D). Furthermore, PTPN2 was identified as a common gene between UC and PSC presenting in both the DisGeNET database and microarray datasets (Fig. 2E). The PTPN2 gene was downregulated in both UC and PSC. Also, we found two SNPs related to PTPN2 gene, including rs62097857 rs12968719, using DisGeNET database.

Functional Enrichment Analysis

The potential biological function of common DEGs was evaluated by GO and KEGG pathway enrichment analyses, and components with P-values < 0.05 were selected .A total of 207, 48 and 8 significant BP, MF, and KEGG pathways were identified (Supplementary File 1). Among the related biological processes, mRNA splicing processes (GO: 0000398 and GO: 0000377) and mRNA processing (GO: 0006397) were mostly associated with the common DEGs which YTHDC1, U2AF2, HNRNPA2B1, SNRPA1, SNU13, and PABPC1 were in these biological processes. Also, common DEGs significantly have roles in regulation of Innate Immune Response which BANF1, CEP63, and PTPN2 had impact on Negative Regulation Of Innate Immune Response (GO:0045824) and CFH, BANF1, and CEP63 had impact on Regulation Of Innate Immune Response (GO:0045088). Moreover, these genes were mainly enriched in RNA bindings (GO:0003723 and GO: 1990247), mRNA binding (GO:0003729) as molecular functions. The KEGG pathway analysis showed that common DEGs were mostly correlated with spliceosome and mRNA surveillance pathway. The top 10 significant BP and MF and 8 KEGG pathways are shown in Fig. 3A-C.

PPI and hub genes identification

The PPI network of common DEGs including 56 nodes and 46 edges was constructed using data from STRING (Fig. 4A). Also, top module with 5 nodes and 10 edges was identified (Fig. 4B). Genes with degree > 5 were identified as hub genes which were PABPC1, HNRNPA2B1, SNU13, NOP56, and SNRPA1.

ceRNA network construction

Using mirTarbase databases, we identified the 94 potential miRNAs involved in regulating the top cluster genes. No miRNAs identified regulating HNRNPA2B1. Furthermore, 200 circRNAs were detected using circBank database, which are related to the 94 potential miRNAs. The ceRNA network was constructed by the 4 mRNAs, 94 miRNAs, and 200 circRNAs (Fig. 5A). The 30 hub nodes identified by cytoHubba based on degree method includes PABPC1 (score: 49), hsa-miR-6884-5p (score: 34), NHP2L1 (score: 27), hsa-miR-4728-5p (score: 25), hsa-miR-1273g-3p (score: 22), hsa-miR-23b-3p (score: 18), SNRPA1 (score: 16), hsa-miR-6797-5p (score: 15), hsa-miR-1249-5p (score: 11), hsa-miR-181a-5p (score: 9), hsa-miR-3202, hsa_circ_0037995, hsa_circ_0037996, and hsa_circ_0037997 (score: 8), hsa-miR-4510 (score: 7), hsa-miR-6133, hsa-miR-6127, hsa-miR-6129, hsa_circ_0041089, hsa_circ_0041099, hsa_circ_0041116, hsa-miR-6883-5p, hsa-miR-6785-5p, and hsa-miR-149-3p (score: 6), hsa-miR-6794-5p, hsa-miR-4716-3p, hsa_circ_0097830, hsa_circ_0138676, hsa_circ_0106143, and hsa_circ_0106142 (score: 5) (Fig. 5B).

Of 94 identified miRNAs, 38 miRNAs were found to be associated with 590 lncRNAs, using the information form miRNet database. We performed an analysis to detect common lncRNAs between those related to each miRNAs, which no common lncRNA was found.

Transcription Factors

We investigated the possible transcription factors for top module genes using the hTFtarget database. As shown in Fig. 6A, we found 224 TFs for HNRNPA2B1, 169 TFs for NHP2L1, 158 TFs for NOP56, 182 TFs for SNRPA1, and 192 TFs for PABPC1, which may be involved in regulating the expression of these genes (Supplementary File1). Further analysis showed that 90 TFs were common between selected genes (Fig. 6B).

Co-expression network

The co-expression network between the PABPC1, SNRPA1, NOP56, NHP2L1, and HNRNPA2B1 hub genes was identified by using data from HIPPIE database (Fig. 7). This co-expression network revealed that 3 main subnetworks that are linked together by several genes. Also, two genes, including CUL3 and DHX15, were found to be associated with all 3 main subnetworks; these associations were stronger for DHX15 gene. DHX15 is a gene that encodes a putative ATP-dependent RNA helicase, which is reported to play a role in pre-mRNA splicing (22). DHX15 belongs to the RNA helicase family and is involved in numerous biological processes. Moreover, the CUL3 gene is responsible for coding the cullin-3 protein. Cullin-3 is a vital component of an E3 ubiquitin ligase complex that functions through the tagging of excess and defective proteins with ubiquitin molecules, making them recognizable for proteasomes, which lead to their degradation. Additionally, this system regulates the levels of proteins that are involved in critical cellular processes, including the timing of cell division and growth (23, 24).

Drug-Gene interaction result

Based on the results from DGIdb, we identified a total of 4 drugs which could potentially have an interaction with PABPC1 gene, including CHEMBL585964, CHEMBL592588, DITOLYLGUANIDINE, and CHEMBL589711 (Fig. 8). Furthermore, no drugs were identified which have an interaction with SNRPA1, NOP56, and NHP2L1. Moreover, we identified 8 potential interacting drugs with PTPN2 gene, including Dipicolinic-Acid, CHEMBL 601167, Cloxyquin, CHEMBL 598477, CHEMBL 28721, CHEMBL 599924, CHEMBL 601757, CHEMBL 578512.

Primary sclerosing cholangitis (PSC) is one of the most important IBD-associated extra-intestinal manifestations (EIMs), which is mostly linked with ulcerative cholangitis (UC), and it is estimated that about 75% of PSC patients also have IBD, primarily UC (25). However, the exact molecular relationship between IBD and PSC remains unclear. Recent GWAS studies indicated that several genes, which are mostly implicated in immune responses, are associated with these disorders (26). This strong connection brought us to design a study to explore the molecular relationship of these two conditions using bioinformatics data. According to the DisGeNET and GEO database, we respectively identified 157 and 56 common DEGs between UC and PSC, and only the PTPN2 gene was common between UC and PSC in both the DisGeNET database and microarray datasets. Also, we identified 22 common SNPs between UC and PSC from the DisGeNET database. Based on our functional analysis, the common DEGs were mostly involved in mRNA processing, mRNA splicing processes, RNA bindings, and mRNA binding.

The protein tyrosine phosphatase nonreceptor 2 (PTPN2) gene encodes for the PTPN2 protein, also called T cell tyrosine phosphatase (TCPTP). PTPN2 protein is a member of the protein tyrosine phosphatases (PTP) family, which regulates the functional activity of their targets through the process of tyrosine phosphorylation, resulting in activating or deactivating cell signaling molecules (27). This protein is involved in a variety of cellular substrates, including protein tyrosine kinase targets such as the insulin receptor, the epidermal growth factor receptor (EGFR), Src family kinases, as well as multiple types of Janus kinases (JAK), and signal transducer and activator of transcription (STAT) family members (28, 29). Therefore, PTPN2 plays an important role in cell proliferation, differentiation, growth, mitotic cell cycle, and oncogenic transformation (30). Additionally, PTPN2 dephosphorylates T cell receptor kinases, which modifies immune reactions and cellular responses to inflammation, notable in the intestine (31, 32). Several studies have identified PTPN2 as a susceptibility gene for IBD and variations in this gene have been associated with an increased risk of developing chronic inflammatory and autoimmune diseases, such as celiac disease (33), IBD (including both Crohn's disease and ulcerative colitis) (33, 34), and PSC (35). These genetic variations may affect the expression or function of PTPN2, leading to dysregulated immune responses and increased susceptibility to IBD. For instance, a loss-of-function mutation in the PTPN2 gene in T cells can lead to enhanced expression and differentiation of naive T helper cells into Th1 and Th17 subtypes while simultaneously reducing the expression of T regulatory cells. Indeed, patients with PTPN2 variants exhibited elevated levels of pro-inflammatory cytokines, such as INF-γ, IL17, and IL22 in both their serum and intestinal mucosal layers (36–38).

Two SNPs of PTPN2, namely rs2847297 and SNP rs12968719, have been suggested to be associated with higher risk for PSC (39, 40). Moreover, a significant correlation between intestinal dysbiosis and exacerbated conditions in individuals with IBD has been unveiled regarding the malfunctioning of PTPN2 caused by the SNP rs1893217 (41). Therefore, given the autoimmune inflammatory nature of both UC and PSC, it seems to be reasonable to suggest that the PTPN2 gene, which significantly influences the inflammation process, is involved in the PSC development in patients with UC. Another study investigated the potential association between the presence of SNP rs1893217 in the PTPN2 gene and the occurrence of intestinal dysbiosis in patients with UC and PSC. The findings revealed importance of the gut mucosa-associated microbiome in PSC patients and suggested that the role of PTPN2 rs1893217 as a genetic risk factor for PSC (42). Our analysis demonstrated two SNPs related to PTPN2, including rs62097857 and rs12968719. However, there was no available study regarding the role of these SNPs in UC and/or PSC.

In the next step, we constructed PPI network for common DEGs that 5 genes were involved in the top module, including PABPC1, SNRPA1, NOP56, NHP2L1, and HNRNPA2B1 which all have an almost common duty in genome expression, and RNA modification. The PABPC1 gene (Poly (A) binding protein cytoplasmic 1), located within chromosome region 8q22.2–23, acts as an RNA binding protein through attaching to the poly(A) tail on mRNA to facilitate the processes of pre-mRNA splicing and maintain the mRNA stability (43). Importantly, PABPC1 was found to participate in an inhibitory translational complex to suppress the overexpression of pro-inflammatory mediators in activated macrophages (44). On the other hand, PABPC1 overexpression has also been linked to cancer progression (45, 46) and mediating cell stress responses (47). The SNRPA1 gene encodes a protein named small nuclear ribonucleoprotein polypeptide A', which plays a critical role as a constituent of the U2 small nuclear ribonucleoprotein (snRNP). This complex identifies the branch-point site in the initial stages of pre-mRNA splicing (48, 49). Additionally, SNRPA1 has been found to inhibit the formation of R-loops, thereby supporting DNA repair processes (48). Previous research has demonstrated that SNRPA1 functions as a component of the spliceosome and is involved in reprogramming of pluripotent stem cells (50). In a study, SNRPA1 was identified as a remarkable factor in inactivating p53, a tumor suppressor gene, in colorectal cancer (51). The NOP56 gene plays a pivotal role in processing ribosomal RNA (rRNA) precursors. Reduced expression of NOP56 can impede rRNA biosynthesis, thereby compromising the overall efficiency of rRNA synthesis (52). Reducing NOP56 expression significantly decreases the growth rate of lung, pancreatic, and colorectal cancer cells harboring KRAS mutations (53).

The NHP2L1 gene plays crucial roles in telomere maintenance and ribosome biogenesis, both of which are essential for cell viability and proper cellular function (54). Indeed, protein NHP2L1 was discovered to interact with the U4 RNA, hence, is involved in spliceosome assembly, splicing, and gene expression (55). Dysfunction or mutations in the NHP2L1 gene can lead to telomere shortening, genomic instability, and impaired protein synthesis, potentially contributing to the development of various diseases, including cancer and genetic disorders (56). The HNRNPA2B1 gene encodes a critical RNA-binding protein, heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1). This protein plays a key role in "reading" RNA methylation modifications and actively participates in downstream processes such as RNA translation and degradation (57). As a result, HNRNPA2B1 plays a multifaceted role in various cellular processes, such as telomere function, RNA biology, splicing, correct localization of transcripts, and loading of exosomes (58). A recent study demonstrated that HNRNPA2B1 exacerbates inflammation by enhancing M1 macrophage polarization, which is mediated by regulating mRNA stability (59). Another investigation showed the role of the HNRNPA2B1 gene in N6-Methyladenosine (m6A) modification of mRNAs, contributing to IBD pathophysiology (60). Despite the lack of research in examining these 5 genes in both UC and PSC, the biological function of these genes, alongside our findings, suggests the potential role of these genes in the UC and PSC relationship.

In further analysis, we identified the 94 potential miRNAs involved in regulating the genes of the top cluster and 200 circRNAs related to the potential miRNAs. We constructed the ceRNA network by the identified mRNAs, miRNAs, and circRNAs and detected 30 RNA molecules with the highest score in this network. Of 94 identified miRNAs, 38 miRNAs were found to be associated with 590 lncRNAs.

A growing body of evidence indicated the essential role of ncRNAs and ceRNA networks in various biological processes, including cell proliferation and growth, apoptosis, immune response, and tissue homeostasis. Therefore, dysregulation of them could contribute to the development of human diseases (61, 62). Regarding UC, the first miRNA profiling study was conducted in 2008 on colonic tissues of IBD patients. These workers identified eleven miRNAs, which showed differential expression compared to the controls. Among them, 3 and 8 miRNAs were significantly downregulated and upregulated, respectively (63). Furthermore, Xu S. et al. showed that the circRNA- and miRNA-associated ceRNA network plays a crucial role in various aspects of UC, including the regulation of inflammatory response, immune response, cell proliferation, apoptosis, and progress to tumor formation. This network's involvement in these processes suggests its significance in the pathogenesis and progression of UC (64). According to the findings of a study by Ouyang et al., circ_0001187 could exacerbate TNF-α-induced inflammation injury in human normal colorectal mucosa cells through affecting the MYD88 pathway via its interaction with miR-1236-3p (65). As we identified hsa-miR-23b-3p as a hub node in our study, Fasseu et al. found this miRNA as a differentially expressed miRNAs in human CD colonic tissue (66). Also, hsa-miR-149-3p which is among 30 hub nodes in the present study, showed a significant decrease in both active and inactive CD patients compared to the healthy controls in Wu et al.’s study (67). Povero et al. conducted a study using whole miRNA-sequencing analysis and identified more than 100 different miRNAs in extracellular vesicles derived from patients with PSC. Among these miRNAs, eleven were found to be differentially expressed in extracellular vesicles from PSC patients. The top five down-regulated miRNAs were miR-7113-5p, miR-4715-5p, miR-221-5p, miR-4444, and miR-150-3p. Conversely, the top upregulated miRNAs included miR-3183, miR-192-5p, miR-122-5p, miR-4465, miR-4784, and miR-4645-3p (68). These differentially expressed miRNAs may provide valuable insights into the molecular mechanisms underlying PSC and potentially serve as diagnostic or therapeutic targets in the future. Nevertheless, none of these miRNAs have been identified as a hub node in our study, which might bring the idea that different miRNAs are involved in connecting PSC to UC. The ongoing research has expanded our knowledge of miRNA and circRNA diversity and their functional significance in various biological processes and diseases. The continuous exploration of miRNAs and circRNAs is crucial for gaining a comprehensive understanding of their regulatory roles and potential therapeutic applications.

We further identified the co-expression network between the hub genes consisting of 3 main subnetworks linked together by several genes. The CUL3 and DHX15 genes were found to be associated with all 3 main subnetworks; these associations were stronger for the DHX15 gene. DHX15 is a gene that encodes a putative ATP-dependent RNA helicase, which is reported to play a role in pre-mRNA splicing (22). DHX15 belongs to the RNA helicase family and is involved in numerous biological processes. Moreover, the CUL3 gene is responsible for coding the cullin-3 protein. Cullin-3 is a vital component of an E3 ubiquitin ligase complex that functions through the tagging of excess and defective proteins with ubiquitin molecules, making them recognizable for proteasomes, which leads to their degradation. Additionally, this system regulates the levels of proteins involved in critical cellular processes, including the timing of cell division and growth (23, 24).

According to our findings, several drugs, including CHEMBL585964, CHEMBL592588, Ditolylguanidine, and CHEMBL589711were found to interact with “PABPC1” gene, one of our top cluster genes. Ditolylguanidine is a nonselective sigma receptor agonist which binds to both sigma₁ and sigma₂ receptors with equal affinity (69). In a study, Johannessen et al. focused on exploring how σ-receptors modulate the voltage-gated sodium channel (Nav1.5) in the heart. Ditolylguanidine as a sigma_1-2 receptor ligand reversibly inhibited Nav1.5 channels to multiple degrees in human embryonic kidney 293 (HEK-293) cells and COS-7 cells (70). Despite the novel finding of this study regarding the potential of the Ditolylguanidine to be utilized in context of regulating PABPC1 gene, according to the literature, no study has been conducted in this area. Also, there is no available evidence regarding other detected drugs in our analysis.

Besides the top cluster genes, we extracted possible gene-drug reactions for PTPN2 as the only common DEG between the DisGeNET and microarray datasets and identified 8 potential interacting drugs with this gene. Between these drugs, Cloxyquin is a specific activator of the two-pore domain potassium channel TRESK [TWIK-related spinal cord K + channel (also known as K2P18.1)], which is linked to typical migraine occurrence and neuronal excitability control (71). In fact, TRESK functions to maintain membrane potential in various cells. In this regard, a recent study revealed the pivotal role of physiological TRESK function in the differentiation of T-cells toward regulatory subtypes, thus presenting a novel pharmacological target with potential implications for the treatment of autoimmune disorders. Also, TRESK is a crucial mediator in translating the signal from the T-cell receptor during the selection process of thymus-derived regulatory T-cells (Treg), ultimately influencing Treg development and function (72). Hence, the activation of this channel by the compound Cloxyquin demonstrates promise as an initial candidate for the development of a novel class of immunomodulatory agents.

At last, our study was constrained by the absence of experimental data to validate these findings and the limited availability of datasets. Although additional verification is needed, our research offers significant insights into potential genes that might play a role in the molecular connection between UC and PSC. This could facilitate the development of diagnostic tools and treatment targets for these conditions.

BP Biological Process

CD Crohn’s Disease

CeRNA Competitive Endogenous RNA

DEG Differential Expressed Gene

DGIdb Drug Gene Interaction Database

EGFR Epidermal Growth Factor Receptor

EIMs Extra-Intestinal Manifestations

FC Fold Change

GEO Gene Expression Omnibus

GI Gastrointestinal

GO Gene Ontology

GWAS Genome-Wide Association Studies

HNRNPA2B1 Heterogeneous nuclear ribonucleoprotein A2/B1

IBD Inflammatory Bowel Disease

JAK Janus Kinases

KEGG Kyoto Encyclopedia of Genes and Genomes

MF Molecular Function

PPI Protein-Protein Interaction

PTP Protein Tyrosine Phosphatases

PSC Primary Sclerosing cholangitis

RMA Robust Multi-array Average

SNPs Single Nucleotide Polymorphisms

STAT Signal Transducer and Activator of Transcription

TCPTP T cell tyrosine phosphatase

TF Transcription Factor

UC Ulcerative Colitis

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

The in silico analyses conducted in this study, as well as the datasets presented, can be accessed through online repositories. The article provides the names of these repositories, along with the corresponding accession numbers, where the data and analyses are available.

Competing interests

The authors declare that they have no competing interests.

Funding

Not applicable.

Authors' contributions

Conceptualization and Methodology: PJ and ZS; Project Administration: ZS; In Silico Data Collection and Analysis: PJ, MR, MP, MRZ, SA ; Draft Writing: AY, MR ; Draft Review and Editing: ZS. All Authors Approved the Final Version to be published; They All Agreed to be Accountable for All Aspects of the Work.

Acknowledgments

The authors would like to acknowledge the mentioned databases, which were instrumental in providing comprehensive and up-to-date data and played a crucial role in our data processing and analysis and allowed us to carry out an in-depth survey and generate valuable insights.

Conflicts of Interest

There is no conflict of interest.

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Bioinformatics Analysis Reveals shared molecular pathways for Relationship between Ulcerative Colitis and Primary Sclerosing Cholangitis

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Materials and Methods

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Competing interests

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Authors' contributions

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