Unveiling the Molecular Landscape: Network Analysis of Genes, Proteins, and Transcription Factors in Primary Progressive and Secondary Progressive Multiple Sclerosis for Unraveling Disease Progression and Prognostic Factors

doi:10.21203/rs.3.rs-3144857/v1

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Unveiling the Molecular Landscape: Network Analysis of Genes, Proteins, and Transcription Factors in Primary Progressive and Secondary Progressive Multiple Sclerosis for Unraveling Disease Progression and Prognostic Factors

https://doi.org/10.21203/rs.3.rs-3144857/v1

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Multiple sclerosis (MS) is a chronic autoimmune disease characterized by the destruction of the myelin sheath in the central nervous system (CNS), leading to various neurological symptoms. The disease has different types, including relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), primary progressive MS (PPMS), and progressive-relapsing MS (PRMS), each with its own clinical characteristics and prognosis. The exact cause of MS is not known, but it is believed to result from a complex interaction between genetic and environmental factors. This study offers a comprehensive analysis of individuals with varying disease durations in multiple sclerosis, comparing those who experienced earlier mortality with those who lived longer. By elucidating the factors influencing disease progression and severity, we anticipate that our findings will contribute to the advancement of knowledge in the field, with the potential to inform future research and clinical practices aimed at improving patient outcomes in MS. In this study, we used microarray data from postmortem brain tissue samples available from NCBI and used a system biology approach to identify differentially expressed genes (DEGs) associated with PPMS and SPMS. We performed protein-protein interaction (PPI) network analysis to identify common proteins and modules involved in the pathogenesis of the two diseases. Additionally, we analyzed the interaction between transcription factors (TFs) and DEGs to identify potential regulatory mechanisms. Furthermore, gene ontology analysis was conducted to investigate the biological processes and pathways affected by the DEGs. Our analysis identified a total of 153 common DEGs between PPMS and SPMS. These DEGs were involved in various biological processes such as cell adhesion, regulation of apoptotic process, inflammatory response, and protein phosphorylation. The PPI network analysis revealed key proteins, including MSN, ROS1, CD4, and NR4A1, which may play important roles in the pathogenesis of both diseases. We also identified TFs that interacted with the DEGs, highlighting their potential regulatory roles. Our study provides insights into the molecular mechanisms underlying PPMS and SPMS. By identifying common DEGs, PPIs, and TFs, we contribute to the understanding of shared pathways and potential therapeutic targets for these neurodegenerative disorders. Further research is warranted to validate and explore the functional significance of these findings.

multiple sclerosis

protein-protein interactions

differentially expressed genes

transcription factors

SPMS

PPMS.

Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system (CNS) (1). It is characterized by the destruction of the myelin sheath that surrounds and protects nerve fibers, resulting in various neurological symptoms (2, 3). MS affects people of all ages, races, and ethnicities, with a higher prevalence in women than in men (4, 5). There are four types of MS, each with different clinical characteristics, disease courses, and prognoses. These types include relapsing-remitting MS (RRMS): This is the most common type of MS, accounting for approximately 85% of cases. It is characterized by recurrent episodes of neurological symptoms (relapses) followed by periods of partial or complete recovery (remission). Secondary progressive MS (SPMS), a type of MS which typically develops in individuals with RRMS after several years. It is characterized by a gradual worsening of symptoms and disability, with or without relapses. The primary progressive MS (PPMS) accounts for approximately 10% of cases and is characterized by a gradual worsening of symptoms and disability from the onset, without relapses. The progressive relapsing MS (PRMS) is the least common type of MS, accounting for less than 5% of cases. It is characterized by a steady worsening of symptoms and disability from the onset, with occasional relapses (6, 7, 8).

MS affects an estimated 2.8 million people worldwide, with a higher prevalence in northern European countries and North America (9). The prevalence of MS varies widely across different populations, with the highest rates reported in Scandinavia and Canada and the lowest rates in Asia and Africa (10, 11). In the United States, MS affects approximately 1 in 1,000 people, with a higher prevalence in women than in men (12).

The exact cause of MS is unknown, but it is believed to result from a complex interaction between genetic and environmental factors. Accordingly, several genes have been implicated in the development of MS, including HLA-DRB1 and, CD226 (12) (13). Regarding environmental factors vitamin D deficiency, smoking, and Epstein-Barr virus infection (14, 15) were linked to the disease. Besides, MS is believed to result from a dysregulation of the immune system, leading to an attack on the myelin sheath and nerve fibers(16). MS is also followed by neurodegeneration, which may contribute to the development of disability over time (17, 18).

By analyzing the networks of gene regulation and protein-protein interactions, it is possible to pinpoint central regulatory components, collections of important molecular variables, and activity modules in various diseases (19, 20, 21). An important source to discover more about cellular function involves the interactome, which measures protein-protein interactions (PPI) at the whole genome level (22). For instance, it has been proposed that genes associated with a disease often interact with one another and frequently form a cluster or module in the PPI network (23, 24).

Global gene expression data from patient postmortem (PM) tissue samples provided the main source of information for studying gene-gene, gene-protein, and protein-protein interactions in whole organism. However, in each of these researches, only one particular condition was taken into consideration at a time, which restricts our ability to understand and compare data from many disorders. Therefore, it would be intriguing to examine all of these diseases at once and compare the underlying molecular pathways, even if there are apparent distinctions in neurodegenerative defects at the molecular level. The discovery of common pathways and variables involved in the onset and progression of many forms of neurodegenerative disorders might be especially aided by such approaches.

In this study, we aimed to investigate the factors contributing to the progression and severity of multiple sclerosis (MS) by comparing individuals who succumbed to the disease at an earlier stage with those who lived longer. By analyzing the data of patients who experienced different disease durations, we sought to identify potential mortality factors and shed light on the underlying mechanisms influencing the course of MS. Here, we have used microarray data gathered from NCBI cases of PM brain samples to use a system biology approach to identify numerous facets of the major neurodegenerative disorders. This study aimed to compare the disease duration in two types of MS and identify potential factors associated with mortality. Understanding the factors that contribute to disease progression and mortality is crucial for developing effective management strategies and improving patient outcomes. Our analysis has identified important genes that may play a role in the occurrence of diseases such as MS. In order to identify potential common mechanisms among these disorders, we have also analyzed the relevant biological pathways and processes among the various types of MS. We also demonstrated a shared protein across two disease-related PPI networks. Additionally, we identified transcription factors (TFs) that may be significant for each condition. Finding some of the reasons for the disease's severity and progression may be aided by this approach. While previous research has investigated various clinical and demographic factors associated with mortality in MS, the impact of disease duration on survival remains relatively understudied. By specifically examining the relationship between disease duration and mortality in PPMS and SPMS, this study aims to shed light on this important aspect of MS progression. By comparing the disease duration between two MS subtypes of PPMS and SPMS, we can gain insights into their distinct disease trajectories and potentially identify factors that contribute to differences in mortality rates.

Data preparation and gene expression analysis

We have used GEO accession number GSE131282 (25) as the primary source of gene expression data. In our work, we considered patients with disease duration of 54 and 47 years in PPMS and SPMS, respectively, as control groups and disease duration of 4 and 6 years in PPMS and SPMS, respectively, as treatment groups. Then by the use of GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool, we created differentially expressed genes (DEGs). Eventually, for every gene list, we removed DEGs which had a p-value of more than 0.05, and log 2 FC between ± 0.6. DEGs of the diseases were checked manually to remove duplicate values, ambiguous names, or missing symbols, which resulted in a DEGs list of unique genes for each disease, and final lists were prepared to use for the next analysis.

Protein-protein interaction networks analysis

Protein-protein interaction (PPI) networks show proteins encoded by genes physically or functionally interact with each other (26). To determine the PPI, the STRING database was utilized in a way that the list of DEGs was deposited in this database, and output, the PPI network, was received (27). To analyze PPI networks, Cytoscape 3.4.0 and Gephi software v 0.9.1 were used for the visualization of networks. To detect the more significant genes, the top 10 percent of nodes that had the highest degree were considered as hub genes. To detect the sub-networks ClusterONE 1.0 plugin, one of the Cytoscape plugins was applied by these limits: minimum size: 5, density: 0.6, p-value < 0.01. Eventually, common proteins between the two networks were determined.

TFs analyzing the list of DEGs

TFs are involved in the regulation of sets of genes, and it is possible that different sets of genes share similar TFs (28). To this end, the ChEA database includes information on protein-DNA interactions that were obtained via ChIP-X tests and were used to make the gene regulatory network to investigate the interaction between TFs and genes. For this purpose, TFs were filtered with p values below 0.05 and pairs of TF target interactions, networks were built, and centrality analysis was then carried out on the network.

Gene ontology analysis

David Database was used to classify DEGs in the involved biological processes and pathways (29). Upregulated and downregulated genes were deposited to the DAVID database separately. Also, the results with p-values greater than 0.05 were deleted, and among the remaining results ten top biological processes harboring the greatest number of DE genes were selected to plot and construct the network.

DEGs in PPMS and SPMS

Samples of patients and controls were compared to identify DEGs in each disease. In the case of PPMS, the number of DEGs was 845 genes, of which 707 were upregulated and 138 were downregulated. While, 840 genes were differentially expressed in SPMS, of which 356 were upregulated, and 484 were downregulated (Fig. 1a). To determine the presence of similar DEGs among these two types of diseases, we have used a Venn diagram to show all overlapping instances (Fig. 1b). As it is represented In Table 1, due to differences in both diseases, we expected to obtain a big variation in the counts of DEGs between the two diseases. Finally, 153 genes were commonly expressed in these two diseases (Fig. 1c). The complete list of common DEGs is presented in Supplementary Table S1.

Among the DEGs shown in Supplementary Table S1, 27 genes were down-regulated (red) (given in Table S2), and 80 genes were up-regulated (violet) (presented in Table S3) in both diseases. However, 46 genes showed an increase in expression in one disease and a decrease in another disease (blue) (Table S4).

PPI network analysis

String database was used through the “ClusterOne” plugin in Cytoscape to identify the possible network modules. It was concluded that in PPMS, two modules related to the translation and regulation of apoptotic process (Fig. 2a) were present, while in SPMS, seven modules were present that are related to the G-protein coupled receptor signaling pathway and extracellular matrix organization (Fig. 2b). Gephi v software was also used to construct the network and present the hub nodes. Then, by comparing the two networks, the common proteins, and their functions were extracted (Fig. 2c).

Annotation and functional enrichment analysis

Gene Ontology analysis (GO) reflects how genes are related to a biological process, organ, function, or pathway(30). The DEGs were submitted to the DAVID database to identify biological processes and pathways. Up-regulated genes were associated with cell adhesion and negative regulation of apoptotic process in PPMS (Fig. 3a). Among the genes that have been down-regulated, the major process is spermatogenesis and cell adhesion (Fig. 3b). On the other hand, Up-regulated genes in SPMS were related to cell adhesion and inflammatory response (Fig. 3c), while down-regulated genes were involved in protein phosphorylation (Fig. 3d).

Also (Fig. 4) illustrates the GO analysis results and the differentially expressed genes (DEGs) in both primary progressive MS (PPMS) and secondary progressive MS (SPMS), including UP-regulated Genes in PPMS (Fig. 4a), Down-regulated Genes in PPMS (Fig. 4b), UP-regulated Genes in SPMS (Fig. 4c), and Down-regulated Genes in SPMS (Fig. 4d). By examining the differential gene expression patterns and their corresponding Gene Ontology annotations in both PPMS and SPMS, we gain insights into the molecular mechanisms and biological processes underlying the progression of these different types of multiple sclerosis. Based on the obtained results, 3 of the top five pathways were common in the UP-regulated Genes, including inflammatory response, cell adhesion, and immune response.

Prediction of TFs for the list of DEGs

Using the ChEA data set of the Enrichr server, we have identified those TFs that interact with the list of DEGs. Our results predicted 10 TFs for PPMS and 11 TFs for SPMS as major regulators of the gene lists (Fig. 5a,b) and (Tables 1,2). Also, common TFs between these two diseases include TP53, RUNX3, NCF4, etc. which have been shown in (Fig. 5c).

Multiple sclerosis is a complex autoimmune disease characterized by chronic inflammation, demyelination, and neurodegeneration within the central nervous system (CNS). It affects over 2.8 million people worldwide and poses significant challenges to patients, healthcare providers, and researchers alike (31, 32).

The clinical course of MS can be classified into different subtypes, including RRMS, SPMS, PPMS, and PRMS, each with its own distinct features (7, 33).

The pathophysiology of MS involves a dysregulated immune response, leading to the infiltration of autoreactive T-cells and B-cells into the CNS. These immune cells initiate an inflammatory cascade that damages the myelin sheath surrounding nerve fibers, resulting in impaired nerve conduction and neurodegeneration(34, 35). The etiology of MS is multifactorial, involving a combination of genetic susceptibility and environmental triggers (36). Various genetic variants have been associated with an increased risk of developing MS, and several environmental factors, such as infections, vitamin D deficiency, and smoking, have been implicated in its pathogenesis (37, 38).

It is anticipated that this study will uncover novel insights into the factors that contribute to the progression and severity of MS. By identifying these factors, we hope to enhance our understanding of the disease's underlying mechanisms and potentially identify targets for intervention and management. Ultimately, this research has the potential to improve patient care and contribute to the development of personalized treatment approaches for individuals with MS. In our study, we have shown that although there is a vast amount of difference between SPMS and PPMS in many aspects, we have found 14 common PPI in both diseases, which shows that there are many similarities between them in the lethality of the disease. Eight of these PPI have been introduced as key factors in MS in the previous studies (Table 1 provides the DOI of related papers). In addition, we have introduced six novel entities and discussed their relation to MS.

Table 1

Common PPI related to SPMS and PPMS
PPI	Full name	Function	Literature evidence
MSN	Moesin	cell adhesion and signaling	10.3389/fimmu.2021.640778
ROS1	ROS proto-oncogene 1, receptor tyrosine kinase	protein kinase	10.1039/c6md00643d
CD4	Cluster of Differentiation 4	T-cell activation and immunity	10.1016/j.it.2004.01.007
NR4A1	Nuclear receptor subfamily 4 group A member 1	Transcriptional regulator, cell differentiation	10.4103/1673-5374.339492
LCP1	Lymphocyte cytosolic protein 1	Actin cytoskeleton regulation	10.1002/pmic.201800355
S100A9	S100 calcium-binding protein A9	Inflammatory protein regulation	10.1111/jcmm.15928
AIF1	Allograft inflammatory factor 1	Inflammatory response regulation	10.1016/j.nbd.2009.11.014
CD44	Cluster of Differentiation 44	Cell adhesion molecule and hyaluronan receptor	10.4049/jimmunol.1004043
GNL3	G protein nucleolar 3	Ribosome biogenesis regulation	This study
PLEC	Plectin	Cytoskeletal protein organization	This study
C1R	Complement C1r subcomponent	Initiates complement activation	This study
C1S	Complement C1s subcomponent	Initiates complement activation	This study
SERPING1	Serpin family G member 1	Regulates complement system activity	This study
ITGB4	Integrin subunit beta 4	Cell adhesion, signaling	This study

MSN (Table 1), an actin-binding protein, has emerged as a key player in MS pathogenesis. It contributes to the migration and activation of immune cells, particularly T cells, facilitating their infiltration into the CNS. This infiltration is a pivotal step in initiating the inflammatory response observed in MS (39). ROS1 (Table 1), a receptor tyrosine kinase, holds promise as a potential target in MS. Its signaling pathways are implicated in the activation and proliferation of immune cells, exacerbating the inflammatory cascade within the CNS(40). CD4⁺ T cells have long been recognized as key drivers of autoimmune responses in MS. These cells, expressing the CD4 surface glycoprotein, release pro-inflammatory cytokines and chemokines, promoting the recruitment and activation of other immune cells (41). NR4A1(Table 1), also known as Nur77, acts as an orchestrator of immune responses. In MS, NR4A1 regulates the activation and survival of autoreactive T cells, contributing to disease progression. Exploring the intricate balance of NR4A1-mediated signaling pathways may uncover novel therapeutic avenues for MS treatment (42). S100A9 (Table 1), a calcium-binding protein expressed in myeloid cells, plays a prominent role in MS pathology. Its contribution lies in the recruitment and activation of immune cells, further fueling inflammation and tissue damage in the CNS. S100A9 is implicated in the recruitment and activation of immune cells, particularly myeloid cells, in MS. This protein contributes to the perpetuation of inflammation within the CNS, leading to tissue damage and neurodegeneration observed in MS (43). LCP1 (Table 1), through its role in cytoskeletal rearrangement and cell motility, influences the migration and infiltration of T cells into the CNS in MS. The infiltration of immune cells, facilitated by LCP1, contributes to the formation of inflammatory lesions and the destruction of myelin, resulting in neurological dysfunction (44). AIF1 (Table 1) is expressed in activated microglia, the resident immune cells of the CNS. In MS, AIF1 expression is upregulated within demyelinated lesions. Activated microglia expressing AIF1 contribute to the inflammatory response and neurodegenerative processes, further exacerbating the progression of MS (45). CD44 (Table 1) is involved in immune cell adhesion and migration. In MS, upregulated CD44 expression facilitates the transmigration of immune cells across the blood-brain barrier and their infiltration into the CNS. This immune cell infiltration contributes to the inflammatory response and subsequent tissue damage in MS (46).

Furthermore, by comparing the two types of MS, we predicted 15 TFs that might be the essential component in MS. Seven of these TFs had been explained in previous investigations, while eight of them have never been examined (Table 2).

Table 2

Common TF related to SPMS and PPMS
TF	Full name	Function	Literature evidence
TCF21	Transcription factor 21	Cardiac development regulator	This study
TRIM28	Tripartite motif-containing 28	transcriptional repressor	This study
DROSHA	Drosha ribonuclease III	mediates primary microRNA processing	This study
ZNF217	Zinc finger protein 217	Transcriptional regulator of oncogenesis	This study
SR1	Serine/arginine-rich splicing factor 1	Hormone-responsive gene regulation	This study
ESR2	Estrogen receptor 2	Hormone-responsive gene regulation	This study
BCAT	Branched-chain amino acid transaminase	Catalyzes amino acid metabolism	This study
RACK7	Receptor for activated C kinase 7	Regulation of protein translation	This study
NR1H3	Nuclear receptor subfamily 1 group H member 3	Cholesterol metabolism regulation and homeostasis	10.1016/j.neuron.2016.04.039
RELA	V-rel avian reticuloendotheliosis viral oncogene	Regulates NF-κB signaling pathway	10.1016/j.jneuroim.2018.04.002
BP1	Homeobox protein DLX6BP1	Neural development and differentiation	10.1016/j.neucli.2022.02.002
SPI1	Spi-1 proto-oncogene	Regulates myeloid cell differentiation	10.1093/brain/awu408
BACH1	BTB domain and CNC homolog 1	Regulates gene expression and stress response	10.1016/j.neuron.2021.12.034
TP53	Tumor protein p53	Tumor suppressor	10.3390/ijms19113652
ERG	ERG, ETS transcription factor	Endothelial Cell Differentiation and Angiogenesis	10.1080/08820139.2018.1433203

In the following section we discuss the possible role of the MS related TFs in details. NR1H3 (Table 2), also known as LXRA (Liver X Receptor Alpha), is a transcription factor that plays a significant role in regulating lipid metabolism and immune responses. It has been implicated in the pathogenesis of MS due to its involvement in modulation of inflammatory processes and lipid homeostasis within the CNS. Dysregulation of NR1H3 signaling has been associated with altered immune cell function and inflammation, contributing to MS pathology (47). RELA (Table 2) is a subunit of the NF-κB transcription factor complex that regulates numerous cellular processes, including inflammation, immune responses, and cell survival. In the context of MS, RELA plays a crucial role in orchestrating the immune response and promoting inflammation within the CNS. Dysregulated RELA signaling can contribute to the chronic inflammatory state observed in MS and the subsequent damage to myelin (48). BP1 (Table 2), also known as DLX4 (Distal-less Homeobox 4), is a transcription factor belonging to the homeobox gene family. While the direct involvement of BP1 in MS is not well-established, studies have shown its dysregulation in other autoimmune diseases. BP1 may influence immune cell development and function, potentially impacting the immune response in MS. Further research is needed to elucidate the specific role of BP1 in MS pathogenesis (49). SPI1 (Table 2), also known as PU.1, is a transcription factor essential for the development and function of hematopoietic cells, including immune cells. SPI1 plays a critical role in regulating immune cell differentiation, activation, and inflammatory responses. Dysregulated SPI1 activity has been associated with autoimmune and inflammatory diseases, including MS. Altered SPI1 expression may impact immune cell function and contribute to the immune dysregulation observed in MS (50). BACH1 (Table 2) is a transcription factor involved in cellular stress responses and oxidative stress regulation. While its direct involvement in MS is not well-elucidated, oxidative stress is known to play a role in MS pathogenesis. BACH1 may modulate oxidative stress levels and influence immune cell function in MS. Further investigation is necessary to determine the precise role of BACH1 in MS (51). TP53 (Table 2), commonly referred to as p53, is a well-known tumor suppressor protein involved in cell cycle regulation, DNA repair, and apoptosis. In the context of MS, TP53 may have a dual role. On the one hand, TP53 activation can trigger apoptosis of autoreactive immune cells, helping to control the autoimmune response. On the other hand, TP53 activation may promote neuroinflammation and tissue damage. The precise involvement of TP53 in MS is complex and requires further investigation (52). ERG (Table 2) is a transcription factor involved in hematopoiesis and endothelial cell development. While its direct role in MS is not yet fully understood, ERG has been implicated in endothelial cell dysfunction and angiogenesis, processes that can contribute to immune cell infiltration and inflammation within the CNS in MS. Further research is needed to uncover the specific involvement of ERG in MS pathogenesis (53). By investigating the factors associated with early mortality in MS, we aim to provide valuable information to clinicians, researchers, and healthcare providers. Identifying these factors will not only deepen our understanding of the disease but also pave the way for further research and the development of targeted therapies. Through collaborative efforts, we can strive towards better outcomes and improved quality of life for individuals living with MS.

Multiple sclerosis represents a multifaceted autoimmune disorder characterized by chronic inflammation and demyelination of the central nervous system. This study aimed to investigate the relationship between disease duration and mortality in two types of multiple sclerosis: PPMS, and SPMS. Understanding the factors that influence disease progression and severity is crucial for developing effective treatment strategies and improving patient outcomes. By examining the characteristics of individuals who experienced a shorter disease duration and comparing them with those who had a longer survival time, we aimed to identify specific factors that may play a significant role in determining the prognosis of MS. By comparing the disease duration in these subtypes, we sought to identify potential factors associated with mortality and better understanding the disease progression in each subtype. Our findings contribute to the existing knowledge on MS prognosis and shed light on the importance of disease duration as a potential factor influencing mortality. By elucidating the distinct disease trajectories and mortality factors in PPMS and SPMS, this study provides valuable insights for clinicians and researchers in developing targeted interventions and personalized treatment approaches. Through an in-depth analysis of protein-protein interactions and potential transcription factors, we discerned shared signaling pathways and potential biomarkers.

Our investigation identified 13 converging PPIs between SPMS and PPMS, revealing an intersection of pathogenic mechanisms. Notably, eight of these PPIs have been previously correlated with MS, reinforcing their significance in driving disease progression. Additionally, we unveiled six novel PPIs that shed light on previously unexplored molecular mechanisms governing MS. Furthermore, our predictive analysis pinpointed 15 potential TFs, seven of which had undergone prior scrutiny in the context of MS. However, the remaining eight TFs represent novel entities in relation to MS and hold great potential as uncharted therapeutic targets.

The identification of common PPIs and potential TFs in both SPMS and PPMS enriches our understanding of the underlying biological underpinnings of multiple sclerosis. These findings carry substantial implications as potential biomarkers, offering prospects for improved diagnostic accuracy, prognostic insights, and personalized treatment strategies. Subsequent experimental validation and clinical investigations are vital steps toward harnessing the clinical utility of these molecular players and elevating the standards of care for individuals affected by MS. Further research is warranted to explore the underlying mechanisms linking disease duration and mortality in MS and to validate these findings in larger and more diverse patient populations. By identifying and addressing the factors contributing to disease progression and mortality, we can ultimately improve patient outcomes and enhance the quality of life for individuals with multiple sclerosis.

TCF21	Transcription factor 21
TRIM28	Tripartite motif-containing 28
DROSHA	Drosha ribonuclease III
ZNF217	Zinc finger protein 217
SR1	Serine/arginine-rich splicing factor 1
ESR2	Estrogen receptor 2
BCAT	Branched-chain amino acid transaminase
RACK7	Receptor for activated C kinase 7
NR1H3	Nuclear receptor subfamily 1 group H member 3
RELA	V-rel avian reticuloendotheliosis viral oncogene
BP1	Homeobox protein DLX6BP1
SPI1	Spi-1 proto-oncogene
BACH1	BTB domain and CNC homolog 1
TP53	Tumor protein p53
ERG	ERG, ETS transcription factor

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

All data used in this study is freely available and can be obtained from NCBI using above-mentioned GSE code “GSE131282”.

Competing of interests

The authors declare that the research was conducted in the absence of any commercial or financial interest.

Funding

This work has been conducted with no specific funding to declare.

Authors’ contributions:

MR and HM conceived the idea. MR and AS conducted the experiment and prepared the results. HF and ZR supervised the procedure, verified the results and overlooked the discussion. HM wrote the first draft, and HF and ZR edited the manuscript. MR and AS prepared the final draft.

Acknowledgments

Not applicable

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No competing interests reported.

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Unveiling the Molecular Landscape: Network Analysis of Genes, Proteins, and Transcription Factors in Primary Progressive and Secondary Progressive Multiple Sclerosis for Unraveling Disease Progression and Prognostic Factors

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