Therapeutic Insights and Immune Pathway Connections Revealed by Core Symptom Gene Network Analysis in Ankylosing Spondylitis

Abstract

Ankylosing spondylitis (AS) exhibits marked clinical heterogeneity that is poorly captured by conventional disease-centric analyses, hindering the development of personalized therapies. We propose a symptom-centered network pharmacology framework that directly links individual clinical symptoms to their underlying molecular mechanisms and therapeutic targets. AS- and symptom-associated genes were collected from GeneCards and prioritized using centrality analysis within protein–protein interaction networks. Symptom relevance was validated using patient-derived transcriptomic datasets. Network proximity between symptom modules and FDA-approved drug targets was assessed. A refined gene set, integrating TNF-associated neighbors and highly central nodes, was subjected to pathway enrichment analysis. Disease-centric analysis yielded a restricted 18-gene core enriched mainly in broad immune pathways. In contrast, the symptom-centered network identified 145 genes associated with specific symptoms such as inflammatory back pain and morning stiffness. Key genes, including PTEN, TLR4, JAK2, NRAS, and NR3C1, were significantly upregulated in AS patients. TNF showed local connectivity but limited global proximity, while IL17A- and JAK inhibitor-related targets were absent. A refined 24-gene module revealed enrichment in interleukin- and cytokine-mediated signaling pathways. Symptom-centered network analysis more effectively captures molecular heterogeneity in AS, providing a robust framework for symptom-specific target discovery and personalized therapeutic strategies.

1. Introduction

Ankylosing spondylitis (AS) is a chronic inflammatory rheumatic disease occurring due to a form of spondyloarthritis (SpA) that primarily affects the axial skeleton, particularly the spine and sacroiliac joints [1]. Patients typically experience back pain, morning stiffness, and progressive restriction of spinal mobility, which can ultimately lead to severe postural deformities and radiographic ankylosis, a condition commonly known as the “bamboo spine” [2,3]. These debilitating musculoskeletal changes significantly impair physical function, reduce work capacity, and diminish a patient’s overall quality of life severely [4]. Furthermore, AS is frequently accompanied by extra-articular manifestations, including acute anterior uveitis, inflammatory bowel disease (IBD), psoriasis, and cardiovascular comorbidities, further complicating disease management [5].

The pathogenesis of AS is a complex, multifactorial process involving a dynamic interplay of genetic predisposition, environmental exposures, and immune dysregulation [6]. The human leukocyte antigen-B27 (HLA-B27) gene is strongly associated with the disease in approximately 80–95% of patients. Nonetheless, only a fraction of HLA-B27-positive individuals develop AS, indicating the involvement of other genetic variants, such as endoplasmic reticulum aminopeptidase 1 (ERAP1), interleukin-23 receptor (IL23R), and tumor necrosis factor superfamily member 15 (TNFSF15) [7,8]. Environmental triggers, particularly disruptions in the gut microbiota and bacterial antigens, are thought to contribute through molecular mimicry with HLA-B27 [9]. Additionally, multiple factors such as smoking, vitamin D deficiency, and biomechanical stress at the entheses have been linked to disease activity and progression [10]. AS is primarily an autoimmune disease with the misfolding of HLA-B27 heavy chains can induce endoplasmic reticulum stress and trigger the unfolded protein response. Similarly, the IL-23/Th17 axis drives an excessive production of IL-17, TNF-α, and other cytokines, leading to chronic inflammation and abnormal bone remodeling characteristic of AS [11,12]. This persistent inflammation at the entheses and axial skeleton eventually triggers both bone resorption and new bone formation, ultimately causing spinal fusion and irreversible structural damage [13].

All treatments for AS focus on alleviating pain and stiffness, maintaining mobility, and preventing complications at the moment. There are non-steroidal anti-inflammatory drugs (NSAIDs), TNF inhibitors, IL-17A inhibitors, and janus kinase (JAK) inhibitors for AS patients [1]. Despite the development of therapeutic agents that target these key pathways, treatment outcomes remain suboptimal [14]. NSAIDs remain the first-line treatment, effectively alleviating pain and stiffness [15]. However, their long-term use is limited by the risk of gastrointestinal, cardiovascular, and renal toxicity [16]. For patients who respond inadequately to NSAIDs, biologic agents targeting TNF have demonstrated significant efficacy in reducing inflammation and delaying structural progression [16]. TNF inhibitors (etanercept, adalimumab, and golimumab), IL-17A inhibitors (secukinumab and ixekizumab), and JAK inhibitors (upadacitinib and tofacitinib) have expanded the therapeutic landscape for AS [17]. Nonetheless, these agents are associated with adverse events, including increased susceptibility to infections, injection-site reactions, and paradoxical inflammatory responses [18,19], with up to 30% of patients experiencing primary or secondary non-responsiveness [20]. TNF inhibitors exert their biological properties by blocking TNF-α activity and thereby reducing joint inflammation [19]. IL-17 inhibitors provide an additional therapeutic option for patients who do not respond adequately to TNF inhibitors [21]. Moreover, the patients who fail to NSAIDs, TNF inhibitors or IL-17A inhibitors, JAK inhibitors serve as alternative treatment options [17]. However, these treatments also carry risks, including upper respiratory tract infections, the exacerbation of inflammatory bowel disease, and long-term risk of malignancies and cardiovascular events [22,23]. The significant variability in treatment response underscores the critical need for personalized medicine, a necessity stemming from the complexity and diversity of AS pathogenesis. This therapeutic variability highlights that current limitations are closely linked to our incomplete understanding of the underlying molecular networks. Given that many complex diseases, including AS, arise from perturbations across interconnected pathways rather than isolated genetic defects, the conventional ‘one drug–one target’ paradigm is insufficient [24]. A broader systems-level framework is therefore essential to capture the multifactorial nature of AS and to identify clinically relevant points of intervention [25,26].

Network pharmacology has emerged as a promising framework to address these challenges by integrating multi-omics data, protein–protein interaction networks, and pathway analyses to identify key nodes and interactions underlying disease processes [27]. This approach enables the simultaneous examination of multiple targets and pathways, offering the potential to elucidate disease mechanisms, predict drug responses, and guide the design of multi-target therapeutic strategies [28]. In the context of AS, network pharmacology provides a unique opportunity to explore the interplay between symptom-associated genes and existing drug targets, thereby uncovering new intervention points and refining current treatment approaches [29]. Nonetheless, disease-centric network analyses, which dominate prior studies, tend to emphasize broad canonical hubs while overlooking the molecular diversity that underlies distinct clinical manifestations. However, most previous network-based investigations of AS have concentrated on disease-level gene sets. This approach obscures the molecular diversity driving distinct clinical manifestations such as inflammatory back pain, morning stiffness, or peripheral arthritis. This gap limits the translational relevance of studies containing therapeutic strategies derived from purely disease-centric analyses, which may fail to capture the symptom-specific mechanisms that directly impact patient quality of life. To address this limitation, we shift the paradigm from a traditional disease-centric model to a novel symptom-centered network pharmacology framework. This study is, to our knowledge, one of the first to computationally dissect the molecular heterogeneity of AS based on its core symptoms. We systematically identified core genes associated with major AS symptoms, validated their expression patterns in patient datasets, and examined their topological relationships with TNF, a principal therapeutic target in AS management. By combining centrality-based network analysis, gene expression validation, and network proximity assessment, we aimed to generate novel insights into the systems-level mechanisms of AS and to inform the development of more precise and effective therapeutic strategies.

2. Materials and Methods

2.1. Gene Set Construction

FDA-approved therapeutic targets for AS were identified through DrugBank (https://go.drugbank.com/, accessed on 5 September 2025). To construct a symptom-relevant gene set, genes associated with representative clinical symptoms of AS (e.g., inflammatory back pain, morning stiffness, peripheral arthritis) were screened from GeneCards (https://genecards.org/, accessed on 5 September 2025) using a relevance score threshold of ≥20. Firstly, symptoms regarding AS were selected based on the WHO ICD-11 framework and ASAS classification criteria. Representative clinical symptoms were defined a priori based on established clinical descriptions of ankylosing spondylitis and axial spondyloarthritis, with emphasis on manifestations directly reflecting patient-reported disease burden. Among potential manifestations, extra-articular features such as uveitis were deliberately excluded, as the present analysis focused on symptom domains directly related to musculoskeletal involvement. Each symptom term was treated as an independent query in GeneCards, and symptom–gene associations were retrieved separately rather than inferred from a single disease-level query. Symptom-associated gene lists were subsequently merged using a union-based strategy to preserve symptom-specific molecular diversity. This approach was intentionally adopted to avoid collapsing heterogeneous symptom signals into a single disease-centric profile prior to network-based refinement. The relevance score threshold of ≥20 was applied to prioritize gene–symptom associations supported by multiple evidence sources integrated within GeneCards and to reduce the inclusion of weak or low-confidence links. For comparison, AS-associated genes were retrieved from GeneCards using the keyword ‘ankylosing spondylitis’. Genes with a relevance score ≥ 20 were selected to ensure robust association. Each symptom term was queried independently in GeneCards, and symptom-associated gene lists were merged using a union-based strategy prior to network refinement. This procedure was adopted to preserve symptom-specific molecular signals and to minimize bias toward generic inflammatory gene enrichment.

2.2. Centrality Analysis

Centrality metrics were calculated for each node (gene) within the symptom-associated gene network using Netminer 4 software (Cyram Inc., Gyeonggi-do, Republic of Korea). Five network centrality indices were employed: Degree, Betweenness, Closeness, Eigenvector (Edge), and Eigenvector (Linked). Degree, betweenness, and closeness centrality were used to capture local connectivity, mediating roles, and global accessibility of genes within the network, respectively. Eigenvector centrality (linked and edge-based) was applied to identify genes with influence amplified by connections to other highly central nodes. Genes ranking in the top 5% for each index were selected, and those overlapping across multiple indices were identified as core symptom-associated genes. The use of a top 5% threshold across multiple centrality measures was intended to reduce bias toward single-metric universal hubs and to identify genes that consistently occupied influential positions within the symptom-associated network. After excluding low-confidence or disconnected nodes from the network, the remaining genes were defined as the core symptom-associated gene set. The disease-associated gene set was mapped to STRING (https://string-db.org/, version 12.0) with a confidence score threshold of 0.7 (Homo sapiens) to construct a protein–protein interaction network. Degree, betweenness, and closeness centrality were calculated as described for the symptom network.

2.3. Gene Expression Pattern Analysis

To validate the biological relevance of the identified core genes, gene expression patterns were analyzed using patient data. The NCBI Gene Expression Omnibus (GEO) dataset GDS5231 (Ankylosing spondylitis: blood) [30], which includes microarray data from AS patients and healthy controls, was selected. Fold changes in gene expression between AS and control samples were calculated for each probe to assess differential expression.

2.4. Network Proximity Analysis

A protein–protein interaction (PPI) network was constructed to evaluate the network-based proximity between the therapeutic target gene TNF and the 145 core genes. PPI data were obtained from the STRING database (https://string-db.org/, version 12.0) with a confidence score threshold of 0.7 for Homo sapiens. The STRING confidence threshold was applied to balance network completeness with reliability, ensuring exclusion of low-confidence interactions while retaining biologically supported connections relevant to symptom-level analysis. The PPI network was visualized using Cytoscape software (version 3.10.3) and STRING App (v.2.2.0) (data source: “STRING: protein query”). The shortest path lengths between TNF and each core gene were computed using the NetworkX Python package (version 3.13). The observed average shortest path was defined as the network proximity score.

2.5. Functional Enrichment Analysis

To capture genes most functionally relevant to drug–symptom interactions, we merged 13 genes directly connected to TNF with 14 highly connected genes from the top 10% of the 145 core genes based on network centrality. After removing redundancies, a total of 24 unique genes were selected. This gene set was then subjected to pathway enrichment using the Reactome Pathway Database (Version 94) (https://reactome.org/, accessed on 8 September 2025), allowing us to identify biologically significant immune and inflammatory signaling pathways associated with AS. To evaluate concordance between frameworks, the overlap between the symptom-core and disease-core gene sets was identified. The intersecting genes were subjected to STRING analysis to confirm connectivity and were also analyzed for functional enrichment using the ShinyGO v0.82 (https://bioinformatics.sdstate.edu/go/, accessed on 8 September 2025).

2.6. Statistics

All quantitative analyses and statistical evaluations were conducted as follows. For differential gene expression, a two-tailed t-test was applied to compare expression levels between AS patients and healthy controls using GDS5231 data, with fold change used to represent expression differences. A threshold of p < 0.05 was considered statistically significant. For network proximity analysis, statistical testing was performed by comparing the observed mean shortest path distance between TNF and the core gene set to a null distribution generated from 10,000 randomly sampled gene sets. The observed proximity was standardized using z-scores calculated from the mean and standard deviation of the null distribution. Corresponding p-values were derived from the cumulative distribution function of the standard normal distribution, with statistical significance set at p < 0.05. Reactome enrichment results were evaluated using false discovery rate (FDR) values, and pathways with FDR < 0.05 were considered significantly enriched.

3. Results

3.1. Limitations of Disease-Centric Network Analysis

To provide a disease-level reference, we also constructed a core gene network from AS-associated genes curated in GeneCards (relevance ≥ 20). This analysis yielded an 18-gene core dominated by canonical hubs such as TNF, IL1B, IL6, IL17A, IL23R, CRP, IL1RN, MMP3, NOD2, HLA-B, ERAP1, ZNF354A, TAP1, TAP2, FRG2C, USP50, LIN54, and ERAP2 (Figure 1). Centrality assessment confirmed these nodes as the most connected and influential within the disease network. However, overlap with the 145 symptom-core genes was limited to only six shared nodes (HLA-B, TNF, NOD2, CRP, IL6, and IL1B) (Figure 2A). Functional enrichment of the disease-core genes resulted in a restricted set of broad immune-related pathways (Figure 2B).

3.2. Identification of Core Symptom Genes Through Centrality and PPI Analysis

Network centrality analysis was performed using five different metrics, which included degree, betweenness, closeness, eigenvector linked, and eigenvector edge (

Table 1. Network centrality analysis.

Top 5% Gene	Degree Score	Top 5% Gene	Betweenness Score	Top 5% Gene	Closeness Score	Top 5% Gene	Eigenvector (Linked) Score	Top 5% Gene	Eigenvector (Edge) Score
TNF	0.875	TNF	0.063414	TGFB1	0.996747	TGFB1	0.028799	TP53	0.093697
IL6	0.833333	IL17A	0.056259	LMNA	0.996406	LMNA	0.028618	TGFB1	0.068743
TGFB1	0.833333	NOD2	0.05239	FBN1	0.996065	CBS	0.028142	PTEN	0.066873
LMNA	0.791667	SGSH	0.042887	BRAF	0.995895	FBN1	0.028111	LMNA	0.063758
TP53	0.75	IL17RA	0.039644	CBS	0.995895	BRAF	0.028103	IL6	0.063713
FBN1	0.75	IL36RN	0.0394	TNF	0.995724	COL1A1	0.028085	TNF	0.062645
IL10	0.708333	FBN1	0.026806	COL1A1	0.995554	TNF	0.028057	PIK3CA	0.062082
IL1B	0.708333	LTA	0.02657	NF1	0.994534	IL6	0.028036	BRAF	0.060777
CRP	0.708333	CARD14	0.026201	ELN	0.994364	CRP	0.027805	FGFR3	0.060252
HLA-DRB1	0.708333	MIR146A	0.023174	PTPN11	0.994194	APOE	0.027788	AKT1	0.060211
BRAF	0.708333	IL23R	0.022281	MMP2	0.993515	HLA-B	0.02774	EGFR	0.059777
CBS	0.708333	TGFB1	0.02178	IL6	0.993007	TP53	0.027735	FBN1	0.058401
IFNG	0.666667	HLA-C	0.021439	IL17A	0.993007	IL10	0.027694	ATM	0.057729
HLA-B	0.666667	LMNA	0.021071	IL10	0.992499	IL1B	0.027556	RYR1	0.05748
H19	0.666667	IL6	0.020554	PTEN	0.992499	ALB	0.027514	INS	0.055908
NF1	0.666667	BRAF	0.020282	PIK3CA	0.992499	IFNG	0.027505	NF1	0.055527
COL1A1	0.666667	CBS	0.020159	AKT1	0.992499	NF1	0.027405	KRAS	0.05526
CXCL8	0.625	NF1	0.019314	SMAD4	0.992499	MMP2	0.027377	COL2A1	0.055182
EGFR	0.625	PTPN11	0.018886	NOTCH3	0.992499	CXCL8	0.027376	COL1A1	0.054745
TLR4	0.625	IL10	0.018678	BMP4	0.992499	TLR4	0.027344	APOE	0.053907
STAT3	0.625	COL1A1	0.018295	CRP	0.99233	ACE	0.02734	CTNNB1	0.053261
IL1A	0.625	TP53	0.018288	ALB	0.991992	IGF1	0.02734	HRAS	0.052865
IL17A	0.625	ELN	0.017428	ACE	0.991992	STAT3	0.027336	ACE	0.052721
ALB	0.625	CRP	0.017164	IGF1	0.991992	MMP9	0.027336	STAT3	0.052613
TERT	0.625	IL1B	0.016873	TP53	0.991823	F2	0.027322	TERT	0.05245
MMP9	0.625	ACE	0.016648	F2	0.991823	INS	0.027188	IGF1	0.052366
APOE	0.625	IGF1	0.016648	APOE	0.991485	TERT	0.027144	IL10	0.052192
ACE	0.625	TGFBR2	0.016532	IL1B	0.991316	PTPN11	0.027051	SMAD4	0.051937
KRAS	0.625	ALB	0.016401	IFNG	0.991147	CD4	0.027035	NTRK1	0.051661
IGF1	0.625	F2	0.016326	CXCL8	0.990979	ELN	0.026904	BRCA2	0.051291
CD4	0.625	BMP4	0.016245	TLR4	0.990979	DMD	0.026834	SQSTM1	0.051186
F2	0.625	MMP2	0.016211	STAT3	0.990979	PTEN	0.026816	SOD1	0.050498
FGFR3	0.625	PTEN	0.016125	MIR21	0.990979	PIK3CA	0.026816	FLNA	0.049436
PTPN11	0.625	PIK3CA	0.016125	MMP9	0.990979	AKT1	0.026816	NOTCH1	0.049432
NTRK1	0.583333	AKT1	0.016125	GLB1	0.990979	SMAD4	0.026816	CBS	0.049166
NOD2	0.583333	SMAD4	0.016125	TERT	0.99081	IL1A	0.026793	VCP	0.047156
BDNF-AS	0.583333	NOTCH3	0.015898	HLA-B	0.990641	IL17A	0.026652	FGFR2	0.046912
IL1RN	0.583333	MIR21	0.015825	IL1A	0.990473	GNAS	0.026591	PTPN11	0.046295
PTEN	0.583333	FGFR3	0.015812	INS	0.990473	SCN4A	0.026585	IL1B	0.046293
MIR21	0.583333	IFNG	0.01579	ICAM1	0.990473	FGFR3	0.026564	BRCA1	0.046259
PIK3CA	0.583333	APOE	0.015527	IL2RA	0.990136	RYR1	0.026534	APP	0.046022
CTNNB1	0.583333	STAT3	0.015489	IL2	0.990136	IL2RA	0.026501	MAPT	0.045921
AKT1	0.583333	MMP9	0.015489	DMD	0.990136	IL2	0.026501	MTHFR	0.045736
INS	0.583333	TLR4	0.015463	NOS3	0.989968	BMP4	0.026437	DMD	0.045493
TTR	0.583333	TERT	0.015418	EDN1	0.989968	LEP	0.026425	CDKN2A	0.045463
TGFBR2	0.583333	IL1A	0.015385	VWF	0.989968	EGFR	0.026395	BMP4	0.045174
COL2A1	0.583333	ICAM1	0.015376	BDNF-AS	0.989631	GFAP	0.026371	IFNG	0.045096
CERNA3	0.583333	CXCL8	0.015368	TGFBR2	0.989631	COMP	0.026289	FGFR1	0.044917
SMAD4	0.583333	CAV1	0.015334	CAV1	0.989463	H19	0.026247	ELN	0.043889
MMP2	0.583333	EGFR	0.015207	EGFR	0.989295	NTRK1	0.026225	CEP290	0.043826
HRAS	0.583333	NOS3	0.015091	GNAS	0.989295	CTNNB1	0.026215	CFTR	0.043625
ELN	0.583333	HLA-B	0.015047	SCN4A	0.989127	COL2A1	0.026177	COL1A2	0.042618
BMP4	0.583333	KRAS	0.015042	MIR223	0.989127	BDNF	0.026176	H19	0.042452
NGF	0.541667	PPARG	0.014997	SMAD3	0.989127	NOD2	0.026163	TRPV4	0.042259
TNFRSF1A	0.541667	EDN1	0.01488	ABCC6	0.989127	NRAS	0.026125	CHEK2	0.042243
CCL2	0.541667	VWF	0.01488	CTNNB1	0.988959	MTHFR	0.026096	NRAS	0.041919
BDNF	0.541667	GNAS	0.014846	PPARG	0.988959	CSF3	0.026083	TGFBR2	0.041912
IL2RA	0.541667	INS	0.014793	RYR1	0.988959	TTR	0.026074	ESR1	0.041776
IL2	0.541667	COL2A1	0.014736	COMP	0.988959	BDNF-AS	0.026058	BDNF-AS	0.041421
SCN4A	0.541667	BDNF-AS	0.014651	NOD2	0.988791	KRAS	0.026042	CDH1	0.041069
PPARG	0.541667	IL2RA	0.014645	FGFR3	0.988791	CERNA3	0.02602	PALB2	0.040956
ICAM1	0.541667	IL2	0.014645	CD36	0.988791	CTLA4	0.026012	SCN4A	0.040686
TRPV4	0.541667	CTNNB1	0.014583	MPO	0.988623	FLNA	0.025935	GNAS	0.040438
MPZ	0.541667	GLB1	0.014549	TGFBR1	0.988623	HSPG2	0.025935	NOD2	0.040004
ATM	0.541667	SMAD3	0.014456	FLNA	0.988623	MPO	0.02593	BDNF	0.039969
SOD1	0.541667	DMD	0.014393	HSPG2	0.988623	VEGFA	0.025894	APC	0.039844
MMP1	0.541667	TGFBR1	0.014379	MFN2	0.988455	TNFRSF11B	0.025886	RET	0.039579
LEP	0.541667	MMP1	0.014144	ACTC1	0.988455	MMP1	0.025865	HLA-B	0.039426
CDKN2A	0.541667	SCN4A	0.014117	NOS2	0.988287	TGFBR2	0.025849	TTR	0.039226
CSF3	0.541667	MIR223	0.014087	ADA	0.988287	EDN1	0.025847	MYH7	0.039081
TNFRSF11B	0.541667	NOS2	0.014045	MMP1	0.988287	VWF	0.025847	CTLA4	0.038822
MTHFR	0.541667	SRC	0.013876	COL1A2	0.988287	MYC	0.02582	PPARG	0.038747
VEGFA	0.541667	ACTC1	0.013866	ELANE	0.988119	TRPV4	0.025808	MTOR	0.038728
GNAS	0.541667	ELANE	0.013851	SRC	0.988119	MPZ	0.025808	ALB	0.038546
DMD	0.541667	MPO	0.01383	TGFB2	0.988119	MIR21	0.025801	TTN	0.038367
NOS3	0.541667	ADA	0.013828	FBN2	0.988119	IL1RN	0.0258	CRP	0.038336
NRAS	0.541667	NOTCH1	0.013811	TGFB3	0.987952	CXCR4	0.025796	CREBBP	0.038064
GBA1	0.541667	TGFB2	0.013759	NOTCH1	0.987952	NOTCH3	0.025795	GBA1	0.03798
FGFR1	0.541667	DSP	0.01359	HMOX1	0.987784	MTOR	0.025729	TLR4	0.037887
PTGS2	0.5	CD36	0.013569	COL2A1	0.987784	ATRX	0.025729	SHH	0.037821
COMT	0.5	ABCC6	0.01354	ATRX	0.987784	CDKN2A	0.025726	MECP2	0.037728
NFKB1	0.5	COL1A2	0.01351	PRKAR1A	0.987617	TNFRSF1A	0.025724	POLG	0.037696
IL4	0.5	GLRA1	0.013501	CCN2	0.987617	GBA1	0.025723	MAP2K1	0.037672
CTLA4	0.5	CD4	0.013412	ACTA1	0.987449	CAV1	0.025718	GFAP	0.037535
ELANE	0.5	CCN2	0.013377	DSP	0.987449	SMAD3	0.02563	MIR21	0.037499
CD40LG	0.5	NTRK1	0.013368	FKTN	0.987449	PRKAR1A	0.025606	DYSF	0.037489
HLA-DQB1	0.5	HMOX1	0.013367	PTGS2	0.987282	ESR1	0.025603	GLB1	0.037471
RYR1	0.5	PTGS2	0.013283	CD4	0.987114	ADA	0.025572	SOX9	0.037466
GJB1	0.5	FBN2	0.013188	SMAD2	0.986947	HRAS	0.025553	ERBB2	0.037256
JAK2	0.5	COMP	0.013053	IL5	0.98678	CD36	0.025547	TSC2	0.037195
ADA	0.5	RYR1	0.01305	LEP	0.98678	GLA	0.025495	HSPG2	0.037161
MPO	0.5	GBA1	0.013018	FGF2	0.98678	GLB1	0.025495	BRIP1	0.03709
CFTR	0.5	LEP	0.013014	GJB2	0.986612	DES	0.025472	MYC	0.037002
MIR223	0.5	ATRX	0.013001	TIMP1	0.986612	COL1A2	0.025468	IGF1R	0.03679
ESR1	0.5	H19	0.012952	MYH3	0.986612	CCL2	0.025453	SMAD3	0.036744
CXCR4	0.5	MTHFR	0.012912	TBX4	0.986612	SOD1	0.025424	SNCA	0.03672
FAS	0.5	MFN2	0.012891	GFAP	0.986278	MFN2	0.025407	ATRX	0.036527
MAPK1	0.5	IL5	0.012851	TPM3	0.986278	ACTB	0.025404	SRC	0.036482
MIR125A	0.5	FKTN	0.012818	RUNX2	0.986278	MECP2	0.025403	EZH2	0.036427
SMAD3	0.5	SMAD2	0.012774	NTRK1	0.986278	TGFB2	0.025379	MMP2	0.036204
SRC	0.5	BDNF	0.012749	SOD1	0.986111	MAP2K1	0.025373	MET	0.036179
GLA	0.5	FGF2	0.012713	MTHFR	0.986111	JAK2	0.02537	SCN9A	0.036126
TGFBR1	0.5	CXCL10	0.012669	DES	0.986111	MAPK1	0.02537	FLNC	0.036034
MAP2K1	0.5	FLNA	0.012665	CTLA4	0.985944	ELANE	0.025347	CXCL8	0.035913
MYC	0.5	HSPG2	0.012665	CXCL10	0.985944	SRC	0.02534	BMP2	0.035792
CDH1	0.5	MYH7	0.012591	APOA1	0.985944	GJA1	0.025334	SLC2A1	0.035779
TGFB2	0.5	PRKAR1A	0.012564	MBTPS2	0.985944	ICAM1	0.02532	FIG4	0.035714
BRCA1	0.5	APOA1	0.012471	PIK3C2A	0.985944	FBN2	0.025306	GJA1	0.035535
GFAP	0.5	SOD1	0.01246	RMRP	0.985944	RET	0.02529	MMP9	0.035522
APOA1	0.5	HRAS	0.012441	BDNF	0.985777	ATM	0.025276	ACTB	0.035301
EDN1	0.5	GJB2	0.012406	MYC	0.985777	PPARG	0.025259	MFN2	0.035176
MTOR	0.5	TIMP1	0.012406	BCL2	0.985443	IDH1	0.02524	TNFRSF11B	0.035156
VWF	0.5	TBX4	0.012406	ERCC4	0.985277	TGFB3	0.025223	PRKAR1A	0.035136
MFN2	0.5	IL1RN	0.012321	GLA	0.98511	BCL2	0.025221	COL5A1	0.035118
MYH7	0.5	TGFB3	0.012296	GJA1	0.98511	NOS3	0.025208	KIT	0.035111
CAV1	0.5	DES	0.012266	COL5A1	0.984777	NGF	0.025206	AR	0.035081
COMP	0.5	GLA	0.012242	CXCR4	0.98461	NAGLU	0.025195	GLA	0.034793
NOTCH3	0.5	JAG1	0.01223	CDKN2A	0.98461	CFTR	0.025177	NLRP3	0.034642
NOTCH1	0.5	RUNX2	0.012116	FBLN5	0.98461	FIG4	0.025158	ATP7A	0.034636
ATRX	0.5	ACTA1	0.01208	POLR3A	0.98461	NFKB1	0.025153	NOTCH3	0.034536
COL1A2	0.5	CDH1	0.012049	ESR1	0.984444	MIR125A	0.025145	TGFB3	0.03436
FGFR2	0.5	CTLA4	0.012017	ENPP1	0.984444	TSC2	0.02513	DES	0.034339
SCN9A	0.458333	COL5A1	0.012013	ABCC9	0.984277	COL5A1	0.025109	MAPK1	0.034212
MEFV	0.458333	GFAP	0.01201	PLG	0.984277	FKRP	0.025104	COL3A1	0.034158
NLRP3	0.458333	CDKN2A	0.011983	MBL2	0.984277	NLRP3	0.025083	SMARCA4	0.034106
ABCB1	0.458333	MYC	0.01196	INPP5E	0.984277	TGFBR1	0.025079	RUNX2	0.034098
TLR2	0.458333	GPHN	0.011954	VCAM1	0.984111	ERCC4	0.02506	IGF2	0.033837
HFE	0.458333	MYH3	0.011879	BSCL2	0.984111	CD40LG	0.025011	GCK	0.033832
IL1R1	0.458333	CXCR4	0.011616	SERPINC1	0.984111	MYH3	0.025006	PTCH1	0.033637
IL13	0.458333	BCL2	0.0116	NLRP3	0.983945	POLG	0.024994	RPGRIP1L	0.033629
IL18	0.458333	ESR1	0.011558	TRPV1	0.983945	VCP	0.024994	VEGFA	0.033441
MIR155	0.458333	TPM3	0.011552	IKBKG	0.983945	COMT	0.02499	PDGFRB	0.033347
RET	0.458333	CCL2	0.011535	CREBBP	0.983945	APOA1	0.024944	SH3TC2	0.033297
NOS2	0.458333	MBTPS2	0.011519	CALCA	0.983778	CCN2	0.024912	VWF	0.033292
IDH1	0.458333	PIK3C2A	0.011519	MIR34A	0.983778	NOTCH1	0.024903	ABCC8	0.033291
COL5A1	0.458333	RMRP	0.011519	COL3A1	0.983778	MYH7	0.024887	FKRP	0.033222
HMOX1	0.458333	TRPV1	0.011496	LOX	0.983778	IL18	0.024881	HNF4A	0.033177
CSF2	0.458333	DVL1	0.0114	MYH11	0.983778	DNMT1	0.024881	FBN2	0.032917
MMP3	0.458333	POLR3A	0.011378	TNXB	0.983778	JAG1	0.02488	ADAR	0.032859
BRCA2	0.458333	CERNA3	0.011356	PIK3R1	0.983612	CREBBP	0.024843	BCL2	0.032827
NAGLU	0.458333	ABCC9	0.011333	NAGLU	0.983612	IFIH1	0.024841	OFD1	0.032701
PRTN3	0.458333	GJA1	0.011302	PIK3CD	0.983612	FBLN5	0.024814	EP300	0.0327
CCND1	0.458333	PLG	0.011301	ERCC8	0.983612	BRCA1	0.024811	MLH1	0.032656
ERBB2	0.458333	MBL2	0.011301	STAT5A	0.983612	MAPT	0.024804	KCNJ11	0.032656
DNMT1	0.458333	INPP5E	0.011301	NR3C1	0.983446	GALNS	0.024804	F2	0.032617
MET	0.458333	MEG3	0.011292	IFIH1	0.98328	MIR223	0.024795	MSH2	0.03252
POLG	0.458333	ERCC4	0.011291	THBD	0.98328	PIK3R1	0.024729	CD4	0.032489
VCP	0.458333	NLRP3	0.011258	RUNX1	0.98328	PIK3CD	0.024729	IL2	0.032465
MECP2	0.458333	COL3A1	0.011153	SHH	0.98328	FGFR1	0.024665	PMM2	0.032394
CD36	0.458333	LOX	0.011153	IFNA1	0.98328	ACTA1	0.024653	GJB2	0.032318
TNFSF11	0.458333	MYH11	0.011153	ERBB2	0.983114	SMARCB1	0.024641	LEP	0.032299
TTN	0.458333	TNXB	0.011153	APOB	0.983114	SCN1A	0.02464	CXCR4	0.032276
FKRP	0.458333	ERBB2	0.011117	SST	0.983114	SMAD2	0.024617	MMP1	0.032165
DES	0.458333	F5	0.011084	PRKAG2	0.983114	ENPP1	0.024589	ENPP1	0.032134
TSC2	0.458333	ENPP1	0.011034	VCL	0.983114	POMC	0.024527	SDHB	0.031936
FIG4	0.458333	CSF3	0.011014	LDLR	0.983114	TTN	0.024503	TGFBR1	0.031924
FLNA	0.458333	FBLN5	0.011006	NEFL	0.982948	RUNX2	0.024473	PIK3R1	0.031854
ACTB	0.458333	NAGLU	0.010967	BAX	0.982948	AIFM1	0.024445	TH	0.0318
HSPG2	0.458333	LDLR	0.010949	SUMF1	0.982948	BRCA2	0.024433	POLR3A	0.031722
GJA1	0.458333	CREBBP	0.010897	F5	0.982782	MET	0.024433	GLI3	0.03172
BCL2	0.458333	PIK3R1	0.010896	TNFRSF1B	0.982782	SHH	0.024423	TSC1	0.031661
CD8A	0.458333	PIK3CD	0.010896	CASP3	0.982782	VDR	0.024415	IL2RA	0.031571
JAG1	0.458333	C9orf72	0.010876	SNCA	0.982616	HBB	0.024415	RAF1	0.031544
GLB1	0.458333	FGFR1	0.010867	SLC2A1	0.982616	TNFSF11	0.02441	ABCC6	0.031492
PRKAR1A	0.458333	VCAM1	0.01086	KCNQ2	0.982616	HMOX1	0.024395	TMEM67	0.031465
FBN2	0.458333	SHH	0.010831	DYSF	0.982616	SOX10	0.024395	ACTA1	0.031448
CCN2	0.458333	APOB	0.010819	LMNB1	0.982616	MEG3	0.024373	COMT	0.031403
SOX9	0.458333	PRKAG2	0.010819	CYP19A1	0.982616	PTGS2	0.024365	PRKN	0.031393
PDGFB	0.458333	VCL	0.010819	ITGB1	0.982616	SCN9A	0.024332	NGF	0.031356
SPP1	0.458333	IKBKG	0.010817	COL5A2	0.982616	MBTPS2	0.024293	MITF	0.031353
MEG3	0.458333	BSCL2	0.010806	ADA2	0.98245	PIK3C2A	0.024293	HNF1A	0.031343
SCN10A	0.416667	CCR5	0.010783	SOD2-OT1	0.982284	RMRP	0.024293	ERCC2	0.031305
PDGFRA	0.416667	SERPINC1	0.010742	ADRB2	0.982284	BSCL2	0.024244	LRP5	0.031266
TRPV1	0.416667	CASP3	0.010741	IGF1R	0.982284	SUMF1	0.024243	IFIH1	0.031207
IFIH1	0.416667	MIR125A	0.010707	GATA1	0.982284	SOX9	0.024217	CAV1	0.031172
STAT1	0.416667	ATM	0.010701	ITGB3	0.982284	TH	0.024203	INSR	0.031145
GCH1	0.416667	TNFRSF1B	0.010689	CD79A	0.982284	FGF2	0.02418	TUBB3	0.031072
IL5	0.416667	JAK2	0.010672	CLCN5	0.982284	FGFR2	0.024176	GAA	0.030926
CCR5	0.416667	MTOR	0.010658	SLC29A3	0.982284	RUNX1	0.02413	LDLR	0.030898
POMC	0.416667	CFTR	0.010619	SAMHD1	0.982119	IFNA1	0.02413	HBB	0.03084
VDR	0.416667	IFIH1	0.010606	AKT2	0.982119	SPP1	0.024116	JAG1	0.030834
PIK3R1	0.416667	THBD	0.010591	CCR5	0.981953	CTSK	0.024091	SMAD2	0.030793
VCAM1	0.416667	RUNX1	0.010547	DST	0.981953	FOS	0.024088	VDR	0.030749
HSPD1	0.416667	IFNA1	0.010547	SYNJ1	0.981953	NF2	0.024066	HLA-DRB1	0.030591
HBB	0.416667	CALCA	0.010504	HMGCR	0.981953	SLC17A5	0.024041	NOS3	0.030536
PIK3CD	0.416667	MIR34A	0.010504	STIM1	0.981953	ANO5	0.024041	FLNB	0.030321
FOS	0.416667	TRPV4	0.0105	C3	0.981953	GJB2	0.024019	SCN1A	0.030128
CXCL10	0.416667	MPZ	0.0105	GDNF	0.981953	TIMP1	0.024019	NEFL	0.030085
SCN1A	0.416667	CYP19A1	0.010482	KNG1	0.981788	TBX4	0.024019	NAGLU	0.030047
TNFRSF1B	0.416667	ITGB1	0.010482	JUN	0.981788	ACTC1	0.023966	VHL	0.03003
SMAD2	0.416667	COL5A2	0.010482	TREX1	0.981457	PDGFB	0.023942	TGFB2	0.030018
GLRA1	0.416667	ERCC8	0.010458	MIR210	0.981457	EZH2	0.023942	MAN2B1	0.030005
MAPT	0.416667	STAT5A	0.010458	TLR3	0.981291	IL5	0.023936	L1CAM	0.02993
TH	0.416667	NR3C1	0.010452	APP	0.981291	VCAM1	0.023931	NPC1	0.029886
ACTA1	0.416667	ALMS1	0.010426	IL2RB	0.981291	G6PD	0.023927	APOA1	0.029832
BSCL2	0.416667	ENG	0.010421	INSR	0.981126	PTCH1	0.023924	PRKAG2	0.029803
DSP	0.416667	CD79A	0.010394	ENO2	0.981126	AARS1	0.023876	GALC	0.029762
AARS1	0.416667	TTR	0.010367	RIGI	0.981126	LAMA2	0.023876	PTGS2	0.029703
SOX10	0.416667	ADA2	0.010358	C9orf72	0.98096	ATP7A	0.023875	ADA	0.029702
SMARCB1	0.416667	HLA-DRB1	0.01033	CASP8	0.98063	IDUA	0.023875	NFKB1	0.029577
CSF1R	0.416667	MAP2K1	0.010319	IL15	0.98063	ACAN	0.023875	LAMA2	0.029471
FLNC	0.416667	TSC2	0.010314	TARDBP	0.98063	ARSB	0.023875	STAT1	0.029434
TGFB3	0.416667	NEFL	0.010308	GNE	0.98063	GNPTAB	0.023875	IDUA	0.02943
AIFM1	0.416667	NFKB1	0.010292	ADAR	0.98063	IDS	0.023875	COL7A1	0.029424
FKTN	0.416667	SUMF1	0.010258	TBP	0.98063	GUSB	0.023875	SMPD1	0.029358
LAMA2	0.416667	BAX	0.010248	TBK1	0.98063	COL9A3	0.023875	SOX10	0.029261
ACTC1	0.416667	NRAS	0.010197	GJC2	0.98063	SDHB	0.023875	WFS1	0.029232
FBLN5	0.416667	APP	0.010145	IFNB1	0.9803	NEU1	0.023875	MPZ	0.029213
COL4A1	0.416667	BGN	0.010144	RELA	0.9803	FUCA1	0.023875	DSP	0.029187
ENPP1	0.416667	SST	0.010106	SELE	0.9803	HGSNAT	0.023875	DNMT1	0.029157
ERCC4	0.416667	SAMHD1	0.010072	CD28	0.9803	MAN2B1	0.023875	SMARCB1	0.029155
GALNS	0.416667	REN	0.010037	PECAM1	0.9803	FLNB	0.023875	GUSB	0.029106
BGLAP	0.416667	NKX2-5	0.010037	KRAS	0.980135	COL4A1	0.023848	FMR1	0.029093
CREBBP	0.416667	FKRP	0.010031	MAPK8	0.980135	COL3A1	0.02383	RUNX1	0.029091
SHH	0.416667	GDNF	0.010024	SYP	0.980135	LOX	0.02383	TNFRSF1A	0.02908
CASP3	0.416667	AKT2	0.010015	TAP2	0.980135	MYH11	0.02383	IDH1	0.029064
FGF2	0.416667	SOD2-OT1	0.009964	DPP4	0.980135	TNXB	0.02383	SLC26A2	0.028997
RUNX2	0.416667	ADRB2	0.009964	PAX1	0.980135	FN1	0.023817	MYH3	0.02899
SLC26A2	0.416667	SNCA	0.009958	GBA1	0.97997	NOS2	0.023796	FKTN	0.028956
MYH3	0.416667	SLC2A1	0.009958	FH	0.97997	SERPINC1	0.023778	MPO	0.028944
NF2	0.416667	KCNQ2	0.009958	CYCS	0.97997	NR3C1	0.023776	ENG	0.028861
PDGFRB	0.416667	DYSF	0.009958	F9	0.97997	PDGFRB	0.023762	CACNA1A	0.028748
CYP27A1	0.416667	LMNB1	0.009958	FGA	0.97997	ABL1	0.023762	KMT2D	0.028688
BMP2	0.416667	CASP8	0.00992	PPARGC1A	0.97997	GCH1	0.023761	MEFV	0.028662
EZH2	0.416667	IGF1R	0.009917	VIM	0.97997	AR	0.023712	COL4A1	0.028636
ENG	0.416667	GATA1	0.009917	ADAMTS13	0.97997	SOX2	0.023712	IL1A	0.028603
RAF1	0.416667	ITGB3	0.009917	EIF2AK2	0.97997	NTRK2	0.023712	CC2D2A	0.028569
PTCH1	0.416667	CLCN5	0.009917	POLR3B	0.97997	PLA2G6	0.02371	CCL2	0.028563
SCN11A	0.375	SLC29A3	0.009917	HSP90AA1	0.97997	CYP19A1	0.02371	MSH6	0.028494
IL23R	0.375	TNFRSF11B	0.009902	AGL	0.97997	ITGB1	0.02371	COMP	0.028486
MVK	0.375	IL2RB	0.009885	F10	0.97997	COL5A2	0.02371	INPP5E	0.028485
FOXP3	0.375	TLR3	0.009876	ELOVL4	0.97997	MMP3	0.023643	PRNP	0.028452
PMP22	0.375	GATA4	0.009867	TG	0.97997	BGLAP	0.023638	ALPL	0.028447
MIR146A	0.375	PDGFB	0.00984	PEX1	0.97997	EPO	0.023628	PAX6	0.028338
ALK	0.375	COMT	0.009804	CDH23	0.97997	TRPV1	0.023627	MKS1	0.028174
CALCA	0.375	COL4A1	0.009792	RNASEH2B	0.97997	SMPD1	0.0236	IKBKG	0.028166
MIF	0.375	KNG1	0.009788	CTSB	0.97997	ALK	0.023597	CASP8	0.028098
LTA	0.375	VEGFA	0.009788	COL7A1	0.97997	KIT	0.023597	NSD1	0.028087
CASP8	0.375	JUN	0.009788	THBS1	0.97997	BTK	0.023597	DYNC1H1	0.028051
KIT	0.375	TREX1	0.009734	RNASEH2A	0.97997	APC	0.023597	NR3C1	0.028016
PIK3CG	0.375	VDR	0.009702	GP1BA	0.97997	CALCA	0.023577	CP	0.027999
MALAT1	0.375	HBB	0.009702	S100B	0.97997	MIR34A	0.023577	CD36	0.027988
ABCA1	0.375	SLC26A2	0.009683	RNASEH2C	0.97997	SAMHD1	0.023559	SMC1A	0.02798
APP	0.375	SPP1	0.009635	PIGL	0.97997	ERBB2	0.023532	MEG3	0.027908
MIR34A	0.375	DST	0.009625	JAG1	0.979146	IGF1R	0.02353	DHCR7	0.027844
NR3C1	0.375	SYNJ1	0.009625	H19	0.977338	GATA1	0.02353	JAK2	0.02784
F5	0.375	HMGCR	0.009625	MEG3	0.977174	ITGB3	0.02353	DNMT3A	0.027816
BTK	0.375	STIM1	0.009625	IL1RN	0.97701	CLCN5	0.02353	BSCL2	0.027794
SPTLC1	0.375	C3	0.009625	CERNA3	0.97619	SLC29A3	0.02353	CHD7	0.027747
PLA2G6	0.375	IL15	0.009607	CSF3	0.97619	IL1R1	0.023529	EDN1	0.027689
SERPINA1	0.375	TUBB3	0.009571	MYH7	0.97619	SELENON	0.023522	MRE11	0.02755
ADIPOQ	0.375	AGT	0.009561	CCL2	0.976027	IGF2	0.023508	ABL1	0.027544
HIF1A	0.375	SERPINE1	0.009561	CDH1	0.976027	ABCC6	0.0235	AARS1	0.027463
MIR221	0.375	PECAM1	0.009469	HRAS	0.975863	DKC1	0.023488	FH	0.027442
MIR140	0.375	MIR210	0.009452	MTOR	0.975863	PIK3CG	0.023486	CYP19A1	0.02743
WAS	0.375	RET	0.009449	NRAS	0.975863	BMP6	0.023486	NEU1	0.027427
ERCC2	0.375	ACTB	0.009427	FKRP	0.9757	ABCC9	0.023478	ATP6V0A2	0.027416
SCN5A	0.375	INSR	0.009396	JAK2	0.975536	FKTN	0.023472	IDS	0.027415
ASAH1	0.375	ENO2	0.009396	TSC2	0.975536	RAF1	0.023461	ALDH18A1	0.027317
ITGAM	0.375	RIGI	0.009396	CFTR	0.975373	PTH	0.023458	GNE	0.02725
MSH2	0.375	FN1	0.009366	MIR125A	0.975373	BMP2	0.023453	FGF8	0.027209
APC	0.375	TBX1	0.009352	MAP2K1	0.975373	ABCB1	0.023451	DNM2	0.027201
THBD	0.375	ACVRL1	0.009352	ACTB	0.975373	ADA2	0.023447	CTSK	0.027182
MLH1	0.375	PAX1	0.009328	TRPV4	0.975209	ENG	0.023368	APOB	0.027119
WNK1	0.375	LTBP4	0.009316	MPZ	0.975209	ASAH1	0.023358	B3GAT3	0.027034
G6PD	0.375	MAPK8	0.009297	TNFRSF11B	0.975209	GH1	0.023328	EGF	0.026997
SAMHD1	0.375	TAP2	0.009297	ALMS1	0.975209	CASR	0.023328	TARDBP	0.02696
BMP6	0.375	DPP4	0.009297	RET	0.975046	CST3	0.023328	TPM2	0.026952
CP	0.375	PDGFRB	0.009292	MECP2	0.97472	IGFBP3	0.023328	HNF1B	0.026941
ADA2	0.375	DKC1	0.00928	FIG4	0.97472	MAF	0.023328	PIK3CD	0.026905
GPHN	0.375	CD28	0.009274	ATM	0.974556	IDH2	0.023328	MYH11	0.0269
KIF1A	0.375	IFNB1	0.009265	FGFR1	0.974556	CTSD	0.023273	TREX1	0.026889
NEFL	0.375	RELA	0.009265	NFKB1	0.974393	FLNC	0.023221	SYNE1	0.026868
ABCC9	0.375	SELE	0.009265	MAPT	0.974393	GATA4	0.023187	PLA2G6	0.026839
REN	0.375	TARDBP	0.00926	COL4A1	0.974393	SST	0.023178	ADA2	0.026823
EPO	0.375	GNE	0.00926	GALNS	0.974393	TPM3	0.023172	MVK	0.026807
SPTAN1	0.375	ADAR	0.00926	VDR	0.97423	FMR1	0.023169	JUN	0.026798
SELENON	0.375	TBP	0.00926	HBB	0.97423	MYH14	0.023163	AIFM1	0.026733
HSPB1	0.375	TBK1	0.00926	TUBB3	0.97423	DNMT3A	0.023162	SCN5A	0.026706
GAA	0.375	GJC2	0.00926	ENG	0.97423	LRP5	0.023162	ICAM1	0.026684
RB1	0.375	HIF1A	0.009248	REN	0.974067	SDHC	0.023162	GALNS	0.026653
MYH14	0.375	EPHX1	0.009239	AIFM1	0.974067	IHH	0.023162	COL5A2	0.026649
VHL	0.375	NGF	0.009231	NKX2-5	0.974067	SPARC	0.023162	GATA4	0.026592
NKX2-5	0.375	ACTA2	0.009222	TTR	0.973904	EBP	0.023162	FBLN5	0.026574
DYNC1H1	0.375	SYP	0.009219	FN1	0.973904	WNT1	0.023162	ERCC4	0.026544
GDAP1	0.375	TPM2	0.009208	CTSK	0.973741	MMP14	0.023162	CTSD	0.026509
IGF2	0.375	FIG4	0.009146	GATA4	0.973741	SGSH	0.023132	FN1	0.026505
SLC17A5	0.375	MECP2	0.00914	AARS1	0.973579	GDNF	0.023123	ACAN	0.026489
TPM3	0.375	TSC1	0.009103	LAMA2	0.973579	SPTAN1	0.023113	CACNA1C	0.026448
CTSK	0.375	TRAF3IP2	0.009052	VEGFA	0.973416	DYNC1H1	0.023113	CDKL5	0.026434
FN1	0.375			PDGFB	0.973416
PTH	0.375
EGR2	0.375
HLA-A	0.375
GNRH1	0.375
ANO5	0.375
CDKN1A	0.375
SERPINC1	0.375
COL9A2	0.375
LDLR	0.375
CYP19A1	0.375
RUNX1	0.375
GJB2	0.375
COL3A1	0.375
FASLG	0.375
ALDH18A1	0.375
GDNF	0.375
POLR3A	0.375
ITGB1	0.375
IFNA1	0.375
LOX	0.375
MYH11	0.375
CD79A	0.375
ABCC6	0.375
COL5A2	0.375
TNXB	0.375
TIMP1	0.375
SUMF1	0.375
MBTPS2	0.375
BGN	0.375
PIK3C2A	0.375
TBX4	0.375
RMRP	0.375
DVL1	0.375
ATP7A	0.375
UBA1	0.375
AR	0.375
IDUA	0.375
ACAN	0.375
ARSB	0.375
GNPTAB	0.375
IDS	0.375
GUSB	0.375
FMR1	0.375
SMPD1	0.375
CTSD	0.375
COL9A3	0.375
SDHB	0.375
NEU1	0.375
FUCA1	0.375
HGSNAT	0.375
SGSH	0.375
TSC1	0.375
MAN2B1	0.375
SOX2	0.375
FLNB	0.375
NTRK2	0.375
ACTA2	0.375
GATA4	0.375
ABL1	0.375
NOTCH2	0.375
DKC1	0.375

). From the top 5% rankings of each metric, a total of 507 unique genes were identified. Among these, 372 genes were found to be ranked within the top 5% in at least two centrality measures. Applying a stricter criterion, 153 genes that consistently ranked within the top 5% across all 5 centrality indices were selected (Figure 3). After validating the network using a PPI analysis confidence score threshold of 0.7 and removing nodes with low confidence or no connectivity, 145 genes were finalized as the core symptom-associated gene set.

3.3. Expression Validation of Core Symptom-Associated Genes

Differential expression analysis was performed with adjustment for multiple testing using the Benjamini–Hochberg false discovery rate (FDR) procedure. Both nominal p-values and FDR-adjusted p-values were calculated, and effect size estimates were used to facilitate interpretation of biological relevance. Among these, five genes (PTEN, TLR4, JAK2, NRAS, and NR3C1) showed nominally increased expression in AS patients compared to healthy controls (Figure 4). PTEN exhibited a fold change of 1.436 (nominal p = 0.000678), followed by TLR4 (fold change = 1.349, nominal p = 0.011243), JAK2 (fold change = 1.285, nominal p = 0.012802), NRAS (fold change = 1.067, nominal p = 0.024315), and NR3C1 (fold change = 1.109, nominal p = 0.028972).

3.4. Network Proximity Analysis of TNF Within the Symptom Gene Landscape

TNF was found to be directly connected to 13 genes, namely TNF, HLA-B, IL10, PTPN11, AKT1, NTRK1, CTNNB1, BDNF, CTLA4, MYC, CD36, ACTB, and NOTCH1, among the 145 core symptom-associated genes, representing 9.0 percent of the network. The average shortest path length between TNF and all core genes was calculated as 1.077. To assess statistical significance, this value was compared against a null distribution generated from 10,000 randomly sampled gene sets. The observed proximity produced a z-score of −0.009 and a p-value of 0.496, indicating that TNF does not show statistically significant global proximity to the core symptom-associated gene set. These results indicate that TNF exhibits prominent local connectivity within the symptom-associated network, despite the absence of statistically significant global proximity across the full gene set.

3.5. Pathway Enrichment of TNF-Linked and Central Symptom-Associated Genes

13 genes directly linked to TNF and 14 genes ranked within the top 10% in network centrality among the 145 core symptom-associated genes were first selected. The 14 genes with high network centrality were FN1, AKT1, IL6, TP53, EGFR, SRC, CTNNB1, STAT3, MMP9, TGFB1, INS, IL1B, NFKB1, and MYC. After removing overlapping entries such as AKT1, CTNNB1, and MYC, which appeared in both groups, a final set of 24 unique genes was constructed. This 24-gene set was then subjected to Reactome pathway enrichment analysis, which identified several statistically significant pathways primarily related to immune function and inflammatory signaling. Notably, the most significantly enriched pathways included Interleukin-4 and Interleukin-13 signaling, Cytokine signaling in the immune system, and Interleukin-10 signaling, all showing high −log₁₀(p-value) scores above 14 (Figure 5).

4. Discussion

The present study demonstrates the utility of a novel symptom-centered network pharmacology framework to explore the molecular basis of AS. By diverging from conventional disease-centric models, which often fail to explain clinical variability, our symptom-level approach successfully captured the molecular heterogeneity of AS with greater granularity. This strategy provides a more precise framework for aligning molecular dysregulation with specific patient-level manifestations, offering a promising path toward precision therapeutics.

The first major finding was the identification of 145 core symptom-associated genes through a combination of centrality analysis and PPI network refinement. Symptom-associated genes were not selected based on symptom frequency or keyword overlap but were prioritized through multi-metric network centrality, minimizing bias from database-driven parameter choices. The biological relevance of this gene set was further supported by validation against the GDS5231 dataset, which revealed significant upregulation of five genes, including PTEN, TLR4, JAK2, NRAS, and NR3C1 in AS patients compared with healthy controls. These genes represent critical mechanistic anchors that bridge molecular dysregulation with clinical manifestations. TLR4, a central mediator of innate immunity, is activated by microbial antigens and reinforces the hypothesis that gut dysbiosis contributes to AS pathogenesis [31]. JAK2 is a key component of the IL-23/Th17 axis, a pathway central to AS inflammation and directly targeted by recently approved JAK inhibitors [32]. PTEN and NRAS link inflammatory signaling to processes of cell survival and tissue remodeling, suggesting that structural changes in AS may involve pathways beyond cytokine-driven inflammation alone [33]. Finally, NR3C1, encoding the glucocorticoid receptor, provides a plausible molecular explanation for the variability in corticosteroid responsiveness observed in clinical practice [34]. Accordingly, the transcriptomic validation should be interpreted as supportive evidence linking network-derived candidates to patient data, rather than as definitive confirmation of disease-specific expression changes.

Beyond gene identification and expression validation, we investigated the network proximity of FDA-approved drug targets to the 145 symptom-associated genes. This analysis was motivated by the recognition that while TNF inhibitors remain the cornerstone of AS therapy, up to 30% of patients exhibit primary or secondary non-responsiveness [35]. Our results showed that TNF occupies a hub-like position within the symptom gene network and is directly connected to 13 of the identified core genes. These local connections emphasize the capacity of TNF inhibition to modulate critical immune-regulatory hubs that shape symptom expression. However, the lack of statistically significant global proximity between TNF and the entire 145-gene set suggests that the therapeutic effects of TNF inhibitors arise from selective rather than widespread network modulation. In contrast, the targets of more recently approved biologics, including IL-17A and JAK inhibitors, were not represented within the symptom-associated network. This finding provides a plausible molecular explanation for the clinical observation that while many patients benefit substantially from TNF blockade, others experience only partial or no improvement [15]. The targets of more recently approved biologics, including IL-17A and JAK inhibitors, were not represented within the symptom-associated network. Although IL-17 and JAK pathways are well-established drivers of AS pathogenesis at the disease level, their absence from the symptom-level gene set may account for the variable treatment responses observed in clinical practice. To address this complexity, we refined our analysis by constructing a 24-gene set that integrated two complementary perspectives: 13 genes directly connected to TNF within the symptom-associated network and 14 genes from the top 10% of centrality rankings among the 145 core genes. This combined set captured both the immediate pharmacological neighborhood of TNF and the broader symptom-related hubs. Enrichment analysis of the 24-gene set revealed significant associations with immune and inflammatory signaling pathways, including Interleukin-4 and Interleukin-13 signaling, Cytokine signaling in the immune system, and Interleukin-10 signaling. These pathways are highly relevant to AS, as they regulate processes such as T-helper cell differentiation, cytokine-mediated communication, and anti-inflammatory feedback. The convergence of TNF-linked and symptom-central genes on these pathways suggests that symptom expression in AS is coordinated by broader immune-modulatory circuits rather than by isolated molecular targets. Within the context of an immune-mediated disease such as ankylosing spondylitis, enrichment of immune and cytokine signaling pathways is biologically expected. Importantly, from a symptom-centered perspective, the clinical relevance of the 24-gene set lies not in pathway novelty but in its ability to link immune regulatory circuits to specific patient-reported symptom manifestations. By integrating TNF-linked genes with highly central symptom-associated nodes, this focused module captures immune pathways that are most directly aligned with patient-reported disease burden, rather than representing a nonspecific inflammatory signature.

This interpretation offers several important insights. First, it provides a molecular rationale for the broad clinical efficacy of TNF inhibitors, despite the absence of statistically significant global proximity: their direct influence on key hubs within immune-inflammatory circuits may be sufficient to disrupt symptom-driving processes. Second, it helps explain the heterogeneity of responses to IL-17A and JAK inhibitors, as these targets may not consistently overlap with the symptom-centric networks that drive clinical manifestations. Third, the integration of TNF-linked and highly central symptom-associated genes highlights potential opportunities for adjunctive or alternative targets that could complement the limitations of current biologics. Previous investigations of AS have typically focused on disease-centric gene sets identified from genome-wide association studies or transcriptomic profiling of patient cohorts [36]. These studies consistently emphasized the roles of HLA-B27, IL-23/Th17 signaling, and bone remodeling pathways, which remain critical to understanding AS pathology [37]. However, by collapsing the disease into a single set of genes, such approaches may obscure the molecular heterogeneity underlying distinct symptom domains. Symptom-centered analysis, by contrast, dissected the molecular correlates of specific clinical manifestations such as inflammatory back pain, morning stiffness, and peripheral arthritis, thereby offering a perspective on how pathogenesis translates into patient experience. It is important in the context of personalized medicine, where the alignment between therapeutic targets and patient-specific symptomatology may determine treatment success or failure.

The multi-step analytical framework adopted in this study, which combined relevance thresholds, multi-metric centrality analysis, expression validation, and drug–symptom proximity assessment, allowed us to minimize spurious associations and enhance the biological reliability of our findings. Importantly, clinical symptoms were explicitly defined and independently operationalized prior to gene collection, allowing symptom-associated molecular signals to be distinguished from generic inflammatory or pain-related biology. A limitation of this study is the reliance on a single integrative database for gene collection, which may bias the analysis toward well-studied targets. However, this potential limitation was partially mitigated by the subsequent multi-layered network refinement strategy. In particular, refining the network into a 24-gene set by integrating TNF-linked and highly central nodes provided a clearer interpretive landscape, ensuring that enrichment results were both mechanistically grounded and clinically meaningful. Even results that did not reach statistical significance in proximity testing contributed valuable insights when considered in the context of clinical heterogeneity, highlighting the selective rather than global influence of existing therapies. At the same time, certain aspects should be interpreted with caution. The reliance on whole-blood transcriptomic data means that expression changes in primary sites of pathology, such as the sacroiliac joints or entheses, may not have been fully captured. Protein–protein interaction data also reflect only currently annotated relationships, potentially missing tissue-specific or dynamic interactions. In addition, the proximity analysis was limited to TNF, as it was the only FDA-approved target overlapping with the symptom network, and therefore does not encompass the full spectrum of therapeutic agents currently used in AS. To further advance these findings, incorporating tissue-specific omics data from inflamed spinal and entheseal sites could provide a more accurate representation of AS pathology. Expanding the analytical framework to include proteomic and epigenetic layers may also clarify how symptom-associated networks are regulated across multiple biological dimensions. In addition, module-based analyses of the identified network could reveal whether distinct clusters of genes correspond to particular symptom domains, thereby informing strategies for more targeted therapeutic interventions. Moreover, extending proximity analyses to include IL-17A and JAK inhibitor targets may also help explain the heterogeneity of treatment responses observed in clinical settings.

While traditional disease-centric frameworks have contributed foundational insights into AS pathogenesis, our findings suggest that they are insufficient to fully capture the molecular complexity of the disease. Enrichment analysis of the disease-core gene set yielded only broad immune-related categories with limited specificity, and its minimal overlap with the symptom-core network highlights the limitations of collapsing clinical heterogeneity into a single gene set. In contrast, the symptom-centric approach preserved pathway diversity and identified immune-regulatory circuits more directly associated with symptom expression. This framework may therefore serve as a more precise and clinically relevant strategy for delineating AS heterogeneity and developing symptom-specific therapeutic interventions. In conclusion, this study demonstrates that while our findings provide novel insights into the molecular architecture of AS symptoms, they also reveal areas where future research is needed to refine and extend the approach. By continuing to align molecular networks with clinical manifestations, symptom-centered network pharmacology may contribute to therapeutic strategies that are not only biologically precise but also better tailored to the lived heterogeneity of AS patients.