Identifying differentially expressed genes, miRNAs and TFs in major depressive disorder by bioinformatics analysis of next generation sequencing data

Identifying differentially expressed genes, miRNAs and TFs in major depressive disorder by bioinformatics analysis of next generation sequencing data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying differentially expressed genes, miRNAs and TFs in major depressive disorder by bioinformatics analysis of next generation sequencing data Basavaraj Vastrad, Chanabasayya Vastrad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6799275/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Major depressive disorder (MDD) is a neuropsychiatric disorder with a high risk of suicide. This study aimed to examine the key genes, microRNAs and TFs associated with MDD. Next generation sequancing dataset (GSE275676) was downloaded from the Gene expression omnibus (GEO) database. The DEGs were screened using DESeq2 in R bioconductor tool. We next performed gene ontology (GO) analysis and REACTOME pathway enrichment analysis using the g:Profiler. Moreover, Cytoscape with IMex interactome was utilized to visualize protein-protein interaction (PPI) network and modules of these DEGs. Subsequantely, miRNA-hub gene regulatory network and TF-hub gene regulatory network were built by Cytoscape to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. Finally, top 10 hub genes in the PPI network were validated by receiver operating characteristic (ROC) curve analysis. We screened 958 DEGs, containing 479 up regulated genes and 479 down regulated genes. GO and REACTOME pathway enrichment analyses revealed that DEGs are mainly enriched in cell communication, synapse, enzyme binding, response to stimulus, cell periphery, signaling receptor binding, neuronal system and immune system. YWHAG, ONECUT1, CALM2, ARRB1, PIK3R1, CDK2, VCAM1, HLA-B, VIM and ERBB2 are the topb hub genes in PPI network and modules. The predicted miRNA-hub gene regulatory network identified miRNAs (hsa-miR-548j-5p, hsa-mir-466, hsa-miR-523-5p and hsa-mir-6829-3p) targeting hub genes and predicted TF-hub gene regulatory network identified TFs (PAX2, JUND, FEV and PPARG). In conclusion, this bioinformatics analysis of NGS data demonstrated that hub genes, miRNAs and TFs might regulate the development of MDD. These hub genes, miRNAs and TFs could be used as new biomarkers for diagnosis and to guide the combination medicine of MDD. Major depressive disorder bioinformatics analysis differential expression genes biomarker therapeutic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Major depressive disorder (MDD) is a common mental disorders that manifests as depressed mood lasting more than 2 weeks and causes emotional distress, functional impairment, health problems, and suicide, among others [Kang and Cho, 2020]. It is estimated to affect more than 250 million of the global population, with no difference in prevalence observed between childhood, adolescence, and adulthood [Bimerew et al. 2024]. Several risk factors have been proposed to cause MDD including genetic inheritance [Lohoff, 2010], environmental variables [Peyrot et al. 2013], oxidative stress [Ait Tayeb et al. 2023], inflammation [Zainal et al. 2021], rheumatoid arthritis [Jiang et al. 2023], Crohn's disease [Persoons et al. 2005], chronic obstructive pulmonary disease [Liu et al. 2024], asthma [Oyamada et al. 2021], obesity [Gibson-Smith et al. 2016], diabetes mellitus [Wang et al. 2019], cardiovascular diseases [Zhang et al. 2021], hypertension [Zhang et al. 2024], Alzheimer's disease [Amidfar et al. 2023], Parkinson's disease [Tran et al. 2021], Huntington's disease [Perlis et al. 2010], bipolar disorder [Goldstein et al. 2015], dementia [Asmer et al. 2018], schizophrenia [Etchecopar-Etchart et al. 2021], epilepsy [Kim et al. 2018], multiple sclerosis [Ehde et al. 2008], autism spectrum disorder [Naheed et al. 2020] and stroke [Li et al. 2012]. As MDD has unclear pathogenesis and an unsatisfactory response to treatment, it is necessary to explore the molecular mechanism of MDD to develop effective target treatments. The underlying complex molecular mechanisms of MDD pose a special challenge to daily clinical practice. Debate on the best strategy for MDD management continues despite great progress in treating MDD in recent decades. Extensive investigation have shown current therapeutic approaches in MDD included monoamine oxidase inhibitors [López-Muñoz and Alamo, 2009], tricyclic antidepressants [Pereira and Hiroaki-Sato, 2018], serotonin reuptake inhibitors [Clevenger et al. 2018], norepinephrine reuptake inhibitors [Pehrson et al. 2015] and ketamine [Zhang et al. 2020] targeting several crucial signaling pathways that predominantly regulate MDD. However, MDD might also can be caused by many unknown causes, such as oxidative stress and inflammation, which cannot be well solved by current drug treatment and MDD is still a complicated incurable mental disorder [Emekdar et al. 2023]. Thus, it is necessary for us to utilize bioinformatics and next generation sequencing (NGS) technology to explore the molecular pathogenesis or potential treatments of MDD. The rapid development of bioinformatics and NGS technology has become an important part of modern medical investigation. The recent bioinformatics analysis of NGS data of human specimens from sufferers and normal individuals enables us to investigate various diseases at diverse levels from somatic mutations and copy number variations to genomic expressions at the transcriptomic level, along with epigene variations [Pujar et al. 2022; Ganekal et al. 2023]. Recently, many specific genes and signaling pathways have been discovered to participate in the progression of MDD. Genes include IL6 [Zhang et al. 2016], M6A [Du et al. 2015], KCNK2 [Liou et al. 2009], HTR1A [Kishi et al. 2009] and FKBP5 [Rao et al. 2016] were altered in MDD, in contrast to normal control. Wnt signaling pathway [Sanchez-Ruiz et al. 2024], mTOR signaling pathway [Jernigan et al. 2011], TLR signaling pathway [Hung et al. 2017], G protein-linked signaling pathways [Tomita et al. 2013] and PI3K/AKT signaling pathway [Guo et al. 2024] suggesting the relevant signaling molecules might be a molecular target for specific diagnosis and therapy for MDD. These finding suggested the important roles of some function genes and signaling pathway in MDD progression. However, the diagnostic value of various genes has not been investigated in MDD. In this investigation, we used bioinformatics analysis of NGS data to investigate the molecular regulatory mechanisms of genes and signaling pathways that influence MDD advancement and explore therapeutic agents that could target genes. We obtained DEGs between MDD and normal control samples from GSE275676 [Mitsuhashi et al. 2024], a NGS data downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) [Clough and Barrett, 2016]. The samples were derived from the post-mortem ventromedial prefrontal cortex of 19 normal control samples and 20 MDD samples. Differently expressed genes (DEGs) were screened by DESeq2 in R bioconductor tool [Love et al. 2014]. The DEGs were performed to Gene Ontology (GO) analysis and REACTOME pathway enrichment analysis. And then a protein-protein interaction (PPI) network and modules was constructed and analysis to screen hub genes. We performed miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed to predict candidate miRNAs and TFs for MDD. Finally, we verified hub gens for diagnostic value with receiver operating characteristic (ROC) curve analysis. This investigation might provide new insight into potential biomarkers and therapeutic targets for MDD. Materials and Methods Next generation sequencing data source The MDD NGS dataset GSE275676 [Mitsuhashi et al. 2024] was downloaded from the GEO database generated by GPL24676 Illumina NovaSeq 6000 (Homo sapiens). GSE275676 including post-mortem ventromedial prefrontal cortex of 19 normal control samples and 20 MDD samples, used to identify MDD of the DEGs. Identification of DEGs We utilized the DESeq2 in R bioconductor tool [Love et al. 2014] to identify DEGs between the normal control samples and 20 MDD samples in the GSE275676 dataset. The criterion for defining differential gene expression was a p-value 0.1458 for up regulated genes and |log2 (fold change)| < -0.274 for down regulated genes, and were visualized in volcano plots using ggplot2 packages in R software. The DEGs with differences were filtered out and the results were displayed by a heat-map using gplot packages in R software. GO and pathway enrichment analyses of DEGs GO enrichment analysis and REACTOME signaling enrichment pathway analysis were performed on the above DEGs by g:Profiler (http://biit.cs.ut.ee/gprofiler/) [Reimand et al. 2007] to understand the biological functions of these DEGs. Gene Ontology (GO) enrichment analysis (http://www.geneontology.org) [Thomas, 2017] was actually associated to the disease and classifies GO terms in three categories such as biological processes (BP), cellular component (CC), and molecular function (MF). The REACTOME (https://reactome.org/) [Fabregat et al. 2018] is a widely used pathway database that genomic data for biological pathways, diseases, and drugs. Adjusted p values < 0.05, which was considered statistically significant as a criteria for selection. Construction of the PPI network and module analysis Online tool IMex interactome ( https://www.imexconsortium.org/curation/ ) [Porras et al. 2020] database was used to construct PPI network based on the DEGs. Cytoscape software18 (v3.10.3) (http://www.cytoscape.org/) [Shannon et al. 2003] was used to visualize the interaction results from the IMex interactome. The Network Analyzer plug-in was used to obtain hub genes according to the node degree [Luo et al. 2017], betweenness [Li et al. 2017], stress [Gilbert et al. 2021] and closeness [Li et al. 2020] methods. The significant modules in the PPI network were identified by the PEWCC [Zaki et al 2013], one plug-in of Cytoscape software. Construction of the miRNA-hub gene regulatory network The hub genes with high degree of connectivity in miRNA-hub gene regulatory network were selected as the promising targets for searching miRNA through the miRNet database (https://www.mirnet.ca/) [Fan et al 2018]. This database contains miRNA- hub gene interaction data from 14 disparate sources including TarBase, miRTarBase, miRecords, miRanda, miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0. The identified target network was visualized using the Cytoscape software [Shannon et al. 2003]. Construction of the TF-hub gene regulatory network The hub genes with high degree of connectivity in TF-hub gene regulatory network were selected as the promising targets for searching TF through the NetworkAnalyst (https://www.networkanalyst.ca/) [Zhou et al 2019]. This database contains TF- hub gene interaction data from disparate source JASPER. The identified target network was visualized using the Cytoscape software [Shannon et al. 2003]. Receiver operating characteristic curve (ROC) analysis For ROC analysis of individual hub gene, outcome (MDD) and individual hub gene expression were collected. The area under the curve (AUC) could be used as an indicator to measure the diagnostic value of hub genes. Using pROC package in R biocontuctoor tool [Robin et al. 2011], we determined the AUC. Firstly, logistic regression was used to determine the diagnostic value. Then, according to the outcome and diagnostic value, we determined the AUC value. The AUC was quantified, with AUCs > 0.8 having statistical significance. Results Identification of DEGs To investigate DEGs in MDD, we downloaded the NGS data in the GSE275676 from GEO datasets. A threshold of P 0.1458 and |log2 (fold change)| < -0.274 was utilized to screen up and down regulated genes. A total of 958 genes were differentially expressed between the MDD and normal control samples in the GSE275676 dataset, including 479 up regulated genes and 479 down regulated genes (Table. 1). The volcano plot of the DEGs was generated (Fig.1). 958 DEGs were shown in the heat map (Fig.2). GO and pathway enrichment analyses of DEGs Then, GO and REACTOME pathway enrichment analyses were implemented with the g:Profiler to explore the potential biofunction of DEGs. The findings revealed that the most enriched GO terms were associated with the cell communication, biological regulation, response to stimulus and multicellular organismal process(BP); synapse, cell projection, cell periphery and endomembrane system (CC); enzyme binding, molecular function regulator activity, signaling receptor binding and amide binding (Table. 2). According to the REACTOME pathway enrichment study, DEGs have a significant role in neuronal system, transmission across chemical synapses, immune system and neutrophil degranulation (Table. 3). Construction of the PPI network and module analysis The PPI network was built to know the potential connection among the DEGs. A PPI network with 4875 nodes and 11005 interacting pairs was constructed (Fig. 3). The identified 10 hub genes included 5 up-regulated genes (YWHAG, ONECUT1, CALM2, ARRB1 and PIK3R1) and 5 down-regulated genes (CDK2, VCAM1, HLA-B, VIM and ERBB2) (Table. 4). We further identified the hub genes according to the node degree, betweenness, stress and closeness method. Through Cytoscape PEWCC, two significant modules from the PPI network complex were found. Functional-annotation clustering showed that module 1 comprised 25 nodes and 64 edges (Fig. 4A). The hub genes in module 1 involved in neuronal system, signal transduction, transmission across chemical synapses, cell communication, biological regulation and signaling by Rho GTPases, MiroGTPases and RHOBTB3. Module 2 is comprising of 17 nodes and 24 edges (Fig. 5B). The hub genes in module 2 involved in immune system, signaling by interleukins, response to stimulus and endomembrane system. Construction of the miRNA-hub gene regulatory network To understand the potential regulation of hub genes, we performed a comprehensive network analysis with miRNA. The miRNA-hub gene regulatory network consists of 2764 nodes (miRNA: 2427; Hub Gene: 336) and 45050 edges (Fig.5). YWHAG can be regulated by 441 miRNAs (ex: hsa-miR-548j-5p), PIK3R1 can be regulated by 406 miRNAs (ex: hsa-mir-466); CALM2 can be regulated by 333 miRNAs (hsa-miR-1911-5p); YWHAH can be regulated by 325 miRNAs (hsa-miR-142-3p); NEDD4L can be regulated by 309 miRNAs (hsa-mir-376c-3p); TNFAIP3 can be regulated by 361 miRNAs (hsa-miR-523-5p); SERPINH1 can be regulated by 348 miRNAs (hsa-mir-6829-3p); CDK2 can be regulated by 338 miRNAs (hsa-miR-34a-5p); VIM can be regulated by 334 miRNAs (hsa-mir-708-3p); ERBB2 can be regulated by 285 miRNAs (hsa-miR-6891-3p) (Table. 5). Construction of the TF-hub gene regulatory network To understand the potential regulation of hub genes, we performed a comprehensive network analysis with TF. The TF-hub gene regulatory network consists of 2764 nodes (TF: 97; Hub Gene: 335) and 2969 edges (Fig.6). NEDD4L can be regulated by 24 TFs (ex: PAX2); HDAC9 can be regulated by 19 TFs (ex: JUND); PIK3R1 can be regulated by 18 TFs (ex: EGR1); MAP1LC3A can be regulated by 14 TFs (ex: CREB1); PRKCZ can be regulated by 13 TFs (ex: HINFP); TNF can be regulated by 51 TFs (ex: FEV); IRF1 can be regulated by 17 TFs (ex: PPARG); ERBB2 can be regulated by 17 TFs (ex: TFAP2C); NFKB2 can be regulated by 14 TFs (ex: GATA2); CDK2 can be regulated by 14 TFs (ex: TEAD1) (Table. 5). Receiver operating characteristic curve (ROC) analysis To access the potential prediction function of hub genes in MDD, we focused ROC curve analyses of 10 MDD related hub genes using the “pROC” package. The AUC values were as follows: YWHAG (AUC = 0.919), ONECUT1 (AUC = 0.909), CALM2 (AUC = 0.915), ARRB1 (AUC = 0.903), PIK3R1 (AUC = 0.905), CDK2 (AUC = 0.917), VCAM1 (AUC = 0.921), HLA-B (AUC = 0.913), VIM (AUC = 0.893) and ERBB2 (AUC = 0.907) (Fig.7). These AUC values implied that all ten MDD related hub genes have high specificity for MDD. Discussion Much progress has been made in investigation on MDD, but it remains one of the most common mental disorders seriously affecting human health worldwide. The incidence of MDD will continue to increase in the coming years [Abdoli et al. 2022]. By understanding and exploring its molecular pathogenesis, we can prevent the occurrence of MDD. NGS data analysis is a powerful technique that can identify the DEGs in specific physiological and pathological states and discover novel diagnostic or therapeutic targets [Devarbhav et al. 2021]. In this investigation, we conducted a systematic bioinformatics analysis of the NGS GEO dataset (GSE275676) to reveal the potential molecular mechanisms of MDD. We screened 958 DEGs from NGS dataset, including 479 up regulated and 479 down regulated genes. CYP2C19 [Bonasser et al. 2024] and LTF (lactotransferrin) [Wang et al. 2025] are involved in the progression of MDD. CYP2C19 [Klomp et al. 2024], MMP1 [Li et al. 2002], CXCL11 [Kochumon et al. 2020] and LTF (lactotransferrin) [Artym et al. 2021] expression is altered in the patients with inflammation. CYP2C19 [Wiese et al. 2012] and MMP1 [Wang et al. 2020] have been proposed as novel biomarkers for rheumatoid arthritis progression. Altered expression of CYP2C19 [Lu et al. 2020], MMP1 [Yi et al. 2021] and CXCL11 [Porter et al. 2008] are associated with chronic obstructive pulmonary disease. Regulation of CYP2C19 [Lang et al. 2015] levels might be a novel treatment option against asthma. CYP2C19 [Thompson et al. 2024], MMP1 [Boumiza et al. 2017], TFAP2B [Albuquerque et al. 2014], CXCL11 [Kochumon et al. 2020] and LTF (lactotransferrin) [Jamka et al. 2020] were previously reported to be significantly associated with obesity. CYP2C19 [Austin-Zimmerman et al. 2021], TFAP2B [Maeda et al. 2005], C2CD4B [Kycia et al. 2018] and LTF (lactotransferrin) [Mohamed et al. 2018] play a pivotal role in diabetes mellitus. CYP2C19 [Maas et al. 2024], MMP1 [Hu et al. 2018], TFAP2B [Xiong et al. 2013] and LTF (lactotransferrin) [Chen et al. 2023] serving as diagnostic markers in cardiovascular diseases. CYP2C19 [Cai et al. 2023] and MMP1 [Kostov et al. 2022] have been reported to be implicated in the hypertension. CYP2C19 [Benedet et al. 2018], MMP1 [Leake et al. 2000], BARHL1 [Barh et al. 2017] and LTF (lactotransferrin) [Tsatsanis et al. 2021] were reported to be regulated in the Alzheimer disease. CYP2C19 [Hidestrand et al. 2001] and MMP1 [Gupta et al. 2014] were demonstrated to be significantly associated with the Parkinson's disease. Altered expression of CYP2C19 [Joas et al. 2023] and MMP1 [Demidenko et al. 2004] have been identified in bipolar disorder. The altered expression of CYP2C19 [Teng et al. 2024] and MMP1 [Bolinskey et al. 2001] are associated with schizophrenia. CYP2C19 [Song et al. 2022] and MMP1 [Dikmen et al. 1983] have been considered a diagnostic markers of epilepsy. CYP2C19 [Hellman et al. 2003], MMP1 [Mirowska-Guzel et al. 2009], CXCL11 [Szczuciński et al. 2011] and LTF (lactotransferrin) [Abdelalim et al. 2024] genes might be used as potential biomarkers to determine the development of multiple sclerosis. CYP2C19 [Bishop et al. 2015] and LTF (lactotransferrin) [Martirosian et al. 2011] can possibly be verified as prognostic indicators of autism spectrum disorder. Previous studies focused on the role of CYP2C19 [Maas et al. 2024], MMP1 [Cárcel-Márquez et al. 2021] and LTF (lactotransferrin) [Xia et al. 2022] in stroke development. Previous studies have indicated the MMP1 [Oliveira et al. 2019], C2CD4B [Di Pietro et al. 2024] and LTF (lactotransferrin) [Ding et al. 2024] plays a crucial role in oxidative stress. Previous animal studies have demonstrated that MMP1 [Meijer et al. 2007] and LTF (lactotransferrin) [Bar-Gil Shitrit et al. 2017] are linked with the development mechanisms of Crohn's disease. MMP1 [Close Kirkwood et al. 2002] was associated with the occurrence and progress of Huntington disease. MMP1 [Chao and Ghorpade, 2009] is associated to the risk of dementia. These research findings concur with those from this investigation, which suggest that significant DEGs might have a role in the pathophysiology of MDD. In this investigation, the GO analysis and REACTOME pathway enrichment analysis of the key DEGs were analyzed by using g:Profiler database. These analyses could help to find enriched genes involved in the regulation of MDD. Studies have shown that the abnormal activation of the signaling pathways include neuronal system [Hamilton et al. 2013], signal transduction [Lin et al. 2023], GPCR downstream signaling [Tomita et al. 2013] and immune system [Jiang et al. 2023] are associated with MDD. Some studies have shown that AVPR1B [Szczepankiewicz et al. 2013], GPR26 [Zhang al. 2011], MCHR2 [Delacrétaz et al. 2015], NRGN (neurogranin) [Wen et al. 2016], HTR2A [Lin et al. 2015], MEF2C [Muench et al. 2018], RGS6 [Stewart et al. 2014], NCALD (neurocalcin delta) [Zhang et al. 2023], GRIN2A [Hu et al. 2018], GRIN3A [Ping et al. 2023], PAK1 [Fuchsova et al. 2016], RASGRF1 [Cheng et al. 2020], HTR7 [Wei et al. 2020], CDKL5 [Barbiero et al. 2022], GRM7 [Niu et al. 2017], HOMER1 [Leber et al. 2017], DLG4 [Kaut et al. 2017], MCHR1 [Roy et al. 2007], VAMP2 [Saito et al. 2006], SYT7 [Lebowitz et al. 2024], NPY1R [Borroto-Escuela et al. 2024], PCLO (piccolo presynaptic cytomatrix protein) [Igata et al. 2017], GRIN2B [Brown et al. 2023], RGS7 [Sutton et al. 2021], GRIA2 [Chiesa et al. 2013], DLG2 [Qin et al. 2020], FBXW7 [Al-Hakeim et al. 2022], CDK5 [Papadopoulou et al. 2015], ARRB1 [Chang et al. 2015], PAK3 [Fuchsova et al. 2016], NRXN1 [Skiba et al. 2021], HDAC9 [Dai et al. 2023], TNF (tumor necrosis factor) [Yao et al. 2020] , CCL2 [Curzytek and Leśkiewicz, 2021], PCSK9 [Habibitabar et al. 2024], VCAM1 [Zhou et al. 2024], WNT1 [Sanchez-Ruiz et al. 2024], C4B [Park et al. 2022], SELL (selectin L) [Yun et al. 2024], APLNR (apelin receptor) [Wang et al. 2020], GFAP (glial fibrillary acidic protein) [Zhang et al. 2024], SERPINA1 [Chan et al. 2024], ABI3BP [Kari et al. 2023], SERPINE1 [Fang et al. 2012], TNFAIP3 [Chen et al. 2017], SPHK1 [Elzaitony et al. 2024], HMOX1 [Dai et al. 2025], NLRC5 [Sun et al. 2023], CASP1 [Inserra et al. 2019], CXCR4 [Ogłodek et al. 2014], TSPO (translocator protein) [Attwells s et al. 2020], B2M [Zhang et al. 2018], ANXA2 [Dai et al. 2023], VIM (vimentin) [O'Leary et al. 2021], MYD88 [Tao et al. 2023], APLN (apelin) [Chibaatar et al. 2024], AMH (anti-Mullerian hormone) [Valenti et al. 2025], AQP4 [Yao et al. 2023], CA3 [Lindqvist et al. 2014], IRF1 [Bialek et al. 2020], IL16 [Almulla et al. 2024], CSF1 [Wohleb et al. 2018], FOXO1 [Zhao et al. 2020], FSTL1 [Xiao et al. 2022], PON3 [Bliźniewska-Kowalska et al. 2022], GPR35 [Cheng et al. 2024], TLR4 [Wei et al. 2025], AGT (angiotensinogen) [López-León et al. 2008], GRM4 [Dadkhah et al. 2017], REST (RE1 silencing transcription factor) [Otsuki et al. 2010], EMP1 [Nakataki et al. 2011] and FADS2 [Sublette et al. 2016] plays a certain role in MDD. Altered expression of AVPR1B [Ferrier et al. 2010], GUCY2C [Tronstad et al. 2018], ERFE (erythroferrone) [Woźniak al. 2023], WIF1 [Terry et al. 2019], TRAF5 [Sun et al. 2024], PLCB1 [Gorenjak et al. 2024], OPTN (optineurin) [Tschurtschenthaler and Adolph, 2018], NPY1R [Chandrasekharan et al. 2022], PRKCD (protein kinase C delta) [Limami et al. 2022], LANCL2 [Tubau-Juni et al. 2024], CASP5 [Smith et al. 2022], TNF (tumor necrosis factor) [Peyrin-Biroulet et al. 2008], CXCL8 [Chen et al. 2021], CCL2 [Maharshak et al. 2010], SERPINA3 [Liu et al. 2024], GZMB (granzyme B) [Lefferts et al. 2021], SOCS3 [Sanati et al. 2022], GBP5 [Che et al. 2024], VCAM1 [Burns et al. 2001], OSM (oncostatin M) [Yang et al. 2024], CHI3L1 [Douadi et al. 2022], C4B [Nissilä et al. 2017], SELL (selectin L) [Bravo et al. 2021], CD44 [Spencer, 2018], LYZ (lysozyme) [Pruzanski et al. 1977], NAT1 [Mahid et al. 2007], GPR65 [Neale et al. 2024], CFB (complement factor B) [Akhlaghpour et al. 2023], S100A4 [Cunningham et al. 2010], RUNX3 [Dybska et al. 2021], TNFAIP3 [Zou et al. 2020], CD74 [Shu et al. 2024], TLR5 [Shen et al. 2022], CD14 [Lakatos et al. 2011], HLA-DRB1 [Newman et al. 2004], ATF3 [Gu et al. 2018], LY75 [Hirayama et al. 2016], CASP1 [Xu et al. 2025], IL15 [Bouchaud et al. 2010], CXCR4 [Dogra et al. 2023], OSMR (oncostatin M receptor) [Du et al. 2020], HLA-B [Asakura et al. 2012], TSPO (translocator protein) [Zhang s et al. 2022], B2M [Bednarz-Misa et al. 2020], TMBIM1 [Orlando et al. 2016], HLA-C [Jung et al. 2016], VIM (vimentin) [Mortensen et al. 2021], MYD88 [Wang et al. 2025], LITAF (lipopolysaccharide induced TNF factor) [Stucchi et al. 2006], ENTPD2 [Feldbrügge et al. 2017], IFIH1 [Santoro et al. 2024], AMH (anti-Mullerian hormone) [Gutiérrez et al. 2024], DKK1 [Kim et al. 2019], IRF1 [Xu et al. 2025], RAB13 [Ohira et al. 2009], IL16 [Hong et al. 2008], CSF1 [Nieto et al. 2017], IFI16 [Vanhove et al. 2015], FOXO1 [Han et al. 2020], PSTPIP1 [André et al. 2010], PON3 [Sanchez et al. 2006], IL10RA [Xue et al. 2020], GPR35 [Song et al. 2023], TLR4 [Wei et al. 2025], TIMP1 [Tran et al. 2024], TRIM22 [Li et al. 2016], PLD2 [Zhou et al. 2016], TAP2 [Kim et al. 2024], AGT (angiotensinogen) [Hume et al. 2016], NFATC1 [Cushing et al. 2023,] MGP (matrix Gla protein) [Vieujean et al. 2024], CCDC80 [Wang et al. 2021] and FADS2 [Liu et al. 2020] promotes Crohn's disease. AVPR1B [Ferrier et al. 2010], NPPA (natriuretic peptide A) [Cheng et al. 2019], EREG (epiregulin) [Harada et al. 2015], GPR26 [Gutiérrez-Rojas al. 2022], ERFE (erythroferrone) [Kautz al. 2021], HTR2A [Xiang et al. 2024], MEF2C [Wang et al. 2024], CAMKK2 [Li et al. 2021], TRAF5 [Gissler et al. 2021], RGS6 [Song et al. 2023], NCALD (neurocalcin delta) [Zhang et al. 2023], PLCB1 [Gorenjak et al. 2024], PAK1 [Chen et al. 2024], RASGRF1 [Cheng et al. 2020], CNTN4 [Wang et al. 2022], CDKL5 [Cortelazzo et al. 2017], GRM7 [Zaki-Dizaji et al. 2024], VAMP2 [Duan et al. 2020], OPTN (optineurin) [Liu et al. 2018], SLC39A10 [Gao et al. 2017], PIK3R1 [Fu et al. 2024], SCN8A [Alrashdi et al. 2021], DCLK1 [Yi et al. 2019], RGS7 [Basak et al. 2023], DLG2 [Keane et al. 2022], SH3RF3 [Patel et al. 2025], FBXW7 [Zhang et al. 2022], PRKCD (protein kinase C delta) [Yang et al. 2019], GIT1 [Song a et al. 2021], MEF2A [Xiong a et al. 2019], CDK5 [Li et al. 2021], ARRB1 [Fang et al. 2021], NRXN3 [Hishimoto et al. 2019], GMFB (glia maturation factor beta) [Fan et al. 2018], IFT20 [Paulson et al. 2021], ATP2B2 [Yin and Zhang, 2025], DLGAP4 [Ding et al. 2024], PRMT8 [Zheng et al. 2022], TRIM37 [Brigant et al. 2016], MYCBP2 [Yuan et al. 2022], HDAC9 [Das and Natarajan, 2020], CASP5 [Eckhart and Fischer, 2024], CXCL3 [Kwon et al. 2024], TNF (tumor necrosis factor) [van Loo and Bertrand, 2003], SERPINA3 [Soman and Asha Nair, 2022], CXCL8 [Haas et al. 2016], CCL2 [Raghu et al. 2017], GZMB (granzyme B) [Xu et al. 2024], CXCL2 [Liu et al. 2021], SOCS3 [Liu et al. 2015], TBX21 [Lau et al. 2017], NKG7 [Ng et al. 2024], PRF1 [Sidore et al. 2021], PCSK9 [Frostegård, 2022], IL21R [Niu et al. 2022], VCAM1 [Burns et al. 2001], OSM (oncostatin M) [Hermans et al. 2022], CHI3L1 [Stephan et al. 2025], RGS1 [Feng et al. 2022], C4B [Rezende et al. 2004], SELL (selectin L) [Rosen, 2004], ACP5 [Yu et al. 2025], CES1 [Scheaffer et al. 2020], CD44 [Govindaraju et al. 2019], BATF2 [van der Geest and Lee, 2025], LYZ (lysozyme) [Moreno-Navarrete et al. 2021], AZGP1 [Na et al. 2017], LILRB2 [Nishiyama et al. 2021], GFAP (glial fibrillary acidic protein) [Siemionow et al. 2009], SFRP4 [Kida et al. 2023], SERPINA1 [Meghadri et al. 2022], GPR65 [Zhang et al. 2023], GBP3 [Zhang et al. 2023], CFI (complement factor I) [Altmann et al. 2020], S100A4 [Ambartsumian et al. 2019], RUNX3 [Lotem et al. 2017], HLA-DRA [Kessal et al. 2018], SERPINE1 [Chen et al. 2021], TNFAIP3 [Momtazi et al. 2019], CD74 [Farr et al. 2020], LAIR1 [Kumawat et al. 2019], TLR5 [Pachathundikandi et al. 2023], NUPR1 [Wang et al. 2006], EPHA2 [Zeng et al. 2023], CD14 [Ogawa et al. 2013], HBB (hemoglobin subunit beta) [Bandeira et al. 2014], GBP1 [Honkala et al. 2020], TNFRSF12A [Li et al. 2024], HLA-DRB1 [Klimenta et al. 2019], SPHK1 [Elzaitony et al. 2024], ATF3 [Liu et al. 2024], HMOX1 [Sebastián et al. 2018], NLRC5 [He et al. 2020], TAP1 [Vasků et al. 2000], ITGB4 [Zhou et al. 2023], CASP1 [Man et al. 2017], IL15 [Perera et al. 2012], S100A11 [Sobolewski et al. 2020], MFAP4 [Sofíudóttir et al. 2024], MLKL (mixed lineage kinase domain like pseudokinase) [Xu et al. 2019], GPX3 [Szczuko et al. 2019], SPX (spexin hormone) [Yortanli et al. 2025], CXCR4 [Gallego et al. 2021], HLA-B [Lenna et al. 2015], IFITM3 [Xiong et al. 2024], TSPO (translocator protein) [Corica et al. 2023], IER3 [Arlt and Schäfer, 2011], EFNA1 [Fukuda et al. 2025], LGALS9 [Braß et al. 2023], TMBIM1 [Gong et al. 2016], ANXA2 [Lim and Hajjar, 2021], NFKB2 [Chawla et al. 2022], VIM (vimentin) [Meng et al. 2023], MYD88 [Luo et al. 2022], LITAF (lipopolysaccharide induced TNF factor) [Zou et al. 2015], WWTR1 [Wang et al. 2016], CLIC4 [Luo et al. 2024], SLC15A3 [Song et al. 2018], ENTPD2 [Feldbrügge et al. 2017], IFIH1 [Amado-Rodríguez et al. 2022], APLN (apelin) [Park et al. 2024], RAB20 [Liang et al. 2012], MYOCD (myocardin) [Ackers-Johnson et al. 2015], MASP1 [Schwaner et al. 2017], AMH (anti-Mullerian hormone) [Okawa et al. 2025], CD109 [Aono et al. 2023], CLIC1 [Zhu et al. 2017], IGF2 [Wu et al. 2025], AQP4 [Lan et al. 2016], DTX3L [Hong et al. 2020], DKK1 [Kim et al. 2019], MYL9 [Kimura et al. 2017], CA3 [Zuo et al. 2025], SCD (stearoyl-CoA desaturase) [Kurikawa et al. 2013], IRF1 [Chen et al. 2023], IL16 [Smith et al. 2018], CSF1 [Lin et al. 2019], TGFBR1 [Li a et al. 2020], HFE (homeostatic iron regulator) [Wessling-Resnick, 2010], PDPN (podoplanin) [Quintanilla et al. 2019], IFI16 [Fan et al. 2023], RARRES2 [Er et al. 2018], FOXO1 [Lundell et al. 2019], PSTPIP1 [Sanz-Cabanillas et al. 2024], HSPB1 [Zhao et al. 2025], FSTL1 [Guo et al. 2016], NPC2 [Hannaford et al. 2013], FGF1 [Dhlamini et al. 2022], GPR35 [Wu et al. 2023], TLR4 [Heine and Zamyatina, 2022], TIMP1 [Schoeps et al. 2023], MAP3K8 [Chiu et al. 2024], HEY2 [Lina et al. 2023], TRIM22 [Wei et al. 2022], LTBR (lymphotoxin beta receptor) [Giles et al. 2018], RHOC (ras homolog family member C) [Wu et al. 2010], NKX3-1 [Le Magnen et al. 2018], F11R [Salifu et al. 2007], PLD2 [Zhou et al. 2016], ARPC1B [Volpi et al. 2019], IGFBP7 [Qiu and Huang, 2024], DDR1 [Borza et al. 2022], S1PR2 [Ren et al. 2017], EDNRB (endothelin receptor type B) [Xu et al. 2022], AGT (angiotensinogen) [Kalupahana et al. 2012], GNG12 [Larson et al. 2010], STK33 [Jiang et al. 2024], ERBB2 [Zeng et al. 2012], PRRX1 [Zhang et al. 2022], NFATC1 [Baumgart et al. 2014], FBLN5 [Hou et al. 2017], GRM4 [Fallarino et al. 2010], SLAMF9 [Cong et al. 2024], SIGLEC7 [Dharmadhikari et al. 2017], STEAP3 [Zhang et al. 2012], AEBP1 [Runtian et al. 2025], DPT (dermatopontin) [Unamuno et al. 2020], BEST3 [Song et al. 2014], SERPINB1 [Lan et al. 2025], BGN (biglycan) [Guo et al. 2019], ELN (elastin) [Wu et al. 2015], FADS2 [Liu et al. 2020] and PSMB8 [Li et al. 2024] were significantly altered in paients with inflammation. The mRNA expression levels of AVPR1B [Enhörning et al. 2016], PIK3C2G [Daimon et al. 2008], GPR26 [Kichi al. 2022], ONECUT1 [Russ-Silsby al. 2023], NPR3 [Saulnier et al. 2011], HTR2A [Bennet et al. 2015], CAMKK2 [Kim et al. 2020], GRIN3A [Zhang et al. 2023], PAK1 [Veluthakal et al. 2018], RASGRF1 [Hoffmann et al. 2012], CHRM3 [Guo et al. 2006], CHN1 [Li et al. 2020], HOMER1 [Lu et al. 2017], VAMP2 [Hirai et al. 2008], PRKCZ (protein kinase C zeta) [Qin et al. 2008], PCLO (piccolo presynaptic cytomatrix protein) [Ma et al. 2008], GRIN2B [Kochetova et al. 2020], PIK3R1 [Li et al. 2022], CACNB2 [Vuori et al. 2019], FBXW7 [Al-Hakeim et al. 2022], CDK5 [Ahmed and Sharma, 2011], MAPK9 [Jaeschke et al. 2005], NPY1R [Lafferty et al. 2020], BCL11A [Tang et al. 2014], HDAC9 [Liu et al. 2024], TNF (tumor necrosis factor) [Akash et al. 2018], CXCL8 [Silva et al. 2020], CCL2 [Guan et al. 2011], GZMB (granzyme B) [El Mesallamy et al. 2014], SLC47A2 [Chen et al. 2022], SOCS3 [Zhang et al. 2022], TBX21 [Petakh et al. 2024], NKG7 [Ji et al. 2023], PCSK9 [Carugo et al. 2022], RORC (RAR related orphan receptor C) [Wang et al. 2003], VCAM1 [Siddiqui et al. 2023], OSM (oncostatin M) [Ikeda et al. 2021], CHI3L1 [Zhu et al. 2025], C4B [Kingery et al. 2012], SELL (selectin L) [Siddiqui et al. 2023], CES1 [Hu et al. 2020], CD44 [Assayag-Asherie et al. 2015], SLC12A3 [Yin et al. 2024], LYZ (lysozyme) [Mirmiranpour et al. 2016], NAT1 [Paz-Rodríguez et al. 2024], SFRP4 [Luo et al. 2020], C4A [Kingery et al. 2012], GFAP (glial fibrillary acidic protein) [Ayala-Guerrero et al. 2022], SERPINH1 [Li et al. 2022], CFB (complement factor B) [Wang et al. 2013], RUNX3 [Jin et al. 2023], SERPINE1 [Fan et al. 2018], TNFAIP3 [Cao et al. 2023], CD74 [Mangano et al. 2024], TLR5 [Pearson et al. 2024], HLA-DPA1 [Varney et al. 2010], CD14 [Hedgpeth et al. 2015], HLA-DRB1 [Zhang et al. 2019], SPHK1 [Liu et al. 2022], ATF3 [Kim et al. 2013], HMOX1 [Rivera-Valdés et al. 2025], HLA-DPB1 [Varney et al. 2010], TAP1 [Li et al. 2014], CASP1 [Yuan et al. 2012], IL15 [Ye, 2015], S100A11 [Wu et al. 2023], MFAP4 [Blindbæk et al. 2017], ACTA2 [Fang et al. 2014], MLKL (mixed lineage kinase domain like pseudokinase) [Kamal et al. 2019], GPX3 [Ling et al. 2020], SPX (spexin hormone) [Gallagher et al. 2024], CXCR4 [Arakura et al. 2017], HLA-B [Jan et al. 2021], IFITM3 [Mehrbod et al. 2017], HLA-DMB [Siegmund et al. 1999], TSPO (translocator protein) [Qiu et al. 2016], HLA-DMA [Siegmund et al. 1999], B2M [Dai et al. 2024], HLA-C [Smigoc Schweiger et al. 2014], VIM (vimentin) [Roefs et al. 2017], MYD88 [Tian et al. 2021], IFIH1 [Zurawek et al. 2015], APLN (apelin) [Xu et al. 2021], MASP1 [Krogh et al. 2017], AMH (anti-Mullerian hormone) [Verdiesen et al. 2021], IGF2 [Cao et al. 2021], AQP4 [Li et al. 2022], DKK1 [Gao et al. 2022], P2RY1 [Dance et al. 2024], SCD (stearoyl-CoA desaturase) [Oballa et al. 2011], HLA-E [Hodgkinson et al. 2000], IRF1 [Colli et al. 2018], IL16 [Ali et al. 2024], CSF1 [Dikilitaş et al. 2022], HFE (homeostatic iron regulator) [Barton and Acton, 2017], IFI16 [Arunachalam et al. 2024,] RARRES2 [Zhao et al. 2018], FOXO1 [Nathanael et al. 2022], HSPB1 [Tokuda et al. 2015], FGF1 [Bu et al. 2025], GPR35 [Vander Molen et al. 2005], TLR4 [Oo, 2024], TIMP1 [Saucedo et al. 2021], F11R [Adedayo et al. 2022], CTSH (cathepsin H) [Fløyel et al. 2018], IGFBP7 [Zhu et al. 2024], DDR1 [Yan et al. 2021], S1PR2 [Liu et al. 2022], TAP2 [Qu et al. 2007], EDNRB (endothelin receptor type B) [Bregar et al. 2018], AGT (angiotensinogen) [Repchuk et al. 2021], ERBB2 [de Kay et al. 2022], NFATC1 [Cai et al. 2024], SIGLEC7 [Dharmadhikari et al. 2017], MGP (matrix Gla protein) [Sardana et al. 2017], ARRDC4 [Liu et al. 2024], SERPINB1 [Kyohara et al. 2022], FADS2 [Shetty and Kumari, 2021] and SLC40A1 [Huang et al. 2024] are correlated with diabetes mellitus. AVPR1B [Ejiohuo et al. 2025], CUX2 [Glaseret al. 2005], MCHR2 [Abou Jamra et al. 2010], NRGN (neurogranin) [Wen et al. 2016], SYT7 [Wang et al. 2021], HTR2A [Tan et al. 2014], NUDT4 [Maheshwari et al. 2025], CAMKK2 [Kaiser et al. 2023], GRIN2A [Itokawa et al. 2003], PLCB1 [Lo Vasco et al. 2013], HTR7 [Wei et al. 2020], CDKL5 [Lucash et al. 2024], GRM7 [Kandaswamy et al. 2014], HOMER1 [Leber et al. 2017], VAMP2 [Abou Jamra et al. 2008], SYT7 [Wang et al. 2021], PRKCZ (protein kinase C zeta) [Saxena et al. 2008], PCLO (piccolo presynaptic cytomatrix protein) [Chen et al. 2021], PIK3R1 [Huang et al. 2020], SCN8A [Wang et al. 2008], CACNB2 [Cheng et al. 2025], GRIA2 [Chiesa et al. 2012], GNAZ (G protein subunit alpha z) [Saito a et al. 1999], NRXN3 [Sudhakar et al. 2025], NDEL1 [Dal Mas et al. 2019], NBEA (neurobeachin) [Jacobsen et al. 2015], LDB2 [Horiuchi et al. 2020], DCTN1 [Hallen et al. 2020], KCNH7 [Strauss et al. 2014], TNF (tumor necrosis factor) [Doğanavşargil Baysal et al. 2019], CXCL8 [Ghoryani et al. 2019], CCL2 [Ghoryani et al. 2019], VCAM1 [Pantovic-Stefanovic et al. 2024], WNT1 [Beasley et al. 2002], CHI3L1 [Sahin et al. 2019], C4A [Melbourne et al. 2018], GFAP (glial fibrillary acidic protein) [Webster et al. 2005], ABI3BP [Kari et al. 2023], HLA-DRB1 [Le Clerc et al. 2021], ITGB4 [Han et al. 2018], TSPO (translocator protein) [Scaini et al. 2019], IGF2 [Ye et al. 2024], AQP4 [Zhao et al. 2020], CA3 [Stern et al. 2020], HLA-E [Mihoub et al. 2004], TLR4 [Aflouk et al. 2024], DDR1 [Aranda et al. 2024], AGT (angiotensinogen) [Meira-Lima et al. 2000], FADS2 [Liu and McNamara, 2011] and RFX4 [Glaser et al. 2005] are key regulators of bipolar disorder. The altered expression of NPPA (natriuretic peptide A) [Cheng et al. 2019], CUX2 [Wang et al. 2018], ERFE (erythroferrone) [Spoto al. 2019], NPPC (natriuretic peptide C) [Lumsden et al. 2010], WIF1 [Ress et al. 2018], NRGN (neurogranin) [Jorgensen et al. 2022], HTR2A [Gao et al. 2020], MEF2C [Qiao et al. 2017], CAMKK2 [Li et al. 2021], TRAF5 [Peng et al. 2023], DKK2 [Wang et al. 2025], PAK1 [Xu et al. 2022], RASGRF1 [Tsai et al. 2020], PRKCB (protein kinase C beta) [Li et al. 2013], CNTN4 [Wang et al. 2022], CDKL5 [Ivaniuk et al. 2023], HOMER1 [Zhang et al. 2022], SPATC1L [Yamada et al. 2017], SYT7 [Sun et al. 2024], CALM3 [Hamrick et al. 2024], SCN3B [Lin et al. 2022], KCNJ3 [Yamada et al. 2019], CDK5R1 [Infante et al. 2024], PIK3R1 [He et al. 2024], ROBO2 [Wang et al. 2023], DCLK1 [Yang et al. 2023], CACNB2 [Parthiban and Sani, 2023], CACNG8 [Ortega et al. 2015], PPP3R1 [Tang et al. 2005], FBXW7 [Zhang et al. 2022], PRKCD (protein kinase C delta) [Miao et al. 2022], GIT1 [Song a et al. 2021], MADD (MAP kinase activating death domain) [Wu a et al. 2012], BEX1 [Accornero a et al. 2017], MEF2A [Muhammad Sulaiman, 2024], FGF12 [Li et al. 2017], CDK5 [Bai et al. 2012], GABARAPL1 [Liu et al. 2025], PAK3 [Chen et al. 2024], SUB1 [Huang et al. 2021], DES (desmin) [Brodehl et al. 2018], ATP2B2 [Wei et al. 2025], CAMK2N1 [Wei et al. 2021], PDZRN3 [Pernot et al. 2022], EEF1A2 [Feng et al. 2021], ADD2 [Chen et al. 2015], CDH10 [Chen et al. 2018], NEK2 [Zhou et al. 2025], LDB2 [Shang et al. 2014], CAMTA2 [Song et al. 2016], TRIM37 [Brigant et al. 2016], HDAC9 [Das and Natarajan, 2020], TNF (tumor necrosis factor) [Yuan et al. 2020], SERPINA3 [Li et al. 2021], CXCL8 [Zhu et al. 2022], CCL2 [Gholamalizadeh et al. 2024], GZMB (granzyme B) [Xu et al. 2024], CXCL2 [Guo et al. 2020], SOCS3 [Han et al. 2020], PCSK9 [Hummelgaard et al. 2023], MS4A6A [Lu-Chen et al. 2025], VCAM1 [Singh et al. 2023], WNT1 [Pang et al. 2021], OSM (oncostatin M) [Ikeda et al. 2021], CHI3L1 [Stephan et al. 2025], C4B [Trouw et al. 2008], SELL (selectin L) [Berardi et al. 2014], LILRB3 [Zhang et al. 2021], CES1 [Marrades et al. 2010], CD44 [El-Dakdouki et al. 2014], LYZ (lysozyme) [Mink et al. 2009], NAT1 [Sangaralingham et al. 2003], AZGP1 [Huscher et al. 2021], APLNR (apelin receptor) [Wang et al. 2020], SFRP4 [Ren et al. 2024], GFAP (glial fibrillary acidic protein) [Kaneko et al. 2009], SERPINH1 [Rusu-Nastase et al. 2022], S100A4 [Cheng et al. 2025], RUNX3 [Jin et al. 2023], ABI3BP [Delfín et al. 2019], SERPINE1 [Long et al. 2024], TNFAIP3 [Hua et al. 2009], CD74 [Li et al. 2023], ADAMTS1 [Karakose et al. 2017], IL12A [Wang et al. 2021], HLA-DPA1 [Liu et al. 2006], EPHA2 [Zeng et al. 2023], COL3A1 [McGrath-Cadell et al. 2024], CD14 [Stanislawski et al. 2021], TNFRSF12A [Yerra et al. 2022], HLA-DRB1 [Kaur et al. 2025], SPHK1 [Józefczuk et al. 2020], ATF3 [Li et al. 2022], HMOX1 [Xu and Bu, 2023], NLRC5 [Chen et al. 2025], HLA-DPB1 [Liu et al. 2006], TAP1 [Kolbus et al. 2012], CASP1 [Zhang et al. 2025], IL15 [Guo et al. 2020], MFAP4 [Wang et al. 2017], ACTA2 [Fang et al. 2014], GPX3 [Decharatchakul et al. 2020], SPX (spexin hormone) [Kumar et al. 2021], CXCR4 [Li et al. 2023], HLA-B [Eyiol et al. 2018], HSPA1B [Yang et al. 2021], IFITM3 [Xiong et al. 2024], TSPO (translocator protein) [Baglini et al. 2024], CDK2 [Su et al. 2021], IER3 [Zhou et al. 2017], BAG3 [Maffioli et al. 2022], DDIT4L [Simonson et al. 2017], B2M [Jheng et al. 2024], TMBIM1 [Gong et al. 2016], ANXA2 [Kayejo et al. 2023], HLA-C [Bonaccorsi et al. 2021], VIM (vimentin) [Håversen et al. 2018], MYD88 [Luo et al. 2022], , WWTR1 [Flinn et al. 2023], HCLS1 [Li et al. 2024], IFIH1 [Dou et al. 2014], APLN (apelin) [Gao and Chen, 2023], CPT1B [Khan et al. 2013], MYOCD (myocardin) [Yuan et al. 2024], MASP1 [Hertle et al. 2016], AMH (anti-Mullerian hormone) [Verdiesen et al. 2022], CLIC1 [Zhu et al. 2017], IGF2 [Lu et al. 2021], SEMA3F [Reichert et al. 2019], AQP4 [Warth et al. 2007], DKK1 [Toth, 2024], CA3 [Su et al. 2021], SCD (stearoyl-CoA desaturase) [Abd Alla et al. 2021], HLA-E [Zidi et al. 2016], IL16 [de Souza et al. 2020], CSF1 [Sjaarda et al. 2018], TGFBR1 [Zhou et al. 2023], PDPN (podoplanin) [Cimini et al. 2019], IFI16 [Chang et al. 2024], FOXO1 [Kazemi Fard et al. 2021], HSPB1 [Zhao et al. 2025], FSTL1 [Ponce-Ruíz et al. 2024], PON3 [Priyanka et al. 2019], AGTRAP (angiotensin II receptor associated protein) [Wakui et al. 2013], GPR35 [Chen et al. 2020], TLR4 [Vaez et al. 2023], TIMP1 [Korzeń et al. 2023], MAP3K8 [Yu et al. 2022], HEY2 [van Walree et al. 2023], LTBR (lymphotoxin beta receptor) [Grandoch et al. 2015], F11R [Babinska et al. 2019], ACKR3 [Duval et al. 2022], PLD2 [Li et al. 2024], IGFBP7 [Katoh et al. 2024], DDR1 [Franco et al. 2008], S1PR2 [Duan et al. 2024], AGT (angiotensinogen) [Daugherty et al. 2024], ERBB2 [Jian et al. 2020], PRRX1 [Fang et al. 2025], FBLN5 [Li et al. 2025], SUSD2 [Bruikman et al. 2020], MGP (matrix Gla protein) [Kumric et al. 2021], AEBP1 [Xue et al. 2024], SLC1A5 [Kennel et al. 2019], KCNE4 [Abbott, 2016], SSPN (sarcospan) [Rahimi Kahmini et al. 2024], SERPINB1 [Lan et al. 2025], BGN (biglycan) [Scuruchi et al. 2020], ELN (elastin) [Wang et al. 2021], FADS2 [Xu et al. 2019], PALLD (palladin, cytoskeletal associated protein) [Liu et al. 2021], ORAI3 [Saliba et al. 2015], SLC40A1 [Feng et al. 2024], PSMB9 [Liu and Delgado, 2024], CCDC80 [Wang et al. 2021] and PSMB8 [Li et al. 2024] were observed to be associated with the progression of cardiovascular diseases. NPPA (natriuretic peptide A) [Li et al. 2022], EREG (epiregulin) [Song et al. 2022], GUCY2C [Lin et al. 2016], CARTPT (CART prepropeptide) [Mahmoudinezhad et al. 2021], NPPC (natriuretic peptide C) [Cabiati et al. 2020], STX1A [Romeo et al. 2008], CAMKK2 [Wang et al. 2024], TRAF5 [Gissler et al. 2021], RGS6 [Kim et al. 2017], NCALD (neurocalcin delta) [Ma et al. 2017], GRIN3A [Zhang et al. 2023], GNG3 [Schwindinger et al. 2009], GRM7 [Ghafouri-Fard et al. 2023], MCHR1 [Högberg et al. 2012], SYT3 [Zhang et al. 2020], HRAS (HRas proto-oncogene, GTPase) [Oba et al. 2018], PIK3R1 [Pinhel et al. 2020], GNB1 [Kleinendorst et al. 2024], DCLK1 [Yang et al. 2023], YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Capobianco et al. 2012], PRKCD (protein kinase C delta) [Osborne et al. 2024], NRXN3 [Albuquerque et al. 2014], PAK3 [Chen et al. 2024], NEK2 [Nuncia-Cantarero et al. 2018], HDAC9 [Yang et al. 2025], TNF (tumor necrosis factor) [Olszanecka-Glinianowicz et al. 2004], CXCL8 [Lima et al. 2021], CCL2 [Wu and Ma, 2024], GZMB (granzyme B) [El Mesallamy et al. 2014], CXCL2 [He et al. 2024], SOCS3 [Emamgholipour et al. 2023], IRX5 [Bjune et al. 2019], VCAM1 [Yu et al. 2017], WNT1 [Pivovarova-Ramich et al. 2021], OSM (oncostatin M) [Piquer-Garcia et al. 2020], CHI3L1 [Teitsdottir et al. 2018], CES1 [Marrades et al. 2010], LYZ (lysozyme) [Moreno-Navarrete et al. 2021], NAT1 [Paz-Rodríguez et al. 2024], APLNR (apelin receptor) [De Los Santos et al. 2023], SFRP4 [Bukhari et al. 2019], GFAP (glial fibrillary acidic protein) [Cano et al. 2014], GPR65 [Bagchi et al. 2024], S100A4 [Xi et al. 2024], SERPINE1 [Su et al. 2023], CD74 [Chan et al. 2015], TLR5 [Pekkala et al. 2015], COL3A1 [Wang et al. 2024], CD14 [Kang et al. 2022], SPHK1 [Nagahashi et al. 2018], ATF3 [Li et al. 2022], LY75 [Makino et al. 2018], CASP1 [Kimura et al. 2016], IL15 [Ye, 2015], S100A11 [Shen et al. 2023], GPX3 [Langhardt et al. 2018], SPX (spexin hormone) [Kumar et al. 2021], CXCR4 [Ahn et al. 2023], IFITM3 [Pomar et al. 2022], TSPO (translocator protein) [Lassance et al. 2015], CDK2 [Colón-Mesa et al. 2021], LGALS9 [Lee et al. 2022], TMBIM1 [Zhao et al. 2021], ANXA2 [Wang et al. 2019], VIM (vimentin) [Roh and Yoo, 2021], LITAF (lipopolysaccharide induced TNF factor) [Ji et al. 2011], APLN (apelin) [De Los Santos et al. 2023], CPT1B [Ratner et al. 2015], AMH (anti-Mullerian hormone) [Zeng et al. 2022], CLIC1 [Zapata et al. 2023], IGF2 [Szydlowska-Gladysz et al. 2024], DKK1 [Yang et al. 2024], SCD (stearoyl-CoA desaturase) [Sampath and Ntambi, 2011], IL16 [Reyes-Farias et al. 2024], FOXO1 [Benchoula et al. 2021], HSPB1 [Pilch et al. 2015], FSTL1 [Liu et al. 2024], PON3 [Salas-Pérez et al. 2022], FGF1 [Wang et al. 2023], AGTRAP (angiotensin II receptor associated protein) [Maeda et al. 2013], TLR4 [Benomar and Taouis, 2019], TIMP1 [Andrade et al. 2012], MAP3K8 [Ballak et al. 2014], F11R [Ong et al. 2008], IGFBP7 [Jędrysik et al. 2024], TRIM56 [Qin et al. 2025], AGT (angiotensinogen) [Repchuk et al. 2021], STK33 [Rask-Andersen et al. 2013], NFATC1 [Chasapi et al. 2020], SIGLEC7 [Rosenstock et al. 2017], AEBP1 [Zhang et al. 2005], CCDC80 [Osorio-Conles et al. 2017], DPT (dermatopontin) [Unamuno et al. 2020], SERPINB1 [Li et al. 2024], BGN (biglycan) [Han et al. 2020], ELN (elastin) [Martinez-Santibanez et al. 2015], FADS2 [Mahmoudinezhad et al. 2023], SIX3 [Yu et al. 2024], MYD88 [Guerrero-Romero et al. 2023] and IFI30 [Turcot et al. 2012] might be considered to be a novel biomarkers for obesity. NPPA (natriuretic peptide A) [Li et al. 2020], NPR3 [Sekiguchi al. 2011], NPPC (natriuretic peptide C) [Špiranec et al. 2018], WIF1 [Kania et al. 2022,] HTR2A [Choi et al. 2020], MEF2C [Sahoo et al. 2016], CHRM3 [Cowley et al. 2018], CHN1 [Liang et al. 2020], PIK3R1 [Zhang et al. 2010], CACNB2 [Soldatov, 2015], FBXW7 [Wang et al. 2022], FGF12 [Yeo et al. 2020], ARRB1 [Sun et al. 2018], ADD2 [Kardia et al. 2007], NEK2 [Zhou et al. 2025], TNF (tumor necrosis factor) [Mehaffey and Majid, 2017], CXCL8 [Kim et al. 2008], CCL2 [Alsheikh et al. 2020], GZMB (granzyme B) [Mao et al. 2018], RXFP2 [Zhang et al. 2025], SOCS3 [Benincasa et al. 2023], PCSK9 [Ye et al. 2024], VCAM1 [Qiu et al. 2022], WNT1 [Młynarczyk et al. 2022], CHI3L1 [Sun et al. 2022], RGS1 [Patel et al. 2018], C4B [Mulvihill et al. 2019], CES1 [Ikonnikova et al. 2022], CD44 [Isobe et al. 2019], CRIP1 [Schweigert et al. 2021], SLC12A3 [Huang et al. 2022], LYZ (lysozyme) [Qvarnstrom et al. 2008], C4A [Magen et al. 2010], GFAP (glial fibrillary acidic protein) [Canissario et al. 2022], S100A4 [Laggner et al. 2022], TNFAIP3 [Xue et al. 2011], CD74 [Le Hiress et al. 2015], TLR5 [Wu et al. 2025], NUPR1 [Zhou et al. 2024], EPHA2 [Murakoshi et al. 2022], COL3A1 [Samokhin et al. 2018], CD14 [Fehrenbach et al. 2021], SCNN1G [Fan et al. 2020], HLA-DRB1 [Li et al. 2012], SPHK1 [Pyne and Pyne, 2017], HMOX1 [Song et al. 2024], HLA-DPB1 [Kominami et al. 2009], CASP1 [Udjus et al. 2019], IL15 [Hilton et al. 2022], MFAP4 [Christensen et al. 2024,] GPX3 [Decharatchakul et al. 2020], CXCR4 [Hiraide et al. 2024], HLA-B [Hu et al. 2024], B2M [Jheng et al. 2024], NFKB2 [Nagata et al. 2024], MYD88 [Qi et al. 2019], CLIC4 [Hatziioanou et al. 2018], IFIH1 [Bouças et al. 2013], APLN (apelin) [Zhu et al. 2015], MYOCD (myocardin) [Sahoo et al. 2016], IGF2 [Yang et al. 2020], AQP4 [González-Marrero et al. 2022], DKK1 [Toth, 2024], HFE (homeostatic iron regulator) [Määttä and Nikkari, 2015], IFI16 [Rao et al. 2024], RARRES2 [Batista et al. 2021], FOXO1 [Li et al. 2023], FGF1 [Tomaszewski et al. 2007], IL10RA [Park et al. 2013], AGTRAP (angiotensin II receptor associated protein) [Wakui et al. 2013], GPR35 [McCarthy et al. 2018], TLR4 [Grylls et al. 2021], TIMP1 [He et al. 2022], CYP4F11 [Gainer et al. 2005], F11R [Ong et al. 2008], PLD2 [Nelson et al. 2017], IGFBP7 [Torres et al. 2023], DDR1 [Bonafiglia et al. 2022], AGT (angiotensinogen) [Repchuk et al. 2021], NUDT1 [Vitry et al. 2021], NFATC1 [Du et al. 2016], MGP (matrix Gla protein) [Chirinos et al. 2018], CCDC80 [Sasagawa et al. 2016], BGN (biglycan) [Sardo et al. 2010], ELN (elastin) [Owens et al. 2017] and PSMB8 [Zhang et al. 2025] have emerged as a potential targets for hypertension. NPPA (natriuretic peptide A) [Lima et al. 2008], PTGER3 [Park et al. 2007], HTR2A [Konjevod et al. 2023], PAK1 [Lu et al. 2018], CHRM3 [Jiménez-Morales et al. 2014], VAMP2 [Ding et al. 2022], RGS7BP [Lee et al. 2011], ROBO2 [Hernandez-Pacheco et al. 2021], GABARAPL1 [Pan et al. 2025], NRXN1 [Margaritte-Jeannin et al. 2023], INPP4A [Aich et al. 2012], CXCL3 [Sokulsky et al. 2020], TNF (tumor necrosis factor) [Berry et al. 2006], CXCL8 [Tang et al. 2016], CCL2 [Luo et al. 2025], GZMB (granzyme B) [Annoni et al. 2015], CXCL2 [Al-Alwan et al. 2013], SOCS3 [Sun et al. 2019], TBX21 [Sharma et al. 2014], VCAM1 [Ando et al. 2001], WNT1 [Yang et al. 2013], CHI3L1 [Huang et al. 2022] , SELL (selectin L) [Monteseirín et al. 2001], CD44 [Katoh et al. 2022], OSM (oncostatin M) [Esnault et al. 2024], LYZ (lysozyme) [Ohbayashi et al. 2016], NAT1 [Brooks et al. 2021], SERPINA1 [Martín-González et al. 2025], S100A4 [Wu et al. 2021], RUNX3 [Zhang et al. 2025], HLA-DRA [Kim et al. 2012], TNFAIP3 [Krusche et al. 2019], TLR5 [Whitehead et al. 2020], EPHA2 [Xu et al. 2022], CD14 [Zhou et al. 2019], HLA-DRB1 [Yao et al. 2016], SPHK1 [Sudhadevi et al. 2024], ATF3 [Wang et al. 2025], HLA-DPB1 [Dekker et al. 1997], TAP1 [Liu et al. 2017], ITGB4 [Ou et al. 2023], CASP1 [Zhang et al. 2023], IL15 [Jonakowsket al. 2017], MFAP4 [Pilecki et al. 2015], HSPA1B [Faisal et al. 2022], TNFSF10 [Weckmann et al. 2011], MYD88 [Yang et al. 2024], IRF1 [Hu et al. 2021], IL16 [Deng and Shi, 2006], CSF1 [Du a et al. 2023], FOXO1 [Wang et al. 2022], FSTL1 [Huang et al. 2022], FGF1 [Li et al. 2021], TLR4 [Wu et al. 2022], TIMP1 [Kim, 2023], PLD2 [Wang et al. 2025], CTSH (cathepsin H) [Faiz et al. 2013], ARPC1B [Papadatou et al. 2021], S1PR2 [Wang et al. 2023], AGT (angiotensinogen) [Pasaje et al. 2011], ERBB2 [Sio et al. 2023], NFATC1 [Yang et al. 2024], ELN (elastin) [Ingram et al. 2016] and SLC40A1 [Wang et al. 2023] makes a vital regulatory role in asthma. CUX2 [Zhang al. 2022], NPTX2 [Xing et al. 2024], STX1A [Luppe et al. 2023], NRGN (neurogranin) [Kalkan et al. 2022], HTR2A [Manna et al. 2012], GABBR2 [Yoo et al. 2017], MEF2C [Raviglione et al. 2021], CAMKK2 [Sayad et al. 2018], GRIN2A [Samanta, 2023], CACNG3 [Everett et al. 2007], PLCB1 [Desprairies et al. 2020], PAK1 [Corriveau et al. 2023], RASGRF1 [Chen et al. 2017], GRM7 [Marafi et al. 2020], CNKSR2 [Higa et al. 2021], SCN3B [Al-Eitan et al. 2019], IQSEC2 [Brant et al. 2022], UNC13A [Su et al. 2024], KCNJ3 [Li et al. 2024], NAPB (NSF attachment protein beta) [Ali et al. 2024], GRIN2B [Wang et al. 2023], SCN8A [Conecker et al. 2024], PRICKLE1 [Ban et al. 2022], YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Cetica et al. 2024], GRIA2 [Latsko et al. 2012], PRICKLE2 [Liu et al. 2025], SCN2A [Zeng et al. 2022], KCNQ3 [Miceli et al. 2015], SYNGAP1 [Wiltrout et al. 2024], PRKCD (protein kinase C delta) [Ma et al. 2024], STX1B [Ke a et al. 2023], MEF2A [Huang et al. 2016], FGF12 [Ohori et al. 2023], CDK5 [Banerjee et al. 2021], DLG3 [He et al. 2024], SAMD12 [Chen et al. 2025], PAK3 [Gambardella et al. 2025], NDEL1 [Gavrilovici et al. 2021], NRXN1 [Rochtus et al. 2019], INPP4A [Özkan et al. 2023], PPFIBP1 [Rosenhahn et al. 2022], EEF1A2 [Mohamed and Klann, 2023], BCL11A [Wang et al. 2022], NBEA (neurobeachin) [Pan et al. 2022], KCNH7 [Wu et al. 2024], TNF (tumor necrosis factor) [Fang et al. 2024], SOCS3 [Wang and Yin, 2025], WNT1 [Huang et al. 2015], CD44 [Kusakabe et al. 2025], LILRB2 [Yue et al. 2021], GFAP (glial fibrillary acidic protein) [Thaele et al. 2025], TNFAIP3 [Zhuo et al. 2022], TLR5 [Zhao et al. 2020], NUPR1 [Cui et al. 2024], HLA-DRB1 [Bui et al. 2023], SPHK1 [Dong et al. 2020], ATF3 [Kang et al. 2024], TAP1 [Layouni et al. 2010], CASP1 [Lv et al. 2025], CXCR4 [Song et al. 2016], HLA-B [Chouchi et al. 2018], TSPO (translocator protein) [Qin et al. 2024], ANXA2 [Ma et al. 2023], VIM (vimentin) [Sitovskaya et al. 2023], MYD88 [Zhu et al. 2019], IFIH1 [Song et al. 2024], APLN (apelin) [Dong et al. 2020], AMH (anti-Mullerian hormone) [Harden et al. 2016], AQP4 [Nowicka, 2023], DKK1 [Busceti et al. 2007], CA3 [Zhang et al. 2017], TGFBR1 [Zheng et al. 2021], FGF1 [Simonato et al. 1998], TLR4 [Zhang et al. 2022], TIMP1 [He et al. 2023], S1PR2 [Dong et al. 2020], GRM4 [Al-Eitan et al. 2019], REST (RE1 silencing transcription factor) [Navarrete-Modesto et al. 2019], MGP (matrix Gla protein) [Wu et al. 2025] and EMP1 [Li et al. 2009] expression might be regarded as an indicator of susceptibility to epilepsy. Altered expression of CUX2 [Barington al. 2018], STX1A [Nakamura et al. 2008], HTR2A [Coskunpina et al. 2023], GABBR2 [Bielopolski et al. 2023], MEF2C [Basu et al. 2024], EPHA5 [Pascolini et al. 2019], GRIN2A [Mangano et al. 2022], HTR7 [Helsmoortel et al. 2016], CHRM3 [Petersen et al. 2013], CDKL5 [Wang et al. 2012], GRM7 [Noroozi et al. 2016], DLG4 [Feyder et al. 2010], IQSEC2 [Brant et al. 2022], NAPB (NSF attachment protein beta) [Ali et al. 2024], GRIN2B [Yalçintepe et al. 2021], CDK5R1 [Lintas et al. 2019], CDH8 [Parfenenko et al. 2024], ARHGAP33 [Schuster et al. 2015], PRICKLE1 [Ban et al. 2022], CACNB2 [Graziano et al. 2021], GRIA2 [Latsko et al. 2012], PRICKLE2 [Sowers et al. 2013], SCN2A [Ma et al. 2022], KCNQ3 [Sands et al. 2019], SYNGAP1 [Wiltrout et al. 2024], FGF12 [Seiffert et al. 2022], MARK1 [Maussion et al. 2008], CDK5 [Gu and Kanungo, 2024], NRXN3 [Feichtinger et al. 2023], NRXN1 [Cooper et al. 2024], CNTN5 [Hadi et al. 2025], CDH9 [Wang et al. 2019], EEF1A2 [Mohamed and Klann, 2023], KIRREL3 [Liu et al. 2015], NBEA (neurobeachin) [Nuytens et al. 2013], TNF (tumor necrosis factor) [Yamauchi et al. 2021], CXCL8 [Anastasescu et al. 2024], SOCS3 [Li et al. 2024], PCSK9 [Salem et al. 2023], VCAM1 [Kameno et al. 2013], WNT1 [Martin et al. 2013], SELL (selectin L) [Arslan et al. 2024], CES1 [Hernandez et al. 2022], BATF2 [Voinsky et al. 2022], GFAP (glial fibrillary acidic protein) [Simone et al. 2023], HLA-DRB1 [Guerini et al. 2024], IL15 [Vojdani et al. 2008], CXCR4 [Kara et al. 2018], HLA-B [Guerini et al. 2024], TSPO (translocator protein) [Tseng et al. 2024], HLA-C [Guerini et al. 2024], APLN (apelin) [Boso et al. 2007], AMH (anti-Mullerian hormone) [Pankhurst et al. 2012], SEMA3F [Li et al. 2019], AQP4 [Davoudi et al. 2023], IL16 [Ahmad et al. 2019], FOXO1 [Guo et al. 2023], TLR4 [Nadeem et al. 2017] and FADS2 [Sun et al. 2018] are associated with prognosis in patients with autism spectrum disorder. ADAMTS20 [Park al. 2016] ARHGAP10 [Sekiguchi al. 2020], NPTX2 [Zhou et al. 2024], NRGN (neurogranin) [Hwang et al. 2021], HTR2A [Tan et al. 2014], MEF2C [Ali et al. 2024], CAMKK2 [Sayad et al. 2018], DLGAP3 [Li et al. 2013], GRIN2A [Sheng et al. 2024], GRIN3A [Takata et al. 2013], PLCB1 [Lo Vasco et al. 2012], PAK1 [Deo et al. 2013], HTR7 [Wei et al. 2009], CHRM3 [Wang et al. 2016], GRM7 [Alrefai et al. 2024], HOMER1 [Leber et al. 2017], DLG4 [Balan et al. 2013], GRIK1 [Hirata et al. 2012], VAMP2 [Kawashima et al. 2008], PPP3CB [Liu et al. 2011], KCNJ3 [Yamada et al. 2012], PCLO (piccolo presynaptic cytomatrix protein) [Chen et al. 2021], GRIN2B [Poltavskaya et al. 2021], PIK3R1 [Huang et al. 2020], MAP1A [Morris et al. 2003], DCLK1 [Håvik et al. 2012], CACNB2 [Juraeva et al. 2014], GRIA2 [Alkelai et al. 2021], DLG2 [Sanders et al. 2022], SCN2A [Suddaby et al. 2019], GIT1 [Fass a et al. 2022], HOMER2 [Gilks a et al. 2010], CDK5 [Cantrup et al. 2012], NRXN3 [Hu et al. 2013], PAK3 [Kim et al. 2017], NDEL1 [Nani et al. 2023], NRXN1 [Sebastian et al. 2023], DES (desmin) [Hsiung et al. 2019], BCL11A [Chen et al. 2020], KIF3B [Alsabban et al. 2020], LDB2 [Horiuchi et al. 2020], HDAC9 [O'Connell et al. 2019], KCNH7 [Wang et al. 2019], TNF (tumor necrosis factor) [He et al. 2022], CXCL8 [Arabska et al. 2022], CCL2 [Xiong et al. 2014], SOCS3 [Ni et al. 2021], PCSK9 [Huang et al. 2022], VCAM1 [Meixensberger et al. 2021], WNT1 [Beasley et al. 2002], CHI3L1 [Hill et al. 2011], C4B [Mayilyan et al. 2008], SELL (selectin L) [Mohite et al. 2017], C4A [Melbourne et al. 2018], GFAP (glial fibrillary acidic protein) [Webster et al. 2005], GRHL3 [Sargazi et al. 2020], BTN3A2 [Wu et al. 2019], CD14 [de la Fontaine et al. 2006], HLA-DRB1 [Seshasubramanian et al. 2020], HMOX1 [Tavitian et al. 2020], ITGB4 [O'Brien et al. 2018], CASP1 [Jia et al. 2025], IL15 [He et al. 2022], GPX3 [Liu et al. 2020], CXCR4 [Borroto-Escuela et al. 2017], HLA-B [Seshasubramanian et al. 2020], HSPA1B [Pae et al. 2005], TSPO (translocator protein) [Iliopoulou et al. 2021], RGS9 [Liou et al. 2009], HLA-C [ISGCWTCCC 2012], MYD88 [Chen et al. 2017], IGF2 [Fernández-Pereira et al. 2022], AQP4 [Wu et al. 2020], DKK1 [Al-Dujaili et al. 2021], IFIT3 [Guo et al. 2022], CA3 [Berry et al. 2018], HLA-E [Mihoub et al. 2004], IL16 [Cheng et al. 2023], PADI2 [Watanabe a et al. 2009], HSPB1 [Kowalczyk et al. 2022], IL10RA [Kapelski et al. 2016], TLR4 [Aflouk et al. 2024], TIMP1 [Rahimi et al. 2017], IGFBP7 [Fernández-Pereira et al. 2022] DDR1 [Aranda et al. 2024], TAP2 [Jun et al. 2004], REST (RE1 silencing transcription factor) [Warburton et al. 2015], SLC1A5 [Deng et al. 2008], FADS2 [Liu and Jin, 2024] and STON2 [Ma et al. 2024]genes are a potential biomarkers for the detection and prognosis of schizophrenia. A previous study reported that EPHA1 [Matsumoto al. 2025], NPTX2 [Massa et al. 2024], NRGN (neurogranin) [Saunders et al. 2023], HTR2A [Wilkosz et al. 2007], GABBR2 [Weng et al. 2024], MEF2C [Ren et al. 2022], CAMKK2 [Gaff et al. 2021], DKK2 [Aghaizu et al. 2023], RGS6 [Spicer et al. 2025], GRIN3A [Lee et al. 2024], HTR7 [Solas et al. 2021], CHRM3 [Sanfilippo et al. 2023], PRKCB (protein kinase C beta) [Zhou et al. 2020], HOMER1 [Urdánoz-Casado et al. 2021], DLG4 [Bustos et al. 2017], GRIK1 [Pang et al. 2025], VAMP2 [González et al. 2025], OPTN (optineurin) [Duan et al. 2023], SYT7 [Zhu et al. 2024], CALM3 [Ibarreta et al. 1997], UNC13A [Agra Almeida Quadros et al. 2024], GRIN2B [Andreoli et al. 2014], CDK5R1 [Shao et al. 2022], PIK3R1 [Li et al. 2023], MAP1A [Cai et al. 2023], RGS7 [Squitti et al. 2023], DLG2 [Prokopenko et al. 2022], SH3RF3 [Patel et al. 2025], PRICKLE2 [Sun et al. 2020], PPP3R1 [Zhou et al. 2021], MADD (MAP kinase activating death domain) [Hassan a et al. 2021], MEF2A [Li et al. 2021], CDK5 [Nikhil et al. 2019], ARRB1 [Sengupta and Mukhopadhyay, 2025], NRXN3 [Hishimoto et al. 2019], PAK3 [McPhie et al. 2003], GMFB (glia maturation factor beta) [Bellver-Sanchis et al. 2024], CNTN5 [Dauar et al. 2025], TLN2 [Gusareva et al. 2018], HDAC9 [Zhang et al. 2024], TNF (tumor necrosis factor) [Zhou et al. 2020], SERPINA3 [Sanfilippo et al. 2025], CXCL8 [Jin et al. 2024], CCL2 [Arfaei et al. 2024], CXCL2 [Ma et al. 2024], TBX21 [Cao et al. 2025], PCSK9 [Zhang et al. 2024], MS4A6A [Lacher et al. 2018], VCAM1 [Salian et al. 2024], WNT1 [Macyczko et al. 2023], CHI3L1 [Connolly et al. 2023], C4B [Zorzetto et al. 2017], PLA2G3 [Martínez-García et al. 2010], SELL (selectin L) [Corsi et al. 2011], CD44 [Liu et al. 2024], LYZ (lysozyme) [Sandin et al. 2016], NAT1 [Johnson et al. 2004], C4A [Zorzetto et al. 2017], LILRB2 [Lao et al. 2021], GFAP (glial fibrillary acidic protein) [Shir et al. 2022], SERPINA1 [Barba et al. 2022], GMPR (guanosine monophosphate reductase) [Liu et al. 2018], HLA-DRA [Branciamore et al. 2023], SERPINE1 [Fang et al. 2012], CD74 [Liu et al. 2024], TLR5 [Herrera-Rivero et al. 2019], ADAMTS1 [Miguel et al. 2005], IL12A [Zhu et al. 2014], EPHA2 [Ma et al. 2022], CD14 [André et al. 2019], HLA-DRB1 [Shigemizu et al. 2024], SPHK1 [Lee et al. 2018], HMOX1 [Gong et al. 2023], IFITM2 [Xiang et al. 2025], CASP1 [Flores et al. 2022], IL15 [Bishnoi et al. 2015], MLKL (mixed lineage kinase domain like pseudokinase) [Motawi et al. 2020], GPX3 [Panyard et al. 2024], CXCR4 [Huang et al. 2024], HLA-B [Roses, 2009], HSPA1B [Gu et al. 2025], IFITM3 [Feng et al. 2025], TSPO (translocator protein) [Fairley et al. 2024], CDK2 [Baumann et al. 1993], BAG3 [Gonzalez-Rodriguez et al. 2021], B2M [Huang et al. 2023], TNFSF10 [Burgaletto et al. 2021], PTPRC (protein tyrosine phosphatase receptor type C) [Huang et al. 2024], ANXA2 [Ye et al. 2024], VIM (vimentin) [Zhang et al. 2025], MYD88 [Chen et al. 2025], CLIC4 [Chen et al. 2024], CD109 [Fessel, 2023], CLIC1 [Xiang et al. 2025], IGF2 [Chen et al. 2025], AQP4 [Mohaupt et al. 2023], DKK1 [Sato et al. 2024], IFIT3 [Garces et al. 2023], CA3 [Dong et al. 2022], SCD (stearoyl-CoA desaturase) [Astarita et al. 2011], IL16 [Yin et al. 2017], CSF1 [Pons a et al. 2021], HFE (homeostatic iron regulator) [Ali-Rahmani et al. 2014], FOXO1 [Lin et al. 2024], FSTL1 [Dai et al. 2023], PON3 [Trentini et al. 2024], FGF1 [Chang et al. 2019], TLR4 [Yang et al. 2020], TIMP1 [Liu et al. 2023], HEY2 [Chen et al. 2019], RHOC (ras homolog family member C) [Tian et al. 2021], PLD2 [Oliveira et al. 2010], DDR1 [Stevenson et al. 2023], S1PR2 [Wang et al. 2023], TAP2 [Bullido et al. 2007], ITPKB (inositol-trisphosphate 3-kinase B) [Salta et al. 2016], ERBB2 [Wang et al. 2017], REST (RE1 silencing transcription factor) [González-Mundo et al. 2020] and AEBP1 [Asadie et al. 2024] are altered expressed in Alzheimer disease. Research has revealed that EPHA1 [Ma al. 2021], NPTX2 [Moran et al. 2008], NPPC (natriuretic peptide C) [Woodward et al. 2017], SV2C [Chang et al. 2024], NRGN (neurogranin) [Koob et al. 2025], HTR2A [Lee et al. 2012], MEF2C [Shulskaya et al. 2024], TRAF5 [Zhang et al. 2021], GRIN2A [Nepal et al. 2019], HOMER1 [Lenka et al. 2024], DLG4 [Lukashevich et al. 2024], VAMP2 [Kim et al. 2021], OPTN (optineurin) [Wise and Cannon, 2016], GRIN2B [Cui et al. 2024], PAK6 [Giusto et al. 2024], CDK5R1 [Das et al. 2012], DLG2 [Zhao et al. 2020], PRKCD (protein kinase C delta) [Gordon a et al. 2016], STX1B [Chang et al. 2019], GIT1 [Zhou a et al. 2023], CDK5 [Alrouji et al. 2024], ARRB1 [Fang et al. 2021], DZIP1 [Valente et al. 2012], EEF1A2 [Khwanraj et al. 2023], TNF (tumor necrosis factor) [Amin et al. 2022], HLA-DRB5 [Su et al. 2021], CCL2 [Xiromerisiou et al. 2022], SOCS3 [Liu et al. 2024], PCSK9 [Jahed et al. 2022], MS4A6A [Fan et al. 2016], VCAM1 [Zheng et al. 2022], WNT1 [Wei et al. 2019], CHI3L1 [Gong et al. 2025], CD44 [Wang et al. 2022], NAT1 [van der Walt et al. 2003], GFAP (glial fibrillary acidic protein) [Lin et al. 2023], SERPINA1 [Halbgebauer et al. 2016], RUNX3 [Huang et al. 2024], HLA-DRA [Mo et al. 2015], TNFAIP3 [Ma et al. 2023], CD14 [Panaro et al. 2008], HLA-DRB1 [Garretti et al. 2023], SPHK1 [Motyl and Strosznajder, 2018], HMOX1 [Tavitian et al. 2020], NLRC5 [Liu et al. 2023], CASP1 [Huan et al. 2023], IL15 [Rentzos et al. 2007], GPX3 [Wang et al. 2025], CXCR4 [Dogra et al. 2023], TSPO (translocator protein) [Xue et al. 2022], BAG3 [Ying et al. 2022], RGS9 [Tekumalla et al. 2001], PTPRC (protein tyrosine phosphatase receptor type C) [Bottero al. 2018], MYD88 [Liu et al. 2023], APLN (apelin) [Angelopoulou et al. 2021], DNAJB1 [Akber et al. 2015], IGF2 [Grunenwald et al. 2025], AQP4 [Lapshina and Ekimova, 2024], CA3 [Legault-Denis et al. 2024], SCD (stearoyl-CoA desaturase) [Tardiff et al. 2022], IL16 [Zhang et al. 2020], CSF1 [Chang a et al. 2019], FOXO1 [Zeng et al. 2019], HSPB1 [Meng et al. 2024], FSTL1 [Cao et al. 2021], FGF1 [Wei et al. 2014], RAB32 [Radefeldt et al. 2025], TLR4 [Roy et al. 2024], TIMP1 [Lorenzl et al. 2002], PLD2 [Mendez-Gomez et al. 2018], ITPKB (inositol-trisphosphate 3-kinase B) [Di Leva et al. 2023], ERBB2 [Jin et al. 2024], SLC1A5 [Liu et al. 2022], PSMB9 [Shani et al. 2023] and PSMB8 [Nguyen et al. 2023] are expressed in Parkinson's Disease. ERFE (erythroferrone) [Youssef al. 2021], HTR2A [Xiang et al. 2024], PAK1 [Fu et al. 2012], RASGRF1 [Abreu et al. 2009], CHRM3 [Fakher et al. 2023], KCNH7 [Martínez et al. 2008], CASP5 [Rui et al. 2018], TNF (tumor necrosis factor) [Lillegraven et al. 2023], CXCL8 [Gowhari Shabgah et al. 2019], HLA-DRB5 [Xu et al. 2021], CCL2 [Moadab et al. 2021], GZMB (granzyme B) [Aubert et al. 2024], CXCL2 [Wang et al. 2021], SOCS3 [Xu et al. 2024], TBX21 [Chae et al. 2009], PCSK9 [Meng et al. 2023], IL21R [Carreño-Saavedra et al. 2023], GBP5 [Haque et al. 2021], VCAM1 [Achudhan et al. 2022], WNT1 [Dinesh et al. 2020], OSM (oncostatin M) [Han et al. 2023], CHI3L1 [Yu et al. 2023], RGS1 [Hu et al. 2019], C4B [Rigby et al. 2012], SELL (selectin L) [Bond and Hay, 1997], CD44 [Gorantla et al. 2021], LYZ (lysozyme) [Xu et al. 2025], AZGP1 [Na et al. 2017], SFRP4 [Miao et al. 2013], GFAP (glial fibrillary acidic protein) [Salari et al. 2023], CFB (complement factor B) [Matola et al. 2023], S100A4 [Šenolt et al. 2015], HLA-DOA [Okada et al. 2016], TNFAIP3 [Tang et al. 2023], CD74 [Sánchez-Zuno et al. 2021], LAIR1 [Zhang et al. 2023], TLR5 [Kim et al. 2014], IL12A [Wang et al. 2015], CD14 [Fuentelsaz-Romero et al. 2021], HLA-DRB1 [Klimenta et al. 2019], SPHK1 [Wang et al. 2021], ATF3 [Hu et al. 2021], HMOX1 [Sun et al. 2024], NLRC5 [Yu et al. 2020], HLA-DPB1 [Yang et al. 2021], TAP1 [Zhang et al. 2002], CASP1 [He et al. 2024], IL15 [Kurowska et al. 2020], S100A11 [Navrátilová et al. 2021], CD58 [Raychaudhuri et al. 2009], GPX3 [Wahl et al. 2025]. CXCR4 [Han et al. 2025], HLA-DMB [Morel et al. 2004], TSPO (translocator protein) [Bruijnen et al. 2019], HLA-DMA [Morel et al. 2004], LGALS9 [Xu et al. 2021], B2M [Söderblom et al. 1996], PTPRC (protein tyrosine phosphatase receptor type C) [Hou et al. 2025], ANXA2 [Xiao et al. 2024], NFKB2 [Manuel Sánchez-Maldonado et al. 2020], HLA-C [Al-Balushi et al. 2025], VIM (vimentin) [Han et al. 2024], MYD88 [Ramirez-Perez et al. 2023], IFIH1 [Martínez et al. 2008], APLN (apelin) [Chang et al. 2021], AMH (anti-Mullerian hormone) [Lopez-Corbeto et al. 2021], CD109 [Song et al. 2019], CLIC1 [Bordean et al. 2021], IGF2 [Martin-Trujillo et al. 2010], AQP4 [Kumar et al. 2025], DKK1 [Zhao et al. 2024], HLA-E [Iwaszko et al. 2015], IRF1 [Yoshida et al. 2012], IL16 [ElAtta et al. 2019], CSF1 [Garcia a et al. 2016], PADI2 [Nava-Quiroz a et al. 2023], HFE (homeostatic iron regulator) [Carini et al. 2023], PDPN (podoplanin) [Takakubo et al. 2017], IFI16 [Alunno et al. 2016], HLA-F [Ravindranath et al. 2023], FOXO1 [Zhang et al. 2021], FSTL1 [Ni et al. 2021], FGF1 [Etori et al. 2023], IL10RA [Yang et al. 2017], TLR4 [Yan et al. 2019], C1R [Breitner et al. 1995], TIMP1 [Schmalz et al. 2019], TRIM22 [Wei et al. 2022], F11R [Fang et al. 2016], DDR2 [Mu et al. 2020], S1PR2 [Wang et al. 2021], TAP2 [Dai et al. 2014], NFATC1 [Zheng et al. 2024], TIGIT (T cell immunoreceptor with Ig and ITIM domains) [Luo et al. 2017], MMEL1 [Huang et al. 2017], CD82 [Neumann et al. 2018] and PSMB9 [Li et al. 2022] play a significant role in the development of rheumatoid arthritis. NPR3 [Chen et al. 2022], NPTX2 [Li et al. 2025], NRGN (neurogranin) [Kuşdoğan et al. 2023], MEF2C [Li et al. 2024], EPHA5 [Overman et al. 2012], GRM7 [Safari et al. 2020], HOMER1 [Tammasse et al. 2024], SYT3 [Lu et al. 2023], PIK3R1 [Li et al. 2022], HOMER2 [Zhu a et al. 2016], CDK5 [Tuo et al. 2018], BCL11A [Hassan et al. 2019], DLGAP4 [Bai et al. 2018], HDAC9 [Markus, 2023], TNF (tumor necrosis factor) [Xue et al. 2022], SERPINA3 [Hu et al. 2024], CXCL8 [He et al. 2018], CCL2 [Li et al. 2024], CXCL2 [Chen et al. 2021], PCSK9 [Moustafa et al. 2021], IL21R [Lee et al. 2016], VCAM1 [Maglinger et al. 2021], OSM (oncostatin M) [Christian et al. 2023], CHI3L1 [Rathcke et al. 2012], C4B [Carr et al. 1993], SELL (selectin L) [Wei et al. 2011], CES1 [Olšerová et al. 2024], CD44 [Shang et al. 2025], APLNR (apelin receptor) [Wu et al. 2017], GFAP (glial fibrillary acidic protein) [Amalia, 2021], SERPINA1 [Malik et al. 2017], GPR65 [Chen et al. 2024], S100A4 [Ji et al. 2025], SERPINE1 [Shilenok et al. 2023], TNFAIP3 [Miao et al. 2018], CD74 [Yang et al. 2017], TLR5 [Gu et al. 2016], CD14 [Olson et al. 2020], TNFRSF12A [Zhao et al. 2024], ATF3 [Huang et al. 2025], HMOX1 [Henrich et al. 2023], CASP1 [Wang et al. 2024], IL15 [Lee et al. 2019], ITGAL (integrin subunit alpha L) [Keum et al. 2013], MLKL (mixed lineage kinase domain like pseudokinase) [Tian et al. 2022], GPX3 [Akhter et al. 2014], CXCR4 [Werner et al. 2020], IFITM3 [Harmon et al. 2022], TSPO (translocator protein) [Tuwar et al. 2023], B2M [Zhen et al. 2023], ANXA2 [Jiang et al. 2025], VIM (vimentin) [Xiao et al. 2021], MYD88 [Qin et al. 2025], SLC15A3 [Yu et al. 2024], APLN (apelin) [Xu et al. 2025], IGF2 [Fei et al. 2025], AQP4 [Chen et al. 2024], DKK1 [Zheng et al. 2023], P2RY1 [Janicki et al. 2017], IRF1 [Alexander et al. 2003], IL16 [Liu et al. 2013], FOXO1 [Guo et al. 2022], FGF1 [Dordoe et al. 2022], IL10RA [Park et al. 2013], TLR4 [Oo, 2024], TIMP1 [Ge et al. 2020], MAP3K8 [Li et al. 2020], S1PR2 [Kim et al. 2015], EDNRB (endothelin receptor type B) [Zhang and Sui, 2014], AGT (angiotensinogen) [Wei et al. 2017], REST (RE1 silencing transcription factor) [Doeppner et al. 2017], FADS2 [Yang et al. 2015], MAGT1 [Gotru et al. 2023], ACTA2 [Moosa et al. 2013] and PSMB8 [Zhang et al. 2025] were altered expressed and associated with stroke. Studies have shown that NPTX2 [Shao et al. 2020], VAMP2 [Costa et al. 2022], OPTN (optineurin) [Nan et al. 2024], PPP3R1 [Peterson et al. 2014], PRMT8 [Zheng et al. 2024], DCTN1 [Hori et al. 2021], TNF (tumor necrosis factor) [Zheng et al. 2022], CCL2 [Dhillon et al. 2008], PCSK9 [Bell et al. 2022], GFAP (glial fibrillary acidic protein) [Cicognola et al. 2021], SERPINA1 [Halbgebauer et al. 2016], CD14 [Pase et al. 2020], IL15 [Rentzos et al. 2007], CXCR4 [Ma et al. 2024], TSPO (translocator protein) [Conte et al. 2023], ANXA2 [Gauthier-Kemper et al. 2011], MYD88 [Chen et al. 2024], APLN (apelin) [Cai et al. 2025], AQP4 [Arighi et al. 2022], CA3 [Cao et al. 2016], IL16 [Ma et al. 2022], FOXO1 [Jiang et al. 2019], RAB32 [Panzavolta et al. 2024], TLR4 [Kilic et al. 2018], TIMP1 [Tuna et al. 2018], IGFBP7 [Agbemenyah et al. 2014], AGT (angiotensinogen) [Kim et al. 2006], SLC40A1 [Zou et al. 2025] and PSMB8 [Ding and Zhu, 2018] altered expression can lead to dementia. NPPC (natriuretic peptide C) [Chun et al. 2000], WIF1 [Salah et al. 2013], MEF2C [Yang et al. 2021], CAMKK2 [Li et al. 2021], RGS6 [Mahata et al. 2021], RASGRF1 [Tsai et al. 2020], PRKCB (protein kinase C beta) [Feng et al. 2020], NLK (nemo like kinase) [Szypowska et al. 2011], SYT7 [Ma et al. 2024], FBXW7 [Li et al. 2022], PRKCD (protein kinase C delta) [Greene a et al. 2014], GIT1 [Song a et al. 2021], MEF2A [Zhang et al. 2014], FGF12 [Liu et al. 2022], CDK5 [Park et al. 2015], CCNC (cyclin C) [Stieg et al. 2019], ARRB1 [Tan et al. 2021], SUB1 [Yu et al. 2016], DES (desmin) [Moneo-Corcuera et al. 2023], BCL11A [Li et al. 2022], HDAC9 [Wan et al. 2021], TNF (tumor necrosis factor) [Sriramula and Francis, 2015], SERPINA3 [Ko et al. 2019], CXCL8 [Kumar et al. 2016], CCL2 [Namlı Kalem et al. 2017], SOCS3 [Chen et al. 2019], PCSK9 [Park et al. 2024], VCAM1 [Dong et al. 2011], WNT1 [Mill et al. 2014], OSM (oncostatin M) [DeMarco et al. 2023], RGS1 [Wu et al. 2017], PLA2G3 [Martínez-García et al. 2010], CD44 [Yusupov et al. 2021], LYZ (lysozyme) [Liu et al. 2006], APLNR (apelin receptor) [Şişli et al. 2022], SFRP4 [Ren et al. 2024], GFAP (glial fibrillary acidic protein) [Passos et al. 2022], S100A4 [Ji et al. 2025], RUNX3 [Kang et al. 2024], SERPINE1 [Yuan et al. 2023], CD74 [Chen et al. 2024], TLR5 [Wei et al. 2016], CD14 [Li et al. 2022], SPHK1 [Pchejetski et al. 2007], ATF3 [Feng et al. 2021], HMOX1 [Abe et al. 2023], NLRC5 [Chen et al. 2017], ITGB4 [Tan et al. 2024], CASP1 [Liu et al. 2021], IL15 [Chen et al. 2019], MFAP4 [Salama et al. 2018], GPX3 [Zhang et al. 2024], CXCR4 [Yi et al. 2024], CDK2 [Zhou et al. 2023], BAG3 [Zhang et al. 2022], B2M [Althubiti et al. 2021], TMBIM1 [Gong et al. 2016], ANXA2 [Arruda et al. 2024], VIM (vimentin) [Håversen et al. 2018], MYD88 [Chen et al. 2024], HCLS1 [Li et al. 2018], SLC15A3 [Luo et al. 2024], APLN (apelin) [Zhou et al. 2016], AMH (anti-Mullerian hormone) [Vale-Fernandes et al. 2024], IGF2 [Yang et al. 2014], SEMA3F [Li et al. 2019], AQP4 [Chen et al. 2024], DKK1 [Yang et al. 2024], CA3 [Yin et al. 2017], IRF1 [Deng et al. 2017], CSF1 [Werner a et al. 2019], HFE (homeostatic iron regulator) [Nandar et al. 2013], IFI16 [Gugliesi et al. 2005], FOXO1 [Li and Gao, 2023], HSPB1 [Liu et al. 2021], FSTL1 [Zhang et al. 2025], NPC2 [Lee et al. 2023], PON3 [Aviram et al. 2004], FGF1 [Dhlamini et al. 2022], AGTRAP (angiotensin II receptor associated protein) [Wakui et al. 2013], GPR35 [Li et al. 2025], TLR4 [Wang et al. 2024], TIMP1 [Oszajca et al. 2021], MAP3K8 [Zhai et al. 2023], HEY2 [Chen et al. 2019], IGFBP7 [Aral et al. 2020], S1PR2 [Cao et al. 2019], AGT (angiotensinogen) [Kamiyama et al. 2013], NUDT1 [Jin et al. 2025], ERBB2 [Sheu et al. 2019], NFATC1 [Zheng et al. 2024], FBLN5 [Ohara et al. 2011], STEAP3 [Wilkinson et al. 2019], MGP (matrix Gla protein) [Karsli et al. 2011], EMP1 [Han et al. 2024], SLC1A5 [Shi et al. 2024], BGN (biglycan) [Szabados et al. 2024], MXRA8 [Miao et al. 2024], ELN (elastin) [Martínez-Revelles et al. 2017], FADS2 [Yi et al. 2024], SLC40A1 [Feng et al. 2024] and IFI30 [Cacialli et al. 2021] are essential for oxidative stress development. Altered expression of NRGN (neurogranin) [Byrne et al. 2018], GABBR2 [Philpott et al. 2016], PRKG2 [Yin et al. 2020], GRIN3A [Marco et al. 2013], NLK (nemo like kinase) [Jiang et al. 2020], OPTN (optineurin) [Schwab et al. 2012], GIT1 [Goehler a et al. 2004], CDK5 [Hernández-Echeagaray et al. 2024], TRIM37 [Qin et al. 2024], TNF (tumor necrosis factor) [Hsiao et al. 2014], WNT1 [Sileo et al. 2022], GFAP (glial fibrillary acidic protein) [Heim et al. 2025], SPHK1 [Di Pardo et al. 2020], ATF3 [Liang et al. 2009], CASP1 [Wang et al. 2005], CXCR4 [Di Pardo et al. 2020], TSPO (translocator protein) [Simmons et al. 2018], IGF2 [Yang et al. 2022], FOXO1 [Liu et al. 2022], TLR4 [Martínez-Gopar et al. 2023], ERBB2 [Moreira Sousa et al. 2013] and REST (RE1 silencing transcription factor) [Orozco-Díaz et al. 2019] were associated with prognosis in Huntington's disease. HTR2A [Maltby et al. 2020], DCLK1 [ŞİmŞek, 2020], NRXN1 [Kattimani et al. 2018], NECTIN1 [Castellanos et al. 2013], DCTN1 [Münch et al. 2007], KCNH7 [Couturier et al. 2009], TNF (tumor necrosis factor) [Ribeiro et al. 2019], SERPINA3 [Fissolo et al. 2021], CXCL8 [Košćak Lukač et al. 2024], HLA-DRB5 [Caillier et al. 2021], CCL2 [Jaime-Pérez et al. 2020], GZMB (granzyme B) [Shi et al. 2023], TBX21 [Akbarian et al. 2018], IL21R [Tzartos et al. 2011], OASL (2'-5'-oligoadenylate synthetase like) [Al-Nashmi et al. 2017], VCAM1 [Petersen et al. 2016], WNT1 [Lengfeld et al. 2017], CHI3L1 [Jatczak-Pawlik et al. 2024], RGS1 [IMSGC. 2010], SELL (selectin L) [Mössner et al. 1996], CD44 [Tredicine et al. 2022], NAT1 [Briggs et al. 2014], C4A [Ingram et al. 2010], GFAP (glial fibrillary acidic protein) [Abdelhak et al. 2024], HLA-DRA [Sahbaz et al. 2025], TNFAIP3 [Hoffjan et al. 2015], IL12A [IMSGC 2010], CD14 [Chuluundorj et al. 2014], GBP1 [Guo et al. 2024], HLA-DRB1 [Alcina et al. 2012], SPHK1 [Wang et al. 2024], HMOX1 [Agúndez et al. 2016], NLRC5 [Torkamandi et al. 2021], HLA-DPB1 [Anagnostouli et al. 2020], TAP1 [Bell and Ramachandran, 1995], CASP1 [Saito et al. 2021], IL15 [Laurent et al. 2020], CD58 [Ahmed et al. 2024], CXCR4 [Galli et al. 2019], HLA-DMB [Bennetts et al. 1999], TSPO (translocator protein) [Herranz et al. 2024], B2M [Alvarez-Cermeño et al. 1987], PTPRC (protein tyrosine phosphatase receptor type C) [Szvetko al. 2025], ANXA2 [Tezuka et al. 2022], HLA-C [Morrison et al. 2010], MYD88 [Zheng et al. 2020], IFIH1 [Enevold et al. 2009], AMH (anti-Mullerian hormone) [Giordano et al. 2024], CLIC1 [Karaaslan et al. 2023], IGF2 [Chesik et al. 2007], SEMA3F [Williams et al. 2007], AQP4 [Gaudioso et al. 2023], IFIT3 [Sun et al. 2024], HLA-E [Nova et al. 2025], IRF1 [Annibali et al. 2018], IL16 [Kouchaki et al. 2022], IFI16 [Guo et al. 2024], FGF1 [Mohan et al. 2014], RAB32 [Haile et al. 2017], TLR4 [Oh et al. 2024], TIMP1 [Ghasemi Sakha et al. 2020], ACKR3 [Hasani Nourian et al. 2021], S1PR2 [Seyedsadr et al. 2019], TAP2 [Moins-Teisserenc et al. 1995], EDNRB (endothelin receptor type B) [Granström et al. 2014], AGT (angiotensinogen) [Hladikova et al. 2011], SIGLEC7 [Malhotra et al. 2013], MMEL1 [Yazdanpanah et al. 2022], FADS2 [Rezapour-Firouzi et al. 2015], IFI30 [Stefanović et al. 2020], TUBA1C [Karimi et al. 2023] and PSMB8 [Shaw and Williams, 2024] are closely involved with multiple sclerosis. CHRM3 [Selivanova et al. 2012], PRKCD (protein kinase C delta) [Li et al. 2022], TNF (tumor necrosis factor) [Yao et al. 2019], CXCL8 [Castellucci et al. 2015], SOCS3 [Tinè et al. 2024], WNT1 [Wu et al. 2020], CHI3L1 [Yu et al. 2020], CD44 [Huang et al. 2025], LYZ (lysozyme) [Ohbayashi et al. 2016], AZGP1 [Shen et al. 2024], S100A4 [Qin et al. 2022], SERPINE1 [Xu et al. 2016], CD74 [Wang et al. 2024], TLR5 [Baranašić et al. 2022], IL12A [Wang et al. 2012], CD14 [Li et al. 2024], TNFRSF12A [Dong et al. 2020], HLA-DRB1 [Díaz-Peña et al. 2021], HMOX1 [Zhou et al. 2017], CASP1 [Chung et al. 2024], IL15 [Liu et al. 2015], MFAP4 [Johansson et al. 2014], GPX3 [Reddy et al. 2018], CXCR4 [Zhang et al. 2023], HLA-C [Mkorombindo et al. 2021], VIM (vimentin) [Nissen et al. 2019], MYD88 [Ju et al. 2022], IGF2 [de Carvalho et al. 2024], AQP4 [Lin et al. 2023], DKK1 [Dai et al. 2023], IL16 [Karauda et al. 2024], FOXO1 [Shen et al. 2024], HSPB1 [Wang et al. 2020], FGF1 [Kranenburg et al. 2005], RAB32 [Wu et al. 2024], TLR4 [Lin et al. 2022], TIMP1 [He et al. 2022], IGFBP7 [Ruan et al. 2017], AGT (angiotensinogen) [Marushchak et al. 2019], FBLN5 [Brandsma et al. 2015], ELN (elastin) [Benjamin et al. 2021] and SLC40A1 [Ju et al. 2025] plays a role in diagnosis of chronic obstructive pulmonary disease. These GO terms and pathway enrichment results suggest that the DEGs detected in the present investigation might be involvement in MDD progression. These results might contribute to a better understanding of the molecular mechanism of MDD development and its associated complications. A PPI network and modules were constructed to analyze further molecular processes underlying MDD progression and its associated complications. Previous studies demonstrated that YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Capobianco et al. 2012], PIK3R1 [Pinhel et al. 2020], CDK2 [Colón-Mesa et al. 2021], VCAM1 [Yu et al. 2017] and VIM (vimentin) [Roh and Yoo, 2021] are involved in progression of obesity. Previous literature has confirmed that YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Cetica et al. 2024], DLG3 [He et al. 2024], SYNGAP1 [Wiltrout et al. 2024], GRIN2B [Wang et al. 2023], GRIN2A [Samanta, 2023], HLA-B [Chouchi et al. 2018], VIM (vimentin) [Sitovskaya et al. 2023] and TAP1 [Layouni et al. 2010] are involved in epilepsy. ONECUT1 [Russ-Silsby al. 2023], CALM2 [Cheng et al. 2014], PIK3R1 [Li et al. 2022], GRIN2B [Kochetova et al. 2020], VCAM1 [Siddiqui et al. 2023], HLA-B [Jan et al. 2021], VIM (vimentin) [Roefs et al. 2017], ERBB2 [de Kay et al. 2022], TAP2 [Qu et al. 2007] and TAP1 [Li et al. 2014] are involved in the pathogenesis of diabetes mellitus. Previous studies have shown CALM2 [Hamrick et al. 2024], PIK3R1 [He et al. 2024], CDK2 [Su et al. 2021], VCAM1 [Singh et al. 2023], HLA-B [Eyiol et al. 2018], VIM (vimentin) [Håversen et al. 2018], ERBB2 [Jian et al. 2020], PSMB8 [Li et al. 2024], PSMB9 [Liu and Delgado, 2024] and TAP1 [Kolbus et al. 2012] are a promising targeted therapy in cardiovascular diseases. ARRB1 [Chang et al. 2015], GRIN2B [Brown et al. 2023], DLG2 [Qin et al. 2020], GRIN2A [Hu et al. 2018], DLG4 [Kaut et al. 2017], VCAM1 [Zhou et al. 2024] and VIM (vimentin) [O'Leary et al. 2021] expression is altered in the neurons of patients with MDD. ARRB1 [Fang et al. 2021], PIK3R1 [Fu et al. 2024], DLG2 [Keane et al. 2022], VCAM1 [Burns et al. 2001], HLA-B [Lenna et al. 2015], VIM (vimentin) [Meng et al. 2023], ERBB2 [Zeng et al. 2012], PSMB8 [Li et al. 2024] and TAP1 [Vasků et al. 2000] have been proposed as biomarkers for inflammation progression. Regulation of ARRB1 [Sun et al. 2018], PIK3R1 [Zhang et al. 2010], VCAM1 [Qiu et al. 2022], HLA-B [Hu et al. 2024] and PSMB8 [Zhang et al. 2025] levels might be a novel treatment option against hypertension. ARRB1 [Sengupta and Mukhopadhyay, 2025], PIK3R1 [Li et al. 2023], GRIN2B [Andreoli et al. 2014], DLG2 [Prokopenko et al. 2022], DLG4 [Bustos et al. 2017], CDK2 [Baumann et al. 1993], VCAM1 [Salian et al. 2024], HLA-B [Roses, 2009], VIM (vimentin) [Zhang et al. 2025], ERBB2 [Wang et al. 2017] and TAP2 [Bullido et al. 2007] have been recognized as a biomarkers of predicting the Alzheimer disease. ARRB1 [Fang et al. 2021], GRIN2B [Cui et al. 2024], DLG2 [Zhao et al. 2020], GRIN2A [Nepal et al. 2019], DLG4 [Lukashevich et al. 2024], VCAM1 [Zheng et al. 2022], ERBB2 [Jin et al. 2024], PSMB8 [Nguyen et al. 2023] and PSMB9 [Shani et al. 2023] have been investigated to participate in the pathogenesis of Parkinson's Disease. ARRB1 [Tan et al. 2021], CDK2 [Zhou et al. 2023], VCAM1 [Dong et al. 2011], VIM (vimentin) [Håversen et al. 2018] and ERBB2 [Sheu et al. 2019] were reported to play a role in the oxidative stress. PIK3R1 [Huang et al. 2020], GRIN2A [Itokawa et al. 2003] and VCAM1 [Pantovic-Stefanovic et al. 2024] were found to be altered expressed in bipolar disorder. A study showed that PIK3R1 [Huang et al. 2020], GRIN2B [Poltavskaya et al. 2021], DLG2 [Sanders et al. 2022], GRIN2A [Sheng et al. 2024], DLG4 [Balan et al. 2013], VCAM1 [Meixensberger et al. 2021], HLA-B [Seshasubramanian et al. 2020] and TAP2 [Jun et al. 2004] are postulated to be a biomarkers of schizophrenia. Study also showed that serum PIK3R1 [Li et al. 2022], VCAM1 [Maglinger et al. 2021], VIM (vimentin) [Xiao et al. 2021], PSMB8 [Zhang et al. 2025] and IRF1 [Alexander et al. 2003] are associated with stroke. SYNGAP1 [Wiltrout et al. 2024], GRIN2B [Yalçintepe et al. 2021], GRIN2A [Mangano et al. 2022], DLG4 [Feyder et al. 2010], VCAM1 [Kameno et al. 2013] and HLA-B [Guerini et al. 2024] are closely associated with the development of autism spectrum disorder. VCAM1 [Burns et al. 2001], HLA-B [Asakura et al. 2012], VIM (vimentin) [Mortensen et al. 2021] and TAP2 [Kim et al. 2024] might play a core role in Crohn's disease. VCAM1 [Ando et al. 2001], ERBB2 [Sio et al. 2023] and TAP1 [Liu et al. 2017] have been demonstrated to be regulated in asthma. VCAM1 [Achudhan et al. 2022], VIM (vimentin) [Han et al. 2024], PSMB9 [Li et al. 2022], TAP2 [Dai et al. 2014] and TAP1 [Zhang et al. 2002] have been shown to influence the genetic risk of rheumatoid arthritis. VCAM1 [Petersen et al. 2016], PSMB8 [Shaw and Williams, 2024], TAP2 [Moins-Teisserenc et al. 1995] and TAP1 [Bell and Ramachandran, 1995] might be involved in the genetic susceptibility to multiple sclerosis. Studies have identified VIM (vimentin) [Nissen et al. 2019] as a genetic locus associated with chronic obstructive pulmonary disease susceptibility. ERBB2 [Moreira Sousa et al. 2013] in genetic risk factor has been shown to increase susceptibility to Huntington's disease. PSMB8 [Ding and Zhu, 2018] has been applied as a dementia biomarker. Our data suggest that hub genes expression is significantly altered in MDD. MiRNAs are a type of small non-coding RNA that regultes mRNA expression, and they are becoming key biomarkers in a range of cellular processes. TFs are a type of proteins that regultes mRNA expression, and they are becoming key biomarkers in a range of cellular processes. Moreover, several studies have shown that miRNAs [Chan et al. 2024] and TFs [Kerman et al. 2012] can be involved in the disease progression of MDD. It is evident that miRNA-hub gene regulatory network and TF-hub gene regulatory network play a key part in the development of MDD. The previous studies suggest that some of the biomarkers, such as YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Capobianco et al. 2012], PIK3R1 [Pinhel et al. 2020], NEDD4L [Lee et al. 2017], HDAC9 [Yang et al. 2025], CDK2 [Colón-Mesa et al. 2021], VIM (vimentin) [Roh and Yoo, 2021], TNF (tumor necrosis factor) [Olszanecka-Glinianowicz et al. 2004], hsa-miR-142-3p [Podraza et al. 2024], JUND [Costantino et al. 2019], EGR1 [Zhang et al. 2013], GATA2 [Menghini et al. 2005] and TEAD1 [Lv et al. 2023] might play a key role in obesity development. YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Cetica et al. 2024], NEDD4L [Vanli-Yavuz et al. 2015], TNFAIP3 [Zhuo et al. 2022], VIM (vimentin) [Sitovskaya et al. 2023], TNF (tumor necrosis factor) [Fang et al. 2024], hsa-miR-34a-5p [Ünalp et al. 2022] and EGR1 [Dong et al. 2023] were found to be associated with epilepsy. PIK3R1 [Li et al. 2022], CALM2 [Cheng et al. 2014], HDAC9 [Liu et al. 2024], PRKCZ (protein kinase C zeta) [Qin et al. 2008], TNFAIP3 [Cao et al. 2023], SERPINH1 [Li et al. 2022], VIM (vimentin) [Roefs et al. 2017], ERBB2 [de Kay et al. 2022], TNF (tumor necrosis factor) [Akash et al. 2018], IRF1 [Colli et al. 2018], hsa-miR-142-3p [Collares et al. 2013], PAX2 [Markowska et al. 2013], EGR1 [Ke et al. 2023] and CREB1 [Shahin et al. 2024] have been identified as an diabetes mellitus risk factors. Some studies have pointed out that PIK3R1 [He et al. 2024], CALM2 [Hamrick et al. 2024, NEDD4L [Li et al. 2022], HDAC9 [Das and Natarajan, 2020], TNFAIP3 [Hua et al. 2009], SERPINH1 [Rusu-Nastase et al. 2022], CDK2 [Su et al. 2021], VIM (vimentin) [Håversen et al. 2018], ERBB2 [Jian et al. 2020], TNF (tumor necrosis factor) [Yuan et al. 2020], hsa-miR-142-3p [Scărlătescu et al. 2022], hsa-miR-34a-5p [Yalım et al. 2023], JUND [Akhmedov et al. 2020], EGR1 [Khachigian, 2024], CREB1 [Zhang and Ge, 2022], TFAP2C [Zeng et al. 2024] and GATA2 [Menghini et al. 2005] are the biomarkers with the most associations in cardiovascular diseases. NEDD4L [Xu et al. 2020], HDAC9 [Dai et al. 2023], TNFAIP3 [Chen et al. 2017], VIM (vimentin) [O'Leary et al. 2021], TNF (tumor necrosis factor) [Yao et al. 2020], IRF1 [Bialek et al. 2020], EGR1 [Wu et al. 2021], CREB1 [Wang et al. 2015] and GATA2 [Choi et al. 2014] are involved in MDD. NEDD4L [Liang et al. 2024], TNFAIP3 [Zou et al. 2020], VIM (vimentin) [Mortensen et al. 2021], TNF (tumor necrosis factor) [Peyrin-Biroulet et al. 2008], IRF1 [Xu et al. 2025], hsa-mir-376c-3p [Jerala et al. 2024], EGR1 [Yu et al. 2012], CREB1 [Diegelmann et al. 2013] and GATA2 [Li et al. 2022] might be involved in the development of Crohn's disease. NEDD4L [Li et al. 2022], HDAC9 [Das and Natarajan, 2020], TNFAIP3 [Momtazi et al. 2019], VIM (vimentin) [Meng et al. 2023], ERBB2 [Zeng et al. 2012], TNF (tumor necrosis factor) [van Loo and Bertrand, 2003], IRF1 [Chen et al. 2023], JUND [Yang et al. 2024], EGR1 [Lehman et al. 2022], CREB1 [Chen et al. 2023], GATA2 [Menghini et al. 2005] and TEAD1 [Tran et al. 2025] are well-known for its regulatory function in inflammation. NEDD4L [Liu et al. 2022], HDAC9 [Zhang et al. 2024], CDK2 [Baumann et al. 1993], VIM (vimentin) [Zhang et al. 2025], ERBB2 [Wang et al. 2017], TNF (tumor necrosis factor) [Zhou et al. 2020], hsa-miR-142-3p [Kumar et al. 2013], hsa-miR-34a-5p [Gascón et al. 2024], EGR1 [He et al. 2022], CREB1 [Li et al. 2012] and HINFP (histone H4) [Gupta and Kumar, 2021] have been identified as a biomarkers that participates in Alzheimer disease. NEDD4L [Niu et al. 2022], TNFAIP3 [Xue et al. 2011], TNF (tumor necrosis factor) [Mehaffey and Majid, 2017], EGR1 [Laggner et al. 2022] and GATA2 [Zhang et al. 2022] are potentially involved in the pathogenesis of hypertension. Study showed that NEDD4L [Su et al. 2024], HDAC9 [Markus, 2023], TNFAIP3 [Miao et al. 2018], VIM (vimentin) [Xiao et al. 2021], TNF (tumor necrosis factor) [Xue et al. 2022], EGR1 [Yang et al. 2015] and GATA2 [Bierman-Chow et al. 2023] were associated with stroke. HDAC9 [O'Connell et al. 2019], TNF (tumor necrosis factor) [He et al. 2022], hsa-miR-34a-5p [Tonk et al. 2024], hsa-mir-708-3p [Li et al. 2024], EGR1 [Hu et al. 2019], CREB1 [Pan et al. 2023] and TEAD1 [Sun et al. 2023] plays an important role in schizophrenia. Number of studies have found that HDAC9 [Wan et al. 2021], CDK2 [Zhou et al. 2023], VIM (vimentin) [Håversen et al. 2018], ERBB2 [Sheu et al. 2019], TNF (tumor necrosis factor) [Sriramula and Francis, 2015], IRF1 [Deng et al. 2017], JUND [Paneni et al. 2013], EGR1 [Guo et al. 2023], CREB1 [Pan et al. 2024] and GATA2 [Huang et al. 2023] are closely associated with oxidative stress. Recent studies have shown that PRKCZ (protein kinase C zeta) [Saxena et al. 2008], TNF (tumor necrosis factor) [Doğanavşargil Baysal et al. 2019] and CREB1 [Wang et al. 2021] are closely related to bipolar disorder. Altered expression of TNFAIP3 [Krusche et al. 2019], ERBB2 [Sio et al. 2023], TNF (tumor necrosis factor) [Berry et al. 2006] and IRF1 [Hu et al. 2021] were observed in asthma. TNFAIP3 [Ma et al. 2023], ERBB2 [Jin et al. 2024], TNF (tumor necrosis factor) [Amin et al. 2022], JUND [Berton et al. 2009], EGR1 [Guo et al. 2023], CREB1 [Liu and Li, 2023], HINFP (histone H4) [Gupta and Kumar, 2021] and GATA2 [Kurzawski et al. 2010] have been reported as a critical regulators in the Parkinson's Disease. Researchres have reported that TNFAIP3 [Tang et al. 2023], VIM (vimentin) [Han et al. 2024], TNF (tumor necrosis factor) [Lillegraven et al. 2023], IRF1 [Yoshida et al. 2012], hsa-mir-708-3p [He et al. 2023], JUND [Ye et al. 2023] and EGR1 [Aicher et al. 1999] are associated with rheumatoid arthritis. Altered expression of TNFAIP3 [Hoffjan et al. 2015], TNF (tumor necrosis factor) [Ribeiro et al. 2019], IRF1 [Annibali et al. 2018] and hsa-miR-34a-5p [Agostini et al. 2024] might predict prognosis of multiple sclerosis. VIM (vimentin) [Nissen et al. 2019], TNF (tumor necrosis factor) [Yao et al. 2019], hsa-miR-34a-5p [Mirra et al. 2023] and EGR1 [Chen et al. 2015] are a key biomarkers in the pathogenesis of chronic obstructive pulmonary disease. ERBB2 [Moreira Sousa et al. 2013], TNF (tumor necrosis factor) [Hsiao et al. 2014], hsa-miR-34a-5p [Hart et al. 2023] and CREB1 [McCourt et al. 2015] were found to be associated with an increased risk for Huntington's disease. TNF (tumor necrosis factor) [Yamauchi et al. 2021] was significantly regulated in the autism spectrum disorder. TNF (tumor necrosis factor) [Zheng et al. 2022] was found to be regulated in dementia. This study might advance the understanding of the molecular mechanism of MDD and suggest that YWHAH, MAP1LC3A, ENFKB2, hsa-miR-548j-5p, hsa-mir-466, hsa-miR-1911-5p, hsa-miR-523-5p, hsa-mir-6829-3p, hsa-miR-6891-3p, FEV (Frog erythrocytic virus) and PPARG (peroxisome proliferator activated receptor gamma) might be novel biomarkers and therapeutic targets for MDD patients. Our study provides perspective in which hub genes, miRNAs and TFs might be a mediators in the MDD pathological process and its associated complications. Taken together, our integrated bioinformatic analysis of NGS data identified biomarkers (YWHAG, ONECUT1, CALM2, ARRB1, PIK3R1, CDK2, VCAM1, HLA-B, VIM, ERBB2, hsa-miR-548j-5p, hsa-mir-466, hsa-miR-523-5p, hsa-mir-6829-3p, PAX2, JUND, FEV and PPARG) that have both great diagnostic and prognostic value for MDD and it provided clues for the underlying molecular mechanism and therapeutic targets for MDD. Further investication is needed to explore their biological function in MDD. Declarations Acknowledgement I thanks very much to Mitsuhashi H, Nagy C, Turecki G, McGill University, Psychiatry, McGill Group for Suicide Studies, Montreal, Quebec, Canada, the authors who deposited their NGS dataset GSE275676, into the public GEO database. Conflict of interest The authors declare that they have no conflict of interest. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent No informed consent because this study does not contain human or animals participants. Availability of data and materials The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE275676) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE275676] Consent for publication Not applicable. 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Complement C4A and C4B Gene Copy Number Study in Alzheimer's Disease Patients. Curr Alzheimer Res. 2017;14(3):303-308. doi:10.2174/1567205013666161013091934 Zou D, Zhou S, Wang H, Gou J, Wang S. Knee Joint Swelling at Presentation: A Case of Pediatric Crohn Disease With a TNFAIP3 Mutation. Pediatrics. 2020;146(6):e20193416. doi:10.1542/peds.2019-3416 Zou J, Guo P, Lv N, Huang D. Lipopolysaccharide-induced tumor necrosis factor-α factor enhances inflammation and is associated with cancer (Review). Mol Med Rep. 2015;12(5):6399-6404. doi:10.3892/mmr.2015.4243 Zou Z, Wu F, Chen L, Yao H, Wang Z, Chen Y, Qi M, Jiang Y, Tang L, Gan X, et al. The J bs-5YP peptide can alleviate dementia in senile mice by restoring the transcription of Slc40a1 to secrete the excessive iron from brain. J Adv Res. 2025;69:51-59. doi:10.1016/j.jare.2024.03.014 Zuo W, Fang S, Xu T, Li Y, Zhao J, Xie X, Wang T, Hou W, Wang M. CA3 Pyramidal Neuron Activation Promotes Cognitive Resilience to Inflammation-Induced Cognitive Inflexibility. CNS Neurosci Ther. 2025;31(2):e70271. doi:10.1111/cns.70271 Zurawek M, Fichna M, Fichna P, Skowronska B, Dzikiewicz-Krawczyk A, Januszkiewicz D, Nowak J. Cumulative effect of IFIH1 variants and increased gene expression associated with type 1 diabetes. Diabetes Res Clin Pract. 2015;107(2):259-266. doi:10.1016/j.diabres.2014.11.008 Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files ExperimentalBrainResearchTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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College of Pharmacy, Gadag 582101, Karnataka, India.Gadag","correspondingAuthor":false,"prefix":"","firstName":"Basavaraj","middleName":"","lastName":"Vastrad","suffix":""},{"id":465786180,"identity":"1f45521b-1a56-4dac-ae1e-a3b32e333898","order_by":1,"name":"Chanabasayya Vastrad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACCRQemw2QYGw8QKQWZpCWNJCWBpK0HAYz8Wrhn9387HFhG0Nif//5Yx9+lJ23W9t+GGhLjU00TkvuHDM3ngnUMuNGMvPMnnO3k7edSQRqOZaW24BDi4FEgpk0bxuDMcMNZmYG3rbbyWYHgFoYGw7j0ZL+DaxF/vxhZsa/beeSzc4/JKQlB2yLnMGBZGZm3rYDdmY3CNgicSOnTJrnnISc4Y1kY2aZc8kJZjeAtiTg8Qv/jPRt0jxlNjxy5w8+ZnxTZmdvdj794YMPNTY4tcAsg7MSwSoT8CtHBfakKB4Fo2AUjIKRAQDSZlsK1y3XFwAAAABJRU5ErkJggg==","orcid":"","institution":"Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India.","correspondingAuthor":true,"prefix":"","firstName":"Chanabasayya","middleName":"","lastName":"Vastrad","suffix":""}],"badges":[],"createdAt":"2025-06-02 06:23:21","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6799275/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6799275/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83893860,"identity":"414426ae-5933-4aa9-ba92-e49e7894567a","added_by":"auto","created_at":"2025-06-04 08:32:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":730007,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of differentially expressed genes. Genes with a significant change of more than two-fold were selected. Green dot represented up regulated significant genes and red dot represented down regulated significant genes.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6799275/v1/ce3e7cd148a27dd7036db529.png"},{"id":83893859,"identity":"1946a35d-1ff4-4f2d-9e7e-eafa18b1be35","added_by":"auto","created_at":"2025-06-04 08:32:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1079912,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A19 = Normal control samples; B1 – B20= MDD samples)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6799275/v1/049e3850af09393fb3321a01.png"},{"id":83895074,"identity":"70bea3a2-5888-41cf-9f3f-081a9de122ef","added_by":"auto","created_at":"2025-06-04 08:40:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3102663,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of DEGs. Up regulated genes are marked in green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6799275/v1/9b37b1615a650d814cea3bff.png"},{"id":83895073,"identity":"5bd65d66-724e-4764-9de8-6b98944cda4c","added_by":"auto","created_at":"2025-06-04 08:40:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":908121,"visible":true,"origin":"","legend":"\u003cp\u003eModules selected from the PPI network. (A) The most significant module was obtained from PPI network with 25 nodes and 64 edges for up regulated genes (B) The most significant module was obtained from PPI network with 17 nodes and 24 edges for down regulated genes. Up regulated genes are marked in parrot green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6799275/v1/5ea97e0657ae106b0b386e96.png"},{"id":83893863,"identity":"0aedb4ae-636d-4535-b129-e2599651c8fb","added_by":"auto","created_at":"2025-06-04 08:32:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3610574,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene - miRNA regulatory network. The light brown color diamond nodes represent the key miRNAs; up regulated genes are marked in green; down regulated genes are marked in red\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6799275/v1/b510c83d73b714d4daff760b.png"},{"id":83893862,"identity":"928605ef-556d-4eb2-820d-4d925d769d0b","added_by":"auto","created_at":"2025-06-04 08:32:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2442239,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene - TF regulatory network. The ash color triangle nodes represent the key TFs; up regulated genes are marked in dark green; down regulated genes are marked in dark red.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6799275/v1/6ee17beb03f716f149d4cb8b.png"},{"id":83893866,"identity":"44d1502e-78ea-4f67-b874-de1d3033bb65","added_by":"auto","created_at":"2025-06-04 08:32:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":620653,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analyses of hub genes. A) YWHAG B) ONECUT1 C) CALM2 D) ARRB1 E) PIK3R1 F) CDK2 G) VCAM1 H) HLA-B I) VIM J) ERBB2\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6799275/v1/c7d1befbdd574a27ab22e9a3.png"},{"id":83895690,"identity":"9534306b-42b1-47eb-b9dc-bb85b07976ec","added_by":"auto","created_at":"2025-06-04 08:48:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17249451,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6799275/v1/19a503c5-c5fa-40e6-ba64-9a863b24f05b.pdf"},{"id":83893861,"identity":"23e63133-b8f2-4a29-a015-03c6d789a9ac","added_by":"auto","created_at":"2025-06-04 08:32:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":183813,"visible":true,"origin":"","legend":"","description":"","filename":"ExperimentalBrainResearchTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6799275/v1/ba20e4f4435ba64aa79ae091.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying differentially expressed genes, miRNAs and TFs in major depressive disorder by bioinformatics analysis of next generation sequencing data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) \u0026nbsp;is a common mental disorders that manifests as depressed mood lasting more than 2 weeks and causes emotional distress, functional impairment, health problems, and suicide, among others [Kang and Cho, 2020]. It is estimated to affect more than 250 million of the global population, with no difference in prevalence observed between childhood, adolescence, and adulthood [Bimerew et al. 2024]. Several risk factors have been proposed to cause MDD including genetic inheritance [Lohoff, 2010], environmental variables [Peyrot et al. 2013], oxidative stress [Ait Tayeb et al. 2023], inflammation [Zainal et al. 2021], rheumatoid arthritis [Jiang et al. 2023], Crohn\u0026apos;s disease [Persoons et al. 2005], chronic obstructive pulmonary disease [Liu et al. 2024], asthma [Oyamada et al. 2021], obesity [Gibson-Smith et al. 2016], diabetes mellitus [Wang et al. 2019], cardiovascular diseases [Zhang et al. 2021], hypertension [Zhang et al. 2024], Alzheimer\u0026apos;s disease [Amidfar et al. 2023], Parkinson\u0026apos;s disease [Tran et al. 2021], Huntington\u0026apos;s disease [Perlis et al. 2010], bipolar disorder [Goldstein et al. 2015], dementia [Asmer et al. 2018], schizophrenia [Etchecopar-Etchart et al. 2021], epilepsy [Kim et al. 2018], multiple sclerosis [Ehde et al. 2008], autism spectrum disorder [Naheed et al. 2020] and stroke [Li et al. 2012]. As MDD has unclear pathogenesis and an unsatisfactory response to treatment, it is necessary to explore the molecular mechanism of MDD to develop effective target treatments.\u003c/p\u003e\n\u003cp\u003eThe underlying complex molecular mechanisms of MDD pose a special challenge to daily clinical practice. Debate on the best strategy for MDD management continues despite great progress in treating MDD in recent decades. Extensive investigation have shown current therapeutic approaches in MDD included \u0026nbsp;monoamine oxidase inhibitors [L\u0026oacute;pez-Mu\u0026ntilde;oz and Alamo, 2009], tricyclic antidepressants [Pereira and Hiroaki-Sato, 2018], serotonin reuptake inhibitors [Clevenger et al. 2018], norepinephrine reuptake inhibitors [Pehrson et al. 2015] and ketamine [Zhang et al. 2020] targeting several crucial signaling pathways that predominantly regulate \u0026nbsp;MDD. However, MDD might also can be caused by many unknown causes, such as oxidative stress and inflammation, which cannot be well solved by current drug treatment and MDD is still a complicated incurable mental disorder [Emekdar et al. 2023]. Thus, it is necessary for us to utilize bioinformatics \u0026nbsp;and next generation sequencing (NGS) technology to explore the molecular pathogenesis or potential treatments of MDD.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The rapid development of bioinformatics and NGS technology has become an important part of modern medical investigation.\u0026nbsp;The recent bioinformatics analysis of NGS data of human specimens from sufferers and normal individuals enables us to investigate various diseases at diverse levels from somatic mutations and copy number variations to genomic expressions at the transcriptomic level, along with epigene variations [Pujar \u0026nbsp;et al. 2022; Ganekal et al. 2023]. Recently, many specific genes and signaling pathways have been discovered to participate in the progression of MDD. Genes include IL6 [Zhang et al. 2016], \u0026nbsp;M6A [Du et al. 2015], \u0026nbsp;KCNK2 [Liou et al. 2009], HTR1A [Kishi et al. 2009] and FKBP5 [Rao et al. 2016] were altered in MDD, in contrast to normal control. Wnt signaling pathway [Sanchez-Ruiz et al. 2024], mTOR signaling pathway [Jernigan et al. 2011], TLR signaling pathway [Hung et al. 2017], G protein-linked signaling pathways [Tomita et al. 2013] and \u0026nbsp;PI3K/AKT signaling pathway \u0026nbsp;[Guo et al. 2024] suggesting the relevant signaling molecules might be a molecular target for specific diagnosis and therapy for MDD. These finding suggested the important roles of some function genes and signaling pathway \u0026nbsp;in MDD progression. However, the diagnostic value of various genes has not been investigated in MDD.\u003c/p\u003e\n\u003cp\u003eIn this investigation, we used bioinformatics analysis of NGS data to investigate the molecular regulatory mechanisms of genes and signaling pathways that influence MDD advancement and explore therapeutic agents that could target genes. We obtained DEGs between MDD and normal control samples from GSE275676 [Mitsuhashi et al. 2024], a NGS data downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) [Clough and Barrett, 2016]. The samples were derived from the post-mortem ventromedial prefrontal cortex of 19 \u0026nbsp; normal control samples and 20 MDD samples. Differently expressed genes (DEGs) were screened by DESeq2 \u0026nbsp;in R bioconductor tool [Love et al. 2014]. The DEGs were performed to Gene Ontology (GO) analysis and REACTOME pathway enrichment analysis. And then a protein-protein interaction (PPI) network and modules was constructed and analysis to screen hub genes. We performed miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed to predict candidate miRNAs and TFs for MDD. Finally, we verified hub gens for diagnostic value with receiver operating characteristic (ROC) curve analysis. This investigation might provide new insight into potential biomarkers and therapeutic targets for MDD.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eNext generation sequencing data source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MDD NGS dataset GSE275676 [Mitsuhashi et al. 2024] was downloaded from the GEO database generated by GPL24676 Illumina NovaSeq 6000 (Homo sapiens). GSE275676 including post-mortem ventromedial prefrontal cortex of 19 \u0026nbsp;normal control samples and 20 MDD samples, used to identify MDD of the DEGs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of DEGs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the DESeq2 \u0026nbsp;in R bioconductor tool [Love et al. 2014] to identify DEGs between the normal control samples and 20 MDD samples in the GSE275676 dataset. The criterion for defining differential gene expression was a p-value \u0026lt; \u0026nbsp;0.05, |log2 (fold change)| \u0026gt; 0.1458 for up regulated \u0026nbsp;genes and |log2 (fold change)| \u0026lt; -0.274 for down regulated \u0026nbsp;genes, and were visualized \u0026nbsp;in volcano plots using ggplot2 packages in R software. The DEGs with differences were filtered out and the results were displayed by a heat-map using gplot \u0026nbsp;packages in R software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO and pathway enrichment analyses of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO enrichment analysis and REACTOME signaling enrichment pathway analysis were performed on the above DEGs by g:Profiler (http://biit.cs.ut.ee/gprofiler/) [Reimand et al. 2007] to understand the biological functions of these DEGs. Gene Ontology (GO) enrichment analysis (http://www.geneontology.org) [Thomas, 2017] was actually associated to the disease and classifies GO terms in three categories such as biological processes (BP), cellular component (CC), and molecular function (MF). The REACTOME (https://reactome.org/) [Fabregat et al. 2018] is a widely used pathway database that genomic data for biological pathways, diseases, and drugs. \u0026nbsp;Adjusted \u003cem\u003ep\u003c/em\u003e values \u0026lt; 0.05, which was considered statistically significant as a criteria for selection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the PPI network and module analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnline tool IMex interactome (\u003ca href=\"https://www.imexconsortium.org/curation/\"\u003ehttps://www.imexconsortium.org/curation/\u003c/a\u003e) [Porras et al. 2020] database was used to construct PPI network based on the DEGs. Cytoscape software18 (v3.10.3) (http://www.cytoscape.org/) [Shannon et al. 2003] \u0026nbsp;was used to visualize the interaction results from the IMex interactome. The Network Analyzer plug-in was used to obtain hub genes according to the node degree [Luo et al. 2017], betweenness [Li et al. 2017], stress [Gilbert et al. 2021] and closeness [Li et al. 2020] methods. The significant modules in the PPI network were identified by the PEWCC [Zaki et al 2013], one plug-in of Cytoscape software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the miRNA-hub gene regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hub genes with high degree of connectivity in miRNA-hub gene regulatory network were selected as the promising targets for searching miRNA through the miRNet database (https://www.mirnet.ca/) [Fan et al 2018]. This database contains miRNA- hub gene interaction data from 14 disparate sources including TarBase, miRTarBase, miRecords, miRanda, miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0. The identified target network was visualized using the Cytoscape software [Shannon et al. 2003].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the TF-hub gene regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hub genes with high degree of connectivity in TF-hub gene regulatory network were selected as the promising targets for searching TF through the NetworkAnalyst (https://www.networkanalyst.ca/) [Zhou et al 2019]. This database contains TF- hub gene interaction data from disparate source JASPER. The identified target network was visualized using the Cytoscape software [Shannon et al. 2003].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic curve (ROC) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor ROC analysis of individual hub gene, outcome (MDD) and individual hub gene expression were collected. The area under the curve (AUC) could be used as an indicator to measure the diagnostic value of hub genes. \u0026nbsp;Using pROC package in R biocontuctoor tool [Robin et al. 2011], we determined the AUC. Firstly, logistic regression was used to determine the diagnostic value. Then, according to the outcome and diagnostic value, we determined the AUC value. The AUC was quantified, with AUCs \u0026gt; 0.8 having statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate DEGs in MDD, we downloaded the NGS data in the GSE275676 from GEO datasets. A threshold of P\u0026lt;0.05, |log2 (fold change)| \u0026gt; 0.1458 and |log2 (fold change)| \u0026lt; -0.274 was utilized to screen up and down regulated genes. A total of 958 genes were differentially expressed between the MDD and normal control samples in the GSE275676 dataset, including 479 up regulated genes and 479 down regulated genes (Table. 1). The volcano plot of the DEGs was generated (Fig.1). 958 DEGs were shown in the heat map (Fig.2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO and pathway enrichment analyses of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThen, GO and REACTOME pathway enrichment analyses were implemented with the g:Profiler to explore the potential biofunction of DEGs. The findings revealed that the most enriched GO terms were associated with the cell communication, biological regulation, response to stimulus and multicellular organismal process(BP); synapse, cell projection, cell periphery and endomembrane system (CC); enzyme binding, molecular function regulator activity, signaling receptor binding and amide binding (Table. 2). \u0026nbsp;According to the REACTOME pathway enrichment study, DEGs have a significant role in neuronal system, transmission across chemical synapses, immune system and neutrophil degranulation (Table. 3). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConstruction of the PPI network and module analysis\u003c/p\u003e\n\u003cp\u003eThe PPI network was built to know the potential connection among the DEGs. A PPI network with 4875 nodes and 11005 interacting pairs was constructed (Fig. 3). The identified 10 hub genes included 5 up-regulated genes (YWHAG, ONECUT1, CALM2, ARRB1 and PIK3R1) and 5 down-regulated genes (CDK2, VCAM1, HLA-B, VIM and ERBB2) (Table. 4). \u0026nbsp;We further identified the hub genes according to the node degree, betweenness, stress \u0026nbsp; and closeness method. Through Cytoscape PEWCC, two significant modules from the PPI network complex were found. Functional-annotation clustering showed that module 1 comprised 25 nodes and 64 edges (Fig. 4A). The hub genes in module 1 involved in neuronal system, signal transduction, transmission across chemical synapses, cell communication, biological regulation and signaling by Rho GTPases, MiroGTPases and RHOBTB3. Module 2 is comprising of 17 nodes and 24 edges \u0026nbsp; (Fig. 5B).\u0026nbsp;The hub genes in\u0026nbsp;module 2\u0026nbsp;involved in\u0026nbsp;immune system, signaling by interleukins, response to stimulus and endomembrane system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the miRNA-hub gene regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand the potential regulation of hub genes, we performed a comprehensive network analysis with miRNA. The miRNA-hub gene regulatory network consists of 2764 nodes (miRNA: 2427; Hub Gene: 336) and 45050 edges (Fig.5). YWHAG can be regulated by 441 miRNAs (ex: hsa-miR-548j-5p), PIK3R1 can be regulated by 406 miRNAs (ex: hsa-mir-466); CALM2 can be regulated by 333 miRNAs (hsa-miR-1911-5p); YWHAH can be regulated by 325 miRNAs (hsa-miR-142-3p); NEDD4L can be regulated by 309 miRNAs (hsa-mir-376c-3p); TNFAIP3 can be regulated by 361 miRNAs (hsa-miR-523-5p); SERPINH1 can be regulated by 348 miRNAs (hsa-mir-6829-3p); CDK2 can be regulated by 338 miRNAs (hsa-miR-34a-5p); VIM can be regulated by 334 miRNAs (hsa-mir-708-3p); ERBB2 can be regulated by 285 miRNAs (hsa-miR-6891-3p) (Table. 5). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the TF-hub gene regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand the potential regulation of hub genes, we performed a comprehensive network analysis with TF. The TF-hub gene regulatory network consists of 2764 nodes (TF: 97; Hub Gene: 335) and 2969 edges (Fig.6). NEDD4L can be regulated by 24 TFs (ex: PAX2); HDAC9 can be regulated by 19 TFs (ex: JUND); PIK3R1 can be regulated by 18 TFs (ex: EGR1); MAP1LC3A can be regulated by 14 TFs (ex: CREB1); PRKCZ can be regulated by 13 TFs (ex: HINFP); TNF can be regulated by 51 TFs (ex: FEV); IRF1 can be regulated by 17 TFs (ex: PPARG); ERBB2 can be regulated by 17 TFs (ex: TFAP2C); NFKB2 can be regulated by 14 TFs (ex: GATA2); CDK2 can be regulated by 14 TFs (ex: TEAD1) (Table. 5). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic curve (ROC) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo access the potential prediction function of hub genes in MDD, we focused ROC curve analyses of 10 MDD related hub genes using the \u0026ldquo;pROC\u0026rdquo; package. The AUC values were as follows: YWHAG (AUC = 0.919), ONECUT1 (AUC = 0.909), CALM2 (AUC = 0.915), ARRB1 (AUC = 0.903), PIK3R1 (AUC = 0.905), \u0026nbsp;CDK2 (AUC = 0.917), VCAM1 (AUC = 0.921), HLA-B (AUC = 0.913), VIM (AUC = 0.893) and ERBB2 (AUC = 0.907) (Fig.7). \u0026nbsp;These AUC values implied that all ten MDD related hub genes have high specificity for MDD.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMuch progress has been made in investigation on MDD, but it remains one of the most common mental disorders seriously affecting human health worldwide. The incidence of MDD will continue to increase in the coming years [Abdoli et al. 2022]. By understanding and exploring its molecular pathogenesis, we can prevent the occurrence of MDD. NGS data analysis is a powerful technique that can identify the DEGs in specific physiological and pathological states and discover novel diagnostic or therapeutic targets [Devarbhav et al. 2021]. In this investigation, we conducted a systematic bioinformatics analysis of the NGS GEO dataset (GSE275676) to reveal the potential molecular mechanisms of MDD. We screened 958 DEGs from NGS dataset, including 479 up regulated and 479 down regulated genes. CYP2C19 [Bonasser et al. 2024] and LTF (lactotransferrin) [Wang et al. 2025] are involved in the progression of MDD. CYP2C19 [Klomp et al. 2024], MMP1 [Li et al. 2002], CXCL11 [Kochumon et al. 2020] and LTF (lactotransferrin) \u0026nbsp; [Artym et al. 2021] expression is altered in the patients with inflammation. CYP2C19 [Wiese et al. 2012] \u0026nbsp; and MMP1 [Wang et al. 2020] have been proposed as novel biomarkers for rheumatoid arthritis progression. Altered expression of CYP2C19 [Lu et al. 2020], MMP1 [Yi et al. 2021] and CXCL11 [Porter et al. 2008] are associated with chronic obstructive pulmonary disease. Regulation of CYP2C19 [Lang et al. 2015] levels might be a novel treatment option against asthma. CYP2C19 [Thompson et al. 2024], MMP1 [Boumiza et al. 2017], TFAP2B \u0026nbsp;[Albuquerque et al. 2014], CXCL11 [Kochumon et al. 2020] and LTF (lactotransferrin) [Jamka et al. 2020] were previously reported to be significantly associated with obesity. CYP2C19 [Austin-Zimmerman et al. 2021], TFAP2B [Maeda et al. 2005], C2CD4B \u0026nbsp;[Kycia et al. 2018] and LTF (lactotransferrin) [Mohamed et al. 2018] play a pivotal role in diabetes mellitus. CYP2C19 [Maas et al. 2024], \u0026nbsp; MMP1 [Hu et al. 2018], TFAP2B [Xiong et al. 2013] and LTF (lactotransferrin) [Chen et al. 2023] serving as diagnostic markers in cardiovascular diseases. CYP2C19 [Cai et al. 2023] and MMP1 [Kostov et al. 2022] have been reported to be implicated in the hypertension. CYP2C19 [Benedet et al. 2018], \u0026nbsp;MMP1 [Leake et al. 2000], BARHL1 [Barh et al. 2017] and LTF (lactotransferrin) [Tsatsanis et al. 2021] were reported to be regulated in the Alzheimer disease. \u0026nbsp;CYP2C19 [Hidestrand et al. 2001] and MMP1 [Gupta et al. 2014] were demonstrated to be significantly associated with the Parkinson\u0026apos;s disease. Altered expression of CYP2C19 [Joas et al. 2023] \u0026nbsp;and \u0026nbsp; MMP1 [Demidenko et al. 2004] have been identified in bipolar disorder. The altered expression of CYP2C19 [Teng et al. 2024] and MMP1 [Bolinskey et al. 2001] are associated with schizophrenia. \u0026nbsp; CYP2C19 [Song et al. 2022] and MMP1 [Dikmen et al. 1983] have been considered a diagnostic markers of epilepsy. CYP2C19 [Hellman et al. 2003], MMP1 [Mirowska-Guzel et al. 2009], CXCL11 [Szczuciński et al. 2011] and LTF (lactotransferrin) [Abdelalim et al. 2024] genes might be used as potential biomarkers to determine the development of multiple sclerosis. CYP2C19 [Bishop et al. 2015] and LTF (lactotransferrin) [Martirosian et al. 2011] can possibly be verified as prognostic indicators of autism spectrum disorder. Previous studies focused on the role of CYP2C19 [Maas et al. 2024], MMP1 [C\u0026aacute;rcel-M\u0026aacute;rquez et al. 2021] and LTF (lactotransferrin) [Xia et al. 2022] in stroke development. \u0026nbsp;Previous studies have indicated the MMP1 [Oliveira et al. 2019], C2CD4B [Di Pietro et al. 2024] and LTF (lactotransferrin) \u0026nbsp;[Ding et al. 2024] plays a crucial role in oxidative stress. Previous animal studies have demonstrated that MMP1 [Meijer et al. 2007] and LTF (lactotransferrin) [Bar-Gil Shitrit et al. 2017] \u0026nbsp;are linked with the development mechanisms of Crohn\u0026apos;s disease. MMP1 [Close Kirkwood et al. 2002] was associated with the occurrence and progress of Huntington disease. \u0026nbsp;MMP1 [Chao and Ghorpade, 2009] is associated to the risk of dementia. These research findings concur with those from this investigation, which suggest that significant DEGs might have a role in the pathophysiology of MDD.\u003c/p\u003e\n\u003cp\u003eIn this investigation, the GO analysis and REACTOME pathway enrichment analysis of the key DEGs were analyzed by using g:Profiler database. These analyses could help to find enriched genes involved in the regulation of MDD. Studies have shown that the abnormal activation of the signaling pathways include neuronal system [Hamilton et al. 2013], signal transduction [Lin et al. 2023], GPCR downstream signaling [Tomita et al. 2013] and immune system [Jiang et al. 2023] are associated with MDD. Some studies have shown that AVPR1B [Szczepankiewicz et al. 2013], GPR26 [Zhang al. 2011], MCHR2 [Delacr\u0026eacute;taz \u0026nbsp;et al. 2015], NRGN (neurogranin) [Wen et al. 2016], \u0026nbsp;HTR2A [Lin et al. 2015], MEF2C [Muench et al. 2018], \u0026nbsp;RGS6 [Stewart et al. 2014], NCALD (neurocalcin delta) [Zhang et al. 2023], GRIN2A [Hu et al. 2018], GRIN3A [Ping et al. 2023], PAK1 [Fuchsova et al. 2016], \u0026nbsp;RASGRF1 [Cheng et al. 2020], HTR7 [Wei et al. 2020], CDKL5 [Barbiero et al. 2022], GRM7 [Niu et al. 2017], HOMER1 [Leber et al. 2017], DLG4 [Kaut et al. 2017], MCHR1 [Roy et al. 2007], VAMP2 [Saito et al. 2006], SYT7 [Lebowitz et al. 2024], NPY1R [Borroto-Escuela et al. 2024], PCLO (piccolo presynaptic cytomatrix protein) [Igata et al. 2017], GRIN2B [Brown et al. 2023], RGS7 [Sutton et al. 2021], GRIA2 [Chiesa et al. 2013], DLG2 [Qin et al. 2020], FBXW7 [Al-Hakeim et al. 2022], CDK5 [Papadopoulou et al. 2015], ARRB1 [Chang et al. 2015], PAK3 [Fuchsova et al. 2016], NRXN1 [Skiba et al. 2021], HDAC9 [Dai et al. 2023], TNF (tumor necrosis factor) [Yao \u0026nbsp;et al. 2020] , CCL2 [Curzytek and Leśkiewicz, 2021], PCSK9 [Habibitabar et al. 2024], VCAM1 [Zhou et al. 2024], WNT1 [Sanchez-Ruiz et al. 2024], C4B [Park et al. 2022], \u0026nbsp; SELL (selectin L) \u0026nbsp;[Yun et al. 2024], APLNR (apelin receptor) [Wang et al. 2020], GFAP (glial fibrillary acidic protein) [Zhang et al. 2024], \u0026nbsp; SERPINA1 [Chan et al. 2024], ABI3BP [Kari et al. 2023], SERPINE1 [Fang et al. 2012], TNFAIP3 [Chen et al. 2017], \u0026nbsp; SPHK1 [Elzaitony et al. 2024], HMOX1 [Dai et al. 2025], NLRC5 [Sun et al. 2023], CASP1 [Inserra et al. 2019], CXCR4 [Ogłodek et al. 2014], TSPO (translocator protein) \u0026nbsp;[Attwells s et al. 2020], \u0026nbsp;B2M [Zhang et al. 2018], ANXA2 [Dai et al. 2023], VIM (vimentin) [O\u0026apos;Leary et al. 2021], \u0026nbsp;MYD88 [Tao et al. 2023], APLN (apelin) \u0026nbsp; [Chibaatar et al. 2024], AMH (anti-Mullerian hormone) [Valenti et al. 2025], \u0026nbsp;AQP4 [Yao et al. 2023], CA3 [Lindqvist et al. 2014], IRF1 [Bialek et al. 2020], IL16 [Almulla et al. 2024], CSF1 [Wohleb et al. 2018], FOXO1 [Zhao et al. 2020], FSTL1 [Xiao et al. 2022], PON3 [Bliźniewska-Kowalska et al. 2022], GPR35 [Cheng et al. 2024], TLR4 [Wei et al. 2025], AGT (angiotensinogen) [L\u0026oacute;pez-Le\u0026oacute;n et al. 2008], GRM4 [Dadkhah et al. 2017], REST (RE1 silencing transcription factor) [Otsuki et al. 2010], \u0026nbsp;EMP1 [Nakataki et al. 2011] \u0026nbsp;and FADS2 [Sublette et al. 2016] \u0026nbsp;plays a certain role in MDD. Altered expression of AVPR1B [Ferrier et al. 2010], GUCY2C [Tronstad et al. 2018], \u0026nbsp;ERFE (erythroferrone) [Woźniak al. 2023], WIF1 [Terry et al. 2019], TRAF5 [Sun et al. 2024], \u0026nbsp;PLCB1 [Gorenjak et al. 2024], OPTN (optineurin) [Tschurtschenthaler and Adolph, 2018], NPY1R [Chandrasekharan et al. 2022], \u0026nbsp;PRKCD (protein kinase C delta) [Limami et al. 2022], LANCL2 [Tubau-Juni et al. 2024], CASP5 [Smith et al. 2022], \u0026nbsp;TNF (tumor necrosis factor) [Peyrin-Biroulet \u0026nbsp;et al. 2008], CXCL8 [Chen et al. 2021], CCL2 [Maharshak et al. 2010], \u0026nbsp;SERPINA3 [Liu et al. 2024], GZMB (granzyme B) [Lefferts et al. 2021], SOCS3 [Sanati et al. 2022], GBP5 [Che et al. 2024], VCAM1 [Burns et al. 2001], OSM (oncostatin M) [Yang et al. 2024], CHI3L1 [Douadi et al. 2022], C4B [Nissil\u0026auml; et al. 2017], SELL (selectin L) \u0026nbsp;[Bravo et al. 2021], \u0026nbsp;CD44 [Spencer, 2018], LYZ (lysozyme) [Pruzanski et al. 1977], NAT1 [Mahid et al. 2007], GPR65 [Neale et al. 2024], CFB (complement factor B) [Akhlaghpour et al. 2023], S100A4 [Cunningham et al. 2010], RUNX3 [Dybska et al. 2021], TNFAIP3 [Zou et al. 2020], CD74 [Shu \u0026nbsp;et al. 2024], TLR5 [Shen et al. 2022], \u0026nbsp;CD14 [Lakatos et al. 2011], HLA-DRB1 [Newman et al. 2004], ATF3 [Gu et al. 2018], LY75 [Hirayama et al. 2016], CASP1 [Xu et al. 2025], IL15 [Bouchaud et al. 2010], CXCR4 [Dogra et al. 2023], \u0026nbsp;OSMR (oncostatin M receptor) [Du et al. 2020], \u0026nbsp;HLA-B [Asakura et al. 2012], TSPO (translocator protein) \u0026nbsp;[Zhang s et al. 2022], B2M [Bednarz-Misa et al. 2020], TMBIM1 [Orlando et al. 2016], HLA-C [Jung et al. 2016], \u0026nbsp;VIM (vimentin) [Mortensen et al. 2021], MYD88 [Wang et al. 2025], LITAF (lipopolysaccharide induced TNF factor) [Stucchi et al. 2006], ENTPD2 [Feldbr\u0026uuml;gge et al. 2017], \u0026nbsp;IFIH1 [Santoro et al. 2024], AMH (anti-Mullerian hormone) [Guti\u0026eacute;rrez et al. 2024], DKK1 [Kim et al. 2019], IRF1 [Xu et al. 2025], RAB13 [Ohira et al. 2009], IL16 [Hong et al. 2008], CSF1 [Nieto et al. 2017], IFI16 [Vanhove et al. 2015], FOXO1 [Han et al. 2020], PSTPIP1 [Andr\u0026eacute; et al. 2010], PON3 [Sanchez et al. 2006], \u0026nbsp;IL10RA [Xue et al. 2020], \u0026nbsp;GPR35 [Song et al. 2023], TLR4 [Wei et al. 2025], TIMP1 [Tran et al. 2024], TRIM22 [Li et al. 2016], PLD2 [Zhou et al. 2016], TAP2 [Kim et al. 2024], AGT (angiotensinogen) [Hume et al. 2016], NFATC1 [Cushing et al. 2023,] MGP (matrix Gla protein) [Vieujean et al. 2024], \u0026nbsp;CCDC80 [Wang et al. 2021] \u0026nbsp;and FADS2 [Liu et al. 2020] promotes Crohn\u0026apos;s disease. AVPR1B [Ferrier et al. 2010], NPPA (natriuretic peptide A) [Cheng et al. 2019], EREG (epiregulin) [Harada et al. 2015], GPR26 [Guti\u0026eacute;rrez-Rojas al. 2022], ERFE (erythroferrone) [Kautz al. 2021], HTR2A [Xiang et al. 2024], MEF2C [Wang et al. 2024], CAMKK2 [Li et al. 2021], TRAF5 [Gissler et al. 2021], RGS6 [Song et al. 2023], NCALD (neurocalcin delta) [Zhang et al. 2023], PLCB1 [Gorenjak et al. 2024], PAK1 [Chen et al. 2024], RASGRF1 [Cheng et al. 2020], CNTN4 [Wang et al. 2022], CDKL5 [Cortelazzo et al. 2017], GRM7 [Zaki-Dizaji et al. 2024], VAMP2 [Duan et al. 2020], \u0026nbsp;OPTN (optineurin) [Liu et al. 2018], \u0026nbsp;SLC39A10 [Gao et al. 2017], PIK3R1 [Fu et al. 2024], SCN8A [Alrashdi et al. 2021], DCLK1 [Yi et al. 2019], RGS7 [Basak et al. 2023], DLG2 [Keane et al. 2022], SH3RF3 [Patel et al. 2025], FBXW7 [Zhang et al. 2022], PRKCD (protein kinase C delta) [Yang et al. 2019], GIT1 [Song a et al. 2021], MEF2A [Xiong a et al. 2019], CDK5 [Li et al. 2021], ARRB1 [Fang et al. 2021], NRXN3 [Hishimoto et al. 2019], \u0026nbsp;GMFB (glia maturation factor beta) [Fan et al. 2018], IFT20 [Paulson et al. 2021], ATP2B2 [Yin and Zhang, 2025], DLGAP4 [Ding \u0026nbsp;et al. 2024], PRMT8 [Zheng et al. 2022], TRIM37 [Brigant et al. 2016], MYCBP2 [Yuan et al. 2022], \u0026nbsp;HDAC9 [Das and Natarajan, 2020], CASP5 [Eckhart and Fischer, 2024], CXCL3 [Kwon \u0026nbsp; et al. 2024], TNF (tumor necrosis factor) [van Loo and Bertrand, 2003], SERPINA3 [Soman and Asha Nair, 2022], CXCL8 [Haas et al. 2016], CCL2 [Raghu et al. 2017], GZMB (granzyme B) [Xu et al. 2024], CXCL2 [Liu et al. 2021], SOCS3 [Liu et al. 2015], TBX21 [Lau et al. 2017], \u0026nbsp; NKG7 [Ng et al. 2024], PRF1 [Sidore et al. 2021], PCSK9 [Frosteg\u0026aring;rd, 2022], IL21R [Niu et al. 2022], VCAM1 [Burns et al. 2001], OSM (oncostatin M) [Hermans et al. 2022], CHI3L1 [Stephan et al. 2025], RGS1 [Feng et al. 2022], C4B [Rezende et al. 2004], SELL (selectin L) \u0026nbsp; [Rosen, 2004], ACP5 [Yu et al. 2025], CES1 [Scheaffer et al. 2020], CD44 [Govindaraju et al. 2019], BATF2 [van der Geest and Lee, 2025], LYZ (lysozyme) [Moreno-Navarrete et al. 2021], AZGP1 [Na et al. 2017], LILRB2 [Nishiyama et al. 2021], GFAP (glial fibrillary acidic protein) [Siemionow et al. 2009], SFRP4 [Kida et al. 2023], \u0026nbsp;SERPINA1 [Meghadri et al. 2022], \u0026nbsp;GPR65 [Zhang et al. 2023], GBP3 [Zhang et al. 2023], CFI (complement factor I) [Altmann et al. 2020], S100A4 [Ambartsumian et al. 2019], RUNX3 [Lotem et al. 2017], HLA-DRA [Kessal et al. 2018], SERPINE1 [Chen et al. 2021], TNFAIP3 [Momtazi et al. 2019], CD74 [Farr \u0026nbsp;et al. 2020], LAIR1 [Kumawat et al. 2019], TLR5 [Pachathundikandi et al. 2023], NUPR1 [Wang et al. 2006], EPHA2 [Zeng et al. 2023], CD14 [Ogawa et al. 2013], \u0026nbsp;HBB (hemoglobin subunit beta) [Bandeira et al. 2014], GBP1 [Honkala et al. 2020], TNFRSF12A [Li et al. 2024], HLA-DRB1 [Klimenta et al. 2019], SPHK1 [Elzaitony et al. 2024], ATF3 [Liu et al. 2024], HMOX1 [Sebasti\u0026aacute;n et al. 2018], NLRC5 [He et al. 2020], TAP1 [Vasků et al. 2000], ITGB4 [Zhou et al. 2023], CASP1 [Man et al. 2017], IL15 [Perera et al. 2012], S100A11 [Sobolewski et al. 2020], MFAP4 [Sof\u0026iacute;ud\u0026oacute;ttir et al. 2024], MLKL (mixed lineage kinase domain like pseudokinase) [Xu et al. 2019], GPX3 [Szczuko et al. 2019], SPX (spexin hormone) [Yortanli et al. 2025], CXCR4 [Gallego et al. 2021], HLA-B [Lenna et al. 2015], IFITM3 [Xiong et al. 2024], TSPO (translocator protein) \u0026nbsp;[Corica et al. 2023], IER3 [Arlt \u0026nbsp;and Sch\u0026auml;fer, 2011], EFNA1 [Fukuda et al. 2025], LGALS9 [Bra\u0026szlig; et al. 2023], TMBIM1 [Gong et al. 2016], ANXA2 [Lim and Hajjar, 2021], NFKB2 [Chawla et al. 2022], VIM (vimentin) [Meng et al. 2023], MYD88 [Luo et al. 2022], LITAF (lipopolysaccharide induced TNF factor) [Zou et al. 2015], WWTR1 [Wang et al. 2016], \u0026nbsp;CLIC4 [Luo et al. 2024], \u0026nbsp;SLC15A3 [Song et al. 2018], ENTPD2 [Feldbr\u0026uuml;gge et al. 2017], IFIH1 [Amado-Rodr\u0026iacute;guez et al. 2022], APLN (apelin) \u0026nbsp; [Park et al. 2024], RAB20 [Liang et al. 2012], MYOCD (myocardin) [Ackers-Johnson et al. 2015], MASP1 [Schwaner et al. 2017], AMH (anti-Mullerian hormone) [Okawa et al. 2025], CD109 [Aono et al. 2023], \u0026nbsp;CLIC1 [Zhu et al. 2017], IGF2 [Wu et al. 2025], AQP4 [Lan et al. 2016], DTX3L [Hong et al. 2020], DKK1 [Kim et al. 2019], MYL9 [Kimura et al. 2017], CA3 [Zuo et al. 2025], SCD (stearoyl-CoA desaturase) [Kurikawa et al. 2013], IRF1 [Chen et al. 2023], IL16 [Smith et al. 2018], \u0026nbsp;CSF1 [Lin et al. 2019], \u0026nbsp;TGFBR1 [Li a et al. 2020], HFE (homeostatic iron regulator) [Wessling-Resnick, 2010], PDPN (podoplanin) [Quintanilla et al. 2019], IFI16 [Fan et al. 2023], RARRES2 [Er et al. 2018], FOXO1 [Lundell et al. 2019], PSTPIP1 [Sanz-Cabanillas et al. 2024], HSPB1 [Zhao et al. 2025], FSTL1 [Guo et al. 2016], NPC2 [Hannaford et al. 2013], FGF1 [Dhlamini et al. 2022], GPR35 [Wu et al. 2023], TLR4 [Heine and Zamyatina, 2022], TIMP1 [Schoeps et al. 2023], MAP3K8 [Chiu et al. 2024], HEY2 [Lina et al. 2023], TRIM22 [Wei et al. 2022], LTBR (lymphotoxin beta receptor) [Giles et al. 2018], RHOC (ras homolog family member C) [Wu et al. 2010], NKX3-1 [Le Magnen et al. 2018], F11R [Salifu et al. 2007], PLD2 [Zhou et al. 2016], ARPC1B [Volpi et al. 2019], IGFBP7 [Qiu and \u0026nbsp; Huang, \u0026nbsp;2024], DDR1 [Borza et al. 2022], S1PR2 [Ren et al. 2017], EDNRB (endothelin receptor type B) [Xu et al. 2022], AGT (angiotensinogen) [Kalupahana et al. 2012], GNG12 [Larson et al. 2010], STK33 [Jiang et al. 2024], ERBB2 [Zeng et al. 2012], PRRX1 [Zhang et al. 2022], NFATC1 [Baumgart et al. 2014], \u0026nbsp; FBLN5 [Hou et al. 2017], GRM4 [Fallarino et al. 2010], SLAMF9 [Cong et al. 2024], SIGLEC7 [Dharmadhikari et al. 2017], STEAP3 [Zhang et al. 2012], AEBP1 [Runtian et al. 2025], DPT (dermatopontin) [Unamuno et al. 2020], BEST3 [Song et al. 2014], SERPINB1 [Lan et al. 2025], BGN (biglycan) [Guo et al. 2019], ELN (elastin) [Wu et al. 2015], FADS2 [Liu et al. 2020] \u0026nbsp;and PSMB8 [Li et al. 2024] were significantly altered in paients with inflammation. The mRNA expression levels of AVPR1B [Enh\u0026ouml;rning et al. 2016], PIK3C2G [Daimon et al. 2008], GPR26 [Kichi al. 2022], ONECUT1 [Russ-Silsby al. 2023], NPR3 [Saulnier et al. 2011], HTR2A [Bennet et al. 2015], CAMKK2 [Kim et al. 2020], GRIN3A [Zhang et al. 2023], PAK1 [Veluthakal et al. 2018], RASGRF1 [Hoffmann et al. 2012], CHRM3 [Guo et al. 2006], CHN1 [Li et al. 2020], HOMER1 [Lu et al. 2017], VAMP2 [Hirai et al. 2008], PRKCZ (protein kinase C zeta) [Qin et al. 2008], PCLO (piccolo presynaptic cytomatrix protein) [Ma et al. 2008], GRIN2B [Kochetova et al. 2020], PIK3R1 [Li et al. 2022], CACNB2 [Vuori et al. 2019], FBXW7 [Al-Hakeim et al. 2022], CDK5 [Ahmed and Sharma, 2011], MAPK9 [Jaeschke et al. 2005], NPY1R [Lafferty et al. 2020], BCL11A [Tang \u0026nbsp;et al. 2014], HDAC9 [Liu et al. 2024], \u0026nbsp;TNF (tumor necrosis factor) [Akash et al. 2018], CXCL8 [Silva et al. 2020], CCL2 [Guan et al. 2011], GZMB (granzyme B) [El Mesallamy et al. 2014], \u0026nbsp;SLC47A2 [Chen et al. 2022], SOCS3 [Zhang et al. 2022], TBX21 [Petakh et al. 2024], NKG7 [Ji et al. 2023], PCSK9 [Carugo et al. 2022], \u0026nbsp;RORC (RAR related orphan receptor C) [Wang et al. 2003], \u0026nbsp;VCAM1 [Siddiqui et al. 2023], OSM (oncostatin M) [Ikeda et al. 2021], \u0026nbsp;CHI3L1 [Zhu et al. 2025], \u0026nbsp;C4B [Kingery et al. 2012], SELL (selectin L) \u0026nbsp;[Siddiqui \u0026nbsp;et al. 2023], \u0026nbsp; CES1 [Hu et al. 2020], CD44 [Assayag-Asherie et al. 2015], SLC12A3 [Yin et al. 2024], LYZ (lysozyme) [Mirmiranpour et al. 2016], NAT1 [Paz-Rodr\u0026iacute;guez et al. 2024], SFRP4 [Luo et al. 2020], \u0026nbsp;C4A [Kingery et al. 2012], GFAP (glial fibrillary acidic protein) [Ayala-Guerrero et al. 2022], SERPINH1 [Li et al. 2022], CFB (complement factor B) [Wang et al. 2013], RUNX3 [Jin et al. 2023], SERPINE1 [Fan et al. 2018], TNFAIP3 [Cao et al. 2023], CD74 [Mangano \u0026nbsp;et al. 2024], \u0026nbsp;TLR5 [Pearson et al. 2024], HLA-DPA1 [Varney et al. 2010], CD14 [Hedgpeth et al. 2015], HLA-DRB1 [Zhang et al. 2019], SPHK1 [Liu et al. 2022], ATF3 [Kim et al. 2013], HMOX1 [Rivera-Vald\u0026eacute;s et al. 2025], HLA-DPB1 [Varney et al. 2010], TAP1 [Li et al. 2014], CASP1 [Yuan et al. 2012], IL15 [Ye, 2015], S100A11 [Wu et al. 2023], MFAP4 [Blindb\u0026aelig;k et al. 2017], ACTA2 [Fang et al. 2014], MLKL (mixed lineage kinase domain like pseudokinase) [Kamal et al. 2019], GPX3 [Ling et al. 2020], SPX (spexin hormone) [Gallagher et al. 2024], CXCR4 [Arakura et al. 2017], HLA-B [Jan et al. 2021], IFITM3 [Mehrbod et al. 2017], HLA-DMB [Siegmund et al. 1999], \u0026nbsp; TSPO (translocator protein) \u0026nbsp;[Qiu et al. 2016], HLA-DMA [Siegmund et al. 1999], B2M [Dai et al. 2024], HLA-C [Smigoc Schweiger et al. 2014], VIM (vimentin) [Roefs et al. 2017], MYD88 [Tian et al. 2021], IFIH1 [Zurawek et al. 2015], \u0026nbsp; APLN (apelin) \u0026nbsp; [Xu et al. 2021], MASP1 [Krogh et al. 2017], AMH (anti-Mullerian hormone) [Verdiesen et al. 2021], IGF2 [Cao et al. 2021], AQP4 [Li et al. 2022], DKK1 [Gao et al. 2022], P2RY1 [Dance et al. 2024], SCD (stearoyl-CoA desaturase) [Oballa et al. 2011], HLA-E [Hodgkinson et al. 2000], IRF1 [Colli et al. 2018], IL16 [Ali et al. 2024], CSF1 [Dikilitaş et al. 2022], HFE (homeostatic iron regulator) [Barton and Acton, 2017], IFI16 [Arunachalam et al. 2024,] RARRES2 [Zhao et al. 2018], FOXO1 [Nathanael et al. 2022], HSPB1 [Tokuda et al. 2015], \u0026nbsp;FGF1 [Bu et al. 2025], GPR35 [Vander Molen et al. 2005], TLR4 [Oo, 2024], TIMP1 [Saucedo et al. 2021], \u0026nbsp;F11R [Adedayo et al. 2022], CTSH (cathepsin H) [Fl\u0026oslash;yel et al. 2018], IGFBP7 [Zhu et al. 2024], DDR1 [Yan et al. 2021], \u0026nbsp;S1PR2 [Liu et al. 2022], TAP2 [Qu et al. 2007], EDNRB (endothelin receptor type B) [Bregar et al. 2018], AGT (angiotensinogen) [Repchuk et al. 2021], \u0026nbsp; ERBB2 [de Kay et al. 2022], \u0026nbsp; NFATC1 [Cai et al. 2024], \u0026nbsp;SIGLEC7 [Dharmadhikari et al. 2017], MGP (matrix Gla protein) [Sardana et al. 2017], ARRDC4 [Liu et al. 2024], \u0026nbsp;SERPINB1 [Kyohara et al. 2022], FADS2 [Shetty and Kumari, 2021] and SLC40A1 \u0026nbsp;[Huang et al. 2024] are correlated with diabetes mellitus. AVPR1B [Ejiohuo et al. 2025], CUX2 [Glaseret al. 2005], \u0026nbsp;MCHR2 [Abou Jamra \u0026nbsp;et al. 2010], NRGN (neurogranin) [Wen et al. 2016], SYT7 [Wang et al. 2021],\u0026nbsp;\u0026nbsp;HTR2A [Tan et al. 2014], NUDT4 [Maheshwari et al. 2025], CAMKK2 [Kaiser et al. 2023], GRIN2A [Itokawa et al. 2003], PLCB1 [Lo Vasco et al. 2013], \u0026nbsp;HTR7 [Wei et al. 2020], CDKL5 [Lucash et al. 2024], GRM7 [Kandaswamy et al. 2014], \u0026nbsp;HOMER1 [Leber et al. 2017], \u0026nbsp;VAMP2 [Abou Jamra et al. 2008], SYT7 [Wang et al. 2021], PRKCZ (protein kinase C zeta) [Saxena et al. 2008], PCLO (piccolo presynaptic cytomatrix protein) [Chen et al. 2021], PIK3R1 [Huang et al. 2020], SCN8A [Wang et al. 2008], CACNB2 [Cheng et al. 2025], GRIA2 [Chiesa et al. 2012], GNAZ (G protein subunit alpha z) [Saito a et al. 1999], \u0026nbsp; NRXN3 [Sudhakar et al. 2025], \u0026nbsp; NDEL1 [Dal Mas et al. 2019], NBEA (neurobeachin) [Jacobsen \u0026nbsp;et al. 2015], LDB2 [Horiuchi et al. 2020], \u0026nbsp;DCTN1 [Hallen et al. 2020], KCNH7 [Strauss et al. 2014], \u0026nbsp;TNF (tumor necrosis factor) [Doğanavşargil Baysal et al. 2019], CXCL8 [Ghoryani et al. 2019], CCL2 [Ghoryani et al. 2019], \u0026nbsp; VCAM1 [Pantovic-Stefanovic et al. 2024], WNT1 [Beasley et al. 2002], CHI3L1 [Sahin et al. 2019], C4A [Melbourne et al. 2018], GFAP (glial fibrillary acidic protein) [Webster et al. 2005], ABI3BP [Kari et al. 2023], HLA-DRB1 [Le Clerc et al. 2021], ITGB4 [Han et al. 2018], TSPO (translocator protein) \u0026nbsp;[Scaini et al. 2019], IGF2 [Ye et al. 2024], AQP4 [Zhao et al. 2020], CA3 [Stern et al. 2020], HLA-E [Mihoub et al. 2004], TLR4 [Aflouk et al. 2024], \u0026nbsp;DDR1 [Aranda et al. 2024], AGT (angiotensinogen) [Meira-Lima et al. 2000], FADS2 [Liu and McNamara, 2011] \u0026nbsp;and\u0026nbsp;RFX4 [Glaser\u0026nbsp;et al. 2005]\u0026nbsp;are key regulators of bipolar disorder. The altered expression of NPPA (natriuretic peptide A) [Cheng et al. 2019], CUX2 [Wang et al. 2018], ERFE (erythroferrone) [Spoto al. 2019], NPPC (natriuretic peptide C) [Lumsden et al. 2010], WIF1 [Ress et al. 2018], NRGN (neurogranin) [Jorgensen et al. 2022], HTR2A [Gao et al. 2020], MEF2C [Qiao et al. 2017], CAMKK2 [Li et al. 2021], \u0026nbsp;TRAF5 [Peng et al. 2023], DKK2 [Wang et al. 2025], PAK1 [Xu et al. 2022], RASGRF1 [Tsai et al. 2020], PRKCB (protein kinase C beta) \u0026nbsp;[Li et al. 2013], \u0026nbsp;CNTN4 [Wang et al. 2022], CDKL5 [Ivaniuk et al. 2023], HOMER1 [Zhang et al. 2022], SPATC1L [Yamada et al. 2017], SYT7 [Sun et al. 2024], CALM3 [Hamrick et al. 2024], SCN3B [Lin et al. 2022], KCNJ3 [Yamada et al. 2019], CDK5R1 [Infante et al. 2024], PIK3R1 [He et al. 2024], ROBO2 [Wang et al. 2023], \u0026nbsp;DCLK1 [Yang et al. 2023], CACNB2 [Parthiban and Sani, 2023], CACNG8 [Ortega et al. 2015], PPP3R1 [Tang et al. 2005], FBXW7 [Zhang et al. 2022], PRKCD (protein kinase C delta) [Miao et al. 2022], GIT1 [Song a et al. 2021], MADD (MAP kinase activating death domain) [Wu a et al. 2012], BEX1 [Accornero a et al. 2017], MEF2A [Muhammad Sulaiman, 2024], \u0026nbsp;FGF12 [Li et al. 2017], CDK5 [Bai et al. 2012], GABARAPL1 [Liu et al. 2025], PAK3 [Chen et al. 2024], SUB1 [Huang et al. 2021], DES (desmin) [Brodehl et al. 2018], ATP2B2 [Wei \u0026nbsp;et al. 2025], CAMK2N1 [Wei \u0026nbsp;et al. 2021], PDZRN3 [Pernot \u0026nbsp;et al. 2022], EEF1A2 [Feng \u0026nbsp;et al. 2021], ADD2 [Chen \u0026nbsp;et al. 2015], CDH10 [Chen \u0026nbsp;et al. 2018], NEK2 [Zhou et al. 2025], LDB2 [Shang et al. 2014], CAMTA2 [Song et al. 2016], TRIM37 [Brigant et al. 2016], HDAC9 [Das and Natarajan, 2020], TNF (tumor necrosis factor) [Yuan et al. 2020], SERPINA3 [Li et al. 2021], CXCL8 [Zhu et al. 2022], \u0026nbsp;CCL2 [Gholamalizadeh et al. 2024], \u0026nbsp;GZMB (granzyme B) [Xu et al. 2024], CXCL2 [Guo et al. 2020], \u0026nbsp;SOCS3 [Han et al. 2020], PCSK9 [Hummelgaard et al. 2023], MS4A6A [Lu-Chen et al. 2025], \u0026nbsp;VCAM1 [Singh et al. 2023], WNT1 [Pang et al. 2021], \u0026nbsp;OSM (oncostatin M) [Ikeda et al. 2021], \u0026nbsp;CHI3L1 [Stephan et al. 2025], C4B [Trouw et al. 2008], SELL (selectin L) \u0026nbsp; [Berardi et al. 2014], LILRB3 [Zhang et al. 2021], \u0026nbsp;CES1 [Marrades et al. 2010], CD44 [El-Dakdouki et al. 2014], LYZ (lysozyme) [Mink et al. 2009], NAT1 [Sangaralingham et al. 2003], AZGP1 [Huscher et al. 2021], APLNR (apelin receptor) [Wang et al. 2020], SFRP4 [Ren et al. 2024], GFAP (glial fibrillary acidic protein) [Kaneko et al. 2009], SERPINH1 [Rusu-Nastase et al. 2022], S100A4 [Cheng et al. 2025], \u0026nbsp;RUNX3 [Jin et al. 2023], ABI3BP [Delf\u0026iacute;n et al. 2019], SERPINE1 [Long et al. 2024], TNFAIP3 [Hua et al. 2009], CD74 [Li \u0026nbsp;et al. 2023], ADAMTS1 [Karakose et al. 2017], IL12A [Wang et al. 2021], HLA-DPA1 [Liu et al. 2006], EPHA2 [Zeng et al. 2023], COL3A1 [McGrath-Cadell et al. 2024], CD14 [Stanislawski et al. 2021], TNFRSF12A [Yerra et al. 2022], HLA-DRB1 [Kaur et al. 2025], SPHK1 [J\u0026oacute;zefczuk et al. 2020], ATF3 [Li et al. 2022], HMOX1 [Xu and Bu, 2023], \u0026nbsp;NLRC5 [Chen et al. 2025], \u0026nbsp;HLA-DPB1 [Liu et al. 2006], \u0026nbsp;TAP1 [Kolbus et al. 2012], CASP1 [Zhang et al. 2025], IL15 [Guo et al. 2020], MFAP4 [Wang et al. 2017], ACTA2 [Fang et al. 2014], GPX3 [Decharatchakul et al. 2020], \u0026nbsp; SPX (spexin hormone) [Kumar et al. 2021], CXCR4 [Li et al. 2023], HLA-B [Eyiol et al. 2018], HSPA1B [Yang et al. 2021], IFITM3 [Xiong et al. 2024], TSPO (translocator protein) \u0026nbsp;[Baglini et al. 2024], CDK2 [Su et al. 2021], IER3 [Zhou et al. 2017], \u0026nbsp;BAG3 [Maffioli et al. 2022], DDIT4L [Simonson et al. 2017], B2M [Jheng et al. 2024], TMBIM1 [Gong et al. 2016], ANXA2 [Kayejo et al. 2023], \u0026nbsp;HLA-C [Bonaccorsi et al. 2021], \u0026nbsp;VIM (vimentin) [H\u0026aring;versen et al. 2018], MYD88 [Luo et al. 2022], , WWTR1 [Flinn et al. 2023], \u0026nbsp;HCLS1 [Li et al. 2024], \u0026nbsp;IFIH1 [Dou et al. 2014], APLN (apelin) \u0026nbsp; [Gao and Chen, 2023], CPT1B [Khan et al. 2013], MYOCD (myocardin) [Yuan et al. 2024], MASP1 [Hertle et al. 2016], AMH (anti-Mullerian hormone) [Verdiesen et al. 2022], CLIC1 [Zhu et al. 2017], IGF2 [Lu et al. 2021], SEMA3F [Reichert et al. 2019], AQP4 [Warth et al. 2007], DKK1 [Toth, 2024], CA3 [Su et al. 2021], SCD (stearoyl-CoA desaturase) [Abd Alla et al. 2021], HLA-E [Zidi et al. 2016], IL16 [de Souza et al. 2020], CSF1 [Sjaarda et al. 2018], TGFBR1 [Zhou et al. 2023], PDPN (podoplanin) [Cimini et al. 2019], IFI16 [Chang et al. 2024], FOXO1 [Kazemi Fard et al. 2021], HSPB1 [Zhao et al. 2025], \u0026nbsp;FSTL1 [Ponce-Ru\u0026iacute;z et al. 2024], PON3 [Priyanka et al. 2019], AGTRAP (angiotensin II receptor associated protein) [Wakui et al. 2013], GPR35 [Chen et al. 2020], TLR4 [Vaez et al. 2023], TIMP1 [Korzeń et al. 2023], MAP3K8 [Yu et al. 2022], HEY2 [van Walree et al. 2023], LTBR (lymphotoxin beta receptor) [Grandoch et al. 2015], F11R [Babinska et al. 2019], ACKR3 [Duval \u0026nbsp; et al. 2022], PLD2 [Li et al. 2024], IGFBP7 [Katoh et al. 2024], \u0026nbsp;DDR1 [Franco et al. 2008], S1PR2 [Duan et al. 2024], AGT (angiotensinogen) [Daugherty et al. 2024], ERBB2 [Jian et al. 2020], PRRX1 [Fang et al. 2025], FBLN5 [Li et al. 2025], SUSD2 [Bruikman et al. 2020], MGP (matrix Gla protein) [Kumric et al. 2021], AEBP1 [Xue et al. 2024], SLC1A5 [Kennel et al. 2019], KCNE4 [Abbott, 2016], SSPN (sarcospan) [Rahimi Kahmini et al. 2024], SERPINB1 [Lan et al. 2025], BGN (biglycan) [Scuruchi et al. 2020], ELN (elastin) [Wang et al. 2021], \u0026nbsp; FADS2 [Xu et al. 2019], \u0026nbsp;PALLD (palladin, cytoskeletal associated protein) [Liu et al. 2021], ORAI3 [Saliba et al. 2015], SLC40A1 \u0026nbsp;[Feng et al. 2024], PSMB9 [Liu and Delgado, 2024], CCDC80 [Wang et al. 2021] and PSMB8 [Li et al. 2024] were observed to be associated with the progression of cardiovascular diseases. NPPA (natriuretic peptide A) [Li et al. 2022], EREG (epiregulin) [Song et al. 2022], GUCY2C [Lin et al. 2016], CARTPT (CART prepropeptide) [Mahmoudinezhad et al. 2021], NPPC (natriuretic peptide C) [Cabiati et al. 2020], STX1A [Romeo et al. 2008], CAMKK2 [Wang et al. 2024], TRAF5 [Gissler et al. 2021], RGS6 [Kim et al. 2017], NCALD (neurocalcin delta) [Ma et al. 2017], GRIN3A [Zhang et al. 2023], \u0026nbsp;GNG3 [Schwindinger et al. 2009], GRM7 [Ghafouri-Fard et al. 2023], \u0026nbsp;MCHR1 [H\u0026ouml;gberg et al. 2012], SYT3 [Zhang et al. 2020], HRAS (HRas proto-oncogene, GTPase) [Oba et al. 2018], PIK3R1 [Pinhel et al. 2020], GNB1 [Kleinendorst et al. 2024], DCLK1 [Yang et al. 2023], YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Capobianco et al. 2012], PRKCD (protein kinase C delta) [Osborne et al. 2024], \u0026nbsp; NRXN3 [Albuquerque et al. 2014], PAK3 [Chen et al. 2024], NEK2 [Nuncia-Cantarero et al. 2018], HDAC9 [Yang et al. 2025], TNF (tumor necrosis factor) [Olszanecka-Glinianowicz et al. 2004], CXCL8 [Lima et al. 2021], CCL2 [Wu and Ma, 2024], \u0026nbsp;GZMB (granzyme B) [El Mesallamy et al. 2014], CXCL2 [He et al. 2024], SOCS3 [Emamgholipour et al. 2023], \u0026nbsp;IRX5 [Bjune et al. 2019], VCAM1 [Yu et al. 2017], WNT1 [Pivovarova-Ramich et al. 2021], OSM (oncostatin M) [Piquer-Garcia et al. 2020], CHI3L1 [Teitsdottir et al. 2018], CES1 [Marrades et al. 2010], LYZ (lysozyme) [Moreno-Navarrete et al. 2021], NAT1 [Paz-Rodr\u0026iacute;guez et al. 2024], APLNR (apelin receptor) [De Los Santos et al. 2023], \u0026nbsp;SFRP4 [Bukhari et al. 2019], GFAP (glial fibrillary acidic protein) [Cano et al. 2014], GPR65 [Bagchi et al. 2024], S100A4 [Xi et al. 2024], SERPINE1 [Su et al. 2023], CD74 [Chan et al. 2015], TLR5 [Pekkala et al. 2015], \u0026nbsp; COL3A1 [Wang et al. 2024], \u0026nbsp;CD14 [Kang et al. 2022], SPHK1 [Nagahashi et al. 2018], ATF3 [Li et al. 2022], LY75 [Makino et al. 2018], CASP1 [Kimura et al. 2016], IL15 [Ye, 2015], S100A11 [Shen et al. 2023], GPX3 [Langhardt et al. 2018], SPX (spexin hormone) [Kumar et al. 2021], CXCR4 [Ahn et al. 2023], IFITM3 [Pomar et al. 2022], TSPO (translocator protein) \u0026nbsp;[Lassance et al. 2015], CDK2 [Col\u0026oacute;n-Mesa et al. 2021], \u0026nbsp; LGALS9 [Lee et al. 2022], TMBIM1 [Zhao et al. 2021], ANXA2 [Wang et al. 2019], VIM (vimentin) [Roh and Yoo, 2021], LITAF (lipopolysaccharide induced TNF factor) [Ji et al. 2011], \u0026nbsp;APLN (apelin) \u0026nbsp; [De Los Santos \u0026nbsp;et al. 2023], CPT1B [Ratner et al. 2015], \u0026nbsp;AMH (anti-Mullerian hormone) [Zeng et al. 2022], CLIC1 [Zapata et al. 2023], IGF2 [Szydlowska-Gladysz et al. 2024], DKK1 [Yang et al. 2024], SCD (stearoyl-CoA desaturase) [Sampath and Ntambi, 2011], \u0026nbsp;IL16 [Reyes-Farias et al. 2024], FOXO1 [Benchoula et al. 2021], HSPB1 [Pilch et al. 2015], FSTL1 [Liu et al. 2024], PON3 [Salas-P\u0026eacute;rez et al. 2022], \u0026nbsp; FGF1 [Wang et al. 2023], AGTRAP (angiotensin II receptor associated protein) [Maeda et al. 2013], TLR4 [Benomar and Taouis, 2019], TIMP1 [Andrade et al. 2012], \u0026nbsp;MAP3K8 [Ballak et al. 2014], F11R [Ong et al. 2008], IGFBP7 [Jędrysik et al. 2024], TRIM56 [Qin et al. 2025], \u0026nbsp;AGT (angiotensinogen) [Repchuk et al. 2021], STK33 [Rask-Andersen et al. 2013], NFATC1 [Chasapi et al. 2020], SIGLEC7 [Rosenstock et al. 2017], AEBP1 [Zhang et al. 2005], \u0026nbsp;CCDC80 [Osorio-Conles et al. 2017], DPT (dermatopontin) [Unamuno et al. 2020], \u0026nbsp; SERPINB1 [Li et al. 2024], BGN (biglycan) [Han et al. 2020], ELN (elastin) [Martinez-Santibanez et al. 2015], \u0026nbsp; FADS2 [Mahmoudinezhad et al. 2023], \u0026nbsp; \u0026nbsp;SIX3 [Yu et al. 2024], MYD88 [Guerrero-Romero et al. 2023] and IFI30 [Turcot et al. 2012] \u0026nbsp;might be considered to be a novel biomarkers for obesity. NPPA (natriuretic peptide A) [Li et al. 2020], \u0026nbsp;NPR3 [Sekiguchi al. 2011], \u0026nbsp;NPPC (natriuretic peptide C) [\u0026Scaron;piranec et al. 2018], WIF1 [Kania et al. 2022,] HTR2A [Choi et al. 2020], MEF2C [Sahoo et al. 2016], CHRM3 [Cowley et al. 2018], CHN1 [Liang et al. 2020], PIK3R1 [Zhang et al. 2010], \u0026nbsp;CACNB2 [Soldatov, 2015], FBXW7 [Wang et al. 2022], FGF12 [Yeo et al. 2020], \u0026nbsp;ARRB1 [Sun et al. 2018], ADD2 [Kardia \u0026nbsp;et al. 2007], NEK2 [Zhou et al. 2025], TNF (tumor necrosis factor) [Mehaffey and Majid, 2017], CXCL8 [Kim et al. 2008], CCL2 [Alsheikh et al. 2020], GZMB (granzyme B) [Mao et al. 2018], \u0026nbsp;RXFP2 [Zhang et al. 2025], \u0026nbsp;SOCS3 [Benincasa et al. 2023], PCSK9 [Ye et al. 2024], VCAM1 [Qiu et al. 2022], WNT1 [Młynarczyk et al. 2022], CHI3L1 [Sun et al. 2022], RGS1 [Patel et al. 2018], C4B [Mulvihill et al. 2019], CES1 [Ikonnikova et al. 2022], CD44 [Isobe et al. 2019], CRIP1 [Schweigert et al. 2021], SLC12A3 [Huang et al. 2022], \u0026nbsp;LYZ (lysozyme) [Qvarnstrom et al. 2008], C4A [Magen et al. 2010], \u0026nbsp;GFAP (glial fibrillary acidic protein) [Canissario et al. 2022], S100A4 [Laggner et al. 2022], TNFAIP3 [Xue et al. 2011], CD74 [Le Hiress \u0026nbsp;et al. 2015], TLR5 [Wu et al. 2025], \u0026nbsp;NUPR1 [Zhou et al. 2024], EPHA2 [Murakoshi et al. 2022], COL3A1 [Samokhin et al. 2018], CD14 [Fehrenbach et al. 2021], SCNN1G [Fan et al. 2020], \u0026nbsp;HLA-DRB1 [Li et al. 2012], SPHK1 [Pyne and Pyne, 2017], HMOX1 [Song et al. 2024], HLA-DPB1 [Kominami et al. 2009], CASP1 [Udjus et al. 2019], IL15 [Hilton et al. 2022], MFAP4 [Christensen et al. 2024,] GPX3 [Decharatchakul et al. 2020], \u0026nbsp; CXCR4 [Hiraide et al. 2024], HLA-B [Hu et al. 2024], B2M [Jheng et al. 2024], NFKB2 [Nagata et al. 2024], MYD88 [Qi et al. 2019], CLIC4 [Hatziioanou et al. 2018], \u0026nbsp;IFIH1 [Bou\u0026ccedil;as et al. 2013], APLN (apelin) \u0026nbsp; [Zhu \u0026nbsp;et al. 2015], MYOCD (myocardin) [Sahoo et al. 2016], IGF2 [Yang et al. 2020], AQP4 [Gonz\u0026aacute;lez-Marrero et al. 2022], DKK1 [Toth, 2024], HFE (homeostatic iron regulator) [M\u0026auml;\u0026auml;tt\u0026auml; and Nikkari, 2015], IFI16 [Rao et al. 2024], RARRES2 [Batista et al. 2021], FOXO1 [Li et al. 2023], FGF1 [Tomaszewski et al. 2007], IL10RA [Park et al. 2013], AGTRAP (angiotensin II receptor associated protein) [Wakui et al. 2013], GPR35 [McCarthy et al. 2018], TLR4 [Grylls et al. 2021], TIMP1 [He et al. 2022], CYP4F11 [Gainer et al. 2005], F11R [Ong et al. 2008], PLD2 [Nelson et al. 2017], IGFBP7 [Torres et al. 2023], DDR1 [Bonafiglia et al. 2022], \u0026nbsp; AGT (angiotensinogen) [Repchuk et al. 2021], NUDT1 [Vitry et al. 2021], \u0026nbsp;NFATC1 [Du et al. 2016], MGP (matrix Gla protein) [Chirinos et al. 2018], \u0026nbsp; CCDC80 [Sasagawa et al. 2016], BGN (biglycan) [Sardo et al. 2010], ELN (elastin) [Owens et al. 2017] \u0026nbsp;and PSMB8 [Zhang et al. 2025] \u0026nbsp;have emerged as a potential targets for hypertension. NPPA (natriuretic peptide A) [Lima et al. 2008], \u0026nbsp;PTGER3 [Park et al. 2007], HTR2A [Konjevod et al. 2023], PAK1 [Lu et al. 2018], CHRM3 [Jim\u0026eacute;nez-Morales et al. 2014], VAMP2 [Ding et al. 2022], RGS7BP [Lee et al. 2011], ROBO2 [Hernandez-Pacheco et al. 2021], GABARAPL1 [Pan et al. 2025], NRXN1 [Margaritte-Jeannin et al. 2023], INPP4A [Aich et al. 2012], CXCL3 [Sokulsky \u0026nbsp;et al. 2020], TNF (tumor necrosis factor) [Berry et al. 2006], CXCL8 [Tang et al. 2016], CCL2 [Luo et al. 2025], GZMB (granzyme B) [Annoni et al. 2015], CXCL2 [Al-Alwan et al. 2013], \u0026nbsp;SOCS3 [Sun et al. 2019], \u0026nbsp;TBX21 [Sharma et al. 2014], VCAM1 [Ando et al. 2001], WNT1 [Yang et al. 2013], CHI3L1 [Huang et al. 2022] , \u0026nbsp;SELL (selectin L) \u0026nbsp;[Monteseir\u0026iacute;n et al. 2001], CD44 [Katoh et al. 2022], \u0026nbsp;OSM (oncostatin M) [Esnault et al. 2024], LYZ (lysozyme) [Ohbayashi et al. 2016], NAT1 [Brooks et al. 2021], SERPINA1 [Mart\u0026iacute;n-Gonz\u0026aacute;lez et al. 2025], S100A4 [Wu et al. 2021], RUNX3 [Zhang et al. 2025], HLA-DRA [Kim et al. 2012], TNFAIP3 [Krusche et al. 2019], TLR5 [Whitehead et al. 2020], EPHA2 [Xu et al. 2022], CD14 [Zhou et al. 2019], HLA-DRB1 [Yao et al. 2016], \u0026nbsp;SPHK1 [Sudhadevi et al. 2024], \u0026nbsp;ATF3 [Wang et al. 2025], HLA-DPB1 [Dekker et al. 1997], TAP1 [Liu et al. 2017], \u0026nbsp;ITGB4 [Ou et al. 2023], \u0026nbsp;CASP1 [Zhang et al. 2023], IL15 [Jonakowsket al. 2017], MFAP4 [Pilecki et al. 2015], HSPA1B [Faisal et al. 2022], TNFSF10 [Weckmann et al. 2011], MYD88 [Yang et al. 2024], IRF1 [Hu et al. 2021], IL16 [Deng and Shi, 2006], CSF1 [Du a et al. 2023], \u0026nbsp; FOXO1 [Wang et al. 2022], FSTL1 [Huang et al. 2022], FGF1 [Li et al. 2021], TLR4 [Wu et al. 2022], TIMP1 [Kim, 2023], \u0026nbsp;PLD2 [Wang et al. 2025], CTSH (cathepsin H) [Faiz et al. 2013], ARPC1B [Papadatou et al. 2021], \u0026nbsp;S1PR2 [Wang et al. 2023], AGT (angiotensinogen) [Pasaje et al. 2011], ERBB2 [Sio et al. 2023], \u0026nbsp;NFATC1 [Yang et al. 2024], ELN (elastin) [Ingram et al. 2016] \u0026nbsp;and SLC40A1 \u0026nbsp;[Wang et al. 2023] makes a vital regulatory role in asthma. CUX2 [Zhang al. 2022], NPTX2 [Xing et al. 2024], STX1A [Luppe et al. 2023], NRGN (neurogranin) [Kalkan et al. 2022], HTR2A [Manna et al. 2012], GABBR2 [Yoo et al. 2017], MEF2C [Raviglione et al. 2021], CAMKK2 [Sayad et al. 2018], GRIN2A [Samanta, 2023], CACNG3 [Everett et al. 2007], PLCB1 [Desprairies et al. 2020], PAK1 [Corriveau et al. 2023], RASGRF1 [Chen et al. 2017], GRM7 [Marafi et al. 2020], \u0026nbsp;CNKSR2 [Higa et al. 2021], SCN3B [Al-Eitan et al. 2019], IQSEC2 [Brant et al. 2022], UNC13A [Su et al. 2024], KCNJ3 [Li et al. 2024], NAPB (NSF attachment protein beta) [Ali et al. 2024], GRIN2B [Wang et al. 2023], SCN8A [Conecker et al. 2024], \u0026nbsp;PRICKLE1 [Ban et al. 2022], YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Cetica et al. 2024], GRIA2 [Latsko et al. 2012], PRICKLE2 [Liu et al. 2025], SCN2A [Zeng et al. 2022], KCNQ3 [Miceli et al. 2015], SYNGAP1 [Wiltrout et al. 2024], PRKCD (protein kinase C delta) [Ma et al. 2024], \u0026nbsp;STX1B [Ke a et al. 2023], \u0026nbsp;MEF2A [Huang et al. 2016], FGF12 [Ohori et al. 2023], CDK5 [Banerjee et al. 2021], \u0026nbsp; DLG3 [He et al. 2024], SAMD12 [Chen et al. 2025], PAK3 [Gambardella et al. 2025], NDEL1 [Gavrilovici et al. 2021], NRXN1 [Rochtus et al. 2019], INPP4A [\u0026Ouml;zkan et al. 2023], PPFIBP1 [Rosenhahn et al. 2022], EEF1A2 [Mohamed and Klann, 2023], BCL11A [Wang \u0026nbsp;et al. 2022], NBEA (neurobeachin) [Pan \u0026nbsp;et al. 2022], \u0026nbsp;KCNH7 [Wu et al. 2024], TNF (tumor necrosis factor) [Fang et al. 2024], SOCS3 [Wang and Yin, 2025], \u0026nbsp;WNT1 [Huang et al. 2015], \u0026nbsp;CD44 [Kusakabe et al. 2025], LILRB2 [Yue et al. 2021], GFAP (glial fibrillary acidic protein) [Thaele et al. 2025], TNFAIP3 [Zhuo et al. 2022], TLR5 [Zhao et al. 2020], NUPR1 [Cui et al. 2024], HLA-DRB1 [Bui et al. 2023], SPHK1 [Dong et al. 2020], \u0026nbsp; ATF3 [Kang et al. 2024], TAP1 [Layouni et al. 2010], CASP1 [Lv et al. 2025], CXCR4 [Song et al. 2016], HLA-B [Chouchi et al. 2018], TSPO (translocator protein) \u0026nbsp;[Qin et al. 2024], ANXA2 [Ma et al. 2023], VIM (vimentin) [Sitovskaya et al. 2023], MYD88 [Zhu et al. 2019], IFIH1 [Song et al. 2024], APLN (apelin) \u0026nbsp; [Dong \u0026nbsp; et al. 2020], AMH (anti-Mullerian hormone) [Harden et al. 2016], AQP4 [Nowicka, 2023], DKK1 [Busceti et al. 2007], CA3 [Zhang et al. 2017], TGFBR1 [Zheng et al. 2021], FGF1 [Simonato et al. 1998], TLR4 [Zhang et al. 2022], TIMP1 [He et al. 2023], S1PR2 [Dong et al. 2020], GRM4 [Al-Eitan et al. 2019], REST (RE1 silencing transcription factor) [Navarrete-Modesto et al. 2019], MGP (matrix Gla protein) [Wu et al. 2025] and EMP1 [Li et al. 2009] expression might be regarded as an indicator of susceptibility to epilepsy. Altered expression of CUX2 [Barington al. 2018], STX1A [Nakamura et al. 2008], \u0026nbsp;HTR2A [Coskunpina et al. 2023], GABBR2 [Bielopolski et al. 2023], MEF2C [Basu et al. 2024], \u0026nbsp;EPHA5 [Pascolini et al. 2019], GRIN2A [Mangano et al. 2022], HTR7 [Helsmoortel et al. 2016], CHRM3 [Petersen et al. 2013], CDKL5 [Wang et al. 2012], GRM7 [Noroozi et al. 2016], DLG4 [Feyder et al. 2010], IQSEC2 [Brant et al. 2022], NAPB (NSF attachment protein beta) [Ali et al. 2024], GRIN2B [Yal\u0026ccedil;intepe et al. 2021], CDK5R1 [Lintas et al. 2019], CDH8 [Parfenenko et al. 2024], ARHGAP33 [Schuster et al. 2015], PRICKLE1 [Ban et al. 2022], CACNB2 [Graziano et al. 2021], GRIA2 [Latsko et al. 2012], \u0026nbsp;PRICKLE2 [Sowers et al. 2013], SCN2A [Ma et al. 2022], KCNQ3 [Sands et al. 2019], SYNGAP1 [Wiltrout et al. 2024], \u0026nbsp;FGF12 [Seiffert et al. 2022], \u0026nbsp;MARK1 [Maussion et al. 2008], CDK5 [Gu and Kanungo, 2024], \u0026nbsp;NRXN3 [Feichtinger et al. 2023], NRXN1 [Cooper et al. 2024], CNTN5 [Hadi \u0026nbsp;et al. 2025], CDH9 [Wang \u0026nbsp;et al. 2019], EEF1A2 [Mohamed and Klann, 2023], \u0026nbsp;KIRREL3 [Liu \u0026nbsp;et al. 2015], \u0026nbsp; NBEA (neurobeachin) [Nuytens \u0026nbsp;et al. 2013], TNF (tumor necrosis factor) [Yamauchi et al. 2021], CXCL8 [Anastasescu et al. 2024], SOCS3 [Li et al. 2024], \u0026nbsp;PCSK9 [Salem et al. 2023], VCAM1 [Kameno et al. 2013], WNT1 [Martin et al. 2013], SELL (selectin L) \u0026nbsp;[Arslan et al. 2024], \u0026nbsp;CES1 [Hernandez et al. 2022], BATF2 [Voinsky et al. 2022], GFAP (glial fibrillary acidic protein) [Simone et al. 2023], HLA-DRB1 [Guerini et al. 2024], IL15 [Vojdani et al. 2008], CXCR4 [Kara et al. 2018], HLA-B [Guerini et al. 2024], TSPO (translocator protein) \u0026nbsp;[Tseng et al. 2024], HLA-C [Guerini et al. 2024], APLN (apelin) \u0026nbsp; [Boso \u0026nbsp;et al. 2007], AMH (anti-Mullerian hormone) [Pankhurst et al. 2012], SEMA3F [Li et al. 2019], AQP4 [Davoudi \u0026nbsp;et al. 2023], IL16 [Ahmad et al. 2019], \u0026nbsp;FOXO1 [Guo et al. 2023], TLR4 [Nadeem et al. 2017] and FADS2 [Sun et al. 2018] \u0026nbsp;are associated with prognosis in patients with autism spectrum disorder. ADAMTS20 [Park al. 2016] ARHGAP10 [Sekiguchi al. 2020], NPTX2 [Zhou et al. 2024], NRGN (neurogranin) [Hwang et al. 2021], HTR2A [Tan et al. 2014], MEF2C [Ali et al. 2024], CAMKK2 [Sayad et al. 2018], \u0026nbsp; DLGAP3 [Li et al. 2013], \u0026nbsp;GRIN2A [Sheng et al. 2024], GRIN3A [Takata et al. 2013], PLCB1 [Lo Vasco et al. 2012], PAK1 [Deo et al. 2013], \u0026nbsp;HTR7 [Wei et al. 2009], CHRM3 [Wang et al. 2016], GRM7 [Alrefai et al. 2024], HOMER1 [Leber et al. 2017], DLG4 [Balan et al. 2013], GRIK1 [Hirata et al. 2012], VAMP2 [Kawashima et al. 2008], PPP3CB [Liu et al. 2011], KCNJ3 [Yamada et al. 2012], PCLO (piccolo presynaptic cytomatrix protein) [Chen et al. 2021], GRIN2B [Poltavskaya et al. 2021], PIK3R1 [Huang et al. 2020], MAP1A [Morris et al. 2003], DCLK1 [H\u0026aring;vik et al. 2012], CACNB2 [Juraeva et al. 2014], GRIA2 [Alkelai et al. 2021], DLG2 [Sanders et al. 2022], SCN2A [Suddaby et al. 2019], GIT1 [Fass a et al. 2022], HOMER2 [Gilks a et al. 2010], CDK5 [Cantrup et al. 2012], NRXN3 [Hu et al. 2013], PAK3 [Kim et al. 2017], NDEL1 [Nani et al. 2023], NRXN1 [Sebastian et al. 2023], \u0026nbsp;DES (desmin) [Hsiung et al. 2019], BCL11A [Chen \u0026nbsp;et al. 2020], KIF3B [Alsabban et al. 2020], LDB2 [Horiuchi et al. 2020], HDAC9 [O\u0026apos;Connell et al. 2019], KCNH7 [Wang et al. 2019], TNF (tumor necrosis factor) [He et al. 2022], CXCL8 [Arabska et al. 2022], CCL2 [Xiong et al. 2014], SOCS3 [Ni et al. 2021], PCSK9 [Huang et al. 2022], VCAM1 [Meixensberger et al. 2021], WNT1 [Beasley et al. 2002], \u0026nbsp;CHI3L1 [Hill et al. 2011], \u0026nbsp;C4B [Mayilyan et al. 2008], \u0026nbsp;SELL (selectin L) \u0026nbsp;[Mohite et al. 2017], C4A [Melbourne et al. 2018], GFAP (glial fibrillary acidic protein) [Webster et al. 2005], GRHL3 [Sargazi et al. 2020], BTN3A2 [Wu \u0026nbsp;et al. 2019], CD14 [de la Fontaine et al. 2006], HLA-DRB1 [Seshasubramanian et al. 2020], HMOX1 [Tavitian et al. 2020], ITGB4 [O\u0026apos;Brien et al. 2018], CASP1 [Jia et al. 2025], IL15 [He et al. 2022], GPX3 [Liu et al. 2020], CXCR4 [Borroto-Escuela et al. 2017], HLA-B [Seshasubramanian et al. 2020], HSPA1B [Pae et al. 2005], TSPO (translocator protein) \u0026nbsp; [Iliopoulou et al. 2021], RGS9 [Liou et al. 2009], HLA-C [ISGCWTCCC 2012], MYD88 [Chen et al. 2017], IGF2 [Fern\u0026aacute;ndez-Pereira et al. 2022], AQP4 [Wu \u0026nbsp;et al. 2020], DKK1 [Al-Dujaili et al. 2021], IFIT3 [Guo et al. 2022], CA3 [Berry et al. 2018], HLA-E [Mihoub et al. 2004], IL16 [Cheng et al. 2023], PADI2 [Watanabe a et al. 2009], \u0026nbsp;HSPB1 [Kowalczyk et al. 2022], IL10RA [Kapelski et al. 2016], TLR4 [Aflouk et al. 2024], TIMP1 [Rahimi et al. 2017], IGFBP7 [Fern\u0026aacute;ndez-Pereira et al. 2022] DDR1 [Aranda et al. 2024], \u0026nbsp;TAP2 [Jun et al. 2004], REST (RE1 silencing transcription factor) [Warburton et al. 2015], SLC1A5 [Deng et al. 2008], FADS2 [Liu and Jin, 2024] and STON2 [Ma et al. 2024]genes are a potential biomarkers for the detection and prognosis of schizophrenia. A previous study reported that EPHA1 [Matsumoto al. 2025], NPTX2 [Massa et al. 2024], \u0026nbsp;NRGN (neurogranin) [Saunders et al. 2023], HTR2A [Wilkosz et al. 2007], GABBR2 [Weng et al. 2024], MEF2C [Ren et al. 2022], CAMKK2 [Gaff et al. 2021], DKK2 [Aghaizu et al. 2023], RGS6 [Spicer et al. 2025], \u0026nbsp;GRIN3A [Lee et al. 2024], HTR7 [Solas et al. 2021], \u0026nbsp;CHRM3 [Sanfilippo et al. 2023], PRKCB (protein kinase C beta) \u0026nbsp; [Zhou et al. 2020], HOMER1 [Urd\u0026aacute;noz-Casado et al. 2021], DLG4 [Bustos et al. 2017], GRIK1 [Pang et al. 2025], VAMP2 [Gonz\u0026aacute;lez et al. 2025], OPTN (optineurin) [Duan et al. 2023], SYT7 [Zhu et al. 2024], CALM3 [Ibarreta et al. 1997], UNC13A [Agra Almeida Quadros et al. 2024], GRIN2B [Andreoli et al. 2014], CDK5R1 [Shao et al. 2022], \u0026nbsp;PIK3R1 [Li et al. 2023], \u0026nbsp;MAP1A [Cai et al. 2023], RGS7 [Squitti et al. 2023], DLG2 [Prokopenko et al. 2022], SH3RF3 [Patel et al. 2025], PRICKLE2 [Sun et al. 2020], PPP3R1 [Zhou et al. 2021], MADD (MAP kinase activating death domain) [Hassan a et al. 2021], MEF2A [Li et al. 2021], CDK5 [Nikhil et al. 2019], ARRB1 [Sengupta and Mukhopadhyay, 2025], NRXN3 [Hishimoto et al. 2019], \u0026nbsp;PAK3 [McPhie et al. 2003], GMFB (glia maturation factor beta) [Bellver-Sanchis et al. 2024], CNTN5 [Dauar \u0026nbsp;et al. 2025], TLN2 [Gusareva \u0026nbsp;et al. 2018], HDAC9 [Zhang et al. 2024], TNF (tumor necrosis factor) [Zhou et al. 2020], SERPINA3 [Sanfilippo et al. 2025], CXCL8 [Jin et al. 2024], CCL2 [Arfaei et al. 2024], CXCL2 [Ma et al. 2024], TBX21 [Cao et al. 2025], PCSK9 [Zhang et al. 2024], MS4A6A [Lacher et al. 2018], VCAM1 [Salian et al. 2024], \u0026nbsp; WNT1 [Macyczko et al. 2023], \u0026nbsp; CHI3L1 [Connolly et al. 2023], C4B [Zorzetto et al. 2017], PLA2G3 [Mart\u0026iacute;nez-Garc\u0026iacute;a et al. 2010], SELL (selectin L) \u0026nbsp;[Corsi et al. 2011], \u0026nbsp;CD44 [Liu et al. 2024], LYZ (lysozyme) [Sandin et al. 2016], NAT1 [Johnson et al. 2004], C4A [Zorzetto et al. 2017], LILRB2 [Lao et al. 2021], \u0026nbsp;GFAP (glial fibrillary acidic protein) [Shir et al. 2022], SERPINA1 [Barba et al. 2022], GMPR (guanosine monophosphate reductase) [Liu et al. 2018], HLA-DRA [Branciamore et al. 2023], SERPINE1 [Fang et al. 2012], CD74 [Liu et al. 2024], TLR5 [Herrera-Rivero et al. 2019],\u0026nbsp; ADAMTS1 [Miguel et al. 2005], IL12A [Zhu et al. 2014], EPHA2 [Ma et al. 2022], CD14 [Andr\u0026eacute; et al. 2019], HLA-DRB1 [Shigemizu et al. 2024], SPHK1 [Lee et al. 2018], HMOX1 [Gong et al. 2023], IFITM2 [Xiang et al. 2025], CASP1 [Flores et al. 2022], IL15 [Bishnoi et al. 2015], \u0026nbsp;MLKL (mixed lineage kinase domain like pseudokinase) [Motawi et al. 2020], GPX3 [Panyard et al. 2024], CXCR4 [Huang et al. 2024], HLA-B [Roses, 2009], HSPA1B [Gu et al. 2025], \u0026nbsp;IFITM3 [Feng et al. 2025], \u0026nbsp;TSPO (translocator protein) \u0026nbsp;[Fairley et al. 2024], CDK2 [Baumann et al. 1993], BAG3 [Gonzalez-Rodriguez et al. 2021], B2M [Huang et al. 2023], \u0026nbsp;TNFSF10 [Burgaletto et al. 2021], PTPRC (protein tyrosine phosphatase receptor type C) [Huang et al. 2024], \u0026nbsp;ANXA2 [Ye et al. 2024], VIM (vimentin) [Zhang et al. 2025], MYD88 [Chen et al. 2025], CLIC4 [Chen et al. 2024], \u0026nbsp;CD109 [Fessel, 2023], CLIC1 [Xiang et al. 2025], IGF2 [Chen et al. 2025], AQP4 [Mohaupt \u0026nbsp; et al. 2023], DKK1 [Sato et al. 2024], IFIT3 [Garces et al. 2023], CA3 [Dong et al. 2022], SCD (stearoyl-CoA desaturase) [Astarita et al. 2011], IL16 [Yin et al. 2017], CSF1 [Pons a et al. 2021], HFE (homeostatic iron regulator) [Ali-Rahmani \u0026nbsp;et al. 2014], FOXO1 [Lin et al. 2024], FSTL1 [Dai et al. 2023], PON3 [Trentini et al. 2024], FGF1 [Chang et al. 2019], TLR4 [Yang et al. 2020], TIMP1 [Liu et al. 2023], HEY2 [Chen et al. 2019], \u0026nbsp;RHOC (ras homolog family member C) [Tian et al. 2021], PLD2 [Oliveira et al. 2010], DDR1 [Stevenson et al. 2023], S1PR2 [Wang et al. 2023], TAP2 [Bullido et al. 2007], ITPKB (inositol-trisphosphate 3-kinase B) [Salta et al. 2016], ERBB2 [Wang et al. 2017], REST (RE1 silencing transcription factor) [Gonz\u0026aacute;lez-Mundo et al. 2020] and AEBP1 [Asadie et al. 2024] \u0026nbsp;are \u0026nbsp;altered expressed in Alzheimer disease. \u0026nbsp;Research has revealed that EPHA1 [Ma al. 2021], NPTX2 [Moran et al. 2008], NPPC (natriuretic peptide C) [Woodward et al. 2017], SV2C [Chang et al. 2024], NRGN (neurogranin) [Koob et al. 2025], HTR2A [Lee et al. 2012], MEF2C [Shulskaya et al. 2024], TRAF5 [Zhang et al. 2021], GRIN2A [Nepal et al. 2019], \u0026nbsp;HOMER1 [Lenka et al. 2024], DLG4 [Lukashevich et al. 2024], VAMP2 [Kim et al. 2021], OPTN (optineurin) [Wise and Cannon, 2016], GRIN2B [Cui et al. 2024], PAK6 [Giusto et al. 2024], CDK5R1 [Das et al. 2012], DLG2 [Zhao et al. 2020], PRKCD (protein kinase C delta) [Gordon a et al. 2016], \u0026nbsp;STX1B [Chang et al. 2019], GIT1 [Zhou a et al. 2023], CDK5 [Alrouji et al. 2024], \u0026nbsp; ARRB1 [Fang et al. 2021], DZIP1 [Valente et al. 2012], EEF1A2 [Khwanraj \u0026nbsp;et al. 2023], TNF (tumor necrosis factor) [Amin et al. 2022], HLA-DRB5 [Su et al. 2021], CCL2 [Xiromerisiou et al. 2022], \u0026nbsp;SOCS3 [Liu et al. 2024], PCSK9 [Jahed et al. 2022], MS4A6A [Fan et al. 2016], VCAM1 [Zheng et al. 2022], WNT1 [Wei et al. 2019], \u0026nbsp;CHI3L1 [Gong et al. 2025], CD44 [Wang et al. 2022], NAT1 [van der Walt et al. 2003], GFAP (glial fibrillary acidic protein) [Lin et al. 2023], SERPINA1 [Halbgebauer et al. 2016], RUNX3 [Huang et al. 2024], HLA-DRA [Mo et al. 2015], TNFAIP3 [Ma \u0026nbsp;et al. 2023], CD14 [Panaro et al. 2008], HLA-DRB1 [Garretti et al. 2023], SPHK1 [Motyl and Strosznajder, 2018], HMOX1 [Tavitian et al. 2020], NLRC5 [Liu et al. 2023], CASP1 [Huan et al. 2023], IL15 [Rentzos et al. 2007], GPX3 [Wang et al. 2025], CXCR4 [Dogra et al. 2023], \u0026nbsp;TSPO (translocator protein) \u0026nbsp;[Xue et al. 2022], BAG3 [Ying et al. 2022], RGS9 [Tekumalla et al. 2001], \u0026nbsp;PTPRC (protein tyrosine phosphatase receptor type C) [Bottero al. 2018], \u0026nbsp;MYD88 [Liu et al. 2023], APLN (apelin) \u0026nbsp; [Angelopoulou \u0026nbsp;et al. 2021], \u0026nbsp; DNAJB1 [Akber et al. 2015], \u0026nbsp;IGF2 [Grunenwald et al. 2025], AQP4 [Lapshina and Ekimova, 2024], CA3 [Legault-Denis et al. 2024], SCD (stearoyl-CoA desaturase) [Tardiff et al. 2022], IL16 [Zhang et al. 2020], CSF1 [Chang a et al. 2019], FOXO1 [Zeng et al. 2019], HSPB1 [Meng et al. 2024], FSTL1 [Cao et al. 2021], \u0026nbsp; FGF1 [Wei et al. 2014], RAB32 [Radefeldt et al. 2025], TLR4 [Roy et al. 2024], TIMP1 [Lorenzl et al. 2002], PLD2 [Mendez-Gomez et al. 2018], \u0026nbsp;ITPKB (inositol-trisphosphate 3-kinase B) [Di Leva et al. 2023], \u0026nbsp;ERBB2 [Jin et al. 2024], \u0026nbsp;SLC1A5 [Liu et al. 2022], PSMB9 [Shani et al. 2023] \u0026nbsp;and PSMB8 [Nguyen et al. 2023] are expressed in Parkinson\u0026apos;s Disease. ERFE (erythroferrone) [Youssef al. 2021], HTR2A [Xiang et al. 2024], PAK1 [Fu et al. 2012], RASGRF1 [Abreu et al. 2009], CHRM3 [Fakher et al. 2023], KCNH7 [Mart\u0026iacute;nez et al. 2008], CASP5 [Rui \u0026nbsp;et al. 2018], TNF (tumor necrosis factor) [Lillegraven et al. 2023], CXCL8 [Gowhari Shabgah et al. 2019], HLA-DRB5 [Xu et al. 2021], CCL2 [Moadab et al. 2021], \u0026nbsp; GZMB (granzyme B) [Aubert et al. 2024], \u0026nbsp; CXCL2 [Wang et al. 2021], SOCS3 [Xu et al. 2024], TBX21 [Chae et al. 2009], PCSK9 [Meng et al. 2023], IL21R [Carre\u0026ntilde;o-Saavedra et al. 2023], \u0026nbsp;GBP5 [Haque et al. 2021], VCAM1 [Achudhan et al. 2022], WNT1 [Dinesh et al. 2020], \u0026nbsp; OSM (oncostatin M) [Han et al. 2023], \u0026nbsp; CHI3L1 [Yu et al. 2023], \u0026nbsp;RGS1 [Hu et al. 2019], \u0026nbsp;C4B [Rigby et al. 2012], SELL (selectin L) \u0026nbsp;[Bond and Hay, 1997], CD44 [Gorantla et al. 2021], LYZ (lysozyme) [Xu et al. 2025], AZGP1 [Na et al. 2017], SFRP4 [Miao et al. 2013], GFAP (glial fibrillary acidic protein) [Salari et al. 2023], CFB (complement factor B) [Matola et al. 2023], S100A4 [\u0026Scaron;enolt et al. 2015], \u0026nbsp;HLA-DOA [Okada et al. 2016], TNFAIP3 [Tang \u0026nbsp;et al. 2023], CD74 [S\u0026aacute;nchez-Zuno et al. 2021], \u0026nbsp;LAIR1 [Zhang et al. 2023], TLR5 [Kim et al. 2014], IL12A [Wang et al. 2015], CD14 [Fuentelsaz-Romero et al. 2021], HLA-DRB1 [Klimenta et al. 2019], SPHK1 [Wang et al. 2021], ATF3 [Hu et al. 2021], HMOX1 [Sun et al. 2024], NLRC5 [Yu et al. 2020], HLA-DPB1 [Yang et al. 2021], TAP1 [Zhang et al. 2002], CASP1 [He et al. 2024], \u0026nbsp;IL15 [Kurowska et al. 2020], S100A11 [Navr\u0026aacute;tilov\u0026aacute; et al. 2021], \u0026nbsp;CD58 [Raychaudhuri et al. 2009], GPX3 [Wahl et al. 2025]. CXCR4 [Han et al. 2025], HLA-DMB [Morel et al. 2004], TSPO (translocator protein) \u0026nbsp;[Bruijnen et al. 2019], HLA-DMA [Morel \u0026nbsp;et al. 2004], LGALS9 [Xu et al. 2021], B2M [S\u0026ouml;derblom et al. 1996], PTPRC (protein tyrosine phosphatase receptor type C) [Hou et al. 2025], ANXA2 [Xiao et al. 2024], NFKB2 [Manuel S\u0026aacute;nchez-Maldonado et al. 2020], HLA-C [Al-Balushi et al. 2025], VIM (vimentin) [Han et al. 2024], MYD88 [Ramirez-Perez et al. 2023], IFIH1 [Mart\u0026iacute;nez et al. 2008], \u0026nbsp;APLN (apelin) \u0026nbsp; [Chang \u0026nbsp; et al. 2021], AMH (anti-Mullerian hormone) [Lopez-Corbeto et al. 2021], CD109 [Song et al. 2019], CLIC1 [Bordean et al. 2021], IGF2 [Martin-Trujillo et al. 2010], AQP4 [Kumar et al. 2025], DKK1 [Zhao et al. 2024], HLA-E [Iwaszko et al. 2015], IRF1 [Yoshida et al. 2012], IL16 [ElAtta et al. 2019], CSF1 [Garcia a et al. 2016], PADI2 [Nava-Quiroz a et al. 2023], HFE (homeostatic iron regulator) [Carini et al. 2023], PDPN (podoplanin) [Takakubo et al. 2017], IFI16 [Alunno et al. 2016], HLA-F [Ravindranath et al. 2023], FOXO1 [Zhang et al. 2021], FSTL1 [Ni et al. 2021], FGF1 [Etori et al. 2023], IL10RA [Yang et al. 2017], \u0026nbsp;TLR4 [Yan et al. 2019], C1R [Breitner et al. 1995], TIMP1 [Schmalz et al. 2019], TRIM22 [Wei et al. 2022], F11R [Fang \u0026nbsp;et al. 2016], DDR2 [Mu et al. 2020], S1PR2 [Wang et al. 2021], TAP2 [Dai et al. 2014], NFATC1 [Zheng et al. 2024], TIGIT (T cell immunoreceptor with Ig and ITIM domains) [Luo et al. 2017], MMEL1 [Huang et al. 2017], \u0026nbsp;CD82 [Neumann et al. 2018] and PSMB9 [Li et al. 2022] play a significant role in the development of rheumatoid arthritis. NPR3 [Chen \u0026nbsp; et al. 2022], NPTX2 [Li et al. 2025], NRGN (neurogranin) [Kuşdoğan et al. 2023], MEF2C [Li et al. 2024], EPHA5 [Overman et al. 2012], GRM7 [Safari et al. 2020], HOMER1 [Tammasse et al. 2024], SYT3 [Lu et al. 2023], \u0026nbsp;PIK3R1 [Li et al. 2022], \u0026nbsp;HOMER2 [Zhu a et al. 2016], CDK5 [Tuo et al. 2018], BCL11A [Hassan \u0026nbsp;et al. 2019], \u0026nbsp;DLGAP4 [Bai et al. 2018], HDAC9 [Markus, 2023], TNF (tumor necrosis factor) [Xue et al. 2022], SERPINA3 [Hu et al. 2024], CXCL8 [He et al. 2018], CCL2 [Li et al. 2024], \u0026nbsp;CXCL2 [Chen et al. 2021], \u0026nbsp;PCSK9 [Moustafa et al. 2021], IL21R [Lee et al. 2016], VCAM1 [Maglinger et al. 2021], OSM (oncostatin M) [Christian et al. 2023], \u0026nbsp; CHI3L1 [Rathcke et al. 2012], C4B [Carr et al. 1993], SELL (selectin L) \u0026nbsp;[Wei et al. 2011], CES1 [Ol\u0026scaron;erov\u0026aacute; et al. 2024], CD44 [Shang et al. 2025], \u0026nbsp;APLNR (apelin receptor) [Wu et al. 2017], GFAP (glial fibrillary acidic protein) [Amalia, 2021], \u0026nbsp;SERPINA1 [Malik et al. 2017], GPR65 [Chen et al. 2024], S100A4 [Ji et al. 2025], SERPINE1 [Shilenok et al. 2023], TNFAIP3 [Miao \u0026nbsp;et al. 2018], \u0026nbsp;CD74 [Yang et al. 2017], TLR5 [Gu et al. 2016], CD14 [Olson et al. 2020], TNFRSF12A [Zhao et al. 2024], ATF3 [Huang et al. 2025], HMOX1 [Henrich et al. 2023], CASP1 [Wang et al. 2024], IL15 [Lee et al. 2019], ITGAL (integrin subunit alpha L) [Keum et al. 2013], MLKL (mixed lineage kinase domain like pseudokinase) [Tian et al. 2022], GPX3 [Akhter et al. 2014], CXCR4 [Werner et al. 2020], \u0026nbsp; IFITM3 [Harmon et al. 2022], TSPO (translocator protein) \u0026nbsp;[Tuwar et al. 2023], B2M [Zhen et al. 2023], \u0026nbsp;ANXA2 [Jiang et al. 2025], VIM (vimentin) [Xiao et al. 2021], MYD88 [Qin et al. 2025], SLC15A3 [Yu et al. 2024], APLN (apelin) \u0026nbsp; [Xu \u0026nbsp;et al. 2025], IGF2 [Fei et al. 2025], AQP4 [Chen et al. 2024], DKK1 [Zheng et al. 2023], P2RY1 [Janicki et al. 2017], IRF1 [Alexander et al. 2003], IL16 [Liu et al. 2013], FOXO1 [Guo et al. 2022], FGF1 [Dordoe et al. 2022], \u0026nbsp;IL10RA [Park et al. 2013], TLR4 [Oo, 2024], TIMP1 [Ge et al. 2020], MAP3K8 [Li et al. 2020], \u0026nbsp;S1PR2 [Kim et al. 2015], \u0026nbsp;EDNRB (endothelin receptor type B) [Zhang and Sui, 2014], AGT (angiotensinogen) [Wei et al. 2017], REST (RE1 silencing transcription factor) [Doeppner et al. 2017], FADS2 [Yang et al. 2015], \u0026nbsp;MAGT1 [Gotru et al. 2023], ACTA2 [Moosa et al. 2013] and PSMB8 [Zhang et al. 2025] \u0026nbsp; \u0026nbsp;were altered expressed and associated with stroke. Studies have shown that NPTX2 [Shao et al. 2020], VAMP2 [Costa et al. 2022], \u0026nbsp;OPTN (optineurin) [Nan et al. 2024], PPP3R1 [Peterson et al. 2014], PRMT8 [Zheng et al. 2024], DCTN1 [Hori et al. 2021], \u0026nbsp;TNF (tumor necrosis factor) [Zheng et al. 2022], CCL2 [Dhillon et al. 2008], \u0026nbsp;PCSK9 [Bell et al. 2022], GFAP (glial fibrillary acidic protein) [Cicognola et al. 2021], SERPINA1 [Halbgebauer et al. 2016], CD14 [Pase et al. 2020], IL15 [Rentzos et al. 2007], CXCR4 [Ma et al. 2024], TSPO (translocator protein) \u0026nbsp;[Conte et al. 2023], ANXA2 [Gauthier-Kemper et al. 2011], MYD88 [Chen et al. 2024], APLN (apelin) \u0026nbsp; [Cai \u0026nbsp; et al. 2025], AQP4 [Arighi et al. 2022], \u0026nbsp; CA3 [Cao et al. 2016], IL16 [Ma et al. 2022], FOXO1 [Jiang et al. 2019], RAB32 [Panzavolta et al. 2024], TLR4 [Kilic et al. 2018], TIMP1 [Tuna et al. 2018], IGFBP7 [Agbemenyah et al. 2014], AGT (angiotensinogen) [Kim et al. 2006], SLC40A1 \u0026nbsp;[Zou et al. 2025] and PSMB8 [Ding and Zhu, 2018] altered expression can lead to dementia. NPPC (natriuretic peptide C) [Chun et al. 2000], WIF1 [Salah et al. 2013], \u0026nbsp;MEF2C [Yang et al. 2021], CAMKK2 [Li et al. 2021], RGS6 [Mahata et al. 2021], RASGRF1 [Tsai et al. 2020], PRKCB (protein kinase C beta) \u0026nbsp;[Feng et al. 2020], NLK (nemo like kinase) [Szypowska et al. 2011], \u0026nbsp; SYT7 [Ma et al. 2024], \u0026nbsp;FBXW7 [Li et al. 2022], PRKCD (protein kinase C delta) [Greene a et al. 2014], \u0026nbsp;GIT1 [Song a et al. 2021], MEF2A [Zhang et al. 2014], FGF12 [Liu et al. 2022], CDK5 [Park et al. 2015], CCNC (cyclin C) [Stieg et al. 2019], ARRB1 [Tan et al. 2021], SUB1 [Yu et al. 2016], DES (desmin) [Moneo-Corcuera et al. 2023], \u0026nbsp; BCL11A [Li \u0026nbsp;et al. 2022], HDAC9 [Wan et al. 2021], TNF (tumor necrosis factor) [Sriramula and Francis, 2015], SERPINA3 [Ko et al. 2019], CXCL8 [Kumar et al. 2016], CCL2 [Namlı Kalem et al. 2017], SOCS3 [Chen et al. 2019], PCSK9 [Park et al. 2024], VCAM1 [Dong et al. 2011], WNT1 [Mill et al. 2014], \u0026nbsp; OSM (oncostatin M) [DeMarco et al. 2023], \u0026nbsp; RGS1 [Wu et al. 2017], PLA2G3 [Mart\u0026iacute;nez-Garc\u0026iacute;a et al. 2010], CD44 [Yusupov et al. 2021], LYZ (lysozyme) [Liu et al. 2006], \u0026nbsp;APLNR (apelin receptor) [Şişli et al. 2022], SFRP4 [Ren et al. 2024], GFAP (glial fibrillary acidic protein) [Passos et al. 2022], \u0026nbsp;S100A4 [Ji et al. 2025], RUNX3 [Kang et al. 2024], SERPINE1 [Yuan et al. 2023], CD74 [Chen et al. 2024], TLR5 [Wei et al. 2016], CD14 [Li et al. 2022], SPHK1 [Pchejetski et al. 2007], ATF3 [Feng et al. 2021], HMOX1 [Abe et al. 2023], NLRC5 [Chen et al. 2017], ITGB4 [Tan et al. 2024], CASP1 [Liu et al. 2021], IL15 [Chen et al. 2019], MFAP4 [Salama et al. 2018], \u0026nbsp;GPX3 [Zhang et al. 2024], CXCR4 [Yi et al. 2024], CDK2 [Zhou \u0026nbsp; et al. 2023], BAG3 [Zhang et al. 2022], B2M [Althubiti et al. 2021], TMBIM1 [Gong et al. 2016], ANXA2 [Arruda et al. 2024], VIM (vimentin) [H\u0026aring;versen et al. 2018], MYD88 [Chen et al. 2024], HCLS1 [Li et al. 2018], \u0026nbsp;SLC15A3 [Luo et al. 2024], APLN (apelin) \u0026nbsp; [Zhou et al. 2016], AMH (anti-Mullerian hormone) [Vale-Fernandes et al. 2024], IGF2 [Yang et al. 2014], SEMA3F [Li et al. 2019], AQP4 [Chen et al. 2024], DKK1 [Yang et al. 2024], CA3 [Yin et al. 2017], IRF1 [Deng et al. 2017], CSF1 [Werner a et al. 2019], HFE (homeostatic iron regulator) [Nandar et al. 2013], IFI16 [Gugliesi et al. 2005], \u0026nbsp;FOXO1 [Li and Gao, 2023], HSPB1 [Liu et al. 2021], FSTL1 [Zhang et al. 2025], NPC2 [Lee et al. 2023], PON3 [Aviram et al. 2004], FGF1 [Dhlamini et al. 2022], AGTRAP (angiotensin II receptor associated protein) [Wakui et al. 2013], \u0026nbsp;GPR35 [Li et al. 2025], TLR4 [Wang et al. 2024], TIMP1 [Oszajca et al. 2021], MAP3K8 [Zhai et al. 2023], HEY2 [Chen et al. 2019], \u0026nbsp; IGFBP7 [Aral et al. 2020], S1PR2 [Cao et al. 2019], AGT (angiotensinogen) [Kamiyama et al. 2013], \u0026nbsp; NUDT1 [Jin et al. 2025], ERBB2 [Sheu et al. 2019], NFATC1 [Zheng et al. 2024], FBLN5 [Ohara et al. 2011], STEAP3 [Wilkinson et al. 2019], MGP (matrix Gla protein) [Karsli et al. 2011], \u0026nbsp;EMP1 [Han et al. 2024], SLC1A5 [Shi et al. 2024], BGN (biglycan) [Szabados et al. 2024], MXRA8 [Miao et al. 2024], ELN (elastin) [Mart\u0026iacute;nez-Revelles et al. 2017], \u0026nbsp;FADS2 [Yi et al. 2024], \u0026nbsp;SLC40A1 \u0026nbsp; [Feng et al. 2024] \u0026nbsp;and IFI30 [Cacialli et al. 2021] \u0026nbsp;are essential for oxidative stress development. Altered expression of NRGN (neurogranin) [Byrne et al. 2018], GABBR2 [Philpott et al. 2016], PRKG2 [Yin et al. 2020], GRIN3A [Marco et al. 2013], NLK (nemo like kinase) [Jiang et al. 2020], OPTN (optineurin) [Schwab et al. 2012], \u0026nbsp;GIT1 [Goehler a et al. 2004], CDK5 [Hern\u0026aacute;ndez-Echeagaray et al. 2024], TRIM37 [Qin et al. 2024], \u0026nbsp;TNF (tumor necrosis factor) [Hsiao et al. 2014], WNT1 [Sileo et al. 2022], \u0026nbsp;GFAP (glial fibrillary acidic protein) [Heim et al. 2025], SPHK1 [Di Pardo et al. 2020], ATF3 [Liang et al. 2009], CASP1 [Wang et al. 2005], CXCR4 [Di Pardo et al. 2020], TSPO (translocator protein) \u0026nbsp;[Simmons et al. 2018], IGF2 [Yang et al. 2022], FOXO1 [Liu et al. 2022], TLR4 [Mart\u0026iacute;nez-Gopar et al. 2023], ERBB2 [Moreira Sousa et al. 2013] and REST (RE1 silencing transcription factor) [Orozco-D\u0026iacute;az et al. 2019] were associated with prognosis in Huntington\u0026apos;s disease. HTR2A [Maltby et al. 2020], DCLK1 [ŞİmŞek, 2020], NRXN1 [Kattimani et al. 2018], NECTIN1 [Castellanos \u0026nbsp;et al. 2013], DCTN1 [M\u0026uuml;nch et al. 2007], KCNH7 [Couturier et al. 2009], \u0026nbsp;TNF (tumor necrosis factor) [Ribeiro et al. 2019], SERPINA3 [Fissolo et al. 2021], CXCL8 [Ko\u0026scaron;ćak Lukač et al. 2024], HLA-DRB5 [Caillier et al. 2021], CCL2 [Jaime-P\u0026eacute;rez et al. 2020], GZMB (granzyme B) [Shi et al. 2023], \u0026nbsp;TBX21 [Akbarian et al. 2018], IL21R [Tzartos et al. 2011], OASL (2\u0026apos;-5\u0026apos;-oligoadenylate synthetase like) [Al-Nashmi et al. 2017], VCAM1 [Petersen et al. 2016], WNT1 [Lengfeld et al. 2017], \u0026nbsp;CHI3L1 [Jatczak-Pawlik et al. 2024], RGS1 [IMSGC. 2010], \u0026nbsp;SELL (selectin L) \u0026nbsp;[M\u0026ouml;ssner et al. 1996], CD44 [Tredicine et al. 2022], \u0026nbsp;NAT1 [Briggs et al. 2014], C4A [Ingram et al. 2010], \u0026nbsp;GFAP (glial fibrillary acidic protein) [Abdelhak et al. 2024], \u0026nbsp;HLA-DRA [Sahbaz et al. 2025], TNFAIP3 [Hoffjan \u0026nbsp;et al. 2015], \u0026nbsp;IL12A [IMSGC 2010], CD14 [Chuluundorj et al. 2014], GBP1 [Guo et al. 2024], HLA-DRB1 [Alcina et al. 2012], SPHK1 [Wang et al. 2024], HMOX1 [Ag\u0026uacute;ndez et al. 2016], \u0026nbsp; NLRC5 [Torkamandi et al. 2021], HLA-DPB1 [Anagnostouli et al. 2020], TAP1 [Bell and Ramachandran, 1995], CASP1 [Saito et al. 2021], IL15 [Laurent et al. 2020], \u0026nbsp;CD58 [Ahmed et al. 2024], CXCR4 [Galli et al. 2019], HLA-DMB [Bennetts et al. 1999], TSPO (translocator protein) \u0026nbsp;[Herranz et al. 2024], B2M [Alvarez-Cerme\u0026ntilde;o et al. 1987], PTPRC (protein tyrosine phosphatase receptor type C) [Szvetko al. 2025], ANXA2 [Tezuka et al. 2022], HLA-C [Morrison et al. 2010], MYD88 [Zheng et al. 2020], \u0026nbsp;IFIH1 [Enevold et al. 2009], AMH (anti-Mullerian hormone) [Giordano et al. 2024], CLIC1 [Karaaslan et al. 2023], \u0026nbsp;IGF2 [Chesik et al. 2007], SEMA3F [Williams et al. 2007], AQP4 [Gaudioso et al. 2023], IFIT3 [Sun et al. 2024], HLA-E [Nova et al. 2025], IRF1 [Annibali et al. 2018], IL16 [Kouchaki et al. 2022], IFI16 [Guo et al. 2024], FGF1 [Mohan et al. 2014], RAB32 [Haile et al. 2017], TLR4 [Oh et al. 2024], TIMP1 [Ghasemi Sakha et al. 2020], ACKR3 [Hasani Nourian \u0026nbsp;et al. 2021], S1PR2 [Seyedsadr et al. 2019], TAP2 [Moins-Teisserenc et al. 1995], \u0026nbsp;EDNRB (endothelin receptor type B) [Granstr\u0026ouml;m et al. 2014], AGT (angiotensinogen) [Hladikova et al. 2011], SIGLEC7 [Malhotra et al. 2013], MMEL1 [Yazdanpanah et al. 2022], \u0026nbsp;FADS2 [Rezapour-Firouzi et al. 2015], IFI30 [Stefanović et al. 2020], TUBA1C [Karimi et al. 2023] \u0026nbsp;and PSMB8 [Shaw and Williams, 2024] are closely involved with multiple sclerosis. CHRM3 [Selivanova et al. 2012], PRKCD (protein kinase C delta) [Li et al. 2022], TNF (tumor necrosis factor) [Yao et al. 2019], CXCL8 [Castellucci et al. 2015], SOCS3 [Tin\u0026egrave; et al. 2024], WNT1 [Wu et al. 2020], CHI3L1 [Yu et al. 2020], CD44 [Huang et al. 2025], LYZ (lysozyme) [Ohbayashi et al. 2016], AZGP1 [Shen et al. 2024], S100A4 [Qin et al. 2022], SERPINE1 [Xu et al. 2016], CD74 [Wang et al. 2024], TLR5 [Barana\u0026scaron;ić et al. 2022], IL12A [Wang et al. 2012], CD14 [Li et al. 2024], TNFRSF12A [Dong et al. 2020], HLA-DRB1 [D\u0026iacute;az-Pe\u0026ntilde;a et al. 2021], HMOX1 [Zhou et al. 2017], CASP1 [Chung et al. 2024], IL15 [Liu et al. 2015], MFAP4 [Johansson et al. 2014], GPX3 [Reddy et al. 2018], CXCR4 [Zhang et al. 2023], HLA-C [Mkorombindo et al. 2021], VIM (vimentin) [Nissen et al. 2019], MYD88 [Ju et al. 2022], IGF2 [de Carvalho et al. 2024], \u0026nbsp;AQP4 [Lin et al. 2023], DKK1 [Dai et al. 2023], IL16 [Karauda et al. 2024], FOXO1 [Shen et al. 2024], HSPB1 [Wang et al. 2020], FGF1 [Kranenburg et al. 2005], RAB32 [Wu et al. 2024], TLR4 [Lin et al. 2022], TIMP1 [He et al. 2022], \u0026nbsp;IGFBP7 [Ruan et al. 2017], AGT (angiotensinogen) [Marushchak et al. 2019], \u0026nbsp; FBLN5 [Brandsma et al. 2015], \u0026nbsp;ELN (elastin) [Benjamin et al. 2021] and SLC40A1 \u0026nbsp; [Ju et al. 2025] plays a role in diagnosis of chronic obstructive pulmonary disease. These GO terms and pathway enrichment results suggest that the DEGs detected in the present investigation might be involvement in MDD progression. These results might contribute to a better understanding of the molecular mechanism of MDD development and its associated complications.\u003c/p\u003e\n\u003cp\u003eA PPI network and modules were constructed to analyze further molecular processes underlying MDD progression and its associated complications. \u0026nbsp;Previous studies demonstrated that YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Capobianco et al. 2012], PIK3R1 [Pinhel et al. 2020], CDK2 [Col\u0026oacute;n-Mesa et al. 2021], VCAM1 [Yu et al. 2017] and VIM (vimentin) [Roh and Yoo, 2021] are involved in progression of obesity. Previous literature has confirmed that YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Cetica et al. 2024], DLG3 [He et al. 2024], SYNGAP1 [Wiltrout et al. 2024], GRIN2B [Wang et al. 2023], GRIN2A [Samanta, 2023], HLA-B [Chouchi et al. 2018], VIM (vimentin) [Sitovskaya et al. 2023] and TAP1 [Layouni et al. 2010] are involved in epilepsy. ONECUT1 [Russ-Silsby al. 2023], CALM2 [Cheng et al. 2014], \u0026nbsp;PIK3R1 [Li et al. 2022], GRIN2B [Kochetova et al. 2020], VCAM1 [Siddiqui et al. 2023], HLA-B [Jan et al. 2021], VIM (vimentin) [Roefs et al. 2017], ERBB2 [de Kay et al. 2022], TAP2 [Qu et al. 2007] and TAP1 [Li et al. 2014] are involved in the pathogenesis of diabetes mellitus. Previous studies have shown CALM2 [Hamrick et al. 2024], \u0026nbsp;PIK3R1 [He et al. 2024], CDK2 [Su et al. 2021], VCAM1 [Singh et al. 2023], HLA-B [Eyiol et al. 2018], \u0026nbsp;VIM (vimentin) [H\u0026aring;versen et al. 2018], ERBB2 [Jian et al. 2020], PSMB8 [Li et al. 2024], \u0026nbsp; PSMB9 [Liu and Delgado, 2024] and TAP1 [Kolbus et al. 2012] are a promising targeted therapy in cardiovascular diseases. ARRB1 [Chang et al. 2015], GRIN2B [Brown et al. 2023], DLG2 [Qin et al. 2020], GRIN2A [Hu et al. 2018], DLG4 [Kaut et al. 2017], VCAM1 [Zhou et al. 2024] and VIM (vimentin) [O\u0026apos;Leary et al. 2021] expression is altered in the neurons of patients with MDD. ARRB1 [Fang et al. 2021], \u0026nbsp;PIK3R1 [Fu et al. 2024], DLG2 [Keane et al. 2022], \u0026nbsp; VCAM1 [Burns et al. 2001], HLA-B [Lenna et al. 2015], VIM (vimentin) [Meng et al. 2023], ERBB2 [Zeng et al. 2012], PSMB8 [Li et al. 2024] and TAP1 [Vasků et al. 2000] have been proposed as biomarkers for inflammation progression. Regulation of ARRB1 [Sun et al. 2018], PIK3R1 [Zhang et al. 2010], VCAM1 [Qiu et al. 2022], HLA-B [Hu et al. 2024] and PSMB8 [Zhang et al. 2025] \u0026nbsp; levels might be a novel treatment option against hypertension. ARRB1 [Sengupta and Mukhopadhyay, 2025], PIK3R1 [Li et al. 2023], GRIN2B [Andreoli et al. 2014], \u0026nbsp;DLG2 [Prokopenko et al. 2022], DLG4 [Bustos et al. 2017], CDK2 [Baumann et al. 1993], VCAM1 [Salian et al. 2024], HLA-B [Roses, 2009], VIM (vimentin) [Zhang et al. 2025], ERBB2 [Wang et al. 2017] and TAP2 [Bullido et al. 2007] have been recognized as a biomarkers of predicting the Alzheimer disease. \u0026nbsp;ARRB1 [Fang et al. 2021], GRIN2B [Cui et al. 2024], DLG2 [Zhao et al. 2020], GRIN2A [Nepal et al. 2019], DLG4 [Lukashevich et al. 2024], VCAM1 [Zheng et al. 2022], ERBB2 [Jin et al. 2024], PSMB8 [Nguyen et al. 2023] and PSMB9 [Shani et al. 2023] have been investigated to participate in the pathogenesis of Parkinson\u0026apos;s Disease. ARRB1 [Tan et al. 2021], CDK2 [Zhou \u0026nbsp; et al. 2023], VCAM1 [Dong et al. 2011], VIM (vimentin) [H\u0026aring;versen et al. 2018] and ERBB2 [Sheu et al. 2019] were reported to play a role in the oxidative stress. PIK3R1 [Huang et al. 2020], GRIN2A [Itokawa et al. 2003] and VCAM1 [Pantovic-Stefanovic et al. 2024] were found to be altered expressed in bipolar disorder. \u0026nbsp;A study showed that PIK3R1 [Huang et al. 2020], GRIN2B [Poltavskaya et al. 2021], DLG2 [Sanders et al. 2022], GRIN2A [Sheng et al. 2024], DLG4 [Balan et al. 2013], VCAM1 [Meixensberger et al. 2021], HLA-B [Seshasubramanian et al. 2020] and TAP2 [Jun et al. 2004] are postulated to be a biomarkers of schizophrenia. Study also showed that serum PIK3R1 [Li et al. 2022], VCAM1 [Maglinger et al. 2021], VIM (vimentin) [Xiao et al. 2021], PSMB8 [Zhang et al. 2025] \u0026nbsp; and IRF1 [Alexander et al. 2003] \u0026nbsp;are associated with stroke. SYNGAP1 [Wiltrout et al. 2024], GRIN2B [Yal\u0026ccedil;intepe et al. 2021], \u0026nbsp; GRIN2A [Mangano et al. 2022], DLG4 [Feyder et al. 2010], VCAM1 [Kameno et al. 2013] and HLA-B [Guerini et al. 2024] are closely associated with the development of autism spectrum disorder. VCAM1 [Burns et al. 2001], HLA-B [Asakura et al. 2012], VIM (vimentin) [Mortensen et al. 2021] and TAP2 [Kim et al. 2024] might play a core role in Crohn\u0026apos;s disease. VCAM1 [Ando et al. 2001], ERBB2 [Sio et al. 2023] and TAP1 [Liu et al. 2017] \u0026nbsp;have been demonstrated to be \u0026nbsp;regulated in asthma. VCAM1 [Achudhan et al. 2022], VIM (vimentin) [Han et al. 2024], PSMB9 [Li et al. 2022], TAP2 [Dai et al. 2014] and TAP1 [Zhang et al. 2002] have been shown to influence the genetic risk of rheumatoid arthritis. VCAM1 [Petersen et al. 2016],\u0026nbsp; PSMB8 [Shaw and Williams, 2024], TAP2 [Moins-Teisserenc et al. 1995] and TAP1 [Bell and Ramachandran, 1995] might be involved in the genetic susceptibility to multiple sclerosis. Studies have identified VIM (vimentin) [Nissen et al. 2019] \u0026nbsp;as a genetic locus associated with chronic obstructive pulmonary disease \u0026nbsp; susceptibility. ERBB2 [Moreira Sousa et al. 2013] in genetic risk factor has been shown to increase susceptibility to Huntington\u0026apos;s disease. PSMB8 [Ding and Zhu, 2018] has been applied as a dementia \u0026nbsp; biomarker. Our data suggest that hub genes expression is significantly altered in MDD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMiRNAs are a type of small non-coding RNA \u0026nbsp;that regultes mRNA expression, and they are becoming key biomarkers in a range of cellular processes. TFs are a type of proteins that regultes mRNA expression, and they are becoming key biomarkers in a range of cellular processes. Moreover, several studies have shown that miRNAs [Chan et al. 2024] \u0026nbsp;and TFs [Kerman et al. 2012] can be involved in the disease progression of MDD. It is evident that miRNA-hub gene regulatory network and TF-hub gene regulatory network play a key part in the development of MDD. \u0026nbsp;The previous studies suggest that some of the biomarkers, such as YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Capobianco et al. 2012], PIK3R1 [Pinhel et al. 2020], NEDD4L [Lee et al. 2017], HDAC9 [Yang et al. 2025], CDK2 [Col\u0026oacute;n-Mesa et al. 2021], VIM (vimentin) [Roh and Yoo, 2021], TNF (tumor necrosis factor) [Olszanecka-Glinianowicz et al. 2004], hsa-miR-142-3p [Podraza et al. 2024], JUND [Costantino et al. 2019], EGR1 [Zhang et al. 2013], GATA2 [Menghini et al. 2005] and TEAD1 \u0026nbsp;[Lv et al. 2023] \u0026nbsp;might play a key role in obesity development. YWHAG (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma) [Cetica et al. 2024], NEDD4L [Vanli-Yavuz et al. 2015], \u0026nbsp;TNFAIP3 [Zhuo et al. 2022], VIM (vimentin) [Sitovskaya et al. 2023], TNF (tumor necrosis factor) [Fang et al. 2024], hsa-miR-34a-5p [\u0026Uuml;nalp et al. 2022] and EGR1 [Dong et al. 2023] were found to be associated with epilepsy. PIK3R1 [Li et al. 2022], CALM2 [Cheng et al. 2014], HDAC9 [Liu et al. 2024], PRKCZ (protein kinase C zeta) [Qin et al. 2008], TNFAIP3 [Cao et al. 2023], SERPINH1 [Li et al. 2022], VIM (vimentin) [Roefs et al. 2017], ERBB2 [de Kay et al. 2022], TNF (tumor necrosis factor) [Akash et al. 2018], IRF1 [Colli et al. 2018], hsa-miR-142-3p [Collares et al. 2013], PAX2 [Markowska et al. 2013], \u0026nbsp; EGR1 [Ke et al. 2023] and CREB1 [Shahin et al. 2024] have been identified as an diabetes mellitus risk factors. Some studies have pointed out that PIK3R1 [He et al. 2024], CALM2 [Hamrick et al. 2024, NEDD4L [Li et al. 2022], HDAC9 [Das and Natarajan, 2020], TNFAIP3 [Hua et al. 2009], SERPINH1 [Rusu-Nastase et al. 2022], CDK2 [Su et al. 2021], VIM (vimentin) [H\u0026aring;versen et al. 2018], ERBB2 [Jian et al. 2020], TNF (tumor necrosis factor) [Yuan et al. 2020], hsa-miR-142-3p [Scărlătescu et al. 2022], hsa-miR-34a-5p [Yalım et al. 2023], JUND [Akhmedov et al. 2020], EGR1 [Khachigian, 2024], CREB1 [Zhang and Ge, 2022], TFAP2C [Zeng et al. 2024] and GATA2 [Menghini et al. 2005] are the biomarkers with the most associations in cardiovascular diseases. \u0026nbsp;NEDD4L [Xu et al. 2020], HDAC9 [Dai et al. 2023], \u0026nbsp;TNFAIP3 [Chen et al. 2017], \u0026nbsp; VIM (vimentin) [O\u0026apos;Leary et al. 2021], TNF (tumor necrosis factor) [Yao \u0026nbsp;et al. 2020], IRF1 [Bialek et al. 2020], \u0026nbsp;EGR1 [Wu et al. 2021], CREB1 [Wang et al. 2015] \u0026nbsp; and GATA2 [Choi et al. 2014] are involved in MDD. NEDD4L [Liang et al. 2024], TNFAIP3 [Zou et al. 2020], VIM (vimentin) [Mortensen et al. 2021], TNF (tumor necrosis factor) [Peyrin-Biroulet \u0026nbsp;et al. 2008], IRF1 [Xu et al. 2025], hsa-mir-376c-3p \u0026nbsp; [Jerala et al. 2024], EGR1 [Yu et al. 2012], CREB1 [Diegelmann et al. 2013] and GATA2 [Li et al. 2022] might be involved in the development of Crohn\u0026apos;s disease. NEDD4L [Li et al. 2022], HDAC9 [Das and Natarajan, 2020], \u0026nbsp;TNFAIP3 [Momtazi et al. 2019], VIM (vimentin) [Meng et al. 2023], ERBB2 [Zeng et al. 2012], TNF (tumor necrosis factor) [van Loo and Bertrand, 2003], \u0026nbsp;IRF1 [Chen et al. 2023], JUND [Yang et al. 2024], EGR1 [Lehman et al. 2022], CREB1 [Chen et al. 2023], GATA2 [Menghini et al. 2005] and TEAD1 \u0026nbsp;[Tran et al. 2025] are well-known for its regulatory function in inflammation. \u0026nbsp; NEDD4L [Liu et al. 2022], HDAC9 [Zhang et al. 2024], \u0026nbsp;CDK2 [Baumann et al. 1993], VIM (vimentin) [Zhang et al. 2025], ERBB2 [Wang et al. 2017], TNF (tumor necrosis factor) [Zhou et al. 2020], \u0026nbsp;hsa-miR-142-3p [Kumar et al. 2013], hsa-miR-34a-5p [Gasc\u0026oacute;n et al. 2024], EGR1 [He et al. 2022], CREB1 [Li et al. 2012] and HINFP (histone H4) [Gupta and Kumar, 2021] have been identified as a biomarkers that participates in Alzheimer disease. NEDD4L [Niu et al. 2022], TNFAIP3 [Xue et al. 2011], TNF (tumor necrosis factor) [Mehaffey and Majid, 2017], EGR1 [Laggner et al. 2022] and GATA2 [Zhang et al. 2022] \u0026nbsp;are potentially involved in the pathogenesis of hypertension. Study showed that NEDD4L [Su et al. 2024], HDAC9 [Markus, 2023], TNFAIP3 [Miao \u0026nbsp;et al. 2018], VIM (vimentin) [Xiao et al. 2021], TNF (tumor necrosis factor) [Xue et al. 2022], EGR1 [Yang et al. 2015] and GATA2 [Bierman-Chow et al. 2023] were associated with stroke. HDAC9 [O\u0026apos;Connell et al. 2019], \u0026nbsp;TNF (tumor necrosis factor) [He et al. 2022], hsa-miR-34a-5p [Tonk et al. 2024], hsa-mir-708-3p [Li et al. 2024], EGR1 [Hu et al. 2019], CREB1 [Pan et al. 2023] and TEAD1 \u0026nbsp; [Sun et al. 2023] \u0026nbsp;plays an important role in schizophrenia. Number of studies have found that HDAC9 [Wan et al. 2021], CDK2 [Zhou \u0026nbsp;et al. 2023], VIM (vimentin) [H\u0026aring;versen et al. 2018], ERBB2 [Sheu et al. 2019], \u0026nbsp;TNF (tumor necrosis factor) [Sriramula and Francis, 2015], IRF1 [Deng et al. 2017], JUND [Paneni et al. 2013], EGR1 [Guo et al. 2023], CREB1 [Pan et al. 2024] and GATA2 \u0026nbsp; [Huang et al. 2023] \u0026nbsp;are closely associated with oxidative stress. Recent studies have shown that PRKCZ (protein kinase C zeta) [Saxena et al. 2008], TNF (tumor necrosis factor) [Doğanavşargil Baysal et al. 2019] and CREB1 [Wang et al. 2021] are closely related to bipolar disorder. \u0026nbsp;Altered expression of TNFAIP3 [Krusche et al. 2019], \u0026nbsp;ERBB2 [Sio et al. 2023], TNF (tumor necrosis factor) [Berry et al. 2006] and IRF1 [Hu et al. 2021] were observed in asthma. TNFAIP3 [Ma \u0026nbsp; et al. 2023], ERBB2 [Jin et al. 2024], TNF (tumor necrosis factor) [Amin et al. 2022], JUND [Berton et al. 2009], EGR1 [Guo et al. 2023], CREB1 [Liu and Li, 2023], \u0026nbsp;HINFP (histone H4) [Gupta and Kumar, 2021] and GATA2 [Kurzawski et al. 2010] have been reported as a critical regulators in the Parkinson\u0026apos;s Disease. Researchres have reported that TNFAIP3 [Tang \u0026nbsp;et al. 2023], VIM (vimentin) [Han et al. 2024], TNF (tumor necrosis factor) [Lillegraven et al. 2023], IRF1 [Yoshida et al. 2012], hsa-mir-708-3p [He et al. 2023], \u0026nbsp;JUND [Ye et al. 2023] and EGR1 [Aicher et al. 1999] are associated with rheumatoid arthritis. \u0026nbsp; Altered expression of TNFAIP3 [Hoffjan \u0026nbsp; et al. 2015], TNF (tumor necrosis factor) [Ribeiro et al. 2019], IRF1 [Annibali et al. 2018] and hsa-miR-34a-5p [Agostini et al. 2024] might predict \u0026nbsp;prognosis of multiple sclerosis. VIM (vimentin) [Nissen et al. 2019], \u0026nbsp;TNF (tumor necrosis factor) [Yao et al. 2019], hsa-miR-34a-5p [Mirra et al. 2023] and EGR1 [Chen et al. 2015] are a key biomarkers in the pathogenesis of chronic obstructive pulmonary disease. \u0026nbsp;ERBB2 [Moreira Sousa et al. 2013], \u0026nbsp;TNF (tumor necrosis factor) [Hsiao et al. 2014], hsa-miR-34a-5p [Hart et al. 2023] and CREB1 [McCourt et al. 2015] \u0026nbsp;were found to be associated with an increased risk for Huntington\u0026apos;s disease. TNF (tumor necrosis factor) [Yamauchi et al. 2021] was significantly regulated in the autism spectrum disorder. TNF (tumor necrosis factor) [Zheng et al. 2022] was found to be regulated in dementia. This study might advance the understanding of the molecular mechanism of MDD and suggest that YWHAH, MAP1LC3A, ENFKB2, hsa-miR-548j-5p, hsa-mir-466, hsa-miR-1911-5p, hsa-miR-523-5p, hsa-mir-6829-3p, hsa-miR-6891-3p, \u0026nbsp;FEV (Frog erythrocytic virus) and PPARG (peroxisome proliferator activated receptor gamma) might be novel biomarkers and therapeutic targets for MDD patients. Our study provides perspective in which hub genes, miRNAs and TFs might be a mediators in the MDD pathological process and its associated complications.\u003c/p\u003e\n\u003cp\u003eTaken together, our integrated bioinformatic analysis of NGS data identified biomarkers (YWHAG, ONECUT1, CALM2, ARRB1, PIK3R1, CDK2, VCAM1, HLA-B, VIM, ERBB2, hsa-miR-548j-5p, hsa-mir-466, hsa-miR-523-5p, hsa-mir-6829-3p, PAX2, JUND, FEV and PPARG) that have both great diagnostic and prognostic value for MDD and it provided clues for the underlying \u0026nbsp; molecular mechanism and therapeutic targets for MDD. Further investication is needed to explore their biological function in MDD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI thanks very much to Mitsuhashi H, Nagy C, Turecki G, McGill University, Psychiatry, McGill Group for Suicide Studies, Montreal, Quebec, Canada, the authors who deposited their NGS dataset GSE275676, into the public GEO database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo informed consent because this study does not contain human or animals participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE275676) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE275676]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB. V. \u0026nbsp; \u0026nbsp;- Writing original draft, and review and editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC. V. \u0026nbsp; - Software and investigation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBasavaraj Vastrad \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChanabasayya \u0026nbsp;Vastrad \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbott GW. KCNE4 and KCNE5: K(+) channel regulation and cardiac arrhythmogenesis. Gene. 2016;593(2):249-260. doi:10.1016/j.gene.2016.07.069\u003c/li\u003e\n\u003cli\u003eAbd Alla J, Jamous YF, Quitterer U. Stearoyl-CoA Desaturase (SCD) Induces Cardiac Dysfunction with Cardiac Lipid Overload and Angiotensin II AT1 Receptor Protein Up-Regulation. 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Diabetes Res Clin Pract. 2015;107(2):259-266. doi:10.1016/j.diabres.2014.11.008 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Major depressive disorder, bioinformatics analysis, differential expression genes, biomarker, therapeutic","lastPublishedDoi":"10.21203/rs.3.rs-6799275/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6799275/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Major depressive disorder (MDD) is a neuropsychiatric disorder with a high risk of suicide. This study aimed to examine the key genes, microRNAs and TFs associated with MDD. Next generation sequancing dataset (GSE275676) was downloaded from the Gene expression omnibus (GEO) database. The DEGs were screened using DESeq2 in R bioconductor tool. We next performed gene ontology (GO) analysis and REACTOME pathway enrichment analysis using the g:Profiler. Moreover, Cytoscape with IMex interactome was utilized to visualize protein-protein interaction (PPI) network and modules of these DEGs. Subsequantely, miRNA-hub gene regulatory network and TF-hub gene regulatory network were built by Cytoscape to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. Finally, top 10 hub genes in the PPI network were validated by receiver operating characteristic (ROC) curve analysis. We screened 958 DEGs, containing 479 up regulated genes and 479 down regulated genes. GO and REACTOME pathway enrichment analyses revealed that DEGs are mainly enriched in cell communication, synapse, enzyme binding, response to stimulus, cell periphery, signaling receptor binding, neuronal system and immune system. YWHAG, ONECUT1, CALM2, ARRB1, PIK3R1, CDK2, VCAM1, HLA-B, VIM and ERBB2 are the topb hub genes in PPI network and modules. The predicted miRNA-hub gene regulatory network identified miRNAs (hsa-miR-548j-5p, hsa-mir-466, hsa-miR-523-5p and hsa-mir-6829-3p) targeting hub genes and predicted TF-hub gene regulatory network identified TFs (PAX2, JUND, FEV and PPARG). In conclusion, this bioinformatics analysis of NGS data demonstrated that hub genes, miRNAs and TFs might regulate the development of MDD. These hub genes, miRNAs and TFs could be used as new biomarkers for diagnosis and to guide the combination medicine of MDD.","manuscriptTitle":"Identifying differentially expressed genes, miRNAs and TFs in major depressive disorder by bioinformatics analysis of next generation sequencing data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 08:32:32","doi":"10.21203/rs.3.rs-6799275/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a01d12c8-5612-4567-869c-61a82031e855","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-04T08:32:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-04 08:32:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6799275","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6799275","identity":"rs-6799275","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}