Ask about this productRelated genes to: SERPINA4 antibody
- Gene:
- SERPINA4 NIH gene
- Name:
- serpin family A member 4
- Previous symbol:
- PI4
- Synonyms:
- KST, KAL, KLST, kallistatin
- Chromosome:
- 14q32.13
- Locus Type:
- gene with protein product
- Date approved:
- 1994-05-11
- Date modifiied:
- 2016-04-12
Related products to: SERPINA4 antibody
Related articles to: SERPINA4 antibody
- The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. This study aims to identify protein biomarkers that differentiate sepsis from non-infectious systemic inflammatory response syndrome (NISIRS), addressing the need for early sepsis diagnosis. - Source: PubMed
Publication date: 2026/04/24
Ruiz-Sanmartín AdolfoRibas VicentSuñol DavidChiscano-Camón LuisMartín LauraBajaña IvánBastida JulianaLarrosa NievesGonzález Juan JoséCarrasco María DoloresCanela NúriaFerrer RicardRuiz-Rodríguez Juan Carlos - Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Serine protease inhibitors (SERPINs) have emerged as potential regulators in tumor progression, yet the specific function and regulatory mechanisms of SERPINA4 in CRC remain poorly characterized. - Source: PubMed
Publication date: 2026/02/28
Zhang XiaoyuLiu YutongTu QianyiZhang DandanDeng JiaqiZhou JingjiaoLv HuiZhou JunQiu FengwuDeng Yang - Hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM) are characterized by left ventricular hypertrophy and diastolic dysfunction. Despite overlapping remodeling features, their distinct mechanisms and therapeutic responses remain unclear. This study integrated genetic, imaging, and proteomic data to identify key mediators underlying β1-adrenergic receptor blockers (β1-blockers)-related therapeutic heterogeneity between HHD and HCM. - Source: PubMed
Publication date: 2026/02/09
Chen ZheTang YifanLi ShuangPan LiangbinWu JiaxuanLiu JiangjiangMa HaitaoWang BinXie Kai - Pancreatic ductal adenocarcinoma (PDA) is an aggressive malignancy that lacks reliable biomarkers to guide treatment decisions. Effective prognostic tools are needed to improve its clinical management. We conducted a comprehensive proteomic analysis on 115 PDA patient samples with matched adjacent normal tissue. A 20-protein diagnostic panel was identified (LGALS1, ANXA2, LGALS3BP, CTSD, S100P, COL12A1, SFN, THBS2, CTHRC1, THBS1, SERPINB5, LAMC2, POSTN, CEACAM6, CTSE, PLEC, PKM, S100A11, TAGLN2, ALDOA). Consensus clustering analysis identified four prognostic proteomic subtypes. Subtypes with poorer prognoses exhibited upregulation of neutrophil degranulation, extracellular matrix remodeling, focal adhesion, Mesenchymal Epithelial Transition, collagen formation, and PI3K-Akt-mTOR-related pathways, indicating a predominance of basal-like and activated stromal features. In tumors with homologous recombination deficiency or Catalogue of Somatic Mutations in Cancer Signature-3, several immune-related proteins were enriched. An 18-protein (PURB, SDCBP2, CD2BP2, GALM, SERPINA3, OAS3, FAN1, ZPR1, KRT2, NUDT2, SMNDC1, SERPINA4, CUTA, WDR36, POSTN, CLEC11A, PEX14, and PI4KA) risk score was developed and validated using multicox regression analyses with LASSO regularization. The risk score demonstrated independent prognostic significance for overall survival and recurrence, and was validated in an independent proteomic dataset generated using a different proteomic technology. This study thus introduces four novel prognostic PDA subtypes, and an 18-protein risk score validated in an independent dataset, which shows promise for improving survival prediction and could serve as a valuable tool for personalized treatment guidance. - Source: PubMed
Aref Adel TGrealey JasonPathan MohashinNoor ZainabAnees AsimAzad A K MSmith Daniela LeeHumphries Erin MBucio-Noble DanielKoh Jennifer M SSykes Erin KWilliams Steven GLyons Ruth JLucas NatashaXavier DylanSahni SumitMittal AnubhavSamra Jaswinder SPearson John VWaddell NicolaKondrashova OlgaChou AngelaSioson LorettaSheen Amy Hains Peter GRobinson Phillip JZhong QingReddel Roger RGill Anthony J - Cardiomyopathy often results in heart failure and mortality, significantly impairing patients' quality of life. Advancements in genomics and proteomics now enable the identification of proteins associated with cardiomyopathy, offering valuable insights for its diagnosis and treatment. However, numerous potential pathogenic proteins remain unidentified, underscoring the need for further exploration of novel drug targets for cardiomyopathy. This study aims to employ Mendelian randomization (MR) to explore genetic associations between plasma proteins and cardiomyopathies, with the objective of identifying potential drug targets. Two-sample MR was employed to investigate causal relationships between cardiomyopathies and plasma proteins, using summary data from genome-wide association studies of different cardiomyopathy subtypes, such as dilated cardiomyopathy, hypertrophic cardiomyopathy (HCM), and restrictive cardiomyopathy (RCM). Cis-protein quantitative trait loci retrieved from the deCODE database served as genetic instruments. Steiger filtering was applied to assess and validate reverse causality. Enrichment analysis was conducted to elucidate potential biological effects, while protein-protein interaction networks were examined to explore interactions among proteins. Molecular docking was employed to evaluate the binding affinity between drugs and their targets. The MR analysis identified 70 significant proteins linked to cardiomyopathy, 12 to dilated cardiomyopathy, 60 to HCM, and 103 to RCM. Intersection analysis revealed 24 significant proteins. Following multiple hypothesis testing, 2 significant proteins (CCL17, SERPINA4) were identified for HCM, and 16 significant proteins (APOL3, C1QL1, CNDP1, CRLF1, CSF2RB, CTSH, GABARAPL2, GP1BA, ICAM5, NPPB, NTM, PDCD5, PTPRS, RNASET2, RTN4R, TCN2) were identified for RCM. Reverse causality testing provided no evidence of reverse causality for any positive genes. Enrichment analysis of protein-protein interaction networks indicated a potential biological role for the positive proteins. Moreover, potential drug targets for treating cardiomyopathy were identified. The genetic associations between plasma proteins and cardiomyopathy were analyzed, leading to the identification of specific proteins as potential biomarkers. Additionally, novel drug targets were identified, providing valuable insights for the diagnosis and treatment of cardiomyopathy. - Source: PubMed
Ji YandiDai GuohuaFan MaoxiaChen ChenLiu RuixiaDong XueyanGao Wulin