SerpinA3 antibody
- Known as:
- SerpinA3 (anti-)
- Catalog number:
- orb43054
- Product Quantity:
- EUR
- Category:
- -
- Supplier:
- Biorbyt biorb
- Gene target:
- SerpinA3 antibody
Related genes to: SerpinA3 antibody
- Gene:
- SERPINA3 NIH gene
- Name:
- serpin family A member 3
- Previous symbol:
- AACT
- Synonyms:
- ACT
- Chromosome:
- 14q32.13
- Locus Type:
- gene with protein product
- Date approved:
- 2001-06-22
- Date modifiied:
- 2016-10-04
Related products to: SerpinA3 antibody
Related articles to: SerpinA3 antibody
- Tamoxifen (TAM) resistance remains a challenge in estrogen receptor-positive breast cancer treatment. Current research suggested that mesenchymal stem cells (MSCs) derived exosomes may mediate chemoresistance, but the underlying mechanisms are unclear. This study investigates how exosomes from tamoxifen-pretreated MSCs (Tt-MSC-exos) promote tamoxifen resistance through the miR-137/SERPINA3 axis. - Source: PubMed
Publication date: 2026/05/21
Huang XiaolanLi TingtingYe ShiqianLan YananZeng LinLiu Yan - Major depressive disorder (MDD) in adolescents lacks objective peripheral biomarkers that may assist diagnosis and biological stratification. Serine protease inhibitor A3 (SERPINA3) is an inflammation-related acute-phase protein, but its circulating levels and clinical relevance in adolescent MDD remain unclear. - Source: PubMed
Publication date: 2026/05/20
Xue YongFan Gui MeiWang YueDong HuiWei Chun HongZhang Bo - Lung cancer remains the leading cause of cancer-related mortality, with poor outcomes driven by late presentation and therapy resistance. Although genes encoding secreted proteins may reflect tumor biology and have biomarker potential, systematic multi-cohort studies identifying and validating prognostically relevant secreted-protein candidates in non-small cell lung cancer (NSCLC) are limited. - Source: PubMed
Publication date: 2026/05/18
Kim JoonLee GeuninYong Seung-HyunKim Eun YoungJo YunjuJeong WoojuRyu DongryeolOh Chang-MyungLee Sang Hoon - Left bundle branch pacing (LBBP) has gained increasing attention as a novel pacing strategy, but its molecular underpinnings in the context of heart failure (HF) remain unclear due to limited LBBP-specific datasets.We integrated three GEO datasets (GSE5406, GSE19303, GSE21610) representing HF transcriptomics and performed batch correction, differential expression analysis, functional enrichment, immune infiltration profiling, weighted gene co-expression network analysis (WGCNA), hub gene identification, and drug-pathway prediction.PCA demonstrated successful batch correction across datasets. Differentially expressed genes (DEGs), including HOPX, NPPA, MYH6, SERPINA3, and ASPN, were identified. Enrichment analyses indicated extracellular matrix remodeling, cardiac development, and cGMP-PKG signaling. Immune analysis showed significant alterations in B cell memory, plasma cells, CD8 T cells, regulatory T cells, NK cells, monocytes, macrophages (M0/M1/M2), dendritic cells, and mast cells. WGCNA highlighted significant modules (MEpink, MElightyellow, MEyellow, MEgreenyellow) associated with treatment response. Hub gene analysis confirmed ASPN, HOPX, MYH6, SERPINA3, and NPPA as key drivers. Drug prediction suggested multiple candidates, including β-blockers, RAAS inhibitors, anti-fibrotic agents, vericiguat, metformin, and SGLT2 inhibitors.This integrative analysis of HF transcriptomics reveals potential immune remodeling, hub genes, and repurposable drugs relevant to LBBP response heterogeneity, providing hypothesis-generating insights and potential therapeutic strategies for validation in LBBP-specific cohort. - Source: PubMed
Publication date: 2026/05/11
Sun XiaTang XiangZhong WeiYuan WeiJin Mingfeng - Pancreatic ductal adenocarcinoma (PDAC) is frequently preceded by new-onset diabetes mellitus (NODM), yet differentiating PDAC-associated DM from type 2 diabetes (T2D) remains clinically challenging. We investigated whether plasma proteomic profiling combined with machine learning could discriminate these conditions. Plasma samples from individuals with PDAC (with and without DM), long-standing T2D, and controls were analyzed by MALDI-TOF mass spectrometry. Spectral features were processed through a nested cross-validation framework to prevent data leakage, and model interpretability was explored using SHAP values. In parallel, low-molecular-weight proteins were characterized by GeLC-MS followed by LC-MS/MS and differential abundance analysis. Machine learning models distinguished PDAC-associated DM from T2D with a balanced accuracy of 85%. Proteomic analyses identified distinct signatures in PDAC- associated DM, including downregulation of erythrocyte-related proteins and PPBP, and upregulation of acute-phase reactants such as FGA, CP, and SERPINA3. Treatment-naïve cases displayed increased circulating epithelial and keratin-associated proteins, which were attenuated after therapy, suggesting dynamic tumor-related remodeling. These findings demonstrate that integrating MALDI-TOF profiling with machine learning can capture plasma signatures associated with PDAC-associated DM. Although exploratory, this approach supports further validation in prospective cohorts aimed at improving PDAC risk stratification among individuals with NODM. SIGNIFICANCE: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a dismal 5-year survival rate, primarily due to late-stage diagnosis. The frequent occurrence of new-onset diabetes mellitus (NODM) as a paraneoplastic syndrome offers a critical window for early detection. However, the clinical challenge of distinguishing PDAC-associated diabetes (PDAC-DM) from type 2 diabetes mellitus (T2D) has hindered the implementation of effective screening strategies. This study addresses this significant clinical problem by leveraging a multi-faceted proteomics approach. We demonstrate that the integration of MALDI-TOF mass spectrometry peptide profiling with machine learning algorithms can accurately discriminate PDAC-DM from T2D with 85% accuracy. Furthermore, we used LC-MS/MS to identify specific low molecular weight proteins that are differentially regulated between these conditions, providing a molecular basis for the observed discrimination. Our work is significant as it presents a novel, high-throughput pipeline for biomarker discovery that combines the scalability of MALDI-TOF with the analytical power of LC-MS/MS and machine learning. The identified plasma signatures hold strong translational potential to improve risk stratification in patients with NODM, ultimately enabling earlier diagnosis of PDAC and improving patient survival prospects. This research directly contributes to the field of clinical proteomics by providing a robust methodological framework and candidate biomarkers for the early detection of one of oncology's most challenging diseases. - Source: PubMed
Publication date: 2026/04/30
Lazari Lucas CardosoDonnarumma Carlos Del CistiaMatheus Luiz Henrique GomesD'Alpino Peixoto Renatade Matos Mozânia ReisValerio Hellen PaulaRosa-Fernandes LiviaOba-Shinjo Sueli MMachado Marcel Cerqueira CésarMachado Marcel Autran CesarMarie Suely K NCorrea-Giannella Maria LuciaPalmisano Giuseppe