WFDC5 antibody
- Known as:
- WFDC5 (anti-)
- Catalog number:
- orb101266
- Product Quantity:
- EUR
- Category:
- -
- Supplier:
- Biorbyt biorb
- Gene target:
- WFDC5 antibody
Related genes to: WFDC5 antibody
- Gene:
- WFDC5 NIH gene
- Name:
- WAP four-disulfide core domain 5
- Previous symbol:
- -
- Synonyms:
- WAP1, dJ211D12.5
- Chromosome:
- 20q13.12
- Locus Type:
- gene with protein product
- Date approved:
- 2003-02-21
- Date modifiied:
- 2016-10-05
Related products to: WFDC5 antibody
Related articles to: WFDC5 antibody
- Human epididymis protein 4 (HE4), a member of the WFDC protein family, has been shown to exhibit abnormal expression in patients with chronic obstructive pulmonary disease (COPD). However, the causal relationship between HE4 and COPD, as well as the involvement of other members of the WFDC family in COPD pathogenesis, remains unclear. This study aims to investigate the potential of WFDC proteins, particularly HE4, as biological markers for COPD through mendelian randomization (MR) and transcriptomic analysis. - Source: PubMed
Li Mei-XueZeng Zhang-FangCao Zhao-Wen - Androgenetic alopecia (AGA) is a chronic form of hair loss influenced by various factors, with increasing focus on the role of immune cell-driven follicular microinflammation. However, the precise immune phenotypes involved and their causal relationship with AGA remain poorly understood. This study aims to identify and validate the causal role of immunophenotypes in AGA using Mendelian randomization (MR) integrated with multi-omics data, and to explore the mediating role of plasma proteins in this relationship. - Source: PubMed
Publication date: 2025/11/05
Du YimeiDu YongkunYu YutaoHuang YuanboFan WeixinWang Lei - Tobacco smoke is a major risk factor for esophageal squamous cell carcinoma (ESCC), yet only a subset of smokers develop this disease, implicating gene-smoking interactions in modulating individual susceptibility. Through integrative transcriptomic analyses of normal and tumor samples from smokers and nonsmokers, we identify four smoke-responsive genes (CXCL14, HORMAD1, WFDC5, and MPZ) as potential contributors to ESCC carcinogenesis. Among these, HORMAD1 is markedly upregulated in ESCC cells upon exposure to cigarette smoke condensate (10 µg/mL), benzo[a]pyrene (3 µM), or 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (10 µM), correlating with activation of error-prone nonhomologous end joining (NHEJ) in response to DNA damage. Notably, smokers with higher HORMAD1 expression levels exhibit enhanced NHEJ but impaired homologous recombination (HR), leading to increased genomic instability. Through a two-stage case-control study involving 5151 ESCC cases and 5963 controls, we identify two regulatory variants of HORMAD1, rs11204679 and rs33924488, significantly associated with ESCC risk through a gene-smoking interaction (p = 0.0027). Both variants confer a protective effect among smokers (OR = 0.80, 95% CI: 0.74-0.87, p = 9.58 × 10 ) but not in nonsmokers (OR = 0.98, 95% CI: 0.90-1.06, p = 0.5950). Mechanistically, the rs11204679 G > C and rs33924488 GA > G- variants attenuate HOXA6 and SOX15 binding at a distal enhancer, respectively, suppressing HORMAD1 expression via long-range chromatin interactions. These findings establish HORMAD1 as a critical mediator of tobacco-related DNA repair dysregulation and a potential biomarker for ESCC risk stratification and precision prevention. - Source: PubMed
Publication date: 2025/08/28
Yue XinyingYang ZifeiMa JialingSu QianqianPan MiaoxinSong LinaLi YuepingLiu ShashaWu YutongChang Jiang - Transcriptomic analyses have revealed hundreds of p53-regulated genes; however, these studies used a limited number of cell lines and p53-activating agents. Therefore, we searched for candidate p53-target genes by employing stress factors and cell lines never before used in a high-throughput search for p53-regulated genes. We performed RNA-Seq on A549 cells exposed to camptothecin, actinomycin D, nutlin-3a, as well as a combination of actinomycin D and nutlin-3a (A + N). The latter two substances synergise upon the activation of selected p53-target genes. A similar analysis was performed on other cell lines (U-2 OS, NCI-H460, A375) exposed to A + N. To identify proteins in cell lysates or those secreted into a medium of A549 cells in control conditions or treated with A + N, we employed mass spectrometry. The expression of selected genes strongly upregulated by A + N or camptothecin was examined by RT-PCR in p53-deficient cells and their controls. We found that p53 participates in the upregulation of: ACP5, APOL3, CDH3, CIBAR2, CRABP2, CTHRC1, CTSH, FAM13C, FBXO2, FRMD8, FRZB, GAST, ICOSLG, KANK3, KCNK6, KLRG2, MAFB, MR1, NDRG4, PTAFR, RETSAT, TMEM52, TNFRSF14, TRANK1, TYSND1, WFDC2, WFDC5, WNT4 genes. Twelve of these proteins were detected in the secretome and/or proteome of treated cells. Our data generated new hypotheses concerning the functioning of p53. Many genes activated by A + N or camptothecin are also activated by interferons, indicating a noticeable overlap between transcriptional programs of p53 and these antiviral cytokines. Moreover, several identified genes code for antagonists of WNT/β-catenin signalling pathways, which suggests new connections between these two cancer-related signalling systems. One of these antagonists is DRAXIN. Previously, we found that its gene is activated by p53. In this study, using mass spectrometry and Western blotting, we detected expression of DRAXIN in a medium of A549 cells exposed to A + N. Thus, this protein functions not only in the development of the nervous system, but it may also have a new cancer-related function. - Source: PubMed
Publication date: 2024/03/07
Łasut-Szyszka BarbaraGdowicz-Kłosok AgnieszkaMałachowska BeataKrześniak MałgorzataBędzińska AgnieszkaGawin MartaPietrowska MonikaRusin Marek - Melanoma is considered to be the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in its prognosis. Herein, we developed a transfer learning-based biomarker discovery model that could aid in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results revealed that the genes found were consistent with those found using other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set. Further novel biomarkers were also found. Our ensemble model achieved an AUC of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB) in melanoma. The results show the utility of a transfer learning approach for biomarker discovery in melanoma. - Source: PubMed
Publication date: 2022/12/07
Miñoza Jose Marie AntonioRico Jonathan AdamZamora Pia Regina FatimaBacolod MannyLaubenbacher ReinhardDumancas Gerard Gde Castro Romulo