Ask about this productRelated genes to: SERPINB13 antibody
- Gene:
- SERPINB13 NIH gene
- Name:
- serpin family B member 13
- Previous symbol:
- PI13
- Synonyms:
- HUR7, hurpin, headpin
- Chromosome:
- 18q21.33
- Locus Type:
- gene with protein product
- Date approved:
- 1995-12-20
- Date modifiied:
- 2016-04-06
Related products to: SERPINB13 antibody
Related articles to: SERPINB13 antibody
- Psoriasis is a multifaceted skin and systemic disorder driven by a complex interplay of genetic, immunological, and environmental factors. Genetic predisposition plays a pivotal role, with the IL-17/IL-23 immune axis recognized as a central pathogenic pathway. Ongoing research, however, continues to uncover additional critical drivers, cytokines, intracellular signaling networks, and potential therapeutic targets. - Source: PubMed
Publication date: 2026/07/13
Wang WeixiangZhang QiangXu Suting - The incidence of bladder cancer (BCa) is rising worldwide. Recent reports suggest a disproportionate increase among younger individuals, raising questions about epidemiologic and genetic contributors to early-onset disease. We aimed to characterize incidence and prevalence trends across younger and older populations and to evaluate germline genetic factors associated with young-onset bladder cancer (YOBC). - Source: PubMed
Publication date: 2026/05/06
Diaz Gabriela MIsali IlahaDavis LauraWang BettyAydogdu CanMcSweeney SeanSindhani MohitNizam AmandaWeight Christopher JWee ChristopherBukavina Laura - Gene mutations and altered epigenetic regulation of gene expression are characteristic features of malignant neoplasms. Combinations of these abnormalities form molecular features of individual tumors. In the large-scale Dependency Map (DepMap) project, the broad panels of human tumor cell lines are being tested for sensitivity to single gene inactivation. Using DepMap data, we have previously identified a set of genes termed supertargets, the deletion of which significantly reduced the survival of cells of a particular tissue origin while minimally impairing the unrelated cell lines. In the present study, we determined the factors of viability (inhibition of proliferation or death) of cell lines in which the supertarget genes have been deleted. We found that, in 79 % of cases, the reduced survival may be caused by epigenetic changes of gene expression. In the remaining 21 % of cases, it is associated with altered gene structure. Three groups containing different types of gene expression alterations can be distinguished. In the first group, the reduced cell survival correlated with a higher expression of the supertarget gene (e. g., SOX10 and HNF1B). In the second group, a gene different from the deleted supertarget was overexpressed (gene pairs: FOXA1 and SPDEF, TP63 and SERPINB13, etc.). The third group was characterized by correlations between low expression of a certain gene and tumor cell sensitivity (e. g., FAM126A and FAM126B, SMARCA2 and SMARCA4). The genetic changes included GOF mutations (KRAS, BRAF genes, etc.), LOF mutations (STAG1, SMARCA2 genes, etc.), gene fusions (BCR-ABL1, PAX3-FOXO1, etc.), and amplification (CPM, BEST3, etc.). Therefore, many different molecular mechanisms act as predictors of tumor cell response to inhibition of supertarget genes. - Source: PubMed
Chetverina D AKozelchuk N YLomaev D VShtil A AErokhin М M - Pterygium is a prevalent ocular disease characterized by abnormal conjunctival tissue proliferation, significantly impacting patients' quality of life. However, the underlying molecular mechanisms driving pterygium pathogenesis remain inadequately understood. This study aimed to investigate gene expression changes following pterygium excision and their association with immune cell infiltration. Clinical samples of pterygium and adjacent relaxed conjunctival tissue were collected for transcriptomic analysis using RNA sequencing combined with bioinformatics approaches. Machine learning algorithms, including LASSO, SVM-RFE, and Random Forest, were employed to identify potential diagnostic biomarkers. GO, KEGG, GSEA, and GSVA were utilized for enrichment analysis. Single-sample GSEA was employed to analyze immune infiltration. The GSE2513 and GSE51995 datasets from the GEO database, along with clinical samples, were selected for validation analysis. Differentially expressed genes (DEGs) were identified from the PRJNA1147595 and GSE2513 datasets, revealing 2437 DEGs and 172 differentially regulated genes (DRGs), respectively. There were 52 co-DEGs shared by both datasets, and four candidate biomarkers (FN1, SPRR1B, SERPINB13, EGR2) with potential diagnostic value were identified through machine learning algorithms. Single-sample GSEA demonstrated increased Th2 cell infiltration and decreased CD8 + T cell presence in pterygium tissues, suggesting a crucial role of the immune microenvironment in pterygium pathogenesis. Analysis of the GSE51995 dataset and qPCR results revealed significantly higher expression levels of FN1 and SPRR1B in pterygium tissues compared to conjunctival tissues, but SERPINB13 and EGR2 expression levels were not statistically significant. Furthermore, we identified four candidate drugs targeting the two feature genes FN1 and SPRR1B. This study provides valuable insights into the molecular characteristics and immune microenvironment of pterygium. The identification of potential biomarkers FN1 and SPRR1B highlights their significance in pterygium pathogenesis and lays a foundation for further exploration aimed at integrating these findings into clinical practice. - Source: PubMed
Publication date: 2025/04/17
Yang JiChen Ya-NanFang Chen-YanLi YanKe Hong-QinGuo Rui-QinXiang PingXiao Yun-LingZhang Li-WeiLiu Hai - The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies. - Source: PubMed
Publication date: 2024/10/11
Zhang XiaoZhang PengpengRen QianheLi JunLin HaoranHuang YumingWang Wei