Ask about this productRelated genes to: GZMM antibody
- Gene:
- GZMM NIH gene
- Name:
- granzyme M
- Previous symbol:
- -
- Synonyms:
- MET1, LMET1
- Chromosome:
- 19p13.3
- Locus Type:
- gene with protein product
- Date approved:
- 1994-10-12
- Date modifiied:
- 2015-11-16
Related products to: GZMM antibody
Related articles to: GZMM antibody
- Sepsis is a systemic inflammatory response syndrome caused by an infection featuring high morbidity and mortality due to complex mechanisms underlying immune dysfunction. In this study, based on the sepsis transcriptome profiles from the GEO datasets (GSE65682, GSE28750, GSE95233, and GSE167363), we used the machine learning method and other computational algorithms, such as differential gene expression analysis, weighted gene coexpression network analyses (WGCNA), and the building of PPI networks to identify four hub genes (DDX24, GZMM, KCNA3, and NCL). The quantitative reverse transcription PCR performed preliminary validation that all four hub genes were significantly downregulated in patients with sepsis. DDX24 had the highest diagnostic performance (AUC > 0.8) for discriminating patients from normal subjects. GZMM was found to be significantly related to the prognoses of patients as well as APACHE II scores, and the downregulated expression pattern might represent T cell and NK cell exhaustion. Analysis based on single-cell RNA sequencing showed that DDX24 and GZMM were mainly expressed in T cells and NK cells, and the expression trends strongly correlate with patient survival. Functional enrichment analysis suggested that the hub genes likely participate in regulation of immune responses, especially those pertaining to T cells. Drug prediction found 25 candidate drugs that will serve as new therapeutic targets for precision medicine to treat sepsis. Overall, the multifaceted study shed light on key roles played by these hub genes (especially DDX24 and GZMM) in the development of sepsis and will be useful references in diagnosing patients and estimating prognosis. - Source: PubMed
Publication date: 2026/04/03
Zhang YiTang LiangWu JuanYang LinLiu WenLiang YiHan JianfangHe ShuangYang Yulian - Patients with breast cancer are susceptible to coronavirus disease (COVID-19), which affects cancer treatment efficacy and prognosis. However, its effects on neoadjuvant chemotherapy (NAC) response and its genetic correlation with breast cancer remain unclear. We aimed to clarify these effects and investigate potential genetic associations and shared pathogenic mechanisms. - Source: PubMed
Publication date: 2025/10/10
Wang YaliZhan JiaqiLiu ShunyiChen MinyanChen LiliLin YuxiangHou JialinYu LiuwenChen XiaobinCai WeifengQiu YibinCai QindongHe PengGuo WenhuiXu ChunsenLin ShunguoFu FangmengWang Chuan - Histopathologic diagnosis of thin, invasive cutaneous melanoma (CM) is only 34-62% accurate. Therefore, we sought to develop a transcriptomic biomarker to distinguish benign from malignant melanocytic neoplasms. We generated a targeted RNA-Sequencing dataset (TempO-Seq) of benign nevi (BN; n = 50) and CM (Breslow depth ≤ 1.0 mm; n = 51) and demonstrated enrichment of immune-related pathways among the 450 differentially expressed genes. Next, we trained a putative transcriptomic biomarker in two datasets, including BN and CM, and one dataset with CM in association with a nevus, macrodissected into CM and nevus regions. We refer to the nevus portion of CM in association with a nevus as progressing nevi (PN), since these nevi progressed to CM. Principal component analysis showed that PN samples clustered in a component intermediate to BN and CM. Ordinal regularized regression selected PYGL, AP000845.1, PHYHIP, WSCD1, FBXO7, TRPM1, SLC4A4, NALCN, FRMD4B, HHATL, COL1A1, CRYM, EPOP, RGS1, KRT6C, IGHG1, CNTN1, MMP11, GZMM, AP001880.1, TTYH3, TMEM132A, and PRAME; these genes were consistently selected in 1000 models using data from bootstrap resamples and had a single model predictive accuracy of at least 0.90 (area under the receiver operator characteristics curve). Linear regression models fit with these 23 genes in the TempO-Seq data, and publicly available microarray datasets from BN, dysplastic nevi, and CM, showed high consistency in the magnitude and directionality of gene expression differences between nevi and CM. Furthermore, immunohistochemical staining showed consistent protein-level changes in MMP11 and PYGL. These results illuminate the potential for a transcriptomic biomarker to differentiate benign from malignant melanocytic neoplasms and improve the accuracy of melanoma diagnosis. - Source: PubMed
Publication date: 2025/10/03
Borden Elizabeth SHastings Colin TPrakash NithishKuo TylerTapia EdgarYozwiak MichaelSagerman PaulVargas de Stefano DanielleBuetow Kenneth HWilson Melissa ACuriel-Lewandrowski ClaraChow Hsiao-Hui SherryLaFleur Bonnie JHastings Karen Taraszka - Sepsis is a life-threatening condition characterized by immune dysregulation, yet the mechanisms underlying T cell dysfunction remain poorly understood. - Source: PubMed
Publication date: 2025/09/13
Li XiangChen ZhibinYao YandongChen MuhuHu Yingchun - This study aims to integrate cross-disease omics data and perform multidimensional analysis to uncover the molecular basis of schizophrenia (SCZ) and sleep disorder (SD) comorbidity and to systematically analyze the potential mechanism of the Hugan Tiaoshen Formula (HGTS) in treating SCZ with SD. Integrate transcriptional data of SCZ and SD from the GEO database, screen disease-shared differential genes. Construct PPI network, identify core targets by topological analysis. Use machine learning algorithms to select cross-disease hub genes. Analyze immune cell infiltration and gene-immune interaction. Conduct molecular docking. Build an SCZ-SD comorbidity mouse model and assess behavioral improvements. Verify key pathway regulatory effects by Western blot and qRT-PCR. Cross-disease analysis identified 25 shared core targets. The constructed "compound-target" network revealed quercetin, β-sitosterol, and ADRB2 as key nodes. The PPI network identified HSPB1, THBS1, and other targets enriched in antigen presentation and PI3K-Akt pathways. Machine learning algorithms highlighted HSPB1, ADRB2, and GZMM as core genes. In SCZ, resting CD4+ memory T cells were positively correlated with HSPB1, while abnormal dendritic cells and low ADRB2 expression were associated with SD. Molecular docking confirmed strong binding between baicalin, β-sitosterol, and the targets. Animal experiments showed that HGTS improved neurological symptoms and sleep structure while regulating the expression of HSPB1, ADRB2, and BDNF. This study reveals shared core targets HSPB1, ADRB2, and GZMM between SCZ and SD. The compound HGTS, through the synergistic action of multiple components such as quercetin and β-sitosterol, improves neurological symptoms and sleep rhythm. - Source: PubMed
Huang ZixuanHuang ZiqiDu ZhiqiangGao XuezhengJiang YingZhou ZhenheZhu Haohao