Ask about this productRelated genes to: TMCC2 Blocking Peptide
- Gene:
- TMCC2 NIH gene
- Name:
- transmembrane and coiled-coil domain family 2
- Previous symbol:
- -
- Synonyms:
- HUCEP11, FLJ38497
- Chromosome:
- 1q32.1
- Locus Type:
- gene with protein product
- Date approved:
- 2005-01-21
- Date modifiied:
- 2014-11-19
Related products to: TMCC2 Blocking Peptide
Related articles to: TMCC2 Blocking Peptide
- Di(2-ethylhexyl) phthalate (DEHP), a widely used plasticizer, crosses the placental barrier and disrupts fetal development, though its precise mechanisms remain poorly defined. To assess its placental effects, pregnant mice were treated daily with 40 μg/kg DEHP, and placental alterations were systematically evaluated through histopathological examination, Western blot analysis, and whole-transcriptome sequencing. DEHP exposure reduced the total placental area, disrupted the decidual and spongiotrophoblast layers, and disorganized vascular structures. Concurrently, protein levels of Bax, p53, and Caspase-3 were significantly elevated, while Bcl-2 expression decreased, indicating mitochondrial apoptosis mediated by the PI3K/Akt-p53 pathway. Transcriptomic profiling further revealed widespread dysregulation of mRNAs and noncoding RNAs, enriched in processes involving the Notch signaling pathway, cholesterol metabolism, hormone response, and oxygen transport. Construction of a ceRNA network suggested that miR-6538 may contribute to placental dysfunction by regulating Tmcc2 and Susd2. These findings elucidate a novel mechanism through which DEHP impairs placental structure and function via apoptotic signaling and RNA regulatory networks, offering important insights into its reproductive toxicity. - Source: PubMed
Publication date: 2025/12/08
Dong YangLi Yin-YinYan Yu-Mei ChenBai YueZhang Xi-FengLiu Jing - Sepsis is a life-threatening condition driven by dysregulated immune responses, resulting in organ dysfunction and high mortality rates. Identifying key genes and pathways involved in sepsis progression is crucial for improving diagnostic and therapeutic strategies. This study analyzed transcriptomic data from 49 samples (37 septic patients across days 0, 1, and 8, and 12 healthy controls) using weighted gene co-expression network analysis (WGCNA) and multi-algorithm feature selection approaches. Differential expression analysis, pathway enrichment, and network analyses were employed to uncover potential biomarkers and molecular mechanisms. WGCNA identified modules such as MEbrown4 and MEblack, which strongly correlated with sepsis progression (r > 0.7, < 0.01). Differential expression analysis highlighted up-regulated genes like CD177 and down-regulated genes like LOC440311. KEGG analysis revealed significant pathways including neuroactive ligand-receptor interaction, PI3K-Akt signaling, and MAPK signaling. Gene ontology analysis showed involvement in immune-related processes such as complement activation and antigen binding. Protein-protein interaction network analysis identified hub genes such as TNFSF10, IGLL5, BCL2L1, and SNCA. Feature selection methods (random forest, LASSO regression, SVM-RFE) consistently identified top predictors like TMCC2, TNFSF10, and PLVAP. Receiver operating characteristic (ROC) analysis demonstrated high predictive accuracy for sepsis progression, with AUC values of 0.973 (TMCC2), 0.969 (TNFSF10), and 0.897 (PLVAP). Correlation analysis linked key genes such as TNFSF10, GUCD1, and PLVAP to pathways involved in immune response, cell death, and inflammation. This integrative transcriptomic analysis identifies critical gene modules, pathways, and biomarkers associated with sepsis progression. Key genes like TNFSF10, TMCC2, and PLVAP genes show strong diagnostic potential, providing novel insights into sepsis pathogenesis and offering promising targets for future therapeutic interventions. Among these, TNFSF10 and PLVAP are known to encode secreted proteins, suggesting their potential as circulating biomarkers. This enhances their translational relevance in clinical diagnostics. - Source: PubMed
Publication date: 2025/05/07
Sun QinghuiZhang Hai-LiWang YichaoXiu HaoLu YufeiHe NaYin Li - Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package "ConsensusClusterPlus" on the bulk RNA-seq data. Hub genes were discerned through the application of the R package "limma" and univariate regression analysis, with the calculation of risk scores carried out using the R package "survminer". To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan-Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages "ESTIMATE" and "GSVA" were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis. - Source: PubMed
Publication date: 2024/08/05
Tao LinfengZhou YanyouWu LifangLiu Jun - Transmembrane and coiled-coil 2 (TMCC2) is a human orthologue of the Drosophila gene dementin, mutant alleles of which cause neurodegeneration with features of Alzheimer's disease (AD). TMCC2 and Dementin further have an evolutionarily conserved interaction with the amyloid protein precursor (APP), a protein central to AD pathogenesis. To investigate if human TMCC2 might also participate in mechanisms of neurodegeneration, we examined TMCC2 expression in late onset AD human brain and age-matched controls, familial AD cases bearing a mutation in APP Val717, and Down syndrome AD. Consistent with previous observations of complex formation between TMCC2 and APP in the rat brain, the dual immunocytochemistry of control human temporal cortex showed highly similar distributions of TMCC2 and APP. In late onset AD cases stratified by APOE genotype, TMCC2 immunoreactivity was associated with dense core senile plaques and adjacent neuronal dystrophies, but not with Aβ surrounding the core, diffuse Aβ plaques or tauopathy. In Down syndrome AD, we observed in addition TMCC2-immunoreactive and methoxy-X04-positive pathological features that were morphologically distinct from those seen in the late onset and familial AD cases, suggesting enhanced pathological alteration of TMCC2 in Down syndrome AD. At the protein level, western blots of human brain extracts revealed that human brain-derived TMCC2 exists as at least three isoforms, the relative abundance of which varied between the temporal gyrus and cerebellum and was influenced by APOE and/or dementia status. Our findings thus implicate human TMCC2 in AD via its interactions with APP, its association with dense core plaques, as well as its alteration in Down syndrome AD. - Source: PubMed
Publication date: 2024/07/31
Hopkins Paul C RTroakes ClaireKing AndrewTear Guy - Previous studies have reported inconsistent associations between platelet count (PLT) and cancer survival. However, whether there is linear causal effect merits in-depth investigations. We conducted a cohort study using the UK Biobank and a two-sample Mendelian randomization (MR) analysis. PLT levels were measured prior to cancer diagnosis. We adopted overall survival (OS) as the primary outcome. Cox models were utilized to estimate the effects of PLTs on survival outcomes at multiple lag times for cancer diagnosis. We employed 34 genetic variants as PLT proxies for MR analysis. Linear and non-linear effects were modeled. Prognostic effects of gene expression harboring the instrumental variants were also investigated. A total of 65 471 cancer patients were included. We identified a significant association between elevated PLTs (per 100 × 10/L) and inferior OS (HR: 1.07; 95% CI: 1.04-1.10; < .001). Similar significant associations were observed for several cancer types. We further observed a U-shaped relationship between PLTs and cancer survival ( < .001). Our MR analysis found null evidence to support a causal association between PLTs and overall cancer survival (HR: 1.000; 95% CI: 0.998-1.001; = .678), although non-linear MR analysis unveiled a potential greater detrimental effect at lower PLT range. Expression of eleven PLT-related genes were associated with cancer survival. Early detection of escalated PLTs indicated possible occult cancer development and inferior subsequent survival outcomes. The observed associations could potentially be non-linear. However, PLT is less likely to be a promising therapeutic target. - Source: PubMed
Publication date: 2024/07/29
Li ChangtaoChen JunhuaHan DeqianShu ChiHuang JunWei LinruLuo HaoranWu QingbinChen XinHe YazhouZhou Yanhong