Ask about this productRelated genes to: TCEAL4 Blocking Peptide
- Gene:
- TCEAL4 NIH gene
- Name:
- transcription elongation factor A like 4
- Previous symbol:
- -
- Synonyms:
- FLJ21174, WEX7
- Chromosome:
- Xq22.2
- Locus Type:
- gene with protein product
- Date approved:
- 2004-08-16
- Date modifiied:
- 2016-02-15
Related products to: TCEAL4 Blocking Peptide
Related articles to: TCEAL4 Blocking Peptide
- High-grade serous ovarian cancer (HGSOC) is the second most lethal gynecologic malignancy, often diagnosed at a late stage due to the lack of reliable early detection strategies. Currently, there are no specific diagnostic or prognostic biomarkers for ovarian cancer (OC). Thus, there is a great need for novel validated biomarkers for OC diagnosis. - Source: PubMed
Publication date: 2026/01/27
Vaicekauskaitė IevaJuodakis JuliusKazlauskaitė PaulinaČiurlienė RūtaSmailytė GiedrėLazutka Juozas RimantasSabaliauskaitė Rasa - Adipose-derived stem cells (ADSCs) are a type of stem cell found in adipose tissue with the capacity to differentiate into multiple lineages, including osteoblasts. The differentiation of ADSCs into osteoblasts underlies osteogenic and pathological cellular basis in osteoporosis, bone damage and repair. - Source: PubMed
Publication date: 2025/03/18
Jin XinLu YiFan Zhihong - As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with "" package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the "" package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including , , , , , and . The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized. - Source: PubMed
Publication date: 2023/02/07
Ye Ling-FangWeng Jia-YiWu Li-Da - An Xq22.2 region upstream of PLP1 has been proposed to underly a neurological disease trait when deleted in 46,XX females. Deletion mapping revealed that heterozygous deletions encompassing the smallest region of overlap (SRO) spanning six Xq22.2 genes (BEX3, RAB40A, TCEAL4, TCEAL3, TCEAL1, and MORF4L2) associate with an early-onset neurological disease trait (EONDT) consisting of hypotonia, intellectual disability, neurobehavioral abnormalities, and dysmorphic facial features. None of the genes within the SRO have been associated with monogenic disease in OMIM. Through local and international collaborations facilitated by GeneMatcher and Matchmaker Exchange, we have identified and herein report seven de novo variants involving TCEAL1 in seven unrelated families: three hemizygous truncating alleles; one hemizygous missense allele; one heterozygous TCEAL1 full gene deletion; one heterozygous contiguous deletion of TCEAL1, TCEAL3, and TCEAL4; and one heterozygous frameshift variant allele. Variants were identified through exome or genome sequencing with trio analysis or through chromosomal microarray. Comparison with previously reported Xq22 deletions encompassing TCEAL1 identified a more-defined syndrome consisting of hypotonia, abnormal gait, developmental delay/intellectual disability especially affecting expressive language, autistic-like behavior, and mildly dysmorphic facial features. Additional features include strabismus, refractive errors, variable nystagmus, gastroesophageal reflux, constipation, dysmotility, recurrent infections, seizures, and structural brain anomalies. An additional maternally inherited hemizygous missense allele of uncertain significance was identified in a male with hypertonia and spasticity without syndromic features. These data provide evidence that TCEAL1 loss of function causes a neurological rare disease trait involving significant neurological impairment with features overlapping the EONDT phenotype in females with the Xq22 deletion. - Source: PubMed
Publication date: 2022/11/10
Hijazi HadiaReis Linda MPehlivan DavutBernstein Jonathan AMuriello MichaelSyverson ErinBonner DevonEstiar Mehrdad AGan-Or ZivRouleau Guy ALyulcheva EkaterinaGreenhalgh LynnTessarech MarineColin EstelleGuichet AgnèsBonneau Dominiquevan Jaarsveld R HLachmeijer A M ARuaud LyseLevy JonathanTabet Anne-ClaudePloski RafalRydzanicz MałgorzataKępczyński ŁukaszPołatyńska KatarzynaLi YidanFatih Jawid MMarafi DanaRosenfeld Jill ACoban-Akdemir ZeynepBi WeiminGibbs Richard AHobson Grace MHunter Jill VCarvalho Claudia M BPosey Jennifer ESemina Elena VLupski James R - BACKGROUND This study aimed to identify important marker genes in lung adenocarcinoma (LACC) and establish a prognostic risk model to predict the risk of LACC in patients. MATERIAL AND METHODS Gene expression and methylation profiles for LACC and clinical information about cases were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, respectively. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between cancer and control groups were selected through meta-analysis. Pearson coefficient correlation analysis was performed to identify intersections between DEGs and DMGs and a functional analysis was performed on the genes that were correlated. Marker genes and clinical factors significantly related to prognosis were identified using univariate and multivariate Cox regression analyses. Risk prediction models were then created based on the marker genes and clinical factors. RESULTS In total, 1975 DEGs and 2095 DMGs were identified. After comparison, 16 prognosis-related genes (EFNB2, TSPAN7, INPP5A, VAMP2, CALML5, SNAI2, RHOBTB1, CKB, ATF7IP2, RIMS2, RCBTB2, YBX1, RAB27B, NFATC1, TCEAL4, and SLC16A3) were selected from 265 overlapping genes. Four clinical factors (pathologic N [node], pathologic T [tumor], pathologic stage, and new tumor) were associated with prognosis. The prognostic risk prediction models were constructed and validated with other independent datasets. CONCLUSIONS An integrated model that combines clinical factors and gene markers is useful for predicting risk of LACC in patients. The 16 genes that were identified, including EFNB2, TSPAN7, INPP5A, VAMP2, and CALML5, may serve as novel biomarkers for diagnosis of LACC and prediction of disease prognosis. - Source: PubMed
Publication date: 2020/10/06
Ke HonggangWu YunyuWang RunjieWu Xiaohong