Ask about this productRelated genes to: CCDC144B Blocking Peptide
- Gene:
- CCDC144B NIH gene
- Name:
- coiled-coil domain containing 144B (pseudogene)
- Previous symbol:
- -
- Synonyms:
- FLJ36492
- Chromosome:
- 17p11.2
- Locus Type:
- pseudogene
- Date approved:
- 2007-08-07
- Date modifiied:
- 2012-11-19
Related products to: CCDC144B Blocking Peptide
Related articles to: CCDC144B Blocking Peptide
- The relationship between the brain and aging remains unclear. Our objective is to explore the causal connections between brain structure,gene expression, and traits associated with aging. Mendelian randomization(MR) analysis was conducted to explore the associations between brain structures and aging-related traits including GrimAge acceleration(GrimAA), PhenoAge acceleration (PhenoAA), HannumAge acceleration(HannumAA), HorvathAge acceleration(HorvathAA), and leukocyte telomere length(LTL). The Linkage Disequilibrium Score Regression(LDSC) method was employed to identify the shared genetic etiology between brain structures and aging. The Summary Data-based Mendelian Randomization(SMR) was utilized to investigate which brain genes have a causal influence on aging. We also examined the expression of the 8 genes derived from the SMR analysis across different cell types in post-mortem human brain specimens. The phenotypes potentially linked to genetics, as indicated by the LDSC outcomes, are as follows:148 phenotypes with GrimAA,150 phenotypes with HannumAA, 160 phenotypes with HorvathAA, 160 phenotypes with PhenoAA,and 110 phenotypes with LTL. Concerning the causal link between brain structures and aging-related traits, 7 brain structures consistently demonstrated a causative effect on GrimAA, while 29 brain structures exerted a causal influence on PhenoAA.Additionally, 7 BIDs revealed a causal relationship with HannumAA. There are 10 and 14 brain structures have a causative effect on HorvathAA and LTL, respectively. SMR revealed that 8 genes(CCDC144B, SHMT1, FAM106A, FAIM, CTD-2303H24.2, EBAG9P1, USP32P2 and OGFOD3) expression in different brain regions affected aging. These genes exhibit different expression patterns in various cells. Our results are in line with the possibility of a causal connection between aging and brain structure. - Source: PubMed
Publication date: 2025/08/28
Li ChengchengTang JiazeCui JunshuanLong NiyaCen WuWu QiboYang MingChu LiangzhaoZhou Xingwang - Transcriptome differences between Hodgkin's lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), and mantle cell lymphoma (MCL), which are all derived from B cell, remained unclear. This study aimed to construct lymphoma-specific diagnostic models by screening lymphoma marker genes. Transcriptome data of HL, DLBCL, and MCL were obtained from public databases. Lymphoma marker genes were screened by comparing cases and controls as well as the intergroup differences among lymphomas. A total of 9 HL marker genes, 7 DLBCL marker genes, and 4 MCL marker genes were screened in this study. Most HL marker genes were upregulated, whereas DLBCL and MCL marker genes were downregulated compared to controls. The optimal HL-specific diagnostic model contains one marker gene (MYH2) with an AUC of 0.901. The optimal DLBCL-specific diagnostic model contains 7 marker genes (LIPF, CCDC144B, PRO2964, PHF1, SFTPA2, NTS, and HP) with an AUC of 0.951. The optimal MCL-specific diagnostic model contains 3 marker genes (IGLV3-19, IGKV4-1, and PRB3) with an AUC of 0.843. The present study reveals the transcriptome data-based differences between HL, DLBCL, and MCL, when combined with other clinical markers, may help the clinical diagnosis and prognosis. - Source: PubMed
Publication date: 2021/04/22
Li Wen-XingDai Shao-XingAn San-QiSun TingtingLiu JustinWang JunLiu Leyna GXun YangYang HuaFan Li-XiaZhang Xiao-LiLiao Wan-QinYou HuaTamagnone LucaLiu FangHuang Jing-FeiLiu Dahai - We endeavored to identify objective blood biomarkers for pain, a subjective sensation with a biological basis, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We studied psychiatric patients, a high risk group for co-morbid pain disorders and increased perception of pain. For discovery, we used a powerful within-subject longitudinal design. We were successful in identifying blood gene expression biomarkers that were predictive of pain state, and of future emergency department (ED) visits for pain, more so when personalized by gender and diagnosis. MFAP3, which had no prior evidence in the literature for involvement in pain, had the most robust empirical evidence from our discovery and validation steps, and was a strong predictor for pain in the independent cohorts, particularly in females and males with PTSD. Other biomarkers with best overall convergent functional evidence for involvement in pain were GNG7, CNTN1, LY9, CCDC144B, and GBP1. Some of the individual biomarkers identified are targets of existing drugs. Moreover, the biomarker gene expression signatures were used for bioinformatic drug repurposing analyses, yielding leads for possible new drug candidates such as SC-560 (an NSAID), and amoxapine (an antidepressant), as well as natural compounds such as pyridoxine (vitamin B6), cyanocobalamin (vitamin B12), and apigenin (a plant flavonoid). Our work may help mitigate the diagnostic and treatment dilemmas that have contributed to the current opioid epidemic. - Source: PubMed
Publication date: 2019/02/12
Niculescu A BLe-Niculescu HLevey D FRoseberry KSoe K CRogers JKhan FJones TJudd SMcCormick M AWessel A RWilliams AKurian S MWhite F A - CD133 is an important, but not exclusive, biomarker of colorectal cancer (CRC) stem cells. - Source: PubMed
Kim Seung TaeSohn InsukDO In-GuJang JiryeonKim Seok HyungJung In HoPark Joon OhPark Young SukTalasaz AmiraliLee JeeyunKim Hee Cheol