Редактирование: CTNNB1

Catenin beta-1 (Beta-catenin) [CTNNB] [OK/SW-cl.35] [PRO2286]

==Publications==

{{medline-entry
|title=Catalog of Lung Cancer Gene Mutations Among Chinese Patients.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/32850378
|abstract= Detailed catalog of lung cancer-associated gene mutations provides valuable information for lung cancer diagnosis and treatment. In China, there has never been a wide-ranging study cataloging lung cancer-associated gene mutations. This study aims to reveal a comprehensive catalog of lung cancer gene mutations in china, focusing on [[EGFR]], [[ALK]], [[KRAS]], HER2, [[PIK3CA]], [[MET]], [[BRAF]], [[HRAS]], and [[CTNNB1]] as major targets. Additionally, we also aim to correlate smoking history, gender, and age distribution and pathological types with various types of gene mutations.   A retrospective data acquisition was conducted spanning 6 years (2013-2018) among all patients who underwent lung cancer surgeries not bronchial or percutaneous lung biopsy at three major tertiary hospitals. Finally, we identified 1,729 patients who matched our inclusion criteria.   1081 patients (62.49%) harbored [[EGFR]] mutation. [[ALK]] ([i]n[/i] = 42, 2.43%), [[KRAS]] ([i]n[/i] = 201, 11.62%), [[CTNNB1]] ([i]n[/i] = 28, 1.62%), [[BRAF]] ([i]n[/i] = 31, 1.79%), [[PIK3CA]] ([i]n[/i] = 51, 2.95%), [[MET]] ([i]n[/i] = 14, 0.81%), HER2 ([i]n[/i] = 47, 2.72%), [[HRAS]] ([i]n[/i] = 3, 0.17%), and other genes([i]n[/i] = 232, 13.4%). Females expressed 55.38% vs. males 44.62% mutations. Among subjects with known smoking histories, 32.82% smokers, 67.15% non-smokers were observed. Generally, 51.80% patients were above 60 years vs. 48.20% in younger patients. Pathological types found includes LUADs 71.11%, SQCCs 1.68%, ASC 0.75%, LCC 0.58%, SCC 0.35%, ACC 0.17%, and SC 0.06%, unclear 25.19%.   We offer a detailed catalog of the distribution of lung cancer mutations. Showing how gender, smoking history, age, and pathological types are significantly related to the prevalence of lung cancer in China.

|keywords=* China
* aging
* gene mutation
* lung cancer
* pathology
* tobacco smoking
|full-text-url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417348
}}
{{medline-entry
|title=[i][[TP53]][/i] Tumor-suppressor Gene Plays a Key Role in [[IGF1]] Signaling Pathway Related to the Aging of Human Melanocytes.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/31092438
|abstract=The insulin-like growth factor 1 ([[IGF1]]) signaling pathway as an aging mechanism related to p53 in human melanogenesis remains unclear. The aim of this study was to investigate the relationship between p53 and [[IGF1]] signaling pathway in young, senescent and H O -treated cells. The protein and gene expression in young, senescent and H O -treated cells were analyzed using western blot and reverse transcription polymerase chain reaction (RT-PCR) assays, respectively. The expression levels of (phosphoinositide 3-kinases) PI3K, v-akt murine thymoma viral oncogene homolog 1 (AKT1), mammalian target of rapamycin, β-catenin ([[CTNNB1]]), acetylated p53 (ac-p53), p53 and p-p21 proteins, related to [[IGF1]] and p53 signaling pathways, were higher in senescent and H O -treated cells than those of young cells. Furthermore, AKT reduced melanogenesis through microphthalmia-associated transcription factor (MITF) inactivation by the inhibition of [[CTNNB1]]. The gene expression levels of PI3K, [[TP53]] and catalase (CAT) in senescent and H O -treated cells were increased compared to young cells. p53 protein plays a key role in the aging of melanocytes via [[IGF1]] signaling pathways.
|mesh-terms=* Aging
* Animals
* Catalase
* Cell Proliferation
* Cellular Senescence
* Gene Expression Regulation
* Humans
* Hydrogen Peroxide
* Insulin-Like Growth Factor I
* Melanocytes
* Mice
* Microphthalmia-Associated Transcription Factor
* Phosphatidylinositol 3-Kinases
* Proto-Oncogene Proteins c-akt
* Signal Transduction
* Tumor Suppressor Protein p53
* beta Catenin
|keywords=* AKT1
* H2O2
* IGF1
* PI3K
* melanocyte
* p53
|full-text-url=https://sci-hub.do/10.21873/anticanres.13363
}}
{{medline-entry
|title=Identification of key genes and transcription factors in aging mesenchymal stem cells by DNA microarray data.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/30641220
|abstract=Mesenchymal stem cells ([[MSC]]s) are multipotent cells that can be widely used in stem cell therapy. However, few studies have revealed the potential mechanisms of the changes in aging [[MSC]]. In this study, microarray data GSE35955 was downloaded from the Gene Expression Omnibus database. Then limma package in R was used to filtrate differentially expressed genes (DEGs), Transcription factors ([[TF]]s) were predicted by DCGL package. After predicting [[TF]]s, protein-protein interaction (PPI) network and [[TF]]-mediated transcriptional regulation network were constructed. The functional and pathway enrichment analysis of screened DEGs, hub genes and [[TF]]s were conducted by the DAVID. Totally 156 up-regulated DEGs and 343 down-regulated DEGs were obtained. 6 hub genes ([[CTNNB1]], [[PPP2R1A]], [[FYN]], [[MAPK1]], [[PIK3C2A]] and [[EP300]]) were obtained from PPI network. 11 [[TF]]s (CREB1, [[[[CUX1]]]], [[EGR1]], [[EP300]], [[FOXC1]], [[HSF2]], [[MEF2A]], [[PLAU]], [[SP1]], [[STAT1]] and USF1) for DEGs were predicted and 2 highly scored co-expression relationships ([[EP300]]-[[PPP2R1A]] and [[STAT1]]-[[FOXC1]]) were acquired from the [[TF]]-mediated transcriptional regulation network. The discovery of the hub genes, [[TF]]s and pathways might contribute to the understanding of genetic and molecular functions of aging-related changes in [[MSC]]. Further validation studies on genes and [[TF]]s such as [[CTNNB1]], [[FYN]], [[PPP2R1A]], [[MAPK1]], [[EP300]] and related biological processes and pathways, including adherens junction, DNA damage caused from oxidative stress, attribution of telomere, [[MSC]] differentiation and epigenetic regulation, are urgent for clinical prevention and treatment.
|mesh-terms=* Adult
* Aged
* Aged, 80 and over
* Aging
* Gene Expression Profiling
* Gene Expression Regulation
* Humans
* Mesenchymal Stem Cells
* Middle Aged
* Mitogen-Activated Protein Kinase 1
* Oligonucleotide Array Sequence Analysis
* Protein Interaction Maps
* Protein Phosphatase 2
* Proto-Oncogene Proteins c-fyn
* Transcription Factors
* beta Catenin
|keywords=* Differentially expressed genes
* Enrichment analysis
* Gene Expression Omnibus
* Hub genes
* Microarray analysis
* Protein-protein interaction network
* Transcriptional regulatory network
|full-text-url=https://sci-hub.do/10.1016/j.gene.2018.12.063
}}
{{medline-entry
|title=Proteomic profiling of follicle fluids after superstimulation in one-month-old lambs.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/29080277
|abstract=Follicular fluid (FF) accumulates in the antrum of the ovarian follicle. In addition, FF provides the microenvironment for oocyte development, oocyte maturation and competence, which are acquired during follicular development. Superstimulatory treatment of 1-month-old lambs can achieve synchronous development of numerous growing follicles. However, these growing follicles are unable to completely mature and ovulate. Furthermore, the oocytes exhibit lower competence compared with those of ewes. In this study, we utilized an isobaric tag for relative and absolute quantification (iTRAQ)-based proteomics analysis and compare protein composition between pre-pubertal and adult superstimulated follicle FF in sheep. In total, 243 differentially expressed proteins were identified, including 155 downregulated and 88 upregulated between lamb and ewe. Gene ontology (GO) and KEGG pathway analysis indicated that the differentially expressed proteins are involved in signal transduction, anatomical structure development, stress response, metabolic pathways, and the complement and coagulation cascades. Many of the proteins known to affect follicle development were observed in lower abundance in FF of lamb (e.g. [[ADAMTS9]], [[CD14]], [[CTNNB1]], [[FST]], [[GCLC]], [[HSPG2]], [[IGFBP2]], [[IGFBP6]], [[INHBA]], [[PRL]], [[PAPPA]], [[POSTN]], [[PRDX1]], [[SERPINA1]], [[SOD3]], [[STC1]], [[VEGFC]], etc.). However, a higher abundance was observed for proteasome proteins. Inadequate amounts of these proteins in FF may be lead to the unique characteristics of follicular development in lamb. These differentially expressed proteins illuminate the age-dependent changes in protein expression in the follicle microenvironment.
|mesh-terms=* Aging
* Animals
* Female
* Follicle Stimulating Hormone
* Follicular Fluid
* Gene Ontology
* Ovarian Follicle
* Proteomics
* Sexual Maturation
* Sheep, Domestic
|keywords=* follicular fluid
* lamb
* proteome
* quantitative proteomic
* superstimulation
|full-text-url=https://sci-hub.do/10.1111/rda.13091
}}
{{medline-entry
|title=P4 medicine and osteoporosis: a systematic review.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/27873024
|abstract=Osteoporosis is the most frequent bone metabolic disease. In order to improve early detection, prediction, prevention, diagnosis, and treatment of the disease, a new model of P4 medicine (personalized, predictive, preventive, and participatory medicine) could be applied. The aim of this work was to systematically review the publications of four different types of "omics" studies related to osteoporosis, in order to discover novel predictive, preventive, diagnostic, and therapeutic targets for better management of the geriatric population. To systematically search the PubMed database, we created specific groups of criteria for four different types of "omics" information on osteoporosis: genomic, transcriptomic, proteomic, and metabolomic. We then analyzed the intersections between them in order to find correlations and common pathways or molecules with important roles in osteoporosis, and with a potential application in disease prediction, prevention, diagnosis, or treatment. Altogether, 180 publications of "omics" studies in the field of osteoporosis were found and reviewed at first selection. After introducing the inclusion and exclusion criteria (the secondary selection), 46 papers were included in the systematic review. The intersection of reviewed papers identified five genes (ESR1, [[IBSP]], [[CTNNB1]], [[SOX4]], and IDUA) and processes like the Wnt pathway, JAK/STAT signaling, and ERK/MAPK, which should be further validated for their predictive, diagnostic, or other clinical value in osteoporosis. Such molecular insights will enable us to fit osteoporosis into the P4 strategy and could increase the effectiveness of disease prediction and prevention, with a decrease in morbidity in the geriatric population.
|mesh-terms=* Aged
* Aged, 80 and over
* Female
* Genetic Markers
* Genetic Predisposition to Disease
* Genetic Testing
* Geriatric Assessment
* Humans
* Male
* Osteoporosis
* Precision Medicine
* Prevalence
* Risk Factors
|keywords=* Geriatrics
* Omics
* Osteoporosis biomarkers
* Personalized medicine
* Systems biology
|full-text-url=https://sci-hub.do/10.1007/s00508-016-1125-3
}}
{{medline-entry
|title=Expression profile analysis of new candidate genes for the therapy of primary osteoporosis.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/26914116
|abstract=Primary osteoporosis is a progressive bone disease that is characterized by a decrease in bone mass and density which can lead to an increased risk of fracture. Most of present treatments are effective for osteoporosis, but have limitations and side-effects. With the aging of the world population is increasing, the incidence of osteoporosis is rising. Therefore, the purpose of this study was to identify new candidate genes used as the therapeutic targets of primary osteoporosis. In this study, microarray data GSE35958 were downloaded from Gene Expression Omnibus, then the differentially expressed genes (DEGs) were identified by limma package. Gene Ontology (GO) and KEGG pathway enrichment analyses were performed for both up- and down-regulated DEGs using DAVID. In addition, the transcription factor analysis was conducted for DEGs. The protein-protein interaction (PPI) network was constructed by STRING and Cytoscape. Finally, CFinder was used to analyze the PPI sub-network. Totally, 327 up-regulated DEGs and 396 down-regulated DEGs were identified. The DEGs such as [[EGFR]] and [[AKT1]] were mainly enriched in the pathway of focal adhesion. [[EGFR]] was also involved in cell adhesion based on GO enrichment analysis. Functional analysis of DEGs indicated that 26 transcription factors were screened. Moreover, [[EGFR]], [[AKT1]] and transcription factor [[CTNNB1]] were the key nodes with high degrees according to PPI network and sub-network. The literature suggested that [[AKT1]], [[EGFR]] and [[CTNNB1]] were closely related to osteoblastic differentiation and osteoclastogenesis. Some crucial DEGs such as [[EGFR]], [[AKT1]] and [[CTNNB1]] might be connected with primary osteoporosis and could be used as therapeutic targets of osteoporosis.
|mesh-terms=* Aging
* Down-Regulation
* Gene Expression
* Gene Expression Profiling
* Humans
* Osteoporosis
* Protein Interaction Maps
* Transcription Factors
* Up-Regulation
* beta Catenin


}}