Recombinant Escherichia coli Heat Shock Protein 70 (HSP70) _ DnaK, Substrate Binding Domain, C-terminus
- Known as:
- Recombinant Escherichia coli Heat Shock Protein 70 (HSP70) _ DnaK, Substrate Binding Domain, C-terminus
- Catalog number:
- 228-10335-1
- Product Quantity:
- 20
- Category:
- -
- Supplier:
- Ray Biotech
- Gene target:
- Recombinant Escherichia coli Heat Shock Protein 70 (HSP70) _ DnaK Substrate Binding Domain C-terminus
Related genes to: Recombinant Escherichia coli Heat Shock Protein 70 (HSP70) _ DnaK, Substrate Binding Domain, C-terminus
- Gene:
- FCN2 NIH gene
- Name:
- ficolin 2
- Previous symbol:
- -
- Synonyms:
- P35, FCNL, EBP-37, ficolin-2
- Chromosome:
- 9q34.3
- Locus Type:
- gene with protein product
- Date approved:
- 1996-07-11
- Date modifiied:
- 2016-10-05
- Gene:
- HSPA1A NIH gene
- Name:
- heat shock protein family A (Hsp70) member 1A
- Previous symbol:
- HSPA1
- Synonyms:
- HSP70-1
- Chromosome:
- 6p21.33
- Locus Type:
- gene with protein product
- Date approved:
- 2001-06-22
- Date modifiied:
- 2016-10-05
- Gene:
- HSPA1B NIH gene
- Name:
- heat shock protein family A (Hsp70) member 1B
- Previous symbol:
- -
- Synonyms:
- HSP70-2
- Chromosome:
- 6p21.33
- Locus Type:
- gene with protein product
- Date approved:
- 1995-08-11
- Date modifiied:
- 2016-10-05
- Gene:
- HSPA1L NIH gene
- Name:
- heat shock protein family A (Hsp70) member 1 like
- Previous symbol:
- -
- Synonyms:
- HSP70-HOM, hum70t
- Chromosome:
- 6p21.33
- Locus Type:
- gene with protein product
- Date approved:
- 1989-05-12
- Date modifiied:
- 2016-10-05
- Gene:
- HSPA6 NIH gene
- Name:
- heat shock protein family A (Hsp70) member 6
- Previous symbol:
- -
- Synonyms:
- HSP70B'
- Chromosome:
- 1q23.3
- Locus Type:
- gene with protein product
- Date approved:
- 1991-07-26
- Date modifiied:
- 2015-11-19
Related products to: Recombinant Escherichia coli Heat Shock Protein 70 (HSP70) _ DnaK, Substrate Binding Domain, C-terminus
Related articles to: Recombinant Escherichia coli Heat Shock Protein 70 (HSP70) _ DnaK, Substrate Binding Domain, C-terminus
- Liver hepatocellular carcinoma (LIHC) is a malignant cancer with high incidence and poor prognosis. To investigate the correlation between hub genes and progression of LIHC and to provided potential prognostic markers and therapy targets for LIHC. Our study mainly used The Cancer Genome Atlas (TCGA) LIHC database and the gene expression profiles of GSE54236 from the Gene Expression Omnibus (GEO) to explore the differential co-expression genes between LIHC and normal tissues. The differential co-expression genes were extracted by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. The Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to annotate the function of differential genes. Then the hub genes were validated using protein-protein interaction (PPI) network. And the expression level and prognostic analysis were performed. The probable associations between the expression of hub genes and both tumor purity and infiltration of immune cells were explored by TIMER. A total of 68 differential co-expression genes were extracted. These genes were mainly enriched in complement activation (biological process), collagen trimer (cellular component), carbohydrate binding and receptor ligand activity (molecular function) and cytokine - cytokine receptor interaction. Then we demonstrated that the 10 hub genes (CFP, CLEC1B, CLEC4G, CLEC4M, FCN2, FCN3, PAMR1 and TIMD4) were weakly expressed in LIHC tissues, the qRT-PCR results of clinical samples showed that six genes were significantly downregulated in LIHC patients compared with adjacent tissues. Worse overall survival (OS) and disease-free survival (DFS) in LIHC patients were associated with the lower expression of CFP, CLEC1B, FCN3 and TIMD4. Ten hub genes had positive association with tumor purity. CFP, CLEC1B, FCN3 and TIMD4 could serve as novel potential molecular targets for prognosis prediction in LIHC. - Source: PubMed
Publication date: 2024/04/29
Sun JiaweiZhang ZizhenCai JiaruLi XiaopingXu Xiaoling - Xanthelasma palpebrarum (XP) is the most common form of cutaneous xanthoma, with a prevalence of 1.1%~4.4% in the population. However, the cause of XP remains largely unknown. In the present study, we used Mendelian randomization to assess the genetic association between plasma lipids, metabolic traits, and circulating protein with XP, leveraging summary statistics from large genome-wide association studies (GWAS). Genetically predicted plasma cholesterol and LDL-C, but not HDL-C or triglyceride, were significantly associated with XP. Metabolic traits, including BMI, fasting glucose, type 2 diabetes, systolic and diastolic blood pressure, were not significantly associated with XP. Furthermore, we found genetically predicted 12 circulating proteins were associated with XP, including FN1, NTM, FCN2, GOLM1, ICAM5, PDE5A, C5, CLEC11A, CXCL1, CCL2, CCL11, CCL13. In conclusion, this study identified plasma cholesterol, LDL-C, and 12 circulating proteins to be putative causal factors for XP, highlighting the role of plasma cholesterol and inflammatory response in XP development. - Source: PubMed
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Hu WentingLiu YaozhongLian CuihongLu Haocheng - Hepatitis B virus (HBV) is the dominant pathogenic factor of hepatocellular carcinoma (HCC) in Asia and Africa. Early identification and clinical diagnosis are crucial for HBV-related HCC. Random forest (RF) and artificial neural network (ANN) were an innovative and highly effective supervised machine learning (ML) algorithm for the early diagnosis and screening of HBV-related HCC. This study aims to identify significant biomarkers and develop a novel genetic model for the efficient diagnosis of HBV-related HCC. - Source: PubMed
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Jiang XiliHu JiyunXie Shucai - Individuals genetically susceptible to high schistosomiasis worm burden may contribute disproportionately to transmission and could be prioritized for control. Identifying genes involved may guide development of therapy. - Source: PubMed
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Nyangiri Oscar AsanyaMulindwa JuliusNamulondo JoyceKitibwa AnnaNassuuna JacentElliott AlisonKimuda Magambo PhillipBoobo AlexNerima BarbaraAdriko MosesDunton Nathan JMadhan Gaganjit KaurKristiansen MarkCasacuberta-Partal MiriamNoyes HarryMatovu Enock - Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS. - Source: PubMed
Publication date: 2023/10/30
Åkesson JuliaHojjati SaraHellberg SandraRaffetseder JohannaKhademi MohsenRynkowski RobertKockum IngridAltafini ClaudioLubovac-Pilav ZelminaMellergård JohanJenmalm Maria CPiehl FredrikOlsson TomasErnerudh JanGustafsson Mika