C1orf43
- Known as:
- C1orf43
- Catalog number:
- 002769A
- Product Quantity:
- 250ul
- Category:
- -
- Supplier:
- ABM
- Gene target:
- C1orf43
Related genes to: C1orf43
- Gene:
- C1orf43 NIH gene
- Name:
- chromosome 1 open reading frame 43
- Previous symbol:
- -
- Synonyms:
- NICE-3, DKFZp586G1722
- Chromosome:
- 1q21.3
- Locus Type:
- gene with protein product
- Date approved:
- 2004-09-14
- Date modifiied:
- 2018-05-11
Related products to: C1orf43
Related articles to: C1orf43
- Atopic dermatitis (AD) and psoriasis (PsO) are chronic inflammatory skin diseases with distinct features, but clinical and molecular overlap exists. These ambiguous phenotypes complicate diagnosis and treatment, and their immunologic basis remains poorly defined. This study aimed to delineate compartment-specific immune profiles in AD-PsO overlap lesions and explore candidate markers associated with molecular stratification. - Source: PubMed
Publication date: 2026/09/25
Lee JongeunChun Yookyung SophieNam Hyo JeongHuh Yun JungKim JaehwanKrueger James GPaik Seung SamKim Hyun JeJin Seon-PilKim Jeong Eun - Mitochondrial dynamics (MD) are crucial in various inflammatory disorders, yet the specific mechanisms involved in psoriasis remain inadequately understood. Thus, this study aims to discover potential biomarkers and explore the mechanisms related to MD in psoriasis by employing bioinformatics methods in conjunction with the Mendelian randomization (MR) approach. In this investigation, datasets associated with psoriasis, specifically (GSE14905, GSE13355, and ukb-a-100), alongside genes pertinent to MD (MDRGs), were employed. The initial step involved the identification of significant module genes associated with MD through weighted gene co-expression network analysis. Subsequently, the identified module genes were cross-referenced with differentially expressed genes discerned between psoriasis and control groups to extract differentially expressed MDRGs. Additionally, MR analysis was conducted to identify potential candidate genes. The definitive potential biomarkers were determined through protein-protein interaction (PPI) networks, machine learning methodologies, receiver operating characteristic analysis, and expression profiling. Finally, gene set enrichment analysis, alongside immune infiltration and immune response assessments, was executed to elucidate the underlying mechanisms by which the potential biomarkers function in the context of psoriasis. There were 3136 key module genes through weighted gene co-expression network analysis and 643 differentially expressed MDRGs by crossing key module genes and 4310 differentially expressed genes. Afterward, 56 candidate genes with causal relationship to psoriasis were selected by MR analysis. Then 19 hub genes from PPI network were used to further screen 6 feature genes by machine learning, and they had a better ability to distinguish psoriasis (area under the curve > 0.7). C1orf43, SNF8, STOML2, and MRPS16 were identified as potential biomarkers in psoriasis, and were co-enriched in pyrimidine metabolism, DNA_replication, and proteasome. Eventually, there were 11 differential immune cells (memory B cells, activated dendritic cells, etc) and 13 differential immune responses (antigen processing and presentation, antimicrobials, etc) between psoriasis and control samples in psoriasis (P < .05). C1orf43, SNF8, STOML2, and MRPS16 were identified as potential biomarkers linked to MD in psoriasis, which provide promising leads for further investigation. These biomarkers require experimental validation to confirm their role in the pathogenesis of psoriasis and their potential as therapeutic targets. - Source: PubMed
Zhang BaolanShi JingJiang JianhangZhang Litao - Lactylation is closely involved in cancer progression, but its role in hepatocellular carcinoma (HCC) is unclear. The present work set out to develop a lactylation-related gene (LRG) signature for HCC. The lactylation score of tumor and normal groups was calculated using the gene set variation analysis (GSVA) package. The single-cell RNA sequencing (scRNA-seq) analysis of HCC was performed in the "Seurat" package. Prognostic LRGs were selected by performing univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses to develop and validate a Riskscore model. Functional enrichment analysis was conducted by gene set enrichment analysis (GSEA) using the "clusterProfiler" package. Genomic characteristics between different risk groups were compared, and tumor mutational burden (TMB) was calculated by the "Maftools" package. Immune cell infiltration was assessed by algorithms of cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT), microenvironment cell populations-counter (MCP-counter), estimating the proportions of immune and cancer cells (EPIC), tumor immune estimation resource (TIMER), and single-sample gene set enrichment analysis (ssGSEA). Immunotherapy response was predicted by the tumor immune dysfunction and exclusion (TIDE) algorithm. Drug sensitivity was analyzed using the "pRRophetic" package. A nomogram was established using the "rms" package. The expressions of the prognostic LRGs in HCC cells were verified by in vitro test, and cell counting kit-8 (CCK-8), wound healing, and transwell assays were carried out to measure the viability, migration, and invasion of HCC cells. The lactylation score, which was higher in the tumor group than in the normal group, has been confirmed as an independent factor for the prognostic evaluation in HCC. Six prognostic LRGs, including two protective genes ( and ) and four risk genes (, , , and ), were identified to develop a Riskscore model with a strong prognostic prediction performance in HCC. The scRNA-seq analysis revealed that was largely expressed in myeloid cells, while , , , , and were mainly expressed in hepatocytes. The high-risk group was primarily enriched in the pathways involved in tumor occurrence and development, with higher T cell infiltration. Moreover, the high-risk group was found to be less responsive to immunotherapy but was more sensitive to chemotherapeutic drugs. By integrating Riskscore and clinical features, a nomogram with a high predictive accuracy was developed. Additionally, , , , and were highly expressed in HCC cells. Silencing inhibited the viability, migration, and invasion of HCC cells. The present work developed a novel LRG gene signature that could be considered a promising therapeutic target and biomarker for HCC. - Source: PubMed
Publication date: 2025/02/14
Yu LiangheShi YanZhi ZhenyuLi ShuangYu WenlongZhang Yongjie - Previous analyses of bulk colon and rectal adenocarcinoma (COAD/READ) RNA-sequence data comparing African ancestry (AA) and European ancestry (EA) groups have reported differentially expressed genes related to the immune response. However, these previous analyses of AA versus EA tissues did not control for mismatch-repair enzyme (MMR)/microsatellite instability (MSI) status, which is also associated with altered expression of immune related genes, and is used to determine eligibility for immune checkpoint inhibitor therapy. - Source: PubMed
Publication date: 2025/10/02
Joseph Dimitri FFu AndrewFlores Ricardo ESharma Dev VLaComb Joseph FClark Julie MLi EllenLiao YunhanYang JieYu QiAdams SeiduOgunwobi Olorunseun OTheisen BrianSteele Nina GChen BinGuillaume Alexandra - Accurate measurement of human epidermal growth factor receptor 2 (HER2) copy number variation (CNV) is very important for guiding the tumor target therapy in breast cancer. Digital PCR (dPCR) is a sensitive and an absolute quantitative method, which can be used to detect HER2 CNV. Three HER2 exon-specific digital PCR assays along with three new reference genes assays (homo sapiens ribonuclease P RNA component H1 (RPPH1), glucose-6-phosphate isomerase (GPI), and chromosome 1 open reading frame 43 (C1ORF43), on different chromosomes) were established and validated by using standard reference material, 8 different cell lines and 110 clinical Formalin-fixed and paraffin-embedded (FFPE) samples. DPCR can achieve precise quantification of HER2 CNV by calculating the ratio of HER2/reference gene. The positive and negative coincidence rates were 98% (53/54) and 95% (53/56), respectively, compared with fluorescence in situ hybridization (FISH) diagnostic result 110 of FFPE samples. The common reference gene CEP17 used for FISH diagnostic was not suitable as single reference gene for HER2 CNV measurements by dPCR. The best practice of HER2 CNV determination by dPCR is to conduct the three duplex assays of H1 (HER2 exon 4) with the proposed three new reference genes, with a positive cut-off value of H1/RPPH1 ≥ 2.0 or H1/averaged reference gene ≥ 2.0. The proposed dPCR method in our study can accurately provide absolute copy number of HER2 and reference gene on an alternative chromosome, thus avoiding false negative caused by polysomy of chromosome 17. The improved molecular typing and diagnosis of breast cancer will better guide clinical medication. - Source: PubMed
Publication date: 2022/12/26
Wang XiaXing DechunLiu ZhengZhang YujingCheng BoSun SuozhuWang QingtaoDong Lianhua