C1ORF132 Gene

Name chromosome 1 open reading frame 132
Description Predicted to be involved in miRNA-mediated post-transcriptional gene silencing. Predicted to be part of RISC complex. [provided by Alliance of Genome Resources, Mar 2025]
Summary
{"type": "root", "children": [{"type": "p", "children": [{"type": "t", "text": "\n Multiple independent studies have demonstrated that the gene C1orf132 plays a dual role in biomedical research. On one hand, the methylation status of C1orf132 has been robustly associated with chronological age in various tissues—including blood, saliva, and hair—and is therefore frequently incorporated into forensic age‐prediction models. In these studies, its CpG methylation levels consistently correlate with donor age, contributing to multivariate prediction panels that yield high accuracy across different populations and even in complex scenarios such as after hematopoietic stem cell transplantation."}, {"type": "fg", "children": [{"type": "fg_fs", "start_ref": "1", "end_ref": "9"}]}, {"type": "t", "text": " On the other hand, beyond serving as a robust epigenetic marker of aging, C1orf132 has been implicated in more specific cellular functions. Notably, one investigation found that the CpG sites within C1orf132—whose gene product encodes the microRNA MIR29B2C—exhibit unique methylation dynamics following allogeneic hematopoietic stem cell transplantation; this observation suggests a potential role in regulating the self‐renewal of hematopoietic stem cells and, consequently, graft function."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "10"}]}, {"type": "t", "text": " Moreover, dysregulation of C1orf132 expression has emerged as part of a prognostic signature in lung adenocarcinoma, further supporting its relevance as a biomarker in oncology."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "11"}]}, {"type": "t", "text": "\n "}]}, {"type": "rg", "children": [{"type": "r", "ref": 1, "children": [{"type": "t", "text": "Yessenia Anaya, Patrick Yew, Katherine A Roberts, et al. "}, {"type": "b", "children": [{"type": "t", "text": "DNA methylation of decedent blood samples to estimate the chronological age of human remains."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Int J Legal Med (2021)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1007/s00414-021-02650-8"}], "href": "https://doi.org/10.1007/s00414-021-02650-8"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "34245337"}], "href": "https://pubmed.ncbi.nlm.nih.gov/34245337"}]}, {"type": "r", "ref": 2, "children": [{"type": "t", "text": "A Freire-Aradas, C Phillips, A Mosquera-Miguel, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Development of a methylation marker set for forensic age estimation using analysis of public methylation data and the Agena Bioscience EpiTYPER system."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Forensic Sci Int Genet (2016)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1016/j.fsigen.2016.06.005"}], "href": "https://doi.org/10.1016/j.fsigen.2016.06.005"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "27337627"}], "href": "https://pubmed.ncbi.nlm.nih.gov/27337627"}]}, {"type": "r", "ref": 3, "children": [{"type": "t", "text": "Sohee Cho, Sang-Eun Jung, Sae Rom Hong, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Independent validation of DNA-based approaches for age prediction in blood."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Forensic Sci Int Genet (2017)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1016/j.fsigen.2017.04.020"}], "href": "https://doi.org/10.1016/j.fsigen.2017.04.020"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "28511095"}], "href": "https://pubmed.ncbi.nlm.nih.gov/28511095"}]}, {"type": "r", "ref": 4, "children": [{"type": "t", "text": "Sang-Eun Jung, Seung Min Lim, Sae Rom Hong, et al. "}, {"type": "b", "children": [{"type": "t", "text": "DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Forensic Sci Int Genet (2019)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1016/j.fsigen.2018.09.010"}], "href": "https://doi.org/10.1016/j.fsigen.2018.09.010"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "30300865"}], "href": "https://pubmed.ncbi.nlm.nih.gov/30300865"}]}, {"type": "r", "ref": 5, "children": [{"type": "t", "text": "Helena Correia Dias, Cristina Cordeiro, Francisco Corte Real, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Age Estimation Based on DNA Methylation Using Blood Samples From Deceased Individuals."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "J Forensic Sci (2020)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1111/1556-4029.14185"}], "href": "https://doi.org/10.1111/1556-4029.14185"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "31490551"}], "href": "https://pubmed.ncbi.nlm.nih.gov/31490551"}]}, {"type": "r", "ref": 6, "children": [{"type": "t", "text": "Jan Fleckhaus, Peter M Schneider "}, {"type": "b", "children": [{"type": "t", "text": "Novel multiplex strategy for DNA methylation-based age prediction from small amounts of DNA via Pyrosequencing."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Forensic Sci Int Genet (2020)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1016/j.fsigen.2019.102189"}], "href": "https://doi.org/10.1016/j.fsigen.2019.102189"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "31648151"}], "href": "https://pubmed.ncbi.nlm.nih.gov/31648151"}]}, {"type": "r", "ref": 7, "children": [{"type": "t", "text": "Helena Correia Dias, Cristina Cordeiro, Janet Pereira, et al. "}, {"type": "b", "children": [{"type": "t", "text": "DNA methylation age estimation in blood samples of living and deceased individuals using a multiplex SNaPshot assay."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Forensic Sci Int (2020)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1016/j.forsciint.2020.110267"}], "href": "https://doi.org/10.1016/j.forsciint.2020.110267"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "32325350"}], "href": "https://pubmed.ncbi.nlm.nih.gov/32325350"}]}, {"type": "r", "ref": 8, "children": [{"type": "t", "text": "Chao Xiao, Shaohua Yi, Daixin Huang "}, {"type": "b", "children": [{"type": "t", "text": "Genome-wide identification of age-related CpG sites for age estimation from blood DNA of Han Chinese individuals."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Electrophoresis (2021)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1002/elps.202000367"}], "href": "https://doi.org/10.1002/elps.202000367"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "33978960"}], "href": "https://pubmed.ncbi.nlm.nih.gov/33978960"}]}, {"type": "r", "ref": 9, "children": [{"type": "t", "text": "Renata Zbieć-Piekarska, Magdalena Spólnicka, Tomasz Kupiec, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Development of a forensically useful age prediction method based on DNA methylation analysis."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Forensic Sci Int Genet (2015)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1016/j.fsigen.2015.05.001"}], "href": "https://doi.org/10.1016/j.fsigen.2015.05.001"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "26026729"}], "href": "https://pubmed.ncbi.nlm.nih.gov/26026729"}]}, {"type": "r", "ref": 10, "children": [{"type": "t", "text": "Magdalena Spólnicka, Renata Zbieć Piekarska, Emilia Jaskuła, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Donor age and C1orf132/MIR29B2C determine age-related methylation signature of blood after allogeneic hematopoietic stem cell transplantation."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Clin Epigenetics (2016)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1186/s13148-016-0257-7"}], "href": "https://doi.org/10.1186/s13148-016-0257-7"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "27602173"}], "href": "https://pubmed.ncbi.nlm.nih.gov/27602173"}]}, {"type": "r", "ref": 11, "children": [{"type": "t", "text": "Fuduan Peng, Ruiping Wang, Yuanyuan Zhang, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Differential expression analysis at the individual level reveals a lncRNA prognostic signature for lung adenocarcinoma."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Mol Cancer (2017)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1186/s12943-017-0666-z"}], "href": "https://doi.org/10.1186/s12943-017-0666-z"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "28587642"}], "href": "https://pubmed.ncbi.nlm.nih.gov/28587642"}]}]}]}
Synonyms MIR29B2CHG, C1orf132
NCBI Gene ID 100128537
API
Download Associations
Predicted Functions View C1ORF132's ARCHS4 Predicted Functions.
Co-expressed Genes View C1ORF132's ARCHS4 Predicted Functions.
Expression in Tissues and Cell Lines View C1ORF132's ARCHS4 Predicted Functions.

Functional Associations

C1ORF132 has 498 functional associations with biological entities spanning 4 categories (organism, disease, phenotype or trait, chemical, cell line, cell type or tissue) extracted from 8 datasets.

Click the + buttons to view associations for C1ORF132 from the datasets below.

If available, associations are ranked by standardized value

Dataset Summary
dbGAP Gene-Trait Associations traits associated with C1ORF132 gene in GWAS and other genetic association datasets from the dbGAP Gene-Trait Associations dataset.
DISEASES Experimental Gene-Disease Association Evidence Scores diseases associated with C1ORF132 gene in GWAS datasets from the DISEASES Experimental Gene-Disease Assocation Evidence Scores dataset.
GEO Signatures of Differentially Expressed Genes for Small Molecules small molecule perturbations changing expression of C1ORF132 gene from the GEO Signatures of Differentially Expressed Genes for Small Molecules dataset.
GEO Signatures of Differentially Expressed Genes for Viral Infections virus perturbations changing expression of C1ORF132 gene from the GEO Signatures of Differentially Expressed Genes for Viral Infections dataset.
GTEx Tissue Gene Expression Profiles tissues with high or low expression of C1ORF132 gene relative to other tissues from the GTEx Tissue Gene Expression Profiles dataset.
GTEx Tissue Sample Gene Expression Profiles tissue samples with high or low expression of C1ORF132 gene relative to other tissue samples from the GTEx Tissue Sample Gene Expression Profiles dataset.
Roadmap Epigenomics Cell and Tissue Gene Expression Profiles cell types and tissues with high or low expression of C1ORF132 gene relative to other cell types and tissues from the Roadmap Epigenomics Cell and Tissue Gene Expression Profiles dataset.
TISSUES Experimental Tissue Protein Expression Evidence Scores tissues with high expression of C1ORF132 protein in proteomics datasets from the TISSUES Experimental Tissue Protein Expression Evidence Scores dataset.