Ankrd34b, also known as ankyrin repeat domain 34B, may play various roles in different biological processes. In murine embryonic stem cell-derived brachyury(+) cells, its up-regulation was noted, suggesting it might be a positive regulator of neurogenesis and a negative regulator of adipogenesis [2]. It has also been associated with different traits in animals, being a prioritized candidate gene for reproduction and growth in Nellore cattle [3].

In normal kidney tissue, age-related DNA methylation analysis identified loci in ANKRD34B as potential epigenetic cancer risk susceptibility loci for renal cancer [1]. In the context of osteoporosis, it was among the genes identified as part of an optimal differential genes combination with high diagnostic effect for predicting female osteoporosis risk [4]. In Alzheimer's disease, ANKRD34B corresponded to a differentially methylated site [5]. In pigs, ANKRD34B was one of the genes with copy number changes in CNVRs, potentially having economic importance in pig breeding [6]. In prostate cancer, its overexpression was validated by RT-qPCR in tumor tissue compared to adjacent normal tissue [7].

In conclusion, Ankrd34b is involved in multiple biological processes and disease conditions, including cancer, osteoporosis, and potentially Alzheimer's disease. Studies across different species, especially in genetic models, have revealed its role in various physiological and pathological states, highlighting its importance in understanding disease mechanisms and potentially in developing new diagnostic or therapeutic strategies.

References:

1. Serth, Jürgen, Peters, Inga, Hill, Bastian, Klintschar, Michael, Kuczyk, Markus Antonius. 2022. Age-Related DNA Methylation in Normal Kidney Tissue Identifies Epigenetic Cancer Risk Susceptibility Loci in the ANKRD34B and ZIC1 Genes. In International journal of molecular sciences, 23, . doi:10.3390/ijms23105327. https://pubmed.ncbi.nlm.nih.gov/35628134/

2. Doss, Michael Xavier, Wagh, Vilas, Schulz, Herbert, Hescheler, Jürgen, Sachinidis, Agapios. 2010. Global transcriptomic analysis of murine embryonic stem cell-derived brachyury(+) (T) cells. In Genes to cells : devoted to molecular & cellular mechanisms, 15, 209-28. doi:10.1111/j.1365-2443.2010.01390.x. https://pubmed.ncbi.nlm.nih.gov/20184659/

3. Ogunbawo, Adebisi R, Mulim, Henrique A, Campos, Gabriel S, Oliveira, Hinayah R. 2024. Genetic Foundations of Nellore Traits: A Gene Prioritization and Functional Analyses of Genome-Wide Association Study Results. In Genes, 15, . doi:10.3390/genes15091131. https://pubmed.ncbi.nlm.nih.gov/39336722/

4. Tang, Hongwei, Han, Qingtian, Yin, Yong. 2022. Screening of Important Markers in Peripheral Blood Mononuclear Cells to Predict Female Osteoporosis Risk Using LASSO Regression Algorithm and SVM Method. In Evolutionary bioinformatics online, 18, 11769343221075014. doi:10.1177/11769343221075014. https://pubmed.ncbi.nlm.nih.gov/35110962/

5. Ren, Jianting, Zhang, Bo, Wei, Dongfeng, Zhang, Zhanjun. 2020. Identification of Methylated Gene Biomarkers in Patients with Alzheimer's Disease Based on Machine Learning. In BioMed research international, 2020, 8348147. doi:10.1155/2020/8348147. https://pubmed.ncbi.nlm.nih.gov/32309439/

6. Dong, K, Pu, Y, Yao, N, Guan, W, Ma, Y. 2015. Copy number variation detection using SNP genotyping arrays in three Chinese pig breeds. In Animal genetics, 46, 101-9. doi:10.1111/age.12247. https://pubmed.ncbi.nlm.nih.gov/25590996/

7. Nikitina, Anastasia S, Sharova, Elena I, Danilenko, Svetlana A, Pushkar, Dmitry Y, Kostryukova, Elena S. . Novel RNA biomarkers of prostate cancer revealed by RNA-seq analysis of formalin-fixed samples obtained from Russian patients. In Oncotarget, 8, 32990-33001. doi:10.18632/oncotarget.16518. https://pubmed.ncbi.nlm.nih.gov/28380430/