{"type": "root", "children": [{"type": "p", "children": [{"type": "t", "text": "\n CT47A3 is a member of the cancer/testis (CT) gene family – a group of genes that are ordinarily expressed only in germ cells but are aberrantly activated in various cancers, particularly in high‐risk, estrogen receptor–negative tumors."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "1"}]}, {"type": "t", "text": " Its expression profile not only makes it an attractive candidate for immunotherapeutic targeting by eliciting spontaneous immune responses, but also positions it as a potential biomarker for cancer diagnosis. In prostate cancer, for instance, cancer/testis antigens such as CT47A3 are being explored for their diagnostic and therapeutic utility, with studies demonstrating differential autoantibody responses that may help distinguish malignant from benign conditions."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "2"}]}, {"type": "t", "text": " Moreover, as comprehensive transcript catalogs like CHESS expand our understanding of the human transcriptome by cataloging both protein‐coding and noncoding sequences, CT47A3 is being considered within the broader landscape of transcriptional activity where discerning functional transcripts from transcriptional noise is critical."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "3"}]}, {"type": "t", "text": " Finally, integrating genomic profiles that include genes like CT47A3 into hierarchical machine learning systems may enhance personalized treatment strategies and improve prognostic predictions in cancer survivors, further underscoring its potential relevance in precision oncology."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "4"}]}, {"type": "t", "text": "\n "}]}, {"type": "rg", "children": [{"type": "r", "ref": 1, "children": [{"type": "t", "text": "Otavia L Caballero, Sami Shousha, Qi Zhao, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Expression of Cancer/Testis genes in ductal carcinoma in situ and benign lesions of the breast."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Oncoscience (2013)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.18632/oncoscience.4"}], "href": "https://doi.org/10.18632/oncoscience.4"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "25593980"}], "href": "https://pubmed.ncbi.nlm.nih.gov/25593980"}]}, {"type": "r", "ref": 2, "children": [{"type": "t", "text": "Henry A Adeola, Muneerah Smith, Lisa Kaestner, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Novel potential serological prostate cancer biomarkers using CT100+ cancer antigen microarray platform in a multi-cultural South African cohort."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Oncotarget (2016)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.18632/oncotarget.7359"}], "href": "https://doi.org/10.18632/oncotarget.7359"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "26885621"}], "href": "https://pubmed.ncbi.nlm.nih.gov/26885621"}]}, {"type": "r", "ref": 3, "children": [{"type": "t", "text": "Mihaela Pertea, Alaina Shumate, Geo Pertea, et al. "}, {"type": "b", "children": [{"type": "t", "text": "CHESS: a new human gene catalog curated from thousands of large-scale RNA sequencing experiments reveals extensive transcriptional noise."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Genome Biol (2018)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1186/s13059-018-1590-2"}], "href": "https://doi.org/10.1186/s13059-018-1590-2"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "30486838"}], "href": "https://pubmed.ncbi.nlm.nih.gov/30486838"}]}, {"type": "r", "ref": 4, "children": [{"type": "t", "text": "Ashraf Abou Tabl, Abedalrhman Alkhateeb, Waguih ElMaraghy, et al. "}, {"type": "b", "children": [{"type": "t", "text": "A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Front Genet (2019)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.3389/fgene.2019.00256"}], "href": "https://doi.org/10.3389/fgene.2019.00256"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "30972106"}], "href": "https://pubmed.ncbi.nlm.nih.gov/30972106"}]}]}]}