Orphan nuclear receptors as drug targets for the treatment of prostate and breast cancers is a research paper published in Cancer Treatment Reviews (2014). On theSindex it has a DataRank of 1.7. It has been cited 50 times, with 31 citing works in its 1-hop citation network.
Nuclear receptors (NRs), a family of 48 transcriptional factors, have been studied intensively for their roles in cancer development and progression. The presence of distinctive ligand binding sites capable of interacting with small molecules has made NRs attractive targets for developing cancer therapeutics. In particular, a number of drugs have been developed over the years to target human androgen- and estrogen receptors for the treatment of prostate cancer and breast cancer. In contrast, orphan nuclear receptors (ONRs), which in many cases lack known biological functions or ligands, are still largely under investigated. This review is a summary on ONRs that have been implicated in prostate and breast cancers, specifically retinoic acid-receptor-related orphan receptors (RORs), liver X receptors (LXRs), chicken ovalbumin upstream promoter transcription factors (COUP-TFs), estrogen related receptors (ERRs), nerve growth factor 1B-like receptors, and ‘‘dosage-sensitive sex reversal, adrenal hypoplasia critical region, on chromosome X, gene 1’’ (DAX1). Discovery and development of small molecules that can bind at various functional sites on these ONRs will help determine their biological functions. In addition, these molecules have the potential to act as prototypes for future drug development. Ultimately, the therapeutic value of targeting the ONRs may go well beyond prostate and breast cancers.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.590
From this paper's citation signal
Citation Network Contribution
1.1
From 29 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 34% comes from its base citations and 66% from the citation network (29 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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