<scp>PINTology</scp>: A short history of the <scp>lncRNA LINC‐PINT</scp> in different diseases
Bibliographic record
Abstract
LINC-PINT is a p53-induced long intergenic noncoding transcript that plays a crucial role in many diseases, especially cancer. This long noncoding RNA (lncRNA) gene produces in total 102 (LNCipedia) alternatively spliced variants (LINC-PINT:1 to LINC-PINT:102). The functions of known variants include RNA transcripts, host transcripts for circular RNA (circRNA) generation and as sources for the translation of short peptides. In most human tumors, LINC-PINT is down-regulated where it serves as a tumor suppressor. However, the diversity of its functions in other maladies signifies its general clinical importance. Current LINC-PINT molecular functions include RNA-protein interactions, miRNA sponging and epigenetic modulation with these mechanisms operating in different cellular contexts to exert effects on biological processes ranging from DNA damage responses, cell cycle and growth arrest, senescence, cell migration and invasion, and apoptosis. Genetic polymorphisms in LINC-PINT have also been functionally associated with cancer and other pathologies including the autoimmune diseases pemphigus foliaceus and arthritis. Hence, LINC-PINT shows great potential as a clinical biomarker, especially for the diagnosis and prognosis of cancer. In this review, we explore the current knowledge highlighting the distinctive molecular functions of LINC-PINT in specific cancers and other disease states. This article is categorized under: RNA in Disease and Development > RNA in Disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".