Clinical implications of lncRNA LINC-PINT in cancer
Bibliographic record
Abstract
Long noncoding RNAs (lncRNAs) possess the potential for therapeutic targeting to treat many disorders, including cancers. Several RNA-based therapeutics (ASOs and small interfering RNAs) have gained FDA approval over the past decade. And with their potent effects, lncRNA-based therapeutics are of emerging significance. One important lncRNA target is LINC-PINT, with its universalized functions and relationship with the famous tumor suppressor gene TP53. Establishing clinical relevance, much like p53, the tumor suppressor activity of LINC-PINT is implicated in cancer progression. Moreover, several molecular targets of LINC-PINT are directly or indirectly used in routine clinical practice. We further associate LINC-PINT with immune responses in colon adenocarcinoma, proposing the potential utility of LINC-PINT as a novel biomarker of immune checkpoint inhibitors. Collectively, current evidence suggests LINC-PINT can be considered for use as a diagnostic/prognostic marker for cancer and several other diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".