ReviewAnnals of medicine2023
Unlocking the predictive potential of long non-coding RNAs: a machine learning approach for precise cancer patient prognosis.
Review in Annals of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Cancer in transition: discovery of tumor-intrinsic transcriptional programs shaping the immune and microenvironmental landscape.Biomarker research · 2026Review
- Pharmacomodulation of G-quadruplexes in long non-coding RNAs dysregulated in colorectal cancer.BMC biology · 2025Article
- A Dataset Curated for the Assessment of G4s in the LncRNAs Dysregulated in Various Human Cancers.Scientific data · 2025Article
- Targeting LINC00665/miR-199b-5p/SERPINE1 axis to inhibit trastuzumab resistance and tumorigenesis of gastric cancer via PI3K/AKt pathway.Non-coding RNA research · 2025Article
- Decoding long non‑coding RNAs: Friends and foes in cancer development (Review).International journal of oncology · 2024Review
- Long non-coding RNAs in drug resistance across the top five cancers: Update on their roles and mechanisms.Heliyon · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The intricate web of cancer biology is governed by the active participation of long non-coding RNAs (lncRNAs), playing crucial roles in cancer cells' proliferation, migration, and drug resistance. Pioneering research driven by machine learning algorithms has unveiled the profound ability of specific combinations of lncRNAs to predict the prognosis of cancer patients. These findings highlight the transformative potential of lncRNAs as powerful therapeutic targets and prognostic markers. In this comprehensive review, we meticulously examined the landscape of lncRNAs in predicting the prognosis of the top five cancers and other malignancies, aiming to provide a compelling reference for future research endeavours. Leveraging the power of machine learning techniques, we explored the predictive capabilities of diverse lncRNA combinations, revealing their unprecedented potential to accurately determine patient outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.