ReviewCancers2020
Functional Screening Techniques to Identify Long Non-Coding RNAs as Therapeutic Targets in Cancer.
Review in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 17 citations in OpenAlex.
- Mechanistic insights into HAGLROS-mediated therapy resistance in ovarian cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Article
- Designing nucleic acid-based therapeutics for cancer treatment: Updates on the state of the art.Molecular therapy : the journal of the American Society of Gene Therapy · 2026Review
- A comprehensive review on lncRNA LOXL1-AS1: molecular mechanistic pathways of lncRNA LOXL1-AS1 in tumorigenicity of cancer cells.Frontiers in oncology · 2024Review
- Involvement of lncRNAs in cancer cells migration, invasion and metastasis: cytoskeleton and ECM crosstalk.Journal of experimental & clinical cancer research : CR · 2023Review
- Engineered Extracellular Vesicle-Delivered CRISPR/CasRx as a Novel RNA Editing Tool.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Pathogenic roles of long noncoding RNAs in melanoma: Implications in diagnosis and therapies.Genes & diseases · 2023Review
- Microinjection of antisense oligonucleotides into living mouse testis enables lncRNA function study.Cell & bioscience · 2021Article
- Functional interplay between long non-coding RNAs and the Wnt signaling cascade in osteosarcoma.Cancer cell international · 2021Review
- NSE, positively regulated by LINC00657-miR-93-5p axis, promotes small cell lung cancer (SCLC) invasion and epithelial-mesenchymal transition (EMT) process.International journal of medical sciences · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Recent technological advancements such as CRISPR/Cas-based systems enable multiplexed, high-throughput screening for new therapeutic targets in cancer. While numerous functional screens have been performed on protein-coding genes to date, long non-coding RNAs (lncRNAs) represent an emerging class of potential oncogenes and tumor suppressors, with only a handful of large-scale screens performed thus far. Here, we review in detail currently available screening approaches to identify new lncRNA drivers of tumorigenesis and tumor progression. We discuss the various approaches of genomic and transcriptional targeting using CRISPR/Cas9, as well as methods to post-transcriptionally target lncRNAs via RNA interference (RNAi), antisense oligonucleotides (ASOs) and CRISPR/Cas13. We discuss potential advantages, caveats and future applications of each method to provide an overview and guide on investigating lncRNAs as new therapeutic targets in cancer.
Indexed as
Identifiers
What OpenQuestion holds
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.