Evidence map›Paper›PMID 42255996›Full record

ReviewBiomedical reports2026

Interaction between lncRNAs and RNA binding proteins, and their potential as drug targets in the therapy of liver cancer (Review).

Jiawei Liu, Dong Chen

Abstract readReview
In one paragraph

Review in Biomedical reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jiawei LiuDepartment of Pharmacy, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China.
Dong ChenCenter for Genome Analysis, Wuhan Ruixing Biotechnology, Co., Ltd., Wuhan, Hubei 430074, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver cancer, including hepatocellular carcinoma (HCC) as the major type, is a serious malignant tumor with high morbidity and mortality worldwide. Long noncoding RNAs (lncRNAs) play an essential role in the pathogenesis and development of liver cancer, as they can cooperate with and modulate other molecules, particularly RNA binding proteins (RBPs), to perform regulatory functions. Conversely, RBPs also deeply influence the functional manner of lncRNAs in cancer cells. Thus, it is critical to decipher how lncRNAs and RBPs interact with each other, affect the expression, localization, structure, modification, or function of their partners, and induce the following biological programs. In the present review, the interactions between lncRNAs and RBPs identified over the past years are examined, and their modes of cooperation and downstream effects on liver cancer progression are explored. Briefly, lncRNA-RBP pairs were classified into different categories according to their mechanisms of interaction and their influence on liver cancer development, including how their interactions affect one another, how they cooperate to regulate targets, and the resulting functional outcomes on liver cancer. In addition, the current state-of-the-art databases and technologies used to identify potential interactions between lncRNAs and RBPs were also emphasized, including crosslinking and immunoprecipitation, RNA immunoprecipitation, RNA pull-down and mass spectrometry. In summary, the present review systematically summarized the regulatory functions of lncRNA-RBP pairs in liver cancer, which suggests the potential to harness RNA-based therapeutics as an alternative treatment modality for liver cancer.

Indexed as

functional mannerliver cancerlong noncoding RNAsRNA binding proteins

Identifiers

PMID42255996
PMCPMC13238478

What OpenQuestion holds

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Registered trials

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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.