Evidence map›Paper›PMID 42147719›Full record

ReviewCancer drug resistance (Alhambra, Calif.)2026

Roles and potential applications of non-coding RNAs in cancer treatment with immune checkpoint inhibitors and immunomodulatory therapies.

Yayu Chen, Zhishuang Ye, Yanping Wang, Shanlan Liang, Daniel Xin Zhang

Abstract readReview
In one paragraph

Review in Cancer drug resistance (Alhambra, Calif.), 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

5 authors.

Yayu ChenDepartment of Health Sciences, Hong Kong Metropolitan University, Hong Kong, China.
Zhishuang YeDepartment of Health Sciences, Hong Kong Metropolitan University, Hong Kong, China.
Yanping WangDepartment of Health Sciences, Hong Kong Metropolitan University, Hong Kong, China.
Shanlan LiangDepartment of Health Sciences, Hong Kong Metropolitan University, Hong Kong, China.
Daniel Xin ZhangDepartment of Health Sciences, Hong Kong Metropolitan University, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-coding RNAs (ncRNAs) have emerged as key regulators of cancer-immune crosstalk, especially in an era when immune checkpoint inhibitors and other immunomodulatory therapies are reshaping the cancer treatment landscape. Accumulating evidence continues to indicate that ncRNAs, including microRNAs, long non-coding RNAs and circular RNAs, critically connect oncological signaling with immune interactions, thereby influencing clinical outcomes. In this review, we summarize how ncRNAs modulate key immune checkpoint axes, particularly programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) and cytotoxic T-lymphocyte antigen 4 (CTLA-4). We also discuss ncRNA networks that are actively involved in modern cancer immunotherapies, such as T cell-based therapies, macrophage and dendritic cell engineering, cytokine therapies, cancer vaccines and oncolytic viruses. Building on these mechanistic insights, we outline the potential of ncRNAs as biomarkers for predicting response and prognosis, as future therapeutic targets to improve and enhance immunotherapy combinations, along with key barriers in the field and emerging solutions. Altogether, the evidence not only highlights ncRNAs as rising stars in precision immuno-oncology, but also motivates future opportunities to incorporate ncRNAs into clinical consideration.

Indexed as

cancerimmune checkpoint inhibitorsimmunotherapyNon-coding RNAtherapy

Identifiers

PMID42147719
PMCPMC13174202

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.