Evidence map›Paper›PMID 41923561›Full record

ArticleJournal of cellular and molecular medicine2026

Novel Prognostic and Predictive miRNA Biomarkers Shape the Landscape of T Cell Dysfunction in Cancer.

Hong-Jiu Wang, Xiao-Ling Wen, Zhe-Yu Wu, Na Wang, Shu-Heng Fu, Fei-Fan Xiong, Jiang-Ying Liang, Deng-Hui Guo, Si-Rui Li, Jie Shen and 2 more

Abstract read
In one paragraph

Article in Journal of cellular and molecular medicine, 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

12 authors.

Hong-Jiu WangSchool of Intelligent Medicine and Technology, Big Data Research Center, Hainan Medical University, Haikou, China.ORCID 0009-0000-4976-005X
Xiao-Ling WenSchool of Intelligent Medicine and Technology, Big Data Research Center, Hainan Medical University, Haikou, China.ORCID 0009-0002-1988-2384
Zhe-Yu WuShanghai Jiao Tong University, Shanghai, China.
Na WangSchool of Intelligent Medicine and Technology, Big Data Research Center, Hainan Medical University, Haikou, China.
Shu-Heng FuSchool of Intelligent Medicine and Technology, Big Data Research Center, Hainan Medical University, Haikou, China.
Fei-Fan XiongSchool of Intelligent Medicine and Technology, Big Data Research Center, Hainan Medical University, Haikou, China.
Jiang-Ying LiangSchool of Intelligent Medicine and Technology, Big Data Research Center, Hainan Medical University, Haikou, China.
Deng-Hui GuoSchool of Intelligent Medicine and Technology, Big Data Research Center, Hainan Medical University, Haikou, China.
Si-Rui LiSchool of Intelligent Medicine and Technology, Big Data Research Center, Hainan Medical University, Haikou, China.
Jie ShenSchool of Intelligent Medicine and Technology, Big Data Research Center, Hainan Medical University, Haikou, China.
Xiao-Ling GaoThe Medical Laboratory Center, Hainan General Hospital, Haikou, China.ORCID 0000-0002-5466-3200
Zhen-Zhen WangSchool of Intelligent Medicine and Technology, Big Data Research Center, Hainan Medical University, Haikou, China.

Funding

Education Department of Hainan Province No.Hnky2022-32Education Department of Hainan Province No.Hnky2025-24Hainan Province Clinical, Medical CenterKey Research and Development Project of Hainan Province No.ZDYF2022SHFZ055National Natural Science Foundation of China No.31701159National Natural Science Foundation of China No.32160179National Natural Science Foundation of China No.32500823Natural Science Foundation of Hainan Province No.621MS041Natural Science Foundation of Hainan Province No.821MS045Natural Science Foundation of Hainan Province No.821MS0777Natural Science Foundation of Hainan Province No.822MS074
6 · The paper itself

Abstract

T cell dysfunction (TCD) plays a critical role in cancer progression and significantly impacts patient outcomes. Despite its importance, the exact molecular mechanisms underlying TCD remain poorly understood. To address this, we constructed a comprehensive pan-cancer landscape of TCD, with a particular focus on identifying miRNA biomarkers that define and predict TCD severity. Our analysis revealed six key miRNAs (miR-203b, miR-214, miR-4772, miR-141, miR-200a, and miR-200b) that were closely associated with varying degrees of TCD. These prognostic miRNAs not only exhibited distinct expression patterns across four identified TCD subtypes (from low to high TCD severity) but also demonstrated strong predictive performance in classifying patients with different levels of TCD. The identified miRNA signatures serve as reliable biomarkers for stratifying patients into high-risk and low-risk groups, with higher TCD levels correlating to poorer overall survival. In addition to miRNA biomarkers, we observed that patients with severe TCD exhibited increased infiltration of immune cells and macrophages and dysregulation of DNA methylation patterns. Patients with higher degrees of TCD displayed low methylation levels, which further contributed to the progression of T cell dysfunction. In summary, our study highlights the pivotal role of miRNA biomarkers in shaping the landscape of T cell dysfunction across cancers. These miRNAs serve as both prognostic indicators and predictive tools, enabling accurate classification of TCD severity and offering new avenues for therapeutic exploration and patient stratification in cancer immunotherapy.

Indexed as

Biomarkers, TumorMicroRNAsNeoplasmsT-LymphocytesDNA MethylationGene Expression ProfilingGene Expression Regulation, NeoplasticHumansPrognosisTumor MicroenvironmentBiomarkers, TumorMicroRNAscancer subtypeimmunocompetencemiRNApan‐cancerprognosis biomarkerT cell dysfunctiontumour microenvironment

Identifiers

PMID41923561
PMCPMC13045257

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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.