Evidence map›Paper›PMID 42050573›Full record

ArticleBiology direct2026

Integrative transcriptomic profiling reveals NK cell exhaustion-associated prognostic genes and identifies CSF1 as a key immunoregulatory target in hepatocellular carcinoma.

Yu Wang, Xiaoyan Xu, Yu Qi, Yiling Zhang, Long Ding, Zhuang Li, Shuqin Long, Guohua Yang, Rui Sun, Xiaohong Guo

Abstract read
In one paragraph

Article in Biology direct, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Yu Wang *Center of Traditional Chinese Medicine Modernization for Liver Diseases, Hubei University of Chinese Medicine, Wuhan, 430065, China.
Xiaoyan Xu *Department of Pathology, College of Basic Medical Sciences, Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region, 010059, China.
Yu QiCenter of Traditional Chinese Medicine Modernization for Liver Diseases, Hubei University of Chinese Medicine, Wuhan, 430065, China.
Yiling ZhangCenter of Traditional Chinese Medicine Modernization for Liver Diseases, Hubei University of Chinese Medicine, Wuhan, 430065, China.
Long DingCenter of Traditional Chinese Medicine Modernization for Liver Diseases, Hubei University of Chinese Medicine, Wuhan, 430065, China.
Zhuang LiCenter of Traditional Chinese Medicine Modernization for Liver Diseases, Hubei University of Chinese Medicine, Wuhan, 430065, China.
Shuqin LongDepartment of Stomatology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China.
Guohua YangWuhan BioEagle Biological Science and Technology Co.,Ltd., Wuhan, 430073, China.
Rui SunDepartment of Stomatology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China. sunrui@sxbqeh.com.cn.
Xiaohong GuoCenter of Traditional Chinese Medicine Modernization for Liver Diseases, Hubei University of Chinese Medicine, Wuhan, 430065, China. judyguo313@hbucm.edu.cn.ORCID 0000-0003-1770-0639

Funding

Natural Science Foundation of China 82174020
6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) features a complex tumor immune microenvironment (TIME) where natural killer cell exhaustion (NKEX) facilitates immune evasion. Its regulatory networks and prognostic value remain insufficiently understood.

methodsWe integrated single-cell (GSE149614) and bulk RNA-seq (TCGA-LIHC) data. Following Seurat-based clustering, we utilized CellChat and pySCENIC for communication and transcription factor analysis. NKEX-associated modules were identified via gene set variation analysis and weighted gene co-expression network analysis. A prognostic signature was developed using LASSO-Cox regression and validated in an external cohort (ICGC). CSF1 was prioritized for validation through clinical immunohistochemistry, siRNA knockdown, and Western Blotting. Candidate compounds identified via reverse network pharmacology were validated through CCK-8 assays, docking, western blotting and quantitative PCR.

resultsSingle-cell analysis revealed pronounced NKEX and disrupted communication in HCC. A four-gene prognostic signature (AKR1B1, SMS, CSF1, CFL1) demonstrated robust predictive performance in both TCGA (1-year AUC: 0.759) and external validation cohorts. High-risk patients showed significantly poorer survival. CSF1 was markedly upregulated in HCC tissues; its silencing inhibited Huh7 cell migration and invasion while upregulating CXCL10 in Huh7 cells and CXCR3 in NK92 cells. Molecular docking and CCK-8 assays justified the dosage and identified punicalagin and evoden as potent inhibitors that significantly suppressed CSF1 expression at both mRNA and protein levels.

conclusionThrough integrated multi-omics and experimental validation, we characterized NKEX in HCC and established a robust prognostic signature. CSF1 emerged as a key immunomodulatory target. Punicalagin and evoden were identified as potential lead compounds to modulate the CSF1, offering a promising strategy to restore antitumor immunity in HCC.

Indexed as

Carcinoma, HepatocellularKiller Cells, NaturalLiver NeoplasmsTranscriptomeGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMacrophage Colony-Stimulating FactorPrognosisCSF1 protein, humanMacrophage Colony-Stimulating FactorHepatocellular carcinomaNK cell exhaustionSingle-cell sequencingTumor immune microenvironmentWGCNA

Identifiers

PMID42050573
PMCPMC13267415

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.