Evidence map›Paper›PMID 42270911›Full record

ArticleDiscover oncology2026

Integration of bulk and single-cell RNA-seq data identifies a cellular senescence-related prognostic signature in liver hepatocellular carcinoma.

Rentong Liu, Bing Li, Yiqian Luo, Xiuwei Yan, Yuanyuan Wang, Song Jiang, Chengjie Gan, Yiwei Cheng

Abstract read
In one paragraph

Article in Discover oncology, 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
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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

8 authors.

Rentong LiuThe 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army, Dalian, 116000, China.
Bing LiThe 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army, Dalian, 116000, China.
Yiqian LuoThe 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army, Dalian, 116000, China.
Xiuwei YanDepartment of Neurosurgery, Cancer Center, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, China.
Yuanyuan WangThe 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army, Dalian, 116000, China.
Song JiangThe 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army, Dalian, 116000, China.
Chengjie GanThe 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army, Dalian, 116000, China.
Yiwei ChengThe 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army, Dalian, 116000, China. 279880793@qq.com.

Funding

The Hospital-level Fund of the 967th Hospital of the Joint Logistic Support Force of the Chinese People's Liberation Army No. 2025-M-02
6 · The paper itself

Abstract

Liver hepatocellular carcinoma (LIHC) is a heterogeneous malignancy with poor prognosis, but the landscape of cellular senescence-related genes (CSRGs) in LIHC remains incompletely characterized. Here, we curated 167 CSRGs and performed integrative bulk and single-cell RNA-seq analyses using TCGA, ICGC, and TISCH datasets. Fifteen prognostic CSRGs were identified and stratified LIHC patients into two distinct molecular subtypes: Cluster 2 exhibited an immune-excluded phenotype with poor prognosis, whereas Cluster 1 represented an immune-desert phenotype. A CSRGs-based risk score was constructed and validated as an independent prognostic factor across cohorts. Single-cell analysis identified EEF1E1 as a key CSRG predominantly expressed in malignant hepatocytes, correlating with advanced clinicopathological features and immune infiltration. Functional experiments demonstrated that EEF1E1 knockdown significantly suppressed LIHC cell migration and invasion. Collectively, this study establishes a robust CSRGs-based prognostic signature. It also identifies EEF1E1 as a functionally relevant gene with oncogenic potential and a potential therapeutic target in LIHC.

Indexed as

Cellular senescenceHepatocellular carcinomaPrognostic signatureTumor microenvironment

Identifiers

PMID42270911
PMCPMC13478048

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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.