Evidence map›Paper›PMID 39859485›Full record

ArticleInternational journal of molecular sciences2025

Cellular Senescence in Hepatocellular Carcinoma: Immune Microenvironment Insights via Machine Learning and In Vitro Experiments.

Xinhe Lu, Yuhang Luo, Yun Huang, Zhiqiang Zhu, Hongyan Yin, Shunqing Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
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

6 authors.

Xinhe LuSchool of Life and Health Sciences, Hainan University, Haikou 570228, China.ORCID 0009-0008-2912-0109
Yuhang LuoSchool of Life and Health Sciences, Hainan University, Haikou 570228, China.
Yun HuangSchool of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Zhiqiang ZhuSchool of Environmental Science and Engineering, Hainan University, Haikou 570228, China.ORCID 0000-0002-7479-9279
Hongyan YinSchool of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China.
Shunqing XuSchool of Environmental Science and Engineering, Hainan University, Haikou 570228, China.

Funding

Collaborative Innovation Center of One Health, Hainan University XTCX2022JKA02Hainan Provincial Natural Science Foundation of China 321RC456Innovation Fund for Scientific and Technological Personnel of Hainan Province KJRC2023B02
6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune microenvironment remain unclear. Three machine learning methods, namely k nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), are utilized to identify eight key HCC cell senescence markers (HCC-CSMs). Consensus clustering revealed molecular subtypes. The single-cell analysis explored the tumor microenvironment, immune checkpoints, and immunotherapy responses. In vitro, RNA interference mediated

Indexed as

Carcinoma, HepatocellularCellular SenescenceLiver NeoplasmsMachine LearningTumor MicroenvironmentBiomarkers, TumorCell Line, TumorGene Expression Regulation, NeoplasticHumansKiller Cells, NaturalSingle-Cell AnalysisSurvivinBiomarkers, TumorBIRC5 protein, humanSurvivinBIRC5cellular senescencehepatocellular carcinomaimmune microenvironmentmachine learningNK cells

Identifiers

PMID39859485
PMCPMC11765518

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.