Evidence map›Paper›PMID 39692931›Full record

ArticleDiscover oncology2024

Identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis.

Chen Chen, Rui Peng, Shengjie Jin, Yuhong Tang, Huanxiang Liu, Daoyuan Tu, Bingbing Su, Shunyi Wang, Guoqing Jiang, Jun Cao and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover oncology, 2024. 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

12 authors.

Chen ChenDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Rui PengDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Shengjie JinDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital, Yangzhou, China.
Yuhong TangDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Huanxiang LiuDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Daoyuan TuDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Bingbing SuDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Shunyi WangDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Guoqing JiangDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Jun CaoDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China. dx120200192@stu.yzu.edu.cn.
Chi ZhangDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China. zc17212850@163.com.
Dousheng BaiDepartment of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China. drbaidousheng@yzu.edu.cn.

Funding

Beijing iGanDan Foundation GDXZ-08-19Cross-cooperation special projects of the NJPH YJCHZ-2021-08National Natural Science Foundation of China Grant No. 82203716Provincial-level discipline leader of the NJPH DTRA202214the "13th five-year Plan" Science and Education strong Health Project Innovation team of Yangzhou LJRC20181the "13th five-year Plan" Science and Education strong Health Project Innovation team of Yangzhou YZCXTD201801the Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX22_3568, KYCX23_3617, SJCX23_2028
6 · The paper itself

Abstract

Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. But the effective biomarkers for HCC metastasis remain underexplored. Here we integrated GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) datasets to screen candidate genes for hepatocellular carcinoma metastasis, a consensus metastasis-derived prognostic signature (MDPS) was constructed by machine learning. Based on the risk scores, HCC patients were stratified into high-risk and low-risk groups. Comprehensive analyses were conducted to investigate various aspects including survival outcomes, clinical characteristics, immune cell infiltration, as well as in vitro experiments. Together, we develop a comprehensive machine learning-based program for constructing a consensus MDPS including four genes (SPP1, TYMS, HMMR and MYCN). Our findings revealed that four genes could serve as efficient prognostic biomarkers and therapeutic targets in HCC. In addition, in vitro experiments showed that HMMR overregulation exacerbated tumor progression, including proliferation, migration and invasion.

Indexed as

BioinformaticsHepatocellular carcinomaMachine learningMetastasisPrognosis

Identifiers

PMID39692931
PMCPMC11655777

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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.