Evidence map›Paper›PMID 40708677›Full record

ArticleJournal of translational internal medicine2024

Computational analyses to reveal the key determinants of the high malignancy level of cholangiocarcinoma.

Xuan Li, Aoran Liu, Xuechen Mu, Zhihang Wang, Jun Xiao, Yinwei Qu, Zhenyu Huang, Ye Zhang, Ying Xu

Abstract read
In one paragraph

Article in Journal of translational internal medicine, 2024. 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. On why cancer cells require a great amount of glucose.Quantitative biology (Beijing, China) · 2026
    Article
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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

9 authors.

Xuan LiKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin Province, China.
Aoran LiuThe First Laboratory of Cancer Institute, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
Xuechen MuCollege of Mathematics, Jilin University, Changchun 130012, Jilin Province, China.
Zhihang WangKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin Province, China.
Jun XiaoKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin Province, China.
Yinwei QuKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin Province, China.
Zhenyu HuangKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, Jilin Province, China.ORCID https://orcid.org/0009-0009-0029-1729
Ye ZhangThe First Laboratory of Cancer Institute, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.ORCID https://orcid.org/0000-0003-3495-5596
Ying XuSchool of Medicine, Southern University of Science and Technology, Shenzhen 518055, Guangdong Province, China.ORCID https://orcid.org/0009-0004-8767-7635

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: Cholangiocarcinoma (CHOL) is a rare and highly aggressive cancer that originates in the bile duct; it has an average five-year survival rate of 9%, which makes it the cancer with the lowest survival rate among all 33 cancer types in the cancer genome atlas (TCGA) Program. The aim of this study is to elucidate the key determinants of the high malignancy level of CHOL through computational and cell-based experimental approaches and, particularly, to investigate how bile acids (BAs) influence CHOL's propensity to metastasize. Methods: Our study analyzed the transcriptomic data from 1835 tissue samples of 7 digestive system cancer types in the TCGA database and compared them with those of 330 control tissue samples. Multiple cellular and molecular factors were considered in the study, including the level of hypoxia, level of immune cell infiltration, degree of cellular dedifferentiation, and level of sialic acid (SA) accumulation on the surface of cancer cells. Using these factors, we developed a multivariable regression model for the five-year survival rate, as reported by the Surveillance, Epidemiology, and End Results (SEER) Program reports, and analyzed how BA biology influences a few of these factors and causes CHOL to have a high malignancy level. Results: CHOL exhibited the highest level of SA accumulation and B-cell infiltration among all cancer types studied. BAs inhibit the cell cycle progression through the receptor Conclusions: BAs are the key contributors to the lowest five-year survival rate of CHOL among the seven cancer types studied here. This finding not only reveals the molecular mechanisms underlying the high malignancy level of CHOL but also provides a new potential target for the diagnosis and treatment of CHOL.

Indexed as

bile acidcancer migrationcholangiocarcinomaFenton reactionsialic acid

Identifiers

PMID40708677
PMCPMC12288948

What Socratic holds

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LicenceCC BY
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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 Socratic graph.