Evidence map›Paper›PMID 32884220›Full record

ArticleWorld journal of gastroenterology2020

Establishment of a pattern recognition metabolomics model for the diagnosis of hepatocellular carcinoma.

Peng-Cheng Zhou, Lun-Quan Sun, Li Shao, Lun-Zhao Yi, Ning Li, Xue-Gong Fan

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
0.8field-weighted citation impact, top 28% of its field
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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.

  1. Pooled it
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  7. [Research advances of metabolomics in early diagnosis of hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2022
    Review
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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

6 authors at 3 institutions in 1 country.

Peng-Cheng ZhouHunan Key Laboratory of Viral Hepatitis and Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha 410008, Hunan Province, China.
Lun-Quan SunCenter for Molecular Medicine, Xiangya Hospital, Central South University, Changsha 410008, Hunan Province, China.
Li ShaoInstitute of Translational Medicine, The Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, Zhejiang Province, China.
Lun-Zhao YiYunnan Food Safety Research Institute, Kunming University of Science and Technology, Kunming 650500, Yunnan Province, China.
Ning LiHunan Key Laboratory of Viral Hepatitis and Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha 410008, Hunan Province, China.
Xue-Gong FanHunan Key Laboratory of Viral Hepatitis and Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha 410008, Hunan Province, China. xgfan@hotmail.com.
Central South University · CNAffiliated Hospital of Hangzhou Normal University · CNKunming University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly diagnosis of hepatocellular carcinoma may help to ensure that patients have a chance for long-term survival; however, currently available biomarkers lack sensitivity and specificity.

aimTo characterize the serum metabolome of hepatocellular carcinoma in order to develop a new metabolomics diagnostic model and identifying novel biomarkers for screening hepatocellular carcinoma based on the pattern recognition method.

methodsUltra-performance liquid chromatography-mass spectroscopy was used to characterize the serum metabolome of hepatocellular carcinoma (

resultsThe concentrations of most metabolites, including proline, were lower in patients with hepatocellular carcinoma, whereas the hydroxypurine levels were higher in these patients. As ordinary analysis models failed to discriminate hepatocellular carcinoma from cirrhosis, pattern recognition analysis was used to establish a pattern recognition model that included hydroxypurine and proline. The leave-one-out cross-validation accuracy and area under the receiver operating characteristic curve analysis were 95.00% and 0.90 [95% Confidence Interval (CI): 0.81-0.99] for the training set, respectively, and 78.95% and 0.84 (95%CI: 0.67-1.00) for the validation set, respectively. In contrast, for α-fetoprotein, the accuracy and area under the receiver operating characteristic curve were 65.00% and 0.69 (95%CI: 0.52-0.86) for the training set, respectively, and 68.42% and 0.68 (95%CI: 0.41-0.94) for the validation set, respectively. The

conclusionHydroxypurine and proline might be novel biomarkers for hepatocellular carcinoma, and this disease could be diagnosed by the metabolomics model based on pattern recognition.

Indexed as

Carcinoma, HepatocellularLiver Neoplasmsalpha-FetoproteinsBiomarkers, TumorHumansMetabolomeMetabolomicsROC Curvealpha-FetoproteinsBiomarkers, TumorBiomarkersHepatocellular carcinomaMetabolomicsPattern recognition

Identifiers

PMID32884220
PMCPMC7445864
OpenAlexW3080434267

What OpenQuestion holds

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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.