Evidence map›Paper›PMID 36973651›Full record

Trial reportBMC gastroenterology2023

A novel predictive model of microvascular invasion in hepatocellular carcinoma based on differential protein expression.

Zhenglu Wang, Lei Cao, Jianxi Wang, Hanlin Wang, Tingting Ma, Zhiqi Yin, Wenjuan Cai, Lei Liu, Tao Liu, Hengde Ma and 3 more

Full text readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in BMC gastroenterology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

13 authors.

Zhenglu Wang *Biological Sample Resource Sharing Center, Tianjin First Central Hospital, Nankai University, Tianjin, China.
Lei Cao *Biological Sample Resource Sharing Center, Tianjin First Central Hospital, Nankai University, Tianjin, China.
Jianxi WangBiological Sample Resource Sharing Center, Tianjin First Central Hospital, Nankai University, Tianjin, China.
Hanlin WangDepartment of Pathology and Laboratory Medicine, University of California in Los Angeles (UCLA), Los Angeles, CA, USA.
Tingting MaBiological Sample Resource Sharing Center, Tianjin First Central Hospital, Nankai University, Tianjin, China.
Zhiqi YinPathology Department, Tianjin First Central Hospital, Nankai University, Tianjin, China.
Wenjuan CaiPathology Department, Tianjin First Central Hospital, Nankai University, Tianjin, China.
Lei LiuResearch Institute of Transplant Medicine, Nankai University, Tianjin, China.
Tao LiuKey Laboratory of Transplant Medicine, Chinese Academy of Medical Sciences, 24 Fukang Road, Nankai, Tianjin, 300192, China.
Hengde MaHPS Gene Technology Co., Ltd., Tianjin, China.
Yamin ZhangOrgan Transplant Department, Tianjin First Central Hospital, Nankai University, Tianjin, China.
Zhongyang ShenResearch Institute of Transplant Medicine, Nankai University, Tianjin, China.
Hong ZhengKey Laboratory of Transplant Medicine, Chinese Academy of Medical Sciences, 24 Fukang Road, Nankai, Tianjin, 300192, China. zhenghongyx@139.com.ORCID http://orcid.org/0000-0001-7386-7084

Funding

Science Fund of the Nature Science Foundation of Tianjin 21JCZDJC00160Tianjin Health Science and Technology Project zc20065Youth Science Fund of the Nature Science Foundation of Tianjin 20JCQNJC01370
6 · The paper itself

Abstract

backgroundThis study aims to construct and verify a nomogram model for microvascular invasion (MVI) based on hepatocellular carcinoma (HCC) tumor characteristics and differential protein expressions, and explore the clinical application value of the prediction model.

methodsThe clinicopathological data of 200 HCC patients were collected and randomly divided into training set and validation set according to the ratio of 7:3. The correlation between MVI occurrence and primary disease, age, gender, tumor size, tumor stage, and immunohistochemical characteristics of 13 proteins, including GPC3, CK19 and vimentin, were statistically analyzed. Univariate and multivariate analyzes identified risk factors and independent risk factors, respectively. A nomogram model that can be used to predict the presence of MVI was subsequently constructed. Then, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were conducted to assess the performance of the model.

resultsMultivariate logistic regression analysis indicated that tumor size, GPC3, P53, RRM1, BRCA1, and ARG were independent risk factors for MVI. A nomogram was constructed based on the above six predictors. ROC curve, calibration, and DCA analysis demonstrated the good performance and the clinical application potential of the nomogram model.

conclusionsThe predictive model constructed based on the clinical characteristics of HCC tumors and differential protein expression patterns could be helpful to improve the accuracy of MVI diagnosis in HCC patients.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsGlypicansHumansNeoplasm InvasivenessNomogramsRetrospective StudiesRisk FactorsGlypicansGPC3 protein, humanDifferential protein expressionHepatocellular carcinomaMicrovascular invasionPredictive modelRisk factors

Identifiers

PMID36973651
PMCPMC10041792

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read14
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 OpenQuestion graph.