Evidence map›Paper›PMID 32724401›Full record

ArticleOncology letters2020

Serum profile of low molecular weight fucosylated glycoproteins for early diagnosis of hepatocellular carcinoma.

Weirong Yao, Kaiyu Wang, Yu Jiang, Zhufeng Huang, Yiyun Huang, Huihui Yan, Suhong Huang, Min Chen, Jian Liao

Open access · diamondAbstract read
In one paragraph

Article in Oncology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 43% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. 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

9 authors at 3 institutions in 1 country.

Weirong YaoInstitute for Laboratory Medicine, The First Hospital of Longhai, Zhangzhou, Fujian 363199, P.R. China.
Kaiyu WangInstitute for Laboratory Medicine, Fuzhou General Hospital of Nanjing Command (The 900th Hospital of Joint Logistic Support Force People's Liberation Army), Fuzhou, Fujian 350003, P.R. China.
Yu JiangClinical Laboratory, Fuzhou Second Hospital (Fuzhou Integrated Traditional Chinese and Modern Medicine Hospital of Fujian Province, Fuzhou Second Hospital Affiliated to Xiamen University), Fuzhou, Fujian 350007, P.R. China.
Zhufeng HuangInstitute for Laboratory Medicine, The First Hospital of Longhai, Zhangzhou, Fujian 363199, P.R. China.
Yiyun HuangInstitute for Laboratory Medicine, The First Hospital of Longhai, Zhangzhou, Fujian 363199, P.R. China.
Huihui YanInstitute for Laboratory Medicine, Fuzhou General Hospital of Nanjing Command (The 900th Hospital of Joint Logistic Support Force People's Liberation Army), Fuzhou, Fujian 350003, P.R. China.
Suhong HuangInstitute for Laboratory Medicine, Fuzhou General Hospital of Nanjing Command (The 900th Hospital of Joint Logistic Support Force People's Liberation Army), Fuzhou, Fujian 350003, P.R. China.
Min ChenInstitute for Laboratory Medicine, Fuzhou General Hospital of Nanjing Command (The 900th Hospital of Joint Logistic Support Force People's Liberation Army), Fuzhou, Fujian 350003, P.R. China.
Jian LiaoInstitute for Laboratory Medicine, Fuzhou General Hospital of Nanjing Command (The 900th Hospital of Joint Logistic Support Force People's Liberation Army), Fuzhou, Fujian 350003, P.R. China.
Fuzhou General Hospital of Nanjing Military Command · CNZhangzhou Municipal Hospital of Fujian Province · CNFuzhou Second Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Our previous study reported a method of using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry to analyze the association between abnormal fucosylation of serum glycoproteins and the progression of hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC). In the present study, the aforementioned method was improved by focusing on fucosylated glycoproteins <10 kD, classification models were established and blind tests were performed on an enlarged sample size (n=299). According to the present results, the classification models had a sensitivity and specificity of 74.31 and 76.32%, respectively, to identify HCC among all serum samples, 81.65 and 83.08%, respectively, to distinguish HCC from HBV-associated cirrhosis and chronic hepatitis Band 88.99 and 84.62%, respectively, to distinguish HCC from HBV-associated cirrhosis. When combined with α-fetoprotein (AFP) measurements (AFP >20 ng/ml), the sensitivity and specificity of the models were significantly elevated to 80.73 and 87.37%, 87.16 and 90.00%, and 92.66 and 93.84%, respectively. In addition, the HBV-HCC vs. HBV-cirrhosis classification model was used to analyze serum samples collected from 9 patients with cirrhosis 1 year before they were diagnosed with HCC, and from 6 patients who had cirrhosis but developed no signs of HCC for the following 3 years. The model identified 7 patients (77.78%) with no significant clinical symptoms of HCC, and gave no false positive results, demonstrating that the classification models established in the present study may be useful for the early diagnosis of HCC. After isolation and purification, two proteins with differential expression were identified as isoform 1 of inter-α-trypsin inhibitor heavy chain 4 precursor, and thymosin β-4-like protein 3. These may be used as candidate markers for HCC diagnosis. Additionally, the present study indicates that defucosylation of serum glycoproteins may occur during the development and progression of HCC.

Indexed as

defucosylationearly diagnosisfucosylationhepatitis B virushepatocellular carcinomaMALDI-TOF MS profiling

Identifiers

PMID32724401
PMCPMC7377157
OpenAlexW3034603647

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.