Evidence map›Paper›PMID 37950277›Full record

ArticleCancer cell international2023

Novel biomarker genes for the prediction of post-hepatectomy survival of patients with NAFLD-related hepatocellular carcinoma.

Yuting Song, Ying Wang, Xin Geng, Xianming Wang, Huisi He, Youwen Qian, Yaping Dong, Zhecai Fan, Shuzhen Chen, Wen Wen and 1 more

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

Article in Cancer cell international, 2023. 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
0.4field-weighted citation impact, top 32% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  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

11 authors at 5 institutions in 1 country.

Yuting Song *Model Animal Research Center, Nanjing University, Nanjing, 210008, China.
Ying Wang *Department of Laboratory Medicine, Shanghai Eastern Hepatobiliary Surgery Hospital, Shanghai, 200438, China.
Xin Geng *National Center for Liver Cancer, Naval Medical University, Shanghai, 201805, China.
Xianming WangNational Center for Liver Cancer, Naval Medical University, Shanghai, 201805, China.
Huisi HeNational Center for Liver Cancer, Naval Medical University, Shanghai, 201805, China.
Youwen QianNational Center for Liver Cancer, Naval Medical University, Shanghai, 201805, China.
Yaping DongNational Center for Liver Cancer, Naval Medical University, Shanghai, 201805, China.
Zhecai FanNational Center for Liver Cancer, Naval Medical University, Shanghai, 201805, China.
Shuzhen ChenNational Center for Liver Cancer, Naval Medical University, Shanghai, 201805, China.
Wen WenNational Center for Liver Cancer, Naval Medical University, Shanghai, 201805, China. wenwen_smmu@163.com.
Hongyang WangModel Animal Research Center, Nanjing University, Nanjing, 210008, China. hywangk@vip.sina.com.
Shanghai Changzheng Hospital · CNEastern Hepatobiliary Surgery Hospital · CNShanghai Cancer Institute · CNModel Animal Research Center · CNNanjing University · CN

Funding

National Natural Science Foundation of China 82072600National Natural Science Foundation of China 82203323Science and Technology Commission of Shanghai Municipality 22Y11908700Shanghai Municipal Health Commission 2019CXJQ03Shanghai Municipal Health Commission 2022XD036
6 · The paper itself

Abstract

backgroundThe incidence and prevalence of nonalcoholic fatty liver disease related hepatocellular carcinoma (NAFLD-HCC) are rapidly increasing worldwide. This study aimed to identify biomarker genes for prognostic prediction model of NAFLD-HCC hepatectomy by integrating text-mining, clinical follow-up information, transcriptomic data and experimental validation.

methodsThe tumor and adjacent normal liver samples collected from 13 NAFLD-HCC and 12 HBV-HCC patients were sequenced using RNA-Seq. A novel text-mining strategy, explainable gene ontology fingerprint approach, was utilized to screen NAFLD-HCC featured gene sets and cell types, and the results were validated through a series of lab experiments. A risk score calculated by the multivariate Cox regression model using discovered key genes was established and evaluated based on 47 patients' follow-up information.

resultsDifferentially expressed genes associated with NAFLD-HCC specific tumor microenvironment were screened, of which FABP4 and VWF were featured by previous reports. A risk prediction model consisting of FABP4, VWF, gender and TNM stage were then established based on 47 samples. The model showed that overall survival in the high-risk score group was lower compared with that in the low-risk score group (p = 0.0095).

conclusionsThis study provided the landscape of NAFLD-HCC transcriptome, and elucidated that our model could predict hepatectomy prognosis with high accuracy.

Indexed as

FABP4Nonalcoholic fatty liver disease related hepatocellular carcinomaRNA-SeqText-miningVWF

Identifiers

PMID37950277
PMCPMC10638756
OpenAlexW4388566853

What OpenQuestion 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 OpenQuestion graph.