Evidence map›Paper›PMID 36849926›Full record

ArticleBMC genomics2023

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma.

Yi Fu, Anfeng Si, Xindong Wei, Xinjie Lin, Yujie Ma, Huimin Qiu, Zhinan Guo, Yong Pan, Yiru Zhang, Xiaoni Kong and 3 more

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

Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
5.3field-weighted citation impact, top 4% 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

16 citing papers in PubMed, 22 citations in OpenAlex.

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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

13 authors at 9 institutions in 1 country.

Yi Fu *Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Anfeng Si *Department of Surgical Oncology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
Xindong Wei *Central Laboratory, Department of Liver Diseases, Shuguang Hospital, Shanghai University of Chinese Traditional Medicine, Shanghai, China.
Xinjie LinShanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Yujie MaShanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Huimin QiuCollaborative Innovation Center for Biomedicines, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Zhinan GuoCollaborative Innovation Center for Biomedicines, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Yong PanDepartment of Infectious Disease, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, China.
Yiru ZhangDepartment of Infectious Disease, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, China.
Xiaoni KongCentral Laboratory, Department of Liver Diseases, Shuguang Hospital, Shanghai University of Chinese Traditional Medicine, Shanghai, China.
Shibo LiDepartment of Infectious Disease, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, China. lsb0398@126.com.
Yanjun ShiAbdominal Transplantation Center, General Surgery, School of Medicine, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China. shiyanjunss@hotmail.com.
Hailong WuShanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China. wuhl@sumhs.edu.cn.
Shanghai University of Medicine and Health Sciences · CNUniversity of Shanghai for Science and Technology · CNWenzhou Medical University · CNNanjing General Hospital of Nanjing Military Command · CNRuijin Hospital · CNShanghai University · CNShanghai University of Sport · CNShuguang Hospital · CNZhoushan Hospital · CN

Funding

2020 "Shanghai University Young Teacher Training Funding Program" A3-2601-20-201001-20the Hundred Teacher Talent Program of Shanghai University of Medicine and Health Sciences B3-0200-20-311008-23the National Natural Science Foundation of China 31870905the Science and Technology Commission of Shanghai Municipality 22ZR1428100the Scientific Program of Shanghai Municipal Health Commission 201940352the University-level Scientific Fund of Shanghai University of Medicine and Health Sciences E3-0200-21-201011-42the Zhejiang Province Major Science and Technology Project for Medicine and Health No.WKJ-ZJ-2329
6 · The paper itself

Abstract

backgroundNear 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence.

methodsData of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted.

resultsA 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC.

conclusionCombined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsRNA, Long Noncodingalpha-FetoproteinsHumansMachine LearningNeoplasm Stagingalpha-FetoproteinsRNA, Long NoncodingEarly recurrenceHepatocellular carcinomaLong non-coding RNA signatureMachine learning

Identifiers

PMID36849926
PMCPMC9972730
OpenAlexW4322493272

What OpenQuestion holds

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LicenceCC BY
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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.