Evidence map›Paper›PMID 34957996›Full record

ArticleCancer biomarkers : section A of Disease markers2022

A long non-coding RNA-based signature predicts early recurrence in hepatocellular carcinoma.

Shuai He, Jin-Feng Li, Hao Tian, Ye Sang, Xiao-Jing Yang, Gui-Xin Guo, Jin-E Yang

Abstract read
In one paragraph

Article in Cancer biomarkers : section A of Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Shuai HeMOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China.
Jin-Feng LiMOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China.
Hao TianMOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China.
Ye SangMOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China.
Xiao-Jing YangMOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China.
Gui-Xin GuoNational Supercomputer Center in Guangzhou, School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China.
Jin-E YangMOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China.
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly recurrence is the main obstacle for long-term survival of hepatocellular carcinoma (HCC) patients after curative resection.

objectiveWe aimed to develop a long non-coding RNA (lncRNA) based signature to predict early recurrence.

methodsUsing bioinformatics analysis and quantitative reverse transcription PCR (RT-qPCR), we screened for lncRNA candidates that were abnormally expressed in HCC. The expression levels of candidate lncRNAs were analyzed in HCC tissues from 160 patients who underwent curative resection, and a risk model for the prediction of recurrence within 1 year (early recurrence) of HCC patients was constructed with linear support vector machine (SVM).

resultsAn lncRNA-based classifier (Clnc), which contained nine differentially expressed lncRNAs including AF339810, AK026286, BC020899, HEIH, HULC, MALAT1, PVT1, uc003fpg, and ZFAS1 was constructed. In the test set, this classifier reliably predicted early recurrence (AUC, 0.675; sensitivity, 72.0%; specificity, 63.1%) with an odds ratio of 4.390 (95% CI, 2.120-9.090). Clnc showed higher accuracy than traditional clinical features, including tumor size, portal vein tumor thrombus (PVTT) in predicting early recurrence (AUC, 0.675 vs 0.523 vs 0.541), and had much higher sensitivity than Barcelona Clinical Liver Cancer (BCLC; 72.0% vs 50.0%), albeit their AUCs were comparable (0.675 vs 0.678). Moreover, combining Clnc with BCLC significantly increased the AUC, compared with Clnc or BCLC alone in predicting early recurrence (all P< 0.05). Finally, logistic and Cox regression analyses suggested that Clnc was an independent prognostic factor and associated with the early recurrence and recurrence-free survival of HCC patients after resection, respectively (all P= 0.001).

conclusionsOur lncRNA-based classifier Clnc can predict early recurrence of patients undergoing surgical resection of HCC.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsRNA, Long NoncodingComputational BiologyHumansPrognosisRNA, Long NoncodingHepatocellular carcinomalncRNA-based classifierprognosisrecurrence

Identifiers

PMID34957996
PMCPMC12364278
OpenAlexW4200102698

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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.