Evidence map›Paper›PMID 33520012›Full record

ArticleDisease markers2020

15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma.

Qiong Zhang, Gang Ning, Hongye Jiang, Yanlin Huang, Jinsong Piao, Zhen Chen, Xiaojun Tan, Jiangyu Zhang, Genglong Liu

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Article in Disease markers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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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

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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

9 citing papers in PubMed.

  1. Biomarker Potential ofInternational journal of medical sciences · 2024
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  5. Development of a Predictive Nomogram for Estimating Medication Nonadherence in Hemodialysis Patients.Medical science monitor : international medical journal of experimental and clinical research · 2022
    Article
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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

9 authors.

Qiong ZhangDepartment of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095 Guangdong Province, China.
Gang NingDepartment of Gastroenterology and Hepatology, Guangzhou Digestive Diseases Center, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180 Guangdong Province, China.
Hongye JiangDepartment of Clinical Laboratory, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, 528300 Guangdong Province, China.
Yanlin HuangDepartment of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630 Guangdong Province, China.
Jinsong PiaoDepartment of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095 Guangdong Province, China.
Zhen ChenDepartment of Clinical Laboratory, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, 528300 Guangdong Province, China.
Xiaojun TanDepartment of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095 Guangdong Province, China.ORCID https://orcid.org/0000-0002-3088-8149
Jiangyu ZhangDepartment of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095 Guangdong Province, China.ORCID https://orcid.org/0000-0003-1714-3684
Genglong LiuDepartment of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095 Guangdong Province, China.ORCID https://orcid.org/0000-0002-9163-4396

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOur study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients.

methodsThe lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a 15-lncRNA-based classifier and compared our classifier to the existing six-lncRNA signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with the TNM staging system by the C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate the clinical value of our nomogram.

resultsFifteen relapse-free survival (RFS-) related lncRNAs were identified, and the classifier, consisting of the identified 15 lncRNAs, could effectively classify patients into the high-risk and low-risk subgroups. The prediction accuracy of the 15-lncRNA-based classifier for predicting 2-year and 5-year RFS was 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, respectively, which was better than the existing six-lncRNA signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than the traditional TNM stage and 15-lncRNA-based classifier. The decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage and 15-lncRNA-based classifier. The results were confirmed externally.

conclusionCompared to the TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

Indexed as

AdultAgedAged, 80 and overBiomarkers, TumorCarcinoma, HepatocellularFemaleHumansLiver NeoplasmsMaleMiddle AgedNeoplasm Recurrence, LocalNeoplasm StagingNomogramsRNA, Long NoncodingSensitivity and SpecificityTranscriptomeBiomarkers, TumorRNA, Long Noncoding

Identifiers

PMID33520012
PMCPMC7817238

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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.