Evidence map›Paper›PMID 34034705›Full record

ArticleBMC cancer2021

Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma.

Dongsheng He, Shengyin Liao, Lifang Cai, Weiming Huang, Xuehua Xie, Mengxing You

Abstract readValidation Study
In one paragraph

Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

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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

6 authors.

Dongsheng HeDepartment of Medical Oncology, The First Hospital of Putian, Teaching Hospital, Fujian Medical University, Putian, 351100, China.
Shengyin LiaoDepartment of Medical Oncology, The First Hospital of Putian, Teaching Hospital, Fujian Medical University, Putian, 351100, China.
Lifang CaiDepartment of Medical Oncology, The First Hospital of Putian, Teaching Hospital, Fujian Medical University, Putian, 351100, China.
Weiming HuangDepartment of Medical Oncology, The First Hospital of Putian, Teaching Hospital, Fujian Medical University, Putian, 351100, China.
Xuehua XieDepartment of Medical Oncology, The First Hospital of Putian, Teaching Hospital, Fujian Medical University, Putian, 351100, China.
Mengxing YouDepartment of Medical Oncology, The First Hospital of Putian, Teaching Hospital, Fujian Medical University, Putian, 351100, China. youmengxing_oncol@163.com.ORCID http://orcid.org/0000-0002-5579-3023

Funding

the project of putian science and technology department No. 2020SY006the youth scientific research project of fujian provincial health and family planning commission 2017-2-80
6 · The paper itself

Abstract

backgroundThe potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients.

methodsThe gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson's correlation coefficient less than - 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual's OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student's t-test.

resultsIn this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage.

conclusionWe identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

Indexed as

NomogramsBiomarkers, TumorCarcinoma, HepatocellularDatasets as TopicDNA MethylationEpigenesis, GeneticGene Expression ProfilingGene Expression Regulation, NeoplasticHepatectomyHumansKaplan-Meier EstimateLiverLiver NeoplasmsNeoplasm StagingPhosphatidylcholine-Sterol O-AcyltransferasePreoperative PeriodBiomarkers, TumorLCAT protein, humanPhosphatidylcholine-Sterol O-AcyltransferaseRibosomal Protein S6 Kinases, 90-kDaRPS6KA6 protein, humanHepatocellular carcinomaMethylation-driven genesNomogramPrognosis

Identifiers

PMID34034705
PMCPMC8146257

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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.