Evidence map›Paper›PMID 32420331›Full record

ArticleBioMed research international2020

Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma.

Yan-Mei Dong, Ming Li, Qi-En He, Yi-Fan Tong, Hong-Zhi Gao, Yi-Zhi Zhang, Ya-Meng Wu, Jun Hu, Ning Zhang, Kai Song

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 41% 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

4 citing papers in PubMed, 6 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

10 authors at 3 institutions in 1 country.

Yan-Mei DongSchool of Chemical Engineering and Technology, Tianjin University, 300072 Tianjin, China.
Ming LiDepartment of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, 300072 Tianjin, China.
Qi-En HeSchool of Chemical Engineering and Technology, Tianjin University, 300072 Tianjin, China.
Yi-Fan TongSchool of Chemical Engineering and Technology, Tianjin University, 300072 Tianjin, China.
Hong-Zhi GaoThe Second Affiliated Hospital of Fujian Medical University, 362000 Quanzhou, Fujian, China.
Yi-Zhi ZhangDepartment of Hematology and Oncology, Karamay Central Hospital, 834000 Karamay, Xinjiang Uygur Autonomous Region, China.
Ya-Meng WuDepartment of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, 300072 Tianjin, China.
Jun HuDepartment of Hematology and Oncology, Karamay Central Hospital, 834000 Karamay, Xinjiang Uygur Autonomous Region, China.
Ning ZhangDepartment of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, 300072 Tianjin, China.ORCID https://orcid.org/0000-0002-1559-111X
Kai SongSchool of Chemical Engineering and Technology, Tianjin University, 300072 Tianjin, China.ORCID https://orcid.org/0000-0001-9195-6160
Tianjin University · CNKaramay Central Hospital · CNFujian Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tobacco exposure is one of the major risks for the initiation and progress of lung cancer. The exact corresponding mechanisms, however, are mainly unknown. Recently, a growing body of evidence has been collected supporting the involvement of DNA methylation in the regulation of gene expression in cancer cells. The identification of tobacco-related signature methylation probes and the analysis of their regulatory networks at different molecular levels may be of a great help for understanding tobacco-related tumorigenesis. Three independent lung adenocarcinoma (LUAD) datasets were used to train and validate the tobacco exposure pattern classification model. A deep selecting method was proposed and used to identify methylation signature probes from hundreds of thousands of the whole epigenome probes. Then, BIMC (biweight midcorrelation coefficient) algorithm, SRC (Spearman's rank correlation) analysis, and shortest path tracing method were explored to identify associated genes at gene regulation level and protein-protein interaction level, respectively. Afterwards, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and GO (Gene Ontology) enrichment analysis were used to analyze their molecular functions and associated pathways. 105 probes were identified as tobacco-related DNA methylation signatures. They belong to 95 genes which are involved in hsa04512, hsa04151, and other important pathways. At gene regulation level, 33 genes are uncovered to be highly related to signature probes by both BIMC and SRC methods. Among them, FARSB and other eight genes were uncovered as Hub genes in the gene regulatory network. Meanwhile, the PPI network about these 33 genes showed that MAGOH, FYN, and other five genes were the most connected core genes among them. These analysis results may provide clues for a clear biological interpretation in the molecular mechanism of tumorigenesis. Moreover, the identified signature probes may serve as potential drug targets for the precision medicine of LUAD.

Indexed as

Adenocarcinoma of LungDatabases, GeneticDNA MethylationDNA, NeoplasmEpigenomeGene Expression Regulation, NeoplasticGene Regulatory NetworksLung NeoplasmsTobacco UseGene Expression ProfilingHumansDNA, Neoplasm

Identifiers

PMID32420331
PMCPMC7201762
OpenAlexW3019174090

What OpenQuestion holds

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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.