ArticlePloS one2012
Dysfunctions associated with methylation, microRNA expression and gene expression in lung cancer.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- A novel method to identify gene interaction patterns.BMC genomics · 2021Article
- The Serum MicroRNA Signatures for Pancreatic Cancer Detection and Operability Evaluation.Frontiers in bioengineering and biotechnology · 2020Article
- Integrative Analysis of Methylation and Gene Expression in Lung Adenocarcinoma and Squamous Cell Lung Carcinoma.Frontiers in bioengineering and biotechnology · 2020Article
- Smoking alters the evolutionary trajectory of non-small cell lung cancer.Experimental and therapeutic medicine · 2019Article
- miR-137 inhibits the proliferation of human non-small cell lung cancer cells by targeting SRC3.Oncology letters · 2017Article
- Article
- Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.BioMed research international · 2014Article
- Application of ThinPrep bronchial brushing cytology in the early diagnosis of lung cancer: a retrospective study.PloS one · 2014Article
- Identification of logic relationships between genes and subtypes of non-small cell lung cancer.PloS one · 2014Article
- Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrating high-throughput data obtained from different molecular levels is essential for understanding the mechanisms of complex diseases such as cancer. In this study, we integrated the methylation, microRNA and mRNA data from lung cancer tissues and normal lung tissues using functional gene sets. For each Gene Ontology (GO) term, three sets were defined: the methylation set, the microRNA set and the mRNA set. The discriminating ability of each gene set was represented by the Matthews correlation coefficient (MCC), as evaluated by leave-one-out cross-validation (LOOCV). Next, the MCCs in the methylation sets, the microRNA sets and the mRNA sets were ranked. By comparing the MCC ranks of methylation, microRNA and mRNA for each GO term, we classified the GO sets into six groups and identified the dysfunctional methylation, microRNA and mRNA gene sets in lung cancer. Our results provide a systematic view of the functional alterations during tumorigenesis that may help to elucidate the mechanisms of lung cancer and lead to improved treatments for patients.
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Registered trials
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.