ArticleBMC cancer2016
On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue.
Article in BMC cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Computational identification of key genetic drivers in COPD: A first step towards uncovering candidate biomarkers in smokers.Biochemistry and biophysics reports · 2025Article
- Classification of lung cancer severity using gene expression data based on deep learning.BMC medical informatics and decision making · 2025Article
- Identification of shared pathogenic signatures of multiple sclerosis and chronic obstructive pulmonary disease: an integrated transcriptomic analysis of blood specimens.Molecular genetics and genomics : MGG · 2024Article
- HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data.Bioinformatics (Oxford, England) · 2024Article
- Endometrial Cancer-Adjacent Tissues Express Higher Levels of Cancer-Promoting Genes than the Matched Tumors.Genes · 2022Article
- Genome-Wide Plasma Cell-Free DNA Methylation Profiling Identifies Potential Biomarkers for Lung Cancer.Disease markers · 2019Article
- Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis.Clinical rheumatology · 2018Review
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7 authors.
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Abstract
backgroundAdenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations. However, the lack of cell architecture in these small tissue samples hampers the process of distinguishing between the two subtypes. Molecular profiling can also be used to discriminate between the two lung cancer subtypes, on condition that the biopsy is composed of at least 50 % of tumor cells. However, for some cases, the tissue composition of a biopsy might be a mix of tumor and tumor-adjacent histologically normal tissue (TAHN). When this happens, a new biopsy is required, with associated cost, risks and discomfort to the patient. To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue.
methodsUsing publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. Then, we evaluated the classification performance of our models by measuring the area under the receiver operating characteristic curve (AUC). Finally, we analyzed the relevance of the selected genes using hierarchical clustering and IPA® software for gene functional analysis.
resultsAll Bayesian models achieved high classification performance (AUC > 0.94), which were confirmed by hierarchical cluster analysis. From the genes selected, 25 (93 %) were found to be related to cancer (19 were associated with ADC or SCC), confirming the biological relevance of our method.
conclusionsThe results from this study confirm that computational methods using tumor and TAHN tissue can serve as a prognostic tool for lung cancer subtype classification. Our study complements results from other studies where TAHN tissue has been used as prognostic tool for prostate cancer. The clinical implications of this finding could greatly benefit lung cancer patients.
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