ArticleBMC genomics2014
TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasis in non-small cell lung cancer.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 96 citations in OpenAlex.
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- PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients.Scientific reports · 2021Article
- An Unsupervised Strategy for Identifying Epithelial-Mesenchymal Transition State Metrics in Breast Cancer and Melanoma.iScience · 2020Article
- Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data.BMC bioinformatics · 2019Article
- Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis.Frontiers in genetics · 2019Article
- TINAGL1 promotes hepatocellular carcinogenesis through the activation of TGF-β signaling-medicated VEGF expression.Cancer management and research · 2019Article
- Detecting Rare Mutations with Heterogeneous Effects Using a Family-Based Genetic Random Field Method.Genetics · 2018Article
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- Tensor decomposition-based and principal-component-analysis-based unsupervised feature extraction applied to the gene expression and methylation profiles in the brains of social insects with multiple castes.BMC bioinformatics · 2018Article
- Activation of PI3K/AKT is involved in TINAG-mediated promotion of proliferation, invasion and migration of hepatocellular carcinoma.Cancer biomarkers : section A of Disease markers · 2018Article
- Tensor decomposition-based unsupervised feature extraction identifies candidate genes that induce post-traumatic stress disorder-mediated heart diseases.BMC medical genomics · 2017Article
- Identification of candidate drugs using tensor-decomposition-based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets.Scientific reports · 2017Article
- Molecular and cellular events during blastocyst implantation in the receptive uterus: clues from mouse models.The Journal of reproduction and development · 2017Review
- Principal Components Analysis Based Unsupervised Feature Extraction Applied to Gene Expression Analysis of Blood from Dengue Haemorrhagic Fever Patients.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-small cell lung cancer (NSCLC) remains lethal despite the development of numerous drug therapy technologies. About 85% to 90% of lung cancers are NSCLC and the 5-year survival rate is at best still below 50%. Thus, it is important to find drugable target genes for NSCLC to develop an effective therapy for NSCLC.
resultsIntegrated analysis of publically available gene expression and promoter methylation patterns of two highly aggressive NSCLC cell lines generated by in vivo selection was performed. We selected eleven critical genes that may mediate metastasis using recently proposed principal component analysis based unsupervised feature extraction. The eleven selected genes were significantly related to cancer diagnosis. The tertiary protein structure of the selected genes was inferred by Full Automatic Modeling System, a profile-based protein structure inference software, to determine protein functions and to specify genes that could be potential drug targets.
conclusionsWe identified eleven potentially critical genes that may mediate NSCLC metastasis using bioinformatic analysis of publically available data sets. These genes are potential target genes for the therapy of NSCLC. Among the eleven genes, TINAGL1 and B3GALNT1 are possible candidates for drug compounds that inhibit their gene expression.
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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.