ArticleScientific data2018
A merged lung cancer transcriptome dataset for clinical predictive modeling.
Article in Scientific data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Meta-Merging the Transcriptomes of Gastric Tumors Redefines the Connections among Molecular and Clinical Subtypes.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Time-Course of Transcriptomic Change in the Lungs of F344 Rats Repeatedly Exposed to a Multiwalled Carbon Nanotube in a 2-Year Test.Nanomaterials (Basel, Switzerland) · 2023Article
- Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.PLoS computational biology · 2023Article
- Molecular Pathology of Lung Cancer.Cold Spring Harbor perspectives in medicine · 2022Review
- Article
- Gene selection using pyramid gravitational search algorithm.PloS one · 2022Article
- Proteogenomics of non-small cell lung cancer reveals molecular subtypes associated with specific therapeutic targets and immune evasion mechanisms.Nature cancer · 2021Article
- A novel 12-gene signature as independent prognostic model in stage IA and IB lung squamous cell carcinoma patients.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2021Article
- Article
- A merged microarray meta-dataset for transcriptionally profiling colorectal neoplasm formation and progression.Scientific data · 2021Article
- Versatile workflow for cell type-resolved transcriptional and epigenetic profiles from cryopreserved human lung.JCI insight · 2021Article
- Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma.Frontiers in oncology · 2021Article
- Recent advances in diagnostic technologies in lung cancer.The Korean journal of internal medicine · 2020Review
- Review
- Compendiums of cancer transcriptomes for machine learning applications.Scientific data · 2019Article
- Pan-cancer analysis connects tumor matrisome to immune response.NPJ precision oncology · 2019Article
- Article
- NETest Liquid Biopsy Is Diagnostic of Lung Neuroendocrine Tumors and Identifies Progressive Disease.Neuroendocrinology · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Gene Expression Omnibus (GEO) database is an excellent public source of whole transcriptomic profiles of multiple cancers. The main challenge is the limited accessibility of such large-scale genomic data to people without a background in bioinformatics or computer science. This presents difficulties in data analysis, sharing and visualization. Here, we present an integrated bioinformatics pipeline and a normalized dataset that has been preprocessed using a robust statistical methodology; allowing others to perform large-scale meta-analysis, without having to conduct time-consuming data mining and statistical correction. Comprising 1,118 patient-derived samples, the normalized dataset includes primary non-small cell lung cancer (NSCLC) tumors and paired normal lung tissues from ten independent GEO datasets, facilitating differential expression analysis. The data has been merged, normalized, batch effect-corrected and filtered for genes with low variance via multiple open source R packages integrated into our workflow. Overall this dataset (with associated clinical metadata) better represents the diseased population and serves as a powerful tool for early predictive biomarker discovery.
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