ArticleBMC bioinformatics2019
Identification of monotonically differentially expressed genes for non-small cell lung cancer.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- SOHLH2-RAD54L axis induces radioresistance by promoting homologous recombination repair in non-small cell lung cancer.Cell death discovery · 2026Article
- Identification of monotonically classifying pairs of genes for ordinal disease outcomes.Bioinformatics advances · 2026Article
- Identification of monotonically expressed long non-coding RNA signatures for breast cancer using variational autoencoders.PloS one · 2023Article
- Article
- Signatures of Co-Deregulated Genes and Their Transcriptional Regulators in Lung Cancer.International journal of molecular sciences · 2022Article
- Computational Identification of Master Regulators Influencing Trypanotolerance in Cattle.International journal of molecular sciences · 2021Article
- Decoding the differentiation of mesenchymal stem cells into mesangial cells at the transcriptomic level.BMC genomics · 2020Article
- Article
- Long noncoding RNA SNHG7 contributes to cell proliferation, migration, invasion and epithelial to mesenchymal transition in non-small cell lung cancer by regulating miR-449a/TGIF2 axis.Thoracic cancer · 2020Article
- Identification of Monotonically Differentially Expressed Genes across Pathologic Stages for Cancers.Journal of oncology · 2020Article
- Article
- Colon Cancer Progression Is Reflected to Monotonic Differentiation in Gene Expression and Pathway Deregulation Facilitating Stage-specific Drug Repurposing.Cancer genomics & proteomicsArticle
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
backgroundMonotonically expressed genes (MEGs) are genes whose expression values increase or decrease monotonically as a disease advances or time proceeds. Non-small cell lung cancer (NSCLC) is a multistage progression process resulting from genetic sequences mutations, the identification of MEGs for NSCLC is important.
resultsWith the aid of a feature selection algorithm capable of identifying MEGs - the MFSelector method - two sets of potential MEGs were selected in this study: the MEGs across the different pathologic stages and the MEGs across the risk levels of death for the NSCLC patients at early stages. For the lung adenocarcinoma (AC) subtypes no statistically significant MEGs were identified across pathologic stages, however dozens of MEGs were identified across the risk levels of death. By contrast, for the squamous cell lung carcinoma (SCC) there were no statistically significant MEGs as either stage or risk level advanced.
conclusionsThe pathologic stage of non-small cell lung cancer patients at early stages has no prognostic value, making the identification of prognostic gene signatures for them more meaningful and highly desirable.
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