ArticleMedical oncology (Northwood, London, England)2017
Identification and characterization of biomarkers and their functions for Lapatinib-resistant breast cancer.
Article in Medical oncology (Northwood, London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 40 citations in OpenAlex.
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- Lapatinib-Resistant HER2+ Breast Cancer Cells Are Associated with Dysregulation of MAPK and p70S6K/PDCD4 Pathways and Calcium Management, Influence of Cryptotanshinone.International journal of molecular sciences · 2025Article
- Gene expression prediction from histology images via hypergraph neural networks.Briefings in bioinformatics · 2024Article
- An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome.BMC bioinformatics · 2024Article
- Case Report: Molecular and microenvironment change upon midostaurin treatment in mast cell leukemia at single-cell level.Frontiers in immunology · 2023Article
- Experimental and computational assessment of the synergistic pharmacodynamic drug-drug interactions of a triple combination therapy in refractory HER2-positive breast cancer cells.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Autophagy-related prognostic signature for survival prediction of triple negative breast cancer.PeerJ · 2022Article
- Exploring the biosynthetic pathway of lignin in Acorus tatarinowii Schott using de novo leaf and rhizome transcriptome analysis.Bioscience reports · 2021Article
- Integrated Transcriptomic Analysis Revealed Hub Genes and Pathways Involved in Sorafenib Resistance in Hepatocellular Carcinoma.Pathology oncology research : POR · 2021Article
- Identification of drivers of breast cancer invasion by secretome analysis: insight into CTGF signaling.Scientific reports · 2020Article
- Identification and characterization of critical genes associated with tamoxifen resistance in breast cancer.PeerJ · 2020Article
- Identification and Validation of Potential Biomarkers and Their Functions in Acute Kidney Injury.Frontiers in genetics · 2020Article
- Pathway-Based Drug Response Prediction Using Similarity Identification in Gene Expression.Frontiers in genetics · 2020Article
- Modeling cancer drug response through drug-specific informative genes.Scientific reports · 2019Article
- Identification of biomarkers and their functions in dasatinib-resistant pancreatic cancer using bioinformatics analysis.Oncology letters · 2019Article
- A combined approach with gene-wise normalization improves the analysis of RNA-seq data in human breast cancer subtypes.PloS one · 2018Article
- LCK as a Potential Therapeutic Target for Acute Rejection after Kidney Transplantation: A Bioinformatics Clue.Journal of immunology research · 2018Article
- Utility of a Novel Three-Dimensional and Dynamic (3DD) Cell Culture System for PK/PD Studies: Evaluation of a Triple Combination Therapy at Overcoming Anti-HER2 Treatment Resistance in Breast Cancer.Frontiers in pharmacology · 2018Article
- Identification of key pathways and genes with aberrant methylation in prostate cancer using bioinformatics analysis.OncoTargets and therapy · 2017Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lapatinib, a novel oral dual tyrosine kinase inhibitor blocking HER1 and HER2 pathways, has presented beneficial effects on breast cancer with positive HER2. However, its efficacy is largely limited by the occurrence of acquired drug resistance. In this study, we aimed to explore the underlying molecular mechanisms of Lapatinib resistance using bioinformatics strategies. The gene expression profile of SKBR3-R (acquired Lapatinib-resistant) and SKBR3 (Lapatinib-sensitive) cell line was downloaded from gene expression omnibus database. Then, the differentially expressed genes (DEGs) were selected using dChip software. Furthermore, gene ontology (GO) and pathway enrichment analyses were carried out by using DAVID database. Finally, the protein-protein interaction network was constructed, and the hub genes in the network were analyzed by using STRING database. A total of 300 DEGs, such as HSPA5, MAP1LC3A and RASSF2, were screened out. GO functional enrichment analysis showed that the genes were associated with cell membrane component-related, stimulus-related and binding-related items. KEGG pathway analysis indicated that three dysfunctional pathways, including PPAR signaling pathway, cytokine-cytokine receptor interaction and pathways in cancer, were enriched. Protein-protein interaction network construction revealed that some hub genes, such as PPARG, TGFBI, TGFBR2, TIMP1, CTGF, UBA52 and JUN, might have an association with Lapatinib resistance. The present study offered new insights into the molecular mechanisms of Lapatinib resistance and identified a series of important hub genes that have the potential to be the targets for treatment of Lapatinib-resistant breast cancer.
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