Dataset Information

Screening and Identification of Key Biomarkers in Acquired Lapatinib-Resistant Breast Cancer.

ABSTRACT: Lapatinib, targeting the human epidermal growth factor receptor family members HER1 and HER2, has been approved by the US Food and Drug Administration for use in metastatic HER2-positive breast cancer. However, resistance to lapatinib remains a common challenge to HER2-positive metastatic breast cancer. Until now, the molecular mechanisms of acquired resistance to lapatinib (ALR) have remained unclear. With no definite biomarkers currently known, we aimed to screen for key biomarkers in ALR. In this research, we identified 55 differentially expressed genes (DEGs, 20 upregulated, 35 downregulated) through bioinformatic analysis using microarray datasets GSE16179, GSE38376, and GSE51889 from the Gene Expression Omnibus (GEO) database. The related gene function was explored using the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. The functional enrichment of the DEGs was analyzed, including negative regulation of the B cell apoptotic process, DNA replication, solute:proton symporter activity, synthesis, and degradation of ketone bodies, and metal sequestration by antimicrobial proteins. Analysis of seven hub genes revealed their concentration mainly in DNA replication and cell cycle. Survival analysis revealed that MCM10 and SPC24 may be related with poor prognosis in patients with ALR. Meanwhile, the prediction model of lapatinib sensitivity was constructed, and emerging role of the model was further analyzed using several webtools. In conclusion, hub genes are involved in the complex mechanisms underlying ALR in breast cancer and provide favorable support for treatment of ALR in future.

SUBMITTER: Bao S

PROVIDER: S-EPMC7500445 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Screening and Identification of Key Biomarkers in Acquired Lapatinib-Resistant Breast Cancer.

Bao Shengnan S Chen Yi Y Yang Fan F Sun Chunxiao C Yang Mengzhu M Li Wei W Huang Xiang X Li Jun J Wu Hao H Yin Yongmei Y

Frontiers in pharmacology 20200904

Lapatinib, targeting the human epidermal growth factor receptor family members HER1 and HER2, has been approved by the US Food and Drug Administration for use in metastatic HER2-positive breast cancer. However, resistance to lapatinib remains a common challenge to HER2-positive metastatic breast cancer. Until now, the molecular mechanisms of acquired resistance to lapatinib (ALR) have remained unclear. With no definite biomarkers currently known, we aimed to screen for key biomarkers in ALR. In ...[more]

PMID: 33013420

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Project description:BackgroundGastric cancer (GC) is one of the most common cancers all over the world, causing high mortality. Gastric cancer screening is one of the effective strategies used to reduce mortality. We expect that good biomarkers can be discovered to diagnose and treat gastric cancer as early as possible.MethodsWe download four gene expression profiling datasets of gastric cancer (GSE118916, GSE54129, GSE103236, GSE112369), which were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between gastric cancer and adjacent normal tissues were detected to explore biomarkers that may play an important role in gastric cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlap genes were conducted by the Metascape online database; the protein-protein interaction (PPI) network was constructed by the STRING online database, and we screened the hub genes of the PPI network using the Cytoscape software. The survival curve analysis was conducted by km-plotter and the stage plots of hub genes were created by the GEPIA online database. PCR, WB, and immunohistochemistry were used to verify the expression of hub genes. A neural network model was established to quantify the predictors of gastric cancer.ResultsThe relative expression level of cadherin-3 (CDH3), lymphoid enhancer-binding factor 1 (LEF1), and matrix metallopeptidase 7 (MMP7) were significantly higher in gastric samples, compared with the normal groups (p<0.05). Receiver operator characteristic (ROC) curves were constructed to determine the effect of the three genes' expression on gastric cancer, and the AUC was used to determine the degree of confidence: CDH3 (AUC = 0.800, P<0.05, 95% CI =0.857-0.895), LEF1 (AUC=0.620, P<0.05, 95%CI=0.632-0.714), and MMP7 (AUC=0.914, P<0.05, 95%CI=0.714-0.947). The high-risk warning indicator of gastric cancer contained 8<CDH3<15 and 10<expression of LEF1<16.ConclusionsCDH3, LEF1, and MMP7 can be used as candidate biomarkers to construct a neural network model from hub genes, which may be helpful for the early diagnosis of gastric cancer.

Dataset Information

Screening and Identification of Key Biomarkers in Acquired Lapatinib-Resistant Breast Cancer.

Publications

Screening and Identification of Key Biomarkers in Acquired Lapatinib-Resistant Breast Cancer.

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