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Potential candidate treatment agents for targeting of cholangiocarcinoma identified by gene expression profile analysis.

ABSTRACT: Cholangiocarcinoma (CCA) remains to be a major health problem in several Asian countries including Thailand. The molecular mechanism of CCA is poorly understood. Early diagnosis is difficult, and at present, no effective therapeutic drug is available. The present study aimed to identify the molecular mechanism of CCA by gene expression profile analysis and to search for current approved drugs which may interact with the upregulated genes in CCA. Gene Expression Omnibus (GEO) was used to analyze the gene expression profiles of CCA patients and normal subjects. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology enrichment analysis was also performed, with the KEGG pathway analysis indicating that pancreatic secretion, protein digestion and absorption, fat digestion and absorption, and glycerolipid metabolism may serve important roles in CCA oncogenesis. The drug signature database (DsigDB) was used to search for US Food and Drug Administration (FDA)-approved drugs potentially capable of reversing the effects of the upregulated gene expression in CCA. A total of 61 antineoplastic and 86 non-antineoplastic drugs were identified. Checkpoint kinase 1 was the most interacting with drug signatures. Many of the targeted protein inhibitors that were identified have been approved by the US-FDA as therapeutic agents for non-antineoplastic diseases, including cimetidine, valproic acid and lovastatin. The current study demonstrated an application for bioinformatics analysis in assessing the potential efficacy of currently approved drugs for novel use. The present results suggest novel indications regarding existing drugs useful for CCA treatment. However, further in vitro and in vivo studies are required to support the current predictions.

SUBMITTER: Chujan S

PROVIDER: S-EPMC6007048 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Potential candidate treatment agents for targeting of cholangiocarcinoma identified by gene expression profile analysis.

Chujan Suthipong S Suriyo Tawit T Ungtrakul Teerapat T Pomyen Yotsawat Y Satayavivad Jutamaad J

Biomedical reports 20180522 1

Cholangiocarcinoma (CCA) remains to be a major health problem in several Asian countries including Thailand. The molecular mechanism of CCA is poorly understood. Early diagnosis is difficult, and at present, no effective therapeutic drug is available. The present study aimed to identify the molecular mechanism of CCA by gene expression profile analysis and to search for current approved drugs which may interact with the upregulated genes in CCA. Gene Expression Omnibus (GEO) was used to analyze ...[more]

PMID: 29930804

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Project description:Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal) samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a) all genes on the microarray platform and b) a list of known disease-related genes (a priori selection). We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms.Highly discriminative expression profiles were produced on both simulated gene expression data and expression data from breast cancer and lymphoma datasets on the basis of ER and BCL-6 expression, respectively. Use of relapse-free status to identify profiles for prognosis prediction resulted in poorly discriminative decision rules. Supervised feature selection resulted in more accurate classifications than random or a priori selection, however, the difference in prediction error decreased as the number of features increased. These results held when decision rules were applied across-datasets to samples profiled on the same microarray platform.Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine learning approaches. These findings are relevant to the use of molecular profiling for the identification of candidate biomarker panels.

Project description:BackgroundOsteoarthritis (OA) is a common skeletal system disease that has been partially attributed to genetic factors. The hand is frequently affected, which seriously affects the patient's quality of life. However, the pathogenetic mechanism of hand osteoarthritis (hand OA) is still elusive.MethodsA genome-wide association study (GWAS) summary of hand OA was obtained from the UK Biobank dataset, which contains data from a total of 452,264 White British individuals, including 37,782 OA patients. The transcriptome-wide association study (TWAS) of hand OA was performed using FUnctional Summary-based ImputatiON (FUSION) with the skeletal muscle and blood as gene expression references. The significant genes identified by TWAS were further subjected to gene set enrichment analysis (GSEA) with the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Furthermore, we compared the genes and gene sets identified by our TWAS with that of a knee OA mRNA expression profile to detect the genes and gene sets shared by TWAS and mRNA expression profiles in OA. The mRNA expression profiles of 18 normal knee cartilages and 20 OA knee cartilages were acquired from the Gene Expression Omnibus database (accession number: GSE114007).ResultsTWAS identified 177 genes with P?<?0.05 for the skeletal muscle, including ANKRD44 (P?=?0.0001), RIC3 (P?=?0.0003), and AC005154.6 (P?=?0.0004). TWAS identified 423 genes with P?<?0.05 for the blood, including CRIM1 (P?=?0.0002), ZNF880 (P?=?0.0002), and NCKIPSD (P?=?0.0003). After comparing the results of the TWAS to those of the mRNA expression profiling of OA, we identified 5 common genes, including DHRS3 (log2fold?=?-?1.85, P?=?3.31?×?10-?9) and SKP2 (log2fold?=?1.36, P?=?1.62?×?10-?8). GSEA of TWAS identified 51 gene ontology (GO) terms for hand OA, for example, protein binding (P?=?0.0003) and cytosol (P?=?0.0020). We also detected 6 common GO terms shared by TWAS and mRNA expression profiling, including protein binding (PTWAS?=?2.54?×?10-?4, PmRNA?=?3.42?×?10-?8), extracellular exosome (PTWAS?=?0.02, PmRNA?=?1.18?×?10-?4), and cytoplasm (PTWAS?=?0.0183, PmRNA?=?0.0048).ConclusionIn this study, we identified 5 candidate genes and 6 GO terms related to hand OA, which may help to uncover the pathogenesis of hand OA. It should be noted that the possible difference in the gene expression profiles between hand OA and knee OA may affect our study results, which should be interpreted with caution.

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Potential candidate treatment agents for targeting of cholangiocarcinoma identified by gene expression profile analysis.

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Potential candidate treatment agents for targeting of cholangiocarcinoma identified by gene expression profile analysis.

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