[국내논문]Alternative Carcinogenicity Screening Assay Using Colon Cancer Stem Cells: A Quantitative PCR (qPCR)-Based Prediction System for Colon Carcinogenesis원문보기
The carcinogenicity of chemicals in the environment is a major concern. Recently, numerous studies have attempted to develop methods for predicting carcinogenicity, including rodent and cell-based approaches. However, rodent carcinogenicity tests for evaluating the carcinogenic potential of a chemic...
The carcinogenicity of chemicals in the environment is a major concern. Recently, numerous studies have attempted to develop methods for predicting carcinogenicity, including rodent and cell-based approaches. However, rodent carcinogenicity tests for evaluating the carcinogenic potential of a chemical to humans are time-consuming and costly. This study focused on the development of an alternative method for predicting carcinogenicity using quantitative PCR (qPCR) and colon cancer stem cells. A toxicogenomic method, mRNA profiling, is useful for predicting carcinogenicity. Using microarray analysis, we optimized 16 predictive gene sets from five carcinogens (azoxymethane, 3,2'-dimethyl-4-aminobiphenyl, N-ethyl-n-nitrosourea, metronidazole, 4-(n-methyl-n-nitrosamino)-1-(3-pyridyl)-1-butanone) used to treat colon cancer stem cell samples. The 16 genes were evaluated by qPCR using 23 positive and negative carcinogens in colon cancer stem cells. Among them, six genes could differentiate between positive and negative carcinogens with a p-value of ${\leq}0.05$. Our qPCR-based prediction system for colon carcinogenesis using colon cancer stem cells is cost- and time-efficient. Thus, this qPCR-based prediction system is an alternative to in vivo carcinogenicity screening assays.
The carcinogenicity of chemicals in the environment is a major concern. Recently, numerous studies have attempted to develop methods for predicting carcinogenicity, including rodent and cell-based approaches. However, rodent carcinogenicity tests for evaluating the carcinogenic potential of a chemical to humans are time-consuming and costly. This study focused on the development of an alternative method for predicting carcinogenicity using quantitative PCR (qPCR) and colon cancer stem cells. A toxicogenomic method, mRNA profiling, is useful for predicting carcinogenicity. Using microarray analysis, we optimized 16 predictive gene sets from five carcinogens (azoxymethane, 3,2'-dimethyl-4-aminobiphenyl, N-ethyl-n-nitrosourea, metronidazole, 4-(n-methyl-n-nitrosamino)-1-(3-pyridyl)-1-butanone) used to treat colon cancer stem cell samples. The 16 genes were evaluated by qPCR using 23 positive and negative carcinogens in colon cancer stem cells. Among them, six genes could differentiate between positive and negative carcinogens with a p-value of ${\leq}0.05$. Our qPCR-based prediction system for colon carcinogenesis using colon cancer stem cells is cost- and time-efficient. Thus, this qPCR-based prediction system is an alternative to in vivo carcinogenicity screening assays.
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문제 정의
The purpose of this study was to identify commonalities in gene expression alterations induced by genotoxic carcinogens (GCs) with similar modes of action. To determine the optimal concentrations of chemicals including GCs and non-carcinogens (NCs) for colon cancer cells, we selected concentrations based on 1/10 of the concentrations used in mice.
However, these arrays require specialized equipment and highly skilled bioinformatics approaches, limiting the evaluation of carcinogenicity test results. In this study, we evaluated GC biomarkers by qPCR, which is a cost-effective approach. Cumulative exposure of human and mouse cells to carcinogens leads to the progression of cellular carcinogenesis and causes distinct genetic changes [14, 16-18].
They clearly differentiated GCs and NCs in distinct cellular signaling pathways. This study presents a unique approach for determining colon cancer carcinogenesis using new GC biomarkers. These measurable biomarkers can be used as new endpoints for detecting carcinogenesis.
제안 방법
From these five datasets, we selected appropriate GC biomarker candidates by the criteria that comply with a p-value (p < 0.05) of the permutation t-test between the qPCR results of GC and NC.
Total RNA was extracted using the Ribospin Kit (GeneAll, Korea) according to the manufacturer’s instructions. The RNA quality was measured with an Agilent 2100 Bioanalyzer using the RNA 6000 Nano Chip (Agilent Technologies, USA), and the RNA quantity was measured with an ND-1000 Spectrophotometer (NanoDrop Technologies, Inc., USA). We used 300 ng of each RNA sample as input in the Affymetrix procedure according to the manufacturer’s instructions.
Microarray analysis of HCT116 colon cancer stem cells treated with five genotoxic carcinogens (GCs) (azoxymethane (AOM), N-ethyl-n-nitrosourea (ENU), metronidazole (MNZ), 4-(n-methyl-n-nitrosamino)-1-(3-pyridyl)-1-butanone (NKK), and 3,2'-dimethyl-4-aminobiphenyl (DMAB)) for 7 days.
Next, we performed microarrays to identify GC biomarker candidates using the GCs AOM, ENU, MNZ, NKK, and DMAB. Gene expression profiles of these compound-treated HCT116 colon cancer stem cells were detected following exposure to each chemical and showed distinct patterns in hierarchical clustering (Fig.
We evaluated the mechanisms functioning in carcinogen-treated colon cancer stem cells and developed an alternative carcinogenicity test. Using qPCR analysis, we selected six predictive genes (PGA5, SCT, MS4A4E, KRTAP4-6, RNPS1, and PLCXD1) from among the 16 genes predicted by microarray analysis based on data following treatment with 17 GCs and 5 NCs. Using DiseaseConnect (http://disease-connect.
We selected appropriate GC biomarker candidates with a p-value (p ≤ 0.05) from the permutation t-test between the qPCR results of GCs and NCs.
After identifying GC biomarker candidates by microarray analysis, we validated these 16 candidates to differentiate GCs and NCs. We performed qPCR analysis using 23 chemicals, including GCs and NCs.
05) from the permutation t-test between the qPCR results of GCs and NCs. Finally, we optimized six genes: PGA5, SCT, MS4A4E, KRTAP4-6, RNPS1, and PLCXD1. We then performed GeneMANIA and GIANT analyses to determine the relationships among the identified candidates.
대상 데이터
The human colon cancer cell line HCT116 was obtained from American Type Culture Collection (Manassas, VA, USA). HCT116 cells were cultured in DMEM medium (Hyclone, USA) supplemented with heat-inactivated 10% (v/v) fetal bovine serum (Sigma-Aldrich, USA).
Datasets, including coexpression, physical interactions, pathway, and genetic interactions, were collected from GeneMANIA. The five dataset-modulated carcinogens were produced from the GeneMANIA database (http://www.genemania.org). GIANT (Genome-scale Integrated Analysis of gene Networks in Tissues) was used for tissue-specific pathway analysis.
GIANT (Genome-scale Integrated Analysis of gene Networks in Tissues) was used for tissue-specific pathway analysis. The dataset is available at http://giant.princeton.edu/.
After excluding “unknown” probe sets that were not identified by gene symbols and functions, 16 genes were selected.
이론/모형
Cell viability was analyzed by performing the CCK-8 assay according to the manufacturer’s instructions.
Cell viability was quantified using a cell counting kit-8 (CCK-8) assay (Promega, USA). Cells were seeded at 5,000 cells per well into 96-well culture plates containing 100 µl of DMEM supplemented with 10% fetal bovine serum, 100 µg/ml penicillin, and 0.
후속연구
In the future, the biomarker candidates can be used to differentiate GCs from NCs. Further studies should be performed to compare the present NC- and GC-specific biomarker candidates.
참고문헌 (22)
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