Dataset Information

Microarray analysis of differentially-expressed genes and linker genes associated with the molecular mechanism of colorectal cancer.

ABSTRACT: Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide and remains the third leading cause of cancer-associated mortality. The present study aimed to fully elucidate the pathogenesis of CRC and identify associated genes in tumor development. Microarray GSE44076, GSE41328 and GSE44861 datasets were downloaded from the Gene Expression Omnibus database and integrated with meta-analysis. Differentially-expressed genes (DEGs) were identified from CRC samples compared with adjacent non-cancerous controls using the Limma package in R, followed by functional analysis using the Database for Annotation, Visualization, and Integrated Discovery online tool. A protein-protein interaction (PPI) network of DEGs and linker genes was constructed using NetBox software and modules were also mined. Functional annotation was performed for modules with a maximum number of nodes. Subsequent to meta-analysis to pool the data, one dataset that included 327 samples involved in 11,081 genes was obtained. A total of 697 DEGs were identified between CRC samples and adjacent non-cancerous controls. In the PPI network, modules 1 and 5 contained the maximum number of nodes. Collagen, type I, ?1 (COL1A1), COL1A2 and matrix metallopeptidase 9 (MMP9) in module 1 and UDP-glucose 6-dehydrogenase (UGDH), aldehyde dehydrogenase 1 family, member A1 (ALDH1A1), fatty acid binding protein 4 (FABP4) and monoglyceride lipase (MGLL) in module 5 exhibited a high degree of connectivity. Functional analysis indicated that the genes in module 1 were involved in extracellular matrix (ECM)-associated functions and that the genes in module 5 were involved in metabolism-related functions. Overall, significant DEGs and linker genes, namely COL1A1, COL1A2, MMP9, UGDH, ALDH1A1, FABP4 and MGLL, play a crucial role in the development of CRC via regulating the ECM and cell metabolism.

SUBMITTER: Shen X

PROVIDER: S-EPMC5103929 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Microarray analysis of differentially-expressed genes and linker genes associated with the molecular mechanism of colorectal cancer.

Shen Xingjie X Yue Meng M Meng Fansheng F Zhu Jingyu J Zhu Xiaoyan X Jiang Yakun Y

Oncology letters 20160912 5

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide and remains the third leading cause of cancer-associated mortality. The present study aimed to fully elucidate the pathogenesis of CRC and identify associated genes in tumor development. Microarray GSE44076, GSE41328 and GSE44861 datasets were downloaded from the Gene Expression Omnibus database and integrated with meta-analysis. Differentially-expressed genes (DEGs) were identified from CRC samples compared with adjacen ...[more]

PMID: 27899990

Similar Datasets

Project description:BackgroundMicroarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human).ResultsWe hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile.ConclusionsFactors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects.

Project description:BACKGROUND: This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework. RESULTS: The performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of Microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets. CONCLUSION: This paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experiments. Two different ranking methods used in module 1 bring diversity in the selection of genes. The conversion of ranks to p-values, the fusion of p-values and FDR analysis aid in the identification of significant genes which cannot be judged based on gene ranking alone. The 3 fold validation, namely, robustness in selection of genes using FDR analysis, clustering, and visualization demonstrate the relevance of the DEGs. Empirical analyses on 50 artificial datasets and 6 real microarray datasets illustrate the efficacy of the proposed approach. The analyses on 3 cancer datasets demonstrate the utility of the proposed approach on microarray datasets with two classes of samples. The scalability of the proposed unified approach to multi-sample (more than two sample classes) microarray datasets is addressed using three sets of Parkinson's Data. Empirical analyses show that the unified framework outperformed other gene selection methods in selecting differentially expressed genes from microarray data.

Dataset Information

Microarray analysis of differentially-expressed genes and linker genes associated with the molecular mechanism of colorectal cancer.

Publications

Microarray analysis of differentially-expressed genes and linker genes associated with the molecular mechanism of colorectal cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure