Integrative Bioinformatics Approaches for Dynamic Time Series and Steady State Transcriptome Microarray Data
Date Issued
2011
Date
2011
Author(s)
Lu, Tzu-Pin
Abstract
Microarray technology has been widely utilized in biological and medical researches in the past two decades. The high-throughput feature facilitates the exploration of dysregulated cellular functions driven by experimental manipulations and identification of potential candidate genes for further validations. However, dealing with those massive data poses an exciting challenge in how to perform an efficient and accurate analysis. To address this issue, various statistical algorithms and mathematical models have been developed. In this dissertation, four bioinformatics approaches were presented and applied on two microarray datasets, three human lymphoblastoid cell lines exposed to radiation treatments and non-smoking female lung cancer patients in Taiwan. The first approach was a dynamic time series analysis, which explored the radiation-induced effects between higher and lower doses in the cells with different p53 status. Template-based clustering and tight clustering were performed to identify differentially expressed genes, and the results exhibited distinct signaling pathways in the three cell lines after 10Gy and iso-survival radiation exposures. After 10Gy radiation treatments, the p53 signaling pathway was triggered in TK6, whereas the NFkB signaling pathway was activated in WTK1 without functional p53 protein. Alternatively, irradiation with iso-survival doses induced down-regulations of many E2F4-related genes in all cell lines in spite of p53 status, which indicated that the E2F4 signaling pathway might serve as important regulators in response to lower dose radiation.
The second approach investigated the gene expression profiles of non-smoking female lung cancer patients in Taiwan. This data set was composed of 60 pairs of tumor and adjacent normal tissue specimens. There were 687 differentially expressed genes in tumor tissue identified by paired t-test and significantly enriched in the pathway of axon guidance signaling. The varying patterns were highly similar to two public lung cancer datasets with both tumor and normal tissues from the same individual, which strengthened that these dysregulated genes were involved in lung tumorigenesis. Among them, the downregulation of SEMA5A in tumor tissue, both at the transcriptional and translational levels, was associated with poor survival outcomes. The results suggested that SEMA5A might be used as a novel biomarker for non-smoking female lung cancer patients.
In the third approach, concurrent analyses of gene expression and copy number variations (CNVs) were performed in 42 pairs of non-smoking lung adenocarcinoma women. The results revealed the genomic landscape of recurrent copy number variated regions and 475 differentially expressed genes associated with CNVs. Among these CNV-driven genes, two important functions, survival regulation via AKT signaling and cytoskeleton reorganization, were significantly enriched. Survival analyses based on these enriched pathways demonstrated effective predictions in three independent microarray datasets, which suggested that those identified genes/pathways with concordant changes in both gene expression and CNV might be used as prognostic biomarkers for lung tumorigenesis.
In the fourth approach, a comprehensive analysis was conducted in 32 pairs of non-smoking female lung adenocarcinoma patients to investigate SNPs, CNVs, methylation alterations, and gene expressions simultaneously. Associated co-varying patterns were observed between genetic modifications and transcriptional dysregulations. Three statistical approaches identified 617 SNP alleles related to CNVs or methylation alterations, and among them, Kruskal-Wallis test indicated 13 SNPs with downstream gene expression changes. Therefore, these SNPs with concordant changes in both DNA and RNA levels deserve more research efforts to elucidate their roles in lung cancer.
In conclusion, these four bioinformatics approaches were effective in addressing biomedical issues and the results are confirmable in external datasets or biological experiments.
Subjects
Microarray
Bioinformatics
Integrated Analysis
Time Series
Radiation Response
Lung Cancer
SDGs
Type
thesis
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