In the past decades, a considerable number of studies have performed meta-analysis on large data collections to prioritize sets of genes, pathways or types/categories of disease focusing on either differential expression, survival analysis, or co-expression networks. However, not many web applications or databases have been developed from these studies thus findings largely remained restricted to the addressed questions and it was not possible for other researchers to use the collected data for the evaluation of novel hypotheses. In this thesis, transcriptomic meta-analysis strategies have been applied to untangle complexities in multiple aspects of cancer research including treatment, diagnosis, and prognosis. Furthermore, three different web-tools have been developed which are not limited to a single type of meta-analysis. In this context, in addition to interesting cancer related findings, novel methodologies have been proposed and tested in the field of meta-analysis and cancer research. First chapter of the thesis presented a general introduction on the concepts of the thesis. Second chapter focused on a pathway comparison strategy based on meta-analysis that was used to reveal concordant/discordant aspects of rapamycin-mediation on transcriptomes of zebrafish and mouse. Analysis has shown that ribosomal terms were significantly upregulated while proteasome was downregulated in both species. Zebrafish has undergone a whole-genome duplication event; I also found out that rapamycin treatment resulted in largely concordant behavior of duplicated gene pairs. In addition, an online database, CompariZome, was developed to evaluate the duplicate zebrafish gene pairs in multiple GEO datasets in zebrafish in comparison to respective human expression datasets. In the third chapter of this thesis, I focused on identification of correlation between a trio of genes, CDH1, HNF4A, and GRHL3, using Cancer Cell Line Encyclopedia (CCLE) dataset to reveal the significance of association between these genes in different cancers, including breast and other epithelial cancers. The findings indicated correlation within the module and has demonstrated the power of meta-analysis using CCLE dataset. In the fourth chapter of this thesis, I focused on understanding the association of CHRNA5, a subunit of cholinergic receptors, with epithelial-to-mesenchymal transition (EMT) as well as epithelial differentiation, TP53 induction, and estrogen (E2) signaling with respect to breast cancer. Meta-analysis of invitro and in-vivo microarray expression datasets showed that CHRNA5, itself, and its positively co-expressed neighbors, were likely secondary targets of E2-signaling; overexpressed in ER- breast cancer patients; and indicators of worse prognosis. Functional annotation revealed that CHRNA5 and its co-expression network was indeed associated with proliferation related pathways. Based on meta-analysis of different cohorts processed in the study, an online database E2S (Estrogen (E2) to Survival) was developed that can facilitate user to query any gene for evaluation of E2-mediated effects, regulation by estrogen receptor (ER), prognostic importance and co-expression network along with functional annotations. In the fifth chapter of this thesis I focused on deciphering the correlation and deregulation between a human parolog pair of genes, i.e., mineralocorticoid and glucocorticoid receptors (MR and GR, respectively) in breast cancer. Meta-analysis of a separate normal/tumor cohort revealed that both genes were downregulated in breast cancer and their expressions were highly positively correlated. However, deregulation analysis predicted that expression of MR and GR was more tightly regulated in normal breast hence its regulation might be lost with the onset of tumorigenesis. Another Shiny database, DualExpBC, was developed to evaluate differential expression of a gene in breast cancer as well as correlation and deregulation of expression between any two input genes in the breast normal/cancer expression cohort. With this thesis, I have developed novel tools and approaches for intra- and inter-species comparative transcriptomics and meta-analysis providing potential diagnostic, prognostic and therapeutic biomarkers.