Background: The development of microarrays permits us to monitor transcriptomes on a genome-wide scale. To validate microarray measurements, quantitative-real time-reverse transcription PCR (Q-RT-PCR) is one of the most robust and commonly used approaches. The new challenge in gene quantification analysis is how to explicitly incorporate statistical estimation in such studies. In the realm of statistical analysis, the various available methods of the probe level normalization for microarray analysis may result in distinctly different target selections and variation in the scores for the correlation between microarray and Q-RT-PCR. Moreover, it remains a major challenge to identify a proper internal control for Q-RT-PCR when confirming microarray measurements.Results: Sixty-six Affymetrix microarray slides using lung adenocarcinoma tissue RNAs were analyzed by a statistical re-sampling method in order to detect genes with minimal variation in gene expression. By this approach, we identifiedDDX5as a novel internal control for Q-RT-PCR. Twenty-three genes, which were differentially expressed between adjacent normal and tumor samples, were selected and analyzed using 24 paired lung adenocarcinoma samples by Q-RT-PCR using two internal controls,DDX5andGAPDH. The percentage correlation between Q-RT-PCR and microarray were 70% and 48% by usingDDX5andGAPDHas internal controls, respectively.Conclusion: Together, these quantification strategies for Q-RT-PCR data processing procedure, which focused on minimal variation, ought to significantly facilitate internal control evaluation and selection for Q-RT-PCR when corroborating microarray data.

Increasing evidence suggests thatPRMT5, a protein arginine methyltransferase, is involved in tumorigenesis. However, no systematic research has demonstrated the cell‐transforming activity ofPRMT5. We investigated the involvement ofPRMT5 in tumor formation. First, we showed thatPRMT5 was associated with many human cancers, through statistical analysis of microarray data in theNCBI GEOdatabase. Overexpression of ectopicPRMT5per seor its specific shRNAenhanced or reduced cell growth under conditions of normal or low concentrations of serum, low cell density, and poor cell attachment. A stable clone that expressed exogenousPRMT5 formed tumors in nude mice, which demonstrated thatPRMT5 is a potential oncoprotein.PRMT5 accelerated cell cycle progression throughG1 phase and modulated regulators ofG1; for example, it upregulated cyclin‐dependent kinase (CDK) 4,CDK6, and cyclinsD1,D2 andE1, and inactivated retinoblastoma protein (Rb). Moreover,PRMT5 activated phosphoinositide 3‐kinase (PI3K)/AKTand suppressed c‐JunN‐terminal kinase (JNK)/c‐Jun signaling cascades. However, only inhibition ofPI3Kactivity, and not overexpression ofJNK, blockedPRMT5‐induced cell proliferation. Further analysis ofPRMT5 expression in 64 samples of human lung cancer tissues by microarray and western blot analysis revealed a tight association ofPRMT5 with lung cancer. Knockdown ofPRMT5 retarded cell growth of lung cancer cell linesA549 andH1299. In conclusion, to the best of our knowledge, we have characterized the cell‐transforming activity ofPRMT5 and delineated its underlying mechanisms for the first time.

Lung adenocarcinoma is the leading cause of cancer-related deaths among both men and women in the world. Despite recent advances in diagnosis and treatment, the mortality rates with an overall 5-year survival of only 15%. This high mortality is probably attributable to early metastasis. Although several well-known markers correlated with poor/metastasis prognosis in lung adenocarcinoma patients by immunohistochemistry was reported, the molecular mechanisms of lung adenocarcinoma development are still not clear. To explore novel molecular markers and their signaling pathways will be crucial for aiding in treatment of lung adenocarcinoma patients. To identify novel lung adenocarcinoma-associated /metastasis genes and to clarify the underlying molecular mechanisms of these targets in lung cancer progression, we created a bioinformatics scheme consisting of integrating three gene expression profile datasets, including pairwise lung adenocarcinoma, secondary metastatic tumors vs. benign tumors, and a series of invasive cell lines. Among the novel targets identified, FLJ10540 was overexpressed in lung cancer tissues and is associated with cell migration and invasion. Furthermore, we employed two co-expression strategies to identify in which pathway FLJ10540 was involved. Lung adenocarcinoma array profiles and tissue microarray IHC staining data showed that FLJ10540 and VEGF-A, as well as FLJ10540 and phospho-AKT exhibit positive correlations, respectively. Stimulation of lung cancer cells with VEGF-A results in an increase in FLJ10540 protein expression and enhances complex formation with PI3K. Treatment with VEGFR2 and PI3K inhibitors affects cell migration and invasion by activating the PI3K/AKT pathway. Moreover, knockdown of FLJ10540 destabilizes formation of the P110-α/P85-α-(PI3K) complex, further supporting the participation of FLJ10540 in the VEGF-A/PI3K/AKT pathway. This finding set the stage for further testing of FLJ10540 as a new therapeutic target for treating lung cancer and may contribute to the development of new therapeutic strategies that are able to block the PI3K/AKT pathway in lung cancer cells.

Background: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level.Results: Here, we build an integrative platform, theEncyclopedia ofHepatocellularCarcinoma genesOnline, dubbed EHCOhttp://ehco.iis.sinica.edu.tw, to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs.Conclusion: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment.

The evolutionarily conserved Aurora family kinases, a family of mitotic serine/threonine kinases, has three members in humans (Aurora-A, -B and -C). Overexpression of Aurora family members, particularly Aurora-A, has been reported in many human cancers and cell lines. In this study, we present evidence based on comparative gene expression analysis via quantitative RT-PCR to delineate the relative contributions of these kinases in 60 cell lines and statistical analysis in five different human cancer microarray datasets. The analysis demonstrated the selective upregulation of these Aurora members in various cancers. In general, Aurora-A exhibited the highest expression levels, with substantially decreased quantities of the Aurora-C transcript detected relative to Aurora-A and -B. Moreover, to characterize the roles of each Aurora member, which share many similarities, we investigated the expression profiles of the family in normal tissues and a panel of different phases of the HeLa cell cycle. Finally, both Aurora-A and -B were overexpressed in a majority of esophageal tumor tissues in comparison to the normal variants. Taken together, the results show that each Aurora member exhibits distinct expression patterns, implying that they are engaged in different biological processes to accomplish more elaborate cell physiological functions in higher organisms.