microRNA expression profiling of nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC) is posing a serious health problem worldwide. The association between its pathogenesis and microRNAs (miRNA) has not been elucidated. In this study, miRNA expression profiling was performed to screen the miRNA expression changes in 8 NPC tissues and 4 normal nasopharyngeal tissues. Thirty-four miRNAs were identified to be differentially expressed; of these, one miRNA (miR-18a) was overexpressed and 33 miRNAs (miR-34b, miR-34c, let-7 family, etc.) were underexpressed in NPC tissues compared to the normal samples. Validation was performed by real-time quantitative PCR for two altered miRNAs (miR-34b and let-7g) and one non-differentially expressed miRNA (miR-30c). Unsupervised hierarchical clustering analysis showed that the aberrant miRNAs were correlated with the clinical stage of NPC patients. In addition to several biological pathways that are well characterised in NPC and which were significantly targeted by the underexpressed miRNAs, two novel pathways, nervous system development and sensory perception of sound, were identified to be strongly associated with NPC development. Furthermore, a c-Myc centered miRNA regulatory network was inferred in NPC. Our study reveals that aberrantly expressed miRNAs play important roles in NPC tumorigenesis and may serve as potential targets for novel therapeutic strategies in the future.

Selection of reliable reference genes for gene expression study in nasopharyngeal carcinoma.

AIM: To construct a system for selecting reference genes (RGs) and to select the most optimal RGs for gene expression studies in nasopharyngeal carcinoma (NPC). METHODS: The total RNAs from 20 NPC samples were each labeled with Cy5-dUTP. To create a common control, the total RNA from 15 nasopharyngeal phlogistic (NP) tissues was mixed and labeled via reverse transcription with Cy3-dUTP. cDNA microarrays containing 14 112 genes were then performed. A mathematical approach was constructed to screen stably expressed genes from the microarray data. Using this method, three genes (YARS, EIF3S7, and PFDN1) were selected as candidate RGs. Furthermore, 7 commonly used RGs (HPRT1, GAPDH, TBP, ACTB, B2M, G6PDH, and HBB) were selected as additional potential RGs. Real-time PCR was used to detect these 10 candidate genes' expression levels and the geNorm program was used to find the optimal RGs for NPC studies. RESULTS: On the basis of the 10 candidate genes' expression stability level, geNorm analysis identified the optimal single RG (YARS or HPRT1) and the most suitable set of RGs (HPRT1, YARS, and EIF3S7) for NPC gene expression studies. In addition, this analysis determined that B2M, G6PDH, and HBB were not appropriate for use as RGs. Interestingly, ACTB was the least stable RG in our study, even though previous studies had indicated that it was one of the most stable RGs. Three novel candidate genes (YARS, EIF3S7, and PFDN1), which were selected from microarray data, were all identified as suitable RGs for NPC research. A RG-selecting system was then constructed, which combines microarray data analysis, a literature screen, real-time PCR, and bioinformatic analysis. CONCLUSION: We construct a RG-selecting system that helps find the optimal RGs. This process, applied to NPC research, determined the single RG (YARS or HPRT1) and the set of RGs (HPRT1, YARS, and EIF3S7) that are the most suitable internal controls.

A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression.

BACKGROUND: The identification of gene differential co-expression patterns between cancer stages is a newly developing method to reveal the underlying molecular mechanisms of carcinogenesis. Most researches of this subject lack an algorithm useful for performing a statistical significance assessment involving cancer progression. Lacking this specific algorithm is apparently absent in identifying precise gene pairs correlating to cancer progression. RESULTS: In this investigation we studied gene pair co-expression change by using a stochastic process model for approximating the underlying dynamic procedure of the co-expression change during cancer progression. Also, we presented a novel analytical method named 'Stochastic process model for Identifying differentially co-expressed Gene pair' (SIG method). This method has been applied to two well known prostate cancer data sets: hormone sensitive versus hormone resistant, and healthy versus cancerous. From these data sets, 428,582 gene pairs and 303,992 gene pairs were identified respectively. Afterwards, we used two different current statistical methods to the same data sets, which were developed to identify gene pair differential co-expression and did not consider cancer progression in algorithm. We then compared these results from three different perspectives: progression analysis, gene pair identification effectiveness analysis, and pathway enrichment analysis. Statistical methods were used to quantify the quality and performance of these different perspectives. They included: Re-identification Scale (RS) and Progression Score (PS) in progression analysis, True Positive Rate (TPR) in gene pair analysis, and Pathway Enrichment Score (PES) in pathway analysis. Our results show small values of RS and large values of PS, TPR, and PES; thus, suggesting that gene pairs identified by the SIG method are highly correlated with cancer progression, and highly enriched in disease-specific pathways. From this research, several gene interaction networks inferred could provide clues for the mechanism of prostate cancer progression. CONCLUSION: The SIG method reliably identifies cancer progression correlated gene pairs, and performs well both in gene pair ontology analysis and in pathway enrichment analysis. This method provides an effective means of understanding the molecular mechanism of carcinogenesis by appropriately tracking down the process of cancer progression.

Cap1p plays regulation roles in redox, energy metabolism and substance transport: an investigation on Candida albicans under normal culture condition.

Cap1p, a transcription factor in Candida albicans, is believed to be required for tolerance to oxidative stress. However, no information is available concerning its function on basal transcriptional profile. In this study, differentially expressed genes between the CAP1-deleted strain and its parental strain under normal culture condition were identified through microarray analysis. Notably, among the 48 down-regulated genes with the deletion of CAP1, there were three clusters, functionally related to intracellular redox, energy metabolism and substance transport. IPF7817, IPF11105 and FDH11, the three putative Cap1p target genes functionally related to redox, were shown to be activated by oxidative stress in a Cap1p-dependent manner. Furthermore, rhodamine 6G efflux analyses demonstrated that Cap1p contributed to the energy-driven efflux. Taken together, these results reveal that Cap1p plays a significant role in redox status regulation, energy metabolism and substance transport under normal culture condition.

Cap1p is involved in multiple pathways of oxidative stress response in Candida albicans.

Cap1p, a transcription factor in Candida albicans, is thought to participate in oxidative stress tolerance, but the pathways involved are still unclear. The study was designed to reveal the possible pathways by examining changes in the transcription profile after H2O2 treatment with both the cap1-deleted strain CJD21 and its parental strain CAI4 using microarray analysis. Of the identified 89 genes differentially expressed in CAI4 after exposure to H2O2, 76 genes were in a Cap1p-dependent expression pattern. We have shown that Cap1p is involved in the oxidative stress response in C. albicans via multiple pathways, including the cellular antioxidant defense system (e.g., thioredoxin reductase, glutathione reductase, glutathione S-transferase), carbohydrate metabolism and energy metabolism (e.g., glucose-6-phosphate dehydrogenase, transaldolase, glyoxalase I, NADH-dependent flavin oxidoreductase), protein degradation (e.g., 26S proteasome regulatory subunit, ubiquitin-specific protease), ATP-dependent RNA helicase (e.g., DEAD box protein ATP-dependent RNA helicase), and resistance pathways (e.g., multidrug resistance protein, ABC transporter essential for cadmium resistance). Real-time reverse transcription-PCR analysis further confirmed the results of microarray. Collectively, this study provides new insight into the biological functions of Cap1p in oxidative stress response.

cDNA array analysis of the differential expression change in virulence-related genes during the development of resistance in Candida albicans.

Candida albicans is the most frequently isolated fungus in immunocompromised patients associated with mucosal and deep-tissue infections. To investigate the correlation between virulence and resistance on a gene expression profile in C. albicans, we examined the changes in virulence-related genes during the development of resistance in C. albicans from bone marrow transplant patients using a constructed cDNA array representing 3096 unigenes. In addition to the genes known to be associated with azole resistance, 16 virulence-related genes were identified, whose differential expressions were newly found to be associated with the resistant phenotype. Differential expressions for these genes were confirmed by RT-PCR independently. Furthermore, the up-regulation of EFG1, CPH2, TEC1, CDC24, SAP10, ALS9, SNF1, SPO72 and BDF1, and the down-regulation of RAD32, IPF3636 and UBI4 resulted in stronger virulence and invasiveness in the resistant isolates compared with susceptible ones. These findings provide a link between the expression of virulence genes and development of resistance during C. albicans infection in bone marrow transplant (BMT) patients, where C. albicans induces hyphal formation and expression change in multiple virulence factors.