Network features suggest new hepatocellular carcinoma treatment strategies.

BACKGROUND: Resistance to therapy remains a major cause of the failure of cancer treatment. A major challenge in cancer therapy is to design treatment strategies that circumvent the higher-level homeostatic functions of the robust cellular network that occurs in resistant cells. There is a lack of understanding of mechanisms responsible for the development of cancer and the basis of therapy-resistance mechanisms. Cellular signaling networks have an underlying architecture guided by universal principles. A robust system, such as cancer, has the fundamental ability to survive toxic anticancer drug treatments or a stressful environment mainly due to its mechanisms of redundancy. Consequently, inhibition of a single component/pathway would probably not constitute a successful cancer therapy. RESULTS: We developed a computational method to study the mechanisms of redundancy and to predict communications among the various pathways based on network theory, using data from gene expression profiles of hepatocellular carcinoma (HCC) of patients with poor and better prognosis cancers. Our results clearly indicate that immune system pathways tightly regulate most cancer pathways, and when those pathways are targeted by drugs, the network connectivity is dramatically changed. We examined the main HCC targeted treatments that are currently being evaluated in clinical trials. One prediction of our study is that Sorafenib combined with immune system treatments will be a more effective combination strategy than Sorafenib combined with any other targeted drugs. CONCLUSIONS: We developed a computational framework to analyze gene expression data from HCC tumors with varying degrees of responsiveness and non-tumor samples, based on both Gene and Pathway Co-expression Networks. Our hypothesis is that redundancy is one of the major causes of drug resistance, and can be described as a function of the network structure and its properties. From this perspective, we believe that integration of the redundant variables could lead to the development of promising new methodologies to selectively identify and target the most significant resistance mechanisms of HCC. We describe three mechanisms of redundancy based on their levels of generalization and study the possible impact of those redundancy mechanisms on HCC treatments.

Evaluation of reference genes for real-time quantitative PCR studies in Candida glabrata following azole treatment.

BACKGROUND: The selection of stable and suitable reference genes for real-time quantitative PCR (RT-qPCR) is a crucial prerequisite for reliable gene expression analysis under different experimental conditions. The present study aimed to identify reference genes as internal controls for gene expression studies by RT-qPCR in azole-stimulated Candida glabrata. RESULTS: The expression stability of 16 reference genes under fluconazole stress was evaluated using fold change and standard deviation computations with the hkgFinder tool. Our data revealed that the mRNA expression levels of three ribosomal RNAs (RDN5.8, RDN18, and RDN25) remained stable in response to fluconazole, while PGK1, UBC7, and UBC13 mRNAs showed only approximately 2.9-, 3.0-, and 2.5-fold induction by azole, respectively. By contrast, mRNA levels of the other 10 reference genes (ACT1, EF1α, GAPDH, PPIA, RPL2A, RPL10, RPL13A, SDHA, TUB1, and UBC4) were dramatically increased in C. glabrata following antifungal treatment, exhibiting changes ranging from 4.5- to 32.7-fold. We also assessed the expression stability of these reference genes using the 2(-ΔΔCT) method and three other software packages. The stability rankings of the reference genes by geNorm and the 2(-ΔΔCT) method were identical to those by hkgFinder, whereas the stability rankings by BestKeeper and NormFinder were notably different. We then validated the suitability of six candidate reference genes (ACT1, PGK1, RDN5.8, RDN18, UBC7, and UBC13) as internal controls for ten target genes in this system using the comparative CT method. Our validation experiments passed for all six reference genes analyzed except RDN18, where the amplification efficiency of RDN18 was different from that of the ten target genes. Finally, we demonstrated that the relative quantification of target gene expression varied according to the endogenous control used, highlighting the importance of the choice of internal controls in such experiments. CONCLUSIONS: We recommend the use of RDN5.8, UBC13, and PGK1 alone or the combination of RDN5.8 plus UBC13 or PGK1 as reference genes for RT-qPCR analysis of gene expression in C. glabrata following azole treatment. In contrast, we show that ACT1 and other commonly used reference genes (GAPDH, PPIA, RPL13A, TUB1, etc.) were not validated as good internal controls in the current model.