Gene Expression Profiling and Biofunction Analysis of HepG2 Cells Targeted by Crocetin

Crocetin is a carotenoid extracted from Gardenia jasminoides, one of the most popular traditional Chinese medicines, which has been used in the prevention and treatment of various diseases. The present study is aimed at clarifying the effect of crocetin on gene expression profiling of HepG2 cells by RNA-sequence assay and further investigating the molecular mechanism underlying the multiple biofunctions of crocetin based on bioinformatics analysis and molecular evidence. Among a total 23K differential genes identified, crocetin treatment upregulated the signals of 491 genes (2.14% of total gene probes) and downregulated the signals of 283 genes (1.24% of total gene probes) by ≥2-fold. The Gene Ontology analysis enriched these genes mainly on cell proliferation and apoptosis (BRD4 and DAXX); lipid formation (EHMT2); cell response to growth factor stimulation (CYP24A1 and GCNT2); and growth factor binding (ABCB1 and ABCG1), metabolism, and signal transduction processes. The KEGG pathway analysis revealed that crocetin has the potential to regulate transcriptional misregulation, ABC transporters, bile secretion, alcoholism, systemic lupus erythematosus (SLE), and other pathways, of which SLE was the most significantly disturbed pathway. The PPI network was constructed by using the STRING online protein interaction database and Cytoscape software, and 21 core proteins were obtained. RT-qPCR datasets serve as the solid evidence that verified the accuracy of transcriptome sequencing results with the same change trend. This study provides first-hand data for comprehensively understanding crocetin targeting on hepatic metabolism and its multiple biofunctions.

Transcriptomic Profile of Distal Middle Cerebral Artery from Moyamoya Disease Patients Reveals a Potential Unique Pathway

BACKGROUND: Moyamoya disease (MMD) is a peculiar disease, characterized by progressive steno-occlusion of the distal ends of bilateral internal carotid arteries and their proximal branches. Numerous studies of MMD investigated as a singular pathway, thus overlooked the complexity of MMD pathobiology. AIM: In this study, we sought to investigate the gene expression in the involved arteries to reveal the novel mechanism of MMD. MATERIALS AND METHODS: Eight middle cerebral artery (MCA) specimens were obtained from six patients underwent surgical procedure superficial temporal artery to MCA (STA-MCA bypass) for MMD and two control patients. We performed RNA extraction and microarray analysis with Agilent Whole Human Genome DNA microarray 4x44K ver.2.0 (Agilent Tech., Inc., Wilmington, DE, USA). RESULTS: From 42,405 gene probes assayed, 921 gene probes were differentially regulated in MCA of patients with MMD. Subsequent pathway analysis with PANTHER database revealed that angiogenesis, inflammation, integrin, platelet-derived growth factor (PDGF), and WNT pathways were distinctly regulated in MMD. Among genes in aforementioned pathways, SOS1 and AKT2 were the mostly distinctly regulated genes and closely associated with RAS pathway. CONCLUSION: The gene expression in MCA of patients with MMD was distinctly regulated in comparison with control MCA; presumably be useful for elucidating MMD pathobiology.

Introduce a New Approach to Detect Genes Associated to Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) represents the most frequent of all oral neoplasms in the world. Genetics plays an important role in the etiopathogenesis of OSCC. However, the investigation of the molecular mechanism of OSCC is still incomplete. In this article, we introduced a new approach to detect OSCC-associated genes, in which we not only compare mean difference, but also variance difference between cases and controls. Based on two OSCC datasets from Gene Expression Omnibus, we identified 456 differentially variable (DV) gene probes, in addition to 2,375 differentially expressed (DE) gene probes. There are 2,193 DE-only probes, 274 DV-only probes, and 182 DE-and-DV probes. DAVID functional analysis showed that genes corresponding to DE-only, DV-only, and DE-and-DV probes were enriched in different KEGG pathways, indicating they play different roles in OSCC. This new approach can be used to investigate the genetic risk factors for other complex human diseases.

Identifying Significant Features in Cancer Methylation Data Using Gene Pathway Segmentation

In order to provide the most effective therapy for cancer, it is important to be able to diagnose whether a patient's cancer will respond to a proposed treatment. Methylation profiling could contain information from which such predictions could be made. Currently, hypothesis testing is used to determine whether possible biomarkers for cancer progression produce statistically significant results. However, this approach requires the identification of individual genes, or sets of genes, as candidate hypotheses, and with the increasing size of modern microarrays, this task is becoming progressively harder. Exhaustive testing of small sets of genes is computationally infeasible, and so hypothesis generation depends either on the use of established biological knowledge or on heuristic methods. As an alternative machine learning, methods can be used to identify groups of genes that are acting together within sets of cancer data and associate their behaviors with cancer progression. These methods have the advantage of being multivariate and unbiased but unfortunately also rapidly become computationally infeasible as the number of gene probes and datasets increases. To address this problem, we have investigated a way of utilizing prior knowledge to segment microarray datasets in such a way that machine learning can be used to identify candidate sets of genes for hypothesis testing. A methylation dataset is divided into subsets, where each subset contains only the probes that relate to a known gene pathway. Each of these pathway subsets is used independently for classification. The classification method is AdaBoost with decision trees as weak classifiers. Since each pathway subset contains a relatively small number of gene probes, it is possible to train and test its classification accuracy quickly and determine whether it has valuable diagnostic information. Finally, genes from successful pathway subsets can be combined to create a classifier of high accuracy.

47 Genes Define Myeloma Cell Acquired Resistance to Bortezomib and Have Profound Prognostic Implications in Multiple Myeloma

The proteasome inhibitor Bortezomib (Bz), the first agent of a new class of drugs in Multiple Myeloma (MM), has shown remarkable activity and forms an integral part of modern MM treatment. Nevertheless, resistance to Bz eventually develops in a significant proportion of patients, with adverse effects on survival. Numerous publications have addressed this issue through in vitro developed models of acquired Bz resistance (BzR). However the results were quite different in each publication, none of the produced Bz myeloma cell lines was provably stable, no common mechanism of resistance could be demonstrated, and hence were of minimal relevance to the clinical setting. In order to address these issues an effort was made for the development of an in vitro model of acquired BzR that would resemble the clinical reality in the most accurate way. Two myeloma cell lines were used, one resembling a multisensitive (JJN3) and the other a multiresistant (U266) drug behavior, that were both sensitive to Bz. An at least 20 fold increase in the 48h Bz IC50 was noted for both cell lines. The increase in the IC50 was able to be verified a year after culturing the cell lines in normal medium thus ensuring a stable resistance phenotype. To delineate the molecular mechanisms that underlie the development of BzR a combined genetic/Gene Expression Profile (GEP) and functional/Proteomics approach was used with emphasis in the common elements of both cell lines. The hypothesis was that if certain pathways are activated in the cells that actually produce the phenotype of BzR they must fulfil two important criteria: 1) They must be present in all the levels of the BzR, 2) The gene changes have to be verified in the level of the gene encoded proteins thus securing their functional importance. GEP of the naïve cell lines along with the GEP of the Bz resistant cells at different levels of BzR (5-fold, 10-fold, 20-fold) were used. The statistical analysis revealed 100 gene probes common in both cell lines that achieved their highest change as soon as BzR was established and remained stable at that level for all later versions (P<0.1, q<0.1) and 115 gene probes common in both cell lines that their change was proportional to the level of BzR (P<0.001, q <0.005). The proteomics analysis of the Bz resistant cell lines at their latest level of resistance (20-fold) revealed 262 proteins common in both cell lines that were up-regulated and 263 common in both cell lines that were down-regulated (change >10% to be considered significant). The intersection of the list of the common genes with the list of the common proteins revealed 47 gene-proteins all but one novel in MM. They can be grouped in distinct biological categories with the most prominent ones being the ROS/Mitochondrial Factor category comprising of 10 gene-proteins, the E3 Ubiquitin Pathway 6 genes-proteins and Translation Regulation 5 genes-proteins. Even more importantly 30 of them have profound survival implications in MM -all of them novel in MM- both for Overall Survival (OS) and Progression Free Survival (PFS) in both Bz (TT3) and non Bz (TT2) containing protocols implying that myeloma cells apply both Bz specific and non-specific mechanisms to acquire BzR. Based on these 30 genes-proteins a GEP risk score (GEP-30) was constructed that was able to achieve remarkable statistical power in both Bz containing and non containing trials of both newly diagnosed (TT2 with and without thalidomide i.e. TT2+ and TT2-, TT3a, TT3b, HOVON, MRC IX, Figure 1A,B,C) and relapsed MM (TT6 , OS: NR vs 1.52 yr P<0.00001, PFS: NR vs 1.13 yr P<0.00001 for low and high risk) Figure 1. KM plots for OS and PFS of GEP-30 for newly diagnosed MM Figure 1. KM plots for OS and PFS of GEP-30 for newly diagnosed MM Figure 1B. Figure 1B. Figure 1C. Figure 1C. Disclosures Stein: University of Arkansas for Medical Sciences: Employment. Barlogie:University of Arkansas for Medical Sciences: Employment. Epstein:University of Arkansas for Medical Sciences: Employment. Heuck:Janssen: Other: Advisory Board; Celgene: Consultancy; Millenium: Other: Advisory Board; Foundation Medicine: Honoraria; University of Arkansas for Medical Sciences: Employment.

Gene Expression Profiling of Extramedullary Disease-Related Toward Identification of a Terminal Disease Pathway in Multiple Myeloma

Introduction Extramedullary disease (EMD) is a primary disease manifestation of MM, which while not seen frequently at presentation increases in incidence at relapse where its incidence seems to be increasing following the introduction of novel agents. Patients with EMD have a shorter overall survival as well as an increased incidence of anemia, thrombocytopenia, elevated serum lactate dehydrogenase, cytogenetic abnormalities, and high-risk features as determined by gene expression profiling. There is also an increased incidence of the high risk MAF subtypes t (14:16 or 14; 20). Understanding the biology of EMD and identifying its present could give important information about how to improve the outcome of this group. In this work we have used GEP analysis of bone marrow derived plasma cells to predict the presence of EMD so that we can identify the genomic risk factors that define the features of a plasma cell clone, which can develop the capacity to metastasize outside the BM. Materials and Methods We focused on patients treated on TT protocols, at the UAMS, Myeloma Institute between 1989 - 2010, a total of 1154 patients, of which 46 developed EMD before the start of therapy (EMD-1), and 91 developed EMD after registration to UAMS for MM treatment EMD-2. Results We show that most EMD2 cases (57.14%) develop within 3 years after initiation of therapy at the UAMS with few cases developing after this time. Predicting the risk of EMD Combining patients with EMD1 and EMD2 diagnosis within 3 years gave a total of 98 EMD cases. We used 824 samples from 1017 myeloma patients who never developed EMD and had follow up at least 3 years as a comparator group. The data were divided into training (n=619 with 66 EMD cases and 553 controls) and test sets (n=303 with 32 EMD cases and 271 controls). Using the training set, we identified 5 significant gene probes (with a q value < 0.001) and made a score to predict cases and controls. The sensitivity and specificity turned out to be 74.24% and 77.40% in the training set, and 56.25% and 76.75% in the test set, respectively. Predicting the time to EMD2 We tested whether we could predict time to EMD2 based on using baseline GEP samples. In this analysis, all EMD2 cases and controls were included. We divided the data into training (n=743 with 61 EMD2 and 682 controls) and test sets (n=365 with 30 EMD2 and 335 controls). By fitting a uniform Cox regression model to each gene in the training set, we identified 68 gene probes that are associated with time to EMD2 (with a q-value <0.1). We then created a score based on the 68 gene probes and identified an optimal cutoff based on the training set. Applying the optimal cutoff to both training and test sets, we found that the new 68-gene high/low risk model is a good predictor on the cumulative incidence of EMD2 (p value < 0.0001). Conclusion We show that EMD2 cases mostly occur within 3 years of diagnosis and a 68 gene based risk score that can predict a cumulative incidence of EMD. Of the 68 genes that are used to develop the prognostic score for EMD, 6 genes are also part of the 70-gene risk score developed by our group. GEP studies can help us identify EMD-specific gene signature that can further help develop target agents. Figure 1. Figure 1. Disclosures Waheed: University of Arkansas for Medical Sciences: Employment. Wang:Cancer Research and Biostatistics: Employment. Qu:Cancer Research and Biostatistics: Employment. Heuck:Millenium: Other: Advisory Board; Foundation Medicine: Honoraria; Janssen: Other: Advisory Board; University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy. Matin:University of Arkansas for Medical Sciences: Employment. Jethava:University of Arkansas for Medical Sciences: Employment. van Rhee:University of Arkansa for Medical Sciences: Employment. Hoering:Cancer Research and Biostatistics: Employment. Barlogie:University of Arkansas for Medical Sciences: Employment. Davies:University of Arkansas for Medical Sciences: Employment; Millenium: Consultancy; Onyx: Consultancy; Celgene: Consultancy; Janssen: Consultancy. Morgan:Weismann Institute: Honoraria; University of Arkansas for Medical Sciences: Employment; CancerNet: Honoraria; MMRF: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.