Analysing differential gene expression in cancer

DDX3X dynamics, glioblastoma's genetic landscape, therapeutic advances, and autophagic interplay

Glioblastoma is one of the most aggressive and deadly forms of cancer, posing significant challenges for the medical community. This review focuses on key aspects of Glioblastoma, including its genetic differences between primary and secondary types. Temozolomide is a major first-line treatment for Glioblastoma, and this article explores its development, how it works, and the issue of resistance that limits its effectiveness, prompting the need for new treatment strategies. Gene expression profiling has greatly advanced cancer research by revealing the molecular mechanisms of tumors, which is essential for creating targeted therapies for Glioblastoma. One important protein in this context is DDX3X, which plays various roles in cancer, sometimes promoting it or otherwise suppressing it. Additionally, autophagy, a process that maintains cellular balance, has complex implications in cancer treatment. Understanding autophagy helps to identify resistance mechanisms and potential treatments, with Chloroquine showing promise in treating Glioblastoma. This review covers the interplay between Glioblastoma, DDX3X, and autophagy, highlighting the challenges and potential strategies in treating this severe disease.

Insights into molecular mechanisms of chemotherapy resistance in cancer

Cancer heterogeneity poses a significant hurdle to the successful treatment of the disease, and is being influenced by genetic inheritance, cellular and tissue biology, disease development, and response to therapy. While chemotherapeutic drugs have demonstrated effectiveness, their efficacy is impeded by challenges such as presence of resilient cancer stem cells, absence of specific biomarkers, and development of drug resistance. Often chemotherapy leads to a myriad of epigenetic, transcriptional and post-transcriptional alterations in gene expression as well as changes in protein expression, thereby leading to massive metabolic reprogramming. This review seeks to provide a detailed account of various transcriptional regulations, proteomic changes, and metabolic reprogramming in various cancer models in response to three primary chemotherapeutic interventions, docetaxel, carboplatin, and doxorubicin. Discussing the molecular targets of some of these regulatory events and highlighting their contribution in sensitivity to chemotherapy will provide insights into drug resistance mechanisms and uncover novel perspectives in cancer treatment.

Acovenoside A as a novel therapeutic approach to boost taxol and carboplatin apoptotic and antiproliferative activities in NSCLC: Interplay of miR-630/miR-181a and apoptosis genes

The aim of the present study is to explore the potential anticancer effect of the cardenolide; acovenoside A against non-small cell lung cancer (NSCLC), understand its molecular mechanism in inducing apoptosis and show the effect of its combination with carboplatin and taxol. MTT assay showed that the combination of acovenoside A with taxol and carboplatin caused 78.9% cytotoxicity reflecting the synergistic effect. The triple combination showed the best growth inhibition efficiency where the number of cells at the G2/M phase was decreased and boosted up apoptotic and necrotic activity. The combination also showed the most remarkable increase in gene expression of Bax and p53 and the least level of Bcl2. The gene expression of miRNA181a and miRNA630 was significantly upregulated in cell lines treated with the combination. The present study has proven that the underlying mechanism of acovenoside A is partially attributed to the upregulation of miR-630 and miR-181a gene expressions which in turn targets the intrinsic apoptosis genes as p53, Bax and Bcl2 as well as caspase 3. The present study is the first to address the valuable effect of using acovenoside A together with carboplatin and taxol in the treatment of NSCLC via exerting apoptotic, antiproliferative, and cytotoxic effects..

An AI-assisted Investigation of Tumor-Associated Macrophages and their Polarization in Colorectal Cancer

Tumor-associated Macrophages (or TAMs) are amongst the most common cells that play a significant role in the initiation and progression of colorectal cancer (CRC). Recently, Ghosh et al. proposed distinguishing signatures for identifying macrophage polarization states, namely, immuno-reactive and immuno-tolerant, using the concept of Boolean implications and Boolean networks. Their signature, called the Signature of Macrophage Reactivity and Tolerance (SMaRT), comprises of 338 human genes (equivalently, 298 mouse genes). However, SMaRT was constructed using datasets that were not specialized towards any particular disease. In this paper, (a) we perform a comprehensive analysis of the SMaRT signature on single-cell human and mouse colorectal cancer RNA-seq datasets; (b) we then adopt a technique akin to transfer learning to construct a "refined" SMaRT signature for investigating TAMs and their polarization in the CRC tumor microenvironment. Towards validation of our refined gene signature, we use (a) 5 pseudo-bulk RNA-seq datasets derived from single-cell human datasets; and (b) 5 large-cohort microarray datasets from humans. Furthermore, we investigate the translational potential of our refined gene signature in problems related to MSS/MSI (4 datasets) and CIMP+/CIMP- status (4 datasets). Overall, our refined gene signature and its extensive validation provide a path for its adoption in clinical practice in diagnosing colorectal cancer and associated attributes.

Risk factors and actionable molecular signatures in COVID-19-associated lung adenocarcinoma and lung squamous cell carcinoma patients

The molecular mechanism of COVID-19's pathogenic effect on lung cancer patients is yet unknown. In this study, we used differential gene expression pattern analysis to try to figure out the possible disease mechanism of COVID-19 and its associated risk factors in patients with the two most common types of non-small-cell lung cancer, lung adenocarcinoma and lung squamous cell carcinoma. We also used network-based approaches to identify potential diagnostic and molecular targets for COVID-19-infected lung cancer patients. Our study showed that lung cancer and COVID-19 patients share 36 genes that are expressed differently and in common. Most of these genes are expressed in lung tissues and are mostly involved in the pathogenesis of different respiratory tract diseases. Additionally, we also found that COVID-19 may affect the expression of several cancer-associated genes in lung cancer patients, such as the oncogenes JUN, TNC, and POU2AF1. Moreover, we also reported that COVID-19 may predispose lung cancer patients to other diseases like acute liver failure and respiratory distress syndrome. Also, our findings in concert with published literature suggest that molecular signatures like hsa-mir-93-5p, CCNB2, IRF1, CD163, and different immune cell-based approaches could help both diagnose and treat this group of patients. Overall, the scientific results of this research will aid in the formulation of suitable management strategies as well as the development of diagnostic and therapeutic methods for COVID-19-infected lung cancer patients.

Clinicopathological and prognostic value of lncRNA TPT1-AS1 in cancer: a systematic review study and meta-analysis

INTRODUCTION

Aberrant expression of lncRNAs in cancer cells can impact their key phenotypes. We aimed to summarize available evidence on clinicopathological and prognostic value of lncRNA TPT1-AS1 in cancer.

METHODS

A systematic search was performed on Medline and Embase databases using relevant key terms covering lncRNA TPT1-AS1, cancer, and clinical outcomes. The effect size estimates and their 95 % confidence interval (CI) were pooled using random-effects models. Meta- analyses were conducted using STATA 16.0 software.

RESULTS

Seventeen articles met our eligibility criteria. Tumor tissue compared to normal tissue showed increased level of lncRNA TPT1-AS1 expression (pooled standardized mean difference (95 % CI): 0.65 (0.52-0.79)). Overexpression of this lncRNA was a significant predictor for poor prognosis (Pooled log-rank test P-value < 0.001); in patients with high-level of lncRNA TPT1-AS1, the risk of death at five years was 1.40 times greater than their counterparts. The pooled Odds ratios for association lncRNA TPT1-AS1 with tumor stage, tumor size, and lymph node metastasis were 1.94 (95 % CI: 0.90-4.19, 8 studies, I² = 79.6 %), 2.33 (95 % CI: 1.31-4.14, 5 studies, I² = 40.0 %), and 1.89 (95 % CI: 1.08-3.36, 5 studies, I² = 61.7 %), respectively. Regarding the identified potential mechanisms, lncRNA TPT1-AS1 plays a role in cancer growth mainly by sponging miRNAs and regulating their downstream targets or controlling the expression of key cell cycle regulators.

CONCLUSION

In cancer patients, elevated expression of lncRNA TPT1-AS1 might be associated with a shorter Overall Survival, advanced stages, larger tumor size, and lymph node metastasis.

Through the Looking Glass: Updated Insights on Ovarian Cancer Diagnostics

Epithelial ovarian cancer (EOC) is the deadliest gynaecological malignancy and the eighth most prevalent cancer in women, with an abysmal mortality rate of two million worldwide. The existence of multiple overlapping symptoms with other gastrointestinal, genitourinary, and gynaecological maladies often leads to late-stage diagnosis and extensive extra-ovarian metastasis. Due to the absence of any clear early-stage symptoms, current tools only aid in the diagnosis of advanced-stage patients, wherein the 5-year survival plummets further to less than 30%. Therefore, there is a dire need for the identification of novel approaches that not only allow early diagnosis of the disease but also have a greater prognostic value. Toward this, biomarkers provide a gamut of powerful and dynamic tools to allow the identification of a spectrum of different malignancies. Both serum cancer antigen 125 (CA-125) and human epididymis 4 (HE4) are currently being used in clinics not only for EOC but also peritoneal and GI tract cancers. Screening of multiple biomarkers is gradually emerging as a beneficial strategy for early-stage diagnosis, proving instrumental in administration of first-line chemotherapy. These novel biomarkers seem to exhibit an enhanced potential as a diagnostic tool. This review summarizes existing knowledge of the ever-growing field of biomarker identification along with potential future ones, especially for ovarian cancer.

Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors

BACKGROUND

Vast amounts of rapidly accumulating biological data related to cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is CancerOmicsNet, an AI-based system to predict the therapeutic effects of multitargeted kinase inhibitors across various cancers. This approach was previously demonstrated to outperform other deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching.

METHODS

CancerOmicsNet integrates multiple heterogeneous data by utilizing a deep graph learning model with sophisticated attention propagation mechanisms to extract highly predictive features from cancer-specific networks. The AI-based system was devised to provide more accurate and robust predictions than data-driven therapeutic discovery using gene signature reversion.

RESULTS

Selected CancerOmicsNet predictions obtained for "unseen" data are positively validated against the biomedical literature and by live-cell time course inhibition assays performed against breast, pancreatic, and prostate cancer cell lines. Encouragingly, six molecules exhibited dose-dependent antiproliferative activities, with pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 being the most potent against the pancreatic cancer cell line Panc 04.03.

CONCLUSIONS

CancerOmicsNet is a promising AI-based platform to help guide the development of new approaches in precision oncology involving a variety of tumor types and therapeutics.

Evaluating the Prognostic and Therapeutic Potentials of the Proteasome 26S Subunit, ATPase (PSMC) Family of Genes in Lung Adenocarcinoma: A Database Mining Approach

This study explored the prognostic and therapeutic potentials of multiple Proteasome 26S Subunit, ATPase (PSMC) family of genes (PSMC1-5) in lung adenocarcinoma (LUAD) diagnosis and treatment. All the PSMCs were found to be differentially expressed (upregulated) at the mRNA and protein levels in LUAD tissues. The promoter and multiple coding regions of PSMCs were reported to be differentially and distinctly methylated, which may serve in the methylation-sensitive diagnosis of LUAD patients. Multiple somatic mutations (alteration frequency: 0.6-2%) were observed along the PSMC coding regions in LUAD tissues that could assist in the high-throughput screening of LUAD patients. A significant association between the PSMC overexpression and LUAD patients' poor overall and relapse-free survival (p < 0.05; HR: >1.3) and individual cancer stages (p < 0.001) was discovered, which justifies PSMCs as the ideal targets for LUAD diagnosis. Multiple immune cells and modulators (i.e., CD274 and IDO1) were found to be associated with the expression levels of PSMCs in LUAD tissues that could aid in formulating PSMC-based diagnostic measures and therapeutic interventions for LUAD. Functional enrichment analysis of neighbor genes of PSMCs in LUAD tissues revealed different genes (i.e., SLIRP, PSMA2, and NUDSF3) previously known to be involved in oncogenic processes and metastasis are co-expressed with PSMCs, which could also be investigated further. Overall, this study recommends that PSMCs and their transcriptional and translational products are potential candidates for LUAD diagnostic and therapeutic measure discovery.

Functional genomics data: privacy risk assessment and technological mitigation

The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research advancement but poses notable privacy challenges, some of which are analogous to those that occur when sharing genetic variant data. However, there are also unique privacy challenges that arise from cryptic information leakage during the processing and summarization of functional genomics data from raw reads to derived quantities, such as gene expression values. Here, we review these challenges and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis.

Delineating the Effects of Passaging and Exposure in a Longitudinal Study of Arsenic-Induced Squamous Cell Carcinoma in a HaCaT Cell Line Model

Cutaneous squamous cell carcinoma (cSCC) is a major deleterious health effect of chronic arsenic (iAs) exposure. The molecular mechanism of arsenic-induced cSCC remains poorly understood. We recently demonstrated that chronic iAs exposure leads to temporally regulated genome-wide changes in profiles of differentially expressed mRNAs and miRNAs at each stage of carcinogenesis (7, 19 and 28 weeks) employing a well-established passage-matched HaCaT cell line model of arsenic-induced cSCC. Here, we performed longitudinal differential expression analysis (miRNA and mRNA) between the different time points (7 vs. 19 weeks and 19 vs. 28 weeks) within unexposed and exposed groups, coupled to expression pairing and pathway analyses to differentiate the relative effects of long-term passaging and chronic iAs exposure. Data showed that 66-105 miRNA [p < 0.05; log2(Fold Change)>I1I] and 2826-4079 mRNA [p < 0.001; log2(Fold Change)>I1I] molecules were differentially expressed depending on the longitudinal comparison. Several mRNA molecules differentially expressed as a function of time, independent of iAs exposure were being targeted by miRNA molecules which were also differentially expressed in a time dependent manner. Distinct pathways were predicted to be modulated as a function of time or iAs exposure. Some pathways were also modulated both by time and exposure. Thus, the HaCaT model can distinguish between the effects of passaging and chronic iAs exposure individually and corroborate our previously published data on effects of iAs exposure compared to unexposed passage matched HaCaT cells. In addition, this work provides a template for cell line based longitudinal chronic exposure studies to follow for optimal efficacy.

Alterations in protein expression and site-specific N-glycosylation of prostate cancer tissues

Identifying molecular alterations occurring during cancer progression is essential for a deeper understanding of the underlying biological processes. Here we have analyzed cancerous and healthy prostate biopsies using nanoLC-MS(MS) to detect proteins with altered expression and N-glycosylation. We have identified 75 proteins with significantly changing expression during disease progression. The biological processes involved were assigned based on protein-protein interaction networks. These include cellular component organization, metabolic and localization processes. Multiple glycoproteins were identified with aberrant glycosylation in prostate cancer, where differences in glycosite-specific sialylation, fucosylation, and galactosylation were the most substantial. Many of the glycoproteins with altered N-glycosylation were extracellular matrix constituents, and are heavily involved in the establishment of the tumor microenvironment.

Self-Assembly of pH-Labile Polymer Nanoparticles for Paclitaxel Prodrug Delivery: Formulation, Characterization, and Evaluation

The efficacy of paclitaxel (PTX) is limited due to its poor solubility, poor bioavailability, and acquired drug resistance mechanisms. Designing paclitaxel prodrugs can improve its anticancer activity and enable formulation of nanoparticles. Overall, the aim of this work is to improve the potency of paclitaxel with prodrug synthesis, nanoparticle formation, and synergistic formulation with lapatinib. Specifically, we improve potency of paclitaxel by conjugating it to α-tocopherol (vitamin E) to produce a hydrophobic prodrug (Pro); this increase in potency is indicated by the 8-fold decrease in half maximal inhibitory concentration (IC50) concentration in ovarian cancer cell line, OVCA-432, used as a model system. The efficacy of the paclitaxel prodrug was further enhanced by encapsulation into pH-labile nanoparticles using Flash NanoPrecipitation (FNP), a rapid, polymer directed self-assembly method. There was an 1100-fold decrease in IC50 concentration upon formulating the prodrug into nanoparticles. Notably, the prodrug formulations were 5-fold more potent than paclitaxel nanoparticles. Finally, the cytotoxic effects were further enhanced by co-encapsulating the prodrug with lapatinib (LAP). Formulating the drug combination resulted in synergistic interactions as indicated by the combination index (CI) of 0.51. Overall, these results demonstrate this prodrug combined with nanoparticle formulation and combination therapy is a promising approach for enhancing paclitaxel potency.

Machine learning based refined differential gene expression analysis of pediatric sepsis

Background

Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches.

Methods

In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure.

Results

Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89.

Conclusions

Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.

Identification of Transcription Factor/Gene Axis in Colon Cancer Using a Methylome Approach

Colon cancer is one of the most commonly diagnosed cancers worldwide. Both environmental and molecular characters can influence its development. DNA methylation has been heralded as a promising marker for use in cancer prevention, diagnosis, and treatment. It has been shown to facilitate cancer progression through multiple mechanisms. Changes in DNA methylation can inhibit or promote the binding of transcription factors (TFs) and further disturb gene regulation. Detection of DNA methylation-mediated regulatory events in colon cancer are critical for mining novel biomarkers. Here, we explore the influence of CpG sites located at promoter regions of differentially expressed genes and identify methylation–gene relationships using expression–methylation quantitative trait loci. We find that promoter methylation sites mainly negatively regulate the corresponding genes. We also identify candidate TFs that can bind to these sites in a sequence-dependent manner. By integrating transcriptome and methylome profiles, we construct a TF–CpG–gene regulatory network for colon cancer, which is used to determine the roles of TFs and methylation in the transcription process. Finally, based on TF–CpG–gene relationships, we design a framework to evaluate patient prognosis, which shows that one TF–CpG–gene triplet is significantly associated with patient survival rate and represents a potential novel biomarker for use in colon cancer prognosis and treatment.

GraphGR: A graph neural network to predict the effect of pharmacotherapy on the cancer cell growth.. [DOI: 10.1101/2020.05.20.107458]

Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to treatment with drugs. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. High prediction accuracies reported in the literature likely result from significant overlaps among training, validation, and testing sets, making many predictors inapplicable to new data. To address these issues, we developed GraphGR, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, GraphGR integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and genedisease associations, into a unified graph structure. In order to construct diverse and information-rich cancer-specific networks, we devised a novel graph reduction protocol based on not only the topological information, but also the biological knowledge. The performance of GraphGR, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches on the same data. Finally, several new predictions are validated against the biomedical literature demonstrating that GraphGR generalizes well to unseen data, i.e. it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. GraphGR is freely available to the academic community at https://github.com/pulimeng/GraphGR.

The Transcriptomic Toolbox: Resources for Interpreting Large Gene Expression Data within a Precision Medicine Context for Metabolic Disease Atherosclerosis

As one of the most widespread metabolic diseases, atherosclerosis affects nearly everyone as they age; arteries gradually narrow from plaque accumulation over time reducing oxygenated blood flow to central and periphery causing heart disease, stroke, kidney problems, and even pulmonary disease. Personalized medicine promises to bring treatments based on individual genome sequencing that precisely target the molecular pathways underlying atherosclerosis and its symptoms, but to date only a few genotypes have been identified. A promising alternative to this genetic approach is the identification of pathways altered in atherosclerosis by transcriptome analysis of atherosclerotic tissues to target specific aspects of disease. Transcriptomics is a potentially useful tool for both diagnostics and discovery science, exposing novel cellular and molecular mechanisms in clinical and translational models, and depending on experimental design to identify and test novel therapeutics. The cost and time required for transcriptome analysis has been greatly reduced by the development of next generation sequencing. The goal of this resource article is to provide background and a guide to appropriate technologies and downstream analyses in transcriptomics experiments generating ever-increasing amounts of gene expression data.

Elucidating the microRNA-203 specific biological processes in glioblastoma cells from comprehensive RNA-sequencing transcriptome profiling

Glioblastoma (GBM) is the most common primary malignant intracranial adult brain tumor. Allelic deletion on chromosome 14q plays an essential role in GBM pathogenesis, and this chromosome 14q site was thought to harbor multiple tumor suppressor genes associated with GBM, a region that also encodes microRNA-203 (miR-203). This study was conducted to identify whole transcriptome profile changes associated with miR-203 expression by high-throughput RNA sequencing. Enrichment analyses for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that miR-203 expression had a strong, negative effect on a number of fundamental and interconnected biological processes involved in cell growth and proliferation. The biological processes mostly influenced were p53 signaling pathway, FoxO signaling pathway, DNA replication, cell cycle, MAPK signaling pathway, and apoptosis. In total, 847 upregulated and 345 downregulated differentially expressed genes were identified in control versus miR-203 expressing glioma cells. After GO enrichment, the downregulated differentially expressed genes such as BCL2, SPARC were found to be mainly enriched in cell cycle regulation and apoptosis processes, whereas the upregulated differentially expressed genes such as CCND1, E2F1 were involved in the DNA replication and cell cycle regulation. We also performed miR-203 target analysis and found BCL2, AKT, SPARC, ROBO1, c-JUN, PDGFA, and CREB were predicted target of miR-203 and miR-203 expression suppressed the protein and mRNA levels of these target genes by western blotting and qRT-PCR analysis. Moreover, co-transfection experiments using a luciferase-based reporter assay demonstrated that miR-203 directly regulated BCL-2 expression and BCL-2 overexpression suppressed miR-203 mediated glioma cell apoptosis. These results indicate that overexpression of miR-203 coordinately regulates several oncogenic pathways in GBM.

DECtp: Calling Differential Gene Expression Between Cancer and Normal Samples by Integrating Tumor Purity Information

Identifying differentially expressed genes (DEGs) between tumor and normal samples is critical for studying tumorigenesis, and has been routinely applied to identify diagnostic, prognostic, and therapeutic biomarkers for many cancers. It is well-known that solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. However, the tumor purity information is more or less ignored in traditional differential expression analyses, which might decrease the power of differential gene identification or even bias the results. In this paper, we have developed a novel differential gene calling method called DECtp by integrating tumor purity information into a generalized least square procedure, followed by the Wald test. We compared DECtp with popular methods like t-test and limma on nine simulation datasets with different sample sizes and noise levels. DECtp achieved the highest area under curves (AUCs) for all the comparisons, suggesting that cancer purity information is critical for DEG calling between tumor and normal samples. In addition, we applied DECtp into cancer and normal samples of 14 tumor types collected from The Cancer Genome Atlas (TCGA) and compared the DEGs with those called by limma. As a result, DECtp achieved more sensitive, consistent, and biologically meaningful results and identified a few novel DEGs for further experimental validation.

Altered integrin expression patterns shown by microarray in human cutaneous melanoma

A large variety of molecular pathways in melanoma progression suggests that no individual molecular alteration is crucial in itself. Our aim was to define the molecular alterations underlying metastasis formation. Gene expression profiling was performed using microarray and qRT-PCR to define alterations between matched primary and metastatic melanoma cell lines. These data were integrated with publicly available unmatched tissue data. The invasiveness of cell lines was determined by Matrigel invasion assays and invasive clones from primary melanoma-derived cell lines were also selected. Two metastatic cell line models were created: the regional lymph node WM983A-WM983A-WM983B and the distant lung WM793B-WM793B-1205Lu metastatic models. The majority of metastasis genes were downregulated and enriched in adhesion and ITGA6-B4 pathways. Upregulation of immune pathways was characteristic of distant metastases, whereas increased Rap1 signaling was specific for regional (sub)cutaneous metastases. qRT-PCR analysis of selected integrins (A2, A3, A4, A9, B5, B8, A6, B1, and B3) highlighted the possible importance of ITGA3/4 and B8 in the metastatic process, distinguishing regional and distant metastases. We identified functionally relevant gene clusters that influenced metastasis formation. Our data provide further evidence that integrin expression patterns may be important in distant metastasis formation.

Identification of Common Genes Refers to Colorectal Carcinogenesis with Paired Cancer and Noncancer Samples

Colorectal cancer is a malignant tumor which harmed human beings' health. The aim of this study was to explore common biomarkers associated with colorectal carcinogenesis in paired cancer and noncancer samples. At first, fifty-nine pairs of colorectal cancer and noncancer samples from three gene expression datasets were collected and analyzed. Then, 181 upregulation and 282 downregulation common differential expression genes (DEGs) were found. Next, functional annotation was performed in the DAVID database with the DEGs. Finally, real-time polymerase chain reaction (PCR) assay was conducted to verify the analyses in sixteen colorectal cancer and individual-matched adjacent mucosa samples. Real-time PCR showed that MCM2, RNASEH2A, and TOP2A were upregulated in colorectal cancer compared with adjacent mucosa samples (MCM2, P < 0.001; RNASEH2A, P < 0.001; TOP2A, P = 0.001). These suggested that 463 DEGs might contribute to colorectal carcinogenesis.

Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes

BACKGROUND

Identification of driver genes related to certain types of cancer is an important research topic. Several systems biology approaches have been suggested, in particular for the identification of breast cancer (BRCA) related genes. Such approaches usually rely on differential gene expression and/or mutational landscape data. In some cases interaction network data is also integrated to identify cancer-related modules computationally.

RESULTS

We provide a framework for the comparative graph-theoretical analysis of networks integrating the relevant gene expression, mutations, and potein-protein interaction network data. The comparisons involve a graph-theoretical analysis of normal and tumor network pairs across all instances of a given set of breast cancer samples. The network measures under consideration are based on appropriate formulations of various centrality measures: betweenness, clustering coefficients, degree centrality, random walk distances, graph-theoretical distances, and Jaccard index centrality.

CONCLUSIONS

Among all the studied centrality-based graph-theoretical properties, we show that a betweenness-based measure differentiates BRCA genes across all normal versus tumor network pairs, than the rest of the popular centrality-based measures. The AUROC and AUPR values of the gene lists ordered with respect to the measures under study as compared to NCBI BioSystems pathway and the COSMIC database of cancer genes are the largest with the betweenness-based differentiation, followed by the measure based on degree centrality. In order to test the robustness of the suggested measures in prioritizing cancer genes, we further tested the two most promising measures, those based on betweenness and degree centralities, on randomly rewired networks. We show that both measures are quite resilient to noise in the input interaction network. We also compared the same measures against a state-of-the-art alternative disease gene prioritization method, MUFFFINN. We show that both our graph-theoretical measures outperform MUFFINN prioritizations in terms of ROC and precions/recall analysis. Finally, we filter the ordered list of the best measure, the betweenness-based differentiation, via a maximum-weight independent set formulation and investigate the top 50 genes in regards to literature verification. We show that almost all genes in the list are verified by the breast cancer literature and three genes are presented as novel genes that may potentialy be BRCA-related but missing in literature.

Reconstructing cancer drug response networks using multitask learning

BACKGROUND

Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer.

RESULTS

The reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug.

CONCLUSIONS

Predictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.

Gene Coexpression Analyses Differentiate Networks Associated with Diverse Cancers Harboring TP53 Missense or Null Mutations

In a variety of solid cancers, missense mutations in the well-established TP53 tumor suppressor gene may lead to the presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumor biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA) from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of coexpression of genes in tumors grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA) and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the TP53 missense- and null mutation-carrying groups that was robust to the choice of the tuning parameter in WGCNA. After comparing these sets of genes across the four cancers, one gene (KIR3DL2) consistently showed differential coexpression patterns between the null and missense groups. KIR3DL2 is known to play an important role in regulating the immune response, which is consistent with our observation that this gene's strongly-correlated partners implicated many immune-related pathways. Examining mutation-type-related changes in correlations between sets of genes may provide new insight into tumor biology.

Global Gene Expression Profiling: A Complement to Conventional Histopathologic Analysis of Neoplasia

Transcriptional profiling of entire tumors has yielded considerable insight into the molecular mechanisms of heterogeneous cell populations within different types of neoplasms. The data thus acquired can be further refined by microdissection methods that enable the analyses of subpopulations of neoplastic cells. Separation of the various components of a neoplasm (i.e., stromal cells, inflammatory infiltrates, and blood vessels) has been problematic, primarily because of a paucity of tools for accurate microdissection. The advent of laser capture microdissection combined with powerful tools of linear amplification of RNA and high-throughput microarray-based assays have allowed the transcriptional mapping of intricate and highly complex networks within pure populations of neoplastic cells. With this approach, specific “molecular signatures” can be assigned to tumors of distinct or even similar histomorphology, thereby aiding the desired objective of pattern recognition, tumor classification, and prognostication. This review highlights the potential benefits of global gene expression profiling of tumor cells as a complement to conventional histopathologic analyses.

The cancer-promoting gene fatty acid-binding protein 5 (FABP5) is epigenetically regulated during human prostate carcinogenesis

The DNA methylation status of CpG islands in the FABP5 promoter is critical for its expression. Epigenetic regulation of FABP5 gene expression plays an important role during human prostate carcinogenesis, along with up-regulation of c-Myc and Sp1.

Cell cycle specific distribution of killin: evidence for negative regulation of both DNA and RNA synthesis

p53 tumor-suppressor gene is a master transcription factor which controls cell cycle progression and apoptosis. killin was discovered as one of the p53 target genes implicated in S-phase control coupled to cell death. Due to its extreme proximity to pten tumor-suppressor gene on human chromosome 10, changes in epigenetic modification of killin have also been linked to Cowden syndrome as well as other human cancers. Previous studies revealed that Killin is a high-affinity DNA-binding protein with preference to single-stranded DNA, and it inhibits DNA synthesis in vitro and in vivo. Here, co-localization studies of RFP-Killin with either GFP-PCNA or endogenous single-stranded DNA binding protein RPA during S-phase show that Killin always adopts a mutually exclusive punctuated nuclear expression pattern with the 2 accessory proteins in DNA replication. In contrast, when cells are not in S-phase, RFP-Killin largely congregates in the nucleolus where rRNA transcription normally occurs. Both of these cell cycle specific localization patterns of RFP-Killin are stable under high salt condition, consistent with Killin being tightly associated with nucleic acids within cell nuclei. Together, these cell biological results provide a molecular basis for Killin in competitively inhibiting the formation of DNA replication forks during S-phase, as well as potentially negatively regulate RNA synthesis during other cell cycle phases.

Loss of connectivity in cancer co-expression networks

Differential gene expression profiling studies have lead to the identification of several disease biomarkers. However, the oncogenic alterations in coding regions can modify the gene functions without affecting their own expression profiles. Moreover, post-translational modifications can modify the activity of the coded protein without altering the expression levels of the coding gene, but eliciting variations to the expression levels of the regulated genes. These considerations motivate the study of the rewiring of networks co-expressed genes as a consequence of the aforementioned alterations in order to complement the informative content of differential expression. We analyzed 339 mRNAomes of five distinct cancer types to find single genes that presented co-expression patterns strongly differentiated between normal and tumor phenotypes. Our analysis of differentially connected genes indicates the loss of connectivity as a common topological trait of cancer networks, and unveils novel candidate cancer genes. Moreover, our integrated approach that combines the differential expression together with the differential connectivity improves the classic enrichment pathway analysis providing novel insights on putative cancer gene biosystems not still fully investigated.

Evolution and Controllability of Cancer Networks: A Boolean Perspective

Cancer forms a robust system capable of maintaining stable functioning (cell sustenance and proliferation) despite perturbations. Cancer progresses as stages over time typically with increasing aggressiveness and worsening prognosis. Characterizing these stages and identifying the genes driving transitions between them is critical to understand cancer progression and to develop effective anti-cancer therapies. In this work, we propose a novel model for the `cancer system' as a Boolean state space in which a Boolean network, built from protein-interaction and gene-expression data from different stages of cancer, transits between Boolean satisfiability states by "editing" interactions and "flipping" genes. Edits reflect rewiring of the PPI network while flipping of genes reflect activation or silencing of genes between stages. We formulate a minimization problem min flip to identify these genes driving the transitions. The application of our model (called BoolSpace) on three case studies-pancreatic and breast tumours in human and post spinal-cord injury (SCI) in rats-reveals valuable insights into the phenomenon of cancer progression: (i) interactions involved in core cell-cycle and DNA-damage repair pathways are significantly rewired in tumours, indicating significant impact to key genome-stabilizing mechanisms; (ii) several of the genes flipped are serine/threonine kinases which act as biological switches, reflecting cellular switching mechanisms between stages; and (iii) different sets of genes are flipped during the initial and final stages indicating a pattern to tumour progression. Based on these results, we hypothesize that robustness of cancer partly stems from "passing of the baton" between genes at different stages-genes from different biological processes and/or cellular components are involved in different stages of tumour progression thereby allowing tumour cells to evade targeted therapy, and therefore an effective therapy should target a "cover set" of these genes. A C/C++ implementation of BoolSpace is freely available at: http://www.bioinformatics.org.au/tools-data.

Identifying potential cancer driver genes by genomic data integration

Cancer is a genomic disease associated with a plethora of gene mutations resulting in a loss of control over vital cellular functions. Among these mutated genes, driver genes are defined as being causally linked to oncogenesis, while passenger genes are thought to be irrelevant for cancer development. With increasing numbers of large-scale genomic datasets available, integrating these genomic data to identify driver genes from aberration regions of cancer genomes becomes an important goal of cancer genome analysis and investigations into mechanisms responsible for cancer development. A computational method, MAXDRIVER, is proposed here to identify potential driver genes on the basis of copy number aberration (CNA) regions of cancer genomes, by integrating publicly available human genomic data. MAXDRIVER employs several optimization strategies to construct a heterogeneous network, by means of combining a fused gene functional similarity network, gene-disease associations and a disease phenotypic similarity network. MAXDRIVER was validated to effectively recall known associations among genes and cancers. Previously identified as well as novel driver genes were detected by scanning CNAs of breast cancer, melanoma and liver carcinoma. Three predicted driver genes (CDKN2A, AKT1, RNF139) were found common in these three cancers by comparative analysis.

MicroRNA 203 Modulates Glioma Cell Migration via Robo1/ERK/MMP-9 Signaling

Glioblastoma (GBM) is the most common and malignant primary adult brain cancer. Allelic deletion on chromosome 14q plays an important role in the pathogenesis of GBM, and this site was thought to harbor multiple tumor suppressor genes associated with GBM, a region that also encodes microRNA-203 (miR-203). In this study, we sought to identify the role of miR-203 as a tumor suppressor in the pathogenesis of GBM. We analyzed the miR-203 expression data of GBM patients in 10 normal and 495 tumor tissue samples derived from The Cancer Genome Atlas data set. Quantitative real-time PCR and in situ hybridization in 10 high-grade GBM and 10 low-grade anaplastic astrocytoma tumor samples showed decreased levels of miR-203 expression in anaplastic astrocytoma and GBM tissues and cell lines. Exogenous expression of miR-203 using a plasmid expressing miR-203 precursor (pmiR-203) suppressed glioma cell proliferation, migration, and invasion. We determined that one relevant target of miR-203 was Robo1, given that miR-203 expression decreased mRNA and protein levels as determined by RT-PCR and Western blot analysis. Moreover, cotransfection experiments using a luciferase-based transcription reporter assay have shown direct regulation of Robo1 by miR-203. We also show that Robo1 mediates miR-203 mediated antimigratory functions as up-regulation of Robo1 abrogates miR-203 mediated antimigratory effects. We also show that miR-203 expression suppressed ERK phosphorylation and MMP-9 expression in glioma cells. Furthermore, we demonstrate that miR-203 inhibits migration of the glioma cells by disrupting the Robo1/ERK/MMP-9 signaling axis. Taken together, these studies demonstrate that up-regulation of Robo1 in response to the decrease in miR-203 in glioma cells is responsible for glioma tumor cell migration and invasion.

Laser ionization mass spectrometry in oral squamous cell carcinoma

Biomarker research in oral squamous cell carcinoma (OSCC) aims for screening/early diagnosis and in predicting its recurrence, metastasis and overall prognosis. This article reviews the current molecular perspectives and diagnosis of oral cancer with proteomics using matrix-assisted laser desorption ionization (MALDI) and surface-enhanced laser desorption ionization (SELDI) mass spectrometry (MS). This method shows higher sensitivity, accuracy, reproducibility and ability to handle complex tissues and biological fluid samples. However, the data interpretation tools of contemporary mass spectrometry still warrant further improvement. Based on the data available with laser-based mass spectrometry, biomarkers of OSCC are classified as (i) diagnosis and prognosis, (ii) secretory, (iii) recurrence and metastasis, and (iv) drug targets. Majority of these biomarkers are involved in cell homeostasis and are either physiologic responders or enzymes. Therefore, proteins directly related to tumorigenesis have more diagnostic value. Salivary secretory markers are another group that offers a favourable and easy strategy for non-invasive screening and early diagnosis in oral cancer. Key molecular inter-related pathways in oral carcinogenesis are also intensely researched with software analysis to facilitate targeted drug therapeutics. The review suggested the need for incorporating 'multiple MS or tandem approaches' and focusing on a 'group of biomarkers' instead of single protein entities, for making early diagnosis and treatment for oral cancer a reality.

Pharmacogenetics and pharmacogenomics: a bridge to individualized cancer therapy

In the past decade, advances in pharmacogenetics and pharmacogenomics (PGx) have gradually unveiled the genetic basis of interindividual differences in drug responses. A large portion of these advances have been made in the field of anticancer therapy. Currently, the US FDA has updated the package inserts of approximately 30 anticancer agents to include PGx information. Given the complexity of this genetic information (e.g., tumor mutation and gene overexpression, chromosomal translocation and germline variations), as well as the variable level of scientific evidence, the FDA recommendation and potential action needed varies among drugs. In this review, we have highlighted some of these PGx discoveries for their scientific values and utility in improving therapeutic efficacy and reducing side effects. Furthermore, examples are also provided for the role of PGx in new anticancer drug development by revealing novel druggable targets.

APG: an Active Protein-Gene network model to quantify regulatory signals in complex biological systems

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information.

Cancer proteomics

Cancer presents high mortality and morbidity globally, largely due to its complex and heterogenous nature, and lack of biomarkers for early diagnosis. A proteomics study of cancer aims to identify and characterize functional proteins that drive the transformation of malignancy, and to discover biomarkers to detect early-stage cancer, predict prognosis, determine therapy efficacy, identify novel drug targets, and ultimately develop personalized medicine. The various sources of human samples such as cell lines, tissues, and plasma/serum are probed by a plethora of proteomics tools to discover novel biomarkers and elucidate mechanisms of tumorigenesis. Innovative proteomics technologies and strategies have been designed for protein identification, quantitation, fractionation, and enrichment to delve deeper into the oncoproteome. In addition, there is the need for high-throughput methods for biomarker validation, and integration of the various platforms of oncoproteome data to fully comprehend cancer biology.

Superficial spreading and nodular melanoma are distinct biological entities: a challenge to the linear progression model

The classification of melanoma subtypes into prognostically relevant and therapeutically insightful categories has been a challenge since the first description of melanoma in the 1800s. One limitation has been the assumption that the two most common histological subtypes of melanoma, superficial spreading and nodular, evolve according to a linear model of progression, as malignant melanocytes spread radially and then invade vertically. However, recent clinical, pathological, and molecular data indicate that these two histological subtypes might evolve as distinct entities. Here, we review the published data that support distinct molecular characterization of superficial spreading and nodular melanoma, the clinical significance of this distinction including prognostic relevance and the therapeutic implications.

The functional and structural characterization of a novel oncogene GIG47 involved in the breast tumorigenesis

Background

A candidate oncogene GIG47, previously known as a neudesin with a neurotrophic activity, was identified by applying the differential expression analysis method.

Methods

As a first step to understand the molecular role of GIG47, we analyzed the expression profile of GIG47 in multiple human cancers including the breast cancer and characterized its function related to human carcinogenesis. Based on this oncogenic role of GIG47, we then embarked on determining the high-resolution structure of GIG47. We have applied multidimensional heteronuclear NMR methods to GIG47.

Results

GIG47 was over-expressed in primary breast tumors as well as other human tumors including carcinomas of the uterine cervix, malignant lymphoma, colon, lung, skin, and leukemia. To establish its role in the pathogenesis of breast cancer in humans, we generated stable transfectants of MCF7 cells. The ectopic expression of GIG47 in MCF7 cells promoted the invasiveness in the presence of 50% serum. In addition, it also resulted in the increased tumorigenicity in in vivo tumor formation assay. The tumorigenesis mechanism involving GIG47 might be mediated by the activation of MAPK and PI3K pathways. These results indicate that GIG47 plays a role in the breast tumorigenesis, thus representing a novel target for the treatment of breast cancer. To facilitate the development of GIG47-targeted therapeutics, we determined the structural configuration of GIG47. The high-resolution structure of GIG47 was obtained by combination of NMR and homology modeling. The overall structure of GIG47 has four α-helices and 6 β-strands, arranged in a β1-α1-β2-β3-α2-β4-α3-α4-β5-β6 topology. There is a potential heme/steroid binding pocket formed between two helices α2 and α3.

Conclusion

The determined three-dimensional structure of GIG47 may facilitate the development of potential anti-cancer agents.

Network biology methods integrating biological data for translational science

The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

Genome-wide DNA methylation profiling of non-small cell lung carcinomas

Background

Non-small cell lung carcinoma (NSCLC) is a complex malignancy that owing to its heterogeneity and poor prognosis poses many challenges to diagnosis, prognosis and patient treatment. DNA methylation is an important mechanism of epigenetic regulation involved in normal development and cancer. It is a very stable and specific modification and therefore in principle a very suitable marker for epigenetic phenotyping of tumors. Here we present a genome-wide DNA methylation analysis of NSCLC samples and paired lung tissues, where we combine MethylCap and next generation sequencing (MethylCap-seq) to provide comprehensive DNA methylation maps of the tumor and paired lung samples. The MethylCap-seq data were validated by bisulfite sequencing and methyl-specific polymerase chain reaction of selected regions.

Results

Analysis of the MethylCap-seq data revealed a strong positive correlation between replicate experiments and between paired tumor/lung samples. We identified 57 differentially methylated regions (DMRs) present in all NSCLC tumors analyzed by MethylCap-seq. While hypomethylated DMRs did not correlate to any particular functional category of genes, the hypermethylated DMRs were strongly associated with genes encoding transcriptional regulators. Furthermore, subtelomeric regions and satellite repeats were hypomethylated in the NSCLC samples. We also identified DMRs that were specific to two of the major subtypes of NSCLC, adenocarcinomas and squamous cell carcinomas.

Conclusions

Collectively, we provide a resource containing genome-wide DNA methylation maps of NSCLC and their paired lung tissues, and comprehensive lists of known and novel DMRs and associated genes in NSCLC.

Averaged differential expression for the discovery of biomarkers in the blood of patients with prostate cancer

BACKGROUND

The identification of a blood-based diagnostic marker is a goal in many areas of medicine, including the early diagnosis of prostate cancer. We describe the use of averaged differential display as an efficient mechanism for biomarker discovery in whole blood RNA. The process of averaging reduces the problem of clinical heterogeneity while simultaneously minimizing sample handling.

METHODOLOGY/PRINCIPAL FINDINGS

RNA was isolated from the blood of prostate cancer patients and healthy controls. Samples were pooled and subjected to the averaged differential display process. Transcripts present at different levels between patients and controls were purified and sequenced for identification. Transcript levels in the blood of prostate cancer patients and controls were verified by quantitative RT-PCR. Means were compared using a t-test and a receiver-operating curve was generated. The Ring finger protein 19A (RNF19A) transcript was identified as having higher levels in prostate cancer patients compared to healthy men through the averaged differential display process. Quantitative RT-PCR analysis confirmed a more than 2-fold higher level of RNF19A mRNA levels in the blood of patients with prostate cancer than in healthy controls (p = 0.0066). The accuracy of distinguishing cancer patients from healthy men using RNF19A mRNA levels in blood as determined by the area under the receiving operator curve was 0.727.

CONCLUSIONS/SIGNIFICANCE

Averaged differential display offers a simplified approach for the comprehensive screening of body fluids, such as blood, to identify biomarkers in patients with prostate cancer. Furthermore, this proof-of-concept study warrants further analysis of RNF19A as a clinically relevant biomarker for prostate cancer detection.

Weighted change-point method for detecting differential gene expression in breast cancer microarray data

In previous work, we proposed a method for detecting differential gene expression based on change-point of expression profile. This non-parametric change-point method gave promising result in both simulation study and public dataset experiment. However, the performance is still limited by the less sensitiveness to the right bound and the statistical significance of the statistics has not been fully explored. To overcome the insensitiveness to the right bound we modified the original method by adding a weight function to the D_n statistic. Simulation study showed that the weighted change-point statistics method is significantly better than the original NPCPS in terms of ROC, false positive rate, as well as change-point estimate. The mean absolute error of the estimated change-point by weighted change-point method was 0.03, reduced by more than 50% comparing with the original 0.06, and the mean FPR was reduced by more than 55%. Experiment on microarray Dataset I resulted in 3974 differentially expressed genes out of total 5293 genes; experiment on microarray Dataset II resulted in 9983 differentially expressed genes among total 12576 genes. In summary, the method proposed here is an effective modification to the previous method especially when only a small subset of cancer samples has DGE.

Expression profiles of carcinosarcoma of the uterine corpus-are these similar to carcinoma or sarcoma?

Uterine carcinosarcoma (CS) is usually classified as uterine endometrial carcinoma (EC). However, CS is more aggressive even compared with high grade EC. CS is also reported to undergo epithelial to mesenchymal transition (EMT). In this study, we compared the gene expression profiles of CS, EC, and uterine sarcoma (US) and evaluated the role of EMT and chromosomal aberrations in CS tumor formation. Frozen tissues of 46 patients (14 CS, 24 EC, and 8 US) were included. The similarity was examined by Gene Set Enrichment Analysis (GSEA), Fisher's exact test, and clustering using "intrinsic gene set". We examined the expression of 39 EMT-related genes and evaluated TGF-beta signaling by phospho-SMAD2/3 (p-SMAD2/3) staining. Chromosomal regions differing between CS and EC were identified by chromosomal GSEA and comparative genomic hybridization (CGH) microarrays. Three statistical methods confirmed that CS resembled US rather than EC. Acquired markers of EMT were upregulated and attenuated markers of EMT were downregulated in CS. Immunohistochemistry showed that carcinomatous region of CS have higher expression of p-SMAD2/3 than EC (P = 0.008). Chromosomal GSEA showed that genes located at 19q13 had higher expression in CS. Furthermore, CGH microarray indicated that the TGFB1 locus at 19q13.1 was amplified in 4 of 7 samples. Based on the expression profile, CS resembles US rather than EC. TGF-beta signaling is activated in CS and chromosomal gains at 19q13, which includes the TGFB1 locus, suggest that this may contribute to high expression of TGF-beta and thereby EMT phenotype of CS.

A new frontier in personalized cancer therapy: mapping molecular changes

Mutations in the genome of a normal cell can affect the function of its many genes and pathways. These alterations could eventually transform the cell from a normal to a malignant state by allowing an uncontrolled proliferation of the cell and formation of a cancer tumor. Each tumor in an individual patient can have hundreds of mutated genes and perturbed pathways. Cancers clinically presenting as the same type or subtype could potentially be very different at the molecular level and thus behave differently in response to therapy. The challenge is to distinguish the key mutations driving the cancer from the background of mutational noise and find ways to effectively target them. The promise is that such a molecular approach to classifying cancer will lead to better diagnostic, prognostic and personalized treatment strategies. This article provides an overview of advances in the molecular characterization of cancers and their applications in therapy.

S-phase-coupled apoptosis in tumor suppression

DNA replication is essential for accurate transmission of genomic information from parental to daughter cells. DNA replication is licensed once per cell division cycle. This process is highly regulated by both positive and negative regulators. Over-replication, under-replication, as well as DNA damage in a cell all induce the activation of checkpoint control pathways such as ATM/ATR, CHK kinases, and the tumor suppressor protein p53, which provide "damage controls" via either DNA repairs or apoptosis. This review focuses on accumulating evidence, with the emphasis on recently discovered Killin, that S-phase checkpoint control is crucial for a mammalian cell to make a life and death decision in order to safeguard genome integrity.

Integrated analysis of mutations, miRNA and mRNA expression in glioblastoma

BACKGROUND

Glioblastoma arises from complex interactions between a variety of genetic alterations and environmental perturbations. Little attention has been paid to understanding how genetic variations, altered gene expression and microRNA (miRNA) expression are integrated into networks which act together to alter regulation and finally lead to the emergence of complex phenotypes and glioblastoma.

RESULTS

We identified association of somatic mutations in 14 genes with glioblastoma, of which 8 genes are newly identified, and association of loss of heterozygosity (LOH) is identified in 11 genes with glioblastoma, of which 9 genes are newly discovered. By gene coexpression network analysis, we identified 15 genes essential to the function of the network, most of which are cancer related genes. We also constructed miRNA coexpression networks and found 19 important miRNAs of which 3 were significantly related to glioblastoma patients' survival. We identified 3,953 predicted miRNA-mRNA pairs, of which 14 were previously verified by experiments in other groups. Using pathway enrichment analysis we also found that the genes in the target network of the top 19 important miRNAs were mainly involved in cancer related signaling pathways, synaptic transmission and nervous systems processes. Finally, we developed new methods to decipher the pathway connecting mutations, expression information and glioblastoma. We identified 4 cis-expression quantitative trait locus (eQTL): TP53, EGFR, NF1 and PIK3C2G; 262 trans eQTL and 26 trans miRNA eQTL for somatic mutation; 2 cis-eQTL: NRAP and EGFR; 409 trans- eQTL and 27 trans- miRNA eQTL for lost of heterozygosity (LOH) mutation.

CONCLUSIONS

Our results demonstrate that integrated analysis of multi-dimensional data has the potential to unravel the mechanism of tumor initiation and progression.