A comparative analysis of gene-expression data of multiple cancer types

Exploring novel protein-based biomarkers for advancing breast cancer diagnosis: A review

This review provides a contemporary examination of the evolving landscape of breast cancer (BC) diagnosis, focusing on the pivotal role of novel protein-based biomarkers. The overview begins by elucidating the multifaceted nature of BC, exploring its prevalence, subtypes, and clinical complexities. A critical emphasis is placed on the transformative impact of proteomics, dissecting the proteome to unravel the molecular intricacies of BC. Navigating through various sources of samples crucial for biomarker investigations, the review underscores the significance of robust sample processing methods and their validation in ensuring reliable outcomes. The central theme of the review revolves around the identification and evaluation of novel protein-based biomarkers. Cutting-edge discoveries are summarised, shedding light on emerging biomarkers poised for clinical application. Nevertheless, the review candidly addresses the challenges inherent in biomarker discovery, including issues of standardisation, reproducibility, and the complex heterogeneity of BC. The future direction section envisions innovative strategies and technologies to overcome existing challenges. In conclusion, the review summarises the current state of BC biomarker research, offering insights into the intricacies of proteomic investigations. As precision medicine gains momentum, the integration of novel protein-based biomarkers emerges as a promising avenue for enhancing the accuracy and efficacy of BC diagnosis. This review serves as a compass for researchers and clinicians navigating the evolving landscape of BC biomarker discovery, guiding them toward transformative advancements in diagnostic precision and personalised patient care.

Open Data for Differential Network Analysis in Glioma

The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. By using selected exemplary tools and open-access resources for cancer research and differential network analysis, we highlight disturbed signaling components in brain cancer subtypes of glioma.

Sex-biased differences in the correlation between epithelial-to-mesenchymal transition-associated genes in cancer cell lines

There is a wide disparity in the incidence, malignancy and mortality of different types of cancer between each sex. The sex-specificity of cancer seems to be dependent on the type of cancer. Cancer incidence and mortality have been demonstrated as sex-specific in a number of different types of cancer, such as liver cancer, whereas sex-specificity is not noticeable in certain other types of cancer, including colon and lung cancer. The present study aimed to elucidate the molecular basis for sex-biased gene expression in cancer. The mRNA expression of the epithelial-to-mesenchymal transition-associated genes was investigated, including E-cadherin (also termed CDH1), vimentin (VIM), discoidin domain receptor 1 (DDR1) and zinc finger E-box binding homeobox 1 (ZEB1) in female- and male-derived cancer cell lines by reverse transcription (RT)-PCR and the Broad-Novartis Cancer Cell Line Encyclopedia (CCLE) database analysis. A negative correlation was observed between DDR1 and ZEB1 only in the female-derived cancer cell lines via RT-PCR analysis. A negative correlation between DDR1 index (defined by the logarithmic value of DDR1 divided by ZEB1, based on the mRNA data from the RT-PCR analysis) and an invasive phenotype was observed in cancer cell lines in a sex-specific manner. Analysis of the CCLE database demonstrated that DDR1 and ZEB1, which are already known to be sex-biased, were negatively correlated in female-derived liver cancer cell lines, but not in male-derived liver cancer cell lines. In contrast, cell lines of colon and lung cancer did not reveal any sex-dependent difference in the correlation between DDR1 and ZEB1. Kaplan-Meier survival curves using the transcriptomic datasets such as Gene Expression Omnibus, European Genome-phenome Archiva and The Cancer Genome Atlas databases suggested a sex-biased difference in the correlation between DDR1 expression pattern and overall survival in patients with liver cancer. The results of the present study indicate that sex factors may affect the regulation of gene expression, contributing to the sex-biased progression of the different types of cancer, particularly liver cancer. Overall, these findings suggest that analyses of the correlation between DDR1 and ZEB1 may prove useful when investigating sex-biased cancers.

A unifying view of the broad-spectrum antiviral activity of RSAD2 (viperin) based on its radical-SAM chemistry

RSAD2 (cig-5), also known as viperin (virus inhibitory protein, endoplasmic reticulum associated, interferon inducible), is a member of the radical S-adenosylmethionine (SAM) superfamily of enzymes. Since the discovery of this enzyme more than a decade ago, numerous studies have shown that it exhibits antiviral activity against a wide range of viruses. However, there is no clear picture demonstrating the mechanism by which RSAD2 restricts the replication process of different viruses, largely because there is no direct evidence describing its in vivo enzymatic activity. As a result, a multifunctionality model has emerged. According to this model the mechanism by which RSAD2 restricts replication of different viruses varies and in many cases is not dependent on the radical-SAM chemistry of RSAD2. If the radical-SAM activity of RSAD2 is not required for its antiviral function, the question worth asking is: why does the cellular defence mechanism induce the expression of the radical-SAM enzyme RSAD2, which is metabolically expensive due to the requirement for a [4Fe-4S] cluster and usage of SAM? Here, in contrast to the multifunctionality view, I put forward a unifying model. I postulate that the radical-SAM activity of RSAD2 modulates cellular metabolic pathways essential for viral replication and/or cell proliferation and survival. As a result, its catalytic activity restricts the replication of a wide range of viruses via a common cellular function. This view is based on recent discoveries hinting towards possible substrates of RSAD2, re-evaluation of previous studies regarding the antiviral activity of RSAD2, and accumulating evidence suggesting a role of human RSAD2 in the metabolic reprogramming of cells.

HEPATOKIN1 is a biochemistry-based model of liver metabolism for applications in medicine and pharmacology

The epidemic increase of non-alcoholic fatty liver diseases (NAFLD) requires a deeper understanding of the regulatory circuits controlling the response of liver metabolism to nutritional challenges, medical drugs, and genetic enzyme variants. As in vivo studies of human liver metabolism are encumbered with serious ethical and technical issues, we developed a comprehensive biochemistry-based kinetic model of the central liver metabolism including the regulation of enzyme activities by their reactants, allosteric effectors, and hormone-dependent phosphorylation. The utility of the model for basic research and applications in medicine and pharmacology is illustrated by simulating diurnal variations of the metabolic state of the liver at various perturbations caused by nutritional challenges (alcohol), drugs (valproate), and inherited enzyme disorders (galactosemia). Using proteomics data to scale maximal enzyme activities, the model is used to highlight differences in the metabolic functions of normal hepatocytes and malignant liver cells (adenoma and hepatocellular carcinoma).

In silico models of cells can provide insight into the causes and effects of disease states and reduce the need for in vivo studies. Here, the authors present a kinetic model of hepatocyte metabolism including energy, carbohydrate, lipid and nitrogen metabolism and hormonal and allosteric regulation of enzymatic activity.

Constructing Bayesian networks by integrating gene expression and copy number data identifies NLGN4Y as a novel regulator of prostate cancer progression

To understand the heterogeneity of prostate cancer (PCa) and identify novel underlying drivers, we constructed integrative molecular Bayesian networks (IMBNs) for PCa by integrating gene expression and copy number alteration data from published datasets. After demonstrating such IMBNs with superior network accuracy, we identified multiple sub-networks within IMBNs related to biochemical recurrence (BCR) of PCa and inferred the corresponding key drivers. The key drivers regulated a set of common effectors including genes preferentially expressed in neuronal cells. NLGN4Y—a protein involved in synaptic adhesion in neurons—was ranked as the top gene closely linked to key drivers of myogenesis subnetworks. Lower expression of NLGN4Y was associated with higher grade PCa and an increased risk of BCR. We show that restoration of the protein expression of NLGN4Y in PC-3 cells leads to decreased cell proliferation, migration and inflammatory cytokine expression. Our results suggest that NLGN4Y is an important negative regulator in prostate cancer progression. More importantly, it highlights the value of IMBNs in generating biologically and clinically relevant hypotheses about prostate cancer that can be validated by independent studies.

Highly preserved consensus gene modules in human papilloma virus 16 positive cervical cancer and head and neck cancers

In this study, we investigated the consensus gene modules in head and neck cancer (HNC) and cervical cancer (CC). We used a publicly available gene expression dataset, GSE6791, which included 42 HNC, 14 normal head and neck, 20 CC and 8 normal cervical tissue samples. To exclude bias because of different human papilloma virus (HPV) types, we analyzed HPV16-positive samples only. We identified 3824 genes common to HNC and CC samples. Among these, 977 genes showed high connectivity and were used to construct consensus modules. We demonstrated eight consensus gene modules for HNC and CC using the dissimilarity measure and average linkage hierarchical clustering methods. These consensus modules included genes with significant biological functions, including ATP binding and extracellular exosome. Eigengen network analysis revealed the consensus modules were highly preserved with high connectivity. These findings demonstrate that HPV16-positive head and neck and cervical cancers share highly preserved consensus gene modules with common potentially therapeutic targets.

Common Subcluster Mining in Microarray Data for Molecular Biomarker Discovery

Molecular biomarkers can be potential facilitators for detection of cancer at early stage which is otherwise difficult through conventional biomarkers. Gene expression data from microarray experiments on both normal and diseased cell samples provide enormous scope to explore genetic relations of disease using computational techniques. Varied patterns of expressions of thousands of genes at different cell conditions along with inherent experimental error make the task of isolating disease related genes challenging. In this paper, we present a data mining method, common subcluster mining (CSM), to discover highly perturbed genes under diseased condition from differential expression patterns. The method builds heap through superposing near centroid clusters from gene expression data of normal samples and extracts its core part. It, thus, isolates genes exhibiting the most stable state across normal samples and constitute a reference set for each centroid. It performs the same operation on datasets from corresponding diseased samples and isolates the genes showing drastic changes in their expression patterns. The method thus finds the disease-sensitive genesets when applied to datasets of lung cancer, prostrate cancer, pancreatic cancer, breast cancer, leukemia and pulmonary arterial hypertension. In majority of the cases, few new genes are found over and above some previously reported ones. Genes with distinct deviations in diseased samples are prospective candidates for molecular biomarkers of the respective disease.

A clustering-based approach for efficient identification of microRNA combinatorial biomarkers

BACKGROUND

MicroRNAs (miRNAs) have great potential serving as tumor biomarkers and therapeutic targets. As the rapid development of high-throughput experimental technology, gene expression experiments have become more and more specialized and diversified. The complex data structure has brought great challenge for the identification of biomarkers. In the meantime, current statistical and machine learning methods for detecting biomarkers have the problem of low reliability and biased criteria.

RESULTS

This study aims to select combinatorial miRNA biomarkers, which have higher sensitivity and specificity than single-gene biomarkers. In order to avoid exhaustive search and redundant information, miRNAs are firstly clustered, then the combinations of representative cluster members are assessed as potential biomarkers. Both the criteria for the partition of clusters and selection of representative members are based on Fisher linear discriminant analysis (FDA). The FDA-based criterion has been demonstrated to be superior to three other criteria in selecting representative members, and also good at refining clusters. In the comparison with eight common feature selection methods, this clustering-based method performs the best with regard to the discriminative ability of selected biomarkers.

CONCLUSIONS

Our experimental results demonstrate that the clustering-based method can identify microRNA combinatorial biomarkers with high accuracy and efficiency. Our method and data are available to the public upon request.

A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data

Background

We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis.

Results

Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ² test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ² test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers.

Conclusions

Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0358-5) contains supplementary material, which is available to authorized users.

A five-gene reverse transcription-PCR assay for pre-operative classification of breast fibroepithelial lesions

Background

Breast fibroepithelial lesions are biphasic tumors and include fibroadenomas and phyllodes tumors. Preoperative distinction between fibroadenomas and phyllodes tumors is pivotal to clinical management. Fibroadenomas are clinically benign while phyllodes tumors are more unpredictable in biological behavior, with potential for recurrence. Differentiating the tumors may be challenging when they have overlapping clinical and histological features especially on core biopsies. Current molecular and immunohistochemical techniques have a limited role in the diagnosis of breast fibroepithelial lesions. We aimed to develop a practical molecular test to aid in distinguishing fibroadenomas from phyllodes tumors in the pre-operative setting.

Methods

We profiled the transcriptome of a training set of 48 formalin-fixed, paraffin-embedded fibroadenomas and phyllodes tumors and further designed 43 quantitative polymerase chain reaction (qPCR) assays to verify differentially expressed genes. Using machine learning to build predictive regression models, we selected a five-gene transcript set (ABCA8, APOD, CCL19, FN1, and PRAME) to discriminate between fibroadenomas and phyllodes tumors. We validated our assay in an independent cohort of 230 core biopsies obtained pre-operatively.

Results

Overall, the assay accurately classified 92.6 % of the samples (AUC = 0.948, 95 % CI 0.913–0.983, p = 2.51E-19), with a sensitivity of 82.9 % and specificity of 94.7 %.

Conclusions

We provide a robust assay for classifying breast fibroepithelial lesions into fibroadenomas and phyllodes tumors, which could be a valuable tool in assisting pathologists in differential diagnosis of breast fibroepithelial lesions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0692-6) contains supplementary material, which is available to authorized users.

Reconstruction of a generic metabolic network model of cancer cells

A promising strategy for finding new cancer drugs is to use metabolic network models to investigate the essential reactions or genes in cancer cells. In this study, we present a generic constraint-based model of cancer metabolism, which is able to successfully predict the metabolic phenotypes of cancer cells. This model is reconstructed by collecting the available data on tumor suppressor genes. Notably, we show that the activation of oncogene related reactions can be explained by the inactivation of tumor suppressor genes. We show that in a simulated growth medium similar to the body fluids, our model outperforms the previously proposed model of cancer metabolism in predicting expressed genes.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Global gene expression analysis of canine cutaneous mast cell tumor: could molecular profiling be useful for subtype classification and prognostication?

Prognosis and therapeutic management of dogs with cutaneous mast cell tumors (MCTs) depend on clinical stage and histological grade. However, the prognostic value of this latter is still questionable. In the present study, MCT transcriptome was analyzed to identify a set of candidate genes potentially useful for predicting the biological behavior of MCTs. Fifty-one canine MCT biopsies were analyzed. Isolated and purified total RNAs were individually hybridized to the Agilent Canine V2 4x44k DNA microarray. The comparison of reference differentiated and undifferentiated MCT transcriptome revealed a total of 597 differentially expressed genes (147 down-regulated and 450 up-regulated). The functional analysis of this set of genes provided evidence that they were mainly involved in cell cycle, DNA replication, p53 signaling pathway, nucleotide excision repair and pyrimidine metabolism. Class prediction analysis identified 13 transcripts providing the greatest accuracy of class prediction and divided samples into two categories (differentiated and undifferentiated), harboring a different prognosis. The Principal Component Analysis of all samples, made by using the selected 13 markers, confirmed MCT classification. The first three components accounted for 99.924% of the total variance. This molecular classification significantly correlated with survival time (p = 0.0026). Furthermore, among all marker genes, a significant association was found between mRNA expression and MCT-related mortality for FOXM1, GSN, FEN1 and KPNA2 (p<0.05). Finally, marker genes mRNA expression was evaluated in a cohort of 22 independent samples. Data obtained enabled to identify MCT cases with different prognosis. Overall, the molecular characterization of canine MCT transcriptome allowed the identification of a set of 13 transcripts that clearly separated differentiated from undifferentiated MCTs, thus predicting outcome regardless of the histological grade. These results may have clinical relevance and warrant future validation in a prospective study.

FUT11 as a potential biomarker of clear cell renal cell carcinoma progression based on meta-analysis of gene expression data

In this paper, we provide a comprehensive summary of available clear cell renal cell carcinoma (ccRCC) microarray data in the form of meta-analysis of genes differentially regulated in tumors as compared to healthy tissue, using effect size to measure the strength of a relationship between the disease and gene expression. We identified 725 differentially regulated genes, with a number of interesting targets, such as TMEM213, SMIM5, or ATPases: ATP6V0A4 and ATP6V1G3, of which limited or no information is available in terms of their function in ccRCC pathology. Downregulated genes tended to represent pathways related to tissue remodeling, blood clotting, vasodilation, and energy metabolism, while upregulated genes were classified into pathways generally deregulated in cancers: immune system response, inflammatory response, angiogenesis, and apoptosis. One hundred fifteen deregulated genes were included in network analysis, with EGLN3, AP-2, NR3C1, HIF1A, and EPAS1 (gene encoding HIF2-α) as points of functional convergence, but, interestingly, 610 genes failed to join previously identified molecular networks. Furthermore, we validated the expression of 14 top deregulated genes in independent sample set of 32 ccRCC tumors by qPCR and tested if it could serve as a marker of disease progression. We found a correlation of high fucosyltransferase 11 (FUT11) expression with non-symptomatic course of the disease, which suggests that FUT11's expression might be potentially used as a biomarker of disease progression.

Identification of human HK genes and gene expression regulation study in cancer from transcriptomics data analysis

The regulation of gene expression is essential for eukaryotes, as it drives the processes of cellular differentiation and morphogenesis, leading to the creation of different cell types in multicellular organisms. RNA-Sequencing (RNA-Seq) provides researchers with a powerful toolbox for characterization and quantification of transcriptome. Many different human tissue/cell transcriptome datasets coming from RNA-Seq technology are available on public data resource. The fundamental issue here is how to develop an effective analysis method to estimate expression pattern similarities between different tumor tissues and their corresponding normal tissues. We define the gene expression pattern from three directions: 1) expression breadth, which reflects gene expression on/off status, and mainly concerns ubiquitously expressed genes; 2) low/high or constant/variable expression genes, based on gene expression level and variation; and 3) the regulation of gene expression at the gene structure level. The cluster analysis indicates that gene expression pattern is higher related to physiological condition rather than tissue spatial distance. Two sets of human housekeeping (HK) genes are defined according to cell/tissue types, respectively. To characterize the gene expression pattern in gene expression level and variation, we firstly apply improved K-means algorithm and a gene expression variance model. We find that cancer-associated HK genes (a HK gene is specific in cancer group, while not in normal group) are expressed higher and more variable in cancer condition than in normal condition. Cancer-associated HK genes prefer to AT-rich genes, and they are enriched in cell cycle regulation related functions and constitute some cancer signatures. The expression of large genes is also avoided in cancer group. These studies will help us understand which cell type-specific patterns of gene expression differ among different cell types, and particularly for cancer.

Frequent alterations of MCPH1 and ATM are associated with primary breast carcinoma: clinical and prognostic implications

BACKGROUND

MCPH1 is a proximal regulator of DNA damage response pathway that is involved in recruitment of phosphorylated ATM to double-stranded DNA breaks.

METHODS

To understand the importance of MCPH1 and ATM in deregulation of DNA damage response pathway in breast carcinoma, we studied m-RNA expression and genetic/epigenetic alterations of these genes in primary breast carcinoma samples.

RESULTS

Our study revealed reduced expression (mRNA/protein) and high alterations (deletion/methylation) (96 %, 121 of 126) of MCPH1 and ATM. Mutation was, however, rare in inactivation of MCPH1. In immunohistochemical analysis, reduced protein expression of MCPH1, ATM and p-ATM were concordant with their molecular alterations (P = 0.03-0.01). Alterations of MCPH1 and deletion of ATM were significantly high in estrogen/progesterone receptor-negative than estrogen/progesterone receptor-positive breast carcinoma samples compared to early or late age of onset tumors, indicating differences in pathogenesis of the molecular subtypes (P = 0.004-0.01). These genes also showed differential association with tumor stage, grade and lymph node status in different subtypes of breast carcinoma (P = 0.00001-0.01). Their coalterations showed significant association with tumor progression and prognosis (P = 0.003-0.05). Interestingly, patients with alterations of these genes or MCPH1 alone had poor outcome after treatment with DNA-interacting drugs and/or radiation (P = 0.01-0.05).

CONCLUSIONS

Inactivation of MCPH1-ATM-associated DNA damage response pathway might have an important role in the development of breast carcinoma with diagnostic, prognostic and therapeutic implications.

A novel five gene signature derived from stem-like side population cells predicts overall and recurrence-free survival in NSCLC

Gene expression profiling has been used to characterize prognosis in various cancers. Earlier studies had shown that side population cells isolated from Non-Small Cell Lung Cancer (NSCLC) cell lines exhibit cancer stem cell properties. In this study we apply a systems biology approach to gene expression profiling data from cancer stem like cells isolated from lung cancer cell lines to identify novel gene signatures that could predict prognosis. Microarray data from side population (SP) and main population (MP) cells isolated from 4 NSCLC lines (A549, H1650, H460, H1975) were used to examine gene expression profiles associated with stem like properties. Differentially expressed genes that were over or under-expressed at least two fold commonly in all 4 cell lines were identified. We found 354 were upregulated and 126 were downregulated in SP cells compared to MP cells; of these, 89 up and 62 downregulated genes (average 2 fold changes) were used for Principle Component Analysis (PCA) and MetaCore™ pathway analysis. The pathway analysis demonstrated representation of 4 up regulated genes (TOP2A, AURKB, BRRN1, CDK1) in chromosome condensation pathway and 1 down regulated gene FUS in chromosomal translocation. Microarray data was validated using qRT-PCR on the 5 selected genes and all showed robust correlation between microarray and qRT-PCR. Further, we analyzed two independent gene expression datasets that included 360 lung adenocarcinoma patients from NCI Director's Challenge Set for overall survival and 63 samples from Sungkyunkwan University (SKKU) for recurrence free survival. Kaplan-Meier and log-rank test analysis predicted poor survival of patients in both data sets. Our results suggest that genes involved in chromosome condensation are likely related with stem-like properties and might predict survival in lung adenocarcinoma. Our findings highlight a gene signature for effective identification of lung adenocarcinoma patients with poor prognosis and designing more aggressive therapies for such patients.

Epstein-barr virus infected gastric adenocarcinoma expresses latent and lytic viral transcripts and has a distinct human gene expression profile

BACKGROUND

EBV DNA is found within the malignant cells of 10% of gastric cancers. Modern molecular technology facilitates identification of virus-related biochemical effects that could assist in early diagnosis and disease management.

METHODS

In this study, RNA expression profiling was performed on 326 macrodissected paraffin-embedded tissues including 204 cancers and, when available, adjacent non-malignant mucosa. Nanostring nCounter probes targeted 96 RNAs (20 viral, 73 human, and 3 spiked RNAs).

RESULTS

In 182 tissues with adequate housekeeper RNAs, distinct profiles were found in infected versus uninfected cancers, and in malignant versus adjacent benign mucosa. EBV-infected gastric cancers expressed nearly all of the 18 latent and lytic EBV RNAs in the test panel. Levels of EBER1 and EBER2 RNA were highest and were proportional to the quantity of EBV genomes as measured by Q-PCR. Among protein coding EBV RNAs, EBNA1 from the Q promoter and BRLF1 were highly expressed while EBNA2 levels were low positive in only 6/14 infected cancers. Concomitant upregulation of cellular factors implies that virus is not an innocent bystander but rather is linked to NFKB signaling (FCER2, TRAF1) and immune response (TNFSF9, CXCL11, IFITM1, FCRL3, MS4A1 and PLUNC), with PPARG expression implicating altered cellular metabolism. Compared to adjacent non-malignant mucosa, gastric cancers consistently expressed INHBA, SPP1, THY1, SERPINH1, CXCL1, FSCN1, PTGS2 (COX2), BBC3, ICAM1, TNFSF9, SULF1, SLC2A1, TYMS, three collagens, the cell proliferation markers MYC and PCNA, and EBV BLLF1 while they lacked CDH1 (E-cadherin), CLDN18, PTEN, SDC1 (CD138), GAST (gastrin) and its downstream effector CHGA (chromogranin). Compared to lymphoepithelioma-like carcinoma of the uterine cervix, gastric cancers expressed CLDN18, EPCAM, REG4, BBC3, OLFM4, PPARG, and CDH17 while they had diminished levels of IFITM1 and HIF1A. The druggable targets ERBB2 (Her2), MET, and the HIF pathway, as well as several other potential pharmacogenetic indicators (including EBV infection itself, as well as SPARC, TYMS, FCGR2B and REG4) were identified in some tumor specimens.

CONCLUSION

This study shows how modern molecular technology applied to archival fixed tissues yields novel insights into viral oncogenesis that could be useful in managing affected patients.

Integrated Analysis Reveals hsa-miR-142 as a Representative of a Lymphocyte-Specific Gene Expression and Methylation Signature

Gene expression profiling has provided insights into different cancer types and revealed tissue-specific expression signatures. Alterations in microRNA expression contribute to the pathogenesis of many types of human diseases. Few studies have integrated all levels of gene expression, miRNA and methylation to uncover correlations between these data types. We performed an integrated profiling to discover instances of miRNAs associated with a gene expression and DNA methylation signature across multiple cancer types. Using data from The Cancer Genome Atlas (TCGA), we revealed a concordant gene expression and methylation signature associated with the microRNA hsa-miR-142 across the same samples. In all cancer types examined, we found a signature of co-expression of a gene set R and methylated sites M, which correlate positively (M+) or negatively (M−) with the expression of hsa-miR-142. The set R consistently contains many genes, such as TRAF3IP3, NCKAP1L, CD53, LAPTM5, PTPRC, EVI2B, DOCK2, LCP2, CYBB and FYB. The signature is preserved across glioblastoma, ovarian, breast, colon, kidney, lung, uterine and rectum cancer. There is 28% overlap of methylation sites in M between glioblastoma (GBM) and ovarian cancer. There is 60% overlap of genes in R between GBM and ovarian (P = 1.3e⁻¹¹). Most of the genes in R are known to be expressed in lymphocytes and haematopoietic stem cells, while M reflects membrane proteins involved in cell-cell adhesion functions. We speculate that the hsa-miR-142 associated signature may signal haematopoietic-specific processes and an accumulation of methylation events triggering a progressive loss of cell-cell adhesion. We also observed that GBM samples belonging to the proneural subtype tend to have underexpressed hsa-miR-142 and R genes, hypomethylated M+ and hypermethylated M−, while the mesenchymal samples have the opposite profile.

A comparative study of gene-expression data of basal cell carcinoma and melanoma reveals new insights about the two cancers

A comparative analysis of genome-scale transcriptomic data of two types of skin cancers, melanoma and basal cell carcinoma in comparison with other cancer types, was conducted with the aim of identifying key regulatory factors that either cause or contribute to the aggressiveness of melanoma, while basal cell carcinoma generally remains a mild disease. Multiple cancer-related pathways such as cell proliferation, apoptosis, angiogenesis, cell invasion and metastasis, are considered, but our focus is on energy metabolism, cell invasion and metastasis pathways. Our findings include the following. (a) Both types of skin cancers use both glycolysis and increased oxidative phosphorylation (electron transfer chain) for their energy supply. (b) Advanced melanoma shows substantial up-regulation of key genes involved in fatty acid metabolism (β-oxidation) and oxidative phosphorylation, with aerobic metabolism being far more efficient than anaerobic glycolysis, providing a source of the energetics necessary to support the rapid growth of this cancer. (c) While advanced melanoma is similar to pancreatic cancer in terms of the activity level of genes involved in promoting cell invasion and metastasis, the main metastatic form of basal cell carcinoma is substantially reduced in this activity, partially explaining why this cancer type has been considered as far less aggressive. Our method of using comparative analyses of transcriptomic data of multiple cancer types focused on specific pathways provides a novel and highly effective approach to cancer studies in general.