Microarray analysis and tumor classification

Multi-omics technologies and molecular biomarkers in brain tumor-related epilepsy

BACKGROUND

Brain tumors are one of the leading causes of epilepsy, and brain tumor-related epilepsy (BTRE) is recognized as the major cause of intractable epilepsy, resulting in huge treatment cost and burden to patients, their families, and society. Although optimal treatment regimens are available, the majority of patients with BTRE show poor resolution of symptoms. BTRE has a very complex and multifactorial etiology, which includes several influencing factors such as genetic and molecular biomarkers. Advances in multi-omics technologies have enabled to elucidate the pathophysiological mechanisms and related biomarkers of BTRE. Here, we reviewed multi-omics technology-based research studies on BTRE published in the last few decades and discussed the present status, development, opportunities, challenges, and prospects in treating BTRE.

METHODS

First, we provided a general review of epilepsy, BTRE, and multi-omics techniques. Next, we described the specific multi-omics (including genomics, transcriptomics, epigenomics, proteomics, and metabolomics) techniques and related molecular biomarkers for BTRE. We then presented the associated pathogenetic mechanisms of BTRE. Finally, we discussed the development and application of novel omics techniques for diagnosing and treating BTRE.

RESULTS

Genomics studies have shown that the BRAF gene plays a role in BTRE development. Furthermore, the BRAF V600E variant was found to induce epileptogenesis in the neuronal cell lineage and tumorigenesis in the glial cell lineage. Several genomics studies have linked IDH variants with glioma-related epilepsy, and the overproduction of D2HG is considered to play a role in neuronal excitation that leads to seizure occurrence. The high expression level of Forkhead Box O4 (FOXO4) was associated with a reduced risk of epilepsy occurrence. In transcriptomics studies, VLGR1 was noted as a biomarker of epileptic onset in patients. Several miRNAs such as miR-128 and miRNA-196b participate in BTRE development. miR-128 might be negatively associated with the possibility of tumor-related epilepsy development. The lncRNA UBE2R2-AS1 inhibits the growth and invasion of glioma cells and promotes apoptosis. Quantitative proteomics has been used to determine dynamic changes of protein acetylation in epileptic and non-epileptic gliomas. In another proteomics study, a high expression of AQP-4 was detected in the brain of GBM patients with seizures. By using quantitative RT-PCR and immunohistochemistry assay, a study revealed that patients with astrocytomas and oligoastrocytomas showed high BCL2A1 expression and poor seizure control. By performing immunohistochemistry, several studies have reported the relationship between D2HG overproduction and seizure occurrence. Ki-67 overexpression in WHO grade II gliomas was found to be associated with poor postoperative seizure control. According to metabolomics research, the PI3K/AKT/mTOR pathway is associated with the development of glioma-related epileptogenesis. Another metabolomics study found that SV2A, P-gb, and CAD65/67 have the potential to function as biomarkers for BTRE.

CONCLUSIONS

Based on the synthesized information, this review provided new research perspectives and insights into the early diagnosis, etiological factors, and personalized treatment of BTRE.

A Critical Analysis of the FDA's Omics-Driven Pharmacodynamic Biomarkers to Establish Biosimilarity

Demonstrating biosimilarity entails comprehensive analytical assessment, clinical pharmacology profiling, and efficacy testing in patients for at least one medical indication, as required by the U.S. Biologics Price Competition and Innovation Act (BPCIA). The efficacy testing can be waived if the drug has known pharmacodynamic (PD) markers, leaving most therapeutic proteins out of this concession. To overcome this, the FDA suggests that biosimilar developers discover PD biomarkers using omics technologies such as proteomics, glycomics, transcriptomics, genomics, epigenomics, and metabolomics. This approach is redundant since the mode-action-action biomarkers of approved therapeutic proteins are already available, as compiled in this paper for the first time. Other potential biomarkers are receptor binding and pharmacokinetic profiling, which can be made more relevant to ensure biosimilarity without requiring biosimilar developers to conduct extensive research, for which they are rarely qualified.

Tumor response to neoadjuvant chemotherapy in molecular breast cancer subtypes in Medellin, Colombia. Retrospective cohort study

Objectives

To describe the frequency of clinical and pathological response in different molecular subtypes of breast cancer, in patients receiving prior neoadjuvant chemotherapy.

Materials and methods

Descriptive retrospective cohort. The study population consisted of women 18 years of age and older with a histological diagnosis of invasive breast cancer stages IIA, IIB, IIIA, IIIB and IIIC, with a classification by molecular subtypes, who had received prior neoadjuvant chemotherapy, seen at a high complexity clinic in Medellin (Colombia), between July 1, 2017, and July 30, 2019. We measured age clinical stage, histological characteristics, molecular classification, and complete clinical and pathological responses by molecular subtype. A descriptive analysis was conducted.

Results

Overall, 255 patients met the inclusion criteria. Mean age was 55.2 years; the clinical stages with the highest prevalence were IIIB (28.6 %) and IIB (26.3 %), and the most frequent by histologic grading were grades 3 (48.2 %) and 2 (37.3 %). Frequency by molecular types was as follows: luminal A (10.2 %), HER2-negative luminal B (39.6 %), triple-negative (23.1%), HER2-positive luminal B (13.7 %), and pure HER2 (13.3 %). Complete clinical response following chemotherapy, by molecular type, was as follows: luminal A (26.9 %), HER2-negative luminal B (37.6 %), HER2-positive luminal B (48.6 %), pure HER2 (41.2 %), triple-negative (45.8 %). Complete pathological response by molecular subtype was achieved in the luminal A (19.2 %), HER2-negative luminal B (32.7 %), HER2-positive luminal B (54.3 %), pure HER2 (50 %) and triple-negative (42.4 %) subtypes.

Conclusions

In clinical practice, breast cancer classification by molecular subtypes is a means to approach the assess the to neoadjuvant chemotherapy. Prospective studies are needed in the region in order to determine the ability to predict overall and disease-free survival based on the complete pathologic response.

Genome-Wide Analysis of Periodontal and Peri-implant Cells and Tissues

-Omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ, or tissue sample, are powerful means of generating comprehensive genome-level data sets on complex diseases. We have systematically assessed the transcriptome, microbiome, miRNome, and methylome of gingival and peri-implant tissues from human subjects and further studied the transcriptome of primary cells ex vivo, or in vitro after infection with periodontal pathogens.Our data offer new insight on the pathophysiology underlying periodontal and peri-implant diseases, a possible route to a better and earlier diagnosis of these highly prevalent chronic inflammatory diseases and thus, to a personalized and efficient treatment approach.Herein, we outline the laboratory steps required for the processing of periodontal cells and tissues for -omics analyses using current microarrays or next-generation sequencing technology.

Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles

The availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied in classifying samples between tumor and normal type for 13 types of tumors with a high success ratio. Using gene expression, ordered by pathways, results in complexity-entropy diagrams. The map allows the clustering of the tumor and normal types samples, with a high success rate for nine of the thirteen, studied cancer types. Further analysis using information distance also shows good discriminating behavior, but, more importantly, allows for discriminating between cancer types. Together, our results allow the classification of tissues without the need to identify relevant genes or impose a particular cancer model. The used procedure can be extended to classification problems beyond the reported results.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

Fluid Flow Stimulation Modulates Expression of S100 Genes in Normal Breast Epithelium and Breast Cancer

INTRODUCTION

S100 proteins are intracellular calcium ion sensors that participate in cellular processes, some of which are involved in normal breast functioning and breast cancer development. Despite several S100 genes being overexpressed in breast cancer, their roles during disease development remain elusive. Human mammary epithelial cells (HMECs) can be exposed to fluid shear stresses and implications of such interactions have not been previously studied. The goal of this study was to analyze expression profiles of S100 genes upon exposing HMECs to fluid flow.

METHODS

HMECs and breast cancer cell lines were exposed to fluid flow in a parallel-plate bioreactor system. Changes in gene expression were quantified using microarrays and qPCR, gene-gene interactions were elucidated using network analysis, and key modified genes were examined in three independent clinical datasets.

RESULTS

S100 genes were among the most upregulated genes upon flow stimulation. Network analysis revealed interactions between upregulated transcripts, including interactions between S100P, S100PBP, S100A4, S100A7, S100A8 and S100A9. Overexpression of S100s was also observed in patients with early stage breast cancer compared to normal breast tissue, and in most breast cancer patients. Finally, survival analysis revealed reduced survival times for patients with elevated expression of S100A7 and S100P.

CONCLUSION

This study shows that exposing HMECs to fluid flow upregulates genes identified clinically to be overexpressed during breast cancer development, including S100A7 and S100P. These findings are the first to show that S100 genes are flow-responsive and might be participating in a fundamental adaptation pathway in normal tissue that is also active in breast cancer.

Fluid flow exposure promotes epithelial-to-mesenchymal transition and adhesion of breast cancer cells to endothelial cells

BACKGROUND

Mechanical interactions between tumor cells and microenvironments are frequent phenomena during breast cancer progression, however, it is not well understood how these interactions affect Epithelial-to-Mesenchymal Transition (EMT). EMT is associated with the progression of most carcinomas through induction of new transcriptional programs within affected epithelial cells, resulting in cells becoming more motile and adhesive to endothelial cells.

METHODS

MDA-MB-231, SK-BR-3, BT-474, and MCF-7 cells and normal Human Mammary Epithelial Cells (HMECs) were exposed to fluid flow in a parallel-plate bioreactor system. Changes in expression were quantified using microarrays, qPCR, immunocytochemistry, and western blots. Gene-gene interactions were elucidated using network analysis, and key modified genes were examined in clinical datasets. Potential involvement of Smads was investigated using siRNA knockdown studies. Finally, the ability of flow-stimulated and unstimulated cancer cells to adhere to an endothelial monolayer, migrate and invade membrane pores was evaluated in flow and static adhesion experiments.

RESULTS

Fluid flow stimulation resulted in upregulation of EMT inducers and downregulation of repressors. Specifically, Vimentin and Snail were upregulated both at the gene and protein expression levels in flow stimulated HMECs and MDA-MB-231 cells, suggesting progression towards an EMT phenotype. Flow-stimulated SNAI2 was abrogated with Smad3 siRNA. Flow-induced overexpression of a panel of cell adhesion genes was also observed. Network analysis revealed genes involved in cell flow responses including FN1, PLAU, and ALCAM. When evaluated in clinical datasets, overexpression of FN1, PLAU, and ALCAM was observed in patients with different subtypes of breast cancer. We also observed increased adhesion, migration and invasion of flow-stimulated breast cancer cells compared to unstimulated controls.

CONCLUSIONS

This study shows that fluid forces on the order of 1 Pa promote EMT and adhesion of breast cancer cells to an endothelial monolayer and identified biomarkers were distinctly expressed in patient populations. A better understanding of how biophysical forces such as shear stress affect cellular processes involved in metastatic progression of breast cancer is important for identifying new molecular markers for disease progression, and for predicting metastatic risk.

Prognostic Nomogram of Prognosis-Related Genes and Clinicopathological Characteristics to Predict the 5-Year Survival Rate of Colon Cancer Patients

Background: The Cancer Genome Atlas (TCGA) has established a genome-wide gene expression profile, increasing our understanding of the impact of tumor heredity on clinical outcomes. The aim of this study was to construct a nomogram using data from the TCGA regarding prognosis-related genes and clinicopathological characteristics to predict the 5-years survival rate of colon cancer (CC) patients. Methods: Kaplan-Meier and Cox regression analyses were used to identify genes associated with the 5-years survival rate of CC patients. Cox regression was used to analyze the relationship between the clinicopathological features and prognostic genes and overall survival rates in patients with CC and to identify independent risk factors for the prognosis of CC patients. A nomogram for predicting the 5-years survival rate of CC patients was constructed by R software. Results: A total of eight genes (KCNJ14, CILP2, ATP6V1G2, GABRD, RIMKLB, SIX2, PLEKHA8P1, and MPP2) related to the 5-years survival of rate CC patients were identified. Age, stage, and PLEKHA8P1 were independent risk factors for the 5-years survival rate in patients with CC. The accuracy, sensitivity and specificity of the nomogram model constructed by age, TNM staging, and PLEKHA8P1 for predicting the 5-years survival of rate CC patients were 83.3, 83.97, and 85.79%, respectively. Conclusion: The nomogram can correctly predict the 5-year survival rate of patients with CC, thus aiding the individualized decision-making process for patients with CC.

Identification of Overexpressed Genes in Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a fatal tumor lacking effective therapies. The characterization of overexpressed genes could constitute a strategy for identifying drivers of tumor progression as targets for novel therapies. Thus, we performed an integrated gene-expression analysis on RNAseq data of 85 MPM patients from TCGA dataset and reference samples from the GEO. The gene list was further refined by using published studies, a functional enrichment analysis, and the correlation between expression and patients' overall survival. Three molecular signatures defined by 15 genes were detected. Seven genes were involved in cell adhesion and extracellular matrix organization, with the others in control of the mitotic cell division or apoptosis inhibition. Using Western blot analyses, we found that ADAMTS1, PODXL, CIT, KIF23, MAD2L1, TNNT1, and TRAF2 were overexpressed in a limited number of cell lines. On the other hand, interestingly, CTHRC1, E-selectin, SPARC, UHRF1, PRSS23, BAG2, and MDK were abundantly expressed in over 50% of the six MPM cell lines analyzed. Thus, these proteins are candidates as drivers for sustaining the tumorigenic process. More studies with small-molecule inhibitors or silencing RNAs are fully justified and need to be undertaken to better evaluate the cancer-driving role of the targets herewith identified.

Advances on diagnostic biomarkers of pancreatic ductal adenocarcinoma: A systems biology perspective

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is usually diagnosed at an advanced stage when curative surgery is no longer an option. Robust diagnostic biomarkers with high sensitivity and specificity for early detection are urgently needed. Systems biology provides a powerful tool for understanding diseases and solving challenging biological problems, allowing biomarkers to be identified and quantified with increasing accuracy, sensitivity, and comprehensiveness. Here, we present a comprehensive overview of efforts to identify biomarkers of PDAC using genomics, transcriptomics, proteomics, metabonomics, and bioinformatics. Systems biology perspective provides a crucial “network” to integrate multi-omics approaches to biomarker identification, shedding additional light on early PDAC detection.

Survival Risk Prediction Models of Gliomas Based on IDH and 1p/19q

Gliomas have been classified into different molecular subtypes based on their molecular features. To explore the prognostic factors of different subtypes of gliomas, we performed a univariate survival analysis based on the RNA-seq data of 653 patients obtained from The Cancer Genome Atlas. We identified 12205 (20.18%), 6125 (10.13%) and 5206 (8.61%) genes associated with the overall survival (OS) of the IDH-wildtype, IDH-mutation 1p/19q intact and IDH-mutation 1p/19q codeletion gliomas, respectively. Pathway enrichment analysis revealed that OS related genes were mainly involved in alcoholism, systemic lupus erythematosus, hematopoietic cell lineage and diabetes. The OS related genes were further selected using Lasso regression, and three prognostic risk score models were constructed to effectively predict the OS of the patients with different subtypes of gliomas. In total, 76 signature genes were identified and were selected to construct the three models. Moreover, neither of the 76 genes overlapped between different models, which suggested the enormous difference among the three subtypes, although some signature genes (SERPINA5, RP11.229A12.2 and RP11.62F24.2) were also identified as the OS related genes in different glioma subtypes. Interestingly, five genes (RP11.229A12.2, RP11.62F24.2, C3orf67, RP11.275H4.1 and TBX3) played opposing roles (protective or risk factor) in different subtypes. Additionally, the prognosis models consisted of a substantial proportion of non-coding RNA (58.74%, 70.13% and 58.11% in the IDH-wildtype, IDH-mutation 1p/19q intact and IDH-mutation 1p/19q codeletion). Furthermore, multivariate analysis integrating clinical variables demonstrated that risk group predicted by the prognostic models was an independent prognostic factor for gliomas. In conclusion, we have constructed and validated three models that have the potential to predict the prognosis of glioma patients. The genes and pathways identified in this study require further investigation for their underlying mechanisms and potential clinical significance in improving the OS of the glioma patients.

The changing role of pathologists from morphologists to molecular pathologists in the era of precision medicine

Pathology is the study of human illness. Throughout centuries of scientific discoveries, pathologic examination of tissue samples has been the gold standard for diagnosis and pathologists have been involved in the elucidation of aetiology, assessment of the biology, clinicopathologic correlation and prediction of prognosis. The advances in science and technology and focused interest in breast cancer research have provided ample opportunities for pathologists to participate in better understanding of the basic fundamental cascade of events leading to tumorigenesis in breast cancer. They also partnered with their clinical colleagues and scientists to find more effective therapeutic options. This change has been possible with recognition of the fact that morphology alone may not be sufficient to tell the entire story of clinical behaviour of all breast cancer patients. In addition, the realization of heterogeneity of breast cancer and the differences in the expression of various biomarkers and the observed differences in response to therapy have resulted in extensive efforts to better define the characters of each breast cancer subtype. It is now generally agreed that breast cancer is not a single disease and not all patients with breast cancer can benefit from the same therapy. These changes have brought new challenges for pathologists. Pathologist are now required to not only provide diagnosis, but also study the precise molecular characterization of each individual breast cancer case and play a significant role in the treatment planning of breast cancer patients. This remarkable change in the role of the pathologist require his/her involvement in the modern taxonomy of this disease and to rise to the challenge of genomic medicine and molecular diagnostics, which are the fastest growing areas of medicine. Emphasis should also been placed to create a new morphomolecular pathology and train our young pathologist to expand beyond morphology and to embrace the power of molecular diagnostics, in order to be able to effectively practise in the era of precision medicine.

Histomorphological and molecular profiling: friends not foes! Morpho-molecular analysis reveals agreement between histological and molecular profiling

AIMS

Whereas current cancer diagnosis largely relies on the well-established organ and tissue typing of tumours, partially complemented by molecular properties, the comprehensive molecular profiling efforts of recent years have stimulated proposals for molecular reclassifications of tumours independently of anatomical origin. Proposals based only on mutational profiles show the least concordance with histotypes, whereas greater concordance is achieved when various genomic and proteomic data are included.

METHODS AND RESULTS

The most comprehensive molecular reclassification of tumours, by Hoadley et  al (Cell, 158, 2014; 929) and Hoadley et  al (Cell, 173, 2018; 291), integrated multi-omics data, and proposes novel molecular tumour classes. To investigate the relationship between the proposed molecular classes and the original histological tumour types, we re-examined the histomorphology of molecularly reclassified cases. Our results show that the claimed molecular reclassification is associated with and explainable by specific histological subtypes in 70% of the reclassified cases.

CONCLUSION

Therefore, in contrast to the proclaimed reclassification and independence of molecular and histological tumour types, our analysis demonstrates that comprehensive molecular profiling, which includes gene expression and methylation as well as proteomic profiling in addition to mutational analyses, is largely consistent with histomorphological tumour properties.

Transcriptome profiling-based identification of prognostic subtypes and multi-omics signatures of glioblastoma

Glioblastoma (GBM) is a lethal tumor, but few biomarkers and molecular subtypes predicting prognosis are available. This study was aimed to identify prognostic subtypes and multi-omics signatures for GBM. Using oncopression and TCGA-GBM datasets, we identified 80 genes most associated with GBM prognosis using correlations between gene expression levels and overall survival of patients. The prognostic score of each sample was calculated using these genes, followed by assigning three prognostic subtypes. This classification was validated in two independent datasets (REMBRANDT and Severance). Functional annotation revealed that invasion- and cell cycle-related gene sets were enriched in poor and favorable group, respectively. The three GBM subtypes were therefore named invasive (poor), mitotic (favorable), and intermediate. Interestingly, invasive subtype showed increased invasiveness, and MGMT methylation was enriched in mitotic subtype, indicating need for different therapeutic strategies according to prognostic subtypes. For clinical convenience, we also identified genes that best distinguished the invasive and mitotic subtypes. Immunohistochemical staining showed that markedly higher expression of PDPN in invasive subtype and of TMEM100 in mitotic subtype (P < 0.001). We expect that this transcriptome-based classification, with multi-omics signatures and biomarkers, can improve molecular understanding of GBM, ultimately leading to precise stratification of patients for therapeutic interventions.

A five-gene signature to predict the overall survival time of patients with esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the six most commonly diagnosed tumor types in the Chinese population. Gene expression profiles help to predict the prognosis of patients with ESCC. Disease recurrence as the survival endpoint has been analyzed in the majority of previous studies; therefore, the aim of the present study was to construct a robust gene signature in order to determine the overall survival (OS) of patients with ESCC. The gene expression and clinical data of patients with ESCC were downloaded from The Cancer Genome Atlas (TCGA) database. Of the selected data (172 samples from surviving patients), 72 samples were randomly selected as modeling data, and verification was conducted using the entire dataset. Data from the Gene Expression Omnibus was analyzed simultaneously, and a venn diagram was constructed to determine the intersection between these two sets of results; a total of 97 genes were found to be associated with OS. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these genes were primarily associated with specific pathways (Homo sapiens), including DNA replication, protein processing in endoplasmic reticulum and influenza A. A five-gene signature was identified with a robust likelihood-based survival modeling approach. Using regression coefficient modeling, a prognostic model consisting of the C-X-C motif chemokine ligand 8, DNA damage inducible transcript 3, RAB27A, member RAS oncogene family, replication factor C subunit 2 and elongation factor for RNA polymerase II 2 genes was constructed and validated. Based on these results, patients were subdivided into high and low-risk groups. Compared with the high-risk group, the OS time of patients in the low-risk group was significantly increased. Furthermore, it was determined that the five genes were all differentially expressed in ESCC tissues compared with normal tissues, indicating the potential role of these genes in ESCC initiation and progression. In another independent cohort, this five-gene signature was further confirmed and was considered as an independent prognostic biomarker for OS prediction in patients with ESCC. In conclusion, the OS of patients with ESCC may be predicted using this five-gene signature, which may be useful in identifying patients with high-risk ESCC.

The Molecular Biologic Basis of Esophageal and Gastric Cancers

Esophageal cancer and gastric cancer are leading causes of cancer-related mortality worldwide. In this article, the authors discuss the molecular biology of esophageal and gastric cancer with a focus on esophageal adenocarcinoma. They review data from The Cancer Genome Atlas project and advances in the molecular stratification and classification of esophageal carcinoma and gastric cancer. They also summarize advances in microRNA, molecular staging, gene expression profiling, tumor microenvironment, and detection of circulating tumor DNA. Finally, the authors summarize some of the implications of understanding the molecular basis of esophageal cancer and future directions in the management of esophageal cancer.

Speeding up the discovery of combinations of differentially expressed genes for disease prediction and classification

BACKGROUND AND OBJECTIVE

Finding combinations (i.e., pairs, or more generally, q-tuples with q ≥ 2) of genes whose behavior as a group differs significantly between two classes has received a lot of attention in the quest for the discovery of simple, accurate, and easily interpretable decision rules for disease classification and prediction. For example, the Top Scoring Pair (TSP) method seeks to find pairs of genes so that the probability of the reversal of the relative ranking of the expression levels of the genes in the two classes is maximized. The computational cost of finding a q-tuple of genes that scores highest under a given metric is O(G^q), where G is the total number of genes. This cost is often problematic or prohibitive in practice (even for q=2), as the number of genes G is often in the order of tens of thousands.

METHODS

In this paper, we show that this computational cost can be significantly reduced by excluding from consideration genes whose behavior is almost identical in the two classes and therefore their inclusion in any q-tuple is rather non-informative. Our criterion for the exclusion of genes is supported by a statistically robust metric, the Area Under the Curve (AUC) of the corresponding Receiver Operating Characteristic (ROC) curve. By filtering out genes whose AUC value is below a user-chosen threshold, as determined by a procedure that we describe in the paper, dramatic reductions in the run times are obtained while maintaining the same classification accuracy.

RESULTS

We have experimentally verified the gains of this approach on several case studies involving ovarian, colon, leukemia, breast and prostate cancers, and diffuse large b-cell lymphoma.

CONCLUSIONS

The proposed method is not only faster (for example, we observed an average 78.65% reduction over the run time of TSP) while maintaining the same classification accuracy, but it can even result in better classification accuracy due to its inherent ability to avoid the so-called "pivot" (non-informative) genes that may intrude in q-tuples chosen otherwise.

HORMAD2 methylation-mediated epigenetic regulation of gene expression in thyroid cancer

This study is aimed to investigate the methylation level of candidate genes and its impact on thyroid carcinoma (THCA) development. Infinium Human Methylation 450 BeadChip Arrays by Illumina (Illumina HM450K) was the most popular CpG microarray platform widely used in biological and medical research. The methylation level of differentially expressed genes and their corresponding CpG sites were analysed by R programme. The expression of HORMAD2 was evaluated by qRT-PCR and Western blot, while the methylation level was examined via methylation-specific PCR. Cell viability, metastasis, cell cycle and apoptosis were detected by MTT assay, transwell and wound healing assay and flow cytometry, respectively, after treatment with 5-aza-2'-deoxycytidine (5-Aza). Tumour formation assay was used to analyse thyroid tumour growth in nude mice in vivo. The methylation levels of all 116 differentially expressed genes were analysed. HORMAD2 was significantly hypermethylated and its mRNA expression was inhibited in THCA cells. After treatment with 5-Aza, HORMAD2 expression was up-regulated in THCA cells and its overexpression can suppress thyroid cancer cell viability, mobility and invasiveness remarkably. Up-regulation of HORMAD2 in THCA cells could prolong G0/G1 phase and shorten S phase to impede cell mitosis as well as promote thyroid cancer cells apoptosis. Furthermore, tumour formation assay showed that increased HORMAD2 level impeded tumour growth in vivo. Hypermethylation of HORMAD2 could induce THCA progression, while hypomethylation of HORMAD2 retard cell growth and mobility and facilitate apoptosis through increasing its mRNA expression.

Identification of differentially expressed genes by means of outlier detection

Background

An important issue in microarray data is to select, from thousands of genes, a small number of informative differentially expressed (DE) genes which may be key elements for a disease. If each gene is analyzed individually, there is a big number of hypotheses to test and a multiple comparison correction method must be used. Consequently, the resulting cut-off value may be too small. Moreover, an important issue is the selection’s replicability of the DE genes. We present a new method, called ORdensity, to obtain a reproducible selection of DE genes. It takes into account the relation between all genes and it is not a gene-by-gene approach, unlike the usually applied techniques to DE gene selection.

Results

The proposed method returns three measures, related to the concepts of outlier and density of false positives in a neighbourhood, which allow us to identify the DE genes with high classification accuracy. To assess the performance of ORdensity, we used simulated microarray data and four real microarray cancer data sets. The results indicated that the method correctly detects the DE genes; it is competitive with other well accepted methods; the list of DE genes that it obtains is useful for the correct classification or diagnosis of new future samples and, in general, it is more stable than other procedures.

Conclusions

ORdensity is a new method for identifying DE genes that avoids some of the shortcomings of the individual gene identification and it is stable when the original sample is changed by subsamples.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2318-8) contains supplementary material, which is available to authorized users.

A Machine Learning Perspective on Personalized Medicine: An Automized, Comprehensive Knowledge Base with Ontology for Pattern Recognition

Personalized or precision medicine is a new paradigm that holds great promise for individualized patient diagnosis, treatment, and care. However, personalized medicine has only been described on an informal level rather than through rigorous practical guidelines and statistical protocols that would allow its robust practical realization for implementation in day-to-day clinical practice. In this paper, we discuss three key factors, which we consider dimensions that effect the experimental design for personalized medicine: (I) phenotype categories; (II) population size; and (III) statistical analysis. This formalization allows us to define personalized medicine from a machine learning perspective, as an automized, comprehensive knowledge base with an ontology that performs pattern recognition of patient profiles.

Gene expression profiling in human corticotroph tumours reveals distinct, neuroendocrine profiles

Adrenocorticotrophic hormone (ACTH)-secreting pituitary adenomas give rise to a severe endocrinological disorder, comprising Cushing's disease, with multifaceted clinical presentation and treatment outcomes. Experimental studies suggest that the disease variability is inherent to the pituitary tumour, thus indicating the need for further studies into tumour biology. The present study evaluated transcriptome expression pattern in a large series of ACTH-secreting pituitary adenoma specimens in order to identify molecular signatures of these tumours. Gene expression profiling of formalin-fixed, paraffin-embedded specimens from 40 human ACTH-secreting pituitary adenomas revealed the significant expression of genes involved in protein biosynthesis and ribosomal function, in keeping with the neuroendocrine cell profile. Unsupervised cluster analysis identified 3 distinct gene profile clusters and several genes were uniquely overexpressed in a given cluster, accounting for different molecular signatures. Of note, gene expression profiles were associated with clinical features, such as the age and size of the tumour. Altogether, the findings of the present study show that corticotroph tumours are characterised by a neuroendocrine gene expression profile and present subgroup-specific molecular features.

Establishment of a nine-gene prognostic model for predicting overall survival of patients with endometrial carcinoma

Endometrial carcinoma (EC) is the most common malignant tumor of the female genital tract in developed countries. The prognosis of early stage EC is favorable, but a subset faces high risk of cancer progression or recurrence. EC has a poor prognosis upon progression to advanced or metastatic stages. Therefore, our goal is to build a robust prognostic model for predicting overall survival (OS) in EC patients. In this study, 1571 genes were identified as being associated with OS based on genomewide expression profiles using a training dataset. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that these genes were involved in various cancer-related signaling pathways. Nine signature genes were further selected using stepwise selection, and their potential role in the development of EC was demonstrated by performing differential expression analysis between EC and normal uterine tissues. A prognostic model that aggregated these nine signature genes was ultimately established and effectively divided EC patients into two risk groups. OS for patients in the high-risk group was significantly poorer compared with that of the low-risk group. This nine-gene model was subsequently validated and evaluated using the TCGA dataset and shown to have a high discriminating power to distinguish EC patients with an elevated risk of mortality based on the FIGO staging system and other prognostic factors. This study provides a novel prognostic model for the identification of EC patients with elevated risk of mortality and will help to improve our understanding of the underlying mechanisms involved in prognostic EC factors.

Differential expression patterns of housekeeping genes increase diagnostic and prognostic value in lung cancer

BACKGROUND

Using DNA microarrays, we previously identified 451 genes expressed in 19 different human tissues. Although ubiquitously expressed, the variable expression patterns of these "housekeeping genes" (HKGs) could separate one normal human tissue type from another. Current focus on identifying "specific disease markers" is problematic as single gene expression in a given sample represents the specific cellular states of the sample at the time of collection. In this study, we examine the diagnostic and prognostic potential of the variable expressions of HKGs in lung cancers.

METHODS

Microarray and RNA-seq data for normal lungs, lung adenocarcinomas (AD), squamous cell carcinomas of the lung (SQCLC), and small cell carcinomas of the lung (SCLC) were collected from online databases. Using 374 of 451 HKGs, differentially expressed genes between pairs of sample types were determined via two-sided, homoscedastic t-test. Principal component analysis and hierarchical clustering classified normal lung and lung cancers subtypes according to relative gene expression variations. We used uni- and multi-variate cox-regressions to identify significant predictors of overall survival in AD patients. Classifying genes were selected using a set of training samples and then validated using an independent test set. Gene Ontology was examined by PANTHER.

RESULTS

This study showed that the differential expression patterns of 242, 245, and 99 HKGs were able to distinguish normal lung from AD, SCLC, and SQCLC, respectively. From these, 70 HKGs were common across the three lung cancer subtypes. These HKGs have low expression variation compared to current lung cancer markers (e.g., EGFR, KRAS) and were involved in the most common biological processes (e.g., metabolism, stress response). In addition, the expression pattern of 106 HKGs alone was a significant classifier of AD versus SQCLC. We further highlighted that a panel of 13 HKGs was an independent predictor of overall survival and cumulative risk in AD patients.

DISCUSSION

Here we report HKG expression patterns may be an effective tool for evaluation of lung cancer states. For example, the differential expression pattern of 70 HKGs alone can separate normal lung tissue from various lung cancers while a panel of 106 HKGs was a capable class predictor of subtypes of non-small cell carcinomas. We also reported that HKGs have significantly lower variance compared to traditional cancer markers across samples, highlighting the robustness of a panel of genes over any one specific biomarker. Using RNA-seq data, we showed that the expression pattern of 13 HKGs is a significant, independent predictor of overall survival for AD patients. This reinforces the predictive power of a HKG panel across different gene expression measurement platforms. Thus, we propose the expression patterns of HKGs alone may be sufficient for the diagnosis and prognosis of individuals with lung cancer.

Immunohistochemical Analysis of ER, PR, HER-2, CK 5/6, p63 and EGFR Antigen Expression in Medullary Breast Cancer

Aims and Background

Recent publications of breast cancer classification based on gene expression profile analyses indicate that medullary breast carcinomas (MBC) may be considered part of the basal-like carcinoma spectrum made up of ER-negative, PR-negative and HER-2-negative cells (“triple-negative phenotype”). On the other hand, there are also data showing that a proportion of MBC and atypical MBC (AMBC) is ER, PR and/or HER-2 positive. Therefore, we have decided to immunohistochemically analyze ER, PR, HER-2 and basal/myoepithelial markers CK5/6, p63 and EGFR expression in our archival paraffin-embedded MBC and AMBC samples from 48 patients.

Methods

Immunohistochemical evaluation of samples which were derived from patients operated on at our two hospitals between 1999 and 2005.

Results

Typical MBC was found in 39 patients and AMBC in 9 patients. The patients ranged in age from 32 to 84 years (median 55). Modified radical mastectomy with axillary dissection was performed in 30/48 patients (63%) while breast segmentectomy with axillary dissection was performed in 18/48 patients (37%). Metastases in axillary lymph nodes were observed in 15/48 patients (31%). ER positivity was present in 3/48 patients (6%), PR positivity in 8/48 (17%), and a positive HER-2 reaction was present in 14/48 patients (29%). CK 5/6 was positive in 20/48, p63 in 24/48 and EGFR in 8/48 patients. Adjuvant therapy was applied in all but 2 patients. Alive were 45/48 (94%) of patients. With the exception of PR expression, 39 patients with typical MBC and 9 patients with AMBC were comparable in the analyzed parameters. Positive HER-2 antigen expression in the analyzed sample was not found to be associated to a statistically significant degree with the MBC or AMBC histological tumor type, tumor size, axillary lymph node metastases, ER and PR status nor with patient survival.

Conclusions

The data from our study seem to be generally comparable with the relatively scarce published data on clinicopathological parameters of MBC and AMBC.

Microarray Analysis of Hypertension

Hypertension is a complex disorder in which multiple genes, pathways, and organ systems simultaneously interact to contribute to the final level of blood pressure. Fully elucidating these interactions is an important area of hypertension research and one in which high-throughput methods such as microarrays can play a key role. With recent advances in microarray technology, reliable and accurate quantification of all known mRNA transcripts in a sample is now routinely performed. In addition, with improved statistical methods and publicly available tools and resources, robust analysis of the large amount of data generated from microarray experiments is now achievable for all research laboratories as will be outlined in this review.

Informatively clustering longitudinal microarrays using binary or survival outcome data

The goal of this research is to discover what groups of genes are associated with the disease process. We use binary and failure time outcomes to inform the clustering of longitudinally-collected microarray data. We propose a linear model with normally distributed cluster-specific random effects for the longitudinal gene expression trajectory. The random effects are linearly related to a latent continuous representation of the outcome, where the probability or hazard of the outcome depends on these latent variables. We apply our method to microarray data collected from trauma patients in the Inflammation and Host Response to Injury project.

A statistical framework for applying RNA profiling to chemical hazard detection

Use of 'omics technologies in environmental science is expanding. However, application is mostly restricted to characterizing molecular steps leading from toxicant interaction with molecular receptors to apical endpoints in laboratory species. Use in environmental decision-making is limited, due to difficulty in elucidating mechanisms in sufficient detail to make quantitative outcome predictions in any single species or in extending predictions to aquatic communities. Here we introduce a mechanism-agnostic statistical approach, supplementing mechanistic investigation by allowing probabilistic outcome prediction even when understanding of molecular pathways is limited, and facilitating extrapolation from results in laboratory test species to predictions about aquatic communities. We use concepts familiar to environmental managers, supplemented with techniques employed for clinical interpretation of 'omics-based biomedical tests. We describe the framework in step-wise fashion, beginning with single test replicates of a single RNA variant, then extending to multi-gene RNA profiling, collections of test replicates, and integration of complementary data. In order to simplify the presentation, we focus on using RNA profiling for distinguishing presence versus absence of chemical hazards, but the principles discussed can be extended to other types of 'omics measurements, multi-class problems, and regression. We include a supplemental file demonstrating many of the concepts using the open source R statistical package.

Identification of gene pairs through penalized regression subject to constraints

Background

This article concerns the identification of gene pairs or combinations of gene pairs associated with biological phenotype or clinical outcome, allowing for building predictive models that are not only robust to normalization but also easily validated and measured by qPCR techniques. However, given a small number of biological samples yet a large number of genes, this problem suffers from the difficulty of high computational complexity and imposes challenges to the accuracy of identification statistically.

Results

In this paper, we propose a parsimonious model representation and develop efficient algorithms for identification. Particularly, we derive an equivalent model subject to a sum-to-zero constraint in penalized linear regression, where the correspondence between nonzero coefficients in these models is established. Most importantly, it reduces the model complexity of the traditional approach from the quadratic order to the linear order in the number of candidate genes, while overcoming the difficulty of model nonidentifiablity. Computationally, we develop an algorithm using the alternating direction method of multipliers (ADMM) to deal with the constraint. Numerically, we demonstrate that the proposed method outperforms the traditional method in terms of the statistical accuracy. Moreover, we demonstrate that our ADMM algorithm is more computationally efficient than a coordinate descent algorithm with a local search. Finally, we illustrate the proposed method on a prostate cancer dataset to identify gene pairs that are associated with pre-operative prostate-specific antigen.

Conclusion

Our findings demonstrate the feasibility and utility of using gene pairs as biomarkers.

Garlic extract in bladder cancer prevention: Evidence from T24 bladder cancer cell xenograft model, tissue microarray, and gene network analysis

There is a growing interest in the use of naturally occurring agents in cancer prevention. This study investigated the garlic extract affects in bladder cancer (BC) prevention. The effect of garlic extract in cancer prevention was evaluated using the T24 BC BALB/C-nude mouse xenograft model. Microarray analysis of tissues was performed to identify differences in gene expression between garlic extract intake and control diet, and gene network analysis was performed to assess candidate mechanisms of action. Furthermore, we investigated the expression value of selected genes in the data of 165 BC patients. Compared to the control group, significant differences in tumor volume and tumor weight were observed in the groups fed 20 mg/kg (p<0.05), 200 mg/kg, and 1000 mg/kg of garlic extract (p<0.01). Genes (645) were identified as cancer prevention-related genes (fold change >2 and p<0.05) by tissue microarray analysis. A gene network analysis of 279 of these genes (p<0.01) was performed using Cytoscape/ClueGo software: 36 genes and 37 gene ontologies were mapped to gene networks. Protein kinase A (PKA) signaling pathway including AKAP12, RDX, and RAB13 genes were identified as potential mechanisms for the activity of garlic extract in cancer prevention. In BC patients, AKAP12 and RDX were decreased but, RAB13 was increased. Oral garlic extract has strong cancer prevention activity in vivo and an acceptable safety profile. PKA signaling process, especially increasing AKAP12 and RDX and decreasing RAB13, are candidate pathways that may mediate this prevention effect.

Simvastatin down-regulates differential genetic profiles produced by organochlorine mixtures in primary breast cell (HMEC)

Women all over the world are exposed to an unavoidable contamination by organochlorine pesticides and other chemical pollutants. Many of them are considered as xenoestrogens and have been associated with the development and progression of breast cancer. We have demonstrated that the most prevalent pesticide mixtures found in healthy women and in women diagnosed with breast cancer modulates the gene expression in human epithelial mammary cells. Statins are well-known cholesterol-depleting agents acting as inhibitors of cholesterol synthesis. Since the early 1990s, it has been known that statins could be successfully used in cancer therapy, including breast cancer, but the exact mechanism behind anti-tumor activity of the statins remains unclear. In the present study we evaluated the effect of simvastatin in the gene expression pattern induced by realistic organochlorine mixtures found in breast cancer patients. The gene expression of 94 genes related with the cell signaling pathways were assessed. Our results indicate that simvastatin exerts a global down regulating effect on successfully determined genes (78.7%), thus attenuating the effects induced by organochlorine mixtures on the gene profile of human mammary epithelial cells. This effect was more evident on genes whose function is the ATP-binding process (that also were particularly up-regulated by pesticide mixtures). We also found that MERTK (a proto-oncogene which is overexpressed in several malignancies) and PDGFRB (a member of the platelet-derived growth factor family whose expression is high in breast-cancer cells that have become resistant to endocrine therapy) were among the genes with a higher differential regulation by simvastatin. Since resistance to treatment with tyrosine kinase inhibitors is closely related to MERKT, our findings would enhance the possible utility of statins in breast cancer treatment, i.e. improving therapeutic results combining statins with tyrosine Kinase inhibitors.

Genome-Wide Analysis of Periodontal and Peri-Implant Cells and Tissues

Omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ or tissue sample, are powerful means of generating comprehensive genome-level data sets on complex diseases. We have systematically assessed the transcriptome, miRNome and methylome of gingival tissues from subjects with different diagnostic entities of periodontal disease, and studied the transcriptome of primary cells ex vivo, or in vitro after infection with periodontal pathogens. Our data further our understanding of the pathobiology of periodontal diseases and indicate that the gingival -omes translate into discernible phenotypic characteristics and possibly support an alternative, "molecular" classification of periodontitis.Here, we outline the laboratory steps required for the processing of periodontal cells and tissues for -omics analyses using current microarrays or next-generation sequencing technology.

Cancer-testis antigen expression in synovial sarcoma: NY-ESO-1, PRAME, MAGEA4, and MAGEA1

Synovial sarcoma (SS) is regarded as a relatively chemosensitive sarcoma, but the prognosis of advanced SSs remains poor. Here we identified highly expressed cancer-testis antigens that could be promising immunotherapy targets for SS, using a previously conducted cDNA microarray, and we assessed the clinicopathological or prognostic relationships of these antigens in SS. We compared the gene expression profiles of 11 SSs with those of 3 normal adipose tissues. Among the up-regulated cancer-testis antigens, we analyzed PRAME, MAGEA1, and MAGEA4 and another cancer-testis antigen (NY-ESO-1) together, by immunohistochemistry and real-time polymerase chain reaction in 108 SSs. Immunohistochemically, NY-ESO-1, PRAME, MAGEA4, and MAGEA1 were positive in 66 (61%), 93 (86%), 89 (82%), and 16 (15%) of 108 SSs, respectively, and 104 (96%) of 108 SSs showed the immunohistochemical expression of at least 1 of NY-ESO-1, PRAME, and MAGEA4. Moreover, the high expression of at least 1 of these 3 antigens was observed in 83% of the SSs. High expression of NY-ESO-1 and MAGEA4 was significantly correlated with the presence of necrosis and advanced clinical stage. The immunohistochemical expression of these cancer-testis antigens was not correlated with prognosis, but the coexpression of NY-ESO-1, PRAME, and MAGEA4 was significantly associated with adverse prognosis. The real-time polymerase chain reaction results were closely related to the immunohistochemical results: NY-ESO-1 (P = .0019), PRAME (P = .039), MAGEA4 (P = .0149), and MAGEA1 (P = .0766). These data support the potential utility of NY-ESO-1, PRAME, and MAGEA4 as immunotherapy targets and ancillary prognostic parameters, suggesting the possible benefit of the combined use of these cancer-testis antigens as an SS immunotherapy target.

Validation of a Gene Expression Test for Mesothelioma Prognosis in Formalin-Fixed Paraffin-Embedded Tissues

A molecular test performed using fresh-frozen tissue was proposed for use in the prognosis of patients with pleural mesothelioma. The accuracy of the test and its properties was assessed under Clinical Laboratory Improvement Amendments-approved guidelines using FFPE tissue from an independent multicenter patient cohort. Concordance studies were performed using matched frozen and FFPE mesothelioma samples. The prognostic value of the test was evaluated in an independent validation cohort of 73 mesothelioma patients who underwent surgical resection. FFPE-based classification demonstrated overall high concordance (83%) with the matched frozen specimens, on removal of cases with low confidence scores, showing sensitivity and specificity in predicting type B classification (poor outcome) of 43% and 98%, respectively. Concordance between research and clinical methods increased to 87% on removal of low confidence cases. Median survival times in the validation cohort were 18 and 7 months in type A and type B cases, respectively (P = 0.002). Multivariate classification adding pathologic staging information to the gene expression score resulted in significant stratification of risk groups. The median survival times were 52 and 14 months in the low-risk (class 1) and intermediate-risk (class 2) groups, respectively. The prognostic molecular test for mesothelioma can be performed on FFPE tissues to predict survival, and can provide an orthogonal tool, in combination with established pathologic parameters, for risk evaluation.

A seven-gene signature predicts overall survival of patients with colorectal cancer

Colorectal cancer (CRC) is a major cause of global cancer mortality. Gene expression profiles can help predict prognosis of patients with CRC. In most of previous studies, disease recurrence was analyzed as the survival endpoint. Thus we aim to build a robust gene signature for prediction of overall survival (OS) in patients with CRC. Fresh frozen CRC tissues from 64 patients were analyzed using Affymetrix HG-U133plus 2.0 gene arrays. By performing univariate survival analysis, 6487 genes were found to be associated with the OS in our cohort. KEGG analysis revealed that these genes were mainly involved in pathways such as endocytosis, axon guidance, spliceosome, Wnt signalling and ubiquitin mediated proteolysis. A seven-gene signature was further selected by a robust likelihood-based survival modelling approach. The prognostic model of seven-gene signature (NHLRC3, ZDHHC21, PRR14L, CCBL1, PTPRB, PNPO, and PPIP5K2) was constructed and weighted by regression coefficient, which divided patients into high- and low-risk groups. The OS for patients in high-risk group was significantly poorer compared with patients in low-risk group. Moreover, all seven genes were found to be differentially expressed in CRC tissues as compared with adjacent normal tissues, indicating their potential role in CRC initiation and progression. This seven-gene signature was further validated as an independent prognostic marker for OS prediction in patients with CRC in other two independent cohorts. In short, we developed a robust seven-gene signature that can predict the OS for CRC patients, providing new insights into identification of CRC patients with high risk of mortality.

Omics Profiling in Precision Oncology

Cancer causes significant morbidity and mortality worldwide, and is the area most targeted in precision medicine. Recent development of high-throughput methods enables detailed omics analysis of the molecular mechanisms underpinning tumor biology. These studies have identified clinically actionable mutations, gene and protein expression patterns associated with prognosis, and provided further insights into the molecular mechanisms indicative of cancer biology and new therapeutics strategies such as immunotherapy. In this review, we summarize the techniques used for tumor omics analysis, recapitulate the key findings in cancer omics studies, and point to areas requiring further research on precision oncology.

Multi-omics landscapes of colorectal cancer subtypes discriminated by an individualized prognostic signature for 5-fluorouracil-based chemotherapy

Until recently, few prognostic signatures for colorectal cancer (CRC) patients receiving 5-fluorouracil (5-FU)-based chemotherapy could be used in clinical practice. Here, using transcriptional profiles for a panel of cancer cell lines and three cohorts of CRC patients, we developed a prognostic signature based on within-sample relative expression orderings (REOs) of six gene pairs for stage II-III CRC patients receiving 5-FU-based chemotherapy. This REO-based signature had the unique advantage of being insensitive to experimental batch effects and free of the impractical data normalization requirement. After stratifying 184 CRC samples with multi-omics data from The Cancer Genome Atlas into two prognostic groups using the REO-based signature, we further revealed that patients with high recurrence risk were characterized by frequent gene copy number aberrations reducing 5-FU efficacy and DNA methylation aberrations inducing distinct transcriptional alternations to confer 5-FU resistance. In contrast, patients with low recurrence risk exhibited deficient mismatch repair and carried frequent gene mutations suppressing cell adhesion. These results reveal the multi-omics landscapes determining prognoses of stage II-III CRC patients receiving 5-FU-based chemotherapy.

Identification of phenotype-relevant differentially expressed genes in breast cancer demonstrates enhanced quantile discretization protocol's utility in multi-platform microarray data integration

Microarray for transcriptomics experiments often suffer from limited statistical power due to small sample size. Quantile discretization (QD) maps expression values for a sample into a series of equivalently sized 'bins' that represent a discrete numerical range, e.g. [Formula: see text]4 to [Formula: see text]4, which enables normalized data from multiple experiments and/or expression platforms to be combined for re-analysis. We found, however, that informal selection of bin numbers often resulted in loss of the underlying correlation structure in the data through assigning of the same numerical value to genes that are in reality expressed at significantly different levels within a sample. Here we report a procedure for determining an optimal bin number for dataset. Applying this to integrated public breast cancer datasets enabled statistical identification of several differentially expressed tumorigenesis-related genes that were not found when analyzing the individual datasets, and also several cancer biomarkers not previously indicated as having utility in the disease. Notably, differential modulation of translational control and protein synthesis via multiple pathways were found to potentially have central roles in breast cancer development and progression. These findings suggest that our protocol has significant utility in making meaningful novel biomedical discoveries by leveraging the large public expression data repositories.

Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age

Objective

By focussing on differences in the mural granulosa cell (MGC) and cumulus cell (CC) transcriptomes from follicles resulting in competent (live birth) and non-competent (no pregnancy) oocytes the study aims on defining a competence classifier expression profile in the two cellular compartments. Design: A case-control study. Setting: University based facilities for clinical services and research. Patients: MGC and CC samples from 60 women undergoing IVF treatment following the long GnRH-agonist protocol were collected. Samples from 16 oocytes where live birth was achieved and 16 age- and embryo morphology matched incompetent oocytes were included in the study.

Methods

MGC and CC were isolated immediately after oocyte retrieval. From the 16 competent and non-competent follicles, mRNA was extracted and expression profile generated on the Human Gene 1.0 ST Affymetrix array. Live birth prediction analysis using machine learning algorithms (support vector machines) with performance estimation by leave-one-out cross validation and independent validation on an external data set.

Results

We defined a signature of 30 genes expressed in CC predictive of live birth. This live birth prediction model had an accuracy of 81%, a sensitivity of 0.83, a specificity of 0.80, a positive predictive value of 0.77, and a negative predictive value of 0.86. Receiver operating characteristic analysis found an area under the curve of 0.86, significantly greater than random chance. When applied on 3 external data sets with the end-point outcome measure of blastocyst formation, the signature resulted in 62%, 75% and 88% accuracy, respectively. The genes in the classifier are primarily connected to apoptosis and involvement in formation of extracellular matrix. We were not able to define a robust MGC classifier signature that could classify live birth with accuracy above random chance level.

Conclusion

We have developed a cumulus cell classifier, which showed a promising performance on external data. This suggests that the gene signature at least partly include genes that relates to competence in the developing blastocyst.

The Influence of the Global Gene Expression Shift on Downstream Analyses

The assumption that total abundance of RNAs in a cell is roughly the same in different cells is underlying most studies based on gene expression analyses. But experiments have shown that changes in the expression of some master regulators such as c-MYC can cause global shift in the expression of almost all genes in some cell types like cancers. Such shift will violate this assumption and can cause wrong or biased conclusions for standard data analysis practices, such as detection of differentially expressed (DE) genes and molecular classification of tumors based on gene expression. Most existing gene expression data were generated without considering this possibility, and are therefore at the risk of having produced unreliable results if such global shift effect exists in the data. To evaluate this risk, we conducted a systematic study on the possible influence of the global gene expression shift effect on differential expression analysis and on molecular classification analysis. We collected data with known global shift effect and also generated data to simulate different situations of the effect based on a wide collection of real gene expression data, and conducted comparative studies on representative existing methods. We observed that some DE analysis methods are more tolerant to the global shift while others are very sensitive to it. Classification accuracy is not sensitive to the shift and actually can benefit from it, but genes selected for the classification can be greatly affected.

Potential biomarkers for esophageal cancer

Esophageal cancer, which consist of esophageal adenocarcinoma and esophageal squamous cell carcinoma, is one of the most common malignant tumors in the world, especially in the south of Iran and China. To find and investigate the biomarkers in the initiation, development and progression of esophageal cancer will help us predict the prognosis of esophageal cancer patients and improve the curative effect and survival rate. Here, we reviewed the potential biomarkers of esophageal cancer in three aspects: Immunohistochemical markers, blood-based markers, miRNA markers and Gene expression profiling. All these biomarkers provided promising therapeutic targets for the diagnosis, treatment, and prognosis of esophageal cancer.

Differentially expressed genes in metastatic advanced Egyptian bladder cancer

BACKGROUND

Bladder cancer is one of the most common cancers worldwide. Gene expression profiling using microarray technologies improves the understanding of cancer biology. The aim of this study was to determine the gene expression profile in Egyptian bladder cancer patients.

MATERIALS AND METHODS

Samples from 29 human bladder cancers and adjacent non-neoplastic tissues were analyzed by cDNA microarray, with hierarchical clustering and multidimensional analysis.

RESULTS

Five hundred and sixteen genes were differentially expressed of which SOS1, HDAC2, PLXNC1, GTSE1, ULK2, IRS2, ABCA12, TOP3A, HES1, and SRP68 genes were involved in 33 different pathways. The most frequently detected genes were: SOS1 in 20 different pathways; HDAC2 in 5 different pathways; IRS2 in 3 different pathways. There were 388 down-regulated genes. PLCB2 was involved in 11 different pathways, MDM2 in 9 pathways, FZD4 in 5 pathways, p15 and FGF12 in 4 pathways, POLE2 in 3 pathways, and MCM4 and POLR2E in 2 pathways. Thirty genes showed significant differences between transitional cell cancer (TCC) and squamous cell cancer (SCC) samples. Unsupervised cluster analysis of DNA microarray data revealed a clear distinction between low and high grade tumors. In addition 26 genes showed significant differences between low and high tumor stages, including fragile histidine triad, Ras and sialyltransferase 8 (alpha) and 16 showed significant differences between low and high tumor grades, like methionine adenosyl transferase II, beta.

CONCLUSIONS

The present study identified some genes, that can be used as molecular biomarkers or target genes in Egyptian bladder cancer patients.

Breast cancer subtypes: morphologic and biologic characterization

Advances in basic science, technology and translational research have created a revolution in breast cancer diagnosis and therapy. Researchers' discoveries of genes defining variability in response to therapy and heterogeneity in clinical presentations and tumor biology are the foundation of the path to personalized medicine. The success of personalized breast cancer care depends on access to pertinent clinical information and risk factors, optimal imaging findings, well-established morphologic features, and traditional and contemporary prognostic/predictive testing. The integration of these entities provides an opportunity to identify patients who can benefit from specific therapies, and demonstrates the link between breast cancer subtypes and their association with different tumor biology. It is critical to recognize specific types of breast cancer in individual patients and design optimal personalized therapy. This article will highlight the roles of morphologic features and established tumor biomarkers on patient outcome.

CrossNorm: a novel normalization strategy for microarray data in cancers

Normalization is essential to get rid of biases in microarray data for their accurate analysis. Existing normalization methods for microarray gene expression data commonly assume a similar global expression pattern among samples being studied. However, scenarios of global shifts in gene expressions are dominant in cancers, making the assumption invalid. To alleviate the problem, here we propose and develop a novel normalization strategy, Cross Normalization (CrossNorm), for microarray data with unbalanced transcript levels among samples. Conventional procedures, such as RMA and LOESS, arbitrarily flatten the difference between case and control groups leading to biased gene expression estimates. Noticeably, applying these methods under the strategy of CrossNorm, which makes use of the overall statistics of the original signals, the results showed significantly improved robustness and accuracy in estimating transcript level dynamics for a series of publicly available datasets, including titration experiment, simulated data, spike-in data and several real-life microarray datasets across various types of cancers. The results have important implications for the past and the future cancer studies based on microarray samples with non-negligible difference. Moreover, the strategy can also be applied to other sorts of high-throughput data as long as the experiments have global expression variations between conditions.

New insights in antibody-mediated rejection

PURPOSE OF REVIEW

In the present review, we aim to describe the state of knowledge concerning antibody-mediated rejection (ABMR) spectrum and diagnosis criteria before analyzing the present and future promising leads regarding ABMR prognosis markers and treatment.

RECENT FINDINGS

Recent studies regarding complement-binding donor-specific antibodies and the molecular approach highlighted the unmet need for stratification tools for prognosis and treatment inside ABMR disease.

SUMMARY

ABMR is the leading cause of kidney allograft failure. The recent expansion of its spectrum is related to the paradigm of a continuous process, leading insidiously to a chronic form of ABMR and to the progressive acknowledgement of new entities (such as vascular ABMR, subclinical ABMR, C4d-negative ABMR). Considering the global picture of ABMR, the Banff classification gradually refined the diagnosis criteria so that it now describes a clinically relevant and coherent entity. Nevertheless, if the diagnosis mainly relies on conventional assessment, such as histological findings and circulating donor-specific antibodies, these criteria face serious limitations in terms of stratification of patients at risk of graft loss inside ABMR disease. Recently, new promising tools have emerged in order to identify long-term outcomes at the time of the diagnosis of rejection. In this regard, donor-specific antibodies' complement-fixing ability and the molecular approach contributed significantly. Currently, however, no clinically relevant surrogate marker of treatment efficiency is currently available.