Combination of gene expression and genome copy number alteration has a prognostic value for breast cancer

Identification of key miRNAs in prostate cancer progression based on miRNA-mRNA network construction

Prostate cancer (PC) is one of the major male cancers. Differential diagnosis of PC is indispensable for the individual therapy, i.e., Gleason score (GS) that describes the grade of cancer can be used to choose the appropriate therapy. However, the current techniques for PC diagnosis and prognosis are not always effective. To identify potential markers that could be used for differential diagnosis of PC, we analyzed miRNA-mRNA interactions and we build specific networks for PC onset and progression. Key differentially expressed miRNAs for each GS were selected by calculating three parameters of network topology measures: the number of their single regulated mRNAs (NSR), the number of target genes (NTG) and NSR/NTG. miRNAs that obtained a high statistically significant value of these three parameters were chosen as potential biomarkers for computational validation and pathway analysis. 20 miRNAs were identified as key candidates for PC. 8 out of 20 miRNAs (miR-25-3p, miR-93-3p, miR-122-5p, miR-183-5p, miR-615-3p, miR-7-5p, miR-375, and miR-92a-3p) were differentially expressed in all GS and proposed as biomarkers for PC onset. In addition, "Extracellular-receptor interaction", "Focal adhesion", and "microRNAs in cancer" were significantly enriched by the differentially expressed target genes of the identified miRNAs. miR-10a-5p was found to be differentially expressed in GS 6, 7, and 8 in PC samples. 3 miRNAs were identified as PC GS-specific differentially expressed miRNAs: miR-155-5p was identified in PC samples with GS 6, and miR-142-3p and miR-296-3p in PC samples with GS 9. The efficacy of 20 miRNAs as potential biomarkers was revealed with a Random Forest classification using an independent dataset. The results demonstrated our 20 miRNAs achieved a better performance (AUC: 0.73) than miRNAs selected with Boruta algorithm (AUC: 0.55), a method for the automated feature extraction. Studying miRNA-mRNA associations, key miRNAs were identified with a computational approach for PC onset and progression. Further experimental validations are needed for future translational development.

Identification of Breast Cancer Subtype-Specific Biomarkers by Integrating Copy Number Alterations and Gene Expression Profiles

Background and Objectives: Breast cancer is a heterogeneous disease categorized into four subtypes. Previous studies have shown that copy number alterations of several genes are implicated with the development and progression of many cancers. This study evaluates the effects of DNA copy number alterations on gene expression levels in different breast cancer subtypes. Materials and Methods: We performed a computational analysis integrating copy number alterations and gene expression profiles in 1024 breast cancer samples grouped into four molecular subtypes: luminal A, luminal B, HER2, and basal. Results: Our analyses identified several genes correlated in all subtypes such as KIAA1967 and MCPH1. In addition, several subtype-specific genes that showed a significant correlation between copy number and gene expression profiles were detected: SMARCB1, AZIN1, MTDH in luminal A, PPP2R5E, APEX1, GCN5 in luminal B, TNFAIP1, PCYT2, DIABLO in HER2, and FAM175B, SENP5, SCAF1 in basal subtype. Conclusions: This study showed that computational analyses integrating copy number and gene expression can contribute to unveil the molecular mechanisms of cancer and identify new subtype-specific biomarkers.

Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data

The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.

In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer

Like other cancer diseases, prostate cancer (PC) is caused by the accumulation of genetic alterations in the cells that drives malignant growth. These alterations are revealed by gene profiling and copy number alteration (CNA) analysis. Moreover, recent evidence suggests that also microRNAs have an important role in PC development. Despite efforts to profile PC, the alterations (gene, CNA, and miRNA) and biological processes that correlate with disease development and progression remain partially elusive. Many gene signatures proposed as diagnostic or prognostic tools in cancer poorly overlap. The identification of co-expressed genes, that are functionally related, can identify a core network of genes associated with PC with a better reproducibility. By combining different approaches, including the integration of mRNA expression profiles, CNAs, and miRNA expression levels, we identified a gene signature of four genes overlapping with other published gene signatures and able to distinguish, in silico, high Gleason-scored PC from normal human tissue, which was further enriched to 19 genes by gene co-expression analysis. From the analysis of miRNAs possibly regulating this network, we found that hsa-miR-153 was highly connected to the genes in the network. Our results identify a four-gene signature with diagnostic and prognostic value in PC and suggest an interesting gene network that could play a key regulatory role in PC development and progression. Furthermore, hsa-miR-153, controlling this network, could be a potential biomarker for theranostics in high Gleason-scored PC.

BIRC5 Genomic Copy Number Variation in Early-Onset Breast Cancer

Background:

Baculoviral inhibitor of apoptosis repeat-containing 5 (BIRC5) gene is an inhibitor of apoptosis that expresses in human embryonic tissues but it is absent in most healthy adult tissues. The copy number of BIRC5 has been indicated to be highly increased in tumor tissues; however, its association with the age of onset in breast cancer is not well understood.

Methods:

Forty tumor tissues of breast cancer were obtained from Tumor Bank of Cancer Institute, Imam Khomeini Hospital, Tehran, Iran. BIRC5 gene copy number variation (CNV) was evaluated using Multiplex Ligation-dependent Probe Amplification (MLPA) and then compared with the age of onset for each patient.

Results:

BIRC5 amplification was seen in 17.5% of cases. Also, a significant association was observed between BIRC5 gene amplification and individuals under 40 years of age (P=0.04).

Conclusion:

BIRC5 gene has the potential to be a marker for the detection and prognosis of cancer at an early age.

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

How interacting pathways are regulated by miRNAs in breast cancer subtypes

BACKGROUND

An important challenge in cancer biology is to understand the complex aspects of the disease. It is increasingly evident that genes are not isolated from each other and the comprehension of how different genes are related to each other could explain biological mechanisms causing diseases. Biological pathways are important tools to reveal gene interaction and reduce the large number of genes to be studied by partitioning it into smaller paths. Furthermore, recent scientific evidence has proven that a combination of pathways, instead than a single element of the pathway or a single pathway, could be responsible for pathological changes in a cell.

RESULTS

In this paper we develop a new method that can reveal miRNAs able to regulate, in a coordinated way, networks of gene pathways. We applied the method to subtypes of breast cancer. The basic idea is the identification of pathways significantly enriched with differentially expressed genes among the different breast cancer subtypes and normal tissue. Looking at the pairs of pathways that were found to be functionally related, we created a network of dependent pathways and we focused on identifying miRNAs that could act as miRNA drivers in a coordinated regulation process.

CONCLUSIONS

Our approach enables miRNAs identification that could have an important role in the development of breast cancer.

Pathway-based expression profile for breast cancer diagnoses

Microarray experiments have made possible to identify breast cancer marker gene signatures. However, gene expression-based signatures present limitations because they do not consider metabolic role of the genes and are affected by genetic heterogeneity across patient cohorts. Considering the activity of entire pathways rather than the expression levels of individual genes can be a way to exceed these limits. We evaluated and compared five methods of pathway-level aggregation of gene expression data. Our results confirmed the important role of pathway expression profile in breast cancer diagnostic classification (accuracy >90%). However, although assessed on a limited number of samples and datasets, this study shows that using dissimilarity representation among patients does not improve the classification of pathway-based expression profiles.

Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential

BACKGROUND

Development of human cancer can proceed through the accumulation of different genetic changes affecting the structure and function of the genome. Combined analyses of molecular data at multiple levels, such as DNA copy-number alteration, mRNA and miRNA expression, can clarify biological functions and pathways deregulated in cancer. The integrative methods that are used to investigate these data involve different fields, including biology, bioinformatics, and statistics.

RESULTS

These methodologies are presented in this review, and their implementation in breast cancer is discussed with a focus on integration strategies. We report current applications, recent studies and interesting results leading to the identification of candidate biomarkers for diagnosis, prognosis, and therapy in breast cancer by using both individual and combined analyses.

CONCLUSION

This review presents a state of art of the role of different technologies in breast cancer based on the integration of genetics and epigenetics, and shares some issues related to the new opportunities and challenges offered by the application of such integrative approaches.

Managing chronic pathologies with a stepped mHealth-based approach in clinical psychology and medicine

Chronic diseases and conditions typically require long-term monitoring and treatment protocols both in traditional settings and in out-patient frameworks. The economic burden of chronic conditions is a key challenge and new and mobile technologies could offer good solutions. mHealth could be considered an evolution of eHealth and could be defined as the practice of medicine and public health supported by mobile communication devices. mHealth approach could overcome limitations linked with the traditional, restricted, and highly expensive in-patient treatment of many chronic pathologies. Possible applications include stepped mHealth approach, where patients can be monitored and treated in their everyday contexts. Unfortunately, many barriers for the spread of mHealth are still present. Due the significant impact of psychosocial factors on disease evolution, psychotherapies have to be included into the chronic disease protocols. Existing psychological theories of health behavior change have to be adapted to the new technological contexts and requirements. In conclusion, clinical psychology and medicine have to face the "chronic care management" challenge in both traditional and mHealth settings.

Integration of mRNA expression profile, copy number alterations, and microRNA expression levels in breast cancer to improve grade definition

Defining the aggressiveness and growth rate of a malignant cell population is a key step in the clinical approach to treating tumor disease. The correct grading of breast cancer (BC) is a fundamental part in determining the appropriate treatment. Biological variables can make it difficult to elucidate the mechanisms underlying BC development. To identify potential markers that can be used for BC classification, we analyzed mRNAs expression profiles, gene copy numbers, microRNAs expression and their association with tumor grade in BC microarray-derived datasets. From mRNA expression results, we found that grade 2 BC is most likely a mixture of grade 1 and grade 3 that have been misclassified, being described by the gene signature of either grade 1 or grade 3. We assessed the potential of the new approach of integrating mRNA expression profile, copy number alterations, and microRNA expression levels to select a limited number of genomic BC biomarkers. The combination of mRNA profile analysis and copy number data with microRNA expression levels led to the identification of two gene signatures of 42 and 4 altered genes (FOXM1, KPNA4, H2AFV and DDX19A) respectively, the latter obtained through a meta-analytical procedure. The 42-based gene signature identifies 4 classes of up- or down-regulated microRNAs (17 microRNAs) and of their 17 target mRNA, and the 4-based genes signature identified 4 microRNAs (Hsa-miR-320d, Hsa-miR-139-5p, Hsa-miR-567 and Hsa-let-7c). These results are discussed from a biological point of view with respect to pathological features of BC. Our identified mRNAs and microRNAs were validated as prognostic factors of BC disease progression, and could potentially facilitate the implementation of assays for laboratory validation, due to their reduced number.

Combined analysis of chromosomal instabilities and gene expression for colon cancer progression inference

BACKGROUND

Copy number alterations (CNAs) represent an important component of genetic variations. Such alterations are related with certain type of cancer including those of the pancreas, colon, and breast, among others. CNAs have been used as biomarkers for cancer prognosis in multiple studies, but few works report on the relation of CNAs with the disease progression. Moreover, most studies do not consider the following two important issues. (I) The identification of CNAs in genes which are responsible for expression regulation is fundamental in order to define genetic events leading to malignant transformation and progression. (II) Most real domains are best described by structured data where instances of multiple types are related to each other in complex ways.

RESULTS

Our main interest is to check whether the colorectal cancer (CRC) progression inference benefits when considering both (I) the expression levels of genes with CNAs, and (II) relationships (i.e. dissimilarities) between patients due to expression level differences of the altered genes. We first evaluate the accuracy performance of a state-of-the-art inference method (support vector machine) when subjects are represented only through sets of available attribute values (i.e. gene expression level). Then we check whether the inference accuracy improves, when explicitly exploiting the information mentioned above. Our results suggest that the CRC progression inference improves when the combined data (i.e. CNA and expression level) and the considered dissimilarity measures are applied.

CONCLUSIONS

Through our approach, classification is intuitively appealing and can be conveniently obtained in the resulting dissimilarity spaces. Different public datasets from Gene Expression Omnibus (GEO) were used to validate the results.