How interacting pathways are regulated by miRNAs in breast cancer subtypes

miR-770-5p-induced cellular switch to sensitize trastuzumab resistant breast cancer cells targeting HER2/EGFR/IGF1R bidirectional crosstalk

Background/aim

Studies highlighted the bidirectional crosstalk between the HER family members in breast cancer as resistance mechanism to anti-HER agents. Cross-signaling between HER2/EGFR and ER/IGF1R could play role in the development of resistance to therapeutics hence stimulating cell growth. To overcome this resistance, combined therapies targeting both pathways simultaneously have been proposed as an effective strategy. The involvement of miRNAs in resistance of targeted therapies like trastuzumab was demonstrated in recent studies. Hence the regulation of miRNAs in resistance state could reverse the cell behaviour to drugs. Previously we found that overexpression of miR-770-5p downregulated AKT and ERK expression through HER2 signaling and potentiated the effect of trastuzumab. In this study we examined the impact of miR-770-5p on trastuzumab resistance.

Materials and methods

Cells were treated with tamoxifen or trastuzumab to examine their role in bidirectional crosstalk. The molecule mechanism of miR-770-5p on HER2/EGFR/IGF1R bidirectional crosstalk was explored by western blot. The expression of miR-770-5p in trastuzumab resistant cells was examined by q-PCR. To investigate the effect of miR-770-5p on cancer cell proliferation in trastuzumab resistance state, resistant cells were analyzed by iCELLigence real-time cell analyzer.

Results

miR-770-5p expression was significantly downregulated in trastuzumab-resistant BT-474 and SK-BR-3 cells. Overexpression of miR-770-5p sensitized the resistant cells to trastuzumab, as evidenced by reduced cell proliferation and increased cell viability. Additionally, in resistant cells, increased expression and activation of EGFR and IGF1R were observed. However, miR-770-5p overexpression resulted in decreased phosphorylation of AKT and ERK, indicating its suppressive role in EGFR/HER2 signaling. Furthermore, miR-770-5p downregulated the expression of IGF1R and mTOR, suggesting its involvement in regulating the escape signaling mediated by IGF1R in resistance.

Conclusion

In conclusion, our findings demonstrate the critical role of miR-770-5p in regulating bidirectional crosstalk and overcoming trastuzumab resistance in breast cancer cells. These results highlight the potential of miR-770-5p as a therapeutic target to improve the efficacy of targeted therapies and address resistance mechanisms in breast cancer.

Upregulated GATA3/miR205-5p Axis Inhibits MFNG Transcription and Reduces the Malignancy of Triple-Negative Breast Cancer

Simple Summary

Triple-negative cancer (TNBC) is a deadly disease that presents a potential health threat to women worldwide. It is the most aggressive and presents a poor prognosis among all breast cancer subgroups. We previously demonstrated that the elevated expression of manic fringe (MFNG) plays a pivotal role in breast cancer. However, the mechanism through which MFNG is regulated remains obscure. The study presented here set out to determine the mechanism by which MFNG expression is regulated in TNBC. Our findings revealed that GATA3 and miR-205-p cooperatively block the transcription of MFNG leading to the inhibition of cell migration and tumor growth in vitro and in vivo. Our study uncovers a novel GATA3/miR-205-p/MFNG feed-forward loop and miR205-5p could be adopted as a potential therapeutic strategy of TNBC.

Abstract

Triple-negative breast cancer (TNBC) accounts for approximately 20% of all breast carcinomas and has the worst prognosis of all breast cancer subtypes due to the lack of an effective target. Therefore, understanding the molecular mechanism underpinning TNBC progression could explore a new target for therapy. While the Notch pathway is critical in the development process, its dysregulation leads to TNBC initiation. Previously, we found that manic fringe (MFNG) activates the Notch signaling and induces breast cancer progression. However, the underlying molecular mechanism of MFNG upstream remains unknown. In this study, we explore the regulatory mechanisms of MFNG in TNBC. We show that the increased expression of MFNG in TNBC is associated with poor clinical prognosis and significantly promotes cell growth and migration, as well as Notch signaling activation. The mechanistic studies reveal that MFNG is a direct target of GATA3 and miR205-5p and demonstrate that GATA3 and miR205-5p overexpression attenuate MFNG oncogenic effects, while GATA3 knockdown mimics MFNG phenotype to promote TNBC progression. Moreover, we illustrate that GATA3 is required for miR205-5p activation to inhibit MFNG transcription by binding to the 3′ UTR region of its mRNA, which forms the GATA3/miR205-5p/MFNG feed-forward loop. Additionally, our in vivo data show that the miR205-5p mimic combined with polyetherimide-black phosphorus (PEI-BP) nanoparticle remarkably inhibits the growth of TNBC-derived tumors which lack GATA3 expression. Collectively, our study uncovers a novel GATA3/miR205-5p/MFNG feed-forward loop as a pathway that could be a potential therapeutic target for TNBC.

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 long non-coding RNAs and RNA binding proteins in breast cancer subtypes

Breast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes.

Triple negative aggressive phenotype controlled by miR-135b and miR-365: new theranostics candidates

Triple negative breast cancer (TNBC) accounts for about a fifth of all breast cancers and includes a diverse group of cancers. The heterogeneity of TNBC and the lack of target receptors on the cell surface make it difficult to develop specific therapeutic treatments. These aspects cause the high negative prognosis of patients with this type of tumor. The analysis of the molecular profiles of TNBC samples has allowed a better characterization of this tumor, supporting the search for new reliable diagnostic markers. To this end, we have developed a bioinformatic approach to integrate networks of genes differentially expressed in basal breast cancer compared to healthy tissues, with miRNAs able to regulate their expression. We studied the role of these miRNAs in TNBC subtype cell lines. We therefore identified two miRNAs, namely miR-135b and miR-365, with a central role in regulating the altered functional pathways in basal breast cancer. These two miRNAs are differentially expressed in human TNBC immunohistochemistry-selected tissues, and their modulation has been shown to play a role in the proliferation of tumor control and its migratory and invasive capacity in TNBC subtype cell lines. From the perspective of personalized medicine, we managed to modulate the expression of the two miRNAs in organotypic cultures, suggesting their possible use as diagnostic and therapeutic molecules. miR-135b and miR-365 have a key role in TNBC, controlling proliferation and invasion. Their detection could be helpful in TNBC diagnosis, while their modulation could become a new therapeutic tool for TNBC.

Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer

Background

Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes.

Methods

In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer.

Findings

We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients.

Interpretation

We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens.

Funding

This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.

Integration of Molecular Docking and In Vitro Studies: A Powerful Approach for Drug Discovery in Breast Cancer

Molecular docking in the pharmaceutical industry is a powerful in silico approach for discovering novel therapies for unmet medical needs predicting drug–target interactions. It not only provides binding affinity between drugs and targets at the atomic level, but also elucidates the fundamental pharmacological properties of specific drugs. The purpose of this review was to illustrate newer and emergent uses of docking when combined with in vitro techniques for drug discovery in metastatic breast cancer. We grouped the selected articles into five main categories; namely, systematic repositioning of drugs, natural drugs, new synthesized molecules, combinations of drugs, and drug latentiation. We focused on new promising drugs that have a good affinity with their targets, thus inducing a favorable biological response. This review suggests that the integration of molecular docking and in vitro studies can accelerate cancer drug discovery showing a good consistency of the results between the two approaches.

Perturbations of pathway co-expression network identify a core network in metastatic breast cancer

Metastases are the main cause of death in advanced breast cancer (BC) patients. Although chemotherapy and hormone therapy are current treatment strategies, drug resistance is frequent and still not completely understood. In this study, a bioinformatics analysis was performed on BC patients to explore the molecular mechanisms associated with BC metastasis. Microarray gene expression profiles of metastatic and non metastatic BC patients were downloaded from Gene Expression Omnibus (GEO) dataset. Raw data were normalized and merged using the Combat tool. Pathways enriched with differently expressed genes were identified and a pathway co-expression network was generated using Pearson's correlation. We identified from this network, which includes 17 pathways and 128 interactions, the pathways that most influence the network efficiency. Moreover, protein interaction network was investigated to identify hub genes of the pathway network. The prognostic role of the network was evaluated with a survival analysis using an independent dataset. In conclusion, the pathway co-expression network could contribute to understanding the mechanism and development of BC metastases.

In Silico Discovery of Candidate Drugs against Covid-19

Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide.

Theranostic application of miR-429 in HER2+ breast cancer

Human epidermal growth factor receptor 2 (HER2) is overexpressed/amplified in one third of breast cancers (BCs), and is associated with the poorer prognosis and the higher metastatic potential in BC. Emerging evidences highlight the role of microRNAs (miRNAs) in the regulation of several cellular processes, including BC.

Methods: Here we identified, by in silico approach, a group of three miRNAs with central biological role (high degree centrality) in HER2+ BC. We validated their dysregulation in HER2+ BC and we analysed their functional role by in vitro approaches on selected cell lines and by in vivo experiments in an animal model.

Results: We found that their expression is dysregulated in both HER2+ BC cell lines and human samples. Focusing our study on the only upregulated miRNA, miR-429, we discovered that it acts as an oncogene and its upregulation is required for HER2+ cell proliferation. It controls the metastatic potential of HER2+ BC subtype by regulating migration and invasion of the cell.

Conclusions: In HER2+ BC oncogenic miR-429 is able to regulate HIF1α pathway by directly targeting VHL mRNA, a molecule important for the degradation of HIF1α. The overexpression of miR-429, observed in HER2+ BC, causes increased proliferation and migration of the BC cells. More important, silencing miR-429 succeeds in delaying tumor growth, thus miR-429 could be proposed as a therapeutic probe in HER2+ BC tumors.

In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis

Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling.

BACKGROUND

Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype.

METHODS

We describe a computational approach that correlates phenotype from magnetic resonance imaging (MRI) of breast cancer (BC) lesions with microRNAs (miRNAs), mRNAs, and regulatory networks, developing a radiomiRNomic map. We validated our approach to the relationships between MRI and miRNA expression data derived from BC patients. We obtained 16 radiomic features quantifying the tumor phenotype. We integrated the features with miRNAs regulating a network of pathways specific for a distinct BC subtype.

RESULTS

We found six miRNAs correlated with imaging features in Luminal A (miR-1537, -205, -335, -337, -452, and -99a), seven miRNAs (miR-142, -155, -190, -190b, -1910, -3617, and -429) in HER2+, and two miRNAs (miR-135b and -365-2) in Basal subtype. We demonstrate that the combination of correlated miRNAs and imaging features have better classification power of Luminal A versus the different BC subtypes than using miRNAs or imaging alone.

CONCLUSION

Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis.

Portrait of Tissue-Specific Coexpression Networks of Noncoding RNAs (miRNA and lncRNA) and mRNAs in Normal Tissues

Genes that encode proteins playing a role in more than one biological process are frequently dependent on their tissue context, and human diseases result from the altered interplay of tissue- and cell-specific processes. In this work, we performed a computational approach that identifies tissue-specific co-expression networks by integrating miRNAs, long-non-coding RNAs, and mRNAs in more than eight thousands of human samples from thirty normal tissue types. Our analysis (1) shows that long-non coding RNAs and miRNAs have a high specificity, (2) confirms several known tissue-specific RNAs, and (3) identifies new tissue-specific co-expressed RNAs that are currently still not described in the literature. Some of these RNAs interact with known tissue-specific RNAs or are crucial in key cancer functions, suggesting that they are implicated in tissue specification or cell differentiation.

In silico perturbation of drug targets in pan-cancer analysis combining multiple networks and pathways

The knowledge of cancer cell response to conventional therapies is crucial in order to choose the correct therapy of patients affected by cancer. The major problem is generally attributed to the lack of specific biological processes able to predict the therapy efficacy. Here, we optimized a computational method for the analysis of gene networks able to detect and quantify the effects of a drug in a pan-cancer study. Overall, our method, using several network topological measures has identified a cancer gene network with a key role in biological processes. The gene network, able to classify with a good performance cancer vs normal samples, was modulated in silico to evaluate the effects of new or approved drugs. This computational model could offer an interesting hint to decipher molecular mechanisms contributing to resistance or inefficacy of drugs.

Potential Role of miRNAs as Theranostic Biomarkers of Epilepsy

Epilepsy includes a group of disorders of the brain characterized by an enduring predisposition to generate epileptic seizures. Although familial epilepsy has a genetic component and heritability, the etiology of the majority of non-familial epilepsies has no known associated genetic mutations. In epilepsy, recent epigenetic profiles have highlighted a possible role of microRNAs in its pathophysiology. In particular, molecular profiling identifies a significant number of microRNAs (miRNAs) altered in epileptic hippocampus of both animal models and human tissues. In this review, analyzing molecular profiles of different animal models of epilepsy, we identified a group of 20 miRNAs commonly altered in different epilepsy-animal models. As emerging evidences highlighted the poor overlap between signatures of animal model tissues and human samples, we focused our analysis on miRNAs, circulating in human biofluids, with a principal role in epilepsy hallmarks, and we identified a group of 8 diagnostic circulating miRNAs. We discussed the functional role of these 8 miRNAs in the epilepsy hallmarks. A few of them have also been proposed as therapeutic molecules for epilepsy treatment, revealing a great potential for miRNAs as theranostic molecules in epilepsy.

Tree-based machine learning algorithms identified minimal set of miRNA biomarkers for breast cancer diagnosis and molecular subtyping

Breast cancer is a complex disease and its effective treatment needs affordable diagnosis and subtyping signatures. While the use of machine learning approach in clinical computation biology is still in its infancy, the prevalent approach in identifying molecular biomarkers remains to be screening of all biomarkers by differential expression analysis. Many of these attempts used miRNAs expression data in breast cancer and amounted to the multitude of differentially expressed miRNAs in this cancer; hence, the minimal set of miRNA biomarkers to classify breast cancer is yet to be identified. Availability of diverse and vast amount of cancer datasets like The Cancer Genome Atlas facilitated the molecular profiling of patients' tumors and introduced new challenges like clinical grade interpretations from big data. In this study, miRNA expression dataset of breast cancer patients from TCGA database was used to develop prediction models from which miRNA biomarkers were identified for diagnosis and molecular subtyping of this cancer. I took the advantage of interpretability of tree-based classification models to extract their rules and identify minimal set of biomarkers in this cancer. Empirical negative control miRNAs in breast cancer obtained and used to normalize the dataset. Tree-based machine learning models trained in my analysis used hsa-miR-139 with hsa-miR-183 to classify breast tumors from normal samples, and hsa-miR4728 with hsa-miR190b to further classify these tumors into three major subtypes of breast cancer. In addition to the proposed biomarkers, the most important miRNAs in breast cancer classification were also presented.

In silico identification of drug target pathways in breast cancer subtypes using pathway cross-talk inhibition

Background

Despite great development in genome and proteome high-throughput methods, treatment failure is a critical point in the management of most solid cancers, including breast cancer (BC). Multiple alternative mechanisms upon drug treatment are involved to offset therapeutic effects, eventually causing drug resistance or treatment failure.

Methods

Here, we optimized a computational method to discover novel drug target pathways in cancer subtypes using pathway cross-talk inhibition (PCI). The in silico method is based on the detection and quantification of the pathway cross-talk for distinct cancer subtypes. From a BC data set of The Cancer Genome Atlas, we have identified different networks of cross-talking pathways for different BC subtypes, validated using an independent BC dataset from Gene Expression Omnibus. Then, we predicted in silico the effects of new or approved drugs on different BC subtypes by silencing individual or combined subtype-derived pathways with the aim to find new potential drugs or more effective synergistic combinations of drugs.

Results

Overall, we identified a set of new potential drug target pathways for distinct BC subtypes on which therapeutic agents could synergically act showing antitumour effects and impacting on cross-talk inhibition.

Conclusions

We believe that in silico methods based on PCI could offer valuable approaches to identifying more tailored and effective treatments in particular in heterogeneous cancer diseases.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1535-2) contains supplementary material, which is available to authorized users.

Radiation-Induced Gene Expression Changes in High and Low Grade Breast Cancer Cell Types

BACKGROUND

There is extensive scientific evidence that radiation therapy (RT) is a crucial treatment, either alone or in combination with other treatment modalities, for many types of cancer, including breast cancer (BC). BC is a heterogeneous disease at both clinical and molecular levels, presenting distinct subtypes linked to the hormone receptor (HR) status and associated with different clinical outcomes. The aim of this study was to assess the molecular changes induced by high doses of ionizing radiation (IR) on immortalized and primary BC cell lines grouped according to Human epidermal growth factor receptor (HER2), estrogen, and progesterone receptors, to study how HR status influences the radiation response. Our genomic approach using in vitro and ex-vivo models (e.g., primary cells) is a necessary first step for a translational study to describe the common driven radio-resistance features associated with HR status. This information will eventually allow clinicians to prescribe more personalized total doses or associated targeted therapies for specific tumor subtypes, thus enhancing cancer radio-sensitivity.

METHODS

Nontumorigenic (MCF10A) and BC (MCF7 and MDA-MB-231) immortalized cell lines, as well as healthy (HMEC) and BC (BCpc7 and BCpcEMT) primary cultures, were divided into low grade, high grade, and healthy groups according to their HR status. At 24 h post-treatment, the gene expression profiles induced by two doses of IR treatment with 9 and 23 Gy were analyzed by cDNA microarray technology to select and compare the differential gene and pathway expressions among the experimental groups.

RESULTS

We present a descriptive report of the substantial alterations in gene expression levels and pathways after IR treatment in both immortalized and primary cell cultures. Overall, the IR-induced gene expression profiles and pathways appear to be cell-line dependent. The data suggest that some specific gene and pathway signatures seem to be linked to HR status.

CONCLUSIONS

Genomic biomarkers and gene-signatures of specific tumor subtypes, selected according to their HR status and molecular features, could facilitate personalized biological-driven RT treatment planning alone and in combination with targeted therapies.

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.

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis

BACKGROUND

Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network.

RESULTS

We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study.

CONCLUSIONS

Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

Identifying the miRNA signature associated with survival time in patients with lung adenocarcinoma using miRNA expression profiles

Lung adenocarcinoma is a multifactorial disease. MicroRNA (miRNA) expression profiles are extensively used for discovering potential theranostic biomarkers of lung cancer. This work proposes an optimized support vector regression (SVR) method called SVR-LUAD to simultaneously identify a set of miRNAs referred to the miRNA signature for estimating the survival time of lung adenocarcinoma patients using their miRNA expression profiles. SVR-LUAD uses an inheritable bi-objective combinatorial genetic algorithm to identify a small set of informative miRNAs cooperating with SVR by maximizing estimation accuracy. SVR-LUAD identified 18 out of 332 miRNAs using 10-fold cross-validation and achieved a correlation coefficient of 0.88 ± 0.01 and mean absolute error of 0.56 ± 0.03 year between real and estimated survival time. SVR-LUAD performs well compared to some well-recognized regression methods. The miRNA signature consists of the 18 miRNAs which strongly correlates with lung adenocarcinoma: hsa-let-7f-1, hsa-miR-16-1, hsa-miR-152, hsa-miR-217, hsa-miR-18a, hsa-miR-193b, hsa-miR-3136, hsa-let-7g, hsa-miR-155, hsa-miR-3199-1, hsa-miR-219-2, hsa-miR-1254, hsa-miR-1291, hsa-miR-192, hsa-miR-3653, hsa-miR-3934, hsa-miR-342, and hsa-miR-141. Gene ontology annotation and pathway analysis of the miRNA signature revealed its biological significance in cancer and cellular pathways. This miRNA signature could aid in the development of novel therapeutic approaches to the treatment of lung adenocarcinoma.

BITS 2015: the annual meeting of the Italian Society of Bioinformatics

This preface introduces the content of the BioMed Central journal Supplements related to the BITS 2015 meeting, held in Milan, Italy, from the 3^th to the 5^th of June, 2015.