Hepatitis B Virus and microRNAs: A Bioinformatics Approach.

In recent decades, microRNAs (miRNAs) have emerged as key regulators of gene expression, and the identification of viral miRNAs (v-miRNAs) within some viruses, including hepatitis B virus (HBV), has attracted significant attention. HBV infections often progress to chronic states (CHB) and may induce fibrosis/cirrhosis and hepatocellular carcinoma (HCC). The presence of HBV can dysregulate host miRNA expression, influencing several biological pathways, such as apoptosis, innate and immune response, viral replication, and pathogenesis. Consequently, miRNAs are considered a promising biomarker for diagnostic, prognostic, and treatment response. The dynamics of miRNAs during HBV infection are multifaceted, influenced by host variability and miRNA interactions. Given the ability of miRNAs to target multiple messenger RNA (mRNA), understanding the viral-host (human) interplay is complex but essential to develop novel clinical applications. Therefore, bioinformatics can help to analyze, identify, and interpret a vast amount of miRNA data. This review explores the bioinformatics tools available for viral and host miRNA research. Moreover, we introduce a brief overview focusing on the role of miRNAs during HBV infection. In this way, this review aims to help the selection of the most appropriate bioinformatics tools based on requirements and research goals.

The Impact of Drug-Drug Interactions on the Toxicity Profile of Combined Treatment with BRAF and MEK Inhibitors in Patients with BRAF-Mutated Metastatic Melanoma.

BACKGROUND: BRAF and MEK inhibition is a successful strategy in managing BRAF-mutant melanoma, even if the treatment-related toxicity is substantial. We analyzed the role of drug-drug interactions (DDI) on the toxicity profile of anti-BRAF/anti-MEK therapy. METHODS: In this multicenter, observational, and retrospective study, DDIs were assessed using Drug-PIN software (V 2/23). The association between the Drug-PIN continuous score or the Drug-PIN traffic light and the occurrence of treatment-related toxicities and oncological outcomes was evaluated. RESULTS: In total, 177 patients with advanced BRAF-mutated melanoma undergoing BRAF/MEK targeted therapy were included. All grade toxicity was registered in 79% of patients. Cardiovascular toxicities occurred in 31 patients (17.5%). Further, 94 (55.9%) patients had comorbidities requiring specific pharmacological treatments. The median Drug-PIN score significantly increased when the target combination was added to the patient's home therapy (p-value < 0.0001). Cardiovascular toxicity was significantly associated with the Drug-PIN score (p-value = 0.048). The Drug-PIN traffic light (p = 0.00821) and the Drug-PIN score (p = 0.0291) were seen to be significant predictors of cardiotoxicity. Patients with low-grade vs. high-grade interactions showed a better prognosis regarding overall survival (OS) (p = 0.0045) and progression-free survival (PFS) (p = 0.012). The survival analysis of the subgroup of patients with cardiological toxicity demonstrated that patients with low-grade vs. high-grade DDIs had better outcomes in terms of OS (p = 0.0012) and a trend toward significance in PFS (p = 0.068). CONCLUSIONS: DDIs emerged as a critical issue for the risk of treatment-related cardiovascular toxicity. Our findings support the utility of DDI assessment in melanoma patients treated with BRAF/MEK inhibitors.

A network approach to define the predictive role of immune profile on tumor response and toxicity of anti PD-1 single agent immunotherapy in patients with solid tumors.

Background: The immune profile of each patient could be considered as a portrait of the fitness of his/her own immune system. The predictive role of the immune profile in immune-related toxicities (irAEs) development and tumour response to treatment was investigated. Methods: A prospective, multicenter study evaluating, through a multiplex assay, the soluble immune profile at the baseline of 53 patients with advanced cancer, treated with immunotherapy as single agent was performed. Four connectivity heat maps and networks were obtained by calculating the Spearman correlation coefficients for each group: responder patients who developed cumulative toxicity (R-T), responders who did not develop cumulative toxicity (R-NT), non-responders who developed cumulative toxicity (NR-T), non-responders who did not develop cumulative toxicity (NR-NT). Results: A statistically significant up-regulation of IL-17A, sCTLA4, sCD80, I-CAM-1, sP-Selectin and sEselectin in NR-T was detected. A clear loss of connectivity of most of the soluble immune checkpoints and cytokines characterized the immune profile of patients with toxicity, while an inversion of the correlation for ICAM-1 and sP-selectin was observed in NR-T. Four connectivity networks were built for each group. The highest number of connections characterized the NR-T. Conclusions: A connectivity network of immune dysregulation was defined for each subgroup of patients, regardless of tumor type. In patients with the worst prognosis (NR-T) the peculiar connectivity model could facilitate their early and timely identification, as well as the design of a personalized treatment approach to improve outcomes or prevent irAEs.

Circular RNA mediated gene regulation in human breast cancer: A bioinformatics analysis.

Circular RNAs (circRNAs) are a new acknowledged class of RNAs that has been shown to play a major role in several biological functions both in physiological and pathological conditions, operating as critical part of regulatory processes, like competing endogenous RNA (ceRNA) networks. The ceRNA hypothesis is a recently discovered molecular mechanism that adds a new key layer of post-transcriptional regulation, whereby various types of RNAs can reciprocally influence each other's expression competing for binding the same pool of microRNAs, even affecting disease development. In this study, we build a network of circRNA-miRNA-mRNA interactions in human breast cancer, called CERNOMA, that is a bipartite graph with one class of nodes corresponding to differentially expressed miRNAs (DEMs) and the other one corresponding to differentially expressed circRNAs (DEC) and mRNAs (DEGs). A link between a DEC (or DEG) and DEM is placed if it is predicted to be a target of the DEM and shows an opposite expression level trend with respect to the DEM. Within the CERNOMA, we highlighted an interesting deregulated circRNA-miRNA-mRNA triplet, including the up-regulated hsa_circRNA_102908 (BRCA1 associated RING domain 1), the down-regulated miR-410-3p, and the up-regulated ESM1, whose overexpression has been already shown to promote tumor dissemination and metastasis in breast cancer.

Immune-related toxicity and soluble profile in patients affected by solid tumors: a network approach.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have particular, immune-related adverse events (irAEs), as a consequence of interfering with self-tolerance mechanisms. The incidence of irAEs varies depending on ICI class, administered dose and treatment schedule. The aim of this study was to define a baseline (T0) immune profile (IP) predictive of irAE development. METHODS: A prospective, multicenter study evaluating the immune profile (IP) of 79 patients with advanced cancer and treated with anti-programmed cell death protein 1 (anti-PD-1) drugs as a first- or second-line setting was performed. The results were then correlated with irAEs onset. The IP was studied by means of multiplex assay, evaluating circulating concentration of 12 cytokines, 5 chemokines, 13 soluble immune checkpoints and 3 adhesion molecules. Indoleamine 2, 3-dioxygenase (IDO) activity was measured through a modified liquid chromatography-tandem mass spectrometry using the high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) method. A connectivity heatmap was obtained by calculating Spearman correlation coefficients. Two different networks of connectivity were constructed, based on the toxicity profile. RESULTS: Toxicity was predominantly of low/moderate grade. High-grade irAEs were relatively rare, while cumulative toxicity was high (35%). Positive and statistically significant correlations between the cumulative toxicity and IP10 and IL8, sLAG3, sPD-L2, sHVEM, sCD137, sCD27 and sICAM-1 serum concentration were found. Moreover, patients who experienced irAEs had a markedly different connectivity pattern, characterized by disruption of most of the paired connections between cytokines, chemokines and connections of sCD137, sCD27 and sCD28, while sPDL-2 pair-wise connectivity values seemed to be intensified. Network connectivity analysis identified a total of 187 statistically significant interactions in patients without toxicity and a total of 126 statistically significant interactions in patients with toxicity. Ninety-eight interactions were common to both networks, while 29 were specifically observed in patients who experienced toxicity. CONCLUSIONS: A particular, common pattern of immune dysregulation was defined in patients developing irAEs. This immune serological profile, if confirmed in a larger patient population, could lead to the design of a personalized therapeutic strategy in order to prevent, monitor and treat irAEs at an early stage.

A Network of MicroRNAs and mRNAs Involved in Melanosome Maturation and Trafficking Defines the Lower Response of Pigmentable Melanoma Cells to Targeted Therapy.

BACKGROUND: The ability to increase their degree of pigmentation is an adaptive response that confers pigmentable melanoma cells higher resistance to BRAF inhibitors (BRAFi) compared to non-pigmentable melanoma cells. METHODS: Here, we compared the miRNome and the transcriptome profile of pigmentable 501Mel and SK-Mel-5 melanoma cells vs. non-pigmentable A375 melanoma cells, following treatment with the BRAFi vemurafenib (vem). In depth bioinformatic analyses (clusterProfiler, WGCNA and SWIMmeR) allowed us to identify the miRNAs, mRNAs and biological processes (BPs) that specifically characterize the response of pigmentable melanoma cells to the drug. Such BPs were studied using appropriate assays in vitro and in vivo (xenograft in zebrafish embryos). RESULTS: Upon vem treatment, miR-192-5p, miR-211-5p, miR-374a-5p, miR-486-5p, miR-582-5p, miR-1260a and miR-7977, as well as GPR143, OCA2, RAB27A, RAB32 and TYRP1 mRNAs, are differentially expressed only in pigmentable cells. These miRNAs and mRNAs belong to BPs related to pigmentation, specifically melanosome maturation and trafficking. In fact, an increase in the number of intracellular melanosomes-due to increased maturation and/or trafficking-confers resistance to vem. CONCLUSION: We demonstrated that the ability of pigmentable cells to increase the number of intracellular melanosomes fully accounts for their higher resistance to vem compared to non-pigmentable cells. In addition, we identified a network of miRNAs and mRNAs that are involved in melanosome maturation and/or trafficking. Finally, we provide the rationale for testing BRAFi in combination with inhibitors of these biological processes, so that pigmentable melanoma cells can be turned into more sensitive non-pigmentable cells.

Genomic and Immune Approach in Platinum Refractory HPV-Negative Head and Neck Squamous Cell Carcinoma Patients Treated with Immunotherapy: A Novel Combined Profile.

INTRODUCTION: Only a minority of patients with platinum refractory head and neck squamous cell carcinoma (PR/HNSCC) gain some lasting benefit from immunotherapy. METHODS: The combined role of the comprehensive genomic (through the FoundationOne Cdx test) and immune profiles of 10 PR/HNSCC patients treated with the anti-PD-1 nivolumab was evaluated. The immune profiles were studied both at baseline and at the second cycle of immunotherapy, weighing 20 circulating cytokines/chemokines, adhesion molecules, and 14 soluble immune checkpoints dosed through a multiplex assay. A connectivity map was obtained by calculating the Spearman correlation between the expression profiles of circulating molecules. RESULTS: Early progression occurred in five patients, each of them showing TP53 alteration and three of them showing a mutation/loss/amplification of genes involved in the cyclin-dependent kinase pathway. In addition, ERB2 amplification (1 patient), BRCA1 mutation (1 patient), and NOTCH1 genes alteration (3 patients) occurred. Five patients achieved either stable disease or partial response. Four of them carried mutations in PI3K/AKT/PTEN pathways. In the only two patients, with a long response to immunotherapy, the tumor mutational burden (TMB) was high. Moreover, a distinct signature, in terms of network connectivity of the circulating soluble molecules, characterizing responder and non-responder patients, was evidenced. Moreover, a strong negative and statistically significant (p-value ≤ 0.05) correlation with alive status was evidenced for sE-selectin at T1. CONCLUSIONS: Our results highlighted the complexity and heterogeneity of HNSCCs, even though it was in a small cohort. Molecular and immune approaches, combined in a single profile, could represent a promising strategy, in the context of precision immunotherapy.

SPINNAKER: an R-based tool to highlight key RNA interactions in complex biological networks.

BACKGROUND: Recently, we developed a mathematical model for identifying putative competing endogenous RNA (ceRNA) interactions. This methodology has aroused a broad acknowledgment within the scientific community thanks to the encouraging results achieved when applied to breast invasive carcinoma, leading to the identification of PVT1, a long non-coding RNA functioning as ceRNA for the miR-200 family. The main shortcoming of the model is that it is no freely available and implemented in MATLAB®, a proprietary programming platform requiring a paid license for installing, operating, manipulating, and running the software. RESULTS: Breaking through these model limitations demands to distribute it in an open-source, freely accessible environment, such as R, designed for an ordinary audience of users that are not able to afford a proprietary solution. Here, we present SPINNAKER (SPongeINteractionNetworkmAKER), the open-source version of our widely established mathematical model for predicting ceRNAs crosstalk, that is released as an exhaustive collection of R functions. SPINNAKER has been even designed for providing many additional features that facilitate its usability, make it more efficient in terms of further implementation and extension, and less intense in terms of computational execution time. CONCLUSIONS: SPINNAKER source code is freely available at https://github.com/sportingCode/SPINNAKER.git together with a thoroughgoing PPT-based guideline. In order to help users get the key points more conveniently, also a practical R-styled plain-text guideline is provided. Finally, a short movie is available to help the user to set the own directory, properly.

A Comparison of Network-Based Methods for Drug Repurposing along with an Application to Human Complex Diseases.

Drug repurposing strategy, proposing a therapeutic switching of already approved drugs with known medical indications to new therapeutic purposes, has been considered as an efficient approach to unveil novel drug candidates with new pharmacological activities, significantly reducing the cost and shortening the time of de novo drug discovery. Meaningful computational approaches for drug repurposing exploit the principles of the emerging field of Network Medicine, according to which human diseases can be interpreted as local perturbations of the human interactome network, where the molecular determinants of each disease (disease genes) are not randomly scattered, but co-localized in highly interconnected subnetworks (disease modules), whose perturbation is linked to the pathophenotype manifestation. By interpreting drug effects as local perturbations of the interactome, for a drug to be on-target effective against a specific disease or to cause off-target adverse effects, its targets should be in the nearby of disease-associated genes. Here, we used the network-based proximity measure to compute the distance between the drug module and the disease module in the human interactome by exploiting five different metrics (minimum, maximum, mean, median, mode), with the aim to compare different frameworks for highlighting putative repurposable drugs to treat complex human diseases, including malignant breast and prostate neoplasms, schizophrenia, and liver cirrhosis. Whilst the standard metric (that is the minimum) for the network-based proximity remained a valid tool for efficiently screening off-label drugs, we observed that the other implemented metrics specifically predicted further interesting drug candidates worthy of investigation for yielding a potentially significant clinical benefit.

SWIMmeR: an R-based software to unveiling crucial nodes in complex biological networks.

SUMMARY: We present SWIMmeR, an open-source version of its predecessor SWIM (SWitchMiner) that is a network-based tool for mining key (switch) genes that are associated with intriguing patterns of molecular co-abundance and may play a crucial role in phenotypic transitions in various biological settings. SWIM was originally written in MATLAB®, a proprietary programming language that requires the purchase of a license to install, manipulate, operate and run the software. Over the last years, SWIM has sparked a widespread interest within the scientific community thanks to the promising results obtained through its application in a broad range of phenotype-specific scenarios, spanning from complex diseases to grapevine berry maturation. This success has created the call for it to be distributed in a freely accessible, open-source, runtime environment, such as R, aimed at a general audience of non-expert users that cannot afford the leading proprietary solution. SWIMmeR is provided as a comprehensive collection of R functions and it also includes several additional features that make it less intensive in terms of computer time and more efficient in terms of usability and further implementation and extension. AVAILABILITY AND IMPLEMENTATION: The SWIMmeR source code is freely available at https://github.com/sportingCode/SWIMmeR.git, along with a practical user guide, including a usage example of its application on breast cancer dataset. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptomics and Metabolomics Integration Reveals Redox-Dependent Metabolic Rewiring in Breast Cancer Cells.

Rewiring glucose metabolism toward aerobic glycolysis provides cancer cells with a rapid generation of pyruvate, ATP, and NADH, while pyruvate oxidation to lactate guarantees refueling of oxidized NAD+ to sustain glycolysis. CtPB2, an NADH-dependent transcriptional co-regulator, has been proposed to work as an NADH sensor, linking metabolism to epigenetic transcriptional reprogramming. By integrating metabolomics and transcriptomics in a triple-negative human breast cancer cell line, we show that genetic and pharmacological down-regulation of CtBP2 strongly reduces cell proliferation by modulating the redox balance, nucleotide synthesis, ROS generation, and scavenging. Our data highlight the critical role of NADH in controlling the oncogene-dependent crosstalk between metabolism and the epigenetically mediated transcriptional program that sustains energetic and anabolic demands in cancer cells.

The role of FOSL1 in stem-like cell reprogramming processes.

Cancer stem-like cells (CSCs) have self-renewal abilities responsible for cancer progression, therapy resistance, and metastatic growth. The glioblastoma stem-like cells are the most studied among CSC populations. A recent study identified four transcription factors (SOX2, SALL2, OLIG2, and POU3F2) as the minimal core sufficient to reprogram differentiated glioblastoma (GBM) cells into stem-like cells. Transcriptomic data of GBM tissues and cell lines from two different datasets were then analyzed by the SWItch Miner (SWIM), a network-based software, and FOSL1 was identified as a putative regulator of the previously identified minimal core. Herein, we selected NTERA-2 and HEK293T cells to perform an in vitro study to investigate the role of FOSL1 in the reprogramming mechanisms. We transfected the two cell lines with a constitutive FOSL1 cDNA plasmid. We demonstrated that FOSL1 directly regulates the four transcription factors binding their promoter regions, is involved in the deregulation of several stemness markers, and reduces the cells' ability to generate aggregates increasing the extracellular matrix component FN1. Although further experiments are necessary, our data suggest that FOSL1 reprograms the stemness by regulating the core of the four transcription factors.

An Overview of the Computational Models Dealing with the Regulatory ceRNA Mechanism and ceRNA Deregulation in Cancer.

Pools of RNA molecules can act as competing endogenous RNAs (ceRNAs) and indirectly alter their expression levels by competitively binding shared microRNAs. This ceRNA cross talk yields an additional posttranscriptional regulatory layer, which plays key roles in both physiological and pathological processes. MicroRNAs can act as decoys by binding multiple RNAs, as well as RNAs can act as ceRNAs by competing for binding multiple microRNAs, leading to many cross talk interactions that could favor significant large-scale effects in spite of the weakness of single interactions. Identifying and studying these extended ceRNA interaction networks could provide a global view of the fine-tuning gene regulation in a wide range of biological processes and tumor progressions. In this chapter, we review current progress of predicting ceRNA cross talk, by summarizing the most up-to-date databases, which collect computationally predicted and/or experimentally validated miRNA-target and ceRNA-ceRNA interactions, as well as the widespread computational methods for discovering and modeling possible evidences of ceRNA-ceRNA interaction networks. These methods can be grouped in two categories: statistics-based methods exploit multivariate analysis to build ceRNA networks, by considering the miRNA expression levels when evaluating miRNA sponging relationships; mathematical methods build deterministic or stochastic models to analyze and predict the behavior of ceRNA cross talk.

Gene network analysis using SWIM reveals interplay between the transcription factor-encoding genes HMGA1, FOXM1, and MYBL2 in triple-negative breast cancer.

Among breast cancer subtypes, triple-negative breast cancer (TNBC) is the most aggressive with the worst prognosis and the highest rates of metastatic disease. To identify TNBC gene signatures, we applied the network-based methodology implemented by the SWIM software to gene expression data of TNBC patients in The Cancer Genome Atlas (TCGA) database. SWIM enables to predict key (switch) genes within the co-expression network, whose perturbations in expression pattern and abundance may contribute to the (patho)biological phenotype. Here, SWIM analysis revealed an interesting interplay between the genes encoding the transcription factors HMGA1, FOXM1, and MYBL2, suggesting a potential cooperation among these three switch genes in TNBC development. The correlative nature of this interplay in TNBC was assessed by in vitro experiments, demonstrating how they may actually modulate the expression of each other.

Polymorphonuclear Myeloid-Derived Suppressor Cells Are Abundant in Peripheral Blood of Cancer Patients and Suppress Natural Killer Cell Anti-Tumor Activity.

Tumor microenvironment (TME) includes a wide variety of cell types and soluble factors capable of suppressing immune-responses. While the role of NK cells in TME has been analyzed, limited information is available on the presence and the effect of polymorphonuclear (PMN) myeloid-derived suppressor cells, (MDSC). Among the immunomodulatory cells present in TME, MDSC are potentially efficient in counteracting the anti-tumor activity of several effector cells. We show that PMN-MDSC are present in high numbers in the PB of patients with primary or metastatic lung tumor. Their frequency correlated with the overall survival of patients. In addition, it inversely correlated with low frequencies of NK cells both in the PB and in tumor lesions. Moreover, such NK cells displayed an impaired anti-tumor activity, even those isolated from PB. The compromised function of NK cells was consequent to their interaction with PMN-MDSC. Indeed, we show that the expression of major activating NK receptors, the NK cytolytic activity and the cytokine production were inhibited upon co-culture with PMN-MDSC through both cell-to-cell contact and soluble factors. In this context, we show that exosomes derived from PMN-MDSC are responsible of a significant immunosuppressive effect on NK cell-mediated anti-tumor activity. Our data may provide a novel useful tool to implement the tumor immunoscore. Indeed, the detection of PMN-MDSC in the PB may be of prognostic value, providing clues on the presence and extension of both adult and pediatric tumors and information on the efficacy not only of immune response but also of immunotherapy and, possibly, on the clinical outcome.

Disruption of redox homeostasis for combinatorial drug efficacy in K-Ras tumors as revealed by metabolic connectivity profiling.

BACKGROUND: Rewiring of metabolism induced by oncogenic K-Ras in cancer cells involves both glucose and glutamine utilization sustaining enhanced, unrestricted growth. The development of effective anti-cancer treatments targeting metabolism may be facilitated by the identification and rational combinatorial targeting of metabolic pathways. METHODS: We performed mass spectrometric metabolomics analysis in vitro and in vivo experiments to evaluate the efficacy of drugs and identify metabolic connectivity. RESULTS: We show that K-Ras-mutant lung and colon cancer cells exhibit a distinct metabolic rewiring, the latter being more dependent on respiration. Combined treatment with the glutaminase inhibitor CB-839 and the PI3K/aldolase inhibitor NVP-BKM120 more consistently reduces cell growth of tumor xenografts. Maximal growth inhibition correlates with the disruption of redox homeostasis, involving loss of reduced glutathione regeneration, redox cofactors, and a decreased connectivity among metabolites primarily involved in nucleic acid metabolism. CONCLUSIONS: Our findings open the way to develop metabolic connectivity profiling as a tool for a selective strategy of combined drug repositioning in precision oncology.

The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes.

Breast cancer (BC) is a heterogeneous and complex disease as witnessed by the existence of different subtypes and clinical characteristics that poses significant challenges in disease management. The complexity of this tumor may rely on the highly interconnected nature of the various biological processes as stated by the new paradigm of Network Medicine. We explored The Cancer Genome Atlas (TCGA)-BRCA data set, by applying the network-based algorithm named SWItch Miner, and mapping the findings on the human interactome to capture the molecular interconnections associated with the disease modules. To characterize BC phenotypes, we constructed protein-protein interaction modules based on "hub genes", called switch genes, both common and specific to the four tumor subtypes. Transcriptomic profiles of patients were stratified according to both clinical (immunohistochemistry) and genetic (PAM50) classifications. 266 and 372 switch genes were identified from immunohistochemistry and PAM50 classifications, respectively. Moreover, the identified switch genes were functionally characterized to select an interconnected pathway of disease genes. By intersecting the common switch genes of the two classifications, we selected a unique signature of 28 disease genes that were BC subtype-independent and classification subtype-independent. Data were validated both in vitro (10 BC cell lines) and ex vivo (66 BC tissues) experiments. Results showed that four of these hub proteins (AURKA, CDC45, ESPL1, and RAD54L) were over-expressed in all tumor subtypes. Moreover, the inhibition of one of the identified switch genes (AURKA) similarly affected all BC subtypes. In conclusion, using a network-based approach, we identified a common BC disease module which might reflect its pathological signature, suggesting a new vision to face with the disease heterogeneity.

Prostate cancer screening research can benefit from network medicine: an emerging awareness.

Up to date, screening for prostate cancer (PCa) remains one of the most appealing but also a very controversial topics in the urological community. PCa is the second most common cancer in men worldwide and it is universally acknowledged as a complex disease, with a multi-factorial etiology. The pathway of PCa diagnosis has changed dramatically in the last few years, with the multiparametric magnetic resonance (mpMRI) playing a starring role with the introduction of the "MRI Pathway". In this scenario the basic tenet of network medicine (NM) that sees the disease as perturbation of a network of interconnected molecules and pathways, seems to fit perfectly with the challenges that PCa early detection must face to advance towards a more reliable technique. Integration of tests on body fluids, tissue samples, grading/staging classification, physiological parameters, MR multiparametric imaging and molecular profiling technologies must be integrated in a broader vision of "disease" and its complexity with a focus on early signs. PCa screening research can greatly benefit from NM vision since it provides a sound interpretation of data and a common language, facilitating exchange of ideas between clinicians and data analysts for exploring new research pathways in a rational, highly reliable, and reproducible way.

Identification of Disease-miRNA Networks Across Different Cancer Types Using SWIM.

MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs) involved in several biological processes and diseases. MiRNAs regulate gene expression at the posttranscriptional level, mostly downregulating their targets by binding specific regions of transcripts through imperfect sequence complementarity. Prediction of miRNA-binding sites is challenging, and target prediction algorithms are usually based on sequence complementarity. In the last years, it has been shown that by adding miRNA and protein coding gene expression, we are able to build tissue-, cell line-, or disease-specific networks improving our understanding of complex biological scenarios. In this chapter, we present an application of a recently published software named SWIM, that allows to identify key genes in a network of interactions by defining appropriate "roles" of genes according to their local/global positioning in the overall network. Furthermore, we show how the SWIM software can be used to build miRNA-disease networks, by applying the approach to tumor data obtained from The Cancer Genome Atlas (TCGA).

BRAFV600E-mutant cancers display a variety of networks by SWIM analysis: prediction of vemurafenib clinical response.

PURPOSE: Several studies have shown that different tumour types sharing a driver gene mutation do not respond uniformly to the same targeted agent. Our aim was to use an unbiased network-based approach to investigate this fundamental issue using BRAFV600E mutant tumours and the BRAF inhibitor vemurafenib. METHODS: We applied SWIM, a software able to identify putative regulatory (switch) genes involved in drastic changes to the cell phenotype, to gene expression profiles of different BRAFV600E mutant cancers and their normal counterparts in order to identify the switch genes that could potentially explain the heterogeneity of these tumours' responses to vemurafenib. RESULTS: We identified lung adenocarcinoma as the tumour with the highest number of switch genes (298) compared to its normal counterpart. By looking for switch genes encoding for kinases with homology sequences similar to known vemurafenib targets, we found that thyroid cancer and lung adenocarcinoma have a similar number of putative targetable switch gene kinases (5 and 6, respectively) whereas colorectal cancer has just one. CONCLUSIONS: We are persuaded that our network analysis may aid in the comprehension of molecular mechanisms underlying the different responses to vemurafenib in BRAFV600E mutant tumours.