Tumor Budding, p53, and DNA Mismatch Repair Markers in Sinonasal Intestinal-Type Adenocarcinoma: A Retrospective Study Supports the Adverse Prognostic Impact of Tumor Budding.

Sinonasal intestinal-type adenocarcinoma (ITAC) is a very rare, closely occupational-related tumor with strong histological similarities to colorectal cancer (CRC). In the latter, tumor budding (TB) is widely recognized as a negative prognostic parameter. The aim of this study was to evaluate the prognostic role of TB in ITAC and to correlate it with other established or emerging biomarkers of the disease, such as p53 and deficient DNA mismatch repair (MMR) system status/microsatellite instability (MSI). We retrospectively analyzed 32 consecutive specimens of patients with ITAC diagnosis treated in two institutions in Northern Italy. We reviewed surgical specimens for TB evaluation (low-intermediate/high); p53 expression and MMR proteins were evaluated via immunohistochemistry. Results were retrospectively stratified using clinical data and patients' outcomes. According to bud counts, patients were stratified into two groups: intermediate/high budding (>4 TB) and low budding (≤4 TB). Patients with high TB (>4) have an increased risk of recurrence and death compared to those with low TB, with a median survival of 13 and 54 months, respectively. On multivariate analysis, considering TB, therapy, and stage as covariates, TB emerged as an independent prognostic factor net of the stage of disease or type of therapy received. No impact of p53 status as a biomarker of prognosis was observed and no alterations regarding MMR proteins were identified. The results of the present work provide further significant evidence on the prognostic role of TB in ITAC and underline the need for larger multicenter studies to implement the use of TB in clinical practice.

Prospective Validation Study of a Novel Integrated Pathway Based on Clinical Features, Magnetic Resonance Imaging Biomarkers, and MicroRNAs for Early Detection of Prostate Cancer.

BACKGROUND: Prostate cancer (PCa) is the most diagnosed cancer in men, with an increasing need to integrate noninvasive imaging and circulating microRNAs beyond prostate-specific antigen for screening and early detection. OBJECTIVE: To validate magnetic resonance imaging (MRI) biomarkers and circulating microRNAs as triage tests for patients directed to prostate biopsy, and to test different diagnostic pathways to compare their performance on patients' outcome, in terms of unnecessary biopsy avoidance. DESIGN, SETTING, AND PARTICIPANTS: A prospective single-center cohort study, enrolling patients with PCa suspicion who underwent MRI, MRI-directed fusion biopsy (MRDB), and circulating microRNAs, was conducted. A network-based analysis was used to identify MRI biomarkers and microRNA drivers of clinically significant PCa. INTERVENTION: MRI, MRDB, and blood sampling. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The decision curve analysis was exploited to assess the performance of the proposed diagnostic pathways and to quantify their benefit in terms of biopsy avoidance. RESULTS AND LIMITATIONS: Overall, 261 men were enrolled and underwent MRDB for PCa detection. A total of 178 patients represented the entire cohort: 55 (30.9%) were negative for PCa, 39 (21.9%) had grade group (GG) 1 PCa, and 84 (47.2%) had GG >1 PCa. The proposed integrated pathway, including clinical data, MRI biomarkers, and microRNAs, provided the best net benefit with a biopsy avoidance rate of about 20% at a low disease probability. The main limitation is the monocentric design in a referral center. CONCLUSIONS: The integrated pathway represents a validated model that sees MRI biomarkers and microRNAs as a prebiopsy triage of patients at a risk for clinically significant PCa. The proposed pathway showed the highest net benefit in terms of unnecessary biopsy avoidance. PATIENT SUMMARY: The proposed integrated pathway for early detection of prostate cancer (PCa) allows accurate patient allocation to biopsy and patients' stratification into risk group categories, reducing overdiagnosis and overtreatment of clinically insignificant PCa.

Linoleic acid potentiates CD8+ T cell metabolic fitness and antitumor immunity.

The metabolic state represents a major hurdle for an effective adoptive T cell therapy (ACT). Indeed, specific lipids can harm CD8+ T cell (CTL) mitochondrial integrity, leading to defective antitumor responses. However, the extent to which lipids can affect the CTL functions and fate remains unexplored. Here, we show that linoleic acid (LA) is a major positive regulator of CTL activity by improving metabolic fitness, preventing exhaustion, and stimulating a memory-like phenotype with superior effector functions. We report that LA treatment enhances the formation of ER-mitochondria contacts (MERC), which in turn promotes calcium (Ca2+) signaling, mitochondrial energetics, and CTL effector functions. As a direct consequence, the antitumor potency of LA-instructed CD8 T cells is superior in vitro and in vivo. We thus propose LA treatment as an ACT potentiator in tumor therapy.

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.

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.

In silico recognition of a prognostic signature in basal-like breast cancer patients.

BACKGROUND: Triple-negative breast cancers (TNBCs) display poor prognosis, have a high risk of tumour recurrence, and exhibit high resistance to drug treatments. Based on their gene expression profiles, the majority of TNBCs are classified as basal-like breast cancers. Currently, there are not available widely-accepted prognostic markers to predict outcomes in basal-like subtype, so the selection of new prognostic indicators for this BC phenotype represents an unmet clinical challenge. RESULTS: Here, we attempted to address this challenging issue by exploiting a bioinformatics pipeline able to integrate transcriptomic, genomic, epigenomic, and clinical data freely accessible from public repositories. This pipeline starts from the application of the well-established network-based SWIM methodology on the transcriptomic data to unveil important (switch) genes in relation with a complex disease of interest. Then, survival and linear regression analyses are performed to associate the gene expression profiles of the switch genes with both the patients' clinical outcome and the disease aggressiveness. This allows us to identify a prognostic gene signature that in turn is fed to the last step of the pipeline consisting of an analysis at DNA level, to investigate whether variations in the expression of identified prognostic switch genes could be related to genetic (copy number variations) or epigenetic (DNA methylation differences) alterations in their gene loci, or to the activities of transcription factors binding to their promoter regions. Finally, changes in the protein expression levels corresponding to the so far identified prognostic switch genes are evaluated by immunohistochemical staining results taking advantage of the Human Protein Atlas. CONCLUSION: The application of the proposed pipeline on the dataset of The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) patients affected by basal-like subtype led to an in silico recognition of a basal-like specific gene signature composed of 11 potential prognostic biomarkers to be further investigated.

Dynamic tracing of sugar metabolism reveals the mechanisms of action of synthetic sugar analogs.

Synthetic sugar analogs are widely applied in metabolic oligosaccharide engineering (MOE) and as novel drugs to interfere with glycoconjugate biosynthesis. However, mechanistic insights on their exact cellular metabolism over time are mostly lacking. We combined ion-pair ultrahigh performance liquid chromatography-triple quadrupole mass spectrometry mass spectrometry using tributyl- and triethylamine buffers for sensitive analysis of sugar metabolites in cells and organisms and identified low abundant nucleotide sugars, such as UDP-arabinose in human cell lines and CMP-sialic acid (CMP-NeuNAc) in Drosophila. Furthermore, MOE revealed that propargyloxycarbonyl (Poc)-labeled ManNPoc was metabolized to both CMP-NeuNPoc and UDP-GlcNPoc. Finally, time-course analysis of the effect of antitumor compound 3Fax-NeuNAc by incubation of B16-F10 melanoma cells with N-acetyl-D-[UL-13C6]glucosamine revealed full depletion of endogenous ManNAc 6-phosphate and CMP-NeuNAc within 24 h. Thus, dynamic tracing of sugar metabolic pathways provides a general approach to reveal time-dependent insights into the metabolism of synthetic sugars, which is important for the rational design of analogs with optimized effects.

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.

Network Analysis Integrating microRNA Expression Profiling with MRI Biomarkers and Clinical Data for Prostate Cancer Early Detection: A Proof of Concept Study.

The MRI of the prostate is the gold standard for the detection of clinically significant prostate cancer (csPCa). Nonetheless, MRI still misses around 11% of clinically significant disease. The aim was to comprehensively integrate tissue and circulating microRNA profiling, MRI biomarkers and clinical data to implement PCa early detection. In this prospective cohort study, 76 biopsy naïve patients underwent MRI and MRI directed biopsy. A sentinel sample of 15 patients was selected for a pilot molecular analysis. Weighted gene coexpression network analysis was applied to identify the microRNAs drivers of csPCa. MicroRNA-target gene interaction maps were constructed, and enrichment analysis performed. The ANOVA on ranks test and ROC analysis were performed for statistics. Disease status was associated with the underexpression of the miRNA profiled; a correlation was found with ADC (r = -0.51, p = 0.02) and normalized ADC values (r = -0.64, p = 0.002). The overexpression of miRNAs from plasma was associated with csPCa (r = 0.72; p = 0.02), and with PI-RADS assessment score (r = 0.73; p = 0.02); a linear correlation was found with biomarkers of diffusion and perfusion. Among the 800 profiled microRNA, eleven were identified as correlating with PCa, among which miR-548a-3p, miR-138-5p and miR-520d-3p were confirmed using the RT-qPCR approach on an additional cohort of ten subjects. ROC analysis showed an accuracy of >90%. Provided an additional validation set of the identified miRNAs on a larger cohort, we propose a diagnostic paradigm shift that sees molecular data and MRI biomarkers as the prebiopsy triage of patients at risk for PCa. This approach will allow for accurate patient allocation to biopsy, and for stratification into risk group categories, reducing overdiagnosis and overtreatment.

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.

Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked Biomarkers in Patients Affected by Non-Small Cell Lung Cancer.

Several studies in recent times have linked gut microbiome (GM) diversity to the pathogenesis of cancer and its role in disease progression through immune response, inflammation and metabolism modulation. This study focused on the use of network analysis and weighted gene co-expression network analysis (WGCNA) to identify the biological interaction between the gut ecosystem and its metabolites that could impact the immunotherapy response in non-small cell lung cancer (NSCLC) patients undergoing second-line treatment with anti-PD1. Metabolomic data were merged with operational taxonomic units (OTUs) from 16S RNA-targeted metagenomics and classified by chemometric models. The traits considered for the analyses were: (i) condition: disease or control (CTRLs), and (ii) treatment: responder (R) or non-responder (NR). Network analysis indicated that indole and its derivatives, aldehydes and alcohols could play a signaling role in GM functionality. WGCNA generated, instead, strong correlations between short-chain fatty acids (SCFAs) and a healthy GM. Furthermore, commensal bacteria such as Akkermansia muciniphila, Rikenellaceae, Bacteroides, Peptostreptococcaceae, Mogibacteriaceae and Clostridiaceae were found to be more abundant in CTRLs than in NSCLC patients. Our preliminary study demonstrates that the discovery of microbiota-linked biomarkers could provide an indication on the road towards personalized management of NSCLC patients.

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.

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.

SWIM tool application to expression data of glioblastoma stem-like cell lines, corresponding primary tumors and conventional glioma cell lines.

BACKGROUND: It is well-known that glioblastoma contains self-renewing, stem-like subpopulation with the ability to sustain tumor growth. These cells - called cancer stem-like cells - share certain phenotypic characteristics with untransformed stem cells and are resistant to many conventional cancer therapies, which might explain the limitations in curing human malignancies. Thus, the identification of genes controlling the differentiation of these stem-like cells is becoming a successful therapeutic strategy, owing to the promise of novel targets for treating malignancies. METHODS: Recently, we developed SWIM, a software able to unveil a small pool of genes - called switch genes - critically associated with drastic changes in cell phenotype. Here, we applied SWIM to the expression profiling of glioblastoma stem-like cells and conventional glioma cell lines, in order to identify switch genes related to stem-like phenotype. RESULTS: SWIM identifies 171 switch genes that are all down-regulated in glioblastoma stem-like cells. This list encompasses genes like CAV1, COL5A1, COL6A3, FLNB, HMMR, ITGA3, ITGA5, MET, SDC1, THBS1, and VEGFC, involved in "ECM-receptor interaction" and "focal adhesion" pathways. The inhibition of switch genes highly correlates with the activation of genes related to neural development and differentiation, such as the 4-core OLIG2, POU3F2, SALL2, SOX2, whose induction has been shown to be sufficient to reprogram differentiated glioblastoma into stem-like cells. Among switch genes, the transcription factor FOSL1 appears as the brightest star since: it is down-regulated in stem-like cells; it highly negatively correlates with the 4-core genes that are all up-regulated in stem-like cells; the promoter regions of the 4-core genes harbor a consensus binding motif for FOSL1. CONCLUSIONS: We suggest that the inhibition of switch genes in stem-like cells could induce the deregulation of cell communication pathways, contributing to neoplastic progression and tumor invasiveness. Conversely, their activation could restore the physiological equilibrium between cell adhesion and migration, hampering the progression of cancer. Moreover, we posit FOSL1 as promising candidate to orchestrate the differentiation of cancer stem-like cells by repressing the 4-core genes' expression, which severely halts cancer growth and might affect the therapeutic outcome. We suggest FOSL1 as novel putative therapeutic and prognostic biomarker, worthy of further investigation.

Interplay Between Long Noncoding RNAs and MicroRNAs in Cancer.

In the last decade noncoding RNAs (ncRNAs) have been extensively studied in several biological processes and human diseases including cancer. microRNAs (miRNAs) are the best-known class of ncRNAs. miRNAs are small ncRNAs of around 20-22 nucleotides (nt) and are crucial posttranscriptional regulators of protein coding genes. Recently, new classes of ncRNAs, longer than miRNAs have been discovered. Those include intergenic noncoding RNAs (lincRNAs) and circular RNAs (circRNAs). These novel types of ncRNAs opened a very exciting field in biology, leading researchers to discover new relationships between miRNAs and long noncoding RNAs (lncRNAs), which act together to control protein coding gene expression. One of these new discoveries led to the formulation of the "competing endogenous RNA (ceRNA) hypothesis." This hypothesis suggests that an lncRNA acts as a sponge for miRNAs reducing their expression and causing the upregulation of miRNA targets. In this chapter we first discuss some recent discoveries in this field showing the mutual regulation of miRNAs, lncRNAs, and protein-coding genes in cancer. We then discuss the general approaches for the study of ceRNAs and present in more detail a recent computational approach to explore the ability of lncRNAs to act as ceRNAs in human breast cancer that has been shown to be, among the others, the most precise and promising.

Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine.

Network medicine relies on different types of networks: from the molecular level of protein⁻protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein⁻protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs-including long non-coding RNAs (lncRNAs) -competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes-called switch genes-critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.