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.

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.

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.

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.

GZMKhigh CD8+ T effector memory cells are associated with CD15high neutrophil abundance in non-metastatic colorectal tumors and predict poor clinical outcome.

CD8+ T cells are a major prognostic determinant in solid tumors, including colorectal cancer (CRC). However, understanding how the interplay between different immune cells impacts on clinical outcome is still in its infancy. Here, we describe that the interaction of tumor infiltrating neutrophils expressing high levels of CD15 with CD8+ T effector memory cells (TEM) correlates with tumor progression. Mechanistically, stromal cell-derived factor-1 (CXCL12/SDF-1) promotes the retention of neutrophils within tumors, increasing the crosstalk with CD8+ T cells. As a consequence of the contact-mediated interaction with neutrophils, CD8+ T cells are skewed to produce high levels of GZMK, which in turn decreases E-cadherin on the intestinal epithelium and favors tumor progression. Overall, our results highlight the emergence of GZMKhigh CD8+ TEM in non-metastatic CRC tumors as a hallmark driven by the interaction with neutrophils, which could implement current patient stratification and be targeted by novel therapeutics.

A gene dosage-dependent effect unveils NBS1 as both a haploinsufficient tumour suppressor and an essential gene for SHH-medulloblastoma.

AIMS: Inherited or somatic mutations in the MRE11, RAD50 and NBN genes increase the incidence of tumours, including medulloblastoma (MB). On the other hand, MRE11, RAD50 and NBS1 protein components of the MRN complex are often overexpressed and sometimes essential in cancer. In order to solve the apparent conundrum about the oncosuppressive or oncopromoting role of the MRN complex, we explored the functions of NBS1 in an MB-prone animal model. MATERIALS AND METHODS: We generated and analysed the monoallelic or biallelic deletion of the Nbn gene in the context of the SmoA1 transgenic mouse, a Sonic Hedgehog (SHH)-dependent MB-prone animal model. We used normal and tumour tissues from these animal models, primary granule cell progenitors (GCPs) from genetically modified animals and NBS1-depleted primary MB cells, to uncover the effects of NBS1 depletion by RNA-Seq, by biochemical characterisation of the SHH pathway and the DNA damage response (DDR) as well as on the growth and clonogenic properties of GCPs. RESULTS: We found that monoallelic Nbn deletion increases SmoA1-dependent MB incidence. In addition to a defective DDR, Nbn+/- GCPs show increased clonogenicity compared to Nbn+/+ GCPs, dependent on an enhanced Notch signalling. In contrast, full NbnKO impairs MB development both in SmoA1 mice and in an SHH-driven tumour allograft. CONCLUSIONS: Our study indicates that Nbn is haploinsufficient for SHH-MB development whereas full NbnKO is epistatic on SHH-driven MB development, thus revealing a gene dosage-dependent effect of Nbn inactivation on SHH-MB development.

An integrative in-silico analysis discloses a novel molecular subset of colorectal cancer possibly eligible for immune checkpoint immunotherapy.

BACKGROUND: Historically, the molecular classification of colorectal cancer (CRC) was based on the global genomic status, which identified microsatellite instability in mismatch repair (MMR) deficient CRC, and chromosomal instability in MMR proficient CRC. With the introduction of immune checkpoint inhibitors, the microsatellite and chromosomal instability classification regained momentum as the microsatellite instability condition predicted sensitivity to immune checkpoint inhibitors, possibly due to both high tumor mutation burden (TMB) and high levels of infiltrating lymphocytes. Conversely, proficient MMR CRC are mostly resistant to immunotherapy. To better understand the relationship between the microsatellite and chromosomal instability classification, and eventually discover additional CRC subgroups relevant for therapeutic decisions, we developed a computational pipeline that include molecular integrative analysis of genomic, epigenomic and transcriptomic data. RESULTS: The first step of the pipeline was based on unsupervised hierarchical clustering analysis of copy number variations (CNVs) versus hypermutation status that identified a first CRC cluster with few CNVs enriched in Hypermutated and microsatellite instability samples, a second CRC cluster with a high number of CNVs mostly including non-HM and microsatellite stable samples, and a third cluster (7.8% of the entire dataset) with low CNVs and low TMB, which shared clinical-pathological features with Hypermutated CRCs and thus defined Hypermutated-like CRCs. The mutational features, DNA methylation profile and base substitution fingerprints of these tumors revealed that Hypermutated-like patients are molecularly distinct from Hypermutated and non-Hypermutated tumors and are likely to develop and progress through different genetic events. Transcriptomic analysis highlighted further differences amongst the three groups and revealed an inflamed tumor microenvironment and modulation Immune Checkpoint Genes in Hypermutated-like CRCs. CONCLUSION: Therefore, our work highlights Hypermutated-like tumors as a distinct and previously unidentified CRC subgroup possibly responsive to immune checkpoint inhibitors. If further validated, these findings can lead to expanding the fraction of patients eligible to 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.

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.

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.

Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality.

BACKGROUND: Prostate magnetic resonance imaging (MRI) is technically demanding, requiring high image quality to reach its full diagnostic potential. An automated method to identify diagnostically inadequate images could help optimize image quality. PURPOSE: To develop a convolutional neural networks (CNNs) based analysis pipeline for the classification of prostate MRI image quality. STUDY TYPE: Retrospective. SUBJECTS: Three hundred sixteen prostate mpMRI scans and 312 men (median age 67). FIELD STRENGTH/SEQUENCE: A 3 T; fast spin echo T2WI, echo planar imaging DWI, ADC, gradient-echo dynamic contrast enhanced (DCE). ASSESSMENT: MRI scans were reviewed by three genitourinary radiologists (V.P., M.D.M., S.C.) with 21, 12, and 5 years of experience, respectively. Sequences were labeled as high quality (Q1) or low quality (Q0) and used as the reference standard for all analyses. STATISTICAL TESTS: Sequences were split into training, validation, and testing sets (869, 250, and 120 sequences, respectively). Inter-reader agreement was assessed with the Fleiss kappa. Following preprocessing and data augmentation, 28 CNNs were trained on MRI slices for each sequence. Model performance was assessed on both a per-slice and a per-sequence basis. A pairwise t-test was performed to compare performances of the classifiers. RESULTS: The number of sequences labeled as Q0 or Q1 was 38 vs. 278 for T2WI, 43 vs. 273 for DWI, 41 vs. 275 for ADC, and 38 vs. 253 for DCE. Inter-reader agreement was almost perfect for T2WI and DCE and substantial for DWI and ADC. On the per-slice analysis, accuracy was 89.95% ± 0.02% for T2WI, 79.83% ± 0.04% for DWI, 76.64% ± 0.04% for ADC, 96.62% ± 0.01% for DCE. On the per-sequence analysis, accuracy was 100% ± 0.00% for T2WI, DWI, and DCE, and 92.31% ± 0.00% for ADC. The three best algorithms performed significantly better than the remaining ones on every sequence (P-value < 0.05). DATA CONCLUSION: CNNs achieved high accuracy in classifying prostate MRI image quality on an individual-slice basis and almost perfect accuracy when classifying the entire sequences. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

Mining sponge phenomena in RNA expression data.

In the last few years, the interactions among competing endogenous RNAs (ceRNAs) have been recognized as a key post-transcriptional regulatory mechanism in cell differentiation, tissue development, and disease. Notably, such sponge phenomena substracting active microRNAs from their silencing targets have been recognized as having a potential oncosuppressive, or oncogenic, role in several cancer types. Hence, the ability to predict sponges from the analysis of large expression data sets (e.g. from international cancer projects) has become an important data mining task in bioinformatics. We present a technique designed to mine sponge phenomena whose presence or absence may discriminate between healthy and unhealthy populations of samples in tumoral or normal expression data sets, thus providing lists of candidates potentially relevant in the pathology. With this aim, we search for pairs of elements acting as ceRNA for a given miRNA, namely, we aim at discovering miRNA-RNA pairs involved in phenomena which are clearly present in one population and almost absent in the other one. The results on tumoral expression data, concerning five different cancer types, confirmed the effectiveness of the approach in mining interesting knowledge. Indeed, 32 out of 33 miRNAs and 22 out of 25 protein-coding genes identified as top scoring in our analysis are corroborated by having been similarly associated with cancer processes in independent studies. In fact, the subset of miRNAs selected by the sponge analysis results in a significant enrichment of annotation for the KEGG32 pathway "microRNAs in cancer" when tested with the commonly used bioinformatic resource DAVID. Moreover, often the cancer datasets where our sponge analysis identified a miRNA as top scoring match the one reported already in the pertaining literature.

High prevalence of asymptomatic SARS-CoV-2 infection in a cohort of liver transplant recipients in central Italy.

Asymptomatic subjects account for 25 to 45% of SARS-CoV-2 infections, and in particular, subjects on mild immunosuppressive therapy may have symptoms masked and could spread virus for an extended period of time. To determine the cumulative incidence of symptomatic and asymptomatic SARS-CoV-2 infections and associated risk factors, we conducted a prospective clinical and serological survey in a cohort of 278 liver transplant recipients (LTRs) from Central Italy. Three different serology tests were performed every 4 months in 259 LTRs between April 2020 and April 2021: one based on raw extract of whole SARS-CoV-2 virus and two on specific viral antigens (nucleoprotein and receptor binding domain) to detect specific IgG, IgM and IgA. Hundred fifteen LTRs who reported symptoms or close contact with a SARS-CoV-2-positive subject, or had a positive serological result underwent molecular testing by standard screening procedures (RT-PCR on naso-pharyngeal swab). Thirty-one past or active SARS-CoV-2 infections were identified: 14 had positive molecular test (64% symptomatic), and 17 had positive serology only (18% symptomatic). SARS-CoV-2 infection was not statistically related to gender, age, obesity, diabetes, renal impairment, type of anti-rejection therapy or time from transplant. Asymptomatic SARS-CoV-2 cases (61.3%) were more frequent in males and in those with glomerular filtrate rate >50 ml/min. Overall, the addition of repeated serology to standard diagnostic molecular protocols increased detection of SARS-CoV-2 infection from 5.1% to 10.9%. Anti-SARS-CoV-2 seroprevalence among our LTRs (11.2%) is comparable to the general population of Central Italy, considered a medium-impact area. Only one asymptomatic subject (6%) was found to carry SARS-CoV-2 in respiratory tract at the time of serological diagnosis.

Polymorphonuclear myeloid-derived suppressor cells impair the anti-tumor efficacy of GD2.CAR T-cells in patients with neuroblastoma.

The outcome of patients affected by high-risk or metastatic neuroblastoma (NB) remains grim, with ≥ 50% of the children experiencing relapse or progression of the disease despite multimodal, intensive treatment. In order to identify new strategies to improve the overall survival and the quality of life of these children, we recently developed and optimized a third-generation GD2-specific chimeric antigen receptor (CAR) construct, which is currently under evaluation in our Institution in a phase I/II clinical trial (NCT03373097) enrolling patients with relapsed/refractory NB. We observed that our CAR T-cells are able to induce marked tumor reduction and even achieve complete remission with a higher efficiency than that of other CAR T-cells reported in previous studies. However, often responses are not sustained and relapses occur. Here, we demonstrate for the first time a mechanism of resistance to GD2.CAR T-cell treatment, showing how polymorphonuclear myeloid-derived suppressor cells (PMN-MDSC) increase in the peripheral blood (PB) of NB patients after GD2.CAR T-cell treatment in case of relapse and loss of response. In vitro, isolated PMN-MDSC demonstrate to inhibit the anti-tumor cytotoxicity of different generations of GD2.CAR T-cells. Gene-expression profiling of GD2.CAR T-cells "conditioned" with PMN-MDSC shows downregulation of genes involved in cell activation, signal transduction, inflammation and cytokine/chemokine secretion. Analysis of NB gene-expression dataset confirms a correlation between expression of these genes and patient outcome. Moreover, in patients treated with GD2.CAR T-cells, the frequency of circulating PMN-MDSC inversely correlates with the levels of GD2.CAR T-cells, resulting more elevated in patients who did not respond or lost response to the treatment. The presence and the frequency of PMN-MDSC in PB of high-risk and metastatic NB represents a useful prognostic marker to predict the response to GD2.CAR T-cells and other adoptive immunotherapy. This study underlines the importance of further optimization of both CAR T-cells and clinical trial in order to target elements of the tumor microenvironment.

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.

SEaCorAl: Identifying and contrasting the regulation-correlation bias in RNA-Seq paired expression data of patient groups.

The Cancer Genome Atlas database offers the possibility of analyzing genome-wide expression RNA-Seq cancer data using paired counts, that is, studies where expression data are collected in pairs of normal and cancer cells, by taking samples from the same individual. Correlation of gene expression profiles is the most common analysis to study co-expression groups, which is used to find biological interpretation of -omics big data. The aim of the paper is threefold: firstly we show for the first time, the presence of a "regulation-correlation bias" in RNA-Seq paired expression data, that is an artifactual link between the expression status (up- or down-regulation) of a gene pair and the sign of the corresponding correlation coefficient. Secondly, we provide a statistical model able to theoretically explain the reasons for the presence of such a bias. Thirdly, we present a bias-removal algorithm, called SEaCorAl, able to effectively reduce bias effects and improve the biological significance of correlation analysis. Validation of the SEaCorAl algorithm is performed by showing a significant increase in the ability to detect biologically meaningful associations of positive correlations and a significant increase of the modularity of the resulting unbiased correlation network.