MOSAIC: An Artificial Intelligence-Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers.

PURPOSE: Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innovative treatment strategies. We have created and implemented MOSAIC, an artificial intelligence (AI)-based framework designed for multimodal analysis, classification, and personalized prognostic assessment in rare cancers. Clinical validation was performed on myelodysplastic syndrome (MDS), a rare hematologic cancer with clinical and genomic heterogeneities. METHODS: We analyzed 4,427 patients with MDS divided into training and validation cohorts. Deep learning methods were applied to integrate and impute clinical/genomic features. Clustering was performed by combining Uniform Manifold Approximation and Projection for Dimension Reduction + Hierarchical Density-Based Spatial Clustering of Applications with Noise (UMAP + HDBSCAN) methods, compared with the conventional Hierarchical Dirichlet Process (HDP). Linear and AI-based nonlinear approaches were compared for survival prediction. Explainable AI (Shapley Additive Explanations approach [SHAP]) and federated learning were used to improve the interpretation and the performance of the clinical models, integrating them into distributed infrastructure. RESULTS: UMAP + HDBSCAN clustering obtained a more granular patient stratification, achieving a higher average silhouette coefficient (0.16) with respect to HDP (0.01) and higher balanced accuracy in cluster classification by Random Forest (92.7% ± 1.3% and 85.8% ± 0.8%). AI methods for survival prediction outperform conventional statistical techniques and the reference prognostic tool for MDS. Nonlinear Gradient Boosting Survival stands in the internal (Concordance-Index [C-Index], 0.77; SD, 0.01) and external validation (C-Index, 0.74; SD, 0.02). SHAP analysis revealed that similar features drove patients' subgroups and outcomes in both training and validation cohorts. Federated implementation improved the accuracy of developed models. CONCLUSION: MOSAIC provides an explainable and robust framework to optimize classification and prognostic assessment of rare cancers. AI-based approaches demonstrated superior accuracy in capturing genomic similarities and providing individual prognostic information compared with conventional statistical methods. Its federated implementation ensures broad clinical application, guaranteeing high performance and data protection.

MUSE-XAE: MUtational Signature Extraction with eXplainable AutoEncoder enhances tumour types classification.

MOTIVATION: Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource to understand the genomic changes during tumorigenesis. Therefore, it is essential to employ precise and accurate methods for their extraction to ensure that the underlying patterns are reliably identified and can be effectively utilized in new strategies for diagnosis, prognosis, and treatment of cancer patients. RESULTS: We present MUSE-XAE, a novel method for mutational signature extraction from cancer genomes using an explainable autoencoder. Our approach employs a hybrid architecture consisting of a nonlinear encoder that can capture nonlinear interactions among features, and a linear decoder which ensures the interpretability of the active signatures. We evaluated and compared MUSE-XAE with other available tools on both synthetic and real cancer datasets and demonstrated that it achieves superior performance in terms of precision and sensitivity in recovering mutational signature profiles. MUSE-XAE extracts highly discriminative mutational signature profiles by enhancing the classification of primary tumour types and subtypes in real world settings. This approach could facilitate further research in this area, with neural networks playing a critical role in advancing our understanding of cancer genomics. AVAILABILITY AND IMPLEMENTATION: MUSE-XAE software is freely available at https://github.com/compbiomed-unito/MUSE-XAE.

Serum ferritin levels can predict long-term outcomes in patients with metabolic dysfunction-associated steatotic liver disease.

OBJECTIVE: Hyperferritinaemia is associated with liver fibrosis severity in patients with metabolic dysfunction-associated steatotic liver disease (MASLD), but the longitudinal implications have not been thoroughly investigated. We assessed the role of serum ferritin in predicting long-term outcomes or death. DESIGN: We evaluated the relationship between baseline serum ferritin and longitudinal events in a multicentre cohort of 1342 patients. Four survival models considering ferritin with confounders or non-invasive scoring systems were applied with repeated five-fold cross-validation schema. Prediction performance was evaluated in terms of Harrell's C-index and its improvement by including ferritin as a covariate. RESULTS: Median follow-up time was 96 months. Liver-related events occurred in 7.7%, hepatocellular carcinoma in 1.9%, cardiovascular events in 10.9%, extrahepatic cancers in 8.3% and all-cause mortality in 5.8%. Hyperferritinaemia was associated with a 50% increased risk of liver-related events and 27% of all-cause mortality. A stepwise increase in baseline ferritin thresholds was associated with a statistical increase in C-index, ranging between 0.02 (lasso-penalised Cox regression) and 0.03 (ridge-penalised Cox regression); the risk of developing liver-related events mainly increased from threshold 215.5 µg/L (median HR=1.71 and C-index=0.71) and the risk of overall mortality from threshold 272 µg/L (median HR=1.49 and C-index=0.70). The inclusion of serum ferritin thresholds (215.5 µg/L and 272 µg/L) in predictive models increased the performance of Fibrosis-4 and Non-Alcoholic Fatty Liver Disease Fibrosis Score in the longitudinal risk assessment of liver-related events (C-indices>0.71) and overall mortality (C-indices>0.65). CONCLUSIONS: This study supports the potential use of serum ferritin values for predicting the long-term prognosis of patients with MASLD.

A prospective, multicenter, three-cohort study evaluating contrast-induced acute kidney injury (CI-AKI) in patients with cirrhosis.

BACKGROUND & AIMS: Nephrotoxicity of intravenous iodinated contrast media (ICM) in cirrhosis is still a debated issue, due to scarce, low-quality and conflicting evidence. This study aims to evaluate the incidence and predisposing factors of acute kidney injury (AKI) in patients with cirrhosis undergoing contrast-enhanced computed tomography (CECT). METHODS: We performed a prospective, multicenter, cohort study including 444 inpatients, 148 with cirrhosis (cohort 1) and 163 without cirrhosis (cohort 3) undergoing CECT and 133 with cirrhosis (cohort 2) unexposed to ICM. Kidney function parameters were assessed at T0, 48-72 h (T1), 5 and 7 days after CECT/enrollment. Urinary neutrophil gelatinase-associated lipocalin (U-NGAL) was measured in 50 consecutive patients from cohort 1 and 50 from cohort 2 as an early biomarker of tubular damage. RESULTS: AKI incidence was not significantly increased in patients with cirrhosis undergoing CECT (4.8%, 1.5%, 2.5% in cohorts 1, 2, 3 respectively, p = n.s.). Most AKI cases were mild and transient. The presence of concomitant infections was the only independent predictive factor of contrast-induced AKI (odds ratio 22.18; 95% CI 2.87-171.22; p = 0.003). No significant modifications of U-NGAL between T0 and T1 were detected, neither in cohort 1 nor in cohort 2 (median ΔU-NGAL: +0.2 [-7.6 to +5.5] ng/ml, +0.0 [-6.8 to +9.5] ng/ml, respectively [p = 0.682]). CONCLUSIONS: AKI risk after CECT in cirrhosis is low and not significantly different from that of the general population or of the cirrhotic population unexposed to ICM. It mostly consists of mild and rapidly resolving episodes of renal dysfunction and it is not associated with tubular kidney injury. Patients with ongoing infections appear to be the only ones at higher risk of AKI. IMPACT AND IMPLICATIONS: Nephrotoxicity due to intravenous iodinated contrast media (ICM) in patients with cirrhosis is still a debated issue, as the available evidence is limited and based on very heterogeneous studies, often conducted on small and retrospective cohorts. In this prospective three-cohort study we found that intravenous administration of ICM was associated with a low risk of AKI, similar to that of the general population and to that of patients with cirrhosis unexposed to ICM. Patients with ongoing infections were the only ones to have a significantly increased risk of contrast-induced AKI. Therefore, the actual recommendations of performing contrast imaging studies cautiously in cirrhosis do not seem to be reasonable anymore, with the exception of infected patients, who have a significantly higher risk of contrast-induced AKI.

Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma.

Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of children worldwide, and information about its prognosis can be pivotal for patients, their families, and clinicians. One of the main goals in the related bioinformatics analyses is to provide stable genetic signatures able to include genes whose expression levels can be effective to predict the prognosis of the patients. In this study, we collected the prognostic signatures for neuroblastoma published in the biomedical literature, and noticed that the most frequent genes present among them were three: AHCY, DPYLS3, and NME1. We therefore investigated the prognostic power of these three genes by performing a survival analysis and a binary classification on multiple gene expression datasets of different groups of patients diagnosed with neuroblastoma. Finally, we discussed the main studies in the literature associating these three genes with neuroblastoma. Our results, in each of these three steps of validation, confirm the prognostic capability of AHCY, DPYLS3, and NME1, and highlight their key role in neuroblastoma prognosis. Our results can have an impact on neuroblastoma genetics research: biologists and medical researchers can pay more attention to the regulation and expression of these three genes in patients having neuroblastoma, and therefore can develop better cures and treatments which can save patients' lives.

Modelling socioeconomic position as a driver of the exposome in the first 18 months of life of the NINFEA birth cohort children.

BACKGROUND: The exposome drivers are less studied than its consequences but may be crucial in identifying population subgroups with unfavourable exposures. OBJECTIVES: We used three approaches to study the socioeconomic position (SEP) as a driver of the early-life exposome in Turin children of the NINFEA cohort (Italy). METHODS: Forty-two environmental exposures, collected at 18 months of age (N = 1989), were classified in 5 groups (lifestyle, diet, meteoclimatic, traffic-related, built environment). We performed cluster analysis to identify subjects sharing similar exposures, and intra-exposome-group Principal Component Analysis (PCA) to reduce the dimensionality. SEP at childbirth was measured through the Equivalised Household Income Indicator. SEP-exposome association was evaluated using: 1) an Exposome Wide Association Study (ExWAS), a one-exposure (SEP) one-outcome (exposome) approach; 2) multinomial regression of cluster membership on SEP; 3) regressions of each intra-exposome-group PC on SEP. RESULTS: In the ExWAS, medium/low SEP children were more exposed to greenness, pet ownership, passive smoking, TV screen and sugar; less exposed to NO2, NOX, PM25abs, humidity, built environment, traffic load, unhealthy food facilities, fruit, vegetables, eggs, grain products, and childcare than high SEP children. Medium/low SEP children were more likely to belong to a cluster with poor diet, less air pollution, and to live in the suburbs than high SEP children. Medium/low SEP children were more exposed to lifestyle PC1 (unhealthy lifestyle) and diet PC2 (unhealthy diet), and less exposed to PC1s of the built environment (urbanization factors), diet (mixed diet), and traffic (air pollution) than high SEP children. CONCLUSIONS: The three approaches provided consistent and complementary results, suggesting that children with lower SEP are less exposed to urbanization factors and more exposed to unhealthy lifestyles and diet. The simplest method, the ExWAS, conveys most of the information and is more replicable in other populations. Clustering and PCA may facilitate results interpretation and communication.

Necroptosis-driving genes RIPK1, RIPK3 and MLKL-p are associated with intratumoral CD3+ and CD8+ T cell density and predict prognosis in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly lethal cancer and the second leading cause of cancer-related deaths worldwide. As demonstrated in other solid neoplasms and HCC, infiltrating CD8+ T cells seem to be related to a better prognosis, but the mechanisms affecting the immune landscape in HCC are still mostly unknown. Necroptosis is a programmed, caspase-independent cell death that, unlike apoptosis, evokes immune response by releasing damage-associated molecular factors. However, in HCC, the relationship between the necroptotic machinery and the tumor-infiltrating lymphocytes has not been fully investigated so far. METHODS: We investigated the association between the main necroptosis-related genes, that is, RIPK1, RIPK3, MLKL-p, and CD3+/CD8+ tumor-infiltrating T cell by RNA-seq data analysis in 371 patients with primary HCC from The Cancer Genome Atlas and then by immunohistochemistry in two independent cohorts of HCC patients from Italy (82) and Japan (86). RESULTS: Our findings highlighted the immunogenetic role of necroptosis and its potential prognostic role in HCC: RIPK1, RIPK3 and MLKL-p were found significantly associated with intratumoral CD3+ and CD8+ T cells. In addition, multivariate survival analysis showed that the expression of RIPK1, RIPK3 and MLKL-p was associated with better overall survival in the two independent cohorts. CONCLUSIONS: Our results confirmed the immunogenetic properties of necroptosis (NCP) in human HCC, showing that tumor-infiltrating lymphocytes (TILs) and, specifically, CD8+ T cells accumulate in tumors with higher expression of the necroptosis-related genes. These results suggest the importance of further studies to better assess the specific composition, as well as the functional features of the immune environment associated with a necroptotic signature in order to explore new possible diagnostic and immunotherapeutic scenarios.

Unravelling the instability of mutational signatures extraction via archetypal analysis.

The high cosine similarity between some single-base substitution mutational signatures and their characteristic flat profiles could suggest the presence of overfitting and mathematical artefacts. The newest version (v3.3) of the signature database available in the Catalogue Of Somatic Mutations In Cancer (COSMIC) provides a collection of 79 mutational signatures, which has more than doubled with respect to previous version (30 profiles available in COSMIC signatures v2), making more critical the associations between signatures and specific mutagenic processes. This study both provides a systematic assessment of the de novo extraction task through simulation scenarios based on the latest version of the COSMIC signatures and highlights, through a novel approach using archetypal analysis, which COSMIC signatures are redundant and more likely to be considered as mathematical artefacts. 29 archetypes were able to reconstruct the profile of all the COSMIC signatures with cosine similarity > 0.8. Interestingly, these archetypes tend to group similar original signatures sharing either the same aetiology or similar biological processes. We believe that these findings will be useful to encourage the development of new de novo extraction methods avoiding the redundancy of information among the signatures while preserving the biological interpretation.

Calcineurin Gamma Catalytic Subunit PPP3CC Inhibition by miR-200c-3p Affects Apoptosis in Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) outpaces all the other forms of the female reproductive system malignancies. MicroRNAs have emerged as promising predictive biomarkers to therapeutic treatments as their expression might characterize the tumor stage or grade. In EOC, miR-200c is considered a master regulator of oncogenes or tumor suppressors. To investigate novel miR-200c-3p target genes involved in EOC tumorigenesis, we evaluated the association between this miRNA and the mRNA expression of several potential target genes by RNA-seq data of both 46 EOC cell lines from Cancer Cell line Encyclopedia (CCLE) and 456 EOC patient bio-specimens from The Cancer Genome Atlas (TCGA). Both analyses showed a significant anticorrelation between miR-200c-3p and the protein phosphatase 3 catalytic subunit γ of calcineurin (PPP3CC) levels involved in the apoptosis pathway. Quantitative mRNA expression analysis in patient biopsies confirmed the inverse correlation between miR-200c-3p and PPP3CC levels. In vitro regulation of PPP3CC expression through miR-200c-3p and RNA interference technology led to a concomitant modulation of BCL2- and p-AKT-related pathways, suggesting the tumor suppressive role of PPP3CC in EOC. Our results suggest that inhibition of high expression of miR-200c-3p in EOC might lead to overexpression of the tumor suppressor PPP3CC and subsequent induction of apoptosis in EOC patients.

Long-term outcomes and predictive ability of non-invasive scoring systems in patients with non-alcoholic fatty liver disease.

BACKGROUND & AIMS: Non-invasive scoring systems (NSS) are used to identify patients with non-alcoholic fatty liver disease (NAFLD) who are at risk of advanced fibrosis, but their reliability in predicting long-term outcomes for hepatic/extrahepatic complications or death and their concordance in cross-sectional and longitudinal risk stratification remain uncertain. METHODS: The most common NSS (NFS, FIB-4, BARD, APRI) and the Hepamet fibrosis score (HFS) were assessed in 1,173 European patients with NAFLD from tertiary centres. Performance for fibrosis risk stratification and for the prediction of long-term hepatic/extrahepatic events, hepatocarcinoma (HCC) and overall mortality were evaluated in terms of AUC and Harrell's c-index. For longitudinal data, NSS-based Cox proportional hazard models were trained on the whole cohort with repeated 5-fold cross-validation, sampling for testing from the 607 patients with all NSS available. RESULTS: Cross-sectional analysis revealed HFS as the best performer for the identification of significant (F0-1 vs. F2-4, AUC = 0.758) and advanced (F0-2 vs. F3-4, AUC = 0.805) fibrosis, while NFS and FIB-4 showed the best performance for detecting histological cirrhosis (range AUCs 0.85-0.88). Considering longitudinal data (follow-up between 62 and 110 months), NFS and FIB-4 were the best at predicting liver-related events (c-indices>0.7), NFS for HCC (c-index = 0.9 on average), and FIB-4 and HFS for overall mortality (c-indices >0.8). All NSS showed limited performance (c-indices <0.7) for extrahepatic events. CONCLUSIONS: Overall, NFS, HFS and FIB-4 outperformed APRI and BARD for both cross-sectional identification of fibrosis and prediction of long-term outcomes, confirming that they are useful tools for the clinical management of patients with NAFLD at increased risk of fibrosis and liver-related complications or death. LAY SUMMARY: Non-invasive scoring systems are increasingly being used in patients with non-alcoholic fatty liver disease to identify those at risk of advanced fibrosis and hence clinical complications. Herein, we compared various non-invasive scoring systems and identified those that were best at identifying risk, as well as those that were best for the prediction of long-term outcomes, such as liver-related events, liver cancer and death.

Temporal Transcriptome Analysis Reveals Dynamic Gene Expression Patterns Driving ß-Cell Maturation.

Newly differentiated pancreatic ß cells lack proper insulin secretion profiles of mature functional ß cells. The global gene expression differences between paired immature and mature ß cells have been studied, but the dynamics of transcriptional events, correlating with temporal development of glucose-stimulated insulin secretion (GSIS), remain to be fully defined. This aspect is important to identify which genes and pathways are necessary for ß-cell development or for maturation, as defective insulin secretion is linked with diseases such as diabetes. In this study, we assayed through RNA sequencing the global gene expression across six ß-cell developmental stages in mice, spanning from ß-cell progenitor to mature ß cells. A computational pipeline then selected genes differentially expressed with respect to progenitors and clustered them into groups with distinct temporal patterns associated with biological functions and pathways. These patterns were finally correlated with experimental GSIS, calcium influx, and insulin granule formation data. Gene expression temporal profiling revealed the timing of important biological processes across ß-cell maturation, such as the deregulation of ß-cell developmental pathways and the activation of molecular machineries for vesicle biosynthesis and transport, signal transduction of transmembrane receptors, and glucose-induced Ca2+ influx, which were established over a week before ß-cell maturation completes. In particular, ß cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal ß cells displayed high basal insulin secretion, which decreased to the low levels found in mature ß cells only a week later. Different genes associated with calcium-mediated processes, whose alterations are linked with insulin resistance and deregulation of glucose homeostasis, showed increased expression across ß-cell stages, in accordance with the temporal acquisition of proper GSIS. Our temporal gene expression pattern analysis provided a comprehensive database of the underlying molecular components and biological mechanisms driving ß-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional ß cells from human embryonic stem/induced pluripotent cell for transplantation-based type 1 diabetes therapy.

MiR-200c-3p Contrasts PD-L1 Induction by Combinatorial Therapies and Slows Proliferation of Epithelial Ovarian Cancer through Downregulation of ß-Catenin and c-Myc.

Conventional/targeted chemotherapies and ionizing radiation (IR) are being used both as monotherapies and in combination for the treatment of epithelial ovarian cancer (EOC). Several studies show that these therapies might favor oncogenic signaling and impede anti-tumor responses. MiR-200c is considered a master regulator of EOC-related oncogenes. In this study, we sought to investigate if chemotherapy and IR could influence the expression of miR-200c-3p and its target genes, like the immune checkpoint PD-L1 and other oncogenes in a cohort of EOC patients' biopsies. Indeed, PD-L1 expression was induced, while miR-200c-3p was significantly reduced in these biopsies post-therapy. The effect of miR-200c-3p target genes was assessed in miR-200c transfected SKOV3 cells untreated and treated with olaparib and IR alone. Under all experimental conditions, miR-200c-3p concomitantly reduced PD-L1, c-Myc and ß-catenin expression and sensitized ovarian cancer cells to olaparib and irradiation. In silico analyses further confirmed the anti-correlation between miR-200c-3p with c-Myc and ß-catenin in 46 OC cell lines and showed that a higher miR-200c-3p expression associates with a less tumorigenic microenvironment. These findings provide new insights into how miR-200c-3p could be used to hold in check the adverse effects of conventional chemotherapy, targeted therapy and radiation therapy, and offer a novel therapeutic strategy for EOC.

Atrial fibrillation after orthotopic heart transplantatation: Pathophysiology and clinical impact.

BACKGROUND: Atrial fibrillation (AF) is a well-established post-cardiac surgery complication. Orthotopic heart transplantation (OHT) represents a peculiar condition where surgical thoracic veins isolation and autonomic denervation occur. This study aims at investigating AF incidence in OHT in order to define its risk factors and to evaluate its prognostic impact. METHODS: 278 patients affected by OHT were recruited in our Cardiac Surgery Unit and retrospectively analyzed, using clinical, surgical and instrumental data. RESULTS: The patients cohort showed 45 post-operative (16.5%) and 20 late AF cases (7.2%). Only paroxysmal AF episodes were observed. Elderly donors and acute rejection resulted as risk factors in patients with post-operative AF episodes, who presented higher all-cause mortality at 11 years post-OHT (p < 0.001, Kaplan Meier analysis). The majority of late AF episodes occurred during hospitalization, due to renal failure or infections and more frequently in male patients; no significant correlation was observed with acute or chronic rejection or other characteristics. CONCLUSION: Pulmonary vein isolation and vagal denervation lead to low AF incidence in OHT recipients. Acute rejection and graft status are the main risk factors for post-operative AF episodes, while other systemic conditions act as late AF triggers. The occurrence of AF episodes is associated with poor outcome and AF should be considered as a marker of clinical frailty.

Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine.

Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases.

ß-Arrestin-1 expression and epithelial-to-mesenchymal transition in laryngeal carcinoma.

AIM: The novel primary end-point of the present study was to ascertain ß-arrestin-1 expression in a cohort of consecutive patients with laryngeal squamous cell carcinoma (LSCC) with information available on their cigarette-smoking habits. A secondary end-point was to conduct a preliminary clinical and pathological investigation into the possible role of ß-arrestin-1 in the epithelial-to-mesenchymal transition (EMT), identified by testing for E-cadherin, Zeb1, and Zeb2 expression, in the setting of LSCC. METHODS: The expression of ß-arrestin-1, E-cadherin, zeb1, and zeb2 was ascertained in 20 consecutive LSCCs. RESULTS: Statistical analysis showed no significant associations between ß-arrestin-1 and EMT (based on the expression of E-cadherin, Zeb1, and Zeb2). The combined effect of nicotine and ß-arrestin-1 was significantly associated with a shorter disease-free survival ( P=0.01) in our series of LSCC. This latter result was also confirmed in an independent, publicly available LSCC cohort ( P=0.047). CONCLUSIONS: Further investigations on larger series (ideally in prospective settings) are needed before we can consider closer follow-up protocols and/or more aggressive treatments for patients with LSCC and a combination of nicotine exposure and ß-arrestin-1 positivity in tumor cells at the time of their diagnosis. Further studies on how ß-arrestin functions in cancer via different signaling pathways might reveal potential targets for the treatment of even advanced laryngeal malignancies.

MiR-21 over-expression and Programmed Cell Death 4 down-regulation features malignant pleural mesothelioma.

BACKGROUND: Differential diagnosis between malignant pleural mesothelioma (MPM) and benign mesothelial conditions is still challenging and there is a lack of useful markers. Programmed cell death 4 (PDCD4) is a well-known tumor suppressor gene in several cancers, its post-transcriptional activity is directly controlled by miR-21, whose over-expression has been recently reported in MPM compared to normal mesothelium. Aim of this study was to test this suppressor gene as a possible new marker of malignant transformation in mesothelial cells, as well as a new prognostic marker. METHODS: PDCD4 nuclear expression was assessed by immunohistochemistry (IHC) in 40 non-neoplastic pleural (NNP) and 40 MPM formalin-fixed and paraffin-embedded specimens. PDCD4 and miR-21 expressions were analyzed by qRT-PCR in all cases. In situ hybridization (ISH) of miR-21 was performed in 5 representative cases of both groups. The prognostic relevance of PDCD4 was assessed in a public available gene expression dataset. RESULTS: IHC showed that PDCD4 nuclear expression was significantly lower in MPM than in NNP. PDCD4 was down-regulated, whereas miR-21 was over-expressed in MPM cases compared to NNP ones. ISH detected miR-21 only in MPM specimens. Down-expression of PDCD4 was found significantly associated with short overall survival in publicly available data. CONCLUSIONS: These findings highlighted a switch between PDCD4 and miR-21 expression in MPM. Further studies should assess the diagnostic reliability of these two markers for MPM in biopsy and effusion specimens.

DNA methylation profiling reveals common signatures of tumorigenesis and defines epigenetic prognostic subtypes of canine Diffuse Large B-cell Lymphoma.

Epigenetic deregulation is a hallmark of cancer characterized by frequent acquisition of new DNA methylation in CpG islands. To gain insight into the methylation changes of canine DLBCL, we investigated the DNA methylome in primary DLBCLs in comparison with control lymph nodes by genome-wide CpG microarray. We identified 1,194 target loci showing different methylation levels in tumors compared with controls. The hypermethylated CpG loci included promoter, 5'-UTRs, upstream and exonic regions. Interestingly, targets of polycomb repressive complex in stem cells were mostly affected suggesting that DLBCL shares a stem cell-like epigenetic pattern. Functional analysis highlighted biological processes strongly related to embryonic development, tissue morphogenesis and cellular differentiation, including HOX, BMP and WNT. In addition, the analysis of epigenetic patterns and genome-wide methylation variability identified cDLBCL subgroups. Some of these epigenetic subtypes showed a concordance with the clinical outcome supporting the hypothesis that the accumulation of aberrant epigenetic changes results in a more aggressive behavior of the tumor. Collectively, our results suggest an important role of DNA methylation in DLBCL where aberrancies in transcription factors were frequently observed, suggesting an involvement during tumorigenesis. These findings warrant further investigation to improve cDLBCL prognostic classification and provide new insights on tumor aggressiveness.

Oncofetal gene SALL4 and prognosis in cancer: A systematic review with meta-analysis.

The Spalt-Like Transcription Factor 4 (SALL4) oncogene plays a central function in embryo-fetal development and is absent in differentiated tissues. Evidence suggests that it can be reactivated in several cancers worsening the prognosis. We aimed at investigating the risk associated with SALL4 reactivation for all-cause mortality and recurrence in cancer using the current literature. A PubMed and SCOPUS search until 1st September 2016 was performed, focusing on perspective studies reporting prognostic parameters in cancer data. In addition, 17 datasets of different cancer types from The Cancer Genome Atlas were considered. A total of 9,947 participants across 40 cohorts, followed-up for about 5 years on average, were analyzed comparing patients showing SALL4 presence (SALL4+, n = 1,811) or absence (SALL4-, n = 8,136). All data were summarised using risk ratios (RRs) for the number of deaths/recurrences and hazard ratios (HRs) for the time-dependent risk related to SALL4+, adjusted for potential confounders. SALL4+ significantly increased overall mortality (RR = 1.34, 95% confidence intervals (CI)=1.21-1.48, p<0.0001, I2=66%; HR=1.4; 95%CI: 1.19-1.65; p<0.0001; I2=63%) and recurrence of disease (RR = 1.25, 95% CI = 1.1-1.42, p=0.0006, I2=62%); HR=1.52; 95% CI: 1.22-1.89, p=0.0002; I2=69%) compared to SALL4-. Moreover, SALL4 remained significantly associated with poor prognosis even using HRs adjusted for potential confounders (overall mortality: HR=1.4; 95%CI: 1.19-1.65; p<0.0001; I2=63%; recurrence of disease: HR=1.52; 95% CI: 1.22-1.89, p=0.0002; I2=69%). These results suggest that SALL4 expression increases both mortality and recurrence of cancer, confirming this gene as an important prognostic marker and a potential target for personalized medicine.

Changes in microRNA expression during disease progression in patients with chronic viral hepatitis.

BACKGROUND & AIMS: MicroRNAs (miRNAs) have been involved in hepatocarcinogenesis, but little is known on their role in the progression of chronic viral hepatitis. Aim of this study was to identify miRNA signatures associated with stages of disease progression in patients with chronic viral hepatitis. METHODS: MiRNA expression profile was investigated in liver biopsies from patients with chronic viral hepatitis and correlated with clinical, virological and histopathological features. Relevant miRNAs were further investigated. RESULTS: Most of the significant changes in miRNA expression were associated with liver fibrosis stages and included the significant up-regulation of a group of miRNAs that were demonstrated to target the master regulators of epithelial-mesenchymal transition ZEB1 and ZEB2 and involved in the preservation of epithelial cell differentiation, but also in cell proliferation and fibrogenesis. In agreement with miRNA data, immunostaining of liver biopsies showed that expression of the epithelial marker E-cadherin was maintained in severe fibrosis/cirrhosis while expression of ZEBs and other markers of epithelial-mesenchymal transition were low or absent. Severe liver fibrosis was also significantly associated with the down-regulation of miRNAs with antiproliferative and tumour suppressor activity. Similar changes in miRNA and target gene expression were demonstrated along with disease progression in a mouse model of carbon tetrachloride (CCl4)-induced liver fibrosis, suggesting that they might represent a general response to liver injury. CONCLUSION: Chronic viral hepatitis progression is associated with the activation of miRNA pathways that promote cell proliferation and fibrogenesis, but preserve the differentiated hepatocyte phenotype.

Improving biomarker list stability by integration of biological knowledge in the learning process.

BACKGROUND: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for biomarker discovery using microarray data often provide results with limited overlap. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the outcomes. Thus, a possible approach to improve list stability is to integrate biological information from genomic databases in the learning process; however, a comprehensive assessment based on different types of biological information is still lacking in the literature. In this work we have compared the effect of using different biological information in the learning process like functional annotations, protein-protein interactions and expression correlation among genes. RESULTS: Biological knowledge has been codified by means of gene similarity matrices and expression data linearly transformed in such a way that the more similar two features are, the more closely they are mapped. Two semantic similarity matrices, based on Biological Process and Molecular Function Gene Ontology annotation, and geodesic distance applied on protein-protein interaction networks, are the best performers in improving list stability maintaining almost equal prediction accuracy. CONCLUSIONS: The performed analysis supports the idea that when some features are strongly correlated to each other, for example because are close in the protein-protein interaction network, then they might have similar importance and are equally relevant for the task at hand. Obtained results can be a starting point for additional experiments on combining similarity matrices in order to obtain even more stable lists of biomarkers. The implementation of the classification algorithm is available at the link: http://www.math.unipd.it/~dasan/biomarkers.html.