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.

SYNDSURV: A simple framework for survival analysis with data distributed across multiple institutions

Data sharing among different institutions represents one of the major challenges in developing distributed machine learning approaches, especially when data is sensitive, such as in medical applications. Federated learning is a possible solution, but requires fast communications and flawless security. Here, we propose SYNDSURV (SYNthetic Distributed SURVival), an alternative approach that simplifies the current state-of-the-art paradigm by allowing different centres to generate local simulated instances from real data and then gather them into a centralised hub, where an Artificial Intelligence (AI) model can learn in a standard way. The main advantage of this procedure is that it is model-agnostic, therefore prediction models can be directly applied in distributed applications without requiring particular adaptations as the current federated approaches do. To show the validity of our approach for medical applications, we tested it on a survival analysis task, offering a viable alternative to train AI models on distributed data. While federated learning has been mainly optimised for gradient-based approaches so far, our framework works with any predictive method, proving to be a comparable way of performing distributed learning without being too demanding towards each participating institute in terms of infrastructural requirements.

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.

Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations

Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are unresolved, and comparative or ab initio tools can provide a structure. Here, we evaluate the impact of model structures, compared to experimental structures, on the predictors of protein stability changes upon single-point mutations, where no significant changes are expected between the original and the mutated structures. We show that there are substantial differences among the computational tools. Methods that rely on coarse-grained representation are less sensitive to the underlying protein structures. In contrast, tools that exploit more detailed molecular representations are sensible to structures generated from comparative modeling, even on single-residue substitutions.

Variability of transient elastography-based spleen stiffness performed at 100 Hz

BACKGROUND

Spleen stiffness measurement (SSM) performed by transient elastography at 100 Hz is a novel technology for the evaluation of portal hypertension in advanced chronic liver disease, but technical aspects are lacking. We aimed to evaluate the intraexamination variability of SSM and to determine the best transient elastography protocol for obtaining robust measurements to be used in clinical practice.

METHODS

We analyzed 253 SSM exams with up to 20 scans for each examination, performed between April 2021 and June 2022. All SSM results were evaluated according to different protocols by dividing data into groups of n measurements (from 2 to 19). Considering as reference the median SSM values across all the 20 measurements, we calculated the distribution of the absolute deviations of each protocol from the reference median. This analysis was repeated 1,000 times by resampling the data. Distributions were also stratified by etiology (chronic liver disease versus clinically significant portal hypertension) and different SSM ranges: < 25 kPa, 25-75, and > 75 kPa.

RESULTS

Overall, we observed that the spleen stiffness exam had less variability if it exceeded 12 measurements, i.e., absolute deviations ≤ 5 kPa at 95% confidence. For exams with higher SSM values (> 75 kPa), as seen in clinically significant portal hypertension, at least 15 measurements are highly recommendable.

CONCLUSIONS

Fifteen scans per examination should be considered for each SSM exam performed at 100 Hz to achieve a low intraexamination variability within a reasonable time in clinical practice.

RELEVANCE STATEMENT

Performing at least 15 scans per examination is recommended for 100 Hz SSM in order to achieve a low intraexamination variability, in particular for values > 75 kPa compatible with clinically significant portal hypertension.

KEY POINTS

• Spleen stiffness measurement by transient elastography is used for stratification in patients with portal hypertension. • At 100 Hz, this method may have intraexamination variability. • A minimum of 15 scans per examination achieves a low intraexamination variability.

The need for multimodal health data modeling: a practical approach for a federated-learning healthcare platform

Federated learning initiatives in healthcare are being developed to collaboratively train predictive models without the need to centralize sensitive personal data. GenoMed4All is one such project, with the goal of connecting European clinical and -omics data repositories on rare diseases through a federated learning platform. Currently, the consortium faces the challenge of a lack of well-established international datasets and interoperability standards for federated learning applications on rare diseases. This paper presents our practical approach to select and implement a Common Data Model (CDM) suitable for the federated training of predictive models applied to the medical domain, during the initial design phase of our federated learning platform. We describe our selection process, composed of identifying the consortium's needs, reviewing our functional and technical architecture specifications, and extracting a list of business requirements. We review the state of the art and evaluate three widely-used approaches (FHIR, OMOP and Phenopackets) based on a checklist of requirements and specifications. We discuss the pros and cons of each approach considering the use cases specific to our consortium as well as the generic issues of implementing a European federated learning healthcare platform. A list of lessons learned from the experience in our consortium is discussed, from the importance of establishing the proper communication channels for all stakeholders to technical aspects related to -omics data. For federated learning projects focused on secondary use of health data for predictive modeling, encompassing multiple data modalities, a phase of data model convergence is sorely needed to gather different data representations developed in the context of medical research, interoperability of clinical care software, imaging, and -omics analysis into a coherent, unified data model. Our work identifies this need and presents our experience and a list of actionable lessons learned for future work in this direction.

Role of the Extracytoplasmic Function Sigma Factor SigE in the Stringent Response of Mycobacterium tuberculosis

Bacteria respond to nutrient starvation implementing the stringent response, a stress signaling system resulting in metabolic remodeling leading to decreased growth rate and energy requirements. A well-characterized model of stringent response in Mycobacterium tuberculosis is the one induced by growth in low phosphate. The extracytoplasmic function (ECF) sigma factor SigE was previously suggested as having a key role in the activation of stringent response. In this study, we challenge this hypothesis by analyzing the temporal dynamics of the transcriptional response of a sigE mutant and its wild-type parental strain to low phosphate using RNA sequencing. We found that both strains responded to low phosphate with a typical stringent response trait, including the downregulation of genes encoding ribosomal proteins and RNA polymerase. We also observed transcriptional changes that support the occurring of an energetics imbalance, compensated by a reduced activity of the electron transport chain, decreased export of protons, and a remodeling of central metabolism. The most striking difference between the two strains was the induction in the sigE mutant of several stress-related genes, in particular, the genes encoding the ECF sigma factor SigH and the transcriptional regulator WhiB6. Since both proteins respond to redox unbalances, their induction suggests that the sigE mutant is not able to maintain redox homeostasis in response to the energetics imbalance induced by low phosphate. In conclusion, our data suggest that SigE is not directly involved in initiating stringent response but in protecting the cell from stress consequent to the low phosphate exposure and activation of stringent response. IMPORTANCE Mycobacterium tuberculosis can enter a dormant state enabling it to establish latent infections and to become tolerant to antibacterial drugs. Dormant bacteria's physiology and the mechanism(s) used by bacteria to enter dormancy during infection are still unknown due to the lack of reliable animal models. However, several in vitro models, mimicking conditions encountered during infection, can reproduce different aspects of dormancy (growth arrest, metabolic slowdown, drug tolerance). The stringent response, a stress response program enabling bacteria to cope with nutrient starvation, is one of them. In this study, we provide evidence suggesting that the sigma factor SigE is not directly involved in the activation of stringent response as previously hypothesized, but it is important to help the bacteria to handle the metabolic stress related to the adaptation to low phosphate and activation of stringent response, thus giving an important contribution to our understanding of the mechanism behind stringent response development.

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, NO_X, PM_25abs, 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.

Challenges in predicting stabilizing variations: An exploration

An open challenge of computational and experimental biology is understanding the impact of non-synonymous DNA variations on protein function and, subsequently, human health. The effects of these variants on protein stability can be measured as the difference in the free energy of unfolding (ΔΔG) between the mutated structure of the protein and its wild-type form. Throughout the years, bioinformaticians have developed a wide variety of tools and approaches to predict the ΔΔG. Although the performance of these tools is highly variable, overall they are less accurate in predicting ΔΔG stabilizing variations rather than the destabilizing ones. Here, we analyze the possible reasons for this difference by focusing on the relationship between experimentally-measured ΔΔG and seven protein properties on three widely-used datasets (S2648, VariBench, Ssym) and a recently introduced one (S669). These properties include protein structural information, different physical properties and statistical potentials. We found that two highly used input features, i.e., hydrophobicity and the Blosum62 substitution matrix, show a performance close to random choice when trying to separate stabilizing variants from either neutral or destabilizing ones. We then speculate that, since destabilizing variations are the most abundant class in the available datasets, the overall performance of the methods is higher when including features that improve the prediction for the destabilizing variants at the expense of the stabilizing ones. These findings highlight the need of designing predictive methods able to exploit also input features highly correlated with the stabilizing variants. New tools should also be tested on a not-artificially balanced dataset, reporting the performance on all the three classes (i.e., stabilizing, neutral and destabilizing variants) and not only the overall results.

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.

Deep learning methods to predict amyotrophic lateral sclerosis disease progression

Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target the patient’s treatment. Predictive models for disease progression are thus of great interest. One of the most extensive and well-studied open-access data resources for ALS is the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) repository. In 2015, the DREAM-Phil Bowen ALS Prediction Prize4Life Challenge was held on PRO-ACT data, where competitors were asked to develop machine learning algorithms to predict disease progression measured through the slope of the ALSFRS score between 3 and 12 months. However, although it has already been successfully applied in several studies on ALS patients, to the best of our knowledge deep learning approaches still remain unexplored on the ALSFRS slope prediction in PRO-ACT cohort. Here, we investigate how deep learning models perform in predicting ALS progression using the PRO-ACT data. We developed three models based on different architectures that showed comparable or better performance with respect to the state-of-the-art models, thus representing a valid alternative to predict ALS disease progression.

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.

Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset

Predicting the difference in thermodynamic stability between protein variants is crucial for protein design and understanding the genotype-phenotype relationships. So far, several computational tools have been created to address this task. Nevertheless, most of them have been trained or optimized on the same and ‘all’ available data, making a fair comparison unfeasible. Here, we introduce a novel dataset, collected and manually cleaned from the latest version of the ThermoMutDB database, consisting of 669 variants not included in the most widely used training datasets. The prediction performance and the ability to satisfy the antisymmetry property by considering both direct and reverse variants were evaluated across 21 different tools. The Pearson correlations of the tested tools were in the ranges of 0.21–0.5 and 0–0.45 for the direct and reverse variants, respectively. When both direct and reverse variants are considered, the antisymmetric methods perform better achieving a Pearson correlation in the range of 0.51–0.62. The tested methods seem relatively insensitive to the physiological conditions, performing well also on the variants measured with more extreme pH and temperature values. A common issue with all the tested methods is the compression of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\Delta \Delta G$\end{document} predictions toward zero. Furthermore, the thermodynamic stability of the most significantly stabilizing variants was found to be more challenging to predict. This study is the most extensive comparisons of prediction methods using an entirely novel set of variants never tested before.

Digital dermoscopy monitoring of melanocytic lesions: Two novel calculators combining static and dynamic features to identify melanoma

BACKGROUND

Early diagnosis is the most effective intervention to improve the prognosis of cutaneous melanoma. Even though the introduction of dermoscopy has improved the diagnostic accuracy, it can still be difficult to distinguish some melanomas from benign melanocytic lesions. Digital dermoscopy monitoring can identify dynamic changes of melanocytic lesions: To date, some algorithms were proposed, but a universally accepted one is still lacking.

OBJECTIVES

To identify independent predictive variables associated with the diagnosis of cutaneous melanoma and develop a multivariable dermoscopic prediction model able to discriminate benign from malignant melanocytic lesions undergoing digital dermoscopy monitoring.

METHODS

We collected dermoscopic images of melanocytic lesions excised after dermoscopy monitoring and carried out static and dynamic evaluations of dermoscopic features. We built two multivariable predictive models based on logistic regression and random forest.

RESULTS

We evaluated 173 lesions (65 cutaneous melanomas and 108 nevi). Forty-two melanomas were in situ, and the median thickness of invasive melanomas was 0.35 mm. The median follow-up time was 9.8 months for melanomas and 9.1 for nevi. The logistic regression and random forest models performed with AUC values of 0.87 and 0.89, respectively, were substantially higher than those of the static evaluation models (ABCD TDS score, 0.57; 7-point checklist, 0.59). Finally, we built two risk calculators, which translate the proposed models into user-friendly applications, to assist clinicians in the decision-making process.

CONCLUSIONS

The present study demonstrates that the integration of dynamic and static evaluations of melanocytic lesions is a safe approach that can significantly boost the diagnostic accuracy for cutaneous melanoma. We propose two diagnostic tools that significantly increase the accuracy in discriminating melanoma from nevi during digital dermoscopy monitoring.

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.

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.

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

Newly differentiated pancreatic β cells lack proper insulin secretion profiles of mature functional β cells. The global gene expression differences between paired immature and mature β 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 β-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 β-cell developmental stages in mice, spanning from β-cell progenitor to mature β 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 β-cell maturation, such as the deregulation of β-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 β-cell maturation completes. In particular, β cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal β cells displayed high basal insulin secretion, which decreased to the low levels found in mature β 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 β-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 β-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional β 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 β-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 β-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 β-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.

Protein Stability Perturbation Contributes to the Loss of Function in Haploinsufficient Genes

Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that the “perturbation” of the protein stability upon a missense variant (in terms of absolute ΔΔG value, i.e., |ΔΔG|) has a significant, but not predictive, correlation with the pathogenicity of that variant. However, here we show that this correlation becomes significantly amplified in haploinsufficient genes. Moreover, the enrichment of pathogenic variants increases at the increasing protein stability perturbation value. These findings suggest that protein stability perturbation might be considered as a potential cofactor in diseases associated with haploinsufficient genes reporting missense variants.

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.

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

AIM

The novel primary end-point of the present study was to ascertain β-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 β-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 β-arrestin-1, E-cadherin, zeb1, and zeb2 was ascertained in 20 consecutive LSCCs.

RESULTS

Statistical analysis showed no significant associations between β-arrestin-1 and EMT (based on the expression of E-cadherin, Zeb1, and Zeb2). The combined effect of nicotine and β-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 β-arrestin-1 positivity in tumor cells at the time of their diagnosis. Further studies on how β-arrestin functions in cancer via different signaling pathways might reveal potential targets for the treatment of even advanced laryngeal malignancies.

Synaptotagmin 4 Regulates Pancreatic β Cell Maturation by Modulating the Ca2+ Sensitivity of Insulin Secretion Vesicles

Islet β cells from newborn mammals exhibit high basal insulin secretion and poor glucose-stimulated insulin secretion (GSIS). Here we show that β cells of newborns secrete more insulin than adults in response to similar intracellular Ca²⁺ concentrations, suggesting differences in the Ca²⁺ sensitivity of insulin secretion. Synaptotagmin 4 (Syt4), a non-Ca²⁺ binding paralog of the β cell Ca²⁺ sensor Syt7, increased by ∼8-fold during β cell maturation. Syt4 ablation increased basal insulin secretion and compromised GSIS. Precocious Syt4 expression repressed basal insulin secretion but also impaired islet morphogenesis and GSIS. Syt4 was localized on insulin granules and Syt4 levels inversely related to the number of readily releasable vesicles. Thus, transcriptional regulation of Syt4 affects insulin secretion; Syt4 expression is regulated in part by Myt transcription factors, which repress Syt4 transcription. Finally, human SYT4 regulated GSIS in EndoC-βH1 cells, a human β cell line. These findings reveal the role that altered Ca²⁺ sensing plays in regulating β cell maturation.

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.

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.

Knowledge Driven Variable Selection (KDVS) - a new approach to enrichment analysis of gene signatures obtained from high-throughput data

Background

High–throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of the selected genes with an a posteriori enrichment analysis, based on biological knowledge. However, this approach comes with some drawbacks. First, gene selection procedure often requires tunable parameters that affect the outcome, typically producing many false hits. Second, a posteriori enrichment analysis is based on mapping between biological concepts and gene expression measurements, which is hard to compute because of constant changes in biological knowledge and genome analysis. Third, such mapping is typically used in the assessment of the coverage of gene signature by biological concepts, that is either score–based or requires tunable parameters as well, limiting its power.

Results

We present Knowledge Driven Variable Selection (KDVS), a framework that uses a priori biological knowledge in HT data analysis. The expression data matrix is transformed, according to prior knowledge, into smaller matrices, easier to analyze and to interpret from both computational and biological viewpoints. Therefore KDVS, unlike most approaches, does not exclude a priori any function or process potentially relevant for the biological question under investigation. Differently from the standard approach where gene selection and functional assessment are applied independently, KDVS embeds these two steps into a unified statistical framework, decreasing the variability derived from the threshold–dependent selection, the mapping to the biological concepts, and the signature coverage. We present three case studies to assess the usefulness of the method.

Conclusions

We showed that KDVS not only enables the selection of known biological functionalities with accuracy, but also identification of new ones. An efficient implementation of KDVS was devised to obtain results in a fast and robust way. Computing time is drastically reduced by the effective use of distributed resources. Finally, integrated visualization techniques immediately increase the interpretability of results. Overall, KDVS approach can be considered as a viable alternative to enrichment–based approaches.

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.

Effect of size and heterogeneity of samples on biomarker discovery: synthetic and real data assessment

MOTIVATION

The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the number of features); 2) heterogeneity of the disease; 3) heterogeneity of experimental protocols and computational pipelines employed in the analysis. In this paper, we focus on the first two issues and assess, both on simulated (through an in silico regulation network model) and real clinical datasets, the consistency of candidate biomarkers provided by a number of different methods.

METHODS

We extensively simulated the effect of heterogeneity characteristic of complex diseases on different sets of microarray data. Heterogeneity was reproduced by simulating both intrinsic variability of the population and the alteration of regulatory mechanisms. Population variability was simulated by modeling evolution of a pool of subjects; then, a subset of them underwent alterations in regulatory mechanisms so as to mimic the disease state.

RESULTS

The simulated data allowed us to outline advantages and drawbacks of different methods across multiple studies and varying number of samples and to evaluate precision of feature selection on a benchmark with known biomarkers. Although comparable classification accuracy was reached by different methods, the use of external cross-validation loops is helpful in finding features with a higher degree of precision and stability. Application to real data confirmed these results.

The transcriptional response in human umbilical vein endothelial cells exposed to insulin: a dynamic gene expression approach

Background

In diabetes chronic hyperinsulinemia contributes to the instability of the atherosclerotic plaque and stimulates cellular proliferation through the activation of the MAP kinases, which in turn regulate cellular proliferation. However, it is not known whether insulin itself could increase the transcription of specific genes for cellular proliferation in the endothelium. Hence, the characterization of transcriptional modifications in endothelium is an important step for a better understanding of the mechanism of insulin action and the relationship between endothelial cell dysfunction and insulin resistance.

Methodology and principal findings

The transcriptional response of endothelial cells in the 440 minutes following insulin stimulation was monitored using microarrays and compared to a control condition. About 1700 genes were selected as differentially expressed based on their treated minus control profile, thus allowing the detection of even small but systematic changes in gene expression. Genes were clustered in 7 groups according to their time expression profile and classified into 15 functional categories that can support the biological effects of insulin, based on Gene Ontology enrichment analysis. In terms of endothelial function, the most prominent processes affected were NADH dehydrogenase activity, N-terminal myristoylation domain binding, nitric-oxide synthase regulator activity and growth factor binding. Pathway-based enrichment analysis revealed “Electron Transport Chain” significantly enriched.

Results were validated on genes belonging to “Electron Transport Chain” pathway, using quantitative RT-PCR.

Conclusions

As far as we know, this is the first systematic study in the literature monitoring transcriptional response to insulin in endothelial cells, in a time series microarray experiment. Since chronic hyperinsulinemia contributes to the instability of the atherosclerotic plaque and stimulates cellular proliferation, some of the genes identified in the present work are potential novel candidates in diabetes complications related to endothelial dysfunction.