Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions.

BACKGROUND: Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course. METHODS: We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data. RESULTS: We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient's risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922). CONCLUSIONS: The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient's risk scores can be used in clinical settings.

Spinal neuronal activity and neuroinflammatory component in a mouse model of CFA-induced vestibulodynia.

Vestibulodynia is a complex pain disorder characterized by chronic discomfort in the vulvar region, often accompanied by tactile allodynia and spontaneous pain. In patients a depressive behaviour is also observed. In this study, we have used a model of vestibulodynia induced by complete Freund's adjuvant (CFA) focusing our investigation on the spinal cord neurons and microglia. We investigated tactile allodynia, spontaneous pain, and depressive-like behavior as key behavioral markers of vestibulodynia. In addition, we conducted in vivo electrophysiological recordings to provide, for the first time to our knowledge, the characterization of the spinal sacral neuronal activity in the L6-S1 dorsal horn of the spinal cord. Furthermore, we examined microglia activation in the L6-S1 dorsal horn using immunofluorescence, unveiling hypertrophic phenotypes indicative of neuroinflammation in the spinal cord. This represents a novel insight into the role of microglia in vestibulodynia pathology. To address the therapeutic aspect, we employed pharmacological interventions using GABApentin, amitriptyline, and PeaPol. Remarkably, all three drugs, also used in clinic, showed efficacy in alleviating tactile allodynia and depressive-like behavior. Concurrently, we also observed a normalization of the altered neuronal firing and a reduction of microglia hypertrophic phenotypes. In conclusion, our study provides a comprehensive understanding of the CFA-induced model of vestibulodynia, encompassing behavioral, neurophysiological and neuroinflammatory aspects. These data pave the way to investigate spinal cord first pain plasticity in vestibulodynia.

Familial Dilated Cardiomyopathy: A Novel MED9 Short Isoform Identification.

Familial dilated cardiomyopathy (DCM) is among the leading indications for heart transplantation. DCM alters the transcriptomic profile. The alteration or activation/silencing of physiologically operating transcripts may explain the onset and progression of this pathological state. The mediator complex (MED) plays a fundamental role in the transcription process. The aim of this study is to investigate the MED subunits, which are altered in DCM, to identify target crossroads genes. RNA sequencing allowed us to identify specific MED subunits that are altered during familial DCM, transforming into human myocardial samples. N = 13 MED subunits were upregulated and n = 7 downregulated. MED9 alone was significantly reduced in patients compared to healthy subjects (HS) (FC = -1.257; p < 0.05). Interestingly, we found a short MED9 isoform (MED9s) (ENSG00000141026.6), which was upregulated when compared to the full-transcript isoform (MED9f). Motif identification analysis yielded several significant matches (p < 0.05), such as GATA4, which is downregulated in CHD. Moreover, although the protein-protein interaction network showed FOG2/ZFPM2, FOS and ID2 proteins to be the key interacting partners of GATA4, only FOG2/ZFPM2 overexpression showed an interaction score of "high confidence" ≥ 0.84. A significant change in the MED was observed during HF. For the first time, the MED9 subunit was significantly reduced between familial DCM and HS (p < 0.05), showing an increased MED9s isoform in DCM patients with respect to its full-length transcript. MED9 and GATA4 shared the same sequence motif and were involved in a network with FOG2/ZFPM2, FOS, and ID2, proteins already implicated in cardiac development.

Machine learning improves the accuracy of graft weight prediction in living donor liver transplantation.

Precise graft weight (GW) estimation is essential for planning living donor liver transplantation to select grafts of adequate size for the recipient. This study aimed to investigate whether a machine-learning model can improve the accuracy of GW estimation. Data from 872 consecutive living donors of a left lateral sector, left lobe, or right lobe to adults or children for living-related liver transplantation were collected from January 2011 to December 2019. Supervised machine-learning models were trained (80% of observations) to predict GW using the following information: donor's age, sex, height, weight, and body mass index; graft type (left, right, or left lateral lobe); computed tomography estimated graft volume and total liver volume. Model performance was measured in a random independent set (20% of observations) and in an external validation cohort using the mean absolute error (MAE) and the mean absolute percentage error and compared with methods currently available for GW estimation. The best-performing machine-learning model showed an MAE value of 50 ± 62 g in predicting GW, with a mean error of 10.3%. These errors were significantly lower than those observed with alternative methods. In addition, 62% of predictions had errors <10%, whereas errors >15% were observed in only 18.4% of the cases compared with the 34.6% of the predictions obtained with the best alternative method ( p < 0.001). The machine-learning model is made available as a web application ( http://graftweight.shinyapps.io/prediction ). Machine learning can improve the precision of GW estimation compared with currently available methods by reducing the frequency of significant errors. The coupling of anthropometric variables to the preoperatively estimated graft volume seems necessary to improve the accuracy of GW estimation.

Targeted genetic analysis unveils novel associations between ACE I/D and APO T158C polymorphisms with D-dimer levels in severe COVID-19 patients with pulmonary embolism.

Only a percentage of COVID-19 patients develop thrombotic complications. We hypothesized that genetic profiles may explain part of the inter-individual differences. Our goal was to evaluate the genotypic distribution of targeted DNA polymorphisms in COVID-19 patients complicated (PE+) or not (PE-) by pulmonary embolism. We designed a retrospective observational study enrolling N = 94 consecutive patients suffering severe COVID-19 with pulmonary embolism (PE+, N = 47) or not (PE-, N = 47) during hospitalization. A panel of N = 13 prothrombotic DNA polymorphisms (FV R506Q and H1299R, FII G20210A, MTHFR C677T and A1298C, CBS 844ins68, PAI-1 4G/5G, GPIIIa HPA-1 a/b, ACE I/D, AGT T9543C, ATR-1 A1166C, FGB - 455G > A, FXIII103G > T) and N = 2 lipid metabolism-related DNA polymorphisms (APOE T 112C and T158C) were investigated using Reverse Dot Blot technique. Then, we investigated possible associations between genotypic subclasses and demographic, clinical, and laboratory parameters including age, obesity, smoking, pro-inflammatory cytokines, drug therapy, and biomarkers of thrombotic risk such as D-dimer (DD). We found that 58.7% of PE+ had homozygous mutant D/D genotype at ACE I/D locus vs. PE- (40.4%) and 87% of PE+ had homozygous mutant C/C genotype at APOE T158C locus vs. PE- (68.1%). In PE+ group, DD levels were significantly higher in D/D and I/D genotypes at ACE I/D locus (P = 0.00066 and P = 0.00023, respectively) and in C/C and T/C genotypes at APOE T158C locus (P = 1.6e-06 and P = 0.0012, respectively) than PE- group. For the first time, we showed significant associations between higher DD levels and ACE I/D and APOE T158C polymorphisms in PE+ vs. PE- patients suggesting potential useful biomarkers of poor clinical outcome.

Role of gut microbiota in neuropathy and neuropathic pain states: A systematic preclinical review.

Gut microbiota has implications in Central Nervous System (CNS) disorders. Our study systematically identified preclinical studies aimed to investigate the possible gut microbiota contribution in neuropathy and neuropathic pain. The systematic review is reported in accordance with PRISMA checklist and guidelines outlined updated to 2020. We included research articles reporting neuropathy-related behavioral evaluations and/or neurological scores coupled to gut microbiota analysis performed by high-throughput technologies in the last ten years. Two investigators performed a search through 3 electronic bibliographic databases for full-text articles (PubMed, Scopus, and EMBASE) and three registries (Prospero, SyRF, and bioRxiv), cross-references, and linear searches. We assessed the methodological quality via the CAMARADES checklist and appraised the heterogeneous body of evidence by narrative synthesis. In total, there were 19 eligible studies. The most of these reports showed significant changes in gut microbiota setting in neuropathy conditions. The major gut microbiome remodeling was through fecal microbiome transplantation. Mechanistic proof of the gut-CNS communication was achieved by measuring inflammatory mediators, metabolic products, or neurotransmitters. As a limitation, we found considerable heterogeneity across eligible studies. We conclude that the current understanding of preclinical findings suggested an association between neuropathy and/or neuropathic pain and gut microbiota modifications. Our analysis provides the basis for further studies targeting microbiota for managing symptoms of neuropathy or other neuroinflammation-based CNS disorders. The systematic review protocol was registered on the international database Prospero under the registration number (257628).

DNA Methylation Profile of the SREBF2 Gene in Human Fetal Aortas.

Increasing evidence suggests that maternal cholesterol represents an important risk factor for atherosclerotic disease in offspring already during pregnancy, although the underlying mechanisms have not yet been elucidated. Eighteen human fetal aorta samples were collected from the spontaneously aborted fetuses of normal cholesterolemic and hypercholesterolemic mothers. Maternal total cholesterol levels were assessed during hospitalization. DNA methylation profiling of the whole SREBF2 gene CpG island was performed (p value <0.05). The Mann-Whitney U test was used for comparison between the 2 groups. For the first time, our study revealed that in fetal aortas obtained from hypercholesterolemic mothers, the SREBF2 gene shows 4 significant differentially hypermethylated sites in the 5'UTR-CpG island. This finding indicates that more effective long-term primary cardiovascular prevention programs need to be designed for the offspring of mothers with hypercholesterolemia. Further studies should be conducted to clarify the epigenetic mechanisms underlying the association between early atherogenesis and maternal hypercholesterolemia during pregnancy.

Evolution of intra-tumoral heterogeneity across different pathological stages in papillary thyroid carcinoma.

BACKGROUND: Intra-tumor heterogeneity (ITH) results from the continuous accumulation of mutations during disease progression, thus impacting patients' clinical outcome. How the ITH evolves across papillary thyroid carcinoma (PTC) different tumor stages is lacking. METHODS: We used the whole-exome sequencing data from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort to track the ITH and assessed its relationship with clinical features through different stages of the PTC progression. We further assayed the expression levels of the specific genes in papillary thyroid cancer cell lines compared to an immortalized normal thyroid epithelial cell line by qRT-PCR. RESULTS: We revealed the timing of mutational processes and the dynamics of the temporal acquisition of somatic events during the lifetime of the PTC. ITH significantly influences the PTC patient's survival rate and, as genetic heterogeneity increases, the prognosis gets worse in advanced tumor stages. ITH also affects the mutational architecture of each clinical stage which is subject to periodic fluctuations. Different mutational processes may cooperate to shape a stage-specific mutational spectrum during the progression from early to advanced tumor stages. Moreover, different evolutionary paths characterize PTC progression across pathological stages due to both mutations recurrently occurring in all stages in hotspot positions and distinct codon changes dominating in different stages. A different expression level of specific genes also exists in different thyroid cancer cell lines. CONCLUSIONS: Our findings suggest ITH as a potential unfavorable prognostic factor in PTC and highlight the dynamic changes in different clinical stages of PTC, providing some clues for the precision medicine and suggesting different diagnostic decisions depending on the clinical stages of patients. Finally, complete clear guidelines to define risk stratification of PTC patients are lacking; thus, this work could contribute to defining patients who need more aggressive treatments and, in turn, could reduce the social burden of this cancer.

Comparative Proteomic Profiling of Secreted Extracellular Vesicles from Breast Fibroadenoma and Malignant Lesions: A Pilot Study.

Extracellular vesicles (EVs) shuttle proteins, RNA, DNA, and lipids crucial for cell-to-cell communication. Recent findings have highlighted that EVs, by virtue of their cargo, may also contribute to breast cancer (BC) growth and metastatic dissemination. Indeed, EVs are gaining great interest as non-invasive cancer biomarkers. However, little is known about the biological and physical properties of EVs from malignant BC lesions, and even less is understood about EVs from non-malignant lesions, such as breast fibroadenoma (FAD), which are clinically managed using conservative approaches. Thus, for this pilot study, we attempted to purify and explore the proteomic profiles of EVs from benign breast lesions, HER2+ BCs, triple-negative BCs (TNBCs), and continuous BC cell lines (i.e., BT-549, MCF-10A, and MDA-MB-231), combining experimental and semi-quantitative approaches. Of note, proteome-wide analyses showed 49 common proteins across EVs harvested from FAD, HER2+ BCs, TNBCs, and model BC lines. This is the first feasibility study evaluating the physicochemical composition and proteome of EVs from benign breast cells and primary and immortalized BC cells. Our preliminary results hold promise for possible implications in precision medicine for BC.

Impact of Breast Tumor Onset on Blood Count, Carcinoembryonic Antigen, Cancer Antigen 15-3 and Lymphoid Subpopulations Supported by Automatic Classification Approach: A Pilot Study.

BACKGROUND: Recent observations showed that systemic immune changes are detectable in case of breast cancer (BC). In this preliminary study, we investigated routinely measured peripheral blood (PB) parameters for malignant BC cases in comparison to benign breast conditions. Complete blood count, circulating lymphoid subpopulation, and serological carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3) levels were considered. METHODS: A total of 127 female patients affected by malignant (n = 77, mean age = 63 years, min = 36, max = 90) BC at diagnosis (naïve patients) or benign breast conditions (n = 50, mean age = 33 years, min = 18, max = 60) were included in this study. For each patient, complete blood count and lymphoid subpopulations (T-helper, T-cytotoxic, B-, NK-, and NKT-cells) analysis on PB samples were performed. Hormonal receptor status, Ki-67 expression, and serological CEA and CA15-3 levels were assessed in the case of patients with malignant BC via statistical analysis. RESULTS: Women with malignant BC disclosed increased circulating T-helper lymphocytes and CD4/CD8 ratio in PB when compared to those affected by benign breast conditions (2.345 vs 1.894, P < .05 Wilcoxon rank-sum test). In the case of malignant BC patients, additive logistic regression method was able to identify malignant BC cases with increased CA15-3 levels (CA15-3 >25 UI/mL) via the hematocrit and neutrophils/lymphocytes ratio values. Moreover, in the case of women with aggressive malignant BC featured by high levels of Ki-67 proliferation marker, an increasing number of correlations were found among blood count parameters and lymphocytes subpopulations by performing a Spearman's correlation analysis. CONCLUSIONS: This preliminary study confirms the ability of malignant BC to determine systemic modifications. The stratification of malignant BC cases according to the Ki-67 proliferation marker highlighted increasing detectable alterations in the periphery of women with aggressive BC. The advent of novel and more sensitive biomarkers, as well as deep immunophenotyping technologies, will provide additional insights for describing the relationship between tumor onset and peripheral alterations.

Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.

BACKGROUND: Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. OBJECTIVE: This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies-version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice. RESULTS: In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. CONCLUSIONS: The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.

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

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

Junctional adhesion molecule-A is down-regulated in anaplastic thyroid carcinomas and reduces cancer cell aggressiveness by modulating p53 and GSK3 α/ß pathways.

Junctional adhesion molecule A (JAM-A) is a transmembrane protein that contributes to different biological process, including the epithelial to mesenchymal transition (EMT). Through an EMT profiler array, we explored the molecular players associated with human thyroid cancer progression and identified JAM-A as one of the genes mostly deregulated. The quantitative real-time polymerase chain reaction and immunohistochemistry analyses showed that downregulation of JAM-A occurred in anaplastic thyroid carcinoma (ATC) compared with normal thyroid (NT) and papillary thyroid carcinoma (PTC) tissues and correlated with extrathyroid infiltration, tumor size, and ATC histotype. In ATC cell lines, JAM-A restoration suppressed malignant hallmarks of transformation including cell proliferation, motility, and transendothelial migration. Accordingly, knockdown of JAM-A enhanced thyroid cancer cell proliferation and motility in PTC cells. Through the proteome profiler human phospho-kinase array, we demonstrated that higher expression of JAM-A was associated with a significant increased level of phosphorylation of p53 and GSK3 α/ß proteins. In conclusion, our findings highlight a novel role of JAM-A in thyroid cancer progression and suggest that JAM-A restoration could have potential clinical relevance in thyroid cancer treatment.

Bringing radiomics into a multi-omics framework for a comprehensive genotype-phenotype characterization of oncological diseases.

Genomic and radiomic data integration, namely radiogenomics, can provide meaningful knowledge in cancer diagnosis, prognosis and treatment. Despite several data structures based on multi-layer architecture proposed to combine multi-omic biological information, none of these has been designed and assessed to include radiomic data as well. To meet this need, we propose to use the MultiAssayExperiment (MAE), an R package that provides data structures and methods for manipulating and integrating multi-assay experiments, as a suitable tool to manage radiogenomic experiment data. To this aim, we first examine the role of radiogenomics in cancer phenotype definition, then the current state of radiogenomics data integration in public repository and, finally, challenges and limitations of including radiomics in MAE, designing an extended framework and showing its application on a case study from the TCGA-TCIA archives. Radiomic and genomic data from 91 patients have been successfully integrated in a single MAE object, demonstrating the suitability of the MAE data structure as container of radiogenomic data.

Transcription alterations of KCNQ1 associated with imprinted methylation defects in the Beckwith-Wiedemann locus.

PURPOSE: Beckwith-Wiedemann syndrome (BWS) is a developmental disorder caused by dysregulation of the imprinted gene cluster of chromosome 11p15.5 and often associated with loss of methylation (LOM) of the imprinting center 2 (IC2) located in KCNQ1 intron 10. To unravel the etiological mechanisms underlying these epimutations, we searched for genetic variants associated with IC2 LOM. METHODS: We looked for cases showing the clinical features of both BWS and long QT syndrome (LQTS), which is often associated with KCNQ1 variants. Pathogenic variants were identified by genomic analysis and targeted sequencing. Functional experiments were performed to link these pathogenic variants to the imprinting defect. RESULTS: We found three rare cases in which complete IC2 LOM is associated with maternal transmission of KCNQ1 variants, two of which were demonstrated to affect KCNQ1 transcription upstream of IC2. As a consequence of KCNQ1 haploinsufficiency, these variants also cause LQTS on both maternal and paternal transmission. CONCLUSION: These results are consistent with the hypothesis that, similar to what has been demonstrated in mouse, lack of transcription across IC2 results in failure of methylation establishment in the female germline and BWS later in development, and also suggest a new link between LQTS and BWS that is important for genetic counseling.

ICF-specific DNMT3B dysfunction interferes with intragenic regulation of mRNA transcription and alternative splicing.

Hypomorphic mutations in DNA-methyltransferase DNMT3B cause majority of the rare disorder Immunodeficiency, Centromere instability and Facial anomalies syndrome cases (ICF1). By unspecified mechanisms, mutant-DNMT3B interferes with lymphoid-specific pathways resulting in immune response defects. Interestingly, recent findings report that DNMT3B shapes intragenic CpG-methylation of highly-transcribed genes. However, how the DNMT3B-dependent epigenetic network modulates transcription and whether ICF1-specific mutations impair this process remains unknown. We performed a transcriptomic and epigenomic study in patient-derived B-cell lines to investigate the genome-scale effects of DNMT3B dysfunction. We highlighted that altered intragenic CpG-methylation impairs multiple aspects of transcriptional regulation, like alternative TSS usage, antisense transcription and exon splicing. These defects preferentially associate with changes of intragenic H3K4me3 and at lesser extent of H3K27me3 and H3K36me3. In addition, we highlighted a novel DNMT3B activity in modulating the self-regulatory circuit of sense-antisense pairs and the exon skipping during alternative splicing, through interacting with RNA molecules. Strikingly, altered transcription affects disease relevant genes, as for instance the memory-B cell marker CD27 and PTPRC genes, providing us with biological insights into the ICF1-syndrome pathogenesis. Our genome-scale approach sheds light on the mechanisms still poorly understood of the intragenic function of DNMT3B and DNA methylation in gene expression regulation.

TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review.

In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular 'omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular 'omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular 'omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.

DeClUt: Decluttering differentially expressed genes through clustering of their expression profiles.

BACKGROUND AND OBJECTIVE: differential expression analysis is one of the most popular activities in transcriptomic studies based on next-generation sequencing technologies. In fact, differentially expressed genes (DEGs) between two conditions represent ideal prognostic and diagnostic candidate biomarkers for many pathologies. As a result, several algorithms, such as DESeq2 and edgeR, have been developed to identify DEGs. Despite their widespread use, there is no consensus on which model performs best for different types of data, and many existing methods suffer from high False Discovery Rates (FDR). METHODS: we present a new algorithm, DeClUt, based on the intuition that the expression profile of differentially expressed genes should form two reasonably compact and well-separated clusters. This, in turn, implies that the bipartition induced by the two conditions being compared should overlap with the clustering. The clustering algorithm underlying DeClUt was designed to be robust to outliers typical of RNA-seq data. In particular, we used the average silhouette function to enforce membership assignment of samples to the most appropriate condition. RESULTS: DeClUt was tested on real RNA-seq datasets and benchmarked against four of the most widely used methods (edgeR, DESeq2, NOISeq, and SAMseq). Experiments showed a higher self-consistency of results than the competitors as well as a significantly lower False Positive Rate (FPR). Moreover, tested on a real prostate cancer RNA-seq dataset, DeClUt has highlighted 8 DE genes, linked to neoplastic process according to DisGeNET database, that none of the other methods had identified. CONCLUSIONS: our work presents a novel algorithm that builds upon basic concepts of data clustering and exhibits greater consistency and significantly lower False Positive Rate than state-of-the-art methods. Additionally, DeClUt is able to highlight relevant differentially expressed genes not otherwise identified by other tools contributing to improve efficacy of differential expression analyses in various biological applications.

Goat milk extracellular vesicles: Separation comparison of natural carriers for theragnostic application.

Goat milk is a complex biological fluid, which in addition to having a high nutritional value, it is an interesting source of extracellular vesicles (EVs). Despite the countless potential applications that they offer in many biological fields, is not easy to compare the different proposed systems, and this is a major limitation for the real translatability of these natural nanoplatforms for theragnostic purposes. Thus, it is useful to further investigate reproducible methods to separate goat milk EVs. The choice of methods but also the preprocessing of milk has an immense impact on the separation, quality, and yield of EVs. Here, we tested four protocols to separate EVs from unpasteurised goat milk: two based on differential ultracentrifugation (DUC) and two on size-exclusion chromatography (SEC). Moreover, we assessed two different approaches of pre-treatment (acidification and precipitation) to facilitate milk protein discharge. To the best of our knowledge, a similar comparison of all performed protocols on raw goat milk has never been published before. Therefore, enriched EV samples were successfully obtained from goat milk using both DUC and SEC. Taken together, our results may be helpful to obtain natural carriers for future theragnostic applications in personalised medicine.