Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies

Knowledge-guided learning methods for integrative analysis of multi-omics data

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction

Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON.

A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data

BACKGROUND

Survival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction.

METHODS

We introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application.

RESULTS

The proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer.

CONCLUSIONS

This study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact:  anirban@klyuniv.ac.in.

Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models

BACKGROUND

Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets.

METHODS

We established and validated genetic prediction models for protein levels in plasma as instruments to investigate the associations between genetically predicted protein levels and AD risk. We studied 71,880 (proxy) cases and 383,378 (proxy) controls of European descent.

RESULTS

We identified 69 proteins with genetically predicted concentrations showing associations with AD risk. The drugs almitrine and ciclopirox targeting ATP1A1 were suggested to have a potential for being repositioned for AD treatment.

CONCLUSIONS

Our study provides additional insights into the underlying mechanisms of AD and potential therapeutic strategies.

Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC.

SIGNIFICANCE

PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.

Integration of incomplete multi-omics data using Knowledge Distillation and Supervised Variational Autoencoders for disease progression prediction

OBJECTIVE

The rapid advancement of high-throughput technologies in the biomedical field has resulted in the accumulation of diverse omics data types, such as mRNA expression, DNA methylation, and microRNA expression, for studying various diseases. Integrating these multi-omics datasets enables a comprehensive understanding of the molecular basis of cancer and facilitates accurate prediction of disease progression.

METHODS

However, conventional approaches face challenges due to the dimensionality curse problem. This paper introduces a novel framework called Knowledge Distillation and Supervised Variational AutoEncoders utilizing View Correlation Discovery Network (KD-SVAE-VCDN) to address the integration of high-dimensional multi-omics data with limited common samples. Through our experimental evaluation, we demonstrate that the proposed KD-SVAE-VCDN architecture accurately predicts the progression of breast and kidney carcinoma by effectively classifying patients as long- or short-term survivors. Furthermore, our approach outperforms other state-of-the-art multi-omics integration models.

RESULTS

Our findings highlight the efficacy of the KD-SVAE-VCDN architecture in predicting the disease progression of breast and kidney carcinoma. By enabling the classification of patients based on survival outcomes, our model contributes to personalized and targeted treatments. The favorable performance of our approach in comparison to several existing models suggests its potential to contribute to the advancement of cancer understanding and management.

CONCLUSION

The development of a robust predictive model capable of accurately forecasting disease progression at the time of diagnosis holds immense promise for advancing personalized medicine. By leveraging multi-omics data integration, our proposed KD-SVAE-VCDN framework offers an effective solution to this challenge, paving the way for more precise and tailored treatment strategies for patients with different types of cancer.

A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data

Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and data integration techniques are powerful tools to achieve this goal. This study aimed to introduce a machine learning method to integrate three types of biological data, and investigate the performance of two other methods, in identifying the survival dependency of patients. The data included TCGA RNA-seq gene expression, DNA methylation, and clinical data from 368 patients with colon cancer also we use an independent external validation data set, containing 232 samples. Three methods including, hyper-parameter optimized autoencoders (HPOAE), normal autoencoder, and penalized principal component analysis (PPCA) were used for simultaneous data integration and estimation under a COX hazards model. The HPOAE was thought to outperform other methods. The HPOAE had the Log Rank Mantel-Cox value of 14.27 ± 2, and a Breslow-Generalized Wilcoxon value of 13.13 ± 1. Ten miRNA, 11 methylated genes, and 28 mRNA all by (importance of marginal cutoff > 0.95) were identified. The study demonstrated that hsa-miR-485-5p targets both ZMYM1 and tp53, the latter of which has been previously associated with cancer in numerous studies. Furthermore, compared to other methods, the HPOAE exhibited a greater capacity for identifying survival subgroups and the genes associated with them in patients with colon cancer. However, all of the results were obtained by computational methods, and clinical and experimental studies are needed to validate these results.

Optimisation Models for Pathway Activity Inference in Cancer

BACKGROUND

With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models.

METHODOLOGY

A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation.

RESULTS

The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction.

Integration of Human and Viral miRNAs in Epstein-Barr Virus-Associated Tumors and Implications for Drug Repurposing

Epstein-Barr virus (EBV) is associated with several tumors, and has substantial relevance for public health. Therapeutics innovation for EBV-related disorders is much needed. In this context, miRNAs are noncoding RNA molecules that play vital roles in EBV infection. miRNA-Seq and RNA-Seq data for EBV-associated clinical samples and cell lines have been generated, but their detailed integrative analyses, and exploitation for drug repurposing against EBV are lacking. Hence, we identified and analyzed the differentially expressed miRNAs (DEmiRs) in EBV-infected cell lines (28) and infected (28) and uninfected human tissue (20) samples using an in-house pipeline. We found significantly enriched host miRNAs like hsa-mir-3651, hsa-mir-1248, and hsa-mir-29c-3p in EBV-infected samples from EBV-associated nasopharyngeal carcinoma and Hodgkin's lymphoma, among others. Furthermore, we also identified significantly enriched novel miRNAs such as hsa-mir-29c-3p, hsa-mir-3651, and hsa-mir-98-3p, which were not previously reported in EBV-related tumors. Differentially expressed mRNAs (DEMs) were identified in EBV-infected cell lines (21) and uninfected human tissue (14) samples. We predicted and selected 1572 DEMs (upregulated) that are targeted by 547 DEmiRs (downregulated). These were further classified into essential (870) and nonessential (702) genes. Moreover, a miRNA-mRNA network was developed for the hub miRNAs. Importantly, we used the DEMs during EBV latent infection types I, II, and III to identify the candidate drugs for repurposing: Glyburide, Levodopa, Nateglinide, and Stiripentol, among others. To the best of our knowledge, this is the first integrative analyses that identified DEmiRs and DEMs as potential therapeutic targets and predicted drugs as potential candidates for repurposing against EBV-related tumors.

Computational method for aromatase-related proteins using machine learning approach

Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like amino acid, dipeptide composition, hybrid and evolutionary profiles in the form of position-specific scoring matrix (PSSM); with maximum accuracy of 87.42%, 84.05%, 85.12%, and 92.02% respectively. Based on the primary sequence, the developed method is highly accurate to predict the aromatase-related proteins. Prediction scores graphs were developed using the known dataset to check the performance of the method. Based on the approach described above, a webserver for predicting aromatase-related proteins from primary sequence data was developed and implemented at https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html. We hope that the developed method will be useful for aromatase protein related research.

InterTADs: integration of multi-omics data on topologically associated domains, application to chronic lymphocytic leukemia

The integration of multi-omics data can greatly facilitate the advancement of research in Life Sciences by highlighting new interactions. However, there is currently no widespread procedure for meaningful multi-omics data integration. Here, we present a robust framework, called InterTADs, for integrating multi-omics data derived from the same sample, and considering the chromatin configuration of the genome, i.e. the topologically associating domains (TADs). Following the integration process, statistical analysis highlights the differences between the groups of interest (normal versus cancer cells) relating to (i) independent and (ii) integrated events through TADs. Finally, enrichment analysis using KEGG database, Gene Ontology and transcription factor binding sites and visualization approaches are available. We applied InterTADs to multi-omics datasets from 135 patients with chronic lymphocytic leukemia (CLL) and found that the integration through TADs resulted in a dramatic reduction of heterogeneity compared to individual events. Significant differences for individual events and on TADs level were identified between patients differing in the somatic hypermutation status of the clonotypic immunoglobulin genes, the core biological stratifier in CLL, attesting to the biomedical relevance of InterTADs. In conclusion, our approach suggests a new perspective towards analyzing multi-omics data, by offering reasonable execution time, biological benchmarking and potentially contributing to pattern discovery through TADs.

The p53 family member p73 in the regulation of cell stress response

During oncogenesis, cells become unrestrictedly proliferative thereby altering the tissue homeostasis and resulting in subsequent hyperplasia. This process is paralleled by resumption of cell cycle, aberrant DNA repair and blunting the apoptotic program in response to DNA damage. In most human cancers these processes are associated with malfunctioning of tumor suppressor p53. Intriguingly, in some cases two other members of the p53 family of proteins, transcription factors p63 and p73, can compensate for loss of p53. Although both p63 and p73 can bind the same DNA sequences as p53 and their transcriptionally active isoforms are able to regulate the expression of p53-dependent genes, the strongest overlap with p53 functions was detected for p73. Surprisingly, unlike p53, the p73 is rarely lost or mutated in cancers. On the contrary, its inactive isoforms are often overexpressed in cancer. In this review, we discuss several lines of evidence that cancer cells develop various mechanisms to repress p73-mediated cell death. Moreover, p73 isoforms may promote cancer growth by enhancing an anti-oxidative response, the Warburg effect and by repressing senescence. Thus, we speculate that the role of p73 in tumorigenesis can be ambivalent and hence, requires new therapeutic strategies that would specifically repress the oncogenic functions of p73, while keeping its tumor suppressive properties intact.

HCNM: Heterogeneous Correlation Network Model for Multi-level Integrative Study of Multi-omics Data for Cancer Subtype Prediction

Integrative analysis of multi-omics data is important for biomedical applications, as it is required for a comprehensive understanding of biological function. Integrating multi-omics data serves multiple purposes, such as, an integrated data model, dimensionality reduction of omic features, patient clustering, etc. For oncological data, patient clustering is synonymous to cancer subtype prediction. However, there is a gap in combining some of the widely used integrative analyses to build more powerful tools. To bridge the gap, we propose a multi-level integration algorithm to identify representative integrative subspace and use it for cancer subtype prediction. The three integrative approaches we implement on multi-omics features are, (1) multivariate multiple (linear) regression of the features from a cohort of patients/samples, (2) network construction using different omics features, and (3) fusion of sample similarity networks across the features. We use a type of multilayer network, called heterogeneous network, as a data model to transition between a network-free (NF) regression model and a network-based (NB) model, which uses correlation networks. The heterogeneous networks consist of intra- and inter-layer graphs. Our proposed heterogeneous correlation network model, HCNM, is central to our algorithm for gene-ranking, integrative subspace identification, and tumor-specific subtypes prediction. The genes of our representative integrative subspace have been enriched with gene-ontology and found to exhibit significant gene-disease association (GDA) scores. The subspace in genes which is less than 5% of the total gene-set of each genomic feature is used with NB fusion integrative model to predict sample subtypes. As the identified integrative subspace data of multi-omics is less prone to noise, bias, and outliers, our experiments show that the subtypes in our results agree with previous benchmark studies and exhibit better classification between poor and good survival of patient cohorts.Clinical relevance: Finding significant cancer-specific genes and subtypes of cancer is vital for early prognosis, and personalized treatment; therefore, improves survival probability of a patient.

Global mapping of cancers: The Cancer Genome Atlas and beyond

Cancer genomes have been explored from the early 2000s through massive exome sequencing efforts, leading to the publication of The Cancer Genome Atlas in 2013. Sequencing techniques have been developed alongside this project and have allowed scientists to bypass the limitation of costs for whole-genome sequencing (WGS) of single specimens by developing more accurate and extensive cancer sequencing projects, such as deep sequencing of whole genomes and transcriptomic analysis. The Pan-Cancer Analysis of Whole Genomes recently published WGS data from more than 2600 human cancers together with almost 1200 related transcriptomes. The application of WGS on a large database allowed, for the first time in history, a global analysis of features such as molecular signatures, large structural variations and noncoding regions of the genome, as well as the evaluation of RNA alterations in the absence of underlying DNA mutations. The vast amount of data generated still needs to be thoroughly deciphered, and the advent of machine-learning approaches will be the next step towards the generation of personalized approaches for cancer medicine. The present manuscript wants to give a broad perspective on some of the biological evidence derived from the largest sequencing attempts on human cancers so far, discussing advantages and limitations of this approach and its power in the era of machine learning.

Serine and one-carbon metabolisms bring new therapeutic venues in prostate cancer

Serine and one-carbon unit metabolisms are essential biochemical pathways implicated in fundamental cellular functions such as proliferation, biosynthesis of important anabolic precursors and in general for the availability of methyl groups. These two distinct but interacting pathways are now becoming crucial in cancer, the de novo cytosolic serine pathway and the mitochondrial one-carbon metabolism. Apart from their role in physiological conditions, such as epithelial proliferation, the serine metabolism alterations are associated to several highly neoplastic proliferative pathologies. Accordingly, prostate cancer shows a deep rearrangement of its metabolism, driven by the dependency from the androgenic stimulus. Several new experimental evidence describes the role of a few of the enzymes involved in the serine metabolism in prostate cancer pathogenesis. The aim of this study is to analyze gene and protein expression data publicly available from large cancer specimens dataset, in order to further dissect the potential role of the abovementioned metabolism in the complex reshaping of the anabolic environment in this kind of neoplasm. The data suggest a potential role as biomarkers as well as in cancer therapy for the genes (and enzymes) belonging to the one-carbon metabolism in the context of prostatic cancer.

Emerging roles of the HECT-type E3 ubiquitin ligases in hematological malignancies

Ubiquitination-mediated proteolysis or regulation of proteins, ultimately executed by E3 ubiquitin ligases, control a wide array of cellular processes, including transcription, cell cycle, autophagy and apoptotic cell death. HECT-type E3 ubiquitin ligases can be distinguished from other subfamilies of E3 ubiquitin ligases because they have a C-terminal HECT domain that directly catalyzes the covalent attachment of ubiquitin to their substrate proteins. Deregulation of HECT-type E3-mediated ubiquitination plays a prominent role in cancer development and chemoresistance. Several members of this subfamily are indeed frequently deregulated in human cancers as a result of genetic mutations and altered expression or activity. HECT-type E3s contribute to tumorigenesis by regulating the ubiquitination rate of substrates that function as either tumour suppressors or oncogenes. While the pathological roles of the HECT family members in solid tumors are quite well established, their contribution to the pathogenesis of hematological malignancies has only recently emerged. This review aims to provide a comprehensive overview of the involvement of the HECT-type E3s in leukemogenesis.

HiG2Vec: hierarchical representations of Gene Ontology and genes in the Poincaré ball

MOTIVATION

Knowledge manipulation of Gene Ontology (GO) and Gene Ontology Annotation (GOA) can be done primarily by using vector representation of GO terms and genes. Previous studies have represented GO terms and genes or gene products in Euclidean space to measure their semantic similarity using an embedding method such as the Word2Vec-based method to represent entities as numeric vectors. However, this method has the limitation that embedding large graph-structured data in the Euclidean space cannot prevent a loss of information of latent hierarchies, thus precluding the semantics of GO and GOA from being captured optimally. On the other hand, hyperbolic spaces such as the Poincaré balls are more suitable for modeling hierarchies, as they have a geometric property in which the distance increases exponentially as it nears the boundary because of negative curvature.

RESULTS

In this article, we propose hierarchical representations of GO and genes (HiG2Vec) by applying Poincaré embedding specialized in the representation of hierarchy through a two-step procedure: GO embedding and gene embedding. Through experiments, we show that our model represents the hierarchical structure better than other approaches and predicts the interaction of genes or gene products similar to or better than previous studies. The results indicate that HiG2Vec is superior to other methods in capturing the GO and gene semantics and in data utilization as well. It can be robustly applied to manipulate various biological knowledge.

AVAILABILITYAND IMPLEMENTATION

https://github.com/JaesikKim/HiG2Vec.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Central vascular ligation and mesentery based abdominal surgery

In the nineteenth century the idea of a correct surgical approach in oncologic surgery moved towards a good lymphadenectomy. In colon cancer the segment is removed with adjacent mesentery, in gastric cancer or pancreatic cancer a good oncologic resection is obtained with adequate lymphadenectomy. Many guidelines propose a minimal lymph node count that the surgeon must obtain. Therefore, it is essential to understand the adequate extent of lymphadenectomy to be performed in cancer surgery. In this review of the current literature, the focus is on "central vascular ligation", understood as radical lymphadenectomy in upper and lower gastrointestinal cancer, the evolution of this approach during the years and the improvement of laparoscopic techniques. For what concerns laparoscopic surgery, the main goal is to minimize post-operative trauma introducing the "less is more" concept whilst preserving attention for oncological outcomes. This review will demonstrate the importance of a scientifically based standardization of oncologic gastrointestinal surgery, especially in relation to the expansion of minimally invasive surgery and underlines the importance to further investigate through new randomized trials the role of extended lymphadenectomy in the new era of a multimodal approach, and most importantly, an era where minimally invasive techniques and the idea of "less is more" are becoming the standard thought for the surgical approach.

Low level of plasminogen increases risk for mortality in COVID-19 patients

The pathophysiology of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and especially of its complications is still not fully understood. In fact, a very high number of patients with COVID-19 die because of thromboembolic causes. A role of plasminogen, as precursor of fibrinolysis, has been hypothesized. In this study, we aimed to investigate the association between plasminogen levels and COVID-19-related outcomes in a population of 55 infected Caucasian patients (mean age: 69.8 ± 14.3, 41.8% female). Low levels of plasminogen were significantly associated with inflammatory markers (CRP, PCT, and IL-6), markers of coagulation (D-dimer, INR, and APTT), and markers of organ dysfunctions (high fasting blood glucose and decrease in the glomerular filtration rate). A multidimensional analysis model, including the correlation of the expression of coagulation with inflammatory parameters, indicated that plasminogen tended to cluster together with IL-6, hence suggesting a common pathway of activation during disease's complication. Moreover, low levels of plasminogen strongly correlated with mortality in COVID-19 patients even after multiple adjustments for presence of confounding. These data suggest that plasminogen may play a pivotal role in controlling the complex mechanisms beyond the COVID-19 complications, and may be useful both as biomarker for prognosis and for therapeutic target against this extremely aggressive infection.

NUAK2 and RCan2 participate in the p53 mutant pro-tumorigenic network

Most inactivating mutations in TP53 gene generates neomorphic forms of p53 proteins that experimental evidence and clinical observations suggest to exert gain-of-function effects. While massive effort has been deployed in the dissection of wild type p53 transcriptional programme, p53 mutant pro-tumorigenic gene network is still largely elusive. To help dissecting the molecular basis of p53 mutant GOF, we performed an analysis of a fully annotated genomic and transcriptomic human pancreatic adenocarcinoma to select candidate players of p53 mutant network on the basis their differential expression between p53 mutant and p53 wild-type cohorts and their prognostic value. We identified NUAK2 and RCan2 whose p53 mutant GOF-dependent regulation was further validated in pancreatic cancer cellular model. Our data demonstrated that p53R270H can physically bind RCan2 gene locus in regulatory regions corresponding to the chromatin permissive areas where known binding partners of p53 mutant, such as p63 and Srebp, bind. Overall, starting from clinically relevant data and progressing into experimental validation, our work suggests NUAK2 and RCan2 as novel candidate players of the p53 mutant pro-tumorigenic network whose prognostic and therapeutic interest might attract future studies.

Integration strategies of multi-omics data for machine learning analysis

Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems.

Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.

Involvement of transcribed lncRNA uc.291 and SWI/SNF complex in cutaneous squamous cell carcinoma

While non-melanoma skin cancers (NMSCs) are the most common tumours in humans, only the sub-type cutaneous squamous cell carcinoma (cSCC), might become metastatic with high lethality. We have recently identified a regulatory pathway involving the lncRNA transcript uc.291 in controlling the expression of epidermal differentiation complex genes via the interaction with ACTL6A, a component of the chromatin remodelling complex SWI/SNF. Since transcribed ultra-conserved regions (T-UCRs) are expressed in normal tissues and are deregulated in tumorigenesis, here we hypothesize a potential role for dysregulation of this axis in cSCC, accounting for the de-differentiation process observed in aggressive poorly differentiated cutaneous carcinomas. We therefore analysed their expression patterns in human tumour biopsies at mRNA and protein levels. The results suggest that by altering chromatin accessibility of the epidermal differentiation complex genes, down-regulation of uc.291 and BRG1 expression contribute to the de-differentiation process seen in keratinocyte malignancy. This provides future direction for the identification of clinical biomarkers in cutaneous SCC. Analysis of publicly available data sets indicates that the above may also be a general feature for SCCs of different origins.

Polymorphism on human aromatase affects protein dynamics and substrate binding: spectroscopic evidence

Human aromatase is a member of the cytochrome P450 superfamily, involved in steroid hormones biosynthesis. In particular, it converts androgen into estrogens being therefore responsible for the correct sex steroids balance. Due to its capacity in producing estrogens it has also been considered as a promising target for breast cancer therapy. Two single-nucleotide polymorphisms (R264C and R264H) have been shown to alter aromatase activity and they have been associated to an increased or decreased risk for estrogen-dependent pathologies. Here, the effect of these mutations on the protein dynamics is investigated by UV/FTIR and time resolved fluorescence spectroscopy. H/D exchange rates were measured by FTIR for the three proteins in the ligand-free, substrate- and inhibitor-bound forms and the data indicate that the wild-type enzyme undergoes a conformational change leading to a more compact tertiary structure upon substrate or inhibitor binding. Indeed, the H/D exchange rates are decreased when a ligand is present. In the variants, the exchange rates in the ligand-free and -bound forms are similar, indicating that a structural change is lacking, despite the single amino acid substitution is located in the peripheral shell of the protein molecule. Moreover, the fluorescence lifetimes data show that the quenching effect on tryptophan-224 observed upon ligand binding in the wild-type, is absent in both variants. Since this residue is located in the catalytic pocket, these findings suggest that substrate entrance and/or retention in the active site is partially compromised in both mutants. A contact network analysis demonstrates that the protein structure is organized in two main clusters, whose connectivity is altered by ligand binding, especially in correspondence of helix-G, where the amino acid substitutions occur. Our findings demonstrate that SNPs resulting in mutations on aromatase surface modify the protein flexibility that is required for substrate binding and catalysis. The cluster analysis provides a rationale for such effect, suggesting helix G as a possible target for aromatase inhibition.

New immunological potential markers for triple negative breast cancer: IL18R1, CD53, TRIM, Jaw1, LTB, PTPRCAP

Breast cancer (BC) is the second leading cause of cancer death in women worldwide, and settings of specific prognostic factors and efficacious therapies are made difficult by phenotypic heterogeneity of BC subtypes. Therefore, there is a current urgent need to define novel predictive genetic predictors that may be useful for stratifying patients with distinct prognostic outcomes. Here, we looked for novel molecular signatures for triple negative breast cancers (TNBCs). By a bioinformatic approach, we identified a panel of genes, whose expression was positively correlated with disease-free survival in TNBC patients, namely IL18R1, CD53, TRIM, Jaw1, LTB, and PTPRCAP, showing specific immune expression profiles linked to survival prediction; most of these genes are indeed expressed in immune cells and are required for productive lymphocyte activation. According to our hypothesis, these genes were not, or poorly, expressed in different TNBC cell lines, derived from either primary breast tumours or metastatic pleural effusions. This conclusion was further supported in vivo, as immuno-histochemical analysis on biopsies of TNBC invasive ductal carcinomas highlighted differential expression of these six genes in cancer cells, as well as in intra- and peri-tumoral infiltrating lymphocytes. Our data open to the possibility that inter-tumour heterogeneity of immune markers might have predictive value; further investigations are recommended in order to establish the real power of cancer-related immune profiles as prognostic factors.

Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer

MOTIVATION

To better understand the molecular features of cancers, a comprehensive analysis using multiomics data has been conducted. Additionally, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW, and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene-gene graph using pathway information by assigning interactions between genes in multiple layers of networks.

RESULTS

: As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene-gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets.

AVAILABILITY

iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The ZNF750-RAC1 axis as potential prognostic factor for breast cancer

The human zinc finger (C2H2-type) protein ZNF750 is a transcription factor regulated by p63 that plays a critical role in epithelial tissues homoeostasis, as well as being involved in the pathogenesis of cancer. Indeed, missense mutations, truncation and genomic deletion have been found in oesophageal squamous cell carcinoma. In keeping, we showed that ZNF750 negatively regulates cell migration and invasion in breast cancer cells; in particular, ZNF750 binds and recruits KDM1A and HDAC1 on the LAMB3 and CTNNAL1 promoters. This interaction, in turn, represses the transcription of LAMB3 and CTNNAL1 genes, which are involved in cell migration and invasion. Given that ZNF750 is emerging as a crucial transcription factor that acts as tumour suppressor gene, here, we show that ZNF750 represses the expression of the small GTPase, Ras-related C3 botulinum toxin substrate 1 (RAC1) in breast cancer cell lines, by directly binding its promoter region. In keeping with ZNF750 controlling RAC1 expression, we found an inverse correlation between ZNF750 and RAC1 in human breast cancer datasets. More importantly, we found a significant upregulation of RAC1 in human breast cancer datasets and we identified a direct correlation between RAC1 expression and the survival rate of breast cancer patient. Overall, our findings provide a novel molecular mechanism by which ZNF750 acts as tumour suppressor gene. Hence, we report a potential clinical relevance of ZNF750/RAC1 axis in breast cancer.

Can COVID-19 pandemic boost the epidemic of neurodegenerative diseases?

The pandemic of Coronavirus Disease 2019 (COVID-19) presents the world with the medical challenge associated with multifactorial nature of this pathology. Indeed COVID-19 affects several organs and systems and presents diversified clinical picture. COVID-19 affects the brain in many ways including direct infection of neural cells with SARS-CoV-2, severe systemic inflammation which floods the brain with pro-inflammatory agents thus damaging nervous cells, global brain ischaemia linked to a respiratory failure, thromboembolic strokes related to increased intravascular clotting and severe psychological stress. Often the COVID-19 is manifested by neurological and neuropsychiatric symptoms that include dizziness, disturbed sleep, cognitive deficits, delirium, hallucinations and depression. All these indicate the damage to the nervous tissue which may substantially increase the incidence of neurodegenerative diseases and promote dementia.

Liquid biopsies and cancer omics

The development of the sequencing technologies allowed the generation of huge amounts of molecular data from a single cancer specimen, allowing the clinical oncology to enter the era of the precision medicine. This massive amount of data is highlighting new details on cancer pathogenesis but still relies on tissue biopsies, which are unable to capture the dynamic nature of cancer through its evolution. This assumption led to the exploration of non-tissue sources of tumoral material opening the field of liquid biopsies. Blood, together with body fluids such as urines, or stool, from cancer patients, are analyzed applying the techniques used for the generation of omics data. With blood, this approach would allow to take into account tumor heterogeneity (since the circulating components such as CTCs, ctDNA, or ECVs derive from each cancer clone) in a time dependent manner, resulting in a somehow "real-time" understanding of cancer evolution. Liquid biopsies are beginning nowdays to be applied in many cancer contexts and are at the basis of many clinical trials in oncology.

Commensal microbes and p53 in cancer progression

Aetiogenesis of cancer has not been fully determined. Recent advances have clearly defined a role for microenvironmental factors in cancer progression and initiation; in this context, microbiome has recently emerged with a number of reported correlative and causative links implicating alterations of commensal microbes in tumorigenesis. Bacteria appear to have the potential to directly alter physiological pathways of host cells and in specific circumstances, such as the mutation of the tumour suppressive factor p53, they can also directly switch the function of a gene from oncosuppressive to oncogenic. In this minireview, we report a number of examples on how commensal microbes alter the host cell biology, affecting the oncogenic process. We then discuss more in detail how interaction with the gut microbiome can affect the function of p53 mutant in the intestinal tumorigenesis.

Serological determinants of COVID-19

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection spreaded rapidly worldwide, as far as it has become a global pandemic. Therefore, the introduction of serological tests for determination of IgM and IgG antibodies has become the main diagnostic tool, useful for tracking the spread of the virus and for consequently allowing its containment. In our study we compared point of care test (POCT) lateral flow immunoassay (FIA) vs automated chemiluminescent immunoassay (CLIA), in order to assess their specificity and sensibility for COVID-19 antibodies detection.

RESULTS

We find that different specificities and sensitivities for IgM and IgG tests. Notably IgM POCT FIA method vs CLIA method (gold standard) has a low sensitivity (0.526), while IgG POCT FIA method vs CLIA method (gold standard) test has a much higher sensitivity (0.937); further, with respect of IgG, FIA and CLIA could arguably provide equivalent information.

CONCLUSIONS

FIA method could be helpful in assessing in short time, the possible contagiousness of subjects that for work reasons cannot guarantee "social distancing".

Cancer predictive studies

The identification of individual or clusters of predictive genetic alterations might help in defining the outcome of cancer treatment, allowing for the stratification of patients into distinct cohorts for selective therapeutic protocols. Neuroblastoma (NB) is the most common extracranial childhood tumour, clinically defined in five distinct stages (1–4 & 4S), where stages 3–4 define chemotherapy-resistant, highly aggressive disease phases. NB is a model for geneticists and molecular biologists to classify genetic abnormalities and identify causative disease genes. Despite highly intensive basic research, improvements on clinical outcome have been predominantly observed for less aggressive cancers, that is stages 1,2 and 4S. Therefore, stages 3–4 NB are still complicated at the therapeutic level and require more intense fundamental research. Using neuroblastoma as a model system, here we herein outline how cancer prediction studies can help at steering preclinical and clinical research toward the identification and exploitation of specific genetic landscape. This might result in maximising the therapeutic success and minimizing harmful effects in cancer patients.

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.

Multi-omics Data and Analytics Integration in Ovarian Cancer

Cancer, which involves the dysregulation of genes via multiple mechanisms, is unlikely to be fully explained by a single data type. By combining different “omes”, researchers can increase the discovery of novel bio-molecular associations with disease-related phenotypes. Investigation of functional relations among genes associated with the same disease condition may further help to develop more accurate disease-relevant prediction models. In this work, we present an integrative framework called Data & Analytic Integrator (DAI), to explore the relationship between different omics via different mathematical formulations and algorithms. In particular, we investigate the combinatorial use of molecular knowledge identified from omics integration methods netDx, iDRW and SSL, by fusing the derived aggregated similarity matrices and by exploiting these in a semi-supervised learner. The analysis workflows were applied to real-life data for ovarian cancer and underlined the benefits of joint data and analytic integration.