Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes.

Breast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic. While these intrinsic subtypes show a significant association with clinical outcomes, their long-term survival prediction beyond 5 years often deviates from expected clinical outcomes. This study aimed to identify naturally occurring long-term prognostic subgroups of BC based on an integrated multi-omics analysis. This study incorporates a clinical cohort of 335 untreated BC patients from the Oslo2 study with long-term follow-up (>12 years). Multi-Omics Factor Analysis (MOFA+) was employed to integrate transcriptomic, proteomic, and metabolomic data obtained from the tumor tissues. Our analysis revealed three prominent multi-omics clusters of BC patients with significantly different long-term prognoses (p = 0.005). The multi-omics clusters were validated in two independent large cohorts, METABRIC and TCGA. Importantly, a lack of prognostic association to long-term follow-up above 12 years in the previously established intrinsic subtypes was shown for these cohorts. Through a systems-biology approach, we identified varying enrichment levels of cell-cycle and immune-related pathways among the prognostic clusters. Integrated multi-omics analysis of BC revealed three distinct clusters with unique clinical and biological characteristics. Notably, these multi-omics clusters displayed robust associations with long-term survival, outperforming the established intrinsic subtypes.

Novel insights into the structural changes induced by disease-associated mutations in TDP-43: a computational approach.

Over the last two decades, the pathogenic aggregation of TAR DNA-binding protein 43 (TDP-43) is found to be strongly associated with several fatal neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTD), etc. While the mutations and truncation in TDP-43 protein have been suggested to be responsible for TDP-43 pathogenesis by accelerating the aggregation process, the effects of these mutations on the bio-mechanism of pathological TDP-43 protein remained poorly understood. Investigating this at the molecular level, we formulized an integrated workflow of molecular dynamic simulation and machine learning models (MD-ML). By performing an extensive structural analysis of three disease-related mutations (i.e., I168A, D169G, and I168A-D169G) in the conserved RNA recognition motifs (RRM1) of TDP-43, we observed that the I168A-D169G double mutant delineates the highest packing of the protein inner core as compared to the other mutations, which may indicate more stability and higher chances of pathogenesis. Moreover, through our MD-ML workflow, we identified the biological descriptors of TDP-43 which includes the interacting residue pairs and individual protein residues that influence the stability of the protein and could be experimentally evaluated to develop potential therapeutic strategies.Communicated by Ramaswamy H. Sarma.

A machine learning approach to unmask novel gene signatures and prediction of Alzheimer's disease within different brain regions.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder whose aetiology is currently unknown. Although numerous studies have attempted to identify the genetic risk factor(s) of AD, the interpretability and/or the prediction accuracies achieved by these studies remained unsatisfactory, reducing their clinical significance. Here, we employ the ensemble of random-forest and regularized regression model (LASSO) to the AD-associated microarray datasets from four brain regions - Prefrontal cortex, Middle temporal gyrus, Hippocampus, and Entorhinal cortex- to discover novel genetic biomarkers through a machine learning-based feature-selection classification scheme. The proposed scheme unraveled the most optimum and biologically significant classifiers within each brain region, which achieved by far the highest prediction accuracy of AD in 5-fold cross-validation (99% average). Interestingly, along with the novel and prominent biomarkers including CORO1C, SLC25A46, RAE1, ANKIB1, CRLF3, PDYN, numerous non-coding RNA genes were also observed as discriminator, of which AK057435 and BC037880 are uncharacterized long non-coding RNA genes.

AE-LGBM: Sequence-based novel approach to detect interacting protein pairs via ensemble of autoencoder and LightGBM.

Protein-protein interactions (PPIs) play a crucial role in biological processes of living organisms. Correct prediction of PPI can prove to be extremely valuable in probing protein functions which can aid in the development of new and powerful therapies for disease prevention. Many experimental studies have been previously performed to investigate PPIs. However, in-vitro techniques to investigate PPIs are resource-extensive and time-consuming. Although various in-silico approaches to predict PPI have been developed in recent years, they could be fallible in terms of accuracy and false-positive rate. To overcome these shortcomings, we propose a novel approach, AE-LGBM to predict the PPIs more accurately. It employs LightGBM classifier and utilizes the Autoencoder, which is an artificial neural network, to efficiently produce lower-dimensional, discriminative, and noise-free features. We incorporate conjoint triad (CT) and Composition-Transition-Distribution (CTD) features into the AE-LGBM framework. On performing ten-fold cross-validation, the prediction accuracies obtained by AE-LGBM for Human and Yeast datasets are 98.7% and 95.4% respectively. AE-LGBM was further evaluated on independent datasets and has achieved excellent accuracies of 100%, 100%, 99.9%, 99.3%, 99.2% on E. coli, M. musculus, C. elegans, H. pylori and H. sapiens respectively. AE-LGBM has also obtained the best accuracy when tested over three important PPI networks namely single-core network (CD9), the multiple-core network (The Ras/Raf/MEK/ERK pathway) and the cross-connection network (Wnt Network). The outstanding generalization ability of AE-LGBM makes it a versatile, efficient, and robust PPIs prediction model.

BCG vaccination policy and preventive chloroquine usage: do they have an impact on COVID-19 pandemic?

Coronavirus disease 2019 (COVID-19) is a severe acute respiratory syndrome caused by Coronavirus 2 (SARS-CoV-2). In the light of its rapid global spreading, on 11 March 2020, the World Health Organization has declared it a pandemic. Interestingly, the global spreading of the disease is not uniform, but has so far left some countries relatively less affected. The reason(s) for this anomalous behavior are not fully understood, but distinct hypotheses have been proposed. Here we discuss the plausibility of two of them: the universal vaccination with Bacillus Calmette-Guerin (BCG) and the widespread use of the antimalarial drug chloroquine (CQ). Both have been amply discussed in the recent literature with positive and negative conclusions: we felt that a comprehensive presentation of the data available on them would be useful. The analysis of data for countries with over 1000 reported COVID-19 cases has shown that the incidence and mortality were higher in countries in which BCG vaccination is either absent or has been discontinued, as compared with the countries with universal vaccination. We have performed a similar analysis of the data available for CQ, a widely used drug in the African continent and in other countries in which malaria is endemic; we discuss it here because CQ has been used as the drug to treat COVID-19 patients. Several African countries no longer recommend it officially for the fight against malaria, due to the development of resistance to Plasmodium, but its use across the continent is still diffuse. Taken together, the data in the literature have led to the suggestion of a possible inverse correlation between BCG immunization and COVID-19 disease incidence and severity.