ConSIG: consistent discovery of molecular signature from OMIC data

Interpretable diagnostic system for multiocular diseases based on hybrid meta-heuristic feature selection

Age-related Macular Degeneration (AMD), Cataract, Diabetic Retinopathy (DR) and Glaucoma are the four most common ocular conditions that affect a person's vision. Early detection in the asymptomatic stages can alleviate vision loss or slow down the progression of these diseases. However, manual diagnosis is a costly and tedious process, especially in mass screening applications. Computer aided diagnosis (CAD) systems serve as an aid to ophthalmologists in efficient diagnosis of ocular diseases. In particular, a generic CAD framework that detects multiple ocular diseases could be immensely beneficial. In the proposed work, a single framework for detection of the above-mentioned ocular diseases has been explored. Specifically, a pool of non-linear handcrafted features are extracted from fundus images, followed by feature selection using a hybrid optimization algorithm, where features are selected using JAYA algorithm (JA) first, followed sequentially by the Harris hawks optimization (HHO) algorithm. The selected features are used to train an extreme gradient boosting (XGB) model for disease classification. Unlike existing systems that restrict non-linear features to single ocular disease detection, the proposed system is the first of its kind for detection of the above-specified multiple ocular diseases in a generic framework, yielding 93 % accuracy, 91.3 % sensitivity, 96.4 % specificity, 90.4 % precision and 90.8 % F1 score. Further, in this study, Shapley additive explanations (SHAP) analysis is employed to gain insight on the impact of the non-linear features on the model's prediction capability. This work clearly demonstrates the importance of explainability that opens the 'black box' nature of machine learning (ML) model and clearly unveils the relationships among the features and the diagnosis. Also, the explainable ML model improves transparency of the model's decision-making process. The proposed algorithm can efficiently assist physicians in diagnosing the ocular diseases using fundus images in clinical practice, and avoids the subjective difference that comes with manual assessment.

Improving the identification of cancer driver modules using deep features learned from multi-omics data

Identifying the cancer driver modules or pathways is crucial to understanding the fundamental mechanisms of cancer occurrence and progression. The rapid abundance of cancer omics data provides unprecedented opportunities to study the driver modules in cancer, and many computational methods have been developed in recent years. However, most existing methods have limitations in considering different types of cancer omics data and cannot effectively learn informative omics features for integrated identification of driver modules. In this paper, we introduce a new integrated framework to accurately identify the cancer driver modules by integrating the protein-protein interaction network, transcriptional regulatory network, gene expression and mutation data in cancer. We first develop a series of methods to learn the deep features of functional connectivity between genes in each omics data and then construct an integrated gene functional coherence network. Furthermore, we present a two-step module mining method to efficiently identify the cancer driver modules from the integrated gene functional coherence network. Systematic experiments in three cancer types demonstrate that the proposed framework can obtain more significant driver modules than most existing methods, and some identified driver modules are associated with clinical survival phenotypes.

Chem(Pro)2: the atlas of chemoproteomic probes labelling human proteins

Chemoproteomic probes (CPPs) have been widely considered as powerful molecular biological tools that enable the highly efficient discovery of both binding proteins and modes of action for the studied compounds. They have been successfully used to validate targets and identify binders. The design of CPP has been considered extremely challenging, which asks for the generalization using a large number of probe data. However, none of the existing databases gives such valuable data of CPPs. Herein, a database entitled 'Chem(Pro)2' was therefore developed to systematically describe the atlas of diverse types of CPPs labelling human protein in living cell/lysate. With the booming application of chemoproteomic technique and artificial intelligence in current chemical biology study, Chem(Pro)2 was expected to facilitate the AI-based learning of interacting pattern among molecules for discovering innovative targets and new drugs. Till now, Chem(Pro)2 has been open to all users without any login requirement at: https://idrblab.org/chemprosquare/.

SYNBIP 2.0: epitopes mapping, sequence expansion and scaffolds discovery for synthetic binding protein innovation

Synthetic binding proteins (SBPs) represent a pivotal class of artificially engineered proteins, meticulously crafted to exhibit targeted binding properties and specific functions. Here, the SYNBIP database, a comprehensive resource for SBPs, has been significantly updated. These enhancements include (i) featuring 3D structures of 899 SBP-target complexes to illustrate the binding epitopes of SBPs, (ii) using the structures of SBPs in the monomer or complex forms with target proteins, their sequence space has been expanded five times to 12 025 by integrating a structure-based protein generation framework and a protein property prediction tool, (iii) offering detailed information on 78 473 newly identified SBP-like scaffolds from the RCSB Protein Data Bank, and an additional 16 401 555 ones from the AlphaFold Protein Structure Database, and (iv) the database is regularly updated, incorporating 153 new SBPs. Furthermore, the structural models of all SBPs have been enhanced through the application of the AlphaFold2, with their clinical statuses concurrently refreshed. Additionally, the design methods employed for each SBP are now prominently featured in the database. In sum, SYNBIP 2.0 is designed to provide researchers with essential SBP data, facilitating their innovation in research, diagnosis and therapy. SYNBIP 2.0 is now freely accessible at https://idrblab.org/synbip/.

MolBiC: the cell-based landscape illustrating molecular bioactivities

The measurement of cell-based molecular bioactivity (CMB) is critical for almost every step of drug development. With the booming application of AI in biomedicine, it is essential to have the CMB data to promote the learning of cell-based patterns for guiding modern drug discovery, but no database providing such information has been constructed yet. In this study, we introduce MolBiC, a knowledge base designed to describe valuable data on molecular bioactivity measured within a cellular context. MolBiC features 550 093 experimentally validated CMBs, encompassing 321 086 molecules and 2666 targets across 988 cell lines. Our MolBiC database is unique in describing the valuable data of CMB, which meets the critical demands for CMB-based big data promoting the learning of cell-based molecular/pharmaceutical pattern in drug discovery and development. MolBiC is now freely accessible without any login requirement at: https://idrblab.org/MolBiC/.

PDCdb: the biological activity and pharmaceutical information of peptide-drug conjugate (PDC)

Peptide-drug conjugates (PDCs) have emerged as a promising class of targeted therapeutics with substantial pharmaceutical advantages and market potentials, which is a combination of a peptide (selective to the disease-relevant target), a linker (stable in circulation but cleavable at target site) and a cytotoxic/radioactive drug (efficacious/traceable for disease). Among existing PDCs, those based on radiopharmaceuticals (a.k.a. radioactive drugs) are valued due to their accurate imaging and targeted destruction of disease sites. It's demanded to accumulate the biological activity and pharmaceutical information of PDCs. Herein, a database PDCdb was thus constructed to systematically describe these valuable data. Particularly, biological activities for 2036 PDCs were retrieved from literatures, which resulted in 1684, 613 and 2753 activity data generated based on clinical trial, animal model and cell line, respectively. Furthermore, the pharmaceutical information for all 2036 PDCs was collected, which gave the diverse data of (a) ADME property, plasma half-life and administration approach of a PDC and (b) chemical modification, primary target, mode of action, conjugating feature of the constituent peptide/linker/drug. In sum, PDCdb systematically provided the biological activities and pharmaceutical information for the most comprehensive list of PDCs among the available databases, which was expected to attract broad interest from related communities and could be freely accessible at: https://idrblab.org/PDCdb/.

Gene selection and cancer classification using interaction-based feature clustering and improved-binary Bat algorithm

In high-dimensional gene expression data, selecting an optimal subset of genes is crucial for achieving high classification accuracy and reliable diagnosis of diseases. This paper proposes a two-stage hybrid model for gene selection based on clustering and a swarm intelligence algorithm to identify the most informative genes with high accuracy. First, a clustering-based multivariate filter approach is performed to explore the interactions between the features and eliminate any redundant or irrelevant ones. Then, by controlling for the problem of premature convergence in the binary Bat algorithm, the optimal gene subset is determined using different classifiers with the Monte Carlo cross-validation data partitioning model. The effectiveness of our proposed framework is evaluated using eight gene expression datasets, by comparison with other recently published algorithms in the literature. Experiments confirm that in seven out of eight datasets, the proposed method can achieve superior results in terms of classification accuracy and gene subset size. In particular, it achieves a classification accuracy of 100% in Lymphoma and Ovarian datasets and above 97.4% in the rest with a minimum number of genes. The results demonstrate that our proposed algorithm has the potential to solve the feature selection problem in different applications with high-dimensional datasets.

Application of artificial intelligence in drug design: A review

Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data

BACKGROUND

Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.

METHOD

In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.

RESULTS

We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites.

CONCLUSION

The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Emotion analysis and recognition in 3D space using classifier-dependent feature selection in response to tactile enhanced audio-visual content using EEG

Traditional media such as text, images, audio, and video primarily target specific senses like vision and hearing. In contrast, multiple sensorial media aims to create immersive experiences by integrating additional sensory modalities such as touch, smell, and taste where applicable. Tactile enhanced audio-visual content leverages the sense of touch in addition to visual and auditory stimuli, aiming to create a more immersive and engaging interaction for users. Previously, tactile enhanced content has been explored in 2D emotional space (valence and arousal). In this paper, EEG data against tactile enhanced audio-visual content is labeled based on a self-assessment manikin scale in 3 dimensions i.e., valence, arousal, and dominance. Statistical significance (with a 95% confidence interval) is also established based on gathered scores, highlighting a significant difference in the arousal and dominance dimension of traditional media and tactile enhanced media. A new methodology is proposed using classifier-dependent feature selection approach to classify valence, arousal, and dominance states using three different classifiers. A highest accuracy of 75%, 73.8%, and 75% is achieved for classifying valence, arousal, and dominance states, respectively. The proposed scheme outperforms previous emotion recognition based studies in response to enhanced multimedia content in terms of accuracy, F-score, and other error parameters.

Tunable Q-factor wavelet transform based lung signal decomposition and statistical feature extraction for effective lung disease classification

The auscultation is a non-invasive and cost-effective method used for the diagnosis of lung diseases, which are one of the leading causes of death worldwide. However, the efficacy of the auscultation suffers from the limitations of the analog stethoscopes and the subjective nature of human interpretation. To overcome these limitations, the accurate diagnosis of these diseases by employing the computer based automated algorithms applied to the digitized lung sounds has been studied for the last decades. This study proposes a novel approach that uses a Tunable Q-factor Wavelet Transform (TQWT) based statistical feature extraction followed by individual and ensemble learning model training with the aim of lung disease classification. During the learning stage various machine learning algorithms are utilized as the individual learners as well as the hard and soft voting fusion approaches are employed for performance enhancement with the aid of the predictions of individual models. For an objective evaluation of the proposed approach, the study was structured into two main tasks that were investigated in detail by using several sub-tasks to comparison with state-of-the-art studies. Among the sub-tasks which investigates patient-based classification, the highest accuracy obtained for the binary classification was achieved as 97.63% (healthy vs. non-healthy), while accuracy values up to 66.32% for three-class classification (obstructive-related, restrictive-related, and healthy), and 53.42% for five-class classification (asthma, chronic obstructive pulmonary disease, interstitial lung disease, pulmonary infection, and healthy) were obtained. Regarding the other sub-task, which investigates sample-based classification, the proposed approach was superior to almost all previous findings. The proposed method underscores the potential of TQWT based signal decomposition that leverages the power of its adaptive time-frequency resolution property satisfied by Q-factor adjustability. The obtained results are very promising and the proposed approach paves the way for more accurate and automated digital auscultation techniques in clinical settings.

Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis

BACKGROUND

Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis.

METHOD

We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan-Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.

RESULTS

Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.

CONCLUSIONS

Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.

Identifying miRNA as biomarker for breast cancer subtyping using association rule

- This paper presents a comprehensive study focused on breast cancer subtyping, utilizing a multifaceted approach that integrates feature selection, machine learning classifiers, and miRNA regulatory networks. The feature selection process begins with the CFS algorithm, followed by the Apriori algorithm for association rule generation, resulting in the identification of significant features tailored to Luminal A, Luminal B, HER-2 enriched, and Basal-like subtypes. The subsequent application of Random Forest (RF) and Support Vector Machine (SVM) classifiers yielded promising results, with the SVM model achieving an overall accuracy of 76.60 % and the RF model demonstrating robust performance at 80.85 %. Detailed accuracy metrics revealed strengths and areas for refinement, emphasizing the potential for optimizing subtype-specific recall. To explore the regulatory landscape in depth, an analysis of selected miRNAs was conducted using MIENTURNET, a tool for visualizing miRNA-target interactions. While FDR analysis raised concerns for HER-2 and Basal-like subtypes, Luminal A and Luminal B subtypes showcased significant miRNA-gene interactions. Functional enrichment analysis for Luminal A highlighted the role of Ovarian steroidogenesis, implicating specific miRNAs such as hsa-let-7c-5p and hsa-miR-125b-5p as potential diagnostic biomarkers and regulators of Luminal A breast cancer. Luminal B analysis uncovered associations with the MAPK signaling pathway, with miRNAs like hsa-miR-203a-3p and hsa-miR-19a-3p exhibiting potential diagnostic and therapeutic significance. In conclusion, this integrative approach combines machine learning techniques with miRNA analysis to provide a holistic understanding of breast cancer subtypes. The identified miRNAs and associated pathways offer insights into potential diagnostic biomarkers and therapeutic targets, contributing to the ongoing efforts to improve breast cancer diagnostics and personalized treatment strategies.

scHD4E: Novel ensemble learning-based differential expression analysis method for single-cell RNA-sequencing data

Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.

Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction

This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques-Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection-play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts.

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data

The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.

Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson's disease

Feature selection and machine learning algorithms can be used to analyze Single Nucleotide Polymorphisms (SNPs) data and identify potential disease biomarkers. Reproducibility of identified biomarkers is critical for them to be useful for clinical research; however, genotyping platforms and selection criteria for individuals to be genotyped affect the reproducibility of identified biomarkers. To assess biomarkers reproducibility, we collected five SNPs datasets from the database of Genotypes and Phenotypes (dbGaP) and explored several data integration strategies. While combining datasets can lead to a reduction in classification accuracy, it has the potential to improve the reproducibility of potential biomarkers. We evaluated the agreement among different strategies in terms of the SNPs that were identified as potential Parkinson's disease (PD) biomarkers. Our findings indicate that, on average, 93% of the SNPs identified in a single dataset fail to be identified in other datasets. However, through dataset integration, this lack of replication is reduced to 62%. We discovered fifty SNPs that were identified at least twice, which could potentially serve as novel PD biomarkers. These SNPs are indirectly linked to PD in the literature but have not been directly associated with PD before. These findings open up new potential avenues of investigation.

FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response

Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

Wekemo Bioincloud: A user-friendly platform for meta-omics data analyses

The increasing application of meta-omics approaches to investigate the structure, function, and intercellular interactions of microbial communities has led to a surge in available data. However, this abundance of human and environmental microbiome data has exposed new scalability challenges for existing bioinformatics tools. In response, we introduce Wekemo Bioincloud-a specialized platform for -omics studies. This platform offers a comprehensive analysis solution, specifically designed to alleviate the challenges of tool selection for users in the face of expanding data sets. As of now, Wekemo Bioincloud has been regularly equipped with 22 workflows and 65 visualization tools, establishing itself as a user-friendly and widely embraced platform for studying diverse data sets. Additionally, the platform enables the online modification of vector outputs, and the registration-independent personalized dashboard system ensures privacy and traceability. Wekemo Bioincloud is freely available at https://www.bioincloud.tech/.

VARIDT 3.0: the phenotypic and regulatory variability of drug transporter

The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.

Identification of telomere-associated gene signatures to predict prognosis and drug sensitivity in glioma

OBJECTIVE

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties, leading to significant mortality and morbidity. Emerging evidence shows telomere maintenance has implicated in glioma susceptibility and prognosis. In this study, we comprehensively analyzed gene signatures related to telomere maintenance in glioma and their predictive values for predicting the prognosis and drug sensitivity in glioma.

METHODS

We initially identified telomere-related genes differentially expressed between low-grade glioma (LGG) and glioblastoma (GBM) and accordingly developed a risk model by univariate and multivariate Cox analysis to assess the expressions of telomere-related genes across the risk groups. Finally, to assess these genes in immune function the anti-tumor medications often used in the clinical treatment of glioma, we computed immune cell infiltration analysis and drug sensitivity analysis.

RESULTS

The consensus clustering analysis identified 20 telomere-related genes which split LGG patients into two distinct subtypes. The patient survival, the expressions of key telomere-related DEGs, and immune cell infiltration significantly differed between Cluster 1 and Cluster 2. The LASSO risk model [riskScore=(0.086)*HOXA7+(0.242)*WEE1+(0.247)*IGF2BP3+(0.052)*DUSP10] showed significant differences regarding the 1-, 3-, 5-year overall survival, immune cell infiltration, and drug sensitivity between high- and low-risk groups. The predictive nomogram constructed to quantify the survival probability of each sample at 1, 3, and 5 years was consistent with the actual patient survival.

CONCLUSION

Our comprehensive characterization of telomere-associated gene signatures in glioma reveals their possible roles in the development, tumor microenvironment, and prognosis. The study provides some suggestive relationships between four telomere-related genes (HOXA7, WEE1, IGF2BP3, and DUSP10) and glioma prognosis.

Voltammetric studies and spectroscopic investigations of the interaction of an anticancer drug bevacizumab-DNA and analytical applications of disposable pencil graphite sensor

A sensitive, simple, fast electrochemical biosensor for the DNA interaction of bevacizumab (BEVA), which is used as a targeted drug in cancer treatment, was developed using the differential pulse voltammetry (DPV) technique with pencil graphite electrode (PGE). In the work, PGE was electrochemically activated in a supporting electrolyte medium of +1.4 V/60 s (PBS pH 3.0). Surface characterization of PGE was carried out by SEM, EDX, EIS, and CV techniques. Determination and electrochemical properties of BEVA were examined with CV and DPV techniques. BEVA gave a distinct analytical signal on the PGE surface at a potential of +0.90 V (vs. Ag/AgCl). In the procedure proposed in this study, BEVA gave a linear response on PGE in PBS (pH 3.0 containing 0.02 M NaCl) (0.1 mg mL-1 - 0.7 mg mL-1) with LOD and LOQ values of 0.026 mg mL-1 and 0.086 μg mL-1, respectively. BEVA was reacted with 20 μg mL-1 DNA in PBS for 150 s and analytical peak signals for adenine and guanine bases were evaluated. The interaction between BEVA-DNA was supported by UV-Vis. Absorption spectrometry and the binding constant was determined as 7.3 × 104.

Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications

Recent research has highlighted the pivotal role of RNA post-transcriptional modifications in the regulation of RNA expression and function. Accurate identification of RNA modification sites is important for understanding RNA function. In this study, we propose a novel RNA modification prediction method, namely Rm-LR, which leverages a long-range-based deep learning approach to accurately predict multiple types of RNA modifications using RNA sequences only. Rm-LR incorporates two large-scale RNA language pre-trained models to capture discriminative sequential information and learn local important features, which are subsequently integrated through a bilinear attention network. Rm-LR supports a total of ten RNA modification types (m6A, m1A, m5C, m5U, m6Am, Ψ, Am, Cm, Gm, and Um) and significantly outperforms the state-of-the-art methods in terms of predictive capability on benchmark datasets. Experimental results show the effectiveness and superiority of Rm-LR in prediction of various RNA modifications, demonstrating the strong adaptability and robustness of our proposed model. We demonstrate that RNA language pretrained models enable to learn dense biological sequential representations from large-scale long-range RNA corpus, and meanwhile enhance the interpretability of the models. This work contributes to the development of accurate and reliable computational models for RNA modification prediction, providing insights into the complex landscape of RNA modifications.

Integrated computational screening and liquid biopsy approach to uncover the role of biomarkers for oral cancer lymph node metastasis

Cancer is an abnormal, heterogeneous growth of cells with the ability to invade surrounding tissue and even distant organs. Worldwide, GLOBOCAN had an estimated 18.1 million new cases and 9.6 million death rates of cancer in 2018. Among all cancers, Oral cancer (OC) is the sixth most common cancer worldwide, and the third most common in India, the most frequent type, oral squamous cell carcinoma (OSCC), tends to spread to lymph nodes in advanced stages. Throughout the past few decades, the molecular landscape of OSCC biology has remained unknown despite breakthroughs in our understanding of the genome-scale gene expression pattern of oral cancer particularly in lymph node metastasis. Moreover, due to tissue variability in single-cohort studies, investigations on OSCC gene-expression profiles are scarce or inconsistent. The work provides a comprehensive analysis of changed expression and lays a major focus on employing a liquid biopsy base method to find new therapeutic targets and early prediction biomarkers for lymph node metastasis. Therefore, the current study combined the profile information from GSE9844, GSE30784, GSE3524, and GSE2280 cohorts to screen for differentially expressed genes, and then using gene enrichment analysis and protein-protein interaction network design, identified the possible candidate genes and pathways in lymph node metastatic patients. Additionally, the mRNA expression of discovered genes was assessed using real-time PCR, and the Human Protein Atlas database was utilized to determine the protein levels of hub genes in tumor and normal tissues. Angiogenesis was been investigated using the Chorioallentoic membrane (CAM) angiogenesis test. In a cohort of OSCC patients, fibronectin (FN1), C-X-C Motif Chemokine Ligand 8 (CXCL8), and matrix metallopeptidase 9 (MMP9) were significantly upregulated, corroborating these findings. Our identified significant gene signature showed greater serum exosome effectiveness in early detection and clinically linked with intracellular communication in the establishment of the premetastatic niche. Also, the results of the CAM test reveal that primary OC derived exosomes may have a function in angiogenesis. As a result, our study finds three potential genes that may be used as a possible biomarker for lymph node metastasis early detection and sheds light on the underlying processes of exosomes that cause a premetastatic condition.

Identification of signature genes and drug candidates for primary plasma cell leukemia: An integrated system biology approach

BACKGROUND

Plasma cell leukemia (PCL) is one of the rare cancer which is characterized by the uncontrolled proliferation of plasma cells in peripheral blood and bone marrow. The aggressive behavior of the disease and high mortality rate among PCL patients makes it a thirst area to be explored.

METHODS

The dataset for PCL was obtained from the GEO database and was analyzed using GEO2R for differentially expressed genes. Further, the functional enrichment analysis was carried out for DEGs using DAVID. The protein-protein interactions (PPI) for DEGs were obtained using STRING 11.5 and were analyzed in Cytoscape 3.7.2. to obtain the key hub genes. These key hub genes were investigated for their interaction with suitable drug candidates using DGIdb, DrugMAP, and Schrodinger's version 2022-1.

RESULTS

Out of the total of 104 DEGs, 39 genes were up-regulated whereas 65 genes were down-regulated. A total of 11 biological processes, 2 cellular components, and 5 molecular functions were enriched along with the 7 KEGG pathways for the DEGs. Further, a total of 11 hub genes were obtained from the PPI of DEGs of which TP53, MAPK1, SOCS1, MBD3, and YES1 were the key hub genes. Oxaliplatin, mitoxantrone, and ponatinib were found to have the highest binding affinity towards the p53, MAPK1, and YES1 proteins respectively.

CONCLUSION

TP53, MAPK1, SOCS1, MBD3, and YES1 are the signature hub genes that might be responsible for the aggressive prognosis of PCL leading to poor survival rate. However, p53, MAPK1, and YES1 can be targeted with oxaliplatin, mitoxantrone, and ponatinib.

FGFR3 mutation characterization identifies prognostic and immune-related gene signatures in bladder cancer

BACKGROUND

Immunotherapy and FGFR3-targeted therapy play an important role in the management of locally advanced and metastatic bladder cancer (BLCA). Previous studies indicated that FGFR3 mutation (mFGFR3) may be involved in the alterations of immune infiltration, which may affect the priority or combination of these two treatment regimes. However, the specific impact of mFGFR3 on the immunity and how FGFR3 regulates the immune response in BLCA to affect prognosis remain unclear. In this study, we aimed to elucidate the immune landscape associated with mFGFR3 status in BLCA, screen immune-related gene signatures with prognostic value, and construct and validate a prognostic model.

METHODS

ESTIMATE and TIMER were used to assess the immune infiltration within tumors in the TCGA BLCA cohort based on transcriptome data. Further, the mFGFR3 status and mRNA expression profiles were analyzed to identify immune-related genes that were differentially expressed between patients with BLCA with wild-type FGFR3 or mFGFR3 in the TCGA training cohort. An FGFR3-related immune prognostic score (FIPS) model was established in the TCGA training cohort. Furthermore, we validated the prognostic value of FIPS with microarray data in the GEO database and tissue microarray from our center. Multiple fluorescence immunohistochemical analysis was performed to confirm the relationship between FIPS and immune infiltration.

RESULTS

mFGFR3 resulted in differential immunity in BLCA. In total, 359 immune-related biological processes were enriched in the wild-type FGFR3 group, whereas none were enriched in the mFGFR3 group. FIPS could effectively distinguish high-risk patients with poor prognosis from low-risk patients. The high-risk group was characterized by a higher abundance of neutrophils; macrophages; and follicular helper, CD4, and CD8 T-cells than the low-risk group. In addition, the high-risk group exhibited higher expression of PD-L1, PD-1, CTLA-4, LAG-3, and TIM-3 than the low-risk group, indicating an immune-infiltrated but functionally suppressed immune microenvironment. Furthermore, patients in the high-risk group exhibited a lower mutation rate of FGFR3 than those in the low-risk group.

CONCLUSIONS

FIPS effectively predicted survival in BLCA. Patients with different FIPS exhibited diverse immune infiltration and mFGFR3 status. FIPS might be a promising tool for selecting targeted therapy and immunotherapy for patients with BLCA.

Methodology to identify a gene expression signature by merging microarray datasets

A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.

Lipophagy-related gene RAB7A is involved in immune regulation and malignant progression in hepatocellular carcinoma

BACKGROUND

RAB7A (RAS-related in Brain 7A) is an important member of the RAS oncogene family. However, the correlation between RAB7A and the development and immune infiltration of hepatocellular carcinoma (HCC) has rarely been studied. Here, we studied the role of RAB7A in HCC through bioinformatics analysis, real-world cohort validation, and in vitro experimental exploration.

MATERIALS AND METHODS

The RAB7A expression level was analyzed through TCGA, HPA and TISIDB databases. TIMER and TISCH were used to analyze the correlation between RAB7A and tumor immune microenvironment. The expression of RAB7A was detected through real-time PCR and western blotting. The cell proliferation was detected by EdU and CCK8. Wound-healing and transwell assays were used to test the invasion and migration ability. Cell cycle distribution and reactive oxygen species (ROS) content were analyzed by flow cytometry. Identification of epithelial-mesenchymal transition (EMT) was performed by immunofluorescence double staining. Immunohistochemistry (IHC) was used to evaluate the correlation between RAB7A and immune checkpoints.

RESULTS

RAB7A is upregulated in most of the tumor types, and the upregulation of RAB7A is associated with a poorer prognosis in many cancers. The results showed that RAB7A was significantly positively correlated with the infiltration of macrophages and cancer-associated fibroblasts (CAFs), but negatively correlated with M2-type macrophages in most tumors. The single-cell atlas also revealed the distribution and proportion of RAB7A in immune cells of HCC. The in vitro experiments suggested that RAB7A was increased in HCC tissue and cell lines. The knockdown of RAB7A inhibited the activation of the PIK3CA-AKT pathway and suppressed the expression of CDK4, CDK6 and CCNA2. Knockdown of RAB7A induced G0/G1 arrest and ROS accumulation in HCC. In addition, overexpression of RAB7A enhanced migration and invasion by inducing EMT. The real-world cohort showed that the expression level of RAB7A was positively correlated with the expression levels of TGFBR1 and PD-L1.

CONCLUSIONS

RAB7A may serve as a potential tumor prognostic and immune infiltration-related biomarker, predicting immunotherapy efficacy in certain cancer types, especially in HCC. Besides, RAB7A was a multi-pathway target involved in the malignant progression of HCC.

Multi-omics characterization of RNA binding proteins reveals disease comorbidities and potential drugs in COVID-19

The COVID-19 has led to a devastating global health crisis, which emphasizes the urgent need to deepen our understanding of the molecular mechanism and identifying potential antiviral drugs. Here, we comprehensively analyzed the transcriptomic and proteomic profiles of 178 COVID-19 patients, ranging from asymptomatic to critically ill. Our analyses found that the RNA binding proteins (RBPs) were likely to be perturbed in infection. Interactome analysis revealed that RBPs interact with virus proteins and the viral interacting RBPs were likely to locate in central regions of human protein-protein interaction network. Functional enrichment analysis revealed that the viral interacting RBPs were likely to be enriched in RNA transport, apoptosis and viral genome replication-related pathways. Based on network proximity analyses of 299 human complex-disease genes and COVID-19-related RBPs in the human interactome, we revealed the significant associations between complex diseases and COVID-19. Network analysis also implicated potential antiviral drugs for treatment of COVID-19. In summary, our integrative characterization of COVID-19 patients may thus help providing evidence regarding pathophysiology and potential therapeutic strategies for COVID-19.

scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies

Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preserving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.

Drug Repurposing and Systems Biology approaches of Enzastaurin can target potential biomarkers and critical pathways in Colorectal Cancer

Colorectal cancer (CRC) is a severe health concern that results from a cocktail of genetic, epigenetic, and environmental abnormalities. Because it is the second most lethal malignancy in the world and the third-most common malignant tumor, but the treatment is unavailable. The goal of the current study was to use bioinformatics and systems biology techniques to determine the pharmacological mechanism underlying putative important genes and linked pathways in early-onset CRC. Computer-aided methods were used to uncover similar biological targets and signaling pathways associated with CRC, along with bioinformatics and network pharmacology techniques to assess the effects of enzastaurin on CRC. The KEGG and gene ontology (GO) pathway analysis revealed several significant pathways including in positive regulation of protein phosphorylation, negative regulation of the apoptotic process, nucleus, nucleoplasm, protein tyrosine kinase activity, PI3K-Akt signaling pathway, pathways in cancer, focal adhesion, HIF-1 signaling pathway, and Rap1 signaling pathway. Later, the hub protein module identified from the protein-protein interactions (PPIs) network, molecular docking and molecular dynamics simulation represented that enzastaurin showed strong binding interaction with two hub proteins including CASP3 (-8.6 kcal/mol), and MCL1 (-8.6 kcal/mol), which were strongly implicated in CRC management than other the five hub proteins. Moreover, the pharmacokinetic features of enzastaurin revealed that it is an effective therapeutic agent with minimal adverse effects. Enzastaurin may inhibit the potential biological targets that are thought to be responsible for the advancement of CRC and this study suggests a potential novel therapeutic target for CRC.

Robust correlation estimation and UMAP assisted topological analysis of omics data for disease subtyping

Deciphering information hidden in the gene expression assays for identifying disease subtypes has significant importance in precision medicine. However, computational limitations thwart this process due to the intricacy of the biological networks and the curse of dimensionality of gene expression data. Therefore, clustering in such scenarios often becomes the first choice of exploratory data analysis to identify natural structures and intrinsic patterns in the data. However, sparse and high dimensional nature of omics data prevents conventional clustering algorithms to discover subtypes that are clinically relevant and statistically significant. Hence, non-linear dimensionality reduction techniques coupled with clustering in such scenarios often becomes imperative to improve the clustering results. In this study, we present a robust pipeline to discover disease subtypes with clinical relevance. Specifically, we focus on discovering patient sub-groups that have a residual life patterns remarkably different from other sub-groups. This is significant because by refining prognosis, subtyping can reduce uncertainty in approximating patients expected outcome. The methodology present is based on robust correlation estimation, UMAP- a non-linear dimensionality reduction method and mapper- a tool from topology. Notably, we suggest a method for improving the robustness of the correlation matrix of gene expression data for improving the clustering results. The performance of the model is evaluated by applying to five cancer datasets obtained through TCGA and comparisons are performed with some state of the art methods of NEMO, RSC-OTRI and SNF with regard to log-rank test and Restricted Life Expectancy Difference. For example in GBM dataset, the minimum separation for any two discovered subtypes is 221 days which is significantly higher than the other methodologies. We also compared the results without using the robust correlation based estimate and observed that robust correlation improves separability between survival curves significantly. From the results we infer that our methodology performs better compared to other methodologies with regard to separating survival curves of patient sub-groups despite using single omics profiles of patients compared to multiple omics profiles of SNF and NEMO. Pathway over-representation analysis is performed on the final clustering results to investigate the biological underpinnings characterizing each subtype.

Transcriptomic screening to identify hub genes and drug signatures for PCOS based on RNA-Seq data in granulosa cells

BACKGROUND

Polycystic ovary syndrome (PCOS) is one of the most incident reproductive diseases, and remains the main cause of female infertility. Granulosa cells play a critical role in normal follicle development and steroid hormones synthesis. In spite of extensive research, no sole medication has been approved by FDA to treat PCOS. This study aimed to investigate the novel therapeutics targets in PCOS, focusing on granulosa cells transcriptome functional analysis with a drug repositioning approach.

METHODS

PCOS microarray and RNA-Seq datasets in granulosa cells were screened and reanalyzed. KEGG pathway enrichment and interaction network analyses were performed and followed by a set of drug signature screening and Poly-pharmacology survey.

RESULTS

545 deregulated genes were identified via filters including p_adj < 0.05 and |log2FC| > 1. Amongst the top 15 KEGG pathways significantly enriched, metabolism of xenobiotics by cytochrome P450, steroid hormone biosynthesis and ovarian steroidogenesis were observed. The Protein-Protein Interaction network identified 18 hub genes amongst this set. Interestingly, most candidate drug signatures have been introduced by databases are either FDA approved or entered into clinical trials, including melatonin, resveratrol and raloxifene. Investigational or experimental introduced drugs obey rules of drug-likeness with almost safe and acceptable ADMET properties. Notably, 21 top target genes of the final drug set were also included in the granulosa significant differentially expressed genes.

CONCLUSION

Results of the current study represent approved, investigational and experimental drug signatures according to the differentially expressed genes in granulosa cells with supported literature reviews. This data might be useful for researchers and clinicians to pave the way for better management of PCOS.

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

Prediction of lung cancer metastasis by gene expression

Tumor metastasis is the main cause of death in cancer patients. Early prediction of tumor metastasis can allow for timely intervention. At present, research on tumor metastasis mainly focuses on manual diagnosis by imaging or diagnosis by computational methods. With the deterioration of the tumor, gene expression levels in blood change greatly. It is feasible to measure the transcripts of key genes to predict whether cancer will metastasize. Therefore, in this paper, we obtained gene expression data from 226 patients from TCGA. These data included 239,322 transcripts. Background screening and LASSO analysis were used to select 31 transcripts as features. Finally, a deep neural network (DNN) was used to determine whether or not lung cancer would metastasize. We compared our methods with several other methods and found that our method achieved the best precision. In addition, in a previous study, we identified 7 genes that play a vital role in lung cancer. We added those gene transcripts into the DNN and found that the AUC and AUPR of the model were increased.

A novel strategy for designing the magic shotguns for distantly related target pairs

Due to its promising capacity in improving drug efficacy, polypharmacology has emerged to be a new theme in the drug discovery of complex disease. In the process of novel multi-target drugs (MTDs) discovery, in silico strategies come to be quite essential for the advantage of high throughput and low cost. However, current researchers mostly aim at typical closely related target pairs. Because of the intricate pathogenesis networks of complex diseases, many distantly related targets are found to play crucial role in synergistic treatment. Therefore, an innovational method to develop drugs which could simultaneously target distantly related target pairs is of utmost importance. At the same time, reducing the false discovery rate in the design of MTDs remains to be the daunting technological difficulty. In this research, effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions. Through comprehensive assessment on 10 target pairs with hierarchical similarity-levels, the proposed strategy turned out to reduce the false discovery rate successfully. Constructed model types with much smaller numbers of inhibitor molecules gained considerable yields and showed better false-hit controllability than before. To further evaluate the generalization ability, an in-depth assessment of high-throughput virtual screening on ChEMBL database was conducted. As a result, this novel strategy could hierarchically improve the enrichment factors for each target pair (especially for those distantly related/unrelated target pairs), corresponding to target pair similarity-levels.

GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion

MOTIVATION

Many studies have shown that IDH mutation and 1p/19q co-deletion can serve as prognostic signatures of glioma. Although these genetic variations affect the expression of one or more genes, the prognostic value of gene expression related to IDH and 1p/19q status is still unclear.

RESULTS

We constructed an ensemble gene pair signature for the risk evaluation and survival prediction of glioma based on the prior knowledge of the IDH and 1p/19q status. First, we separately built two gene pair signatures IDH-GPS and 1p/19q-GPS and elucidated that they were useful transcriptome markers projecting from corresponding genome variations. Then, the gene pairs in these two models were assembled to develop an integrated model named Glioma Prognostic Gene Pair Signature (GPGPS), which demonstrated high area under the curves (AUCs) to predict 1-, 3- and 5-year overall survival (0.92, 0.88 and 0.80) of glioma. GPGPS was superior to the single GPSs and other existing prognostic signatures (avg AUC = 0.70, concordance index = 0.74). In conclusion, the ensemble prognostic signature with 10 gene pairs could serve as an independent predictor for risk stratification and survival prediction in glioma. This study shed light on transferring knowledge from genetic alterations to expression changes to facilitate prognostic studies.

AVAILABILITY AND IMPLEMENTATION

Codes are available at https://github.com/Kimxbzheng/GPGPS.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Potential shared gene signatures and molecular mechanisms between atherosclerosis and depression: Evidence from transcriptome data

BACKGROUND

Atherosclerosis and depression contribute to each other; however, mechanisms linking them at the genetic level remain unexplored. This study aimed to identify shared gene signatures and related pathways between these comorbidities.

METHODS

Atherosclerosis-related datasets were downloaded from the Gene Expression Omnibus database. Differential and weighted gene co-expression network analyses were employed to identify atherosclerosis-related genes. Depression-related genes were downloaded from the DisGeNET database, and the overlaps between atherosclerosis-related genes and depression-related genes were characterized as crosstalk genes. The functional enrichment analysis and protein-protein interaction network were performed in these gene sets. Subsequently, the Boruta algorithm and Recursive Feature Elimination algorithm were performed to identify feature-selection genes. A support vector machine was constructed to measure the accuracy of calculations, and two external validation sets were included to verify the results.

RESULTS

Based on two atherosclerosis-related datasets (GSE28829 and GSE43292), 165 genes were determined as atherosclerosis-related genes. Meanwhile, 1478 depression-related genes were obtained. After intersecting, 24 crosstalk genes were identified, and two pathways, "lipid and atherosclerosis" and "tryptophan metabolism," were revealed as mutual pathways according to the enrichment analysis results. Through the protein-protein interaction network, Molecular Complex Detection plugin, and cytoHubba plugin, PTPRC and MMP9 were identified as the hub gene. Moreover, SLC22A3, CASP1, AMPD3, and PIK3CG were recognized as feature-selection genes. Based on two external validation sets, CASP1 and MMP9 were finally determined as the critical crosstalk genes.

CONCLUSIONS

"Lipid and atherosclerosis" and "tryptophan metabolism" were possibly the pathways of atherosclerosis secondary to depression and depression due to atherosclerosis, respectively. CASP1 and MMP9 were revealed as the most pivotal candidates linking atherosclerosis and depression by mediating these two pathways. Further experimentation is needed to confirm these conclusions.

Identification and analysis of C17orf53 as a prognostic signature for hepatocellular carcinoma

C17orf53 is a novel gene for DNA synthesis and homologous recombination. However, the exact role of C17orf53 in hepatocellular carcinoma (HCC) remains unclear. In this study, we analyzed it using a set of public datasets. UALCAN, Human Protein Atlas (HPA), Kaplan‒Meier Plotter, Tumor Immune Estimation Resource (TIMER), cBioPortal, GEPIA, GeneMANIA, and LinkedOmics were used. Functional analysis was conducted in SK-Hep-1 cells by using small interfering RNA (siRNA). C17orf53 was highly expressed and predicted unfavorable survival in HCC patients. Moreover, it showed positive correlations with the abundance of B cells, macrophages and dendritic cells. In addition, we identified 126 genes that were positively correlated with C17orf53 and its coeffector minichromosome maintenance 8 (MCM8). These genes were mainly enriched in the cell cycle, DNA replication and Fanconi anemia pathways. Knockdown of C17orf53 significantly inhibited the proliferation of SK-Hep-1 cells and decreased the expression of MCM8, cyclin D1 and proliferating cell nuclear antigen (PCNA). Overall, C17orf53 is a novel prognostic signature for HCC.

A systematic review on the state-of-the-art strategies for protein representation

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

The mechanism and biomarker function of Cavin-2 in lung ischemia-reperfusion injury

BACKGROUND

Lung Ischemia Reperfusion injury(LIRI) is one of the most predominant complications of ischemic lung disease. Cavin-2 emerged as a regulator of a variety of cellular processes, including endocytosis, lipid homeostasis, signal transduction and tumorigenesis, but the function of Cavin-2 in LIRI is unknown. The purpose of this study was to determine the predictive potential of Cavin-2 in protecting lung ischemia-reperfusion injury and its corresponding mechanisms.

METHODS

We found the strong relationship between Cavin-2 and multiple immune-related genes by deep learning method. To reveal the mechanism of Cavin-2 in LIRI, the LIRI SD rat model was constructed to detect the expression of Cavin-2 in the lung tissue of SD rats after LIRI, and the expression of Cavin-2 in lung cell lines was also detected. The expression of IL-6, IL-10 and MDA in cells after Cavin-2 over-expression or knockdown was examined under hypoxic conditions. The expression levels of p-AKT, p-STAT3 and p-ERK1/2 were measured in over-expressing Cavin-2 cells under hypoxic-ischemia conditions, and then the corresponding blockers of AKT, STAT3 and ERK1/2 were given to verify, whether they play a protective role in LIRI.

RESULTS

After hypoxia, the expression of Cavin-2 in rat lung tissues was significantly increased, and the cellular activity and IL-10 in Cavin-2 over-expressing cells were significantly higher than that of the control group, while IL-6 and MDA were significantly lower than that of the control group, while the above results were reversed in Cavin-2 knockdown cells; Meanwhile, the phosphorylation levels of AKT, STAT3, and ERK1/2 were significantly increased in Cavin-2 over-expression cells after hypoxia. When AKT, STAT3, and ERK1/2 specific blockers were given, they lost their protective effect against LIRI.

CONCLUSIONS

Cavin-2 shows biomarker potential in protecting lung from ischemia-reperfusion injury through the survivor activating factor enhancement (SAFE) and reperfusion injury salvage kinase (RISK) pathway.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

Liver transcriptomics reveals microRNA features of the host response in a mouse model of dengue virus infection

BACKGROUND

Organ dysfunction, especially liver injury, caused by dengue virus (DENV) infection has been associated with fatal cases in dengue patients around the world. However, the pathophysiological mechanisms of liver involvement in dengue remain unclear. There is accumulating evidence that miRNAs are playing an important role in regulating viral pathogenesis, and it can help in diagnostic and anti-viral therapies development.

METHODS

We collected liver tissues of DENV-infected for small RNA sequencing to identify significantly different express miRNAs during dengue virus infection, and the identified target genes of these miRNAs were annotated by biological function and pathway enrichment.

RESULTS

31 significantly altered miRNAs were identified, including 16 up-regulated and 15 down-regulated miRNAs. By performing a series of miRNA prediction and signaling pathway enrichment analyses, the down-regulated miRNAs of mmu-miR-484, mmu-miR-1247-5p and mmu-miR-6538 were identified to be the crucial miRNAs. Further analysis revealed that the inflammation and immune responses involving Hippo, PI3K-Akt, MAPK, Wnt, mTOR, TGF-beta, Tight junction, and Platelet activation were modulated collectively by these three key miRNAs during DENV infection. These pathways are considered to be closely associated with the pathogenic mechanism and treatment strategy of dengue patients.

CONCLUSION

The miRNAs identified by sequencing, especially miR-484 may be the potential therapeutic targets for liver involvement in dengue patients which involves the regulation of vascular permeability and expression of inflammatory cytokines.

DRESIS: the first comprehensive landscape of drug resistance information

Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.