The emergence of large-scale human genome projects, advances in DNA sequencing technologies, and the massive volume of electronic medical records EMR shift the transformation of healthcare research ...into the next paradigm, namely ‘Precision Medicine.’ This new clinical system model uses patients' genomic profiles and disparate healthcare data sources to a greater extent and provides personalized deliverables. As an advanced analytical technique, deep learning models significantly impact precision medicine because they can process voluminous amounts of diversified data with improved accuracy. Two salient features of deep learning models, namely processing a massive volume of multi-model data at multiple levels of abstraction and the ability to identify inherent features from the input data on their own, attract the implication of deep learning techniques in precision medicine research. The proposed review highlights the importance of deep learning-based analytical models in handling diversified and disparate big data sources of precision medicine. To augment further, state-of-the-art precision medicine research based on the taxonomy of deep learning models has been reviewed along with their research outcomes. The diversified data inputs used in research attempts, their applications, benchmarking data repositories, and usage of various evaluation measures for accuracy estimations are highlighted in this review. This review also brings out some promising analytical avenues of precision medicine research that give directions for future exploration.
•Focuses on the transformation of healthcare research into the precision medicine based clinical research.•Discusses the implications of diversified data sources (structured, semi-structured, unstructured) in precision medicine.•Emphasizes the need for deep learning-based analytical solutions for precision medicine-based healthcare services.
In peripheral blood, cell‐free DNA (cfDNA) contains circulating tumor DNA (ctDNA), which indicates molecular abnormalities in metastatic breast tumor tissue. The sequencing of cfDNA of Metastatic ...Breast Cancer (MBC) patients allows assessment of therapy response and noninvasive treatment. In the proposed study, clinically significant alterations in PIK3CA and TP53 genes associated with MBC resulting in a missense substitution of His1047Arg and Arg282Trp from an next‐generation sequencing‐based multi‐gene panel were reported in a cfDNA of a patient with MBC. To investigate the impact of the reported mutation, we used molecular docking, molecular dynamics simulation, network analysis, and pathway analysis. Molecular Docking analysis determined the distinct binding pattern revealing H1047R‐ATP complex has a higher number of Hydrogen bonds (H‐bonds) and binding affinity with a slight difference compared to the PIK3CA‐ATP complex. Following, molecular dynamics simulation for 200 ns, of which H1047R‐ATP complex resulted in the instability of PIK3CA. Similarly, for TP53 mutant R282W, the zinc‐free state (apo) and zinc‐bounded (holo) complexes were investigated for conformational change between apo and holo complexes, of which the holo complex mutant R282W was unstable. To validate the conformational change of PIK3CA and TP53, 80% mutation of H1047R in the kinase domain of p110α expressed ubiquitously in PIK3CA protein that alters PI3K pathway, while R282W mutation in DNA binding helix (H2) region of P53 protein inhibits the transcription factor in P53 pathway causing MBC. According to our findings, the extrinsic (hypoxia, oxidative stress, and acidosis); intrinsic factors (MYC amplification) in PIK3CA and TP53 mutations will provide potential insights for developing novel therapeutic methods for MBC therapy.
Non-small-cell lung cancer (NSCLC) is the most common type of lung cancer. NSCLC accounts for 84% of all lung cancer cases. In recent years, advances in pathway understanding, methods for discovering ...novel genetic biomarkers, and new drugs designed to inhibit the signaling cascades have enabled clinicians to personalize therapy for NSCLC.
The primary aim of this study is to identify the genes associated with NSCLC that harbor pathogenic variants that could be causative for NSCLC. The second aim is to investigate their roles in different pathways that lead to NSCLC.
We examined exome-sequencing datasets from 54 NSCLC patients to characterize the variants associated with NSCLC.
Our findings revealed that 17 variants in 14 genes were considered highly pathogenic, including CDKN2A, ERBB2, FOXP1, IDH1, JAK3, KMT2D, K-Ras, MSH3, MSH6, POLE, RNF43, TCF7L2, TP53, and TSC1. Gene set enrichment analysis revealed the involvement of transmembrane receptor protein tyrosine kinase activity, protein binding, ATP binding, phosphatidylinositol-4,5-bisphosphate 3-kinase, and Ras guanyl-nucleotide exchange factor activity. Pathway analysis of these genes yielded different cancer-related pathways, including colorectal, prostate, endometrial, pancreatic, PI3K-Akt signaling pathways, and signaling pathways regulating pluripotency of stem cells. Module 1 from protein-protein interactions (PPIs) identified genes that harbor pathogenic SNPs. Three of the most deleterious SNPs are ERBB2 (rs1196929947), K-Ras (rs121913529), and POLE (rs751425952). Interestingly, one patient has a pathogenic K-Ras variant (rs121913529) co-occurred with the missense variant (rs752054698) inTSC1 gene.
This study maps highly pathogenic variants associated with NSCLC and investigates their contributions to the pathogenesis of NSCLC. This study sheds light on the potential applications of precision medicine in patients with NSCLC.
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•In this study, we identified highly pathogenic variants and explored their roles in NSCLC development and proliferation.•Our findings revealed that 17 nsSNPs located in 14 genes were considered highly pathogenic mutation.•WES analysis also revealed that pathogenic K-Ras mutation G12V co-occurred with the missense (Y704C) from TSC1.
This study systematically reviews the Artificial Intelligence (AI) methods developed to resolve the critical process of COVID-19 gene data analysis, including diagnosis, prognosis, biomarker ...discovery, drug responsiveness, and vaccine efficacy. This systematic review follows the guidelines of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Embase, Web of Science, and Scopus databases to identify the relevant articles from January 2020 to June 2022. It includes the published studies of AI-based COVID-19 gene modeling extracted through relevant keyword searches in academic databases. This study included 48 articles discussing AI-based genetic studies for several objectives. Ten articles confer about the COVID-19 gene modeling with computational tools, and five articles evaluated ML-based diagnosis with observed accuracy of 97% on SARS-CoV-2 classification. Gene-based prognosis study reviewed three articles and found host biomarkers detecting COVID-19 progression with 90% accuracy. Twelve manuscripts reviewed the prediction models with various genome analysis studies, nine articles examined the gene-based in silico drug discovery, and another nine investigated the AI-based vaccine development models. This study compiled the novel coronavirus gene biomarkers and targeted drugs identified through ML approaches from published clinical studies. This review provided sufficient evidence to delineate the potential of AI in analyzing complex gene information for COVID-19 modeling on multiple aspects like diagnosis, drug discovery, and disease dynamics. AI models entrenched a substantial positive impact by enhancing the efficiency of the healthcare system during the COVID-19 pandemic.
Circular RNAs (circRNAs) are regulatory elements that are involved in orchestrating gene expression and protein functions and are implicated in various biological processes including cancer. Notably, ...breast cancer has a significant mortality rate and is one of the most common malignancies in women. CircRNAs have been demonstrated to contribute to the pathogenesis of breast cancer including its initiation, progression, metastasis, and resistance to drugs. By acting as miRNA sponges, circRNAs can indirectly influence gene expression by disrupting miRNA regulation of their target genes, ultimately altering the course of cancer development and progression. Additionally, circRNAs can interact with proteins and modulate their functions including signaling pathways involved in the initiation and development of cancer. Recently, circRNAs can encode peptides that play a role in the pathophysiology of breast cancer and other diseases and their potential as diagnostic biomarkers and therapeutic targets for various cancers including breast cancer. CircRNAs possess biomarkers that differentiate, such as stability, specificity, and sensitivity, and can be detected in several biological specimens such as blood, saliva, and urine. Moreover, circRNAs play an important role in various cellular processes including cell proliferation, differentiation, and apoptosis, all of which are integral factors in the development and progression of cancer. This review synthesizes the functions of circRNAs in breast cancer, scrutinizing their contributions to the onset and evolution of the disease through their interactions with exosomes and cancer-related intracellular pathways. It also delves into the potential use of circRNA as a biomarker and therapeutic target against breast cancer. It discusses various databases and online tools that offer crucial circRNA information and regulatory networks. Lastly, the challenges and prospects of utilizing circRNAs in clinical settings associated with breast cancer are explored.
Mucopolysaccharidoses VI (Maroteaux Lamy syndrome) is a metabolic disorder due to the loss of enzyme activity of N-acetyl galactosamine-4-sulphatase arising from mutations in the ARSB gene. The ...mutated ARSB is the origin for the accumulation of GAGs within the lysosome leading to severe growth deformities, causing lysosomal storage disease. The main focus of this study is to identify the deleterious variants by applying bioinformatics tools to predict the conservation, pathogenicity, stability, and effect of the ARSB variants. We examined 170 missense variants, of which G137V and G144R were the resultant variants predicted detrimental to the progression of the disease. The native along with G137V and G144R structures were fixed as the receptors and subjected to Molecular docking with the small molecule Odiparcil to analyze the binding efficiency and the varied interactions of the receptors towards the drug. The interaction resulted in similar docking scores of - 7.3 kcal/mol indicating effective binding and consistent interactions of the drug with residues CYS117, GLN118, THR182, and GLN517 for native, along with G137V and G144R structures. Molecular Dynamics were conducted to validate the stability and flexibility of the native and variant structures on ligand binding. The overall study indicates that the drug has similar therapeutic towards the native and variant based on the higher binding affinity and also the complexes show stability with an average of 0.2 nm RMS value. This can aid in the future development therapeutics for the Maroteaux Lamy syndrome.
The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health ...infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures. The transcriptomics data contains immune-related genes collected from multiple tissues (blood, nasal, and buccal) with accession number: GSE183071. Extensive bioinformatics work was carried out to identify the potential candidate markers, including differential expression analysis, protein interactions, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment studies. The overlapping investigation found SERPING1, the gene that encodes a glycosylated plasma protein C1-INH, in all three datasets. Furthermore, the immuno-informatics study was conducted on the C1-INH protein. 5DU3, the protein identifier of C1-INH, was fetched to identify the antigenicity, major histocompatibility (MHC) Class I and II binding epitopes, allergenicity, toxicity, and immunogenicity. The screening of peptides satisfying the vaccine-design criteria based on the metrics mentioned above is performed. The drug-gene interaction study reported that Rhucin is strongly associated with SERPING1. HSIC-Lasso (Hilbert-Schmidt independence criterion-least absolute shrinkage and selection operator), a model-free biomarker selection technique, was employed to identify the genes having a nonlinear relationship with the target class. The gene subset is trained with supervised machine learning models by a leave-one-out cross-validation method. Explainable artificial intelligence techniques perform the model interpretation analysis.
Breast cancer biomarkers that detect marginally advanced stages are still challenging. The detection of specific abnormalities, targeted therapy selection, prognosis, and monitoring of treatment ...effectiveness over time are all made possible by circulating free DNA (cfDNA) analysis. The proposed study will detect specific genetic abnormalities from the plasma cfDNA of a female breast cancer patient by sequencing a cancer-related gene panel (MGM455 - Oncotrack Ultima), including 56 theranostic genes (SNVs and small INDELs). Initially, we determined the pathogenicity of the observed mutations using PredictSNP, iStable, Align-GVGD, and ConSurf servers. As a next step, molecular dynamics (MD) was implemented to determine the functional significance of SMAD4 mutation (V465M). Lastly, the mutant gene relationships were examined using the Cytoscape plug-in GeneMANIA. Using ClueGO, we determined the gene's functional enrichment and integrative analysis. The structural characteristics of SMAD4 V465M protein by MD simulation analysis further demonstrated that the mutation was deleterious. The simulation showed that the native structure was more significantly altered by the SMAD4 (V465M) mutation. Our findings suggest that SMAD4 V465M mutation might be significantly associated with breast cancer, and other patient-found mutations (AKT1-E17K and TP53-R175H) are synergistically involved in the process of SMAD4 translocate to nuclease, which affects the target gene translation. Therefore, this combination of gene mutations could alter the TGF-β signaling pathway in BC. We further proposed that the SMAD4 protein loss may contribute to an aggressive phenotype by inhibiting the TGF-β signaling pathway. Thus, breast cancer's SMAD4 (V465M) mutation might increase their invasive and metastatic capabilities.
Communicated by Ramaswamy H. Sarma
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmissions are occurring rapidly; it is raising the alarm around the globe. Though vaccines are currently available, the evolution and ...mutations in the SARS-CoV-2 threaten available vaccines' significance. The drugs are still undergoing clinical trials, and certain medications are approved for "emergency use" or as an "off-label" drug during the pandemic. These drugs have been effective yet accommodating side effects, which also can be lethal. Complementary and alternative medicine is highly demanded since it embraces a holistic approach. Since ancient times, natural products have been used as drugs to treat various diseases in the medical field and are still widely practiced. Medicinal plants contain many active compounds that serve as the key to an effective drug design. The Kabasura kudineer and Nilavembu kudineer are the two most widely approved formulations to treat COVID-19. However, the mechanism of these formulations is not well known. The proposed study used a network pharmacology approach to understand the immune-boosting mechanism by the Kabasura kudineer, Nilavembu kudineer, and JACOM in treating COVID-19. The plants and phytochemical chemical compounds in the Kabasura kudineer, Nilavembu kudineer, and JACOM were obtained from the literature. The Swiss target prediction algorithm was used to predict the targets for these phytochemical compounds. The common genes for the COVID-19 infection and the drug targets were identified. The gene-gene interaction network was constructed to understand the interactions between these common genes and enrichment analyses to determine the biological process, molecular functions, cellular functions, pathways involved, etc. Finally, virtual screening and molecular docking studies were performed to identify the most potential targets and significant phytochemical compounds to treat the COVID-19. The present study identified potential targets as ACE, Cathepsin L, Cathepsin B, Cathepsin K, DPP4, EGFR, HDAC2, IL6, RIPK1, and VEGFA. Similarly, betulinic acid, 5″-(2⁗-Hydroxybenzyl) uvarinol, antofine, (S)-1'-methyloctyl caffeate, (Z)-3-phenyl-2-propenal, 7-oxo-10α-cucurbitadienol, and PLX-4720 collectively to be potential treatment agents for COVID-19.
Serine/threonine kinases called cyclin-dependent kinases (CDKs) interact with cyclins and CDK inhibitors (CKIs) to control the catalytic activity. CDKs are essential controllers of RNA transcription ...and cell cycle advancement. The ubiquitous overactivity of the cell cycle CDKs is caused by a number of genetic and epigenetic processes in human cancer, and their suppression can result in both cell cycle arrest and apoptosis. This review focused on CDKs, describing their kinase activity, their role in phosphorylation inhibition, and CDK inhibitory proteins (CIP/KIP, INK 4, RPIC). We next compared the role of different CDKs, mainly p21, p27, p57, p16, p15, p18, and p19, in the cell cycle and apoptosis in cancer cells with respect to normal cells. The current work also draws attention to the use of CDKIs as therapeutics, overcoming the pharmacokinetic barriers of pan-CDK inhibitors, analyze new chemical classes that are effective at attacking the CDKs that control the cell cycle (cdk4/6 or cdk2). It also discusses CDKI's drawbacks and its combination therapy against cancer patients. These findings collectively demonstrate the complexity of cancer cell cycles and the need for targeted therapeutic intervention. In order to slow the progression of the disease or enhance clinical outcomes, new medicines may be discovered by researching the relationship between cell death and cell proliferation.
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