Bioinformatics and Biology Insights最新文献

{"title":"Investigating Lasofoxifene Efficacy Against the Y537S + F404V Double-Mutant Estrogen Receptor Alpha Using Molecular Dynamics Simulations.","authors":"El Mehdi Bouricha, Mohammed Hakmi","doi":"10.1177/11779322241288703","DOIUrl":"10.1177/11779322241288703","url":null,"abstract":"<p><p>Estrogen receptor alpha (ERα) plays a critical role in breast cancer (BC) progression, with endocrine therapy being a key treatment for ERα + BC. However, resistance often arises due to somatic mutations in the ERα ligand-binding domain (LBD). Lasofoxifene, a third-generation selective estrogen receptor modulator, has shown promise against Y537S and D538G mutations. However, the emergence of a novel F404 mutation in patients with pre-existing LBD mutations raises concerns about its impact on lasofoxifene efficacy. This study investigates the impact of the dual Y537S and F404V mutations on lasofoxifene's efficacy. Using molecular dynamics simulations and molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) free energy calculations, we found that the dual mutation reduces lasofoxifene binding affinity and binding free energy, disrupts crucial protein-ligand interactions, and induces significant conformational changes in the ligand-binding pocket. These alterations are likely due to the loss of the pi-pi stacking interaction in the F404V mutation. These findings suggest a potential reduction in lasofoxifene efficacy due to the dual mutation. Further experimental validation is required to confirm these results and fully understand the impact of dual mutations on lasofoxifene's effectiveness in ERα + metastatic BC.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241288703"},"PeriodicalIF":2.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Taxonomic Distribution, Phylogenetic Relationship, and Domain Conservation of CRISPR-Associated Cas Proteins.","authors":"Weerakkody Ranasinghe, Dorcie Gillette, Alexis Ho, Hyuk Cho, Madhusudan Choudhary","doi":"10.1177/11779322241274961","DOIUrl":"https://doi.org/10.1177/11779322241274961","url":null,"abstract":"<p><p>CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) is a naturally occurring genetic defense system in bacteria and archaea. It is comprised of a series of DNA sequence repeats with spacers derived from previous exposures to plasmid or phage. Further understanding and applications of CRISPR system have revolutionized our capacity for gene or genome editing of prokaryotes and eukaryotes. The CRISPR systems are classified into 3 distinct types: type I, type II, and type III, each of which possesses an associated signature protein, Cas3, Cas9, and Cas10, respectively. As the CRISPR loci originated from earlier independent exposures of foreign genetic elements, it is likely that their associated signature proteins may have evolved rapidly. Also, their functional domain structures might have experienced different selective pressures, and therefore, they have differentially diverged in their amino acid sequences. We employed genomic, phylogenetic, and structure-function constraint analyses to reveal the evolutionary distribution, phylogenetic relationship, and structure-function constraints of Cas3, Cas9, and Cas10 proteins. Results reveal that all 3 Cas-associated proteins are highly represented in the phyla <i>Bacteroidetes</i>, <i>Firmicutes</i>, and <i>Proteobacteria</i>, including both pathogenic and non-pathogenic species. Genomic analysis of homologous proteins demonstrates that the proteins share 30% to 50% amino acid identity; therefore, they are low to moderately conserved and evolved rapidly. Phylogenetic analysis shows that 3 proteins originated monophyletically; however, the evolution rates were different among different branches of the clades. Furthermore, structure-function constraint analysis reveals that both Cas3 and Cas9 proteins experiences low to moderate levels of negative selection, and several protein domains of Cas3 and Cas9 proteins are highly conserved. To the contrary, most protein domains of Cas10 proteins experience neutral or positive selection, which supports rapid genetic divergence and less structure-function constraints.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241274961"},"PeriodicalIF":2.3,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11468465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Strategy to Compare Single-Cell RNA Sequencing Data Sets Provides Phenotypic Insight into Cellular Heterogeneity Underlying Biological Similarities and Differences Between Samples.","authors":"Dan C Wilkinson, Elizabeth Tallman, Mishal Ashraf, Tatiana Gelaf Romer, Jeehoon Lee, Benjamin Burnett, Pierre R Bushel","doi":"10.1177/11779322241280866","DOIUrl":"https://doi.org/10.1177/11779322241280866","url":null,"abstract":"<p><p>Single-cell RNA sequencing (scRNA-seq) allows for an unbiased assessment of cellular phenotypes by enabling the extraction of transcriptomic data. An important question in downstream analysis is how to evaluate biological similarities and differences between samples in high dimensional space. This becomes especially complex when there is cellular heterogeneity within the samples. Here, we present scCompare, a computational pipeline for comparison of scRNA-seq data sets. Phenotypic identities from a known data set are transferred onto another data set using correlation-based mapping to average transcriptomic signatures from each cluster of cells' annotated phenotype. Statistically derived lower cutoffs for phenotype inclusivity allow for cells to be unmapped if they are distinct from the known phenotypes, facilitating potential novel cell type detection. In a comparison of our tool using scRNA-seq data sets from human peripheral blood mononuclear cells (PBMCs), we show that scCompare outperforms single-cell variational inference (scVI) in higher precision and sensitivity for most of the cell types. scCompare was used on a cardiomyocyte data set where it confirmed the discovery of a distinct cluster of cells that differed between the 2 protocols for differentiation. Further use of scCompare on cell atlas data sets revealed insights into the cellular heterogeneity underpinning biological diversity between samples. In addition, we used a cell atlas to better understand the effect of key parameters used in the scCompare pipeline. We envision that scCompare will be of value to the research community when comparing large scRNA-seq data sets.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241280866"},"PeriodicalIF":2.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11457179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Architectures of Medical Images Revealed by Registration of Multiple Modalities.","authors":"Sam Freesun Friedman, Gemma Elyse Moran, Marianne Rakic, Anthony Phillipakis","doi":"10.1177/11779322241282489","DOIUrl":"10.1177/11779322241282489","url":null,"abstract":"<p><p>The advent of biobanks with vast quantities of medical imaging and paired genetic measurements creates huge opportunities for a new generation of genotype-phenotype association studies. However, disentangling biological signals from the many sources of bias and artifacts remains difficult. Using diverse medical images and time-series (ie, magnetic resonance imagings [MRIs], electrocardiograms [ECGs], and dual-energy X-ray absorptiometries [DXAs]), we show how registration, both spatial and temporal, guided by domain knowledge or learned <i>de novo</i>, helps uncover biological information. A multimodal autoencoder comparison framework quantifies and characterizes how registration affects the representations that unsupervised and self-supervised encoders learn. In this study we (1) train autoencoders before and after registration with nine diverse types of medical image, (2) demonstrate how neural network-based methods (VoxelMorph, DeepCycle, and DropFuse) can effectively learn registrations allowing for more flexible and efficient processing than is possible with hand-crafted registration techniques, and (3) conduct exhaustive phenotypic screening, comprised of millions of statistical tests, to quantify how registration affects the generalizability of learned representations. Genome- and phenome-wide association studies (GWAS and PheWAS) uncover significantly more associations with registered modality representations than with equivalently trained and sized representations learned from native coordinate spaces. Specifically, registered PheWAS yielded 61 more disease associations for ECGs, 53 more disease associations for cardiac MRIs, and 10 more disease associations for brain MRIs. Registration also yields significant increases in the coefficient of determination when regressing continuous phenotypes (eg, 0.36 ± 0.01 with ECGs and 0.11 ± 0.02 for DXA scans). Our findings reveal the crucial role registration plays in enhancing the characterization of physiological states across a broad range of medical imaging data types. Importantly, this finding extends to more flexible types of registration, such as the cross-modal and the circular mapping methods presented here.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241282489"},"PeriodicalIF":2.3,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Potential Key Genes for the Comorbidity of Myasthenia Gravis With Thymoma by Integrated Bioinformatics Analysis and Machine Learning.","authors":"Hui Liu, Geyu Liu, Rongjing Guo, Shuang Li, Ting Chang","doi":"10.1177/11779322241281652","DOIUrl":"https://doi.org/10.1177/11779322241281652","url":null,"abstract":"<p><strong>Background: </strong>Thymoma is a key risk factor for myasthenia gravis (MG). The purpose of our study was to investigate the potential key genes responsible for MG patients with thymoma.</p><p><strong>Methods: </strong>We obtained MG and thymoma dataset from GEO database. Differentially expressed genes (DEGs) were determined and functional enrichment analyses were conducted by R packages. Weighted gene co-expression network analysis (WGCNA) was used to screen out the crucial module genes related to thymoma. Candidate genes were obtained by integrating DEGs of MG and module genes. Subsequently, we identified several candidate key genes by machine learning for diagnosing MG patients with thymoma. The nomogram and receiver operating characteristics (ROC) curves were applied to assess the diagnostic value of candidate key genes. Finally, we investigated the infiltration of immunocytes and analyzed the relationship among key genes and immune cells.</p><p><strong>Results: </strong>We obtained 337 DEGs in MG dataset and 2150 DEGs in thymoma dataset. Biological function analyses indicated that DEGs of MG and thymoma were enriched in many common pathways. Black module (containing 207 genes) analyzed by WGCNA was considered as the most correlated with thymoma. Then, 12 candidate genes were identified by intersecting with MG DEGs and thymoma module genes as potential causes of thymoma-associated MG pathogenesis. Furthermore, five candidate key genes (<i>JAM3</i>, <i>MS4A4A</i>, <i>MS4A6A</i>, <i>EGR1</i>, and <i>FOS</i>) were screened out through integrating least absolute shrinkage and selection operator (LASSO) regression and Random forest (RF). The nomogram and ROC curves (area under the curve from 0.833 to 0.929) suggested all five candidate key genes had high diagnostic values. Finally, we found that five key genes and immune cell infiltrations presented varying degrees of correlation.</p><p><strong>Conclusions: </strong>Our study identified five key potential pathogenic genes that predisposed thymoma to the development of MG, which provided potential diagnostic biomarkers and promising therapeutic targets for MG patients with thymoma.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241281652"},"PeriodicalIF":2.3,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11437577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Study of Algorithms Detecting Differential Rhythmicity in Transcriptomic Data.","authors":"Lin Miao, Douglas E Weidemann, Katherine Ngo, Benjamin A Unruh, Shihoko Kojima","doi":"10.1177/11779322241281188","DOIUrl":"10.1177/11779322241281188","url":null,"abstract":"<p><p>Rhythmic transcripts play pivotal roles in driving the daily oscillations of various biological processes. Genetic or environmental disruptions can lead to alterations in the rhythmicity of transcripts, ultimately impacting downstream circadian outputs, including metabolic processes and even behavior. To statistically compare the differences in transcript rhythms between 2 or more conditions, several algorithms have been developed to analyze circadian transcriptomic data, each with distinct features. In this study, we compared the performance of 7 algorithms that were specifically designed to detect differential rhythmicity (DODR, LimoRhyde, CircaCompare, compareRhythms, diffCircadian, dryR, and RepeatedCircadian). We found that even when applying the same statistical threshold, these algorithms yielded varying numbers of differentially rhythmic transcripts, most likely because each algorithm defines rhythmic and differentially rhythmic transcripts differently. Nevertheless, the output for the differential phase and amplitude were identical between dryR and compareRhyhms, and diffCircadian and CircaCompare, while the output from LimoRhyde2 was highly correlated with that from diffCircadian and CircaCompare. Because each algorithm has unique requirements for input data and reports different information as an output, it is crucial to ensure the compatibility of input data with the chosen algorithm and assess whether the algorithm's output fits the user's needs when selecting an algorithm for analysis.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241281188"},"PeriodicalIF":2.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11440551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142336251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and Functional Annotation of Hypothetical Proteins of Pan-Drug-Resistant <i>Providencia rettgeri</i> Strain MRSN845308 Toward Designing Antimicrobial Drug Targets.","authors":"Dipta Chandra Pal, Tasnimul Arabi Anik, Atiq Abrar Rahman, S M Mahfujur Rahman","doi":"10.1177/11779322241280580","DOIUrl":"10.1177/11779322241280580","url":null,"abstract":"<p><p><i>Providencia rettgeri</i> has increasingly been responsible for several infections, including urinary tract, post-burn wounds, neonatal sepsis, and others. The emergence of drug-resistant isolates of <i>P rettgeri</i>, accompanied by intrinsic and acquired antibiotic resistance, has exacerbated the challenge of treating such infections, necessitating the development of novel therapeutics. Hypothetical proteins (HPs) form a major portion of cellular proteins and can be targeted by these novel therapeutics. In this study, 410 HPs from a pan-drug-resistant (PDR) <i>P rettgeri</i> strain (MRSN845308) were functionally annotated and characterized by physicochemical properties, localization, virulence, essentiality, druggability, and functionality. Among 410 HPs, the VirulentPred 2.0 tool and VICMpred combinedly predicted 33 HPs as virulent, whereas 48 HPs were highly interacting proteins based on the STRING v12 database. BlastKOALA and eggNOG-mapper v2.1.12 predicted 13 HPs involved in several metabolic pathways like Riboflavin metabolism and Lipopolysaccharide biosynthesis. Overall, 83 HPs were selected as primary drug targets; however, only 80 remained after nonhomology searching and essentiality analysis. In addition, all were detected as novel drug targets according to DrugBank 5.1.12. Considering the potential of membrane and extracellular proteins, 29 HPs (extracellular, outer, and inner membrane) were selected based on the combined prediction from PSORTb v3.0.3, CELLO v.2.5, BUSCA, SOSUIGramN, and PSLpred. According to the prevalence of those HPs in different strains of <i>P rettgeri</i> sequences in National Center for Biotechnology Information Identical Protein Groups (NCBI-IPG), 5 HPs were selected as final drug targets. In addition, 5 other HPs annotated as transporter proteins were also added to the list. As no crystal structures of our targets are present, 3-dimensional structures of selected HPs were predicted by the AlphaFold Server powered by AlphaFold 3. Our findings might facilitate a better understanding of the mechanism of virulence and pathogenesis, and up-to-date annotations can make uncharacterized HPs easy to identify as targets for novel therapeutics.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241280580"},"PeriodicalIF":2.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142379980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GOTermViewer: Visualization of Gene Ontology Enrichment in Multiple Differential Gene Expression Analyses.","authors":"Milene Volpato, Mark Hull, Ian M Carr","doi":"10.1177/11779322241271550","DOIUrl":"10.1177/11779322241271550","url":null,"abstract":"<p><p>Gene ontology phrases are a widely used set of hierarchical terms that describe the biological properties of genes. These terms are then used to annotate individual genes, making it possible to determine the likely physiological properties of groups of genes such as a list of differentially expressed genes. Consequently, their ability to predict changes in biological features and functions based on alterations in gene expression has made gene ontology terms popular in the wide range of bioinformatic fields, such as differential gene expression and evolutionary biology. However, while they make the analysis easier, it is seldom easy to convey the results in a readily understandable manner. A number of applications have been developed to visualize gene ontology (GO) term enrichment; however, these solutions tend to focus on the display of aggregated results from a single analysis, making them unsuitable for the analysis of a series of experiments such as a time course or response to different drug treatments. As multiple pair wise comparisons are becoming a common feature of RNA profiling experiments, the absence of a mechanism to easily compare them is a significant problem. Consequently, to overcome this obstacle, we have developed GOTermViewer, an application that displays GO term enrichment data as determined by GOstats such that changes in physiological response across a number of individual analyses across a time course or range of drug treatments can be visualized.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241271550"},"PeriodicalIF":2.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142307116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcriptomic Profiles of AKAP12 Deficiency in Mouse Corpus Callosum.","authors":"Tomonori Hoshino, Hajime Takase, Hidehiro Ishikawa, Gen Hamanaka, Shintaro Kimura, Norito Fukuda, Ji Hyun Park, Hiroki Nakajima, Hisashi Shirakawa, Akihiro Shindo, Kyu-Won Kim, Irwin H Gelman, Josephine Lok, Ken Arai","doi":"10.1177/11779322241276936","DOIUrl":"https://doi.org/10.1177/11779322241276936","url":null,"abstract":"<p><p>A-kinase anchor protein 12 (AKAP12), a scaffold protein, has been implicated in the central nervous system, including blood-brain barrier (BBB) function. Although its expression level in the corpus callosum is higher than in other brain regions, such as the cerebral cortex, the role of AKAP12 in the corpus callosum remains unclear. In this study, we investigate the impact of AKAP12 deficiency by transcriptome analysis using RNA-sequencing (RNA-seq) on the corpus callosum of AKAP12 knockout (KO) mice. We observed minimal changes, with only 13 genes showing differential expression, including <i>Akap12</i> itself. Notably, <i>Klf2</i> and <i>Sgk1</i>, genes potentially involved in BBB function, were downregulated in AKAP12 KO mice and expressed in vascular cells similar to <i>Akap12</i>. These changes in gene expression may affect important biological pathways that may be associated with neurological disorders. Our findings provide an additional data set for future research on the role of AKAP12 in the central nervous system.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241276936"},"PeriodicalIF":2.3,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11439161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classifications: A Network-Based Approach.","authors":"Roberta Coletti, Mónica Leiria de Mendonça, Susana Vinga, Marta B Lopes","doi":"10.1177/11779322241271535","DOIUrl":"https://doi.org/10.1177/11779322241271535","url":null,"abstract":"<p><p>Tumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official World Health Organization (WHO) classification of the central nervous system (CNS). These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this context, the search for new potential diagnostic and prognostic targets, characteristic of each glioma type, is crucial to support the development of novel therapies. Based on The Cancer Genome Atlas (TCGA) glioma RNA-sequencing data set updated according to the 2016 and 2021 WHO guidelines, we proposed a 2-step variable selection approach for biomarker discovery. Our framework encompasses the graphical lasso algorithm to estimate sparse networks of genes carrying diagnostic information. These networks are then used as input for regularized Cox survival regression model, allowing the identification of a smaller subset of genes with prognostic value. In each step, the results derived from the 2016 and 2021 classes were discussed and compared. For both WHO glioma classifications, our analysis identifies potential biomarkers, characteristic of each glioma type. Yet, better results were obtained for the WHO CNS classification in 2021, thereby supporting recent efforts to include molecular data on glioma classification.</p>","PeriodicalId":9065,"journal":{"name":"Bioinformatics and Biology Insights","volume":"18 ","pages":"11779322241271535"},"PeriodicalIF":2.3,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11403688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}