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Shared genetic association between inflammatory bowel disease and acute myeloid leukemia: insights from mendelian randomization and transcriptomic analyses

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INFLAMMATION RESEARCH 2025年第1期74卷 1-22页

作者： Zhou, Yanqun Zhang, Xiongfeng Yin, Shangjin Yao, Yuhong Chen, Tao Huang, Liming Liu, Zenghui Guizhou Univ Chinese Med Clin Med Sch 2 83 Feishan St Guiyang 550003 Guizhou Peoples R China Guizhou Univ Chinese Med Affiliated Hosp 2 Dept Hematol Guiyang Peoples R China Guizhou Univ Chinese Med Affiliated Hosp 2 Dept Nephrol Guiyang Peoples R China Guangzhou Univ Tradit Chinese Med Affiliated Hosp 1 Dept Hematol 16 Jichang Rd Guangzhou 510405 Guangdong Peoples R China

BackgroundObservational studies suggest that a history of inflammatory bowel disease (IBD) is associated with the onset of acute myeloid leukemia (AML), often attributed to drug use. However, these findings are inconsistent. This study aimed to assess the causal relationship between IBD and AML, identify shared pathogenesis, and discover diagnostic and prognostic markers and potential therapeutic ***-sample Mendelian randomization (MR) was employed to analyze genetic associations between IBD [ulcerative colitis (UC) and Crohn's disease (CD)] and AML. transcriptomic data from gene expression omnibus (GEO) identified differentially expressed genes (DEGs) in UC, AML, and controls. Weighted Gene Co-expression Network Analysis (WGCNA) and enrichment analyses [Gene Multiple Association Network Integration Algorithm (GeneMANIA), Kyoto Encyclopedia of Genes and Genomes (KEGG), Ractom pathway] and Gene Ontology (GO) explored shared genetic pathways. Receiver Operating Characteristic (ROC) curve and survival analyses screened diagnostic and prognostic markers. Cibersort and GSVA were employed to analyze the proportion of immune cells in UC and AML datasets, as well as to assess the association of specific genes with immune infiltration. The Drug Signatures database (DSigDB) and Autodock molecular docking identified potential therapeutic small *** analysis revealed a causal association between UC and the onset of AML. Differential expression and WGCNA analyses identified 23 co-driver genes regulated by Signal Transducer and Activator of Transcription 3 (STAT3) and Activating Transcription Factor 4 (AFT4), enriched in immune, inflammatory, and cell proliferation pathways. Tissue Inhibitor of Metalloproteinases 1 (TIMP1) and F2R-Like Trypsin Receptor 1 (F2RL1) were identified as practical diagnostic and prognostic markers for AML, with high TIMP1 and low F2RL1 expression promoting an immunosuppressive and inflammatory tumor microenvironment. Querceti

关键词： Acute myeloid leukemia Inflammatory bowel disease Mendelian randomization transcriptomic data Prevention

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Analysis of the cross-study replicability of tuberculosis gene signatures using 49 curated human transcriptomic datasets

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Tuberculosis 2025年 153卷 102649-102649页

作者： Wang, Xutao Harper, Katie Sinha, Pranay Johnson, W. Evan Patil, Prasad Department of Biostatistics Boston University School of Public Health Boston United States Division of Computational Biomedicine Boston University School of Medicine Boston United States Section of Infectious Diseases Department of Medicine Boston University School of Medicine Boston United States Division of Infectious Disease Center for Data Science Rutgers New Jersey Medical School NJ United States

Background: Tuberculosis (TB) is the leading cause of infectious disease mortality worldwide. Numerous host blood-based gene expression signatures have been proposed in the literature as alternative tools for diagnosing TB infection. However, the generalizability of these signatures to different patient contexts is not well-characterized. There is a pressing need for a well-curated database of TB gene expression studies for the systematic assessment of existing and newly developed TB gene signatures. Results: We built curatedTBdata, a manually-curated database of 49 human TB transcriptomic studies. This data resource is freely available through GitHub and as an R Bioconductor package that allows users to validate new and existing biomarkers without the challenges of harmonizing heterogeneous studies. We demonstrate the use of this data resource with cross-study comparisons for 72 human host blood-based TB gene signatures. For the comparison of subjects with active TB from healthy controls, 19 gene signatures had weighted mean AUC of 0.90 or greater, with the highest result of 0.94. In active TB disease versus latent TB infection, 7 gene signatures had weighted mean AUC of 0.90 or greater, with a maximum of 0.93. Conclusions: The curatedTBdata data package offers a comprehensive resource of curated human blood-based gene expression and clinically annotated data. This resource will facilitate the development of new signatures that are generalizable across cohorts or more applicable to specific subsets of patients. © 2025 Elsevier Ltd

关键词： Gene set scoring methods Gene signature transcriptomic data Tuberculosis

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Identifying cancer prognosis genes through causal learning

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BRIEFINGS IN BIOINFORMATICS 2025年第1期26卷 bbae721页

作者： Wu, Siwei Yin, Chaoyi Wang, Yuezhu Sun, Huiyan Jilin Univ Sch Artificial Intelligence 3003 Qianjin St Changchun 130012 Peoples R China Jilin Univ Int Ctr Future Sci 3003 Qianjin St Changchun 130012 Peoples R China Jilin Univ Engn Res Ctr Knowledge Driven Human Machine Intell 3003 Qianjin St Changchun 130012 Peoples R China

Accurate identification of causal genes for cancer prognosis is critical for estimating disease progression and guiding treatment interventions. In this study, we propose CPCG (Cancer Prognosis's Causal Gene), a two-stage framework identifying gene sets causally associated with patient prognosis across diverse cancer types using transcriptomic data. Initially, an ensemble approach models gene expression's impact on survival with parametric and semiparametric hazard models. Subsequently, an iterative conditional independence test combined with graph pruning is utilized to infer the causal skeleton, thereby pinpointing prognosis-related genes. Experiments on transcriptomic data from 18 cancer types sourced from The Cancer Genome Atlas Project demonstrate CPCG's effectiveness in predicting prognosis under four evaluation metrics. Validations on 24 additional datasets covering 12 cancer types from the Gene Expression Omnibus and the Chinese Glioma Genome Atlas Project further demonstrate CPCG's robustness and generalizability. CPCG identifies a concise but reliable set of genes, obviating the need for gene combination enumeration for survival time estimation. These genes are also proved closely linked to crucial biological processes in cancer. Moreover, CPCG constructs a stable causal skeleton and exhibits insensitivity to the order of data shuffling. Overall, CPCG is a powerful tool for extracting cancer prognostic biomarkers, offering interpretability, generalizability, and robustness. CPCG holds promise for facilitating targeted interventions in clinical treatment strategies.

关键词： cancer prognosis generalizable and robust predictions causal structure learning compact gene set transcriptomic data

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BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018年第3期15卷 850-860页

作者： Pirayre, Aurelie Couprie, Camille Duval, Laurent Pesquet, Jean-Christophe IFP Energies Nouvelles 1 & 4 Ave Bois Preau F-92852 Rueil Malmaison France Facebook AI Res F-75002 Paris France Cent Supelec F-92295 Chatenay Malabry France

Discovering meaningful gene interactions is crucial for the identification of novel regulatory processes in cells. Building accurately the related graphs remains challenging due to the large number of possible solutions from available data. Nonetheless, enforcing a priori on the graph structure, such as modularity, may reduce network indeterminacy issues. BRANE Clust (Biologically-Related A priori Network Enhancement with Clustering) refines gene regulatory network (GRN) inference thanks to cluster information. It works as a post-processing tool for inference methods (i.e., CLR, GENIE3). In BRANE Clust, the clustering is based on the inversion of a system of linear equations involving a graph-Laplacian matrix promoting a modular structure. Our approach is validated on DREAM4 and DREAM5 datasets with objective measures, showing significant comparative improvements. We provide additional insights on the discovery of novel regulatory or co-expressed links in the inferred Escherichia co//network evaluated using the STRING database. The comparative pertinence of clustering is discussed computationally (SIMoNe, WGCNA, X-means) and biologically (RegulonDB). BRANE Clust software is available at: http://***-mlvir/pirayre/***

关键词： Gene regulatory network clustering combinatorial optimization transcriptomic data DREAM challenge

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A Novel Approach of transcriptomic microRNA Analysis Using Text Mining Methods: An Early Detection of Multiple Sclerosis Disease

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IEEE ACCESS 2021年 9卷 120024-120033页

作者： Ali, Nehal M. Shaheen, Mohamed Mabrouk, Mai S. Aborizka, Mohamed A. Arab Acad Sci Technol & Maritime Transport Coll Comp & Informat Technol Cairo 12677 Egypt Arab Acad Sci Technol & Maritime Transport Coll Comp & Informat Technol Alexandria 21532 Egypt Misr Univ Sci & Technol Fac Comp Sci Dept Biomed Engn Cairo 11371 Egypt

Multiple sclerosis is an autoimmune disease that causes psychological impacts and severe physical disabilities, including motor disabilities and partial blindness. This work introduces an early detection method for multiple sclerosis disease by analyzing transcriptomic microRNA data. By transforming this phenotype classification problem into a text mining problem, multiple sclerosis disease biomarkers can be obtained. To our knowledge, text mining methods have not been introduced previously in transcriptomic data analysis of multiple sclerosis disease. Hence, this work presents a complete predictive model by combining consecutive transcriptomic data preprocessing procedures, followed by the proposed KmerFIDF method as a feature extraction method and linear discriminant analysis for dimensionality reduction. Predictive machine learning methods can then be obtained accordingly. This study describes experimental work on a transcriptomic dataset of noncoding microRNA sequences denoted from relapsing-remitting multiple sclerosis patients before fingolimod treatment and after six consecutive months of treatment. The experimental results of the predictive methods with the proposed model report sensitivity, specificity, F1-score, and average accuracy scores of 96.4, 96.47, 95.6, and 97% with random forest, 92.89, 92.78, 93.2, and 94% with support vector machine and 91.95, 92.2, 93.1, and 94% with logistic regression, respectively. These promising results support the introduced model and the proposed KmerFIDF method in transcriptomic data analysis. Moreover, comparative experiments are conducted with two referenced studies. The obtained results show that the average reported accuracy scores of the proposed model outperform the referenced literature work.

关键词： RNA Text mining Diseases Multiple sclerosis Proteins DNA Sequential analysis KmerFIDF miRNA analysis multiple sclerosis machine learning text mining transcriptomic data

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Early multi-cancer detection through deep learning: An anomaly detection approach using Variational Autoencoder

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JOURNAL OF BIOMEDICAL INFORMATICS 2024年 160卷 104751页

作者： Sado, Innocent Tatchum Fitime, Louis Fippo Pelap, Geraud Fokou Tinku, Claude Meudje, Gaelle Mireille Bouetou, Thomas Univ Yaounde I Natl Adv Sch Engn Yaounde Dept Comp Engn Lab Informat Syst & Signal Proc Yaounde Cameroon Univ Dschang Dept Math & Comp Sci URIFIA Dschang Cameroon Smart Digital Strategy SARL Co Yaounde Cameroon

Cancer is a disease that causes many deaths worldwide. The treatment of cancer is first and foremost a matter of detection, a treatment that is most effective when the disease is detected at an early stage. With the evolution of technology, several computer-aided diagnosis tools have been developed around cancer;several image-based cancer detection methods have been developed too. However, cancer detection faces many difficulties related to early detection which is crucial for patient survival rate. To detect cancer early, scientists have been using transcriptomic data. However, this presents some challenges such as unlabeled data, a large amount of data, and image-based techniques that only focus on one type of cancer. The purpose of this work is to develop a deep learning model that can effectively detect as soon as possible, specifically in the early stages, any type of cancer as an anomaly in transcriptomic data. This model must have the ability to act independently and not be restricted to any specific type of cancer. To achieve this goal, we modeled a deep neural network (a Variational Autoencoder) and then defined an algorithm for detecting anomalies in the output of the Variational Autoencoder. The Variational Autoencoder consists of an encoder and a decoder with a hidden layer. With the TCGA and GTEx data, we were able to train the model for six types of cancer using the Adam optimizer with decay learning for training, and a two-component loss function. Asa result, we obtained the lowest value of accuracy 0.950, and the lowest value of recall 0.830. This research leads us to the design of a deep learning model for the detection of cancer as an anomaly in transcriptomic data.

关键词： Variational Autoencoder Anomaly detection transcriptomic data Cancer Early detection

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Cirscan: a shiny application to identify differentially active sponge mechanisms and visualize circRNA-miRNA-mRNA networks

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BMC BIOINFORMATICS 2024年第1期25卷 1-14页

作者： Fraboulet, Rose-Marie Si Ahmed, Yanis Aubry, Marc Corre, Sebastien Galibert, Marie-Dominique Blum, Yuna Univ Rennes CNRS INSERM Equipe Labellisee Ligue Natl CancIGDR Inst Genet F-35000 Rennes France Univ Rennes 1 INSERM OSS Oncogenesis Stress Signaling CLCC Eugene MarquisUMR S 1242 S 1242 F-35000 Rennes France CHU Rennes Hosp Univ Rennes Dept Mol Genet & Genom F-35000 Rennes France

BackgroundNon-coding RNAs represent a large part of the human transcriptome and have been shown to play an important role in disease such as cancer. However, their biological functions are still incompletely understood. Among non-coding RNAs, circular RNAs (circRNAs) have recently been identified for their microRNA (miRNA) sponge function which allows them to modulate the expression of miRNA target genes by taking on the role of competitive endogenous RNAs (ce-circRNAs). Today, most computational tools are not adapted to the search for ce-circRNAs or have not been developed for the search for ce-circRNAs from user's transcriptomic *** this study, we present Cirscan (CIRcular RNA Sponge CANdidates), an interactive Shiny application that automatically infers circRNA-miRNA-mRNA networks from human multi-level transcript expression data from two biological conditions (e.g. tumor versus normal conditions in the case of cancer study) in order to identify on a large scale, potential sponge mechanisms active in a specific condition. Cirscan ranks each circRNA-miRNA-mRNA subnetwork according to a sponge score that integrates multiple criteria based on interaction reliability and expression level. Finally, the top ranked sponge mechanisms can be visualized as networks and an enrichment analysis is performed to help its biological interpretation. We showed on two real case studies that Cirscan is capable of retrieving sponge mechanisms previously described, as well as identifying potential novel circRNA sponge *** can be considered as a companion tool for biologists, facilitating their ability to prioritize sponge mechanisms for experimental validations and identifying potential therapeutic targets. Cirscan is implemented in R, released under the license GPL-3 and accessible on GitLab (https://***/geobioinfo/cirscan_Rshiny). The scripts used in this paper are also provided on Gitlab (https://***/geobioinfo/cirscan_paper).

关键词： Circular RNAs Sponge mechanism transcriptomic data Regulation network Cancer

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Assessment of single-nucleotide variant discovery protocols in RNA-seq data from human cells exposed to mycotoxins

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TOXICOLOGY MECHANISMS AND METHODS 2023年第3期33卷 215-221页

作者： Alonso-Garrido, M. Lozano, M. Riffo-Campos, A. L. Font, G. Vila-Donat, P. Manyes, L. Univ Valencia Fac Pharm Lab Food Chem & Toxicol Av Vicent Andres Estelles S-N Burjassot 46100 Spain Univ Valencia Epidemiol & Environm Hlth Joint Res Unit Univ Jaume 1 FISABIO Valencia Spain Univ La Frontera Millennium Nucleus Sociomed SocioMed & Vicerrecto Temuco Chile Univ Valencia Dept Comp Sci ETSE Valencia Spain

Food and feed contamination by nonlegislated mycotoxins beauvericin (BEA) and enniatin B (ENB) is a worldwide health concern in the present. The principal objective of this work is to assess some of the existing protocols to discover the single nucleotide variants (SNVs) in transcriptomic data obtained by RNA-seq from Jurkat cells in vitro samples individually exposed to BEA and ENB at three concentration levels (1.5, 3 and 5 mu M). Moreover, previous transcriptomic results will be compared with new findings obtained using a different protocol. SNVs rs201003509 in BEA exposed cells and the rs36045790 in ENB were found in the differentially expressed genes in all doses compared to controls by means of the Genome Analysis Toolkit (GATK) Best Practices workflow. SNV-RNA-seq complementary pipeline did not show any SNV. Concerning gene expression, discrepant results were found for 1.5 mu M BEA exposed cells compared with previous findings. However, 354 overlapped differentially expressed genes (DEGs) were identified in the three ENB concentrations used, with 147 matches with respect to the 245 DEGs found in the previous results. In conclusion, the two discovery SNVs protocols based on variant calling from RNA-seq used in this work displayed very different results and there were SNVs found manually not identified by any pipeline. Additionally, the new gene expression analysis reported comparable but non identical DEGs to the previous transcriptomic results obtained from these RNA-seq data.

关键词： Beauvericin enniatin B single-nucleotide variants Jurkat cells transcriptomic data

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ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology

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BMC BIOINFORMATICS 2023年第1期24卷 1-25页

作者： Paklao, Thummarat Suratanee, Apichat Plaimas, Kitiporn Chulalongkorn Univ Fac Sci Adv Virtual & Intelligent Comp AV Ctr Dept Math & Comp Sci Bangkok 10330 Thailand King Mongkuts Univ Technol North Bangkok Fac Appl Sci Dept Math Bangkok 10800 Thailand Chulalongkorn Univ Fac Sci Omics Sci & Bioinformat Ctr Bangkok 10330 Thailand

BackgroundFlux Balance Analysis (FBA) is a key metabolic modeling method used to simulate cellular metabolism under steady-state conditions. Its simplicity and versatility have led to various strategies incorporating transcriptomic and proteomic data into FBA, successfully predicting flux distribution and phenotypic results. However, despite these advances, the untapped potential lies in leveraging gene-related connections like co-expression patterns for valuable *** fill this gap, we introduce ICON-GEMs, an innovative constraint-based model to incorporate gene co-expression network into the FBA model, facilitating more precise determination of flux distributions and functional pathways. In this study, transcriptomic data from both Escherichia coli and Saccharomyces cerevisiae were integrated into their respective genome-scale metabolic models. A comprehensive gene co-expression network was constructed as a global view of metabolic mechanism of the cell. By leveraging quadratic programming, we maximized the alignment between pairs of reaction fluxes and the correlation of their corresponding genes in the co-expression network. The outcomes notably demonstrated that ICON-GEMs outperformed existing methodologies in predictive accuracy. Flux variabilities over subsystems and functional modules also demonstrate promising results. Furthermore, a comparison involving different types of biological networks, including protein-protein interactions and random networks, reveals insights into the utilization of the co-expression network in genome-scale metabolic ***-GEMs introduce an innovative constrained model capable of simultaneous integration of gene co-expression networks, ready for board application across diverse transcriptomic data sets and multiple organisms. It is freely available as open-source at https://***/ThummaratPaklao/***.

关键词： Flux balance analysis (FBA) Constraint-based approach Gene co-expression network Escherichia coli Saccharomyces cerevisiae Quadratic programming Metabolic flux analysis Metabolic engineering transcriptomic data Genome-scale metabolic model

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Deciphering early responsive signature genes in rice blast disease: an integrated temporal transcriptomic study

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JOURNAL OF APPLIED GENETICS 2024年第4期65卷 665-681页

作者： Antony, Ajitha Veerappapillai, Shanthi Karuppasamy, Ramanathan Vellore Inst Technol Sch Bio Sci & Technol Dept Biotechnol Vellore 632014 Tamil Nadu India

Rice blast disease, caused by Magnaporthe oryzae, reigns as the top-most cereal killer, jeopardizing global food security. This necessitates the timely scouting of pathogen stress-responsive genes during the early infection stages. Thus, we integrated time-series microarray (GSE95394) and RNA-Seq (GSE131641) datasets to decipher rice transcriptome responses at 12- and 24-h post-infection (Hpi). Our analysis revealed 1580 differentially expressed genes (DEGs) overlapped between datasets. We constructed a protein-protein interaction (PPI) network for these DEGs and identified significant subnetworks using the MCODE plugin. Further analysis with CytoHubba highlighted eight plausible hub genes for pathogenesis: RPL8 (upregulated) and RPL27, OsPRPL3, RPL21, RPL9, RPS5, OsRPS9, and RPL17 (downregulated). We validated the expression levels of these hub genes in response to infection, finding that RPL8 exhibited significantly higher expression compared with other downregulated genes. Remarkably, RPL8 formed a distinct cluster in the co-expression network, whereas other hub genes were interconnected, with RPL9 playing a central role, indicating its pivotal role in coordinating gene expression during infection. Gene Ontology highlighted the enrichment of hub genes in the ribosome and protein translation processes. Prior studies suggested that plant immune defence activation diminishes the energy pool by suppressing ribosomes. Intriguingly, our study aligns with this phenomenon, as the identified ribosomal proteins (RPs) were suppressed, while RPL8 expression was activated. We anticipate that these RPs could be targeted to develop new stress-resistant rice varieties, beyond their housekeeping role. Overall, integrating transcriptomic data revealed more common DEGs, enhancing the reliability of our analysis and providing deeper insights into rice blast disease mechanisms.

关键词： Magnaporthe oryzae transcriptomic data Protein-Protein Interaction MCODE CytoHubba Gene Ontology

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