SEARCH · Search PubMed

Results for “Integrative omics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources

PubMed2026

Integrative Multi-Omics Analysis Identifies Thrombosis-Associated Molecular Features Linked to Germline Susceptibility and Immune Cell Communication in Gastric Cancer.

Emerging evidence indicates that coagulation-related molecular programs are associated with thrombosis, tumor progression, and molecular dysregulation in gastric cancer (GC). However, thrombosis-associated molecular features in GC and their potential links to inherited susceptibility remain insufficiently understood. Integrated analyses of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were performed to identify thrombosis-associated genes and establish a machine learning-based prognostic signature. Genome-wide association study (GWAS), expression quantitative trait loci (eQTL), transcriptome-wide association study (TWAS), and Mendelian randomization (MR) analyses were conducted to investigate susceptibility-associated transcriptional programs in GC. Functional assays were used to evaluate candidate genes associated with malignant phenotypes. Single-cell RNA sequencing (scRNA-seq) and cell-cell communication analyses were further performed to characterize cell-type-specific expression patterns and potential intercellular interactions. A total of 22 differentially expressed thrombosis-associated genes were identified, and a prognostic signature comprising 14 genes was established. The signature stratified patients into high- and low-risk groups and showed prognostic performance in both the training and validation cohorts. Integrative GWAS, eQTL, and TWAS analyses identified susceptibility-associated transcriptional programs that were positively correlated with the thrombosis-associated risk score. Silencing ACTN2 and CRYAB significantly reduced GC cell migration and invasion. scRNA-seq analysis revealed relatively high CRYAB expression in neutrophils, and CellChat analysis suggested potential neutrophil-B cell interactions involving COLLAGEN-related signaling. This integrative multi-omics study identified a thrombosis-associated molecular signature linked to prognosis and germline susceptibility-associated transcriptional programs in GC. ACTN2 and CRYAB may represent candidate genes associated with GC cell migration and invasion, while single-cell analysis suggested potential immune-related communication features.

Humans↗

PubMed2026

Integrated multi-omics analyses identify an RAS-SLC11A2-associated molecular framework linking iron metabolism with PCOS-related cardiometabolic risk.

INTRODUCTION: PCOS is a common endocrine disorder with elevated cardiometabolic risk, yet the role of the renin-angiotensin system (RAS)-iron metabolism axis in this comorbidity remains unclear. We explored its underlying mechanisms and evaluated the therapeutic potential of gentiopicroside. METHODS: Integrated multi-omics analyses combining transcriptomics, single-cell RNA sequencing, Mendelian randomization, machine learning, molecular docking, and in vitro functional assays were performed to identify shared molecular pathways and therapeutic targets across PCOS, hypertension, NAFLD, and T2DM. RESULTS: SLC11A2 was consistently dysregulated in PCOS transcriptomic datasets, and associated with iron metabolism, inflammatory response and oxidative stress pathways. Genetic analyses validated RAS-related regulation in hypertension susceptibility and revealed shared genetic architecture between PCOS and cardiometabolic traits. Network and single-cell analyses characterized SLC11A2-associated molecular patterns in disease-relevant cell types; machine learning identified disease-classifying molecular signatures. Gentiopicroside alleviated inflammatory and oxidative stress phenotypes, including reduced IL-6 expression and reactive oxygen species accumulation. CONCLUSION: This study defines an RAS-SLC11A2 molecular framework linking iron metabolism dysregulation to PCOS-related cardiometabolic risk, elucidating the mechanisms connecting ovarian dysfunction, inflammation, oxidative stress and hypertension, and supports gentiopicroside as a promising therapeutic candidate.

Humans↗

PubMed2026

Integrated multi-omics analysis reveals a pH-driven metabolic and translational switch in Ureaplasma parvum.

Human ureaplasmas are minimal-genome bacteria and pathobionts of the urogenital tract. They must adapt to fluctuating pH conditions despite the absence of canonical transcriptional regulatory systems. However, the mechanisms underlying these responses remain unclear. This study aimed to construct a system-level model of pH adaptation in this minimal pathogen. We used an integrated multi-omics platform combining proteomics, metabolomics, and RNA modification profiling to construct a system-level model of pH adaptation. The results revealed a bifurcated strategy governed by the differential activation of preexisting, co-regulated functional modules. Under neutral pH conditions (pH 7), Ureaplasma parvum activated energy metabolism and upregulated ATP synthesis while forming a stress-counteracting proteostasis pathway. This may suggest a biological energy state under high stress conditions. Conversely, under acidic stress (pH 5), it activated biosynthesis/translation, showing significant upregulation of ribosomal proteins and accumulation of translation precursors and the polyamine spermidine. This may represent a state of expanded translational capacity. This adaptive switch is accompanied by dynamic reorganization of the epitranscriptome, highlighting the importance of post-transcriptional regulation. This study suggests mechanisms by which minimal organisms achieve adaptive plasticity through sophisticated post-transcriptional and metabolic control, providing a new framework for understanding Ureaplasma physiology and the biology of genome-reduced organisms.IMPORTANCEMinimal bacteria challenge canonical views of cellular regulation. In organisms with radically reduced genomes and sparse transcription factors, how adaptive plasticity is achieved remains a core question. Our study proposes a model in which a simple physicochemical cue-extracellular pH-selects among prewired cellular programs, while post-transcriptional and epitranscriptomic layers fine-tune execution. The findings of this study suggest a multi-omics scheme for how organisms adapt to environmental changes and ensure survival without inducing new circuits or complex transcriptional regulation. Conceptually, it proposes regulation via RNA modifications in processes, such as metabolism, proteostasis, and translation. This framework may be generalizable to other genome-reduced microorganisms. Beyond microbiology, it provides design principles for synthetic biology and offers a mechanistic interpretation of phenotypic tolerance to stress factors. It may encourage the use of pH-linked epitranscriptome signals as measurable indicators of cellular state.

Hydrogen-Ion Concentration↗

PubMed2025

Integration of multiple omics reveals key targets and cellular mechanisms for intervention in sarcopenia.

BACKGROUND: Sarcopenia, an age-related syndrome characterized by progressive loss of muscle mass, strength, and function, presents a significant global health burden with limited therapeutic interventions. This study integrates genomic causality, multi-tissue omics, and cellular mediation analyses to identify and prioritize mechanistically grounded therapeutic targets. METHODS: A multi-tiered analytical framework was applied, beginning with two-sample Mendelian randomization (MR) to infer causal relationships between 4907 plasma proteins (cis-pQTLs from 35,559 individuals) and sarcopenia traits in Pan-UK Biobank participants. Bayesian colocalization and transcriptomic validation in human sarcopenia muscle biopsies were employed to prioritize targets. Cellular mediation analysis quantified contributions of immune and stromal cell subtypes to protein-trait pathways using transcriptomic deconvolution. RESULTS: MR identified 1237 plasma proteins causally associated with sarcopenia traits, with six targets (HGFAC, GATM, HMOX2, F2, LMAN2L, HPGDS) validated through colocalization, transcriptomic expression, and sarcopenia-related dysregulation. Cellular mediation revealed immune mechanisms underlying HGFAC's effects, with CD4+ regulatory T cells mediating 3.49 % of its impact on sarcopenia traits. Prothrombin exhibited muscle-protective effects independent of coagulation. CONCLUSION: This study establishes a causal map linking plasma proteins to sarcopenia through immune-stromal interactions. The integration of MR, multi-omics validation, and cellular mediation prioritizes six proteins as actionable targets, supporting repurposing of thrombin inhibitors and development of immunometabolic therapies. The framework bridges genomic causality with cellular pathophysiology, advancing precision strategies for age-related muscle decline.

Humans↗

PubMed2024

Understanding the biological processes of kidney carcinogenesis: an integrative multi-omics approach.

Biological mechanisms related to cancer development can leave distinct molecular fingerprints in tumours. By leveraging multi-omics and epidemiological information, we can unveil relationships between carcinogenesis processes that would otherwise remain hidden. Our integrative analysis of DNA methylome, transcriptome, and somatic mutation profiles of kidney tumours linked ageing, epithelial-mesenchymal transition (EMT), and xenobiotic metabolism to kidney carcinogenesis. Ageing process was represented by associations with cellular mitotic clocks such as epiTOC2, SBS1, telomere length, and PBRM1 and SETD2 mutations, which ticked faster as tumours progressed. We identified a relationship between BAP1 driver mutations and the epigenetic upregulation of EMT genes (IL20RB and WT1), correlating with increased tumour immune infiltration, advanced stage, and poorer patient survival. We also observed an interaction between epigenetic silencing of the xenobiotic metabolism gene GSTP1 and tobacco use, suggesting a link to genotoxic effects and impaired xenobiotic metabolism. Our pan-cancer analysis showed these relationships in other tumour types. Our study enhances the understanding of kidney carcinogenesis and its relation to risk factors and progression, with implications for other tumour types.

Kidney Neoplasms↗

PubMed2026

Integrative multi-omics analysis unravels the metabolic landscape and reveals serum biomarkers for early diagnosis of hyperuricemia.

BACKGROUND: Hyperuricemia (HUA) is a major risk factor for gout and multiple metabolic disorders. Although serum uric acid (UA) is the gold standard for HUA diagnosis, it fails to reflect early metabolic disturbances and shows limited predictive value for asymptomatic HUA. This study sought to elucidate the pathological mechanisms underlying HUA and identify novel diagnostic biomarkers beyond UA. METHODS: This study enrolled 195 patients with HUA and 98 healthy controls. Global metabolomics and proteomics profiling were performed to characterize molecular alterations underlying HUA. Based on the biological relevance of the shared dysregulated pathways, a pathway correlation network was constructed to elucidate the pathological mechanisms driving HUA initiation and progression. Furthermore, diagnostic biomarkers for HUA were identified using machine learning algorithms, and were validated with an external cohort. RESULTS: HUA patients exhibited distinct metabolic and proteomic profiles compared with healthy controls. Integrated multi-omics pathway analysis revealed that peroxisome proliferators-activated receptor signaling pathway, arachidonic acid metabolism, purine metabolism, pyrimidine metabolism and sphingolipid signaling pathway were significantly dysregulated in HUA. Among them, arachidonic acid metabolism was identified as a hub pathway involved in HUA progression. Furthermore, a metabolite panel consisting of cysteine-S-sulfate, glycerophosphocholine and 4-hydroxyphenylpyruvic acid was screened by machine learning and validated in an independent cohort, which showed slightly higher diagnostic performance for HUA than UA. CONCLUSIONS: This study reveals the core metabolic and protein regulatory networks of HUA, and identifies a novel serum metabolite panel for the diagnosis of HUA. These findings provide new insights for improved clinical diagnosis and management.

Humans↗

PubMed2026

Integration of multi-omics data uncovers novel germline susceptibility candidates in early-onset colorectal cancer.

Colorectal cancer (CRC) is increasingly diagnosed in individuals under 50 years of age, yet the underlying genetic predisposition remains largely unexplained, particularly in mismatch repair (MMR)-proficient cases. This study aimed to identify novel hereditary CRC susceptibility genes by integrating germline and tumour whole-exome sequencing (WES) with transcriptomic profiling across a cohort of early-onset CRC (EOCRC) patients. Tumours were categorised using Consensus Molecular Subtypes (CMS) classification and analysed for mutational signature and burden. We used a novel 'All vs One' multi-omic integration approach to identify loss-of-function rare germline variants with concordant gene expression alterations in tumour tissue. Five candidate genes (ADCY4, NOXO1, CDHR2, ARHGAP10, EEF2K) were prioritised based on this approach and potential biological relevance in CRC. These findings highlight the molecular heterogeneity of EOCRC and demonstrate the utility of multi-omic approaches in refining germline variant interpretation. Integrating tumour transcriptomics enhances gene discovery efforts and supports a more comprehensive understanding of CRC heritability in younger individuals.

Humans↗

PubMed2026

BRIDGE: an interactive application for multi-omics data analysis, visualization and integration.

SUMMARY: BRIDGE is a Shiny-based application that provides an accessible, modular platform for individual and integrative multi-omics analysis. Using an independent SQLite database backend, it offers a local, private, and user-friendly environment that requires no prior computational expertise. The application supports proteomics, phospho-proteomics, and RNA-seq analyses through a comprehensive suite of visualization and analytical modules, together with an integrated multi-omics analysis pipeline. Built-in caching and asynchronous processing improve responsiveness, enabling efficient exploration, analysis, and visualization of multi-omics datasets on moderate hardware. AVAILABILITY AND IMPLEMENTATION: BRIDGE is implemented in R using Shiny and is freely available as a Docker container at https://ghcr.io/paulilab/bridge. A public demonstration server with example datasets is available at https://bridge.imp.ac.at. Code and datasets are also available at https://github.com/paulilab/BRIDGE and under DOI: https://doi.org/10.5281/zenodo.20215824.

Multiomics↗

PubMed2025

JASMINE: A powerful representation learning method for enhanced analysis of incomplete multi-omics data.

Integrative analysis of multi-omics data provides a more comprehensive and nuanced view of a subject's biological state. However, high-dimensionality and ubiquitous modality missingness present significant analytical challenges. Existing methods for incomplete multi-omics data are scarce, do not fully leverage both modality-specific and shared information, and produce task-biased representations. We propose JASMINE, a self-supervised representation learning method for incomplete multi-omics data that preserves both modality-specific and joint information and enhances sample similarity structure. JASMINE produces embeddings that achieve superior performance across multiple tasks for two different incomplete multi-omics datasets while requiring only a single round of training per dataset.

missing data↗

PubMed2026

Trimethylamine-producing microbe Bacillus megaterium KCTC 3007 promotes antitumor immunity in endometrial cancer via type I interferon response pathways.

BACKGROUND: Endometrial cancer (ECa) is one of the most common gynecologic malignancies, with limited therapeutic responses in metastatic or recurrent cases. The bacterial microbiota has emerged as a key modulator of carcinogenesis and antitumor immunity. However, the role of endometrial microbiota in ECa pathogenesis and prognosis remains poorly understood. METHODS: We performed comprehensive multi-omics analysis integrating metatranscriptomics, transcriptomics, and targeted metabolomics from 60 ECa and 18 benign patients. RNA sequencing enabled simultaneous profiling of active tissue-resident microbiota and host gene expression. Serum metabolomics was conducted on all patients. Identified microbial-metabolite associations were validated through in vitro co-culture experiments using peripheral blood mononuclear cells (PBMCs), cancer cell lines, RNA sequencing, and live cell imaging. RESULTS: ECa patients exhibited significantly altered microbial diversity and composition compared to benign controls. Through integrated multi-omics analysis, we identified Bacillus megaterium (BM) KCTC 3007 as a beneficial microbe associated with prolonged recurrence-free survival. In an exploratory analysis of ECa subtypes, Cupriavidus taiwanensis and Marinomonas primoryensis showed potential links to poor prognosis, although these observations warrant caution due to the limited size of certain subgroups. Tissue BM abundance positively correlated with serum trimethylamine N-oxide (TMAO) levels, particularly in postmenopausal women. In vitro experiments demonstrated that BM KCTC 3007 enhanced antitumor immunity by promoting interleukin and type I interferon expression, expanding CD8 + T cell populations, and increasing immune cell-tumor cell interactions. RNA sequencing revealed activation of interferon alpha response and immune cell proliferation pathways, with IFNAR1 identified as a key upstream regulator. TMAO treatment recapitulated these immune-activating effects, enhancing CD8 + T cell responses and preferentially inducing pyroptotic cancer cell death. CONCLUSIONS: We provide the first evidence that tissue-resident BM KCTC 3007 promotes antitumor immunity in ECa through TMAO production and subsequent type I interferon-mediated immune activation. This integrated multi-omics approach establishes a complete microbe-metabolite-host mechanistic pathway and highlights the therapeutic potential of TMAO-producing probiotic strains for ECa treatment. Video Abstract.

Female↗

PubMed2026

Integrating multi-omics technologies to decipher microbiome functions.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Multiomics↗

PubMed2026

Polystyrene microplastics induce auditory neurotoxicity in mammals: Integrated multi-omics profiling reveals oxidative damage and synaptic molecular dysregulation.

Microplastics (MPs) are ubiquitous environmental pollutants, yet their neurotoxic effects on the auditory system remain poorly understood. This study develops an integrated multi-level analytical framework combining auditory neurophysiology, behavioral assessment, tissue biochemistry, transcriptomics, and proteomics to investigate polystyrene (PS)-MPs-induced auditory neurotoxicity in rats. PS-MPs infiltrate the auditory system and significantly impair auditory processing, with central dysfunction emerging earlier and more prominently than peripheral alterations. Multi-omics analyses reveal coordinated suppression of glutamatergic synapse and Wnt signaling pathways in the cochlear nucleus. Mechanistically, PS-MPs perturb the crosstalk between glutamatergic synaptic and Wnt signaling, promoting AMPA receptor (AMPAR) internalization and potentially affecting synaptic plasticity-related processes and neuronal responsiveness. In parallel, PS-MPs trigger oxidative stress, apoptosis, and glial activation, reflecting pronounced neuroinflammatory and redox imbalance. In primary cochlear nucleus neurons (PCNNs), these mechanisms were further validated in vitro, where activation of Wnt signaling by Wnt3a significantly alleviated oxidative injury and reduced AMPAR internalization. Collectively, these findings provide comprehensive preclinical evidence for the neurotoxic potential of MPs and reveal a previously unrecognized PS-MPs-induced auditory neurotoxicity, although further studies are needed for human relevance. Results from the rat model further implicate Wnt-mediated signaling as a potential modulatory pathway underlying MPs-induced synaptic molecular alterations and redox dysfunction.

Animals↗

PubMed2026

Integrative multi-omics identifies DOC2A as a novel pharmacological target for bipolar disorder.

BACKGROUND: Current bipolar disorder (BD) therapies suffer from limited efficacy and adverse effects, necessitating mechanistically grounded targets. METHODS: We integrated BD genome-wide association study data (158,036 cases; 2,796,499 controls) with brain proteomics (ROSMAP and Banner dorsolateral prefrontal cortex, n = 376 and 152) to perform proteome-wide association studies (PWAS). Bayesian colocalization and summary-data-based Mendelian randomization (SMR) prioritized causal genes. Cell-type-specific transcriptomics validated dysregulation in iPSC-derived neurons, astrocytes, and postmortem hippocampus/prefrontal cortex. Weighted gene co-expression networks (WGCNAs), functional enrichment, and molecular docking assessed functional pathways and druggability. RESULTS: PWAS identified eight BD-associated genes (false discovery rate < 0.05), with DOC2A emerging as the top candidate. Colocalization (H4 > 0.8) and SMR supported a causal association of DOC2A with BD, with no pleiotropy (heterogeneity in dependent instruments P > 0.01); DOC2A expression decreased in BD across neurons (P = 4.26 × 10-2), astrocytes (P = 2.09 × 10-2), hippocampus (P = 9.80 × 10-3, t = -2.738), and prefrontal cortex (P = 1.44 × 10-2, t = -2.580); WGCNA positioned DOC2A as a key regulator (module membership/gene significance P < 0.05) of co-expression networks enriched for BD-associated processes including neurotransmitter secretion and postsynaptic actin cytoskeleton organization (P < 0.05); molecular docking revealed favorable-affinity binding (ΔG < -4 kcal/mol) between DOC2A and BD-related drugs and neuroprotective compounds. CONCLUSIONS: Our convergent multi-omics framework highlights DOC2A dysregulation as a key contributor to synaptic dysfunction in BD and nominates it as a promising therapeutic target. The demonstrated interaction with existing neuroactive compounds provides immediate translational avenues.

Bipolar Disorder↗

PubMed2026

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n = 11), persistent FGR (n = 9), and healthy controls (n = 13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n = 69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p < 0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular signatures reflecting the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

PubMed2026

Esketamine multi-omic biomarker evaluation in major depressive disorder (EMBER-MDD): concept, objectives and methodologies of a non-clinical investigator-initiated study.

Treatment resistance (TR) in major depressive disorder (MDD) affects a substantial minority of patients and is hard to recognize early, delaying intensified care. The Esketamine multi-omic biomarker evaluation in MDD (EMBER-MDD) is a non-interventional, investigator-initiated, in-vitro study within the EU Psych-STRATA programme, analyzing biospecimens collected in the randomized INTENSIFY study and the mirror OBS-TR cohort after participants complete treatment. EMBER-MDD aims to discover individual-omic and integrated multi-omic (hypothesis-free) biomarkers and signatures associated with TR risk, and molecular correlates of clinical response to esketamine nasal spray versus treatment as usual (TAU). Biomaterials will derive from approximately 420 adults with MDD (estimated n = 210 esketamine; n = 210 TAU) and include whole blood, RNA-stabilized whole blood, plasma and serum, sampled at baseline and, when feasible, during and after treatment (up to ~ 5,040 aliquots stored at - 80 °C). Genomics will use baseline DNA genotyping on Illumina Infinium GSA v3.0+MD arrays; epigenomics will profile genome-wide DNA methylation across time points using MethylationEPIC v2.0; transcriptomics will employ mRNA-seq (NovaSeq X/ X Plus); and proteomics/ metabolomics will be generated using high-throughput Olink and/ or Biocrates platforms. Each layer will undergo state-of-the-art preprocessing and analyses (e.g., GWAS/ PRS, EWAS, differential expression, WGCNA, pathway and network analyses), followed by integrative strategies including QTL mapping (meQTL/ eQTL/ pQTL/ mQTL) and intermediate-fusion machine learning with nested cross-validation, explainable AI (SHAP/ LIME) and treatment-effect modelling. All outputs are research-only and will not support individual efficacy, tolerability, or clinical decision-making. The study will deliver robust biosignatures and mechanistic hypotheses to guide future validation and inform stratified, molecularly guided intervention strategies in subsequent prospective trials. Trial registration number: 2023-506617-21-00 and 2025-178-f-S.

Humans↗

PubMed2025

Integrated multi-omics analysis reveals TMEM147 as an immunosuppressive prognostic biomarker in LUAD.

TMEM147, an endoplasmic reticulum (ER) membrane protein, is implicated in lung adenocarcinoma (LUAD) progression, although its precise role remains unclear. To elucidate its function, this study integrated bioinformatics analyses with experimental validation. First, TMEM147 expression was assessed using TCGA and GEO datasets, with validation performed in LUAD cell lines. Survival analysis evaluated its prognostic significance. Subsequently, Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and single-sample gene set enrichment analysis (ssGSEA) were employed to identify associated functional pathways and interactions within the tumor immune microenvironment. Transcription factor binding predictions and in vitro functional assays (migration, invasion, proliferation) further characterized TMEM147's role. Results indicated that TMEM147 was significantly upregulated in LUAD and correlated with poor outcomes in patients. FLI1 was predicted as a key transcriptional regulator of TMEM147. Furthermore, TMEM147 expression influenced immune cell infiltration profiles and was associated with pathways involved in ribonucleoprotein biogenesis and oxidative phosphorylation (OXPHOS). Importantly, silencing TMEM147 significantly reduced cancer cell migration, invasion, and proliferation. These findings collectively suggest that TMEM147 promotes LUAD progression and holds potential as both a prognostic biomarker and a therapeutic target.

Bioinformatics analysis↗

PubMed2025

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).

Deep Learning↗

PubMed2026

Integrative multi-omics analyses suggest a candidate microbial metabolite-associated host gene network in ulcerative colitis.

Ulcerative colitis (UC) is associated with gut microbial dysbiosis, but the host molecular alterations potentially linked to microbially derived metabolites remain incompletely understood. We integrated Mendelian randomization (MR), microbial metabolite annotation, computational target prediction, colonic transcriptomics, network analysis, and machine learning. MiBioGen microbiome GWAS data were used as exposures and FinnGen Release 12 ULCERENTER as the outcome. Metabolites linked to MR-prioritized taxa were retrieved from GutMGene, and human targets were predicted using SwissTargetPrediction and SEA. UC-related genes were defined by integrating differential expression analysis and WGCNA and then intersected with predicted metabolite targets. MR prioritized one family and eight genera showing nominal genetically supported associations with UC, but none remained significant after Benjamini-Hochberg FDR correction. Three prioritized genera were linked to 15 microbe-metabolite records, corresponding to 13 unique metabolites; nine were retained for target prediction, yielding 277 unique predicted human targets. Transcriptomic analysis identified 1,530 DEGs and a 312-gene MEgrey60 module, with 273 overlapping genes, producing 1,569 unique UC-related genes. Their intersection with the 277 predicted targets yielded 47 candidate genes. Enrichment analyses highlighted mainly metabolic and lipid-related processes. Random Forest showed the highest mean AUC across the two independent external benchmarking cohorts, and SHAP prioritized EPHX1, HSD17B2, IGFBP5, and MMP10. IBDome analysis showed inflammation-associated expression differences in these genes. This study provides a genomics-informed, hypothesis-generating framework that prioritizes candidate microbe-metabolite-host relationships in UC for future experimental validation.

Humans↗