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PubMed2026

An integrated proteomics and transcriptomics analysis highlights concordance between protein turnover and carbohydrate transport and metabolism as key functional categories during the growth of Trichophyton rubrum.

Dermatophytes are a class of keratinophilic skin fungi that invade host skin, hair, and nails to acquire nutrients. An integrated multi-omics approach utilizing liquid chromatography-tandem mass spectrometry and RNA-seq after growth in a protein-rich soy medium was employed to capture the major subset of secreted protein families of Trichophyton rubrum. The secretome consisted mainly of proteases and cell wall-degrading enzymes, with subtilisins (Sub6 and Sub7), metallopeptidase (LAP2), and chitinase having the most abundant peptides. Transcriptional profiling indicated fungal adaptation in protein-rich media to process the protein nutrients through modulation of metabolism and general cellular function pathways. Correlation analysis between proteomics and transcriptomics data using functional KOG categories shows high concordance of KOG categories O (posttranslational modification, protein turnover, and chaperones), P (inorganic ion transport and metabolism), and G (carbohydrate transport and metabolism), as per cosine similarity analysis.IMPORTANCEDermatophytes are keratinophilic skin fungal pathogens that invade host skin, hair, and nails to acquire nutrients. There is an epidemic-like increase in infections, as well as an increase in antimicrobial resistance among dermatophytes, as witnessed over the last decade. There is hence a need to understand the key pathways and virulence factors required during growth and infection. We present an integrated multi-omics analysis (proteomics and transcriptomics data) using a vector-based similarity approach to show high concordance of KOG functional categories belonging to posttranslational modification, protein turnover, carbohydrate transport, and metabolism.

Proteomics↗

PubMed2026

Next-Generation Disease Profiling by Integrating Histopathology with Spatial Multi-Omics Data.

The field of pathology has experienced several transformative changes in recent years with the advent of digital pathology and spatial multi-omics. These technologies have enhanced every aspect of pathology practice, from streamlining daily workflows to generating high-fidelity multi-omics data that provide pathologists with novel tools to refine disease profiling and clinical diagnosis. Each layer of multimodal data (genomic, metabolomic, proteomic, or transcriptomic) has uncovered a distinct facet of disease pathologies, and combined with machine learning/artificial intelligence-based data analysis and pattern recognition models, has provided holistic understanding of regulatory mechanisms underpinning them. However, high-dimensional data have far exceeded the volume, scale, and complexity of immunostaining methods implemented by pathologists and, thus, have generated significant challenges related to deconvolution, interpretation, and clinical translation. Furthermore, these multimodal studies have predominantly relied on computational methods to process data and extract disease-relevant insights, thus raising questions around relevance or role of a pathologist in this new era of multi-omics. This review will provide a perspective on the evolving fields of molecular histopathology and spatial -omics, leveraging them to approach disease profiling, and redefining the role of a pathologist during this process.

Humans↗

PubMed2026

Bridging the gap: multi-omic insights into exercise responses in postmenopausal women.

Postmenopausal women represent the fastest-growing demographic at risk of sarcopenia and cardiometabolic disease, yet exercise biology research remains disproportionately derived from male or hormone-replete phenotypes. Menopause constitutes a chronic endocrine perturbation characterized by sustained reductions in estrogen and progesterone, and altered androgen balance, superimposed on the acute and chronic perturbations induced by exercise. This hormonal shift modifies substrate metabolism, inflammation, redox balance, and recovery capacity, factors that shape molecular responses to exercise across tissues and time. Here, we synthesize current evidence on exercise responses in postmenopausal females across genomics, epigenomics, transcriptomics, proteomics, and metabolomics/lipidomics. Across omics layers, direct data in postmenopausal cohorts remain limited, with frequent underreporting of menopausal status, hormone therapy exposure, circulating hormone concentrations, medication use, and biosampling timing relative to exercise and hormone dosing. We outline a menopause-aware framework for exercise-omics that prioritizes endocrine stratification, repeated sampling across exercise and recovery, and integrative multi-omics approaches linking molecular responses to functional outcomes. We also outline minimum reporting standards to improve reproducibility, inclusivity, and translational relevance. Advancing menopause-aware exercise-omics will be essential for developing precision exercise strategies that improve health span and functional independence in later life.

Humans↗

PubMed2026

CancerOmicsStudio (CoS): a web server for integrative and interpretable analysis of multi-omics cancer data.

MOTIVATION: Large-scale omics resources, including The Cancer Genome Atlas, Genomics of Drug Sensitivity in Cancer, and the Cancer Dependency Map, have become essential for cancer research. However, these datasets are distributed across different platforms, formats and analysis frameworks, which limits their practical use by researchers without extensive computational expertise. RESULTS: We developed CancerOmicsStudio (CoS), a web server for integrative and interpretable analysis of multi-omics cancer data across 33 cancer types. CoS provides five major modules: CosAI, Traditional Analysis, Drug Sensitivity, CRISPR Dependency and Single-Cell Tumor Microenvironment. The Traditional Analysis module supports expression comparison, diagnostic evaluation, survival analysis, enrichment analysis and gene correlation. The Drug Sensitivity and CRISPR Dependency modules enable systematic evaluation of gene-drug response associations and gene essentiality in cancer cell lines. The Single-Cell Tumor Microenvironment module supports tumor microenvironment analysis at single-cell resolution. In total, approximately 1.23 million results have been precomputed to enable rapid retrieval. CosAI further allows users to submit natural-language queries and obtain results through a Real-time Analysis as Retrieval framework, with responses summarized by a lightweight language model. AVAILABILITY AND IMPLEMENTATION: CancerOmicsStudio is freely available at Zenodo (doi: 10.5281/zenodo.18744990) and https://cos.wanglab.bio.

Humans↗

PubMed2026

Omics in optic neuropathies: From molecular landscapes to personalized therapeutics.

Optic neuropathies comprise a heterogeneous group of disorders involving transient or permanent injury to retinal ganglion cells (RGCs) and their axons. Clinically, these neurodegenerative conditions manifest as dyschromatopsia, decreased visual acuity, and visual field defects, and in severe cases may ultimately lead to blindness and disability. The marked heterogeneity across disease subtypes, incompletely understood etiologies, and complex pathogenic mechanisms pose substantial challenges to precise diagnosis and effective treatment. Recent advances in omics technologies - including genomics, transcriptomics, proteomics, metabolomics, lipidomics, single-cell and spatial sequencing, and integrative multi-omics approaches - have ushered optic nerve degenerative disease research into an era of high-resolution comprehensive investigation. In this review, we summarize representative applications of omics approaches to elucidate genetic alterations, signaling dysregulation, metabolic reprogramming, and immune responses in optic neuropathies. We further discuss the emerging potential of multi-omics in identifying early diagnostic biomarkers and informing individualized therapeutic strategies. Finally, we provide a forward-looking perspective on the future trajectory of omics technologies and their prospects in both fundamental research and clinical translation, with the overarching aim of accelerating the bench-to-bedside transition in this critical eye disease field.

biomarkers↗

PubMed2025

CCDC137 knockdown suppresses bladder cancer progression by downregulating SCD.

BACKGROUND: The Coiled-coil domain-containing (CCDC) family, due to its unique protein structural domain and broad involvement in diverse biological processes, has emerged as a focus in oncology research. Nevertheless, its clinical significance and function in bladder cancer (BLCA) remain poorly defined. METHODS: Machine learning algorithms were employed to identify pivotal CCDC genes in the cancer genome atlas (TCGA), and a prognostic model was subsequently constructed. Multi-omics data encompassing pan-cancer cohorts, single-cell sequencing, and spatial transcriptomics were integrated to characterize the expression patterns and prognostic significance of Coiled-coil domain-containing 137 (CCDC137), a previously uncharacterized CCDC family member in BLCA. Tissue microarray confirmed CCDC137 abnormal expression in bladder carcinoma specimens. The effect of CCDC137 knockdown on BLCA progression was evaluated through CCK8 assay, clonogenic formation, wound healing, Transwell, and subcutaneous xenograft models. RNA sequencing, quantitative RT-PCR, and western blot were utilized to delineate its regulatory network. RESULTS: A prognostic model incorporating 10 CCDC genes was successfully established in the TCGA-BLCA cohort. Then, we found that CCDC137 exhibited pan-cancer overexpression and usually correlation with poor clinical outcomes. Immunohistochemistry further substantiated its dysregulation in bladder carcinoma. Integrated multi-omics analyses suggested associations between CCDC137 expression and a tumor immunosuppressive microenvironment. CCDC137 knockdown significantly suppressed bladder cancer cell proliferation and migratory capacity in vitro. Correspondingly, subcutaneous xenograft tumor growth was inhibited in vivo. Moreover, decreased expression of stearoyl-CoA desaturase (SCD), a key lipid metabolic enzyme, accompanied CCDC137 depletion. These findings collectively suggest a cancer-promoting role for CCDC137 in bladder carcinoma. CONCLUSIONS: This systematic investigation combining multi-omics bioinformatics analyses and experimental validation demonstrates the role of CCDC137 in bladder carcinoma progression, providing novel mechanistic insights into the pathogenesis of BLCA and offering a theoretical foundation for therapeutic targeting of CCDC137 in urothelial malignancies.

Urinary Bladder Neoplasms↗

PubMed2026

Regulatory Evolution and the Genetic Basis of Human Brain Expansion.

The evolution of the human brain is characterized by profound changes in structure and function, despite relatively limited divergence in protein-coding genes compared to other primates. This paradox has led to increasing recognition of gene regulatory elements (GREs) as primary drivers of evolutionary innovation. In this review, we synthesize current knowledge on the role of conserved noncoding elements (CNEs), human accelerated regions (HARs), and transposable element (TE)-derived sequences in shaping gene regulatory networks (GRNs) underlying brain development. Comparative analyses across humans and closely related primates, including the chimpanzee, gorilla, and orangutan, reveal that while core regulatory architectures are highly conserved, subtle changes in regulatory elements drive species-specific gene expression patterns. We highlight how CNEs provide a stable regulatory framework, whereas HARs and TE-derived elements introduce lineage-specific modifications that fine-tune neurodevelopmental processes. Advances in functional genomics, including CRISPR-based perturbations, massively parallel reporter assays, and single-cell multi-omics, have enabled direct interrogation of regulatory function, linking sequence variation to cellular phenotypes. Furthermore, we discuss how regulatory evolution contributes to both cognitive innovation and susceptibility to neurological disorders. Despite significant progress, challenges remain in establishing causal relationships between regulatory variation and phenotypic outcomes. Future integration of multi-omics data and comparative models will be essential for resolving these complexities. Together, this review provides a comprehensive framework for understanding the molecular basis of primate brain evolution through the lens of gene regulation.

Brain evolution↗

PubMed2026

Deep visual multi-omics profiling links morphology and molecular programs in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma exhibits striking intra-tumoral heterogeneity at morphological and genetic levels, complicating treatment and contributing to disease progression. CcRCCs with rhabdoid differentiation are highly aggressive tumors characterized by distinct histopathologies. However, the relationship between morphology, underlying molecular alterations, and tumor behavior remains largely unclear. Here, we present Deep Visual Multi-Omics, an approach integrating digital pathology, morphology-guided single-cell isolation, and ultra-sensitive multi-omics profiling to link cell morphologies to their molecular underpinnings. Across five tumors, we profiled ~40,000 AI-classified and expert-curated cells. We identified progressive molecular dysregulation across cells with increasing histopathological grade coexisting within heterogeneous tumors as well as distinct molecular alterations associated with aggressive rhabdoid ccRCC cells, including signatures consistent with enhanced FOXM1-driven proliferation, altered cell-matrix interactions, and a putative immunomodulatory phenotype. Notably, rhabdoid cells exhibited elevated expression of IFN-beta, PD-L1, CD38, ITGB2, and integrin signaling, suggesting that they themselves may act as a source of signals influencing the local immune microenvironment. Besides providing new insights into the biology of ccRCC and highlighting avenues for future translational studies, this illustrates the potential of Deep Visual Multi-omics to dissect cancer heterogeneity and characterize high-risk cell populations.

Humans↗

PubMed2026

Large-scale multi-omics enhance risk prediction for type 2 diabetes.

BACKGROUND: Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA1c measurements. METHODS: We analysed data from 42,840 UK Biobank participants without diagnosed diabetes at baseline. The study population was split into a derivation set (Phase 1 metabolomics release, N = 23,108) to fit models and an independent validation set (Phase 2 release, N = 19,732) to evaluate performance. Data for a PRS for type 2 diabetes, 11 metabolites, and 15 proteins were added to the CDRS to develop multi-omics prediction models. Model performance was evaluated using Harrell's C-index and the net reclassification index (NRI). RESULTS: During 10 years of follow-up, 1090 participants developed incident type 2 diabetes. Among individual omics layers, proteomics contributed the greatest improvement in predictive performance, increasing the C-index from 0.862 (clinical CDRS) to 0.884 (ΔC-index; + 0.022; P < 0.001), with a continuous NRI of 42.0%. The full multi-omics model further significantly increased the C-index compared to a model combining the clinical CDRS with proteomics data (C-index, 0.891; ΔC-index; + 0.007; P < 0.001). CONCLUSION: Integrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. Several of the selected proteins and metabolites are on cardiovascular disease pathways, highlighting the link between diabetes and cardiovascular risk. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins. External validation and cost-effectiveness analyses are needed to support clinical adoption.

Humans↗

PubMed2025

PLSKO: a robust knockoff generator to control false discovery rate in omics variable selection.

MOTIVATION: Integrating the knockoff framework with any variable-selection method delivers stringent false discovery rate (FDR) control without recourse to p-values, offering a powerful alternative for differential expression analysis of high-throughput omics datasets. However, existing knockoff generators rely on restrictive modelling assumptions or coarse approximations that often inflate the FDR when applied to real-world data. RESULTS: We introduce Partial Least Squares Knockoff (PLSKO), an efficient, assumption-free generator that remains robust across diverse omics platforms. Our extensive simulations show that PLSKO is the only method to maintain FDR control with sufficient power in complex non-linear settings. Our semi-simulation studies drawn from RNA-seq, proteomics, metabolomics, and microbiome experiments confirm PLSKO generates valid knockoff variables. In pre-eclampsia multi-omics case studies, we combine PLSKO with Aggregation Knockoff to address the randomness of knockoffs and improve power, and demonstrate the method's ability to recover biologically meaningful features. AVAILABILITY AND IMPLEMENTATION: Our proposed algorithm is available on Github (https://github.com/guannan-yang/PLSKO) and Zenodo (https://doi.org/10.5281/zenodo.16879594).

Algorithms↗

PubMed2025

Integration of multi-source gene interaction networks and omics data with graph attention networks to identify novel disease genes.

MOTIVATION: The pathogenesis of diseases is closely associated with genes, and the discovery of disease genes holds significant importance for understanding disease mechanisms and designing targeted therapeutics. However, biological validation of all genes for diseases is expensive and challenging. RESULTS: In this study, we propose DGP-AMIO, a computational method based on graph attention networks, to rank all unknown genes and identify potential novel disease genes by integrating multi-omics and gene interaction networks from multiple data sources. DGP-AMIO outperforms other methods significantly on 20 disease datasets, with an average AUROC and AUPR exceeding 0.9. The superior performance of DGP-AMIO is attributed to the integration of multiomics and gene interaction networks from multiple databases, as well as triGAT, a proposed GAT-based method that enables precise identification of disease genes in directed gene networks. Enrichment analysis conducted on the top 100 genes predicted by DGP-AMIO and literature research revealed that a majority of enriched GO terms, KEGG pathways and top genes were associated with diseases supported by relevant studies. We believe that our method can serve as an effective tool for identifying disease genes and guiding subsequent experimental validation efforts. AVAILABILITY AND IMPLEMENTATION: DGP-AMIO is publicly available at https://github.com/yangkaiyuan1027/DGP-AMIO.

Gene Regulatory Networks↗

PubMed2025

soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.

The rapid advancement of spatial multi-omics technologies has opened new avenues for dissecting tissue architecture with unprecedented resolution. However, inherent disparities across omics modalities, such as differences in biological hierarchy and resolution, pose significant challenges for integrative analysis. To address this, we present soFusion, a method for representation learning on spatial multi-omics data that enables automated identification of tissue compartmentalization. soFusion employs a graph convolutional network (GCN) to extract latent embeddings from spatial omics profiles. To simultaneously capture both cross-modality relationships and modality-specific features, we introduce a novel strategy for intra- and inter-omics feature learning. Moreover, modality-specific decoders are designed to preserve the unique information embedded in each omics type. We evaluated soFusion on multiple datasets including gene expression, protein expression, and epigenetic features. Across all benchmarks, soFusion consistently outperformed existing methods in delineating anatomical structures and identifying spatial domains with improved continuity and reduced noise. Collectively, soFusion offers an effective solution for spatial multi-omics integration, substantially enhancing the robustness of spatial domain identification.

Humans↗

PubMed2026

Multi-cohort integration and machine learning identify CPVL as a novel oncogenic driver in gastric cancer.

BACKGROUND: Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, and the prognosis of advanced GC remains poor. Systematic identification of robust biomarkers through multi-cohort integration and computational prioritization may facilitate the discovery of novel therapeutic targets. AIM: To identify key genes associated with gastric cancer progression through integrative multi-omics analysis and to elucidate the biological functions and molecular mechanisms of the top-prioritized candidate gene. METHODS: Comprehensive bioinformatics analyses integrating The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) datasets were performed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), Cox regression, and eight machine-learning algorithms to systematically identify and prioritize GC-associated hub genes. Among the identified candidates, CPVL was selected for further validation based on its diagnostic and prognostic performance. CPVL expression and clinical relevance were validated by independent datasets and immunohistochemistry. Lentiviral constructs were used to overexpress or silence CPVL in GC cell lines. Functional assays were performed, including CCK-8, colony formation, EdU incorporation, and flow cytometry, to assess cell proliferation and cell-cycle distribution. Western blotting and JAK2 inhibitor (AZD1480) rescue experiments were performed to elucidate the underlying mechanisms, and a nude mouse xenograft model was used to evaluate tumorigenicity in vivo. RESULTS: Multi-cohort screening identified five hub genes (CPVL, AADAC, BCAT1, CPXM1, and FBN1). Among them, CPVL exhibited the highest diagnostic accuracy (AUC = 0.895) and the strongest correlation with poor overall survival, and was therefore selected for mechanistic investigation. CPVL expression was markedly upregulated in GC tissues and cell lines. Functional assays demonstrated that CPVL promotes GC cell proliferation and accelerates G1/S-phase transition. Mechanistically, CPVL activated the JAK2/STAT3 signaling pathway, upregulating Cyclin D1 and CDK4 while downregulating p27. Treatment with the JAK2 inhibitor AZD1480 partially reversed these effects. In vivo, CPVL knockdown significantly inhibited tumor growth. CONCLUSION: Through systematic multi-cohort integration and machine-learning prioritization, CPVL was identified as a novel oncogenic driver in gastric cancer. CPVL promotes tumor growth via activation of the JAK2/STAT3 pathway and regulation of the Cyclin D1/CDK4/p27 axis, highlighting its potential as a diagnostic biomarker and therapeutic target.

Biomarker↗

PubMed2026

ARID5A RNA-binding coordinates microglial defense and ferroptosis in iPSC-derived models.

RNA-binding proteins (RBPs) are key regulators of gene expression that shape cellular function in health and disease. However, the roles of RBPs in immune cells within the central nervous system (CNS) remain poorly understood. Here, we identify ARID5A as an RBP highly expressed in microglia and uncover its RNA-mediated regulatory functions using integrated multi-omics analyses of its RNA, DNA, and protein interactions. ARID5A regulates the splicing and translation of its RNA targets, many of which are integral to lysosomal, immune, and iron metabolism pathways. We confirm the functional relevance of this ARID5A-dependent RNA regulatory network by demonstrating that ARID5A modulates lysosomal activity, cytokine secretion, iron accumulation, and ferroptosis in iPSC-derived microglia. We further demonstrate that knockdown of microglial ARID5A reduces neuronal ferroptosis in co-cultures, underscoring the interconnected nature of these pathways. Moreover, in microglia harboring the TREM2-T66M mutation, ARID5A depletion restores dysregulated lysosomal and metabolic functions. Our results highlight the importance of protein-RNA interactions in regulating microglial cell biology.

Microglia↗

PubMed2025

Omics in Nonsteroidal Anti-Inflammatory Drugs-Exacerbated Respiratory Disease: Current Evidence From the Upper and Lower Airways.

Nonsteroidal anti-inflammatory drugs (NSAID)-exacerbated respiratory disease (N-ERD) is a mainly type 2 inflammatory condition that combines asthma, nasal polyps, and hypersensitivity to NSAIDs. Its pathogenesis involves both upper and lower airways, yet most studies to date have examined these compartments separately. It remains unclear whether the molecular mechanisms in the nose, sinuses, and lungs are distinct or overlapping-an important gap, given that clinical manifestations of N-ERD involve both sites. In this review, we summarize available omics studies-transcriptomics, proteomics, metabolomics, and epigenomics-performed on upper and lower airways in patients with N-ERD. While omics approaches have revealed new molecular insights, comparisons across studies are limited by heterogeneity in design, controls, and methodology. We emphasize the need for integrated multi-omics analyses and standardized frameworks to better characterize the disease across airways. Such efforts are essential for identifying robust biomarkers and therapeutic targets and for moving toward a systems-level understanding of N-ERD.

Humans↗

PubMed2024

Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique.

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells. Here, we present a protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique called moETM. We describe steps for data preprocessing, multi-omics integration, inclusion of prior pathway knowledge, and cross-omics imputation. As a demonstration, we used the single-cell multi-omics data collected from bone marrow mononuclear cells (GSE194122) as in our original study. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.

Deep Learning↗

PubMed2025

OmnibusX: A unified platform for accessible multi-omics analysis.

OmnibusX is an integrated, privacy-centric platform that enables code-free multi-omics data analysis by bridging computational methodologies with user-friendly interfaces. Designed to overcome challenges posed by fragmented analytical tools and high computational barriers, OmnibusX consolidates workflows for diverse technologies - including bulk RNA-seq, single-cell RNA-seq, single-cell ATAC-seq, and spatial transcriptomics - into a single, cohesive application. The application integrates established open-source tools such as Scanpy, DESeq2, SciPy, and scikit-learn into transparent, reproducible pipelines, offering users control over analytical parameters. Additionally, OmnibusX features proprietary modules, including a highly accurate cell-type prediction engine and an interactive plotting editor for generating publication-quality visualizations. Available as a standalone desktop application and an enterprise edition for centralized server deployment, OmnibusX ensures all data processing is conducted locally, eliminating external data transfer and usage tracking. By lowering technical barriers and enhancing reproducibility, OmnibusX aims to accelerate biological discovery and foster robust, data-driven collaborations. A fully documented trial version is accessible at: https://omnibusx.com/apps.

Computational Biology↗

PubMed2026

Multi-omics Investigations of Immune Microenvironment of Human Colorectal Cancer.

BACKGROUND/AIM: Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide. Although immunotherapy has improved outcomes for a subset of patients, its limited efficacy in many cases highlights the need for a more comprehensive understanding of the CRC immune microenvironment. This study aimed to characterize the molecular landscape of the CRC immune microenvironment using an integrated multi-omics approach and to identify candidate regulatory molecules associated with immune remodelling. MATERIALS AND METHODS: We integrated structural variation, DNA methylation, chromatin accessibility, proteomic, and phosphoproteomic data generated from an in-house CRC cohort with transcriptomic data from The Cancer Genome Atlas (TCGA). Analyses focused on 1,539 immune-related genes (IRGs) associated with CD4+ T cells, B cells, and natural killer (NK) cells. Multi-layered genomic and proteomic analyses were performed to identify altered immune-related pathways, hub genes, candidate transcription factors, and upstream kinases. RESULTS: Higher infiltration of CD4+ T cells, B cells, and NK cells was associated with CRC. IRGs exhibited widespread alterations across genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic levels. IL10, LEP, ITGAM, and EGFR emerged as candidate hub genes. EGFR phosphorylation at S991 and T693 was significantly decreased in CRC. STAT2 and HSF1 were identified as candidate upstream transcription factors, while CDK2 emerged as a candidate upstream kinase associated with immune infiltration and immune checkpoint expression. CONCLUSION: This study provides a systematic multi-omics characterization of immune microenvironment remodelling in CRC and identifies candidate molecular regulators that may serve as potential targets for future immunotherapy research.

Humans↗