Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “omics analysis”

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.

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

At least 415 records · Page 23Linked to original sources
PubMed2026

Integrated single-cell and spatial transcriptomic analyses reveal malignant epithelial glycolytic heterogeneity and spatial niche remodeling during colorectal cancer progression.

Colorectal cancer (CRC) progression is shaped by metabolic reprogramming and complex interactions within the tumor microenvironment. However, the cellular heterogeneity, spatial organization, and clinical relevance of glycolytic activity in CRC remain incompletely understood. In this study, we integrated single-cell RNA sequencing, bulk transcriptomics, and spatial transcriptomics data to systematically characterize glycolytic heterogeneity in CRC. Glycolytic activity was quantified using five independent scoring methods, consistently showing that epithelial cells exhibited the highest glycolytic activity across the two single-cell cohorts. Stratification of CopyKAT-verified aneuploid malignant epithelial cells into high-glycolysis (HG) and low-glycolysis (LG) subgroups by glycolysis scores revealed that HG cells exhibited higher stemness scores and chromosomal copy number variations. Cell-cell communication analysis revealed that, compared with LG cells, HG cells exhibited increased interaction frequency and strength with immune and stromal populations, indicating enhanced malignant epithelial-microenvironment crosstalk. Spatial transcriptomics analyses further revealed that glycolytic activity varied across normal colorectal tissue, primary CRC, and colorectal liver metastases, accompanied by progressive remodeling of epithelial-associated spatial niches and MIF-mediated intercellular communication. Bulk transcriptomic analysis identified a glycolysis-related prognostic signature with robust predictive performance, which served as an independent prognostic factor for overall survival in CRC cohorts. Collectively, these findings indicate that glycolytic heterogeneity is a key feature of CRC malignant epithelial cells and is closely associated with tumor progression, microenvironmental remodeling, and clinical outcomes.

Humans↗
PubMed2026

Moving Beyond Morphology to Multiplexed Molecular Imaging as the Next Frontier in Diagnostic Pathology.

Diagnostic pathology has long relied on the morphologic interpretation of hematoxylin and eosin-stained tissues to guide diagnosis and assess prognostic features. Although pathologists intuitively recognize spatial patterns and architectural organization, these assessments remain largely qualitative and difficult to quantify systematically. Immunohistochemistry and immunofluorescence have introduced molecular specificity but are limited in multiplexing capacity, whereas bulk genomic and transcriptomic assays provide high molecular depth but lose spatial context by averaging signals across heterogeneous cell populations. Recent advances in spatial proteomics-including mass spectrometry-based imaging and cyclic immunofluorescence-now enable multiplexed, single-cell protein analysis within intact tissue architecture. These technologies have revealed complex immune and stromal microenvironments, spatially organized biomarkers predictive of therapeutic response, and molecular gradients underlying disease progression. By integrating histologic and molecular information, spatial proteomics bridges traditional microscopy with high-dimensional omics, allowing quantitative, spatially resolved insights into tissue organization and disease mechanisms. This review summarizes recent developments in multiplexed spatial proteomics from both scientific and pathologic perspectives, highlighting how these technologies extend beyond morphology to quantify histologic patterns, refine biomarker discovery, and facilitate clinical translation. The review also examines translational challenges and barriers to clinical implementation, including costs, standardization requirements, and workflow integration.

Humans↗
PubMed2003

Interplay of transcriptomics and proteomics.

Despite the obvious attractions of parallel profiling of transcripts and proteins on a global 'omic' scale, there are practical and biological differences involved in their application. Transcriptomics is now a robust, high-throughput, cost-effective technology capable of simultaneously quantifying tens of thousands of defined mRNA species in a miniaturized, automated format. Conversely, proteomic analysis is currently much more limited in breadth and depth of coverage owing to variations in protein abundance, hydrophobicity, stability, size and charge. Nevertheless, transcriptomic and proteomic data can be compared and contrasted provided the studies are carefully designed and interpreted. Differential splicing, post-translational modifications and data integration are among some of the future challenges to tackle.

Biotechnology↗
PubMed2025

Whole-genome Sequence Analysis Revealed Novel Subjective Cognitive Decline-associated Genes in 10,763 Chinese.

Subjective cognitive decline (SCD) is widely regarded as a potential preclinical stage of Alzheimer's disease (AD), yet its genetic basis remains poorly understood. To address this gap, we investigated genetic biomarkers associated with SCD using whole-genome sequencing (WGS) in 10,763 Chinese participants from the Healthy Zhejiang One Million People Cohort (HOPE Cohort). The discovery stage included 9284 samples, with 1479 samples used for validation. Using a two-stage design, we systematically investigated both common and rare variants associated with SCD. In rare variant analyses, we identified and replicated an association between the upstream region of SEPHS2 and SCD. SEPHS2 is involved in selenophosphate synthesis, and a Mendelian randomization analysis reveals that its expression levels in both blood and brain cerebellum are associated with AD. Additionally, we identified CLVS2, which encodes a protein primarily expressed in neuronal cells, as a potential regulator for SCD based on missense rare variants. Multi-omics evidence suggests that both SEPHS2 and CLVS2 may play roles in neurodegenerative diseases. For common variants, we validated 8 known loci related to cognitive decline, 3 of which originated from the only existing SCD genetic study conducted under a migraine background. Overall, our WGS-based study fills the gap in SCD research by providing vital genetic evidence from an East Asian population and offers insights into the pathogenic mechanisms of SCD.

Aged↗
PubMed2026

Genetic and epigenetic underpinnings of biological aging: a multi-omics study integrating Mendelian randomization, spatial transcriptomics, and drug target discovery.

Inflammaging represents a hallmark of biological aging, yet the causal inflammatory mediators driving multi-dimensional epigenetic aging and their effector genes remain poorly characterized at the genetic level. We developed a four-tier analytical framework integrating causal screening, multi-omics effector gene mapping, spatial transcriptomics, and drug target evaluation. Two-sample Mendelian randomization (MR) of 91 circulating inflammatory proteins against six aging phenotypes identified IL-12B, IFNG, and IL-2 as the most robust pro-aging mediators with consistent effects across independent outcomes. Using multi-omics summary-based MR (SMR) as the core analytical engine, we integrated four-layer whole-blood molecular QTL resources eQTL (eQTLGen, n = 31,684), sQTL (GTEx, n = 755), pQTL (INTERVAL + SCALLOP, n = 34,232), and mQTL (McRae et al., n = 1,980) - with GWAS summary statistics for four epigenetic age acceleration measures. At a stringent threshold (P_SMR < 1&#xd7;10&#x207b;&#xb9;&#xb2;), seven high-confidence effector genes were identified: NHLRC1, TPMT, SELP, and RIPPLY3 for IEAA; ZNF373A and PLDN for HannumAA; and EDARADD for PhenoAA. The chromosome 6p21 NHLRC1-TPMT locus, overwhelmingly driven by methylation QTL signals (-log&#x2081;&#x2080;P = 26.06), emerged as the dominant genetic node of epigenetic aging. Spatial projection via gsMap onto a mouse E16.5 embryo atlas (121,767 cells) revealed preferential enrichment in smooth muscle and lung, with EDARADD showing marked specificity in mucosal epithelium. Cross-database drug target mining classified TPMT and SELP as repurposable known targets and NHLRC1 as a high-priority novel druggable candidate. This study provides multi-omics convergent causal evidence for inflammation-driven epigenetic aging and delivers genetically anchored targets for precision anti-aging intervention.

Aging↗
PubMed2024

Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment.

INSTRUCTION: Colorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients with CRC to identify how these interactions impact the prognosis and effectiveness of immunotherapy, focusing on patient outcomes and the potential for predicting treatment response. METHODS: Using datasets from multiple cohorts, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we conducted an in-depth multi-omics study to uncover the relationship between ferroptosis regulators and fatty acid metabolism in CRC. Through unsupervised clustering, we discovered unique patterns that link ferroptosis and fatty acid metabolism, and further investigated them in the context of immune cell infiltration and pathway analysis. We developed the FeFAMscore, a prognostic model created using a combination of machine learning algorithms, and assessed its predictive power for patient outcomes and responsiveness to treatment. The FeFAMscore signature expression level was confirmed using RT-PCR, and ACAA2 progression in cancer was further verified. RESULTS: This study revealed significant correlations between ferroptosis regulators and fatty acid metabolism-related genes with respect to tumor progression. Three distinct patient clusters with varied prognoses and immune cell infiltration were identified. The FeFAMscore demonstrated superior prognostic accuracy over existing models, with a C-index of 0.689 in the training cohort and values ranging from 0.648 to 0.720 in four independent validation cohorts. It also responses to immunotherapy and chemotherapy, indicating a sensitive response of special therapies (e.g., anti-PD-1, anti-CTLA4, osimertinib) in high FeFAMscore patients. CONCLUSION: Ferroptosis regulators and fatty acid metabolism-related genes not only enhance immune activation, but also contribute to immune escape. Thus, the FeFAMscore, a novel prognostic tool, is promising for predicting both the prognosis and efficacy of immunotherapeutic strategies in patients with CRC.

Ferroptosis↗
PubMed2026

Clinicopathological response and survival outcomes of HER2-low versus HER2-zero early breast Cancer: A systematic review and Meta-analysis.

BACKGROUND: Breast cancer is the most common malignant tumor in women. Human epidermal growth factor receptor 2 (HER2) is a key biomarker for classification and treatment. A subgroup with HER2-low expression has been identified, but existing evidence is heterogeneous. This systematic review and meta-analysis compared pathological response and survival outcomes between HER2-low and HER2-zero early-stage breast cancer to clarify prognostic features. METHODS: This study followed PRISMA guidelines and was registered in PROSPERO (CRD420251120506). PubMed, Embase, Web of Science, ClinicalTrials.gov, and major oncology conferences were searched through September 2025. Cohort studies of early-stage breast cancer comparing HER2-low (IHC 1+/2+ and ISH-negative) vs. HER2-zero with extractable pCR, DFS, or OS data were included. Studies involving HER2-positive patients or inconsistent definitions were excluded. Meta-analyses were performed using RevMan 5.3. RESULTS: Twenty-eight studies involving 115,182 patients were included. HER2-low patients showed significantly lower pCR rates (OR&#xa0;=&#xa0;0.58, 95% CI: 0.52-0.65). DFS favored HER2-low (multivariate HR&#xa0;=&#xa0;0.75, 95% CI: 0.69-0.83), especially in HR+ tumors, with a weaker effect in HR- cases. OS also favored HER2-low (HR&#xa0;=&#xa0;0.80, 95% CI: 0.72-0.89), mainly driven by the HR- subgroup; no OS difference was seen in HR+ tumors. Sensitivity analyses and funnel plots indicated robust results with no apparent publication bias. Overall study quality was high (17 high-quality, 11 moderate-quality). CONCLUSION: HER2-low early breast cancer shows lower pCR after neoadjuvant therapy but better long-term survival. These findings support the clinical relevance of HER2-low as a biologically meaningful subgroup within HER2-negative disease, while its status as a stable and independent subtype still requires further validation through prospective studies, standardized testing, and multi-omics investigation.

Humans↗
PubMed2025

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight.

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data.

Humans↗
PubMed2025

Catalyzing computational biology research at an academic institute through an interest network.

Biology has been transformed by the rapid development of computing and the concurrent rise of data-rich approaches such as, omics or high-resolution imaging. However, there is a persistent computational skills gap in the biomedical research workforce. Inherent limitations of classroom teaching and institutional core support highlight the need for accessible ways for researchers to explore developments in computational biology. An analysis of the Scripps Research Genomics Core revealed increases in the total number and diversity of experiments: the share of experiments other than bulk RNA- or DNA-sequencing increased from 34% to 60% within 10 years, requiring more tailored computational analyses. These challenges were tackled by forming a volunteer-led affinity group of approximately 300 academic biomedical researchers interested in computational biology, referred to as the Computational Biology and Bioinformatics (CBB) affinity group. This adaptive group has provided continuing education and networking opportunities through seminars, workshops, and coding sessions while evolving along with the needs of its members. A survey of CBB's impact confirmed the group's events increased the members' exposure to computational biology educational and research events (79% respondents) and networking opportunities (61% respondents). Thus, volunteer-led affinity groups may be a viable complement to traditional institutional resources for enhancing the application of computing in biomedical research.

Computational Biology↗
PubMed2025

A Multi-omics Exploration Revealing SLIT2 as a Prime Therapeutic Target for Peripheral Facial Paralysis: Integrating Single-Cell Transcriptomics and Plasma Proteome Data.

Peripheral facial paralysis (PFP) is a common neurological disorder characterized by facial-nerve dysfunction. Identifying therapeutic targets and understanding the molecular and cellular mechanisms underlying PFP are crucial for developing effective treatment strategies. This study combined Mendelian randomization (MR) analysis and single-cell RNA sequencing (scRNA-seq) to explore potential therapeutic candidates and their roles in PFP pathophysiology. The MR analysis included 1925 publicly available plasma protein cis-heritability instruments. Instrumental variables were selected for MR analysis to identify plasma proteins associated with PFP, followed by colocalization analysis to evaluate shared genetic variants between the identified proteins and PFP. After the initial identification of plasma proteins associated with Bell's palsy using MR analysis, a rat model of facial-nerve injury was established to further dissect underlying mechanisms at cellular and molecular levels. Using scRNA-seq technology, we delved deeply into cellular Heterogeneity and dynamic changes in gene expression in the facial-nerve nucleus tissues under both injured and control conditions, thereby achieving a systematic study ranging from macroscopic genetic associations to microscopic cellular functions. Finally, expression patterns were preliminarily validated by performing in vitro immunofluorescence analysis on the facial-nerve nucleus samples of SD rats. The MR analysis results identified 30 plasma proteins significantly associated with PFP, with nine target genes showing differential expression in the scRNA-seq data. Colocalization analysis demonstrated that slit guidance Ligand 2 (SLIT2), semaphorin 4D (SEMA4D), EGF containing fibulin extracellular matrix protein 1 (EFEMP1), and sprouty related EVH1 domain containing 2 (SPRED2) shared causal variants with PFP. SLIT2 was highly expressed in the microglia and inhibitory neurons in the experimental group, whereas SEMA4D showed elevated expression across multiple glial cell types in the same group. In contrast, EFEMP1 and SPRED2 showed distinct expression patterns in fibroblasts and oligodendrocytes. The role of SLIT2 has been previously well-documented in many central nervous system diseases. However, for the first time, this study detected SLIT2 alteration after facial-nerve injury. Altered intercellular signaling, particularly enhanced SLIT2-ROBO signaling between neurons and glial cells, was observed in the PFP group. Pseudotime analysis revealed dynamic SLIT2 expression during microglia and inhibitory neuron differentiation, mirroring changes in ROBO1 expression. Immunofluorescence analysis of rat facial-nerve nucleus samples verified that SLIT2 protein levels were significantly increased in the facial-nerve nuclei of injured samples. In conclusion, despite the fact that this study is primarily founded on animal models and despite notable differences existing between animals and humans in terms of the facial motor nucleus, this study successfully identified SLIT2 as potential therapeutic targets for PFP. The SLIT2-ROBO axis stands out as a particularly promising candidate. SLIT2 may play a role in modulating neuroimmune interactions and promoting nerve repair. These findings provide a foundation for future clinical studies and targeted interventions to enhance recovery from PFP. Future research should focus on human sample validation to enhance clinical translation.

Animals↗
PubMed2026

Integrative multi-omics quantitative trait loci prioritize CASP7 as a candidate protective gene for cataract.

Cataracts are the leading cause of vision loss worldwide. Despite surgery being the only effective treatment, its economic burden highlights the necessity of exploring the pathogenesis of cataracts. In this study, we analyzed 4 large-scale GWAS (genome-wide association study) datasets for cataracts and performed SMR analysis along with heterogeneity in dependent instruments (HEIDI) testing to explore the effects of methylation, expression, and protein QTLs on cataracts. We further validated shared genetic variants through COLOC analysis. Additionally, we searched datasets related to cataracts from the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses. By integrating summary-based Mendelian randomization (SMR) results with bioinformatics findings, CASP7 showed a consistent protective-direction association with cataract risk (mQTL: OR [95% CI]&#x2005;=&#x2005;0.959 [0.941-0.977], FDR-adjusted P&#x2005;=&#x2005;.039; eQTL: OR [95% CI]&#x2005;=&#x2005;0.897 [0.860-0.937], FDR-adjusted P&#x2005;=&#x2005;.0046; pQTL: OR [95% CI]&#x2005;=&#x2005;0.597 [0.483-0.738], FDR-adjusted P&#x2005;=&#x2005;.00083). GEO-based analyses provided transcriptomic support for CASP7 involvement in cataract-related lens biology. These findings prioritize CASP7 as a genetically supported candidate protective gene associated with cataract risk. Because this study is based on public summary-level and transcriptomic datasets, the results should be interpreted cautiously and require functional validation in human lens-relevant systems.

Quantitative Trait Loci↗
PubMed2005

Expansion of the BioCyc collection of pathway/genome databases to 160 genomes.

The BioCyc database collection is a set of 160 pathway/genome databases (PGDBs) for most eukaryotic and prokaryotic species whose genomes have been completely sequenced to date. Each PGDB in the BioCyc collection describes the genome and predicted metabolic network of a single organism, inferred from the MetaCyc database, which is a reference source on metabolic pathways from multiple organisms. In addition, each bacterial PGDB includes predicted operons for the corresponding species. The BioCyc collection provides a unique resource for computational systems biology, namely global and comparative analyses of genomes and metabolic networks, and a supplement to the BioCyc resource of curated PGDBs. The Omics viewer available through the BioCyc website allows scientists to visualize combinations of gene expression, proteomics and metabolomics data on the metabolic maps of these organisms. This paper discusses the computational methodology by which the BioCyc collection has been expanded, and presents an aggregate analysis of the collection that includes the range of number of pathways present in these organisms, and the most frequently observed pathways. We seek scientists to adopt and curate individual PGDBs within the BioCyc collection. Only by harnessing the expertise of many scientists we can hope to produce biological databases, which accurately reflect the depth and breadth of knowledge that the biomedical research community is producing.

Animals↗
PubMed2026

Mutual Information-based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG.

Mutual information (MI)-based approaches have increasingly been applied to cancer genomics; however, their use for genome-wide prognostic biomarker discovery remains relatively underexplored. The present article summarizes the conceptual workflow of Mutual Information-based Prognostic Omics Gene (MI-POG) based on previously published applications in breast cancer, lower-grade glioma, and other cancer datasets. The framework consists of clinical endpoint discretization, genome-wide MI-based screening, candidate ranking, and downstream validation using conventional survival-analysis approaches. Previous MI-POG applications identified solute carrier family 20 member 1 (SLC20A1) as a prognostic biomarker in hormone receptor-positive breast cancer. Elevated SLC20A1 expression was associated with unfavorable survival outcomes and was independently validated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Methodological analyses demonstrated how survival endpoints can be integrated into an information-theoretic framework through fixed-time outcome discretization, enabling model-independent assessment of molecular-clinical dependencies. Applications across multiple cancer datasets suggested the potential applicability of the framework across biologically distinct tumor types, although further validation will be required to establish its robustness and generalizability. In conclusion, MI-POG can be formalized as an information-theoretic framework for genome-wide identification of prognostic biomarkers by quantifying molecular-clinical dependencies using mutual information. Representative applications from previously published studies suggest that MI-POG may complement conventional survival-analysis approaches and provide a useful strategy for biomarker discovery, although additional benchmarking and prospective validation will be required.

Humans↗
PubMed2026

Comprehensive In Silico Analysis Identifies MSTO1 and LIG1 as Candidate Biomarkers With Diagnostic and Prognostic Relevance in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and remains a major cause of cancer-related mortality worldwide. Its poor clinical outcomes are largely attributed to late-stage diagnosis and the limited accuracy of currently available diagnostic and prognostic biomarkers. Therefore, identifying novel molecular markers with improved sensitivity, specificity, and therapeutic relevance is essential for enhancing early detection and guiding personalized treatment strategies. AIMS: To identify and prioritize novel candidate HCC biomarkers with diagnostic and prognostic value and potential therapeutic vulnerability using integrated multi-omics, survival, functional dependency, and tumor microenvironment analyses. METHODS AND RESULTS: We examined the mRNA and protein expression levels of 8 DEGs in HCC tissues in the TCGA and CPTAC datasets using UALCAN, which showed that MSTO1 and LIG1 were overexpressed consistently in HCC relative to normal liver tissues. Moreover, elevated expression levels of these genes were significantly associated with higher tumor grade and advanced stage. Kaplan-Meier plotter survival data confirmed that increased expression of MSTO1 and LIG1 was associated with poorer overall survival. The DepMap CRISPR knockout data confirmed a functional dependency of both genes in HCC cell lines. CBioPortal analyses provided characterization of genomic alterations and enabled enrichment analysis of co-expressed genes, and the TCGA-UALCAN pan-cancer analyses supported the assessment of tissue specificity across tumor types. TIMER3 analyses linked candidate gene expression with immune cell infiltration patterns. Diagnostic performance by ROC analysis showed excellent discrimination for MSTO1 (AUC&#x2009;=&#x2009;0.987) and good discrimination for LIG1 (AUC&#x2009;=&#x2009;0.897). Multivariate Cox regression with Benjamini-Hochberg FDR correction across the eight genes supported MSTO1 as a candidate independent prognostic factor after adjustment for tumor stage, grade, etiology, age, and sex (HR&#x2009;=&#x2009;1.29, p&#x2009;=&#x2009;0.035), whilst LIG1 showed no independent prognostic value. Promoter methylation of MSTO1 and ADH4, assessed via UALCAN, showed that both genes were significantly differentially methylated in the promoter region of primary HCC tissues compared with normal liver tissues. Our study also confirmed the biological and clinical relevance of established HCC biomarkers: TERT, IRAK1, and ADH4. CONCLUSION: MSTO1 and LIG1 emerged as candidate diagnostic biomarkers in HCC. Additionally, MSTO1 showed a candidate prognostic association with overall survival that remained significant after adjusting for tumor stage, grade, and etiology, as well as patients' age, but not after further adjustment for AFP status. Functional data also highlighted MSTO1 as a candidate therapeutic dependency. On the other hand, LIG1 showed no independent prognostic association in either multivariate model. Their differential expression and functional essentiality in HCC cell lines highlighted their value for further experimental and independent-cohort validation before potential integration into biomarker development pipelines aimed at improving early detection and targeted therapy in HCC.

Humans↗
PubMed2026

Exercise-associated epigenetic remodeling and TCR repertoire dynamics in Lynch syndrome carriers.

Lynch syndrome (LS) carriers are at elevated cancer risk. Emerging evidence suggests that exercise may serve as a non-pharmacologic preventive strategy, yet the epigenetic and immunological mechanisms underlying its protective effects in this population remain unclear. Here, we perform integrative multi-omics profiling of DNA methylation, gene expression, and the T cell receptor (TCR) repertoire in LS carriers undergoing a 52-week aerobic cycling intervention. We identify compartment-specific DNA methylation changes, including innate immune activation in cfDNA and oncogenic pathway repression in tissue. Integrative transcriptomic analysis highlights ISL1 as a key exercise-repressed, epigenetically regulated gene, and identifies FLCN as a colorectal cancer (CRC)-associated methylation target. TCR analysis reveals an exercise-associated increase in systemic repertoire diversity and tissue-specific clonal convergence, thus suggesting antigen-driven recruitment. Collectively, these findings uncover epigenetic and immune remodeling as potential mechanisms of exercise-mediated protection in LS.

Lynch syndrome↗
PubMed2005

Prostate cancer and the genomic revolution: Advances using microarray analyses.

The emerging technology of microarray analysis allows the establishment of molecular portraits of prostate cancer and the discovery of novel genes involved in the carcinogenesis process. Many novel genes have already been identified using this technique, and functional analyses of these genes are currently being tested. The combination of microarray analysis with other recently developed high-throughput techniques, such as proteomics, tissue arrays, and gene promoter-methylation, especially using tissue microdissection methods, will provide us with more comprehensive insights into how prostate cancer develops and responds to gene-targeted therapies. Animal models of prostate cancer are being characterized by high throughput techniques to better define the similarities and differences between those models and the human disease, and to determine whether particular models may be useful for specific targeted therapies in pre-clinical studies. Although profiling of mRNA expression provides important information of gene expression, the development of proteomic technologies will allow for an even more precise global insight into cellular signaling and structural alterations during prostate carcinogenesis. Not only will the "omic" revolution change basic science, but it will lead to a new era of molecular medicine.

Animals↗
PubMed2026

Integrative Transcriptomic and Proteomic Profiling Identifies S100P as a Potential Functional Biomarker for Sessile Serrated Lesions.

BACKGROUND: Sessile serrated lesions (SSLs) account for 15% of colorectal cancers (CRCs) but detection remains difficult due to flat morphology, mucinous features, and subtle histology. AIMS: This study aimed to identify novel and functionally relevant biomarkers of SSLs using transcriptomic screening and multi-omics validation. METHODS: Paired SSL and normal mucosa specimens (n&#x2009;=&#x2009;6) underwent RNA sequencing. Differentially expressed genes (DEGs) were filtered for membrane or secretory proteins and validated across TCGA and adenoma transcriptomes. Functional significance was assessed using CRISPR dependency profiling, proteotranscriptomic concordance, pharmacogenomic sensitivity, and connectivity map analysis. RESULTS: We identified 216 upregulated genes in SSLs, including 68 encoding secretory/membrane proteins that better discriminated SSLs from controls and were enriched for adhesion and neuronal signaling while suppressing TNF&#x3b1;-NF&#x3ba;B inflammatory pathways. Cross-cohort comparison revealed five overlapping candidates between SSLs and TCGA CMS1 tumors. Among them, S100P emerged as the primary biomarker candidate, showing consistent upregulation in SSLs and CMS1 tumors while remaining low in normal mucosa and conventional adenomas. TFF1 also showed RNA-level upregulation but appeared more context-dependent. S100P demonstrated strong RNA-protein concordance in CRC cell-line profiling, supporting its detectability as a biomarker candidate. Pharmacogenomic profiling of LS411N cells revealed marked sensitivity to SN-38 and fluoropyrimidines, consistent with serrated CRC vulnerabilities. Connectivity map analysis identified perturbations, including MAPK1 and histone acetyltransferase suppression, that may reverse parts of the SSL transcriptional program. CONCLUSION: These findings prioritize S100P as a promising biomarker candidate for SSLs that warrants further validation in larger cohorts and clinically applicable platforms.

Humans↗