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PubMed1992

Protein secondary structure prediction using logic-based machine learning.

Many attempts have been made to solve the problem of predicting protein secondary structure from the primary sequence but the best performance results are still disappointing. In this paper, the use of a machine learning algorithm which allows relational descriptions is shown to lead to improved performance. The Inductive Logic Programming computer program, Golem, was applied to learning secondary structure prediction rules for alpha/alpha domain type proteins. The input to the program consisted of 12 non-homologous proteins (1612 residues) of known structure, together with a background knowledge describing the chemical and physical properties of the residues. Golem learned a small set of rules that predict which residues are part of the alpha-helices--based on their positional relationships and chemical and physical properties. The rules were tested on four independent non-homologous proteins (416 residues) giving an accuracy of 81% (+/- 2%). This is an improvement, on identical data, over the previously reported result of 73% by King and Sternberg (1990, J. Mol. Biol., 216, 441-457) using the machine learning program PROMIS, and of 72% using the standard Garnier-Osguthorpe-Robson method. The best previously reported result in the literature for the alpha/alpha domain type is 76%, achieved using a neural net approach. Machine learning also has the advantage over neural network and statistical methods in producing more understandable results.

Amino Acid Sequence↗

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↗

PubMed2025

Paternally Expressed Gene 10 Promoter Methylation Level as a Predictor of HBeAg Seroconversion in Chronic Hepatitis B Patients.

The management of chronic hepatitis B (CHB) encounters challenges like suboptimal antiviral response and the lack of predictive biomarkers. In this study, the role of paternally expressed gene 10 (PEG10) in hepatitis B e antigen (HBeAg) seroconversion (HBeAg SC) was explored to identify a therapeutic target and predictive model. In total, 349 participants were recruited, and 141 HBeAg-positive patients were followed up after 48 weeks of antiviral therapy. Key genes were screened by machine learning algorithms (BORUTA, RF and LASSO). PEG10 mRNA, promoter methylation and plasma levels were examined. The effect of PEG10 was assessed by logistic regression, and HBeAg SC was predicted by nomograms. HBeAg-positive patients showed markedly elevated PEG10 mRNA expression (p < 0.001), which correlated strongly with major virological markers such as HBV DNA (r = 0.520, p < 0.001), HBeAg (r = 0.490, p < 0.001) and HBsAg (r = 0.400, p < 0.001). In addition, HBeAg-positive patients exhibited a significant reduction in PEG10 promoter methylation levels compared with controls (p < 0.001). According to logistic regression analysis, PEG10 promoter methylation status was an independent predictor of HBeAg SC. The predictive nomogram incorporating PEG10 promoter methylation ratio (PMR), albumin (ALB), aspartate aminotransferase (AST) and HBeAg demonstrated excellent clinical predictive value (area under curve (AUC) = 0.895,95% confidence interval (CI): 0.808 ~ 0.963). The methylation status of the PEG10 promoter represents a promising biomarker for the prediction of HBeAg SC in patients with CHB. CLINICAL TRIAL REGISTRATION: Not applicable.

Humans↗

PubMed2025

Distinct immune-metabolic phenotypes underlie poor coronary collateral circulation.

BACKGROUND: Coronary collateral circulation (CCC) significantly impacts myocardial perfusion and clinical outcomes in coronary artery disease patients, yet the underlying molecular heterogeneity remains inadequately characterized. OBJECTIVE: To identify distinct molecular phenotypes in patients with poor CCC, validate these phenotypes using clinical parameters, and evaluate their prognostic implications. METHODS: This study enrolled 149 patients (80 with good CCC and 69 with poor CCC) for high-throughput proteomic profiling. Unsupervised consensus clustering identified molecular subtypes within poor CCC patients, followed by differential expression analysis and KEGG pathway enrichment. Boruta feature selection was implemented, and multiple machine learning algorithms were tested on clinical data, with XGBoost optimization (accuracy 80.0%, F1-score 80.31%) and SHAP value interpretation. External validation was performed using the MIMIC database. Kaplan-Meier analysis and Cox regression models assessed major adverse cardiovascular events (MACE). RESULTS: Two distinct phenotypes emerged among poor CCC patients: Cluster 1 (n = 39, Complement-Driven Vascular Remodeling [CDVR]) and Cluster 2 (n = 30, Immuno-Thrombotic Myocardial Dysfunction [ITMD]). An XGBoost model incorporating fasting glucose, eosinophil percentage, and HbA1c achieved excellent discrimination (AUC > 0.91). External validation confirmed the phenotype-specific clinical patterns. Notably, Cluster 2 demonstrated significantly higher MACE incidence compared to Cluster 1 (Log-rank p < 0.05), with KEGG analysis revealing significant upregulation of platelet activation, diabetic cardiomyopathy, and metabolic pathways in the ITMD phenotype. CONCLUSION: Poor CCC encompasses distinct immune-metabolic phenotypes that can be accurately classified using integrated proteomic-clinical modeling. This classification enables more precise risk stratification and may guide personalized therapeutic strategies for coronary artery disease patients with inadequate collateralization.

Humans↗

PubMed2026

Lactylation-related immune-metabolic dysregulation defines prognostic and therapeutic stratification in lung adenocarcinoma.

BACKGROUND: Lactylation links lactate metabolism with inflammatory signaling and immune regulation in tumors. However, its cellular distribution and translational value in lung adenocarcinoma (LUAD) remain unclear. METHODS: Single-cell RNA-sequencing datasets GSE189357 and GSE171145 were integrated to characterize lactylation-related activity, intercellular communication, and malignant epithelial cell states in LUAD. Single-cell-derived lactylation-related differentially expressed genes were mapped to TCGA-LUAD and multiple GEO cohorts. Univariate Cox regression and machine learning algorithms were used to construct a lactylation-related prognostic signature (LRPS). The associations of LRPS with prognosis, immunotherapy response, drug sensitivity, genomic alterations, immune infiltration, and inflammation- and metabolism-related pathways were evaluated. KRT7 was further validated using virtual knockout analysis, spatial transcriptomics, and in vitro and in vivo experiments. RESULTS: lactylation-related transcriptional activity showed heterogeneous distribution across LUAD cell populations and was associated with altered cell-cell communication. In malignant epithelial cells, LRTS-high and LRTS-low states exhibited distinct metabolic, inflammatory, and tumor-related pathway activities. LRPS showed stable prognostic performance in TCGA-LUAD and multiple GEO cohorts and remained an independent prognostic factor. Low LRPS was associated with greater potential benefit from immunotherapy, whereas different LRPS groups displayed distinct drug sensitivity, genomic alteration, and immune microenvironment patterns. KRT7 was highly expressed in LUAD and associated with poor prognosis. KRT7 knockdown suppressed LUAD cell proliferation, migration, invasion, colony formation, and tumor growth in vivo. CONCLUSIONS: This study identifies lactylation-related immune-metabolic dysregulation as a clinically relevant feature of LUAD and develops a single-cell-guided LRPS for prognosis and therapeutic stratification. KRT7 emerged as an LRPS-related functional candidate with experimentally supported roles in malignant LUAD phenotypes.

Immunotherapy↗

PubMed2026

Integrative multi-omics and machine learning identify the SPI1-METTL16-PLIN4 axis as a candidate driver of steatosis in HepG2 cells.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a prevalent metabolic disorder with limited therapeutic options. This study aimed to identify potential regulators and explore their functional roles in a cellular model of NAFLD. METHODS: WGCNA was performed on the hepatic transcriptomic dataset GSE126848 (31 NAFLD vs. 26 controls), followed by integration with serum proteomic data from 12 NAFLD patients and 12 healthy controls. Hub genes were prioritized using three machine learning algorithms. Functional validation was conducted in a HepG2 cellular steatosis model induced by high fructose (3.2 g/L) and oleic acid (400 μM) for 48 h. Lipid accumulation was assessed by Oil Red O staining and triglyceride/total cholesterol measurement. Inflammation was evaluated by TNF-α and IL-6 secretion (ELISA), and oxidative stress by ROS levels (flow cytometry). The binding interaction between METTL16 and PLIN4 mRNA was validated by RNA immunoprecipitation (RIP)-quantitative PCR. METTL16-mediated m6A modification of PLIN4 was assessed by Methylated RIP (MeRIP)-quantitative PCR. Transcriptional regulation of METTL16 by SPI1 was examined by chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays. RESULTS: Integrative analysis identified PLIN4 as a core hub gene. PLIN4 was upregulated in the HepG2 steatosis model (P < 0.001). PLIN4 knockdown alleviated lipid droplet accumulation (P < 0.001), reduced TNF-α and IL-6 secretion (P < 0.01), and decreased ROS levels (P < 0.001) in fructose/oleic acid-treated HepG2 cells. Mechanistically, METTL16 mediated its m6A modification to enhance PLIN4 mRNA stability. Furthermore, SPI1 was found to transcriptionally activate METTL16 by binding to its promoter (P < 0.001). PLIN4 re-expression partially reversed the protective effects of SPI1 knockdown on lipid accumulation (P = 0.01), inflammation (P < 0.05), and oxidative stress (P < 0.001). CONCLUSION: This study identifies the SPI1/METTL16/PLIN4 axis as a potential regulatory mechanism contributing to in vitro steatosis, inflammation, and oxidative stress in steatotic HepG2 cells.

Humans↗

PubMed2025

Establishment of a prognostic model based on ER stress-related cell death genes and proposing a novel combination therapy in acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a highly heterogeneous malignancy, presenting significant challenges in accurately predicting patient prognosis. Dysregulation of endoplasmic reticulum (ER) stress and resistance to programmed cell death (PCD) are hallmarks of AML cells. However, the prognostic significance of the interplay between ER stress and cell death pathways in AML remains largely unexplored. METHODS: We analyzed RNA sequencing and clinical data from 887 AML patients across 4 cohorts to develop an ER stress-related cell death index (ERCDI) using 10 machine-learning algorithms with 117 unique combinations. Survival and time-dependent Receiver Operating Characteristic Curve (ROC) analyses were performed to assess the model's efficacy. Clinical characteristics, the tumor immune microenvironment, and drug sensitivity differences between the high- and low-risk groups were also analyzed. The CMap database was used to identify potential therapeutic drugs. In vitro and in vivo experiments, including CCK-8, colony formation, flow cytometry, Transwell assays, and xenograft mouse models, were conducted to evaluate the effects of the target genes and candidate drugs. RESULTS: The ERCDI demonstrated strong prognostic and predictive performance for prognosis in AML patients. Furthermore, the ERCDI effectively predicted immunotherapy and chemotherapy outcomes and was associated with the immune features of the different risk groups. DNA damage-inducible transcript 4 protein (DDIT4), a key gene associated with ERCDI, is related to poor prognosis in AML patients with high expression. Additionally, the knockdown of DDIT4 significantly inhibited AML cell proliferation, induced cell apoptosis, and promoted cell cycle arrest. Chaetocin was subsequently identified as a candidate compound for AML treatment. Subsequent experiments suggested that combining chaetocin and venetoclax is a potentially promising therapeutic strategy for AML. CONCLUSION: The ERCDI provides personalized risk assessment and treatment recommendations for individual AML patients. The combined use of chaetocin and venetoclax can potentially be repurposed for AML therapy.

Humans↗

PubMed2025

Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration.

OBJECTIVE: Diagnosis of hepatocellular carcinoma (HCC) remains challenging for clinicians. Machine learning approaches and big data analyses are viable strategies for identifying HCC diagnostic markers. MATERIALS AND METHODS: In this study, we downloaded mRNA expression profiles of HCC from the GEO database and used random forest and machine learning algorithms, such as least absolute shrinkage and selection operator, to screen for reliable diagnostic genes. Disease Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis enrichment analyses were performed to explore differential gene functions and disease pathways. CIBERSORT was performed to calculate the immune cell infiltration of HCC and the correlation between diagnostic genes and immune cells. Cell experiments were performed to evaluate the function of R-spondin 3 (RSPO3) in HCC cells. Immunohistochemical staining was used to evaluate the protein expression of CD138, CD206 and iNOS. RESULTS: The results indicated that extracellular matrix protein 1 (ECM1), Niemann-Pick C1-Like 1 (NPC1L1) and RSPO3 were down-regulated in HCC compared with the normal group (p < 0.05), which was validated in clinical tissue samples. Moreover, ECM1, NPC1L1 and RSPO3 had high diagnostic values (AUC > 0.75) for HCC in both training and test groups. Immuno-infiltration analysis revealed that ECM1 and RSPO3 were highly positively correlated with neutrophil and macrophage M2 levels, whereas they were negatively correlated with Tregs. RSPO3-si affected cell proliferation and apoptosis in HCC. Furthermore, RSPO3 exhibited a positive correlation with tumour progression, the proportion of plasma cells and M2 macrophages in mice, while showing a negative association with M1 macrophages. CONCLUSION: The present study identified ECM1, NPC1L1 and RSPO3 as new diagnostic biomarkers for HCC based on normal and diseased samples from HCC, meanwhile the pro-oncogenic function of RSPO3 and its regulation on immune infiltration have been confirmed.

Carcinoma, Hepatocellular↗

PubMed2026

Plasma Exosome Metabolomics Reveal Stage-Specific Alterations in Elderly Women With Premetabolic and Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) is a chronic disorder that poses a major threat to global health. Exosomes have emerged as promising biomarkers for diagnosing and monitoring chronic diseases. However, stage-specific alterations in the exosomal metabolome during MetS development remain poorly understood. This study aimed to characterize the plasma exosomal metabolome and explore candidate exosomal biomarkers in individuals with MetS. METHODS: This study included 20 patients with MetS, 23 individuals with pre-MetS, and 45 healthy controls. Plasma exosomes were isolated and analyzed using untargeted liquid chromatography-mass spectrometry-based metabolomics. Differential metabolites were defined by a dual-threshold, that is, p < 0.05 from t-test and variable importance in projection > 1 from partial least squares discriminant analysis, with fold change indicating their expression changes. Further, we employed machine learning algorithms to predict MetS status. RESULTS: We identified 27 differential metabolites between the pre-MetS and control groups, mainly enriched in histidine metabolism and the tricarboxylic acid cycle. Of these, 12 metabolites were upregulated, and 15 were downregulated, with 1-methylhistidine and isocitrate playing central regulatory roles. Comparison between the MetS and control groups revealed 45 differentially expressed metabolites, mainly enriched in thiamine metabolism, including 13 upregulated and 32 downregulated. In the pre-MetS group, cladribine showed the highest area under the curve (AUC) (0.743, p < 0.05), whereas 3-methylxanthine yielded the largest AUC (0.714, p < 0.05) in the MetS group. CONCLUSION: Our study characterized stage-dependent alterations in the plasma exosome-derived metabolome in MetS and suggests that exosomal metabolomics may provide complementary molecular information on early MetS metabolic perturbations.

exosomal features↗

PubMed2026

Proteomic profiling of bone for the estimation of post-mortem interval and post-mortem submersion interval: a systematic review.

Accurate estimation of the Post-Mortem Interval (PMI) and Post-Mortem Submersion Interval (PMSI) remains a persistent challenge in forensic science, especially when traditional morphological and entomological methods fail due to advanced decomposition or in aquatic environments. Proteomic profiling of bone tissues has recently emerged as a promising approach, leveraging the predictable degradation patterns of bone proteins to estimate time since death more reliably. This systematic review, conducted in accordance with PRISMA guidelines, analyzed 24 peer-reviewed studies focusing on the application of proteomic techniques to bone tissue for PMI and PMSI estimation. The included studies were evaluated based on sample type, analytical techniques used, identified biomarkers, environmental conditions assessed, and the overall reliability and reproducibility of the findings. The review found that specific bone proteins, particularly collagen, osteocalcin, fetuin-A, etc. exhibited consistent degradation patterns that correlated strongly with elapsed post-mortem time. Cortical bone was identified as a more stable and informative matrix compared to trabecular bone. Mass spectrometry, especially LC-MS/MS, emerged as the predominant analytical technique due to its high sensitivity and accuracy in detecting low-abundance proteins over extended PMIs and PMSIs. However, protein degradation rates were significantly influenced by environmental variables such as temperature, humidity, soil pH, and microbial activity. This review also emphasizes the transformative role of bone proteomics in advancing forensic science while identifying key gaps that must be addressed to achieve global standardization and practical implementation in diverse forensic contexts. The integration of proteomics with other emerging technologies, such as machine learning algorithms and computational modeling, may further enhance the precision of PMI and PMSI estimation in future applications.

Postmortem Changes↗

PubMed2026

Machine learning-based integration develops a novel lysosome-related prognostic signature associated with prognosis and immune infiltration landscape in acute myeloid leukemia.

BACKGROUND: Lysosomes are essential for intracellular degradation and recycling, and changes in their function significantly contribute to tumor growth. Nonetheless, the exact role of lysosome-related genes (LRGs) in the pathogenesis of acute myeloid leukemia (AML) is still inadequately comprehended. METHODS: Differentially expressed LRGs (DE-LRGs) between AML and control groups were identified using AML-related data extracted from the Gene Expression Omnibus (GEO). The LRGs-related prognostic genes were identified and the risk model was established using univariate COX regression analysis and machine learning algorithms, based on the data obtained from The Cancer Genome Atlas (TCGA). Subsequently, we performed comprehensive analyses regarding clinical features, functional pathways, immune microenvironment, and chemotherapeutic drugs sensitivity between the high- and low-risk groups. Reverse transcription Quantitative polymerase chain reaction (RT-qPCR) and western blot were adopted to validate the expression of prognostic genes in human bone marrow-derived cell line HS-27 A and human AML cell line MOLM-13. RESULTS: Through comprehensive analysis, a risk model was developed utilizing ten LRGs (ATP6V0E2, CALCRL, TMEM165, GZMB, HCK, TCIRG1, CD1D, GPRASP1, ABCA1, and NAGA), and this model was further validated using GEO datasets. Significant differences in clinical characteristics, functional pathways, immune microenvironment characteristics, and chemotherapeutic drug sensitivity were observed between the two risk groups In vitro validation experiment illustrated that the expression trends of ATP6V0E2, TMEM165, and ABCA1 were consistent with our bioinformatics analysis. CONCLUSION: Our study demonstrates that lysosome-associated signature might forecast the prognosis of AML patients and offer guidance for subsequent immunotherapy and chemotherapy strategies.

Acute myeloid leukemia↗

PubMed2026

Essence: A benchmarking-validated transformer framework for early diagnosis of Parkinson's disease using cerebrospinal fluid protein biomarkers.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms. The lack of objective molecular biomarkers limits early diagnosis and personalized treatment. Here, we propose Essence, a benchmarking-validated framework integrating cerebrospinal fluid (CSF) proteomics with traditional and deep learning models to identify robust protein signatures for PD. Using data from two independent cohorts, 1266 high-confidence proteins are quantified, among which 178 exhibit differential abundance between PD and healthy controls (HC). Through systematic benchmarking of ten machine learning algorithms and four neural architectures, the Transformer model consistently outperforms alternatives across multiple feature selection strategies, achieving an area under the receiver operating characteristic curve (AUC) of 1.0000 with only 35 features. Functional analyses of the top-ranked 35 proteins reveal enrichment in neuroinflammatory, synaptic, and oxidative stress-related pathways. Importantly, spatial transcriptomic profiling based on the Allen Brain Atlas shows region-specific expression of these biomarkers in PD-relevant brain structures, including the striatum, subthalamic nucleus, hippocampus, and white matter tracts. This anatomical alignment supports the functional relevance of the identified markers and highlights their potential utility in early-stage diagnosis and mechanistic understanding of PD.

Benchmarking↗

PubMed2026

Clinical applications of digital twin technology in In Vitro Fertilisation.

BACKGROUND: Digital twin technology, originating from aerospace and manufacturing industries, has emerged as a transformative tool in healthcare. In vitro fertilisation (IVF) faces persistent challenges including suboptimal embryo selection, unpredictable treatment outcomes, and limited personalisation of protocols. Despite advances in assisted reproductive technology, existing literature exhibits fragmentation: artificial intelligence applications in embryo selection, ovarian stimulation, and endometrial assessment have been developed independently without systematic integration into comprehensive treatment frameworks. Digital twin technology offers unprecedented opportunities to create virtual replicas of biological systems, enabling real-time monitoring, predictive modelling, and personalised treatment strategies. AIM: This narrative review aims to critically examine the current applications of digital twin technology in IVF, evaluate its potential benefits and limitations, synthesize existing evidence into an integrative conceptual model, and identify future directions for implementation in reproductive medicine. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases. A narrative review approach was selected over systematic review to accommodate the heterogeneity of evidence types in this emerging field, including theoretical frameworks, simulation studies, and proof-of-concept implementations that would be excluded from systematic reviews. Search terms included "digital twin," "IVF," "in vitro fertilisation," "assisted reproductive technology," "embryo selection," and "predictive modelling." Studies published between 2015 and 2025 were included, focusing on original research articles, systematic reviews, and proof-of-concept studies describing digital twin applications in reproductive medicine. RESULTS: Digital twin technology in IVF demonstrates significant potential across multiple domains including embryo development simulation, ovarian response prediction, endometrial receptivity modelling, and personalised stimulation protocols. Current applications integrate artificial intelligence, machine learning algorithms, time-lapse imaging, and omics data to create comprehensive virtual models. Early evidence suggests improvements in embryo selection accuracy, ovarian response prediction, and treatment protocol optimization, though large-scale randomized controlled trials remain limited. Implementation challenges include data integration complexity, computational requirements, regulatory considerations, and validation requirements. CONCLUSION: Digital twin technology represents a paradigm shift in IVF practice, offering personalised, predictive, and precision medicine approaches. This review synthesizes existing evidence to propose an integrative conceptual model for digital twin implementation across the IVF treatment spectrum, identifies critical knowledge gaps, and establishes research priorities to advance clinical translation. Despite current limitations, continued advancement promises improved success rates and patient outcomes.

Humans↗

PubMed2026

MULTIPREVENT: Integrated screening for smoking-related multimorbidity using low-dose chest computed tomography.

OBJECTIVES: Tobacco consumption, combined with individual genetic predispositions, contributes to an age-dependent risk not only for lung cancer but also for other non-communicable diseases (NCDs) such as cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), osteoporosis, and diabetes. The MULTIPREVENT project aims to validate whether low-dose computed tomography (LDCT) of the chest, combined with simple biomarkers, functional tests, and genomic profiling, can serve as an effective tool for comprehensive health assessment and risk prediction of multimorbidity in adults. STUDY DESIGN: The study is based on a prospective epidemiological design involving 3000 participants from the MOLTEST-BIS lung cancer screening cohort (2016-2018). These participants, aged 50-79 years (during MOLTEST-BIS) and with a smoking history of at least 30 pack-years, will undergo two follow-up assessments in 2025-2027 and 2030-2032. METHODS: Each follow-up includes LDCT, spirometry, standardized blood pressure measurement, anthropometric evaluation, biomarker assessment (lipid profile, lipoprotein(a), glycated haemoglobin), and health-related questionnaires. Genetic profiling will be performed using the Illumina Infinium Global Screening Arrays approach to identify inherited predispositions to major NCDs. All data, clinical, imaging (including radiomics), molecular, and genetic, will be integrated through machine learning algorithms to develop AI-based risk prediction models. RESULTS: The MULTIPREVENT study is expected to generate a wide range of scientific, clinical, and infrastructural results that will serve as a foundation for future public health initiatives in integrated prevention. CONCLUSIONS: By linking imaging and biochemical markers, genetic susceptibility, and clinical parameters within a longitudinal design, MULTIPREVENT will establish data-driven, AI-supported prevention strategies aimed at reducing morbidity and mortality among adults exposed to tobacco. The project will also serve as a model for population-based multimorbidity prevention programs.

Humans↗

PubMed2025

Polygenic risk scores for rheumatoid arthritis and idiopathic pulmonary fibrosis and associations with RA, interstitial lung abnormalities, and quantitative interstitial abnormalities among smokers.

OBJECTIVE: Genome-wide association studies (GWAS) facilitate construction of polygenic risk scores (PRSs) for rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF). We investigated associations of RA and IPF PRSs with RA and high-resolution chest computed tomography (HRCT) parenchymal lung abnormalities. METHODS: Participants in COPDGene, a prospective multicenter cohort of current/former smokers, had chest HRCT at study enrollment. Using genome-wide genotyping, RA and IPF PRSs were constructed using GWAS summary statistics. HRCT imaging underwent visual inspection for interstitial lung abnormalities (ILA) and quantitative CT (QCT) analysis using a machine-learning algorithm that quantified percentage of normal lung, interstitial abnormalities, and emphysema. RA was identified through self-report and DMARD use. We investigated associations of RA and IPF PRSs with RA, ILA, and QCT features using multivariable logistic and linear regression. RESULTS: We analyzed 9,230 COPDGene participants (mean age 59.6 years, 46.4 % female, 67.2 % non-Hispanic White, 32.8 % Black/African American). In non-Hispanic White participants, RA PRS was associated with RA diagnosis (OR 1.32 per unit, 95 %CI 1.18-1.49) but not ILA or QCT features. Among non-Hispanic White participants, IPF PRS was associated with ILA (OR 1.88 per unit, 95 %CI 1.52-2.32) and quantitative interstitial abnormalities (adjusted β=+0.50 % per unit, p = 7.3 × 10-8) but not RA. There were no statistically significant associations among Black/African American participants. CONCLUSIONS: RA and IPF PRSs were associated with their intended phenotypes among non-Hispanic White participants but performed poorly among Black/African American participants. PRS may have future application to risk stratify for RA diagnosis among patients with ILD or for ILD among patients with RA.

Humans↗

PubMed2026

A machine learning-derived and functionally validated circadian rhythm signature predicts clinical outcomes and in silico drug sensitivity in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) displays considerable heterogeneity in clinical outcomes, highlighting the need for reliable prognostic biomarkers. While the aberrant expression of circadian rhythm-related genes has been implicated in cancer pathogenesis, its comprehensive role in CRC progression and predicted therapeutic vulnerabilities remains inadequately characterized. METHODS: Bulk and single-cell RNA-sequencing data were integrated from multiple CRC cohorts. A circadian rhythm signature (CRS) was developed through machine learning algorithms and validated for prognostic value. Comprehensive analyses of tumor microenvironment, genomic alterations, and drug sensitivity were performed. Furthermore, the biological function of the core gene, BHLHE40, was validated in CRC cell lines through CCK-8, EdU, and wound healing assays. RESULTS: Single-cell analysis demonstrated an elevated expression signature of circadian rhythm-related genes in dendritic cells. The optimized CRS, comprising 14 circadian rhythm-related genes, successfully categorized patients into high- and low-risk groups. Patients with a high CRS showed markedly poorer overall survival and computationally inferred immunosuppressive features, including reduced CD8+ T cell infiltration and increased M2 macrophage polarization. Genomic analysis revealed enhanced mutation burden in TP53 and alterations in RTK-RAS/WNT pathways. Notably, in vitro assays confirmed that BHLHE40 is significantly overexpressed in CRC cells. Knockdown of BHLHE40 markedly inhibited tumor cell proliferation and migration. Drug sensitivity profiling identified bexarotene and SMER-3 as potential therapeutic options for high-CRS patients. A nomogram integrating CRS with clinical parameters demonstrated superior predictive accuracy for 1-, 3-, and 5-year survival. CONCLUSIONS: The CRS represents a promising prognostic biomarker that reflects tumor immune status and genomic features, providing valuable insights for personalized treatment strategies in CRC.

Circadian rhythm↗

PubMed2025

dbscATAC: a resource of single-cell super-enhancers/enhancers and gene markers derived from scATAC-seq data.

MOTIVATION: scATAC-seq enables high-resolution mapping of cis-regulatory elements. It has been widely applied to uncover cell-type-specific regulatory networks and complement scRNA-seq analysis in numerous studies. However, a large number of datasets generated by scATAC-seq remain underutilized due to limited exploration of super-enhancers/typical enhancers and gene markers. A comprehensive resource enabling cell-type-specific annotation of cis-regulatory elements and their dynamic enhancer-gene linkages remains an urgent unmet need for scATAC-seq. RESULTS: We present dbscATAC, a specialized single-cell database for annotating super-enhancers, gene markers, and enhancer-gene interactions derived from scATAC-seq data. Using improved machine learning algorithms, we identified 213 835 super-enhancers across 520 tissue/cell types from three species, as well as 347 484 gene markers, 13 470 526 enhancers, and 10 402 346 enhancer-gene interactions derived from 1 668 076 single cells spanning 1028 tissue/cell types in 13 species. An easy-to-use online platform with multiple analytic modules and hierarchical query options was developed for searching, browsing and visualizing single-cell super-enhancers, enhancers, and gene markers. dbscATAC provides a comprehensive resource to facilitate the exploration of enhancer landscapes, gene regulation, and cell-type-specific characteristics in single-cell epigenomics. AVAILABILITY AND IMPLEMENTATION: The database with all the super-enhancer/enhancer annotation data is available at http://singlecelldb.com/dbscATAC/index.php. And the source code of dbscATAC for prediction of SEs, enhancers, and gene markers are available at https://github.com/EvansGao/dbscATAC. The source code, tissue/cell type description, and data summary can be downloaded at DOI: 10.6084/m9.figshare.28706414.scATAC-seq, Database, Super-enhancers/enhancers, Gene markers.

Enhancer Elements, Genetic↗

PubMed2022

Improving risk indexes for Alzheimer's disease and related dementias for use in midlife.

Knowledge of a person's risk for Alzheimer's disease and related dementias (ADRDs) is required to triage candidates for preventive interventions, surveillance, and treatment trials. ADRD risk indexes exist for this purpose, but each includes only a subset of known risk factors. Information missing from published indexes could improve risk prediction. In the Dunedin Study of a population-representative New Zealand-based birth cohort followed to midlife (N = 938, 49.5% female), we compared associations of four leading risk indexes with midlife antecedents of ADRD against a novel benchmark index comprised of nearly all known ADRD risk factors, the Dunedin ADRD Risk Benchmark (DunedinARB). Existing indexes included the Cardiovascular Risk Factors, Aging, and Dementia index (CAIDE), LIfestyle for BRAin health index (LIBRA), Australian National University Alzheimer's Disease Risk Index (ANU-ADRI), and risks selected by the Lancet Commission on Dementia. The Dunedin benchmark was comprised of 48 separate indicators of risk organized into 10 conceptually distinct risk domains. Midlife antecedents of ADRD treated as outcome measures included age-45 measures of brain structural integrity [magnetic resonance imaging-assessed: (i) machine-learning-algorithm-estimated brain age, (ii) log-transformed volume of white matter hyperintensities, and (iii) mean grey matter volume of the hippocampus] and measures of brain functional integrity [(i) objective cognitive function assessed via the Wechsler Adult Intelligence Scale-IV, (ii) subjective problems in everyday cognitive function, and (iii) objective cognitive decline measured as residualized change in cognitive scores from childhood to midlife on matched Weschler Intelligence scales]. All indexes were quantitatively distributed and proved informative about midlife antecedents of ADRD, including algorithm-estimated brain age (β's from 0.16 to 0.22), white matter hyperintensities volume (β's from 0.16 to 0.19), hippocampal volume (β's from -0.08 to -0.11), tested cognitive deficits (β's from -0.36 to -0.49), everyday cognitive problems (β's from 0.14 to 0.38), and longitudinal cognitive decline (β's from -0.18 to -0.26). Existing indexes compared favourably to the comprehensive benchmark in their association with the brain structural integrity measures but were outperformed in their association with the functional integrity measures, particularly subjective cognitive problems and tested cognitive decline. Results indicated that existing indexes could be improved with targeted additions, particularly of measures assessing socioeconomic status, physical and sensory function, epigenetic aging, and subjective overall health. Existing premorbid ADRD risk indexes perform well in identifying linear gradients of risk among members of the general population at midlife, even when they include only a small subset of potential risk factors. They could be improved, however, with targeted additions to more holistically capture the different facets of risk for this multiply determined, age-related disease.

Alzheimer’s disease↗