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Background: Insomnia persists as a prevalent sleep disorder among middle-aged and older adults, significantly impacting quality of life and increasing susceptibility to age-related diseases. It is classified into objective insomnia (O-IN) and paradoxical insomnia (P-IN), where subjective and objective sleep assessments diverge. Current treatment regimens for both patient groups yield unsatisfactory outcomes. Consequently, investigating the neurophysiological distinctions between P-IN and O-IN is imperative for devising novel precision interventions aligned with primary prediction, targeted prevention, and personalized medicine (PPPM) principles.Working hypothesis and methodology.Given the emerging influence of gut microbiota (GM) on sleep physiology via the gut-brain axis, our study focused on characterizing the GM profiles of a well-characterized cohort of 96 Italian postmenopausal women, comprising 54 insomniac patients (18 O-IN and 36 P-IN) and 42 controls, through 16S rRNA amplicon sequencing. Associations were explored with general and clinical history, sleep patterns, stress, hematobiochemical parameters, and nutritional patterns. Results: Distinctive GM profiles were unveiled between O-IN and P-IN patients. O-IN patients exhibited prominence in the Coriobacteriaceae family, including Collinsella and Adlercreutzia, along with Erysipelotrichaceae, Clostridium, and Pediococcus. Conversely, P-IN patients were mainly discriminated by Bacteroides, Staphylococcus, Carnobacterium, Pseudomonas, and respective families, along with Odoribacter. Conclusions: These findings provide valuable insights into the microbiota-mediated mechanism of O-IN versus P-IN onset. GM profiling may thus serve as a tailored stratification criterion, enabling the identification of women at risk for specific insomnia subtypes and facilitating the development of integrated microbiota-based predictive diagnostics, targeted prevention, and personalized therapies, ultimately enhancing clinical effectiveness. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00369-1.

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Objective: The study aims to identify distinct population-specific comorbidity progression patterns, timely detect potential comorbidities, and gain better understanding of the progression of comorbid conditions among patients. Methods: This work presents a comorbidity progression analysis framework that utilizes temporal comorbidity networks (TCN) for patient stratification and comorbidity prediction. We propose a TCN construction approach that utilizes longitudinal, temporal diagnosis data of patients to construct their TCN. Subsequently, we employ the TCN for patient stratification by conducting preliminary analysis, and typical prescription analysis to uncover potential comorbidity progression patterns in different patient groups. Finally, we propose an innovative comorbidity prediction method by utilizing the distance-matched temporal comorbidity network (TCN-DM). This method identifies similar patients with disease prevalence and disease transition patterns and combines their diagnosis information with that of the current patient to predict potential comorbidity at the patient's next visit. Results: This study validated the capability of the framework using a real-world dataset MIMIC-III, with heart failure (HF) as interested disease to investigate comorbidity progression in HF patients. With TCN, this study can identify four significant distinctive HF subgroups, revealing the progression of comorbidities in patients. Furthermore, compared to other methods, TCN-DM demonstrated better predictive performance with F1-Score values ranging from 0.454 to 0.612, showcasing its superiority. Conclusions: This study can identify comorbidity patterns for individuals and population, and offer promising prediction for future comorbidity developments in patients.

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This research seeks to assess the potential of regionally integrated health management for specific sub-populations, including the incorporation of self-management initiatives. It will achieve this by conducting a thorough stratification analysis of hospital data, utilizing the Adjusted Clinical Groups (ACG) classification system. The approach involves a retrospective review of healthcare data spanning five years, which includes patient demographics, health outcomes, and healthcare utilization metrics. We intend to use the ACG method to classify the patient population into pertinent groups that mirror their health requirements and resource use. The insights obtained from this analysis will be used to create a localized adaptation of the Kaiser Permanente Pyramid Model of Care. This adaptation aims to identify the distribution of costs among patients treated in the Rivierenland Hospital. We anticipate that stratifying data with the ACG method will identify distinct multimorbid subgroups. These subgroups will have unique healthcare requirements. Early interventions and customized health management strategies, based on these insights, could enhance health outcomes and resource efficiency for high-risk patients. This analysis will serve as a foundation for constructive discussions with hospital management and clinical staff, fostering a deeper comprehension of the patients' burden of disease. It might also foster multidisciplinary collaboration opportunities between medical specialties as with regional healthcare partners such as general practitioners (GPs), mental health and other long-term care organizations. Moreover, we anticipate that self-care initiatives, supported by customized health information, will encourage increased patient engagement and strategies for enhancing lifestyle improvements. This strategy is expected to enable the personalization of advanced care planning based on individual needs profiles, thereby improving the management of complex and chronic conditions, and encouraging self-care practices. Our anticipated findings highlight the potential benefits of a data-informed approach to advancing healthcare outcomes and present opportunities for future investigations to refine and implement such integrated care models across the region.

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Background: The role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy. Methods: This study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to develop an effective MCMGS from candidate genes. Subsequently, an immunological assessment of MCMGS was conducted to provide new insights into the evaluation of immunotherapy responses and prognostic assessments. Additionally, we utilized Gene Set Enrichment Analysis (GSEA) and pathway analysis to explore the biological pathways and mechanisms associated with MCMGS. Results: MCMGS incorporated 13 marker genes and was successful in segregating patients into distinct high- and low-risk categories. Prognostic efficacy was confirmed by survival analysis incorporating MCMGS scores, alongside clinical parameters such as age, T stage, and Gleason scores. High MCMGS scores were correlated with upregulated pathways in fatty acid metabolism and ß-alanine metabolism, while low scores correlated with DNA repair mechanisms, homologous recombination, and cell cycle progression. Patients classified as low-risk displayed increased sensitivity to drugs, indicating the utility of MCMGS in forecasting responses to immune checkpoint inhibitors. Conclusion: The combination of MCMGS with a robust machine learning methodology demonstrates considerable promise in guiding personalized risk stratification and informing therapeutic decisions for patients with PCA.

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Fibroblast activation protein (FAP) is mainly found on the surface of activated fibroblasts but is not expressed on the surface of inactive fibroblasts. Selective FAP inhibitors (FAPI), which are coupled to a radioactive tracer, can be used to quantify profibrotic and proinflammatory fibroblasts in patients using FAPI positron emission tomography (PET) computed tomography (CT). Following initial applications in neoplastic diseases, FAPI-PET/CT is also increasingly being applied in rheumatological diseases. The first studies have shown that in patients with systemic sclerosis (SSc) FAPI accumulates in actively fibrotically remodeled pulmonary and myocardial areas, that a high FAPI accumulation is associated with the risk of short-term progression and that this accumulation in the lungs regresses after successful treatment. In cases of immunoglobulin 4 (IgG4)-associated diseases (IgG4 rheumatic disease, RD), the FAPI signal correlates with the histological accumulation of activated fibroblasts and a poorer response to treatment to inhibit inflammation. Fibroblasts in chronically inflamed tissue, such as patients with inflammatory joint diseases, vasculitis or myositis, also express FAP and can be quantified by FAPI-PET/CT. The treatment-induced change of the phenotype from a destructive IL-6+/MMP3+THY1+ fibroblast subtype to an inflammation inhibiting CD200+DKK3+ subtype can be mechanistically demonstrated using FAPI-PET/CT. These studies provide indications that FAPI-PET/CT enables quantification of the tissue response in patients with fibrosing and chronic inflammatory diseases and can be used for patient stratification; however, further studies are essential for validation of the use of FAPI-PET/CT as a molecular imaging marker.

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BACKGROUND: The immune response of critically ill patients, such as those with sepsis, severe trauma, or major surgery, is heterogeneous and dynamic, but its characterization and impact on outcomes are poorly understood. Until now, the primary challenge in advancing our understanding of the disease has been to concurrently address both multiparametric and temporal aspects. METHODS: We used a clustering method to identify distinct groups of patients, based on various immune marker trajectories during the first week after admission to ICU. In 339 severely injured patients, we initially longitudinally clustered common biomarkers (both soluble and cellular parameters), whose variations are well-established during the immunosuppressive phase of sepsis. We then applied this multi-trajectory clustering using markers composed of whole blood immune-related mRNA. RESULTS: We found that both sets of markers revealed two immunotypes, one of which was associated with worse outcomes, such as increased risk of hospital-acquired infection and mortality, and prolonged hospital stays. This immunotype showed signs of both hyperinflammation and immunosuppression, which persisted over time. CONCLUSION: Our study suggest that the immune system of critically ill patients can be characterized by two distinct longitudinal immunotypes, one of which included patients with a persistently dysregulated and impaired immune response. This work confirms the relevance of such methodology to stratify patients and pave the way for further studies using markers indicative of potential immunomodulatory drug targets.

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Objective: Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a variety of disease symptoms and an unpredictable clinical course. To improve treatment outcome, stratification based on immunological manifestations commonly seen in patients with SLE such as autoantibodies, type I interferon (IFN) signature and neutrophil extracellular trap (NET) release may help. It is assumed that there is an association between these immunological phenomena, since NET release induces IFN production and IFN induces autoantibody formation via B-cell activation. Here we studied the association between autoantibodies, the IFN signature, NET release, and clinical manifestations in patients with SLE. Methods: We performed principal component analysis (PCA) and hierarchical clustering of 57 SLE-related autoantibodies in 25 patients with SLE. We correlated each autoantibody to the IFN signature and NET inducing capacity. Results: We observed two distinct clusters: one cluster contained mostly patients with a high IFN signature. Patients in this cluster often present with cutaneous lupus, and have higher anti-dsDNA concentrations. Another cluster contained a mix of patients with a high and low IFN signature. Patients with high and low NET inducing capacity were equally distributed between the clusters. Variance between the clusters is mainly driven by antibodies against histones, RibP2, RibP0, EphB2, RibP1, PCNA, dsDNA, and nucleosome. In addition, we found a trend towards increased concentrations of autoantibodies against EphB2, RibP1, and RNP70 in patients with an IFN signature. We found a negative correlation of NET inducing capacity with anti-FcER (r = -0.530; p = 0.007) and anti-PmScl100 (r = -0.445; p = 0.03). Conclusion: We identified a subgroup of patients with an IFN signature that express increased concentrations of antibodies against DNA and RNA-binding proteins, which can be useful for further patient stratification and a more targeted therapy. We did not find positive associations between autoantibodies and NET inducing capacity. Our study further strengthens the evidence of a correlation between RNA-binding autoantibodies and the IFN signature.

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BACKGROUND CONTEXT: Associations between magnetic resonance imaging (MRI)-detected lumbar intervertebral disc degeneration (LDD) and LBP are often of modest magnitude. This association may be larger in specific patient subgroups. PURPOSE: To examine whether the association between LDD and LBP is modified by underlying genetic predispositions to pain. STUDY DESIGN: Cross-sectional study in UK Biobank (UKB) and Twins UK. PATIENT SAMPLES: A genome-wide association study (GWAS) of the number of anatomical chronic pain locations was conducted in 347,538 UKB participants. The GWAS was used to develop a genome-wide polygenic risk score (PRS) in a holdout sample of 30,000 UKB participants. The PRS model was then used in analyses of 645 TwinsUK participants with standardized LDD MRI assessments. OUTCOME MEASURES: Ever having had LBP associated with disability lasting ≥1 month (LBP1). METHODS: Using the PRS as a proxy for "genetically-predicted propensity to pain", we stratified TwinsUK participants into PRS quartiles. A "basic" model examined the association between an LDD summary score (LSUM) and LBP1, adjusting for covariates. A "fully-adjusted" model also adjusted for PRS quartile and LSUM x PRS quartile interaction terms. RESULTS: In the basic model, the odds ratio (OR) of LBP1 was 1.8 per standard deviation of LSUM (95% confidence interval [CI] 1.4-2.3). In the fully-adjusted model, there was a statistically significant LSUM-LBP1 association in quartile 4, the highest PRS quartile (OR=2.5 [95% CI 1.7-3.7], p=2.6×10-6), and in quartile 3 (OR=2.0, [95% CI 1.3-3.0]; p=.002), with small-magnitude and/or nonsignificant associations in the lowest 2 PRS quartiles. PRS quartile was a significant effect modifier of the LSUM-LBP1 association (interaction p≤.05). CONCLUSIONS: Genetically-predicted propensity to pain modifies the LDD-LBP association, with the strongest association present in people with the highest genetic propensity to pain. Lumbar MRI findings may have stronger connections to LBP in specific subgroups of people.

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Background: Huntington's disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient's quality of life. Despite this clear genetic course, high variability of HD patients' symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care. Methods: Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits. Results: Results demonstrate two distinct subtypes, one large cluster (n = 7122) showing a relative stable disease progression and a second, smaller cluster (n = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients' first visit only. Conclusion: In summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients' disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals' treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. This greatly aligns with the goal of shifting towards 3P medicine. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00368-2.

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Background: Alternative splicing (AS) occurs in the process of gene post-transcriptional process, which is very important for the correct synthesis and function of protein. The change of AS pattern may lead to the change of expression level or function of lung cancer-related genes, and then affect the occurrence and development of lung cancers. The specific AS pattern might be used as a biomarker for early warning and prognostic assessment of a cancer in the framework of predictive, preventive, and personalized medicine (PPPM; 3PM). AS events of immune-related genes (IRGs) were closely associated with tumor progression and immunotherapy. We hypothesize that IRG-AS events are significantly different in lung adenocarcinomas (LUADs) vs. controls or in lung squamous cell carcinomas (LUSCs) vs. controls. IRG-AS alteration profiling was identified to construct IRG-differentially expressed AS (IRG-DEAS) signature models. Study on the selective AS events of specific IRGs in lung cancer patients might be of great significance for further exploring the pathogenesis of lung cancer, realizing early detection and effective monitoring of lung cancer, finding new therapeutic targets, overcoming drug resistance, and developing more effective therapeutic strategies, and better used for the prediction, diagnosis, prevention, and personalized medicine of lung cancer. Methods: The transcriptomic, clinical, and AS data of LUADs and LUSCs were downloaded from TCGA and its SpliceSeq databases. IRG-DEAS events were identified in LUAD and LUSC, followed by their functional characteristics, and overall survival (OS) analyses. OS-related IRG-DEAS prognostic models were constructed for LUAD and LUSC with Lasso regression, which were used to classify LUADs and LUSCs into low- and high-risk score groups. Furthermore, the immune cell distribution, immune-related scores, drug sensitivity, mutation status, and GSEA/GSVA status were analyzed between low- and high-risk score groups. Also, low- and high-immunity clusters and AS factor (SF)-OS-related-AS co-expression network and verification of cell function of CELF6 were analyzed in LUAD and LUSC. Results: Comprehensive analysis of transcriptomic, clinical, and AS data of LUADs and LUSCs identified IRG-AS events in LUAD (n = 1607) and LUSC (n = 1656), including OS-related IRG-AS events in LUAD (n = 127) and LUSC (n = 105). A total of 66 IRG-DEAS events in LUAD and 89 IRG-DEAS events in LUSC were identified compared to controls. The overlapping analysis between IRG-DEASs and OS-related IRG-AS events revealed 14 OS-related IRG-DEAS events for LUAD and 16 OS-related IRG-DEAS events for LUSC, which were used to identify and optimize a 12-OS-related-IRG-DEAS signature prognostic model for LUAD and an 11-OS-related-IRG-DEAS signature prognostic model for LUSC. These two prognostic models effectively divided LUAD or LUSC samples into low- and high-risk score groups that were closely associated with OS, clinical characteristics, and tumor immune microenvironment, with significant gene sets and pathways enriched in the two groups. Moreover, weighted gene co-expression network (WGCNA) and nonnegative matrix factorization method (NMF) analyses identified four OS-relevant subtypes of LUAD and six OS-relevant subtypes of LUSC, and ssGSEA identified five immunity-relevant subtypes of LUAD and five immunity-relevant subtypes of LUSC. Interestingly, splicing factors-OS-related-AS network revealed hub molecule CELF6 was significantly related to the malignant phenotype in lung cancer cells. Conclusions: This study established two reliable IRG-DEAS signature prognostic models and constructed interesting splicing factor-splicing event networks in LUAD and LUSC, which can be used to construct clinically relevant immune subtypes, patient stratification, prognostic prediction, and personalized medical services in the PPPM practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00366-4.

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Background: DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC. Method: Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified. Results: A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (p < 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC. Conclusions: This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00359-3.

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Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC]: 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC: 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P < 0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N = 143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.

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Background: Statins are widely used to reduce the risk of cardiovascular disease (CVD). Patients with end-stage renal disease (ESRD) on hemodialysis have significantly increased risk of developing CVD. Statin treatment in these patients however did not show a statistically significant benefit in large trials on a patient cohort level. Methods: We generated gene expression profiles for statins to investigate the impact on cellular programs in human renal proximal tubular cells and mesangial cells in-vitro. We subsequently selected biomarkers from key statin-affected molecular pathways and assessed these biomarkers in plasma samples from the AURORA cohort, a double-blind, randomized, multi-center study of patients on hemodialysis or hemofiltration that have been treated with rosuvastatin. Patient clusters (phenotypes) were created based on the identified biomarkers using Latent Class Model clustering and the associations with outcome for the generated phenotypes were assessed using Cox proportional hazards regression models. The multivariable models were adjusted for clinical and biological covariates based on previously published data in AURORA. Results: The impact of statin treatment on mesangial cells was larger as compared with tubular cells with a large overlap of differentially expressed genes identified for atorvastatin and rosuvastatin indicating a predominant drug class effect. Affected molecular pathways included TGFB-, TNF-, and MAPK-signaling and focal adhesion among others. Four patient clusters were identified based on the baseline plasma concentrations of the eight biomarkers. Phenotype 1 was characterized by low to medium levels of the hepatocyte growth factor (HGF) and high levels of interleukin 6 (IL6) or matrix metalloproteinase 2 (MMP2) and it was significantly associated with outcome showing increased risk of developing major adverse cardiovascular events (MACE) or cardiovascular death. Phenotype 2 had high HGF but low Fas cell surface death receptor (FAS) levels and it was associated with significantly better outcome at 1 year. Conclusions: In this translational study, we identified patient subgroups based on mechanistic markers of statin therapy that are associated with disease outcome in patients on hemodialysis.

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Background: Head and neck squamous cell carcinoma (HNSCC) is the most common type and accounts for 90% of all head and neck cancer cases. Despite advances in early diagnosis and treatment strategies-chemotherapy, surgical resection, and radiotherapy-5-year survival remains grim. For patients with early-stage HNSCC, accurately predicting clinical outcomes is challenging. Considering the pivotal role of the immune system in HNSCC, we developed a reliable immune-related gene signature (IRGS) and explored its predictive accuracy in patients with early-stage HNSCC. Methods: We examined immune gene expression profiles and clinical information from 230 early-stage HNSCC specimens, including 100 cases from The Cancer Genome Atlas (TCGA), 49 cases from the Gene Expression Omnibus (GEO; GSE65858), and 81 cases from an independent clinical cohort. The prognostic signature was constructed using Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) Cox algorithm. We also explored the IRGS-related biological pathways and immune landscape using bioinformatics analysis. Results: A nine-immune-gene signature was generated to significantly stratify patients into high and low-risk groups. High risk patients exhibited shorter survival time [hazard ratio (HR) =13.795, 95% confidence interval (CI): 3.275-58.109, P<0.001]. The signature demonstrated robust prognostic ability in the training and validation sets and could independently predict overall survival (OS) and relapse-free survival (RFS). Subsequently, the receiver operating characteristic (ROC) curve and C-index confirmed the signature's predictive accuracy compared to clinical parameters. Additionally, cases classified as low risk showed more immune cell infiltration than high-risk cases. Conclusions: Our novel IRGS is a reliable and robust classifier for accurate patient stratification and prognostic evaluation. Future studies will attempt to affirm the signature's clinical application to early-stage HNSCC.

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Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact:  anirban@klyuniv.ac.in.

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The co-occurrence and familial clustering of neurodevelopmental disorders and immune disorders suggest shared genetic risk factors. Based on genome-wide association summary statistics from five neurodevelopmental disorders and four immune disorders, we conducted genome-wide, local genetic correlation and polygenic overlap analysis. We further performed a cross-trait GWAS meta-analysis. Pleotropic loci shared between the two categories of diseases were mapped to candidate genes using multiple algorithms and approaches. Significant genetic correlations were observed between neurodevelopmental disorders and immune disorders, including both positive and negative correlations. Neurodevelopmental disorders exhibited higher polygenicity compared to immune disorders. Around 50%-90% of genetic variants of the immune disorders were shared with neurodevelopmental disorders. The cross-trait meta-analysis revealed 154 genome-wide significant loci, including 8 novel pleiotropic loci. Significant associations were observed for 30 loci with both types of diseases. Pathway analysis on the candidate genes at these loci revealed common pathways shared by the two types of diseases, including neural signaling, inflammatory response, and PI3K-Akt signaling pathway. In addition, 26 of the 30 lead SNPs were associated with blood cell traits. Neurodevelopmental disorders exhibit complex polygenic architecture, with a subset of individuals being at a heightened genetic risk for both neurodevelopmental and immune disorders. The identification of pleiotropic loci has important implications for exploring opportunities for drug repurposing, enabling more accurate patient stratification, and advancing genomics-informed precision in the medical field of neurodevelopmental disorders.

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Objective: Developing new therapies for knee osteoarthritis (KOA) requires improved prediction of disease progression. This study evaluated the prognostic value of clinical clusters and machine-learning derived quantitative 3D bone shape B-score for predicting total and partial knee replacement (KR). Design: This retrospective study used longitudinal data from the Osteoarthritis Initiative. A previous study used patients' clinical profiles to delineate phenotypic clusters. For these clusters, the distribution of B-scores was assessed (employing Tukey's method). The value of both cluster allocation and B-score for KR-prediction was then evaluated using multivariable Cox regression models and Kaplan-Meier curves for time-to-event analyses. The impact of using B-score vs. cluster was evaluated using a likelihood ratio test for the multivariable Cox model; global performances were assessed by concordance statistics (Harrell's C-index) and time dependent receiver operating characteristic (ROC) curves. Results: B-score differed significantly for the individual clinical clusters (p â€‹< â€‹0.001). Overall, 9.4% of participants had a KR over 9 years, with a shorter time to event in clusters with high B-score at baseline. Those clusters were characterized clinically by a high rate of comorbidities and potential signs of inflammation. Both phenotype and B-score independently predicted KR, with better prediction if combined (P â€‹< â€‹0.001). B-score added predictive value in groups with less pain and radiographic severity but limited physical activity. Conclusions: B-scores correlated with phenotypes based on clinical patient profiles. B-score and phenotype independently predicted KR surgery, with higher predictive value if combined. This can be used for patient stratification in drug development and potentially risk prediction in clinical practice.

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Relevance: The proteasome is a crucial mechanism that regulates protein fate and eliminates misfolded proteins, playing a significant role in cellular processes. In the context of lung cancer, the proteasome's regulatory function is closely associated with the disease's pathophysiology, revealing multiple connections within the cell. Therefore, studying proteasome inhibitors as a means to identify potential pathways in carcinogenesis and metastatic progression is crucial in in-depth insight into its molecular mechanism and discovery of new therapeutic target to improve its therapy, and establishing effective biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized treatment for lung squamous carcinoma in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine). Methods: This study identified differentially expressed proteasome genes (DEPGs) in lung squamous carcinoma (LUSC) and developed a gene signature validated through Kaplan-Meier analysis and ROC curves. The study used WGCNA analysis to identify proteasome co-expression gene modules and their interactions with the immune system. NMF analysis delineated distinct LUSC subtypes based on proteasome gene expression patterns, while ssGSEA analysis quantified immune gene-set abundance and classified immune subtypes within LUSC samples. Furthermore, the study examined correlations between clinicopathological attributes, immune checkpoints, immune scores, immune cell composition, and mutation status across different risk score groups, NMF clusters, and immunity clusters. Results: This study utilized DEPGs to develop an eleven-proteasome gene-signature prognostic model for LUSC, which divided samples into high-risk and low-risk groups with significant overall survival differences. NMF analysis identified six distinct LUSC clusters associated with overall survival. Additionally, ssGSEA analysis classified LUSC samples into four immune subtypes based on the abundance of immune cell infiltration with clinical relevance. A total of 145 DEGs were identified between high-risk and low-risk score groups, which had significant biological effects. Moreover, PSMD11 was found to promote LUSC progression by depending on the ubiquitin-proteasome system for degradation. Conclusions: Ubiquitinated proteasome genes were effective in developing a prognostic model for LUSC patients. The study emphasized the critical role of proteasomes in LUSC processes, such as drug sensitivity, immune microenvironment, and mutation status. These data will contribute to the clinically relevant stratification of LUSC patients for personalized 3P medical approach. Further, we also recommend the application of the ubiquitinated proteasome system in multi-level diagnostics including multi-omics, liquid biopsy, prediction and targeted prevention of chronic inflammation and metastatic disease, and mitochondrial health-related biomarkers, for LUSC 3PM practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00352-w.

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Challenge in the framework of Predictive Preventive and Personalised Medicine: In recent years, we have been witnessing a change in the performance of hospital pharmacists, aimed at increasing their participation in the pharmacotherapeutic process of patients. The drug cycle, characterised as multidisciplinary, is very complex. It is essential for the multidisciplinary team to have a broad vision of the medication system in order to guarantee safety and quality.Considering the challenges of current healthcare systems and paradigm shift from reactive to predictive medicine, a new professional environment should be created to promote the implementation of Predictive, Preventive and Personalised Medicine in healthcare. Objectives and study design: To optimise care times in multipurpose outpatient hospital both in the preparation of ready-to-use medications and in the dispensing of medications for home treatment.To increase the confidence and value of hospital pharmacists in the process of patient and family care.The design of the study was carried out by the following:-Coordinating the schedules of the multi-pathology day hospital with the software and records of Medication Preparation in the pharmacy service.-Opening a Pharmacy Outpatient Clinic associated with the multi-pathology day hospital.-Planning and scheduling patient treatments. Achievements: With the implementation of this programme, the visibility of hospital pharmacists in the multidisciplinary team was increased.This Pharmacy Outpatient Clinic allowed the coordination of the pharmaceutical care process in the day hospital.This project increased the credibility of the Pharmacy Service in the improvement of the integral process of the medicine. Predictive approach: The presence of pharmacists in the multi-pathology day hospital has a predictive approach. A change is made in the workflow that allows to generate a speed of intervention by acting before prescribing, dispensing and administering the treatment to the patient. Targeted prevention: The presence of pharmacists in the multipurpose day hospital unit and their collaboration with other professionals and the patient bring about a selective prevention that decreases the possibility of medication errors occurring. Personalisation of medical services: With the individualised dispensing of treatments, a step forward is taken in the personalisation of medical services, which avoids medication errors in labelling and administration and improves safety in the overall medication circuit in the hospital.