Predict Disease Outcome Research Articles

Differential Expression of BCL2 and IGFBP2 in Childhood Immune Thrombocytopenic Purpura Clinical Subtypes: Implications for Predicting Disease Progression and Apoptotic Regulation.

Immune thrombocytopenic purpura (ITP), which poses challenges in treatment response, is an autoimmune-mediated bleeding disorder with an extremely complex pathogenesis and unpredictable clinical progression. Dysregulation of apoptotic pathways may influence both the pathogenesis and prognosis of ITP. This study aimed to evaluate the expression patterns of the apoptotic protein insulin-like growth factor-binding protein 2 (IGFBP2) and the anti-apoptotic protein B-cell lymphoma 2 (BCL2) as potential predictive or prognostic biomarkers for disease progression in childhood ITP. The expression levels of BCL2 and IGFBP2 were assessed in peripheral blood samples from 40 pediatric ITP patients and 30 age- and sex-matched healthy controls using enzyme-linked immunosorbent assays. Plasma levels of BCL2 and IGFBP2 were higher in ITP patients than in control subjects. Although the difference in IGFBP2 expression was not statistically significant (p = 0.910), BCL2 expression was significantly elevated (p < 0.001). Notably, chronic ITP patients had significantly lower levels of both IGFBP2 and BCL2 markers compared to patients who achieved spontaneous recovery (p < 0.001). BCL2 and IGFBP2 appear to be promising noninvasive biomarkers for predicting disease outcomes in newly diagnosed ITP, emphasizing the need for validation in large-scale, multicenter longitudinal studies.

Impact of Vaccination Status on Chest CT Findings and Disease Outcomes in COVID-19 Era: A Retrospective Study

Background: Computerized Tomography (CT) was extensively used in the COVID-19 era to confirm the diagnosis and follow the patient's response. The vaccine was rapidly introduced to break the disease chain of infection. The current study primarily aimed to examine the relationship between vaccination status and pulmonary CT findings. Moreover, it also aimed to validate the role of CT scan along with other patient criteria in predicting disease outcomes. Methods: A retrospective cohort study was conducted at the radiology department of two Iraqi hospitals in Baghdad. The study enrolled all hospitalized patients with a confirmed COVID-19 diagnosis older than 18 years old. Their data regarding demographic criteria, vaccination criteria (the status and types), and radiological CT-scan parameters (including CT finding and severity score index) were collected Results: It was found that 23 percent of COVID-19 patients were immunized. Most of the unvaccinated cases were older than 45 years and were females. There was a significant correlation between the degree and severity of lung involvement and the vaccination status (p &lt; 0.001). The worst radiological sign for severity was the ground glass appearance. The vaccine type showed significant changes in chest CT. Pfizer had the worst severity score, followed by Sinopharm in vaccinated cases. The overall mortality was 4.5%. Moreover, the vaccine significantly reduced mortality among vaccinated vs. non-vaccinated cases (p = 0.03). By logistic regression, the CT score reliably predicted mortality with an odds ratio of 1.31 (1.18 to 1.45; p &lt; 0.001). Conclusion: Vaccines were found to be significantly effective in protecting vaccinated people against severe infection and limiting lung injury, as evidenced by CT scores. Vaccines had a trend effect on reducing mortality. Moreover, CT scores were reliable in predicting the disease outcome.

Determination of oxidative stress and copeptin levels of COVID-19 according to the clinical course.

COVID-19, caused by the novel coronavirus SARS-CoV-2, is characterized by hyperinflammation, which can trigger oxidative stress. At the same time, COVID-19 is accompanied by both psychological and physical stress. Copeptin, a novel stress marker, has been shown to predict disease outcomes in stress-induced diseases. In this study, we aimed to explore the potential of copeptin, with inflammatory and oxidative stress markers, in distinguishing between different clinical courses of COVID-19. This case-control study included 75 participants: 25 COVID-19 patients hospitalized in the intensive care unit (ICU), 25 non-ICU COVID-19 patients, and 25 healthy individuals. 64% of the ICU patients received corticosteroid treatment for 4-10 days before sampling. Serum concentrations of the study parameters were assessed by enzyme-linked immunosorbent assay (ELISA) and compared between the study groups. Serum IL-6 levels (p<0.001) were significantly higher in ICU patients compared to non-ICU patients and the control group. Serum MDA (p<0.001) and 4-HNE (p=0.027) concentrations were significantly lower in the ICU group in comparison with the other groups. Copeptin was not statistically significant. MDA (p=0.040) and 4-HNE (p=0.017) levels were significantly lower in the treated ICU group than the untreated one. Serum IL-6 levels were noticeably associated with COVID-19 severity. Corticosteroid therapy administration seemed to influence MDA and 4-HNE levels in the treated group, with no obvious influence on IL-6 and copeptin in the same cohort. This data suggests that in SARS-CoV-2 infection, corticosteroids may act through a rapid non-genomic mechanism.

P0340 Endo-Histo Foundational Fusion Model: A Novel Artificial Intelligence Approach for Predicting Histologic Remission and Early Response to Therapy in a Phase 2 Ulcerative Colitis Clinical Trial

Abstract Background Artificial Intelligence (AI)- enabled endoscopy and histology offer accurate, objective, and rapid assessment of disease activity in Ulcerative Colitis (UC). Emerging multi-source AI models integrating diverse datasets may enhance standardised disease evaluation and outcome prediction. This study aimed to develop a novel AI model fusing endoscopic and histological findings to improve the assessment of disease remission and predict early response to therapy in UC clinical trials. Methods A novel multimodal AI fusion algorithm was developed by integrating paired endoscopic videos and histological whole-slide images (WSIs) from the phase 2 clinical trial of Mirikizumab in UC (NCT02589665). The endoscopy branch of the model was trained with 291 white-light videos, using a convolutional neural network to select informative frames and the BioMedCLIP foundational model to extract features. The histology branch utilised the CONCH foundational model and was fine-tuned on 291 WSIs to obtain patch-level features. Features from both modalities were aggregated and fused using multi-head self-attention. The model’s ability to assess histological remission and response to therapy at weeks 12 and 52 was evaluated. Histological remission was defined as Geboes ≤2B.0, while a response to therapy at different time points was based on histological remission or improvement (Geboes &amp;lt;3.1). Results The fusion model outperformed single-modality assessments for histological remission, achieving a sensitivity of 89.72% (95% CI: 82.35–94.76), specificity of 89.67% (95% CI: 84.34–93.67), and accuracy of 89.69% (95% CI: 85.61–92.94). It demonstrated remarkable performance in assessing response to therapy at 12 and 52 weeks, with sensitivity of 97.96% (95% CI: 89.15–99.95), specificity of 86.84% (95% CI: 71.91–95.59) and accuracy of 93.10% (95% CI 85.59 – 97.43) for histological remission at week 52. Substantial agreement was observed between the AI fusion model and central readout. Conclusion This innovative multimodal fusion AI model enhances the assessment of histological remission and accurately predicts response to therapy. By potentially standardising central readouts and enabling automated disease assessment, this novel tool marks a significant advancement towards precision medicine in clinical trials.

Prognostic Value of Cerebrospinal Fluid and Serum Neurofilament Light Chain in Amyotrophic Lateral Sclerosis: A Correlation Study.

The diagnostic and prognostic values of serum neurofilament light chain (sNfL), in comparison to cerebrospinal fluid (CSF) neurofilament light chain (cNfL), and other clinical parameters in amyotrophic lateral sclerosis (ALS) at the time of diagnosis remain elusive. We examine paired serum and CSF samples from 80 ALS patients and 21 control subjects, all obtained at the time of diagnosis. Additional serum samples were collected from 51 other ALS patients. NfL concentrations were quantified using the single molecule array (Simoa) technique. Our findings demonstrate a robust correlation between NfL levels in matched CSF and serum samples. Notably, both sNfL (p < 0.0001) and cNfL (p < 0.0001) exhibited significantly elevated levels in ALS patients compared to controls. Furthermore, baseline sNfL concentrations, as well as cNfL levels, emerged as predictive indicators of subsequent disease progression rate (sNfL: p < 0.0001, cNfL: p = 0.0005) and overall survival (sNfL: p = 0.0073, cNfL: p = 0.0044). Employing a Cox regression model, we identified baseline sNfL level (HR = 1.01, p = 0.013), and diagnostic delay (HR = 0.94, p = 0.003) as independent prognostic factors for mortality. Furthermore, we constructed a nomogram model that incorporates both sNfL and pertinent clinical variables, which substantially enhances the accuracy of predicting disease outcomes (Concordance Index, 0.808). Our study underscores the robust correlation between sNfL and cNfL in ALS patients and establishes baseline sNfL as a potent and independent prognostic marker for mortality.

A comparison of disseminated intravascular coagulation scoring systems and their performance to predict mortality in sepsis patients: A systematic review and meta-analysis.

Disseminated intravascular coagulation (DIC) is a common complication in sepsis patients which exacerbates patient outcomes. The prevalence and outcomes of DIC in sepsis is wide-ranging and highly depends on the severity of the disease and diagnostic approaches utilized. Varied diagnostic criteria of DIC have been developed and their performance in diagnosis and prognosis is not consistent. Therefore, this study aimed to determine the score positivity rate and performance of different DIC scoring systems in predicting mortality in sepsis patients. Four databases, including Medline (through PubMed), Scopus, Embase, and Web of Science were searched for studies that determined DIC in sepsis patients using the three scoring systems namely: the International Society on Thrombosis and Hemostasis DIC (ISTH-DIC) criteria, the Japanese association for acute medicine DIC (JAAM-DIC) criteria, and the sepsis-induced coagulopathy (SIC) criteria. A random-effect meta-analysis was performed with a 95% confidence interval (CI). Subgroup analysis was conducted in view of geographic region and sepsis stages. the protocol was submitted to the Prospective Register for Systematic Reviews (PROSPERO) with an identifier (CRD42023409614). Twenty-one studies, published between 2009 and 2024, comprising 9319 sepsis patients were included. The pooled proportion of cases diagnosed as positive using ISTH-DIC criteria, JAAM-DIC criteria, and SIC were 28% (95% CI: 24-34%), 55% (95% CI:42-70%), and 57% (95% CI: 52-78%), respectively. The pooled mortality rates were 44% (95% CI:33-53%), 37% (95% CI: 29-46%), and 35% (95% CI: 29-41%), respectively. The pooled sensitivity and specificity of ISTH-DIC to predict mortality were 0.43 (95% CI: 0.34-0.52), and 0.81 (95% CI: 0.74-0.87), respectively, while for JAAM-DIC it was 0.73 (95% CI: 0.57-0.85) and 0.46 (95% CI: 0.28-0.65), respectively. Pooled sensitivity and specificity for SIC were 0.71 (95% CI: 0.57-0.82) and 0.49 (95% CI: 0.31-0.66), respectively. The SIC and JAAM-DIC scores exhibited higher sensitivity to identify patients with coagulopathy and predict patient outcomes, and thus are valuable to identify patients for possible treatment at an early stage. The ISTH-DIC score perhaps identified patients at later stages and demonstrated better specificity to predict disease outcomes. Thus, early identification of patients using the SIC and JAAM-DIC scores and later confirmation using the ISTH-DIC score would be beneficial approach for improved management of patients with sepsis.

Personalized multifactorial risk assessment in neoadjuvant-treated breast carcinoma

Abstract Purpose Due to biological heterogeneity of breast carcinoma, predicting the individual response to neoadjuvant treatment (NAT) is complex. Consequently, there are no comprehensive, generally accepted practices to guide post-treatment follow-up. We present clinical and histopathological criteria to advance the prediction of disease outcome in NA-treated breast cancer. Methods A retrospective consecutive cohort of 257 NA-treated Finnish breast cancer patients with up to 13-year follow-up and the corresponding tissue samples of pre- and post-NAT breast and metastatic specimen were evaluated for prognostic impacts. All relevant clinical and biomarker characteristics potentially correlated with tumor response to NAT, course of disease, or outcome of breast cancer were included in the statistical analyses. Results The results highlight the intensified characterization of distinguished prognostic factors and previously overlooked histological features, e.g., mitotic and apoptotic activity. Particularly, decreased PR indicated 3.8-fold (CI 1.9–7.4, p = 0.0001) mortality risk, and a &gt; 10.5-year shorter survival for the majority, &gt; 75% of patients (Q1). Clinically applicable prognostic factors both preceding and following NAT were identified and compiled into heat maps to quantify mortality and recurrence risks. Combinations of risk factors for aggressive disease were exemplified as an interactive tool (bcnatreccalc.utu.fi) to illustrate the spectrum of disease outcomes. Conclusion The results emphasize the value of comprehensive evaluation of conventional patient and biomarker characteristics, especially concerning re-assessment of biomarkers, risk-adapted surveillance, and personalized treatment strategies. Future personalized NA-treatment strategies might benefit from models combining risk-adapted surveillance data and post-NAT re-assessed biomarkers.

Metabolic markers derived from 18 F-FDG PET/CT in suspected recurrent ovarian carcinoma: predictive value for disease burden and prognosis.

This study aims to assess the role of 18 F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) in suspected recurrent ovarian carcinoma. Several clinical and PET parameters were assessed to evaluate disease burden and prognosis. We did a single-center, retrospective study in patients with suspected recurrent ovarian carcinoma who underwent 18 F-FDG PET/CT. The disease burden on the scan was evaluated. We calculated several semiquantitative markers, including standard uptake values (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Survival analysis was done with clinical parameters, CA-125 levels, disease distribution, and metabolic markers. Fifty-two patients were included in the study. Half of the patients had suspected recurrence within 12 months of primary diagnosis. PET/CT scan suggested disease in 35 (67.3%) patients. Multiple metastatic sites were noted in 21 (40.4%) patients. Extra-abdominal metastases were seen in 15 (28.8%) patients. Eight patients had 18 F-FDG avid disease despite a low CA-125 level (<35 IU). Young patients (<50 years), extra-abdominal disease, multiple metastases, and higher restaging were associated with poor outcomes. Meanwhile, treatment history, CA-125 level, and post-PET/CT treatment had no significant effect on survival. MTV@40% SUV (>17.21) and TLG@40% SUV (>68.7) had the sensitivity of 87.5% and 75% for predicting disease outcome. Recurrent ovarian carcinoma commonly presents with multiple metastasis and extra-abdominal metastases. 18 F-FDG PET/CT-guided patterns of disease distribution were significant markers for poor prognosis. Disease burden on PET/CT-derived semiquantitative parameters was associated with poor outcomes.

The Association Between Crohn’s Disease and Patient Response to Yeast: A Review of the Literature

Inflammatory bowel disease represents a wide range of pathologies and disease states including Crohn’s Disease (CD) and Ulcerative colitis (UC). Despite extensive research, the exact immunopathogenesis of Crohn’s disease remains unclear, but a variety of studies over the years have pointed to yeast as a potential source antigen of uncertain significance. The aim of this review is to summarize the current literature investigating the association between Crohn’s disease patients and their responses to yeast. To do this, we performed a literature review by looking at clinical and translational research regarding the immunopathogenesis of Crohn’s disease, yeast and its associated biomarkers, and overall patient response to dietary yeast published between 1 January 1990 and 1 October 2024 that were indexed on PubMed and Google Scholar with the majority written in English. It was found that antibodies against Saccharomyces cerevisiae (ASCA) have proven to be highly specific for CD during the workup of IBD and may have diagnostic value for the purpose of excluding ulcerative colitis. For CD patients, there appears to be a dysregulated immune response to antigens like yeast that results in abnormal mucosal permeability and thus increases antigen presentation to the immune system. In addition, ASCA and its immunoglobulin modifications have been shown to have significant potential in the prediction of CD onset and disease course. Interestingly, although other abnormally structured antibodies can be found in CD patients’ serum for years preceding diagnosis, there appears to be no relation between ASCA and dietary yeast sensitivity by CD patients. In conclusion, significant research efforts have been made in recent years to explore the role of diet in the disease course and management of patients with Crohn’s disease. The immunological role of antigens including yeast in CD is complex and may represent an important pathogenetic factor in addition to influencing the specific phenotype of the disease. Unfortunately, no single specific diet is superior for the management of IBD, and individualized patient treatment by experts in the field is best for adjunctive therapy. New studies characterizing the microbiome of CD patients and also using immune markers/gene modifications to predict disease outcomes have shown to be quite promising. However, further research is required to investigate the CD yeast response and its role in the pathogenesis, diagnosis and treatment of CD.

Exploring the role of health-related quality of life measures in predictive modelling for oncology: a systematic review.

Health related quality of life (HRQoL) is increasingly assessed in oncology research and routine care, which has led to the inclusion of HRQoL in prediction models. This review aims to describe the current state of oncological prediction models incorporating HRQoL.A systematic literature search for the inclusion of HRQoL in prediction models in oncology was conducted. Selection criteria were a longitudinal study design and inclusion of HRQoL data in prediction models as predictor, outcome, or both. Risk of bias was assessed using the PROBAST tool and quality of reporting was scored with an adapted TRIPOD reporting guideline.From 4747 abstracts, 98 records were included in this review. High risk of bias was found in 71% of the publications. HRQoL was mainly incorporated as predictor (78% (55% predictor only, 23% both predictor and outcome)), with physical functioning and symptom domains selected most frequently as predictor. Few models (23%) predicted HRQoL domains by other or baseline HRQoL domains. HRQoL was used as outcome in 21% of the publications, with a focus on predicting symptoms. There were no difference between AI-based (16%) and classical methods (84%) in model type selection or model performance when using HRQoL data.This review highlights the role of HRQoL as a tool in predicting disease outcomes. Prediction of and with HRQoL is still in its infancy as most of the models are not fully developed. Current models focus mostly on the physical aspects of HRQoL to predict clinical outcomes, and few utilize AI-based methods.

Standardization of hemoglobin A2 and hemoglobin F: Achievements and perspectives.

The establishment of reference systems for the standardization of hemoglobin A2 (HbA2) and fetal hemoglobin (HbF), both critical for improving diagnostic accuracy in conditions such as β-thalassemia and sickle cell disease, are described. Efforts were led by the IFCC and other groups to address and reduce the variability in laboratory measurements of these hemoglobins. This document outlines the production of certified reference materials (CRMs) for HbA2 and the development of a reference measurement procedure using isotope dilution mass spectrometry. Similarly, standardizing HbF is essential for supporting diagnostic and therapeutic strategies, particularly in managing sickle cell disease. HbF levels can predict disease outcomes and guide treatment plans. Significant challenges remain in achieving consistent measurement across laboratories, and the process for standardization for this minor hemoglobin has just begun. We are confident that the implementation of these reference systems will provide improved accuracy and traceability in the future.

Progress in Lactate Metabolism and Its Regulation via Small Molecule Drugs.

Lactate, once viewed as a byproduct of glycolysis and a metabolic "waste", is now recognized as an energy-providing substrate and a signaling molecule that modulates cellular functions under pathological conditions. The discovery of histone lactylation in 2019 marked a paradigm shift, with subsequent studies revealing that lactate can undergo lactylation with both histone and non-histone proteins, implicating it in the pathogenesis of various diseases, including cancer, liver fibrosis, sepsis, ischemic stroke, and acute kidney injury. Aberrant lactate metabolism is associated with disease onset, and its levels can predict disease outcomes. Targeting lactate production, transport, and lactylation may offer therapeutic potential for multiple diseases, yet a systematic summary of the small molecules modulating lactate and its metabolism in various diseases is lacking. This review outlines the sources and clearance of lactate, as well as its roles in cancer, liver fibrosis, sepsis, ischemic stroke, myocardial infarction, and acute kidney injury, and summarizes the effects of small molecules on lactate regulation. It aims to provide a reference and direction for future research.

BK Polyomavirus Infection in Kidney Transplantation: A Comprehensive Review of Current Challenges and Future Directions.

BK polyomavirus (BKPyV) infection of the kidney graft remains a major clinical issue in the field of organ transplantation. Risk factors for BKPyV-associated nephropathy (BKPyVAN) and molecular tools for determining viral DNA loads are now better defined. BKPyV DNAemia in plasma, in particular, plays a central role in diagnosing active infection and managing treatment decisions. However, significant gaps remain in the development of reliable biomarkers that can anticipate BKPyV viremia and predict disease outcomes. Biomarkers under active investigation include urine-based viral load assays, viral antigen detection, and immune responses against BKPyV, which may offer more precise methods for monitoring disease progression. In addition, treatment of BKPyVAN is currently based on immunosuppression minimization, while the role of adjunctive therapies remains an area of active research, highlighting the need for more personalized treatment regimens. Ongoing clinical trials are also exploring the efficacy of T-cell-based immunotherapies. The clinical management of BKPyV infection, based on proactive virological monitoring, immune response assessment, integrated histopathology, and timely immunosuppression reduction, is likely to reduce the burden of disease and improve outcomes in kidney transplantation.

Advances in Immune Biomarkers for Predicting Disease Outcomes and Therapeutic Responses

Advances in Immune Biomarkers for Predicting Disease Outcomes and Therapeutic Responses

A deep learning feature importance test framework for integrating informative high-dimensional biomarkers to improve disease outcome prediction.

Many human diseases result from a complex interplay of behavioral, clinical, and molecular factors. Integrating low-dimensional behavioral and clinical features with high-dimensional molecular profiles can significantly improve disease outcome prediction and diagnosis. However, while some biomarkers are crucial, many lack informative value. To enhance prediction accuracy and understand disease mechanisms, it is essential to integrate relevant features and identify key biomarkers, separating meaningful data from noise and modeling complex associations. To address these challenges, we introduce the High-dimensional Feature Importance Test (HdFIT) framework for machine learning models. HdFIT includes a feature screening step for dimension reduction and leverages machine learning to model complex associations between biomarkers and disease outcomes. It robustly evaluates each feature's impact. Extensive Monte Carlo experiments and a real microbiome study demonstrate HdFIT's efficacy, especially when integrated with advanced models like deep neural networks. Our framework shows significant improvements in identifying crucial features and enhancing prediction accuracy, even in high-dimensional settings.

Longitudinal Microbiome-based Interpretable Machine Learning for Identification of Time-Varying Biomarkers in Early Prediction of Disease Outcomes.

Information generated from longitudinally-sampled microbial data has the potential to illuminate important aspects of development and progression for many human conditions and diseases. Identifying microbial biomarkers and their time-varying effects can not only advance our understanding of pathogenetic mechanisms, but also facilitate early diagnosis and guide optimal timing of interventions. However, longitudinal predictive modeling of highly noisy and dynamic microbial data (e.g., metagenomics) poses analytical challenges. To overcome these challenges, we introduce a robust and interpretable machine-learning-based longitudinal microbiome analysis framework, LP-Micro, that encompasses: (i) longitudinal microbial feature screening via a polynomial group lasso, (ii) disease outcome prediction implemented via machine learning methods (e.g., XGBoost, deep neural networks), and (iii) interpretable association testing between time points, microbial features, and disease outcomes via permutation feature importance. We demonstrate in simulations that LP-Micro can not only identify incident disease-related microbiome taxa but also offers improved prediction accuracy compared to existing approaches. Applications of LP-Micro in two longitudinal microbiome studies with clinical outcomes of childhood dental disease and weight loss following bariatric surgery yield consistently high prediction accuracy. The identified critical early predictive time points are informative and aligned with clinical expectations.

Disease activity at the onset of diagnosis as a predictor of disease outcomes in a cohort of patients with systemic lupus erythematosus: A post hoc retrospective analysis of the COMOSLE-EGYPT study.

Systemic lupus erythematosus (SLE) has a non-uniform course directly reflected in changes in disease activity and anticipation of damage. To determine the impact of disease activity at the onset of disease diagnosis, measured by the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) disease activity score, on different disease parameters and outcomes. This multicentre, retrospective cohort study included 823 SLE patients. Disease damage was measured by the Systemic Lupus International Collaborating Clinics Damage Index (SLICC), and comorbidities were measured by the Charlson Comorbidity Index (CCI). According to the mean SLEDAI at onset of disease diagnosis, patients were classified into two groups: I included patients with a mean SLEDAI ≤ 10 (non-severe disease activity), and II included patients with a mean SLEDAI > 10 (severe disease activity). SLEDAI at onset of disease diagnosis was a predictor of damage and comorbidities. A higher SLEDAI score at onset of disease diagnosis was associated with damage accrual. Patients who are younger at disease onset are more likely to have more severe disease. Severe disease activity at the onset of disease diagnosis was also associated with future comorbidity occurrences, but it was not significantly associated with mortality. SLEDAI at the onset of disease diagnosis could be a prognostic marker predicting the damage, which may help in the identification of patients who are at higher risk of adverse outcomes. Special care should be directed towards patients who are younger at disease onset as they may have a higher disease activity at diagnosis. Key Points • This is a unique study as it is the first to focus on the impact of SLE disease activity at the onset of disease diagnosis measured by SLEDAI disease activity score on different disease parameters and outcomes. • Previous studies, though these are scarce, have highlighted the impact of disease activity throughout the disease course and not specifically at the beginning of the SLE disease.

Hepatocellular senescence induces multi-organ senescence and dysfunction via TGFβ

Cellular senescence is not only associated with ageing but also impacts physiological and pathological processes, such as embryonic development and wound healing. Factors secreted by senescent cells affect their microenvironment and can induce spreading of senescence locally. Acute severe liver disease is associated with hepatocyte senescence and frequently progresses to multi-organ failure. Why the latter occurs is poorly understood. Here we demonstrate senescence development in extrahepatic organs and associated organ dysfunction in response to liver senescence using liver injury models and genetic models of hepatocyte-specific senescence. In patients with severe acute liver failure, we show that the extent of hepatocellular senescence predicts disease outcome, the need for liver transplantation and the occurrence of extrahepatic organ failure. We identify the TGFβ pathway as a critical mediator of systemic spread of senescence and demonstrate that TGFβ inhibition in vivo blocks senescence transmission to other organs, preventing liver senescence induced renal dysfunction. Our results highlight the systemic consequences of organ-specific senescence, which, independent of ageing, contributes to multi-organ dysfunction.

CNSC-08. PROGNOSTIC BIOMARKER DISCOVERY THROUGH PROTEOMIC ANALYSIS OF GLIOBLASTOMA MULTIFORME

Abstract BACKGROUND Glioblastoma multiforme (GBM) is an aggressively malignant brain tumor known for its high recurrence rates and dismal survival outcomes. Despite advancements in surgical and therapeutic strategies, the prognosis for GBM patients remains poor. This study applies proteomic analysis to postoperative GBM tumor specimens to identify molecular markers that could enhance prognosis prediction. Through the analysis of protein expression profiles before and after tumor recurrence, we identified significant alterations in protein pathways via Gene Set Enrichment Analysis (GSEA), highlighting potential biomarkers for disease outcome prediction. METHODS We employed Sequential Window Acquisition of All Theoretical Fragment Ion Spectra (SWATH-MS) proteomics to analyze protein samples from both primary and recurrent GBM patients. Gene Set Enrichment Analysis (GSEA) was utilized to compare protein expression profiles and discern significantly altered pathways. RESULTS Analysis revealed 778 differentially expressed proteins (DEPs), with notable alterations in pathways such as glycolysis, interleukin signaling, and MAPK signaling. Proteins such as TOLLIP, ENO1, and ITGA2B emerged as potential prognostic markers. Validation through an online tumor prognostic analysis platform (ToPP), integrated with The Cancer Genome Atlas (TCGA) data, facilitated the development of a prognostic risk score model. This model effectively categorized patients into high and low-risk groups based on their protein expression profiles, enhancing the prediction of patient outcomes. The proteins and pathways identified offer new insights into the molecular dynamics of GBM postoperatively. The association between protein expression profiles and patient prognosis underscores the potential of these markers in developing customized therapeutic strategies. Additionally, the prognostic risk score model serves as a valuable tool for improving prognostic accuracy and could guide personalized treatment plans. CONCLUSION Proteomic profiling of GBM tumors significantly contributes to uncovering the molecular mechanisms driving this aggressive cancer and facilitates the development of dependable prognostic tools. These findings not only affirm the critical role of proteomics in cancer research but also advance personalized medicine strategies in GBM management.

Priority-Elastic net for binary disease outcome prediction based on multi-omics data

BackgroundHigh-dimensional omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential to improve predictive models. However, the data integration process faces several challenges, including data heterogeneity, priority sequence in which data blocks are prioritized for rendering predictive information contained in multiple blocks, assessing the flow of information from one omics level to the other and multicollinearity.MethodsWe propose the Priority-Elastic net algorithm, a hierarchical regression method extending Priority-Lasso for the binary logistic regression model by incorporating a priority order for blocks of variables while fitting Elastic-net models sequentially for each block. The fitted values from each step are then used as an offset in the subsequent step. Additionally, we considered the adaptive elastic-net penalty within our priority framework to compare the results.ResultsThe Priority-Elastic net and Priority-Adaptive Elastic net algorithms were evaluated on a brain tumor dataset available from The Cancer Genome Atlas (TCGA), accounting for transcriptomics, proteomics, and clinical information measured over two glioma types: Lower-grade glioma (LGG) and glioblastoma (GBM).ConclusionOur findings suggest that the Priority-Elastic net is a highly advantageous choice for a wide range of applications. It offers moderate computational complexity, flexibility in integrating prior knowledge while introducing a hierarchical modeling perspective, and, importantly, improved stability and accuracy in predictions, making it superior to the other methods discussed. This evolution marks a significant step forward in predictive modeling, offering a sophisticated tool for navigating the complexities of multi-omics datasets in pursuit of precision medicine’s ultimate goal: personalized treatment optimization based on a comprehensive array of patient-specific data. This framework can be generalized to time-to-event, Cox proportional hazards regression and multicategorical outcomes. A practical implementation of this method is available upon request in R script, complete with an example to facilitate its application.