Search Results (116)

Introduction: Early-onset colorectal cancer (EOCRC) is rising rapidly, particularly among the Hispanic/Latino (H/L) populations, who face disproportionately poor outcomes. The transforming growth factor-beta (TGF-β) signaling pathway plays a critical role in colorectal cancer (CRC) progression by mediating epithelial-to-mesenchymal transition (EMT), immune evasion, and metastasis. However, integrative analyses linking TGF-β alterations to clinical features remain limited—particularly for diverse populations—hindering translational research and the development of precision therapies. To address this gap, we developed AI-HOPE-TGFbeta (Artificial Intelligence agent for High-Optimization and Precision Medicine focused on TGF-β), the first conversational artificial intelligence (AI) agent designed to explore TGF-β dysregulation in CRC by integrating harmonized clinical and genomic data via natural language queries. Methods: AI-HOPE-TGFbeta utilizes a large language model (LLM), Large Language Model Meta AI 3 (LLaMA 3), a natural language-to-code interpreter, and a bioinformatics backend to automate statistical workflows. Tailored for TGF-β pathway analysis, the platform enables real-time cohort stratification and hypothesis testing using harmonized datasets from the cBio Cancer Genomics Portal (cBioPortal). It supports mutation frequency comparisons, odds ratio testing, Kaplan–Meier survival analysis, and subgroup evaluations across race/ethnicity, microsatellite instability (MSI) status, tumor stage, treatment exposure, and age. The platform was validated by replicating findings on the SMAD4, TGFBR2, and BMPR1A mutations in EOCRC. Exploratory queries were conducted to examine novel associations with clinical outcomes in H/L populations. Results: AI-HOPE-TGFbeta successfully recapitulated established associations, including worse survival in SMAD4-mutant EOCRC patients treated with FOLFOX (fluorouracil, leucovorin and oxaliplatin) (p = 0.0001) and better outcomes in early-stage TGFBR2-mutated CRC patients (p = 0.00001). It revealed potential population-specific enrichment of BMPR1A mutations in H/L patients (OR = 2.63; p = 0.052) and uncovered MSI-specific survival benefits among SMAD4-mutated patients (p = 0.00001). Exploratory analysis showed better outcomes in SMAD2-mutant primary tumors vs. metastatic cases (p = 0.0010) and confirmed the feasibility of disaggregated ethnicity-based queries for TGFBR1 mutations, despite small sample sizes. These findings underscore the platform’s capacity to detect both known and emerging clinical–genomic patterns in CRC. Conclusions: AI-HOPE-TGFbeta introduces a new paradigm in cancer bioinformatics by enabling natural language-driven, real-time integration of genomic and clinical data specific to TGF-β pathway alterations in CRC. The platform democratizes complex analyses, supports disparity-focused investigation, and reveals clinically actionable insights in underserved populations, such as H/L EOCRC patients. As a first-of-its-kind system studying TGF-β, AI-HOPE-TGFbeta holds strong promise for advancing equitable precision oncology and accelerating translational discovery in the CRC TGF-β pathway. Full article

Medulloblastoma (MB) is a malignant brain tumor that requires intense multimodal treatment. There is significant treatment-related morbidity associated with MB, and overall prognosis varies between the subgroups of the disease. These tumors were previously risk-stratified based solely on histopathological features. However, advancements in oncologic molecular research have led to novel changes to MB tumor classification, which also affects the prognosis and treatment strategies for individual patients. The WHO CNS5 now recognizes four main molecular subgroups of MB. Each subgroup contains its own genomic heterogeneity that correlates with a unique way to risk stratify patients, determine overall prognosis, and inform treatment. These discoveries have already impacted the implications and outcomes of current treatments based on the subgroup of patients. Ongoing research to better understand this classification system has paved the way for the development of molecular targeted therapy. Full article

Prostate cancer (PCa) is the second most common cancer and the second leading cause of cancer-related mortality among men. Gene expression analysis has been crucial in understanding tumor biology and providing disease progression markers. Cell surface glycoproteins and those in the extracellular matrix play significant roles in the PCa microenvironment by promoting migration, invasion, and metastasis. The molecular and histopathological heterogeneity of prostate tumors necessitates a new marker discovery to better stratify patients at risk for poor prognosis. In this study, our objectives were to investigate and characterize the localization and expression of SLIT/ROBO in PCa samples from transgenic mice and human tumor samples, aiming to identify novel prognostic markers and potential therapeutic targets. We conducted histopathological, immunohistochemical, and bioinformatics analyses on prostate tumors from two knockout mice models (Pb-Cre4/Pten^f/f and Pb-Cre4/Trp53^f/f;Rb1^f/f) and human prostate tumors. Transcriptomic analyses revealed special changes in the expression of genes related to the SLIT/ROBO neural signaling pathway. We further characterized the gene and protein expression of the SLIT/ROBO pathway in knockout animal samples, and protein expression in the PCa samples of patients with different Gleason scores. Public datasets with clinical data from patients (The Human Protein Atlas, cBioPortal, SurvExpress and CamcAPP) were used to validate the gene and protein expression of SLIT1, SLIT2, ROBO1, and ROBO4, correlating these alterations with the prognosis of subgroups of patients. Our findings highlight potential biomarkers of the SLIT/ROBO pathway with prognostic and predictive value, as well as promising therapeutic targets for PCa. Full article

Background and Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by joint inflammation and pain. Metabolomics approaches, which are high-throughput profiling of small molecule metabolites in plasma or serum in RA patients, have so far provided biomarker discovery in the literature for clinical subgroups, risk factors, and predictors of treatment response using classical statistical approaches or machine learning models. Despite these recent developments, an explainable artificial intelligence (XAI)-based methodology has not been used to identify RA metabolomic biomarkers and distinguish patients with RA. This study constructed a XAI-based EBM model using global plasma metabolomics profiling to identify metabolites predictive of RA patients and to develop a classification model that can distinguish RA patients from healthy controls. Materials and Methods: Global plasma metabolomics data were analysed from RA patients (49 samples) and healthy individuals (10 samples). SMOTE technique was used for class imbalance in data preprocessing. EBM, LightGBM, and AdaBoost algorithms were applied to generate a discriminatory model between RA and controls. Comprehensive performance metrics were calculated, and the interpretability of the optimal model was assessed using global and local feature descriptions. Results: A total of 59 samples were analysed, 49 from RA patients, and 10 from healthy subjects. The EBM generated better results than LightGBM and AdaBoost by attaining an AUC of 0.901 (95% CI: 0.847–0.955) with 87.8% sensitivity which helps prevent false negative early RA diagnosis. The primary biomarkers EBM-based XAI identified were N-acetyleucine, pyruvic acid, and glycerol-3-phosphate. EBM global explanation analysis indicated that elevated pyruvic acid levels were significantly correlated with RA, whereas N-acetyleucine exhibited a nonlinear relationship, implying possible protective effects at specific concentrations. Conclusions: This study underscores the promise of XAI and evidence-based medicine methodology in developing biomarkers for RA through metabolomics. The discovered metabolites offer significant insights into RA pathophysiology and may function as diagnostic biomarkers or therapeutic targets. Incorporating EBM methodologies integrated with XAI improves model transparency and increases the therapeutic applicability of predictive models for RA diagnosis/management. Furthermore, the transparent structure of the EBM model empowers clinicians to understand and verify the reasoning behind each prediction, thereby fostering trust in AI-assisted decision-making and facilitating the integration of metabolomic insights into routine clinical practice. Full article

Background and Objectives: The pancreas receives blood through a complex network of multiple branches, primarily originating from the celiac trunk (CeT) and the superior mesenteric artery (SMA). This blood supply is structured into three main arterial groups, each serving different regions of the pancreas to effectively support its endocrine and exocrine functions. Materials and Methods: The databases Medline, Scopus, Web of Science, Google Scholar, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Latin American and the Caribbean Literature in Health Sciences (LILACS) were searched until January 2025. Methodological quality was evaluated using an assurance tool for anatomical studies (AQUA). Pooled prevalence was estimated using a random effects model. Results: A total of sixteen studies met the established selection criteria in this study for meta-analysis. Pancreatic irrigation variants presented a prevalence of 11.2% (CI: 7–14%) and a heterogeneity of 88.2%. The other studies were analyzed by subgroups, showing statistically significant differences in the following subgroups: (1) sample type—a larger sample of images analyzed in the included studies (p = 0.312), which did not show statistically significant differences; (2) geographical region (p = 0.041), which showed a greater presence in the Asian population studied, and this was statistically significant; and (3) sex (male or female) (p = 0.12), where there were no statistically significant differences. Conclusions: The discovery of variations in pancreatic irrigation is common due to the numerous blood vessels involved in supplying this vital organ. Understanding different vascular patterns (such as those from the splenic and mesenteric arteries) is crucial for surgical interventions on the pancreas. For transplant patients, a thorough vascular analysis of both the donor and recipient is essential. Variations can impact blood flow and compatibility, potentially leading to transplant rejection if not addressed. To enhance outcomes, it is recommended to develop more accurate imaging tools for pre-surgical analysis, necessitating ongoing research in this area. Full article

The Patient Rule Induction Method is a data mining technique used for identifying patterns in datasets, particularly focusing on discovering regions of the chosen input space where the response variable is unusually high or low. It falls in the subgroup discovery field, where finding small groups is more relevant for the explainability of the results, although it is not a classification technique, per se. In this paper, we introduce a new framework for breast cancer classification based on the PRIM. This new method involves, first, the random choice of different input spaces for each class label; second, the organization and pruning of the rules using metarules; and finally, it also includes the proposition of a way to handle the class overlapping and, hence, define the final classifier. The framework is tested on five real-life breast cancer datasets compared to three often-used algorithms for breast cancer classification: XG Boost, Logistic Regression, and Random Forest. Across the four metrics and datasets, both our PRIM-based framework and Random Forest demonstrate robust performance, with our framework showing notable accuracy and recall. XGBoost maintains strong F1-scores across the board, indicating balanced precision and recall. On the other hand, Logistic Regression, while competent, generally underperforms compared to the other algorithms, especially in terms of accuracy and recall, achieving 94.1% accuracy against 96.8% and 85.4% recall against 94.2% for the PRIM-based framework on the Wisconsin dataset. Full article

The immune system plays a crucial role in the pathogenesis of neurodegenerative diseases. Here, we explored whether blood immune cell profiles are already altered in healthy individuals with a genetic predisposition to amyotrophic lateral sclerosis (ALS) or Alzheimer’s disease (AD). Using multicolor flow cytometry, we analyzed 92 immune cell phenotypes in the blood of 448 healthy participants from the Berlin Aging Study II. We calculated polygenic risk scores (PGSs) using genome-wide significant SNPs from recent large genome-wide association studies on ALS and AD. Linear regression analyses were then performed of the immune cell types on the PGSs in both the overall sample and a subgroup of older participants (>60 years). While we did not find any significant associations between immune cell subtypes and ALS and AD PGSs when controlling for the false discovery rate (FDR = 0.05), we observed several nominally significant results (p < 0.05) with consistent effect directions across strata. The strongest association was observed with CD57+ CD8+ early-memory T cells and ALS risk (p = 0.006). Other immune cell subtypes associated with ALS risk included PD-1+ CD8+ and CD57+ CD4+ early-memory T cells, non-classical monocytes, and myeloid dendritic cells. For AD, naïve CD57+ CD8+ T cells and mature NKG2A+ natural killer cells showed nominally significant associations. We did not observe major immune cell changes in individuals at high genetic risk of ALS or AD, suggesting they may arise later in disease progression. Additional studies are required to validate our nominally significant findings. Full article

Background: Despite antihypertensive treatment, some high-risk hypertensive patients still experience major adverse cardiovascular events (MACEs). Current risk stratification tools may underestimate the presence of metabolites in hypertension and thereby risk of MACEs. Objectives: We aimed to explore the potential value of gut microbiota-derived metabolite phenylacetylglutamine (PAGln) in risk stratification of hypertension. Methods: We measured plasma PAGln levels using liquid chromatography tandem mass spectrometry in 1543 high-risk hypertensive patients, dividing them into a discovery cohort (n = 792) and a validation cohort (n = 751). After follow-up, the Kaplan–Meier curve and the Cox regression model were utilized to determine the correlation between PAGln and MACEs (death, non-fatal ischemic stroke and hemorrhagic stroke, non-fatal acute coronary syndrome and unplanned revascularization). We examined the predictive performance of PAGln in different subgroups and evaluated the incremental predictive value of PAGln as an addition to the ASCVD risk assessment model. Results: Among all high-risk hypertensive patients, 148 patients experienced MACEs after a mean follow-up of 3.02 years. In both cohorts, after adjusting other confounding risk factors, PAGln remained an independent risk factor the MACEs in hypertensive patients. Patients with plasma PAGln ≥ 1.047 μmol/L have a higher risk of MACEs. PAGln concentration provided incremental predictive value to the ASCVD risk model, with better performance in the discovery cohort. It was most effective in female, patients with a systolic blood pressure (SBP) ≥ 130 mmHg and taking angiotensin-converting enzyme inhibitors (ACEIs). Conclusions: PAGln was associated with an increased risk of MACEs in hypertension, especially in women or in subgroups with SBP ≥ 130 mmHg and taking ACEIs. PAGln should be considered as an independent predictor in risk stratification to improve prognosis. Full article

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide. The discovery of specific driver mutations has revolutionized the treatment landscape of oncogene-addicted NSCLC through targeted therapies, significantly improving patient outcomes. However, immune checkpoint inhibitors (ICIs) have demonstrated limited effectiveness in this context. Emerging evidence, though, reveals significant heterogeneity among different driver mutation subgroups, suggesting that certain patient subsets may benefit from ICIs, particularly when combined with other therapeutic modalities. In this review, we comprehensively examine the current evidence on the efficacy of immunotherapy in oncogene-addicted NSCLC. By analyzing recent clinical trials and preclinical studies, along with an overview of mechanisms that may reduce immunotherapy efficacy, we explored potential strategies to address these challenges, to provide insights that could optimize immunotherapy approaches and integrate them effectively into the treatment algorithm for oncogene-addicted NSCLC. Full article

Aortic aneurysm, the pathological dilatation of the aorta at distinct locations, can be attributed to many different genetic and environmental factors. The resulting pathobiological disturbances generate a complex interplay of processes affecting cells and extracellular molecules of the tunica interna, media and externa. In short, aortic aneurysm can affect processes involving the extracellular matrix, lipid trafficking/atherosclerosis, vascular smooth muscle cells, inflammation, platelets and intraluminal thrombus formation, as well as various endothelial functions. Many of these processes are interconnected, potentiating one another. Newer discoveries, including the involvement of odorant olfactory receptors in these processes, have further shed light on disease initiation and pathology. Olfactory receptors are a varied group of G protein coupled-receptors responsible for the recognition of chemosensory information. Although they comprise many different subgroups, some of which are not well-characterized or identified in humans, odorant olfactory receptors, in particular, are most commonly associated with recognition of olfactory information. They can also be ectopically localized and thus carry out additional functions relevant to the tissue in which they are identified. It is thus the purpose of this narrative review to summarize and present pathobiological processes relevant to the initiation and propagation of aortic aneurysm, while also incorporating evidence associating these ectopically functioning odorant olfactory receptors with the overall pathology. Full article

Melanoma is one of the most malignant cancers, and the global incidence of cutaneous melanoma is increasing. While melanomas are highly prone to metastasize if diagnosed late, early detection and treatment significantly reduce the risk of mortality. Identifying patients at higher risk of metastasis, who might benefit from early adjuvant therapies, is particularly important, especially with the advent of new melanoma treatments. Therefore, there is a pressing need to develop additional prognostic biomarkers for melanoma to improve early stratification of patients and accurately identify high-risk subgroups, ultimately enabling more effective personalized treatments. Recent advances in melanoma therapy, including targeted treatments and immunotherapy, have underscored the importance of biomarkers in determining prognosis and predicting treatment response. The clinical application of these markers holds the potential for significant advancements in melanoma management. Various tumor-derived genetic, proteomic, and cellular components are continuously released into the bloodstream of cancer patients. These molecules, including circulating tumor DNA and RNA, proteins, tumor cells, and immune cells, are emerging as practical and precise liquid biomarkers for cancer. In the current era of effective molecular-targeted therapies and immunotherapies, there is an urgent need to integrate these circulating biomarkers into clinical practice to facilitate personalized treatment. This review highlights recent discoveries in circulating melanoma biomarkers, explores the challenges and potentials of emerging technologies for liquid biomarker discovery, and discusses future directions in melanoma biomarker research. Full article

Machine-learning algorithms have made significant strides, achieving high accuracy in many applications. However, traditional models often need large datasets, as they typically peel substantial portions of the data in each iteration, complicating the development of a classifier without sufficient data. In critical fields like healthcare, there is a growing need to identify and analyze small yet significant subgroups within data. To address these challenges, we introduce a novel classifier based on the patient rule-induction method (PRIM), a subgroup-discovery algorithm. PRIM finds rules by peeling minimal data at each iteration, enabling the discovery of highly relevant regions. Unlike traditional classifiers, PRIM requires experts to select input spaces manually. Our innovation transforms PRIM into an interpretable classifier by starting with random input space selections for each class, then pruning rules using metarules, and finally selecting definitive rules for the classifier. Tested against popular algorithms such as random forest, logistic regression, and XG-Boost, our random PRIM-based classifier (R-PRIM-Cl) demonstrates comparable robustness, superior interpretability, and the ability to handle categorical and numeric variables. It discovers more rules in certain datasets, making it especially valuable in fields where understanding the model’s decision-making process is as important as its predictive accuracy. Full article

Subgroup discovery (SD) is a data mining technique that allows us to obtain the properties of each element given a particular population; these properties are of interest for a specific study, finding the most important or significant subgroups of the population. Also, the larger the population, the more successful the analysis and the creation of the subgroups, since, on this basis, the possibility of finding more unusual characteristics among the elements of the population is greater. The principal purpose of SD is not to obtain a predictive function, but to achieve a result that users can comprehend and interpret easily, and at the same time provide a more complete and suggestive description of the data. In this paper, we present an application of this technique to the medical field to analyze the opinions of physicians on the decreasing rates of autopsies in Mexican hospitals, utilizing five SD algorithms. The results obtained are the rules that allow for the comparison of medical opinions in three hospitals. Full article

Flavonoid alkaloids represent an interesting subgroup of the alkaloid family. Several plants containing flavonoid alkaloids are used in folk medicine for the treatment of various diseases. The interesting biological properties of flavonoid alkaloids make them attractive candidates for lead compounds in drug discovery. Capitavine, or 5,7-dihydroxy-6-(1-methylpiperidin-2-yl)flavone, and related compounds, belong to piperidine–flavonoid alkaloids, possessing a piperidine ring connected to the C6-position of flavonoid skeleton, while buchenavianine is C8 piperidine-bonded analog. Capitavine derivatives were isolated mainly from Buchenavia capitata, while buchenavianine derivatives are present mainly in B. macrophylla. It was found that the chloroform extract of the leaves of B. capitata showed anti-HIV activity. The biological activity of capitavine and buchenavianine derivatives needs to be investigated in terms of their pharmacokinetic properties and toxicity, which are important factors in finding potential drug candidates. The present in silico study using SwissADME, Osiris, and Molinspiration software shows that studied capitavine-derived flavonoid alkaloids exhibit considerable bioactivity for the GPCR ligand (0.12 to 0.20), as enzyme inhibitors (0.17 to 0.22) and as nuclear receptor ligands (0.07 to 0.28). All compounds exhibit good gastrointestinal absorption and low risks of being irritants, tumorigenic, or having a reproductive effect. The risk of mutagenicity was calculated for two compounds related to buchenavianine, and at this point the role of 5-methoxy group appears to be crucial for the low risk of mutagenicity. Full article

Since the new guidelines for endometrial cancer risk classification have been published, many reviews have proposed a critical re-evaluation. In this review, we look back to how the molecular classification system was built and its evolution in time to highlight the major flaws, particularly the biases stemming from the inherent limitations of the cohorts involved in the discoveries. A significant drawback in some cohorts is the inclusion criteria, as well as the retrospective nature and the notably sparse numbers, especially in the POLEmut (nonsynonymous mutation in EDM domain of POLE) risk groups, all of which impact the reliability of outcomes. Additionally, a disregard for variations in follow-up duration leads to a non-negligible bias, which raises a substantial concern in data interpretation and guideline applicability. Finally, according to the results that we obtained through a re-analysis of the confirmation cohort, the p53abn (IHC positive for p53 protein) subgroup, which is predominant in non-endometrioid histology (73–80%), loses its predictivity power in the endometrioid cohort of patients. The exclusion of non-endometrioid subtypes from the cohort led to a complete overlap of three molecular subgroups (all except POLEmut) for both overall and progression-free survival outcomes, suggesting the need for a more histotype-specific approach. In conclusion, this review challenges the current ESGO/ESTRO/ESP guidelines on endometrial cancer risk classification and highlights the limitations that must be addressed to better guide the clinical decision-making process. Full article