Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 11 appraisals
Biology of reproduction
Noninvasive pre-transfer identification of women with recurrent implantation failure using a plasma microRNA-based machine learning model
Recurrent implantation failure (RIF) refers to patients who have undergone at least three implantation failures with several high-quality embryos. Identifying patients with RIF in advance is crucial for effective clinical managements. However, uterine biopsy, the primary research approach, is not only invasive but also poses a risk to the integrity of the endometrial microenvironment. Here, we provided a novel, safe, and immediate methodology for RIF populations identification through plasma microRNA (miRNA) profiles. Peripheral blood samples of 163 women were collected and sequenced to quantify the relative expression levels of plasma miRNAs. Through a comparative analysis of the temporal expression between patients with RIF and those with successful embryo implantation during the peri-implantation period, we determined that the RIF-associated miRNA signature was established at the early stage of the window of implantation. By global screening, we identified 217 miRNAs that were differentially expressed between groups on the ovulation day (D0), using a threshold of |fold change| ≥ 1.5. Through refining selection criteria, 10 eligible miRNAs were ultimately selected and further validated through real-time quantitative polymerase chain reaction. These miRNAs were employed to construct a logistic regression-based RIF model. The model demonstrated robust performance in the validation and test cohorts, with area under the curve of 0.947 and 0.912, respectively. We also demonstrated that miRNAs maintain relative stability within a specific phase, making them suitable as clinical biomarkers. This study provides a noninvasive methodology for identifying women with RIF and suggests a potential role in predicting RIF risk before embryo transfer.
16 July 2026
Read appraisal →Gene
Utilization of next generation sequencing from glioma patient liquid biopsy to determine survival prognostics: A systematic review
Glioma is a malignant cancer that affects the central nervous system. Early detection of glioma is still difficult due to the hard-to-reach location of the cancer. The use of next generation sequencing is one of the new developments that can be used for diagnosis, however the use of this modality for prognosis is still rarely discussed. This systematic review aims to determine the benefits of using next generation sequencing (NGS) on the assessment of ctDNA or cfDNA concentration, progression free survival (PFS), and overall survival (OS). This systematic review was made using data from six databases, namely PubMed, PMC, ProQuest, Google Schoolar, ScienceDirect, and SCOPUS, and was compiled based on guidelines from PRISMA. Fourteen studies were included in this systematic review with a total of 663 glioma patients. The results of ctDNA measurements generated through the NGS method with liquid biopsy samples show that the higher the grading and sampling when the tumor progresses can increase the ctDNA value. When assessing ctDNA concentrations at baseline when samples were taken, most studies showed a worse prognosis for PFS and OS survival time. However, the grouping of patients in the current studies is still highly variable so further studies with standardized ctDNA cut off values are needed for more precise determination of prognosis.
7 June 2026
Read appraisal →Medicine
Machine learning identifies a NETs-related four-gene diagnostic signature for abdominal aortic aneurysm
Abdominal aortic aneurysm (AAA) is a chronic degenerative disease characterized by localized aortic dilation and persistent inflammation. While neutrophil extracellular traps (NETs) are increasingly recognized as key drivers of vascular inflammation and aneurysm progression, the transcriptomic landscape of NETs-related genes (NRGs) in AAA remains inadequately characterized. This study aimed to identify reliable diagnostic biomarkers and explore the immune heterogeneity of AAA to facilitate early risk stratification. We integrated transcriptomic datasets from the Gene Expression Omnibus to elucidate the role of dysregulated NRGs. A comprehensive bioinformatics pipeline was employed, combining weighted gene co-expression network analysis with differential expression profiling to screen for AAA-specific NRGs. Rigorous feature selection was conducted through the intersection of 3 machine learning algorithms - least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest - to derive a diagnostic signature. The model was constructed using the GSE232911 cohort and validated in an independent external cohort. A robust 4-gene diagnostic signature comprising CXCR4, GZMB, ITGA6, and CD47 was identified. This signature demonstrated a favorable diagnostic performance, achieving an area under the curve of 0.920 in the training cohort. The model maintained consistent discriminatory ability in the external validation cohort, primarily driven by the high discriminative ability of CXCR4 and GZMB. Consensus clustering based on these hub genes revealed 2 distinct molecular subtypes, with Cluster 2 characterized by significant enrichment of neutrophils and innate immune pathways, suggesting intense NETosis activity. Furthermore, drug prediction analyses identified candidate therapeutic compounds, including Eugenol and Tretinoin, offering potential avenues for targeting the NETs-associated molecular landscape. Our findings underscore the pivotal role of NETs-mediated inflammation in AAA pathogenesis and validate a robust 4-gene signature for early diagnosis. By delineating immune-related molecular subtypes and identifying potential drug candidates, this study provides a foundational framework for precision risk stratification and the development of targeted nonsurgical therapies for aneurysm management.
25 May 2026
Read appraisal →Genome medicine
DNA methylation biomarkers-based pan-cancer classifier: predictive modeling for cancer classification
BACKGROUND: Machine-learning (ML) driven molecular diagnostics based on omics data has a potential to revolutionize personalized medicine. However, implementation of ML into diagnostic protocols is hindered by methodological challenges which often lead to inflated performance assessment of models during development followed by poor performance of these models in implementation phase. Here, we aimed to develop and validate a pan-cancer classification framework based on DNA methylation data, that addresses methodological challenges of omics data powered ML. METHODS: We curated a primary dataset of DNA methylation profiles for 10756 samples, that included 54 healthy and cancer tissue types and validation dataset comprising data for 2306 samples from 28 independent studies. The classification framework was build using custom biomarkers selection strategy based on effect size metric that considers variance and class imbalance. The ML models were trained, tuned and evaluated using nested cross-validation approach. Local outlier factor algorithm was built into the inference pipelines to identify and filter samples displaying technical or biological anomalies. Additionally, for methodological validation of our framework we used methylation profiles for 3905 central nervous system (CNS) tumors. RESULTS: We found that relatively simple ML models outperformed complex algorithms such as deep neural network. A logistic regression classifier achieved a balanced accuracy (BACC) of 0.90 to classify 54 cancer and healthy tissue types using methylation levels at 1208 CpG sites. Similarly, our CNS tumor classifier also based on logistic regression algorithm reached a BACC of 0.94 across 59 CNS tumor subtypes. The anomaly filtering improved performance across all categories of samples tested. CONCLUSIONS: Our study demonstrates that DNA methylation profiling, when combined with carefully controlled ML practices allows for development of robust solutions that might substantially increase the efficacy of oncological diagnosis. Finally, we deployed our inference pipelines for public access via secure web platform - https://opp.pum.edu.pl/ .
21 May 2026
Read appraisal →Clinica chimica acta; international journal of clinical chemistry
AI in the Prediction of Hepatic Fibrosis Progression Using Non-Coding RNAs
Hepatic fibrosis is a dynamic and progressive condition that can lead to cirrhosis and hepatocellular carcinoma (HCC) if left untreated. Appropriate assessment of the disease progression of fibrosis is critical for early intervention and individualized treatment regimens. Traditional biopsy techniques are invasive and prone to sampling errors, highlighting the need for less invasive predictive techniques. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as key regulators of hepatic fibrogenesis and as a possible biomarker for disease staging and prognosis. The emergence of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized the comprehensive large-scale analysis of transcriptomic data, enhancing the identification of ncRNA biomarkers and predictive modeling. The AI-based algorithms have been found to be more precise in anticipating fibrosis progression by means of integrating multi-omics data, ncRNA interaction networks, and by improving non-invasive diagnostic tools. This review involves the analysis of AI and ncRNA research in hepatic fibrosis, highlighting recent discoveries, possible challenges, and future opportunities. We address the necessity of standardization of data and clinical validation, as well as discuss the role of AI in identifying biomarkers of ncRNA, predicting the stage of fibrosis and risk stratification. ncRNA analysis with AI has a tremendous potential of transforming the diagnostics and prognostics of hepatic fibrosis, enabling precision hepatology.
17 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Integrating liquid biopsies and artificial intelligence for early cancer detection: A systematic review and meta-analysis
INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection. METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated. RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity. CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.
16 May 2026
Read appraisal →Signal transduction and targeted therapy
HDAC inhibitor tucidinostat and metronomic capecitabine plus endocrine therapy for patients with HR-positive HER2-negative advanced breast cancer after CDK4/6 inhibitors treatment: clinical findings and exploratory circulating tumor cell and ctDNA biomarker analyses of a multicenter, phase 2 study (SYSUCC-020 trial)
Treatment options for patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer (ABC) that progresses after CDK4/6 inhibitor (CDK4/6i) continue to evolve, and no single regimen has been established as the preferred standard of care. We conducted a phase 2 trial (SYSUCC-020, NCT05411380) to investigate the efficacy and safety of a novel combination of tucidinostat and metronomic capecitabine (mCAP) with endocrine therapy (ET) in patients following progression on CDK4/6i. This trial adopted a Simon two-stage design: eligible patients received tucidinostat plus metronomic capecitabine together with either an aromatase inhibitor (Cohort 1) or fulvestrant (Cohort 2), selected according to prior ET. Seventy-two female patients were screened and sixty-six patients were included in the efficacy and safety analyses. The median follow-up was 13.88 months (Interquartile range, 8.67-20.67) and the objective response rate was 25.8%, with 17 partial responses. The median progression-free survival (PFS) was 5.39 months (95% CI, 4.07-7.69), namely 6.93 months (95% CI, 4.76-12.22) in Cohort 1 and 3.98 months (95% CI, 2.79-7.59) in Cohort 2. Most patients (93.9%) experienced at least one adverse event. In addition, exploratory analyses of biomarkers indicated that the baseline TP53 mutation status (Wild type vs. mutated, 7.64 months vs. 3.55 months) and circulating tumor cell (CTC) status (CTC-negative vs. CTC-positive, 7.59 months vs. 3.78 months) were associated with the median PFS. Our study demonstrated that tucidinostat combined with mCAP and ET is efficacious and well-tolerated in patients with HR-positive HER2-negative ABC previously treated with CDK4/6i.
17 Apr 2026
Read appraisal →HGG advances
Gene-specific pathogenicity predictor for chromatin remodeling BAF complex-associated neurodevelopmental disorders
Advancements in whole-genome sequencing have increased the number of variants of uncertain significance (VUS) identified in human genomes. This has created a diagnostic bottleneck for genetic counselors tasked with sifting through these variants and determining those most likely to be causative for a patient's clinical presentation. Machine learning (ML) tools can aid in identifying pathogenic variants from VUS, but there is a need for gene-specific algorithms that predict pathogenic variants with high accuracy. To address this need, we present a workflow for developing gene-specific, ensemble-learning ML tools, that leverage outputs from other algorithms, locations of variants within the gene, and evolutionary conservation data to make a prediction of pathogenicity. Variants in SMARCA2 and SMARCA4 that are associated with rare neurodevelopmental diseases were used to screen 15 ML algorithms. A random forest learner was tuned to yield a final accuracy of 0.93 on holdout data. Generalizing this predictor to other BRG1/BRM-associated factor (BAF) complex proteins resulted in a sharp decline in performance. We trained a final predictor for all genes in the study to create a predictor that identifies pathogenic variants in these BAF subunits with an accuracy of 0.91 on holdout data. This predictor specific to BAF complex proteins performs with higher accuracy and area under the precision-recall curve than any other predictor. The decline in performance when generalized to other proteins emphasizes the need for the gene-specific calibration of predictors. Our workflow for the development of such models provides a quick, computationally inexpensive route for improving the ML tools available to genetic counselors.
12 Apr 2026
Read appraisal →PLoS medicine
Spatial omics and AI for clinically actionable cancer biomarkers
Integrating spatial omics with artificial intelligence is likely to advance biomarker research and diagnostics, with the potential to pair mechanistic insight into spatial target biology. By developing scalable, reproducible quantification in routine pathology, it can help bridge discovery, validation, and real-world clinical implementation.
11 Apr 2026
Read appraisal →Clinical genetics
Clinical Feasibility of Long-Read WGS for DNA Methylation Signature Analysis
DNA methylation (DNAm) signatures have emerged as valuable diagnostic biomarkers for rare genetic disorders. To date, the most widely used approach for establishing and validating these signatures has relied on array-based technologies. However, in clinical diagnostics, there is a growing shift from short-read sequencing (SRS) toward long-read sequencing (LRS) technologies. Recent advances in platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) enable direct assessment of DNAm from native DNA, offering improved resolution and reduced technical bias compared to array-based technologies. In this study, we compared DNAm profiles generated by LRS with those obtained from DNAm arrays. DNAm profiles of two individuals with pathogenic KMT2D variants were analyzed using DNAm arrays, LRS using PacBio and ONT, and ONT multiplexed sequencing with adaptive sampling. A support vector machine (SVM) classifier trained on array data, as well as the public classification platform EpigenCentral, yielded correct predictions for all LRS samples, underscoring the potential of LRS platforms in DNAm-based diagnostics. Our results suggest that DNAm profiles generated by LRS align well with DNAm signatures established using DNAm arrays, supporting their feasibility in clinical and research applications with the added benefit of simultaneous methylation and sequence analysis.
4 Apr 2026
Read appraisal →Genes (Basel)
The Evolving Role of Artificial Intelligence in Medical Genetics: Advancing Healthcare, Research, and Biosafety Management
The integration of artificial intelligence (AI) with medical genetics is transforming healthcare by addressing the analytical challenges posed by the vast complexity of multi-omics data. This review explores the synergistic convergence of these fields, highlighting AI's transformative role in enhancing diagnostic precision, enabling non-invasive molecular profiling through imaging-genetics, and advancing predictive and personalized medicine via polygenic risk scores and pharmacogenomics. AI is also emerging as a powerful generative tool in therapeutic design, accelerating drug discovery, protein engineering, and precision gene editing. However, this powerful synergy introduces significant ethical, regulatory, and biosecurity challenges, including data privacy, algorithmic bias, and the dual-use risks of AI-enabled genetic engineering. The future envisions a responsible co-evolution, with multimodal AI and the concept of the Digital Twin driving precision medicine, underpinned by interdisciplinary collaboration to ensure fairness, transparency, and societal trust. This article charts the current landscape and proposes actionable directions, emphasizing the need for robust governance to harness AI's potential while mitigating its risks for the benefit of human health.
1 Dec 2025
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