Aim: Kinship analysis in trace amounts and degraded biological samples has consistently posed a challenge in forensic practice. With shorter amplicons and no stutter peak, Insertion/Deletion polymorphisms (InDels) significantly improve kinship analyses of deceased individuals and their potential living relatives. However, room for improvement remains in identifying 2nd-degree and more distant kinships. To address this issue, a kinship analysis workflow based on machine learning (ML) models was proposed.
Methods: Based on multiple kinship parameters including identity-by-state (IBS) scores, k coefficients, proportion identity-by-descent (IBD), and likelihood ratio (LR) values, this pilot study applied a recently validated InDel locus to preliminarily develop an ML workflow for forensic kinship multi-classification.
Results: In the binary classification of 2nd-degree relatives and unrelated pairs, the LR cutoff threshold workflow and the ML workflow achieved a similar accuracy of 0.9194. However, the ML method had a conclusiveness rate (CR) of 1.0, compared to 0.7066 for the LR workflow. In the multiclass task, the LR-based workflow had a macro F1 score of 0.6955/0.5212 and a CR of 0.7375/0.7046 for single and dual thresholds methods, respectively. However, the ML-based workflow showed that the optimal model - feature combination (XGBoost-IBD+LR) could classify all samples conclusively, with a macro F1 score of 0.9020.
Conclusion: In summary, the ML workflow enhanced the kinship analysis efficiency based on the InDel genotyping system by combining multiple parameters, aiming to provide a more flexible and efficient solution for large-scale database screening.
Aim: A disintegrin and metalloproteinase domain 9 (ADAM9) is involved in various human diseases, including cone-rod dystrophy, Alzheimer’s disease, cancer, and viral infections. However, its comprehensive expression profile and therapeutic potential across cancers remain poorly understood.
Methods: Pan-cancer analyses of ADAM9 expression, mutation status, and prognostic value were conducted using The Cancer Genome Atlas (TCGA) and cBioPortal datasets. The differences between cancer and normal tissues, as well as survival associations, were examined. Mutation landscapes were characterized, and in vitro assays were performed in prostate, breast, and lung cancer cell lines treated with increasing concentrations of Thymoquinone Derivative FL12 (TQFL12). ADAM9 messenger RNA (mRNA) and protein levels were determined by quantitative polymerase chain reaction and Western blotting, respectively.
Results:ADAM9 expression was elevated in multiple cancers, but decreased in Kidney chromophobe (KICH) and Thyroid carcinoma (THCA). High ADAM9 expression correlated with longer overall survival (OS) in Colon adenocarcinoma (COAD), while predicting poorer OS in Breast Invasive Carcinoma (BRCA), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC), KICH, Liver hepatocellular carcinoma (LIHC), Brain Lower Grade Glioma (LGG), Mesothelioma (MESO), Pancreatic Adenocarcinoma (PAAD), and Uveal Melanoma (UVM), suggesting its role as an unfavorable prognostic biomarker in cancers. ADAM9 also exhibited frequent mutations, with mutated cases showing improved progression-free and disease-specific survival, implying favorable prognostic relevance. Notably, TQFL12 - a novel compound synthesized in our laboratory - significantly suppressed ADAM9 protein expression in a dose-dependent manner in 22RV1, MDA-MB-231, and H1975 cells without altering mRNA levels, suggesting that the regulatory effect may occur at the post-translational or translational level.
Conclusions: These results highlight ADAM9 as a potential prognostic marker and therapeutic target, while identifying TQFL12 as a promising inhibitor across multiple cancers.
Brain disorders, including neurodegenerative, psychiatric and oncologic disorders, represent a major global public health challenge, yet their underlying pathogenic mechanisms remain incompletely understood. Accumulating evidence suggests that mitochondrial dysfunction, oxidative stress, impaired energy metabolism, and neurotransmitter imbalance contribute to the etiology of these disorders. Restoring mitochondrial and metabolic function emerges as a potential therapeutic strategy. α-ketoglutarate (AKG), a key intermediate in the tricarboxylic acid cycle, plays diverse roles in cellular energy metabolism, amino acid biosynthesis, redox regulation and epigenetic control. Preclinical studies indicate that exogenous AKG supplementation or targeted modulation of AKG-related metabolic pathways can influence mitochondrial homeostasis as well as cellular metabolic and epigenetic states. However, the biological effects of AKG appear to be context dependent, varying across disease states and metabolic conditions. This review synthesizes current evidence on the molecular mechanisms through which AKG regulates mitochondrial, metabolic, and epigenetic processes in the nervous system, highlighting its distinct and sometimes divergent roles across neurological conditions. By integrating findings from diverse disease contexts, this review aims to critically assess the therapeutic potential of targeting AKG-related pathways in brain disorders and to outline key challenges and priorities for future translational research.
Osimertinib, a third-generation, mutant-selective, irreversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, has fundamentally reshaped the treatment landscape of EGFR-mutated non-small cell lung cancer (NSCLC). Originally approved for T790M-positive disease after progression on earlier-generation tyrosine kinase inhibitors (TKIs), osimertinib has since become the standard-of-care first-line therapy for advanced EGFR exon 19 deletion/L858R-positive NSCLC and the first targeted adjuvant therapy in resected early-stage disease. Its superior systemic efficacy, favorable safety profile, and exceptional central nervous system (CNS) penetration distinguish it from all predecessor agents. However, inevitably acquired resistance, driven by heterogeneous on-target tertiary EGFR mutations, off-target bypass pathway activation, and histological transformation, remains the principal clinical challenge. The postosimertinib treatment era is now being shaped by mesenchymal-epithelial transition factor (MET)-targeted combinations, antibody-drug conjugates, EGFR-MET bispecific antibodies, fourth-generation EGFR TKIs, and frontline intensification strategies. This review synthesizes the current evidence on the clinical indications of osimertinib, efficacy and CNS control, resistance mechanisms and their line-dependent patterns, postprogression management algorithms, combination strategies, guideline evolution, and future directions, providing a comprehensive framework for clinical decision-making and research prioritization.
Extraembryonic mesenchymal cells/mesoderm cells (Exmes/EXMC) are mesenchymal-like populations located outside the embryo proper during early amniote development. They line the amnion and yolk sac, form the placental villous core, and build the connecting stalk. They are essential for extraembryonic tissue support, early hematopoiesis and vascular development. Yet their developmental origins and lineage relationships remain partially resolved, particularly across species. In rodents, EXMC arise during gastrulation from newly specified mesoderm emerging from the primitive streak. In primates including humans, by contrast, extraembryonic mesenchymal cells (Exmes) are detectable in the post-implantation but pre-gastrulation conceptus, before the formation of gastrulation-derived mesoderm. Here, focusing on the peri-implantation to early-organogenesis time window (CS3-CS9 in primates; E4.5-E8.5 in rodents), we review histological, genetic, and single-cell multi-omic evidence on the emergence, diversification, and functional specialization of pre-gastrulation Exmes/post-gastrulation EXMC, with particular attention to their roles in yolk sac and placental hematopoiesis, vascularization, and tissue homeostasis. We further discuss insights from non-human primate models and human stem cell-based embryo models that generate Exmes/EXMC-like populations in vitro. Finally, we outline key challenges for resolving the origins of these cells, defining conserved and species-specific regulatory programs, and leveraging Exmes/EXMC biology to understand pregnancy loss, placental disorders, early hematopoietic and vascular development.
Aim: This study aims to identify key genes and regulatory mechanisms associated with benign prostatic hyperplasia (BPH) using expression quantitative trait locus (eQTL)-based Mendelian randomization and integrative bioinformatics analyses.
Methods: Cis-expression quantitative trait locus (cis-eQTL) data were obtained from the eQTLGen Consortium. Genome-wide association study (GWAS) summary statistics for BPH were retrieved from the FinnGen biobank and the IEU OpenGWAS database. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Mendelian randomization and colocalization analyses were performed to prioritize BPH-associated genes. Subsequent analyses included gene set enrichment
Results: A total of 105 and 11 BPH-associated genes were identified in the training and validation datasets, respectively. Colocalization analysis further prioritized five key genes, including C2 (Complement Component 2), GUCY1B2 (Guanylate Cyclase 1 Soluble Subunit Beta 2), OLFM4 (Olfactomedin 4), ITPR1 (Inositol 1,4,5-Trisphosphate Receptor Type 1), and KLHL36 (Kelch Like Family Member 36). Functional analyses indicated that these genes were involved in multiple BPH-related pathways, including tumor protein p53 (p53), mechanistic target of rapamycin (mTOR), transforming growth factor-beta (TGF-β), and interleukin-17 (IL-17) signaling. These genes were also associated with immune cell infiltration, immune-related factors, transcriptional regulation, miRNA interactions, metabolic pathways, and disease-related gene networks.
Conclusion: Five candidate genes associated with BPH were identified, and their potential regulatory mechanisms were characterized through integrative genetic and transcriptomic analyses. These findings provide new insights into the molecular basis of BPH and may inform future biomarker development and mechanism-driven therapeutic strategies, although further experimental validation remains necessary.
Aim: High-quality genomic resources from underrepresented populations are essential for understanding human genetic origins, population structure, and demographic history. The Ewenki, an ethnolinguistic minority mainly inhabiting the high-latitude, cold regions of Northeast China, remain insufficiently characterized at the genome-wide level. This study aimed to investigate the population structure, ancestral composition, and demographic history of the Ewenki to provide insights into human genetic evolution in Northeast Asia.
Methods: We generated genome-wide single-nucleotide polymorphism (SNP) data from 46 Ewenki individuals in Inner Mongolia and merged them with public modern and ancient genomic datasets. Population structure and demographic history were reconstructed through Principal component analysis (PCA), model-based ADMIXTURE, fineSTRUCTURE haplotype clustering, f3/f4 statistics, and qpWave/qpAdm modeling to infer ancestry composition and admixture events.
Results: The Ewenki occupy a genetically distinct position within Northeast Asia and show close genetic affinities with Chinese Mongolic, Tungusic-speaking, and ancient Northeast Asian populations. f3/f4 statistics revealed shared genetic drift and admixture signals linking the Ewenki to ancient Northeast Asian, Siberian, and Yellow River Basin-related populations. qpWave and qpAdm analyses further indicated that the Ewenki can be modeled primarily as a mixture of ancient Northeast Asian/Siberian-related ancestry and ancient Yellow River Basin farmer-related ancestry, reflecting long-term population interactions and admixture in Northeast Asia.
Conclusion: The Ewenki share significant genetic similarities with Tungusic-speaking populations, mainly resulting from admixture between ancient Northeast Asian groups and Yellow River Basin farmers.
Aim: Genetic factors are major contributors to neurodevelopmental disorders such as autism spectrum disorder. Genetically modified animal models are widely used, yet behavioral phenotyping often relies on coarse metrics that may overlook subtle but meaningful abnormalities. Here, using Shank3B knockout (KO) mice as an example, we developed a machine learning-based behavioral analysis pipeline to enhance sensitivity and precision in genotype-related behavioral screening.
Methods: Adult male knockout and wild-type littermates were tested in standard behavioral paradigms, including the three-chamber social test, grooming assay, open field, and elevated plus maze. Videos were processed using markerless pose estimation to extract high-resolution behavioral features. Multidimensional features were analyzed with dimensionality reduction and unsupervised clustering, and genotype discrimination was evaluated using supervised classifiers.
Results:Shank3B KO mice showed reduced sociability and social novelty preference, increased repetitive grooming, reduced exploration, and elevated anxiety-like behavior. Fine-grained behavioral features revealed altered behavioral structure and transition patterns across tasks. Unsupervised clustering consistently separated genotypes into distinct behavioral states, and machine learning classifiers accurately predicted genotype based on behavioral features.
Conclusion: This study demonstrates that fine-scale, machine learning-assisted analysis applied to conventional behavioral tests facilitates the detection of genotype-specific phenotypes. The proposed pipeline provides a scalable framework for more precise behavioral screening of genetically modified models and supports translational studies of neurodevelopmental disorders.
The Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated (CRISPR/Cas) system, with its programmable nucleic acid recognition and trans-cleavage-mediated signal amplification capabilities, has evolved from a gene-editing tool into a core technology for next-generation molecular biosensing. This review systematically elucidates the molecular mechanisms of this technology, focusing on the trans-cleavage activity of key proteins such as Cas12, Cas13, and Cas14, in conjunction with the functional characteristics of tool enzymes like Cas9, and the fluorescence, electrochemical, and colorimetric signal readout strategies they employ. Furthermore, this article provides a detailed summary of the application advancements of CRISPR biosensors in the rapid test of pathogens, including viruses, bacteria, and parasites, and early tumor screening, which encompasses the highly sensitive detection of various nucleic acid biomarkers, including gene mutation, methylation, fusion gene, and microRNA. Furthermore, this review summarizes the technological evolution of device integration, from fully automated, closed microfluidics to portable systems, to achieve point-of-care testing. Finally, the article proposes future development directions, including the integration of this technology with nanomaterials and artificial intelligence. This review aims to provide a systematic reference for the further development and application transformation of CRISPR molecular biosensing technology.