Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal interstitial lung disease characterized by excessive extracellular matrix (ECM) deposition and tissue stiffening. Matrix stiffness is a key driver of fibrosis, yet diagnostic biomarkers directly linked to this physical property are lacking. This study aimed to identify robust matrix stiffness-related diagnostic biomarkers and potential therapeutic targets for IPF using an integrated machine learning approach. Gene expression profiles were obtained from the GEO database (Training set: GSE33566; Validation set: GSE93606). Differentially expressed genes (DEGs) were intersected with a matrix stiffness-related gene set. Three machine learning algorithms (SVM-RFE, LASSO, and Naive Bayes) were employed to screen diagnostic feature genes. A diagnostic nomogram was constructed and evaluated. Functional enrichment (GO/KEGG/GSEA), immune infiltration (ssGSEA), and molecular docking analyses were performed to explore biological functions and predict therapeutic drugs. Eighteen matrix stiffness-related DEGs were identified. Through machine learning screening, GSN and ARG1 were determined as robust key genes, exhibiting high diagnostic accuracy (AUC>0.7) in both training and validation cohorts. Functional analysis revealed that GSN is involved in actin cytoskeleton regulation, while ARG1 participates in immune response modulation. Both genes showed strong positive correlations with the infiltration of macrophages and neutrophils. Furthermore, molecular docking identified RA-2 as a potential therapeutic agent targeting ARG1 with high binding affinity (-9.2kcal/mol). We identified GSN and ARG1 as novel matrix stiffness-related diagnostic biomarkers for IPF, linking mechanotransduction to immune microenvironment remodeling. The diagnostic nomogram offers high clinical predictive value, and RA-2 emerged as a putative ARG1-targeting compound with favorable docking energy and warrants further experimental validation as a potential antifibrotic agent.

The increasing incidence of adolescent depression represents a serious public health concern. Despite clear diagnostic criteria, the wide range of symptoms and their overlap with other psychiatric disorders make it difficult to provide effective personalized treatment in adolescents. The integration of resting-state functional magnetic resonance imaging (rs-fMRI) and machine learning has shown promise in identifying diagnostic biomarkers and shedding light on personalized treatments in adult depression. However, equivalent studies in adolescent depression are lacking. Therefore, the present study aimed to identify diagnostic rs-fMRI biomarkers for adolescent depression. Phenotypic and rs-fMRI data of 127 adolescents (64 adolescents with depression; 63 healthy controls) were acquired from the Boston Adolescent Neuroimaging of Depression and Anxiety dataset. Partial correlation was used to compute the functional connectome of the whole brain. Repeated nested cross validation with Boruta feature selection and support vector machine was employed to build a classification model to discriminate adolescents with depression from healthy controls. The classification model identified 46 fine-scale connectivity features of the functional connectome as co-biomarkers in adolescent depression. The connectivity between the right medial/superior temporal gyrus and left pars triangularis/rostral middle frontal gyrus, as well as between the right medial orbitofrontal/ rostral anterior cingulate cortex and right precuneus/isthmus cingulate gyrus were identified as the most important features in adolescent depression. The identification of a novel neuroimaging composite-biomarker panel here sheds light on depression diagnosis in adolescence. The retention of anatomical resolution within these composite biomarkers may facilitate the development of individualized neuromodulation treatment strategies.

Exosome-related lactylation gene signature defines diagnostic biomarkers of periodontitis through integrative bulk and single-cell transcriptomics.

Microbial crosstalk along the oral-gut axis: organ-specific oncogenic adaptations of Porphyromonas gingivalis and Fusobacterium nucleatum.

Pancreas-brain axis: exploring the pathogenic mechanisms of pancreatic encephalopathy.

Albumin as a glycoprotein biomarker in congenital disorders of glycosylation.

Mitochondrial energy metabolism-associated Predictive model constriction for coronary artery disease(CAD) of acute myocardial infarction(AMI) patients via integrative bioinformatic analysis.

Superoxide dismutase impacts extracellular vesicle shedding and uptake.

Βreast cancer (BC) is the most common malignant tumor among women. Its significant heterogeneity and complex molecular mechanisms pose major clinical challenges, including limited therapeutic efficacy and drug resistance. Recently, microRNAs (miRs) have been recognized as key post‑transcriptional regulators involved in tumorigenesis and tumor progression through multiple pathways. Among these, the miR‑200 family (miR‑200a, miR‑200b, miR‑200c, miR‑429 and miR‑141) has attracted considerable attention due to its pivotal role in BC. The present review systematically summarizes the genomic characteristics, expression regulation mechanisms and biological functions of the miR‑200 family in BC. Special emphasis is given to their roles in epithelial‑mesenchymal transition, cell proliferation, apoptosis, maintenance of stemness, and remodeling of the tumor microenvironment. Furthermore, members of the miR‑200 family have potential as diagnostic and prognostic biomarkers and are closely linked to chemotherapy resistance. The present review aims to provide novel insights and a theoretical foundation for the diagnosis, treatment, and deeper investigation of BC by comprehensively examining the functional mechanisms of the miR‑200.

Exploring miRNAs in Leishmania infection: from immune modulation to theranostic potential.

MIR31HG Promotes the Onset and Progression of Peri-Implantitis by Regulating the miR-641 Levels.

From prostate specific antigen to genomic signatures: Advances in biomarkers for prostate cancer diagnosis and prognosis.

Multi-omics profiling of ACOX3 unveils pan-cancer clinical biomarker potential.

Artificial intelligence and multi-omics convergence in breast cancer: Revolutionizing diagnosis, prognostication, and precision oncology.

Recent advances in understanding the pathogenesis, diagnosis, and treatment of tuberous sclerosis complex (TSC)-associated Lymphangioleiomyomatosis.

The online version contains supplementary material available at 10.1007/s13205-026-04720-3.

Plasma untargeted lipidomics based on UHPLC-Orbitrap-MS reveals potential biomarkers and the pathogenesis involved in Mycoplasma bovis pneumonia.

Ovulation-derived extracellular vesicles exhibit sustained oncogenic influence on the exposed fallopian tube fimbrial cells after drainage into peritoneal cavity.

tRF-Gly-CCC-012 enhances malignant process in pancreatic cancer via the HNRNPC/PHGDH axis.

m6A-metabolite axes in depression: METTL14 network dysregulation and RBM15 lipid protection.