Research Articles
Lipid Metabolism and Small Extracellular Vesicle Biogenesis in Cancer: Mechanisms, Biomarkers, and Therapeutic Targeting
This article synthesizes current research on the critical interface between lipid metabolism and small extracellular vesicle (sEV) biogenesis in cancer.
Non-Coding RNAs in Hepatocellular Carcinoma: Molecular Classification, Clinical Applications, and Future Therapeutics
Hepatocellular carcinoma (HCC) is a global health challenge with high mortality, often due to late-stage diagnosis and limited treatment options.
Biomolecular Condensates and Protein Aggregates in Human Disease: From Basic Mechanisms to Therapeutic Targeting
This article provides a comprehensive exploration of biomolecular condensates and protein aggregates, highlighting their dual roles in physiological processes and disease pathogenesis.
Protein Side-Chain Rotamers: From Statistical Foundations to AI-Driven Prediction in Drug Discovery
This article provides a comprehensive overview of the statistical conformations of protein side-chain rotamers, a critical field for understanding protein structure and function.
UHPLC-MS/MS Lipidomics in Metabolic Disease: Unraveling Lipid Pathways in Diabetes and Hyperuricemia
Ultra-High-Performance Liquid Chromatography-Tandem Mass Spectrometry (UHPLC-MS/MS) has emerged as a powerful tool for uncovering lipidomic signatures in complex metabolic disorders.
Molecular Underpinnings of Genetic and Rare Diseases: From Genomic Discovery to Precision Therapies
This article provides a comprehensive analysis of the molecular mechanisms, diagnostic technologies, and therapeutic innovations shaping the understanding and treatment of genetic and rare diseases.
Decoding Hypoxia Tolerance: From Molecular Sensors to Therapeutic Innovations
This review synthesizes current research on the physiological and molecular mechanisms of hypoxia tolerance, a critical area for understanding disease pathology and developing therapeutic interventions.
Independent Validation of m6A-Related lncRNA Signatures for Predicting Overall Survival in Cancer
This article provides a comprehensive resource for researchers and drug development professionals on the independent validation of prognostic signatures based on m6A-related long non-coding RNAs (lncRNAs). It covers the foundational biology of m6A-lncRNA interactions, details the methodological pipeline for signature construction and validation from public databases like TCGA and ICGC, addresses common troubleshooting and optimization challenges, and critically reviews validation strategies and comparative performance against other biomarkers. The content synthesizes recent evidence from multiple cancers, including colorectal, pancreatic, and lung adenocarcinoma, to establish best practices for developing clinically applicable prognostic tools that predict overall survival and inform therapeutic responses.
Optimizing m6A-Related lncRNA Prognostic Models: A Comprehensive Guide to Enhancing ROC Curve Performance
This article provides a comprehensive framework for researchers and bioinformaticians aiming to develop and optimize prognostic models based on m6A-related long non-coding RNAs (lncRNAs). It covers the entire pipeline from foundational biology and data acquisition to advanced model construction, performance troubleshooting, and rigorous validation. Focusing specifically on enhancing the predictive accuracy as measured by Receiver Operating Characteristic (ROC) curve analysis, the guide synthesizes current methodologies, including LASSO Cox regression and deep learning approaches, and emphasizes the critical link between model performance and clinical applicability in cancer prognosis and therapeutic response prediction.
Controlling False Discovery Rates in m6A-Related lncRNA Signature Studies: A Guide for Robust Biomarker Development
This article provides a comprehensive guide for researchers and drug development professionals on implementing rigorous false discovery rate (FDR) control in studies of N6-methyladenosine (m6A)-related long non-coding RNA (lncRNA) signatures. As these signatures emerge as powerful prognostic and predictive biomarkers across multiple cancers, including breast, lung, gastric, and colorectal cancers, proper FDR control is paramount for generating translatable findings. We explore the foundational concepts of m6A-lncRNA interactions, detail methodological frameworks for FDR control during signature identification and validation, address common troubleshooting scenarios, and present comparative validation approaches. This resource aims to enhance the reliability and clinical applicability of m6A-lncRNA research by establishing robust statistical standards.