A Mechanism-Centered Text-Mining Landscape of Mitochondrial Bioenergetics and Signaling in Disease Research

Mitochondria are increasingly recognized as integrated bioenergetic and signaling hubs across disease contexts, but the rapidly expanding literature remains fragmented across mechanisms, diseases, and analytical vocabularies. This study mapped the disease-oriented literature on mitochondrial bioenergetics and signaling from 2014 to 2025 using a mechanism-centered text-mining framework integrating dictionary-based annotation and topic modeling. Records retrieved from Web of Science, Scopus, and PubMed were harmonized into a final corpus of 166,462 title–abstract records. Dictionary-based annotation was used to identify disease and mitochondrial mechanism signals, followed by disease–mechanism co-occurrence, lift-based enrichment, exploratory drug/compound annotation, non-negative matrix factorization (NMF), and structural topic modeling (STM). Publication output increased approximately 2.6-fold over the study period. The literature was organized around a central mechanistic backbone involving ROS/redox biology, cell death pathways, bioenergetics/OXPHOS, mitochondrial dysfunction/homeostasis, and quality-control processes. Cancer, cardiometabolic/metabolic disease, and neurodegeneration/neurological injury were the dominant disease contexts. Enrichment analysis revealed disease-characteristic mitochondrial signatures, while NMF identified a 20-topic thematic structure and STM showed increasing emphasis on mitochondrial dysfunction, immune-inflammatory signaling, omics-based prognostic signatures, therapeutic delivery systems, and cancer progression/resistance. Overall, mitochondrial disease research is shifting toward an integrated, application-oriented framework in which mitochondria are positioned as bioenergetic, signaling, immune-regulatory, and therapeutic-response hubs.