Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective

Frontiers in Genetics

Genetics

Mauricio Saez¹  Alberto J. M. Martin²  Verónica Latapiat³  Inti Pedroso⁴ 

[1] Centro de Oncología de Precisión, Facultad de Medicina y Ciencias de la Salud, Universidad Mayor, Santiago, Chile;Laboratorio de Investigación en Salud de Precisión, Departamento de Procesos Diagnósticos y Evaluación, Facultad de Ciencias de la Salud, Universidad Católica de Temuco, Temuco, Chile;Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago, Chile;Escuela de Ingeniería, Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile;Programa de Doctorado en Genómica Integrativa, Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile;Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile;Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago, Chile;Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile;

关键词: personalized medicine;    omics;    transcriptomic;    co-expression;    networks;    diseases;   

DOI  :  10.3389/fgene.2023.1209416

 received in 2023-04-20, accepted in 2023-07-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

This perspective highlights the potential of individualized networks as a novel strategy for studying complex diseases through patient stratification, enabling advancements in precision medicine. We emphasize the impact of interpatient heterogeneity resulting from genetic and environmental factors and discuss how individualized networks improve our ability to develop treatments and enhance diagnostics. Integrating system biology, combining multimodal information such as genomic and clinical data has reached a tipping point, allowing the inference of biological networks at a single-individual resolution. This approach generates a specific biological network per sample, representing the individual from which the sample originated. The availability of individualized networks enables applications in personalized medicine, such as identifying malfunctions and selecting tailored treatments. In essence, reliable, individualized networks can expedite research progress in understanding drug response variability by modeling heterogeneity among individuals and enabling the personalized selection of pharmacological targets for treatment. Therefore, developing diverse and cost-effective approaches for generating these networks is crucial for widespread application in clinical services.

【 授权许可】

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Copyright © 2023 Latapiat, Saez, Pedroso and Martin.