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Análisis integrador multi-plataforma de expresión génica y aprendizaje automático para el descubrimiento de

Haseeb Nisar^1,2, Ali Islam³, Amna Arshad²

¹IRC for Finance and Digital Economy, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.

Biomarkers in medicine

|February 18, 2026

Resumen

Los investigadores identificaron PLOD1 como un gen clave en el glioblastoma (GBM). La alta expresión de PLOD1 se asocia con un mal pronóstico y puede servir como biomarcador diagnóstico para este agresivo tumor cerebral.

Área de la Ciencia:

Oncología
Genómica
Bioinformática

Sus antecedentes:

El glioblastoma (GBM) es un tumor cerebral agresivo con un mal pronóstico.
La identificación de genes críticos es esencial para comprender el desarrollo del glioma.

Objetivo del estudio:

Identificar genes cruciales para el desarrollo del glioma.
Evaluar el potencial diagnóstico y terapéutico de estos genes en el GBM.

Principales métodos:

Análisis integrado de conjuntos de datos de microarrays y RNA-Seq.
Se utilizaron la regresión LASSO y siete clasificadores de aprendizaje automático para la selección de características.
Se validó el rendimiento del clasificador en conjuntos de datos no vistos y biomarcadores mediante qPCR.

Principales resultados:

Se identificaron PLOD1, COL4A2 y COL5A2 como genes clave.
PLOD1 se asoció significativamente con un mal pronóstico del glioblastoma.
La alta expresión de PLOD1 se correlaciona fuertemente con el GBM, lo que indica su potencial como biomarcador diagnóstico.

Conclusiones:

Un enfoque integrador de aprendizaje automático descubrió eficazmente biomarcadores para el GBM.
Los hallazgos respaldan estrategias de tratamiento personalizadas para pacientes con glioblastoma.

Palabras clave:

Glioblastoma Secuenciación de ARN biomarcadores aprendizaje automático microarray

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