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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
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Predicción de Cáncer Colorrectal Basada en Expresión Génica Mediante Aprendizaje Automático y Análisis SHAP

Yulai Yin1, Zhen Yang1, Xueqing Li1,2

  • 1School of Medicine, Nankai University, Tianjin 300071, China.

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Resumen

Se desarrolló un novedoso modelo de diagnóstico genético para el cáncer colorrectal (CCR) utilizando aprendizaje automático. Este modelo de diez genes muestra un alto rendimiento predictivo, ofreciendo potencial para la detección e intervención tempranas del CCR.

Palabras clave:
cáncer colorrectalgenes diferencialesaprendizaje automáticoaleatorización mendelianamodelo predictivo

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Área de la Ciencia:

  • Genómica
  • Bioinformática
  • Oncología

Sus antecedentes:

  • El cáncer colorrectal (CCR) representa un desafío significativo para la salud mundial.
  • El diagnóstico preciso y temprano es crucial para un tratamiento eficaz y mejores resultados para los pacientes.
  • Los marcadores genéticos ofrecen una vía prometedora para desarrollar herramientas de diagnóstico precisas.

Objetivo del estudio:

  • Desarrollar y validar un modelo de diagnóstico genético robusto para el cáncer colorrectal (CCR).
  • Identificar genes clave asociados con el CCR a través de análisis bioinformáticos integrados.
  • Aprovechar el aprendizaje automático para predecir el riesgo y el diagnóstico del CCR.

Principales métodos:

  • Análisis de expresión génica diferencial utilizando la base de datos TCGA.
  • Análisis de aleatorización mendeliana con datos de eQTL y resultados de CCR.
  • Desarrollo y validación de un modelo de diagnóstico utilizando nueve algoritmos de aprendizaje automático, incluido XGBoost.
  • Selección de genes basada en la expresión diferencial, análisis de aleatorización e importancia del modelo de aprendizaje automático.

Principales resultados:

  • Se identificaron 3716 genes expresados diferencialmente (DEGs) y 121 genes asociados con CCR mediante aleatorización mendeliana.
  • Se estableció una firma final de diez genes (RIF1, GDPD5, DBNDD1, RCCD1, CLDN5, ASCL2, IFITM3, IFITM1, SMPDL3A, SUCLG2).
  • El modelo XGBoost logró un AUC de 0.990, y el modelo final mostró AUCs de 0.9875 (entrenamiento) y 0.9601 (validación).
  • IFITM1 y DBNDD1 se identificaron como los genes más influyentes.

Conclusiones:

  • El perfil de expresión génica en el CCR refleja una mayor proliferación celular, metabolismo y evasión inmune.
  • El modelo de diagnóstico genético de diez genes desarrollado demuestra un fuerte rendimiento predictivo para el CCR.
  • Este modelo tiene un potencial significativo para el diagnóstico temprano, la intervención y las estrategias de prevención de tercer nivel del CCR.