{"ok":true,"nota":{"id":"602d4f0d4b4b79a2","slug":"con-65-genes-una-ia-distingue-cancer-de-mama-temprano-de-avanzado-602d4f","titulo":"Con 65 genes, una IA distingue cáncer de mama temprano de avanzado","resumen":"Analizaron datos de expresión génica de 1.075 tumores de mama del proyecto TCGA usando un panel de 65 genes asociados al cáncer. Entrenaron un clasificador de aprendizaje automático para separar estadios tempranos (I-II) de tardíos (III-IV) y estudiaron qué genes aportaban la señal.","fuente":"biorxiv","fuenteNombre":"bioRxiv · preprints","color":"#be2628","autor":"Biswas, S., Huda, T.","url":"https://www.biorxiv.org/content/10.64898/2026.10.18.759962v1?rss=1","imagen":"/hoy/portada/602d4f0d4b4b79a2","tema":"salud","subtemas":["cancer de mama","aprendizaje automatico","expresion genica","pronostico","tcga"],"entidades":["bioRxiv","TCGA","UCSC Xena"],"formato":"dato","entretenimiento":false,"pubTs":"2026-10-22T00:00:00.000Z","indexTs":"2026-10-23T06:48:38.804Z","hash":"2e08fe5ee6d24d9f432f635754bf8cfb1187653524ce5f6b11fa6bb9ae0a5237","tsa":{"time":"Oct 23 06:48:59 2026 GMT","provider":"freetsa.org","sobre":"sello"},"stats":{"views":0,"likes":0,"clicks":0,"dwell":0},"tipo":"normal","registro":"hallazgo","porQueImporta":"El estadio del tumor es uno de los datos que más define el tratamiento y el pronóstico de una paciente. Que el perfil genético del tumor primario contenga esa información abre la puerta a herramientas de apoyo al diagnóstico en el futuro. Sin embargo, el estudio usa una base de datos de Estados Unidos y no prueba nada en pacientes argentinas. Un resultado así necesita validación en otras cohortes antes de cambiar cualquier decisión médica.","quienFirma":"Preprint en bioRxiv (institución principal no identificada en el resumen)","confianza":"preprint","consulta":null},"sello":{"hash":"2e08fe5ee6d24d9f432f635754bf8cfb1187653524ce5f6b11fa6bb9ae0a5237","sello":"2bc5c5fb455213afa50ed4aad84d46fee6f021118dd2070bf8f36c0807ee5532","beacon":{"drand":{"cadena":"8990e7a9aaed2ffed73dbd7092123d6f289930540d7651336225dc172e51b2ce","ronda":6576903,"aleatorio":"8e583525a288ba13cde66d0d6539d282d867cec58f75a0217a855687a27f9649","tiempo":"2026-10-23T06:48:30.000Z"},"nist":{"cadena":2,"pulso":1995651,"salida":"AA82134D649B7B25F10D77FD67053808011261EF94B49E048C931DF7D357E824C95C66604E50CE6E37F934A39EE493177DEA66983586EA3E4667145C3C8919D6","tiempo":"2026-10-23T06:48:00.000Z"},"noAntes":"2026-10-23T06:48:30.000Z"},"verificacion":{"drand":true,"nist":true},"tsa":{"time":"Oct 23 06:48:59 2026 GMT","provider":"freetsa.org","sobre":"sello"},"original":{"titulo":"An Interpretable Machine Learning Framework for Early- versus Late-Stage Classification of Breast Cancer from Gene Expression, with Co-expression Network Analysis","descripcion":"Anatomical stage is a principal determinant of treatment and prognosis in breast cancer, yet it is unclear how much information about stage is carried by the transcriptome of the primary tumour. We asked whether an a priori panel of 65 cancer-related genes can distinguish early-stage (AJCC Stage I-II) from late-stage (Stage III-IV) breast cancer, and which genes contribute to any signal. RNA-seq expression and pathologic stage for The Cancer Genome Atlas breast invasive carcinoma cohort (TCGA-BRCA) were obtained from the UCSC Xena platform. After restricting the data to primary tumours with a known stage, 1,075 tumours were analysed (805 early, 270 late) and split into stratified training (n","pubDate":"2026-10-22","url":"https://www.biorxiv.org/content/10.64898/2026.10.18.759962v1?rss=1"},"indexado":"2026-10-23T06:48:38.804Z"}}