RESUMEN
Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.
RESUMEN
Cryptococcus neoformans causes fatal fungal meningoencephalitis. Here, we study the roles played by fungal kinases and transcription factors (TFs) in blood-brain barrier (BBB) crossing and brain infection in mice. We use a brain infectivity assay to screen signature-tagged mutagenesis (STM)-based libraries of mutants defective in kinases and TFs, generated in the C. neoformans H99 strain. We also monitor in vivo transcription profiles of kinases and TFs during host infection using NanoString technology. These analyses identify signalling components involved in BBB adhesion and crossing, or survival in the brain parenchyma. The TFs Pdr802, Hob1, and Sre1 are required for infection under all the conditions tested here. Hob1 controls the expression of several factors involved in brain infection, including inositol transporters, a metalloprotease, PDR802, and SRE1. However, Hob1 is dispensable for most cellular functions in Cryptococcus deuterogattii R265, a strain that does not target the brain during infection. Our results indicate that Hob1 is a master regulator of brain infectivity in C. neoformans.