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Gonzalez-Isunza, G.

Publications and source records attributed to Gonzalez-Isunza, G..

2 recordsLinked to original sources

Topological data analysis identifies PURPL as a prognostic gene in breast cancer Luminal A patients

IntroductionLuminal A is the most common breast cancer molecular subtype and is generally associated with a favorable five-year prognosis. However, a substantial subset of patients experience relapse, suggesting that biologically distinct high-risk tumors exist within this subtype. MethodsWe applied Topological Analysis of Array Comparative Genomic Hybridization (TAaCGH) to identify recurrent copy number alterations associated with Luminal A breast cancer. We subsequently evaluated the long non-coding RNA PURPL using copy number, gene expression, and survival data in the independent TCGA, METABRIC, and SCAN-B cohorts. Associations with clinical outcome were examined using multivariable Cox regression and TP53-stratified analyses. ResultsPURPL copy number gain was associated with significantly worse survival in TCGA and METABRIC, while elevated PURPL expression was associated with significantly worse survival in TCGA and SCAN-B. Elevated PURPL expression remained associated with survival after adjustment for clinical covariates. In analyses stratified by TP53 mutation status, high PURPL expression remained associated with worse survival in TP53 wild-type tumors. Although PURPL has previously been implicated in cancer-related mechanisms, its copy number alteration, expression, and association with clinical outcome have not been systematically evaluated in Luminal A breast cancer. ConclusionThese findings identify PURPL as a candidate lncRNA within a recurrent copy number alteration associated with adverse clinical outcome in Luminal A breast cancer and support further mechanistic investigation of its role in this subtype.

cancer biology↗

Using machine learning to detect coronaviruses potentially infectious to humans

Establishing the host range for novel viruses remains a challenge. Here, we address the challenge of identifying non-human animal coronaviruses that may infect humans by creating an artificial neural network model that learns from the binding of the spike protein of alpha and beta coronaviruses to their host receptor. The proposed method produces a human-Binding Potential (h-BiP) score that distinguishes, with high accuracy, the binding potential among human coronaviruses. Two viruses, previously unknown to bind human receptors, were identified: Bat coronavirus BtCoV/133/2005 (a MERS related virus) and Rhinolophus affinis coronavirus isolate LYRa3 a SARS related virus. We further analyze the binding properties of these viruses using molecular dynamics. To test whether this model can be used for surveillance of novel coronaviruses, we re-trained the model on a set that excludes SARS-COV-2 viral sequences. The results predict the binding of SARS-CoV-2 with a human receptor, indicating that machine learning methods are an excellent tool for the prediction of host expansion events.

microbiology↗