bioRxivBioinformaticsPreprint
Prognostic and biological roles of Parkinson's disease genes in cancer
Sara Fiume, Francesca Molinari, Benedetta VaiiD, Sara PolettiiD, Giovanni Citterio, Ferdinando FiumaraiD, Stéphanie PapiniD, Paolo PaganettiiD, Maurizio CallariiD, Luca ColnaghiiD
Abstract
BackgroundIncreasing evidence suggests significant associations between Parkinson disease (PD) and cancer risks, with epidemiological studies revealing a complex relationship. PD patients exhibit lower risks of lung, genitourinary, and gastrointestinal cancers but higher risks of melanoma and brain cancers. Despite these observations, the underlying mechanisms between PD and cancers are poorly understood. ObjectivesWe aimed to identify molecular signatures that could provide insight into this complex connection by assessing the association of PD-related genes with patient survival and the cancer-specific co-expression networks they are involved in. MethodsTo explore this, we analyzed transcriptomic data from 18 cancer types in the TCGA dataset (n=6,088) and 16 cancer types in the DepMap dataset (n=682). We focused on seven genes causally implicated in PD (SNCA, PINK1, LRRK2, PRKN/PARK2, PARK7, GBA1, and ATP13A2) and conducted in silico analyses, to evaluate their associations with survival and correlation with genes, pathways and response to drugs in the context of cancer. ResultsOur findings revealed that the expression levels of the genes correlated with overall survival in a cancer-specific manner, often influenced by the TP53 genetic status. These genes were also associated with key cancer hallmarks such as genomic instability and cell proliferation. Additionally, novel associations were identified linking these genes to drug responses in a context-specific manner. ConclusionsThis study suggests that PD and cancer may be linked by biological pathways, sometimes associated with cancer hallmarks. These findings provide potential insights into druggable targets and the shared molecular mechanisms underlying PD and cancer.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
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