Prostate cancer is the most widely diagnosed cancer among men in developed countries and the second most diagnosed cancer in men worldwide. Current methods fail to reliably discriminate indolent tumours from those with metastatic potential. This uncertainty leads many clinicians and/or patients to choose to complete radical therapy, even when the chance of benefit is small or absent. A potential alternative strategy involves the identification of a ‘field-change’ specifically within benign tissue that is associated with adverse pathological features or clinical outcome.
Increasing evidence over the last decade indicates that altered adipose tissue homeostasis may be an important contributor to the development and/or progression of a number of solid organ tumours, including prostate cancer. Given the potential role of the local fat depot in prostate cancer progression, as well as the ongoing need for new strategies to improve risk stratification at the time of diagnosis, we investigated the possibility that alterations in periprostatic adipose may be predictive for disease risk. We identified an altered immune signature between low and high grade samples. Furthermore, using machine learning on RNAseq data, we identified a three-gene signature that includes IGHA1, OLFM4 and RERGL. Such signature was validated with qRT-PCR leading to the confident stratification of an extended cohort of 59 patients.