An Amplified Fatty Acid-Binding Protein Gene Cluster in Prostate Cancer: Emerging Roles in Lipid Metabolism and Metastasis

SIMPLE SUMMARY: Prostate cancer is the second most common cancer in men. In many cases, prostate cancer grows very slowly and remains confined to the prostate. These localized cancers can usually be cured. However, prostate cancer can also metastasize to other organs of the body, which often results in death of the patient. We found that a cluster of genes involved in accumulation and utilization of fats exists in multiple copies and is expressed at much higher levels in metastatic prostate cancer compared to localized disease. These genes, called fatty acid-binding protein (or FABP) genes, individually and collectively, promote properties associated with prostate cancer metastasis. We propose that levels of these FABP genes may serve as an indicator of prostate cancer aggressiveness, and that inhibiting the action of FABP genes may provide a new approach to prevent and/or treat metastatic prostate cancer. ABSTRACT: Treatment for early stage and localized prostate cancer (PCa) is highly effective. Patient survival, however, drops dramatically upon metastasis due to drug resistance and cancer recurrence. The molecular mechanisms underlying PCa metastasis are complex and remain unclear. It is therefore crucial to decipher the key genetic alterations and relevant molecular pathways driving PCa metastatic progression so that predictive biomarkers and precise therapeutic targets can be developed. Through PCa cohort analysis, we found that a fatty acid-binding protein (FABP) gene cluster (containing five FABP family members) is preferentially amplified and overexpressed in metastatic PCa. All five FABP genes reside on chromosome 8 at 8q21.13, a chromosomal region frequently amplified in PCa. There is emerging evidence that these FABPs promote metastasis through distinct biological actions and molecular pathways. In this review, we discuss how these FABPs may serve as drivers/promoters for PCa metastatic transformation using patient cohort analysis combined with a review of the literature.