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Secretome analysis of cancer-associated fibroblasts from prostate cancer to identify potential therapeutic targets

Abikar, A.; Mohammad, M. M. S.; Athalye, R. R.; Nadig, N.; Tamboli, N.; Babu, V.; Ramaiah, K.; Ranganathan, P.

2024-12-12 cancer biology
10.1101/2024.12.09.627457 bioRxiv
Show abstract

Tumor microenvironment (TME) is a complex entity comprising of several cell types secreted factors as well as an extracellular matrix. A dynamic interaction between tumor cells and their environment profoundly influences tumor survival, aggressiveness, and progression. Cancer- associated fibroblasts (CAFs) are one of the major cellular components of TME and serve as a major source of various secreted factors. These factors are known to modulate tumor survival and progression, as well as their response to therapy. Despite the importance of the TME factors on various aspects of tumor cell behavior, to date factors unique to CAFs that could be potential therapeutic targets are not identified in most systems. This study was aimed at identifying such factors from CAFs which may impact tumor behavior such as the ability to metastasize, response to therapy, relapse, etc. This would aid in identifying therapeutic targets originating from the TME. Furthermore, targeting those factors along with conventional chemotherapeutic drugs is likely to enhance the overall efficacy of the therapy. This study has used fibroblasts derived from Benign Prostatic Hyperplasia (BPH) and prostate cancer for comparing the secretome using a quantitative proteomics approach. 66 proteins unique to CAFs and 24 unique to control (BPH) fibroblasts have been identified. Besides 236 proteins are differentially expressed between control and cancer- associated fibroblasts. Using in-silico approaches the potential processes that may be influenced by the differentially expressed proteins have also been identified. This study has identified both qualitative and quantitative differences between the secretomes of normal and cancer-associated fibroblasts with further validation, this paves the way for identifying therapeutic targets.

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