Identifying clear cell renal cell carcinoma coexpression networks associated with opioid signaling and survival

Joseph R. Scarpa, Renzo G. DiNatale, Roy Mano, Andrew W. Silagy, Fengshen Kuo, Takeshi Irie, Patrick J. McCormick, Gregory W. Fischer, A. Ari Hakimi, Joshua S. Mincer

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

While opioids constitute the major component of perioperative analgesic regimens for surgery in general, a variety of evidence points to an association between perioperative opioid exposure and longer term oncologic outcomes. The mechanistic details underlying these effects are not well understood. In this study, we focused on clear cell renal cell carcinoma (ccRCC) and utilized RNA sequencing and outcome data from both The Cancer Genome Atlas, as well as a local patient cohort to identify survival-associated gene coexpression networks. We then projected drug-induced transcriptional profiles from in vitro cancer cells to predict drug effects on these networks and recurrence-free, cancer-specific, and overall survival. The opioid receptor agonist, leu-enkephalin, was predicted to have antisurvival effects in ccRCC, primarily through Th2 immune- and NRF2-dependent macrophage networks. Conversely, the antagonist, naloxone, was predicted to have prosurvival effects, primarily through angiogenesis, fatty acid metabolism, and hemopoesis pathways. Eight coexpression networks associated with survival endpoints in ccRCC were identified, and master regulators of the transition from the normal to disease state were inferred, a number of which are linked to opioid pathways. These results are the first to suggest a mechanism for opioid effects on cancer outcomes through modulation of survival-associated coexpression networks. While we focus on ccRCC, this methodology may be employed to predict opioid effects on other cancer types and to personalize analgesic regimens in patients with cancer for optimal outcomes. Significance: This study suggests a possible molecular mechanism for opioid effects on cancer outcomes generally, with implications for personalization of analgesic regimens.

Original languageEnglish
Pages (from-to)1101-1110
Number of pages10
JournalCancer Research
Volume81
Issue number4
DOIs
StatePublished - 15 Feb 2021
Externally publishedYes

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