Low-Intensity Focused Ultrasound Targeted Microbubble Destruction Enhanced Paclitaxel Sensitivity by Decreasing Autophagy in Paclitaxel-Resistant Ovarian Cancer.

Abstract

Ultrasound targeted microbubble destruction (UTMD) was introduced as a promising method to improve anti-tumor therapeutic efficacy, while minimizing side effects to healthy tissues. Nevertheless, the acoustical phenomenon behind the UTMD as well as the exact mechanisms of autophagy action involved in the increased anti-cancer response are still not fully understood. Therefore, we examined the drug resistance-reversing effects of low-intensity focused ultrasound with microbubble (LIFU+MB) in paclitaxel (PTX)-resistant ovarian cancer cells. Cell viability was evaluated using CCK8 (Cell Counting Kit-8), apoptosis was detected by flow cytometry, quantitative real-time PCR and Western blot were used to detect the expressions of mRNA and protein, and autophagy was observed by transmission electron microscopy (TEM). We revealed that the level of autophagy was increased (p < 0.05) in PTX-resistant ovarian cancer cells. Treatment of LIFU+MB combined with PTX can notably inhibit proliferation as well as increase apoptosis (p < 0.01) in drug-resistant cells. We proposed that LIFU+MB might affect the sensitivity of ovarian cancer cells to PTX by modulating autophagy. To verify the hypothesis, we analyzed the autophagy level of drug-resistant cells after the treatment of LIFU+MB and found that autophagy was significantly inhibited. Altogether, our findings demonstrated that LIFU+MB could reverse PTX resistance in ovarian cancer via inhibiting autophagy, which provides a novel strategy to improve chemosensitivity in ovarian cancer.

• Authors: Gonglin Fan, Jiale Qin, Xiaofeng Fu, Xing Si, Liqiang Li, Keji Yang, Beibei Wang, Haiya Lou, Jiang Zhu
• Journal: Frontiers in oncology (Front Oncol) Vol. 12 Pg. 823956 ( 2022) ISSN: 2234-943X [Print] Switzerland
• PMID: 35574313 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Fan, Qin, Fu, Si, Li, Yang, Wang, Lou and Zhu.