hsa_circ_0003222 accelerates stemness and progression of non-small cell lung cancer by sponging miR-527.
Changhui LiJia-Qi ZhangXiaohua YangCheng HuTianqing ChuRunbo ZhongYinchen ShenFang HuFeng PanJianlin XuJun LuXiaoxuan ZhengHai ZhangWei NieBao-Hui HanXueyan ZhangPublished in: Cell death & disease (2021)
The relationship between circular RNA (circRNA) and cancer stem cells (CSCs) is uncertain. We have investigated the combined influence of CSCs, circRNA (hsa_circ_0003222), and immune checkpoint inhibitors in NSCLC progression and therapy resistance. We constructed lung CSCs (LCSCs; PC9 and A549). The effects of hsa_circ_0003222 in vitro were determined by cell counting, colony and sphere formation, and Transwell assays. A tumor xenograft model of metastasis and orthotopic model were built for in vivo analysis. We found that hsa_circ_0003222 was highly expressed in NSCLC tissues and LCSCs. Higher levels of hsa_circ_0003222 were associated with the stage, metastasis, and survival rate of patients with NSCLC. Reduced levels of hsa_circ_0003222 decreased tumor cell proliferation, migration, invasion, stemness-like properties, and chemoresistance. The silencing of hsa_circ_0003222 was found to downregulate PHF21B expression and its downstream, β-catenin by relieving the sponging effect of miR-527. Moreover, silencing hsa_circ_0003222 alleviated NSCLC resistance to anti-programmed cell death-ligand 1 (PD-L1)-based therapy in vivo. Our data demonstrate the significant role of hsa_circ_0003222 in NSCLC cell stemness-like properties. The manipulation of circRNAs in combination with anti-PD-L1 therapy may alleviate NSCLC stemness and progression.
Keyphrases
- cancer stem cells
- small cell lung cancer
- cell proliferation
- advanced non small cell lung cancer
- stem cells
- epithelial mesenchymal transition
- cell therapy
- long non coding rna
- brain metastases
- gene expression
- single cell
- poor prognosis
- signaling pathway
- wastewater treatment
- long noncoding rna
- big data
- mass spectrometry
- epidermal growth factor receptor
- bone marrow
- mesenchymal stem cells
- machine learning
- single molecule