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A Novel Necroptosis-Related miRNA Signature for Predicting the Prognosis of Breast Cancer Metastasis. DISEASE MARKERS 2022; 2022:3391878. [PMID: 35371342 PMCID: PMC8975690 DOI: 10.1155/2022/3391878] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/03/2022] [Accepted: 03/07/2022] [Indexed: 12/12/2022]
Abstract
Objective Necroptosis was recently identified as a form of programmed cell death that plays an essential role in breast cancer metastasis. MicroRNAs (miRNAs) have long been recognized to affect cell death and tumor growth. In this study, we aimed to screen for necroptosis-associated miRNAs that predict breast cancer metastasis. Method This study used The Cancer Genome Atlas (TCGA) public database to obtain miRNA expression data and associated clinical data from breast cancer patients and then retrieved miRNA data related to necrosis and apoptosis. Next, using Cox regression model analysis (univariate or multivariate) as well as a comparison analysis (differential analysis), a prognostic multi-miRNA molecular marker was established. Finally, prognosis-related miRNAs were utilized to identify target genes, and the functions of the target genes were analyzed for enrichment to investigate the probable mechanisms of the miRNAs. Results Ten miRNAs were screened through differential analysis to build models: hsa-miR-148a-3p, hsa-miR-223-3p, hsa-miR-331-3p, has-miR-181a-5p, hsa-miR-181b-5p, hsa-miR-181c-5p, hsa-miR-181d-5p, hsa-miR-200a-5p, hsa-miR-141-3p, and hsa-miR-425-5p. The multivariate Cox regression model was an independent prognostic factor (univariate Cox regression results: HR = 3.2642, 95%CI = 1.5773 − 6.7554, P = 0.0014; multivariate Cox regression results: HR = 3.1578, 95%CI = 1.5083 − 6, P = 0.0023). The survival curve of the risk score also revealed that patients with a high risk score had a poor prognosis (P = 2e − 04). The receiver operating characteristic (ROC) curve showed that the model has a certain prediction ability. Batch survival analysis of the miRNAs in the model was conducted and showed that hsa-miR-331-3p (P = 0.0182) was strongly associated with prognosis. Twenty-three predicted target genes were obtained, and Gene Ontology (GO) enrichment analysis showed that these target genes were strongly enriched in transcriptional initiation and cell membrane trafficking. Conclusion Our research identified a novel miRNA marker for predicting breast cancer patient prognosis and lays the groundwork for future research on necroptosis-related genes.
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Zhao C, Yang Y, Cui X, Shan Y, Xue J, Jiang D, Sun J, Li N, Li Z, Yang A. Self-Powered Electrical Impulse Chemotherapy for Oral Squamous Cell Carcinoma. MATERIALS (BASEL, SWITZERLAND) 2022; 15:2060. [PMID: 35329513 PMCID: PMC8954269 DOI: 10.3390/ma15062060] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 02/27/2022] [Accepted: 03/06/2022] [Indexed: 01/13/2023]
Abstract
Oral squamous cell carcinoma (OSCC) is a common oral cancer of the head and neck, which causes tremendous physical and mental pain to people. Traditional chemotherapy usually results in drug resistance and side effects, affecting the therapy process. In this study, a self-powered electrical impulse chemotherapy (EIC) method based on a portable triboelectric nanogenerator (TENG) was established for OSCC therapy. A common chemotherapeutic drug, doxorubicin (DOX), was used in the experiment. The TENG designed with zigzag structure had a small size of 6 cm × 6 cm, which could controllably generate the fixed output of 200 V, 400 V and 600 V. The electrical impulses generated by the TENG increased the cell endocytosis of DOX remarkably. Besides, a simply and ingeniously designed microneedle electrode increased the intensity of electric field (EF) between two adjacent microneedle tips compared with the most used planar interdigital electrode at the same height, which was more suitable for three-dimensional (3D) cells or tissues. Based on the TENG, microneedle electrode and DOX, the self-powered EIC system demonstrated a maximal apoptotic cell ratio of 22.47% and a minimum relative 3D multicellular tumor sphere (MCTS) volume of 160% with the drug dosage of 1 μg mL-1.
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Affiliation(s)
- Chaochao Zhao
- Department of Biomedical Engineering, School of Medicine, Foshan University, Foshan 528225, China; (C.Z.); (J.S.); (N.L.)
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China; (Y.Y.); (X.C.); (Y.S.); (J.X.); (D.J.); (Z.L.)
| | - Yuan Yang
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China; (Y.Y.); (X.C.); (Y.S.); (J.X.); (D.J.); (Z.L.)
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 101400, China
| | - Xi Cui
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China; (Y.Y.); (X.C.); (Y.S.); (J.X.); (D.J.); (Z.L.)
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 101400, China
| | - Yizhu Shan
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China; (Y.Y.); (X.C.); (Y.S.); (J.X.); (D.J.); (Z.L.)
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 101400, China
| | - Jiangtao Xue
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China; (Y.Y.); (X.C.); (Y.S.); (J.X.); (D.J.); (Z.L.)
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Dongjie Jiang
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China; (Y.Y.); (X.C.); (Y.S.); (J.X.); (D.J.); (Z.L.)
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 101400, China
| | - Jinyan Sun
- Department of Biomedical Engineering, School of Medicine, Foshan University, Foshan 528225, China; (C.Z.); (J.S.); (N.L.)
| | - Na Li
- Department of Biomedical Engineering, School of Medicine, Foshan University, Foshan 528225, China; (C.Z.); (J.S.); (N.L.)
| | - Zhou Li
- Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China; (Y.Y.); (X.C.); (Y.S.); (J.X.); (D.J.); (Z.L.)
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 101400, China
| | - Anping Yang
- Department of Biomedical Engineering, School of Medicine, Foshan University, Foshan 528225, China; (C.Z.); (J.S.); (N.L.)
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