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Deboever N, Jones CM, Yamashita K, Ajani JA, Hofstetter WL. Advances in diagnosis and management of cancer of the esophagus. BMJ 2024; 385:e074962. [PMID: 38830686 DOI: 10.1136/bmj-2023-074962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Esophageal cancer is the seventh most common malignancy worldwide, with over 470 000 new cases diagnosed each year. Two distinct histological subtypes predominate, and should be considered biologically separate disease entities.1 These subtypes are esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC). Outcomes remain poor regardless of subtype, with most patients presenting with late stage disease.2 Novel strategies to improve early detection of the respective precursor lesions, squamous dysplasia, and Barrett's esophagus offer the potential to improve outcomes. The introduction of a limited number of biologic agents, as well as immune checkpoint inhibitors, is resulting in improvements in the systemic treatment of locally advanced and metastatic esophageal cancer. These developments, coupled with improvements in minimally invasive surgical and endoscopic treatment approaches, as well as adaptive and precision radiotherapy technologies, offer the potential to improve outcomes still further. This review summarizes the latest advances in the diagnosis and management of esophageal cancer, and the developments in understanding of the biology of this disease.
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Affiliation(s)
- Nathaniel Deboever
- Department of Thoracic and Cardiovascular Surgery, MD Anderson Cancer Center, Houston, TX, USA
| | - Christopher M Jones
- Early Cancer Institute, Department of Oncology, University of Cambridge, Cambridge, UK
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Kohei Yamashita
- Department of Gastrointestinal Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Jaffer A Ajani
- Department of Gastrointestinal Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Wayne L Hofstetter
- Department of Thoracic and Cardiovascular Surgery, MD Anderson Cancer Center, Houston, TX, USA
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Li WT, Jin X, Song SJ, Wang C, Fu C, Jiang W, Bai J, Shi ZZ. Blocking SLC7A11 attenuates the proliferation of esophageal squamous cell carcinoma cells. Anim Cells Syst (Seoul) 2024; 28:237-250. [PMID: 38741950 PMCID: PMC11089935 DOI: 10.1080/19768354.2024.2346981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
The role of ferroptosis-associated gene SLC7A11 in esophageal cancer progression is largely unknown, therefore, the effects of blocking SLC7A11 on esophageal squamous cell carcinoma (ESCC) cells are evaluated. Results showed that SLC7A11 was overexpressed in ESCC tissues both in mRNA and protein levels. Blocking SLC7A11 using Erastin suppressed the proliferation and colony formation of ESCC cells, decreased cellular ATP levels, and improved ROS production. Sixty-three SLC7A11-binding proteins were identified using the IP-MS method, and these proteins were enriched in four signaling pathways, including spliceosome, ribosome, huntington disease, and diabetic cardiomyopathy. The deubiquitinase inhibitors PR-619, GRL0617, and P 22077 could reduce at least 40% protein expression level of SLC7A11 in ESCC cells, and PR-619 and GRL0617 exhibited suppressive effects on the cell viability and colony formation ability of KYSE30 cells, respectively. Erastin downregulated GPX4 and DHODH and also reduced the levels of β-catenin, p-STAT3, and IL-6 in ESCC cells. In conclusion, SLC7A11 was overexpressed in ESCC, and blocking SLC7A11 using Erastin mitigated malignant phenotypes of ESCC cells and downregulated key ferroptosis-associated molecules GPX4 and DHODH. The therapeutic potential of targeting SLC7A11 should be further evaluated in the future.
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Affiliation(s)
- Wen-Ting Li
- Medical School, Kunming University of Science and Technology, Kunming, People’s Republic of China
| | - Xin Jin
- Medical School, Kunming University of Science and Technology, Kunming, People’s Republic of China
| | - Sheng-Jie Song
- Medical School, Kunming University of Science and Technology, Kunming, People’s Republic of China
| | - Chong Wang
- Medical School, Kunming University of Science and Technology, Kunming, People’s Republic of China
| | - Chuang Fu
- Medical School, Kunming University of Science and Technology, Kunming, People’s Republic of China
| | - Wen Jiang
- Department of Thoracic Surgery, The First People's Hospital of Yunnan Province & The Affiliated Hospital of Kunming University of Science and Technology, Kunming, People’s Republic of China
| | - Jie Bai
- Medical School, Kunming University of Science and Technology, Kunming, People’s Republic of China
| | - Zhi-Zhou Shi
- Medical School, Kunming University of Science and Technology, Kunming, People’s Republic of China
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Jiang H, Chen R, Li Y, Hao C, Song G, Hua Z, Li J, Wang Y, Wei W. Performance of Prediction Models for Esophageal Squamous Cell Carcinoma in General Population: A Systematic Review and External Validation Study. Am J Gastroenterol 2024; 119:814-822. [PMID: 38088388 PMCID: PMC11062607 DOI: 10.14309/ajg.0000000000002629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 11/30/2023] [Indexed: 02/02/2024]
Abstract
INTRODUCTION Prediction models for esophageal squamous cell carcinoma (ESCC) need to be proven effective in the target population before they can be applied to population-based endoscopic screening to improve cost-effectiveness. We have systematically reviewed ESCC prediction models applicable to the general population and performed external validation and head-to-head comparisons in a large multicenter prospective cohort including 5 high-risk areas of China (Fei Cheng, Lin Zhou, Ci Xian, Yang Zhong, and Yan Ting). METHODS Models were identified through a systematic review and validated in a large population-based multicenter prospective cohort that included 89,753 participants aged 40-69 years who underwent their first endoscopic examination between April 2017 and March 2021 and were followed up until December 31, 2022. Model performance in external validation was estimated based on discrimination and calibration. Discrimination was assessed by C-statistic (concordance statistic), and calibration was assessed by calibration plot and Hosmer-Lemeshow test. RESULTS The systematic review identified 15 prediction models that predicted severe dysplasia and above lesion (SDA) or ESCC in the general population, of which 11 models (4 SDA and 7 ESCC) were externally validated. The C-statistics ranged from 0.67 (95% confidence interval 0.66-0.69) to 0.70 (0.68-0.71) of the SDA models, and the highest was achieved by Liu et al (2020) and Liu et al (2022). The C-statistics ranged from 0.51 (0.48-0.54) to 0.74 (0.71-0.77), and Han et al (2023) had the best discrimination of the ESCC models. Most models were well calibrated after recalibration because the calibration plots coincided with the x = y line. DISCUSSION Several prediction models showed moderate performance in external validation, and the prediction models may be useful in screening for ESCC. Further research is needed on model optimization, generalization, implementation, and health economic evaluation.
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Affiliation(s)
- Hao Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ru Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Yanyan Li
- Cancer Center, Feicheng People's Hospital, Feicheng, China
| | - Changqing Hao
- Department of Endoscopy, Linzhou Cancer Hospital, Linzhou, China
| | - Guohui Song
- Department of Epidemiology, Cancer Institute/Hospital of Ci County, Handan, China
| | - Zhaolai Hua
- Cancer Institute of Yangzhong City/People's Hospital of Yangzhong City, Yangzhong, China
| | - Jun Li
- Cancer Prevention and Treatment Office, Yanting Cancer Hospital, Mianyang, China
| | - Yuping Wang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Wenqiang Wei
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
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Cui Y, Wu Y, Zhu Y, Liu W, Huang L, Hong Z, Zhang M, Zheng X, Sun G. The possible molecular mechanism underlying the involvement of the variable shear factor QKI in the epithelial-mesenchymal transformation of oesophageal cancer. PLoS One 2023; 18:e0288403. [PMID: 37428781 DOI: 10.1371/journal.pone.0288403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 06/26/2023] [Indexed: 07/12/2023] Open
Abstract
OBJECTIVE Based on the GEO, TCGA and GTEx databases, we reveal the possible molecular mechanism of the variable shear factor QKI in epithelial mesenchymal transformation (EMT) of oesophageal cancer. METHODS Based on the TCGA and GTEx databases, the differential expression of the variable shear factor QKI in oesophageal cancer samples was analysed, and functional enrichment analysis of QKI was performed based on the TCGA-ESCA dataset. The percent-spliced in (PSI) data of oesophageal cancer samples were downloaded from the TCGASpliceSeq database, and the genes and variable splicing types that were significantly related to the expression of the variable splicing factor QKI were screened out. We further identified the significantly upregulated circRNAs and their corresponding coding genes in oesophageal cancer, screened the EMT-related genes that were significantly positively correlated with QKI expression, predicted the circRNA-miRNA binding relationship through the circBank database, predicted the miRNA-mRNA binding relationship through the TargetScan database, and finally obtained the circRNA-miRNA-mRNA network through which QKI promoted the EMT process. RESULTS Compared with normal control tissue, QKI expression was significantly upregulated in tumour tissue samples of oesophageal cancer patients. High expression of QKI may promote the EMT process in oesophageal cancer. QKI promotes hsa_circ_0006646 and hsa_circ_0061395 generation by regulating the variable shear of BACH1 and PTK2. In oesophageal cancer, QKI may promote the production of the above two circRNAs by regulating variable splicing, and these circRNAs further competitively bind miRNAs to relieve the targeted inhibition of IL-11, MFAP2, MMP10, and MMP1 and finally promote the EMT process. CONCLUSION Variable shear factor QKI promotes hsa_circ_0006646 and hsa_circ_0061395 generation, and downstream related miRNAs can relieve the targeted inhibition of EMT-related genes (IL11, MFAP2, MMP10, MMP1) and promote the occurrence and development of oesophageal cancer, providing a new theoretical basis for screening prognostic markers of oesophageal cancer patients.
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Affiliation(s)
- Yishuang Cui
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, China
- Department of Hebei Key Laboratory of Medical-Industrial Integration Precision Medicine, Tangshan, Hebei Province, China
| | - Yanan Wu
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, China
- Department of Hebei Key Laboratory of Medical-Industrial Integration Precision Medicine, Tangshan, Hebei Province, China
| | - Yingze Zhu
- Department of Hebei Key Laboratory of Medical-Industrial Integration Precision Medicine, Tangshan, Hebei Province, China
- School of Clinical Medicine, North China University of Science and Technology, Tangshan, Hebei Province, China
- Affiliated Hospital, North China University of Science and Technology, Tangshan, Hebei Province, China
| | - Wei Liu
- Department of Hebei Key Laboratory of Medical-Industrial Integration Precision Medicine, Tangshan, Hebei Province, China
- School of Clinical Medicine, North China University of Science and Technology, Tangshan, Hebei Province, China
- Affiliated Hospital, North China University of Science and Technology, Tangshan, Hebei Province, China
| | - Lanxiang Huang
- Department of Hebei Key Laboratory of Medical-Industrial Integration Precision Medicine, Tangshan, Hebei Province, China
- School of Clinical Medicine, North China University of Science and Technology, Tangshan, Hebei Province, China
- Affiliated Hospital, North China University of Science and Technology, Tangshan, Hebei Province, China
| | - Ziqian Hong
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, China
- Department of Hebei Key Laboratory of Medical-Industrial Integration Precision Medicine, Tangshan, Hebei Province, China
| | - Mengshi Zhang
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, China
- Department of Hebei Key Laboratory of Medical-Industrial Integration Precision Medicine, Tangshan, Hebei Province, China
| | - Xuan Zheng
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, China
- Department of Hebei Key Laboratory of Medical-Industrial Integration Precision Medicine, Tangshan, Hebei Province, China
| | - Guogui Sun
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, China
- Department of Hebei Key Laboratory of Medical-Industrial Integration Precision Medicine, Tangshan, Hebei Province, China
- School of Clinical Medicine, North China University of Science and Technology, Tangshan, Hebei Province, China
- Affiliated Hospital, North China University of Science and Technology, Tangshan, Hebei Province, China
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