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논문 기본 정보

자료유형
학술저널
저자정보
Wang Xiu (Department of General Practice, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, P.R. China) Zhang Zhenhu (Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, P.R. China) Shi Yamin (School of Foreign Languages, Shandong University of Finance and Economics, Jinan 250014, P. R. China) Zhang Wenjuan (Department of Surgical, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, P.R. China) Su Chongyi (Department of Emergency, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, P.R. China) Wang Dong (Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, P.R. China)
저널정보
한국미생물생명공학회 Journal of Microbiology and Biotechnology Journal of Microbiology and Biotechnology Vol.34 No.5
발행연도
2024.5
수록면
1,164 - 1,177 (14page)
DOI
10.4014/jmb.2310.10052

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Esophageal squamous cell carcinoma (ESCC) is among the most common malignant tumors of the digestive tract, with the sixth highest fatality rate worldwide. The ESCC-related dataset, GSE20347, was downloaded from the Gene Expression Omnibus (GEO) database, and weighted gene coexpression network analysis was performed to identify genes that are highly correlated with ESCC. A total of 91 transcriptome expression profiles and their corresponding clinical information were obtained from The Cancer Genome Atlas database. A mitochondria-associated risk (MAR) model was constructed using the least absolute shrinkage and selection operator Cox regression analysis and validated using GSE161533. The tumor microenvironment and drug sensitivity were explored using the MAR model. Finally, in vitro experiments were performed to analyze the effects of hub genes on the proliferation and invasion abilities of ESCC cells. To confirm the predictive ability of the MAR model, we constructed a prognostic model and assessed its predictive accuracy. The MAR model revealed substantial differences in immune infiltration and tumor microenvironment characteristics between high- and low-risk populations and a substantial correlation between the risk scores and some common immunological checkpoints. AZD1332 and AZD7762 were more effective for patients in the low-risk group, whereas Entinostat, Nilotinib, Ruxolutinib, and Wnt.c59 were more effective for patients in the high-risk group. Knockdown of TYMS significantly inhibited the proliferation and invasive ability of ESCC cells in vitro. Overall, our MAR model provides stable and reliable results and may be used as a prognostic biomarker for personalized treatment of patients with ESCC

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