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南京医科大学学报(自然科学版)                                  第43卷第7期
               ·954 ·                     Journal of Nanjing Medical University(Natural Sciences)   2023年7月


             ·临床研究·

              m1A相关基因构建的胰腺癌预后模型的综合分析



              时 坚 ,刘雪昂 ,朱 岩 ,胡 冉 ,马文静 ,朱 毅                     1,2*
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                                               4
                              1,2
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                     1,2
               南京医科大学第一附属医院胰腺中心,江苏                南京    210029;南京医科大学胰腺研究所,江苏            南京   210029;南京医科大
              1                                               2                                      3
              学第一附属医院病理科,公共实验中心,江苏 南京                   210029
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             [摘    要] 目的:基于 m1A 相关基因在多个数据库中差异表达情况与临床参数建立新的胰腺癌风险评估模型,为胰腺癌治
              疗和预后分析提供理论依据。方法:通过分析 m1A 相关基因在 TCGA⁃GTEx 数据库中的差异表达,利用 Cox 分析与 LASSO 回
              归构建胰腺癌预后风险模型,搭配 GEO 数据库和本中心胰腺癌病例的基因表达和临床数据验证该模型的准确性与敏感性。
              结果:筛选出CRLS1与C7orf50两个基因共同构成风险模型,本研究系统验证了该模型有预测胰腺癌预后的显著效能,该模型
              联合肿瘤突变负荷(tumor mutation burden,TMB)具有更强的预后预测能力。结论:该模型是预测胰腺癌患者预后的新工具。
             [关键词] 胰腺癌;m1A;风险模型;预后
             [中图分类号] R735.9                    [文献标志码] A                       [文章编号] 1007⁃4368(2023)07⁃954⁃12
              doi:10.7655/NYDXBNS20230708



              Comprehensive analysis of pancreatic cancer prognostic model constructed by m1A
              related genes

                     1,2           1,2        3       4           3       1,2*
              SHI Jian ,LIU Xue’ang ,ZHU Yan ,HU Ran ,MA Wenjing ,ZHU Yi
              1                                                                               2
               Pancreas Centre,the First Affiliated Hospital of Nanjing Medical University,Nanjing 210029;Pancreas Institute,
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              Nanjing Medical University,Nanjing 210029;Department of Pathology,Public Laboratory Center,the First
              Affiliated Hospital of Nanjing Medical University,Nanjing 210029,China

             [Abstract] Objective:To establish a new pancreatic cancer risk assessment model based on the differential expression of m1A⁃related
              genes in multiple databases and clinical parameters,and provide theoretical basis for pancreatic cancer treatment and prognosis
              analysis. Methods:Differential analysis of m1A⁃related genes was performed in the TCGA⁃GTEx database. Cox analysis and LASSO
              regression were used to construct a pancreatic cancer prognosis risk model,and the accuracy and sensitivity of the model were verified
              by gene expression and clinical data from the GEO database and pancreatic cancer cases in our center. Results:CRLS1 and C7orf50
              were selected to form the risk model. The study systematically verified that the model had significant efficacy in indicating the
              prognosis of pancreatic cancer,and the model combined with tumor mutation burden(TMB)had a stronger prognostic indication
              ability. Conclusion:A new risk model is constructed to indicate the prognosis of pancreatic cancer patients.
             [Key words] pancreatic cancer;m1A;risk model;prognosis
                                                                            [J Nanjing Med Univ,2023,43(07):954⁃965]




                  胰腺癌(pancreatic cancer,PC)是一种恶性程度              略,预后往往较差,即使是经过根治性手术治疗的
              极高的消化道恶性肿瘤,发病率和病死率不断上                             胰腺癌患者仍会在 5 年内出现肿瘤复发 。因此,
                                                                                                     [1]
              升,已成为我国第六大致死原因。胰腺癌起病隐                             迫切需要开发新的预后评估方法来预测患者的临
              匿,易复发和转移,由于缺乏有效的预测和治疗策                            床预后。
                                                                     研究发现,不同生物体的RNA中存在超过160种

             [基金项目] 江苏省“六大人才高峰”人才项目(WSW⁃032)                    转录后 RNA 修饰,其中包括非编码 RNA(如核糖体
                                                                                                        [2]
              ∗                                                 RNA、转运 RNA 和核内小 RNA 等)和 mRNA 。这
              通信作者(Corresponding author),E⁃mail:zhuyijssry@126.com
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