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Identification of immune- and oxidative stress-related signature genes as potential targets for mRNA vaccines for pancreatic cancer patients.
Li, Jiaxu; Han, Yongjiao; Zhao, Ning; Lv, Liping; Ma, Ping; Zhang, Yangyang; Li, Mingyuan; Sun, Hua; Deng, Jiang; Zhang, Yanyu.
Affiliation
  • Li J; Institute of Health Service and Transfusion Medicine, Beijing, PR China.
  • Han Y; Beijing Key Laboratory of Blood Safety and Supply Technologies, Beijing, PR China.
  • Zhao N; College of Biotechnology, Tianjin University of Science & Technology, Tianjin, PR China.
  • Lv L; Institute of Health Service and Transfusion Medicine, Beijing, PR China.
  • Ma P; Beijing Key Laboratory of Blood Safety and Supply Technologies, Beijing, PR China.
  • Zhang Y; College of Biotechnology, Tianjin University of Science & Technology, Tianjin, PR China.
  • Li M; Institute of Health Service and Transfusion Medicine, Beijing, PR China.
  • Sun H; Beijing Key Laboratory of Blood Safety and Supply Technologies, Beijing, PR China.
  • Deng J; Institute of Health Service and Transfusion Medicine, Beijing, PR China.
  • Zhang Y; Beijing Key Laboratory of Blood Safety and Supply Technologies, Beijing, PR China.
Medicine (Baltimore) ; 103(27): e38666, 2024 Jul 05.
Article in En | MEDLINE | ID: mdl-38968513
ABSTRACT
Adenocarcinoma of the pancreas (PAAD) is one of the deadliest malignant tumors, and messenger ribonucleic acid vaccines, which constitute the latest generation of vaccine technology, are expected to lead to new ideas for the treatment of pancreatic cancer. The Cancer Genome Atlas-PAAD and Genotype-Tissue Expression data were merged and analyzed. Weighted gene coexpression network analysis was used to identify gene modules associated with tumor mutational burden among the genes related to both immunity and oxidative stress. Differentially expressed immune-related oxidative stress genes were screened via univariate Cox regression analysis, and these genes were analyzed via nonnegative matrix factorization. After immune infiltration analysis, least absolute shrinkage and selection operator regression combined with Cox regression was used to construct the model, and the usefulness of the model was predicted based on the receiver operating characteristic curve and decision curve analysis curves after model construction. Finally, metabolic pathway enrichment was analyzed using gene set enrichment analysis combined with Kyoto Encyclopedia of Genes and Genomes and gene ontology biological process analyses. This model consisting of the ERAP2, mesenchymal-epithelial transition factor (MET), CXCL9, and angiotensinogen (AGT) genes can be used to help predict the prognosis of pancreatic cancer patients more accurately than existing models. ERAP2 is involved in immune activation and is important in cancer immune evasion. MET binds to hepatocyte growth factor, leading to the dimerization and phosphorylation of c-MET. This activates various signaling pathways, including MAPK and PI3K, to regulate the proliferation, invasion, and migration of cancer cells. CXCL9 overexpression is associated with a poor patient prognosis and reduces the number of CD8 + cytotoxic T lymphocytes in the PAAD tumor microenvironment. AGT is cleaved by the renin enzyme to produce angiotensin 1, and AGT-converting enzyme cleaves angiotensin 1 to produce angiotensin 2. Exposure to AGT-converting enzyme inhibitors after pancreatic cancer diagnosis is associated with improved survival. The 4 genes identified in the present study - ERAP2, MET, CXCL9, and AGT - are expected to serve as targets for messenger ribonucleic acid vaccine development and need to be further investigated in depth.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pancreatic Neoplasms / Oxidative Stress / MRNA Vaccines Limits: Humans Language: En Journal: Medicine (Baltimore) Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pancreatic Neoplasms / Oxidative Stress / MRNA Vaccines Limits: Humans Language: En Journal: Medicine (Baltimore) Year: 2024 Document type: Article