The Mutation Portraits of Oncogenes and Tumor Supressor Genes in Predicting the Overall Survival in Pancreatic Cancer: A Bayesian Network Meta-Analysis

Aryanti, Citra; Uwuratuw, Julianus Aboyaman; Labeda, Ibrahim; Raharjo, Warsinggih; Lusikooy, Ronald Erasio; Rauf, Murny Abdul; Mappincara, Andi; Sampetoding, Samuel; Kusuma, M. Ihwan; Syarifuddin, Erwin

doi:10.31557/APJCP.2023.24.8.2895

The Mutation Portraits of Oncogenes and Tumor Supressor Genes in Predicting the Overall Survival in Pancreatic Cancer: A Bayesian Network Meta-Analysis

Document Type : Research Articles

Authors

¹ Digestive Surgery Training Program, Department of Surgery, Hasanuddin University, Dr. Wahidin Sudirohusodo General Hospital, Makassar, Sulawesi Selatan, Indonesia.

² Division of Digestive Surgery, Department of Surgery, Hasanuddin University, Dr. Wahidin Sudirohusodo General Hospital, Makassar, Sulawesi Selatan, Indonesia.

10.31557/APJCP.2023.24.8.2895

Abstract

Introduction: In pancreatic cancer, the carcinogenesis can not be separated from genetics mutations. The portraits of genes alterations majorily including oncogenes (KRAS, HER2, PD-L1) and tumor supressor genes (P53, CDKN2A, SMAD4). Besides being notorious a screening marker, the genetic mutations were related to the prognosis of pancreatic cancer. The aim of this study is to determine the genetic mutations portrait in predicting the overall survival in pancreatic cancer. Methods: The network meta analysis (NMA) was registered in PROSPERO (CRD42023397976) and conducted in accordance with the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) in addition of NMA extension guidance. Comprehensive searches were done including all studies which reported the overall survival of pancreatic cancer subjects with KRAS, HER2, PD-L1, P53, CDKN2A, SMAD4. Data were collected and analysis will be done based on Bayesian method, Markov Chain Monte Carlo algorithm, using BUGSnet package in R studio. Transivity was controlled by methods and consistency of the NMA will be fitted by deviance information criterion. Data analysis in NMA were presented in Sucra plot, league table, and forest plot. Results: Twenty-four studies were included in this NMA with 4613 total subjects. The NMA was conducted in random-effects, consistent, and convergence model. Relative to control, the genetic mutation of SMAD4 (HR 1.84; 95%CI 1.39-2.46), HER2 (HR 1.76; 95%CI 1.14-2.71), and KRAS (HR 1.7; 95%CI 1.19-2.48) were significant to have worse survival. The mutations of PD-L1, P53, and CDKN2A also showed poor survival, but not statistically significant compared to control. Conclusion: In pancreatic cancer, the mutation of SMAD4 predicted the worst overall survival, compared to control, also mutation of HER2, KRAS, PD-L1, P53, and CDKN2A.

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