Detailed Abstract
[BP Oral Presentation 3]
[BP OP 3-4] Development and validation of a robust predictive scoring system for Pancreatic ductal adenocarcinoma patients with glucose metabolic disorder: (The mGMRS Score)
Zijian ZHANG*1, Yuyuan CHEN1, Jian XU1, Xing HAO2, Qi ZHOU3, Di TANG1
1Department of General Surgery, The Seventh Affiliated Hospital of Sun yat-Sen University, China
2The First Clinical Medical College, Xinxiang Medical University, China
3Department of General Surgery, Huiya Hospital of The First Affiniated Hospital, Sun yat-Sen University, China
Introduction : Pancreatic ductal adenocarcinoma(PDAC) is one of the most deadly cancers worldwide. Most of cancer cells are irregularly highly dependent on glucose metabolism. And some of epidemiological research showed the positive relationship between Type II diabetes and PDAC. However, the glucose related prognostic biomarkers are still being explored.
Methods : Our study established a impoved scoring system that can predict PDAC patients’ prognosis. RNA expression data of tumor tissues from TCGA, ICGC and GEO were explored. An Optimal subset-LASSO Cox regression combined model was used to choose prognostic genes.
Results : A 3-mRNA signature that can distinguish high modified Glucose Metabolic Risk Score(mGMRS) patients from low risk patients with significant differences in overall survival and progression-free survival (OS and PFS). We further validated this scoring system using both internal data and two independent cohorts and found that mGMRS remained significant in different subgroups. Multivariate cox regression analysis showed that mGMRS can be an independent prognostic marker in all cohorts. Receiver operating characteristic curve and C-index analysis showed that mGMRS performed a better prognostic efficiency. Moreover, the gene set enrichment analysis (GSEA) analysis showed that several non-metabolism pathways are significantly associated with high GRRS, which means glucose-related genes could participate in tumorigenesis not only in metabolism way.
Conclusions : In conclusion, mGMRS could provide a novel method to evaluate clinical prognosis and enable individualized treatment for PDAC patients especially who has disorder of glucose metabolism. New therapeutic targets or drugs combination may be developed upon the functional analysis of the classifier genes and related pathways.
Methods : Our study established a impoved scoring system that can predict PDAC patients’ prognosis. RNA expression data of tumor tissues from TCGA, ICGC and GEO were explored. An Optimal subset-LASSO Cox regression combined model was used to choose prognostic genes.
Results : A 3-mRNA signature that can distinguish high modified Glucose Metabolic Risk Score(mGMRS) patients from low risk patients with significant differences in overall survival and progression-free survival (OS and PFS). We further validated this scoring system using both internal data and two independent cohorts and found that mGMRS remained significant in different subgroups. Multivariate cox regression analysis showed that mGMRS can be an independent prognostic marker in all cohorts. Receiver operating characteristic curve and C-index analysis showed that mGMRS performed a better prognostic efficiency. Moreover, the gene set enrichment analysis (GSEA) analysis showed that several non-metabolism pathways are significantly associated with high GRRS, which means glucose-related genes could participate in tumorigenesis not only in metabolism way.
Conclusions : In conclusion, mGMRS could provide a novel method to evaluate clinical prognosis and enable individualized treatment for PDAC patients especially who has disorder of glucose metabolism. New therapeutic targets or drugs combination may be developed upon the functional analysis of the classifier genes and related pathways.
SESSION
BP Oral Presentation 3
Room B 7/28/2020 8:32 AM - 8:39 AM