Establishment of a prognostic model for pancreatic cancer based on vesicle-mediated transport protein-related genes

Abstract

This study attempted to build a prognostic riskscore model for pancreatic cancer (PC) patients based on vesicle-mediated transport protein-related genes (VMTGs). We initially conducted differential expression analysis and Cox regression analysis, followed by the construction of a riskscore model to classify PC patients into high-risk (HR) and low-risk (LR) groups. The GEO GSE62452 dataset further validated the model. Kaplan-Meier survival analysis was employed to analyze the survival rate of the HR group and LR group. Cox analysis confirmed the independent prognostic ability of the riskscore model. Additionally, we evaluated immune status in both HR and LR groups, utilizing data from the GDSC database to predict drug response among PC patients. We identified six PC-specific genes from 724 VMTGs. Survival analysis revealed that the survival rate of the HR group was lower than that of the LR group (P<0.05). Cox analysis confirmed that the prognostic riskscore model could independently predict the survival status of PC patients (P<0.001). Immunological analysis revealed that the ESTIMATE score, immune score, and stroma score of the HR group were considerably lower than those of the LR group, and the tumor purity score of the HR group was higher. The IC50 values of Gemcitabine, Irinotecan, Oxaliplatin, and Paclitaxel in the LR group were considerably lower than those in the HR group (P<0.001). In summary, the VMTG-based prognostic riskscore model could stratify PC risk and effectively predict the survival of PC patients.

Keywords:

• Pancreatic cancer
• vesicle-mediated transport proteins
• survival prediction
• tumor immunity
• drug sensitivity

Author contributions

1. Conception and design: Yanfang Cao

2. Administrative support: Xianfei Zhou

3. Provision of study materials: Renwei Xing, Yang Zhang

4. Collection and assembly of data: Renwei Xing, Fan Yang

5. Data analysis and interpretation: Fan Yang, Yang Zhang

6. Manuscript writing: Yanfang Cao, Xianfei Zhou

7. Final approval of manuscript: All authors

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data and materials in the current study are available from the corresponding author on reasonable request.

Additional information

Funding

This research was supported by Natural Science Foundation of Zhejiang Province [Grant No. LQ23H160005]; Science and Technology Program of Taizhou [Grant No.21ywb42].