Basic Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 28, 2024; 30(36): 4057-4070
Published online Sep 28, 2024. doi: 10.3748/wjg.v30.i36.4057
Construction and validation of a pancreatic cancer prognostic model based on genes related to the hypoxic tumor microenvironment
Fan Yang, Na Jiang, Xiao-Yu Li, Xing-Si Qi, Zi-Bin Tian, Ying-Jie Guo
Fan Yang, Na Jiang, Xiao-Yu Li, Xing-Si Qi, Zi-Bin Tian, Ying-Jie Guo, Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
Co-corresponding authors: Zi-Bin Tian and Ying-Jie Guo.
Author contributions: Yang F, Guo YJ, and Tian ZB designed the study; Yang F, Guo YJ, Tian ZB and Jiang N performed the experiments, and acquired and analyzed the data; Yang F, Guo YJ, Li XY and Qi XS prepared the figures and tables; Yang F, Guo YJ, and Tian ZB reviewed and edited the manuscript. All authors reviewed and approved the final version of the article. Guo YJ and Tian ZB are designated as co-corresponding authors due to their nearly equal contributions across various aspects of the project. They played crucial roles at multiple key stages of the study, including research conception, methodology design, data analysis, and manuscript preparation. Throughout the research process, they jointly managed communication with the journal and addressed related issues, ensuring the smooth progress and accurate representation of the research findings. Designating them as co-corresponding authors not only reflects their shared commitment to the work but also ensures that their contributions are equally recognized and valued. The designation of co-corresponding authors highlights the collaborative spirit and high professional standards within the team and strengthens the scientific rigor and integrity of the paper.
Supported by National Natural Science Foundation of China, No. 82100581.
Institutional review board statement: The manuscript did not involve human participants, animal subjects, or any other materials that require ethical review.
Institutional animal care and use committee statement: The manuscript did not involve animal subjects.
Conflict-of-interest statement: The authors declare that they have no competing interests, and all authors confirm its accuracy.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at guoyingjie305@163.com.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ying-Jie Guo, MD, Doctor, Department of Gastroenterology, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao 266000, Shandong Province, China. guoyingjie305@163.com
Received: July 14, 2024
Revised: August 22, 2024
Accepted: September 5, 2024
Published online: September 28, 2024
Processing time: 67 Days and 14 Hours
Abstract
BACKGROUND

Pancreatic cancer is one of the most lethal malignancies, characterized by poor prognosis and low survival rates. Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy, often failing to capture the complexity of the disease. The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment. This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer.

AIM

To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules.

METHODS

This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2, PLAU, and CCNA2. The results were validated in an independent dataset. This study also examined the correlations between the model risk score and various clinical features, components of the immune microenvironment, chemotherapeutic drug sensitivity, and metabolism-related pathways. Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines.

RESULTS

The prognostic model demonstrated significant predictive value, with the risk score showing a strong correlation with clinical features: It was significantly associated with tumor grade (G) (bP < 0.01), moderately associated with tumor stage (T) (aP < 0.05), and significantly correlated with residual tumor (R) status (bP < 0.01). There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs. Furthermore, the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer.

CONCLUSION

The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.

Keywords: Pancreatic cancer; Hypoxia; Prognostic model; Immune microenvironment; Metabolism pathway

Core Tip: In this study, a prognostic model based on the expression levels of the hypoxia-related genes CAPN2, PLAU, and CCNA2 was developed for pancreatic cancer. Compared with traditional methods, the model demonstrated superior predictive accuracy, and the model risk score was strongly correlated with clinical features such as cancer stage and tumor size. The risk score was also significantly associated with chemotherapy drug sensitivity and metabolic pathway activity. These findings highlight the model's potential to enhance personalized treatment selection and improve prognosis.