Establishment of a Prognostic Model for Pancreatic Cancer Based on Hypoxia-Related Genes

• Published in: Technology in cancer research & treatment 2024-01, Vol.23, p.15330338241288687
• Main Authors: Wu, Yangdong, Zhou, Jianrui, Kou, Qingyan, Sun, Lin, Ma, Yuan, Yang, Tingting, Hu, Xiao
• Format: Article
• Language: English
• Subjects: Antigens, Neoplasm - genetics; Biomarkers, Tumor - genetics; Carbonic Anhydrase IX - genetics; Carbonic Anhydrase IX - metabolism; Cell Line, Tumor; Computational Biology - methods; Databases, Genetic; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Glucose Transporter Type 1 - genetics; Glucose Transporter Type 1 - metabolism; Humans; Hypoxia - genetics; Pancreatic Neoplasms - genetics; Pancreatic Neoplasms - mortality; Pancreatic Neoplasms - pathology; Prognosis; Transcriptome; Tumor Microenvironment - genetics
• ISSN: 1533-0346; 1533-0338; 1533-0338
• DOI: 10.1177/15330338241288687

Objectives Pancreatic cancer presents a formidable challenge with its aggressive nature and dismal prognosis, often hampered by elusive early symptoms. The tumor microenvironment (TME) emerges as a pivotal player in pancreatic cancer progression and treatment responses, characterized notably by hypoxia and immunosuppression. In this study, we aimed to identify hypoxia-related genes and develop a prognostic model for pancreatic cancer leveraging these genes. Methods Through analysis of gene expression data from The Cancer Genome Atlas (TCGA) and subsequent GO/KEGG enrichment analysis, hypoxia-related pathways were identified. We constructed a prognostic model using lasso regression and validated it using an independent dataset. Results Our results showed that expression levels of PLAU, SLC2A1, and CA9 exhibited significant associations with prognosis in pancreatic cancer. The prognostic model, built upon these genes, displayed robust predictive accuracy and was validated in an independent dataset. Furthermore, we found a correlation between the risk score of the prognostic model and clinical parameters of pancreatic cancer patients. At the same time, we also explored the relationship between the established hypoxia-related prognostic model and the immune microenvironment at the single-cell level. RT-qPCR results showed notable differences in the expression of hypoxia pathway-related genes between normal PANC-1 and hypoxic-treated PANC-1 cells. Conclusion Our study provides insights into the role of the hypoxic microenvironment in pancreatic cancer and offers a promising prognostic tool for clinical application.