Identification and characterization of interferon-γ signaling-based personalized heterogeneity and therapeutic strategies in patients with pancreatic cancer

Frontiers in Oncology

Oncology

Hewen Guan¹  Zhiqiang Wu²  Qihang Yuan²  Shilin Xia³  Dong Shang³  Xu Chen³  Xueying Shi³  Jie Ren⁴  Jiaao Sun⁵ 

[1] Department of Dermatology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China;Department of General Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China;Department of General Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China;Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China;Department of Oncology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China;Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China;

关键词: pancreatic cancer;    pan-cancer;    interferon-γ (IFN-γ) signaling pathway;    IFN-γ-related genes;    prediction model;   

DOI  :  10.3389/fonc.2023.1227606

 received in 2023-05-23, accepted in 2023-10-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundInterferon-γ (IFN-γ) is a key cytokine with diverse biological functions, including antiviral defense, antitumor activity, immune regulation, and modulation of cellular processes. Nonetheless, its role in pancreatic cancer (PC) therapy remains debated. Therefore, it is worthwhile to explore the role of Interferon-γ related genes (IFN-γGs) in the progression of PC development.MethodologyTranscriptomic data from 930 PC were sourced from TCGA, GEO, ICGC, and ArrayExpress, and 93 IFN-γGs were obtained from the MSigDB. We researched the characteristics of IFN-γGs in pan-cancer. Subsequently, the cohort of 930 PC was stratified into two distinct subgroups using the NMF algorithm. We then examined disparities in the activation of cancer-associated pathways within these subpopulations through GSVA analysis. We scrutinized immune infiltration in both subsets and probed classical molecular target drug sensitivity variations. Finally, we devised and validated a novel IFN-γ related prediction model using LASSO and Cox regression analyses. Furthermore, we conducted RT-qPCR and immunohistochemistry assays to validate the expression of seven target genes included in the prediction model.ResultsWe demonstrated the CNV, SNV, methylation, expression levels, and prognostic characteristics of IFN-γGs in pan-cancers. Notably, Cluster 2 demonstrated superior prognostic outcomes and heightened immune cell infiltration compared to Clusters 1. We also assessed the IC50 values of classical molecular targeted drugs to establish links between IFN-γGs expression levels and drug responsiveness. Additionally, by applying our prediction model, we segregated PC patients into high-risk and low-risk groups, identifying potential benefits of cisplatin, docetaxel, pazopanib, midostaurin, epothilone.B, thapsigargin, bryostatin.1, and AICAR for high-risk PC patients, and metformin, roscovitine, salubrinal, and cyclopamine for those in the low-risk group. The expression levels of these model genes were further verified through HPA website data and qRT-PCR assays in PC cell lines and tissues.ConclusionThis study unveils IFN-γGs related molecular subsets in pancreatic cancer for the first time, shedding light on the pivotal role of IFN-γGs in the progression of PC. Furthermore, we establish an IFN-γGs related prognostic model for predicting the survival of PC, offering a theoretical foundation for exploring the precise mechanisms of IFN-γGs in PC.

【 授权许可】

Unknown   
Copyright © 2023 Chen, Yuan, Guan, Shi, Sun, Wu, Ren, Xia and Shang