A Novel Immune-Related Gene Prognostic Index (IRGPI) in Pancreatic Adenocarcinoma (PAAD) and Its Implications in the Tumor Microenvironment

Shujing Zhou; Attila Gábor Szöllősi; Xufeng Huang; Yi-Che Chang-Chien; András Hajdu

doi:10.3390/cancers14225652

A Novel Immune-Related Gene Prognostic Index (IRGPI) in Pancreatic Adenocarcinoma (PAAD) and Its Implications in the Tumor Microenvironment

Cancers (Basel). 2022 Nov 17;14(22):5652. doi: 10.3390/cancers14225652.

Authors

Shujing Zhou^{1

2}, Attila Gábor Szöllősi³, Xufeng Huang^{1

4}, Yi-Che Chang-Chien⁵, András Hajdu¹

Affiliations

¹ Department Data Science and Visualization, Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary.
² Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.
³ Department of Immunology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.
⁴ Faculty of Dentistry, University of Debrecen, 4032 Debrecen, Hungary.
⁵ Department of Pathology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.

Abstract

Purpose: Pancreatic adenocarcinoma (PAAD) is one of the most lethal malignancies, with less than 10% of patients surviving more than 5 years. Existing biomarkers for reliable survival rate prediction need to be enhanced. As a result, the objective of this study was to create a novel immune-related gene prognostic index (IRGPI) for estimating overall survival (OS) and to analyze the molecular subtypes based on this index. Materials and procedures: RNA sequencing and clinical data were retrieved from publicly available sources and analyzed using several R software packages. A unique IRGPI and optimum risk model were developed using a machine learning algorithm. The prediction capability of our model was then compared to that of previously proposed models. A correlation study was also conducted between the immunological tumor microenvironment, risk groups, and IRGPI genes. Furthermore, we classified PAAD into different molecular subtypes based on the expression of IRGPI genes and investigated their features in tumor immunology using the K-means clustering technique.

Results: A 12-gene IRGPI (FYN, MET, LRSAM1, PSPN, ERAP2, S100A1, IL20RB, MAP3K14, SEMA6C, PRKCG, CXCL11, and GH1) was established, and verified along with a risk model. OS prediction by our model outperformed previous gene signatures. According to the findings of our correlation studies, different risk groups and IRGPI genes were found to be tightly related to tumor microenvironments, and PAAD could be further subdivided into immunologically distinct molecular subtypes based on the expression of IRGPI genes.

Conclusion: The current study constructed and verified a unique IRGPI. Furthermore, our findings revealed a connection between the IRGPI and the immunological microenvironment of tumors. PAAD was differentiated into several molecular subtypes that might react differently to immunotherapy. These findings could provide new insights for precision and translational medicine for more innovative immunotherapy strategies.

Keywords: gene signature; machine learning; molecular subtypes; pancreatic cancer; tumor microenvironment.

Grants and funding

This work is funded in part by the project TKP2021-NKTA-34, implemented with the support provided by the National Research, Development, and Innovation Fund of Hungary under the TKP2021-NKTA funding scheme.