Open Access

ARTICLE

Prognostic-related genes for pancreatic cancer typing and immunotherapy response prediction based on single-cell sequencing data and bulk sequencing data

by XUEFENG WANG^1,#, SICONG JIANG^2,#, XINHONG ZHOU³, XIAOFENG WANG⁴, LAN LI⁵, JIANJUN TANG^1,*

1 Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanchang University, National Regional Center for Respiratory Medicine, Nanchang, 330006, China
2 Division of Thoracic and Endocrine Surgery, University Hospitals and University of Geneva, Geneva, Switzerland
3 Department of Hepatobiliary Surgery, Xiantao First People’s Hospital of Yangtze University, Xiantao, 433099, China
4 Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510062, China
5 Department of Radiation Oncology, The Third Affiliated Hospital of Kunming Medical University/Yunnan Cancer Hospital, Kunming, 650118, China

* Corresponding Author: JIANJUN TANG. Email:
# Xuefeng Wang and Sicong Jiang contributed equally to the work and should be regarded as joint first authors

(This article belongs to the Special Issue: Role of Reactive Oxygen Species and DNA Damage in Tumor Immunological Responses)

Oncology Research 2023, 31(5), 697-714. https://doi.org/10.32604/or.2023.029458

Received 20 February 2023; Accepted 18 April 2023; Issue published 21 July 2023

Abstract

Background: Pancreatic cancer is associated with high mortality and is one of the most aggressive of malignancies, but studies have not fully evaluated its molecular subtypes, prognosis and response to immunotherapy of different subtypes. The purpose of this study was to explore the molecular subtypes and the key genes associated with the prognosis of pancreas cancer patients and study the clinical phenotype, prognosis and response to immunotherapy using single-cell seq data and bulk RNA seq data, and data retrieved from GEO and TCGA databases. Methods: Single-cell seq data and bioinformatics methods were used in this study. Pancreatic cancer data were retrieved from GEO and TCGA databases, the molecular subtypes of pancreatic cancer were determined using the six cGAS-STING related pathways, and the clinical phenotype, mutation, immunological characteristics and pathways related to pancreatic cancer were evaluated. Results: Pancreatic cancer was classified into 3 molecular subtypes, and survival analysis revealed that patients in Cluster3 (C3) had the worst prognosis, whereas Cluster1 (C1) had the best prognosis. The clinical phenotype and gene mutation were statistically different among the three molecular subtypes. Analysis of immunotherapy response revealed that most immune checkpoint genes were differentially expressed in the three subtypes. A lower risk of immune escape was observed in Cluster1 (C1), indicating higher sensitivity to immunotherapeutic drugs and subjects in this Cluster are more likely to benefit from immunotherapy. The pathways related to pancreatic cancer were differentially enriched among the three subtypes. Five genes, namely SFRP1, GIPR, EMP1, COL17A and CXCL11 were selected to construct a prognostic signature. Conclusions: Single-cell seq data were to classify pancreatic cancer into three molecular subtypes based on differences in clinical phenotype, mutation, immune characteristics and differentially enriched pathways. Five prognosis-related genes were identified for prediction of survival of pancreatic cancer patients and to evaluate the efficacy of immunotherapy in various subtypes.

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Supplementary Material

Supplementary Material File

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