已发表论文

单细胞测序和转录组分析探究了胰腺癌中与 Midnolin 相关的免疫微环境变化,并构建了联合预后模型

 

Authors Guan X, Xu L, Liu J, Fei H, Wang C 

Received 23 November 2024

Accepted for publication 22 February 2025

Published 26 February 2025 Volume 2025:18 Pages 2975—2990

DOI https://doi.org/10.2147/JIR.S503326

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Professor Ning Quan

Xiao Guan,1,* Lei Xu,2,* Jinsong Liu,3,* He Fei,1,* Chengfeng Wang1 

1Department of Pancreatic and Gastric Surgery, Cancer Hospital Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 2Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China; 3Department of VIP Medical, Cancer Hospital Chinese Academy of Medical Sciences, Beijing, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Chengfeng Wang, Department of Pancreatic and Gastric Surgery, Cancer Hospital Chinese Academy of Medical Sciences, Beijing, People’s Republic of China, Email drwangcf1962@163.com

Background: Pancreatic cancer has one of the worst prognoses of any malignant tumor. The value of MIDN, midnolin-related genes and midnolin-related immune infiltrating cells (MICs) in the prognosis of pancreatic cancer remains unknown.
Methods: Single-cell analysis were used to identify midnolin-related genes. Immune cell infiltration was obtained using CIBERSORT. The prognostic midnolin-related genes were identified through the utilization of Cox regression and the least absolute selection operator (LASSO) approach. The combined prognostic model was created using multifactorial Cox regression analysis. Survival analyses, immune microenvironment assessments, drug sensitivity checks were performed to evaluate the combined model performance. Finally, cellular experiments were carried out to confirm MIDN significance in pancreatic cancer.
Results: The combined model was constructed based on MIDN expression, prognostic model of 10 midnolin-related genes and M1 cell infiltration. Most immune checkpoint-related genes were expressed at greater levels in the low-risk group, suggesting a greater chance of immunotherapy’s benefits. The most significant model gene, MIDN, was shown to have a function by cellular tests. In pancreatic cancer, MIDN knockdown drastically decreased pancreatic cancer cell lines’ activity, proliferation, and invasive potential.
Conclusion: The combined model helped assess the prognosis of pancreatic cancer and offered fresh perspectives on immunotherapy in particular.

Keywords: pancreatic cancer, midnolin, prognosis, immune landscape, bioinformatics