SPP1 + macrophages facilitate pancreatic cancer progression via ITGB6-mediated interactions: evidence from integrated multi-omics analysis and experimental validation.

Journal: Immunologic research
Published Date:

Abstract

Basement membranes (BMs) and tumor-associated macrophages (TAMs) are crucial stromal components in pancreatic cancer (PC), critically influencing disease progression. Bulk and single-cell RNA-seq (scRNA-seq) data were acquired from publicly available databases. Through integration of multiple machine learning algorithms, we developed and validated a BM-related subtype and prognostic signature in PC cohorts. The expression profiles of BM-related genes in PC were verified using experimental approaches. We further investigated the functional mechanisms of a core gene in PC progression. Additionally, we characterized the TAM landscape in PC, revealing distinct TAM subsets associated with tumor progression and their dynamic interactions with BM components. Based on BM-related gene expression profiles, PC samples were stratified into two distinct subtypes. Our integrated prognostic signature combining LASSO and survival-SVM algorithms demonstrated robust performance in predicting PC outcomes and clinical characteristics. LAMA3, ITGA3, and ITGB6 showed higher expression in PC specimens versus normal controls. Functional experiments confirmed that ITGB6 knockdown markedly suppressed PC progression. Through integrative analysis of multiple scRNA-seq datasets of PC, we established a single-cell landscape of TAMs and ductal cells, respectively. SPP1 + TAMs correlated with poor PC prognosis and facilitated tumor progression through ITGB6-mediated interactions. In this study, we established novel PC subtypes and constructed a prognostic signature based on BM-related genes. An atlas of TAMs was constructed in PC. SPP1 + macrophages drove pancreatic cancer progression via ITGB6-mediated interactions.

Authors

  • Jiachen Ge
    Department of Hepatobiliary Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, 450003, People's Republic of China.
  • Jianping Cai
    College of Computer Science and Big Data, Fuzhou University, Fuzhou, 350000, China.
  • Gaolei Zhang
    Department of Hepatobiliary Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, 450003, People's Republic of China.
  • Deyu Li
    School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China; Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan, Shanxi 030006, China.
  • Lianyuan Tao
    Department of Hepatobiliary Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, 450003, People's Republic of China. tly2007tly@hotmail.com.