Research Paper Volume 15, Issue 23 pp 14109—14140
Machine learning algorithm integrates bulk and single-cell transcriptome sequencing to reveal immune-related personalized therapy prediction features for pancreatic cancer
- 1 Department of General Surgery, Taiyuan Central Hospital, Taiyuan 030009, Shanxi, P.R. China
- 2 University of Shanghai for Science and Technology, Shanghai 200093, P.R. China
- 3 Department of General Surgery, Peking University First Hospital, Beijing 100034, P.R. China
- 4 Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, P.R. China
- 5 Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, P.R. China
- 6 State Key Laboratory of Oncogenes and Related Genes, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
Received: July 17, 2023 Accepted: November 3, 2023 Published: December 12, 2023
https://doi.org/10.18632/aging.205293How to Cite
Copyright: © 2023 Zang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Pancreatic cancer (PC) is a digestive malignancy with worse overall survival. Tumor immune environment (TIME) alters the progression and proliferation of various solid tumors. Hence, we aimed to detect the TIME-related classifier to facilitate the personalized treatment of PC. Based on the 1612 immune-related genes (IRGs), we classified patients into Immune_rich and Immune_desert subgroups via consensus clustering. Patients in distinct subtypes exhibited a difference in sensitivity to immune checkpoint blockers (ICB). Next, the immune-related signature (IRS) model was established based on 8 IRGs (SYT12, TNNT1, TRIM46, SMPD3, ANLN, AFF3, CXCL9 and RP1L1) and validated its predictive efficiency in multiple cohorts. RT-qPCR experiments demonstrated the differential expression of 8 IRGs between tumor and normal cell lines. Patients who gained lower IRS score tended to be more sensitive to chemotherapy and immunotherapy, and obtained better overall survival compared to those with higher IRS scores. Moreover, scRNA-seq analysis revealed that fibroblast and ductal cells might affect malignant tumor cells via MIF-(CD74+CD44) and SPP1-CD44 axis. Eventually, we identified eight therapeutic targets and one agent for IRS high patients. Our study screened out the specific regulation pattern of TIME in PC, and shed light on the precise treatment of PC.
Abbreviations
ADEX: Aberrantly Differentiated Endocrine Exocrine; AUC: area under the curve; CAFs: cancer-associated fibroblasts; CCLE: Cell Line Encyclopedia; CTRP: Cancer Therapeutics Response Portal; C-index: concordance index; DEGs: differential expression genes; ECM: extracellular matrix; ESTIMATE: Estimation of STromal and Immune cells in MAlignant Tumours using Expression data; GEO: Gene Expression Omnibus; GO: Gene Ontology; GSEA: Gene set enrichment analysis; GTEx: Genotype-Tissue Expression; HCC: hepatocellular carcinoma; IPS: immunophenoscore; IRGs: immune-related genes; IRS: immune-related signature; KEGG: Kyoto Encyclopedia of Genes and Genomes; KNN: k-nearest neighbors; MD: Minimal Depth; ML: machine learning; MMP: matrix metalloproteinase; MsigDB: Molecular Signatures Database; ORLs: overall response rates; OSCC: oral squamous cell carcinoma; PAMG: pancreatic adenocarcinoma molecular gradient; PC: Pancreatic cancer; PC: principal components; PCA: Principal component analysis; PDS: Pathway Deregulation Score; RSF: random survival forest; ssGSEA: single sample Gene Set Enrichment Analysis; SubMap: Subclass mapping; SVM: support vector machine; TCGA: The Cancer Genome Atlas; TCIA: the cancer immunome group atlas; TGFβ: transforming growth factor-β; TIDE: Tumor Immune Dysfunction and Exclusion; TIME: Tumor immune microenvironment; TIMER: Tumor Immune Estimation Resource; TMB: tumor mutation burden; Tregs: regulatory T cells; TRIM: the tripartite motif.