Pan-cancer single cell and spatial transcriptomics analysis deciphers the molecular landscapes of senescence related cancer-associated fibroblasts and reveals its predictive value in neuroblastoma via integrated multi-omics analysis and machine learning

• Published in: Frontiers in immunology Vol. 15; p. 1506256
• Main Authors: Li, Shan, Luo, Junyi, Liu, Junhong, He, Dawei
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 05.12.2024
• Subjects: Biomarkers, Tumor - genetics; cancer-associated fibroblasts; Cancer-Associated Fibroblasts - metabolism; Cellular Senescence - genetics; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humans; Immunology; Janus Kinase 1 - genetics; Janus Kinase 1 - metabolism; Machine Learning; multi-omics analysis; Multiomics; neuroblastoma; Neuroblastoma - diagnosis; Neuroblastoma - genetics; pan-cancer analysis; Predictive Value of Tests; Prognosis; senescence; Single-Cell Analysis; Transcriptome; Tumor Microenvironment - genetics; prognostic prediction; senescence; cancer-associated fibroblasts; neuroblastoma; multi-omics analysis; machine learning; pan-cancer analysis
• DOI: 10.3389/fimmu.2024.1506256
• ISSN: 1664-3224

Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly contribute to reshaping the tumor microenvironment (TME), and no research has systematically explored the molecular landscapes of senescence related CAFs (senes CAF) in NB. We utilized pan-cancer single cell and spatial transcriptomics analysis to identify the subpopulation of senes CAFs via senescence related genes, exploring its spatial distribution characteristics. Harnessing the maker genes with prognostic significance, we delineated the molecular landscapes of senes CAFs in bulk-seq data. We established the senes CAFs related signature (SCRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms to precisely diagnose stage 4 NB and to predict prognosis in NB. Based on risk scores calculated by prognostic SCRS, patients were categorized into high and low risk groups according to median risk score. We conducted comprehensive analysis between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single cell level. Ultimately, we explore the biological function of the hub gene JAK1 in pan-cancer multi-omics landscape. Through integrated analysis of pan-cancer spatial and single-cell transcriptomics data, we identified distinct functional subgroups of CAFs and characterized their spatial distribution patterns. With marker genes of senes CAF and leave-one-out cross-validation, we selected RF algorithm to establish diagnostic SCRS, and SuperPC algorithm to develop prognostic SCRS. SCRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and clinic variables. We stratified NB patients into high and low risk group, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a different mutation landscape, and an enhanced sensitivity to immunotherapy. Single cell analysis reveals biologically cellular variations underlying model genes of SCRS. Spatial transcriptomics delineated the molecular variant expressions of hub gene JAK1 in malignant cells across cancers, while immunohistochemistry validated the differential protein levels of JAK1 in NB. Based on multi-omics analysis and ML algorithms, we successfully developed the SCRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular landscapes of senes CAF and clinical utilization of SCRS.