NDLI: A novel CAF-cancer cell crosstalk-related gene prognostic index based on machine learning: prognostic significance and prediction of therapeutic response in head and neck squamous cell carcinoma

Content Provider	Springer Nature : BioMed Central
Author	Xu, Yuming Li, Junda Wang, Jinming Deng, Feilong
Abstract	Background Cancer-associated fibroblast (CAF)-cancer cell crosstalk (CCCT) plays an important role in tumor microenvironment shaping and immunotherapy response. Current prognostic indexes are insufficient to accurately assess immunotherapy response in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to develop a CCCT-related gene prognostic index (CCRGPI) for assessing the prognosis and response to immune checkpoint inhibitor (ICI) therapy of HNSCC patients. Methods Two cellular models, the fibroblast-cancer cell indirect coculture (FCICC) model, and the fibroblast-cancer cell organoid (FC-organoid) model, were constructed to visualize the crosstalk between fibroblasts and cancer cells. Based on a HNSCC scRNA-seq dataset, the R package CellChat was used to perform cell communication analysis to identify gene pairs involved in CCCT. Least absolute shrinkage and selection operator (LASSO) regression was then applied to further refine the selection of these gene pairs. The selected gene pairs were subsequently subjected to stepwise regression to develop CCRGPI. We further performed a comprehensive analysis to determine the molecular and immune characteristics, and prognosis associated with ICI therapy in different CCRGPI subgroups. Finally, the connectivity map (CMap) analysis and molecular docking were used to screen potential therapeutic drugs. Results FCICC and FC-organoid models showed that cancer cells promoted the activation of fibroblasts into CAFs, that CAFs enhanced the invasion of cancer cells, and that CCCT was somewhat heterogeneous. The CCRGPI was developed based on 4 gene pairs: IGF1-IGF1R, LGALS9-CD44, SEMA5A-PLXNA1, and TNXB-SDC1. Furthermore, a high CCRGPI score was identified as an adverse prognostic factor for overall survival (OS). Additionally, a high CCRGPI was positively correlated with the activation of the P53 pathway, a high TP53 mutation rate, and decreased benefit from ICI therapy but was inversely associated with the abundance of various immune cells, such as CD4+ T cells, CD8+ T cells, and B cells. Moreover, Ganetespib was identified as a potential drug for HNSCC combination therapy. Conclusions The CCRGPI is reliable for predicting the prognosis and immunotherapy response of HSNCC patients and may be useful for guiding the individualized treatment of HNSCC patients.
Related Links	https://translational-medicine.biomedcentral.com/counter/pdf/10.1186/s12967-024-05447-6.pdf
Ending Page	20
Page Count	20
Starting Page	1
File Format	HTM / HTML
ISSN	14795876
DOI	10.1186/s12967-024-05447-6
Journal	Journal of Translational Medicine
Issue Number	1
Volume Number	22
Language	English
Publisher	BioMed Central
Publisher Date	2024-07-09
Access Restriction	Open
Subject Keyword	Biomedicine Medicine Public Health Head and neck squamous cell carcinoma Immunotherapy Machine learning Prognosis Biomarker Cancer-associated fibroblasts Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Biochemistry, Genetics and Molecular Biology Medicine
Journal Impact Factor	6.1/2023
5-Year Journal Impact Factor	6.3/2023

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