Integrating Single-cell RNA-seq to construct a Neutrophil prognostic model for predicting immune responses in cervical carcinoma
Authors: Qian Hengjun , Xieyidai Abuduhailili , Han Songtao , LV Xiang , Deng Yuqin , Feng Yangchun , Wang Ruozheng 
Published: December 2023

Abstract
Objective: The objective of this study is to comprehensively investigate the communication network within the tumor immune microenvironment (Tumor Immune Microenvironment, TIME) of cervical carcinoma (CC). This involves elucidating the intricate relationships among cells to gain a profound understanding of the interactions between immune cells and tumor cells，as well as the assessment of neutrophil differentiation characteristics and the selection of prognostic genes, the primary goal is to establish a risk model with the ability to predict patients' immune responses and prognosis. Additionally, this model seeks to uncover innovative diagnostic and therapeutic targets for cervical carcinoma, thereby furnishing clinicians with dependable strategies for treatment.
Methods: Using single-cell RNA sequencing data (scRNA-seq) obtained from CC samples (E-MTAB-11948), this study employed the Seurat(4.3.0) package to integrate data, remove batch effects, and annotate cell types. A cell communication network was constructed using the iTAKL(0.1.0) package for the analysis of intercellular communication. Neutrophil subpopulations were analyzed utilizing the Monocle2(2.26.0) package to discern various cellular states and conduct pathway analysis using KEGG/GO annotations.
Results: we successfully distinguished and further categorized 32 cell populations into 9 major cell types, encompassing T cells, B cells, Mast cells, Neutrophils, Epithelial cells, Endothelial cells, Monocytes, Fibroblasts, and Smooth muscle cells. Furthermore, we ascertained that five subgroups of Neutrophils, each representing diverse differentiation states, exhibit close associations with immune regulatory and metabolic pathways.From our analysis of intersecting genes in the TCGA-CESC dataset, we successfully identified four prognostic genes: C5AR1, HSPA5, CXCL2, and OLR1. The stability of our prognostic risk model has been reiterated through internal and external validation, demonstrating its high consistency, differentiation, and clinical applicability. Notably, the CIRBESORT analysis divulged diminished immune cell content within the tumor immune microenvironment of the high-risk group, correlating with an unfavorable prognosis. Low expression of C5AR1 and high expression of HSPA5, CXCL2, and OLR1 were significantly associated with shorter survival and poorer prognosis.Conclusion: This study elucidated the intricate regulatory network governing the immune microenvironment in CC and comprehensively analyzed intercellular interactions.highlighting the significant roles of C5AR1, HSPA5, CXCL2, and OLR1 in predicting patient prognosis and responsiveness to immunotherapy. These findings offer novel insights and potential strategies for identifying fresh treatment targets in CC.
Conclusion: This study elucidated the regulatory network of immune microenvironment in CC, and analyzed the interaction between cells. the key roles of C5AR1, HSPA5, CXCL2 and OLR1 in predicting patient prognosis and response to immunotherapy were revealed. This provides new insights and possible strategies for finding new CC treatment targets.