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Purpose Colon cancer is one of the most common digestive tract malignancies. Studies have shown that neutrophils can interact with immune cells and immune factors to affect the prognosis of patients. Methods We first determined the infiltration level of neutrophils in tumors using CIBERSORT and identified key genes in the final risk model by Spearman correlation analysis and subsequent Cox analysis. The risk score of each patient was obtained by multiplying the Cox regression coefficient by the gene expression level, and patients were divided into two groups according to the median. Differences in OS and PFS were assessed by KM survival analysis, and model accuracy was validated in another independent dataset. Finally, the differences in immune infiltration and immunotherapy were evaluated by immunoassay. Results We established and validated a risk scoring model based on neutrophil-related genes in two independent datasets; the patients in the high-risk group had a poorer prognosis than those in the low-risk group. A new nomogram was constructed and validated by combining clinical characteristics and the risk score model to better predict patient OS and PFS. Immune analysis showed that patients in the high-risk group had immune cell infiltration level, immune checkpoint levels, and tumor mutational burden and were more likely to benefit from immunotherapy. Conclusion The low-risk group had relatively better OS and PFS than the high-risk group in the neutrophil-related gene-based risk model. Patients in the high-risk group presented higher immune infiltration levels and tumor mutational burden and thus may be more responsive to immunotherapy.

Background: This study aimed to explore the interactions and relationships of genes and recognize the hub genes associated with prognosis in follicular lymphoma (FL) treated with first-line rituximab combined with chemotherapy.Method: RNA sequencing data of dataset GSE65135 (n=24) were included in differentially expressed genes (DEGs) analysis. Weighted gene co-expression network analysis (WGCNA) was applied for exploring the coexpression network and identifying hub genes. Validation of hub genes expression and prognosis were applied in dataset GSE119214 (n=137) and patient cohort of Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College (n=32), respectively, by analyzing RNAseq expression data and serum protein concentration quantified by ELISA. CIBERSORT was applied for tumor-infiltrating immune cells (TIICs) subset analysis. Results: A total of 3260 DEGs were obtained, with 1861 genes upregulated and 1399 genes downregulated. Using WGCNA and Cytoscape analysis, eight hub genes, PLA2G2D, MMP9, PTGDS, CCL19, NFIB, YAP1, RGL1, and TIMP3 were identified. Kaplan-Meier analysis and multivariate COX regression analysis indicated that CCL19 independently associated with overall survival (OS) for FL patients treated with rituximab and chemotherapy (HR = 0.47, 95%CI [0.25-0.86], p = 0.014). Higher Serum CCL19 concentration was associated with longer progression-free survival (PFS, p=0.014) and OS (p=0.039). TIICs subset analysis showed that CCL19 expression had a positive correlation with monocytes and macrophages M1, and a negative correlation with naïve B cells and plasma cells. Conclusion: CCL19 expression was associated with survival outcomes and might be a potential prognostic biomarker for FL treated with first-line immunochemotherapy.