Department of Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China.
Department of General Surgery, Shanghai Eighth People's Hospital, 8 Caobao Road, Shanghai, 200235, China.
Clin Exp Med. 2024 May 25;24(1):112. doi: 10.1007/s10238-024-01378-0.
Liver metastasis stands as the primary contributor to mortality among patients diagnosed with colorectal cancer (CRC). Neutrophil extracellular traps (NETs) emerge as pivotal players in the progression and metastasis of cancer, showcasing promise as prognostic biomarkers. Our objective is to formulate a predictive model grounded in genes associated with neutrophil extracellular traps and identify novel therapeutic targets for combating CRLM. We sourced gene expression profiles from the Gene Expression Omnibus (GEO) database. Neutrophil extracellular trap-related gene set was obtained from relevant literature and cross-referenced with the GEO datasets. Differentially expressed genes (DEGs) were identified through screening via the least absolute shrinkage and selection operator regression and random forest modeling, leading to the establishment of a nomogram and subtype analysis. Subsequently, a thorough analysis of the characteristic gene CYP4F3 was undertaken, and our findings were corroborated through immunohistochemical staining. We identified seven DEGs (ATG7, CTSG, CYP4F3, F3, IL1B, PDE4B, and TNF) and established nomograms for the occurrence and prognosis of CRLM. CYP4F3 is highly expressed in CRC and colorectal liver metastasis (CRLM), exhibiting a negative correlation with CRLM prognosis. It may serve as a potential therapeutic target for CRLM. A novel prognostic signature related to NETs has been developed, with CYP4F3 identified as a risk factor and potential target for CRLM.
肝脏转移是结直肠癌（CRC）患者死亡的主要原因。中性粒细胞胞外诱捕网（NETs）是癌症进展和转移的关键因素，有望成为预后生物标志物。我们的目标是基于与中性粒细胞胞外诱捕网相关的基因构建一个预测模型，并确定治疗 CRC LM 的新的治疗靶点。我们从基因表达综合数据库（GEO）中获取基因表达谱。从相关文献中获取中性粒细胞胞外诱捕网相关基因集，并与 GEO 数据集交叉引用。通过最小绝对值收缩和选择算子回归和随机森林建模筛选差异表达基因（DEGs），建立列线图和亚型分析。随后，对特征基因 CYP4F3 进行了深入分析，并通过免疫组织化学染色进行了验证。我们鉴定了七个 DEGs（ATG7、CTSG、CYP4F3、F3、IL1B、PDE4B 和 TNF），并建立了用于预测 CRLM 发生和预后的列线图。CYP4F3 在 CRC 和结直肠肝转移（CRLM）中高表达，与 CRLM 预后呈负相关。它可能是 CRLM 的潜在治疗靶点。已经开发出一种与 NETs 相关的新的预后标志物，其中 CYP4F3 被确定为 CRLM 的风险因素和潜在靶点。
