关键词: 5-FU-Related genes 5FRRDEGs Colon adenocarcinoma Gene signature Immune infiltration Prognostic model

来  源:   DOI:10.1016/j.heliyon.2024.e34535   PDF(Pubmed)

Abstract:
UNASSIGNED: Drug resistance is the primary obstacle to advanced tumor therapy and the key risk factor for tumor recurrence and death. 5-Fluorouracil (5-FU) chemotherapy is the most common chemotherapy for individuals with colorectal cancer, despite numerous options.
UNASSIGNED: The Gene Expression Omnibus database was utilized to extract expression profile data of HCT-8 human colorectal cancer wild-type cells and their 5-FU-induced drug resistance cell line. These data were used to identify 5-FU resistance-related differentially expressed genes (5FRRDEGs), which intersected with the colorectal adenocarcinoma (COAD) transcriptome data provided by the Cancer Genome Atlas Program database. A prognostic signature containing five 5FRRDEGs (GOLGA8A, KLC3, TIGD1, NBPF1, and SERPINE1) was established after conducting a Cox regression analysis. We conducted nomogram development, drug sensitivity analysis, tumor immune microenvironment analysis, and mutation analysis to assess the therapeutic value of the prognostic qualities.
UNASSIGNED: We identified 166 5FRRDEGs in patients with COAD. Subsequently, we created a prognostic model consisting of five 5FRRDEGs using Cox regression analysis. The patients with COAD were divided into different risk groups by risk score; the high-risk group demonstrated a worse prognosis than the low-risk group.
UNASSIGNED: In summary, the 5FRRDEG-based prognostic model is an effective tool for targeted therapy and chemotherapy in patients with COAD. It can accurately predict the survival prognosis of these patients as well as to provide the direction for exploring the resistance mechanism underlying COAD.
摘要:
耐药性是肿瘤晚期治疗的主要障碍,也是肿瘤复发和死亡的关键危险因素。5-氟尿嘧啶(5-FU)化疗是结直肠癌患者最常见的化疗药物,尽管有很多选择。
利用基因表达综合数据库提取HCT-8人结直肠癌野生型细胞及其5-FU诱导的耐药细胞系的表达谱数据。这些数据用于鉴定5-FU抗性相关的差异表达基因(5FRRDEGs),与癌症基因组图集计划数据库提供的结直肠腺癌(COAD)转录组数据相交。包含五个5FRRDEGs(GOLGA8A,进行Cox回归分析后,建立KLC3,TIGD1,NBPF1和SERPINE1)。我们进行了列线图开发,药物敏感性分析,肿瘤免疫微环境分析,和突变分析以评估预后质量的治疗价值。
我们在COAD患者中确定了1665FRRDEG。随后,我们使用Cox回归分析建立了由5个5个FRRDEGs组成的预后模型.根据风险评分将COAD患者分为不同的风险组;高危组的预后较差。
总之,基于5FRRDEG的预后模型是COAD患者靶向治疗和化疗的有效工具.它可以准确预测这些患者的生存预后,并为探索COAD的耐药机制提供方向。
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