【佳学基因检测】mRNAsi 相关代谢风险评分模型通过机器学习识别结直肠癌患者的不良预后、免疫逃避背景和低化疗反应

肿瘤基因检测公司排名解码

探索看到《Front Immunol》在. 2022 Aug 23;13:950782.发表了一篇题目为《mRNAsi 相关代谢风险评分模型通过机器学习识别结直肠癌患者的不良预后、免疫逃避背景和低化疗反应》肿瘤靶向药物治疗基因检测临床研究文章。该研究由Meilin Weng, Ting Li, Jing Zhao, Miaomiao Guo, Wenling Zhao, Wenchao Gu, Caihong Sun, Ying Yue, Ziwen Zhong, Ke Nan, Qingwu Liao, Minli Sun, Di Zhou, Changhong Miao等完成。促进了肿瘤的精准治疗与个性化用药的发展，进一步强调了基因信息检测与分析的重要性。

肿瘤靶向药物及精准治疗临床研究内容关键词：

机器学习,结直肠癌,免疫逃避,免疫疗法, mRNAsi,代谢,风险评分模型,干性

肿瘤靶向治疗基因检测临床应用结果

结直肠癌 (CRC) 是消化系统中最致命的癌症之一。尽管癌症干细胞和代谢重编程对肿瘤进展和耐药性有重要影响，但它们对CRC预后的综合影响仍不清楚。因此，我们生成了一个 21 基因 mRNA 干性指数相关的代谢风险评分模型，该模型在癌症基因组图谱和基因表达综合数据库（1323 名患者）中进行了检查，并使用中山医院队列（200 名患者）进行了验证。高风险组表现出更多的免疫浸润；更高水平的免疫抑制检查点，例如 CD274、肿瘤突变负荷和对化疗药物的耐药性；对免疫治疗可能有更好的反应；预后较差；且肿瘤淋巴结转移的晚期阶段高于低危组。风险评分和临床特征相结合可有效预测总生存期。中山队列验证了高危评分组与CRC的恶性进展、较差的预后、较差的辅助化疗反应性相关，并形成了免疫逃避环境。该工具可以在 CRC 和筛查对免疫治疗有反应的 CRC 患者中提供更准确的风险分层。结直肠癌;免疫逃避；免疫疗法； mRNAsi;代谢;风险评分模型；干性。

肿瘤发生与复发转移国际数据库描述：

Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains unclear. Therefore, we generated a 21-gene mRNA stemness index-related metabolic risk score model, which was examined in The Cancer Genome Atlas and Gene Expression Omnibus databases (1323 patients) and validated using the Zhongshan Hospital cohort (200 patients). The high-risk group showed more immune infiltrations; higher levels of immunosuppressive checkpoints, such as CD274, tumor mutation burden, and resistance to chemotherapeutics; potentially better response to immune therapy; worse prognosis; and advanced stage of tumor node metastasis than the low-risk group. The combination of risk score and clinical characteristics was effective in predicting overall survival. Zhongshan cohort validated that high-risk score group correlated with malignant progression, worse prognosis, inferior adjuvant chemotherapy responsiveness of CRC, and shaped an immunoevasive contexture. This tool may provide a more accurate risk stratification in CRC and screening of patients with CRC responsive to immunotherapy.Keywords: Machine learning; colorectal cancer; immune evasion; immunotherapy; mRNAsi; metabolism; risk score model; stemness.

• 上一篇：【佳学基因检测】缺氧诱导的巢蛋白通过靶向转录因子 Nrf2、STAT3 和 SOX2 调节肺癌的活力和代谢
• 下一篇：【佳学基因检测】miR-205在前列腺癌中的表达及与患者预后的关系

• 收藏
• 挑错
• 推荐
• 打印