Du Fenqi, Wu Xiangxin, He Yibo, Zhao Shihui, Xia Mingyu, Zhang Bomiao, Tong Jinxue, Xia Tianyi
Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin Medial University, Harbin, Heilongjiang Province, People's Republic of China.
Ganzhou Cancer Hospital, Ganzhou, Jiangxi Province, People's Republic of China.
Appl Biochem Biotechnol. 2025 Feb;197(2):714-734. doi: 10.1007/s12010-024-05049-4. Epub 2024 Sep 2.
Colon cancer ranked third among the most frequently diagnosed cancers worldwide. Amino acid metabolic reprogramming was related to the occurrence and development of colon cancer. We looked for the amino acid metabolism genes (AMGs) associated with amino acid metabolism from molecular signatures database as prognostic markers and constructed amino acid metabolism scoring model (AMS). According to AMS, the patients were divided into high AMS and low AMS groups, and the prognostic characteristics, molecular phenotypes, somatic cell mutation characteristics, immune cell infiltration characteristics, and immunotherapy effect of the two groups were systematically analyzed. Finally, the compounds targeting AMGs were also screened. We screen out 6 prognostic AMGs (P < 0.05) and construct an AMS model based on them. K-M curve indicated that OS in low AMS group was significantly higher than that in high group (P < 0.05), which were validated in multiple datasets. And different AMS groups had different molecular phenotypes, somatic cell mutation characteristics and immune cell infiltration characteristics. Low AMS group had a better effect for immunotherapy. In addition, we predicted potential therapeutic compounds that could bind to AMGs target proteins. AMS model can be used as a hierarchical tool to evaluate the prognosis, immune infiltration characteristics and immunotherapy response ability of colon cancer. And the compounds screened based on AMGs may become new anti-tumor drugs.
结肠癌在全球最常被诊断出的癌症中排名第三。氨基酸代谢重编程与结肠癌的发生和发展相关。我们从分子特征数据库中寻找与氨基酸代谢相关的氨基酸代谢基因（AMGs）作为预后标志物，并构建了氨基酸代谢评分模型（AMS）。根据AMS，将患者分为高AMS组和低AMS组，并系统分析了两组的预后特征、分子表型、体细胞突变特征、免疫细胞浸润特征和免疫治疗效果。最后，还筛选了靶向AMGs的化合物。我们筛选出6个预后AMGs（P<0.05）并基于它们构建了AMS模型。K-M曲线表明，低AMS组的总生存期显著高于高AMS组（P<0.05），这在多个数据集中得到了验证。并且不同的AMS组具有不同的分子表型、体细胞突变特征和免疫细胞浸润特征。低AMS组对免疫治疗有更好的效果。此外，我们预测了可能与AMGs靶蛋白结合的潜在治疗化合物。AMS模型可作为一种分层工具来评估结肠癌的预后、免疫浸润特征和免疫治疗反应能力。并且基于AMGs筛选出的化合物可能成为新的抗肿瘤药物。
