Cao Lichao, Wei Shangqing, Yin Zongyi, Chen Fang, Ba Ying, Weng Qi, Zhang Jiahao, Zhang Hezi
School of Life Sciences, Northwest University, 710127, Xi'an, Shaanxi Province, China.
Shenzhen University General Hospital, 518071, Shenzhen, Guangdong Province, China.
Heliyon. 2024 Jan 15;10(2):e24713. doi: 10.1016/j.heliyon.2024.e24713. eCollection 2024 Jan 30.
Colon cancer is one of the most common cancers, with 30-50 % of patients returning or metastasizing within 5 years of treatment. Increasingly, researchers have highlighted the influence of microbes on cancer malignant activity, while no studies have explored the relationship between colon cancer and the microbes in tumors. Here, we used tissue and blood samples from 67 colon cancer patients to identify pathogenic microorganisms associated with the diagnosis and prediction of colon cancer and evaluate the predictive performance of each pathogenic marker and its combination based on the next-generation sequencing data by using random forest algorithms. The results showed that we constructed a database of 13,187 pathogenic microorganisms associated with human disease and identified 2 pathogenic microorganisms (_32630 and _57078) associated with colon cancer diagnosis, and the constructed diagnostic prediction model performed well for tumor tissue samples and blood samples. In summary, for the first time, we provide new molecular markers for the diagnosis of colon cancer based on the expression of pathogenic microorganisms in order to provide a reference for improving the effective screening rate of colon cancer in clinical practice and ameliorating the personalized treatment of colon cancer patients.
结肠癌是最常见的癌症之一，30%至50%的患者在治疗后5年内复发或转移。越来越多的研究人员强调了微生物对癌症恶性活动的影响，然而尚无研究探讨结肠癌与肿瘤内微生物之间的关系。在此，我们使用67例结肠癌患者的组织和血液样本，通过随机森林算法基于下一代测序数据鉴定与结肠癌诊断和预测相关的致病微生物，并评估每个致病标志物及其组合的预测性能。结果表明，我们构建了一个包含13187种与人类疾病相关的致病微生物的数据库，鉴定出2种与结肠癌诊断相关的致病微生物（_32630和_57078），并且构建的诊断预测模型对肿瘤组织样本和血液样本均表现良好。总之，我们首次基于致病微生物的表达为结肠癌诊断提供了新的分子标志物，以便为提高临床实践中结肠癌的有效筛查率和改善结肠癌患者的个性化治疗提供参考。
