Zheng Yupeng, Yang Mian, Yi Hongyi, Peng Tao, Sun Jiaze, Yu Jiazi
Department of Colon Anorectal Surgery, Ningbo Medical Center LiHuiLi Hospital, Ningbo, China.
Transl Cancer Res. 2025 May 30;14(5):3096-3112. doi: 10.21037/tcr-2024-2268. Epub 2025 May 16.
Colorectal cancer (CRC) is a major cause of cancer-related deaths worldwide. Understanding the genetic and molecular alterations in CRC can improve patient outcomes. Circulating tumor cells (CTCs) are crucial in cancer metastasis and progression. Analyzing the differentially expressed genes (DEGs) between CTCs and CRC may provide us with new therapeutic strategies. Therefore, this study aims to analyze these DEGs to construct a prognostic risk model that predicts the outcomes of CRC patients and guides clinical treatment.
We analyzed The Cancer Genome Atlas (TCGA) database to identify 1,727 DEGs between CRC and normal samples, and GSE82198 data to find 3,564 DEGs between CTCs and primary CRC samples. Using enrichment analysis, least absolute shrinkage and selection operator (LASSO) regression, and stepwise Cox regression, we derived eight model genes to construct a prognostic risk model. Various algorithms were employed in the immune microenvironment analysis. Integrating clinical factors with risk grouping, we developed a nomogram. We assessed chemotherapy sensitivity and epithelial-mesenchymal transition (EMT) scores in high-/low-risk groups and explored model gene expression at the single-cell level.
We constructed a prognostic risk model for CRC based on eight DEGs of CTCs. The model effectively predicted treatment outcomes and correlated closely with actual prognosis. Through immune microenvironment analysis, we revealed differences in immune cell infiltration and checkpoint gene expression among different risk groups. Moreover, patients in the high-risk group showed higher sensitivity to chemotherapy drugs compared to those in the low-risk group.
The prognosis model based on CTCs' DEGs can effectively predict patient outcomes, facilitating precision treatment for patients. This model holds significant guiding implications for immunotherapy and chemotherapy in CRC, offering potential strategies for the clinical treatment of CRC.
结直肠癌（CRC）是全球癌症相关死亡的主要原因。了解CRC中的基因和分子改变可以改善患者预后。循环肿瘤细胞（CTC）在癌症转移和进展中至关重要。分析CTC与CRC之间的差异表达基因（DEG）可能为我们提供新的治疗策略。因此，本研究旨在分析这些DEG，构建一个预测CRC患者预后并指导临床治疗的预后风险模型。
我们分析了癌症基因组图谱（TCGA）数据库，以识别CRC与正常样本之间的1727个DEG，并分析GSE82198数据，以找到CTC与原发性CRC样本之间的3564个DEG。通过富集分析、最小绝对收缩和选择算子（LASSO）回归以及逐步Cox回归，我们得出了八个模型基因来构建预后风险模型。免疫微环境分析采用了各种算法。将临床因素与风险分组相结合，我们开发了一种列线图。我们评估了高/低风险组中的化疗敏感性和上皮-间质转化（EMT）评分，并在单细胞水平上探索了模型基因表达。
我们基于CTC的八个DEG构建了CRC的预后风险模型。该模型有效地预测了治疗结果，并与实际预后密切相关。通过免疫微环境分析，我们揭示了不同风险组之间免疫细胞浸润和检查点基因表达的差异。此外，与低风险组相比，高风险组的患者对化疗药物表现出更高的敏感性。
基于CTC的DEG的预后模型可以有效地预测患者的预后，促进对患者的精准治疗。该模型对CRC的免疫治疗和化疗具有重要的指导意义，为CRC的临床治疗提供了潜在的策略。
