First Clinical Medical College, General Hospital of Ningxia Medical University, 804 Shengli Road, Yinchuan, 750004, China.
Department of Anal-Colorectal Surgery, General Hospital of Ningxia Medical University, 804 Shengli Road, Yinchuan, 750004, China.
Sci Rep. 2024 Oct 18;14(1):24426. doi: 10.1038/s41598-024-75270-y.
Globally, CRC ranks as a principal cause of mortality, with projections indicating a substantial rise in both incidence and mortality by the year 2040. The immunological responses to cancer heavily rely on the function of CD4Tconv. Despite this critical role, prognostic studies on CRC-related CD4Tconv remain insufficient. In this investigation, transcriptomic and clinical data were sourced from TCGA and GEO. Initially, we pinpointed CD4TGs using single-cell datasets. Prognostic genes were then isolated through univariate Cox regression analysis. Building upon this, 101 machine learning algorithms were employed to devise a novel risk assessment framework, which underwent rigorous validation using Kaplan-Meier survival analysis, univariate and multivariate Cox regression, time-dependent ROC curves, nomograms, and calibration plots. Furthermore, GSEA facilitated the examination of these genes' potential roles. The RS derived from this model was also analyzed for its implications in the TME, and its potential utility in immunotherapy and chemotherapy contexts. A novel prognostic model was developed, utilizing eight CD4TGs that are significantly linked to the outcomes of patients with CRC. This model's RS showcased remarkable predictive reliability for the overall survival rates of CRC patients and strongly correlated with malignancy levels. RS serves as an autonomous prognostic indicator, capable of accurately forecasting patient prognoses. Based on the median value of RS, patients were categorized into subgroups of high and low risk. The subgroup with higher risk demonstrated increased immune infiltration and heightened activity of genes associated with immunity. This investigation's establishment of a CD4TGs risk model introduces novel biomarkers for the clinical evaluation of CRC risks. These biomarkers may enhance therapeutic approaches and, in turn, elevate the clinical outcomes for patients with CRC by facilitating an integrated treatment strategy.
全球范围内，CRC 是主要的死亡原因之一，预计到 2040 年，CRC 的发病率和死亡率将大幅上升。癌症的免疫反应在很大程度上依赖于 CD4Tconv 的功能。尽管 CD4Tconv 具有如此关键的作用，但 CRC 相关 CD4Tconv 的预后研究仍然不足。在这项研究中，我们从 TCGA 和 GEO 数据库中获取了转录组和临床数据。首先，我们使用单细胞数据集确定了 CD4TGs。然后通过单变量 Cox 回归分析分离出预后基因。在此基础上，我们使用 101 种机器学习算法构建了一个新的风险评估框架，并通过 Kaplan-Meier 生存分析、单变量和多变量 Cox 回归、时间依赖性 ROC 曲线、列线图和校准图对其进行了严格的验证。此外，GSEA 有助于研究这些基因的潜在作用。该模型得到的 RS 也被分析了其在 TME 中的作用，以及在免疫治疗和化疗中的潜在应用。我们开发了一种新的预后模型，该模型利用 8 个与 CRC 患者结局显著相关的 CD4TGs。该模型的 RS 对 CRC 患者的总生存率具有显著的预测可靠性，并与恶性程度高度相关。RS 是一个自主的预后指标，能够准确预测患者的预后。根据 RS 的中位数，患者被分为高风险和低风险亚组。高风险亚组的免疫浸润增加，与免疫相关的基因活性增强。本研究建立的 CD4TGs 风险模型为 CRC 风险的临床评估提供了新的生物标志物。这些生物标志物可以增强治疗方法，并通过促进综合治疗策略，提高 CRC 患者的临床结局。
