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<p>Our previous study revealed that an elevated ratio of red cell distribution width to lymphocyte ratio (RLR) might be linked to unfavorable poorer outcomes among colorectal cancer (CRC) patients. Nevertheless, additional research is necessary to authenticate these findings and ascertain the clinical relevance of RLR in the context of CRC. A total of 1,541 patients diagnosed with CRC were retrospectively included in this study. The association between clinical-pathological data and overall survival (OS) was examined using Kaplan-Meier curves. A machine learning approach was employed to identify prognostic indicators, construct a risk model, determine independent factors influencing OS, and assess the model’s accuracy. In CRC patients, RLR exhibited the highest average area under the curve, sensitivity, and specificity, as well, as the highest youden, except for CEA. Subsequently, the three indicators, preoca199, preoCEA, and preoca125, were normalized for CRC patients. Using the weight matrix in the SVM classification model, the 24 indicators were ranked, identifying the top 5 important indicators. The final models included CEA (HR = 1.0032, <i>P</i> = 0.0025), RLR (HR = 1.018, <i>P</i> = 0.0157), T-stage (HR = 1.4456, <i>P</i> &lt; 0.001), N-stage (HR = 1.2092, <i>P</i> = 0.0962), and Stage (HR = 1.7024, <i>P</i> = 0.0012). Notably, OS was significantly prolonged in the group with low levels of CEA and low RLR. RLR could be a novel prognostic marker for OS in patients with CRC.</p>

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A novel prognostic biomarker for colorectal cancer: the ratio of red cell distribution width to lymphocytes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月9日

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  • Jiahao Huang,
  • Zongxuan Huang,
  • Guangquan Qin,
  • Huage Zhong,
  • Jialiang Gan,
  • Yun Guo

摘要

Our previous study revealed that an elevated ratio of red cell distribution width to lymphocyte ratio (RLR) might be linked to unfavorable poorer outcomes among colorectal cancer (CRC) patients. Nevertheless, additional research is necessary to authenticate these findings and ascertain the clinical relevance of RLR in the context of CRC. A total of 1,541 patients diagnosed with CRC were retrospectively included in this study. The association between clinical-pathological data and overall survival (OS) was examined using Kaplan-Meier curves. A machine learning approach was employed to identify prognostic indicators, construct a risk model, determine independent factors influencing OS, and assess the model’s accuracy. In CRC patients, RLR exhibited the highest average area under the curve, sensitivity, and specificity, as well, as the highest youden, except for CEA. Subsequently, the three indicators, preoca199, preoCEA, and preoca125, were normalized for CRC patients. Using the weight matrix in the SVM classification model, the 24 indicators were ranked, identifying the top 5 important indicators. The final models included CEA (HR = 1.0032, P = 0.0025), RLR (HR = 1.018, P = 0.0157), T-stage (HR = 1.4456, P < 0.001), N-stage (HR = 1.2092, P = 0.0962), and Stage (HR = 1.7024, P = 0.0012). Notably, OS was significantly prolonged in the group with low levels of CEA and low RLR. RLR could be a novel prognostic marker for OS in patients with CRC.