Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.
Department of Internal Medicine-Pediatrics, Marshfield Clinic Health System, Marshfield, WI 54449, USA.
Carcinogenesis. 2023 Dec 2;44(8-9):650-661. doi: 10.1093/carcin/bgad062.
Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC.
The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression.
HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ± 0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis.
Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.
肝细胞癌（HCC）是美国发病率逐年上升、死亡率较高的主要癌症类型之一。微小 RNA（miRNA）已成为癌症患者有价值的预后指标。为了确定预测 HCC 患者生存的 miRNA 特征，我们使用 122 名 HCC 患者的 miRNA 表达谱开发了一种基于机器学习的 HCC 生存估计方法 HCCse。
HCCse 方法采用最优特征选择算法与支持向量回归相结合的方法设计。
HCCse 确定了一个稳健的 miRNA 特征，由 32 个 miRNA 组成，获得了 HCC 患者实际和估计生存时间之间的平均相关系数（R）和平均绝对误差（MAE）为 0.87±0.02 和 0.73 年；Jackknife 检验分别获得了实际和估计生存时间之间的 R 和 MAE 为 0.73 和 0.97 年。鉴定的特征有 7 个预后 miRNA（hsa-miR-146a-3p、hsa-miR-200a-3p、hsa-miR-652-3p、hsa-miR-34a-3p、hsa-miR-132-5p、hsa-miR-1301-3p 和 hsa-miR-374b-3p）和 4 个诊断 miRNA（hsa-miR-1301-3p、hsa-miR-17-5p、hsa-miR-34a-3p 和 hsa-miR-200a-3p）。值得注意的是，这三个 miRNA 中的三个，hsa-miR-200a-3p、hsa-miR-1301-3p 和 hsa-miR-17-5p，也与肿瘤分期有关，进一步强调了它们的临床相关性。此外，我们进行了通路富集分析，发现鉴定的 miRNA 特征的靶基因显著富集在乙型肝炎途径中，表明其可能参与 HCC 的发病机制。
我们研究开发了基于机器学习的 HCCse 方法，使用 miRNA 表达谱预测 HCC 患者的生存。我们确定了一个稳健的 miRNA 特征，由 32 个 miRNA 组成，具有预后和诊断价值，突出了它们在 HCC 管理中的临床相关性和在 HCC 发病机制中的潜在作用。
