Table_2_Construction and systematic evaluation of a machine learning-based cuproptosis-related lncRNA score signature to predict the response to immunotherapy in hepatocellular carcinoma.docx

IntroductionHepatocellular carcinoma (HCC) is a common malignant cancer with a poor prognosis. Cuproptosis and associated lncRNAs are connected with cancer progression. However, the information on the prognostic value of cuproptosis-related lncRNAs is still limited in HCC.
MethodsWe isolated the transcriptome and clinical information of HCC from TCGA and ICGC databases. Ten cuproptosis-related genes were obtained and related lncRNAs were correlated by Pearson’s correlation. By performing lasso regression, we created a cuproptosis-related lncRNA prognostic model based on the cuproptosis-related lncRNA score (CLS). Comprehensive analyses were performed, including the fields of function, immunity, mutation and clinical application, by various R packages.
ResultsTen cuproptosis-related genes were selected, and 13 correlated prognostic lncRNAs were collected for model construction. CLS was positively or negatively correlated with cancer-related pathways. In addition, cell cycle and immune related pathways were enriched. By performing tumor microenvironment (TME) analysis, we determined that T-cells were activated. High CLS had more tumor characteristics and may lead to higher invasiveness and treatment resistance. Three genes (TP53, CSMD1 and RB1) were found in high CLS samples with more mutational frequency. More amplification and deletion were detected in high CLS samples. In clinical application, a CLS-based nomogram was constructed. 5-Fluorouracil, gemcitabine and doxorubicin had better sensitivity in patients with high CLS. However, patients with low CLS had better immunotherapeutic sensitivity.
ConclusionWe created a prognostic CLS signature by machine learning, and we comprehensively analyzed the signature in the fields of function, immunity, mutation and clinical application.

引言 肝细胞癌（Hepatocellular carcinoma, HCC）是一类预后不良的常见恶性肿瘤。铜死亡（Cuproptosis）及其相关长链非编码RNA（long non-coding RNA, lncRNA）与肿瘤进展密切相关，但目前关于铜死亡相关lncRNA在HCC中的预后价值的研究信息仍较为有限。
方法 我们从TCGA与ICGC数据库中获取了HCC患者的转录组数据及临床信息。筛选得到10个铜死亡相关基因，并通过Pearson相关分析筛选与其关联的lncRNAs。基于Lasso回归分析，我们构建了以铜死亡相关lncRNA评分（Cuproptosis-related lncRNA score, CLS）为核心的预后模型。借助多款R软件包开展了涵盖功能、免疫、突变及临床应用等维度的综合分析。
结果 共筛选得到10个铜死亡相关基因，并收集到13个与其相关的预后性lncRNAs用于模型构建。CLS与多条肿瘤相关通路呈正负向相关。此外，细胞周期及免疫相关通路存在显著富集。通过肿瘤微环境（Tumor Microenvironment, TME）分析发现，T细胞处于激活状态。高CLS组肿瘤恶性特征更为显著，且可能具有更高的侵袭性与治疗抵抗性。高CLS样本中，TP53、CSMD1及RB1这3个基因的突变频率更高，同时检测到更多的基因扩增与缺失事件。在临床应用层面，我们构建了基于CLS的列线图（Nomogram）。5-氟尿嘧啶（5-Fluorouracil）、吉西他滨（Gemcitabine）及多柔比星（Doxorubicin）在高CLS患者中展现出更优的药物敏感性；而低CLS患者则对免疫治疗具有更高的响应敏感性。
结论 本研究通过机器学习方法构建了基于CLS的预后特征模型，并从功能、免疫、突变及临床应用多个维度对该特征模型开展了全面系统的分析。