基于机器学习的铜死亡相关 lncRNA 评分模型的构建及其在预测肝细胞癌免疫治疗反应中的系统评价。

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

机构信息

Oncology Department, Deyang People's Hospital, Deyang, China.

Intensive care Unit, Deyang People's Hospital, Deyang, China.

出版信息

Front Immunol. 2023 Jan 25;14:1097075. doi: 10.3389/fimmu.2023.1097075. eCollection 2023.

DOI:10.3389/fimmu.2023.1097075

PMID:36761763

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9905126/

Abstract

INTRODUCTION

Hepatocellular 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.

METHODS

We 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.

RESULTS

Ten 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 (, and ) 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.

CONCLUSION

We created a prognostic CLS signature by machine learning, and we comprehensively analyzed the signature in the fields of function, immunity, mutation and clinical application.

摘要

简介

肝细胞癌（HCC）是一种预后较差的常见恶性肿瘤。铜死亡和相关的长链非编码 RNA（lncRNA）与癌症进展有关。然而，铜死亡相关 lncRNA 在 HCC 中的预后价值信息仍然有限。

方法

我们从 TCGA 和 ICGC 数据库中分离 HCC 的转录组和临床信息。获得了 10 个铜死亡相关基因，并通过 Pearson 相关性分析相关 lncRNA。通过执行 Lasso 回归，我们基于铜死亡相关 lncRNA 评分（CLS）创建了一个铜死亡相关 lncRNA 预后模型。通过各种 R 包进行了功能、免疫、突变和临床应用等综合分析。

结果

选择了 10 个铜死亡相关基因，并收集了 13 个相关预后 lncRNA 进行模型构建。CLS 与癌症相关通路呈正相关或负相关。此外，细胞周期和免疫相关通路被富集。通过进行肿瘤微环境（TME）分析，我们确定 T 细胞被激活。高 CLS 具有更多的肿瘤特征，可能导致更高的侵袭性和治疗耐药性。在高 CLS 样本中发现了 3 个基因（、和），其突变频率更高。在高 CLS 样本中检测到更多的扩增和缺失。在临床应用中，构建了基于 CLS 的列线图。在高 CLS 患者中，5-氟尿嘧啶、吉西他滨和阿霉素的敏感性更好。然而，低 CLS 患者对免疫治疗的敏感性更高。