基于 cisplatin 耐药相关 ceRNA 网络的小细胞肺癌两基因预后模型的鉴定。

The identification of a two-gene prognostic model based on cisplatin resistance-related ceRNA network in small cell lung cancer.

机构信息

Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, People's Republic of China.

University of Science and Technology of China, Hefei, Anhui, People's Republic of China.

出版信息

BMC Med Genomics. 2023 May 15;16(1):103. doi: 10.1186/s12920-023-01536-5.

DOI:10.1186/s12920-023-01536-5

PMID:37189142

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10184403/

Abstract

BACKGROUND

Small cell lung cancer (SCLC) is a very malignant tumor with rapid growth and early metastasis. Platinum-based chemo-resistance is the major issue for SCLC treatment failure. Identifying a new prognostic model will help to make an accurate treatment decision for SCLC patients.

METHODS

Using the genomics of drug sensitivity in cancer (GDSC) database, we identified cisplatin resistance-related lncRNAs in SCLC cells. Based on the competing endogenous RNA (ceRNA) network, we identified the mRNAs correlated with the lncRNAs. Using Cox and LASSO regression analysis, a prognostic model was established. The survival prediction accuracy was evaluated by receiver operating characteristic (ROC) curve and Kaplan-Meier analysis. GSEA, GO, KEGG and CIBERSORT tools were used for functional enrichment and immune cells infiltration analysis.

RESULTS

We first screened out 10 differentially expressed lncRNAs between cisplatin resistant and sensitive SCLC cells from GDSC database. Based on ceRNA network, 31 mRNAs were identified with a correlation with the 10 lncRNAs. Furthermore, two genes (LIMK2 and PI4K2B) were identified by Cox and LASSO regression analysis to construct a prognostic model. Kaplan-Meier analysis indicated that the high-risk group had a poor overall survival compared with the low-risk group. The predicted area under the ROC curve (AUC) was 0.853 in the training set, and the AUC was 0.671 in the validation set. In the meanwhile, the low expression of LIMK2 or the high expression of PI4K2B in SCLC tumors was also significantly associated with poor overall survival in both training and validation sets. Functional enrichment analysis showed that the low-risk group was enriched in the apoptosis pathway and high immune infiltration of T cells. Finally, an apoptosis-related gene Cathepsin D (CTSD) was identified to be up-regulated in the low-risk group, and its higher expression correlated with better overall survival in SCLC.

CONCLUSION

We established a prognostic model and potential biomarkers (LIMK2, PI4K2B and CTSD), which could help to improve the risk stratification of SCLC patients.

摘要

背景

小细胞肺癌（SCLC）是一种生长迅速、早期转移的恶性肿瘤。铂类药物耐药是 SCLC 治疗失败的主要问题。确定新的预后模型将有助于为 SCLC 患者做出准确的治疗决策。

方法

利用癌症药物敏感性基因组学（GDSC）数据库，我们鉴定了 SCLC 细胞中与顺铂耐药相关的 lncRNA。基于竞争性内源 RNA（ceRNA）网络，我们鉴定了与 lncRNA 相关的 mRNAs。采用 Cox 和 LASSO 回归分析建立预后模型。通过受试者工作特征（ROC）曲线和 Kaplan-Meier 分析评估生存预测准确性。采用 GSEA、GO、KEGG 和 CIBERSORT 工具进行功能富集和免疫细胞浸润分析。

结果

我们首先从 GDSC 数据库中筛选出顺铂耐药和敏感 SCLC 细胞之间差异表达的 10 个 lncRNA。基于 ceRNA 网络，鉴定出与这 10 个 lncRNA 相关的 31 个 mRNAs。此外，通过 Cox 和 LASSO 回归分析，确定了两个基因（LIMK2 和 PI4K2B）来构建预后模型。Kaplan-Meier 分析表明，高危组的总生存期明显低于低危组。训练集的 ROC 曲线下预测面积（AUC）为 0.853，验证集的 AUC 为 0.671。同时，在训练集和验证集中，SCLC 肿瘤中 LIMK2 低表达或 PI4K2B 高表达均与总生存期不良显著相关。功能富集分析表明，低危组富集于细胞凋亡途径和 T 细胞高浸润。最后，鉴定到一个与细胞凋亡相关的基因 Cathepsin D（CTSD）在低危组中上调，其高表达与 SCLC 患者的总生存期改善相关。