Chen Qiang, Hu Zunqi, Zhang Xin, Wei Ziran, Fu Hongbing, Yang DeJun, Cai Qingping
Department of Gastrointestinal Surgery, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Huangpu District, Shanghai 200003, China.
Open Med (Wars). 2021 Apr 3;16(1):540-552. doi: 10.1515/med-2021-0241. eCollection 2021.
This study aimed to develop a multi-long noncoding RNA (lncRNA) signature for the prediction of gastric cancer (GC) based on differential gene expression between recurrence and nonrecurrence patients.
By repurposing microarray expression profiles of RNAs from The Cancer Genome Atlas (TCGA), we performed differential expression analysis between recurrence and nonrecurrence patients. A prognostic risk prediction model was constructed based on data from TCGA database, and its reliability was validated using data from Gene Expression Omnibus database. Furthermore, the lncRNA-associated competing endogenous RNA (ceRNA) network was constructed, namely, DIANA-LncBasev2 and starBase database.
We identified 363 differentially expressed RNAs (317 mRNAs, 18 lncRNAs, and 28 microRNAs [miRNAs]). Principal component analysis showed that the seven-feature lncRNAs screened by support vector machine-recursive feature elimination algorithm was more informative for predicting recurrence of GC in comparison with the eight-feature lncRNAs screened by random forest-out-of-bag algorithm. Four of the seven-feature lncRNAs including LINC00843, SNHG3, C21orf62-AS1, and MIR99AHG were chosen to develop a four-lncRNA risk score model. This risk score model was able to distinguish patients with high and low risk of recurrence, and was tested in two independent validation sets. The ceRNA network of this four-lncRNA signature included 10 miRNAs and 178 mRNAs. The mRNAs significantly related to the Wnt-signaling pathway and relevant biological processes.
A useful four-lncRNA signature recurrence was established to distinguish GC patients with high and low risk of recurrence. Regulating the relevant miRNAs and Wnt pathway might partly affect GC metastasisby.
本研究旨在基于复发和未复发患者之间的差异基因表达，开发一种用于预测胃癌（GC）的多长链非编码RNA（lncRNA）特征。
通过重新利用来自癌症基因组图谱（TCGA）的RNA微阵列表达谱，我们对复发和未复发患者进行了差异表达分析。基于TCGA数据库的数据构建了预后风险预测模型，并使用来自基因表达综合数据库的数据验证了其可靠性。此外，构建了lncRNA相关的竞争性内源性RNA（ceRNA）网络，即DIANA-LncBasev2和starBase数据库。
我们鉴定出363个差异表达的RNA（317个mRNA、18个lncRNA和28个微小RNA [miRNA]）。主成分分析表明，与通过随机森林袋外算法筛选的八特征lncRNA相比，通过支持向量机递归特征消除算法筛选的七特征lncRNA对预测GC复发更具信息量。选择七个特征lncRNA中的四个，包括LINC00843、SNHG3、C21orf62-AS1和MIR99AHG，来开发一个四lncRNA风险评分模型。该风险评分模型能够区分复发风险高和低的患者，并在两个独立的验证集中进行了测试。这个四lncRNA特征的ceRNA网络包括10个miRNA和178个mRNA。这些mRNA与Wnt信号通路和相关生物学过程显著相关。
建立了一个有用的四lncRNA特征复发模型，以区分复发风险高和低的GC患者。调节相关的miRNA和Wnt通路可能部分影响GC转移。
