Department of Colorectal & Anal Surgery, First Hospital Bethune of Jilin University, No. 71, Xinmin Street, Chaoyang District, Changchun, 130000, Jilin, China.
BMC Cancer. 2020 Oct 7;20(1):968. doi: 10.1186/s12885-020-07163-y.
Colon adenocarcinoma (COAD) patients who develop recurrence have poor prognosis. Our study aimed to establish effective prognosis prediction model based on competing endogenous RNAs (ceRNAs) for recurrence of COAD.
COAD expression profilings downloaded from The Cancer Genome Atlas (TCGA) were used as training dataset, and expression profilings of GSE29623 retrieved from Gene Expression Omnibus (GEO) were set as validation dataset. Differentially expressed RNAs (DERs) between non-recurrent and recurrent specimens in training dataset were screened, and optimum prognostic signature DERs were revealed to establish prognostic score (PS) model. Kaplan-Meier survival analysis was conducted for PS model, and GEO dataset was used for validation. Prognosis prediction efficiencies were evaluated by area under curve (AUC) and C-index. Meanwhile, ceRNA regulatory network was constructed by using signature mRNAs, lncRNAs and miRNAs.
We identified 562 DERs including 42 lncRNAs, 36 miRNAs, and 484 mRNAs. PS prediction model, consisting of 17 optimum prognostic signature DERs, showed that high risk group had significantly poorer prognosis (5-year AUC = 0.951, C-index = 0.788), which also validated in GSE29623. Prognosis prediction model incorporating multi-RNAs with pathologic distant metastasis (M) and pathologic primary tumor (T) (5-year AUC = 0.969, C-index = 0.812) had better efficiency than clinical prognosis prediction model (5-year AUC = 0.712, C-index = 0.680). In the constructed ceRNA regulatory network, lncRNA NCBP2-AS1 could interact with hsa-miR-34c and hsa-miR-363, and lncRNA LINC00115 could interact with hsa-miR-363 and hsa-miR-4709. SIX4, GRAP, NKAIN4, MMAA, and ERVMER34-1 are regulated by hsa-miR-4709.
Prognosis prediction model incorporating multi-RNAs with pathologic M and pathologic T may have great value in COAD prognosis prediction.
发生复发的结肠腺癌（COAD）患者预后较差。本研究旨在建立基于竞争内源性 RNA（ceRNA）的 COAD 复发有效预后预测模型。
使用从癌症基因组图谱（TCGA）下载的 COAD 表达谱作为训练数据集，并从基因表达综合数据库（GEO）检索 GSE29623 的表达谱作为验证数据集。筛选训练数据集中非复发和复发标本之间的差异表达 RNA（DER），并揭示最优预后特征 DER 以建立预后评分（PS）模型。进行 PS 模型的 Kaplan-Meier 生存分析，并使用 GEO 数据集进行验证。通过曲线下面积（AUC）和 C 指数评估预后预测效率。同时，利用特征 mRNA、lncRNA 和 miRNA 构建 ceRNA 调控网络。
我们鉴定了 562 个 DER，包括 42 个 lncRNA、36 个 miRNA 和 484 个 mRNA。由 17 个最优预后特征 DER 组成的 PS 预测模型表明，高风险组的预后明显较差（5 年 AUC=0.951，C 指数=0.788），这在 GSE29623 中也得到了验证。纳入多 RNA 与病理远处转移（M）和病理原发肿瘤（T）的预后预测模型（5 年 AUC=0.969，C 指数=0.812）比临床预后预测模型（5 年 AUC=0.712，C 指数=0.680）具有更好的效率。在构建的 ceRNA 调控网络中，lncRNA NCBP2-AS1 可与 hsa-miR-34c 和 hsa-miR-363 相互作用，lncRNA LINC00115 可与 hsa-miR-363 和 hsa-miR-4709 相互作用。SIX4、GRAP、NKAIN4、MMAA 和 ERVMER34-1 受 hsa-miR-4709 调控。
纳入多 RNA 与病理 M 和病理 T 的预后预测模型可能在 COAD 预后预测中具有重要价值。
