Current studies indicate that long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers and implicated with prognosis in gastric cancer (GC).
We intended to generate a multi-lncRNA signature to improve prognostic prediction of GC. By analyzing ten paired GC and adjacent normal mucosa tissues, 339 differentially expressed lncRNAs were identified as the candidate prognostic biomarkers in GC.
Then we used LASSO Cox regression method to build a 12-lncRNA signature and validated it in another independent GEO dataset. An innovative 12-lncRNA signature was established, and it was significantly associated with the disease free survival (DFS) in the training dataset.
By applying the 12-lncRNA signature, the training cohort patients could be categorized into high-risk or low-risk subgroup with significantly different DFS (HR = 4.52, 95%CI= 2.49-8.20, P < 0.0001). Similar results were obtained in another independent GEO dataset (HR=1.58, 95%CI=1.05 - 2.38, P=0.0270). Further analysis showed that the prognostic value of this 12-lncRNA signature was independent of AJCC stage and postoperative chemotherapy.
Receiver operating characteristic (ROC) analysis showed that the area under receiver operating characteristic curve (AUC) of combined model reached 0.869. Additionally, a well-performed nomogram was constructed for clinicians. Moreover, single-sample gene-set enrichment analysis (ssGSEA) showed that a group of pathways related to drug resistance and cancer metastasis significantly enriched in the high risk patients.
A useful innovative 12-lncRNA signature was established for prognostic evaluation of GC.
It might complement clinicopathological features and facilitate personalized management of GC.
GC is a common and highly lethal malignancy, being the fourth most common cancer and the second leading cause of cancer death in the world 1. Although the tendency of incidence rates declines, it is still concerned worldwide with the highest estimated mortality rates in Eastern Asian 2.
Surgery is the only curative treatment strategy and conventional chemotherapy has shown limited efficacy. Despite the recent therapeutic advances, the overall outcome of GC remains undesirable 3, 4. For the risk stratification of GC, the TNM Staging System has been widely used, which is developed and maintained by American Joint Committee on Cancer (AJCC) and adopted by the Union International Committee on Cancer (UICC).
Although TNM staging system is of great value clinically, it has not adequate prognostic and predictive capabilities to guide patient management 5, 6. Thus, new biomarkers are needed to discriminate the high-risk patients with GC and consequently improve personalized cancer care.
|AJCC||American Joint Committee on Cancer|
|AUC||area under receiver operating characteristic. CI: confidence interval|
|DCA||decision curve analysis|
|DFS||disease free survival|
|GEO||Gene Expression Ominus|
|lincRNA||large intergenic non-coding RNAs|
|lncRNAs||long non-coding RNAs|
|ROC||receiver operating characteristic|
|ssGSEA||single-sample gene-set enrichment analysis|
|UICC||Union International Committee on Cancer.|
文章主要工作就是构建了一个 12-lncRNA signature，但是构建过程分散在了Methods和Results里，这里帮大家总结一下，具体的内容回到文章里看，文章篇幅不长。
6.使用 SVM-RFE 对训练集进行特征选择，筛选特征 lncRNA
7.预测 lncRNA 的靶基因
实战系列（三）模仿4分胃癌发病机制和预后关键基因的文章 （寻找预后key gene，是最普遍的预后建模方法）