Abstract
BACKGROUND: Colon adenocarcinoma (COAD) is one of the common gastrointestinal malignant diseases, with high mortality rate and poor prognosis due to delayed diagnosis. This study aimed to construct a prognostic prediction model for patients with colon adenocarcinoma (COAD) recurrence. METHODS: Differently expressed RNAs (DERs) between recurrence and non-recurrence COAD samples were identified based on expression profile data from the NCBI Gene Expression Omnibus (GEO) repository and The Cancer Genome Atlas (TCGA) database. Then, recurrent COAD discriminating classifier was established using SMV-RFE algorithm, and receiver operating characteristic curve was used to assess the predictive power of classifier. Furthermore, the prognostic prediction model was constructed based on univariate and multivariate Cox regression analysis, and Kaplan-Meier survival curve analysis was used to estimate this model. Furthermore, the co-expression network of DElncRNAs and DEmRNAs was constructed followed by GO and KEGG pathway enrichment analysis. RESULTS: A total of 54 optimized signature DElncRNAs were screened and SMV classifier was constructed, which presented a high accuracy to distinguish recurrence and non-recurrence COAD samples. Furthermore, six independent prognostic lncRNAs signatures (LINC00852, ZNF667-AS1, FOXP1-IT1, LINC01560, TAF1A-AS1, and LINC00174) in COAD patients with recurrence were screened, and the prognostic prediction model for recurrent COAD was constructed, which possessed a relative satisfying predicted ability both in the training dataset and validation dataset. Furthermore, the DEmRNAs in the co-expression network were mainly enriched in glycan biosynthesis, cardiac muscle contraction, and colorectal cancer. CONCLUSIONS: Our study revealed that six lncRNA signatures acted as an independent prognostic biomarker for patients with COAD recurrence.