Abstract

BackgroundColon adenocarcinoma (COAD) is the most common colon cancer exhibiting high mortality. Due to their association with cancer progression, long noncoding RNAs (lncRNAs) are now being used as prognostic biomarkers. In the present study, we used relevant clinical information and expression profiles of lncRNAs originating from The Cancer Genome Atlas database, aiming to construct a prognostic lncRNA signature to estimate the prognosis of patients.MethodsThe samples were randomly spilt into training and validation cohorts. In the training cohort, prognosis-related lncRNAs were selected from differentially expressed lncRNAs using the univariate Cox analysis. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were employed for identifying prognostic lncRNAs. The prognostic signature was constructed by these lncRNAs.ResultsThe prognostic model was able to calculate each COAD patient’s risk score and split the patients into groups of low and high risks. Compared to the low-risk group, the high-risk group had significant poor prognosis. Next, the prognostic signature was validated in the validation, as well as all cohorts. The receiver operating characteristic (ROC) curve and c-index were determined in all cohorts. Moreover, these prognostic lncRNA signatures were combined with clinicopathological risk factors to construct a nomogram for predicting the prognosis of COAD in the clinic. Finally, seven lncRNAs (CTC-273B12.10, AC009404.2, AC073283.7, RP11-167H9.4, AC007879.7, RP4-816N1.7, and RP11-400N13.2) were identified and validated by different cohorts. The Kyoto Encyclopedia of Genes and Genomes analysis of the mRNAs co-expressed with the seven prognostic lncRNAs suggested four significantly upregulated pathways, which were AGE-RAGE, focal adhesion, ECM-receptor interaction, and PI3K/Akt signaling pathways.ConclusionThus, our study verified that the seven lncRNAs mentioned can be used as biomarkers to predict the prognosis of COAD patients and design personalized treatments.

Highlights

  • Colon cancer refers to a frequently occurring gastrointestinal malignant tumor, and it remains the third most common cause of cancer mortalities (Siegel et al, 2017)

  • To improve the accuracy of diagnosis and provide directions for personalized treatment of Colon adenocarcinoma (COAD), it is crucial to discover novel prognostic biomarkers and more precise methods that can differentiate between patients with low and high risks of poor prognosis

  • Accumulating evidence has shown that Long non-coding RNA (lncRNA) exhibit restrictive cancerspecific and tissue-specific expression patterns; the emphasis on the identification of molecular biomarkers has been shifted from mRNA and microRNA to lncRNA (Zhang et al, 2016a; Zhang et al, 2016b)

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Summary

Introduction

Colon cancer refers to a frequently occurring gastrointestinal malignant tumor, and it remains the third most common cause of cancer mortalities (Siegel et al, 2017). To improve the accuracy of diagnosis and provide directions for personalized treatment of COAD, it is crucial to discover novel prognostic biomarkers and more precise methods that can differentiate between patients with low and high risks of poor prognosis. Colon adenocarcinoma (COAD) is the most common colon cancer exhibiting high mortality Due to their association with cancer progression, long noncoding RNAs (lncRNAs) are being used as prognostic biomarkers. We used relevant clinical information and expression profiles of lncRNAs originating from The Cancer Genome Atlas database, aiming to construct a prognostic lncRNA signature to estimate the prognosis of patients. The receiver operating characteristic (ROC) curve and c-index were determined in all cohorts These prognostic lncRNA signatures were combined with clinicopathological risk factors to construct a nomogram for predicting the prognosis of COAD in the clinic. Our study verified that the seven lncRNAs mentioned can be used as biomarkers to predict the prognosis of COAD patients and design personalized treatments

Methods
Results
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