The Function and Prognostic Value of RNA-Binding Proteins in Colorectal Adenocarcinoma Were Analyzed Based on Bioinformatics of Smart Medical Big Data

Colon cancer is the third most frequent cancer in the world and is mainly adenocarcinoma in terms of pathological type. It has been confirmed that the dysregulation of RNA-binding proteins (RBPs) significantly participates in the occurrence and development of numerous malignant tumors. Therefore, we...

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Bibliographic Details
Published in:Journal of healthcare engineering 2021, Vol.2021, p.5536330-15
Main Authors: Zhao, Haoran, Wang, Peng, Liu, Haishi, Li, Yueyang, Luo, Qian, Li, Man, Zhang, Yubao, Song, Zengfu
Format: Article
Language:English
Subjects:
Adenocarcinoma - diagnosis
Adenocarcinoma - genetics
Big Data
Biomarkers, Tumor - genetics
Colorectal Neoplasms - diagnosis
Colorectal Neoplasms - genetics
Computational Biology
Gene Expression Regulation, Neoplastic
Humans
Prognosis
RNA-Binding Proteins - genetics
RNA-Binding Proteins - metabolism
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Summary:Colon cancer is the third most frequent cancer in the world and is mainly adenocarcinoma in terms of pathological type. It has been confirmed that the dysregulation of RNA-binding proteins (RBPs) significantly participates in the occurrence and development of numerous malignant tumors. Therefore, we analyzed the RBPs associated with colon adenocarcinoma (COAD) to assess their possible biological effects and prognostic value. A total of 398 COAD tissue datasets and 39 normal tissue datasets were retrieved from the TCGA data resource and screened out the RBPs, which are differentially expressed between tumor tissues and nontumor tissues. Then, bioinformatics analyses based on smart medical big data were conducted on these RBPs. Overall, 181 differentially expressed RBPs were uncovered, consisting of 121 upregulated RBPs and 60 downregulated RBPs. Finally, we selected 7 prognostic-related RBPs with research prospects and constructed a prognostic model according to the median risk score. There were remarkable differences in OS between the high-risk and low-risk groups. In addition, the performance of the prognostic model was evaluated and verified with other COAD patient data in the TCGA database. The results showed that the area under the ROC curve (AUC) for the train group was 0.744 and the one for the test group was 0.661, confirming that the model assesses patients’ prognosis to some extent. And based on 7 hub RBPs, we constructed a nomogram as a reference for evaluating the survival rate of COAD patients.
ISSN:2040-2295
2040-2309
DOI:10.1155/2021/5536330