Summary
Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Several studies have indicated that rectal cancer is significantly different from colon cancer in terms of treatment, prognosis, and metastasis. Recently, the differential mRNA expression of colon cancer and rectal cancer has received a great deal of attention. The current study aimed to identify significant differences between colon cancer and rectal cancer based on RNA sequencing (RNA-seq) data via support vector machines (SVM). Here, 393 CRC samples from the The Cancer Genome Atlas (TCGA) database were investigated, including 298 patients with colon cancer and 95 with rectal cancer. Following the random forest (RF) analysis of the mRNA expression data, 96 genes such as HOXB13, PRAC, and BCLAF1 were identified and utilized to build the SVM classification model with the Leave-One-Out Cross-validation (LOOCV) algorithm. In the training (n=196) and the validation cohorts (n=197), the accuracy (82.1 % and 82.2 %, respectively) and the AUC (0.87 and 0.91, respectively) indicated that the established optimal SVM classification model distinguished colon cancer from rectal cancer reasonably. However, additional experiments are required to validate the predicted gene expression levels and functions.
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The authors declare that there is no conflict of interest with any financial organization or corporation or individual that can inappropriately influence this work.
This work was supported by the Six Talent Peaks Project in Jiangsu Province (No. 2014-wsw-017); Beijing Medical Award Foundation (No. YJHYXKYJJ-432); Foundation of Social Development Project of the Science and Technology Department of Jiangsu Province (No. BE2015719); Social Development Key Research and Development Plan of Jiangsu Province (No. BE2017694) and The Foundation of Nanjing Medical University (No. 2017NJMUZD140).
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Zhang, Y., Wu, Y., Gong, Zy. et al. Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data. CURR MED SCI 41, 368–374 (2021). https://doi.org/10.1007/s11596-021-2356-8
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DOI: https://doi.org/10.1007/s11596-021-2356-8