Skip to main content
Log in

Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data

  • Published:
Current Medical Science Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018,68(6):394–424

    Article  PubMed  Google Scholar 

  2. Pietrzyk L, Torres A, Maciejewski R, et al. Obesity and Obese-related Chronic Low-grade Inflammation in Promotion of Colorectal Cancer Development. Asian Pac J Cancer Prev, 2015,16(10):4161–4168

    Article  PubMed  Google Scholar 

  3. Keum N, Giovannucci E. Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies. Nat Rev Gastroenterol Hepatol, 2019,16(12): 713–732

    Article  PubMed  Google Scholar 

  4. Siegel RL, Miller KD, Goding Sauer A, et al. Colorectal cancer statistics, 2020. CA Cancer J Clin, 2020,70(3):145–164

    Article  PubMed  Google Scholar 

  5. Li M, Li JY, Zhao AL, et al. Colorectal cancer or colon and rectal cancer: Clinicopathological comparison between colon and rectal carcinomas. Oncology, 2007, 73(1–2):52–57

    Article  CAS  PubMed  Google Scholar 

  6. Minoo P, Zlobec I, Peterson M, et al. Characterization of rectal, proximal and distal colon cancers based on clinicopathological, molecular and protein profiles. Int J Oncol, 2010,37(3):707–718

    Article  CAS  PubMed  Google Scholar 

  7. Ichimasa K, Kudo SE, Miyachi H, et al. Comparative clinicopathological characteristics of colon and rectal T1 carcinoma. Oncol Lett, 2017,13(2):805–810

    Article  PubMed  Google Scholar 

  8. O’Rahilly R, Müller F. Basic human anatomy: A regional study of human structure. W.B. Saunders company, 1983.

  9. Pihl E, Hughes ES, McDermott FT, et al. Lung recurrence after 24 curative surgery for colorectal cancer. Dis Colon Rectum, 1987,30(6):417–419

    Article  CAS  PubMed  Google Scholar 

  10. Kapiteijn E, Marijnen CA, Nagtegaal ID, et al. Preoperative radiotherapy combined with total mesorectal excision for resectable rectal cancer. N Engl J Med, 2001,345(9):638–646

    Article  CAS  PubMed  Google Scholar 

  11. Sauer R, Becker H, Hohenberger W, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med, 2004,351(17):1731–1740

    Article  CAS  PubMed  Google Scholar 

  12. Tamas K, Walenkamp AM, de Vries EG, et al. Rectal and colon cancer: Not just a different anatomic site. Cancer Treat Rev, 2015,41(8):671–679

    Article  CAS  PubMed  Google Scholar 

  13. Jimenez IG, Ladron AL, Pancorbo DM, et al. Influence of the localization of the primary tumor in the survival of patients with metastatic colon-rectal cancer treated with bevacizumab. J Clin Onco, 2014,32:546

    Google Scholar 

  14. Loud JT, Murphy J. Cancer Screening and Early Detection in the 21(st) Century. Semin Oncol Nurs, 2017,33(2):121–128

    Article  PubMed  PubMed Central  Google Scholar 

  15. Dekker E, Tanis PJ, Vleugels JLA, et al. Colorectal cancer. Lancet, 2019,394(10207):1467–1480

    Article  PubMed  Google Scholar 

  16. Kandoth C, McLellan MD, Vandin F, et al. Mutational landscape and significance across 12 major cancer types. Nature, 2013,502(7471):333–339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Vargas AJ, Harris CC. Biomarker development in the precision medicine era: lung cancer as a case study. Nat Rev Cancer, 2016,16(8):525–537

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Cancer Genome Atlas Network. Comprehensive Molecular Characterization of Human Colon and Rectal Cancer. Nature, 2012,487(7407):330–337

    Article  CAS  Google Scholar 

  19. Bosman FT, Yan P, Tejpar S, et al. Tissue biomarker development in a multicentre trial context: a feasibility study on the PETACC3 stage II and III colon cancer adjuvant treatment trial. Clin Cancer Res, 2009,15(17): 5528–5533

    Article  CAS  PubMed  Google Scholar 

  20. Sanz-Pamplona R, Cordero D, Berenguer A, et al. Gene expression differences between colon and rectum tumors. Clin Cancer Res, 2011,17(23):7303–7312

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Yamada A, Yu P, Lin W, et al. A RNA-Sequencing approach for the identification of novel long non-coding RNA biomarkers in colorectal cancer. Sci Rep, 2018,8(1):575

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Oh BY, Cho J, Hong HK, et al. Exome and transcriptome sequencing identifies loss of PDLIM2 in metastatic colorectal cancers. Cancer Manag Res, 2017,9:581–589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lee JR, Kwon CH, Choi Y, et al. Transcriptome analysis of paired primary colorectal carcinoma and liver metastases reveals fusion transcripts and similar gene expression profiles in primary carcinoma and liver metastases. BMC Cancer, 2016,16:539

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Weitschek E, Lauro SD, Cappelli E, et al. CamurWeb: a classification software and a large knowledge base for gene expression data of cancer. BMC Bioinformatics, 2018,19:354

    Article  PubMed  PubMed Central  Google Scholar 

  25. Brown MPS, Grundy WN, Lin D, et al. Support Vector Machine Classification of Microarray Gene Expression Data. University of California, Santa Cruz, Technical Report UCSC-CRL-99–09, 1999

    Google Scholar 

  26. Liaw A, Wiener M. Classification and Regression by randomForest. R News, 2002,2(3):18–22

    Google Scholar 

  27. Vapnik VN. The Nature of Statistical Learning Theory. IEEE T Neural Network, 1997,8:1564

    Article  Google Scholar 

  28. Becker N, Toedt G, Lichter P, et al. Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data. BMC Bioinformatics, 2011,12:138

    Article  PubMed  PubMed Central  Google Scholar 

  29. Breiman L. Random Forests. Machine Learning, 2001,45:5–32

    Article  Google Scholar 

  30. Gabere MN, Hussein MA, Aziz MA. Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer. Onco Targets Ther, 2016,9:3313–3325

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhi J, Sun J, Wang Z, et al. Support vector machine classifier for prediction of the metastasis of colorectal cancer. Int J Mol Med, 2018,41(3):1419–1426

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Li JN, Zhao L, Wu J, et al. Differences in gene expression profiles and carcinogenesis pathways between colon and rectal cancer. J Dig Dis, 2012,13:24–32

    Article  PubMed  CAS  Google Scholar 

  33. Slattery ML, Curtin K, Wolff RK, et al. A comparison of colon and rectal somatic DNA alterations. Dis Colon Rectum, 2009,52:1304–1311

    Article  PubMed  PubMed Central  Google Scholar 

  34. Slattery ML, Pellatt DF, Mullany LE, et al. Differential gene expression in colon tissue associated with diet, lifestyle, and related oxidative stress. PLoS One, 2015, 10(7):e0134406

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Slattery ML, Friedman GD, Potter JD, et al. Adescription of age, sex, and site distributions of colon carcinoma in three geographic areas. Cancer, 1996,78(8):1666–1670

    Article  CAS  PubMed  Google Scholar 

  36. Fleshner P, Slater G, Aufses AH Jr. Age and sex distribution of patients with colorectal cancer. Dis Colon Rectum, 1989,32(2):107–111

    Article  CAS  PubMed  Google Scholar 

  37. Lu Z, Chen H, Jiao X, et al. Prediction of immune checkpoint inhibition with immune oncology-related gene expression in gastrointestinal cancer using a machine learning classifier. J Immunother Cancer, 2020,8(2):e000631

    Article  PubMed  PubMed Central  Google Scholar 

  38. Yang WL, Lee YE, Chen MH, et al. In-silico drug screening and potential target identification for hepatocellular carcinoma using Support Vector Machines based on drug screening result. Gene, 2013, 518(1):201–208

    Article  CAS  PubMed  Google Scholar 

  39. Kristiansen I, Stephan C, Jung K, et al. Sensitivity of HOXB13 as a diagnostic immunohistochemical marker of prostatic origin in prostate cancer metastases: comparison to PSA, Prostein. Androgen Receptor, ERG, NKX3.1, PSAP, and PSMA. Int J Mol Sci, 2017,18(6):E1151

    Article  PubMed  CAS  Google Scholar 

  40. Cantile M, Pettinato G, Procino A, et al. In vivo expression of the whole HOX gene network in human breast cancer, Eur J Cancer, 39(2003):257–264

  41. Maeda K, Hamada J, Takahashi Y, et al. Altered expressions of HOX genes in human cutaneous malignant melanoma. Int J Cancer, 2005,114(3):436–441

    Article  CAS  PubMed  Google Scholar 

  42. Zhao Y, Yamashita T, Ishikawa M. Regulation of tumor invasion by HOXB13 gene over expressed in human endometrial cancer. Oncol Rep, 2005,13(4):721–726

    CAS  PubMed  Google Scholar 

  43. Jung C, Kim RS, Zhang H, et al. HOXB13 is downregulated in colorectal cancer to confer TCF4-mediated transactivation. Br J Cancer, 2005,92(12):2233–2239

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Barros E, Savage KS, Harkin DP. 314: BCLAF1; a multi-faceted protein involved DNA repair, apoptosis and autophagy. Eur J Cancer, 2014,50:S75

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bo Shen.

Additional information

Conflict of Interest Statement

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).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11596-021-2356-8

Key words

Navigation