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Integrated genomic analysis of recurrence-associated small non-coding RNAs in oesophageal cancer
  1. Hee-Jin Jang1,2,3,
  2. Hyun-Sung Lee1,2,4,5,
  3. Bryan M Burt4,
  4. Geon Kook Lee5,
  5. Kyong-Ah Yoon5,6,
  6. Yun-Yong Park2,
  7. Bo Hwa Sohn2,
  8. Sang Bae Kim2,
  9. Moon Soo Kim1,
  10. Jong Mog Lee1,
  11. Jungnam Joo7,
  12. Sang Cheol Kim8,
  13. Ju Sik Yun9,
  14. Kook Joo Na9,
  15. Yoon-La Choi10,
  16. Jong-Lyul Park11,
  17. Seon-Young Kim11,
  18. Yong Sun Lee12,
  19. Leng Han13,14,
  20. Han Liang13,
  21. Duncan Mak15,
  22. Jared K Burks15,
  23. Jae Ill Zo16,
  24. David J Sugarbaker4,
  25. Young Mog Shim16,
  26. Ju-Seog Lee2
  1. 1Center for Lung Cancer, Research Institute and Hospital, National Cancer Center, Goyang, Gyeonggi, Republic of Korea
  2. 2Division of Cancer Medicine, Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
  3. 3Department of Molecular Oncology, The Graduate School of Medicine, Seoul National University, Seoul, Republic of Korea
  4. 4Division of Thoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
  5. 5Lung Cancer Branch, Research Institute and Hospital, National Cancer Center, Goyang, Gyeonggi, Republic of Korea
  6. 6College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
  7. 7Biometric Research Branch, Research Institute and Hospital, National Cancer Center, Goyang, Gyeonggi, Republic of Korea
  8. 8Department of Biomedical Informatics, Center for Genome Science, National Institute of Health, KCDC, Choongchung-Buk-do, Republic of Korea
  9. 9Lung and Esophageal Cancer Clinic, Department of Thoracic and Cardiovascular Surgery, Chonnam National University Hwasun Hospital, Hwasun, Jeollanamdo, Republic of Korea
  10. 10Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
  11. 11Department of Functional Genomics, University of Science and Technology, Medical Genomics Research Center, KRIBB, Daejeon, Republic of Korea
  12. 12Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA
  13. 13Division of Quantitative Sciences, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
  14. 14Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, Texas, USA
  15. 15Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
  16. 16Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
  1. Correspondence to Dr Hyun-Sung Lee, Division of Thoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA; Hyun-Sung.Lee{at}bcm.edu; Dr Young Mog Shim, Department of Thoracic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea; ymshim{at}skku.edu; Dr Ju-Seog Lee, Department of Systems Biology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; jlee{at}mdanderson.org

Abstract

Objective Oesophageal squamous cell carcinoma (ESCC) is a heterogeneous disease with variable outcomes that are challenging to predict. A better understanding of the biology of ESCC recurrence is needed to improve patient care. Our goal was to identify small non-coding RNAs (sncRNAs) that could predict the likelihood of recurrence after surgical resection and to uncover potential molecular mechanisms that dictate clinical heterogeneity.

Design We developed a robust prediction model for recurrence based on the analysis of the expression profile data of sncRNAs from 108 fresh frozen ESCC specimens as a discovery set and assessment of the associations between sncRNAs and recurrence-free survival (RFS). We also evaluated the mechanistic and therapeutic implications of sncRNA obtained through integrated analysis from multiple datasets.

Results We developed a risk assessment score (RAS) for recurrence with three sncRNAs (microRNA (miR)-223, miR-1269a and nc886) whose expression was significantly associated with RFS in the discovery cohort (n=108). RAS was validated in an independent cohort of 512 patients. In multivariable analysis, RAS was an independent predictor of recurrence (HR, 2.27; 95% CI, 1.26 to 4.09; p=0.007). This signature implies the expression of ΔNp63 and multiple alterations of driver genes like PIK3CA. We suggested therapeutic potentials of immune checkpoint inhibitors in low-risk patients, and Polo-like kinase inhibitors, mammalian target of rapamycin (mTOR) inhibitors, and histone deacetylase inhibitors in high-risk patients.

Conclusion We developed an easy-to-use prognostic model with three sncRNAs as robust prognostic markers for postoperative recurrence of ESCC. We anticipate that such a stratified and systematic, tumour-specific biological approach will potentially contribute to significant improvement in ESCC treatment.

  • RNA EXPRESSION
  • OESOPHAGEAL CANCER
  • SURGICAL ONCOLOGY
  • CANCER IMMUNOBIOLOGY
  • GENE EXPRESSION

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