Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients
Abstract Background Previous investigations of transcriptomic signatures of cancer patient survival and post-therapy relapse have focused on tumor tissue. In contrast, here we show that in colorectal cancer (CRC) transcriptomes derived from normal tissues adjacent to tumors (NATs) are better predict...
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-023-04053-2 |
| _version_ | 1797831966376067072 |
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| author | Jinho Kim Hyunjung Kim Min-Seok Lee Heetak Lee Yeon Jeong Kim Woo Yong Lee Seong Hyeon Yun Hee Cheol Kim Hye Kyung Hong Sridhar Hannenhalli Yong Beom Cho Donghyun Park Sun Shim Choi |
| author_facet | Jinho Kim Hyunjung Kim Min-Seok Lee Heetak Lee Yeon Jeong Kim Woo Yong Lee Seong Hyeon Yun Hee Cheol Kim Hye Kyung Hong Sridhar Hannenhalli Yong Beom Cho Donghyun Park Sun Shim Choi |
| author_sort | Jinho Kim |
| collection | DOAJ |
| description | Abstract Background Previous investigations of transcriptomic signatures of cancer patient survival and post-therapy relapse have focused on tumor tissue. In contrast, here we show that in colorectal cancer (CRC) transcriptomes derived from normal tissues adjacent to tumors (NATs) are better predictors of relapse. Results Using the transcriptomes of paired tumor and NAT specimens from 80 Korean CRC patients retrospectively determined to be in recurrence or nonrecurrence states, we found that, when comparing recurrent with nonrecurrent samples, NATs exhibit a greater number of differentially expressed genes (DEGs) than tumors. Training two prognostic elastic net-based machine learning models—NAT-based and tumor-based in our Samsung Medical Center (SMC) cohort, we found that NAT-based model performed better in predicting the survival when the model was applied to the tumor-derived transcriptomes of an independent cohort of 450 COAD patients in TCGA. Furthermore, compositions of tumor-infiltrating immune cells in NATs were found to have better prognostic capability than in tumors. We also confirmed through Cox regression analysis that in both SMC-CRC as well as in TCGA-COAD cohorts, a greater proportion of genes exhibited significant hazard ratio when NAT-derived transcriptome was used compared to when tumor-derived transcriptome was used. Conclusions Taken together, our results strongly suggest that NAT-derived transcriptomes and immune cell composition of CRC are better predictors of patient survival and tumor recurrence than the primary tumor. |
| first_indexed | 2024-04-09T14:00:10Z |
| format | Article |
| id | doaj.art-8e84462bd82d4cb0b0c61ffa12e5ebda |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-04-09T14:00:10Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-8e84462bd82d4cb0b0c61ffa12e5ebda2023-05-07T11:22:15ZengBMCJournal of Translational Medicine1479-58762023-03-0121111510.1186/s12967-023-04053-2Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patientsJinho Kim0Hyunjung Kim1Min-Seok Lee2Heetak Lee3Yeon Jeong Kim4Woo Yong Lee5Seong Hyeon Yun6Hee Cheol Kim7Hye Kyung Hong8Sridhar Hannenhalli9Yong Beom Cho10Donghyun Park11Sun Shim Choi12Precision Medicine Center, Future Innovation Research Division, Seoul National University Bundang HospitalDivision of Biomedical Convergence, College of Biomedical Science, Institute of Bioscience & Biotechnology, Kangwon National UniversityDivision of Biomedical Convergence, College of Biomedical Science, Institute of Bioscience & Biotechnology, Kangwon National UniversityPrecision Medicine Center, Future Innovation Research Division, Seoul National University Bundang HospitalSamsung Genome Institute, Samsung Medical CenterDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of MedicineDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of MedicineDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of MedicineInstitute for Future Medicine, Samsung Medical CenterCancer Data Science Lab, Center for Cancer Research, National Cancer InstituteDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of MedicineGeninus Inc.Division of Biomedical Convergence, College of Biomedical Science, Institute of Bioscience & Biotechnology, Kangwon National UniversityAbstract Background Previous investigations of transcriptomic signatures of cancer patient survival and post-therapy relapse have focused on tumor tissue. In contrast, here we show that in colorectal cancer (CRC) transcriptomes derived from normal tissues adjacent to tumors (NATs) are better predictors of relapse. Results Using the transcriptomes of paired tumor and NAT specimens from 80 Korean CRC patients retrospectively determined to be in recurrence or nonrecurrence states, we found that, when comparing recurrent with nonrecurrent samples, NATs exhibit a greater number of differentially expressed genes (DEGs) than tumors. Training two prognostic elastic net-based machine learning models—NAT-based and tumor-based in our Samsung Medical Center (SMC) cohort, we found that NAT-based model performed better in predicting the survival when the model was applied to the tumor-derived transcriptomes of an independent cohort of 450 COAD patients in TCGA. Furthermore, compositions of tumor-infiltrating immune cells in NATs were found to have better prognostic capability than in tumors. We also confirmed through Cox regression analysis that in both SMC-CRC as well as in TCGA-COAD cohorts, a greater proportion of genes exhibited significant hazard ratio when NAT-derived transcriptome was used compared to when tumor-derived transcriptome was used. Conclusions Taken together, our results strongly suggest that NAT-derived transcriptomes and immune cell composition of CRC are better predictors of patient survival and tumor recurrence than the primary tumor.https://doi.org/10.1186/s12967-023-04053-2Colorectal cancerNormal tissues adjacent to tumorsRecurrenceElastic net-based machine learningTumor-infiltrating immune cells |
| spellingShingle | Jinho Kim Hyunjung Kim Min-Seok Lee Heetak Lee Yeon Jeong Kim Woo Yong Lee Seong Hyeon Yun Hee Cheol Kim Hye Kyung Hong Sridhar Hannenhalli Yong Beom Cho Donghyun Park Sun Shim Choi Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients Journal of Translational Medicine Colorectal cancer Normal tissues adjacent to tumors Recurrence Elastic net-based machine learning Tumor-infiltrating immune cells |
| title | Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients |
| title_full | Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients |
| title_fullStr | Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients |
| title_full_unstemmed | Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients |
| title_short | Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients |
| title_sort | transcriptomes of the tumor adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients |
| topic | Colorectal cancer Normal tissues adjacent to tumors Recurrence Elastic net-based machine learning Tumor-infiltrating immune cells |
| url | https://doi.org/10.1186/s12967-023-04053-2 |
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