Prediction of Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer From Gene Expression Profiling Using Cox-Boost Regression Model
Backgrounds: Distant metastasis in breast cancer patients contributes to increased breast cancer mortality, highlighting the urgent need for effective predictive strategies. Understanding metastasis mechanisms and identifying relevant biomarkers are crucial for improving patient outcomes and informi...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-11-01
|
| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.1177/11769351241297493 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846148675058794496 |
|---|---|
| author | Omid Hamidi Payam Amini Leili Tapak Yasaman Zohrab Beigi Saeid Afshar Irina Dinu |
| author_facet | Omid Hamidi Payam Amini Leili Tapak Yasaman Zohrab Beigi Saeid Afshar Irina Dinu |
| author_sort | Omid Hamidi |
| collection | DOAJ |
| description | Backgrounds: Distant metastasis in breast cancer patients contributes to increased breast cancer mortality, highlighting the urgent need for effective predictive strategies. Understanding metastasis mechanisms and identifying relevant biomarkers are crucial for improving patient outcomes and informing targeted therapies. This study employed a high-dimensional regression model to identify biomarkers linked to distant metastasis-free survival in breast cancer patients, with the goal of enhancing prognostic accuracy and guiding clinical decisions. Methods: We utilized the publicly available breast cancer dataset (GSE2034), which includes gene expression profiles for 22 283 genes across 286 samples. To identify relevant genes, we applied Cox-Boost regression and a random forest (RF) model. We then explored the association between the selected genes and metastasis-free survival outcomes using quantile regression, chosen for its ability to assess the impact of these genes across different survival quantiles ( P < .05). This approach complements the Cox-Boost model by providing a more detailed understanding of gene-survival relationships at various points in the survival distribution, thereby strengthening the robustness of our findings. Results: We identified 222 significant transcripts using univariate Cox regression models. By applying Cox-Boost, both with and without adjustment for ER+/− status, we identified 7 genes associated with time-to-relapse/metastasis in breast cancer patients: SNU13, CLINT1, ACBD3, NEK2, COL2A1, WFDC1, and RACGAP1. A similar approach was used for ER-positive patients. Patients were classified as high or low risk for metastasis based on the median prognostic index calculated from the identified genes ( P < .001). The top-ranked genes associated with high/low risk groups using RF were RACGAP1, NEK2, CCNA2, DTL, ACBD3, ARL6IP5, WFDC1, and PDCD4. Conclusions: We identified eleven key genes, including SNU13, CLINT1, ACBD3, NEK2, COL2A1, WFDC1, and RACGAP1, as well as CCNA2, DTL, ARL6IP5, and PDCD4, that are related to the risk of distant metastasis and may be used as biomarkers to predict distant metastasis of breast cancer. |
| format | Article |
| id | doaj-art-6bb7968637ce46a88b7292458e5abc3e |
| institution | Kabale University |
| issn | 1176-9351 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj-art-6bb7968637ce46a88b7292458e5abc3e2024-11-30T11:03:19ZengSAGE PublishingCancer Informatics1176-93512024-11-012310.1177/11769351241297493Prediction of Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer From Gene Expression Profiling Using Cox-Boost Regression ModelOmid Hamidi0Payam Amini1Leili Tapak2Yasaman Zohrab Beigi3Saeid Afshar4Irina Dinu5Department of Science, Hamedan University of Technology, Hamedan, IranSchool of Medicine, Keele University, Keele, Staffordshire, UKModeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, IranDepartment of Biology, Yazd University, Yazd, IranDepartment of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Hamadan University of Medical Sciences, Hamadan, IranSchool of Public Health, Health Academy, University of Alberta, Edmonton, AB, CanadaBackgrounds: Distant metastasis in breast cancer patients contributes to increased breast cancer mortality, highlighting the urgent need for effective predictive strategies. Understanding metastasis mechanisms and identifying relevant biomarkers are crucial for improving patient outcomes and informing targeted therapies. This study employed a high-dimensional regression model to identify biomarkers linked to distant metastasis-free survival in breast cancer patients, with the goal of enhancing prognostic accuracy and guiding clinical decisions. Methods: We utilized the publicly available breast cancer dataset (GSE2034), which includes gene expression profiles for 22 283 genes across 286 samples. To identify relevant genes, we applied Cox-Boost regression and a random forest (RF) model. We then explored the association between the selected genes and metastasis-free survival outcomes using quantile regression, chosen for its ability to assess the impact of these genes across different survival quantiles ( P < .05). This approach complements the Cox-Boost model by providing a more detailed understanding of gene-survival relationships at various points in the survival distribution, thereby strengthening the robustness of our findings. Results: We identified 222 significant transcripts using univariate Cox regression models. By applying Cox-Boost, both with and without adjustment for ER+/− status, we identified 7 genes associated with time-to-relapse/metastasis in breast cancer patients: SNU13, CLINT1, ACBD3, NEK2, COL2A1, WFDC1, and RACGAP1. A similar approach was used for ER-positive patients. Patients were classified as high or low risk for metastasis based on the median prognostic index calculated from the identified genes ( P < .001). The top-ranked genes associated with high/low risk groups using RF were RACGAP1, NEK2, CCNA2, DTL, ACBD3, ARL6IP5, WFDC1, and PDCD4. Conclusions: We identified eleven key genes, including SNU13, CLINT1, ACBD3, NEK2, COL2A1, WFDC1, and RACGAP1, as well as CCNA2, DTL, ARL6IP5, and PDCD4, that are related to the risk of distant metastasis and may be used as biomarkers to predict distant metastasis of breast cancer.https://doi.org/10.1177/11769351241297493 |
| spellingShingle | Omid Hamidi Payam Amini Leili Tapak Yasaman Zohrab Beigi Saeid Afshar Irina Dinu Prediction of Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer From Gene Expression Profiling Using Cox-Boost Regression Model Cancer Informatics |
| title | Prediction of Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer From Gene Expression Profiling Using Cox-Boost Regression Model |
| title_full | Prediction of Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer From Gene Expression Profiling Using Cox-Boost Regression Model |
| title_fullStr | Prediction of Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer From Gene Expression Profiling Using Cox-Boost Regression Model |
| title_full_unstemmed | Prediction of Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer From Gene Expression Profiling Using Cox-Boost Regression Model |
| title_short | Prediction of Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer From Gene Expression Profiling Using Cox-Boost Regression Model |
| title_sort | prediction of distant metastasis of lymph node negative primary breast cancer from gene expression profiling using cox boost regression model |
| url | https://doi.org/10.1177/11769351241297493 |
| work_keys_str_mv | AT omidhamidi predictionofdistantmetastasisoflymphnodenegativeprimarybreastcancerfromgeneexpressionprofilingusingcoxboostregressionmodel AT payamamini predictionofdistantmetastasisoflymphnodenegativeprimarybreastcancerfromgeneexpressionprofilingusingcoxboostregressionmodel AT leilitapak predictionofdistantmetastasisoflymphnodenegativeprimarybreastcancerfromgeneexpressionprofilingusingcoxboostregressionmodel AT yasamanzohrabbeigi predictionofdistantmetastasisoflymphnodenegativeprimarybreastcancerfromgeneexpressionprofilingusingcoxboostregressionmodel AT saeidafshar predictionofdistantmetastasisoflymphnodenegativeprimarybreastcancerfromgeneexpressionprofilingusingcoxboostregressionmodel AT irinadinu predictionofdistantmetastasisoflymphnodenegativeprimarybreastcancerfromgeneexpressionprofilingusingcoxboostregressionmodel |