Estrogen receptor—positive breast cancer survival prediction and analysis of resistance–related genes introduction
Background In recent years, ER+ and HER2- breast cancer of adjuvant therapy has made great progress, including chemotherapy and endocrine therapy. We found that the responsiveness of breast cancer treatment was related to the prognosis of patients. However, reliable prognostic signatures based on ER...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2021-10-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/12202.pdf |
| _version_ | 1797420892180971520 |
|---|---|
| author | Chen Shuai Fengyan Yuan Yu Liu Chengchen Wang Jiansong Wang Hongye He |
| author_facet | Chen Shuai Fengyan Yuan Yu Liu Chengchen Wang Jiansong Wang Hongye He |
| author_sort | Chen Shuai |
| collection | DOAJ |
| description | Background In recent years, ER+ and HER2- breast cancer of adjuvant therapy has made great progress, including chemotherapy and endocrine therapy. We found that the responsiveness of breast cancer treatment was related to the prognosis of patients. However, reliable prognostic signatures based on ER+ and HER2- breast cancer and drug resistance-related prognostic markers have not been well confirmed, This study in amied to establish a drug resistance-related gene signature for risk stratification in ER+ and HER2- breast cancer. Methods We used the data from The Cancer Genoma Atlas (TCGA) breast cancer dataset and gene expression database (Gene Expression Omnibus, GEO), constructed a risk profile based on four drug resistance-related genes, and developed a nomogram to predict the survival of patients with I-III ER+ and HER2- breast cancer. At the same time, we analyzed the relationship between immune infiltration and the expression of these four genes or risk groups. Results Four drug resistance genes (AMIGO2, LGALS3BP, SCUBE2 and WLS) were found to be promising tools for ER+ and HER2- breast cancer risk stratification. Then, the nomogram, which combines genetic characteristics with known risk factors, produced better performance and net benefits in calibration and decision curve analysis. Similar results were validated in three separate GEO cohorts. All of these results showed that the model can be used as a prognostic classifier for clinical decision-making, individual prediction and treatment, as well as follow-up. |
| first_indexed | 2024-03-09T07:09:13Z |
| format | Article |
| id | doaj.art-411eda30d2504bafab376d7461e2825f |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:09:13Z |
| publishDate | 2021-10-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-411eda30d2504bafab376d7461e2825f2023-12-03T09:18:03ZengPeerJ Inc.PeerJ2167-83592021-10-019e1220210.7717/peerj.12202Estrogen receptor—positive breast cancer survival prediction and analysis of resistance–related genes introductionChen Shuai0Fengyan Yuan1Yu Liu2Chengchen Wang3Jiansong Wang4Hongye He5Department of Breast and Thyroid Surgery, Yiyang Central Hospital, Yiyang, Hunan, ChinaHunan Normal University of Medicine, Changsha, Hunan, ChinaHunan Provincial People’s Hospital, Changsha, Hunan, ChinaHunan Provincial People’s Hospital, Changsha, Hunan, ChinaHunan Provincial People’s Hospital, Changsha, Hunan, ChinaDepartment of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, ChinaBackground In recent years, ER+ and HER2- breast cancer of adjuvant therapy has made great progress, including chemotherapy and endocrine therapy. We found that the responsiveness of breast cancer treatment was related to the prognosis of patients. However, reliable prognostic signatures based on ER+ and HER2- breast cancer and drug resistance-related prognostic markers have not been well confirmed, This study in amied to establish a drug resistance-related gene signature for risk stratification in ER+ and HER2- breast cancer. Methods We used the data from The Cancer Genoma Atlas (TCGA) breast cancer dataset and gene expression database (Gene Expression Omnibus, GEO), constructed a risk profile based on four drug resistance-related genes, and developed a nomogram to predict the survival of patients with I-III ER+ and HER2- breast cancer. At the same time, we analyzed the relationship between immune infiltration and the expression of these four genes or risk groups. Results Four drug resistance genes (AMIGO2, LGALS3BP, SCUBE2 and WLS) were found to be promising tools for ER+ and HER2- breast cancer risk stratification. Then, the nomogram, which combines genetic characteristics with known risk factors, produced better performance and net benefits in calibration and decision curve analysis. Similar results were validated in three separate GEO cohorts. All of these results showed that the model can be used as a prognostic classifier for clinical decision-making, individual prediction and treatment, as well as follow-up.https://peerj.com/articles/12202.pdfER+ and HER2- breast cancerDrug resistance-related geneRisk signature |
| spellingShingle | Chen Shuai Fengyan Yuan Yu Liu Chengchen Wang Jiansong Wang Hongye He Estrogen receptor—positive breast cancer survival prediction and analysis of resistance–related genes introduction PeerJ ER+ and HER2- breast cancer Drug resistance-related gene Risk signature |
| title | Estrogen receptor—positive breast cancer survival prediction and analysis of resistance–related genes introduction |
| title_full | Estrogen receptor—positive breast cancer survival prediction and analysis of resistance–related genes introduction |
| title_fullStr | Estrogen receptor—positive breast cancer survival prediction and analysis of resistance–related genes introduction |
| title_full_unstemmed | Estrogen receptor—positive breast cancer survival prediction and analysis of resistance–related genes introduction |
| title_short | Estrogen receptor—positive breast cancer survival prediction and analysis of resistance–related genes introduction |
| title_sort | estrogen receptor positive breast cancer survival prediction and analysis of resistance related genes introduction |
| topic | ER+ and HER2- breast cancer Drug resistance-related gene Risk signature |
| url | https://peerj.com/articles/12202.pdf |
| work_keys_str_mv | AT chenshuai estrogenreceptorpositivebreastcancersurvivalpredictionandanalysisofresistancerelatedgenesintroduction AT fengyanyuan estrogenreceptorpositivebreastcancersurvivalpredictionandanalysisofresistancerelatedgenesintroduction AT yuliu estrogenreceptorpositivebreastcancersurvivalpredictionandanalysisofresistancerelatedgenesintroduction AT chengchenwang estrogenreceptorpositivebreastcancersurvivalpredictionandanalysisofresistancerelatedgenesintroduction AT jiansongwang estrogenreceptorpositivebreastcancersurvivalpredictionandanalysisofresistancerelatedgenesintroduction AT hongyehe estrogenreceptorpositivebreastcancersurvivalpredictionandanalysisofresistancerelatedgenesintroduction |