Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes Expression
Objective To screen out significant differential genes for predicting the effect of neoadjuvant chemotherapy (NAC) and select the most suitable breast cancer patients for NAC. Methods A total of 60 breast cancer patients' samples before and after NAC were collected for high-throughput RNA-Seq....
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| Format: | Article |
| Language: | zho |
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Magazine House of Cancer Research on Prevention and Treatment
2021-12-01
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| Series: | Zhongliu Fangzhi Yanjiu |
| Subjects: | |
| Online Access: | http://www.zlfzyj.com/EN/10.3971/j.issn.1000-8578.2021.21.0414 |
| _version_ | 1819283509205794816 |
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| author | LU Mei YANG Xiaojuan ZOU Jieya GUO Rong WANG Xin ZHANG Qian DENG Xuepeng TAO Jianfen NIE Jianyun YANG Zhuangqing |
| author_facet | LU Mei YANG Xiaojuan ZOU Jieya GUO Rong WANG Xin ZHANG Qian DENG Xuepeng TAO Jianfen NIE Jianyun YANG Zhuangqing |
| author_sort | LU Mei |
| collection | DOAJ |
| description | Objective To screen out significant differential genes for predicting the effect of neoadjuvant chemotherapy (NAC) and select the most suitable breast cancer patients for NAC. Methods A total of 60 breast cancer patients' samples before and after NAC were collected for high-throughput RNA-Seq. We selected AHNAK, CIDEA, ADIPOQ and AKAP12 as the candidate genes that related to tumor chemotherapeutic resistance. We analyzed the correlation of AHNAK, CIDEA, ADIPOQ, AKAP12 expression levels with the effect of NAC by logistic regression analysis, constructed a prediction model and demonstrated the model by the nomogram. Results AHNAK, CIDEA, ADIPOQ and AKAP12 expression were up-regulated in the residual tumor tissues of non-pCR group after NAC(P < 0.05). Compared with pCR group, non-pCR group presented higher expression levels of AHNAK, CIDEA, ADIPOQ and AKAP12 (P < 0.05). The high expression levels of AHNAK, CIDEA, ADIPOQ and AKAP12 significantly reduced the pCR rate of NAC for breast cancer (P < 0.05). Our prediction model which AHNAK, CIDEA, ADIPOQ and AKAP12 were involved in showed a good fitting effect with H1 test (χ2=6.3967, P=0.4945) and the ROC curve (AUC 0.8249, 95%CI: 0.722-0.9271). Conclusion AHNAK, CIDEA, ADIPOQ and AKAP12 may be novel marker genes for NAC effect on breast cancer. The efficacy prediction model based on this result is expected to be a new method to select the optimal patients of breast cancer for neoadjuvant chemotherapy. |
| first_indexed | 2024-12-24T01:32:36Z |
| format | Article |
| id | doaj.art-f0ff3a8cd67a4d89a16f5ee581c136a9 |
| institution | Directory Open Access Journal |
| issn | 1000-8578 |
| language | zho |
| last_indexed | 2024-12-24T01:32:36Z |
| publishDate | 2021-12-01 |
| publisher | Magazine House of Cancer Research on Prevention and Treatment |
| record_format | Article |
| series | Zhongliu Fangzhi Yanjiu |
| spelling | doaj.art-f0ff3a8cd67a4d89a16f5ee581c136a92022-12-21T17:22:18ZzhoMagazine House of Cancer Research on Prevention and TreatmentZhongliu Fangzhi Yanjiu1000-85782021-12-0148121071107710.3971/j.issn.1000-8578.2021.21.04148578.2021.21.0414Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes ExpressionLU Mei0YANG Xiaojuan1ZOU Jieya2GUO Rong3WANG Xin4ZHANG Qian5DENG Xuepeng6TAO Jianfen7NIE Jianyun8YANG Zhuangqing9Department Ⅲ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, ChinaDepartment Ⅲ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, ChinaDepartment Ⅲ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, ChinaDepartment Ⅱof Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, ChinaDepartment Ⅲ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, ChinaDepartment Ⅲ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, ChinaDepartment Ⅲ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, ChinaDepartment Ⅲ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, ChinaDepartment Ⅲ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, ChinaDepartment Ⅲ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, ChinaObjective To screen out significant differential genes for predicting the effect of neoadjuvant chemotherapy (NAC) and select the most suitable breast cancer patients for NAC. Methods A total of 60 breast cancer patients' samples before and after NAC were collected for high-throughput RNA-Seq. We selected AHNAK, CIDEA, ADIPOQ and AKAP12 as the candidate genes that related to tumor chemotherapeutic resistance. We analyzed the correlation of AHNAK, CIDEA, ADIPOQ, AKAP12 expression levels with the effect of NAC by logistic regression analysis, constructed a prediction model and demonstrated the model by the nomogram. Results AHNAK, CIDEA, ADIPOQ and AKAP12 expression were up-regulated in the residual tumor tissues of non-pCR group after NAC(P < 0.05). Compared with pCR group, non-pCR group presented higher expression levels of AHNAK, CIDEA, ADIPOQ and AKAP12 (P < 0.05). The high expression levels of AHNAK, CIDEA, ADIPOQ and AKAP12 significantly reduced the pCR rate of NAC for breast cancer (P < 0.05). Our prediction model which AHNAK, CIDEA, ADIPOQ and AKAP12 were involved in showed a good fitting effect with H1 test (χ2=6.3967, P=0.4945) and the ROC curve (AUC 0.8249, 95%CI: 0.722-0.9271). Conclusion AHNAK, CIDEA, ADIPOQ and AKAP12 may be novel marker genes for NAC effect on breast cancer. The efficacy prediction model based on this result is expected to be a new method to select the optimal patients of breast cancer for neoadjuvant chemotherapy.http://www.zlfzyj.com/EN/10.3971/j.issn.1000-8578.2021.21.0414breast neoplasmsneoadjuvant chemotherapygene expressionefficacy prediction model |
| spellingShingle | LU Mei YANG Xiaojuan ZOU Jieya GUO Rong WANG Xin ZHANG Qian DENG Xuepeng TAO Jianfen NIE Jianyun YANG Zhuangqing Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes Expression Zhongliu Fangzhi Yanjiu breast neoplasms neoadjuvant chemotherapy gene expression efficacy prediction model |
| title | Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes Expression |
| title_full | Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes Expression |
| title_fullStr | Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes Expression |
| title_full_unstemmed | Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes Expression |
| title_short | Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes Expression |
| title_sort | efficacy prediction model for neoadjuvant chemotherapy on breast cancer based on differential genes expression |
| topic | breast neoplasms neoadjuvant chemotherapy gene expression efficacy prediction model |
| url | http://www.zlfzyj.com/EN/10.3971/j.issn.1000-8578.2021.21.0414 |
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