A novel disulfidptosis-associated expression pattern in breast cancer based on machine learning
Background: Breast cancer (BC), the leading cause of cancer-related deaths among women, remains a serious threat to human health worldwide. The biological function and prognostic value of disulfidptosis as a novel strategy for BC treatment via induction of cell death remain unknown.Methods: Gene mut...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1193944/full |
| _version_ | 1797791904681689088 |
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| author | Zhitang Wang Xianqiang Du Weibin Lian Jialin Chen Chengye Hong Liangqiang Li Debo Chen |
| author_facet | Zhitang Wang Xianqiang Du Weibin Lian Jialin Chen Chengye Hong Liangqiang Li Debo Chen |
| author_sort | Zhitang Wang |
| collection | DOAJ |
| description | Background: Breast cancer (BC), the leading cause of cancer-related deaths among women, remains a serious threat to human health worldwide. The biological function and prognostic value of disulfidptosis as a novel strategy for BC treatment via induction of cell death remain unknown.Methods: Gene mutations and copy number variations (CNVs) in 10 disulfidptosis genes were evaluated. Differential expression, prognostic, and univariate Cox analyses were then performed for 10 genes, and BC-specific disulfidptosis-related genes (DRGs) were screened. Unsupervised consensus clustering was used to identify different expression clusters. In addition, we screened the differentially expressed genes (DEGs) among different expression clusters and identified hub genes. Moreover, the expression level of DEGs was detected by RT-qPCR in cellular level. Finally, we used the least absolute shrinkage and selection operator (LASSO) regression algorithm to establish a prognostic feature based on DEGs, and verified the accuracy and sensitivity of its prediction through prognostic analysis and subject operating characteristic curve analysis. The correlation of the signature with the tumor immune microenvironment and tumor stemness was analyzed.Results: Disulfidptosis genes showed significant CNVs. Two clusters were identified based on three DRGs (DNUFS1, LRPPRC, SLC7A11). Cluster A was found to be associated with better survival outcomes(p < 0.05) and higher levels of immune cell infiltration(p < 0.05). A prognostic signature of four disulfidptosis-related DEGs (KIF21A, APOD, ALOX15B, ELOVL2) was developed by LASSO regression analysis. The signature showed a good prediction ability. In addition, the prognostic signature in this study were strongly related to the tumor microenvironment (TME), tumor immune cell infiltration, tumor mutation burden (TMB), tumor stemness, and drug sensitivity.Conclusion: The prognostic signature we constructed based on disulfidptosis-DEGs is a good predictor of prognosis in patients with BC. This prognostic signature is closely related to TME, and its potential correlation provides clues for further studies. |
| first_indexed | 2024-03-13T02:25:27Z |
| format | Article |
| id | doaj.art-c134246521714eb8887fe69ca941f787 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-13T02:25:27Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-c134246521714eb8887fe69ca941f7872023-06-30T04:27:09ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-06-011410.3389/fgene.2023.11939441193944A novel disulfidptosis-associated expression pattern in breast cancer based on machine learningZhitang WangXianqiang DuWeibin LianJialin ChenChengye HongLiangqiang LiDebo ChenBackground: Breast cancer (BC), the leading cause of cancer-related deaths among women, remains a serious threat to human health worldwide. The biological function and prognostic value of disulfidptosis as a novel strategy for BC treatment via induction of cell death remain unknown.Methods: Gene mutations and copy number variations (CNVs) in 10 disulfidptosis genes were evaluated. Differential expression, prognostic, and univariate Cox analyses were then performed for 10 genes, and BC-specific disulfidptosis-related genes (DRGs) were screened. Unsupervised consensus clustering was used to identify different expression clusters. In addition, we screened the differentially expressed genes (DEGs) among different expression clusters and identified hub genes. Moreover, the expression level of DEGs was detected by RT-qPCR in cellular level. Finally, we used the least absolute shrinkage and selection operator (LASSO) regression algorithm to establish a prognostic feature based on DEGs, and verified the accuracy and sensitivity of its prediction through prognostic analysis and subject operating characteristic curve analysis. The correlation of the signature with the tumor immune microenvironment and tumor stemness was analyzed.Results: Disulfidptosis genes showed significant CNVs. Two clusters were identified based on three DRGs (DNUFS1, LRPPRC, SLC7A11). Cluster A was found to be associated with better survival outcomes(p < 0.05) and higher levels of immune cell infiltration(p < 0.05). A prognostic signature of four disulfidptosis-related DEGs (KIF21A, APOD, ALOX15B, ELOVL2) was developed by LASSO regression analysis. The signature showed a good prediction ability. In addition, the prognostic signature in this study were strongly related to the tumor microenvironment (TME), tumor immune cell infiltration, tumor mutation burden (TMB), tumor stemness, and drug sensitivity.Conclusion: The prognostic signature we constructed based on disulfidptosis-DEGs is a good predictor of prognosis in patients with BC. This prognostic signature is closely related to TME, and its potential correlation provides clues for further studies.https://www.frontiersin.org/articles/10.3389/fgene.2023.1193944/fullbreast cancerdisulfidptosisprognostic signaturetumor microenvironmentexpression pattern |
| spellingShingle | Zhitang Wang Xianqiang Du Weibin Lian Jialin Chen Chengye Hong Liangqiang Li Debo Chen A novel disulfidptosis-associated expression pattern in breast cancer based on machine learning Frontiers in Genetics breast cancer disulfidptosis prognostic signature tumor microenvironment expression pattern |
| title | A novel disulfidptosis-associated expression pattern in breast cancer based on machine learning |
| title_full | A novel disulfidptosis-associated expression pattern in breast cancer based on machine learning |
| title_fullStr | A novel disulfidptosis-associated expression pattern in breast cancer based on machine learning |
| title_full_unstemmed | A novel disulfidptosis-associated expression pattern in breast cancer based on machine learning |
| title_short | A novel disulfidptosis-associated expression pattern in breast cancer based on machine learning |
| title_sort | novel disulfidptosis associated expression pattern in breast cancer based on machine learning |
| topic | breast cancer disulfidptosis prognostic signature tumor microenvironment expression pattern |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1193944/full |
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