Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes
Background Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer that is characterized by high malignancy and rapid progression. Upregulation of glycolysis is a hallmark of tumor growth, and correlates with the progression of breast cancer. We aimed to establish a model to p...
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PeerJ Inc.
2020-11-01
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| author | Xiaoping Li Qihe Yu Jishang Chen Hui Huang Zhuangsheng Liu Chengxing Wang Yaoming He Xin Zhang Weiwen Li Chao Li Jinglin Zhao Wansheng Long |
| author_facet | Xiaoping Li Qihe Yu Jishang Chen Hui Huang Zhuangsheng Liu Chengxing Wang Yaoming He Xin Zhang Weiwen Li Chao Li Jinglin Zhao Wansheng Long |
| author_sort | Xiaoping Li |
| collection | DOAJ |
| description | Background Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer that is characterized by high malignancy and rapid progression. Upregulation of glycolysis is a hallmark of tumor growth, and correlates with the progression of breast cancer. We aimed to establish a model to predict the prognosis of patients with breast IDC based on differentially expressed glycolysis-related genes (DEGRGs). Methods Transcriptome data and clinical data of patients with breast IDC were from The Cancer Genome Atlas (TCGA). Glycolysis-related gene sets and pathways were from the Molecular Signatures Database (MSigDB). DEGRGs were identified by comparison of tumor tissues and adjacent normal tissues. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen for DEGRGs with prognostic value. A risk-scoring model based on DEGRGs related to prognosis was constructed. Receiver operating characteristic (ROC) analysis and calculation of the area under the curve (AUC) were used to evaluate the performance of the model. The model was verified in different clinical subgroups using an external dataset (GSE131769). A nomogram that included clinical indicators and risk scores was established. Gene function enrichment analysis was performed, and a protein-protein interaction network was developed. Results We analyzed data from 772 tumors and 88 adjacent normal tissues from the TCGA database and identified 286 glycolysis-related genes from the MSigDB. There were 185 DEGRGs. Univariate Cox regression and LASSO regression indicated that 13 of these genes were related to prognosis. A risk-scoring model based on these 13 DEGRGs allowed classification of patients as high-risk or low-risk according to median score. The duration of overall survival (OS) was longer in the low-risk group (P < 0.001), and the AUC was 0.755 for 3-year OS and 0.726 for 5-year OS. The results were similar when using the GEO data set for external validation (AUC for 3-year OS: 0.731, AUC for 5-year OS: 0.728). Subgroup analysis showed there were significant differences in OS among high-risk and low-risk patients in different subgroups (T1-2, T3-4, N0, N1-3, M0, TNBC, non-TNBC; all P < 0.01). The C-index was 0.824, and the AUC was 0.842 for 3-year OS and 0.808 for 5-year OS from the nomogram. Functional enrichment analysis demonstrated the DEGRGs were mainly involved in regulating biological functions. Conclusions Our prognostic model, based on 13 DEGRGs, had excellent performance in predicting the survival of patients with IDC of the breast. These DEGRGs appear to have important biological functions in the progression of this cancer. |
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| spelling | doaj.art-f2140e766eb847bca2f08625be62ffb42023-12-03T11:34:35ZengPeerJ Inc.PeerJ2167-83592020-11-018e1024910.7717/peerj.10249Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genesXiaoping Li0Qihe Yu1Jishang Chen2Hui Huang3Zhuangsheng Liu4Chengxing Wang5Yaoming He6Xin Zhang7Weiwen Li8Chao Li9Jinglin Zhao10Wansheng Long11Department of Gastrointestinal Surgery, Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong, ChinaDepartment of Oncology, Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong, ChinaDepartment of Breast Surgery, Yangjiang people’s Hospital, Yangjiang, Guangdong, ChinaDepartment of Breast Surgery, Jiangmen Maternity & Chile Health Care Hospital, Jiangmen, Guangdong, ChinaDepartment of Radiology, Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong, ChinaDepartment of Gastrointestinal Surgery, Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong, ChinaDepartment of Gastrointestinal Surgery, Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong, ChinaClinical Experimental Center, Jiangmen Key Laboratory of Clinical Biobanks and Translational Research, Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong, ChinaDepartment of Breast and Thyroid Surgery, Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong, ChinaDepartment of Gastrointestinal Surgery, Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong, ChinaDepartment of Gastrointestinal Surgery, Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong, ChinaDepartment of Radiology, Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong, ChinaBackground Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer that is characterized by high malignancy and rapid progression. Upregulation of glycolysis is a hallmark of tumor growth, and correlates with the progression of breast cancer. We aimed to establish a model to predict the prognosis of patients with breast IDC based on differentially expressed glycolysis-related genes (DEGRGs). Methods Transcriptome data and clinical data of patients with breast IDC were from The Cancer Genome Atlas (TCGA). Glycolysis-related gene sets and pathways were from the Molecular Signatures Database (MSigDB). DEGRGs were identified by comparison of tumor tissues and adjacent normal tissues. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen for DEGRGs with prognostic value. A risk-scoring model based on DEGRGs related to prognosis was constructed. Receiver operating characteristic (ROC) analysis and calculation of the area under the curve (AUC) were used to evaluate the performance of the model. The model was verified in different clinical subgroups using an external dataset (GSE131769). A nomogram that included clinical indicators and risk scores was established. Gene function enrichment analysis was performed, and a protein-protein interaction network was developed. Results We analyzed data from 772 tumors and 88 adjacent normal tissues from the TCGA database and identified 286 glycolysis-related genes from the MSigDB. There were 185 DEGRGs. Univariate Cox regression and LASSO regression indicated that 13 of these genes were related to prognosis. A risk-scoring model based on these 13 DEGRGs allowed classification of patients as high-risk or low-risk according to median score. The duration of overall survival (OS) was longer in the low-risk group (P < 0.001), and the AUC was 0.755 for 3-year OS and 0.726 for 5-year OS. The results were similar when using the GEO data set for external validation (AUC for 3-year OS: 0.731, AUC for 5-year OS: 0.728). Subgroup analysis showed there were significant differences in OS among high-risk and low-risk patients in different subgroups (T1-2, T3-4, N0, N1-3, M0, TNBC, non-TNBC; all P < 0.01). The C-index was 0.824, and the AUC was 0.842 for 3-year OS and 0.808 for 5-year OS from the nomogram. Functional enrichment analysis demonstrated the DEGRGs were mainly involved in regulating biological functions. Conclusions Our prognostic model, based on 13 DEGRGs, had excellent performance in predicting the survival of patients with IDC of the breast. These DEGRGs appear to have important biological functions in the progression of this cancer.https://peerj.com/articles/10249.pdfBreast invasive ductal carcinomaGlycolysisNomogramFunction enrichment analysis |
| spellingShingle | Xiaoping Li Qihe Yu Jishang Chen Hui Huang Zhuangsheng Liu Chengxing Wang Yaoming He Xin Zhang Weiwen Li Chao Li Jinglin Zhao Wansheng Long Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes PeerJ Breast invasive ductal carcinoma Glycolysis Nomogram Function enrichment analysis |
| title | Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes |
| title_full | Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes |
| title_fullStr | Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes |
| title_full_unstemmed | Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes |
| title_short | Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes |
| title_sort | prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis related genes |
| topic | Breast invasive ductal carcinoma Glycolysis Nomogram Function enrichment analysis |
| url | https://peerj.com/articles/10249.pdf |
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