Triple-Negative Breast Cancer Analysis Based on Metabolic Gene Classification and Immunotherapy
Bibliographic Data
| ID | 22079476 |
|---|---|
| Authors | Yu Zhou (0009-0001-5712-6902, First Affiliated Hospital of Jiamusi University), Yingqi Che, Zhongze Fu (Qiqihar Medical University), Henan Zhang (0000-0002-5205-7434, First Affiliated Hospital of Jiamusi University), Huiyu Wu, Hui-Yu Wu (0000-0002-7504-8872, Harbin Medical University, corresponding author) |
| Year | 2022 |
| Volume | 10 |
| Pages | 902378-902378 |
| Publication date | 2022-07-06 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.902378 |
| PMID | 35875026 |
| OpenAlex | W4283837069 |
| Language | EN |
| References cited | 63 |
Triple negative breast cancer (TNBC) has negative expression of ER, PR and HER-2. TNBC shows high histological grade and positive rate of lymph node metastasis, easy recurrence and distant metastasis. Molecular typing based on metabolic genes can reflect deeper characteristics of breast cancer and provide support for prognostic evaluation and individualized treatment. Metabolic subtypes of TNBC samples based on metabolic genes were determined by consensus clustering. CIBERSORT method was applied to evaluate the score distribution and differential expression of 22 immune cells in the TNBC samples. Linear discriminant analysis (LDA) established a subtype classification feature index. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were generated to validate the performance of prognostic metabolic subtypes in different datasets. Finally, we used weighted correlation network analysis (WGCNA) to cluster the TCGA expression profile dataset and screen the co-expression modules of metabolic genes. Consensus clustering of the TCGA cohort/dataset obtained three metabolic subtypes (MC1, MC2, and MC3). The ROC analysis showed a high prognostic performance of the three clusters in different datasets. Specifically, MC1 had the optimal prognosis, MC3 had a poor prognosis, and the three metabolic subtypes had different prognosis. Consistently, the immune characteristic index established based on metabolic subtypes demonstrated that compared with the other two subtypes, MC1 had a higher IFNγ score, T cell lytic activity and lower angiogenesis score, T cell dysfunction and rejection score. TIDE analysis showed that MC1 patients were more likely to benefit from immunotherapy. MC1 patients were more sensitive to immune checkpoint inhibitors and traditional chemotherapy drugs Cisplatin, Paclitaxel, Embelin, and Sorafenib. Multiclass AUC based on RNASeq and GSE datasets were 0.85 and 0.85, respectively. Finally, based on co-expression network analysis, we screened 7 potential gene markers related to metabolic characteristic index, of which CLCA2, REEP6, SPDEF, and CRAT can be used to indicate breast cancer prognosis. Molecular classification related to TNBC metabolism was of great significance for comprehensive understanding of the molecular pathological characteristics of TNBC, contributing to the exploration of reliable markers for early diagnosis of TNBC and predicting metastasis and recurrence, improvement of the TNBC staging system, guiding individualized treatment
Biology · Breast cancer · Cancer · Computational biology · Immunotherapy · Proportional hazards model · Receiver operating characteristic · Survival analysis · Triple-negative breast cancer · Cancer Immunotherapy and Biomarkers · Cancer, Lipids, and Metabolism · Ferroptosis and cancer prognosis · Medicine · Internal Medicine · Oncology
| Highly cited | No |
|---|