Abstract
Breast cancer is a devastating malignancy, among which the luminal A (LumA) breast cancer is the most common subtype. In the present study, we used a comprehensive bioinformatics approach in the hope of identifying novel prognostic biomarkers for LumA breast cancer patients. Transcriptomic profiling of 611 LumA breast cancer patients was downloaded from TCGA database. Differentially expressed genes (DEGs) between tumor samples and controls were first identified by differential expression analysis, before being used for the weighted gene co-expression network analysis. The subsequent univariate Cox regression and LASSO algorithm were used to uncover key prognostic genes for constructing multivariate Cox regression model. Patients were stratified into high-risk and low-risk groups according to the risk score, and subjected to multiple downstream analyses including survival analysis, gene set enrichment analysis (GSEA), inference on immune cell infiltration and analysis of mutation burden. Receiving operator curve analysis was also performed. A total of 7071 DEGs were first identified by edgeR package, pink module was found significantly associated with invasive lobular carcinoma (ILC). 105 prognostic genes and 9 predictors were identified, allowing the identification of a 5-key prognostic genes (LRRC77P, CA3, BAMBI, CABP1, ATP8A2) after intersection. These 5 genes, and the resulting Cox model, displayed good prognostic performance. Furthermore, distinct differences existed between two risk-score stratified groups at various levels. The identified 5-gene prognostic model will help deepen the understanding of the molecular and immunological mechanisms that affect the survival of LumA-ILC patients and guide and proper monitoring of these patients.
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All data generated or analysis are included in this published article.
Abbreviations
- LumA:
-
Luminal A
- LumB:
-
Luminal B
- ER+:
-
Estrogen receptor
- HER2−:
-
Human epidermal growth receptor 2
- ILC:
-
Invasive lobular carcinoma
- IDC:
-
Invasive ductal carcinoma
- WGCNA:
-
Weighted gene co-expression network analysis
- DEGs:
-
Differentially expressed genes
- ME:
-
Module eigengene
- OS:
-
Overall survival
- PPI:
-
Protein–protein-interaction
- LASSO:
-
Least absolute shrinkage and selection operator
- GSEA:
-
Gene set enrichment analysis
- ES:
-
Enrichment score
- TIICs:
-
Tumor infiltrating immune cells
- CKM:
-
Creatine kinase, muscle
- MYH2:
-
Myosin heavy chain 2
- DHRS7C:
-
Dehydrogenase/Reductase 7C
- STRIT1:
-
Small transmembrane regulator of ion transport 1
- MIR1-1HG:
-
MIR1-1 host gene
- PPP1R3A:
-
Protein phosphatase 1 regulatory subunit 3A
- MUC2:
-
Mucin 2
- CARTPT:
-
CART prepropeptide
- UCN3:
-
Urocortin 3
- SNP:
-
Single-nucleotide polymorphism
- TNBC:
-
Triple-negative breast cancer
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This work was supported by the Shantou Medical Science and Technology Planning Project [Grant Numbers 210521236491457, 210625106490696], the Medical Scientific Research Foundation of Guangdong Province, China [grant numbers A2021432, B2021448], the Special Fund Project of Guangdong Science and Technology [Grant Numbers 210728156901524, 210728156901519] and the Undergraduate Innovation Training Project of Shantou University [Grant Number 31/38/47/54].
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ZYL and YHC designed the study, analyzed and interpreted the data as well as wrote the manuscript. TZ, YL, JHZ, WL, YKC, JC and JZ contributed to the analysis and interpretation of data. ZYL designed the study and supervised the work. All the authors have read and approved the final version of the submitted manuscript.
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10709_2022_157_MOESM1_ESM.tif
Supplementary file1 (TIF 4449 kb). Supplementary Figure S1. Disease-free survival analysis of the the prognostic predictors
10709_2022_157_MOESM2_ESM.tif
Supplementary file2 (TIF 4556 kb). Supplementary Figure S2. Progression specific survival analysis of the the prognostic predictors
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Chen, YH., Zhang, TF., Liu, YY. et al. Identification of a 5-gene-risk score model for predicting luminal A-invasive lobular breast cancer survival. Genetica 150, 299–316 (2022). https://doi.org/10.1007/s10709-022-00157-7
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DOI: https://doi.org/10.1007/s10709-022-00157-7