ArticleWorld journal of surgical oncology2020
Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis.
Article in World journal of surgical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.
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20 citing papers in PubMed.
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- Revelation of comprehensive cell profiling of primary and metastatic tumour ecosystems in oral squamous cell carcinoma by single-cell transcriptomic analysis.International journal of medical sciences · 2024Article
- The role of interleukin‑7 serum level as biological marker in breast cancer: a cross‑sectional, observational, and analytical study.World journal of surgical oncology · 2022Observational
- Screening potential immune signatures for early-stage basal-like/triple-negative breast cancer.World journal of surgical oncology · 2022Article
- Overexpressed VDAC1 in breast cancer as a novel prognostic biomarker and correlates with immune infiltrates.World journal of surgical oncology · 2022Article
- Identification and validation of a novel prognostic signature based on transcription factors in breast cancer by bioinformatics analysis.Gland surgery · 2022Article
- Potential of Mitochondrial Ribosomal Genes as Cancer Biomarkers Demonstrated by Bioinformatics Results.Frontiers in oncology · 2022Review
- A circadian rhythm-related gene signature for prognosis, invasion and immune microenvironment of breast cancer.Frontiers in genetics · 2022Article
- Prognostic value of immune-related lncRNA pairs in patients with bladder cancer.World journal of surgical oncology · 2021Article
- Comprehensive analysis of the cancer driver genes in breast cancer demonstrates their roles in cancer prognosis and tumor microenvironment.World journal of surgical oncology · 2021Article
- In silico analysis of differentially expressed genesets in metastatic breast cancer identifies potential prognostic biomarkers.World journal of surgical oncology · 2021Article
- MRPL13 Act as a Novel Therapeutic Target and Could Promote Cell Proliferation in Non-Small Cell Lung Cancer.Cancer management and research · 2021Article
- Molecular Features of Glioma Determined and Validated Using Combined TCGA and GTEx Data Analyses.Frontiers in oncology · 2021Article
- Correction to: Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis.World journal of surgical oncology · 2020Article
- Identification of Breast Cancer Subtypes Based on Endoplasmic Reticulum Stress-Related Genes and Analysis of Prognosis and Immune Microenvironment in Breast Cancer Patients.Technology in cancer research & treatmentArticle
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study intended to determine important genes related to the prognosis and recurrence of breast cancer.
methodsGene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed.
resultsBased on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which, ABCA3, CCL22, FOXJ1, IL1RN, KCNIP3, MAP2K6, and MRPL13, were significantly expressed in both groups in the two data sets. ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 were good prognostic factors, while KCNIP3 and MRPL13 were poor prognostic factors.
conclusionABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 may serve as good prognostic factors, while KCNIP3 and MRPL13 may be poor prognostic factors for the prognosis of breast cancer.
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