scholarly journals The Expression and Prognostic Significance of Major MicroRNA Genes in Breast Cancer Based on Bioinformatics Analysis

2022 ◽  
Vol 11 (01) ◽  
pp. 1-30
Author(s):  
Qiongge Wang ◽  
Jiuli Hu ◽  
Junwei Liang ◽  
Lanfang Liu ◽  
Shuoyang Xiao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bioinformatics Analysis ◽  
Prognostic Significance ◽  
Microrna Genes

scholarly journals Bioinformatics analysis revealing prognostic significance of RRM2 gene in breast cancer

2019 ◽  
Vol 39 (4) ◽  
Author(s):  
Wei-xian Chen ◽  
Liang-gen Yang ◽  
Ling-yun Xu ◽  
Lin Cheng ◽  
Qi Qian ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Survival Data ◽  
Bioinformatics Analysis ◽  
Prognostic Significance ◽  
Prognostic Index ◽  
Disease Free Survival ◽  
Cancer Prognosis ◽  
Breast Cancer Patients ◽  
Free Survival ◽  
Metastatic Relapse

Abstract Background: Ribonucleotide reductase M2 subunit (RRM2) plays vital roles in many cellular processes such as cell proliferation, invasiveness, migration, angiogenesis, senescence, and tumorigenesis. However, the prognostic significance of RRM2 gene in breast cancer remains to be investigated. Methods:RRM2 expression was initially evaluated using the Oncomine database. The relevance between RRM2 level and clinical parameters as well as survival data in breast cancer was analyzed using the Kaplan–Meier Plotter, PrognoScan, and Breast Cancer Gene-Expression Miner (bc-GenExMiner) databases. Results:RRM2 was overexpressed in different subtypes of breast cancer patients. Estrogen receptor (ER) and progesterone receptor (PR) were negatively correlated with RRM2 expression. Conversely, the Scarff–Bloom–Richardson (SBR) grade, Nottingham prognostic index (NPI), human epidermal growth factor receptor-2 (HER-2) status, nodal status, basal-like status, and triple-negative status were positively related to RRM2 level in breast cancer samples with respect to normal tissues. Patients with increased RRM2 showed worse overall survival, relapse-free survival, distant metastasis-free survival, disease-specific survival, and disease-free survival. RRM2 also exerted positive effect on metastatic relapse event. Besides, a positive correlation between RRM2 and KIF11 genes was confirmed. Conclusion: Bioinformatics analysis revealed that RRM2 might be used as a predictive biomarker for prognosis of breast cancer. Further studies are needed to more precisely elucidate the value of RRM2 in evaluating breast cancer prognosis.

scholarly journals Bioinformatics analysis of prognostic value of TRIM13 gene in breast cancer

2019 ◽  
Vol 39 (3) ◽  
Author(s):  
Wei-xian Chen ◽  
Lin Cheng ◽  
Ling-yun Xu ◽  
Qi Qian ◽  
Yu-lan Zhu
Keyword(s):  
Breast Cancer ◽  
Cancer Treatment ◽  
Cancer Patients ◽  
Bioinformatics Analysis ◽  
Prognostic Significance ◽  
Breast Cancer Treatment ◽  
Breast Cancer Patients ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Metastatic Relapse

Abstract Background: Tripartite motif 13 (TRIM13) plays a significant role in various biological processes including cell growth, apoptosis, transcriptional regulation, and carcinogenesis. However, the prognostic significance of TRIM13 gene in breast cancer treatment remains largely unclear. Methods: We performed a bioinformatics analysis of the clinical parameters and survival data as it relates to TRIM13 in breast cancer patients using several online databases including Oncomine, bcGenExMiner, PrognoScan, and UCSC Xena. Results: We found that TRIM13 was lower-expressed in different subtypes of breast cancer with respect to normal tissues. Estrogen receptor and progesterone receptor status were positively correlated with TRIM13 level; whereas, the Scarff–Bloom–Richardson grade, Nottingham prognostic index, nodal status, basal-like status, and triple-negative status were negatively related to TRIM13 expression in breast cancer patients with respect to normal individuals. Lower TRIM13 expression correlated with worse distant metastasis free survival, relapse free survival, disease specific survival, and metastatic relapse free survival. We also confirmed a positive correlation between TRIM13 and RAB11FIP2 gene expression. Conclusion: Bioinformatics analysis revealed that TRIM13 may be adopted as a promising predictive biomarker for prognosis of breast cancer. More in-depth experiments and clinical trials are needed to validate the value of TRIM13 in breast cancer treatment.

scholarly journals Bioinformatics analysis of prognostic significance of COL10A1 in breast cancer

2020 ◽  
Vol 40 (2) ◽  
Author(s):  
Mingdi Zhang ◽  
Hongliang Chen ◽  
Maoli Wang ◽  
Fang Bai ◽  
Kejin Wu
Keyword(s):  
Breast Cancer ◽  
Bioinformatics Analysis ◽  
Prognostic Significance ◽  
Growth Factor Receptor ◽  
Disease Free Survival ◽  
Predictive Biomarker ◽  
Expression Level ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Metastatic Relapse

Abstract Background: Collagen type X alpha 1 (COL10A1) is overexpressed in diverse tumors and displays vital roles in tumorigenesis. However, the prognostic value of COL10A1 in breast cancer remains unclear. Methods: The expression of COL10A1 was analyzed by the Oncomine database and UALCAN cancer database. The relationship between COL10A1 expression level and clinical indicators including prognostic data in breast cancer were analyzed by the Kaplan–Meier Plotter, PrognoScan, and Breast Cancer Gene-Expression Miner (bc-GenExMiner) databases. Results: COL10A1 was up-regulated in different subtypes of breast cancer. Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2) status and nodal status were positively correlated with COL10A1 expression. Conversely, age, the Scarff–Bloom–Richardson (SBR) grade, basal-like status, and triple-negative status were negatively related to COL10A1 level in breast cancer samples compared with normal tissues. Patients with increased COL10A1 expression level showed worse overall survival (OS), relapse-free survival (RFS), distant metastasis-free survival (DMFS) and disease-free survival (DFS). COL10A1 was positively correlated with metastatic relapse-free survival. GSEA analysis revealed that enrichment of TGF-β signaling pathway. 15-leucine-rich repeat containing membrane protein (LRRC15) is a correlated gene of COL10A1. Conclusion: Bioinformatics analysis revealed that COL10A1 might be considered as a predictive biomarker for prognosis of breast cancer. Further experiments and clinical trials are essential to elucidate the value of COL10A1 in breast cancer treatment.

scholarly journals Mining the prognostic significance of the GINS2 gene in human breast cancer using bioinformatics analysis

2020 ◽  
Vol 20 (2) ◽  
pp. 1300-1310
Author(s):  
Shibo Yu ◽  
Lizhe Zhu ◽  
Peiling Xie ◽  
Siyuan Jiang ◽  
Ke Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Human Breast Cancer ◽  
Bioinformatics Analysis ◽  
Prognostic Significance ◽  
Human Breast

Prognostic significance of body mass index in breast cancer patients with hormone receptor-positive tumours after curative surgery

2013 ◽  
Vol 36 (6) ◽  
pp. 297 ◽  
Author(s):  
Peng Xing ◽  
Ji-Guang Li ◽  
Feng Jin ◽  
Ting-Ting Zhao ◽  
Qun Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Body Mass Index ◽  
Body Mass ◽  
Hormone Receptor ◽  
Univariate Analysis ◽  
Prognostic Significance ◽  
Axillary Lymph ◽  
Nodal Stage ◽  
Hormone Receptor Positive ◽  
High Bmi

Purpose: Obesity has been recognized as a significant risk factor for postmenopausal breast cancer. The aim of this study is to investigate the prognostic significance of body mass index (BMI) in hormone receptor-positive, operable breast cancer. Methods: In this retrospective cohort study, 1,192 consecutive patients with curative resection of primary breast cancer were enrolled. Patients were assigned to two groups according to BMI: normal or underweight (BMI < 23.0 kg/m2) and overweight or obese (BMI ≥23.0 kg/m2). Associations among BMI and clinicopathological characteristics and prognosis of patients were assessed. Results: A high BMI was significantly (P < 0.01) correlated with age, nodal stage, ALNR, ER positivity, PR positivity and menopausal status at diagnosis. Univariate analysis revealed that BMI, pathologic T stage, nodal stage, axillary lymph node ratio (ALNR) and adjuvant radiotherapy history were significantly (P < 0.05) associated with disease-free survival and overall survival, irrespective of tumour hormone receptor status. Multivariate analysis revealed BMI as an independent prognostic factor in all cases and in hormone receptor-positive cases. Conclusion: A high BMI (≥23.0 kg/m^2) is independently associated with poor prognosis in hormone receptor-positive breast cancer.

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