Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis

Chen, Gang; Yu, Mingwei; Cao, Jianqiao; Zhao, Huishan; Dai, Yuanping; Cong, Yizi; Qiao, Guangdong

doi:10.1080/21655979.2021.1960775

Cited by 30 publications

(22 citation statements)

References 94 publications

(104 reference statements)

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“…Others have shown that overexpression of CDC20 indicates unfavorable prognosis and poor response to endocrine therapy in ER + breast cancer (Alfarsi et al, 2019;Tang et al, 2019); in contrast, we discovered that CDC20 was related to worse prognosis only in the TNBC subtype, and we think that the different datasets that we analyzed result in the inconsistency. Other genes such as BUB1, NUF2, CDC20, ASPM, KIF2C, and PRC1 have biological relevance to breast cancer progression, and PLK1, NDC80, and CCNB2 only to TNBC progression, and these genes predict worse prognosis (Wang et al, 2015;Tang et al, 2019;Yang et al, 2019;Lv et al, 2020;Ren et al, 2020;Chen et al, 2021;Jiang et al, 2021;Koyuncu et al, 2021); likewise, we also found that these genes correlated negatively with prognosis in the TNBC subtype. Moreover, it is also reported that high expression levels of AURKB, CDC6, and ECT2 suggest a poor prognosis for breast cancer (Mahadevappa et al, 2017;Daulat et al, 2019;Huang et al, 2019;Xiu et al, 2019), whereas we only found that CDC6 was negatively positively related to overall survival, and we consider that it is also the different datasets and analysis methods that cause the disparity.…”

Section: Discussionsupporting

confidence: 72%

A genomic and transcriptomic study toward breast cancer

Wang

Shang²,

Yao³

et al. 2022

Front. Genet.

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Background: Breast carcinoma is well recognized to be having the highest global occurrence rate among all cancers, being the leading cause of cancer mortality in females. The aim of this study was to elucidate breast cancer at the genomic and transcriptomic levels in different subtypes so that we can develop more personalized treatments and precision medicine to obtain better outcomes.Method: In this study, an expression profiling dataset downloaded from the Gene Expression Omnibus database, GSE45827, was re-analyzed to compare the expression profiles of breast cancer samples in the different subtypes. Using the GEO2R tool, different expression genes were identified. Using the STRING online tool, the protein–protein interaction networks were conducted. Using the Cytoscape software, we found modules, seed genes, and hub genes and performed pathway enrichment analysis. The Kaplan–Meier plotter was used to analyze the overall survival. MicroRNAs and transcription factors targeted different expression genes and were predicted by the Enrichr web server.Result: The analysis of these elements implied that the carcinogenesis and development of triple-negative breast cancer were the most important and complicated in breast carcinoma, occupying the most different expression genes, modules, seed genes, hub genes, and the most complex protein–protein interaction network and signal pathway. In addition, the luminal A subtype might occur in a completely different way from the other three subtypes as the pathways enriched in the luminal A subtype did not overlap with the others. We identified 16 hub genes that were related to good prognosis in triple-negative breast cancer. Moreover, SRSF1 was negatively correlated with overall survival in the Her2 subtype, while in the luminal A subtype, it showed the opposite relationship. Also, in the luminal B subtype, CCNB1 and KIF23 were associated with poor prognosis. Furthermore, new transcription factors and microRNAs were introduced to breast cancer which would shed light upon breast cancer in a new way and provide a novel therapeutic strategy.Conclusion: We preliminarily delved into the potentially comprehensive molecular mechanisms of breast cancer by creating a holistic view at the genomic and transcriptomic levels in different subtypes using computational tools. We also introduced new prognosis-related genes and novel therapeutic strategies and cast new light upon breast cancer.

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Section: Discussionsupporting

confidence: 72%

A genomic and transcriptomic study toward breast cancer

Wang

Shang²,

Yao³

et al. 2022

Front. Genet.

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“…BRCA is recognized as one of the most frequent malignancies, which is the leading cause of cancer deaths in women (Bray et al, 2018). Despite the advances in early screenings and medical therapies, the prognosis of BRCA patients remains disappointing because of drug resistance (Chen et al, 2021). Thus, developing the novel biomarkers and exploring the molecular pathogenesis of BRCA for improving the diagnosis and prognosis of this disease is urgently required.…”

Section: Discussionmentioning

confidence: 99%

“…At present, surgery, chemotherapy, radiotherapy, and hormone therapy are available approaches for BRCA treatment (Liu and Ye, 2017). Despite rapid improvement in target therapy methods, the prognosis is still unsatisfactory due to drug resistance and recurrence (Chen et al, 2021). Immunotherapy has emerged as a revolutionary strategy in various tumors, but whether this method would represent a role in BRCA treatment is an open question (de la Cruz-Merino et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

FN1 overexpression is correlated with unfavorable prognosis and immune infiltrates in breast cancer

Zhang

Luo

2022

Front. Genet.

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Objective: To investigate the correlation of fibronectin 1 (FN1) expression with prognosis and tumor-infiltrating immune cells in breast cancer (BRCA).Methods: FN1 mRNA and protein expressions were analyzed through Tumor Immune Estimation Resource (TIMER), Gene Set Cancer Analysis (GSCA), Human Protein Atlas (HPA) databases, and immunohistochemical analysis. The clinicopathological characteristics and genetic factors affecting the FN1 mRNA expression were assessed by various public databases. Then, we analyzed the prognostic value of FN1 in BRCA by Kaplan-Meier plotter, receiver operating characteristic, and Cox regression analyses. Further, the UCSC Xena database was used to retrieve TCGA-BRCA expression profiles for functional enrichment analysis and immune cell infiltration analysis. The potential drugs for the BRCA patients with high- FN1 expression were identified using the connectivity map analysis.Results: FN1 was upregulated in BRCA tissues compared with normal tissues. High FN1 mRNA expression was correlated with poor clinical outcomes and had good performance in predicting the survival status of BRCA patients. Further, Cox regression analysis showed that FN1 was an independent prognostic factor for predicting the overall survival of patients with BRCA. Moreover, hypermethylation of FN1 contributed to a better prognosis for BRCA patients. Functional enrichment analyses revealed the ECM-receptor interaction pathway and focal adhesion as the common pathways. Moreover, FN1 showed a significant association with tumor-infiltrating immune cells and immune checkpoint inhibitors. Several drugs such as telmisartan, malotilate, and seocalcitol may have therapeutic effects in BRCA patients with high FN1 expression.Conclusion: FN1 might serve as a novel prognostic biomarker and a novel therapeutic target for BRCA. Besides, the association of FN1 with immune cells and immune checkpoint inhibitors may provide assistance for BRCA treatment.

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“…The functions of AURKA in regulating mitosis, spindle composition and centrosome have been well researched [ 37 ]. In addition, studies have reported that AURKA from the serine/threonine kinase family was overexpressed in various cancers and can be used as a prognostic marker for breast cancer and advanced serous ovarian cancer [ 38 , 39 ]. Consistent with previous research, in the present study, AURKA showed a high expression in the DLBCL tissues and DLBCL cells, suggesting that AURKA may play a significant role in the DLBCL.…”

Section: Discussionmentioning

confidence: 99%