scholarly journals The Alterations and Potential Roles of MCMs in Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Xinyu Liu ◽  
Ying Liu ◽  
Qiangshan Wang ◽  
Siqi Song ◽  
Lingjun Feng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Network ◽  
Relapse Free Survival ◽  
Minichromosome Maintenance ◽  
Free Survival ◽  
Oncomine Database ◽  
Kaplan Meier ◽  
Eukaryotic Dna ◽  
Coexpressed Genes ◽  
Kaplan Meier Plotter

The minichromosome maintenance (MCM) protein family plays a key role in eukaryotic DNA replication and has been confirmed to be associated with the occurrence and progression of many tumors. However, the expression levels, functions, and prognostic values of MCMs in breast cancer (BC) have not been clearly and systematically explained. In this article, we studied the transcriptional levels of MCMs in BC based on the Oncomine database. Kaplan-Meier plotter was used to analyze prognostic value of MCMs in human BC patients. Furthermore, we constructed a MCM coexpression gene network and performed functional annotation analysis through DAVID to reveal the functions of MCMs and coexpressed genes. The data showed that the expression of MCM2–8 and MCM10 but not MCM1 and MCM9 was upregulated in BC. Kaplan-Meier plotter analysis revealed that high transcriptional levels of MCM2, MCM4–7, and MCM10 were significantly related to low relapse-free survival (RFS) in BC patients. In contrast, high levels of MCM1 and MCM9 predicted high RFS for BC patients. This study suggests that MCM2, MCM4–7, and MCM10 possess great potential to be valuable prognostic biomarkers for BC and that MCM1 and MCM9 may serve as potential treatment targets for BC patients.

Systemic analysis of the expression levels and prognosis of breast cancer-related cadherins

2021 ◽  
pp. 153537022110104
Author(s):  
Mingfei Xu ◽  
Chaoyue Liu ◽  
Lulan Pu ◽  
Jinrong Lai ◽  
Jingjia Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Patients ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Expression Levels ◽  
Protein Levels ◽  
Kaplan Meier ◽  
Cancer Tissues ◽  
Breast Tissues ◽  
Kaplan Meier Plotter

Cadherins form connection between cells, facilitate communication, and serve as essential agents in the progression of multiple cancers. Over 100 cadherins have been identified and they are mainly divided into four groups: classical cadherins (CDHs), protocadherins (PCDHs), desmosomal (DSC), and cadherin-related proteins. Accumulating evidence has indicated that several members of the cadherins are involved in breast cancer development. Nevertheless, the expression profiles and corresponding prognostic outcomes of these breast cancer-related cadherins are yet to be analyzed. Here, we examined the expression levels and prognostic potential of these breast cancer-related cadherins from the specific databases viz. oncomine, gene expression profiling interactive analysis, human protein atlas, UALCAN, Kaplan–Meier Plotter, and cBioPortal. We found that the CDH2/11 levels were higher in breast cancer tissues, compared to healthy breast tissues, whereas with CDH3-5, PCDH8/10, and DSC3, the levels were lower in the former than in the latter. Additionally, for CDH1/6/13/17/23, PCDH7, and FAT4, trancript level alterations between breast cancer and healthy tissues varied across different databases. The CDH1 protein levels were elevated in breast cancer tissues versus healthy breast tissues, whereas the protein levels of CDH3/11 and PCDH8/10 were reduced in breast cancer, compared to healthy breast tissues. For CDH15 and CDH23, the expression levels paralleled tumor stage. Survival analysis, using the Kaplan–Meier Plotter database, demonstrated that elevated CDH1-3 levels correlated with diminished relapse-free survival in breast cancer patients. Alternately, enhanced CDH4-6/15/17/23, PCDH10, DSC3, and FAT4 levels estimated a rise in relapse-free survival of breast cancer patients. These data suggest CDH1-3 to be a promising target for breast cancer precision therapy and CDH4-6/15/17/23, PCDH10, DSC3, and FAT4 to be novel biomarkers for breast cancer prognosis.

scholarly journals Identification of Flap Endonuclease 1 With Diagnostic and Prognostic Value in Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Min Wu ◽  
Pan Zhang ◽  
Penghui Wang ◽  
Zhen Fang ◽  
Yaqin Zhu
Keyword(s):  
Breast Cancer ◽  
Prognostic Marker ◽  
Prognostic Value ◽  
Diagnostic Value ◽  
Roc Curve Analysis ◽  
Free Survival ◽  
Flap Endonuclease 1 ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

ObjectiveThis study aims to identify the potential value of flap endonuclease 1 (FEN1) as a diagnostic and prognostic marker for breast cancer (BC).MethodsELISA was used to measure serum FEN1 levels and ECLIA for CA153 and CEA levels. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value. Oncomine and UALCAN databases were used to analyze the differences in FEN1 mRNA and protein expressions. Kaplan-Meier Plotter database was then used to assess the prognostic value.ResultsBioinformatics analysis showed that the FEN1 mRNA and protein levels were significantly higher in BC tissues than in normal tissues. FEN1 was detected in culture medium of BC cell lines and serum FEN1 concentrations were significantly increased in BC patients than in cancer-free individuals. Besides, FEN1 exhibited higher diagnostic accuracy (AUC values>0.800) than CA153 and CEA for distinguishing BC patients, especially early BC, from the healthy and benign groups, or individually. Additionally, serum FEN1 levels were significantly associated with the stage (P=0.001) and lymph invasion (P=0.016), and serum FEN1 levels were increased with the development of BC. Furthermore, serum FEN1 levels were significantly decreased in post-operative patients than in pre-operative patients (P=0.016). Based on the Kaplan-Meier Plotter database, the survival analysis indicated that FEN1 overexpression was associated with poor prognoses for overall survival (OS), relapse-free survival (RFS), and distant metastasis-free survival (DMFS) in BC patients.ConclusionFEN1 might be a novel diagnostic and prognostic marker for BC.

Gasdermin D as a potential prognosis and treatment response prediction biomarker for invasive breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12548-e12548
Author(s):  
Xianghou Xia ◽  
Wenjuan Yin ◽  
Jiefei Mao ◽  
Jiejie Hu ◽  
Dehong Zou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Invasive Breast Cancer ◽  
Response Prediction ◽  
Chemotherapy Response ◽  
Breast Cancer Patients ◽  
Free Survival ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter ◽  
Treatment Response Prediction

e12548 Background: Pyroptosis is a type of inflammatory cell death mediated by gasdermins. Pyroptosis is critical for macrophage against pathogen infection. Recently growing evidences show that pyroptosis may affect development and progression of many cancers. We aim to explore the expression and related function of pyroptosis executioner Gasdermin D (GSDMD) in breast cancer. Methods: We investigated the expression level of GSDMD using TNM plotter and Breast Cancer landscape proteome with TCGA, GTEx and TARGET databases, and the prognostic value of GSDMD in invasive breast cancer using Kaplan-Meier plotter with TCGA, GEO and EGA databases. The treatment response prediction values of GSDMD in invasive breast were calculated using ROC-plotter with GEO database. Further validation of the prognostic value and chemotherapy response prediction value of GSDMD were carried out with immunohistochemical staining on tissues from 165 cases of breast cancer patients receiving neoadjuvant chemotherapy in our cancer center. Results: TNM plotter and breast cancer landscape proteome portal analysis shows that overall expression level of GSDMD in invasive breast cancer tissue is 1.67 folds higher than it is in breast normal tissues ( p=1.05*e-06). Expression of GSDMD in LuminalB subtype (p=0.019) and Her2 subtype(p=0.04) is significantly higher than it is in TNBC subtype. Calculations with Kaplan-Meier plotter show expression of GSDMD is negatively correlated with overall survival(OS), HR=0.61(0.4−0.95) p=0.027 and relapse free survival (RFS), HR =0.65(0.58−0.63), p=8.7*e-14 and distant metastasis free survival (DMFS) HR =0.75(0.61−0.91), p=0.0038 in breast cancer patients. ROC-plotter calculations show high GSDMD expression is a powerful endocrine therapy (AUC=0.731 p=6*e-09 ) and chemotherapy response (AUC=0.64 p=8*e-05 ) predictor based on 5-year RFS in overall breast cancer patients. Our IHC staining analysis shows consistent prognostic and chemotherapy prediction value of GSDMD expression in TNBC patients. Conclusions: In conclusion, our findings suggest that high expression of GSDMD is positively correlated with prognosis and therapeutic response in breast cancer. GSDMD is a promising prognostic marker and therapeutic response predictor in invasive breast cancer.

scholarly journals Mining expression and prognosis of FOLR1 in ovarian cancer by using Oncomine and Kaplan-Meier plotter

Pteridines ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 158-164
Author(s):  
Qingyuan Su ◽  
Qingyuan Lv ◽  
Ruijin Wu
Keyword(s):  
Ovarian Cancer ◽  
Mrna Expression ◽  
Ovarian Tumor ◽  
Ovarian Tissue ◽  
Progression Free Survival ◽  
Free Survival ◽  
Normal Ovarian Tissue ◽  
Oncomine Database ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Objective: To further explore folate receptor 1 (FOLR1) gene expression in ovarian cancer and its association with patients’ prognosis by deep mining the Oncomine and Kaplan-Meier plotter databases. Methods: FOLR1 mRNA expression data of ovarian cancer were retrieved from the Oncomine database and further analyzed by comparing tumor to healthy tissue. The prognostic value of FOLR1 in ovarian cancer was analyzed by Kaplan-Meier Plotter, an online survival analysis database. Results A total of 439 studies were included in the Oncomine database in multiple types of cancers. Of the 439 studies, there were 54 with statistical differences for the expression of FOLR1, 19 with increased expression of FOLR1 and 35 with decreased expression comparing ovarian cancer to normal ovary tissue. After searching the Oncomine database, six datasets were discovered comparing the mRNA expression in ovarian tumor to healthy tissue. FOLR1 mRNA expression in ovarian tumor was significantly higher than that of normal ovarian tissue (all p<0.05). The Kaplan-Meier Plotter database analyzed the correlation between FOLR1 expression and ovarian cancer patient’s prognosis. A significant difference of progression-free survival between FOLR1 high and low expressing groups was found in ovarian cancer patients (HR=1.14, 95%CI: 1.00-1.29, p=0.043). However, the overall survival was not statistically different between high and low FOLR1 expressing patients (HR=0.95, 95%CI: 0.84-1.09, p=0.48). Conclusion FOLR1 mRNA was found to be highly expressed in ovarian tumor compared to normal ovarian tissue. Elevated FOLR1 mRNA expression was associated with the poor progression-free survival.

scholarly journals Neural Network Analysis of Follow-Up Data in Primary Breast Cancer

2000 ◽  
Vol 15 (1) ◽  
pp. 116-122 ◽  
Author(s):  
N. Harbeck ◽  
R. Kates ◽  
K Ulm ◽  
H. Graeff ◽  
M. Schmitt
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Risk Group ◽  
Neural Network Analysis ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Kaplan Meier ◽  
The Neural Network ◽  
Pai 1

This paper reports on the performance of a recently developed neural network environment incorporating likelihood-based optimization and complexity reduction techniques in the analysis of breast cancer follow-up data with the goal of building up a clinical decision support system. The inputs to the neural network include classical factors such as grading, age, tumor size, estrogen and progesterone receptor measurements, as well as tumor biological markers such as PAI-1 and uPA. The network learns the structural relationship between these factors and the follow-up data. Examples of neural models for relapse-free survival are presented, which are based on data from 784 breast cancer patients who received their primary therapy at the Department of Obstetrics and Gynecology, Technische Universität München, Germany. The performance of the neural analysis as quantified by various indicators (likelihood, Kaplan-Meier curves, log-rank tests) was very high. For example, dividing the patients into two equally sized groups based on the neural score (i.e., cutoff = median score) leads to an estimated difference in relapse-free survival of 40% or better (80% vs. 40%) after 10 years in Kaplan-Meier analysis. Evidence for factor interactions as well as for time-varying impacts is presented. The neural network weights included in the models are significant at the 5% level. The use of neural network analysis and scoring in combination with strong tumor biological factors such as uPA and PAI-1 appears to result in a very effective risk group discrimination. Considerable additional comparison of data from different patient series will be required to establish the generalization capability more firmly. Nonetheless, the improvement of risk group discrimination represents an important step toward the use of neural networks for decision support in a clinical framework and in making the most of biological markers.

scholarly journals Overexpression of CENPF correlates with poor prognosis and tumor bone metastasis in breast cancer

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Jingbo Sun ◽  
Jingzhan Huang ◽  
Jin Lan ◽  
Kun Zhou ◽  
Yuan Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bone Metastasis ◽  
Western Blot ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Free Survival ◽  
Gene Set ◽  
Oncomine Database ◽  
Kaplan Meier

Abstract Background Centromere Protein F (CENPF) associates with the centromere–kinetochore complex and influences cell proliferation and metastasis in several cancers. The role of CENPF in breast cancer (BC) bone metastasis remains unclear. Methods Using the ONCOMINE database, we compared the expression of CENPF in breast cancer and normal tissues. Findings were confirmed in 60 BC patients through immunohistochemical (IHC) staining. Microarray data from GEO and Kaplan–Meier plots were used analyze the overall survival (OS) and relapse free survival (RFS). Using the GEO databases, we compared the expression of CENPF in primary lesions, lung metastasis lesions and bone metastasis lesions, and validated our findings in BALB/C mouse 4T1 BC models. Based on gene set enrichment analysis (GSEA) and western blot, we predicted the mechanisms by which CENPF regulates BC bone metastasis. Results The ONCOMINE database and immunohistochemical (IHC) showed higher CENPF expression in BC tissue compared to normal tissue. Kaplan–Meier plots also revealed that high CENPF mRNA expression correlated to poor survival and shorter progression-free survival (RFS). From BALB/C mice 4T1 BC models and the GEO database, CENPF was overexpressed in primary lesions, other target organs, and in bone metastasis. Based on gene set enrichment analysis (GSEA) and western blot, we predicted that CENPF regulates the secretion of parathyroid hormone-related peptide (PTHrP) through its ability to activate PI3K–AKT–mTORC1. Conclusion CENPF promotes BC bone metastasis by activating PI3K–AKT–mTORC1 signaling and represents a novel therapeutic target for BC treatment.

scholarly journals Paclitaxel Promotes Tumor-Infiltrating Macrophages in Breast Cancer

2020 ◽  
Vol 19 ◽  
pp. 153303382094582
Author(s):  
Jun Shen ◽  
Cong Chen ◽  
Zhaoqing Li ◽  
Shufang Hu
Keyword(s):  
Breast Cancer ◽  
Progression Free Survival ◽  
Cancer Prognosis ◽  
Migration And Invasion ◽  
P Value ◽  
Free Survival ◽  
Kaplan Meier ◽  
The Relationship ◽  
Kaplan Meier Plotter ◽  
Paclitaxel Treatment

Objective: Breast cancer remains the most threatening triggers of cancer death in women. Drug resistance inevitably leads to the weakness of treatment for breast cancer. Macrophages, as one of the most abundant immune cells in tumor immune-infiltrating microenvironment, involves in cell survival, migration, and invasion of breast cancer. Methods: In this study, we compared the proportions of macrophages in patients with breast cancer with and without paclitaxel treatment, and investigated the targeted genes associated with macrophages for paclitaxel response. To explore the relationship between drug-related genes and breast cancer prognosis, survival analysis based on the drug-related genes were performed by website of Kaplan-Meier plotter with the threshold of significant P value < .05. Results: Compared to the normal samples, we revealed that paclitaxel significantly enhanced the ratio of macrophages in the tumor microenvironment. Furthermore, the expression of 3 drug-related genes (IFT46, PEX11A, and TMEM223) were significantly negatively associated with the proportions of macrophages. And it is worth to notice that PEX11A and TMEM223 were associated with better progression-free survival outcomes of patients with breast cancer. Moreover, PEX11A was associated with longer overall survival time of breast cancer. Conclusion: Taken all together, all the findings support to gain a better understanding to the development of more effective therapies targeted with paclitaxel.

scholarly journals Identification of ATFs in Human Breast Cancer by Integrated Bioinformatic Analysis

2021 ◽  
Author(s):  
chenchen Geng ◽  
Qian Pu ◽  
Shuxu Tian ◽  
Wenwen Geng ◽  
Haiyan Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bioinformatic Analysis ◽  
Free Survival ◽  
Expression Levels ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Protein Expressions ◽  
Human Protein Atlas ◽  
Activating Transcription Factor ◽  
Kaplan Meier Plotter

Abstract Background: To obtain a thorough comprehension of the profile and prognosis of activating transcription factor (ATF) family members in breast cancer.Method: We searched Oncomine, GEPIA, cBioPortal, Kaplan-Meier plotter, and CancerSEA databases to assess expression level, prognostic value, and functions of ATFs in breast cancer. Results: In breast cancer, we found that the expression levels of genes like ATF1, ATF5, and ATF6, were higher than in normal tissues. While the expression levels of ATF3, ATF4, ATF7 were lower in the former than in the latter. Similarly, the ATFs protein expressions were consistent with this in the Human Protein Atlas database. High expressions of ATF2, ATF4, and ATF6-7 were associated with good relapse-free survival. Increased expressions of ATF4 and ATF7 had high overall survival. Conversely, the mRNA expression of ATF1 was negatively correlated with distant metastasis-free survival. Similarly, high expression of ATF2 had reduced post-progression survival. Conclusions: ATF1 was a target of potential therapeutic interest for breast cancer, and ATF4 and ATF6-7 were potential prognostic factors in evaluating breast cancer.

Prognostic and diagnostic roles of prolyl 4-hydroxylase subunit α members in breast cancer

2021 ◽  
Author(s):  
Mingjie Li ◽  
Qianyun Wang ◽  
Qinqin Zheng ◽  
Lin Wu ◽  
Bin Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Stage ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
Poor Survival ◽  
Free Survival ◽  
Kaplan Meier ◽  
Human Protein Atlas ◽  
Cancer Genome Atlas ◽  
Kaplan Meier Plotter

Aim: We aimed to evaluate the diagnostic and prognostic values of P4HAs in breast cancer (BC) patients. Materials & methods: Kaplan–Meier plotter was used to evaluate the prognostic values of P4HAs and correlations between their expression and clinical characteristics were assessed based on The Cancer Genome Atlas and the Human Protein Atlas. Results: The current study showed that P4HAs were highly expressed in BC patients with clinical stage I compared with nontumor control and elevated P4HAs were correlated with poor survival outcomes. Subtypes analysis revealed that P4HA1 and P4HA2 were most expressed in HER2+ subtypes patients. Univariate analysis displayed that elevated P4HA1 and P4HA3 correlated with unfavorable recurrence-free survival in mutated TP53 patients. Conclusion: This study indicated the diagnostic and prognostic roles of P4HAs members and broadened the biomarker fields of early diagnosis and prognostic monitoring of BC patients.

scholarly journals Bioinformatics analysis of the transcriptional expression of minichromosome maintenance proteins as potential indicators of survival in patients with cervical cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Baojie Wu ◽  
Shuyi Xi
Keyword(s):  
Cervical Cancer ◽  
Dna Replication ◽  
Mrna Expression ◽  
Interaction Network ◽  
Hub Genes ◽  
Clinical Value ◽  
Minichromosome Maintenance ◽  
Kaplan Meier ◽  
Coexpressed Genes ◽  
Kaplan Meier Plotter

Abstract Background As major regulators of DNA replication in eukaryotes, minichromosome maintenance (MCM) proteins play an important role in the initiation and extension of DNA replication. MCMs and their related genes may be new markers of cell proliferation activity, which is of great significance for the diagnosis and prognosis of cervical cancer. Methods To explore the role of MCMs and their related genes in cervical cancer, various bioinformatics methods were performed. First, the ONCOMINE and UALCAN databases were used to analyze the mRNA expression of different MCMs. The Human Protein Atlas database was used to analyze the protein expression of MCMs in normal and tumor tissues. The potential clinical value of MCMs was evaluated using the UALCAN, Kaplan-Meier plotter and cBioPortal databases. Then, the related genes and key coexpressed genes of MCMs were screened using GEPIA2 and cBioPortal analysis. For these genes, we used Metascape and the DAVID database to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, construct the related molecular interaction network, and obtain the key subnetworks and related hub genes. The Kaplan-Meier plotter database was used for survival analysis of cervical cancer patients to evaluate and predict the potential clinical value of the hub genes. Moreover, multiple gene comparisons of the expression of MCMs and related genes in different cancer types also showed the clinical significance of these potential targets. Results The mRNA and protein expression of MCMs increased in tumor tissue. Overexpression of MCM2/3/4/5/6/7/8/10 was found to be significantly associated with clinical cancer stage. Higher mRNA expression levels of MCM3/5/6/7/8 were found to be significantly associated with longer overall survival, and higher mRNA expression of MCM2/3/4/5/6/7/8 was associated with favorable OS. In addition, a high mutation rate of MCMs (71%) was observed. MCM2, MCM4, MCM8, MCM3 and MCM7 were the five genes with the most genetic alterations. In addition, the coexpressed genes and related genes of MCMs were successfully screened for enrichment analysis. These genes were significantly enriched in important pathways, such as the DNA replication, cell cycle, mismatch repair, spliceosome, and Fanconi anemia pathways. A protein-protein interaction network was successfully constructed, and a total of 13 hub genes (CDC45, ORC1, RPA1, CDT1, TARDBP, RBMX, SRSF3, SRSF1, RFC5, RFC2, MSH6, DTL, and MSH2) from 4 key subnetworks were obtained. These genes and MCM2/3/4/5/6/7/8 might have potential clinical value for the survival and prognosis of cervical cancer patients. Conclusions These findings promoted the understanding of the MCM protein family and clinically related molecular targets for cervical epithelial neoplasia and cervical cancer. Our results were helpful to evaluate the potential clinical value of MCMs and related genes in patients with cervical cancer.

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