scholarly journals Association Between Depression and Breast Cancer: TNF/TNFRSF1β and LEP/LEPR Axis

2022 ◽  
Author(s):  
Jiaying Lin ◽  
Guangman Cui ◽  
Wenwei Jiang ◽  
Zhousheng Lin ◽  
Xinyue Lan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Interaction Network ◽  
Epidemiological Studies ◽  
Gene Expression Omnibus ◽  
Inflammatory Factors ◽  
Biological Mechanism ◽  
Breast Cancer Patients ◽  
Protein Protein Interaction ◽  
Genes Encoding ◽  
Pathway Analyses

Abstract Depression contributes to enhanced initiation, development and metastasis of breast cancer. Despite epidemiological studies and experimental data suggest that depression and breast cancer may share a common biological mechanism, the results from these studies remain inconsistent. Here, we fully focus on the underlying biological mechanism behind the adverse effects of depression against breast cancer patients, and highlight the practical therapeutic intervention and improving quality of life. Publicly available datasets deposited in the Gene Expression Omnibus (GEO) were downloaded. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses of the differentially expressed genes (DEGs), which were extracted by using R tools, were performed. The protein-protein interaction network of the target DEGs was constructed using Cytoscape software and the hub genes were identified. In our study, we found that genes encoding proinflammatory cytokine, such as IL-1β and TNF, had significantly increased expression in depression. Following chronically stimulated by TNFα and IL-1β (usually for 14-18 days), inflammatory cancer-associated fibroblasts (CAFs) had elevated expression of inflammatory genes. Furthermore, the TNF/TNFRSF1β and LEP/LEPR regulatory axes were proven to be hub pathways of the crosstalk between depression and breast cancer. Our findings demonstrate that inflammatory factors are messengers linking depression and breast cancer, and provided further guidance in clinical medication.

scholarly journals An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sepideh Dashti ◽  
Mohammad Taheri ◽  
Soudeh Ghafouri-Fard
Keyword(s):  
Breast Cancer ◽  
Clinical Importance ◽  
Interaction Network ◽  
R Package ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ncbi Gene Expression Omnibus ◽  
Kaplan Meier ◽  
Microarray Datasets

Abstract Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein–protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA–mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan–Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan–Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA–lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.

scholarly journals Pharmacological mechanisms on the anti-breast cancer property of Coix lacryma-jobi: A network-based analysis

2021 ◽  
Author(s):  
Angelu Mae A Ferrer ◽  
Janella Rayne A David ◽  
Arvin A Taquiqui ◽  
Arci A Bautista ◽  
Custer C Deocaris ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Rna Polymerase Ii ◽  
Bioactive Compounds ◽  
Target Genes ◽  
Interaction Network ◽  
Regulation Of Transcription ◽  
Protein Protein Interaction ◽  
Anti Cancer ◽  
Pathway Analyses ◽  
Go Terms

Breast cancer is considered as one of the three most common cancers around the world and the second leading cause of cancer deaths among women. Coix lachrymal jobi, commonly known as Jobs tears or adlay has been reported to possess anti-cancer properties. Despite evidences provided by clinical data, the usage of Coix lacryma-jobi in treating cancer, particularly breast cancer, has been scarce. Thus, this study was conducted to determine the pharmacological mechanisms underlying its anti-breast cancer property using various network pathway analyses. Bioactive compounds from Coix lacryma-jobi and its potential target genes were obtained from SymMap. Breast cancer-related target genes were collected from CTD. Protein-protein interaction network was analyzed using the STRING database. GO and KEGG pathway enrichment analyses were performed using DAVID to further explore the mechanisms of Coix lacryma-jobi in treating breast cancer. PPI and compound-target-pathway were visualized using Cytoscape. A total of 26 bioactive compounds, 201 corresponding targets, 36625 breast cancer-associated targets were obtained, and 200 common targets were considered potential therapeutic targets. The 9 bioactive compounds identified were berberine, oleic acid, beta-sitosterol, sitosterol, linoleic acid, berberrubine, jatrorrhizine, thalifendine, and stigmasterol. The identified 5 core targets were ESR1, JUN, MAPK14, and RXRA. Coix lacryma-jobi targets enriched in GO terms were mostly involved in regulation of transcription from RNA polymerase II promoter, drug response, steroid hormone receptor activity, and protein binding. This study elucidates on the pharmacological underpinnings on the potency of adlay against breast cancer. Its subsequent drug development will be worth a step forward for a breast cancer-free society.

scholarly journals Decision theory for precision therapy of breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael Kenn ◽  
Dan Cacsire Castillo-Tong ◽  
Christian F. Singer ◽  
Rudolf Karch ◽  
Michael Cibena ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Decision Theory ◽  
Data Science ◽  
Gene Expression Omnibus ◽  
Hormone Receptor Status ◽  
Breast Cancer Patients ◽  
Receptor Status ◽  
Kaplan Meier ◽  
Precision Therapy

AbstractCorrectly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

TGFBR1 Polymorphism and Risk of Breast Cancer in Iranian Women

2015 ◽  
Vol 30 (4) ◽  
pp. 414-417 ◽  
Author(s):  
Elahe Kamali ◽  
Simin Hemmati ◽  
Forouzan Safari ◽  
Manoochehr Tavassoli
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Transforming Growth Factor Beta ◽  
Lower Risk ◽  
Transforming Growth Factor ◽  
Age At Onset ◽  
Epidemiological Studies ◽  
Breast Cancer Patients ◽  
Iranian Women ◽  
Risk Of Cancer

Numerous epidemiological studies have evaluated the association between transforming growth factor beta receptor type 1 ( TGFBR1) polymorphisms and the risk of cancer; however, the results remain inconclusive and controversial. To determine the association between breast cancer risk and the *6A polymorphism of the TGFBR1 gene, a case-control study of 280 breast cancer patients and 280 controls was performed in Iranian women. Our study demonstrates that women who carry the TGFBR1*6A allele are at lower risk of developing breast cancer. The highest protection against breast cancer was observed in 6A/6A homozygotes (OR = 0.32, p = 0.04). A lower frequency of the TGFBR1*6A allele in breast cancer patients may be an important genetic determinant that contributes to a lower risk of breast cancer in Iranian women. The results also showed that the allelic length of TGFBR1 polymorphisms had no significant association with the age at onset or the grade of disease, nor with the expression of progesterone and estrogen receptors and HER2.

scholarly journals Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Wei Han ◽  
Biao Huang ◽  
Xiao-Yu Zhao ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Metastatic Melanoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Subtype Identification ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

scholarly journals High Moesin Expression Is a Predictor of Poor Prognosis of Breast Cancer: Evidence From a Systematic Review With Meta-Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoli Hu ◽  
Yang Liu ◽  
Zhitong Bing ◽  
Qian Ye ◽  
Chengcheng Li
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Systematic Review ◽  
Cancer Patients ◽  
Poor Prognosis ◽  
Data Extraction ◽  
Meta Analysis ◽  
Gene Expression Omnibus ◽  
Breast Cancer Patients ◽  
Node Metastases

Owing to metastases and drug resistance, the prognosis of breast cancer is still dismal. Therefore, it is necessary to find new prognostic markers to improve the efficacy of breast cancer treatment. Literature shows a controversy between moesin (MSN) expression and prognosis in breast cancer. Here, we aimed to conduct a systematic review and meta-analysis to evaluate the prognostic relationship between MSN and breast cancer. Literature retrieval was conducted in the following databases: PubMed, Web of Science, Embase, and Cochrane. Two reviewers independently performed the screening of studies and data extraction. The Gene Expression Omnibus (GEO) database including both breast cancer gene expression and follow-up datasets was selected to verify literature results. The R software was employed for the meta-analysis. A total of 9 articles with 3,039 patients and 16 datasets with 2,916 patients were ultimately included. Results indicated that there was a significant relationship between MSN and lymph node metastases (P &lt; 0.05), and high MSN expression was associated with poor outcome of breast cancer patients (HR = 1.99; 95% CI 1.73–2.24). In summary, there is available evidence to support that high MSN expression has valuable importance for the poor prognosis in breast cancer patients.Systematic Review Registrationhttps://inplasy.com/inplasy-2020-8-0039/.

Identification and validation of a hub gene prognostic index for hepatocellular carcinoma

2021 ◽  
Author(s):  
Q Shi ◽  
Z Meng ◽  
XX Tian ◽  
YF Wang ◽  
WH Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Prognostic Index ◽  
Interaction Network ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Cox Regression Analysis ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Significant Independent Prognostic Factor

Aims: We aim to provide new insights into the mechanisms of hepatocellular carcinoma (HCC) and identify key genes as biomarkers for the prognosis of HCC. Materials & methods: Differentially expressed genes between HCC tissues and normal tissues were identified via the Gene Expression Omnibus tool. The top ten hub genes screened by the degree of the protein nodes in the protein–protein interaction network also showed significant associations with overall survival in HCC patients. Results: A prognostic model containing a five-gene signature was constructed to predict the prognosis of HCC via multivariate Cox regression analysis. Conclusion: This study identified a novel five-gene signature ( CDK1, CCNB1, CCNB2, BUB1 and KIF11) as a significant independent prognostic factor.

Uncovering potential lncRNAs and nearby mRNAs in systemic lupus erythematosus from the Gene Expression Omnibus dataset

Epigenomics ◽  
2019 ◽  
Vol 11 (16) ◽  
pp. 1795-1809 ◽  
Author(s):  
Haiyu Cao ◽  
Dong Li ◽  
Huixiu Lu ◽  
Jing Sun ◽  
Haibin Li
Keyword(s):  
Gene Expression ◽  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Noncoding Rnas ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Systemic Lupus ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Aim: The aim of this study was to find potential differentially expressed long noncoding RNAs (lncRNAs) and mRNAs in systemic lupus erythematosus. Materials & methods: Differentially expressed lncRNAs and mRNAs were obtained in the Gene Expression Omnibus dataset. Functional annotation of differentially expressed mRNAs was performed, followed by protein–protein interaction network analysis. Then, the interaction network of lncRNA-nearby targeted mRNA was built. Results: Several interaction pairs of lncRNA-nearby targeted mRNA including NRIR-RSAD2, RP11-153M7.5-TLR2, RP4-758J18.2-CCNL2, RP11-69E11.4-PABPC4 and RP11-496I9.1-IRF7/ HRAS/ PHRF1 were identified. Measles and MAPK were significantly enriched signaling pathways of differentially expressed mRNAs. Conclusion: Our study identified several differentially expressed lncRNAs and mRNAs. And their interactions may play a crucial role in the process of systemic lupus erythematosus.

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