Plasma HSP90AA1 Predicts the Risk of Breast Cancer Onset and Distant Metastasis

Liu, Haizhou; Zhang, Zihan; Huang, Yi; Wei, Wen-Qi; Ning, Shufang; Li, Jilin; Liang, Xiaofeng; Liu, Kaisheng; Zhang, Litu

doi:10.3389/fcell.2021.639596

Cited by 45 publications

(32 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Preliminary evidence suggests that the anticancer effects of sorafenib in HCC patients are related to these mechanisms. e results of previous studies have confirmed that SLC41A3, SEC61A1, LRP4, PPM1G, and HSP90AA1 play important roles in cancer progression [28][29][30][31][32][33][34][35][36][37][38][39]. SLC41A3 is a member of solute carrier family 41 and is involved in many cellular processes.…”

Section: Discussionmentioning

confidence: 77%

A Risk Model Based on Sorafenib-Response Target Genes Predicts the Prognosis of Patients with HCC

Liu

Zeng

et al. 2022

Journal of Oncology

View full text Add to dashboard Cite

Sorafenib is used to treat digestive system tumors in patients who do not respond to or cannot tolerate surgery. However, the roles and inhibitory mechanisms of sorafenib against hepatocellular carcinoma (HCC) are unclear. Differentially expressed genes in tissues from responders and nonresponders to sorafenib were investigated using the HCC GSE109211 data set. Biological functions and mechanisms were studied using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. The expression levels of differential expressed target genes were identified in HCC tissues, using The Cancer Genome Atlas database, and their prognostic and diagnostic values were explored using survival and receiver operating characteristic curve analysis. A nomogram and risk model of sorafenib-response target genes enabled the evaluation of the prognosis of patients with HCC. The relationship between risk scores and levels of infiltrating immune cells was visualized via correlation analysis. We identified 1620 sorafenib-response target genes involved in the PPAR signaling pathway, antigen processing and presentation, and ferroptosis. SLC41A3, SEC61A1, LRP4, PPM1G, and HSP90AA1 were independent risk factors for a poor prognosis for patients with HCC and had diagnostic value. A risk model based on SLC41A3, SEC61A1, LRP4, PPM1G, and HSP90AA1 expression showed that patients with HCC in the high-risk group had a worse prognosis. Consensus-clustering analysis (performed with K set to 2) distinguished two clusters (the cluster 1 and cluster 2 groups). Patients in cluster 1 survived significantly longer than those in cluster 2. The risk score correlated with the levels of T cells, cytotoxic lymphocytes, CD8+ T cells, macrophages, memory B cells, follicular helper T cells, and other immune cells. The high risk based on the sorafenib-response targets SLC41A3, SEC61A1, LRP4, PPM1G, and HSP90AA1 represented the poor prognosis for patients with HCC and significantly correlated with the levels of immune infiltrating cells in HCC.

show abstract

Section: Discussionmentioning

confidence: 77%

A Risk Model Based on Sorafenib-Response Target Genes Predicts the Prognosis of Patients with HCC

Liu

Zeng

et al. 2022

Journal of Oncology

View full text Add to dashboard Cite

show abstract

“…In the course of this work, we discovered that heat shock protein 90-alpha (HSP90AA1) is the most highlighted gene in the network and based on the available data, HSP90AA1 is an evolutionary conserved protein which has a prominent role in processes such as DNA damage, inflammation and tumorigenesis. there is a considerable body of evidence that shows plasma levels of HSP90AA1 has clinical benefit in prediction of onset and risk of metastasis in breast cancer patients [ 51 ]. In the present work, it also became apparent that HSP90AA1 may has a role in prediction of response to therapy in breast cancer.…”

Section: Resultsmentioning

confidence: 99%

Gene selection using pyramid gravitational search algorithm

et al. 2022

View full text Add to dashboard Cite

Genetics play a prominent role in the development and progression of malignant neoplasms. Identification of the relevant genes is a high-dimensional data processing problem. Pyramid gravitational search algorithm (PGSA), a hybrid method in which the number of genes is cyclically reduced is proposed to conquer the curse of dimensionality. PGSA consists of two elements, a filter and a wrapper method (inspired by the gravitational search algorithm) which iterates through cycles. The genes selected in each cycle are passed on to the subsequent cycles to further reduce the dimension. PGSA tries to maximize the classification accuracy using the most informative genes while reducing the number of genes. Results are reported on a multi-class microarray gene expression dataset for breast cancer. Several feature selection algorithms have been implemented to have a fair comparison. The PGSA ranked first in terms of accuracy (84.5%) with 73 genes. To check if the selected genes are meaningful in terms of patient’s survival and response to therapy, protein-protein interaction network analysis has been applied on the genes. An interesting pattern was emerged when examining the genetic network. HSP90AA1, PTK2 and SRC genes were amongst the top-rated bottleneck genes, and DNA damage, cell adhesion and migration pathways are highly enriched in the network.

show abstract

“…In our study, HSP90AA1 was highly expressed in breast cancer tissues, and the low expression of HSP90AA1 indicated a good prognosis for patients with breast cancer. Moreover, a study by Liu et al ( 29 ) showed that pre-treatment plasma HSP90AA1 levels in combination with other markers could be an effective means of predicting breast cancer metastasis rates. MAPK14 is one of the four p38 MAPKs and a member of the MAP kinase family.…”

Section: Discussionmentioning

confidence: 99%

A network-based pharmacological study on the mechanism of action of muscone in breast cancer

Zhao¹,

Tao²,

Wang³

et al. 2022

Transl Cancer Res TCR

View full text Add to dashboard Cite

Background The purpose of this study was to investigate the mechanism of action of muscone on breast cancer using network pharmacology and molecular docking techniques. Methods Targets of muscone acid action were collected using the PubChem and SwissTargetPrediction databases. Relevant target sets of breast cancer were collected using the GeneCards database, and the intersection of the drug-disease targets was used as the potential target of muscone action in breast cancer. The STRING database was used to construct a target protein-protein interaction (PPI) network, and the data were imported into Cytoscape 3.7.1 for topological network analysis to obtain the core target genes of muscone in breast cancer. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID database. The correlation of core gene expression with breast cancer survival was analyzed using the online Kaplan-Meier plotter tool. Molecular docking of core target genes to muscone was performed using AutoDock Vina. Results A total of 18 common targets of muscone and breast cancer were obtained through target intersection. The PPI map and topology analysis revealed that androgen receptor (AR), progesterone receptor (PGR), matrix metalloproteinase 9 (MMP9), prostaglandin-endoperoxide synthase 2 (PTGS2), heat shock protein 90 alpha family class A member 1 (HSP90AA1), mitogen-activated protein kinase 14 (MAPK14), and cytochrome P450 family 19 subfamily A member 1 (CYP19A1) might be the key targets of muscone acting on breast cancer. The GO enrichment analysis identified 60 terms, while the KEGG pathway enrichment analysis identified 7 signaling pathways, including steroid hormone biosynthesis, ovarian steroidogenesis, cancer pathways, and the tumor necrosis factor (TNF) signaling pathway. The results of survival stage analysis showed that the binding activity between muskone and key targets was better than other targets. The molecular docking results showed that muscone had the highest docking affinity for the key target CYP19A1 gene at −7.0 kJ/moL. Conclusions Muscone might exert anti–breast cancer effects through cancer pathways, ovarian steroidogenesis, and TNF signaling pathways and has the potential to be developed as a clinical agent.

show abstract

Plasma HSP90AA1 Predicts the Risk of Breast Cancer Onset and Distant Metastasis

Cited by 45 publications

References 42 publications

A Risk Model Based on Sorafenib-Response Target Genes Predicts the Prognosis of Patients with HCC

A Risk Model Based on Sorafenib-Response Target Genes Predicts the Prognosis of Patients with HCC

Gene selection using pyramid gravitational search algorithm

A network-based pharmacological study on the mechanism of action of muscone in breast cancer

Contact Info

Product

Resources

About