Dataset Information

Comprehensive analysis identifies as a critical prognostic prediction gene in breast cancer.

ABSTRACT:

Background

Aurora kinases (AURKs) family plays a vital role not only in cell division but also in tumorigenesis. However, there are still rare systematic analyses of the diverse expression patterns and prognostic value of the AURKs family in breast cancer (BC). Systematic bioinformatics analysis was conducted to explore the biological role, prognostic value, and immunologic function of AURKs family in BC.

Methods

The expression, prognostic value, and clinical functions of AURKs family in BC were evaluated with several bioinformatics web portals: ONCOMINE Gene Expression Profiling Interactive Analysis, Kaplan-Meier plotter, cBioPortal, Metascape, GeneMANIA, and LinkedOmics; and the result was verified using human tissues.

Results

The expression of AURKA and AURKB were upregulated in BC in subgroup analyses based on tumor stage (all P < 0.05). BC patients with high AURKA and AURKB expression had a worse overall survival, relapse-free survival, and distant metastasis-free survival (all P < 0.05). Verification experiment revealed that AURKA and AURKB were upregulated in BC ( P < 0.05). AURKA and AURKB were specifically associated with several tumor-associated kinases (polo-like kinase 1 and cyclin-dependent kinase 1), miRNAs (miR-507 and miR-381), and E2F transcription factor 1. Moreover, AURKA and AURKB were correlated with immune cell infiltration. Functional enrichment analysis revealed that AURKA and AURKB were involved in the cell cycle signaling pathway, platinum drug resistance signaling pathway, ErbB signaling pathway, Hippo signaling pathway, and nucleotide-binding and oligomerization domain-like receptor signaling pathway.

Conclusions

Aurora kinases AURKA and AURKB could be employed as novel prognostic biomarkers or promising therapeutic targets for BC.

SUBMITTER: Hu T

PROVIDER: S-EPMC9771277 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comprehensive analysis identifies as a critical prognostic prediction gene in breast cancer.

Hu Ting T Wang Xu X Xia Yun Y Wu Lu L Ma Yuxi Y Zhou Rui R Zhao Yanxia Y

Chinese medical journal 20220920 18

<h4>Background</h4>Aurora kinases (AURKs) family plays a vital role not only in cell division but also in tumorigenesis. However, there are still rare systematic analyses of the diverse expression patterns and prognostic value of the AURKs family in breast cancer (BC). Systematic bioinformatics analysis was conducted to explore the biological role, prognostic value, and immunologic function of AURKs family in BC.<h4>Methods</h4>The expression, prognostic value, and clinical functions of AURKs fa ...[more]

PMID: 36113844

Similar Datasets

Project description:Breast cancer is one of the primary threats to women's health worldwide. However, the molecular mechanisms underlying the development of breast cancer remain to be fully elucidated. The present study aimed to investigate specific target gene expression profiles in breast cancer tissues in general and in different breast cancer stages, as well as to explore their functions in tumor development. For integrated analysis, a total of 5 gene expression profiling datasets for 3 different stages of breast cancer (stages I-III) were downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information. Pre-processing of these datasets was performed using the Robust Multi-array Average algorithm and global renormalization was performed for all studies. Differentially expressed genes between breast cancer patients and controls were estimated using the empirical Bayes algorithm. The Database for Annotation, Visualization and Integrated Discovery web server was used for analyzing the enrichment of the differentially expressed genes in Gene Ontology terms of the category biological process and in Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, breast cancer target genes were downloaded from the Thomson Reuters Integrity Database. We merged these target genes with the genes in breast cancer datasets. Analysis of anti-breast cancer gene networks was performed using the Genome-scale Integrated Analysis of Gene Networks in Tissues web server. The results demonstrated that the normal functions of the cell cycle, cell migration and cell adhesion were altered in all stages of breast cancer. Furthermore, 12 anti-breast cancer genes were identified to be dysregulated in at least one of the three stages. Among all of these genes, ribonucleotide reductase regulatory subunit M2 (RRM2) exhibited the highest degree of interaction with other interacting genes. Analysis of the network interactions revealed that the transcription factor of RRM2 is crucial for cancer development. Other genes, including mucin 1, progesterone receptor and cyclin-dependent kinase 5 regulatory subunit associated protein 3, also exhibited a high degree of interaction with the associated genes. In conclusion, several key anti-breast cancer genes identified in the present study are mainly associated with the regulation of the cell cycle, cell migration, cell adhesion and other cancer-associated cell functions, particularly RRM2.

Project description:BackgroundTriple-negative breast cancer (TNBC) is a special subtype of breast cancer. Transient Receptor Potential (TRP) channel superfamily has emerged as a novel and interesting target in a variety of tumors. However, the association of TRP channel-related genes with TNBC is still unclear.MethodsThe The Cancer Genome Atlas (TCGA)-TNBC and GSE58812 datasets were downloaded from the public database. The differentially expressed TRP channel-related genes (DETGs) were screened by limma package, and mutations of the above genes were analyzed. Subsequently, new molecular subtypes in TNBC-based DETGs were explored by consensus clustering analysis. In addition, Lasso-Cox regression analysis was used to divide it into two robust risk subtypes: high-risk group and low-risk group. The accuracy and distinguishing ability of above models were verified by a variety of methods, including Kaplan-Meier survival analysis, ROC analysis, calibration curve, and PCA analysis. Meanwhile, CIBERSORT algorithm was used to excavate status of immune-infiltrating cells in TNBC tissues. Last, we explored the therapeutic effect of drugs and underlying mechanisms of risk subgroups by pRRophetic package and GSEA algorithm, respectively.ResultsA total of 19 DETGs were identified in 115 TNBC and 113 normal samples from TCGA database. In addition, missense mutation and SNP were the most common variant classification. According to Lasso-Cox regression analysis, the risky formula performed best when nine genes were used: TRPM5, TRPV2, HTR2B, HRH1, P2RY2, MAP2K6, NTRK1, ADCY6, and PRKACB. Subsequently, Kaplan-Meier survival analysis, ROC analysis, calibration curve, and Principal Components Analysis (PCA) analysis showed an excellent accuracy for predicting OS using risky formula in each cohort (P < 0.05). Specifically, high-risk group had a shorter OS compared with low-risk group. In addition, T-cell CD4 memory activated and macrophages M1 were enriched in normal tissues, whereas Tregs were increased in tumor tissues. Note that the low-risk group was better therapeutic effect to docetaxel, doxorubicin, cisplatin, paclitaxel, and gemcitabine than the high-risk group (P < 0.05). Last, in vitro assays, Quantitative Real-time PCR (qRT-PCR) indicated that TRPM5 was significantly highly expressed in MDA-MB-231 and MDA-MB-468 cells compared with that in MCF-10A cells (P < 0.01).ConclusionWe identified a risky formula based on expression of TRP channel-related genes that can predict prognosis, therapeutic effect, and status of tumor microenvironment for patients with TNBC.

Dataset Information

Comprehensive analysis identifies as a critical prognostic prediction gene in breast cancer.

Background

Methods

Results

Conclusions

Publications

Comprehensive analysis identifies as a critical prognostic prediction gene in breast cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets