Dataset Information

Construction of prognostic microRNA signature for human invasive breast cancer by integrated analysis.

ABSTRACT: Background:Despite the advances in early detection and treatment methods, breast cancer still has a high mortality rate, even in those patients predicted to have a good prognosis. The purpose of this study is to identify a microRNA signature that could better predict prognosis in breast cancer and add new insights to the current classification criteria. Materials and methods:We downloaded microRNA sequencing data along with corresponding clinicopathological data from The Cancer Genome Atlas (TCGA). Of 1,098 breast cancer patients identified, 253 patients with fully characterized microRNA profiles were selected for analysis. A three-microRNA signature was generated in the training set. Subsequently, the performance of the signature was confirmed in a validation set. After construction of the signature, we conducted additional experiments, including flow cytometry and the Cell Counting Kit-8 assay, to illustrate the correlation of this microRNA signature with breast cancer cell cycle, apoptosis, and proliferation. Results:Three microRNAs (hsa-mir-31, hsa-mir-16-2, and hsa-mir-484) were identified to be significantly and independently correlated with patient prognosis, and performed with good stability. Our results suggest that higher expression of hsa-mir-484 indicated worse prognosis, while higher expression of hsa-mir-31 and hsa-mir-16-2 indicated better prognosis. Moreover, additional experiments confirmed that this microRNA signature was related to breast cancer cell cycle and proliferation. Conclusion:Our results indicate a three-microRNA signature that can accurately predict the prognosis of breast cancer, especially in basal-like and hormone receptor-positive breast cancer subtypes. We recommend more aggressive therapy and more frequent follow-up for high-risk groups.

SUBMITTER: Shi W

PROVIDER: S-EPMC6430069 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Construction of prognostic microRNA signature for human invasive breast cancer by integrated analysis.

Shi Wei W Dong Fang F Jiang Yujia Y Lu Linlin L Wang Changwen C Tan Jie J Yang Wen W Guo Hui H Ming Jie J Huang Tao T

OncoTargets and therapy 20190315

<h4>Background</h4>Despite the advances in early detection and treatment methods, breast cancer still has a high mortality rate, even in those patients predicted to have a good prognosis. The purpose of this study is to identify a microRNA signature that could better predict prognosis in breast cancer and add new insights to the current classification criteria.<h4>Materials and methods</h4>We downloaded microRNA sequencing data along with corresponding clinicopathological data from The Cancer Ge ...[more]

PMID: 30936717

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Project description:Cancer Dependency Map (CDM) genes comprise an extensive series of genome-scale RNAi-based loss-of-function tests; hence, it served as a method based on the CRISPR-Cas9 technique that could assist scientists in investigating potential gene functions. These CDM genes have a role in tumor cell survival and proliferation, suggesting that they may be used as new therapeutic targets for some malignant tumors. So far, there have been less research on the involvement of CDM genes in breast cancer, and only a tiny percentage of CDM genes have been studied. In this study, information of patients with breast cancer was extracted from The Cancer Genome Atlas (TCGA), from which differentially expressed CDM genes in breast cancer were determined. A variety of bioinformatics techniques were used to assess the functions and prognostic relevance of these confirmed CDM genes. In all, 290 CDM genes were found differentially expressed. Six CDM genes (SRF, RAD51, PMF1, EXOSC3, EXOC1, and TSEN54) were found to be associated with the prognosis of breast cancer samples. Based on the expression of the identified CDM genes and their coefficients, a prognosis model was constructed for prediction, according to which patients with breast cancer were separated into two risk groups. Those with high risk had substantially poorer overall survival (OS) than patients in the other risk group in the TCGA training set, TCGA testing set, and an external cohort from Gene Expression Omnibus (GEO) database. The area under the receiver operating characteristic (ROC) curve for this prognostic signature was, respectively, 0.717 and 0.635 for TCGA training and testing sets, demonstrating its reliability in predicting the prognosis of patients with breast cancer. We next created a nomogram using the six CDM genes discovered to create a therapeutically useful model. The Human Protein Atlas database was used to acquire all immunohistochemistry staining images of the discovered CDM genes. The proportions of tumor-infiltrating immune cells, as well as the expression levels of checkpoint genes, varied substantially between the two risk groups, according to the analyses of immune response. In conclusion, the findings of this research may aid in the understanding of the prognostic value and biological roles of CDM genes in breast cancer.

Project description:Mounting evidence has demonstrated that a lot of miRNAs are overexpressed or downregulated in colorectal cancer (CRC) tissues and play a crucial role in tumorigenesis, invasion, and migration. The aim of our study was to screen new biomarkers related to CRC prognosis by bioinformatics analysis. By using the R language edgeR package for the differential analysis and standardization of miRNA expression profiles from The Cancer Genome Atlas (TCGA), 502 differentially expressed miRNAs (343 up-regulated, 159 down-regulated) were screened based on the cut-off criteria of p < 0.05 and |log2FC|>1, then all the patients (421) with differentially expressed miRNAs and complete survival time, status were then randomly divided into train group (212) and the test group (209). Eight miRNAs with p < 0.005 were revealed in univariate cox regression analysis of train group, then stepwise multivariate cox regression was applied for constituting a five-miRNA (hsa-miR-5091, hsa-miR-10b-3p, hsa-miR-9-5p, hsa-miR-187-3p, hsa-miR-32-5p) signature prognostic biomarkers with obviously different overall survival. Test group and entire group shown the same results utilizing the same prescient miRNA signature. The area under curve (AUC) of receiver operating characteristic (ROC) curve for predicting 5 years survival in train group, test group, and whole cohort were 0.79, 0.679, and 0.744, respectively, which demonstrated better predictive power of prognostic model. Furthermore, Univariate cox regression and multivariate cox regression considering other clinical factors displayed that the five-miRNA signature could serve as an independent prognostic factor. In order to predict the potential biological functions of five-miRNA signature, target genes of these five miRNAs were analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and Gene Ontology (GO) enrichment analysis. The top 10 hub genes (ESR1, ADCY9, MEF2C, NRXN1, ADCY5, FGF2, KITLG, GATA1, GRIA1, KAT2B) of target genes in protein protein interaction (PPI) network were screened by string database and Cytoscape 3.6.1 (plug-in cytoHubba). In addition, 19 of target genes were associated with survival prognosis. Taken together, the current study showed the model of five-miRNA signature could efficiently function as a novel and independent prognosis biomarker and therapeutic target for CRC patients.

Dataset Information

Construction of prognostic microRNA signature for human invasive breast cancer by integrated analysis.

Publications

Construction of prognostic microRNA signature for human invasive breast cancer by integrated analysis.

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