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Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis.

ABSTRACT: BACKGROUND:Lymph node metastasis is a significant problem in breast cancer, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism and to explore the key RNAs and pathways that mediate lymph node metastasis in breast cancer. METHODS:GSE100453 and GSE38167 were downloaded from the Gene Expression Omnibus (GEO) database and 569 breast cancer statistics were also downloaded from the TCGA database. Differentially expressed miRNAs were calculated by using R software and GEO2R. Gene ontology and Enriched pathway analysis of target mRNAs were analyzed by using the Database for Database of Annotation Visualization and Integrated Discovery (DAVID) and R software. The protein-protein interaction (PPI) network was performed according to Metascape, String, and Cytoscape software. RESULTS:In total, 6 differentially expressed miRNAs were selected, and 499 mRNAs were identified after filtering. The research of the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that mRNAs enriched in certain tumor pathways. Also, certain hub mRNAs were highlighted after constructed and analyzed the PPI network. A total of 3 out of 6 miRNAs had a significant relationship with the overall survival (P?

SUBMITTER: Gao G

PROVIDER: S-EPMC7523764 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis.

Gao Guangyu G Shi Xinya X Yao Zhen Z Shen Jiaofeng J Shen Liqin L

Medicine 20200901 39

<h4>Background</h4>Lymph node metastasis is a significant problem in breast cancer, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism and to explore the key RNAs and pathways that mediate lymph node metastasis in breast cancer.<h4>Methods</h4>GSE100453 and GSE38167 were downloaded from the Gene Expression Omnibus (GEO) database and 569 breast cancer statistics were also downloaded from the TCGA database. Differentially expre ...[more]

PMID: 32991406

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Project description:Breast cancer has become the malignant tumor with the highest incidence in women. Axillary lymph node dissection (ALND) is an effective method of maintaining regional control; however, it is associated with a significant risk of complications. Meanwhile, whether the patients need ALND or not is according to sentinel lymph node biopsy (SLNB). However, the false-negative results of SLNB had been reported. Automated breast volume scanning (ABVS) is a routine examination in breast cancer. A real-world cohort consisting of 245 breast cancer patients who underwent ABVS examination were enrolled, including 251 tumor lesions. The ABVS manifestations were analyzed with the SLNB results, and the ALND results for selecting the lymph node metastasis were related to ABVS features. Finally, a nomogram was used to construct a breast cancer axillary lymph node tumor burden prediction model. Breast cancer patients with a molecular subtype of luminal B type, a maximum lesion diameter of ≥5 cm, tumor invasion of the Cooper's ligament, and tumor invasion of the nipple had heavy lymph node tumor burden. Molecular classification, tumor size, and Cooper's ligament status were used to construct a clinical prediction model of axillary lymph node tumor burden. The consistency indexes (or AUC) of the training cohort and the validation cohort were 0.743 and 0.711, respectively, which was close to SLNB (0.768). The best cutoff value of the ABVS nomogram was 81.146 points. After combination with ABVS features and SLNB, the AUC of the prediction model was 0.889, and the best cutoff value was 178.965 points. The calibration curve showed that the constructed nomogram clinical prediction model and the real results were highly consistent. The clinical prediction model constructed using molecular classification, tumor size, and Cooper's ligament status can effectively predict the probability of heavy axillary lymph node tumor burden, which can be the significant supplement to the SLNB. Therefore, this model may be used for individual decision-making in the diagnosis and treatments of breast cancer.

Project description:BackgroundmiRNAs and genes can serve as biomarkers for the prognosis and therapy of cervical tumors whose metastasis into lymph nodes is closely associated with disease progression and poor prognosis.MethodsR software and Bioconductor packages were employed to identify differentially expressed miRNAs (DEMs) from The Cancer Genome Atlas (TCGA) database. GEO2R detected differentially expressed genes (DEGs) in the GSE7410 dataset originating from the Gene Expression Omnibus (GEO). A Cox proportional hazard regression model was established to select prognostic miRNA biomarkers. Online tools such as TargetScan and miRDB predicted target genes, and overlapping DEGs and target genes were defined as consensus genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) function annotations were performed to discern the potential functions of consensus genes. STRING and Cytoscape screened key genes and constructed a regulatory network.ResultsA combination of four miRNAs (down-regulated miR-502 and miR-145, up-regulated miR-142 and miR-33b) was identified as an independent prognostic signature of cervical cancer. A total of 94 consensus genes were significantly enriched in 7 KEGG pathways and 19 GO function annotations including the cAMP signaling pathway, the plasma membrane, integral components of the plasma membrane, cell adhesion, etc. The module analysis suggested that CXCL12, IGF1, PTPRC CDH5, RAD51B, REV3L, and WDHD1 are key genes that significantly correlate with cervical cancer lymph node metastasis.ConclusionsThis study demonstrates that a four-miRNA signature can be a prognostic biomarker, and seven key genes are significantly associated with lymph node metastasis in cervical cancer patients. These miRNAs and key genes have the potential to be therapeutic targets for cervical cancer. Among them, two miRNAs (miR-502 and miR-33b) and two key genes (PTPRC and CDH5) were first reported to be potential novel biomarkers for cervical cancer. The current study further characterizes the progression of lymph node metastasis and mechanism of cervical tumors; therefore, it provides a novel diagnostic indicator and therapeutic targets for future clinical treatments.

Dataset Information

Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis.

Publications

Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis.

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