Dataset Information

Bioinformatics analysis for the identification of key genes and long non-coding RNAs related to bone metastasis in breast cancer.

ABSTRACT: The molecular mechanism of bone metastasis in breast cancer is largely unknown. Herein, we aimed to identify the key genes and long non-coding RNAs (lncRNAs) related to the bone metastasis of breast cancer using a bioinformatics approach. We screened differentially expressed genes and lncRNAs between normal breast and breast cancer bone metastasis samples using the GSE66206 dataset from the Gene Expression Omnibus. We also constructed a differentially expressed lncRNA-mRNA interaction network and analyzed the node degrees to identify the driving genes. After finding potential pathogenic modules of breast cancer bone metastasis, we identified breast cancer bone metastasis-related modules and functional enrichment analysis of the genes and lncRNAs in the modules. Based on the above analysis, we constructed a differentially expressed lncRNA-mRNA network related to bone metastasis in breast cancer and identified core driver genes, including BNIP3 and the lncRNA RP11-317-J19.1. The role of core driver genes and lncRNAs in the network implies their biological functions in regulating bone development and remodeling. Thus, targeting the core driver genes and lncRNAs in the network may be a promising therapeutic strategy to manage bone metastasis.

SUBMITTER: Teng X

PROVIDER: S-EPMC8312419 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Bioinformatics analysis for the identification of key genes and long non-coding RNAs related to bone metastasis in breast cancer.

Teng Xu X Yang Tianshu T Huang Wei W Li Weishi W Zhou Lin L Wang Zihang Z Feng Yajuan Y Zhang Jingyao J Yin Xin X Wang Pei P Li Gen G Yu Hefeng H Chen Zhongqiang Z Fan Dongwei D

Aging 20210705 13

The molecular mechanism of bone metastasis in breast cancer is largely unknown. Herein, we aimed to identify the key genes and long non-coding RNAs (lncRNAs) related to the bone metastasis of breast cancer using a bioinformatics approach. We screened differentially expressed genes and lncRNAs between normal breast and breast cancer bone metastasis samples using the GSE66206 dataset from the Gene Expression Omnibus. We also constructed a differentially expressed lncRNA-mRNA interaction network an ...[more]

PMID: 34226298

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Project description:BackgroundBone is the most common site of metastatic breast cancer, and it is a leading cause of breast cancer-related death. This study aimed to explore bone metastasis-related long non-coding RNAs (lncRNAs) in breast cancer.MethodsFour mRNA datasets and two lncRNA datasets of bone metastasis, lung metastasis and liver metastasis of breast cancer were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) in group of bone metastasis vs lung metastasis and bone metastasis vs liver metastasis, as well as the overlap of the two groups, were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) network construction of DEmRNAs were conducted. The cis nearby-targeted DEmRNAs of DElncRNAs were obtained. Quantitative real-time polymerase chain reactions (qRT-PCR) was used to detect the expression levels of selected DEmRNAs and DElncRNAs. LOC641518-lymphoid enhancer-binding factor 1 (LEF1) pair was selected to verify its role in migration and invasion capability of breast cancer cells by wounding healing assay and transwell invasion assay.ResultsA total of 237 DEmRNAs were obtained in bone metastasis compared with both lung metastasis and liver metastasis. A total of three DElncRNAs in bone metastasis compared with both lung metastasis and liver metastasis were obtained. A total of seven DElncRNA-nearby-targeted DEmRNA pairs and 15 DElncRNA-nearby-targeted DEmRNA pairs in group of bone metastasis vs lung metastasis and bone metastasis vs liver metastasis, were detected, respectively. Four cis LncRNA-mRNA interaction pairs were identified, which are LOC641518-LEF1, FLJ35024-Very Low Density Lipoprotein Receptor (VLDLR), LOC285972-Retinoic Acid Receptor Responder 2 (RARRES2) and LOC254896-TNF receptor superfamily member 10c (TNFRSF10C). qRT-PCR using clinical samples from our hospital confirms the bioinformatics prediction. siRNA knocking down LOC641518 down-regulates LEF1 mRNA expression, and reduces the migration and invasion capability of breast cancer cells.ConclusionsWe concluded that four LncRNA-mRNA pairs, including LOC641518-LEF1, may play a central role in breast cancer bone metastasis.

Project description:BackgroundOsteosarcoma (OS) metastasis is the most common cause of cancer-related mortality, however, no sufficient clinical biomarkers have been identified. In this study, we identified five genes to help predict metastasis at diagnosis.MethodsWe performed weighted gene co-expression network analysis (WGCNA) to identify the most relevant gene modules associated with OS metastasis. An important machine learning algorithm, the support vector machine (SVM), was employed to predict key genes for classifying the OS metastasis phenotype. Finally, we investigated the clinical significance of key genes and their enriched pathways.ResultsEighteen modules were identified in WGCNA, among which the pink, red, brown, blue, and turquoise modules demonstrated good preservation. In the five modules, the brown and red modules were highly correlated with OS metastasis. Genes in the two modules closely interacted in protein-protein interaction networks and were therefore chosen for further analysis. Genes in the two modules were primarily enriched in the biological processes associated with tumorigenesis and development. Furthermore, 65 differentially expressed genes were identified as common hub genes in both WGCNA and protein-protein interaction networks. SVM classifiers with the maximum area under the curve were based on 30 and 15 genes in the brown and red modules, respectively. The clinical significance of the 45 hub genes was analyzed. Of the 45 genes, 17 were found to be significantly correlated with survival time. Finally, 5/17 genes, including ADAP2 (P = 0.0094), LCP2 (P = 0.013), ARHGAP25 (P = 0.0049), CD53 (P = 0.016), and TLR7 (P = 0.04) were significantly correlated with the metastatic phenotype. In vitro verification, western blotting, wound healing analyses, transwell invasion assays, proliferation assays, and colony formation assays indicated that ARHGAP25 promoted OS cell migration, invasion, proliferation, and epithelial-mesenchymal transition.ConclusionWe identified five genes, namely ADAP2, LCP2, ARHGAP25, CD53, and TLR7, as candidate biomarkers for the prediction of OS metastasis; ARHGAP25 inhibits MG63 OS cell growth, migration, and invasion in vitro, indicating that ARHGAP25 can serve as a promising specific and prognostic biomarker for OS metastasis.

Dataset Information

Bioinformatics analysis for the identification of key genes and long non-coding RNAs related to bone metastasis in breast cancer.

Publications

Bioinformatics analysis for the identification of key genes and long non-coding RNAs related to bone metastasis in breast cancer.

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