Dataset Information

Identification of tumorigenicity-associated genes in osteosarcoma cell lines based on bioinformatic analysis and experimental validation.

ABSTRACT: Osteosarcoma is the most common primary malignant tumor of bone. Tumorigenic investigation of osteosarcoma cell lines may facilitate preclinical studies of targeted therapy. Therefore, the aim of this study was to explore the tumorigenicity-associated genes in osteosarcoma cells. We found that 138 genes were highly expressed and 86 genes were lowly expressed in highly tumorigenic osteosarcoma cell lines (143B, MNNG/HOS, and SJSA-1) compared with poorly tumorigenic osteosarcoma cell lines (MG-63, Saos-2, and U-2 OS). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that highly expressed genes were associated with amino acids and energy metabolism, while lowly expressed genes were associated with cell cycle and DNA replication. Gene Ontology (GO) analysis showed that highly expressed genes were associated with endoplasmic reticulum stress response and aggrephagy, whereas lowly expressed genes were correlated with extracellular matrix assembly and DNA damage response. Further analysis identified six highly expressed genes and six lowly expressed genes. Three of highly expressed genes (DDX10, FOXA2, and HEY1) were correlated with poor prognosis, while three of lowly expressed genes (CYP26B1, GP1BB, and IFI44) showed the opposite trend in patients with osteosarcoma. Knockdown of HEY1 significantly inhibited the tumorigenicity of 143B cells in BALB/c nude mice.

SUBMITTER: Jiang S

PROVIDER: S-EPMC7150450 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of tumorigenicity-associated genes in osteosarcoma cell lines based on bioinformatic analysis and experimental validation.

Jiang Shaojie S Zhou Fei F Zhang Yanhua Y Zhou Weiping W Zhu Linghua L Zhang Miaofeng M Luo Jingfeng J Ma Rui R Xu Xiufang X Zhu Jiying J Dong Xue X Zhang Shuangling S Fang Jie J Sun Jihong J Yang Xiaoming X

Journal of Cancer 20200326 12

Osteosarcoma is the most common primary malignant tumor of bone. Tumorigenic investigation of osteosarcoma cell lines may facilitate preclinical studies of targeted therapy. Therefore, the aim of this study was to explore the tumorigenicity-associated genes in osteosarcoma cells. We found that 138 genes were highly expressed and 86 genes were lowly expressed in highly tumorigenic osteosarcoma cell lines (143B, MNNG/HOS, and SJSA-1) compared with poorly tumorigenic osteosarcoma cell lines (MG-63, ...[more]

PMID: 32284759

Similar Datasets

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most lethal cancer, mainly attributing to its high tendency to metastasis. Vascular invasion provides a direct path for solid tumor metastasis. Mounting evidence has demonstrated that microRNAs (miRNAs) are related to human cancer onset and progression including invasion and metastasis.MethodsIn search of invasion-metastasis-associated miRNAs in HCC, microarray dataset GSE67140 was downloaded from the Gene Expression Omnibus database. Differentially expressed miRNAs (DE-miRNAs) were obtained by R software package and the potential target genes were predicted by miRTarBase. The database for annotation, visualization and integrated discovery (DAVID) was introduced to perform functional annotation and pathway enrichment analysis for these potential targets of DE-miRNAs. Protein-protein interaction (PPI) network was established by STRING database and visualized by Cytoscape software. The effects of the miR-494-3p and miR-126-3p on migration and invasion of HCC cell lines were evaluated by conducting wound healing assay and transwell assay.ResultsA total of 138 DE-miRNAs were screened out, including 57 upregulated miRNAs and 81 downregulated miRNAs in human HCC tumors with vascular invasion compared with human HCC tumors without vascular invasion. 762 target genes of the top three upregulated and downregulated miRNAs were predicted, and they were involved in HCC-related pathways, such as pathway in cancer, focal adhesion and MAPK signaling pathway. In the PPI network, the top 10 hub nodes with higher degrees were identified as hub genes, such as TP53 and MYC. Through constructing the miRNA-hub gene network, we found that most of hub genes could be potentially modulated by miR-494-3p and miR-126-3p. Of note, miR-494-3p and miR-126-3p was markedly upregulated and downregulated in HCC cell lines and tissues, respectively. In addition, overexpression of miR-494-3p could significantly promote HCC migration and invasion whereas overexpression of miR-126-3p exerted an opposite effect.ConclusionsTargeting miR-494-3p and miR-126-3p may provide effective and promising approaches to suppress invasion and metastasis of HCC.

Project description:Breast cancer is the leading cause of cancer-associated deaths among females. In recent decades, microRNAs (miRNAs), a type of short non-coding RNA that regulates gene expression at the post-transcription level, have been reported to participate in the regulation of many hub genes associated with tumorigenesis, tumor progression, and metastasis. However, the precise mechanism by which miRNAs regulate breast cancer metastasis remains poorly discussed, which limits the opportunity for the development of novel, effective therapeutic targets. Here, we aimed to determine the miR-622-related principal regulatory mechanism in cancer. First, we found that miR-622 was significantly related to a poor prognosis in various cancers. By utilizing an integrated miRNA prediction process, we identified 77 promising targets and constructed a protein-protein interaction network. Furthermore, enrichment analyses, including GO and KEGG pathway analyses, were performed to determine the potential function of miR-622, which revealed regulation networks and potential functions of miR-622. Then, we identified a key cluster comprised of six hub genes in the protein-protein interaction network. These genes were further chosen for pan-cancer expression, prognostic and predictive marker analyses based on the TCGA and GEO datasets to mine the potential clinical values of these hub genes. To further validate our bioinformatic results, the regulatory axis of miR-622 and RNF8, one of the hub genes recently reported to promote breast cancer cell EMT process and breast cancer metastasis, was selected as in vitro proof of concept. In vitro, we demonstrated the direct regulation of RNF8 by miR-622 and found that the predicted miR-622-RNF8 axis could regulate RNF8-induced epithelial-mesenchymal transition, cell migration, and cell viability. These results were further demonstrated with rescue experiments. We established a closed-loop miRNA-target-phenotype research model that integrated the bioinformatic analysis of the miRNA target genes and experimental validation of the identified key miRNA-target-phenotype axis. We not only identified the hub target genes of miR-622 in silico but also revealed the regulatory mechanism of miR-622 in breast cancer cell EMT process, viability, and migration in vitro for the first time.

Project description:BackgroundDedifferentiated liposarcoma (DDLPS) is one of the most deadly types of soft tissue sarcoma. To date, there have been few studies dedicated to elucidating the molecular mechanisms behind the disease; therefore, the molecular mechanisms behind this malignancy remain largely unknown.Materials and methodsMicroarray profiles of 46 DDLPS samples and nine normal fat controls were extracted from Gene Expression Omnibus (GEO). Quality control for these microarray profiles was performed before analysis. Hierarchical clustering and principal component analysis were used to distinguish the general differences in gene expression between DDLPS samples and the normal fat controls. Differentially expressed genes (DEGs) were identified using the Limma package in R. Next, the enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained using the online tool DAVID (http://david.abcc.ncifcrf.gov/). A protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. Furthermore, the hub genes within the PPI network were identified.ResultsAll 55 microarray profiles were confirmed to be of high quality. The gene expression pattern of DDLPS samples was significantly different from that of normal fat controls. In total, 700 DEGs were identified, and 83 enriched GO terms and three KEGG pathways were obtained. Specifically, within the DEGs of DDLPS samples, several pathways were identified as being significantly enriched, including the PPAR signaling pathway, cell cycle pathway, and pyruvate metabolism pathway. Furthermore, the dysregulated PPI network of DDLPS was constructed, and 14 hub genes were identified. Characteristic of DDLPS, the genes CDK4 and MDM2 were universally found to be up-regulated and amplified in gene copy number.ConclusionThis study used bioinformatics to comprehensively mine DDLPS microarray data in order to obtain a deeper understanding of the molecular mechanism of DDLPS.

Dataset Information

Identification of tumorigenicity-associated genes in osteosarcoma cell lines based on bioinformatic analysis and experimental validation.

Publications

Identification of tumorigenicity-associated genes in osteosarcoma cell lines based on bioinformatic analysis and experimental validation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets