Screening candidate microRNA‐mRNA network for predicting the response to chemoresistance in osteosarcoma by bioinformatics analysis

Dai, Peng; He, Yancheng; Luo, Guosong; Deng, Jiaqi; Jiang, Nan; Fang, Tingting; Li, Yujuan; Cheng, Ying

doi:10.1002/jcb.28938

Cited by 10 publications

(8 citation statements)

References 47 publications

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“…Similarly, they also identified 15 differentially expressed miRNAs (DEMs) in GSE39040, and functional enrichment analysis showed that upregulated DEMs were mainly enriched in cell growth and response to growth factor, and downregulated DEMs were involved in cytokine receptor activity. Moreover, using GEO database, Dai et al (21) screened candidate genes for predicting the response to chemoresistance in osteosarcoma by miRNA-mRNA network. However, the differentially selected genes in these studies are all based on a single dataset, and the small size of samples will cause the instability of results.…”

Section: Discussionmentioning

confidence: 99%

Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy

et al. 2020

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Osteosarcoma is one of the most aggressive malignant bone tumors worldwide. Although great advancements have been made in its treatment owing to the advent of neoadjuvant chemotherapy, the problem of lung metastasis is a major obstacle in the improvement of survival outcomes. Thus, the aim of the present study is to screen novel and key biomarkers, which may act as potential prognostic markers and therapeutic targets in osteosarcoma. We utilized the robust rank aggregation (RRA) method to integrate three osteosarcoma microarray datasets downloaded from the Gene Expression Omnibus (GEO) database, and we identified the robust differentially expressed genes (DEGs) between primary and metastatic osteosarcoma tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functions of robust DEGs. The results of enrichment analysis showed that the robust DEGs were closely associated with osteosarcoma development and progression. Immune cell infiltration analysis was also conducted by CIBERSORT algorithm, and we found that macrophages are the most principal infiltrating immune cells in osteosarcoma, especially macrophages M0 and M2. Then, the protein–protein interaction network and key modules were constructed by Cytoscape, and 10 hub genes were selected by plugin cytoHubba from the whole network. The survival analysis of hub genes was also carried out based on the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The integrated bioinformatics analysis was utilized to provide new insight into osteosarcoma development and metastasis and identified EGR1 , CXCL10 , MYC , and CXCR4 as potential biomarkers for prognosis of osteosarcoma.

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Section: Discussionmentioning

confidence: 99%

Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy

et al. 2020

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“…Pertinent references were searched, and two related published risk models were selected including an eight-gene (PMID: 31333788) [45], a four-gene (PMID: 31146489) [59], a ten-gene (PMID: 31090103) [60], and a nineteengene (PMID: 30604867) [61] signature risk model. To make the models comparable, a multi-factor Cox regression was run and the RiskScore of the training set samples was recalculated according to the corresponding genes in the model of the present study.…”

Section: Comparative Study Of Other Risk Modelsmentioning

confidence: 99%

Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

Zhu

Hou²,

et al. 2020

Cancer Cell Int

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Background: Osteosarcoma (OS) is a common malignant bone tumor originating in the interstitial tissues and occurring mostly in adolescents and young adults. Energy metabolism is a prerequisite for cancer cell growth, proliferation, invasion, and metastasis. However, the gene signatures associated with energy metabolism and their underlying molecular mechanisms that drive them are unknown. Methods: Energy metabolism-related genes were obtained from the TARGET database. We applied the "NFM" algorithm to classify putative signature gene into subtypes based on energy metabolism. Key genes related to progression were identified by weighted co-expression network analysis (WGCNA). Based on least absolute shrinkage and selection operator (LASSO) Cox proportional regression hazards model analyses, a gene signature for the predication of OS progression and prognosis was established. Robustness and estimation evaluations and comparison against other models were used to evaluate the prognostic performance of our model. Results: Two subtypes associated with energy metabolism was determined using the "NFM" algorithm, and significant modules related to energy metabolism were identified by WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the genes in the significant modules were enriched in kinase, immune metabolism processes, and metabolism-related pathways. We constructed a seven-gene signature consisting of SLC18B1, RBMXL1, DOK3, HS3ST2, ATP6V0D1, CCAR1, and C1QTNF1 to be used for OS progression and prognosis. Upregulation of CCAR1, and C1QTNF1 was associated with augmented OS risk, whereas, increases in the expression SCL18B1, RBMXL1, DOK3, HS3ST2, and ATP6VOD1 was correlated with a diminished risk of OS. We confirmed that the seven-gene signature was robust, and was superior to the earlier models evaluated; therefore, it may be used for timely OS diagnosis, treatment, and prognosis. Conclusions: The seven-gene signature related to OS energy metabolism developed here could be used in the early diagnosis, treatment, and prognosis of OS.

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“…Recent high-throughput studies are contributing to an improved understanding of the molecular mechanism concerning various phenotypes, such as carcinogenesis (Sun et al 2018 ), metastasis (Tian et al 2017 ), and chemoresistance (Dai et al 2019 ). These cancer-associated phenotypes are at least partly controlled by differentially expressed genes (DEGs), including long non-coding RNA (lncRNA), microRNA (miRNA), and protein-coding gene (PCG).…”

Section: Introductionmentioning

confidence: 99%

Association of CASC18/miR-20a-3p/TGFB2 ceRNA axis with occult lymph node metastasis in tongue squamous cell carcinoma

Zhou

Liu

et al. 2021

Mol Med

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Background Tongue squamous cell carcinoma (TSCC) ranks as the most prevalent malignancy in the oral cavity. TSCC patients with occult lymph node metastasis (OLNM) are thought to be at risk of worse outcome. However, regulatory mechanisms underlying OLNM remain less investigated. Methods In the present study, CASC18/miR-20a-3p/TGFB2 axis was identified and evaluated by bioinformatic and qRT-PCR analyses. Effects of CASC18 knockdown on cell migration and invasion were determined by wound healing and transwell assays. Western blot, ELISA, RNA pulldown and luciferase reporter assays were performed for mechanism verification. Results CASC18 was identified up-regulating in TSCC tumours, and especially in those from patients with OLNM. Importantly, we found higher CASC18 expression was positively correlated with the presence of OLNM and worse outcome of TSCC patients. Furthermore, we demonstrated that CASC18 knockdown repressed cell migration and invasion through inhibiting epithelial-mesenchymal transition, which could be partly rescued by miR-20a-3p inhibitor. Regarding the molecular mechanism, we further confirmed that CASC18 functioned as a ceRNA to sponge miR-20a-3p to enhanceTGFB2 expression and secretion. Conclusion In conclusion, we have reported a novel CASC18/miR-20a-3p/TGFB2 ceRNA axis in OLNM of TSCC. Our findings will contribute to a deeper understanding of the molecular mechanism of OLNM in TSCC, and facilitate the development of diagnostic methods for assisting treatment decision-making.

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Screening candidate microRNA‐mRNA network for predicting the response to chemoresistance in osteosarcoma by bioinformatics analysis

Cited by 10 publications

References 47 publications

Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy

Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy

Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

Association of CASC18/miR-20a-3p/TGFB2 ceRNA axis with occult lymph node metastasis in tongue squamous cell carcinoma

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