Antitumor effect of focal adhesion kinase inhibitor <scp>PF</scp>562271 against human osteosarcoma <i>in vitro</i> and <i>in vivo</i>

Hu, Chuanzhen; Xu, Chen; Wen, Junxiang; Gong, Li; Liu, Zhuochao; Wang, Jun; Liang, Jing; Hu, Fangqiong; Zhou, Qi; Li, Wei; Shen, Yuhui; Zhang, Wei-Bin

doi:10.1111/cas.13256

Cited by 35 publications

(21 citation statements)

References 41 publications

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“…Focal adhesion is a key regulator of multi-cellular signaling pathways in cell proliferation, cycle regression, migration, apoptosis, and survival [33]. Hu et al [34] assessed the effects of the small-molecule focal adhesion inhibitor PF562271 on OS cells and showed that it diminished OS cell volume, weight, and angiogenesis and concluded that inhibition of focal adhesion could therefore be a target for OS treatment.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…Focal adhesion expression has been suggested to be associated with cell migration and normally indicates a poor prognosis (36). Focal adhesion and the ECM have been commonly associated in osteosarcomas, with these factors investigated as potential antitumor targets (37,38). In the present study, 4 differentially expressed genes ( ALCAM, SDC1, CDH2 and HLA-DMA ) associated with the 9 lncRNAs in the risk assessment model were significantly involved in CAMs.…”

Section: Discussionmentioning

confidence: 99%

A risk assessment model for the prognosis of osteosarcoma utilizing differentially expressed lncRNAs

Sun

Zhao

2018

Mol Med Report

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The present study was conducted to establish a risk assessment model for evaluating osteosarcoma prognosis based on prognosis-associated long non-coding RNA (lncRNA) expression. Human osteosarcoma expression profiles were obtained from the NCBI GEO and EBI ArrayExpress databases and differently expressed lncRNAs between good and poor prognosis groups were evaluated using Student's t-test and Wilcoxon rank test in R (v. 3.1.0). A multivariate Cox regression was used to establish a risk assessment system based on lncRNA expression levels, with the associated regression coefficients used as the weight. Survival analysis and receiver operating characteristic (ROC) curves were constructed to verify the accuracy of the risk assessment model. Associations between the prognosis, risk assessment model and clinical features were also investigated using univariate and multivariate Cox regression analyses. Furthermore, differentially expressed genes associated with the lncRNAs in the risk assessment model were identified, and functional enrichment analysis was performed. A total of 9 from the 211 differentially expressed lncRNAs were selected to establish the risk assessment model. The risk assessment model exhibited a good prognostic prediction ability, with high area under the curve values in the training and validation sets. Additionally, the calculated risk score based on the 9 selected lncRNAs was identified to be an independent prognostic factor for osteosarcoma. Furthermore, differentially expressed genes were primarily enriched in the cell cycle, oxidative phosphorylation and cell adhesion processes. The present study described a risk assessment model based on 9 significantly differentially expressed lncRNAs, which was identified to have a high accuracy in potentially predicting patient prognosis.

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“…After a 14-day incubation, the media were aspirated from each well and the cells were stained with 1% crystal violet for 30 min. The number of colonies in each well was counted with plates tested in duplicate as described in a previous study 39 .…”

Section: Methodsmentioning

confidence: 99%

CXCR1/Akt signaling activation induced by mesenchymal stem cell-derived IL-8 promotes osteosarcoma cell anoikis resistance and pulmonary metastasis

Han

et al. 2018

Cell Death Dis

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The loss of appropriate cell adhesion normally induces apoptosis via a process termed anoikis. The aim of this study was to investigate the effects of mesenchymal stem cells (MSCs) in the cancer microenvironment on the anoikis resistance and pulmonary metastasis of osteosarcoma (OS) cells, and to evaluate the critical role of the interleukin (IL)-8/C-X-C chemokine receptor (CXCR) 1/Akt-signaling pathway in these processes. Metastatic OS subtype cells, which did or did not interact with MSC-conditioned medium (MSC-CM) in vitro, were isolated from the pulmonary site and named Saos2-lung-M. Both MSC-CM and IL-8 treatment increased the anoikis resistance of Saos2 cells in vitro. Moreover, exogenous MSC-CM promoted the survival and metastasis of Saos2 cells in nude mice. Saos2-lung-M cells were more malignant and resistant to anoikis than parental cells. MSCs secreted IL-8, thereby protecting OS cells from anoikis. Blocking the IL-8/CXCR1/Akt pathway via CXCR1 knockdown inhibited the pulmonary metastasis of Saos2-lung-MSCs and prolonged the survival of tumor-bearing mice. In conclusion, MSCs enhanced OS cell resistance to anoikis and pulmonary metastasis via regulation of the IL-8/CXCR1/Akt pathway. These findings suggest that MSCs can “select for” OS cells with high metastatic potential in vivo, and highlight CXCR1 as a key target in the regulation of pulmonary metastasis of OS cells.

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Antitumor effect of focal adhesion kinase inhibitor PF562271 against human osteosarcoma in vitro and in vivo

Cited by 35 publications

References 41 publications

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

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

A risk assessment model for the prognosis of osteosarcoma utilizing differentially expressed lncRNAs

CXCR1/Akt signaling activation induced by mesenchymal stem cell-derived IL-8 promotes osteosarcoma cell anoikis resistance and pulmonary metastasis

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