Syndecan-1 knockdown inhibits glioma cell proliferation and invasion by deregulating a c-src/FAK-associated signaling pathway

Shi, Shuang; Zhong, Dong; Xiao, Yao; Wang, Bing; Wang, Wentao; Zhang, Fu'an; Huang, Hao

doi:10.18632/oncotarget.16733

Cited by 29 publications

(19 citation statements)

References 39 publications

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“…Syndecan-1 is a cell surface heparan sulfate proteoglycan and its expression has been shown to be correlated with tumor cell differentiation in various cancers [52]. In addition, its knockdown has been shown to inhibit glioma cell proliferation and invasion and has been suggested as a therapeutic target for glioma [53]. These results support our expectation that the LGG-related gene set not only involves drug response related genes, but also includes those that play important roles in glioma and may act as diagnostic biomarkers or therapeutic targets.…”

Section: Resultsmentioning

confidence: 99%

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Huang

Bhope

Lim

et al. 2019

Preprint

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1Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and 2 molecular profiles obtained prior to administration of the drug, can play a significant role 3 in individualized medicine. Machine learning models have the potential to address this 4 issue, but training them requires data from a large number of patients treated with each 5 drug, limiting their feasibility. While large databases of drug response and molecular 6 profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear 7 whether preclinical samples can be used to predict CDR of real patients. 8 9We designed a systematic approach to evaluate how well different algorithms, trained on 10 gene expression and drug response of CCLs, can predict CDR of patients. Using data from 11 two large databases, we evaluated various linear and non-linear algorithms, some of 12 which utilized information on gene interactions. Then, we developed a new algorithm 13 called TG-LASSO that explicitly integrates information on samples' tissue of origin with 14 gene expression profiles to improve prediction performance. Our results showed that 15 regularized regression methods provide significantly accurate prediction. However, 16including the network information or common methods of including information on the 17 tissue of origin did not improve the results. On the other hand, TG-LASSO improved the 18 predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. 19Additionally, TG-LASSO identified genes associated with the drug response, including 20 known targets and pathways involved in the drugs' mechanism of action. Moreover, 21 utilizes a new approach for explicitly incorporating the tissue of origin of samples in the 43 prediction task. Our results show that TG-LASSO outperforms all other algorithms and can 44 accurately distinguish resistant and sensitive patients for the majority of the tested drugs. 45Follow-up analysis reveal that this method can also identify biomarkers of drug sensitivity 46 in each cancer type. 47

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Section: Resultsmentioning

confidence: 99%

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Huang

Bhope

Lim

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Syndecan-1 is a cell surface heparan sulfate proteoglycan and its expression has been shown to be correlated with tumor cell differentiation in various cancers [56]. In addition, its knockdown has been shown to inhibit glioma cell proliferation and invasion and has been suggested as a therapeutic target for glioma [57]. These results support our expectation that the LGG-related gene set not only involves drug response related genes, but also includes those that play important roles in glioma and may act as diagnostic biomarkers or therapeutic targets.…”

Section: Functional and Pathway Enrichment Analysis Of Lgg Related Genesmentioning

confidence: 99%

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

et al. 2020

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Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients. We designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples' tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide better prediction performance. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs' mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.

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“…Syndecan family is a new found evolutionarily conserved transmembrane proteoglycan which can regulate cell proliferation, migration, adhesion, angiogenesis, and modulate cell-cell and cell-matrix interactions during wound reparation [ 5 , 6 ]. Syndecan-1 (SDC1) is an important member of syndecan family, which expression has been associated with prognosis and treatment response in a various tumor types, including solid tumors and hematolymphoid malignancies [ 7 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

Clinicopathological and prognostic significance of SDC1 overexpression in breast cancer

Cui

Xuan

et al. 2017

Oncotarget

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BackgroundBreast cancer is the leading cause of cancer death among global women, and its early diagnosis and treatment are very urgent. Syndecan-1 (SDC1) is a heparin sulfate proteoglycan, which has been linked with the prognosis and treatment response in a various tumor type. To investigate whether SDC1 can serve as a prognostic indictor in breast cancer, bioinformatic analyses were performed in the present study.MethodsSDC1 expression was assessed using Oncomine analysis. Kaplan-Meier Plotter and bc-GenExMiner were performed to identify the prognostic roles of SDC1 in breast cancer. COSMIC analysis and cBioPortal database were performed to analysis the mutations of SDC1. The heat map and methylation status of SDC1 were identified by performing the UCSC.ResultsWe found that SDC1 was more frequently overexpressed in breast cancer than their normal tissues and its expression might be negatively related with some CpG sites. Meanwhile, pooled data suggested that SDC1 mRNA expression is associated worse prognosis of breast cancer. Following data mining in multiple big databases confirmed a positive correlation between SDC1 mRNA expression and PLAU mRNA expression in breast cancer tissues. In addition, high SDC1 expression is associated with increased risked of age, nodal, HER2 and higher SBR grade status.ConclusionOur findings suggest that overexpressed SDC1 was identified in breast cancer than in matched normal tissues and is associated with methylation status of SDC1 promoter. Additionally, SDC1 is positively associated with PLAU and might act as a potential prognostic indicator for breast cancer.

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Syndecan-1 knockdown inhibits glioma cell proliferation and invasion by deregulating a c-src/FAK-associated signaling pathway

Cited by 29 publications

References 39 publications

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Clinicopathological and prognostic significance of SDC1 overexpression in breast cancer

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