Advances in targeted therapy for osteosarcoma based on molecular classification

Chen, Yingqian; Liu, Runzhi; Wang, Wei; Wang, Chen; Zhang, Ning; Shao, Xuejing; He, Qiaojun

doi:10.1016/j.phrs.2021.105684

Cited by 43 publications

(29 citation statements)

References 82 publications

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“…Sorafenib (SORA), a tyrosine kinase inhibitor, was previously reported to inhibit growth, angiogenesis, and metastatic ability of osteosarcoma pre-clinical models via the ERK1/2, MCL-1, and ERM pathways [ 1 ]. Apatinib (APAT), an inhibitor of VEGFR, is used for relapsed and unresectable osteosarcoma after the failure of first-line or second-line chemotherapy in a clinical trial (NCT02711007) [ 2 ], and contributes to the clinical treatment of doxorubicin [ 33 ]. By calculating the IC50, it was found that the efficacy of these three targeted drugs was similar in 2D and 3D models ( Fig.…”

Section: Resultsmentioning

confidence: 99%

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Multi-omics analysis based on 3D-bioprinted models innovates therapeutic target discovery of osteosarcoma

Lin

Yang

Yuan

et al. 2022

Bioactive Materials

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Section: Resultsmentioning

confidence: 99%

“…Osteosarcoma is the most common primary bone malignancy. Despite recent therapeutic advances, the use of broad-spectrum tyrosine kinase inhibitor sorafenib [ 1 ], VEGFR inhibitor apatinib [ 2 ], recombinant human endostatin endostar, etc. [ 3 ], osteosarcoma still does harm to children and young adults [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-omics analysis based on 3D-bioprinted models innovates therapeutic target discovery of osteosarcoma

Lin

Yang

Yuan

et al. 2022

Bioactive Materials

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“…The most common site of distant metastasis is the lung, and lung-only metastases account for approximately 82% of metastatic cases (Gill and Gorlick, 2021). The standard therapy for osteosarcoma patients includes surgery resection and chemotherapy, which has led to a dramatic increase in the overall survival of patients with a localized disease, and the five-year survival rate increased from less than 20 to 70% over the past 30 years (Belayneh et al, 2021;Chen et al, 2021). However, unfortunately, 20-30% of osteosarcoma patients are metastatic or recurrent cases, with the five-year survival rate being less than 20% and remaining stagnant (Saraf et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A transmembrane protein family gene signature for overall survival prediction in osteosarcoma

Zeng

et al. 2022

Front. Genet.

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The transmembrane (TMEM) protein family is constituted by a large number of proteins that span the lipid bilayer. Dysregulation of TMEM protein genes widely occurs and is associated with clinical outcomes of patients with multiple tumors. Nonetheless, the significance of TMEM genes in the prognosis prediction of patients with osteosarcoma remains largely unclear. Here, we comprehensively analyzed TMEM protein family genes in osteosarcoma using public resources and bioinformatics methods. Prognosis-related TMEM protein family genes were identified by the univariate Cox regression analysis and were utilized to construct a signature based on six TMEM protein family genes (TMEM120B, TMEM147, TMEM9B, TMEM8A, TMEM59, and TMEM39B) in osteosarcoma. The prognostic signature stratified patients into high- and low-risk groups, and validation in the internal and external cohorts confirmed the risk stratification ability of the signature. Functional enrichment analyses of differentially expressed genes between high- and low-risk groups connected immunity with the prognostic signature. Moreover, we found that M2 and M0 macrophages were the most abundant infiltrated immune cell types in the immune microenvironment, and samples of the high-risk group showed a decreased proportion of M2 macrophages. Single-sample gene set enrichment analysis revealed that the scores of neutrophils and Treg were markedly lower in the high-risk group than these in the low-risk group in The Cancer Genome Atlas and GSE16091 cohorts. As for the related immune functions, APC co-inhibition and cytolytic activity exhibited fewer active levels in the high-risk group than that in the low-risk group in both cohorts. Of the six TMEM genes, the expression of TMEM9B was lower in the high-risk group than in the low-risk group and was positively associated with the overall survival of osteosarcoma patients. In conclusion, our TMEM protein family gene-based signature is a novel and clinically useful prognostic biomarker for osteosarcoma patients, and TMEM9B might be a potential therapeutic target in osteosarcoma.

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“…It is mainly used for image classification. It derives the features and learns essential features in the convolutional layers, then categorizes the output as the presence or absence of osteosarcoma metastasizes by the fully connected layers [ 8 ]. This learning technique, known as a supervised learning algorithm, can be employed for training the CNN model to accomplish effective results by the use of high-quality images in the dataset [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model

2022

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Osteosarcoma is a kind of bone cancer which generally starts to develop in the lengthy bones in the legs and arms. Because of an increase in occurrence of cancer and patient-specific treatment options, the detection and classification of cancer becomes a difficult process. The manual recognition of osteosarcoma necessitates expert knowledge and is time consuming. An earlier identification of osteosarcoma can reduce the death rate. With the development of new technologies, automated detection models can be exploited for medical image classification, thereby decreasing the expert’s reliance and resulting in timely identification. In recent times, an amount of Computer-Aided Detection (CAD) systems are available in the literature for the segmentation and detection of osteosarcoma using medicinal images. In this view, this research work develops a wind driven optimization with deep transfer learning enabled osteosarcoma detection and classification (WDODTL-ODC) method. The presented WDODTL-ODC model intends to determine the presence of osteosarcoma in the biomedical images. To accomplish this, the osteosarcoma model involves Gaussian filtering (GF) based on pre-processing and contrast enhancement techniques. In addition, deep transfer learning using a SqueezNet model is utilized as a featured extractor. At last, the Wind Driven Optimization (WDO) algorithm with a deep-stacked sparse auto-encoder (DSSAE) is employed for the classification process. The simulation outcome demonstrated that the WDODTL-ODC technique outperformed the existing models in the detection of osteosarcoma on biomedical images.

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Advances in targeted therapy for osteosarcoma based on molecular classification

Cited by 43 publications

References 82 publications

Multi-omics analysis based on 3D-bioprinted models innovates therapeutic target discovery of osteosarcoma

Multi-omics analysis based on 3D-bioprinted models innovates therapeutic target discovery of osteosarcoma

A transmembrane protein family gene signature for overall survival prediction in osteosarcoma

Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model

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