A liquid biopsy is currently an interesting tool for measuring tumor material with the advantage of being non-invasive. The overexpression of vimentin and ezrin genes was associated with epithelial-mesenchymal transition (EMT), a key process in metastasis and progression in osteosarcoma (OS). In this study, we identified other OS-specific genes by calculating differential gene expression using the Gene Expression Omnibus (GEO) database, confirmed by using quantitative reverse transcription-PCR (qRT-PCR) to detect OS-specific genes, including VIM and ezrin in the buffy coat, which were obtained from the whole blood of OS patients and healthy donors. Furthermore, the diagnostic model for OS detection was generated by utilizing binary logistic regression with a multivariable fractional polynomial (MFP) algorithm. The model incorporating VIM, ezrin, and COL5A2 genes exhibited outstanding discriminative ability, as determined by the receiver operating characteristic curve (AUC = 0.9805, 95% CI 0.9603, 1.000). At the probability cut-off value of 0.3366, the sensitivity and the specificity of the model for detecting OS were 98.63% (95% CI 90.5, 99.7) and 94.94% (95% CI 87.5, 98.6), respectively. Bioinformatic analysis and qRT-PCR, in our study, identified three candidate genes that are potential diagnostic and prognostic genes for OS.
This study aimed to analyze burden of STS and GIST in population and survival rate which represented the current situation of treatment in Thailand. The data was collected from five population-based cancer registries around the country for the period 2001 through 2015. The Segi world standard population was used to calculated age-standardized incidence rates (ASR). Standardized rate ratios (SRR) were used to compare populations. Joinpoint Trend Analysis was used to assess changes in incidence. STATA was used to examine patient survival rates. During the study period, 4080 cases of STS and 457 cases of GIST were reported. The ASR of STS and GIST was 2.14/100,000 person-years and 0.22/100,000 person-years, respectively. The most common histological types of STS were unspecified sarcoma (24.8%), leiomyosarcoma (19.0%) and liposarcoma (11.4%). The overall ASR of STS in Thailand was relatively low compared to Western countries. The five-year survival rate was 62.6% for STS and 63.4% for GIST, which was comparable to the rates reported in other countries. This is the first report of STS and GIST from PBCRs in Thailand. Based on current healthcare service, an overall survival rates of STS and GIST are comparable to those reported from others.
Background Current techniques to identify circulating-tumor cells (CTCs) in osteosarcoma (OS), which are an indication of a poor prognosis in cases of intermediate levels of metastasis, are complicated and time-consuming. This study investigated the efficacy of quantitative reverse transcription PCR (qRT-PCR), a molecular technique that is available in most laboratories, for detection of CTCs in buffy coat samples of OS patients and healthy donors. Methods Previously published reports on data-reviewing and retrieval of data by calculation of differential gene expression from the Gene Expression Omnibus (GEO) database repository were reviewed identify candidate genes. Following analysis of the expression of the candidate genes identified a diagnostic model for detection of specific gene expression was derived using binary logistic regression with a multivariable fractional polynomial (MFP) algorithm. Results A model incorporating VIM, ezrin, COL1A2, and PLS3 exhibited an outstanding discriminative ability as determined by the receiver operating characteristic curve (AUC = 0.9896, 95%CI 0.9695, 1.000). At the probability cut-off value 0.2943, the sensitivity and the specificity of the model for detection of OS were 100% (95%CI 94.8, 100.0) and 96.49% (95%CI 87.9, 99.6), respectively. Conclusion The qRT-PCR can identify the existence of OS circulating cells by detection of potential candidate genes (VIM, Ezrin, COL1A2 and PLS3). Thus, these genes are worthy to be considered diagnostic biomarkers and alternative micro-metastasis predictors for OS.
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