AFP-producing Xp11 translocation renal cell carcinoma: Case report and review of the literature

Zhang, Huizhi; Wang, Suying

doi:10.1016/j.currproblcancer.2020.100689

Cited by 7 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tumor antigens and ectopic hormones, such as CEA, AFP, NSE, CA50, CA724, CA125, etc., are currently the most often utilized tumor biomarkers in clinic [27][28][29][30][31]. CEA is a broad-spectrum tumor marker that can be used to assess e cacy, monitor disease progression, and predict prognosis in lung cancer, colorectal cancer, breast cancer, and other cancers [32][33][34].…”

Section: Discussionmentioning

confidence: 99%

A simple and available measurement of onco-exosome dsDNA to protein ratio as a potential tumor marker

Sun

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: Exosomes have great potential as new biomarkers in liquid biopsy. However, due to the limitations of exosome extraction and component analysis procedures, further clinical applications of exosomes are hampered. Carcinoembryonic antigen (CEA) is a commonly used broad-spectrum tumor marker that is strongly expressed in a variety of malignancies. Results: In this study, CEA+ exosomes were directly separated from serum using immunomagnetic beads, and the nucleic acid to protein ultraviolet absorption ratio (NPr) of CEA+ exosomes was determined. It was found that the NPr of CEA+ exosomes in tumor group was higher than that of healthy group. We further analyzed the exosome-derived nucleic acid components using fluorescent staining and found that the concentration ratio of double-stranded DNA to protein (dsDPr) in CEA+ exosomes was also significantly different between the two groups, with a sensitivity of 100% and a specificity of 41.67% for the diagnosis of pan-cancer. The AUC of dsDPr combined with NPr was 0.87 and the ACU of dsDPr combined with CA242 could reach 0.94, showing good diagnostic performance for pan-cancer. Conclusions: This study demonstrates that the dsDPr of CEA+ exosomes can effectively distinguish exosomes derived from tumor patients and healthy individuals, which can be employed as a simple and cost-effective non-invasive screening technology to assist tumor diagnosis.

show abstract

Section: Discussionmentioning

confidence: 99%

A simple and available measurement of onco-exosome dsDNA to protein ratio as a potential tumor marker

Sun

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Tumor antigens and ectopic hormones, such as CEA, AFP, NSE, CA50, CA724, CA125, etc., are currently the most often utilized tumor biomarkers in clinic [ 32 – 36 ]. CEA overexpress in several type of tumor which associate with cell proliferation and tumor progression is a broad-spectrum tumor marker that can be used to assess efficacy, monitor disease progression, and predict prognosis in lung cancer, colorectal cancer, breast cancer, and other cancers [ 37 – 40 ].…”

Section: Discussionmentioning

confidence: 99%

A simple and available measurement of onco-sEV dsDNA to protein ratio as a potential tumor marker

Sun

Zhang

et al. 2023

BMC Cancer

View full text Add to dashboard Cite

Background Small extracellular vesicles (sEVs) have great potential as new biomarkers in liquid biopsy. However, due to the limitations of sEVs extraction and component analysis procedures, further clinical applications of sEVs are hampered. Carcinoembryonic antigen (CEA) is a commonly used broad-spectrum tumor marker that is strongly expressed in a variety of malignancies. Results In this study, CEA+ sEVs were directly separated from serum using immunomagnetic beads, and the nucleic acid to protein ultraviolet absorption ratio (NPr) of CEA+ sEVs was determined. It was found that the NPr of CEA+ sEVs in tumor group was higher than that of healthy group. We further analyzed the sEV-derived nucleic acid components using fluorescent staining and found that the concentration ratio of double-stranded DNA to protein (dsDPr) in CEA+ sEVs was also significantly different between the two groups, with a sensitivity of 100% and a specificity of 41.67% for the diagnosis of pan-cancer. The AUC of dsDPr combined with NPr was 0.87 and the ACU of dsDPr combined with CA242 could reach 0.94, showing good diagnostic performance for pan-cancer. Conclusions This study demonstrates that the dsDPr of CEA+ sEVs can effectively distinguish sEVs derived from tumor patients and healthy individuals, which can be employed as a simple and cost-effective non-invasive screening technology to assist tumor diagnosis.

show abstract

“…Secondly, the deficiency of external validation using the data from the local validation group patients could not assess the accuracy of the selected XGB models in diagnosing BM in RCC patients and therefore establish the other clinical utility analysis such as probability density functions (PDF) and clinical utility curves (CUC). Additionally, we were unable to obtain some effective indicators for predicting RCC-BM from SEER, such as transforming growth factor- β (TGF- β ) [ 35 ], fibroblast growth factor (FGF) [ 1 ], insulin-like growth factor [ 36 ], bone morphogenetic protein [ 37 ], AFP [ 38 ], CA-199 [ 31 ], and Fuhrman nuclear grade [ 39 ]. Future studies are needed to incorporate tumor characteristics, laboratory results, and treatment regimens to establish a higher dimensional predictive model.…”

Section: Discussionmentioning

confidence: 99%

Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients

Liu

Yin

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Purpose. Since the prognosis of renal cell carcinoma (RCC) patients with bone metastasis (BM) is poor, this study is aimed at using big data to build a machine learning (ML) model to predict the risk of BM in RCC patients. Methods. A retrospective study was conducted on 40,355 RCC patients in the SEER database from 2010 to 2017. LASSO regression and multivariate logistic regression analysis was performed to determine independent risk factors of RCC-BM. Six ML algorithm models, including LR, GBM, XGB, RF, DT, and NBC, were used to establish risk models for predicting RCC-BM. The prediction performance of ML models was weighed by 10-fold cross-validation. Results. The study investigated 40,355 patients diagnosed with RCC in the SEER database, where 1,811 (4.5%) were BM patients. Independent risk factors for BM were tumor grade, T stage, N stage, liver metastasis, lung metastasis, and brain metastasis. Among the RCC-BM risk prediction models established by six ML algorithms, the XGB model showed the best prediction performance ( AUC = 0.891 ). Therefore, a network calculator based on the XGB model was established to individually assess the risk of BM in patients with RCC. Conclusion. The XGB risk prediction model based on the ML algorithm performed a good prediction effect on BM in RCC patients.

show abstract

AFP-producing Xp11 translocation renal cell carcinoma: Case report and review of the literature

Cited by 7 publications

References 10 publications

A simple and available measurement of onco-exosome dsDNA to protein ratio as a potential tumor marker

A simple and available measurement of onco-exosome dsDNA to protein ratio as a potential tumor marker

A simple and available measurement of onco-sEV dsDNA to protein ratio as a potential tumor marker

Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients

Contact Info

Product

Resources

About