The Matrix Metalloprotease Pump-1 (MMP-7, Matrilysin): A Candidate Marker/Target for Ovarian Cancer Detection and Treatment

Tumor Biology ◽  
1999 ◽  
Vol 20 (2) ◽  
pp. 88-98 ◽  
Author(s):  
Hirotoshi Tanimoto ◽  
Lowell J. Underwood ◽  
Kazushi Shigemasa ◽  
Tim H. Parmley ◽  
Yinxiang Wang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Detection ◽  
Matrix Metalloprotease ◽  
Candidate Marker ◽  
The Matrix

ANALYSIS OF UTERINE LAVAGE FOR EARLY OVARIAN CANCER DETECTION

2020 ◽  
Author(s):  
Diana Žilovič ◽  
Rūta Čiurlienė ◽  
Ieva Vaicekauskaitė ◽  
Rasa Sabaliauskaitė ◽  
Sonata Jarmalaitė
Keyword(s):  
Ovarian Cancer ◽  
Cancer Detection ◽  
Early Ovarian Cancer ◽  
Uterine Lavage

scholarly journals A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Jiang Wu ◽  
Yanju Ji ◽  
Ling Zhao ◽  
Mengying Ji ◽  
Zhuang Ye ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Detection ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Analysis Method ◽  
Data Set ◽  
Specificity And Sensitivity ◽  
Svm Model ◽  
Tof Ms

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data.Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity.Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively.Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

Effect of High Dose External Irradiation on the Matrix Metalloprotease-2 Expression in a Rat Carotid Artery Injury Model

2008 ◽  
Vol 38 (4) ◽  
pp. 212
Author(s):  
Tae Kyoon Kim ◽  
Yong Joo Kim ◽  
Chan Seok Park ◽  
Hun-Jun Park ◽  
Dong-Bin Kim ◽  
...  
Keyword(s):  
Carotid Artery ◽  
External Irradiation ◽  
High Dose ◽  
Matrix Metalloprotease ◽  
Carotid Artery Injury ◽  
Artery Injury ◽  
Injury Model ◽  
The Matrix

Early ovarian cancer detection by deep learning: Two-dimensional comprehensive serum glycopeptide spectra analysis

2020 ◽  
Vol 159 ◽  
pp. 79-80
Author(s):  
M. Mikami ◽  
K. Tanabe ◽  
K. Matsuo ◽  
M. Ikeda ◽  
M. Hayashi ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Two Dimensional ◽  
Spectra Analysis ◽  
Early Ovarian Cancer

scholarly journals Synthesis and Properties of CurNQ for the Theranostic Application in Ovarian Cancer Intervention

Molecules ◽  
2020 ◽  
Vol 25 (19) ◽  
pp. 4471
Author(s):  
Lara G. Freidus ◽  
Pradeep Kumar ◽  
Thashree Marimuthu ◽  
Priyamvada Pradeep ◽  
Viness Pillay ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Detection ◽  
Ovarian Cancer Cell ◽  
In Vitro Cytotoxicity ◽  
Cancer Targeting ◽  
The Novel ◽  
Cytotoxicity Assays ◽  
Ovarian Cancer Cell Lines ◽  
Cancer Intervention

Synthesis of a novel theranostic molecule for targeted cancer intervention. A reaction between curcumin and lawsone was carried out to yield the novel curcumin naphthoquinone (CurNQ) molecule (2,2′-((((1E,3Z,6E)-3-hydroxy-5-oxohepta-1,3,6-triene-1,7-diyl) bis(2-methoxy-4,1-phenylene))bis(oxy))bis(naphthalene-1,4-dione). CurNQ’s structure was elucidated and was fully characterized. CurNQ was demonstrated to have pH specific solubility, its saturation solubility increased from 11.15 µM at pH 7.4 to 20.7 µM at pH 6.8. This pH responsivity allows for cancer targeting (Warburg effect). Moreover, CurNQ displayed intrinsic fluorescence, thus enabling imaging and detection applications. In vitro cytotoxicity assays demonstrated the chemotherapeutic properties of CurNQ as CurNQ reduced cell viability to below 50% in OVCAR-5 and SKOV3 ovarian cancer cell lines. CurNQ is a novel theranostic molecule for potential targeted cancer detection and treatment.

scholarly journals Ovarian Cancer Biomarkers: Current Options and Future Promise

2008 ◽  
Vol 6 (8) ◽  
pp. 795-802 ◽  
Author(s):  
Christine M. Coticchia ◽  
Jiang Yang ◽  
Marsha A. Moses
Keyword(s):  
Ovarian Cancer ◽  
Disease Progression ◽  
Cancer Detection ◽  
Therapeutic Efficacy ◽  
Cancer Biomarkers ◽  
Cancer Diagnostics ◽  
Early Disease ◽  
Molecular Approaches ◽  
Ovarian Cancer Diagnostics ◽  
Potential Biomarkers

As more effective, less toxic cancer drugs reach patients, the need for accurate and reliable cancer diagnostics and prognostics has become widely appreciated. Nowhere is this need more dire than in ovarian cancer; here most women are diagnosed late in disease progression. The ability to sensitively and specifically predict the presence of early disease and its status, stage, and associated therapeutic efficacy has the potential to revolutionize ovarian cancer detection and treatment. This article reviews current ovarian cancer diagnostics and prognostics and potential biomarkers that are being studied and validated. Some of the most recent molecular approaches being used to identify genes and proteins are presented, which may represent the next generation of ovarian cancer diagnostics and prognostics.

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