Panel of potential lncRNA biomarkers can distinguish various types of liver malignant and benign tumors

Magnetic Resonance Spectroscopy (MRS) is used in diagnostic imaging for disease metabolism evaluation. The H MRS is highly used because of the abundance, high sensitivity, etc. The various clinical implementation includes whole-brain MRS is used in measuring metabolites of different brain areas simultaneously. The breast MRS is used in malignant and benign tumors differentiation by the total choline compound. The prostate MRS is used to map the metabolites like citrate, choline, and creatinine. For spinal cord MRS, the myoinositol and N acetyl aspartate were 31 23 1 considered markers for various diseases. The MRS uses nuclei like P, Na, and H for metabolic and biochemical evaluation of cardiac muscles. The liver MRS spectrum has mainly methylene group of lipid, methyl groups of choline, and water. The MRS measures choline, creatinine, lactate, and lipid peaks in uterine leiomyoma and myometrium. Hence there are organ-specic metabolites used as a reference to map the metabolic process by using spectroscopy, making it one of the commonly preferred technique.

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A Comparison of the Changes in Carbohydrate, Fat, and Protein Metabolism Occurring with Malignant and Benign Tumors and the Impact of Nutritional Support

Cancer and Nutrition ◽

10.1007/978-1-4757-9561-5_3 ◽

1991 ◽

pp. 69-95 ◽

Cited By ~ 1

Author(s):

Antonio C. L. Campos ◽

Dan L. Waitzberg ◽

Michael M. Meguid

Keyword(s):

Protein Metabolism ◽

Nutritional Support ◽

Benign Tumors ◽

The Impact ◽

Malignant And Benign Tumors

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KPNA2 expression is a potential marker for differential diagnosis between osteosarcomas and other malignant bone tumor mimickers

10.21203/rs.3.rs-29911/v1 ◽

2020 ◽

Author(s):

Lucen Jiang ◽

Jianghuan Liu ◽

Qingzhu Wei ◽

Yiyang Wang

Keyword(s):

Bone Tumor ◽

Bone Tumors ◽

Diagnostic Value ◽

Benign Tumors ◽

Malignant Bone Tumor ◽

Malignant Bone Tumors ◽

Potential Marker ◽

Tumors Of Bone ◽

Bone Sarcomas ◽

Malignant And Benign Tumors

Abstract Background Karyopherin α 2 (KPNA2), a member of the Karyopherin α family, has been observed in several cancers but lack substantial investigation in malignant bone tumors. The purpose of the current study was to evaluate KPNA2 expression level and its utility as a novel diagnostic biomarker in osteosarcomas and their malignant bone tumor mimickers, such as chondrosarcomas and Ewing sarcomas.Method We investigated the expression of KPNA2 protein by immunohistochemistry on paraffin embedded surgical specimens from 217 patients with malignant and benign tumors of bone, including 81 osteosarcomas, 42 chondrosarcomas, 9 Ewing sarcomas, 28 osteoid osteoma, 20 osteochondroma and 37 Chondroblastoma. Immunoreactivity was scored semi quantitatively based on stain extent and intensity.Results Seventy one of 81 (87.7%) osteosarcomas, zero of 42 (0%) chondrosarcomas and one of 9 (11.1%) Ewing sarcomas showed immunoreactivity for KPNA2. Negative KPNA2 expression was observed in all of benign bone tumors. Much more positive expression of KPNA2 was found in osteosarcomas as compared with chondrosarcomas and Ewing sarcomas. The sensitivity and specificity of KPNA2 immuno-expression for osteosarcoma was 87.7% and 100%, respectively. In several subtypes of osteosarcomas, immunohistochemical expression of KNA2 was more frequent in osteoblastic (94.5%), with 39 (70.9%) showing strong-intensity staining. KPNA2 positivity was observed in eleven of 13 (84.6%) chondroblastic, three of 6 (50%) fibroblastic, three of 4 (75%) telangiectatic and two of 3 (66.7%) giant cell-rich osteosarcoma. Stronger-intensity staining was observed in osteoblastic osteosarcoma.Conclusion KPNA2 is most frequently expressed in osteosarcomas, particularly in osteoblastic and chondroblastic tumors, but is rarely positive in chondrosarcomas and Ewing sarcomas. This feature may have diagnostic value since it is very useful for distinguishing between osteosarcomas and other bone sarcomas mimickers. This report supports KPNA2 as a novel marker for the diagnosis of osteosarcoma.

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Catecholamine metabolism in the tissue of malignant and benign tumors of the sympathetic nervous system

Journal of Pediatric Surgery ◽

10.1016/0022-3468(71)90944-4 ◽

1971 ◽

Vol 6 (6) ◽

pp. 800-801

Author(s):

A. Zimmermann

Keyword(s):

Nervous System ◽

Sympathetic Nervous System ◽

Benign Tumors ◽

Catecholamine Metabolism ◽

Sympathetic Nervous ◽

Malignant And Benign Tumors

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Amide proton transfer (APT) imaging of parotid tumors: Differentiation of malignant and benign tumors

European Journal of Radiology ◽

10.1016/j.ejrad.2020.109047 ◽

2020 ◽

Vol 129 ◽

pp. 109047 ◽

Cited By ~ 1

Author(s):

Takeshi Kamitani ◽

Koji Sagiyama ◽

Osamu Togao ◽

Yuzo Yamasaki ◽

Tomoyuki Hida ◽

...

Keyword(s):

Proton Transfer ◽

Amide Proton ◽

Benign Tumors ◽

Amide Proton Transfer ◽

Parotid Tumors ◽

Apt Imaging ◽

Malignant And Benign Tumors

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Regulation of PI 3-K, PTEN, p53, and mTOR in Malignant and Benign Tumors Deficient in Tuberin

Genes & Cancer ◽

10.1177/1947601912445376 ◽

2011 ◽

Vol 2 (11) ◽

pp. 1051-1060 ◽

Cited By ~ 14

Author(s):

S. L. Habib ◽

A. Yadav ◽

L. Mahimainathan ◽

A. J. Valente

Keyword(s):

Benign Tumors ◽

Malignant And Benign Tumors

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Intravoxel Incoherent Motion Diffusion for Identification of Breast Malignant and Benign Tumors Using Chemometrics

BioMed Research International ◽

10.1155/2017/3845409 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Fengnong Chen ◽

Pulan Chen ◽

Hamed Hamid Muhammed ◽

Juan Zhang

Keyword(s):

Support Vector Machine ◽

Diffusion Coefficient ◽

Discriminant Analysis ◽

Cross Validation ◽

Binary Classification ◽

Intravoxel Incoherent Motion ◽

Support Vector ◽

Benign Tumors ◽

Accurate Identification ◽

Malignant And Benign Tumors

The aim of the paper is to identify the breast malignant and benign lesions using the features of apparent diffusion coefficient (ADC), perfusion fraction f, pseudodiffusion coefficient D⁎, and true diffusion coefficient D from intravoxel incoherent motion (IVIM). There are 69 malignant cases (including 9 early malignant cases) and 35 benign breast cases who underwent diffusion-weighted MRI at 3.0 T with 8 b-values (0~1000 s/mm2). ADC and IVIM parameters were determined in lesions. The early malignant cases are used as advanced malignant and benign tumors, respectively, so as to assess the effectiveness on the result. A predictive model was constructed using Support Vector Machine Binary Classification (SVMBC, also known Support Vector Machine Discriminant Analysis (SVMDA)) and Partial Least Squares Discriminant Analysis (PLSDA) and compared the difference between them both. The D value and ADC provide accurate identification of malignant lesions with b=300, if early malignant tumor was considered as advanced malignant (cancer). The classification accuracy is 93.5% for cross-validation using SVMBC with ADC and tissue diffusivity only. The sensitivity and specificity are 100% and 87.0%, respectively, r2cv=0.8163, and root mean square error of cross-validation (RMSECV) is 0.043. ADC and IVIM provide quantitative measurement of tissue diffusivity for cellularity and are helpful with the method of SVMBC, getting comprehensive and complementary information for differentiation between benign and malignant breast lesions.

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