Seldi-tof MS Profiling of Plasma Proteins in Ovarian Cancer

Wu, Shao Pai; Lin, Yu-Shen; Lai, Hung Cheng; Chu, Tang-Yuan; Kuo, Yu Liang; Liu, Hang Seng

doi:10.1016/s1028-4559(09)60186-8

Cited by 22 publications

(11 citation statements)

References 34 publications

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“…These three identifying peaks had a sensitivity of 84% and specificity of 89%. 67 Although proteomic patterns can be used as "finger prints" to differentiate cancer from normal cases, biomarker discovery requires protein identification within the patterns. Thanks to higher resolution mass spectroscopy and the incorporation of immuno-MS, reverse phase protein arrays (RPPA) and nanometrology-based procedures, researchers are currently identifying hundreds of proteins that are differentially expressed in EOC patients by analyzing ascites, urine and plasma.…”

Section: Proteomicsmentioning

confidence: 99%

Biomarkers for ovarian cancer detection and therapy

Dutta

Wang²,

Phalen³

et al. 2010

Cancer Biology & Therapy

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Section: Proteomicsmentioning

confidence: 99%

Biomarkers for ovarian cancer detection and therapy

Dutta

Wang²,

Phalen³

et al. 2010

Cancer Biology & Therapy

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“…The superior performance of the kernel-method-based is also shown in several case studies such as bancruptcy prediction [4] and image processing [5]. Although the performance of model that is developed by [3] is superior compare to previous studies [1,2] the improvement space still remains.…”

Section: Introductionmentioning

confidence: 94%

“…They used the data from Qilu Hospital, China. Another prediction model was performed by [2] with sensitivity of 84% and specificity of 89%. They [2] used SELDI-TOF MS data and artificial intelligence approach.…”

Section: Introductionmentioning

confidence: 99%

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Ovarian Cancer Identification using One-Pass Clustering and k-Nearest Neighbors

2013

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Abstrak Tingkat kesembuhan pasien dapat ditingkatkan jika kanker ovarium dapat dideteksi lebih awal. Identifikasi deteksi dini kanker ovarium menggunakan profil ekspresi protein (SELDI-TOF MS Kata kunci: kanker ovarium, klasterisasi one-pass clustering, k-nearest neighbors Abstract The identification of ovarian cancer using protein expression profile (SELDI-TOF-MS) is important to assists early detection of ovarian cancer. The chance to save patient's life is greater when

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“…It analyses small volumes of clinical samples without destroying the proteins to be detected and is capable of examining proteins and peptides, which are not available for conventional methods [6,7]. At present, there are many exciting SELDI applications, which have been described by numerous laboratories, especially in studying cancers of various organs, including prostate [10], liver [11], pancreas [12], ovary [13], breast [14], lung [15], colon [16] and others [17,18].…”

Section: Introductionmentioning

confidence: 99%

Potential role of proteomics in the diagnosis of lymphoma: a meta‐analysis

Jiang

et al. 2012

Int J Lab Hematology

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KeywordsSurface-enhanced laser desorption/ ionisation time-of-flight mass spectrometry, lymphomas, systematic review S U M M A RYSurface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) has been approved for identifying biomarkers and diagnosing many diseases such as lymphomas. It is arguable whether the SELDI technique has its value of diagnostic accuracy for lymphomas. The purpose of our study is to determine the diagnostic accuracy of SELDI-TOF-MS for diagnosing lymphomas. The Cochrane Central Register of Controlled Trials, MEDLINE, Pub Med, EMBASE, the Chinese Biomedical Literature Database, the China Academic Journals Full-text Database, and the Chinese Scientific Journals Database were searched systematically for potential studies. Reference lists of included studies and review articles were also reviewed. All studies that reported data on patients with a confirmed diagnosis of lymphomas and that compared the measurement of SELDI-TOF-MS with pathology standard were considered for inclusion. Eleven studies were included in the systematic review. The ranges of the diagnostic value of SELDI-TOF-MS for lymphoma were as follows: sensitivity (SEN) was 0.69-0.96; specificity (SPE) was 0.70-1.00; positive likelihood ratio (PLR) was 2.99 -96.09; negative likelihood ratio (NLR) was 0.04-0.35; and diagnostic odds ratio (DOR) was 18.13-1250.71, respectively. Further, we analysed serum samples as a subgroup, and the pooled endpoints were as follows: pooled SEN was 0.89 (0.85-0.91); pooled SPE was 0.91 (0.88-0.93); pooled PLR was 12.35 (5.36-28.44); pooled NLR was 0.13 (0.09-0.20); and pooled DOR was 101.04 (39.57-258.04), respectively. SELDI-TOF-MS showed high accuracy in identifying lymphoma and could be a useful screening tool for diagnosing lymphoma patients.

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Seldi-tof MS Profiling of Plasma Proteins in Ovarian Cancer

Cited by 22 publications

References 34 publications

Biomarkers for ovarian cancer detection and therapy

Biomarkers for ovarian cancer detection and therapy

Ovarian Cancer Identification using One-Pass Clustering and k-Nearest Neighbors

Potential role of proteomics in the diagnosis of lymphoma: a meta‐analysis

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