Detection of hypopharyngeal squamous cell carcinoma using serum proteomics.

Conclusions. The combination of surface-enhanced laser desorption/ionization (SELDI) with bioinformatics tools could help find serum proteome biomarkers and establish a predictive model for early detection of hypopharyngeal squamous cell carcinoma (HSCC). Objectives. Proteomic profiling of serum usi...

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Publicado en:Acta Oto-Laryngologica Vol. 126; no. 8; pp. 853 - 861
Autores principales: Zhou L, Cheng L, Tao L, Jia X, Lu Y, Liao P
Formato: research tables/charts tracings Journal Article
Publicado: Taylor & Francis Ltd Aug2006
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Taylor & Francis Ltd
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        atl: Detection of hypopharyngeal squamous cell carcinoma using serum proteomics.
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        au:
          Zhou L
          Cheng L
          Tao L
          Jia X
          Lu Y
          Liao P
        affil: Department of Otorlaryngology-Head and Neck Surgery, Fudan University Affiliated Eye, Ear, Nose and Throat Hospital, 83 FenYang Road, 200031 Shanghai, China; zhlwc@online.sh.cn
      sug:
        subj:
          Carcinoma, Squamous Cell Diagnosis
          Pharyngeal Neoplasms Diagnosis
          Proteomics Utilization
          Adult
          Aged
          Aged, 80 and Over
          Algorithms
          Blood Proteins
          Blood Analysis
          Case Control Studies
          Decision Trees
          Descriptive Statistics
          Funding Source
          Middle Age
          Sensitivity and Specificity
          Human
          Adult: 19-44 years
          Aged: 65+ years
          Aged, 80 & over
          Middle Aged: 45-64 years
      ab: Conclusions. The combination of surface-enhanced laser desorption/ionization (SELDI) with bioinformatics tools could help find serum proteome biomarkers and establish a predictive model for early detection of hypopharyngeal squamous cell carcinoma (HSCC). Objectives. Proteomic profiling of serum using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is an emerging technique to identify new biomarkers in biological fluids and to establish clinically useful diagnostic computational models. We used it to find new potential biomarkers and to establish a predictive model for early detection of HSCC. Materials and methods. One hundred serum samples including 48 from HSCC patients and 52 from normal controls which were divided into a training set and a blind testing set were treated on WCX2 and IMAC3 protein chip, and serum protein or peptide patterns were detected by SELDI-TOF-MS. The data of spectra were analyzed by Biomarker Wizard software to screen serum proteome biomarkers of HSCC. A decision tree classification algorithm and blind validation were determined by Biomarker Pattern Software (BPS). Results. Ranging from 2 to 30 kDa, 45 potential biomarkers could differentiate HSCC patients from normal controls (p <0.05). Among them four candidate protein peaks with m/z values of 7796, 4216, 5927, and 5361Da were selected to establish a predictive model by BPS with sensitivity of 94% and specificity of 89%. A sensitivity of 92% and specificity of 82% were validated in the blind testing set.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
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        Journal Article
      ougenre: Article
    language: English
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