We are analyzing https://link.springer.com/article/10.1186/bcr548.

Title:
Expression profiling to predict outcome in breast cancer: the influence of sample selection | Breast Cancer Research
Description:
Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-α status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-α-positive and estrogen receptor-α-negative tumors.
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Keywords {🔍}

expression, gene, tumors, breast, status, erα, set, cancer, clinical, data, outcome, genes, veer, prognostic, prognosis, article, years, pubmed, vant, predict, estrogen, google, scholar, patients, cas, profiling, profiles, study, van, tumor, recurrences, sample, meltzer, analysis, size, information, research, sporadic, independent, studies, identified, test, correlation, groups, distant, nature, privacy, cookies, gruvberger, predicting,

Topics {✒️}

estrogen receptor-α-negative tumors estrogen receptor-α-positive estrogen receptor-α status estrogen receptor [er]-α gene expression profiling lymph node-negative patients discriminate er-α status gene expression patterns er-α-negative tumors gene expression profiles gene expression monitoring estrogen receptor-α estrogen receptor status er-α-positive tumors gene expression data human breast cancer prognostic gene set privacy choices/manage cookies distinct expression profiles er-α-negative full size image pathological markers human breast tumours expression profiling er-α-positive er-α status recent study multidimensional scaling analysis gene expression histopathological prognostic factors primary breast tumors primary surgical resection breast tumor cohorts significant outcome discriminator = estrogen receptor breast tumors based european economic area mårten fernö traditional histopathological methods artificial neural networks random permutation tests dimensional euclidean space cutaneous malignant melanoma perou cm kit mutations exhibit sporadic breast cancers microarray analysis conditions privacy policy sample set comprised er-α subgroups

Schema {🗺️}

WebPage:
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         headline:Expression profiling to predict outcome in breast cancer: the influence of sample selection
         description:Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-α status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-α-positive and estrogen receptor-α-negative tumors.
         datePublished:2002-12-04T00:00:00Z
         dateModified:2002-12-04T00:00:00Z
         pageStart:1
         pageEnd:4
         sameAs:https://doi.org/10.1186/bcr548
         keywords:
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            estrogen receptor
            gene expression
            microarray
            prognosis
            Cancer Research
            Oncology
            Surgical Oncology
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                        type:PostalAddress
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               type:Person
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               affiliation:
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ScholarlyArticle:
      headline:Expression profiling to predict outcome in breast cancer: the influence of sample selection
      description:Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-α status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-α-positive and estrogen receptor-α-negative tumors.
      datePublished:2002-12-04T00:00:00Z
      dateModified:2002-12-04T00:00:00Z
      pageStart:1
      pageEnd:4
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         breast cancer
         estrogen receptor
         gene expression
         microarray
         prognosis
         Cancer Research
         Oncology
         Surgical Oncology
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                     name:Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
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            affiliation:
                  name:Lund University
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                  address:
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                     type:PostalAddress
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         type:PostalAddress
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         type:PostalAddress
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         name:Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden
         type:PostalAddress
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      address:
         name:Department of Oncology, The Jubileum Institute, Lund University Hospital, Lund, Sweden
         type:PostalAddress
      name:The Jubileum Institute, Lund University Hospital
      address:
         name:Department of Oncology, The Jubileum Institute, Lund University Hospital, Lund, Sweden
         type:PostalAddress
      name:Lund University
      address:
         name:Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden
         type:PostalAddress
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      address:
         name:Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
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               type:PostalAddress
            type:Organization
      name:Markus Ringnér
      affiliation:
            name:Lund University
            address:
               name:Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden
               type:PostalAddress
            type:Organization
            name:National Institutes of Health
            address:
               name:Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
               type:PostalAddress
            type:Organization
      name:Patrik Edén
      affiliation:
            name:Lund University
            address:
               name:Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden
               type:PostalAddress
            type:Organization
      name:Åke Borg
      affiliation:
            name:The Jubileum Institute, Lund University Hospital
            address:
               name:Department of Oncology, The Jubileum Institute, Lund University Hospital, Lund, Sweden
               type:PostalAddress
            type:Organization
      name:Mårten Fernö
      affiliation:
            name:The Jubileum Institute, Lund University Hospital
            address:
               name:Department of Oncology, The Jubileum Institute, Lund University Hospital, Lund, Sweden
               type:PostalAddress
            type:Organization
      name:Carsten Peterson
      affiliation:
            name:Lund University
            address:
               name:Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden
               type:PostalAddress
            type:Organization
      name:Paul S Meltzer
      affiliation:
            name:National Institutes of Health
            address:
               name:Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
               type:PostalAddress
            type:Organization
      email:[email protected]
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      name:Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden
      name:Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, USA
      name:Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden
      name:Department of Oncology, The Jubileum Institute, Lund University Hospital, Lund, Sweden
      name:Department of Oncology, The Jubileum Institute, Lund University Hospital, Lund, Sweden
      name:Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden
      name:Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, USA

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