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We are analyzing https://link.springer.com/article/10.1186/s13148-024-01657-3.

Title:
Head and neck cancer of unknown primary: unveiling primary tumor sites through machine learning on DNA methylation profiles | Clinical Epigenetics
Description:
Background The unknown tissue of origin in head and neck cancer of unknown primary (hnCUP) leads to invasive diagnostic procedures and unspecific and potentially inefficient treatment options for patients. The most common histologic subtype, squamous cell carcinoma, can stem from various tumor primary sites, including the oral cavity, oropharynx, larynx, head and neck skin, lungs, and esophagus. DNA methylation profiles are highly tissue-specific and have been successfully used to classify tissue origin. We therefore developed a support vector machine (SVM) classifier trained with publicly available DNA methylation profiles of commonly cervically metastasizing squamous cell carcinomas (n = 1103) in order to identify the primary tissue of origin of our own cohort of squamous cell hnCUP patient’s samples (n = 28). Methylation analysis was performed with Infinium MethylationEPIC v1.0 BeadChip by Illumina. Results The SVM algorithm achieved the highest overall accuracy of tested classifiers, with 87%. Squamous cell hnCUP samples on DNA methylation level resembled squamous cell carcinomas commonly metastasizing into cervical lymph nodes. The most frequently predicted cancer localization was the oral cavity in 11 cases (39%), followed by the oropharynx and larynx (both 7, 25%), skin (2, 7%), and esophagus (1, 4%). These frequencies concord with the expected distribution of lymph node metastases in epidemiological studies. Conclusions On DNA methylation level, hnCUP is comparable to primary tumor tissue cancer types that commonly metastasize to cervical lymph nodes. Our SVM-based classifier can accurately predict these cancers’ tissues of origin and could significantly reduce the invasiveness of hnCUP diagnostics and enable a more precise therapy after clinical validation.
Website Age:
28 years and 1 months (reg. 1997-05-29).

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  • Education
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Custom-built

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🌠 Phenomenal Traffic: 5M - 10M visitors per month


Based on our best estimate, this website will receive around 7,642,828 visitors per month in the current month.

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How Does Link.springer.com Make Money? {💸}

We find it hard to spot revenue streams.

Websites don't always need to be profitable; some serve as platforms for education or personal expression. Websites can serve multiple purposes. And this might be one of them. Link.springer.com could be secretly minting cash, but we can't detect the process.

Keywords {🔍}

primary, methylation, cancer, samples, article, pubmed, dna, neck, lymph, hncup, google, scholar, head, tumor, node, data, sites, classifier, squamous, cell, cohort, study, unknown, tissue, cervical, file, profiles, carcinoma, table, additional, metastases, fig, cas, analysis, metastasis, cases, clinical, patients, site, svm, size, origin, diagnostic, cup, cpg, central, accuracy, nodes, umap, full,

Topics {✒️}

genome–wide methylation profiling article download pdf squamous cell carcinomas ai-assisted molecular profiling squamous cell carcinoma keywords “lymph node cancer genome atlas dna methylation-based classification full size table optimal cross-array normalization small esophageal carcinomas p16/hpv-negative cases cervically metastasizing scc p16/hpv-positive hncup explore potential variations machine learning-based classifier privacy choices/manage cookies cervical lymph nodes dna methylation profiles eur arch otorhinolaryngol dna methylation profiling sample size underscores translational cancer research lymph node metastases single-cell methods [42] markus wirth lymph node metastasis full access cervical node dissection fdg-pet-ct support vector machine lymph node extirpation dna methylation level placental dna methylation lymph node tissue uniform manifold approximation tissue dna methylation dna methylation analysis methylation profiling identifies oropharyngeal carcinoma metastasizes resected laryngeal carcinoma phenomenon commonly observed p53 gene status neck lymph nodes creative commons licence limited sample size small primary tumor german cancer consortium lymph node samples local ethics committee

Questions {❓}

  • Cancer of unknown primary site: real entity or misdiagnosed disease?

Schema {🗺️}

WebPage:
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         headline:Head and neck cancer of unknown primary: unveiling primary tumor sites through machine learning on DNA methylation profiles
         description:The unknown tissue of origin in head and neck cancer of unknown primary (hnCUP) leads to invasive diagnostic procedures and unspecific and potentially inefficient treatment options for patients. The most common histologic subtype, squamous cell carcinoma, can stem from various tumor primary sites, including the oral cavity, oropharynx, larynx, head and neck skin, lungs, and esophagus. DNA methylation profiles are highly tissue-specific and have been successfully used to classify tissue origin. We therefore developed a support vector machine (SVM) classifier trained with publicly available DNA methylation profiles of commonly cervically metastasizing squamous cell carcinomas (n = 1103) in order to identify the primary tissue of origin of our own cohort of squamous cell hnCUP patient’s samples (n = 28). Methylation analysis was performed with Infinium MethylationEPIC v1.0 BeadChip by Illumina. The SVM algorithm achieved the highest overall accuracy of tested classifiers, with 87%. Squamous cell hnCUP samples on DNA methylation level resembled squamous cell carcinomas commonly metastasizing into cervical lymph nodes. The most frequently predicted cancer localization was the oral cavity in 11 cases (39%), followed by the oropharynx and larynx (both 7, 25%), skin (2, 7%), and esophagus (1, 4%). These frequencies concord with the expected distribution of lymph node metastases in epidemiological studies. On DNA methylation level, hnCUP is comparable to primary tumor tissue cancer types that commonly metastasize to cervical lymph nodes. Our SVM-based classifier can accurately predict these cancers’ tissues of origin and could significantly reduce the invasiveness of hnCUP diagnostics and enable a more precise therapy after clinical validation.
         datePublished:2024-03-25T00:00:00Z
         dateModified:2024-03-25T00:00:00Z
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            HNSCC
            DNA methylation
            Classifier
            Human Genetics
            Gene Function
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      headline:Head and neck cancer of unknown primary: unveiling primary tumor sites through machine learning on DNA methylation profiles
      description:The unknown tissue of origin in head and neck cancer of unknown primary (hnCUP) leads to invasive diagnostic procedures and unspecific and potentially inefficient treatment options for patients. The most common histologic subtype, squamous cell carcinoma, can stem from various tumor primary sites, including the oral cavity, oropharynx, larynx, head and neck skin, lungs, and esophagus. DNA methylation profiles are highly tissue-specific and have been successfully used to classify tissue origin. We therefore developed a support vector machine (SVM) classifier trained with publicly available DNA methylation profiles of commonly cervically metastasizing squamous cell carcinomas (n = 1103) in order to identify the primary tissue of origin of our own cohort of squamous cell hnCUP patient’s samples (n = 28). Methylation analysis was performed with Infinium MethylationEPIC v1.0 BeadChip by Illumina. The SVM algorithm achieved the highest overall accuracy of tested classifiers, with 87%. Squamous cell hnCUP samples on DNA methylation level resembled squamous cell carcinomas commonly metastasizing into cervical lymph nodes. The most frequently predicted cancer localization was the oral cavity in 11 cases (39%), followed by the oropharynx and larynx (both 7, 25%), skin (2, 7%), and esophagus (1, 4%). These frequencies concord with the expected distribution of lymph node metastases in epidemiological studies. On DNA methylation level, hnCUP is comparable to primary tumor tissue cancer types that commonly metastasize to cervical lymph nodes. Our SVM-based classifier can accurately predict these cancers’ tissues of origin and could significantly reduce the invasiveness of hnCUP diagnostics and enable a more precise therapy after clinical validation.
      datePublished:2024-03-25T00:00:00Z
      dateModified:2024-03-25T00:00:00Z
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         HNSCC
         DNA methylation
         Classifier
         Human Genetics
         Gene Function
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               name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
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            name:Technical University of Munich
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            name:Technical University of Munich
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               name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
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               name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
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            name:Technical University of Munich
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               name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
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            name:Technical University of Munich
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               name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
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      name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
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      name:Institute of Pathology, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Institute of Pathology, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Institute of Pathology, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Institute of Pathology, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Partner Site Munich and German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
      name:Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Center for Translational Cancer Research, TranslaTUM, Technical University of Munich, Munich, Germany
      name:Partner Site Munich and German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
      name:Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Center for Translational Cancer Research, TranslaTUM, Technical University of Munich, Munich, Germany
      name:Partner Site Munich and German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
      name:Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany
      name:Partner Site Munich and German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany

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