Integrative public data-mining pipeline for the validation of novel independent prognostic biomarkers for lung adenocarcinoma

Ghandili, Susanne; Oqueka, Tim; Schmitz, Melanie; Janning, Melanie; Koerbelin, Jakob; Westphalen, C. Benedikt; P Haen, Sebastian; Loges, Sonja; Bokemeyer, Carsten; Klose, Hans und K Hennigs, Jan (2020): Integrative public data-mining pipeline for the validation of novel independent prognostic biomarkers for lung adenocarcinoma. In: Biomarkers in Medicine, Bd. 14, Nr. 17: S. 1651-1662

Abstract

Aim: We aimed to develop a candidate-based integrative public data mining strategy for validation of novel prognostic markers in lung adenocarcinoma. Materials & methods: An in silico approach integrating meta-analyses of publicly available clinical information linked RNA expression, gene copy number and mutation datasets combined with independent immunohistochemistry and survival datasets. Results: After validation of pipeline integrity utilizing data from the well-characterized prognostic factor Ki-67, prognostic impact of the calcium- and integrin-binding protein, CIB1, was analyzed. CIB1 was overexpressed in lung adenocarcinoma which correlated with pathological tumor and pathological lymph node status and impaired overall/progression-free survival. In multivariate analyses, CIB1 emerged as UICC stage-independent risk factor for impaired survival. Conclusion: Our pipeline holds promise to facilitate further identification and validation of novel lung cancer-associated prognostic markers.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1752-0363
• Sprache: Englisch