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

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.