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

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.