Integrative public data-mining pipeline for the validation of novel independent prognostic biomarkers for lung adenocarcinoma.
Susanne GhandiliTim OquekaMelanie SchmitzMelanie JanningJakob KörbelinC Benedikt WestphalenSebastian P HaenSonja LogesCarsten BokemeyerHans KloseJan K HennigsPublished in: Biomarkers in medicine (2021)
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.
Keyphrases
- copy number
- free survival
- lymph node
- mitochondrial dna
- binding protein
- big data
- prognostic factors
- electronic health record
- healthcare
- genome wide
- meta analyses
- systematic review
- mental health
- poor prognosis
- randomized controlled trial
- data analysis
- rna seq
- molecular docking
- gene expression
- early stage
- bioinformatics analysis
- adverse drug
- social media