Identifying Molecular Biomarker for the Lung Squamous Cell Carcinoma by Integrating Multifactorial Data

Lacking of diagnostic biomarker for early diagnosis leads to the poor survival rate of lung squamous cell carcinoma (LUSC). Nowadays, development of high throughput technologies provides a critical timing for identifying molecular biomarkers by integrating multifactorial data. In this work, we have integrated the survival data and multifactorial data (transcription factors, microRNAs and gene ontology terms) to analyze the underling progression mechanism of the LUSC and attempt to identify the novel survival-associated biomarkers. We found 298 candidate survival-associated genes correlated with patient survival data using univariate Cox proportional hazards regression model. These survival-associated genes have been significantly regulated by 18 transcription factors and 20 microRNAs, enriched within 19 gene ontology terms. Integrating these information, we identified five survival-associated genes (BAX, BCL6, APP, IL10, BBC3) simultaneously correlation with LUSC survival data, indicating novel biomarkers for earlier detection of LUSC.