Identification of diagnostic and prognostic lncRNA biomarkers in oral squamous carcinoma by integrated analysis and machine learning.

Background Patients with oral squamous carcinoma (OSCC) present difficulty in precise diagnosis and poor prognosis. Objective We aimed to identify the diagnostic and prognostic indicators in OSCC and provide basis for molecular mechanism investigation of OSCC. Methods We collected sequencing data and clinical data from TCGA database and screened the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) in OSCC. Machine learning and modeling were performed to identify the optimal diagnostic markers. In order to determine lncRNAs with prognostic value, survival analysis was performed through combing the expression profiles with the clinical data. Finally, co-expressed DEmRNAs of lncRNAs were identified by interacted network construction and functional annotated by GO and KEGG analysis. Results A total of 1114 (345 up- and 769 down-regulated) DEmRNAs and 156 (86 up- and 70 down-regulated) DElncRNAs were obtained in OSCC. Following the machine learning and modeling, 15 lncRNAs were identified to be the optimal diagnostic indicators of OSCC. Among them, FOXD2.AS1 was significantly associated with survival rate of patients with OSCC. In addition, Focal adhesion and ECM-receptor interaction pathways were found to be involved in OSCC. Conclusions FOXD2.AS1 might be a prognostic marker for OSCC and our study may provide more information to the further study in OSCC.