A novel model for identification of prognostic indicator for clinical outcome of squamous cell lung carcinoma
- 199 Downloads
Squamous cell carcinoma of the lung (SCCL) is the most common and aggressive lung tumor with poor clinical outcome. Identification and development of potential genes in prognostic process could be beneficial for clinical management. Sequencing data of 300 SCCL samples at level 3 were downloaded from The Cancer Genome Atlas (TCGA) data portal. Single-factor survival analysis was performed by the Kaplan-Meier method. Functional annotation was conducted on the high-frequency genes filtered out by 1000 times of the least absolute shrinkage and selectionator operator regression analysis. Meanwhile, multi-factor survival analysis was conducted and ROC curve were produced. Risk coefficient and expression level of each gene were used in the division of high-risk and low-risk genes. The number of high-risk genes of each sample was obtained, and the survival condition of different samples was analyzed. Finally, the number of optimal high-risk genes was obtained. Seven thousand nine hundred ninety-eight differential expressed mRNAs were obtained, and 2041 potential prognostic genes were screened out. Twenty one of the 22 high-frequency genes were showed to have significant impact on prognostic process. Single-factor analysis was performed on the 22 models, and eight efficient models were obtained, and seven among them were proven to be significant. By random testing, ≥5 genes and ≥6 genes were proven to be most stable and ≥6 genes were finally recognized as the beneficial indicator to distinguish lung squamous cell carcinoma. Twenty-two potential genes differentially expressed in lung squamous cell carcinoma were identified as potential prognostic indicator in clinical outcome, and the novel model in this study could be applied in other cancer types.
KeywordsLung squamous cell carcinoma TCGA Survival analysis LASSO regression
- 7.Zudaire I, Lozano MD, Vazquez M, Pajares MJ, Agorreta J, Pio R, Zulueta JJ, Yankelevitz D, Henschke C, Montuenga LM. Molecular characterization of small peripheral lung tumors based on the analysis of fine needle aspirates. 2008.Google Scholar
- 17.Heagerty PJ, Lumley T, Pepe MS. Time-dependent roc curves for censored survival data and a diagnostic marker. Biometrics 2000: 337–344.Google Scholar
- 24.Nevins JR, Harpole D, Potti A, West M, Dressman H. Prediction of lung cancer tumor recurrence. Google Patents, 2007.Google Scholar