Analysis And Mining Of Lung Cancer Prognostic Markers Based On Bioinformatics Methods

Background:Lung cancer is the most frequent malignancy and the leading cause of cancer-related death all over the world.Five-year survival rate for lung cancer patients ranges from 4%to 17%depending on disease stage and regional differences.Non-small cell lung cancer(NSCLC),which is mainly comprised of lung adenocarcinoma,lung squamous cell carcinoma and large cell carcinoma,is the most common pathological type of lung cancer and responsible for 85%to 90%of all lung cancer.Integrins function as bridges between the extracellular matrix(ECM)and the cytoskeleton and work as radars to detect changes in the cellular microenvironment,which enables cells to react according the external milieu.They play a crucial role in the regulation process of cell proliferation,migration,differentiation,tumor invasion and metastasis.Several studies have reported that ITGA11,ITGB4 and ITGB8 shown aberrant expression profiles in different cancers,which might be involved in cancer cell invasion and metastasis.However,the expression profiles and clinical significance of ITGA11,ITGB4 and ITGB8 in NSCLC remains unclear.Objective:To analyze the expression pattern and prognosis value of ITGA11,ITGB4 and ITGB8 in lung adenocarcinoma(LUAD)and lung squamous cell carcinoma(LUSC)using online databases,platforms,and various datasets.Methods:The ONCOMINE database and GEPIA platform were used to analyze the expression levels of ITGA11,ITGB4 and ITGB8 in LUAD and LUSC.In addition,we downloaded and analyzed the original expression data of TCGA-LAUD and TCGA-LUSC datasets and a mass of gene expression datasets from GEO database to further validate the expression profiles of ITGA11,ITGB4 and ITGB8.Moreover,Kaplan-Meier survival analysis and cox regression analysis were performed in R to explore the prognostic values of ITGA11,ITGB4 and ITGB8 in LUAD and LUSC.Results:The expression levels of ITGA11 and ITGB4 were significantly higher in tumor tissues compared with normal tissues derived from ONCOMINE and GEPIA analysis results,while the expression level of ITGB8 was significantly regulated in LUSC.ITGB4 was significantly associated with OS in LUAD and could be an independent prognostic factor for patients with LUAD.Conclusion:Compared with normal lung tissues,the expression levels of ITGA11 and ITGB4 were significantly higher in LUAD and LUSC tissues.ITGB8 was significantly upregulated in LUSC.Besides,ITGB4 was significantly related with the overall survival in LUAD and might serve as a potential prognostic biomarker for patients with LUAD.Bacl^round: Lung cancer is the leading cause of cancer-related deaths worldwide and remains the important malignant tumor that threatening human life.Lung adenocarcinoma(LUAD)accounts for nearly 40% of lung cancer and its incidence is still increasing.Although the treatment design and strategies have improved a lot these years,the prognosis of LAUD patients is still poor.This study aimed to construct a more accurately prognosis prediction model for patients with LUAD which could help making individualized management and hold promise for improving survival for LUAD patients.Methods: The gene expression datasets of GSE75037 and GSE32863 and TGCALUAD were downloaded and preprocessed for the study.The corresponding gene annotation files were used for transforming the probes into gene symbol and extracting genes.The differentially expressed genes were obtained using limma and DESeq2 package,respectively.Then,we conduct Gene ontology(GO)enrichment and KEGG(Kyoto Encyclopedia of Genes and Genomes)pathway analysis for the overlapped upregulated and down-regulated differentially expression genes.The univariate and multivariate cox hazard ratio regression model was performed to analyzed and extract the significantly prognostic genes within the overlapped differentially expressed genes* Finally,the prognostic prediction model for patients with LUAD was developed based on a risk score method.Kaplan-Meier survival analysis and time dependent receiver characteristics curves were used for assessing the prognostic value of the model.Results: The overlapped up-regulated and down-regulated genes were 295 and 484,respectively.Univariate and Multivariate cox regression analysis revealed that 11 genes were significantly associated with overall survival in LUAD patients.The prognostic model could efficiently predict prognosis for patients with LUAD.Besides,this prognostic model was an independent prognostic factor for LUAD.Conclusion: The risk score prognosis model based on 11 genes could efficiently make survival prediction for LUAD and serve as an independent prognostic factor.In addition,a prediction model by combining the risk score prognostic model with tumor stage could more accurately evaluate the prognosis of LUAD patients.