Abstract
Background and Goals. To identify a multigene signature model for prognosis of non-small-cell lung cancer (NSCLC) patients, we first found 2146 consensus differentially expressed genes (DEGs) in NSCLC overlapped in Gene Expression Omnibus (GEO) and TCGA lung adenocarcinoma (LUAD) datasets using integrated analysis. We constructed a weighted gene coexpression network (WGCN) using the consensus DEGs and identified the module significantly associated with pathological M stage and consisted of 61 genes. After univariate Cox regression analysis and subsequent stepwise model selection by the Akaike information criterion (AIC) and multivariate Cox hazard model analysis, an mRNA signature model which calculated prognostic score was generated: prognostic score = (−0.2491 × EXPRRAGB) + (−0.0679 × EXPRSPH9) + (−0.2317 × EXPRPS6KL1) + (−0.1035 × EXPRXFP1) + 0.1571 × EXPRRM2 + 0.1104 × EXPRTL1, where EXP is the fragments per kilobase million (FPKM) value of the mRNA included in the model. The prognostic model separated NSCLC patients in the TCGA-LUAD dataset into the low- and high-risk score groups with a median prognostic score of 0.972. Higher scores predicted higher risk. The area under ROC curve (AUC) was 0.994 or 0.776 in predicting the 1- to 10-year survival of NSCLC patients. The prognostic performance of this prognostic model was validated by an independent GSE11969 dataset of NSCLC adenocarcinoma with AUC values between 0.822 and 0.755 in predicting 1- to 10-year survival of NSCLC. These results suggested that the six-gene signature functioned as an independent biomarker to predict the overall survival of NSCLC adenocarcinoma.
Highlights
Lung cancer (LC) is one of the leading causes of cancerassociated deaths worldwide [1, 2]
A total of 14 Gene Expression Omnibus (GEO) datasets regarding Non-small-cell lung cancer (NSCLC) were collected, including GSE19188, GSE30219, GSE10072, GSE7670, GSE2514, GSE32863, GSE21933, GSE40275, GSE12472, GSE80796, GSE8500, GSE85841, GSE19027, and GSE11969. e TCGA-lung adenocarcinoma (LUAD) RNAseq data and clinical data of the NSCLC and ANT samples were downloaded from the TCGA data portal (Table 1) [13,14,15]. ese datasets were processed and analyzed by following the workflow in Figure 1. is workflow was set up based on the published literature [16, 17]
EXPRXFP1) + 0.1571 × EXPRRM2 + 0.1104 × EXPRTL1, where EXP is the fragments per kilobase million (FPKM) value of the mRNA included in the model
Summary
Lung cancer (LC) is one of the leading causes of cancerassociated deaths worldwide [1, 2]. A multiparameter molecular signature provides wider insights into the heterogeneous nature of cancer including NSCLC and may more reliably predict survival and benefit from chemotherapy of cancer patients than a single prognostic biomarker and/or staging system. To identify a multigene signature model for prognosis of non-small-cell lung cancer (NSCLC) patients, we first found 2146 consensus differentially expressed genes (DEGs) in NSCLC overlapped in Gene Expression Omnibus (GEO) and TCGA lung adenocarcinoma (LUAD) datasets using integrated analysis. E prognostic model separated NSCLC patients in the TCGA-LUAD dataset into the low- and high-risk score groups with a median prognostic score of 0.972. E prognostic performance of this prognostic model was validated by an independent GSE11969 dataset of NSCLC adenocarcinoma with AUC values between 0.822 and 0.755 in predicting 1- to 10-year survival of NSCLC. Higher scores predicted higher risk. e area under ROC curve (AUC) was 0.994 or 0.776 in predicting the 1- to 10-year survival of NSCLC patients. e prognostic performance of this prognostic model was validated by an independent GSE11969 dataset of NSCLC adenocarcinoma with AUC values between 0.822 and 0.755 in predicting 1- to 10-year survival of NSCLC. ese results suggested that the six-gene signature functioned as an independent biomarker to predict the overall survival of NSCLC adenocarcinoma
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
