Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

Abstract

Background: The development of human tumors is associated with the abnormal expression of various functional genes, and a massive tumor-based database needs to be deeply mined. Based on a multigene prediction model, access to urgent prognosis of patients has become possible. Materials and Methods: We selected three RNA expression profiles (GSE32863, GSE10072, and GSE43458) from the lung adenocarcinoma (LUAD) database of the Gene Expression Omnibus (GEO) and analyzed the differentially expressed genes (DEGs) between tumor and normal tissue using GEO2R program. After that, we analyzed the transcriptome data of 479 LUAD samples (54 normal tissue samples and 425 cancer tissue samples) and their clinical follow-up data from the (TCGA) database. Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) were used to assess the prediction model. Multivariate Cox analysis was used to identify independent predictors. TCGA pancreatic adenocarcinoma datasets were used to establish a nomogram model. Results: We found 98 significantly prognosis-related genes using KM and COX analysis, among which six genes were found to be the DEGs in GEO. Using multivariate analysis, it was found that a single gene could not be used as an independent predictor of prognosis. However, the risk score calculated by weighting these six genes could serve as an independent prognosis predictor. COX analysis performed with multiple covariates such as age, gender, tumor stage, and TNM typing showed that risk score could still be utilized as an independent risk factor for patient survival rate (p = 0.013) and had an applicable reliability (area under the curve, AUC = 0.665). By combining risk score and various clinical features, the nomogram model was constructed, which had been proven to have high consistency for the prediction of 3-and 5-year survival rate (concordance = 0.751) and high accuracy as tested by ROC (AUC = 0.71;AUC = 0.708). Conclusion: We proposed a method to predict the prognosis of LUAD by weighting multiple genes and constructed a nomogram model suitable for the prognostic evaluation of LUAD, which could provide a new tool for the identification of therapeutic targets and the efficacy evaluation of LUAD.

Functional Effects of Four or Fewer Critical Genes Linked to Lung Cancers and New Subtypes Detected by A New Machine Learning Classifier

“…On the other hand, gene expression profiling holds promise to improve clinical stratification and paves the way for individualized therapy. Zhou et al 14 used a nomogram model to identify six key genes which were combined with various clinical features. The nomogram model led to high consistency for the prediction of 3-and 5-year survival rate (concordance = 0.751) and high accuracy as tested by ROC (AUC = 0.71; AUC = 0.708).…”

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

“…Analyzing GEO datasets of different populations may find different hub genes. A GEO data analysis of the American LUAD population found six hub genes ( VIPR1, FCN3, CA4, CRTAC1, CYP4B1 , and NEDD9 ) related to prognosis ( Jiawei et al, 2020 ). Another study on LUAD populations in Japan and USA found eight hub genes ( GPX3, TCN1, ASPM, PCP4, CAV2, S100P, COL1A1, and SPOK2 ) ( Tu et al, 2021 ).…”

Construction and Validation of a Novel Immune-Related Gene Pairs-Based Prognostic Model in Lung Adenocarcinoma

“… 1 Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer worldwide, and the 5-year survival rate of LUAD patients is less than 20% and the prognosis is poor. 2 , 3 The classical treatments include surgical resection, postoperative chemotherapy, and postoperative radiotherapy. Since severe damage to the organism caused by radiotherapy and chemotherapy, various targeted drugs aiming at different genetic variants have emerged.…”