Development and validation of prognostic models based on cell cycle-related signatures for predicting the prognosis of patients with lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) represents the most prevalent histological subtype within lung cancer. Nevertheless, the risk of postoperative metastasis and recurrence remains a substantial concern. We aimed to build the cell cycle-related competing endogenous RNA (ceRNA) networks and potential prognosis prediction models of LUAD, which might provide a valuable reference for studying the prognosis of LUAD.

Methods: The RNA sequencing data of LUAD were procured from The Cancer Genome Atlas (TCGA) database and the differentially expressed RNAs were identified from the Ensembl genome browser 96 database [P<0.05 and |log2 fold change (FC)| >1]. The gene expression profile data were acquired from the Gene Expression Omnibus (GEO) repository. A gene set variation analysis was carried out to determine the differentially expressed genes (DEGs) (P<0.05) and a cell cycle-related ceRNA network of LUAD was constructed based on the DEGs. Least absolute shrinkage and selection operator (LASSO) analysis was conducted to acquire the optimized gene combination, a risk score (RS) prognostic risk prediction model was generated subsequently, and a Kaplan-Meier curve was developed to evaluate the efficacy of the RS model. Moreover, we constructed the 3- and 5-year prognostic models of nomogram using R3.6.1 "rms" package, the C-index was counted for accessing predictive capacity. Receiver operating characteristic (ROC) curves were used to evaluate the multiple prognostic risk prediction model.

Results: In total, we identified 240 DEGs and constructed the cell cycle-related ceRNA network of LUAD from datasets GSE50081 and GSE37745. Six optimal genes (ADRB2, IL1A, PIK3R2, CKD1, CCNB1 and CHRNA5) related to prognostic were obtained. The C-index values for 3- and 5-year prognostic nomogram models were 0.7665 and 0.7104, respectively, indicating highly accurate predictive capabilities. The area under the curve (AUC) of the combination of RS and clinical factors prognostic risk prediction model was 0.869 in TCGA and 0.770 in GSE50081 dataset.

Conclusions: This research identified six prognostic biomarkers and built the prognostic prediction models of LUAD, which may enhance the comprehension of disease biology, serve as an effective prognostic tool for LUAD and drive novel therapy development potentially.