An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma

Abstract

Aging is an inevitable process characterized by a decline in many physiological activities, and has been known as a significant risk factor for many kinds of malignancies, but there are few studies about aging-related genes (ARGs) in lung squamous carcinoma (LUSC). We designed this study to explore the prognostic value of ARGs and establish an ARG-based prognosis signature for LUSC patients. RNA-sequencing and corresponding clinicopathological data of patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492). Furthermore, the GSE73403 dataset (n = 69) validated the prognostic performance of this ARG signature. Immunohistochemistry (IHC) staining was used to verify the expression of the ARGs in the signature. A five ARG-based signature, including A2M, CHEK2, ELN, FOS, and PLAU, was constructed in the TCGA dataset, and stratified patients into low- and high-risk groups with significantly different overall survival (OS) rates. The ARG risk score remained to be considered as an independent indicator of OS in the multivariate Cox regression model for LUSC patients. Then, a prognostic nomogram incorporating the ARG risk score with T-, N-, and M-classification was established. It achieved a good discriminative ability with a C-index of 0.628 (95% confidence interval [CI]: 0.586–0.671) in the TCGA cohort and 0.648 (95% CI: 0.535–0.762) in the GSE73403 dataset. Calibration curves displayed excellent agreement between the actual observations and the nomogram-predicted survival. The IHC staining discovered that these five ARGs were overexpression in LUSC tissues. Besides, the immune infiltration analysis in the TCGA cohort represented a distinctly differentiated infiltration of anti-tumor immune cells between the low- and high-risk groups. We identified a novel ARG-related prognostic signature, which may serve as a potential biomarker for individualized survival predictions and personalized therapeutic recommendation of anti-tumor immunity for patients with LUSC.

Determination of a prediction model for therapeutic response and prognosis based on chemokine signaling-related genes in stage I–III lung squamous cell carcinoma

“…Furthermore, we compared the performance of the present signature with those of the other previously reported gene signatures in LUSC. The RMS curve showed that the performance of the present signature was similar to that of the other four signatures (Supplementary Figure S3C) (Huang et al, 2021;Li et al, 2022;Zhai et al, 2022). These results suggest that the CSRG signature can serve as a candidate prognostic biomarker for survival in patients with stage I-III LUSC.…”

A Novel Prognosis Signature Based on Ferroptosis-Related Gene DNA Methylation Data for Lung Squamous Cell Carcinoma

“…e risk prognosis model based on 31 methylation sites of FRGs was superior to the model based on clinicopathologic features in the prediction of 1-, 3-, 5-, and 10-year OS in LUSC. In addition, the accuracy and efficiency of this risk prognosis model were significantly superior to other published models [17,18,[27][28][29][30][31] for LUSC patients.…”

“…In addition, the nomogram was tested by proportional hazard assumption to analyze that whether the risk score included in our model was independent prognostic indicators. To evaluate the accuracy and effectiveness of our model, 7 prognostic models of LUSC from published papers [17,18,[27][28][29][30][31] were reviewed and studied with a comparison. Net reclassification index (NRI) and integrated discrimination improvement (IDI) are the common algorithms used to assess the model in recent years [32,33].…”

Mining Database to Identify Aging-Related Molecular Subtype and Prognostic Signature in Lung Adenocarcinoma

“…Another study also constructed a prognostic signature based on 9-aging related genes for acute myeloid leukemia [ 26 ]. Moreover, another bioinformatics study also developed an aging-related signature, which had a good performance in risk stratification and prognosis prediction in lung squamous carcinoma [ 27 ].…”