A novel ferroptosis-related lncRNA signature for prognosis prediction in gastric cancer

Abstract

Background: Gastric cancer (GC) is a common malignant cancer with a poor prognosis. Ferroptosis has been shown to play crucial roles in GC development. Long non-coding RNAs (lncRNAs) is also associated with tumor progression in GC. This study aimed to screen the prognostic ferroptosis-related lncRNAs and to construct a prognostic risk model for GC.

Methods: Ferroptosis-related lncRNAs from The Cancer Genome Atlas (TCGA) GC expression data was downloaded. First, single factor Cox proportional hazard regression analysis was used to select seven prognostic ferroptosis-related lncRNAs from TCGA database. And then, the selected lncRNAs were further included in the multivariate Cox proportional hazard regression analysis to establish the prognostic model. A nomogram was constructed to predict individual survival probability. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the risk model.

Results: We constructed a prognostic ferroptosis-related lncRNA signature in this study. Kaplan-Meier curve analysis revealed a significantly better prognosis for the low-risk group than for the high-risk group (P = 2.036e-05). Multivariate Cox proportional risk regression analysis demonstrated that risk score was an independent prognostic factor [hazard ratio (HR) = 1.798, 95% confidence interval (CI) =1.410-2.291, P < 0.001]. A nomogram, receiver operating characteristic curve, and principal component analysis were used to predict individual prognosis. Finally, the expression levels of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines and normal cell lines were tested by qRT-PCR.

Conclusions: This risk model was shown to be a novel method for predicting prognosis for GC patients.

Keywords: Bioinformatics; Ferroptosis; Gastric cancer; Long non-coding RNA; Prognosis.

Fig. 1

Workflow of the prognostic risk model analysis

Fig. 2

Outcomes of the four ferroptosis-related-lncRNA model in all the samples. (A) HR and 95% CI of the seven top lncRNAs using univariate Cox regression. (B) The distribution of the four ferroptosis-related lncRNA expression profiles of patients in all samples. (C) The distributions of the risk scores in all samples. (D) The distribution of the follow-up time in all samples

Fig. 3

Correlation between the four ferroptosis-related lncRNAs and clinical features. The relationship between the expression of the four ferroptosis-related lncRNAs and (A) T, (B) N, (C) gender, (D) M, (E) age, and (F) grade. NS: Not Significant, *: P < 0.05. Note: T: Tumor, classified into T1, T2, T3, T4; N: Node, classified into N1, N2, N3; M: metastasis, classified into M0, M1

Fig. 4

Univariate and multiple regression analysis of the ferroptosis-related lncRNA signature for GC. Results of the (A) univariate and (B) Multivariate Cox regression analysis show the effects of clinical factors and risk score in all samples. Results of the (C) survival analysis show the prognosis of high-risk and low-risk patients. (D) The ROC for risk-score, age, grade, stage, T, N, and M with OS for GC cohorts

Fig. 5

Principal component analysis. Results of the principal component analysis between low-risk and high-risk groups based on the expression of all genes (A), ferroptosis-related genes (B), and lncRNAs (C), and the four lncRNAs of the prognostic model (D)

Fig. 6

An individualized prediction model for determining the overall survival (OS) of GC patients. (A) Nomogram construction for predicting the 1-, 2- and 3- year OS of GC patients. (B - D) Calibration curve analysis

Fig. 7

The expression of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines