Systematic analysis of ferroptosis-related long non-coding RNA predicting prognosis in patients with lung squamous cell carcinoma

Abstract

Background: Ferroptosis is a novel iron-dependent cell death, and an increasing number of studies have shown that long non-coding RNA (lncRNAs) are involved in the ferroptosis process. However, studies on ferroptosis-related lncRNAs in lung squamous cell carcinoma (LUSC) are limited. In addition, the prognostic role of ferroptosis-related lncRNAs and their relationship with the immune microenvironment and methylation of LUSC is unclear. This study aimed to investigate the potential prognostic value of ferroptosis-related lncRNAs and their involved biological functions in LUSC.

Methods: The Cancer Genome Atlas (TCGA) database and the FerrDb website were used to obtain ferroptosis-related genes for LUSC. The "limma" R package and Pearson analysis were used to find ferroptosis-related lncRNAs. The biological functions of the characterized lncRNAs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We evaluated the prognostic power of this model using Kaplan-Meier analysis, receiver operating characteristic (ROC), and decision curve analysis (DCA). Univariate and multifactor Cox (proportional-hazards) risk model and a nomogram were produced using risk models and clinicopathological parameters for further verification. In addition, the relationship between characterized lncRNAs and tumor immune infiltration and methylation was also discussed.

Results: We identified 29 characterized lncRNAs to produce prognostic risk models. Kaplan-Meier analysis revealed the high-risk group was associated with poor prognosis in LUSC (P<0.001), and ROC (AUC =0.658) and DCA suggested that risk models could predict prognosis. Univariate and multifactorial Cox as well as nomogram further validated the prognostic model (P<0.001). Gene set enrichment analysis (GSEA) showed that the high-risk group was associated with pro-tumor pathways and high-frequency mutations in TP53 were present in both groups. Single sample gene set enrichment analysis (ssGSEA) showed significant differences in immune cell infiltration subtypes and corresponding functions between the two groups. Some immune checkpoint and methylation-related genes were significantly different between the two groups (P<0.05).

Conclusions: We investigated the potential mechanisms of LUSC development from the perspective of ferroptosis-related lncRNAs, providing new insights into LUSC research, and identified 29 lncRNAs as biomarkers to predict the prognosis of LUSC patients.

Keywords: Ferroptosis; long non-coding RNA (lncRNA); lung squamous cell carcinoma (LUSC); prognostic signature.

Figure 1

Flow chart for bioinformatics analysis. TCGA, The Cancer Genome Atlas; LUSC, lung squamous cell carcinoma; lncRNA, long non-coding RNA; FDR, false discovery rate; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 2

Functional enrichment analysis of ferroptosis-related DEGs. (A) GO analysis results showed the enriched biological processes, cell components, and molecular functions associated with DEGs. (B) KEGG pathway analysis results showed the enriched signaling pathways associated with DEGs. NADPH, nicotinamide adenine dinucleotide phosphate; BP, biological process; CC, cellular component; MF, molecular function; HIF-1, hypoxia-inducible factor-1; DEG, differentially expressed gene; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 3

Prognostic analysis of the ferroptosis-related lncRNAs in LUSC patients based on TCGA. (A) Kaplan-Meier survival curve analysis showed the survival time of patients with high-risk scores based on the ferroptosis-related lncRNAs prognostic signature was significantly poorer than those with low-risk scores (P<0.001). (B) ROC curve analysis showed the prognostic accuracy of ferroptosis-related lncRNAs prognostic risk scores and clinicopathological parameters such as age, gender, and stage. (C) Patients were ranked according to the risk score, and the correlation between survival time and risk scores was demonstrated using scatter plots. Heatmap shows the correlation between characteristic lncRNAs and the risk model. (D) The time-dependent ROC curves for 1-, 2-, and 3-year OS predictions by the risk score model in the TCGA-LUSC cohort. (E) The DCA of ferroptosis-related lncRNAs prognostic risk scores and clinicopathological parameters such as age, gender, and stage. AUC, the area under the curve; lncRNA, long non-coding RNA; LUSC, lung squamous cell carcinoma; TCGA, The Cancer Genome Atlas; ROC, receiver operating characteristic; DCA, decision curve analysis; OS, overall survival.

Figure 4

Estimation of the accuracy of the ferroptosis-related lncRNAs prognostic signature in LUSC patients. Univariate Cox regression analysis showed the correlation between OS and various clinicopathological parameters such as age, gender, and stage, and ferroptosis-related lncRNAs prognostic signature risk score. The remaining parameters (P<0.05) were associated with OS in addition to gender, where the risk score was statistically significant (P<0.001). Multifactor Cox regression analysis showed age, stage, and risk score were prognostic indicators for OS rates of LUSC patients, in which the risk score was statistically significant (P<0.001). lncRNA, long non-coding RNA; LUSC, lung squamous cell carcinoma; OS, overall survival.

Figure 5

Heatmap for ferroptosis-related lncRNAs prognostic signature and clinicopathological features. N, M, T, stage, age, and gender are shown as patient annotations. lncRNA, long non-coding RNA.

Figure 6

Construction of the prognostic nomogram with ferroptosis-related lncRNAs prognostic signature risk score, and clinicopathological features. The red arrow is a sample that predicted the 1-, 3-, and 5-year survival rates of LUSC patients. Adjusted P values are shown as: *, P<0.05; **, P<0.01; ***, P<0.001. lncRNA, long non-coding RNA; LUSC, lung squamous cell carcinoma.

Figure 7

Molecular characteristics of patients in the high- and low-risk group. (A,B). The GSEA analysis in the high- and low-risk group to enrich characteristic gene sets (P<0.05, FDR <0.29). (C,D) Significantly mutated genes in LUSC patients in the high- and low-risk group. The top 10 mutated genes in each group ranked by mutation rate are shown. The mutation rate is shown on the right, and the mutation counts are shown on the top. TMB, tumor mutation burden; NA, no answer; GSEA, gene set enrichment analysis; FDR, false discovery rate; LUSC, lung squamous cell carcinoma.

Figure 8

Heatmap for TIICs based on TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCP-counter, XCELL, and EPIC algorithms among high- and low-risk groups. TIICs, tumor-infiltrating immune cells; TIMER, Tumor Immune Estimation Resource; MCP-counter, Microenvironment Cell Populations-counter.

Figure 9

Immune characteristics of ferroptosis-related lncRNAs prognostic signature. (A) ssGSEA for the association between immune cell subpopulations and related functions. (B) Expression of immune checkpoints between the two groups. Adjusted P values are shown as: ns, not significant; *, P<0.05; **, P<0.01; ***, P<0.001. APC, antigen-presenting cell; CCR, chemokine receptor; HLA, human leukocyte antigen; MHC, major histocompatibility complex; IFN, interferon; lncRNA, long non-coding RNA; ssGSEA, single-sample Gene Set Enrichment Analysis.

Figure 10

Expression of m6A-related genes between different groups. The expression differences of YTHDF1, YTHDC1, HNRNPC, YTHDC2, METTL3, ALKBH5, and FTO were statistically significant. Adjusted P values are shown as: ns, not significant; *, P<0.05; **, P<0.01; ***, P<0.001.