Construction of a cuproptosis-tricarboxylic acid cycle-associated lncRNA model to predict the prognosis of non-small cell lung cancer

Introduction

The main pathological types of non-small cell lung cancer (NSCLC) are lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) (1). In addition, NSCLC makes up 85% of the total lung cancer incidence (2). Currently, cancer-related deaths are dominated by lung cancer, and only 21.7% of patients diagnosed with this disease survive for more than 5 years (3). Therefore, accurate predictions of NSCLC prognosis can help provide different treatment options.

On the basis numerous molecular-level studies using tumor cells, NSCLC treatment has changed from conventional radiotherapy and chemotherapy combined with surgery to molecular-targeted immunotherapy. In patients with epidermal growth factor receptor (EGFR)-mutated NSCLC, tyrosine kinase inhibitors have become the first-line treatment (4-6). These drugs can regulate the tumor microenvironment (TME) to enhance the ability of T cells to kill cancer cells (7), thereby achieving good efficacy. Accordingly, immunotherapy effectiveness in NSCLC patients is closely associated with TME (8). Therefore, studying the TME is beneficial for predicting the responsiveness of patients with NSCLC to immunotherapy.

Recently, a new method of programmed cell death has been reported, and this copper-dependent mechanism is known as cuproptosis. Copper is a trace element required for human biological functions, and an appropriate amount of copper, functioning as a cofactor, is required for metabolic processes in the body, such as superoxide disproportionation, oxygen free radical scavenging, pigmentation, and catecholamine metabolism. When copper ion homeostasis is disrupted, metabolism becomes abnormal. An excess of copper ions promotes the formation of tumors through the regulation of mitochondrial respiration, the immune system, antioxidant defense, and apoptosis (9). Antioxidant-1 stimulates the proliferation of NSCLC cells by transporting copper ions (10), leading to a significant increase in serum copper levels in patients with lung cancer (11). Further, water-soluble copper (II) complexes increase copper ion concentrations in the body and jointly promote lung cancer cell death, along with human copper transporter 1 (CTR1) (12). Copper ion metabolism imbalances are closely linked to lung cancer development and its immune microenvironment. Copper ions can affect the occurrence of LUAD and the TME through lysyl oxidase-like 2 (LOXL2), solute carrier family 31, member 2 (SLC31A2), and superoxide dismutase 3 (SOD3) (13). Further, high concentrations of copper ions in patients with lung cancer were found to promote tumor angiogenesis and metastasis (11,14), whereas Copper ion reduction can increase the infiltration of Immune cell (15) and improve cytotoxicity against tumor cells in the body. The downregulation of amine oxidase copper containing 3 expression in lung cancer cells can reduce the migration of CD4⁺ T cells to the lung tissue, thus promoting lung cancer progression (16). Studies on copper ion complexes as anticancer agents for the treatment of cancer have also been performed (17,18). Further, glucose restriction therapy can upregulate expression of the copper ion transporter CTR1, thus delaying the progression of NSCLC (19). Jiang et al. also found that copper nanoparticle reagents can inhibit lung cancer cell migration and improve cisplatin resistance by regulating the TME (20). In addition, it has been established that copper ions can serve as prognostic factors for patients with lung cancer. Specifically, studies have shown that copper levels in patients with advanced lung cancer are higher than those in patients with early lung cancer, suggesting that an increase in copper ion levels is related to the risk of lung cancer (21,22).

Cuproptosis is closely associated with the tricarboxylic acid (TCA) cycle, as excessive copper ions can bind to fatty acylation-associated components of TCA to inhibit the TCA cycle. This results in the loss of iron-sulfur (Fe-S)-cluster proteins, which then triggers protein-associated toxic stress and finally accelerates cell death (23). Excess acetyl-CoA in the TCA cycle competitively inhibits dihydrolipoamide s-acetyltransferase (DLAT) activity, resulting in cuproptosis, and these two factors influence each other. Copper ions can also regulate tumor growth by targeting TCA-cycle proteins. Tsvetkov et al. found that copper directly binds lipid acylated components of the TCA cycle, resulting in the aggregation of lipid acylated proteins and the loss of Fe-S-cluster proteins, which then increases toxic protein stress and ultimately accelerates cell death (23). Active TCA circulation is closely associated with lung cancer (24), and a direct relationship exists between TCA cycle gene expression and NSCLC occurrence and prognosis (25-27). Simultaneously, lactic acid participates in NSCLC tumor cell metabolism as a carbon source for the TCA cycle (28). Copper ions as well as the TCA cycle all play a role in NSCLC.

Long non-coding RNAs (lncRNAs) are transcripts of non-coding proteins that are longer than 200 nucleotides (29) and are closely related to proliferation, migration, and prognosis in NSCLC (30,31). Therefore, they have gradually emerged as novel biomarkers for predicting cancer prognosis. For example, lncRNA translation regulatory long non-coding RNA 1 (TRERNA1) can promote the proliferation of NSCLC cells by regulating forkhead box l1 (FOXL1) expression. In patients with this disease, TRERNA1 is linked to a poor prognosis (32). Numerous studies have also found that lncRNAs associated with cuproptosis can be used as prognostic factors for cancers, such as liver cancer, renal clear cell carcinoma, and soft tissue sarcoma (33-35). Cuproptosis-TCA-related lncRNAs are associated with tumor immunotherapeutic response and have an important role in predicting immunotherapeutic response. Binxiang Chu constructed a new risk-prognostic model and found that cuprotosis-associated lncRNAs were involved in the development of sarcoma and assisted in the formation of the tumor immune microenvironment, which could identify the prognosis of sarcoma patients (36). Four cuprotosis-TCA-associated lncRNAs, including GIHCG and AC145343.1, are high-risk lncRNAs in patients with hepatocellular carcinoma, and they are involved in the regulation of immune cell infiltration, and patients with their high expression are more responsive to immunosuppressants (33). AC145343.1, a cuproptosis-TCA-associated lncRNA, is associated with the TP53 mutation, is involved in the immune cell infiltration regulation and predicts the prognosis of patients with hepatocellular carcinoma. Cuproptosis-associated lncRNAs may affect the ability of tumor cells to escape immune surveillance by regulating the expression of immune infiltration-related molecules (37,38). If the expression of lncRNA XIST is downregulated in uterine corpus endometrial carcinoma (UCEC), high levels of XIST have a higher survival rate, and the XIST/miR-125a-5p/CDKN2A regulatory axis may be involved in the progression of UCEC (39). However, there are few studies on the association among cuproptosis, cuproptosis-TCA-related lncRNAs, the tumor immune microenvironment (TIME), and NSCLC prognosis. Therefore, a comprehensive analysis of cuproptosis-TCA-related lncRNAs would be beneficial for evaluating the treatment and prognosis of NSCLC and can provide new biomarkers for individualized immunotherapy of NSCLC.

Cuproptosis-TCA-associated lncRNAs in NSCLC were identified to construct a relatively accurate model to predict patient prognosis. This study was also undertaken to understand the role of cuproptosis-TCA-associated lncRNAs in the tumor immune microenvironment of NSCLC. Thus, this study elucidated the molecular mechanism by which cuproptosis-TCA-related lncRNAs affect NSCLC. And new immunotherapy theories will be developed from this research. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-660/rc).

Methods

Data source processing

We used The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/repository) to download the transcriptome studies and clinical data of 1,149 patients with NSCLC. RNA-seq data were normalized to transcripts per kilobase million values. TCGA database was used to download simple nucleotide variation data. In total, additional 123 patients were excluded for lack of clinical information, and clinical data from 1,026 patients with NSCLC were obtained (Table S1). Nineteen cuproptosis-related motifs (Table S2) were obtained from the document search (23,40-44). At the same time, the gene set enrichment analysis (GSEA) database (https://www.gsea-msigdb.org/gsea/index.jsp) was used to download 18 TCA-related genes (ACO2, CS, DLD, DLST, FH, IDH2, IDH3A, IDH3B, IDH3G, MDH2, OGDH, SDHA, SDHB, SDHC, SDHD, SUCLA2, SUCLG1 and SUCLG2), and after the removal of duplicated genes, 35 cuproptosis-TCA-related genes were obtained. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Retrieval and identification of cuproptosis-TCA-related lncRNA data

To identify cuproptosis-TCA-related lncRNAs, we made use of pearson’s correlation analysis to evaluate the correlations between cuproptosis-TCA-related genes and lncRNAs using the “Perl” language. We set the criteria as a Pearson correlation coefficient >0.04 and a P value <0.001. Overlapping lncRNAs were identified as cuproptosis-TCA-related.

Construction and validation of cuproptosis-TCA-associated lncRNA signatures

Twenty-four tumor normal samples were excluded from the clinical data, and 1,002 selected patients were placed randomly in training and testing groups (Figure 1). In the training group, univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression, and multivariate Cox regression analyses (P<0.05) were used to construct the cuproptosis-TCA-related lncRNA signatures. Finally, the multivariate Cox recurrence coefficient (β) was used to calculate the risk score, which was equal to ∑icoefficient(lncRNAi) × expression(lncRNAi). The training, test, and total patients were divided into high- and low-risk groups based on the median risk score. By using the R packages “timeROC” and “survival”, We use time-dependent receiver operating characteristic (time-ROC) curve analysis and Kaplan-Meier survival analysis to compare the overall survival (OS) of the training, trial, and low-risk group from all patients with NSCLC, as well as the clinically graded survival of patients with NSCLC. The signature’s prognostic accuracy was assessed using univariate and multivariate Cox analyses.

Figure 1 Flowchart of study. Clinical samples of 1,149 cases in NSCLC were downloaded, and bioinformatics methods were used to identify 11 cuproptosis-TCA-related lncRNAs associated with the prognosis of NSCLC patients. Based on this prognostic model, NSCLC patients were divided into high- and low-risk groups, and the differences between the two were identified in terms of OS, biologic function, tumor immune microenvironment, and immunotherapeutic responsiveness. NSCLC, non-small cell lung cancer; TCA, tricarboxylic acid; lncRNAs, long noncoding RNAs; OS, overall survival; TCGA, The Cancer Genome Atlas; LASSO, least absolute shrinkage and selection operator.

Prediction of nomogram structure

We then used the “rms” R package to establish a hybrid nomogram model that combined the cuproptosis-TCA-associated lncRNA risk score with clinicopathological features to predict 1-, 3-, and 5-year OS in patients with NSCLC. A calibration curve and consistency index were then used to determine the predictive capability of the nomogram.

Functional enrichment analysis

According to the risk score, all clinical patients were divided into high- and low-risk groups. The R package “enrichplot”, “org.Hs.eg.db”, “ggplot2” and “clusterProfiler” was used to analyze the Gene Ontology (GO) enrichment analysis comparing the high- and low-risk groups following the criteria |log₂fold change (FC)| >2.0, based on a P value <0.01, in addition to Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, based on a P value <0.05.

Microenvironment analysis of tumor-free patients with NSCLC in high- and low-risk groups

Twenty-eight types of tumor-infiltrating cell gene sets were downloaded from the TISIDB database (http://cis.hku.hk/TISIDB/download.php). Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), Tumor Immune Estimation Resource (TIMER), Cell type enrichment analysis based on gene expression (Xcell), Quantitative Tissue Infiltrating Immune Estimation by RNA-seq (quanTIseq), Microenvironment Cell Populations-counter (MCP counter), Estimation of the Proportions of Immune and Cancer cells (EPIC), and Single-sample Gene Set Enrichment Analysis (ssGSEA) algorithms were applied to estimate the infiltration level of immune cell populations. For comparison of the differences between high- and low-risk groups in tumor immune cells and immune functions, the ssGSEA algorithm in gene set enrichment analysis (GSVA) was used.

Analysis of genetic variation in high- and low-risk groups

Using nucleotide variation data from patients with NSCLC downloaded from TCGA database, the R package “maftools” was used to evaluate genetic variation in the high- and low-risk groups of patients with NSCLC.

Immunotherapy-related analysis

Tumor mutation burden (TMB) reflects cancer mutations. The Tumor Immune Dysfunction and Exclusion (TIDE) computational framework was used to analyze TMB and TIDE scores in patients with NSCLC to predict the response to immunotherapy in high-risk groups. Waterfall plot showing the relationship between NSCLC patient risk score and TMB. Violin plot showing the relationship between NSCLC patient score and TIDE.

Cell culture

Human bronchial epithelial cell line (BEAS-2B) was purchased from Hunan Starfish Biotechnology Co. Ltd. and these cells were cultured in Starfish matching human bronchial epithelial cell complete medium (item No. TCH-G132). The NSCLC cell line NCI-H1299, which was gifted by the Hunan Key Laboratory of Chinese Medicine Founder’s Research on Conversion Medicine, was cultured in RPMI-1640 medium supplemented with 10% fetal calf serum and 1% (penicillin-streptomycin) double antiseptic sterilizing solution in RPMI-1640 medium at 37 ℃ under 5% CO₂. The sample was incubated in RPMI-1640 medium supplemented with 10% fetal bovine serum and 1% (penicillin-streptomycin) double antiseptic solution at 37 ℃ under 5% CO₂.

Total RNA extraction and real-time quantitative polymerase chain reaction (qRT-PCR)

To assess the expression level of AC004943.2 and LINC00996 between normal and cancer cells, total RNA was isolated from cells using TRIzol reagent. The complementary DNA (cDNA) was synthesized by reverse transcription using NovoScript^® Plus All-in-one 1st Strand cDNA Synthesis SuperMix (gDNA Purge) (Lot No. 05305501). Then qRT-PCR was performed on SYBR Premix Ex Taq (novoprotein). Relative expression of lncRNA was normalized to the internal reference of GAPDH.

LINC00996 forward: 5'-CTCCCATCTTTTCTGCCGGT-3'; reverse: 5'-GATTGTGTCGGAAGCGGTTG-3'. AC004943.2 forward: 5'-CACTTCTGCAGGAACACCGA-3'; reverse: 5'-GTGGAGACTGAATGGCCCTC-3'. GAPDH forward: 5'-GTGAAGGTCGGAGTCAACGG-3'; reverse: 5'-GCAACAATATCCACTTTACCAGAGT-3'.

Statistical analysis

R software was used for all statistical analyses (v4.2.1). The validity of ROC curve analysis and Kaplan-Meier survival analysis was used to predict survival outcomes. The association between high- and low-risk groups of NSCLC and survival outcomes, as well as other clinical characteristics, was investigated using Cox proportional modeling. If the level of statistical significance was not clearly indicated, P<0.05 was used.

Results

After enrolling 1,002 patients, they were divided into a training group and a test group. In the training group, the cuproptosis-TCA-related lncRNAs prognostic model was constructed. Each patient’s risk score was calculated based on this prognostic model. The OS of high-low risk patients in the training group, the test group, and all patient groups was finally compared (Figure 1).

Construction of cuproptosis-TCA-associated lncRNA signature for NSCLC

Pearson correlation coefficients were calculated comparing lncRNA expression and cuproptosis-TCA-related gene expression in NSCLC, and corFilter =0.4 and pvalueFilter =0.001 were used as the criteria to obtain correlation results, as shown in Figure 2A. As a result, 28 genes, including ATP7A, FDX1, and GCSH, were significantly correlated with 1,460 copper-associated death and TCA-related lncRNAs. First, 62 cuproptosis-TCA-related lncRNAs associated with the prognosis of patients with NSCLC were identified using univariate Cox regression analysis in the training group from TCGA (P<0.05). LASSO regression analysis with 1,000 iterations was then adopted. Twenty-eight cuproptosis-TCA-associated lncRNAs were selected for prognostic assessments of the patients (Figure 2B,2C). Finally, 11 cuproptosis-TCA-associated lncRNAs were identified via multivariate Cox regression analysis. LncRNA risk scores were calculated as follows (Figure 2D): risk rating = AC022165.1 ´ 0.47718 + AC004943.2 ´ 0.52590 + AC009226.1 ´ 0.68043 − LINC00996 ´ 0.29947 − AC105389.2 ´ 0.38091 − AC025178.1 ´ 0.27865 − AC092807.3 ´ 0.79728 − AL122020.2 ´ 0.89487 − SNHG30 ´ 0.21668 − LINC02635 ´ 0.21324 − AP003032.1 ´ 0.21425. Analysis revealed that 35 cuproptosis-TCA-related genes were significantly correlated with 11 cuproptosis-TCA-related lncRNAs (Figure 2E). For example, the expression of AC004943.2 and GCSH were positively correlated.

Figure 2 Identification of cuproptosis-TCA-related lncRNAs in NSCLC. (A) Identification of 1,460 cuproptosis-TCA-related lncRNAs associated with 28 cuproptosis-related genes. (B) According to the minimum criteria, 28 cuproptosis-TCA-related lncRNAs were screened using the LASSO regression model. (C) In LASSO regression, the coefficient of cuproptosis-TCA-related lncRNA was calculated. (D) Multivariate Cox regression analysis of 11 cuproptosis-TCA-associated lncRNAs and OS in patients with NSCLC shown in the forest map. (E) Eleven cuproptosis-TCA-related lncRNAs and 35 cuproptosis-TCA-related genes were analyzed via Pearson analysis. NSCLC, non-small cell lung cancer; TCA, tricarboxylic acid; lncRNAs, long non-coding RNAs; OS, overall survival; LASSO, least absolute shrinkage and selection operator; CI, confidence interval.

Validation of cuproptosis-TCA-associated lncRNAs in NSCLC

To verify the accuracy of the cuproptosis-TCA-related lncRNA labels, the aforementioned risk-scoring formula was used for the risk assessment based on all NSCLC patients in TCGA cohort, TCGA training, and TCGA test datasets. The risk states and lncRNA expression levels of TCGA queue (Figure 3A), training (Figure 3B), and test datasets (Figure 3C) were obtained. According to a Kaplan-Meier analysis, patients with low-risk scores were more likely to survive longer than those with high-risk scores in TCGA cohort (P<0.001), TCGA training (P<0.001), and TCGA test (P=0.01) datasets. This indicated that the prognosis of patients with high-risk NSCLC was worse than that of patients with low-risk NSCLC (Figure 3D-3F). Time-ROC analysis further showed that this feature had strong predictive ability; the AUCs of the cohort from TCGA were 0.682, 0.649, and 0.611, respectively, the AUCs of the training cohort were 0.710, 0.732, and 0.737, respectively, and the AUCs of the test cohort were 0.655, 0.569, and 0.483, respectively (Figure 3G-3I). Finally, we found that this feature might be an independent prognostic factor for patients with NSCLC. Clinical features and cuproptosis-TCA-associated lncRNA risk scores were also evaluated with single-factor and multi-factor Cox regression models. Univariate Cox regression analysis suggested that there was a significant impact of this factor on the prognosis of NSCLC patients [total TCGA dataset: hazard ratio (HR) =1.115, 95% confidence interval (CI): 1.071–1.160, P<0.001; Figure 4A]. Multivariate Cox regression analysis further showed that the cuproptosis-TCA-related lncRNA risk score was an independent predictor of OS for patients with NSCLC [total dataset: HR =1.121, 95% CI: 1.074–1.170; P<0.001; Figure 4B]. All genes involved in the distinction between the high- and low-risk groups (Figure 4C), 19 cuproptosis-TCA-related genes (Figure 4D), 1,460 cuproptosis-TCA-associated lncRNAs (Figure 4E), and 11 cuproptosis-TCA-associated lncRNA labels (Figure 4F) were used for principal component analysis. The results showed a wide range of gene expression changes between high- and low-risk patients.

Figure 3 Verification of prognostic predictive ability and risk scores of 11 cuproptosis-TCA-associated lncRNA signatures in NSCLC. (A-C) Risk score distribution and expression heat maps of cuproptosis-TCA-associated lncRNAs in all patients with NSCLC, the training group, and the test group. (D-F) OS of high- and low-risk patients among all patients with NSCLC, the training group, and the test group. (G-I) A ROC curve verified the predictive ability of risk scores in all patients with NSCLC, the training group, and the test group. NSCLC, non-small cell lung cancer; TCA, tricarboxylic acid; lncRNAs, long non-coding RNAs; OS, overall survival; ROC, receiver operating characteristic; AUC, area under the curve.

Figure 4 Relationship between the cuproptosis-TCA-related lncRNA risk score and clinical characteristics of patients with NSCLC. A univariate (A) and multivariate (B) Cox regression forest map was used to analyze the effects of cuproptosis-TCA-related lncRNA risk scores and clinical characteristics on the prognosis of patients with NSCLC. Principal component analysis results are shown. Gene expression levels in high- and low-risk patients were compared based on (C) the expression of all genes tested, (D) cuproptosis-TCA-related genes, (E) cuproptosis-TCA-related lncRNAs, and (F) 11 lncRNAs with prognostic characteristics. NSCLC, non-small cell lung cancer; TCA, tricarboxylic acid; lncRNAs, long non-coding RNAs; CI, confidence interval; PC, principal component.

Establishment of a nomogram survival-prediction model

Because the cuproptosis-TCA-related lncRNA risk score could not intuitively predict OS in patients with clinical NSCLC, we combined the cuproptosis-TCA-related lncRNA risk score with clinicopathological features. A hybrid nomogram model was established to predict the 1-, 3-, and 5-year OS rates (Figure 5A). Predictors included risk score, age, and tumor grade. Simultaneously, calibration diagram indicated that the proposed model performed similarly to the ideal model (Figure 5B).

Figure 5 Cuproptosis-TCA-associated lncRNA mixed nomogram for the diagnosis of NSCLC. (A) Mixed nomogram model to predict 1-, 3-, and 5-year OS for all patients with NSCLC. (B) Nomogram calibration curves to predict 1-, 3-, and 5-year OS for all patients with NSCLC. **, P<0.01; ***, P<0.001. NSCLC, non-small cell lung cancer; TCA, tricarboxylic acid; OS, overall survival; lncRNA, Long non-coding RNA.

Functional enrichment analysis

To further investigate the mechanism underlying the effects of cuproptosis-TCA-related lncRNAs in NSCLC, GO (Figure S1) and KEGG (Figure S2) enrichment analyses were performed on differential genes in the high- and low-risk groups of patients with NSCLC. The cuproptosis-TCA-related lncRNA model was not only related to copper ion metabolism and transport but also to cellular and humoral immune responses, neuroactive ligand-receptor interactions, the IL-17 signaling pathway, and tryptophan metabolism.

Analysis of tumor immune microenvironment and immunotherapy response in patients with high- and low-risk NSCLC

In order to determine whether TME differs between patients with low- and high-risk NSCLC, 28 tumor-infiltrating immune cell gene sets were downloaded, and correlation analysis was performed using ssGSEA based on the c software package. The results showed that type 2 helper T cells, CD4⁺ T cells, natural killer T cells, CD56 (bright) natural killer cells, effector memory CD4 T cells, neutrophils, memory CD8⁺ T cells, and CD8⁺ T cells contributed a greater percentage to immune microenvironment in patients in the high-risk group. However, immature dendritic cells, B cells, mast cells, and eosinophils accounted for a lower proportion in patients in the high-risk group (Figure 6A). ssGSEA was also used to analyze differences in immune functions between the high- and low-risk groups, which showed that CCR, parainflammation, and MHC class I were further enriched in the high-risk group (Figure 6B). Changes in the distribution of somatic mutations in high- and low-risk patients were also analyzed. We found that 462 (95.06%) of the 486 high-risk samples had mutations, and the three genes with the highest mutation rates were TP53, TTN, and MUC16 (Figure 6C). Among the 486 low-risk samples, 440 (90.53%) had mutations, and the top three genes with the highest mutation rates were TP53, TTN, and MUC16 (Figure 6D). Mutation rates of TP53, TTN, and MUC16 in the high-risk group were higher than those in the low-risk group.

Figure 6 Comparison of tumor immune cell infiltration, somatic mutation spectrum, TMB, and TIDE scores in patients with high- and low-risk NSCLC. (A) Analysis of the difference in immune cell infiltration between high- and low-risk groups. (B) Analysis of differences in immune functions between high- and low-risk groups. (C) Analysis of gene mutations in high-risk group. (D) Analysis of gene mutations in low-risk patients. (E) Comparison of TMB between high- and low-risk groups. (F) The K-M curves of H-TMB and L-TMB patients. (G) The K-M curves of H-TMB patients and L-TMB patients in the high- and low-risk groups. (H) Violin plot showing the difference in TIDE scores between high- and low-risk groups. *, P<0.05; **, P<0.01; ***, P<0.001. NSCLC, non-small cell lung cancer; TMB, tumor mutation burden; TIDE, Tumor Immune Dysfunction and Exclusion; K-M, Kaplan-Meier.

The TMB can be used as an important indicator of the therapeutic effects of immune checkpoint blockade (ICB). An analysis of the mutation data showed that the TMB was higher in high-risk patients than that in low-risk patients, suggesting that immunotherapy may be better suited for high-risk patients (Figure 6E). Overall, patients with a higher TMB had higher survival rates (Figure 6F). Further comparisons were performed between the groups with high- and low-risk TMBs. Results showed that patients in the low-risk group with a high TMB had the highest survival rate, whereas those in the high-risk group with a low TMB had the lowest (Figure 6G). TIDE is a computational framework that simulates two major mechanisms of tumor immune escape and provides predictive outcomes related to immunotherapy. To further demonstrate that this risk score is predictive of immunotherapy outcomes, TIDE scores were calculated. We found that TIDE scores were inversely associated with risk scores, suggesting better immunotherapy outcomes in the high-risk groups (Figure 6H).

Expression levels of AC004943.2 and LINC00996 in BEAS-2B, NCI-H520, and NCI-H1299

The expression of high-risk lncRNA AC004943.2 and low-risk lncRNA LINC00996 was examined in lung normal epithelial cells (BEAS-2B) and NSCLC cells (NCI-H520 and NCI-H1299). The results of the qRT-PCR analysis demonstrated that AC004943.2 is markedly upregulated in the NCI-H520 and NCI-H1299 cell lines, whereas its expression is significantly downregulated in the BEAS-2B cell line (P<0.05) (Figure 7A). In contrast, LINC00996 displays elevated expression levels in BEAS-2B, with downregulation observed in both NCI-H520 and NCI-H1299 (P<0.05, P<0.001) (Figure 7B).

Figure 7 Expression levels of AC004943.2 and LINC00996 in NSCLC. The expression levels of AC004943.2 (A) and LINC00996 (B) as determined by qRT-PCR across BEAS-2B, NCI-H520, and NCI-H1299 cell lines. Statistical comparisons to the BEAS-2B group indicate ***, P<0.001 and *, P<0.05. NSCLC, non-small cell lung cancer; qRT-PCR, real-time quantitative polymerase chain reaction.

Discussion

In this study, 11 lncRNAs associated with cuproptosis and TCA in NSCLC were identified, including 3 high-risk lncRNAs and 8 low-risk lncRNAs. Further, a difference was noted in their expression between the high- and low-risk NSCLC groups, and the high-risk group had a short OS. By constructing an ROC curve and nomogram model, we found that these parameters could be used as independent predictors of NSCLC prognostic risk. In addition, by analyzing the differences in biological functions, the TME, and response to immunotherapy between the high- and low-risk groups, there was a better response to immunotherapy in the high-risk group of NSCLC than in the low-risk group.

Twenty-eight cuproptosis- and TCA-related lncRNA genes that were significantly correlated with OS in NSCLC were identified. These 28 genes included genes related to cuproptosis regulation, such as GLS, DLAT, and LIPT1, which were discovered by Tsvetkov et al. (23). The GLS gene encodes glutaminase, which helps the essential amino acid glutamine participate in the TCA cycle. Further, GLS promotes the proliferation of NSCLC cells through an oxidative stress response (45), and the inhibition of GLS expression can improve the survival of patients with prostate cancer (39). High expression of the DLAT glycolysis-associated gene is associated with the tumor volume and poor prognosis in patients with NSCLC (46). Meanwhile, LIPT1 participates in fatty acylation during the TCA cycle and affects the occurrence and prognosis of lung cancer (47). These genes are associated with the development and prognosis of lung cancer.

Several studies have found that lncRNAs, as important regulatory factors in biological processes, can regulate gene expression to contribute to tumor development and occurrence (48-50). A newly identified NSCLC-associated lncRNA, AL139294.1, enters the cell via extracellular vesicle (EV) transport and activates the Wnt and NF-κB2 pathways to promote cancer cell proliferation, differentiation, and migration (51). ACOXL-AS1, a cuproptosis-related lncRNA in the prognostic model of endometrial cancer, contributes to the proliferation of cancer cells by regulating the expression level of the miRror-421 to the cuproptosis related gene MTF1 expression level (52). Cuproptosis-associated lncRNA LINC01614 upregulates the expression level of SLC31A1, which regulates the degree of immune cell infiltration in breast cancer and predicts the prognosis of breast cancer patients (53). However, few studies have investigated the role of TCA-related lncRNAs in NSCLC. Using correlation analysis based on TCGA database, we found 1,460 cuproptosis-TCA-associated lncRNAs and created a prognostic model using these markers. In this study, we identified 11 TCA-associated lncRNA signatures associated with cuproptosis. AC004943.2, AC022165.1, and AC009226.1 were determined to be high-risk lncRNAs, whereas LINC02635, SNHG30, and LINC00996 were low-risk lncRNAs. AC004943.2 affects the tumor immunosuppressive microenvironment by activating the PTK2/PI3K pathway through sponge miR-135a-5p upregulation of protein tyrosine kinase 2 (PTK2) expression (54). Meanwhile AC004943.2 is a predictor in the cuproptosis-associated lncRNA model in head and neck squamous cell carcinoma (HNSCC) and osteosarcoma (55,56). AC004943.2 has been found to be upregulated in laryngeal squamous cell carcinoma, which promotes tumorigenesis, suggesting it as a potential biomarker for laryngeal squamous cell carcinoma (57). And in this study, AC004943.2 was highly expressed in NSCLC cells, verifying that it can be used as a predictor of NSCLC prognosis. LINC00996 is a tumor immune-infiltrating cell-associated lncRNA, which is lowly expressed in HNSCC high-risk patients and correlates with immunotherapy sensitivity (58). LINC00996, as an intermittent hypoxia-associated signal, is lowly expressed in LUAD, and acts as a tumor suppressor to inhibit tumor development and metastasis through multiple biological pathways, predicting the prognosis of LUAD (59,60), and also as a prognostic predictor of bladder, colorectal, and cervical cancers (61-63). Meanwhile, Yan identified LINC00996 as a potential target for NSCLC immunology (64). In this study, LINC00996 was lowly expressed in NSCLC, suggesting that it is a protective lncRNA for NSCLC patients. In addition, AC009226.1 can be used as one of the prognostic predictors for LUAD (65). However, the roles and molecular mechanisms of AC022165.1 and LINC02635 in NSCLC have not been reported. Therefore, in this study, it was determined that this model can be considered an independent prognostic factor for NSCLC and can be used to predict patient therapeutic responses to ICB therapy. Subsequently, a prediction nomogram was generated by combining the risk score of patients with NSCLC with age, sex, and tumor grade to further improve its usefulness and make the risk score easier to use.

Tumor immune microenvironments are influenced by lncRNAs, as demonstrated in several studies (66,67). Accordingly, our study showed that this model is bound up with the tumor immune microenvironment. We also found that the proportions of memory B cells and CD4⁺ T cells were significantly increased in high-risk patients, suggesting that cuproptosis regulates the development of NSCLC by increasing the infiltration of these immune cells. However, these preliminary findings need to be further verified, both in vivo and in vitro. The high-risk group also exhibited rich immunization-related features, such as CCR, parainflammation, and MHC class I.

The analysis of immune cell infiltration and immune functions in samples alone cannot reflect the effect of ICB treatment on patients with NSCLC, and a comprehensive analysis and evaluation of clinical characteristics is needed. Researchers found that patients with a higher TMB have a higher proportion of neoantigens and therefore respond better to immunotherapy (68-70). Our study showed that the TMB of high-risk patients was higher than that of low-risk patients, indicating a better immunotherapeutic effect in high-risk NSCLC patients. We also found that 462 (95.06%) of the 486 high-risk samples had mutations, and among 486 low-risk samples, 440 (90.53%) had mutations. The top three genes with the highest mutation rates in the two groups were TP53, TTN, and MUC16, and the mutation rates of these three genes in the high-risk group were higher than those in the low-risk group. This provides an indication that a high TP53 mutation burden can be beneficial for immunotherapy. This conclusion is consistent with that of previous studies. Wang et al. showed that a high TP53 mutation rate is conducive to ICB treatment in patients with NSCLC (71). Further, TIDE, a framework for computing two major mechanisms of tumor immune escape, can be used to predict immunotherapeutic responses (72,73). Here, this was applied to demonstrate the predictive power of the immunotherapy risk scores. We found a negative association between TIDE and risk scores, further suggesting that patients at a high risk for NSCLC might have better immunotherapy outcomes.

This prognostic prediction model provides a new biological predictive marker for NSCLC patient prognosis as well as responsiveness to immunotherapy-cuprotosis-TCA. The model is able to capture complex molecular features associated with tumors and is suitable for NSCLC prognostic assessment and treatment response prediction. These molecular features often go beyond the ability of traditional gene expression profiling to identify potential therapeutic targets. The biggest drawback of this model is that it is not yet possible to accurately predict the responsiveness of individual patients to specific different targeted drugs and chemotherapeutic agents. Meanwhile, how to effectively translate these models into clinical diagnostic or therapeutic tools still requires a lot of validation studies, which is also the biggest problem facing this model.

We must recognize the limitations of the current study. We lack in-depth mechanistic exploration of the key lncRNAs associated with cuprotosis-TCA in prognostic models. Further hard work on this topic is being carried out by our team.

Conclusions

We found that 11 cuproptosis-TCA-related lncRNAs could effectively predict the prognosis of patients with NSCLC, as independent factors, and the demonstrated on the TME of NSCLC. Simultaneously, they can predict the immunotherapy response. In summary, this is a new theoretical basis for understanding the molecular mechanism involved in the occurrence of NSCLC, which could lead to individualized treatments and prognostic assessments for patients suffering from the disease.

Acknowledgments

Funding: This work was supported by the Excellent Youth Project of Scientific Research Project of Hunan Provincial Education Department (No. 21B0392), the Natural Science Foundation of Changsha City (No. kq2202271), the Key Project of First-class Discipline of Integrative Medicine of Hunan University of Chinese Medicine (No. 2021ZXYJH11), the Natural Science Foundation Project of Hunan University of Chinese Medicine in 2022 (No. 23) and the 2021 Hunan University Student Innovation and Entrepreneurship Training Program (No. S202110541054).

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-660/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-660/dss

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Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-660/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

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