Identification and integration analysis of a novel prognostic signature associated with cuproptosis-related ferroptosis genes and relevant lncRNA regulatory axis in lung adenocarcinoma

Introduction

Until 2022, lung cancer has the highest global mortality rate among cancers, killing about 350 people each day [1]. Non-small cell lung carcinoma (NSCLC) accounts for approximately 85% of all lung cancer patients [2]. The incidence of LUAD, the most predominant subtype of NSCLC, has been increasing year by year [3]. Although the clinical management strategies for LUAD are continuously updated, there are still problems such as low early diagnosis rates and unsatisfactory long-term survival of patients [4]. In this regard, there is an urgent need to find a new clinical model that can accurately diagnose and assess the prognosis of LUAD, and further explore the molecular mechanisms related to the development of the disease, which may provide a new idea for the subsequent targeted therapy.

Ferroptosis, a novel type of programmed cell death [5], distinguishes itself from other cell death modalities such as apoptosis and necrosis and occurs mainly due to the presence of divalent iron ions that accelerate the process of lipid peroxidation of saturated fatty acids in the body, resulting in oxidative stress in cells, which further induces cell death [6]. The iron metabolism has a dual effect on tumor development, i.e., an increase in iron content within a certain range is not conducive to the control of tumor cell growth and multiplication [7], but when the intracellular iron concentration exceeds a threshold, the ferroptosis effect that is triggered at this time has a positive effect on tumor control [8]. A number of studies have shown that various tumor suppressors can upregulate the sensitivity of tumor cells to iron death, such as p53 and BRCA1-Associated Protein 1 (BAP1) [9, 10], both of which can inhibit the expression of SLC7A11 coupled with ferroptosis mechanism to exert tumor suppressive effects. As an essential co-factor in the body, copper, similar to iron, plays an important role in biological processes such as participation in mitochondrial respiration and regulation of signaling pathways [11]. Cuproptosis death is a novel mode of cell death proposed by recent studies, and its mechanism of occurrence depends on intracellular copper accumulation [12]. During the development of cuproptosis, excess copper ion carriers were found to bind to fatty tricarboxylic acid cycle proteins to trigger protein aggregation, leading to acute proteotoxic stress [13]. The discovery of cuproptosis may become a new mechanism for the treatment of tumors in clinical practice, which will guide the future research direction of tumor diagnosis and treatment [14].

Current studies have confirmed the involvement of ferroptosis and cuproptosis in the development of many cancers and both are considered to be important factors strongly associated with cancer progression [15, 16]. It was found in clinical studies on LUAD that the occurrence of ferroptosis has significant implications for the treatment of patients with the advanced and drug-resistant diseases [17], in which cuproptosis also showed a positive prognostic effect [18]. Collectively, it is reasonable to speculate that the combination of the two death modalities may provide better control of tumor progression. Recent studies have shown that CRFGs have superior performance in prognosis and immune infiltration in hepatocellular carcinoma [19]. In the study about colorectal cancer, we can find that the prognostic significance of CRFGs in cancer has higher reliability [20]. The research results mentioned above prove that CRFGs are relevant in the exploration of clinical treatment of malignant tumors. However, the relevant studies of CRFGs in LUAD have not been addressed, thus the combined therapeutic mechanism of ferroptosis and cuproptosis in LUAD needs to be further discovered. Therefore, the study of CRFGs in LUAD may also provide new ideas for the diagnosis and treatment of this disease.

The lncRNA also plays a crucial role in the development of cancer by interacting with DNA, RNA, and proteins to regulate gene expression in both cis or trans transcription, the organization of nuclear structural domains and at the post-transcriptional level [21]. It is now generally accepted that lncRNA can identify cancer cell pathology and identify tumor subtypes, with important prognostic value, and is a biomarker for a variety of cancers, which can be used to guide patients in therapy selection [22]. In the available studies, lncRNA was found to have a significant impact on the progression and metastasis of LUAD. It has been demonstrated that the JPX/miR-33a-5p/Twist1 axis can activate Wnt/β-catenin signaling to promote metastasis in LUAD [23]. Meanwhile, LINC00472 was identified as a protective factor for LUAD [24]. In contrast, the molecular regulatory mechanism of CRFGs is still unclear, which enlightens us to conduct an in-depth investigation of lncRNA to lay the foundation for clinical studies of LUAD.

In the present study, we constructed a clinical prediction model about CRFGs in LUAD by bioinformatics using the gene signature of cuproptosis-related ferroptosis as the main object of analysis and validated it using an external dataset for correlation. Based on the model, we established potential prognostic biomarkers. We also further explored the regulatory mechanisms of the two combined death modes by related lncRNA and the potential impact on the prognosis of LUAD. The workflow of this study is shown in Figure 1.

Figure 1. The flow chart of this study.

Results

Acquisition and enrichment analysis of CRFGs

In the Genecards database, 302 genes related to ferroptosis were obtained (Supplementary Table 1), and 16 genes (FDX1, LIPT1, LIAS, DLD, MTF1, GLS, CDKN2A, DLAT, PDHA1, PDHB, DBT, GCSH, DLST, SLC31A1, ATP7A, and ATP7B) closely related to copper death were collated from the latest studies on cuproptosis [13].

Gene differential expression analysis was performed on 516 LUAD samples and 59 normal samples in the TCGA database, volcano maps (Figure 2A) and gene heat maps (Figure 2B) were constructed, and a total of 2590 genes showing differential expression were acquired, of which 928 genes were up-regulated in LUAD compared to normal samples, and the remaining 1662 genes were down-regulated (Supplementary Table 2).

Figure 2. Acquisition and enrichment analysis of LUAD-related CRFGs. (A) Volcano plot, blue dots indicate genes significantly down-regulated in expression in LUAD samples compared to normal samples, and red dots indicate genes significantly up-regulated. (B) Heat map. (C) PPI network diagram of CRFGs, ferroptosis-related genes are shown with pink nodes and green nodes representing cuproptosis-related genes. (D) Venn diagram constructed by LUAD and CRFGs. (E) GO and KEGG enrichment analysis of LUAD-related CRFGs, with the longer columns representing higher enrichment.

The PPI network was constructed from the genes associated with ferroptosis and cuproptosis (Figure 2C), and the network contained a total of 270 nodes with 16 genes associated with cuproptosis and 254 genes associated with ferroptosis. Venn diagrams were plotted for the 2590 differential genes and 270 CRFGs in LUAD, and a total of 44 common genes were found (Figure 2D). The top twenty results with the highest gene enrichment in CRFGs in LUAD are shown in Figure 2E.

Potential prognostic genes and mutational landscape

A univariate Cox regression analysis was performed on 44 CRFGs (Table 1), and 15 potentially independent prognostic genes were filtered out at a p-value <0.05, which showed differential expression in LUAD compared with normal tissues (Figure 3A). The mutational landscape of these 15 potentially prognostic genes was further analyzed using TCGA data to visualize the top 10 genes with the highest degree of mutation, with the results shown in Figure 3B, where the gene with the highest mutation density is the LIFR.

Table 1. OS-related univariate Cox regression analysis of 44 CRFGs.

Uni_Cox	Pvalue	Hazard ratio (95% CI)
TFAP2A	0.00005	1.23695(1.11607,1.37092)
CCT3	0.00025	1.51739(1.21406,1.8965)
CDC25A	0.00029	1.31458(1.13381,1.52417)
HLF	0.00069	0.8406(0.76035,0.92932)
ENO1	0.00154	1.44234(1.1498,1.80932)
LIFR	0.00168	0.80991(0.71011,0.92373)
GCLC	0.00247	1.14134(1.04769,1.24336)
SLC7A11	0.00891	1.11886(1.02855,1.2171)
HELLS	0.01939	1.20561(1.03069,1.4102)
CA9	0.02291	1.07088(1.00953,1.13597)
ACSL4	0.02331	1.19584(1.02461,1.39569)
CAV1	0.03002	1.11989(1.01101,1.24048)
SCD	0.04025	1.13687(1.00572,1.28513)
ALOX15	0.04032	0.90049(0.81464,0.99538)
GPX3	0.04371	0.87952(0.77637,0.99638)
TFRC	0.05972	1.12552(0.99517,1.27294)
GJA1	0.06981	1.1034(0.99206,1.22722)
TLR4	0.09284	0.87035(0.74022,1.02335)
MAP1LC3C	0.09328	0.90838(0.81195,1.01626)
NEDD4L	0.11818	0.87487(0.7398,1.0346)
CYBB	0.14379	0.92893(0.8415,1.02544)
ACSL1	0.16507	0.89012(0.75523,1.04911)
SLC39A8	0.17102	0.91088(0.79694,1.04112)
CDKN2A	0.19075	1.05277(0.97471,1.13708)
PTGS2	0.22003	1.04517(0.97392,1.12163)
CP	0.24934	1.04482(0.96971,1.12575)
ALOX5	0.28193	0.93514(0.8276,1.05664)
ETV4	0.40547	0.95551(0.85836,1.06366)
SLC40A1	0.43914	0.95399(0.84668,1.0749)
DDR2	0.4647	0.94663(0.81721,1.09656)
WWTR1	0.54997	1.05991(0.87583,1.28268)
HCAR1	0.58266	0.97332(0.88383,1.07188)
CYGB	0.60552	1.05051(0.87133,1.26654)
FBLN1	0.62394	0.96973(0.85759,1.09653)
JAM3	0.64954	0.964(0.823,1.12916)
LCN2	0.69188	1.01353(0.94835,1.0832)
ALAS2	0.71757	1.07262(0.73362,1.56827)
JUN	0.76688	0.97822(0.84569,1.13152)
PDK4	0.81162	0.98725(0.88837,1.09714)
IL6	0.82012	1.01138(0.91741,1.11497)
ZFP36	0.83636	1.01291(0.89686,1.14397)
LPCAT3	0.8883	0.98608(0.81083,1.19919)
ATF3	0.96585	0.9975(0.88946,1.11867)
EFEMP1	0.97338	0.99808(0.89172,1.11713)

Figure 3. The prognostic signature associated with CRFGs. (A) Differential expression of 15 potentially prognostic CRFGs in LUAD samples and the normal samples. (B) Mutation landscape map of potentially prognostic CRFGs, showing SNV and genomic mutation types. (C, D) Prognostic signature established by LASSO Cox regression analysis. (E) Risk score distribution, patient survival status and CRFGs expression profile calculated by the prognostic signature. (F) Survival curves of LUAD patients in low- and high-risk groups. (G) ROC curves of the prognostic signature at 1, 3, and 5 years. *P < 0.05, **P < 0.01.

Establishment and validation of a prognostic signature associated with CRFGs

Based on these 15 potential independent prognostic genes of LUAD above, we performed LASSO Cox regression analysis to construct a prognostic signature associated with CFRGs. Finally, 13 genes were included in the prognostic signature (Figure 3C, 3D). The specific formulation of the prognostic signature: Riskscore=(0.2392)*CCT3+(0.0875)*ENO1+(0.1088)*TFAP2A+(0.0275)*CA9+(0.0302)*SLC7A11+(0.0251)*GCLC+(0.1293)*CAV1+(-0.1273)*GPX3+(-0.1325)*LIFR+(-0.017)*HLF+(0.0557)*ACSL4+(-0.0103)*ALOX15+(0.0144)*SCD. The risk plots and Kaplan-Meier survival analysis curves for the prognostic signature associated with CRFGs in Figure 3E–3G show that the high-risk group has a shorter survival time compared to the low-risk group. The 1-, 3-, and 5-year survival probability of the risk score was represented by the AUC values of 0.716, 0.726, and 0.623, respectively, in the TCGA cohort. These results indicate that the prognostic model constructed in this experiment possesses good stability.

To further validate the prognostic performance of our model, two datasets, GSE41271 and GSE31210, were selected in the GEO database as external validation cohorts for the prognostic performance of the model. As shown in Figure 4A, 4B, the validation results of both external cohorts demonstrate the generalizability and reliability of our constructed prognostic signature regarding CRFGs in LUAD. By DCA we found that the prognostic signature associated with CRFGs had better clinical utility compared with other single prognostic signatures for cuproptosis or ferroptosis (Figure 4C–4E).

Figure 4. Validation of the prognostic signature. Validation of the prognostic signature in the external datasets (A) GSE41271 and (B) GSE31210. DCA curve was used to assess the clinical utility of the prognostic signature associated with CRFGs versus a single cuproptosis or ferroptosis model at (C) 1 year, (D) 3 years, and (E) 5 years of OS. Model1 represents the CRFGs-related model, Model2 represents the cuproptosis-related model, and Model3 represents the ferroptosis-related model.

To further evaluate the potential clinical use of prognostic models associated with CRFGs, univariate Cox regression analysis (Figure 5A) and multivariate Cox regression analysis (Figure 5B) of prognostic signature together with associated clinical factors were performed and a nomogram was constructed (Figure 5C). The calibration curves of this nomogram show that the observed and predicted values have a high consistency (Figure 5D).

Figure 5. The prognostic signature-related nomogram. (A) Univariate Cox regression analysis. (B) Multivariate Cox regression analysis. (C) Nomogram for predicting the 1-, 3-, and 5-year OS of LUAD patients in the TCGA cohort. (D) Calibration curve to validate the established nomogram.

Potential prognostic biomarkers

To explore potential prognostic molecular mechanisms closely associated with LUAD progression, we performed a multivariate Cox regression analysis in the TCGA cohort combining age, gender, pTNM-stage, and smoking these clinical characteristics and 13 CRFGs. TFAP2A (p=0.02847), CAV1 (p=0.00854), LIFR (p=0.00273), age (p=0.04739), and pTNM-stage (p<0.0001) were shown to be potential prognostic factors closely associated with LUAD (Figure 6A). TFAP2A, CAV1, and LIFR were identified as potential prognostic biomarkers for further analysis.

Figure 6. Potential prognostic biomarkers acquisition and immunoassay for CRFG in LUAD. (A) Multifactorial Cox regression analysis of CRFGs and associated clinical characteristics. (B) Immune scores in LUAD and normal tissues, the horizontal coordinate represents the types of immune cell infiltration and the vertical coordinate represents the distribution of immune scores in the two groups. (C) Heat map of correlation between potential prognostic biomarkers and immune scores, horizontal coordinates represent the type of immune cell infiltration, vertical coordinates represent genes. (D) Heat map of the correlation between the potential prognostic biomarkers and common immune checkpoints, horizontal coordinates represent immune checkpoints and vertical coordinates represent biomarkers.

TME analysis

In the TCGA cohort, we calculated the degree of immune cell infiltration of seven common immune cells in LUAD tissues by the EPIC algorithm, and in Figure 6B, it can be found that the degree of infiltration of most immune cells was increased in LUAD compared to normal tissues, except for T cell CD8+. We then investigated the degree of immune infiltration of three potentially prognostic biomarkers and found that TFAP2A expression showed a negative correlation with the degree of B cell, Endothelial cell, and T cell CD8+ infiltration and a positive correlation with T cell CD4+ infiltration (Figure 6C).

We also researched the association between the eight universal immune checkpoints and the expression of these three potentially prognostic biomarkers, as shown in Figure 6D, where all three biomarkers have some association with the immune checkpoints.

The core gene identification and analysis

The results of the analysis of OS and PFS of the three biomarkers (Figure 7A, 7B) and Kaplan-Meier survival curves (Figure 7C–7E) showed that the high expression of TFAP2A was detrimental to survival in the LUAD patients.

Figure 7. Survival analysis of potential prognostic biomarkers and identification of the core genes. Results of (A) OS and (B) PFS survival analysis for the potential prognostic biomarkers. (C) Relationship between LIFR expression and OS. (D) Relationship between TFAP2A expression and OS. (E) Relationship between TFAP2A expression and PFS. Correlation studies of potential prognostic genes with MSI, in which (F) CAV1 was significantly associated with MSI, while (G) LIFR was non-significantly associated with MSI as well as (H) TFAP2A was significantly associated with MSI. (I) Lollipop charts of the mutation landscape of TFAP2A, showing a somatic mutation rate is 0.98%. (J) Protein expression of TFAP2A in normal and LUAD tissues in pathological sections, indicating that TFAP2A was highly expressed in LUAD samples.

High levels of Microsatellite instability (MSI), which predispose to the accumulation of mutations in cancer and an increase in tumor mutational burden (TMB), are detrimental to the control of cancer progression [25]. In this regard, we performed MSI scores for the three potential prognostic biomarkers. In Figure 7F–7H, it can be seen that CAV1 (p=0.003) and TFAP2A (p=0.008) expression showed a correlation with MSI score whereas LIFR (p=0.294) expression was not significantly correlated with it. Combining the results of the above analysis, we suggest that TFAP2A may act as a core gene and be closely related to the development of LUAD. Figure 7I shows the distribution of gene mutations in the TFAP2A. We also used pathological sections of TFAP2A protein expression to demonstrate core gene-related immunohistochemistry through the HPA database (Figure 7J), and it was seen that TFAP2A expression levels were significantly higher in LUAD tissues compared to normal tissues. These results further validate the upregulation of TFAP2A expression in LUAD tissues.

Exploration and validation of lncRNA-related regulatory axis

To further elucidate the regulatory mechanisms associated with the core gene in LUAD, we conducted an exploration of the lncRNA-related regulatory axis. Relevant miRNAs about TFAP2A were obtained from the four databases (mirDIP, miRDB, miRWalk and StarBase) we selected for the search, and the specific results can be found in Supplementary Table 3. MiR-200c-3p and miR-616-3p were obtained from the comprehensive analysis as upstream miRNAs of TFAP2A (Figure 8A). In the TCGA cohort, both miRNAs showed increased expression compared to normal samples (Figure 8B, 8C). Next, the upstream lncRNAs of the obtained core miRNAs were predicted in the LncBase database, and then Kaplan-Meier analysis was performed to plot the survival curves. It should be noted that the prediction result of miR-616-3p was poor, so we selected miR-200c-3p as the subject of the follow-up study. As seen in Figure 8D–8G, the expression of four lncRNAs, LINC00324 (p=0.00741), LINC00240 (p=0.000789), LINC00973 (p=0.0029) and LINC02073 (p=0.00764), were closely associated with the prognostic outcome of LUAD. Their specific AUC values can be obtained from Supplementary Figure 1. We also analyzed the expression of the four lncRNAs mentioned above in LUAD and normal samples, and it could be found that they all had significant expression differences (Figure 8H–8K). After discussion and analysis of these four lncRNAs, we selected LINC00324, which has some research background and relevant expression results in the relevant GSE dataset, to establish the regulatory axis. To further validate the regulatory mechanism of the LINC00324/miR-200c-3p/TFAP2A regulatory axis constructed in this study, we selected four datasets (GSE43458, GSE32863, GSE30219, and GSE27262) in the GEO database as external validation cohorts. In Figure 8L–8O, it can be seen that our selected relevant lncRNA regulatory axis has good outcomes in the dataset. Based on the above validation results, we hypothesized that lncRNAs may play an important role in LUAD progression through the LINC00324/miR-200c-3p/TFAP2A regulatory axis.

Figure 8. Results of the lncRNA-related regulatory axis. (A) Venn diagram using data from four databases: mirDIP, miRDB, miRWalk and StarBase. (B) Differential expression of miR-200c-3p, (C) miR-616-3p in LUAD samples and normal samples. Relationship between (D) LINC00324, (E) LINC00240, (F) LINC00973 and (G) LINC02073 expression levels and OS. Box plot of differential gene expression between (H) LINC00324, (I) LINC00240, (J) LINC00973, (K) LINC02073 in LUAD samples and normal samples. External dataset validation of expression differences of LINC00324. (L) GSE43458; (M) GSE32863; (N) GSE30219; (O) GSE27262.

Abbreviations

LUAD: lung adenocarcinoma; CRFGs: cuproptosis-related ferroptosis genes; TCGA: The Cancer Genome Atlas; LASSO: least absolute shrinkage and selection operator; ROC: receiver operating characteristic; DCA: decision curve analysis; TME: Tumor microenvironment; LncRNA: long non-coding RNA; MSI: microsatellite instability; NSCLC: non-small cell lung carcinoma; BAP1: BRCA1-Associated Protein 1; GEO: Gene Expression Omnibus; PPI: protein-protein interaction; DEGs: differentially expressed genes; HR: hazard ratio; CI: confidence interval; OS: overall survival; PFS: progression-free survival; TMB: tumor mutational burden; CXR: chest X-ray; GBE1: glycogen branching enzyme; ICB: immune checkpoint blockade; CeRNA: crosstalk-competing endogenous RNA.

Author Contributions

Conceptualization, TW and XJ; methodology, TW, XJ and YL; software, XJ, YL and YR; validation, JW; writing—original draft preparation, TW, XJ and YL; writing—review and editing, TW, XJ and YL; visualization, YR; supervision, TW. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

No funding was provided for this study.

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