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American Journal of Cancer Research logoLink to American Journal of Cancer Research
. 2023 Nov 15;13(11):5306–5319.

A risk model based on ferroptosis- and cuproptosis-related lncRNAs predicts prognosis and immune microenvironment in lung adenocarcinoma by bioinformatics analysis and experimental verification

Xiaoying Xu 1,2,*, Xinzhi Feng 3,*, Pengju Zhang 4, Xiaoyan Lin 1,2
PMCID: PMC10695791  PMID: 38058804

Abstract

Ferroptosis and cuproptosis are both novel types of cell death. Long noncoding RNAs (lncRNAs) are associated with multiple cancers. Notably, bioinformatics study of ferroptosis- and cuproptosis-related lncRNAs (FCLs) in lung adenocarcinoma (LUAD) has not been elucidated. In this study, we used univariate Cox, multivariate Cox, and least absolute shrinkage and selection operator Cox (LASSO-Cox) analyses to screen three FCLs, namely AC079193.2, AC090559.1, and AL512363.1. We then showed that these three FCLs were tumor-specific and correlated with ferroptosis and cuproptosis using qRT-PCR. Next, a prognostic risk model consisting of high- and low-risk cohorts was successfully constructed based on The Cancer Genome Atlas-LUAD data. The high-risk group consistently demonstrated poor prognosis. The accuracy of the model was evaluated using AUC, C-index curves, and nomograms. Furthermore, KEGG and GO analysis with R software showed significant enrichment in immune functions and metabolic pathways. Hereto, the immune function and immune cell expression results were more pronounced in the low-risk versus high-risk group. In conclusion, the prognostic risk model comprised of three FCLs effectively predicted patient outcomes and is associated with the immune microenvironment in LUAD.

Keywords: Bioinformatics analysis, lung adenocarcinoma, ferroptosis & cuproptosis-related lncRNA, risk model, prognosis, immune microenvironment

Introduction

As of 2022, lung cancer has the second-highest estimated incidence and the highest projected mortality among all cancers worldwide. It consists of two primary subtypes: non-small-cell lung cancer and small-cell lung cancer [1,2]. Lung adenocarcinoma (LUAD) is a common histologic subtype of non-small-cell lung cancer, seen in non-smoking patients with driver gene mutations [3]. Despite many available treatments, the therapeutic outcomes for LUAD remain poor, and the 5-year survival probability remains relatively low, especially for patients with advanced cancer [4].

Ferroptosis, an iron-dependent type of cell death with abnormal metabolism and biochemical processes, was first reported by Dixon et al. in 2012 [5]. Unlike other forms of cell death such as apoptosis, autophagy, necroptosis, and pyroptosis, ferroptosis mainly involves a combination of iron toxicity, lipid peroxidation, and plasma membrane damage [6,7]. Inducers of ferroptosis include various substances, including erastin, lanperisone, sorafenib, and artemisinin. For this study, erastin was selected as the primary inducer. Erastin triggers oxidative, iron-dependent cell death by reducing glutathione levels through direct inhibition of the cystine/glutamate antiporter system Xc-, leading to the activation of the endoplasmic reticulum stress response [8].

Copper is a trace element essential for life in various biological processes in humans. It plays a dual role as a nutrient and a toxin [9]. Copper is directly involved in the tricarboxylic acid cycle and binds with lipoylated molecules to promote iron (Fe)-sulfur (S) cluster protein loss, ultimately leading to cell death [10]. Elesclomol is a potent copper ionophore, and when it binds to copper ions (elesclomol-Cu (II)), it rapidly transports Cu2+ to the mitochondria to induce a form of cell death known as cuproptosis [11]. Cuproptosis is novel cell death that differs from other well-documented forms such as apoptosis, necroptosis, pyroptosis, and ferroptosis [12].

Long noncoding ribonucleic acid (lncRNA), initially reported in 1991 [13], refers to a class of RNA molecules with a length > 200 nt. These lncRNAs are primarily located in the nucleus and are involved in biological processes, including cell cycle, metabolism, cellular drug resistance, immune microenvironment, and development of multiple malignant tumors [14,15]. LncRNAs are strongly associated with many cancers, including LUAD [16]. However, ferroptosis- and cuproptosis-related lncRNAs (FCLs) in LUAD have not been reported.

Previous research has separately explored risk models related to cuproptosis and ferroptosis. However, a prognostic model that combines both cuproptosis and ferroptosis has not been investigated. Given the complex pathogenesis, high recurrence rate, and increased drug resistance associated with LUAD, it is imperative to conduct more theoretical research to advance the development of cutting-edge treatments in clinical practice. In this study, we successfully identified three FCLs that are tumor-specific and strongly associated with the processes of ferroptosis and cuproptosis. The expression patterns of these three FCLs were consistent across various datasets, including The Cancer Genome Atlas (TCGA) database, cell lines, and clinical tissue samples. Moreover, we have established a risk prognostic model based on TCGA data. We also assessed the correlation between this risk model and functional annotations, the immune environment, and drug sensitivity. This novel model can predict overall survival (OS) in patients and provides a theoretical foundation for the advancement of clinical treatments in LUAD.

Materials and methods

Data acquisition

Transcriptome data for LUAD, including 59 normal cases and 598 tumor cases, and corresponding clinical information were obtained from the TCGA database (http://portal.gdc.cancer.gov/). Cuproptosis-related genes (CRGs) were obtained in a previous study [17]. Ferroptosis-related genes (FRGs) were acquired from the FerrDb V2 database (www.zhounan.org/ferrdb/current/). Clinical tissues were collected from Shandong Provincial Hospital. The study was approved by the Ethics Committee of the Affiliated Shandong Province Hospital of Shandong First Medical University.

Identification of FCLs

Pearson’s correlation algorithm was used to identify FCLs based on their associations with FRGs (|cor| > 0.2, P < 0.001) and CRGs (|cor| > 0.4, P < 0.001). The intersection of these two sets formed the FCL dataset, comprising 206 lncRNAs. The relationships between FCLs and FRGs, as well as CRGs, were analyzed using R. Univariate Cox (Uni-Cox) analysis was applied to screen prognosis-related FCLs (P < 0.05). The best prognosis-related FCLs were filtered using the least absolute shrinkage and selection operator Cox (LASSO-Cox) algorithm and multivariate Cox (multi-cox) analysis. Then, the TCGA-LUAD dataset was randomly categorised into two groups: a training group to construct the FCL signature and a test group for assessing its accuracy. The risk score for the FCL signature was calculated using a formula that considered the expressions of AC079193.2, AL5123663.1, and AC090559.1, each assigned a specific coefficient (i.e., expression AC079193.2*-1.72742139871253 + expression AL5123663.1*-0.29753666108855 + expression AC090559.1*-0.23143589070073).

Cell culture

The BEAS-2B, 95D, and A549 cell lines were gifted by Jiang Yifan, a senior researcher at Shandong First Medical University. The BEAS-2B cell line was cultured in Dulbecco’s Modified Eagle Medium (Gibco, Billings, MT, USA) and 12% fetal bovine serum (FBS; Cell-box, Shenzhen, China), whereas 95D and A549 cell lines were cultured in RPMI 1640 (Gibco) and 12% FBS. All cell cultures were maintained at 37°C and 5% CO2 in a cell incubator.

Quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) analysis

Elesclomol and erastin were both procured from MCE (Monmouth Junction, NJ, USA). The medium was replaced after elesclomol (0 nM, 5 nM, 10 nM, 15 nM, 20 nM, and 25 nM) pulse treatment for 2 h in the presence of 1 µM copper (II) chloride or after erastin (0 µM, 5 µM, 10 µM, 15 µM, 20 µM, and 25 µM) treatment for 24 h, respectively. After 24 h, the total RNA was extracted using TRIzol (Tiangen, Beijing, China). Complementary deoxyribonucleic acid (cDNA) was obtained using the HisScript ® III 1st Strand cDNA Synthesis Kit (Vazyme, Nanjing, China). qRT-PCR was performed using the ChamQ Universal SYBR qPCR Master Mix (Vazyme). Finally, the 2-ΔΔCt algorithm was applied to calculate relative FCL expression. Detailed primer information is presented in Supplementary Table 1.

Construction and examination of the risk prognostic model

The Kaplan-Meier method was used to determine the OS of patients in the two risk cohorts and across multiple clinical cohorts. The risk model accuracy and FCL prognostic value were validated using receiver operating characteristic (ROC) curves, area under the curve (AUC) analysis, C-index curve analysis, and principal component analysis (PCA). Additionally, the independent prognostic factors in the risk model were explored using Uni-Cox and Multi-Cox analyses. Finally, a nomogram was used to predict the 1-, 3-, and 5-year OS based on the risk model, and calibration was performed to test the accuracy of the nomogram prediction.

Gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) analyses

R software was employed for GO and KEGG (false discovery rate < 0.05) analyses between the two risk groups. The GO analysis comprises biological processes, cellular components, and molecular functions.

Analysis of the tumor immune microenvironment

R software was used to calculate the correlation between the two risk groups and immune-related function, immune check points, and m6A-related genes, respectively. Five methods, namely TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL and EPIC, were used to explore the relationship between FCLs and immune cells in the two risk groups.

Statistical analysis

All bioinformatics-related figures were obtained by R (v4.1.3 and v4.2.1) software, while Perl (5.30.0.1) software was used for program execution. All experiment-related statistical graphs were drawn by GraphPad Prism (v9.0.0.121). Statistical significance was defined as p-value < 0.05 (*P < 0.05, **P < 0.01, ***P < 0.001).

Results

Screening of FCLs

The flowchart for the study is displayed in Figure 1. Coexpression analysis for LUAD was used to identify 1,096 ferroptosis-related lncRNAs (FRLs) and 668 cuproptosis-related lncRNAs (CRLs), with the common portion comprising 206 FCLs (Figure 2A). Uni-Cox analysis revealed 18 statistically significant FCLs (Figure 2D). Next, three CRLs were subjected to the LASSO-Cox analysis and multi-Cox analysis to determine the CRL characteristics (Figure 2B, 2C). Heatmaps were generated to visualize the associations based on the Multi-Cox algorithm between FCLs (AC079193.2, AC090559.1, and AL512363.1) and FRGs (Figure 2E; Supplementary Table 2), as well as CRGs (Figure 2F; Supplementary Table 3).

Figure 1.

Figure 1

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The flowchart of this study. Datasets were obtained from TCGA database. Pearson algorithm was used to screen FCLs. Subsequently, we detected levels of three FCL in cell lines and clinical tissues. Next, we established risk model and verified its accuracy. Then, functional analysis showed that multiple immune-related function was enriched. Finally, we conducted immune landscape, including immune function analysis and immune checkpoint.

Figure 2.

Figure 2

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Screening of FCLs. (A) Venn diagram of FRLs and CRLs. (B, C) The distribution of FCLs screened by lasso-cox regression algorithm. (D) The forest diagram of 18 FCLs screened by uni-cox regression algorithm. The heatmap of three prognostic FCL and nine FRGs (E), and 19 CRGs (F).

Expression of FCLs

According to the TCGA database, tumor tissues exhibited lower expression of AC079193.2 and AC090559.1 compared with normal tissues, which was associated with a worse prognosis, while AL512363.1 expression showed the opposite trend (Figure 3A-F). The mRNA levels of these three FCLs were also detected in cell lines and clinical tissues via qRT-PCR, with a similar trend to the TCGA database (Figure 3G, 3H). These levels were higher in the 95D cells than in the A549 cells. We used two drugs (erastin: 0, 5 μM, 10 μM, 15 μM, 20 μM, and 25 μM; elesclomol: 0, 5 nM, 10 nM, 15 nM, 20 nM, and 25 nM) to test the tolerance of cells. The CCK8 results showed that the 95D cells had higher resistance to both drugs than the A54 cells (Figure 3I, 3J). We also examined FRG and CRG expression in cell lines, with similar results to the TCGA database. Compared with the normal lung cell line (BEAS-2B), GPX4 and GCSH had greater expression, and SLC40A1, TFRC, NLRP3, and FDX1 had an opposite trend in lung cancer cell lines (95D and A549) (Figure 3K, 3L).

Figure 3.

Figure 3

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Expression of FCLs. A-C. Expression of three FCLs from TCGA. D-F. Survival curves of three FCLs in normal breast tissues and tumor tissues from TCGA. G, H. Expression of three FCLs three in cell lines and clinical tissues. I, J. CCK8 assay of cell sensitivity to erastin and elesclomol-Cu. K, L. Expression of FRGs and CRGs in cell lines.

Building a prognostic risk model

We confirmed that FCLs were tumor-specific and associated with FRLs and CRLs by experimental validation. Next, we obtained clinical data from the TCGA database and randomly divided them into a training and test cohort (Supplementary Table 4), with 254 and 253 patients, respectively. According to the median expression of the risk score, each cohort was separated into high- and low-expression groups to establish a prognostic risk model. Patient risk scores (Figure 4A-C) and survival time (Figure 4D-F) are shown for the entire cohort, training cohort, and test cohort. The trends of the three FCL expressions were the same in all three groups (Figure 4G-I). The high-risk group was associated with a shorter OS compared to the low-risk group (Figure 4J-L), indicating that the risk model was successfully built, and a high-risk score was associated with a poorer prognosis.

Figure 4.

Figure 4

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Construction of prognostic model. A-C. Risk score of patients in three groups. D-F. Survival status of patients in three groups. G-I. Heatmap of three FCLs in three groups. J-L. OS of patients in three groups.

Assessment of risk model

The clinical significance of various indicators was predicted using Uni-Cox and multivariate Cox regression analyses. Five indicators, including stage, TNM classifications, and risk score, were deeply significant using Uni-Cox analysis (Figure 5A). Multi-Cox regression suggested a statistically significant difference between T classification and risk score; however, there were no significant differences for the remaining clinical indicators (Figure 5B). The accuracy of the model was analyzed using ROC and C-index curves. AUC and C-index curves estimated the accuracy of prediction for risk score, age, stage, gender, T classification, N classification, and M classification (0.691, 0.49, 0.55, 0.713, 0.652, 0.501, and 0.666, respectively) (Figure 5C, 5E). The AUCs at 1-, 3-, and 5-years were estimated at 0.691, 0.579, and 0.611, respectively (Figure 5D). A nomogram was used to evaluate the OS (at 1-, 3-, and 5-years) of patients with LUAD. The risk score of patients was 652, and the 1-, 3-, and 5-year survival probability were estimated at 94.9%, 82.9%, and 65.8%, respectively (Figure 5F, 5G). Thus, T classification and risk score were identified as independent prognostic factors for patients with LUAD.

Figure 5.

Figure 5

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Prognostic evaluation of multiple clinical characteristics. Age, Gender, Stage, T classification, N classification, M classification and Risk Score were predicted by using univariate Cox analysis (A) and multivariate Cox analysis (B). (C, E) The ROC curve and C-index of clinical features. (D) The accuracy of the model prediction by ROC curve in entire set. (F, G) The nomogram predicted prognosis, and calibration tested the accuracy of prediction.

PCA and functional annotation analyses

PCA was conducted to assess four components for both high- and low-risk subgroups. These components were as follows: all genes (Figure 6A), CRGs and FRGs (Figure 6B), FCLs (Figure 6C), and risk-associated lncRNAs (Figure 6D). As shown in Figure 6D, the risk-associated lncRNAs constructed for the two risk groups could distinctly distinguish the two cohorts. The KEGG pathway analysis revealed 10 pathways, including hematopoietic cell lineage, primary immunodeficiency, amoebiasis, asthma, neutrophil extracellular trap formation, cell adhesion molecules, leishmaniasis, staphylococcus aureus infection, complement and coagulation cascades, and allograft rejection (Figure 6E). GO analysis revealed that the FCLs were related to immune responses and NADP+ activity, such as NADP activity, T cell receptor complex, and immune response-regulating signaling pathway (Figure 6F; Supplementary Table 5).

Figure 6.

Figure 6

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PCA and functional annotations. PCA of (A) All genes. (B) FRGs and CRGs. (C) FCLs. (D) Risk lncRNAs. Loop diagram of (E) KEGG pathway and (F) GO analyses.

Exploration of the immune landscape

We used five different methods (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC) to predict the correlation between the two risk groups and immune cells, which revealed that B cell and T cell CD4+ (common part) had significant associations in both risk cohorts (Figure 7A). As presented in Figure 7B, we observed a strong association between FCLs and immune functions in the two risk groups. These immune functions included type II Interferon (IFN) response (P < 0.001), type I IFN response (P < 0.05), HLA (P < 0.001), APC co-stimulation (P < 0.001), APC co-inhibition (P < 0.001), CCR (P < 0.001), parainflammation (P < 0.001), MHC class I (P < 0.05), cytolytic activity (P < 0.01), inflammation-promoting (P < 0.01), check-point (P < 0.001), and T cell co-stimulation (P < 0.001). In addition, we assessed the correlation between m6A-related gene expression and risk groups and found eight genes (YTHDC2, RBM15, HNRNPC, YTHDC1, FTO, METTL3, METTL14, and ZC3H13) associated with risk cohorts (Figure 7C). Finally, the analysis in Figure 7D indicated that 43 immune checkpoints (P < 0.05) were associated with FCLs in high- and low-risk groups.

Figure 7.

Figure 7

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Immune-related functional introduction. A. The fraction of immune cells between the two risk cohorts using seven methods. B. Immune function of two risk groups. C. m6A-related genes expression between the two risk cohorts. D. Expression of immune checkpoints.

Discussion

LUAD is one of the most common malignant tumors with high incidence and mortality worldwide [1]. Ferroptosis, a phenomenon first reported in 2012, and cuproptosis, a newly recognized process in 2022 [5,12], are distinct forms of cell death. Each type has its own unique inducers, e.g., erastin for ferroptosis and elesclomol-Cu (II) for cuproptosis [7,11]. Furthermore, lncRNAs are associated with ferroptosis and cuproptosis [17,18]; however, the mechanism of FCLs in tumors, especially in LUAD, is still unclear. Therefore, investigating the role of FCLs and their use in the construction of a risk model might provide a new therapeutic direction for patients with LUAD.

Risk models of FRL have been reported in the published literature [19-22]; likewise, prognostic models of CRL can also be found in the published study [23-26]. However, an FCL-related prognostic model has not been reported. Herein, we first identified three FCLs (AC079193.2, AC090559.1, and AL512363.1) for LUAD through the LASSO-Cox algorithm based on the TCGA database. After we confirmed that FCLs were tumor-specific and relevant to ferroptosis and cuproptosis, a prognostic risk model was established. The results showed that the risk score and T classification could serve as independent prognostic parameters. A nomogram was used to assess the OS for patients with LUAD, which might act as a supplementary prediction tool. Next, we also found that there were strong internal connections between risk cohorts and multiple immune functions, immune cells, as well as m6A-related genes, and especially immune checkpoints. Finally, the correlation between the risk groups and drug sensitivity was computed. The data showed that risk scores were negative with six clinical drugs for LUAD. Taken together, the FCL signature could potentially predict the prognosis of patients with LUAD, with high-risk scores linked to poorer OS.

Among the three FCLs used in the risk score, high-risk lncRNA AL512363.1 was linked to a worse prognosis, whereas the low-risk lncRNAs AC079193.2 and AC090559.1 with associated with better OS. Notably, AC090559.1 is a ferroptosis- and pyroptosis-related lncRNA that could predict patient prognosis for LUAD [19,27]. In other studies, AC090559.1 has also been identified as a necroptosis-related lncRNA that is down-regulated in osteosarcoma, and associated with a poor prognosis [28]. In contrast, AC079193.2 and AL512363.1 have not been studied.

In this study, we detected the expression of three FRGs and three CRGs in two cell lines. Among these genes, GPX4 plays a crucial role in ferroptosis, and its protein levels can be inhibited through MTORC1 using drugs, thus enhancing the susceptibility of lung cancer cells to ferroptosis [29]. Notably, Jia et al. proved that GPX4 activated the cGAS-STING signaling pathway and modulated STING-dependent immunopathology [30]. SLC40A1, on the other hand, is an iron transporter that plays an essential role in early embryonic development [31]. Overexpression of TFRC in neuroblastoma cells induces sensitivity to ferroptosis [32]. Cuproptosis-related FDX1, acting as the direct target of elesclomol, could rescue copper-independent cell death [33]. NLRP3 could lead to the formation of a complex that was the platform of caspase-1 activation and mediating the release of IL-1β, IL-18, and GSDMD-induced cell death [34]. Lastly, the protein-coding transcript variant 1 of GCSH, when overexpressed, imparts an additional increase in vitality to breast cancer cells [35].

The tumor immune microenvironment is recognized as a significant factor in cancer development and is central to immunotherapy [36]. GO analysis revealed that FCLs were related to immune response and NADP+ activity; however, there was no direct link to ferroptosis or cuproptosis. This could be due to the moderate Pearson correlation coefficient of 0.4 among the screen lncRNA, indicating that their correlation might not be very strong. The FCL risk model, however, was strongly associated with multiple immune functions, including IFN responses, HLA, MHC class I, and immune checkpoints. IFN response determines dynamic tumor-immune interaction in the process of cancer development and metastasis. IFN also plays a dual role leading to immune checkpoint blockade response [37]. The degree of heterozygosity in the HLA gene family was correlated with survival probability in patients with non-small-cell lung cancer treated with immune checkpoint inhibition [38]. In 2019, Burr et al. proved that the deletion of MHC class I in multiple tumor cells could decrease immunotherapy sensitivity [39]. In our data, immune checkpoints like PD1 and CTLA4 were more active in the low-risk group than in the high-risk group. Previous research has shown that CTAL4 treatment can reverse tumor growth mediated by CD4 T cytotoxicity [40]. In the past decade, anti-PD1/PDL1 immunotherapy has become the standard treatment for stage III-IV non-small-cell lung cancer [41].

Furthermore, the analysis involving five methods to assess the relationship between risk groups and immune cells suggested that seven immune cells, including B cells, T cells, natural killer (NK) cells, monocytes, macrophages, myeloid dendritic cells, and mast cells, had significant associations with risk groups. These immune cells have been implicated in various aspects of tumor immunity, such as memory T cell metabolism improvement [42]; mature dendritic cell activation and migration [43]; NK cell involvement in immunotherapy [44]; antibody production of B cells involved in resistance to severe acute respiratory syndrome coronavirus 2 invasions [45]; and the dual role of macrophages in pathogen neutralization and immune response modulation [46]. Finally, the analysis of m6A-related genes in relation to the risk cohorts revealed that eight genes had higher expression in the low-risk group compared to the high-risk group. M6A-related genes and lncRNAs are known to be associated with ferroptosis and cuproptosis [47,48]. It has also been shown that cisplatin and doxorubicin, common anti-cancer drugs, had higher sensitivity in low-risk groups compared to high-risk groups. Kuerban et al. proved that doxorubicin-loaded bacterial outer-membrane could improve anti-cancer efficacy to obviously decrease side effects [49].

This study does have a few limitations. First, the data used for analysis was only obtained from the TCGA database, and further research involving a clinical cohort would be beneficial. Second, experimental validation and in vivo studies are lacking, which would provide a deeper understanding of the mechanisms involved.

In conclusion, this study confirmed the tumor-specific nature of three screened FCLs, namely AC079193.2, AC090559.1, and AL512363.1, and established their relevance to ferroptosis and cuproptosis. By integrating these cell death processes, a prognostic risk model was constructed. This model was able to predict patient OS and assess the immune microenvironment. Our findings might provide a potential opportunity for clinical applications in patients with LUAD.

Acknowledgements

We appreciate the support of Shandong Provincial Hospital and Shandong First Medical University. The research was supported by National Natural Science Foundation of China (Grant No. 8197102168). We thank Bullet Edits Limited for editing the language of the manuscript.

Disclosure of conflict of interest

None.

Supplementary Tables 1-4

ajcr0013-5306-f8.pdf (175.5KB, pdf)

Supplementary Table 5

ajcr0013-5306-f9.xlsx (61KB, xlsx)

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