Bioinformatic prediction of miR-320a as a potential negative regulator of CDGSH iron-sulfur domain 2 (CISD2), involved in lung adenocarcinoma bone metastasis via MYC activation, and associated with tumor immune infiltration

Introduction

According to the International Agency for Research on Cancer in 2022, lung cancer (LC) is the most commonly diagnosed cancer worldwide (with an incidence of 12.4%), and a leading cause of cancer-related deaths (with a mortality rate of 18.7%) (1). The 5-year overall survival (OS) rates for LC range from 14% to 17% (2) with metastasis serving as the primary cause of death (3). The most common type of LC is lung adenocarcinoma (LUAD), which accounts for approximately 55% of cases (4). The 5-year OS rate of patients with late-stage disease is less than 15% (4).

Skeletal involvement is a common occurrence in LUAD metastasis and often presents as osteolytic bone metastasis (BM) (5,6). BM in LC typically presents with bone pain, nerve compression, and hypercalcemia, and significantly affects the quality of life of patients (7). The heterogeneity of tumor cells and the development of resistance to conventional therapies complicate treatment efforts, emphasizing the need for personalized comprehensive treatments tailored to individual patients.

The identification of molecular indices and differential genes for diagnosis could help to assess the biomolecular mechanisms related to LUAD BM, thus advancing the efficacy of gene-targeted therapies. Currently, various studies involving tumor markers (e.g., proteins, peptides, metabolites, and nucleic acids) are underway. Variations in tumor markers can correspond to different stages of cancer development (8), providing a valuable complement to imaging techniques. However, while these markers show promise, their limited specificity and sensitivity in certain contexts highlight the need for further research and development.

One approach to gene-targeted therapy involves inducing programmed cell death in cancer cells. Ferroptosis is a type of programmed cell death that is characterized by the production and accumulation of iron-dependent lipid peroxidation (9,10). A growing body of literature has shown the potential role of ferroptosis in BM (11). For example, semiconductor polymer nanoinductors have been used to induce ferroptosis and amplify oxidative damage, offering a synergistic approach for treating BM (11). In 2023, Zhou et al. (12) used bioinformatics to identify ferroptosis-related differentially expressed genes (DEGs) in LUAD BM, providing new targets for monitoring and treating BM. Additionally, whether used individually or in combination, the serum markers alkaline phosphatase (ALP) and neuron-specific enolase (NSE) both demonstrated an area under the curve >0.70.

However, the specific mechanisms of ferroptosis in LUAD BM remain unclear. This study evaluated the DEGs in LUAD-BM tissues compared to normal lung tissues. The identified DEGs were subsequently intersected with the ferroptosis dataset to acquire the ferroptosis-associated DEGs. Further, to determine essential bio-indices and establish the pathogenesis of LUAD BM at the molecular level, upstream regulatory microRNAs (miRNAs or miRs) were investigated. Finally, we integrated clinical data from Quzhou People’s Hospital LC patients to explore clinical factors for evaluating LUAD BM. In doing so, we sought to further delineate the role of ferroptosis in LUAD-BM patients. We present this article in accordance with the STREGA reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1188/rc).

Methods

Identifying the DEGs in LUAD BM

The gene expression dataset, GSE10799, which comprises both normal and LUAD-BM tumor tissues, was acquired from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi) database using GEOquery. Additional probes corresponding to multiple molecules were removed. When encountering probes for the same molecule, only those with the highest signal were retained.

For the differential expression analysis, an e-tool, Gene Expression Omnibus 2 R (GEO2R; https://www.ncbi.nlm.nih.gov/geo/geo2r/) was used. Genes that were differentially expressed in LUAD BM were retained if they met the following criteria: |log2 (fold change)|>0.5, with an adjusted P value <0.05.

Ferroptosis-related genes

Ferroptosis-related genes were acquired from the ferroptosis database (http://www.zhounan.org/ferrdb). The dataset comprises 259 genes, including 108 driver genes and 69 suppressor genes, and 111 genetic markers (13).

Identifying ferroptosis-related DEGs in LUAD BM

The genes acquired from the ferroptosis database were cross-referenced with GSE10799 to identify the potential ferroptosis-related DEGs, which are hereafter referred to as the candidate genes. Venn diagrams and heat maps were prepared using Venny 2.1 and Heml software (http://www.liuxiaoyuyuan.cn/), respectively.

Identifying candidate gene-gene interactions

Using GeneMANIA database (http://www.genemania.org) and the STRING database (https://string-db.org/), the key genes were first entered as the query. Both databases offer a wide range of options for analyzing and predicting gene interactions, including protein-protein interactions (PPIs), protein-DNA interactions, genetic interactions, common pathway involvement, physiological and biochemical reactions, similarity in gene and protein expression patterns, shared protein domains, and phenotype associations. Taking into account these different types of interactions and sources of evidence, a comprehensive network of interactions was constructed. After constructing a PPI network, we focused on the key nodes and edges within the network, especially those nodes that have significant interactions with multiple genes or proteins, as they may play a central role in biological processes. Then, we further analyzed the network’s topology, including the degree distribution of nodes, clustering coefficients, and path lengths, to reveal the overall organizational principles and potential functional modules of the network. Through network analysis, a group of genes and proteins closely related to key genes can be identified, which may play a synergistic role in common biological pathways or processes. This information provides important clues for further understanding the functions and regulatory mechanisms of key genes.

Relationships between candidate genes and immune cells

Using the Tumor IMmune Estimation Resource (TIMER) (https://cistrome.shinyapps.io/timer/) and The Cancer Genome Atlas (TCGA) databases, the expression of the candidate ferroptosis-related DEGs in LUAD was assessed and portrayed using bar graphs. Kaplan-Meier survival curves were generated from the Gene Expression Profiling Interactive Analysis (GEPIA) website (http://gepia.cancer-pku.cn/).

Gene set enrichment analysis (GSEA)

The online functional analysis was carried out using the DEGs via the Metascape database (https://metascape.org/gp/index.html#/main/step1).

Prediction of the upstream regulated MiRNAs of the candidate genes

To determine whether the candidate genes were modulated by upstream genes, the miRNAs that interact with the 3’untranslated region (3’UTR) end of the messenger RNA (mRNA) were identified from the TargetScan database (https://www.targetscan.org/vert_80/).

Testing the association between the candidate miRNAs and essential genes in TCGA

To evaluate whether the select candidate genes were regulated by upstream genes, we further explored the possible miRNAs associated with the 3’UTR end binding of the mRNA of the select candidate genes through the TargetScan database.

Inclusion of LUAD patients’ clinical data

A retrospective analysis of 40 LUAD patients admitted to the Quzhou People’s Hospital from May 2021 to May 2023 was performed. To be eligible for inclusion in this study, the patients had to meet the following inclusion criteria: (I) have pathologically confirmed LUAD; (II) have complete clinical data; and (III) have complied and cooperated with the relevant tests. Patients were excluded from the study if they met any of the following exclusion criteria: (I) had received glucocorticoid or bisphosphonate treatment within 3 months of enrollment; (II) had another severe lung disease, including asthma; and/or (III) had another concurrent cancer. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of Quzhou People’s Hospital (No. 2023-015) and informed consent was taken from all the patients.

Tissue microarray construction and immunohistochemistry

Specimens of surgically resected LUAD and adjacent tissue (5 cm from the cancer tissue) were collected, paraffin-embedded, sliced (4-µm thick), dewaxed in xylene, rehydrated using gradient ethanol concentration, subjected to an antigen retrieval process by heating in citrate buffer for 20 minutes, treated with hydrogen peroxide (3%) for 10 minutes at ambient temperature to block the activity of endogenous peroxidase, and then blocked using 10% fetal bovine serum for 10 minutes at ambient temperature. The samples were then treated overnight with CISD2 antibody (PA5-53398, Thermo Fisher Scientific, Waltham, MA, USA) at 4 ℃, followed by horseradish peroxidase-conjugated goat anti-rabbit immunoglobulin G secondary antibody (Zhongshan Golden Bridge Biotechnology Co., Ltd., Beijing, China) at ambient temperature for 20 minutes, stained with 3,3'-diaminobenzidine, and coverslipped with neutral resin. The scores were based on the percentages of positively stained cells as follows: 4 (>75%), 3 (51–75%), 2 (25–50%), 1 (<25%), and 0 (negative). The staining intensity was scored as follows: 3 (strong positive), 2 (moderate positive), 1 (weak positive), and 0 (negative or no staining). For each specimen, the final score represented the product of the two scores. After assessing the average of these scores, samples with a score <4 were defined as having a negative expression of the select candidate gene CISD2.

Statistical analysis

R (version 3.6.3; R Foundation for Statistical Computing, Vienna, Austria) was employed for the statistical assessment and visualization. The possible cellular mechanisms of the crucial gene CISD2 were assessed via a GSEA. To assess the patient survival rate, the Kaplan-Meier test was employed, while the log-rank statistical test was used for significance testing. Statistical analyses in the rest of the studies were automatically calculated by the specific online databases. The statistically significant threshold was set as a log-rank P value <0.05 or a P value <0.05.

Results

Screening of the DEGs

We acquired the microarray expression dataset GSE10799 from the GEO database. By comparing the tumor tissue to the normal tissue, volcano and principal component analysis plots of the DEGs were constructed (Figure 1A,1B, respectively). Subsequently, these DEGs were intersected with the ferroptosis-associated genes to obtain a Venn diagram of the two data sets (Figure 1C). Ultimately, the following 15 ferroptosis-related DEGs in LUAD BM were identified: TP63, DPP4, LURAP1L, EGFR, HSD17B11, ISCU, CISD2, CHMP5, PEBP1, IDH1, TNFAIP3, LONP1, ELAVL1, MT3, and SLC2A8.

Figure 1 Identification of DEGs. (A) Volcano plot and (B) PCA plot of the DEGs in GSE10799 (blue represents low expression DEGs, red represents high expression DEGs, and gray section represents genes that are not significantly and differentially expressed); (C) Venn diagram showing the intersection between ferroptosis-associated genes and DEGs in lung cancer BM. PCA, principal component analysis; DEGs, differentially expressed genes; BM, bone metastasis.

Expression of the candidate genes in LUAD

The LUAD-TCGA dataset was used to identify the expression of the aforementioned 15 candidate genes, which revealed that TP63, LURAP1L, HSD17B11, ISCU, PEBP1, and TNFAIP3 were significantly downregulated in the LUAD tissues compared to the normal tissues, while DPP4, CISD2, IDH1, LONP1, and ELAVL1 were significantly upregulated in the LUAD tissues compared to the normal tissues (P<0.05) (Figure 2).

Figure 2 Expression of candidate DEGs in lung adenocarcinoma in TCGA. *, P<0.05; **, P<0.01; ***, P<0.001, statistically significant difference. DEGs, differentially expressed genes; TCGA, The Cancer Genome Atlas; TPM, transcripts per million.

Prognostic value of the candidate genes in LUAD patients with BM

The potential impact of candidate gene expression on the clinical prognosis of patients with LUAD in TCGA database was examined. Patients with low CISD2, ELAVL1, IDH1, and LONP1 expression had better OS rates (Figure 3A). The low expression of CISD2, ELAVL1, and IDH1 was also associated with significantly better DSS and a significantly better PFI (P<0.05). LUAD patients with low expression of CISD2, ELAVL1, and IDH1 have better DSS and PFI (Figure 3B,3C). Combining the expression of candidate genes in LUAD and their role in prognosis, as well as previous research reports (14,15), we selected CISD2 as the most promising candidate gene to perform further investigations.

Figure 3 Correlation between candidate DEGs and the prognosis of lung adenocarcinoma patients. (A) OS; (B) DSS; and (C) PFI. DEGs, differentially expressed genes; OS, overall survival; DSS, disease-specific survival; PFI, progression-free interval.

Determining the genes and proteins that interact with CISD2

A gene-gene interaction network of CISD2 was established, and neighboring genes were altered via GeneMania (Figure 4A), and a PPI network of CISD2 was constructed (Figure 4B).

Figure 4 Screening the CISD2-interacting proteins and genes. (A) Gene-gene interaction network of CISD2; (B) PPI network of CISD2. PPI, protein-protein interaction.

Association between CISD2 and immune cells

The potential relationship between CISD2 expression and the immune cell infiltration of LUAD tumors was assessed. CISD2 levels were negatively associated with B and cluster of differentiation (CD)4 + T cell infiltration levels, but were positively associated with dendritic, CD8+ T, neutrophil, and macrophage cell infiltration levels (Figure 5A). To further assess the influence of CISD2 on the tumor microenvironment (TME), the associations between CISD2 and immune cell types were evaluated. CISD2 levels were negatively correlated with the infiltration of most immune cells, including B cells, plasmacytoid dendritic cells, natural killer cells, and T follicular helper cells, and were positively correlated with the infiltration level of T helper cells, T helper 2 cells, and gamma delta T cells (Figure 5B,5C). Based on these findings, we speculate that the expression of CISD2 may be associated with tumor immune infiltration in LUAD.

Figure 5 Correlation between CISD2 levels and immune cell infiltration levels. (A) Correlation between CISD2 levels and the infiltration of different immune cell types in lung adenocarcinoma in the TIMER and (B, C) TCGA database. *, P<0.05; **, P<0.01; ***, P<0.001; ns, no significant. TIMER, Tumor IMmune Estimation Resource; TCGA, The Cancer Genome Atlas.

Pathway prediction based on the GSEA

Using Metascape, an online GSEA functional analysis was carried out, which revealed that CISD2 may affect the biological processes of LUAD by affecting the cell cycle, reactive mitochondrial translation, and DNA damage repair, and activating the MYC and P53 signaling pathways (Figure 6).

Figure 6 Possible relevant pathways from the GSEA. The Gene Ear Biology Biological Process gene set from MSigDB was used. 1,600 random sample permutations were carried out. NES, normalized enrichment score; FDR, false discovery rate; GSEA, gene set enrichment analysis.

Upstream regulation of CISD2 by MiRNAs

To determine whether CISD2 was modulated by upstream genes, the top ten predicted miRNAs that bind the 3’UTR end of CISD2 mRNA were identified (Table S1).

In the TCGA database, exploring the expression of the above candidate miRNAs in LUAD, we found that only hsa-miR-335-5p and hsa-miR-320a were downregulated in tumor tissues, while hsa-miR-186-5p, hsa-miR-199a-3p, hsa-miR-199b-3p, hsa-miR-320b, hsa-miR-320c, hsa-miR-320d, hsa-miR-154-5p, and hsa-miR-495-3p were upregulated in tumor tissues, all with statistically significant differences (P<0.05) (Figure 7).

Figure 7 Expression of candidate miRNAs in LUAD in TCGA database. *, P<0.05; **, P<0.01; ***, P<0.001. TCGA, The Cancer Genome Atlas; miRNAs, microRNAs; LUAD, lung adenocarcinoma; RPM, reads per million.

We further analyzed the correlation between the above 10 candidates and CISD2 in TCGA database. According to the competing endogenous RNA (ceRNA) hypothesis, miRNAs inhibit the expression of their target mRNAs by binding to them. Therefore, in the ceRNA network, there had to be a negative association between the miRNA and target mRNA. Combining the above-mentioned candidate miRNA expression results, correlation intensity results, and previous research reports, we ascertained that HSA-miR-320A was the most potent upstream modulator of miRNA (Figure 8).

Figure 8 The association between the candidate miRNA and CISD2 in TCGA database. miRNA, microRNA; TCGA, The Cancer Genome Atlas; RPM, reads per million.

Clinical outcomes

Positive expression of CISD2 in LUAD tissues

Of the 40 patients included in the study, 18 had BM, which had an incidence rate of 45.00%. The patients were divided into the BM group and non-BM group. In the BM group, CISD2 was positively expressed in 11 cases (61.11%) with an immunohistochemistry score of 3.77±2.35. In the non-BM group, CISD2 was positively expressed in six cases (27.27%) with an immunohistochemistry score of 2.29±1.68. The positive expression rate of CISD2 and the immunohistochemistry scores were significantly more increased in the BM group than the non-BM group (P<0.05) (Figure 9 and Table 1).

Figure 9 Immunohistochemical results of CISD2 in lung adenocarcinoma tissues and paracancerous tissues (×200). *, P<0.05.

Univariate analysis of factors influencing LUAD

The serum levels of NSE, ALP, carcinoembryonic antigen (CEA), and carbohydrate antigen 125 (CA125) were higher in the BM group than the non-BM group (P<0.05). No statistically significant differences were found in terms of gender, Eastern Cooperative Oncology Group (ECOG) score, age, and smoking status (P>0.05) (see Table 2 for more detailed data).

Binary logistic multivariate regression analysis

The factors identified as significantly different in the above univariate analysis were included in the binary logistic multivariate regression analysis. The results indicated that serum ALP and CISD2 expression are risk factors for LUAD (Table 3).

Discussion

In China, LC ranks first in terms of new cases and deaths, and the probability of BM is as high as 10–15%. LUAD is the most frequent pathological subtype of LC (16). Clinically, the diagnosis of LC BM mainly relies on imaging and bone puncture biopsy. Due to limitations of sensitivity, low specificity, and radiation, it is difficult to achieve early discovery and early diagnosis of BM (17). At present, the specific molecular mechanism related to the metastasis of LUAD is unclear. Therefore, in clinical practice, effective molecular tumor indices have essential reference value in the early diagnosis, prognosis evaluation, and recurrence monitoring of tumor patients.

Ferroptosis, which is a distinctive iron-dependent type of programmed cell death, is crucially involved in various pathological and physiological processes, including immunity and cancer. Relevant studies have explored its implications in tumor metastasis. For example, ferroptosis nano-therapy can simultaneously block hematogenous and lymphatic metastasis (18). BACH1-mediated ferroptosis modulates lymphatic metastasis by suppressing monounsaturated fatty-acid biosynthesis (19). In nasopharyngeal cancer, extracellular vesicles derived from platelets suppress ferroptosis and promote distant metastasis (20). KLF2 regulates ferroptosis through GPX4 to inhibit the ability of cancer cells to invade and migrate in clear cell renal cell carcinoma (21). However, currently, research on the activity of ferroptosis in LC BM is limited.

This study conducted a bioinformatics analysis to identify the ferroptosis-related DEGs in LC BM and also conducted a functional enrichment analysis. The intersection of the DEGs from the LC-BM dataset GSE10799 and ferroptosis-related genes yielded 15 candidate genes. Based on their expression in the TCGA-LUAD dataset and their impact on prognosis, three final candidate genes were identified (i.e., CISD2, ELAVL1, and IDH1). These genes have been investigated in the context of LUAD. For instance, in non-small-cell LC (NSCLC), the downregulation of CISD2 has been shown to have prognostic value and to inhibit tumor development by inducing mitochondrial dysfunction (22). Conversely, the upregulation of CISD2 increases reactive oxygen species homeostasis, leading to tumor development and poor prognosis in LUAD (15).

ELAVL1 expression is upregulated by exosomal lncRNA FOXD3-AS1, thereby activating the PI3K/Akt pathway, which in turn increases the 5-fluorouracil resistance, invasion, and proliferation of LUAD cells (23). Considering the characteristic mechanisms of ferroptosis and previous research findings, we identified CISD2 as the optimal select candidate gene. Through TCGA, GeneMania, and STRING databases, the protein genes that interacted with CISD2 were identified, providing reference points for further mechanistic analyses.

In the TIMER database, the expression of CISD2 was negatively linked with B and CD4+ T cell infiltration levels, but positively associated with macrophage, neutrophil, CD8+ T, and dendritic cell infiltration levels. While previous research has described the influence of CISD2 on the tumor immune microenvironment in colorectal cancer and gliomas (24,25), our findings also suggest CISD2 may affect immune infiltration in LUAD. Various types of immune cells play crucial roles in the BM of LUAD. CD8+T cells can infiltrate the BM site, directly killing metastatic cancer cells and inhibiting the progression of BM. Additionally, CD4+T cells may enhance the cytotoxic effect on BM cancer cells by promoting the activation and proliferation of CD8+T cells. These CD4+ T cells also help regulate the immune response, maintain immune system balance, and prevent excessive immune reactions that could damage the body (26). Neutrophils can secrete pro-inflammatory factors that enhance the invasive capabilities of tumor cells, promoting the survival and spread of cancer cells within bone tissue (27). Tumor-associated macrophages (TAMs) may promote angiogenesis and matrix degradation in bone metastases by secreting various growth factors and cytokines, thereby creating favorable conditions for the growth and spread of bone metastatic cancer cells. Additionally, TAMs can weaken the body’s anti-tumor immune response by inhibiting the activation of immune cells (28). Additionally, the GSEA revealed that CISD2 may impact various biological processes in LC by participating in cell-cycle regulation, reactive mitochondrial translation, DNA damage repair, MYC activation, and the P53 signaling pathway.

Our findings about these potential mechanisms align with previous findings on the development and progression of LC (29-32). For instance, research has shown that CKAP2L promotes NSCLC progression by regulating transcription elongation (29). TRIP13 enhances LC cell growth and migration through the AKT/mTORC1/c-MYC signaling pathway (30). The miR-296-3p/PRKCA/FAK/Ras/c-MYC feedback loop modulated by the HDGF/DDX5/β-catenin complex contributes to LUAD (31). P53-induced gene 3 activates the FAK/Src pathway, promoting cell migration and invasion in LUAD (32).

Additionally, in an analysis of data from both the TargetScan and TCGA databases, hsa-miR-320a was identified as the optimal upstream regulatory miRNA of CISD2. hsa-miR-320a inhibits the expression of CISD2 by directly binding to its 3’UTR region, and there is a negative correlation between the two. Research on hsa-miR-320a in LUAD supports its role as a tumor suppressor and prognostic factor (33). It is upregulated during the reversal of platinum resistance in LUAD, offering a novel approach to studying the pathogenesis of LUAD (34). MiR-320a effectively inhibits the capability of LUAD cells to proliferate and migrate by regulating the STAT3 signaling pathway (35). Combining these findings, we speculate that miR-320a, as a negative regulator of CISD2, may participate in LUAD BM by activating MYC and could be associated with tumor immune infiltration. These speculations provide valuable references for subsequent experimental investigations.

Our retrospective analysis of 40 real-world LUAD patients revealed that the positive expression rate of CISD2 and the immunohistochemistry scores of BM patients were significantly increased compared to those of patients who did not have BM. The multivariate analysis identified serum ALP and CISD2 expression as significant risk factors for BM in LUAD patients. Elevated ALP levels have been previously associated with BM and metastatic activity (36), which supports our findings. The identification of CISD2 as a risk factor is particularly novel, as the role of this gene in BM has not been extensively explored in LUAD. These research findings have important implications for public health and clinical practice. In public health, our study identifies CISD2 as a potential therapeutic target for LUAD BM, providing clues for developing new anti-cancer therapies. Clinically, CISD2 serves as a potential biomarker for patients with LUAD BM, aiding doctors in more accurately assessing patient conditions, personalizing treatment plans, and monitoring disease progression.

Conclusions

This study used biological information technology to analyze and determine the potential ferroptosis select candidate gene CISD2 in the pathogenesis of LUAD to provide novel targets for LUAD-BM treatment. However, this study has certain limitations, such as a small sample size, insufficient mechanism validation, and the absence of multicenter clinical trials. To address these, we plan to expand the sample size to improve statistical power, conduct further experiments to explore CISD2’s role in LUAD BM and its relationship with immune infiltration and miR-320a regulation, and collaborate with multiple institutions on multicenter clinical trials to validate CISD2’s predictive value and therapeutic potential in the future.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the STREGA reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1188/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1188/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1188/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1188/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). The study was approved by the Ethics Committee of Quzhou People’s Hospital (No. 2023-015) and informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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