A disulfidptosis-related lncRNA prognostic model identifies MALINC1 as a regulator of platinum drug sensitivity in colon adenocarcinoma

Introduction

Colorectal cancer is the second most deadly and third most diagnosed cancer globally according to the most recent published Global Cancer Statistics (1,2), contributing an enormous burden to the health care system in developed and developing countries (3,4). Colon adenocarcinoma (COAD) is the most prevalent type of colorectal cancer with poor prognosis despite the unprecedented medical progress achieved (5-7).

Targeting tumor cell death is, and has always been, an important goal and approach of tumor chemotherapy (8). A growing body of evidence, especially from clinical and fundamental research over the past decade or so, has indicated that regulation of specific signaling pathways involved in cell differentiation and fate can significantly inhibit tumor progression (9), including COAD (5,10,11). In this setting, targeting components involved in the execution cascades of some new forms of cell death [e.g. pyroptosis (12), ferroptosis (13,14), cuproptosis (15), disulfidptosis (16,17) and triaptosis (18,19)] in tumor cells has provided new perspectives and positive insights for anti-cancer therapy (9).

Epigenetic modifications represent a highly significant alteration in tumors, including COAD (20). Indeed, epigenetic modifications is also closely associated with various forms of programmed cell death, mainly manifested in regulation of key senor or executing proteins (9). Study (9) has focused on how non-coding RNA, e.g. long non-coding RNA (lncRNA), regulates various cell deaths, bridging the gap between lncRNA and pathogenesis, treatment and prognosis of COAD. With the help of massive biological data, numerous computational models have been developed to evaluate the prognosis of COAD to find and identify potential new drug targets of COAD (21,22), whereas few of which have become mainstream drug therapy methods, suggesting that the development of targeted drugs with COAD epigenetics still requires efforts.

Here, we explore the relationship between lncRNAs and disulfidptosis, a recently characterized form of regulated cell death linked to actin cytoskeleton integrity (23,24). Using transcriptomic data from The Cancer Genome Atlas (TCGA), we developed a prognostic signature based on the disulfidptosis-related lncRNAs in COAD. Within this signature, MALINC1 emerged as a potential alarming lncRNA associated with poor survival in COAD. Functional experiments in COAD cell lines HCT8 and SW480 suggested that MALINC1 silencing significantly suppressed cell proliferation, reduced intracellular levels of free sulfhydryl groups and the reduced glutathione (GSH). Simultaneously, MALINC1 knockdown markedly upregulated the key player of disulfidptosis SLC7A11, whose inhibitor largely restored proliferative potential. In addition, MALINC1 dysfunction sensitized cancer cells to platinum-based chemotherapy. Together, these findings establish a prognostic framework for COAD and provide theoretical support for identifying MALINC1 as a potential therapeutic target. We present this article in accordance with the MDAR and TRIPOD reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0225/rc).

Methods

Data collection and preparation

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Total 448 transcriptomes and matched clinical details of COAD patients, the latter including patient’s age, gender, tumor tumor-node-metastasis (TNM) stage, years of follow-up, and survival outcome, were retrieved from the TCGA repository. R software (version 4.2.2) and Perl (version 5.32.1.1) were used.

Disulfidptosis-related lncRNAs identification and a prognostic disulfidptosis-related lncRNA signature construction

Analysis of co-expression network consisting of 10 disulfidptosis core genes (8) and all lncRNAs was used to scan disulfidptosis-related lncRNAs at criterion of r>0.4 for the correlation coefficient and a significance level of P<0.001. To construct a prognostic disulfidptosis-related lncRNA signature, samples were initially assigned to a training set (n=224) and verification set (n=224) randomly, then corresponding gene expression signatures from the training set were fed into univariate Cox regression algorithm to identify candidate lncRNAs with high prognostic value via a significant criterion of P<0.05. Multivariate Cox regression was employed to choose the most pertinent predictors, whose coefficients were utilized to establish prognostic formula. Final risk score was determined as follows:

Riskscore=∑i=1NExpi×Coei[1]

Verification of the prognostic model and independent prognostic factor identification

In the training, verification set and combined sets, each sample was labelled with risk score, followed by dividing into high- and low-risk groups, with the median value serving as the cutoff threshold. The Kaplan-Meier (KM) survival curves were produced by R software packages “survival” and “survminer” to compare the two groups’ survival rates, and area under the curve (AUC) of receiver operating characteristic (ROC) from the prognostic model was used to evaluate accuracy of model. Univariate and multivariate Cox regression analysis were conducted to evaluate whether the risk score could be an independent prognostic factor.

Principal component analysis (PCA)

The R package “scatterplot3” was used to perform PCA examine, in which main feature components of the corresponding high-dimensional gene expression data could be identified.

Single cell RNA sequencing data analysis

Publicly accessible single-cell RNA transcriptomes from COAD and control human donors were used to check the six lncRNAs expression pattern. Data were downloaded from Zenodo repository (https://zenodo.org/record/7872684), then transcriptomes of colon epithelial compartments and matched metadata were extracted, followed by visualization of expression pattern of indicated lncRNAs in global and specified contexts.

Cell line and culture

Cancerous HCT8 cells, SW480 cells and normal NCM460 colon epithelial cells were obtained from the Cancer Institute, Central South University. The cultured cells were supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin solution and incubated at 37 ℃ in humid air with 5% CO₂.

RNA extraction, reverse transcription, and qRT-PCR

For RNA extraction, cells were treated with TRIzol reagent (CWBio, Taizhou, China) and the total RNA was extracted with trichloromethane. Next, the extracted RNA was washed with isopropyl alcohol and 75% ethanol. For reverse transcription, complementary DNA (cDNA) was synthesized from 1 µg of total RNA using the PrimeScript RT Kit (TaKaRa, Japan). qRT-PCR was performed on a qRT-PCR machine (Life Technologies, USA) and analyzed using the QuantStudio Design & Analysis Software v1.5.1. The relative RNA abundance was calculated using the standard 2^−ΔΔCt method. The primers used are listed in Table S1.

Small interfering RNA (siRNA) transfection

The siRNA sequences used in this study were purchased from RiboBio, and siRNA transfection was performed according to the manufacturer’s protocol. Briefly, cells in the exponential growth phase were plated in 6-well culture plates. When the cell density reached almost 40%, Lipofectamine RNAiMAX reagent and Opti-MEM reduced serum medium were used for siRNA transfection. The sequences are listed in Table S2.

Free thiol content assay

HCT8 or SW480 cells in logarithmic growth phase were seeded into six-well plates at a density of 100,000 cells per well, transfected with siRNA targeting MALINC1 for 48 hours, and then harvested and assayed according to the manufacturer’s protocol of the thiol detection kit (AKAO010M, Beijing Boxbio Science & Technology Co., Ltd.). The resulting data were subjected to statistical analysis.

GSH content assay

HCT8 or SW480 cells were seeded in six-well plates, transfected with siMALINC1, and cultured for 48 hours. Reduced GSH levels were then measured using a GSH assay kit (BC1175, Solarbio) according to the manufacturer’s instructions. The resulting data were subjected to statistical analysis.

Reagent and antibodies

Fetal Bovine Serum (Cat# FND500) was sourced from ExCell Bio (Shanghai, China), and SDS-PAGE Loading Buffer (Cat#WB2001) from New Cell & Molecular Biotech (Suzhou, China). The anti-SLC7A11 antibody (Cat# ab175186) was sourced from Abcam (Cambridge, UK). Antibodies against GAPDH (Cat# GB11002) were purchased from Servicebio (Wuhan, China). Erastin (S7242) was purchased from Selleck Chemicals (Houston, TX, USA).

Western blotting analysis

After the indicated treatments, cells were lysed in RIPA buffer (Beyotime Biotechnology, Shanghai, China) supplemented with a protease inhibitor cocktail and phosphatase inhibitors. The lysates were centrifuged at 12,000 rpm for 15 min at 4 ℃. Protein concentrations in the supernatants were determined using the Bradford method (Beyotime Biotechnology). Subsequently, 20 µg of protein per sample was separated by SDS-PAGE and transferred onto 0.2 µm PVDF membranes. The membranes were blocked with 5% non-fat milk for 1 h at room temperature, incubated with primary antibodies overnight at 4 ℃, and then with secondary antibodies for 1 h at room temperature. Protein signals were detected using an enhanced chemiluminescence kit.

Cell Counting Kit-8 (CCK-8) assay

Cell viability was assessed using a CCK-8 assay kit (Bimake, TX, USA). For the experiment, 1×10⁵ cells were seeded per well in a 96-well plate and treated with various drug or siRNA. Following treatment, 10 µL of CCK-8 reagent was added to each well and incubated for 1 hour. Absorbance was measured at 450 nm. Cell viability was calculated using the formula:

Cell Viability (%) = (A (Compound) – A (Blank)) / (A (Control) – A (Blank)) × 100%.

Colony-formation assay

A total of 600 cells per well were plated in a six-well plate and subjected to the indicated treatments for 48 hours. The medium was refreshed every 2 days. After staining with 0.5% crystal violet (Beyotime Biotechnology, Shanghai, China) and washing with PBS, the plates were imaged. Colonies containing more than 100 cells were counted.

Cell counting assay

HCT8 or SW480 cells in logarithmic growth phase were seeded into 10-cm cell culture dishes at a density of 8,000 cells per dish. After cell attachment, the cells were transfected with MALINC1 siRNA for 48 hours. On days 2, 4, 6, and 8 post-treatment, cells were digested and resuspended to generate single-cell suspensions. Cell counting was performed using an optical microscope with a 10× objective. Each sample was counted in triplicate, and the mean value was recorded as the final cell density.

Statistical analysis

Data from all experiments were processed and analyzed using GraphPad Prism (version 9.0). Results were presented as the mean ± standard deviation (SD) and were derived from at least three independent experiments. Group comparisons were conducted using a two-tailed independent sample Student’s t-test or two-way analysis of variance (ANOVA). A P value of less than 0.05 was deemed statistically significant, with specific annotations as follows: *P<0.05, **P<0.01, and ***P<0.001.

Results

Exhibition of sample metadata from TCGA repository

We retrieved RNA sequencing data of 448 COAD samples from the TCGA repository, which were then randomly and equally divided into a training set (n=224) and a verification set (n=224) for subsequent predictive model construction. As shown in Table 1, no statistically significant difference in various metadata (e.g., age, gender, pathological stage) of COAD patients between the two sets was found.

Disulfidptosis-related lncRNA identification and the prognostic model developing

The correlation between disulfidptosis core genes and all lncRNAs was used to scan disulfidptosis-related lncRNAs. Briefly, through a literature review, we selected 10 disulfidptosis core genes from the transcriptome data of all samples (25), all of which are protein-coding genes. We then extracted their expression patterns from the samples, and a co-expression analysis identified a total of 921 lncRNAs. The upset plot in Figure 1A classified the associations between these lncRNAs and the disulfidptosis core genes, showing that the number of lncRNAs correlated with the expression of the NCKAP1 gene is the highest. We tentatively define these lncRNAs as disulfidptosis-related. To construct a prognostic disulfidptosis-related lncRNA signature, we initially identified 10 candidate lncRNAs with high prognostic value using univariate Cox regression analysis in a preset training set (see the Methods section for details) (Figure 1B), in which six lncRNAs including AL354993.2, AP003555.1, MALINC1, AC013652.1, AC074212.1 and AC007128.1 had a hazard ratio (HR) greater than 1, serving as poor prognostic factors, and other 4 lncRNAs namely MIR223HG, AC120193.1, SNHG16 and AC095055.1, with HR <1, serving as protective factors. Further, we confirmed six key lncRNAs most closely related to survival prediction using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression, which were all from abovementioned 10 lncRNAs, and developed prognostic model for disulfidptosis-related lncRNAs. Each sample’s risk score was determined using the following formula:

Figure 1 Disulfidptosis-related lncRNA identification and the creation of a model to predict survival using key disulfidptosis-related lncRNA based signature from training set. (A) Upset plot showing 921 disulfidptosis-related lncRNAs identified using disulfidptosis core genes. (B) Ten lncRNAs identified via univariate cox regression and their individual hazard ratio. (C) Correlations between disulfidptosis core genes and six key lncRNAs identified via multivariate cox regression. lncRNA, long non-coding RNA.

Risk score = (−1.544 × AC120193.1) + (0.6315 × AP003555.1) + (−1.093 × AC095055.1) + (0.8789 × MALINC1) + (1.169 × AC013652.1) + (0.8108 × AC074212.1).

Figure 1C shows the correlation between these six lncRNAs and the disulfidptosis core genes, along with their statistical P values.

The prognostic model can predict survival of patients with COAD in verification set and combined sets

To validate our disulfidptosis-related lncRNA prognostic model, verification set and combined sets were used. We calculated the risk score of each TCGA sample in the respective sets using the formula, and then classified the samples in each set into the high-risk group and low-risk group accordingly (see the Methods section for details). The high-risk groups in training, verification, and combined sets showed poorer overall survival than individual low-risk groups (Figure 2A-2C). The correlation heatmap in Figure 2D-2F demonstrates how these six lncRNAs comprising the model’s creation differed in their expression levels between the high- and low-risk groups.

Figure 2 Verification of the prognostic model. Kaplan-Meier plot for overall survival of the high- and low-risk score samples in (A) combined sets, (B) training and (C) verification set. Heatmaps of expression of these six lncRNAs in (D) all patients, (E) training set, and (F) validation set. lncRNA, long non-coding RNA.

Further efficiency assessment of the prognostic model confirmed the signature as an independent prognostic indicator for COAD. Firstly, ROC curves showed that the risk score possessed good predictive performance in assessing 1-, 3-, and 5-year overall survival of COAD, as shown in Figure 3A. Then, compared to clinicopathological features like age (AUC =0.655), gender (AUC =0.506), and tumor TNM stage (AUC =0.704), risk score (AUC =0.722) represented the best predictive performance (Figure 3B), indicating that calculated risk score had an acceptable diagnostic precision similar to tumor TNM staging system. Finally, Cox regression analysis (Figure 3C,3D) confirmed the signature as an independent prognostic indicator for COAD.

Figure 3 Risk score of samples as a potential independent prognostic factor. (A,B) ROC curves and corresponding AUC values of the prognostic model for predicting (A) survival at 1-, 3-, and 5-year periods and (B) 1-year survival compared with clinicopathological characteristics. (C) Univariate Cox regression and (D) multivariate Cox regression analysis of risk score and clinicopathological characteristics. AUC, area under the curve; ROC, receiver operating characteristic; TNM, tumor-node-metastasis.

Consistent with these findings, difference in progression-free time between the high and low-risk groups was uncovered in Figure 4A. PCA analysis was then performed in all samples using the six disulfidptosis-related lncRNA profiles, all gene expression data, 10 disulfidptosis gene expression data, and 921 disulfidptosis-related lncRNA profiles to investigate the efficiency to group COAD samples (Figure 4B-4E). Differentiated sample grouping only exists in the context of the six disulfidptosis-related lncRNA profiles but not other three profiles, suggesting the robustness of the abovementioned six lncRNAs in grouping COAD samples.

Figure 4 Risk score of samples could categorize COAD samples. (A) Progression-free survival in high- and low-risk score samples. (B-E) PCA in high- and low-risk groups. (B) PCA of six lncRNAs in the model. (C) PCA of all genes. (D) PCA of 10 disulfidptosis core genes. (E) PCA of all lncRNAs associated with disulfidptosis. COAD, colon adenocarcinoma; lncRNA, long non-coding RNA; PCA, principal component analysis.

To confirm whether tumor TNM stage influents on the predictive power of prognostic signature, we grouped samples to early stage (TNM stage I–II) and late stage (TNM stage III–IV) group (Figure S1A,S1B). Results from survival analysis showed that the established model was not affected by TNM stages suggesting the robustness of our disulfidptosis-related lncRNA prognostic model. Overall, these approaches allowed for identifying a prognostic disulfidptosis-related lncRNA signature that may help predict the course of COAD patients’ lives.

Silencing MALINC1 may trigger disulfidptosis in COAD cell lines

Given that four out of these six key lncRNAs predicted an unfavorable prognosis for COAD patients, we subsequently sought to further investigate the expression patterns of these four lncRNAs in other COAD scenarios. Figure 5A showed that compared to NCM460, a normal colon epithelial cell line, three out of the four candidates, AP003555.1, MALINC1, and AC013652.1, exhibited higher expression level in HCT8 and SW480 ileocecal adenocarcinoma epithelial cell line, with MALINC1 showing the most significant increase. A published single-cell sequencing study describing cell heterogeneity of colon tissues or organoid models from COAD patients (26) also uncovered the differential expression of MALINC1 in colon epithelial cell lineages (Figure 5B-5D), interestingly, the other three lncRNAs predictive of poor prognosis in COAD patients were not detected in this single-cell dataset (data not shown). MALINC1, namely LINC01024, is a novel lncRNA that is controlled by E2F1 transcription and functions in cell cycle progression; specifically, the silencing of MALINC1 in non-synchronous cells causes a reduction in the number of cells in G1, accompanied by an increase in the number of cells in all other phases of the cell cycle, especially in G2/M (27). In other words, when MALINC1 expression is low, fewer cells exit the mitotic checkpoint stasis and start the M phase of the next cell cycle. We speculated that MALINC1 could also affect proliferation of cancer cells. Based on efficient knockdown of MALINC1 by small interfering RNA-mediated silencing (siMALINC1) (Figure 5E and Figure S2A), significant reduction in cell viability (Figure 5F and Figure S2B), growth rate (Figure 5G and Figure S2C), and impaired clonogenicity (Figure 5H and Figure S2D) was observed in MALINC1-silenced HCT8 and SW480. Notably, MALINC1-silenced HCT8 and SW480 cells exhibited markedly reduced levels of intracellular free sulfhydryl groups and reduced GSH (Figure 5I,5J and Figure S2E,S2F). Immunoblotting further revealed that silencing MALINC1 significantly upregulated disulfidptosis-associated protein SLC7A11 (Figure 5K and Figure S2G). Moreover, treatment with the SLC7A11 inhibitor Erastin largely restored proliferative potential (Figure 5L and Figure S2H). These findings suggest that MALINC1 silencing exerts its antiproliferative function probably by triggering cancer cell disulfidptosis.

Figure 5 Silencing MALINC1 may trigger disulfidptosis in COAD. (A) Indicated lncRNA expression profiling in HCT8, SW480 and NCM460 (NCM) colon epithelial cells. (B,C) Single cell RNA profiling of MALINC1 in normal and cancerous colon epithelial cells from primary sites and organoid origins. (D) Percentage of MALINC1⁺ epithelial of total epithelial in indicated conditions. (E) Silencing efficiency of indicated small interfering RNA targeting MALINC1 on HCT8 cells. (F) Cell viability of MALINC1-silenced HCT8 cells at 48 and 72 hours. (G) Cell number of MALINC1-silenced HCT8 cells. (H) Colony formation capacity of MALINC1-silenced HCT8 cells (staining method: ). (I) Intracellular relative free thiol content of MALINC1-silenced HCT8 cells. (J) Measurement of reduced glutathione levels in HCT8 cells following MALINC1 silencing. (K) Immunoblotting analysis of SLC7A11 protein levels in HCT8 cells after MALINC1 knockdown. (L) Proliferation assay of HCT8 cells subjected to MALINC1 silencing and subsequent treatment with the SLC7A11 inhibitor Erastin. ***, P<0.001. COAD, colon adenocarcinoma; lncRNA, long non-coding RNA; NCM, UMAP, Uniform Manifold Approximation and Projection.

Silencing MALINC1 improves platinum drug sensitivity in COAD cell lines

To evaluate whether targeting MALINC1 could treat or help treat colorectal cancer, we fed MALINC1-silenced HCT8 cells with platinum-based drugs cisplatin (Cis) and oxaliplatin, followed by assessing drug cytotoxicity using CCK-8 proliferation assays, direct cell counting, and colony formation assays. Results showed that siMALINC1 pretreated HCT8 cells exhibited higher sensitivity to Cis at 10 nM (Figure 6A), and to oxaliplatin at 10, 20 or 40 nM (Figure 6B). The half-maximal inhibitory concentration (IC₅₀) values of Cis and oxaliplatin in the siMALINC1-pretreated HCT8 cells were displayed in Figure 6C. Similarly, the siMALINC1 + Cis group showed a marked reduction in colony numbers (Figure 6D,6E). Taken together, these findings indicated that silencing MALINC1 significantly enhances the chemosensitivity of HCT8 cells to Cis, providing experimental support for a MALINC1-based combination therapy strategy.

Figure 6 Silencing MALINC1 enhances platinum drug sensitivity in HCT8 cells. (A,B) Cell viability of cisplatin (A) or oxaliplatin (B) treated MALINC1-silenced HCT8 cells at indicated concentration for 48 or 72 hours. (C) IC₅₀ of cisplatin and oxaliplatin in MALINC1-silenced HCT8 cells. Cell number (D) and colony formation capacity (E) of cisplatin (5 nM) or oxaliplatin (2.5 nM) treated MALINC1-silenced HCT8 cells (staining method: ). Cis, cisplatin; IC₅₀, half-maximal inhibitory concentration; OS, oxaliplatin; Cis, cisplatin.

Discussion

LncRNA refers to a class of non-coding RNA molecules exceeding 200 nucleotides in length, which play crucial regulatory roles in various essential cellular biological processes, including chromatin remodeling, transcriptional regulation, and signal transduction. In recent years, extensive studies have demonstrated that lncRNAs significantly influence cell cycle progression, differentiation, apoptosis, and tumorigenesis, with their dysregulated expression closely associated with the initiation, progression, and metastasis of numerous malignancies. However, current research on lncRNAs in cell death regulation has primarily focused on classical cell death modalities such as apoptosis, necroptosis, and ferroptosis. Notably, whether lncRNAs participate in regulating disulfidptosis—a newly identified form of programmed cell death—remains unexplored. This research gap suggests that the potential relationship between lncRNAs and disulfidptosis may represent a novel direction for future studies in cell death mechanisms and cancer therapeutics. In this study, we employed bioinformatics approaches to screen for relevant lncRNAs and preliminarily validated their involvement in disulfidptosis, thereby laying a foundation for subsequent investigations.

The COAD prognostic model we developed, together with our validation results in the HCT8 cell line, identified MALINC1 as a disulfidptosis-related lncRNA in COAD. Following multiple transcriptional perturbation experiments, MALINC1 was further implicated as a potential regulator of disulfidptosis in COAD. Although the precise molecular mechanism through which MALINC1 mediates disulfidptosis in colon cancer cells remains incompletely defined, our in vitro findings at least indicate that silencing MALINC1 suppresses cancer cell proliferation by reducing intracellular free thiol levels and increasing SLC7A11 expression. Interestingly, our findings also revealed that knockdown of MALINC1 in vitro sensitize cancer cells to platinum-based drugs, confirming the potential anti-tumor properties of MALINC1-targeted therapy. Colorectal cancer derived single-cell profiling showed that tumoroid epithelial exhibited higher MALINC1 expression whereas primary cancerous epithelial not, suggesting that potential influence of cancerous epithelial heterogeneity on MALINC1-targeted anti-tumor therapeutic efficacy remains a critical issue that we need to address.

Conclusions

We constructed a prognostic signature based on six disulfidptosis-associated lncRNAs in COAD. This model shows promise for stratifying patient prognosis and evaluating responses to chemotherapy. Collectively, silencing MALINC1 suppresses COAD cell proliferation probably by inducing disulfidptosis, while also enhancing the sensitivity of cancer cells to platinum-based drugs. These findings not only provide a prognostic tool for clinical outcomes but also highlight MALINC1 as a potential therapeutic target, offering a new direction for the development of novel treatment strategies in COAD.

Acknowledgments

We gratefully acknowledge The Cancer Genome Atlas (TCGA) repository, which made the data available, and we would like to express our sincere thanks to all the editors, reviewers and other staff who participated in reviewing and producing this paper.

Footnote

Reporting Checklist: The authors have completed the MDAR and TRIPOD reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0225/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0225/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0225/prf

Funding: This work was supported by the Central South University (No. 2024XQLH067).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0225/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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

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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