RNA sequencing and bioinformatics analysis have identified MAST1 as a potential biomarker and therapeutic target for cervical cancer

Introduction

Cervical cancer (CC) remains a leading malignancy threatening women’s health, with a high incidence and mortality worldwide (1). Globally, approximately 661,021 new cases and 348,189 CC-related deaths are recorded each year (2). The World Health Organization’s (WHO) global strategy for CC elimination sets a target of fewer than four new cases per 100,000 women annually (3). In China—particularly in less-developed regions—limited screening coverage and low human papillomavirus (HPV) vaccination uptake yield an age-standardised incidence of 13.8 and a mortality rate of 4.5 per 100,000 women (4), both of which substantially exceed the WHO target.

CC development is driven by multiple etiological factors, including persistent infection with high-risk HPV subtypes (5), genetic susceptibility (6), and environmental risk exposures (7). Although surgery, radiotherapy, and platinum-based chemotherapy constitute the standard therapeutic arsenal, their efficacy in advanced or recurrent disease remains unsatisfactory (8). Indeed, patients with recurrent or metastatic CC have a 5-year overall survival as low as 16.5% (9). Consequently, a deeper understanding of the key oncogenes and molecular mechanisms underlying CC carcinogenesis is imperative for the development of more effective diagnostic and therapeutic strategies.

Microtubule-associated serine/threonine kinase 1 (MAST1) is a member of the MAST kinase family that functions as a scaffold protein linking actin-myosin complexes with the microtubule cytoskeleton (10). Recent studies have demonstrated that MAST1 confers cisplatin resistance by triggering CRAF-independent reactivation of MEK1, thereby potentiating pro-survival signaling (11). Elevated MAST1 expression strongly correlates with cisplatin resistance, and pharmacologic MAST1 inhibition reverses this chemoresistance in murine models (12,13). Elucidating the ubiquitin-proteasome system-mediated regulation of MAST1 protein turnover, therefore, represents a promising avenue for MAST1-targeted cancer therapy and for overcoming cisplatin resistance (14). Although cisplatin remains a first-line agent for CC treatment, the potential involvement of MAST1 dysregulation in CC pathogenesis has not yet been investigated.

With the advancement of next-generation sequencing technologies, RNA sequencing (RNA-Seq) has become a powerful tool for analyzing cancer transcriptomes (15), fundamentally changing our ability to comprehensively analyze gene expression patterns in various cell types within tissue samples. Recently, it has been widely used in transcriptome profiling of various solid tumors, including gastric cancer and liver cancer (16,17). Our understanding of CC has evolved from morphological pathology to molecular levels, making it significant to explore the mechanisms underlying CC to discover potential molecular biomarkers and stimulate the development of future targeted therapies.

Therefore, we conducted transcriptome sequencing to examine the differential expression profiles between cancerous tissues and adjacent non-cancerous tissues. Using bioinformatics approaches, we performed functional enrichment analyses of the differentially expressed RNAs and identified the most significantly different genes through the TCGA database, further exploring their functional regulation at the cellular and molecular levels to provide new targets and evidence for CC screening, treatment, and prognosis in the future. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-957/rc).

Methods

Data acquisition and statement of ethics

Between January 2023 and December 2023, three CC patients were recruited, and tumor and adjacent normal tissue samples from each patient were obtained for whole transcriptome sequencing at Shanghai Bohao Biotechnology Co., Ltd. (the detailed clinical characteristics of patients 1–3 are presented in Table 1). Inclusion criteria: (I) all patients had a clear diagnosis. The pathological characteristics of tumor and adjacent normal tissue specimens were confirmed by two experienced pathologists from Suzhou Ninth People’s Hospital. The adjacent tissue sampling site was at least 0.5 cm away from the tumor edge of the same specimen. (II) Clinical staging was performed according to the International Federation of Gynecology and Obstetrics (FIGO) guidelines for CC (2018). (III) No prior radiotherapy, chemotherapy, or other treatments before sample collection. (IV) No history of other malignancies. Exclusion criteria: (I) cervical carcinoma in situ; (II) patients with stage IIb or higher with no surgical indication; (III) preoperative radiotherapy, chemotherapy, or immunotherapy; (IV) mental disorders. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Suzhou Ninth People’s Hospital (No. KY2021-045-01) and informed consent was taken from all the patients.

RNA extraction

To extract total RNA from the samples, we used the TM miRNA extraction kit and performed electrophoresis quality control using the Agilent 4200 TapeStation to ensure sample quality. We then purified the samples using the RNAClean XP Kit (Beckman Coulter, Cat A63987, Kraemer Boulevard Brea, Orange County, CA, USA) and the RNase-Free DNase set (QIAGEN, Cat#79254, GmBH, Hilden, Germany).

Analysis of differential gene expression and functional enrichment

To identify differentially expressed genes (DEGs) between two different samples, we normalized gene expression levels using fragments per kilobase of exon model per million mapped reads (FPKM). DEG analysis between samples was performed using edge R, and P values were adjusted for multiple hypothesis testing. The threshold for P values was determined by controlling the false discovery rate (FDR), and adjusted P values (q-values) were calculated. Fold-change was also calculated based on FPKM values. In this study, we used fold-change ≥2 and FDR <0.01 as the criterion for selecting differential genes. A larger Fold Change indicates a greater difference in gene expression between samples, while a smaller FDR value indicates a lower FDR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differential genes were performed using the R package “cluster Profiler.”

Reverse transcription quantitative polymerase chain reaction (RT-qPCR)

Total RNA was isolated from tissues using Trizol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. qPCR analysis was performed using the FastStart Universal SYBR Green Master (ROX) Kit (Roche, Indianapolis, USA), with GAPDH as an internal control to normalize the relative expression of MAST1. Real-time quantitative PCR was conducted with the following conditions: pre-incubation at 94 ℃ for 10 minutes, followed by 40 cycles at 94 ℃ for 10 seconds, 60 ℃ for 15 seconds, 72 ℃ for 15 seconds, and a melting curve program from 60 to 95 ℃. Primer sequences used are listed in Table 2. Relative expression levels were calculated using the 2^−ΔΔCt method. All experiments were performed in triplicate.

Western blotting

Total proteins were extracted from cells using radioimmunoprecipitation assay buffer (RIPA) lysis buffer (R0010, Solarbio, Beijing, China). Protein concentrations were measured using the bicinchoninic acid assay (BCA) assay kit (C503021, Bioworld, Nanjing, China), and proteins were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Proteins were then transferred to polyvinylidene fluoride (PVDF) membranes. After blocking with 5% non-fat milk at room temperature for 1 hour, the membranes were incubated overnight at 4 ℃ with primary antibodies against MAST1 (13305-1-AP, Proteintech, Rosemont, IL, USA), E-cadherin (3195, CST, Danvers, MA, USA), Vimentin (5741, CST), N-cadherin (13116, CST), phosphorylated-Akt (p-Akt) (4060, CST), phosphorylated-mechanistic target of rapamycin (p-mTOR) (5536, CST), Akt (9272, CST), and mTOR (2983, CST). Then, the secondary antibodies were incubated at room temperature for 2 h. After washing three times with phosphate-buffered saline, the membranes were incubated with enhanced chemiluminescence (ECL) reagent, and protein bands were visualized using a gel imaging system. Band intensities were analyzed using ImageJ software.

Cell culture

Human Siha, Hela, C33A, and Caski CC cell lines were purchased from the American Type Culture Collection (ATCC, Manassas, Prince William County, VA, USA). CC cells were cultured in RPMI 1640 medium (Sigma, St. Louis, MO, USA) containing 10% fetal bovine serum (FBS) (Gibco, Grand Island, NY, USA). All cells were maintained in a 37 ℃, 5% CO₂ incubator. Cells in the logarithmic growth phase were used for further studies.

Cells were digested with trypsin and divided into small interfering RNA (siRNA)-NC and siRNA-MAST1 groups. The siRNA (catalogue number TN3096-1) was purchased from Synbio Technologies (Suzhou, China). Cells were seeded into 96-well plates 1 day before transfection and cultured at 37 ℃, 5% CO₂ for 24 hours. When cell confluency reached 80%, siRNA-NC and siRNA-MAST1 were transfected into the cells according to the Lipofectamine 2000 transfection reagent protocol. Cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) high glucose medium without serum and antibiotics for 4–6 hours, then replaced with DMEM high glucose medium containing serum and antibiotics for an additional 24–48 hours before collecting cell suspensions for subsequent experiments.

Immunohistochemical (IHC) analysis

Fresh frozen CC and adjacent normal tissues were prepared into paraffin blocks. Sections were deparaffinized in xylene, dehydrated in graded ethanol, and washed with phosphate-buffered saline (PBS) three times. Endogenous peroxidase was inactivated with H₂O₂ at room temperature for 10 minutes, followed by washing with distilled water three times. Sections were immersed in 0.01 M citrate buffer and subjected to high-pressure treatment for 3 minutes. They were then cooled for 20 minutes and washed with PBS three times. Blocking was performed with 5% bovine serum albumin (BSA) at room temperature for 10 minutes, then excess liquid was removed. Sections were incubated overnight at 4 ℃ with rabbit anti-MAST1 polyclonal antibody (1:200), followed by washing with PBS three times for 2 minutes each. Sections were then incubated with biotinylated goat anti-rabbit IgG (1:100) at 37 ℃ for 30 minutes, washed with PBS three times for 2 minutes each, and developed with 3,3'-diaminobenzidine (DAB) solution. Sections were observed under a microscope, washed with distilled water, counterstained with hematoxylin for 2 minutes, and dehydrated, cleared, and mounted. Positive cells were identified by the presence of brown-yellow or brown granules in the cytoplasm and/or nucleus. Images were analyzed using Image Pro Plus 6.0 software, with three randomly selected fields per slide, and absorbance (A) values were measured.

Transwell assay

Logarithmic phase cells were digested with trypsin and resuspended in serum-free DMEM complete medium, adjusting the concentration to 2×10⁵ cells/mL. 200 µL of cell suspension was placed in the upper chamber of the Transwell, with 700 µL of complete medium in the lower chamber. After 24 hours, cells were stained with a crystal violet methanol solution for 10 minutes, washed with PBS, air-dried, and then observed under a microscope at 200× magnification. Five random fields were selected for photographing and counting, and the results were analyzed.

Statistical analysis

Statistical data were processed using GraphPad Prism 9.0 software. Statistical analyses were performed using Student’s t-test and one-way analysis of variance (ANOVA), and the experimental results were expressed as the mean ± standard deviation (SD). P value <0.05 was considered statistically significant. Each experiment was independently repeated three times to ensure the reliability of the results.

Results

Identification of differentially expressed genes in CC

To identify differentially expressed genes in CC tissues, we performed transcriptome sequencing on samples from three patients by comparing CC and adjacent normal tissues, and detected a total of 50,868 genes. Among these, 40 genes were significantly upregulated (red dots) and 62 genes were significantly downregulated (blue dots) in CC tissues compared to adjacent normal tissues (Figure 1A,1B). Subsequently, GO and KEGG analyses were conducted to explore the functions of these DEGs (as shown in Figure 1C,1D). The GO analysis examined the functions of the differentially expressed genes from three aspects: biological process (GO_BP), cellular component (GO_CC), and molecular function (GO_MF), revealing that the differentially expressed genes were primarily associated with the extracellular matrix. In the KEGG pathway analysis, these genes were mainly enriched in pathways such as glutamatergic synapse, axon guidance, and cancer.

Figure 1 Identification of DEGs. (A) Volcano plot of DEGs, with red dots indicating upregulated RNAs and blue dots indicating downregulated RNAs. (B) Heatmap of DEGs, with colors ranging from red to blue representing expression levels from high to low. (C) Gene Ontology analysis of DEGs, with dot size representing gene counts and color representing adjusted P values. (D) KEGG analysis of DEGs, with dot size representing gene counts and color representing adjusted P values. DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Validation of transcriptome sequencing and bioinformatics analysis results by RT-qPCR to select MAST1

In this study, by integrating the TCGA database with transcriptome sequencing results from tissue samples, we identified nine significantly different genes, namely MAST1, MISP, MUC13, OLR1, PAD13, PAQR4, RBMS2, SIM2, and ROS1 (Table 3). Among them, the upregulated genes include MAST1, MISP, MUC13, OLR1, PAQR4, SIM2, and ROS1; the downregulated genes are PAD13 and RBMS2. RT-qPCR was used to validate the RNA levels of the statistically significant genes MAST1, MISP, PAQR4, and the markedly differentially expressed genes OLR1 and MUC13 in 10 newly sampled pairs of CC tissues, with results shown in Figure 2. The results showed that only MAST1 and MISP were significantly higher in cancerous tissues than in normal adjacent tissues (Figure 2A,2B), consistent with the sequencing results and bioinformatics analysis (P<0.01, P<0.05). However, PAQR4, OLR1, and MUC13 were not statistically significant (Figure 2C-2E) (P>0.05). The TCGA database also showed that MAST1 expression in cancerous tissues was significantly higher than in normal adjacent tissues (Figure 2F).

Figure 2 Validation of differential gene expression by RT-qPCR experiments and TCGA database. (A) RT-qPCR expression scatter plot of MAST1 in 10 clinical samples; (B) RT-qPCR expression scatter plot of MISP in 10 clinical samples; (C) RT-qPCR expression scatter plot of PAQR4 in 10 clinical samples; (D) RT-qPCR expression scatter plot of OLR1 in 10 clinical samples; (E) RT-qPCR expression scatter plot of MUC13 in 10 clinical samples; (F) expression difference of MAST1 in cancer and adjacent tissues in the TCGA database. *, P<0.05; **, P<0.01; ns, not significant. RT-qPCR, reverse transcription quantitative polymerase chain reaction; TCGA, The Cancer Genome Atlas.

Validation of MAST1 in CC tissues

A systematic literature review indicates that MAST1 has been reported to modulate tumorigenesis, progression, and chemoresistance in various malignancies (12,13,18,19); however, its role in CC remains largely unexplored, and the underlying mechanisms are undefined. Furthermore, integrated analysis of TCGA data and our pilot functional assays demonstrated that MAST1 exerts a more pronounced influence on the biological behavior of CC cells. We therefore selected MAST1 for in-depth investigation. To validate MAST1 as a novel biomarker for CC, we used Western blotting and immunohistochemistry (IHC) to assess samples from four patients with CC and their adjacent healthy tissues, as shown in Figure 3A. Detection of MAST1 expression at the protein level in CC and adjacent tissues revealed that MAST1 expression in CC tissues was significantly higher than that in the control group (P<0.05). IHC staining of cancerous and adjacent tissue sections showed that MAST1 was significantly expressed in CC tissues (Figure 3B).

Figure 3 Western blot and IHC detection of MAST1 expression in cervical cancer and adjacent tissues. (A) Western blot validation of the difference in MAST1 expression between cervical cancer and adjacent tissues. *, P<0.05. (B) IHC detection of the difference in MAST1 expression between cervical cancer tissues and adjacent tissues (left scale bar 10 µm, right figure scale bar 5 µm). IHC, immunohistochemical.

Biological function of MAST1 expression in CC Cells

Initially, we identified MAST1 expression in four CC cell lines (Siha, Hela, C33A, Caski) and found that MAST1 expression was significant only in Hela cells (P<0.05) (Figure 4A), leading us to choose Hela cells for siRNA cell line establishment. To enhance the experiment’s success rate, two siRNA sequences were designed for transfection, and Western Blot analysis confirmed a significant reduction in MAST1 expression in Hela cells post-transfection (Figure 4B). Compared with the negative control group, the number of Hela cells invading the lower chamber was significantly reduced after MAST1 knockdown, and the migration detected by the Transwell experiment was significantly inhibited (Figure 4C).

Figure 4 Knockdown of MAST1 inhibits the proliferation and migration of Hela cells. (A) Expression of MAST1 in four cervical cancer cell lines; (B) Western blot detection of MAST1 knockdown efficiency in Hela cells; (C) Transwell experiment detecting the effect of MAST1 knockdown on the migration ability of Hela cells and statistical graph (crystal violet; scale bar 200 µm). *, P<0.05; **, P<0.01; ***, P<0.001.

The impact of MAST1 on signaling pathways according to the KEGG analysis

In the initial phase, we identified signaling pathways potentially involving MAST1 in CC. The expression of different downstream signaling pathway molecules was detected by Western Blot to verify whether it has an effect. Figure 5 illustrates that MAST1 silencing downregulated the p-AKT and p-P38 signaling pathways, with no significant impact on T-AKT, Notch1, T-P38, p-NF-κB, T-NF-κB, SP1, p-ERK, T-ERK, p-SRC, T-SRC, or other signaling pathways not listed here.

Figure 5 The impact of MAST1 downregulation on downstream signaling pathways. Western blot detection of the signaling pathways affected after MAST1 downregulation.

Discussion

Although numerous studies have confirmed that high-risk HPV genotypes can promote the progression of cervical normal cells into invasive lesions (20), the presence of HPV-negative patients with rapid progression and poor prognosis suggests that the specific pathogenesis of CC remains unclear (21). In recent years, the advent of high-throughput sequencing technology and large databases has enabled researchers to screen for potential biomarkers in cancer and adjacent tissues, facilitating the prediction and treatment of cancer in clinical work (22,23). This study selected patients with pathologically confirmed cervical squamous cell carcinoma and compared the differential genes between their cancer tissues and adjacent tissues through transcriptome sequencing, combined with the TCGA database and experimental validation, indicating that MAST1 can serve as a potential biomarker and therapeutic target for CC.

Microtubule-associated serine/threonine-protein kinase 1 (MAST1) is a member of the microtubule-associated serine/threonine-protein kinase family. Studies have confirmed that MAST1 triggers the reactivation of MEK1 in a CRAF-independent manner, promoting survival signals and leading to cisplatin resistance (24). MAST1 also plays an important role in the treatment process of various other tumors, and high expression of MAST1 is positively correlated with cisplatin resistance (12,13), Some scholars speculate that deciphering the molecular mechanisms of the ubiquitin proteasome system regulating MAST1 protein metabolism may be an effective way to promote cancer treatment based on MAST1 and overcome cisplatin resistance (14), and the specific role of MAST1 in the occurrence and development of tumors is still unclear. By comparing the expression of cancer tissues and adjacent tissues, this study found that the expression of MAST1 was significantly increased, and there is currently no study proving whether the change in MAST1 expression is related to the pathogenesis of CC, so understanding the role of MAST1 in CC is of great significance.

In this study, high-throughput sequencing technology, combined with Western blot detection, revealed that the level of MAST1 in cervical squamous cell carcinoma tissues was significantly higher than in adjacent tissues, and this result was also verified by IHC. We speculate that MAST1 may serve as a diagnostic or prognostic assessment molecule for CC, providing a certain reference value and significance for clinical medication. However, considering the small sample size, more data are needed to validate this conclusion. The invasion ability of the Hela cell line was significantly reduced after MAST1 knockdown, suggesting that MAST1 may play a role as an oncogenic factor in the occurrence and development of CC.

To further investigate the downstream signaling pathways of MAST1, we focused on the AKT, Notch1, P38, NF-κB, SP1, ERK, and SRC signaling pathways based on the KEGG enrichment analysis results from the first part. The results showed that silencing of MAST1 downregulated the p-AKT and p-P38 signaling pathways. This suggests that MAST1 may influence the occurrence, development, invasion, and migration processes of CC through the p-AKT and p-P38 pathways.

It is well known that the activated PI3K/AKT signaling pathway promotes epithelial-to-mesenchymal transition (EMT), migration, and invasion of tumor cells and is also crucial for the potential response to cancer therapy (25,26). AKT influences cell morphology, cell adhesion, migratory and invasive capacity by modulating downstream targets, exerts anti-apoptotic effects through negative regulation of Bcl-2 family proteins (27), and affects the EMT (28,29). In CC, the HPV E6/E7 oncoproteins induce the transformation of HPV-infected cells, leading to degradation and destabilization of p53 and pRb, as well as genetic instability, proliferation, apoptosis resistance, and metabolic alterations via activation of the PI3K/Akt/mTOR pathway (30). Studies have identified the PI3K/AKT pathway as a potential therapeutic target in CC (31). In our study, MAST1 modulates the malignant phenotype of CC cells through the AKT pathway.

The human mitogen-activated protein kinase (MAPK) is a conserved eukaryotic signaling pathway, including three classic pathways: ERK, JNK, and P38 (32). Among them, the P38/MAPK signaling pathway plays an important role in environmental stress signals and can be stimulated and induced by various factors such as inflammatory factors, cytokines, and DNA damage agents (33,34). Therefore, P38 is a versatile kinase that can regulate cell proliferation, differentiation, stress response, cell migration, and survival (35,36). Although many studies have confirmed its anti-tumor effects, some studies have also shown that it may lead to cancer progression by enhancing tumor cell survival, promoting tumor cell migration, or increasing chemotherapy resistance (37,38). At the same time, P38 can also mediate inflammation in the tumor microenvironment, such as inducing the expression of VEGF and HIF1α (33,39), and plays an important role in promoting tumor proliferation, metastasis, and angiogenesis. Our results demonstrate that MAST1 knockdown markedly reduces p-P38 protein levels. Thus, it is reasonable to propose that silencing MAST1 impairs P38 phosphorylation and suppresses the MAPK signaling pathway, thereby attenuating CC progression.

This part preliminarily explores the role of MAST1 in regulating the invasive function of CC cell lines and detects the changes in downstream signaling pathways after the change of MAST1 expression. Although using adjacent normal tissues as controls reduced inter-individual variability, tissue-specific limitations remain. Future studies will strive to overcome these constraints by including cervical tissue samples from healthy individuals to more comprehensively elucidate the role of MAST1 in CC. Additionally, the small sample size and the absence of animal experiments represent further limitations of our study. Although validation in the TCGA database supports the overall reliability of our RNA-sequencing findings, the clinical predictive, diagnostic, and prognostic value of MAST1 still requires rigorous investigation in larger clinical cohorts.

Conclusions

This study identifies MAST1 as a differentially expressed gene highly expressed in CC tissues through transcriptome sequencing, bioinformatics analysis, and multiple experimental validations. These results indicate that MAST1 affects CC occurrence and development via the p-AKT and p-P38 pathways, and can serve as a potential biomarker and therapeutic target for CC, providing new ideas and experimental evidence for CC diagnosis, prognosis assessment, and targeted therapy.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-957/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-957/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-957/prf

Funding: This study was funded by the hospital-level project of the Ninth People’s Hospital of Suzhou (Nos. YK202426 and YK202202), the Science and Education Project Fund of Suzhou Wujiang District Health Committee (No. WWK202201), the Suzhou Applied Basic Research Science and Technology Innovation Program (No. SYWD2024201), and the Science and Technology Development Fund of the Affiliated Hospital of Xuzhou Medical University (No. XYFY202423).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-957/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 and its subsequent amendments. All cervical cancer patients providing tissue samples were informed of the study protocol and gave written informed consent. The study was approved by the Ethics Committee of Suzhou Ninth People’s Hospital (No. KY2021-045-01).

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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