Unveiling the Anticancer Mechanisms of Prodigiosin by inhibiting of CDK1, TOP2A, and AURKB Expression in Cervical Carcinoma

Unveiling the Anticancer Mechanisms of Prodigiosin by inhibiting of CDK1, TOP2A, and AURKB Expression in Cervical Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Unveiling the Anticancer Mechanisms of Prodigiosin by inhibiting of CDK1, TOP2A, and AURKB Expression in Cervical Carcinoma Zhenkun Zhu, Chunfan Jiang, Chunxiang Xiang, Qianbao Chen, Mei Yang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3829039/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Prodigiosin (PG) demonstrates a selective targeting effect on tumor cells. However, its role in cervical carcinoma is still being studied. In this study, we aim to study the specific targets and mechanism of PG in cervical carcinoma. We employed GO enrichment and KEGG analysis to identify core genes in CC patients. To corroborate the expression levels of these core genes, we used staining and RT-PCR on both normal and tumor tissues. Following this, the specific effects of PG on Hela, H8, and A549 cells were compared. After PG treatment, cell viability was evaluated using a CCK8 assay at various PG concentrations. Apoptosis in Hela cells was determined through flow cytometry post-PG treatment, and the expression of target genes was measured via RT-PCR. Our analysis highlighted CDK1, TOP2A, and AURKB emerging as core genes. The expression of CDK1, TOP2A, and AURKB, both at the protein and gene levels, was found to be higher in cervical carcinoma tissues compared to controls. Furthermore, lower PG concentrations diminished the viability of Hela and A549 cells without significantly impacting H8 cells. PG was observed to induce apoptosis in Hela cells by reducing the expression of CDK1, TOP2A, and AURKB genes. Biological sciences/Cancer Health sciences/Oncology Prodigiosin cervical carcinoma Gene expression omnibus Target genes CDK1 TOP2A AURKB Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Cervical carcinoma, representing the second most common and prevalent malignancy among women globally, inflicts significant economic and medical burdens on families. 1 The etiology of cervical carcinoma is multifaceted, encompassing genetic and environmental factors. 2 Recent statistics predict approximately 4,290 cervical carcinoma-related fatalities and 13,800 new cases in the United States for the year 2020 alone. 3 Currently, various chemical drugs such as bevacizumab, topotecan, and cisplatin constitute the first-line treatment for cervical carcinoma. However, their use is often marred by severe side effects and the emergence of drug resistance. 4 Consequently, the development of new therapeutic methods is of utmost importance. We have identified a natural compound, PG, 5 which exhibits a targeted apoptosis-inducing effect on cancer cells. Thus, the exploration of PG's anticancer mechanism and potential clinical applications holds promise. Building on the above, PG is a dark red bioactive secondary metabolite synthesized by Actinomycetes, Serratia marcescens, and other bacteria. 6 It exhibits a range of biological properties, including antibacterial, antiprotozoal, anti-malarial, immunosuppressive, and anticancer activities. Extensive research affirms that PG triggers apoptosis in various human cancer cells, while demonstrating comparatively lower toxicity towards normal cells. 7 Recent findings in the context of breast cancer reveal that PG can inhibit the phosphorylation of LRP6, DVL2, and GSK3β, thereby blocking Wnt/ β -catenin signal transduction and diminishing the expression of cyclin D1, consequently slowing tumor progression. 8 Furthermore, PG exhibits cytotoxic effects on multidrug-resistant human cancer cells. Studies have reported that PG can induce autophagic death in Dox-S and Dox-R lung cancer cells by inhibiting the Akt/PI3K-p85/mTOR signaling pathway, suggesting its potential as a therapeutic option for lung cancer. 9 Nonetheless, comprehensive research into the therapeutic role of PG in cervical carcinoma is currently lacking. Recognizing the importance of molecular-level understanding in cancer therapeutics, 10 high-throughput platform-based microarrays have emerged as effective tools for investigating the roles of various genes in disease mechanisms. These gene chips have found extensive application in diverse biological and medical research fields, particularly for identifying DEGs. 11 With the aid of bioinformatics analysis, it is possible to pinpoint oncogenes and TSGs exhibiting abnormal methylation patterns and differential expression in cervical carcinoma tissues. Furthermore, this analysis facilitates the elucidation of associated pathways and functions. Such knowledge contributes to the development of biological markers and therapeutic targets, thereby enabling more precise diagnosis and treatment strategies for cervical carcinoma. In our present study, we leverage these technological advancements to further understand the role of PG in cervical carcinoma. Our initial step involves the analysis of cervical carcinoma target genes using the GEO database. Subsequently, we conduct experimental verification of these genes. This dual-approach not only aids in the exploration of the mechanism behind PG-induced apoptosis in cervical carcinoma cells but also provides clarity on its potential therapeutic targets. 2. Materials and Methods 2.1. Patient Tissue Samples and Data Information This research was approved by the Ethics Committee of Xiang yang Central Hospital, Hubei, China ( Ethics Number: 2023-022 ). 10 CC tissues and 10 normal cervical tissues were collected and informed consent was obtained from all subjects and/or their legal guardians. We confirm that all methods were performed in accordance with the relevant guidelines and regulations. All selected cases were histologically verified by pathologists, with none of the patients having undergone chemotherapy or radiation therapy prior to surgery. The normal tissue samples were sourced from women who had hysterectomies due to non-malignant conditions. Subsequently, all the collected tissues were preserved in paraffin blocks. 2.2. Data information We sourced cervical carcinoma-related datasets GSE127265, GSE9750, and GSE173097 from the NCBI GEO ( https://www.ncbi.nlm.nih.gov/geo/ ). These datasets were based on three different platforms. The first, GSE23126, operated on the GPL23126 platform [Clariom_D_Human] Affymetrix Human Clariom D Assay. This dataset facilitated the identification of differentially expressed genes in cervical carcinoma patients through comparative transcriptome analysis, utilizing seven samples of cervical carcinoma and three normal cervix samples. The second, GSE9750, employed the GPL570 platform [HG-U133 Plus 2] Affymetrix Human Genome U133 Plus 2.0 Array, with data collected from 33 samples of cervical carcinoma and 24 normal cervix samples. The third, GSE23126, was based on the GPL173097 platform Agilent-045997 Arraystar human lncRNA microarray V3. This platform enabled the identification of a metabolic-related risk signature that predicts the prognosis in cervical carcinoma and correlates with immune infiltration, using data from five samples of cervical carcinoma and six normal cervix samples. We used the R package GEO-query to download, process the data, construct the expression matrix, and match each probe to its corresponding gene symbol. 2.3. Identification of DEGs We utilized the 'limma' package to explore DEGs within the GSE127265, GSE9750, and GSE173097 datasets. 12 DEGs were identified using the cutoff criteria |logFC|>1 and FDR < 0.05. Once these DEGs were determined, we proceeded to perform a series of analyses. This included PCA plots, Volcano plots, and hierarchical cluster analysis, conducted with the assistance of R packages ggplot2 and heatmap, respectively. Furthermore, to identify core genes, we constructed Venn diagrams utilizing the online tool available ( http://bioinfogp.cnb.csic.es/tools/venny/) . 13 2.4. Function Enrichment Analysis We utilized the online DAVID database ( https://david.ncifcrf.gov/summary.jsp ) for functional exploration of the DEGs. 14 This tool enabled us to delve into the gene functions extensively. Furthermore, to investigate the principal functions of the selected genes and their involvement in various signaling pathways, we carried out GO functional and KEGG analyses. 2.5. PPI Network Construction and Hub Genes Selection To scrutinize the interactions among the DEGs identified earlier, we constructed a PPI network. This was achieved by using the search tool for interacting genes (STRING) available on the STRING database ( https://string-db.org/cgi/input.pl) . 15 Following this, we visualized the obtained PPI network using the CytoHubba plugin in Cytoscape software. Subsequently, we employed the MCC algorithm to identify the top 10 most crucial genes in the network, designating them as the hub genes. 16 , 17 2.6. Cell Cultivation We obtained the HeLa cell line, a human cervical carcinoma strain, from the Cancer Center of the Chinese Academy of Medical Sciences. The cells were cultured in flasks maintained at 37°C under a humid atmosphere with 5% CO 2 . For this process, we used DMEM (Gibco, USA) enhanced with 10% FBS (Invitrogen, USA) and 1% penicillin-streptomycin solution (containing 100 U/mL penicillin and 100 µg/mL streptomycin). PG (CAS No.: 82-89-3) for the experiments was sourced from MCE (Shanghai, China). 2.7. Cell viability assay HeLa cells were seeded in 96-well plates at a density of 1×10 5 cells/mL, using 100 µL of DMEM medium supplemented with 10% FBS in each well. After 24 h, the cells were exposed to varying concentrations of luciferin: 0, 1, 10, 50, and 100 µM for another 24 h period. Cell viability was then assessed using the Cell Counting Kit-8 (CCK8, MEC, Shanghai, China). 2.8. DAPI Staining To observe nuclear morphological changes in apoptotic cells, we employed DAPI staining. HeLa cells, at a density of 1×10 5 cells/well, were cultured overnight in 24-well plates using DMEM medium supplemented with 10% FBS. Following this, they were exposed to varying concentrations of luciferin (0.1, 1, 2, 10, 50, and 100 µM) for 24 h. Subsequently, the medium was discarded and cells underwent two washes with cold PBS. They were then fixed in 100% ethanol at room temperature for 20 minutes and washed with PBS twice more. Finally, we visualized the cells under a fluorescent microscope (IX70-SIF2 Olympus; Olympus). 2.9. Apoptosis Flow-Cytometry Assay To evaluate PG-induced apoptosis, we employed double staining using Annexin V-FITC and PI. HeLa cells were cultured for 24 h in DMEM medium supplemented with 10% FBS and 1 µM luciferin in 4 cm 2 dishes. The cells were subsequently harvested, washed twice with PBS, and a count of 5 × 10 5 cells was resuspended in binding buffer. These were then stained with Annexin V-FITC and PI for 20 minutes using the Annexin V-FITC Apoptosis Detection Kit (Biyuntian, Nanjing, China). Post-staining, flow cytometry analysis was promptly performed (FACScan; BD Biosciences, Milano, Italy). On the flow cytometry readouts, cells in the Q2 (upper right), Q3 (lower left), and Q4 (lower right) quadrants signify early apoptotic, surviving, and late apoptotic cells, respectively. 2.10 Real-time PCR Analysis We utilized real-time PCR to assess gene expression in sample tissues and in PG-treated HeLa and A549 cells. The cells were washed with cold PBS and treated with trypsin. Subsequent to this, the cell suspension underwent centrifugation at 1000 × g for 10 minutes. The resulting cell pellet was resuspended in 1000 µL of ice-cold PBS and then centrifuged again under similar conditions. The harvested cells were stored at -80°C for further use. Total RNA extraction from tissues and cells was carried out within 24 h of treatment, using TRIzol reagent per the manufacturer's instructions. RT-PCR was conducted with Biosharp One-Step RT-PCR (BL698A; Invitrogen) and Biosharp Taq DNA Polymerase Kit (BL699A; Invitrogen). The expression of target genes in control and treated samples, normalized to GAPDH mRNA, was determined using the 2 −ΔCt method. The primers used for CDK1 were (forward: 5′-GGAGAAGGTACCTATGGAGTTGTG-3′, reverse: 5′-AGCACATCCTGAAGACTGACTAT-3′), for TOP2A (forward: 5′-ACGGAATGACAAGCGAGAAGTAA-3′, reverse: 5′-GCCAAAGCTGAGCATTGTAAA-3′), for AURKB (forward: 5′- TGCATCACACAACGAGACCTATC-3′, reverse: 5′-GAGTGAATGACAGGGACCATCAG-3′) and for GAPDH (forward: 5′-GGAGTCCACTGGCGTCTTCA-3′, reverse: 5′- GTCATGAGTCCTTCCACGATACC-3′). 2.11. Statistical Analysis Each experiment was conducted at least three times, and consistently yielded similar results. Data are represented as mean ± SD. To compare two groups, we used a t-test. When comparing more than two groups, ANOVA was implemented. If ANOVA identified a significant difference, then multiple-comparison tests were subsequently employed. We considered a p-value of less than 0.05 as statistically significant. 3. Results 3.1. Identification and Analysis of Differentially Expressed Genes in cervical carcinoma In the conducted research, the limma package within the R software environment was utilized for the identification of differentially expressed genes (DEGs) across normal control and cervical cancer sample groups. Analysis of the GSE127265 expression matrix yielded 3,246 DEGs, encompassing 1,687 upregulated and 1,559 downregulated genes, which were visually represented through volcano plots and heatmaps (Fig. 1 A, B). In a similar vein, the GSE9750 expression matrix revealed 1,591 DEGs, including 1,228 genes that were upregulated and 363 that were downregulated (Fig. 1 C, D). Furthermore, evaluation of the GSE173097 expression matrix resulted in the identification of 3,093 DEGs, comprising 1,864 upregulated and 1,229 downregulated genes (Fig. 1 E, F). To elucidate the common genetic expression patterns across these datasets, Venn diagrams were constructed, highlighting the intersection of GSE127265, GSE9750, and GSE173097(Fig. 1 G). This approach led to the identification of 106 genes that overlapped among these datasets, which were subsequently earmarked as core genes for in-depth subsequent analyses. 3.2. Analysis of DEGs' Functional Enrichment We employed GO function and KEGG signaling pathway analyses to understand the roles and pathways associated with the DEGs. Our GO enrichment analysis indicated that the core genes are mainly involved in biological processes such as cell division. These genes are also prevalent in cellular components like chromosomal regions and DNA replication, and they play a role in molecular functions such as integrin binding, chemokine receptor binding, and DNA catalytic activity (Fig. 2 A). Additionally, our KEGG pathway enrichment analysis suggested that the core genes are primarily active in signaling pathways such as the cell cycle, DNA replication, and the p53 signaling pathway (Fig. 2 B). This indicates that DEGs play a crucial role in modulating these vital biological processes. 3.3. PPI Network Development and Hub Gene Identification A radial graph was employed to illustrate the biological processes and pathways associated with the DEGs. Utilizing the 106 ascertained DEGs, we established a Protein-Protein Interaction (PPI) network using the STRING database (Fig. 3 A). We identified the top 10 genes, characterized by high MCC, as hub genes within the PPI network through the CytoHubba plugin's MCC algorithm in Cytoscape software. The three genes, namely CDK1, TOP2A, and AURKB, with the most pronounced scores, were pinpointed as the pivotal genes within the PPI network (Fig. 3 B). 3.4. Association of CDK1, TOP2A, and AURKB Expression with Clinicopathological Features in cervical carcinoma In this investigation, the expression profiles of CDK1, TOP2A, and AURKB were scrutinized in a set of 10 cervical cancer samples using Immunohistochemistry (IHC) to explore their potential implications in the etiology and progression of cervical cancer. Relative to normal cervical tissue, a marked augmentation in CDK1 expression was observed in the cytoplasm, whereas TOP2A and AURKB exhibited pronounced nuclear expression in the cancerous cervical tissues (Fig. 4 A, B, C, D). Furthermore, gene expression levels of CDK1, TOP2A, and AURKB showed a significant escalation in these cervical cancer samples (Fig. 4 E, F, G). Noteworthily, these expression patterns were in alignment with established database records. The confluence of these results underscores the potential of CDK1, TOP2A, and AURKB as biomarkers for the prediction and understanding of cervical cancer. 3.5. Selective Cytotoxic Effects of PG on HeLa, H8, and A549 Cell Lines To delineate the cytotoxicity of PG on distinct cell types, we examined its impact on the HeLa cervical carcinoma cell line, the H8 normal human cervical epithelial cell line, and the A549 lung cancer cell line. Upon exposure to PG concentrations ranging from 1-200 µM, HeLa cells exhibited marked concentration and time-dependent growth inhibition relative to untreated counterparts (Fig. 5 A, B). In contrast, H8 cells, emblematic of normal cervical epithelial cells, remained largely unaffected at lower PG concentrations (Fig. 5 C), although elevated PG levels induced a decrement in their viability over prolonged durations (Fig. 5 D). This pattern underscores the selective cytotoxic potency of PG on HeLa cells. In a parallel observation, the A549 cell line, representative of a cancer phenotype, manifested significant growth suppression post PG treatment spanning 1-200 µM concentrations (Fig. 5 E, F). 3.6. PG Induces Apoptosis in HeLa Cells To explore the potential apoptotic effects of PG on HeLa cells, we employed DAPI staining and Annexin V FITC/PE double staining techniques (Fig. 6 A, B, C, D, E). Following a 24 h exposure to PG, an approximate 30% of HeLa cells demonstrated apoptosis relative to the control group. Such findings underscore PG's possible efficacy in inducing apoptosis in HeLa cells. 3.7. Impact of PG Treatment on CDK1/TOP2A/AURKB Expression in HeLa Cells To further elucidate the influence of PG treatment on the expression levels of CDK1/TOP2A/AURKB at the protein and gene levels in HeLa cells, we undertook Western blot and RT-PCR assays. The results revealed a notable reduction in both protein and mRNA levels of CDK1/TOP2A/AURKB in the PG-treated cells compared to the controls (Fig. 7 A, B, C, D, E). 4. Discussion Cis is one of the most effective anticancer drugs for neoadjuvant therapy and advanced cervical carcinoma. Its prolonged use, however, can diminish drug sensitivity, leading to drug resistance. 18 Separately, PG has been identified to have targeted cytotoxicity towards cancer cells, allowing the selective elimination of these malignant cells while sparing normal cells. Notably, PG has been reported to induce apoptosis in various tumor cells, including UCCs, U87MG, HT-29 and MCF-7. 19 – 22 Our research echoes these findings, revealing that PG can trigger apoptosis in Hela cells—a finding that aligns with the results presented by Lin PB et al. 23 This apoptotic action in Hela cells involves the activation of molecules like Bcl-2, Bax, and caspase-3, although the precise mechanism remains to be elucidated. In addition, while low concentrations of PG showed no toxicity towards H8 cells, it did cause morphological alterations in A549 cells, 24 further underscoring its targeted impact on tumor cells. In our research, we extracted 106 differentially co-expressed genes from the GEO database. These DEGs predominantly feature in processes like cell division, proliferation, chromosomal regions, integrin binding, cell cycle, DNA replication, and the p53 signaling pathway. Utilizing the CytoHubba plugin of the Cytoscape software, we identified 10 pivotal genes: CDK1, TOP2A, AURKB, RRM2, MAD2L1, BUB1B, CDKN3, BUB1, KIF11, and CCNB2. A deeper dive into their expression profiles indicated that, relative to normal cervical tissues, all these genes showed pronounced upregulation in cervical carcinoma samples. Notably, CDK1, TOP2A, and AURKB stood out due to their vivid representation, and there was a marked correlation between their expression and survival outcomes in cervical carcinoma patients. CDK1, TOP2A, and AURKB are pivotal genes for DNA replication. Post-PG treatment, a reduction in their expression levels was noted in both HeLa and A549 cells. Specifically, CDK1 facilitates the G2/M transition by orchestrating the centrosome cycle and initiating mitosis, underscoring its importance in the eukaryotic cell cycle. 25 Interestingly, elevated levels of CDK1 have been detected in breast cancer tissues. Diminishing its expression has been linked to increased apoptosis in these cancer cells. 26 Topoisomerase IIA, on the other hand, modulates the DNA topological state during transcription and is intrinsically linked to processes like DNA replication, chromosome segregation, and condensation. 27 Elevated expression of TOP2A, more profound in lung adenocarcinoma tissues than in their normal counterparts, correlates with enhanced proliferation, migration, and invasion of lung cancer cells in vitro. 28 Aurora B, also recognized as AURKB, is fundamental for chromosome segregation and cytokinesis. 29 Intriguingly, elevated Aurora B levels were observed in ovarian cancer A2780 cells. 30 Suppressing its expression led to an augmented proportion of G2/M phase cells and polyploidy in these cells, a scenario followed by increased cell apoptosis and hindered proliferation. This study reveals PG's concentration-dependent inhibitory effect on cancer cell proliferation. At non-cytotoxic levels, PG compromises HeLa cell viability and quantity. At cytotoxic concentrations, PG disrupts key cancer cell growth pathways, including protein kinase signaling and mitochondrial cell death pathways, triggering apoptosis. 31 PG's interaction with cancer cell DNA facilitates oxidative DNA cleavage, leading to oxidation, breakage, and apoptosis. 32 In conclusion, our bioinformatics analysis unveiled the DEGs associated with cervical carcinoma, along with their related biological processes and pathways. Through validation with the GEO database, genes CDK1, TOP2A, and AURKB emerged as potential biomarkers for the precise diagnosis and treatment of cervical carcinoma. We also ascertained that PG can both trigger apoptosis in HeLa cells and inhibit the overexpression of pivotal genes such as CDK1, TOP2A, and AURKB. Thus, our results lay the groundwork for considering PG as a therapeutic option for cervical carcinoma. However, our study isn't without limitations. While we pinpointed the targets of PG in cervical carcinoma cells, an in-depth exploration of the underlying molecular mechanisms remains a future endeavor. Furthermore, our research was confined to human cervical carcinoma cell lines. To fully comprehend PG's in vivo effects on cervical carcinoma, subsequent investigations should integrate animal models, enriching the depth and breadth of this line of research. Abbreviations tumor suppressor genes (TSGs); Gene Expression Omnibus (GEO); Principal Component Analysis (PCA); Protein-Protein Interaction (PPI); Dulbecco's Modified Eagle's Medium (DMEM); Prodigiosin (PG); Fetal Bovine Serum (FBS); phosphate-buffered saline (PBS); propidium iodide (PI); one-way analysis of variance (ANOVA); Gene Ontology (GO); Kyoto Encyclopedia of Genes and Genomes (KEGG); Maximal Clique Centrality (MCC); differentially expressed genes (DEGs); Cisplatin (cis); Immunohistochemistry (IHC) Declarations Declaration of interest The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. Funding This research was supported by National Natural Science Foundation of China (No. 81972449), Foundation of Hubei University of Arts and Science (XK2019046) and Science and Technology Plan of Xiangyang (2022YL07B). Author Contribution CJ and MY; methodology, CX; experiment and data analysis, ZZ and MT; resources, HX; writing—original draft preparation, ZZ; writing—review and editing, MT and QC. All authors contributed to the analysis of the data and reviewed the manuscript. Data availability All data generated or analyzed during this study are included in this published article and its supplementary information files. References Maimaitirexiati G, Tian P, Maimaiti H, et al. Expression and correlation analysis of Skp2 and CBX7 in cervical carcinoma. J Clin Pathol 2022;75:851–6. Cohen PA, Jhingran A, Oaknin A, et al. cervical carcinoma. Lancet 2019;393:169–82. Asthana S, Busa V, Labani S. Oral contraceptives use and risk of cervical carcinoma-A systematic review & meta-analysis. Eur J Obstet Gynecol Reprod Biol 2020;247:163–75. Rosen VM, Guerra I, McCormack M, et al. 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CDK1-mediated mitotic phosphorylation of PBK is involved in cytokinesis and inhibits its oncogenic activity. Cell Signal 2017;39:74–83. Rocha FV, Farias RL, Lima MA, et al. Computational studies, design and synthesis of Pd(II)-based complexes: Allosteric inhibitors of the Human Topoisomerase-IIα. J Inorg Biochem 2019;199:110725. Kou F, Sun H, Wu L, et al. TOP2A Promotes lung adenocarcinoma cells' malignant progression and predicts poor prognosis in lung adenocarcinoma. J Cancer 2020;11:2496–508. Zhao Z, Jin G, Yao K, et al. Aurora B kinase as a novel molecular target for inhibition the growth of osteosarcoma. Mol Carcinog 2019;58:1056–67. Shi J, Xu G, Zhu W, et al. Design and synthesis of 1,4,5,6-tetrahydropyrrolo[3,4-c]pyrazoles and pyrazolo[3,4-b]pyridines for Aurora-A kinase inhibitors. Bioorg Med Chem Lett 2010;20:4273–8. Stankovic N, Senerovic L, Ilic-Tomic T, et al. Properties and applications of undecylprodigiosin and other bacterial prodigiosins. Appl Microbiol Biotechnol 2014;98:3841–58. Darshan N, Manonmani HK. Prodigiosin and its potential applications. J Food Sci Technol 2015; 52 :5393–407. Additional Declarations No competing interests reported. Supplementary Files Rawdata.rar fulllengthgelsandblots.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3829039","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":268932185,"identity":"07d52fae-10ec-4264-a940-c48ad3a27ab4","order_by":0,"name":"Zhenkun Zhu","email":"","orcid":"","institution":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Zhenkun","middleName":"","lastName":"Zhu","suffix":""},{"id":268932186,"identity":"5650acb0-953f-488b-95b3-ddb7dddf4581","order_by":1,"name":"Chunfan Jiang","email":"","orcid":"","institution":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Chunfan","middleName":"","lastName":"Jiang","suffix":""},{"id":268932187,"identity":"30e0cad0-ed28-4168-af47-870feb83be49","order_by":2,"name":"Chunxiang Xiang","email":"","orcid":"","institution":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Chunxiang","middleName":"","lastName":"Xiang","suffix":""},{"id":268932188,"identity":"e6961824-4f43-46f3-8f2b-e89ec205d72b","order_by":3,"name":"Qianbao Chen","email":"","orcid":"","institution":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Qianbao","middleName":"","lastName":"Chen","suffix":""},{"id":268932189,"identity":"f661fd1c-4bbd-4276-accc-274048eb5d5c","order_by":4,"name":"Mei Yang","email":"","orcid":"","institution":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Yang","suffix":""},{"id":268932190,"identity":"4bf800e2-4d58-492d-928d-1e3f9a233973","order_by":5,"name":"Mengjun Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYFCCAyDCxo6fgbGBWC2HQURasmQD8VqYwfoYNxwgVoNu4/mDnwt+HWY2Pn+47cEPBjs5XUKWmR04zCw9sy+dz+xGYrthD0OysRkh64BaGKR5e6yZzW4wtknwMBxI3EaEFubfvD3MjJv7D7ZJ/iFSC5s0zw9nxg0MiW3SxNpiZs3bkJYscQOoRcaAGL/cOPj4Ns8fYFT2H38m+abCTo6gFgYJoArGNhjPgJByEOBvABJ/iFE5CkbBKBgFIxYAAADORW8bBiwlAAAAAElFTkSuQmCC","orcid":"","institution":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science","correspondingAuthor":true,"prefix":"","firstName":"Mengjun","middleName":"","lastName":"Tang","suffix":""},{"id":268932191,"identity":"129fd073-5078-43a9-8d0e-2314ac4da319","order_by":6,"name":"Hui Xing","email":"","orcid":"","institution":"Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Xing","suffix":""}],"badges":[],"createdAt":"2024-01-02 09:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3829039/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3829039/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50175407,"identity":"a5bdc9c2-0236-4686-bf45-5984fb8de44c","added_by":"auto","created_at":"2024-01-25 16:21:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2118894,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential gene expression in cervical carcinoma across multiple datasets.\u003c/p\u003e\n\u003cp\u003e(A) DEG analysis from GSE127265. (B) Hierarchical clustering of DEGs in GSE127265. (C) DEG analysis from GSE9750. (D) Hierarchical clustering of DEGs in GSE9750. (E) DEG analysis from GSE173097. (F) Hierarchical clustering of DEGs in GSE173097. (G) Co-expression patterns across GSE127265, GSE9750, and GSE173097 datasets.\u003c/p\u003e","description":"","filename":"Fig1..png","url":"https://assets-eu.researchsquare.com/files/rs-3829039/v1/fb7fb282d3a6c2b86153a0d2.png"},{"id":50175408,"identity":"0d4f58f8-fcdc-4812-b3d5-230f36fabf6d","added_by":"auto","created_at":"2024-01-25 16:21:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141328,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional characterization and pathway analysis of differentially co-expressed genes. (A) GO functional annotation for 106 differential genes. The vertical axis represents the signaling pathway, the horizontal axis represents the gene count within each pathway, and color denotes enrichment significance. (B) KEGG pathway analysis for the 106 differential genes. Circle size corresponds to the number of enriched genes, while color indicates the significance of enrichment.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3829039/v1/f70134b51a665f9746f78aad.png"},{"id":50176061,"identity":"78d361fa-9f79-41c9-bc57-36b4bb09af46","added_by":"auto","created_at":"2024-01-25 16:29:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":312211,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of core genes within the PPI network of co-expressed genes.\u003c/p\u003e\n\u003cp\u003e(A) PPI network formation for co-expressed genes. (B) Identification of the top three core genes. The colors red, orange, and yellow correspond to genes with decreasing MCC values.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3829039/v1/198382310faa9f434c4f40b3.png"},{"id":50175412,"identity":"27dae403-67f1-4121-96a6-bb1db0f44031","added_by":"auto","created_at":"2024-01-25 16:21:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1405683,"visible":true,"origin":"","legend":"\u003cp\u003eCDK1, TOP2A, and AURKB gene and protein expressions in cervical carcinoma specimens. (A) H\u0026amp;E staining. (B-D) Immunohistochemical staining images for CDK1, TOP2A, and AURKB in normal cervical and cancerous tissues, respectively. (E-G) Gene expression levels of CDK1, TOP2A, and AURKB in both normal and cancerous cervical tissues. Data represent mean values from three independent experiments: **p \u0026lt; 0.01; ***p \u0026lt; 0.001 in comparison to corresponding control values. Scale bar: 40 μm.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3829039/v1/5daba9c197200a8ca25de22d.png"},{"id":50175410,"identity":"d22ccbae-8569-48cd-bb4d-5777a1836c50","added_by":"auto","created_at":"2024-01-25 16:21:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":95467,"visible":true,"origin":"","legend":"\u003cp\u003ePG's impact on the viability of HeLa, H8, and A549 cells.\u003c/p\u003e\n\u003cp\u003e(A-B) CCK8 assay depicting HeLa cell viability post-luciferin treatment at 24 h and 48 h. (C-D) CCK8 assay illustrating H8 cell viability after luciferin exposure at 24 and 48 h. (E-F) CCK8 assay showing A549 cell viability following luciferin treatment at 24 and 48 h. Data represent mean ± SEM from three independent experiments: *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001 relative to respective control values.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3829039/v1/1f33eddcc9731811d2bebee1.png"},{"id":50175411,"identity":"87c50a7f-a096-4abd-921e-c8c82da2db00","added_by":"auto","created_at":"2024-01-25 16:21:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":360657,"visible":true,"origin":"","legend":"\u003cp\u003eApoptosis assessment in PG-treated HeLa cells using flow cytometry. (A, B, C, D, E) Depict the flow cytometry results. Data represent the mean from three independent experiments: ***p \u0026lt; 0.001 in comparison to corresponding control value.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-3829039/v1/6d547bc885201cee1e9eb109.png"},{"id":50176063,"identity":"14f6a43b-06a5-46e1-94be-d460f1c5410e","added_by":"auto","created_at":"2024-01-25 16:29:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":75184,"visible":true,"origin":"","legend":"\u003cp\u003eCDK1, TOP2A, and AURKB expression in HeLa cells post-PG treatment.\u003c/p\u003e\n\u003cp\u003eAfter PG treatment, CDK1, TOP2A, and AURKB protein and gene levels in HeLa cells were reduced in comparison to control (A-E). Data represent the mean from three independent experiments: *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001 relative to corresponding control values.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-3829039/v1/0f0482ee2895670b4baf245b.png"},{"id":51416852,"identity":"f7900b32-8c74-46b7-b2c0-0a5ce312f3d2","added_by":"auto","created_at":"2024-02-21 07:46:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3117482,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3829039/v1/a6964039-c67a-4523-8dd4-a4d940e231ae.pdf"},{"id":50175414,"identity":"2ab926d7-f0d5-43f0-beba-a2c57ee1321e","added_by":"auto","created_at":"2024-01-25 16:21:26","extension":"rar","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":15597,"visible":true,"origin":"","legend":"","description":"","filename":"Rawdata.rar","url":"https://assets-eu.researchsquare.com/files/rs-3829039/v1/c5651816932514549f004c7b.rar"},{"id":50175415,"identity":"375d6239-5ddc-445c-a90e-16ad1e88bd1d","added_by":"auto","created_at":"2024-01-25 16:21:26","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":169933,"visible":true,"origin":"","legend":"","description":"","filename":"fulllengthgelsandblots.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3829039/v1/619ac7c7efe7b9715059a04e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling the Anticancer Mechanisms of Prodigiosin by inhibiting of CDK1, TOP2A, and AURKB Expression in Cervical Carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCervical carcinoma, representing the second most common and prevalent malignancy among women globally, inflicts significant economic and medical burdens on families.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The etiology of cervical carcinoma is multifaceted, encompassing genetic and environmental factors.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Recent statistics predict approximately 4,290 cervical carcinoma-related fatalities and 13,800 new cases in the United States for the year 2020 alone.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Currently, various chemical drugs such as bevacizumab, topotecan, and cisplatin constitute the first-line treatment for cervical carcinoma. However, their use is often marred by severe side effects and the emergence of drug resistance.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Consequently, the development of new therapeutic methods is of utmost importance. We have identified a natural compound, PG,\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e which exhibits a targeted apoptosis-inducing effect on cancer cells. Thus, the exploration of PG's anticancer mechanism and potential clinical applications holds promise.\u003c/p\u003e \u003cp\u003eBuilding on the above, PG is a dark red bioactive secondary metabolite synthesized by Actinomycetes, Serratia marcescens, and other bacteria.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e It exhibits a range of biological properties, including antibacterial, antiprotozoal, anti-malarial, immunosuppressive, and anticancer activities. Extensive research affirms that PG triggers apoptosis in various human cancer cells, while demonstrating comparatively lower toxicity towards normal cells.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Recent findings in the context of breast cancer reveal that PG can inhibit the phosphorylation of LRP6, DVL2, and GSK3β, thereby blocking Wnt/\u003cem\u003eβ\u003c/em\u003e-catenin signal transduction and diminishing the expression of cyclin D1, consequently slowing tumor progression.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Furthermore, PG exhibits cytotoxic effects on multidrug-resistant human cancer cells. Studies have reported that PG can induce autophagic death in Dox-S and Dox-R lung cancer cells by inhibiting the Akt/PI3K-p85/mTOR signaling pathway, suggesting its potential as a therapeutic option for lung cancer.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Nonetheless, comprehensive research into the therapeutic role of PG in cervical carcinoma is currently lacking.\u003c/p\u003e \u003cp\u003eRecognizing the importance of molecular-level understanding in cancer therapeutics,\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e high-throughput platform-based microarrays have emerged as effective tools for investigating the roles of various genes in disease mechanisms. These gene chips have found extensive application in diverse biological and medical research fields, particularly for identifying DEGs.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e With the aid of bioinformatics analysis, it is possible to pinpoint oncogenes and TSGs exhibiting abnormal methylation patterns and differential expression in cervical carcinoma tissues. Furthermore, this analysis facilitates the elucidation of associated pathways and functions. Such knowledge contributes to the development of biological markers and therapeutic targets, thereby enabling more precise diagnosis and treatment strategies for cervical carcinoma.\u003c/p\u003e \u003cp\u003eIn our present study, we leverage these technological advancements to further understand the role of PG in cervical carcinoma. Our initial step involves the analysis of cervical carcinoma target genes using the GEO database. Subsequently, we conduct experimental verification of these genes. This dual-approach not only aids in the exploration of the mechanism behind PG-induced apoptosis in cervical carcinoma cells but also provides clarity on its potential therapeutic targets.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Patient Tissue Samples and Data Information\u003c/h2\u003e \u003cp\u003eThis research was approved by the Ethics Committee of Xiang yang Central Hospital, Hubei, China (\u003cb\u003eEthics Number: 2023-022\u003c/b\u003e). 10 CC tissues and 10 normal cervical tissues were collected and informed consent was obtained from all subjects and/or their legal guardians. \u003cb\u003e We confirm that all methods were performed in accordance with the relevant guidelines and regulations.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAll selected cases were histologically verified by pathologists, with none of the patients having undergone chemotherapy or radiation therapy prior to surgery. The normal tissue samples were sourced from women who had hysterectomies due to non-malignant conditions. Subsequently, all the collected tissues were preserved in paraffin blocks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data information\u003c/h2\u003e \u003cp\u003eWe sourced cervical carcinoma-related datasets GSE127265, GSE9750, and GSE173097 from the NCBI GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These datasets were based on three different platforms. The first, GSE23126, operated on the GPL23126 platform [Clariom_D_Human] Affymetrix Human Clariom D Assay. This dataset facilitated the identification of differentially expressed genes in cervical carcinoma patients through comparative transcriptome analysis, utilizing seven samples of cervical carcinoma and three normal cervix samples. The second, GSE9750, employed the GPL570 platform [HG-U133 Plus 2] Affymetrix Human Genome U133 Plus 2.0 Array, with data collected from 33 samples of cervical carcinoma and 24 normal cervix samples. The third, GSE23126, was based on the GPL173097 platform Agilent-045997 Arraystar human lncRNA microarray V3. This platform enabled the identification of a metabolic-related risk signature that predicts the prognosis in cervical carcinoma and correlates with immune infiltration, using data from five samples of cervical carcinoma and six normal cervix samples. We used the R package GEO-query to download, process the data, construct the expression matrix, and match each probe to its corresponding gene symbol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Identification of DEGs\u003c/h2\u003e \u003cp\u003eWe utilized the 'limma' package to explore DEGs within the GSE127265, GSE9750, and GSE173097 datasets.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e DEGs were identified using the cutoff criteria |logFC|\u0026gt;1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Once these DEGs were determined, we proceeded to perform a series of analyses. This included PCA plots, Volcano plots, and hierarchical cluster analysis, conducted with the assistance of R packages ggplot2 and heatmap, respectively. Furthermore, to identify core genes, we constructed Venn diagrams utilizing the online tool available (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfogp.cnb.csic.es/tools/venny/)\u003c/span\u003e\u003cspan address=\"http://bioinfogp.cnb.csic.es/tools/venny/)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Function Enrichment Analysis\u003c/h2\u003e \u003cp\u003eWe utilized the online DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/summary.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/summary.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for functional exploration of the DEGs.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e This tool enabled us to delve into the gene functions extensively. Furthermore, to investigate the principal functions of the selected genes and their involvement in various signaling pathways, we carried out GO functional and KEGG analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. PPI Network Construction and Hub Genes Selection\u003c/h2\u003e \u003cp\u003eTo scrutinize the interactions among the DEGs identified earlier, we constructed a PPI network. This was achieved by using the search tool for interacting genes (STRING) available on the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/cgi/input.pl)\u003c/span\u003e\u003cspan address=\"https://string-db.org/cgi/input.pl)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003csup\u003e15\u003c/sup\u003e Following this, we visualized the obtained PPI network using the CytoHubba plugin in Cytoscape software. Subsequently, we employed the MCC algorithm to identify the top 10 most crucial genes in the network, designating them as the hub genes.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Cell Cultivation\u003c/h2\u003e \u003cp\u003e We obtained the HeLa cell line, a human cervical carcinoma strain, from the Cancer Center of the Chinese Academy of Medical Sciences. The cells were cultured in flasks maintained at 37\u0026deg;C under a humid atmosphere with 5% CO\u003csub\u003e2\u003c/sub\u003e. For this process, we used DMEM (Gibco, USA) enhanced with 10% FBS (Invitrogen, USA) and 1% penicillin-streptomycin solution (containing 100 U/mL penicillin and 100 \u0026micro;g/mL streptomycin). PG (CAS No.: 82-89-3) for the experiments was sourced from MCE (Shanghai, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Cell viability assay\u003c/h2\u003e \u003cp\u003eHeLa cells were seeded in 96-well plates at a density of 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/mL, using 100 \u0026micro;L of DMEM medium supplemented with 10% FBS in each well. After 24 h, the cells were exposed to varying concentrations of luciferin: 0, 1, 10, 50, and 100 \u0026micro;M for another 24 h period. Cell viability was then assessed using the Cell Counting Kit-8 (CCK8, MEC, Shanghai, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. DAPI Staining\u003c/h2\u003e \u003cp\u003eTo observe nuclear morphological changes in apoptotic cells, we employed DAPI staining. HeLa cells, at a density of 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/well, were cultured overnight in 24-well plates using DMEM medium supplemented with 10% FBS. Following this, they were exposed to varying concentrations of luciferin (0.1, 1, 2, 10, 50, and 100 \u0026micro;M) for 24 h. Subsequently, the medium was discarded and cells underwent two washes with cold PBS. They were then fixed in 100% ethanol at room temperature for 20 minutes and washed with PBS twice more. Finally, we visualized the cells under a fluorescent microscope (IX70-SIF2 Olympus; Olympus).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Apoptosis Flow-Cytometry Assay\u003c/h2\u003e \u003cp\u003eTo evaluate PG-induced apoptosis, we employed double staining using Annexin V-FITC and PI. HeLa cells were cultured for 24 h in DMEM medium supplemented with 10% FBS and 1 \u0026micro;M luciferin in 4 cm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e dishes. The cells were subsequently harvested, washed twice with PBS, and a count of 5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells was resuspended in binding buffer. These were then stained with Annexin V-FITC and PI for 20 minutes using the Annexin V-FITC Apoptosis Detection Kit (Biyuntian, Nanjing, China). Post-staining, flow cytometry analysis was promptly performed (FACScan; BD Biosciences, Milano, Italy). On the flow cytometry readouts, cells in the Q2 (upper right), Q3 (lower left), and Q4 (lower right) quadrants signify early apoptotic, surviving, and late apoptotic cells, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Real-time PCR Analysis\u003c/h2\u003e \u003cp\u003eWe utilized real-time PCR to assess gene expression in sample tissues and in PG-treated HeLa and A549 cells. The cells were washed with cold PBS and treated with trypsin. Subsequent to this, the cell suspension underwent centrifugation at 1000 \u0026times; g for 10 minutes. The resulting cell pellet was resuspended in 1000 \u0026micro;L of ice-cold PBS and then centrifuged again under similar conditions. The harvested cells were stored at -80\u0026deg;C for further use. Total RNA extraction from tissues and cells was carried out within 24 h of treatment, using TRIzol reagent per the manufacturer's instructions. RT-PCR was conducted with Biosharp One-Step RT-PCR (BL698A; Invitrogen) and Biosharp Taq DNA Polymerase Kit (BL699A; Invitrogen). The expression of target genes in control and treated samples, normalized to GAPDH mRNA, was determined using the 2\u003csup\u003e\u0026minus;ΔCt\u003c/sup\u003e method. The primers used for CDK1 were (forward: 5\u0026prime;-GGAGAAGGTACCTATGGAGTTGTG-3\u0026prime;, reverse: 5\u0026prime;-AGCACATCCTGAAGACTGACTAT-3\u0026prime;), for TOP2A (forward: 5\u0026prime;-ACGGAATGACAAGCGAGAAGTAA-3\u0026prime;, reverse: 5\u0026prime;-GCCAAAGCTGAGCATTGTAAA-3\u0026prime;), for AURKB (forward: 5\u0026prime;- TGCATCACACAACGAGACCTATC-3\u0026prime;, reverse: 5\u0026prime;-GAGTGAATGACAGGGACCATCAG-3\u0026prime;) and for GAPDH (forward: 5\u0026prime;-GGAGTCCACTGGCGTCTTCA-3\u0026prime;, reverse: 5\u0026prime;- GTCATGAGTCCTTCCACGATACC-3\u0026prime;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. Statistical Analysis\u003c/h2\u003e \u003cp\u003eEach experiment was conducted at least three times, and consistently yielded similar results. Data are represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. To compare two groups, we used a t-test. When comparing more than two groups, ANOVA was implemented. If ANOVA identified a significant difference, then multiple-comparison tests were subsequently employed. We considered a p-value of less than 0.05 as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Identification and Analysis of Differentially Expressed Genes in cervical carcinoma\u003c/h2\u003e \u003cp\u003eIn the conducted research, the limma package within the R software environment was utilized for the identification of differentially expressed genes (DEGs) across normal control and cervical cancer sample groups. Analysis of the GSE127265 expression matrix yielded 3,246 DEGs, encompassing 1,687 upregulated and 1,559 downregulated genes, which were visually represented through volcano plots and heatmaps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). In a similar vein, the GSE9750 expression matrix revealed 1,591 DEGs, including 1,228 genes that were upregulated and 363 that were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D). Furthermore, evaluation of the GSE173097 expression matrix resulted in the identification of 3,093 DEGs, comprising 1,864 upregulated and 1,229 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F). To elucidate the common genetic expression patterns across these datasets, Venn diagrams were constructed, highlighting the intersection of GSE127265, GSE9750, and GSE173097(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). This approach led to the identification of 106 genes that overlapped among these datasets, which were subsequently earmarked as core genes for in-depth subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Analysis of DEGs' Functional Enrichment\u003c/h2\u003e \u003cp\u003eWe employed GO function and KEGG signaling pathway analyses to understand the roles and pathways associated with the DEGs. Our GO enrichment analysis indicated that the core genes are mainly involved in biological processes such as cell division. These genes are also prevalent in cellular components like chromosomal regions and DNA replication, and they play a role in molecular functions such as integrin binding, chemokine receptor binding, and DNA catalytic activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Additionally, our KEGG pathway enrichment analysis suggested that the core genes are primarily active in signaling pathways such as the cell cycle, DNA replication, and the p53 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). This indicates that DEGs play a crucial role in modulating these vital biological processes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3. PPI Network Development and Hub Gene Identification\u003c/h2\u003e \u003cp\u003eA radial graph was employed to illustrate the biological processes and pathways associated with the DEGs. Utilizing the 106 ascertained DEGs, we established a Protein-Protein Interaction (PPI) network using the STRING database (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). We identified the top 10 genes, characterized by high MCC, as hub genes within the PPI network through the CytoHubba plugin's MCC algorithm in Cytoscape software. The three genes, namely CDK1, TOP2A, and AURKB, with the most pronounced scores, were pinpointed as the pivotal genes within the PPI network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Association of CDK1, TOP2A, and AURKB Expression with Clinicopathological Features in cervical carcinoma\u003c/h2\u003e \u003cp\u003eIn this investigation, the expression profiles of CDK1, TOP2A, and AURKB were scrutinized in a set of 10 cervical cancer samples using Immunohistochemistry (IHC) to explore their potential implications in the etiology and progression of cervical cancer. Relative to normal cervical tissue, a marked augmentation in CDK1 expression was observed in the cytoplasm, whereas TOP2A and AURKB exhibited pronounced nuclear expression in the cancerous cervical tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B, C, D). Furthermore, gene expression levels of CDK1, TOP2A, and AURKB showed a significant escalation in these cervical cancer samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, F, G). Noteworthily, these expression patterns were in alignment with established database records. The confluence of these results underscores the potential of CDK1, TOP2A, and AURKB as biomarkers for the prediction and understanding of cervical cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Selective Cytotoxic Effects of PG on HeLa, H8, and A549 Cell Lines\u003c/h2\u003e \u003cp\u003eTo delineate the cytotoxicity of PG on distinct cell types, we examined its impact on the HeLa cervical carcinoma cell line, the H8 normal human cervical epithelial cell line, and the A549 lung cancer cell line. Upon exposure to PG concentrations ranging from 1-200 \u0026micro;M, HeLa cells exhibited marked concentration and time-dependent growth inhibition relative to untreated counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). In contrast, H8 cells, emblematic of normal cervical epithelial cells, remained largely unaffected at lower PG concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), although elevated PG levels induced a decrement in their viability over prolonged durations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). This pattern underscores the selective cytotoxic potency of PG on HeLa cells. In a parallel observation, the A549 cell line, representative of a cancer phenotype, manifested significant growth suppression post PG treatment spanning 1-200 \u0026micro;M concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6. PG Induces Apoptosis in HeLa Cells\u003c/h2\u003e \u003cp\u003eTo explore the potential apoptotic effects of PG on HeLa cells, we employed DAPI staining and Annexin V FITC/PE double staining techniques (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B, C, D, E). Following a 24 h exposure to PG, an approximate 30% of HeLa cells demonstrated apoptosis relative to the control group. Such findings underscore PG's possible efficacy in inducing apoptosis in HeLa cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Impact of PG Treatment on CDK1/TOP2A/AURKB Expression in HeLa Cells\u003c/h2\u003e \u003cp\u003eTo further elucidate the influence of PG treatment on the expression levels of CDK1/TOP2A/AURKB at the protein and gene levels in HeLa cells, we undertook Western blot and RT-PCR assays. The results revealed a notable reduction in both protein and mRNA levels of CDK1/TOP2A/AURKB in the PG-treated cells compared to the controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B, C, D, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCis is one of the most effective anticancer drugs for neoadjuvant therapy and advanced cervical carcinoma. Its prolonged use, however, can diminish drug sensitivity, leading to drug resistance.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Separately, PG has been identified to have targeted cytotoxicity towards cancer cells, allowing the selective elimination of these malignant cells while sparing normal cells. Notably, PG has been reported to induce apoptosis in various tumor cells, including UCCs, U87MG, HT-29 and MCF-7.\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Our research echoes these findings, revealing that PG can trigger apoptosis in Hela cells\u0026mdash;a finding that aligns with the results presented by Lin PB et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e This apoptotic action in Hela cells involves the activation of molecules like Bcl-2, Bax, and caspase-3, although the precise mechanism remains to be elucidated. In addition, while low concentrations of PG showed no toxicity towards H8 cells, it did cause morphological alterations in A549 cells,\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e further underscoring its targeted impact on tumor cells.\u003c/p\u003e \u003cp\u003eIn our research, we extracted 106 differentially co-expressed genes from the GEO database. These DEGs predominantly feature in processes like cell division, proliferation, chromosomal regions, integrin binding, cell cycle, DNA replication, and the p53 signaling pathway. Utilizing the CytoHubba plugin of the Cytoscape software, we identified 10 pivotal genes: CDK1, TOP2A, AURKB, RRM2, MAD2L1, BUB1B, CDKN3, BUB1, KIF11, and CCNB2. A deeper dive into their expression profiles indicated that, relative to normal cervical tissues, all these genes showed pronounced upregulation in cervical carcinoma samples. Notably, CDK1, TOP2A, and AURKB stood out due to their vivid representation, and there was a marked correlation between their expression and survival outcomes in cervical carcinoma patients.\u003c/p\u003e \u003cp\u003eCDK1, TOP2A, and AURKB are pivotal genes for DNA replication. Post-PG treatment, a reduction in their expression levels was noted in both HeLa and A549 cells. Specifically, CDK1 facilitates the G2/M transition by orchestrating the centrosome cycle and initiating mitosis, underscoring its importance in the eukaryotic cell cycle.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Interestingly, elevated levels of CDK1 have been detected in breast cancer tissues. Diminishing its expression has been linked to increased apoptosis in these cancer cells.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Topoisomerase IIA, on the other hand, modulates the DNA topological state during transcription and is intrinsically linked to processes like DNA replication, chromosome segregation, and condensation.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Elevated expression of TOP2A, more profound in lung adenocarcinoma tissues than in their normal counterparts, correlates with enhanced proliferation, migration, and invasion of lung cancer cells in vitro.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Aurora B, also recognized as AURKB, is fundamental for chromosome segregation and cytokinesis.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Intriguingly, elevated Aurora B levels were observed in ovarian cancer A2780 cells.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Suppressing its expression led to an augmented proportion of G2/M phase cells and polyploidy in these cells, a scenario followed by increased cell apoptosis and hindered proliferation. This study reveals PG's concentration-dependent inhibitory effect on cancer cell proliferation. At non-cytotoxic levels, PG compromises HeLa cell viability and quantity. At cytotoxic concentrations, PG disrupts key cancer cell growth pathways, including protein kinase signaling and mitochondrial cell death pathways, triggering apoptosis. \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e PG's interaction with cancer cell DNA facilitates oxidative DNA cleavage, leading to oxidation, breakage, and apoptosis. \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn conclusion, our bioinformatics analysis unveiled the DEGs associated with cervical carcinoma, along with their related biological processes and pathways. Through validation with the GEO database, genes CDK1, TOP2A, and AURKB emerged as potential biomarkers for the precise diagnosis and treatment of cervical carcinoma. We also ascertained that PG can both trigger apoptosis in HeLa cells and inhibit the overexpression of pivotal genes such as CDK1, TOP2A, and AURKB. Thus, our results lay the groundwork for considering PG as a therapeutic option for cervical carcinoma. However, our study isn't without limitations. While we pinpointed the targets of PG in cervical carcinoma cells, an in-depth exploration of the underlying molecular mechanisms remains a future endeavor. Furthermore, our research was confined to human cervical carcinoma cell lines. To fully comprehend PG's in vivo effects on cervical carcinoma, subsequent investigations should integrate animal models, enriching the depth and breadth of this line of research.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003etumor suppressor genes (TSGs); Gene Expression Omnibus (GEO); Principal Component Analysis (PCA); Protein-Protein Interaction (PPI); Dulbecco\u0026apos;s Modified Eagle\u0026apos;s Medium (DMEM); Prodigiosin (PG); Fetal Bovine Serum (FBS); phosphate-buffered saline (PBS); propidium iodide (PI); one-way analysis of variance (ANOVA); Gene Ontology (GO); Kyoto Encyclopedia of Genes and Genomes (KEGG); Maximal Clique Centrality (MCC); differentially expressed genes (DEGs); Cisplatin (cis); Immunohistochemistry (IHC)\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of interest\u003c/h2\u003e \u003cp\u003eThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by National Natural Science Foundation of China (No. 81972449), Foundation of Hubei University of Arts and Science (XK2019046) and Science and Technology Plan of Xiangyang (2022YL07B).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCJ and MY; methodology, CX; experiment and data analysis, ZZ and MT; resources, HX; writing\u0026mdash;original draft preparation, ZZ; writing\u0026mdash;review and editing, MT and QC. All authors contributed to the analysis of the data and reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMaimaitirexiati G, Tian P, Maimaiti H, et al. Expression and correlation analysis of Skp2 and CBX7 in cervical carcinoma. J Clin Pathol 2022;75:851\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen PA, Jhingran A, Oaknin A, et al. cervical carcinoma. Lancet 2019;393:169\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsthana S, Busa V, Labani S. Oral contraceptives use and risk of cervical carcinoma-A systematic review \u0026amp; meta-analysis. 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Mol Carcinog 2019;58:1056\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi J, Xu G, Zhu W, et al. Design and synthesis of 1,4,5,6-tetrahydropyrrolo[3,4-c]pyrazoles and pyrazolo[3,4-b]pyridines for Aurora-A kinase inhibitors. Bioorg Med Chem Lett 2010;20:4273\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStankovic N, Senerovic L, Ilic-Tomic T, et al. Properties and applications of undecylprodigiosin and other bacterial prodigiosins. Appl Microbiol Biotechnol 2014;98:3841\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarshan N, Manonmani HK. Prodigiosin and its potential applications. J Food Sci Technol 2015;\u003cem\u003e52\u003c/em\u003e:5393\u0026ndash;407.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prodigiosin, cervical carcinoma, Gene expression omnibus, Target genes, CDK1, TOP2A, AURKB","lastPublishedDoi":"10.21203/rs.3.rs-3829039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3829039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProdigiosin (PG) demonstrates a selective targeting effect on tumor cells. However, its role in cervical carcinoma is still being studied. In this study, we aim to study the specific targets and mechanism of PG in cervical carcinoma. We employed GO enrichment and KEGG analysis to identify core genes in CC patients. To corroborate the expression levels of these core genes, we used staining and RT-PCR on both normal and tumor tissues. Following this, the specific effects of PG on Hela, H8, and A549 cells were compared. After PG treatment, cell viability was evaluated using a CCK8 assay at various PG concentrations. Apoptosis in Hela cells was determined through flow cytometry post-PG treatment, and the expression of target genes was measured via RT-PCR. Our analysis highlighted CDK1, TOP2A, and AURKB emerging as core genes. The expression of CDK1, TOP2A, and AURKB, both at the protein and gene levels, was found to be higher in cervical carcinoma tissues compared to controls. Furthermore, lower PG concentrations diminished the viability of Hela and A549 cells without significantly impacting H8 cells. PG was observed to induce apoptosis in Hela cells by reducing the expression of CDK1, TOP2A, and AURKB genes.\u003c/p\u003e","manuscriptTitle":"Unveiling the Anticancer Mechanisms of Prodigiosin by inhibiting of CDK1, TOP2A, and AURKB Expression in Cervical Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 16:21:21","doi":"10.21203/rs.3.rs-3829039/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d16255d7-d8fc-4b05-a1c7-d997f8d3e16f","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28333455,"name":"Biological sciences/Cancer"},{"id":28333456,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2024-02-21T07:45:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 16:21:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3829039","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3829039","identity":"rs-3829039","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}