hIL-24: A Promising Therapeutic Target for Cervical Cancer Running Title: Targeting hIL-24 in Cervical Cancer

hIL-24: A Promising Therapeutic Target for Cervical Cancer Running Title: Targeting hIL-24 in Cervical Cancer | 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 Research Article hIL-24: A Promising Therapeutic Target for Cervical Cancer Running Title: Targeting hIL-24 in Cervical Cancer Min Song, Hongtao Yuan, Jie Zhang, Jing Wang, Jianhua Yu, Wei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3560710/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 Objective: This study aimed to identify potential therapeutic targets for cervical cancer by analyzing global gene expression data to pinpoint key differentially expressed genes (DEGs) associated with the disease. Methods: Gene expression datasets from GEO, TCGA, and GTEx databases were analyzed to identify DEGs in cervical cancer. Weighted Gene Co-expression Network Analysis (WGCNA) was used to uncover disease-specific genes, and machine learning techniques, including LASSO regression and random forest, were employed to refine the search for pivotal genes. Results: The study successfully identified DEGs related to cervical cancer, with human IL-24 (hIL-24) emerging as a critical gene through WGCNA and machine learning predictions. Experimental validation demonstrated that hIL-24 suppressed Siha cervical cancer cell proliferation, migration, and invasion, and induced apoptosis, underscoring its potential as a therapeutic target. Conclusion: The comprehensive analysis of global gene expression data highlighted hIL-24 as a key gene in cervical cancer, suggesting its potential as a viable therapeutic target. These findings provide valuable insights into the role of hIL-24 in cervical cancer pathogenesis and have the potential to guide the development of novel treatment strategies in the field of oncology. WGCNA Machine learning Cervical cancer Differentially expressed genes (DEGs) hIL-24 Gene expression data Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cervical cancer remains a significant global health concern, particularly in developing nations, where it is associated with high incidence and mortality rates [ 1 , 2 , 3 ]. Despite recent advancements in prevention and treatment, the aggressive nature of this disease, characterized by rapid proliferation, invasion, and metastasis, poses persistent therapeutic challenges [ 3 , 4 , 5 ]. Conventional treatments such as surgery, radiotherapy, and chemotherapy often come with undesirable side effects and a significant risk of recurrence [ 1 , 6 , 7 , 8 ]. Consequently, there is an urgent need to explore more effective and less detrimental treatment modalities [ 9 ]. In this context, gene therapy has emerged as a promising avenue in medical research, attracting considerable attention due to its potential for providing targeted treatment strategies [ 10 , 11 , 12 ]. Interleukin-24 (hIL-24) is known for its diverse biological activities, including its ability to induce tumor cell death in various cancer types [ 13 , 14 , 15 ]. While previous studies have demonstrated hIL-24's capacity to inhibit tumor cell proliferation and induce apoptosis, its therapeutic application in cervical cancer remains underexplored [ 16 , 17 , 18 ]. Given the limited understanding of hIL-24's role in cervical cancer, this study seeks to address this knowledge gap by examining its specific effects on Siha cervical cancer cells. We generated the pcDNA3.1 (+)-hIL-24 recombinant plasmid and introduced it into Siha cells using lipofection [ 19 ]. PCR and MTT assays were employed to assess the gene's impact on cell proliferation [ 20 ], and the Transwell assay was used to quantify its effects on cell invasion and migration [ 21 , 22 ]. This research aims to comprehensively evaluate the inhibitory influence of hIL-24 on Siha cell growth and its regulatory effects on cell invasion and migration. Our findings aspire to establish a novel theoretical and practical framework for the application of hIL-24 in cervical cancer therapy. By elucidating the mechanism of action of hIL-24, we aim to improve treatment efficacy and safety for cervical cancer patients, thus contributing to the broader field of oncology and gene therapy. Materials and Methods GEO database chip and TCGA/GTEx transcriptome data download We obtained 309 transcriptome data samples from cervical cancer patients, which included 306 cervical cancer tissue samples and 3 adjacent normal tissue samples, from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ). Download transcriptome data samples from the Genotype-Tissue Expression (GTEx) database for 10 normal cervical tissues. To obtain the datasets GSE63514 and GSE192804, access the Gene Expression Omnibus (GEO) database at https://www.ncbi.nlm.nih.gov/gds . The dataset GSE63514 includes 28 samples of cervical cancer (CC) tissues and 24 samples of normal cervical tissues, while GSE192804 consists of 6 samples of CC tissues and 6 samples of normal cervical tissues [ 23 , 24 ]. Differential gene screening Differential mRNA expression was filtered using the "limma" package in R. A P-value less than 0.05 was defined as the screening criterion for TCGA. In addition, a criterion of absolute log2 fold change greater than 2 and a P-value less than 0.05 was used for further filtering. In this study, we generated a volcano plot using the ggplot2 R package developed by Hadley Wickham ( http://had.co.nz/ggplot2/ ). To create a Venn diagram, utilize the Xiantao Academic Database available at ( https://www.xiantaozi.com/ ) [ 25 , 26 ]. Weighted Gene Co-Expression Network Analysis (WGCNA) analysis The expression clustering and phenotype association analysis will be conducted on gene data from the TCGA database using the 'WGCNA' package in the R language. The most relevant gene modules associated with cervical cancer will be identified, and the genes within these modules will be extracted for further analysis [ 27 ]. Functional enrichment analysis We performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the differentially expressed genes (DEGs) in the CC samples. The SangerBox database ( http://sangerbox.com/home.html ) was utilized for this analysis. Additionally, we generated visualizations [ 28 , 29 ]. Cell culture SiHa cells derived from human cervical cancer were procured from Beyotime Biotechnology Co., Ltd. in Shanghai, China. The cells were cultured in DMEM medium supplemented with 10% fetal bovine serum and 1% (100×) penicillin-streptomycin. The culturing conditions included a temperature of 37°C and 5% carbon dioxide [ 21 , 30 ]. Plasmid extraction The pcDNA3.1 (+)-hIL-24 plasmid was synthesized by Shanghai Biotechnology Co., Ltd. in Shanghai, China, and subsequently sequenced by Shanghai Gene Engineering Co., Ltd. in the same city. The plasmid extraction and purification reagents were purchased from Beyotime Biotechnology Co., Ltd. (Shanghai, China). DH5α E. coli cells transformed with the pcDNA3.1(+)-hIL-24 plasmid were inoculated into LB medium supplemented with ampicillin. The cultures were then incubated overnight at 37°C with shaking at 250 rpm. Plasmids should be extracted in accordance with the instructions provided by the plasmid extraction kit [ 31 , 32 ]. Cell transfection The Lipo8000™ transfection reagent was procured from Shanghai Beyotime Biotechnology Co., Ltd. Approximately 500,000 cells were seeded per well into a 6-well plate the day before transfection and cultured in an antibiotic-free medium for 18–24 hours. The cells were cultivated until the following day, achieving a cell density of approximately 70–80%. Replace each well with 2 mL of fresh medium without antibiotics during transfection. Combine 2.5 µg of DNA with 4 µL of Lipo8000™ transfection reagent in 125 µL of DMEM medium. Add 125 µL of the mixture to each well, followed by a 48-hour incubation period [ 33 ]. Cell growth inhibition assay The density of transfected SiHa cells and non-transfected SiHa cells was adjusted to 2.5×10 4 cells/ml in the DMEM culture medium. Subsequently, they were seeded onto a 96-well plate with 200 µl of cell suspension per well. Following a 72-hour incubation period at 37℃ and 5% carbon dioxide, 20 µL of MTT solution was added to each well and incubated for another 4 hours at 37℃. Remove the culture medium, add 150 µL of DMSO and thoroughly mix for 10 minutes. The absorbance of each well should be measured at a wavelength of 568 nm using an instrument equipped with an enzyme label [ 34 , 35 ]. Cell migration experiment This study aims to investigate the impact of hIL-24 on the migratory capacity of SiHa cells through the utilization of the Transwell assay. The transfected SiHa cells and untransfected SiHa cells were centrifuged at 1000 rpm for 5 minutes. Remove the supernatant and wash the cells twice with PBS. Transfer the cells into the DMEM culture medium and then resuspend them. The cell concentration should be adjusted to 3×10 5 /ml using a hemocytometer for further use. Add 800 µL of DMEM culture medium (10% penicillin-streptomycin) to a 24-well plate and place the Transwell chamber inside. A 200 µL cell suspension was inoculated into each group's upper chamber of the Transwell system and incubated at 37°C for 48 hours. Afterward, the insert was removed, and the chamber was washed with PBS. Cell fixation was performed by incubating the cells in a 70% ethanol solution for 1 hour, followed by staining with a 0.5% crystal violet staining solution at room temperature for 20 minutes. Following the PBS washing, use a clean cotton swab to remove the non-migrated cells on one side of the chamber. Observe and photograph cells under a microscope [ 36 , 37 , 38 , 39 ]. Invasion assay of cells Before the experiment, the Matrigel gel should be melted and diluted with serum-free medium in a ratio of 1:3. The Transwell chamber should be placed in a 24-well plate. Next, 100 µL of diluted matrix gel should be applied evenly onto the polycarbonate membrane at the bottom of each well. The plate should then be incubated at 37 ℃ in a 5% carbon dioxide incubator for 30 minutes. The following steps are identical to those of the cell migration experiment [ 40 , 41 , 42 ]. Apoptosis experiment The transfected SiHa cells should be centrifuged at 1200 rpm for 5 minutes. After centrifugation, remove the supernatant and then resuspend the cells in PBS. The cells should be washed twice using sterile PBS and centrifuged at 1200 rpm for 5 minutes. Cells were analyzed using flow cytometry, following the instructions provided by Shanghai BioCloud Biotechnology Co., Ltd. for the Annexin V-FITC Apoptosis Detection Kit [ 43 , 44 , 45 ]. Statistical analysis Our research uses R language, version 4.2.1. The compilation of R language is performed using the integrated development environment RStudio. The current version of RStudio is 2022.12.0-353. For file processing, we utilized Perl version 5.30.0. In addition, we used GraphPad Prism software, version 8.0. Performing statistical analysis using SPSS 17.0 software. Once the normality of the data has been tested, multiple comparisons will be conducted using analysis of variance (ANOVA). Inter-group comparisons are performed using t-tests. Data is represented as mean ± standard deviation. A p-value below 0.05 indicates statistical significance [ 46 , 47 ]. Results A global database reveals the essential genes for cervical cancer through a comprehensive analysis of GEO, TCGA, and GTEx According to data from the World Health Organization (WHO) on global cancer incidence and mortality rates and global statistical analysis results from the Global Cancer Observatory database, cervical cancer (CC) ranks as the fourth most prevalent cancer in women, making it a substantial public health concern. Cervical cancer is one of the most prevalent cancers among middle-aged women in most countries [ 2 , 48 ]. Hence, developing novel treatment strategies for cervical cancer is vital for improving the overall prognosis of patients. To investigate the crucial genes involved in cervical cancer, we retrieved data from the GTEx database. We obtained transcriptome data for 31 diverse tissue types across genders (Fig. 1 ). Through differential analysis, we identified 1804 genes that exhibited differential expression in cervical cancer compared to the adjacent normal cervical tissues. This analysis used transcriptomic data from TCGA and GTEx (Fig. 2 A). Furthermore, by utilizing the GSE63514 dataset, we obtained 510 genes that displayed differential expression in cervical cancer compared to normal cervical tissues (Fig. 2 B). Furthermore, we identified 1702 genes that exhibited a differential expression in cervical cancer compared to normal cervical tissue, using data from the GSE192804 dataset (Fig. 2 C). The results demonstrate our successful acquisition of transcriptome data from the TCGA and GTEx databases and the retrieval of two microarray datasets from the GEO database. Furthermore, differential analysis has allowed us to individually identify distinct genes for each dataset. WGCNA uncovers genes characteristic of cervical cancer: A thorough investigation of modules associated with the disease To effectively identify disease-related gene characteristics closely associated with CC, we performed a weighted gene co-expression network analysis (WGCNA) using TCGA and GTEx databases. We obtained nine gene modules: black, blue, brown, green, grey, magenta, red, turquoise, and yellow. Among these modules, the turquoise module exhibited the highest proportion of gene importance (Fig. 3 A). The correlation analysis results between the module genes and CC revealed a negative correlation between the turquoise module genes and CC. This result suggests that the turquoise module genes may exert inhibitory effects on cervical cancer (Fig. 3 B). To identify potential overlap, we conducted an intersection analysis between 988 disease-associated genes extracted from the turquoise module and the differentially expressed genes identified from GEO, TCGA, and GTEx databases. This analysis led to the identification of six genes that intersected across these datasets (Fig. 3 C). Furthermore, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on six overlapping genes. GO enrichment analysis showed that the differentially expressed genes in the CC samples were primarily enriched in biological processes, including tissue development, extracellular structure organization, and wound healing (Fig. 4 A). The KEGG enrichment analysis revealed that the differentially expressed genes in the CC samples were primarily enriched in signaling pathways, including ECM-receptor interaction, Human papillomavirus infection, and Focal adhesion (Fig. 4 B). The results above indicate that we identified six genes closely associated with cervical cancer through WGCNA co-expression and differential analyses. Machine learning is vital in disease gene screening, specifically utilizing LASSO and random forest algorithms Subsequently, the expression levels of the six genes in the GSE192804 dataset were extracted, and a multivariate Cox study with LASSO regression was conducted. This analysis identified five disease-associated genes, namely ACOX2, IL-24, SPP1, CRYAB, and ANKRD22 (Fig. 5 A-B). In addition, we utilized the random forest algorithm to assess gene importance, identifying IL24 as one disease-associated gene (Fig. 5 C). Finally, by performing the intersection, we identified a single gene associated with cervical cancer: IL-24 (Fig. 5 D). Based on the results above, we have successfully identified the genes associated with cervical cancer. hIL-24: A comprehensive study of the newly discovered multi-functional anti-cancer protein and its interaction with Siha cells To further elucidate the impact of human IL-24 (hIL-24) on cervical cancer, an overexpression plasmid of hIL-24 (designated as pcDNA3.1 (+)-hIL-24) was constructed and subsequently transfected into Siha cervical cancer cells to evaluate its effects on their biological functions. MTT analysis revealed that Siha cells' average optical density (OD) value in the pcDNA3.1 (+)-hIL-24 plasmid group decreased to 1.0127 compared to the transfection reagent and empty vector groups. This finding highlights the crucial role hIL-24 in inhibiting the growth of Siha cells (Fig. 6 A; Table 1). Furthermore, cell migration experiments demonstrated an inhibitory effect of hIL-24 on the migration ability of Siha cells. The average number of migrated cells in the pcDNA3.1 (+)-hIL-24 plasmid group was 105, markedly lower than the other groups (Fig. 6 B, Fig. 7 A, and Table 2). Further invasive experiments confirmed that overexpression of hIL-24 substantially reduced the invasiveness of Siha cells. The invasive ability of cells transfected with pcDNA3.1(+)-hIL-24 plasmid was the lowest, with an average of 90.5 cells (Fig. 6 C, Fig. 7 B, Table 3). Finally, flow cytometry analysis revealed an increase in the apoptotic rate of Siha cells upon treatment with hIL-24. The pcDNA3.1 (+)-hIL-24 plasmid group showed an apoptotic rate of 12.81% (Fig. 6 D; Fig. 7 C; Table 4). These comprehensive data strongly suggest that human interleukin-24 (hIL-24) inhibits the growth, migration, and invasion ability of Siha cells and promotes apoptosis in these cells. This result demonstrates the tremendous potential of hIL-24 in the treatment of cervical cancer. Discussion The primary objective of this study was to comprehensively investigate the impact of hIL-24 on Siha cells, a subtype of cervical cancer. We focused on assessing its effects on cell growth inhibition, migration reduction, invasion attenuation, and apoptosis enhancement. The insights gained from this study significantly contribute to our understanding of cervical cancer at the molecular level and pave the way for innovative therapeutic approaches. Considering the ongoing global threat posed by cervical cancer to women's health, there is an urgent need for novel treatment modalities [ 49 , 50 , 51 , 52 ]. MTT assays confirmed that hIL-24 notably inhibits the proliferation of Siha cells, in line with existing literature documenting its tumor-suppressive properties [ 53 , 54 , 55 , 56 ]. Furthermore, our study provides insights into the signaling pathways and molecular mechanisms underpinning hIL-24's tumor-suppressive actions, laying the foundation for future investigations. We also demonstrated that hIL-24 significantly reduces the migratory and invasive capabilities of Siha cells, which are crucial for the management and prevention of tumor metastasis [ 57 , 58 , 59 ]. Compared to other studies, our experimental protocol was highly stringent, enhancing the robustness of our findings and supporting the potential of hIL-24 in inhibiting metastasis. In addition, flow cytometry analyses validated hIL-24's role in promoting apoptosis in Siha cells, further underscoring its therapeutic promise [ 60 , 61 ]. Our study's experimental design is rigorous, and our results are substantiated by stringent statistical tests, ensuring high reliability. Nonetheless, the limited sample size may necessitate further validation within a broader cohort. The study's limitations encompass the small cohort size and the absence of multicentric and diverse cell line validation. To comprehensively evaluate hIL-24's therapeutic potential in cervical cancer, future research should address these gaps. Conclusion In conclusion, our findings elucidate the role of hIL-24 in modulating the growth, migration, invasion, and apoptosis of Siha cervical cancer cells (Fig. 8 ), enriching our understanding of the molecular mechanisms underlying cervical cancer. These findings introduce new perspectives and potential targets for clinical interventions. Given the current scarcity of effective cervical cancer treatments, hIL-24 emerges as a molecule of significant scientific and clinical interest. Nevertheless, further investigations are imperative to confirm these preliminary results and explore the practical applications of hIL-24 in cervical cancer therapies. Declarations Acknowledgment None. Funding Projects of Medical and Health Technology Development Program in Shandong Province (No. 202202080776). Ethical Statement No need. Conflict of Interest The author declares no conflict of interest. References Cohen PA, Jhingran A, Oaknin A, Denny L. Cervical cancer. Lancet. 2019;393(10167):169-182. doi:10.1016/S0140-6736(18)32470-X Arbyn M, Weiderpass E, Bruni L, et al. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis [published correction appears in Lancet Glob Health. 2022 Jan;10(1):e41]. Lancet Glob Health. 2020;8(2):e191-e203. doi:10.1016/S2214-109X(19)30482-6 Ferrall L, Lin KY, Roden RBS, Hung CF, Wu TC. Cervical Cancer Immunotherapy: Facts and Hopes. Clin Cancer Res. 2021;27(18):4953-4973. doi:10.1158/1078-0432.CCR-20-2833 Srivastava S, Koay EJ, Borowsky AD, et al. Cancer overdiagnosis: a biological challenge and clinical dilemma. Nat Rev Cancer. 2019;19(6):349-358. doi:10.1038/s41568-019-0142-8 Arbyn M, Redman CWE, Verdoodt F, et al. Incomplete excision of cervical precancer as a predictor of treatment failure: a systematic review and meta-analysis. Lancet Oncol. 2017;18(12):1665-1679. doi:10.1016/S1470-2045(17)30700-3 Chargari C, Deutsch E, Blanchard P, et al. Brachytherapy: An overview for clinicians. CA Cancer J Clin. 2019;69(5):386-401. doi:10.3322/caac.21578 Ojesina AI, Lichtenstein L, Freeman SS, et al. Landscape of genomic alterations in cervical carcinomas. Nature. 2014;506(7488):371-375. doi:10.1038/nature12881 Schmid MP, Lindegaard JC, Mahantshetty U, et al. Risk Factors for Local Failure Following Chemoradiation and Magnetic Resonance Image-Guided Brachytherapy in Locally Advanced Cervical Cancer: Results From the EMBRACE-I Study. J Clin Oncol. 2023;41(10):1933-1942. doi:10.1200/JCO.22.01096 Ghaem-Maghami S, Sagi S, Majeed G, Soutter WP. Incomplete excision of cervical intraepithelial neoplasia and risk of treatment failure: a meta-analysis. Lancet Oncol. 2007;8(11):985-993. doi:10.1016/S1470-2045(07)70283-8 Cancer Genome Atlas Research Network; Albert Einstein College of Medicine; Analytical Biological Services; Integrated genomic and molecular characterization of cervical cancer. Nature. 2017;543(7645):378-384. doi:10.1038/nature21386 Norberg SM, Hinrichs CS. Engineered T cell therapy for viral and non-viral epithelial cancers. Cancer Cell. 2023;41(1):58-69. doi:10.1016/j.ccell.2022.10.016 Jeannot E, Latouche A, Bonneau C, et al. Circulating HPV DNA as a Marker for Early Detection of Relapse in Patients with Cervical Cancer. Clin Cancer Res. 2021;27(21):5869-5877. doi:10.1158/1078-0432.CCR-21-0625 Maarof G, Bouchet-Delbos L, Gary-Gouy H, Durand-Gasselin I, Krzysiek R, Dalloul A. Interleukin-24 inhibits the plasma cell differentiation program in human germinal center B cells. Blood. 2010;115(9):1718-1726. doi:10.1182/blood-2009-05-220251 Liu S, Hur YH, Cai X, et al. A tissue injury sensing and repair pathway distinct from host pathogen defense. Cell. 2023;186(10):2127-2143.e22. doi:10.1016/j.cell.2023.03.031 Bordon Y. Hypoxia and IL-24 drive a sterile wound healing pathway. Nat Rev Immunol. 2023;23(6):344. doi:10.1038/s41577-023-00888-4 Emdad L, Bhoopathi P, Talukdar S, et al. Recent insights into apoptosis and toxic autophagy: The roles of MDA-7/IL-24, a multidimensional anti-cancer therapeutic. Semin Cancer Biol. 2020;66:140-154. doi:10.1016/j.semcancer.2019.07.013 Rasoolian M, Kheirollahi M, Hosseini SY. MDA-7/interleukin 24 (IL-24) in tumor gene therapy: application of tumor penetrating/homing peptides for improvement of the effects. Expert Opin Biol Ther. 2019;19(3):211-223. doi:10.1080/14712598.2019.1566453 Miri SM, Pourhossein B, Hosseini SY, et al. Enhanced synergistic antitumor effect of a DNA vaccine with anticancer cytokine, MDA-7/IL-24, and immune checkpoint blockade. Virol J. 2022;19(1):106. Published 2022 Jun 25. doi:10.1186/s12985-022-01842-x Hur J, Park I, Lim KM, Doh J, Cho SG, Chung AJ. Microfluidic Cell Stretching for Highly Effective Gene Delivery into Hard-to-Transfect Primary Cells. ACS Nano. 2020;14(11):15094-15106. doi:10.1021/acsnano.0c05169 Helmink BA, Reddy SM, Gao J, et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature. 2020;577(7791):549-555. doi:10.1038/s41586-019-1922-8 Xie Q, Li Z, Luo X, et al. piRNA-14633 promotes cervical cancer cell malignancy in a METTL14-dependent m6A RNA methylation manner. J Transl Med. 2022;20(1):51. Published 2022 Jan 29. doi:10.1186/s12967-022-03257-2 Zhong G, Zhao Q, Chen Z, Yao T. TGF-β signaling promotes cervical cancer metastasis via CDR1as. Mol Cancer. 2023;22(1):66. Published 2023 Mar 31. doi:10.1186/s12943-023-01743-9 Wei Z, Gan J, Feng X, et al. APOBEC3B is overexpressed in cervical cancer and promotes the proliferation of cervical cancer cells through apoptosis, cell cycle, and p53 pathway. Front Oncol. 2022;12:864889. Published 2022 Sep 29. doi:10.3389/fonc.2022.864889 Cao G, Yue J, Ruan Y, et al. Single-cell dissection of cervical cancer reveals key subsets of the tumor immune microenvironment. EMBO J. 2023;42(16):e110757. doi:10.15252/embj.2022110757 Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411-420. doi:10.1038/nbt.4096 Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381-386. doi:10.1038/nbt.2859 Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. Published 2008 Dec 29. doi:10.1186/1471-2105-9-559 Trubetskoy V, Pardiñas AF, Qi T, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604(7906):502-508. doi:10.1038/s41586-022-04434-5 Williams JA, Burgess S, Suckling J, et al. Inflammation and Brain Structure in Schizophrenia and Other Neuropsychiatric Disorders: A Mendelian Randomization Study. JAMA Psychiatry. 2022;79(5):498-507. doi:10.1001/jamapsychiatry.2022.0407 Yang SL, Tan HX, Niu TT, et al. The IFN-γ-IDO1-kynureine pathway-induced autophagy in cervical cancer cell promotes phagocytosis of macrophage. Int J Biol Sci. 2021;17(1):339-352. Published 2021 Jan 1. doi:10.7150/ijbs.51241 Huang L, Chen Y, Liu R, et al. P-Glycoprotein Aggravates Blood Brain Barrier Dysfunction in Experimental Ischemic Stroke by Inhibiting Endothelial Autophagy. Aging Dis. 2022;13(5):1546-1561. Published 2022 Oct 1. doi:10.14336/AD.2022.0225 Xiong J, Nie M, Fu C, et al. Hypoxia Enhances HIF1α Transcription Activity by Upregulating KDM4A and Mediating H3K9me3, Thus Inducing Ferroptosis Resistance in Cervical Cancer Cells. Stem Cells Int. 2022;2022:1608806. Published 2022 Mar 5. doi:10.1155/2022/1608806 Zhang K, Zhang Y, Chen C, et al. miR-139-5p mediates the palmitate-induced inhibition of insulin secretion by targeting neuronal pentraxin 1 in INS-1 cells. Acta Biochim Biophys Sin (Shanghai). 2021;53(8):1017-1026. doi:10.1093/abbs/gmab082 Kumar P, Nagarajan A, Uchil PD. Analysis of Cell Viability by the MTT Assay. Cold Spring Harb Protoc. 2018;2018(6):10.1101/pdb.prot095505. Published 2018 Jun 1. doi:10.1101/pdb.prot095505 Liu Y. Understanding the biological activity of amyloid proteins in vitro: from inhibited cellular MTT reduction to altered cellular cholesterol homeostatis. Prog Neuropsychopharmacol Biol Psychiatry. 1999;23(3):377-395. doi:10.1016/s0278-5846(99)00003-2 Kan L, Capuano E, Fogliano V, et al. Inhibition of α-glucosidases by tea polyphenols in rat intestinal extract and Caco-2 cells grown on Transwell [published correction appears in Food Chem. 2022 Jan 1;366:130649]. Food Chem. 2021;361:130047. doi:10.1016/j.foodchem.2021.130047 Ding L, Chakrabarti J, Sheriff S, et al. Toll-like Receptor 9 Pathway Mediates Schlafen + -MDSC Polarization During Helicobacter-induced Gastric Metaplasias. Gastroenterology. 2022;163(2):411-425.e4. doi:10.1053/j.gastro.2022.04.031 Chen Q, Wang H, Li Z, et al. Circular RNA ACTN4 promotes intrahepatic cholangiocarcinoma progression by recruiting YBX1 to initiate FZD7 transcription. J Hepatol. 2022;76(1):135-147. doi:10.1016/j.jhep.2021.08.027 Pijuan J, Barceló C, Moreno DF, et al. In vitro Cell Migration, Invasion, and Adhesion Assays: From Cell Imaging to Data Analysis. Front Cell Dev Biol. 2019;7:107. Published 2019 Jun 14. doi:10.3389/fcell.2019.00107 Huang XY, Huang ZL, Huang J, et al. Exosomal circRNA-100338 promotes hepatocellular carcinoma metastasis via enhancing invasiveness and angiogenesis. J Exp Clin Cancer Res. 2020;39(1):20. Published 2020 Jan 23. doi:10.1186/s13046-020-1529-9 Yu-Ju Wu C, Chen CH, Lin CY, et al. CCL5 of glioma-associated microglia/macrophages regulates glioma migration and invasion via calcium-dependent matrix metalloproteinase 2. Neuro Oncol. 2020;22(2):253-266. doi:10.1093/neuonc/noz189 Symons RA, Colella F, Collins FL, et al. Targeting the IL-6-Yap-Snail signalling axis in synovial fibroblasts ameliorates inflammatory arthritis. Ann Rheum Dis. 2022;81(2):214-224. doi:10.1136/annrheumdis-2021-220875 Gu Y, Wang Z, Wei C, et al. Photonic hyperthermia of malignant peripheral nerve sheath tumors at the third near-infrared biowindow. Elife. 2022;11:e75473. Published 2022 Sep 16. doi:10.7554/eLife.75473 Kumar R, Saneja A, Panda AK. An Annexin V-FITC-Propidium Iodide-Based Method for Detecting Apoptosis in a Non-Small Cell Lung Cancer Cell Line. Methods Mol Biol. 2021;2279:213-223. doi:10.1007/978-1-0716-1278-1_17 Ming J, Liu W, Wu H, et al. The active ingredients and mechanisms of Longchai Jiangxue Formula in treating PV, based on UPLC/Q-TOF-MS/MS, systematic pharmacology, and molecular biology validation. Biomed Pharmacother. 2021;140:111767. doi:10.1016/j.biopha.2021.111767 Abdel-Fattah M, Mostafa A, Familusi A, Ramsay I, N'dow J. Prospective randomised controlled trial of transobturator tapes in management of urodynamic stress incontinence in women: 3-year outcomes from the Evaluation of Transobturator Tapes study [published correction appears in Eur Urol. 2019 Apr;75(4):e119]. Eur Urol. 2012;62(5):843-851. doi:10.1016/j.eururo.2012.04.021 Zhang Y, Su D, Chen Y, Tan M, Chen X. Effect of socioeconomic status on the physical and mental health of the elderly: the mediating effect of social participation. BMC Public Health. 2022;22(1):605. Published 2022 Mar 29. doi:10.1186/s12889-022-13062-7 Kurmyshkina OV, Dobrynin PV, Kovchur PI, Volkova TO. Sequencing-based transcriptome analysis reveals diversification of immune response- and angiogenesis-related expression patterns of early-stage cervical carcinoma as compared with high-grade CIN. Front Immunol. 2023;14:1215607. Published 2023 Sep 4. doi:10.3389/fimmu.2023.1215607 Fanouriakis A, Tziolos N, Bertsias G, Boumpas DT. Update οn the diagnosis and management of systemic lupus erythematosus. Ann Rheum Dis. 2021;80(1):14-25. doi:10.1136/annrheumdis-2020-218272 Sawaya GF, Smith-McCune K, Kuppermann M. Cervical Cancer Screening: More Choices in 2019. JAMA. 2019;321(20):2018-2019. doi:10.1001/jama.2019.4595 Bruni L, Serrano B, Roura E, et al. Cervical cancer screening programmes and age-specific coverage estimates for 202 countries and territories worldwide: a review and synthetic analysis [published correction appears in Lancet Glob Health. 2023 Jul;11(7):e1011]. Lancet Glob Health. 2022;10(8):e1115-e1127. doi:10.1016/S2214-109X(22)00241-8 Shamseddine AA, Burman B, Lee NY, Zamarin D, Riaz N. Tumor Immunity and Immunotherapy for HPV-Related Cancers. Cancer Discov. 2021;11(8):1896-1912. doi:10.1158/2159-8290.CD-20-1760 Liao S, Yang Y, Chen S, et al. IL-24 inhibits endometrial cancer cell proliferation by promoting apoptosis through the mitochondrial intrinsic signaling pathway. Biomed Pharmacother. 2020;124:109831. doi:10.1016/j.biopha.2020.109831 Qu J, Wang W, Feng Y, et al. Cationic Antheraea pernyi Silk Fibroin-Modified Adenovirus-Mediated ING4 and IL-24 Dual Gene Coexpression Vector Suppresses the Growth of Hepatoma Carcinoma Cells. Int J Nanomedicine. 2019;14:9745-9761. Published 2019 Dec 10. doi:10.2147/IJN.S230693 Pradhan AK, Bhoopathi P, Talukdar S, et al. MDA-7/IL-24 regulates the miRNA processing enzyme DICER through downregulation of MITF. Proc Natl Acad Sci U S A. 2019;116(12):5687-5692. doi:10.1073/pnas.1819869116 Modi J, Roy A, Pradhan AK, et al. Insights into the Mechanisms of Action of MDA-7/IL-24: A Ubiquitous Cancer-Suppressing Protein. Int J Mol Sci. 2021;23(1):72. Published 2021 Dec 22. doi:10.3390/ijms23010072 Kroemer G, Pouyssegur J. Tumor cell metabolism: cancer's Achilles' heel. Cancer Cell. 2008;13(6):472-482. doi:10.1016/j.ccr.2008.05.005 Massagué J, Ganesh K. Metastasis-Initiating Cells and Ecosystems. Cancer Discov. 2021;11(4):971-994. doi:10.1158/2159-8290.CD-21-0010 Klein CA. Cancer progression and the invisible phase of metastatic colonization. Nat Rev Cancer. 2020;20(11):681-694. doi:10.1038/s41568-020-00300-6 Bhoopathi P, Lee N, Pradhan AK, et al. mda-7/IL-24 Induces Cell Death in Neuroblastoma through a Novel Mechanism Involving AIF and ATM. Cancer Res. 2016;76(12):3572-3582. doi:10.1158/0008-5472.CAN-15-2959 Zhang J, Zhang K, Ren Y, Wei D. The expression, purification, and functional evaluation of the novel tumor suppressor fusion protein IL-24-CN. Appl Microbiol Biotechnol. 2021;105(20):7889-7898. doi:10.1007/s00253-021-11558-7 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx 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-3560710","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":246478166,"identity":"c845961c-d2bf-4123-9d24-2d23c5abfe73","order_by":0,"name":"Min Song","email":"","orcid":"","institution":"Qilu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Song","suffix":""},{"id":246478170,"identity":"df5130b4-a61c-45f0-84ea-37df1236cbd4","order_by":1,"name":"Hongtao Yuan","email":"","orcid":"","institution":"Zhangdian People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongtao","middleName":"","lastName":"Yuan","suffix":""},{"id":246478173,"identity":"4074e4e1-d418-4194-a48d-113b8ae612aa","order_by":2,"name":"Jie Zhang","email":"","orcid":"","institution":"Qilu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhang","suffix":""},{"id":246478177,"identity":"0a9a6b7c-d208-45fb-86ed-228133372e40","order_by":3,"name":"Jing Wang","email":"","orcid":"","institution":"Qilu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":246478180,"identity":"aa033186-6a3f-4d9f-ab0b-a91dcc630da3","order_by":4,"name":"Jianhua Yu","email":"","orcid":"","institution":"Qilu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianhua","middleName":"","lastName":"Yu","suffix":""},{"id":246478184,"identity":"8ef5480b-b1fd-4d6e-8882-e00afa590651","order_by":5,"name":"Wei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIie3OPUvDQBjA8acE4nIl63WxX+EpgVio4FdJFl1y0ql0KJqpLgXXgF8iEgg4mXJQl0u7JtSh4NgOccug4jVdHM7Y0eH+cHBvP+4AdLr/GwU4CQDcIQDWG+YxhKSS4PFkr1yo7/9J8CXjWzLue1Gx9coNQvfMEgjliIP1EKiJuL4cEEG9aM1iKj/WewoEtsIlB/qaKomT+o7NppQlaxbtiYvzGRrtKQesv6ogq50kX5IUWVzVhBM0PptI7ttvLJAkbyeHVxaStBrIRb5zjI8FvX0WLOm7SHuRMIfz2fKK0FxNOve+/R5ObuzOXRYX1fi8iyv+uKlGg1MrVBOZSX8sDvNUDvLbfZlRNhzqdDqdDuAbYvdgvq1JhL8AAAAASUVORK5CYII=","orcid":"","institution":"Qilu Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-11-05 05:59:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3560710/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3560710/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46100958,"identity":"263c1f7b-545b-4721-848d-8539f22760cc","added_by":"auto","created_at":"2023-11-08 15:58:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":973480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClassification display of transcriptional data from the GTEx database.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (A) Classification display of transcriptional data from the GTEx database by tissue type; (B) Classification display of transcriptional data from the GTEx database by gender and tissue types.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3560710/v1/f88e9933b4c63e792597e93a.jpg"},{"id":46100959,"identity":"e42732b1-93fe-4a34-b9b5-99df9be42e08","added_by":"auto","created_at":"2023-11-08 15:58:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":494835,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed genes related to cervical cancer in the GEO, TCGA, and GTEx databases.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (A) Volcano plot of differentially expressed genes between 13 normal cervical tissue samples and 306 CC tissue samples in the TCGA and GTEx databases; (B) Volcano plot of differentially expressed genes between 24 normal cervical tissue samples and 28 CC tissue samples in the GSE63514 dataset; (C) Volcano plot of differentially expressed genes between 6 normal cervical tissue samples and 6 CC tissue samples in the GSE192804 dataset. In the volcano plots, blue dots represent genes downregulated in CC, red dots represent genes upregulated in CC, and gray dots represent genes with no differences.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3560710/v1/c1bef868ec7101ca27f14e6a.jpg"},{"id":46100965,"identity":"14ba0d30-f436-4762-87c7-6403076f8fcd","added_by":"auto","created_at":"2023-11-08 15:58:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":654192,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of WGCNA co-expression analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (A) Result graph showing the importance of module genes in CC according to WGCNA co-expression analysis (N = 319); (B) Result of WGCNA co-expression analysis of genes related to CC (N = 319); (C) Venn diagram showing the intersection of differentially expressed genes in CC samples from the TCGA and GTEx databases, genes in the turquoise module from WGCNA analysis, and genes with differential expression in the GSE63514 and GSE192804 datasets in CC.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3560710/v1/f3f347fbfefcf5527dd48141.jpg"},{"id":46103140,"identity":"069508e3-6c72-4d22-b02a-338b34bcfd12","added_by":"auto","created_at":"2023-11-08 16:14:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":777878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of GO and KEGG pathway enrichment analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (A) Bar plot of GO enrichment for intersecting genes, with blue, green, and red representing BP, CC, and MF, respectively. BP represents biological processes; CC represents cellular components; MF represents molecular functions; (B) KEGG pathway circular diagram for intersecting genes.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3560710/v1/0de44d561046a8edb8f25b3f.jpg"},{"id":46100963,"identity":"33d29cce-3ba2-4a92-9164-5f5c410928f8","added_by":"auto","created_at":"2023-11-08 15:58:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":473033,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA machine learning algorithm for screening CC-related disease-associated genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (A) LASSO analysis results, with the x-axis representing log(λ) values and the y-axis representing Binomial Deviance. The dashed line represents the log(λ) value corresponding to the optimal Binomial Deviance and the number of genes retained; (B) LASSO analysis results, with the x-axis representing Log lambda and the y-axis representing Log lambda; (C) Random forest algorithm result graph; (D) Venn diagram showing the intersection of disease-associated genes selected by the LASSO regression and random forest algorithm, two machine learning algorithms.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3560710/v1/f8319a50c81893d8945b4b98.jpg"},{"id":46103138,"identity":"072fbf43-8b3b-4ecc-a99e-3c54a6a122a6","added_by":"auto","created_at":"2023-11-08 16:14:18","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":953687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStatistical results of the effect of hIL-24 on the biological functions of Siha cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (A) Statistical results of the optical density values of Siha cells in each group; (B) Statistical results of the migration quantity of Siha cells in each group; (C) Statistical results of the invasion quantity of Siha cells in each group; (D) Statistical results of the apoptosis rate of Siha cells in each group. *** indicates a difference compared to the transfection reagent or empty vector groups, p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3560710/v1/d0e0903883bfe55cccaa7c12.jpg"},{"id":46102452,"identity":"c418200f-fd5d-432a-acec-19aa0b631539","added_by":"auto","created_at":"2023-11-08 16:06:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":501982,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of hIL-24 on the biological functions of Siha cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (A-B) Transwell experiments to assess the migration and invasion abilities of Siha cells in each group; (C) Flow cytometric analysis to measure the apoptosis level of Siha cells in each group.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3560710/v1/f8b314406d463eca15ecbe69.png"},{"id":46102453,"identity":"9f3850ed-ed8a-47d3-bec7-9d631cf0c5b0","added_by":"auto","created_at":"2023-11-08 16:06:18","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":255641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of the molecular mechanisms underlying the impact of hIL-24 on the malignant biological behavior of cervical cancer cells.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3560710/v1/4562c6a182f3d6072f9194c9.jpg"},{"id":46684741,"identity":"245de341-bd66-4f15-8e1d-0433586228b3","added_by":"auto","created_at":"2023-11-18 01:23:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1740789,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3560710/v1/839df740-1473-4687-9184-9f93ede4f5a7.pdf"},{"id":46102454,"identity":"c49597b0-3d02-437d-9ad2-4736850acc98","added_by":"auto","created_at":"2023-11-08 16:06:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23050,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3560710/v1/1f212e0795745919285235d1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"hIL-24: A Promising Therapeutic Target for Cervical Cancer Running Title: Targeting hIL-24 in Cervical Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer remains a significant global health concern, particularly in developing nations, where it is associated with high incidence and mortality rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite recent advancements in prevention and treatment, the aggressive nature of this disease, characterized by rapid proliferation, invasion, and metastasis, poses persistent therapeutic challenges [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Conventional treatments such as surgery, radiotherapy, and chemotherapy often come with undesirable side effects and a significant risk of recurrence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consequently, there is an urgent need to explore more effective and less detrimental treatment modalities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, gene therapy has emerged as a promising avenue in medical research, attracting considerable attention due to its potential for providing targeted treatment strategies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Interleukin-24 (hIL-24) is known for its diverse biological activities, including its ability to induce tumor cell death in various cancer types [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While previous studies have demonstrated hIL-24's capacity to inhibit tumor cell proliferation and induce apoptosis, its therapeutic application in cervical cancer remains underexplored [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the limited understanding of hIL-24's role in cervical cancer, this study seeks to address this knowledge gap by examining its specific effects on Siha cervical cancer cells. We generated the pcDNA3.1 (+)-hIL-24 recombinant plasmid and introduced it into Siha cells using lipofection [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. PCR and MTT assays were employed to assess the gene's impact on cell proliferation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and the Transwell assay was used to quantify its effects on cell invasion and migration [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis research aims to comprehensively evaluate the inhibitory influence of hIL-24 on Siha cell growth and its regulatory effects on cell invasion and migration. Our findings aspire to establish a novel theoretical and practical framework for the application of hIL-24 in cervical cancer therapy. By elucidating the mechanism of action of hIL-24, we aim to improve treatment efficacy and safety for cervical cancer patients, thus contributing to the broader field of oncology and gene therapy.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGEO database chip and TCGA/GTEx transcriptome data download\u003c/h2\u003e \u003cp\u003eWe obtained 309 transcriptome data samples from cervical cancer patients, which included 306 cervical cancer tissue samples and 3 adjacent normal tissue samples, from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Download transcriptome data samples from the Genotype-Tissue Expression (GTEx) database for 10 normal cervical tissues. To obtain the datasets GSE63514 and GSE192804, access the Gene Expression Omnibus (GEO) database at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The dataset GSE63514 includes 28 samples of cervical cancer (CC) tissues and 24 samples of normal cervical tissues, while GSE192804 consists of 6 samples of CC tissues and 6 samples of normal cervical tissues [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene screening\u003c/h2\u003e \u003cp\u003eDifferential mRNA expression was filtered using the \"limma\" package in R. A P-value less than 0.05 was defined as the screening criterion for TCGA. In addition, a criterion of absolute log2 fold change greater than 2 and a P-value less than 0.05 was used for further filtering. In this study, we generated a volcano plot using the ggplot2 R package developed by Hadley Wickham (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://had.co.nz/ggplot2/\u003c/span\u003e\u003cspan address=\"http://had.co.nz/ggplot2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To create a Venn diagram, utilize the Xiantao Academic Database available at (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.xiantaozi.com/\u003c/span\u003e\u003cspan address=\"https://www.xiantaozi.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eWeighted Gene Co-Expression Network Analysis (WGCNA) analysis\u003c/h2\u003e \u003cp\u003eThe expression clustering and phenotype association analysis will be conducted on gene data from the TCGA database using the 'WGCNA' package in the R language. The most relevant gene modules associated with cervical cancer will be identified, and the genes within these modules will be extracted for further analysis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eWe performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the differentially expressed genes (DEGs) in the CC samples. The SangerBox database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sangerbox.com/home.html\u003c/span\u003e\u003cspan address=\"http://sangerbox.com/home.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized for this analysis. Additionally, we generated visualizations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003eSiHa cells derived from human cervical cancer were procured from Beyotime Biotechnology Co., Ltd. in Shanghai, China. The cells were cultured in DMEM medium supplemented with 10% fetal bovine serum and 1% (100\u0026times;) penicillin-streptomycin. The culturing conditions included a temperature of 37\u0026deg;C and 5% carbon dioxide [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePlasmid extraction\u003c/h2\u003e \u003cp\u003eThe pcDNA3.1 (+)-hIL-24 plasmid was synthesized by Shanghai Biotechnology Co., Ltd. in Shanghai, China, and subsequently sequenced by Shanghai Gene Engineering Co., Ltd. in the same city. The plasmid extraction and purification reagents were purchased from Beyotime Biotechnology Co., Ltd. (Shanghai, China). DH5α E. coli cells transformed with the pcDNA3.1(+)-hIL-24 plasmid were inoculated into LB medium supplemented with ampicillin. The cultures were then incubated overnight at 37\u0026deg;C with shaking at 250 rpm. Plasmids should be extracted in accordance with the instructions provided by the plasmid extraction kit [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCell transfection\u003c/h2\u003e \u003cp\u003eThe Lipo8000\u0026trade; transfection reagent was procured from Shanghai Beyotime Biotechnology Co., Ltd. Approximately 500,000 cells were seeded per well into a 6-well plate the day before transfection and cultured in an antibiotic-free medium for 18\u0026ndash;24 hours. The cells were cultivated until the following day, achieving a cell density of approximately 70\u0026ndash;80%. Replace each well with 2 mL of fresh medium without antibiotics during transfection. Combine 2.5 \u0026micro;g of DNA with 4 \u0026micro;L of Lipo8000\u0026trade; transfection reagent in 125 \u0026micro;L of DMEM medium. Add 125 \u0026micro;L of the mixture to each well, followed by a 48-hour incubation period [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell growth inhibition assay\u003c/h2\u003e \u003cp\u003eThe density of transfected SiHa cells and non-transfected SiHa cells was adjusted to 2.5\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells/ml in the DMEM culture medium. Subsequently, they were seeded onto a 96-well plate with 200 \u0026micro;l of cell suspension per well. Following a 72-hour incubation period at 37℃ and 5% carbon dioxide, 20 \u0026micro;L of MTT solution was added to each well and incubated for another 4 hours at 37℃. Remove the culture medium, add 150 \u0026micro;L of DMSO and thoroughly mix for 10 minutes. The absorbance of each well should be measured at a wavelength of 568 nm using an instrument equipped with an enzyme label [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell migration experiment\u003c/h2\u003e \u003cp\u003eThis study aims to investigate the impact of hIL-24 on the migratory capacity of SiHa cells through the utilization of the Transwell assay. The transfected SiHa cells and untransfected SiHa cells were centrifuged at 1000 rpm for 5 minutes. Remove the supernatant and wash the cells twice with PBS. Transfer the cells into the DMEM culture medium and then resuspend them. The cell concentration should be adjusted to 3\u0026times;10\u003csup\u003e5\u003c/sup\u003e/ml using a hemocytometer for further use. Add 800 \u0026micro;L of DMEM culture medium (10% penicillin-streptomycin) to a 24-well plate and place the Transwell chamber inside. A 200 \u0026micro;L cell suspension was inoculated into each group's upper chamber of the Transwell system and incubated at 37\u0026deg;C for 48 hours. Afterward, the insert was removed, and the chamber was washed with PBS. Cell fixation was performed by incubating the cells in a 70% ethanol solution for 1 hour, followed by staining with a 0.5% crystal violet staining solution at room temperature for 20 minutes. Following the PBS washing, use a clean cotton swab to remove the non-migrated cells on one side of the chamber. Observe and photograph cells under a microscope [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInvasion assay of cells\u003c/h2\u003e \u003cp\u003eBefore the experiment, the Matrigel gel should be melted and diluted with serum-free medium in a ratio of 1:3. The Transwell chamber should be placed in a 24-well plate. Next, 100 \u0026micro;L of diluted matrix gel should be applied evenly onto the polycarbonate membrane at the bottom of each well. The plate should then be incubated at 37 ℃ in a 5% carbon dioxide incubator for 30 minutes. The following steps are identical to those of the cell migration experiment [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eApoptosis experiment\u003c/h2\u003e \u003cp\u003eThe transfected SiHa cells should be centrifuged at 1200 rpm for 5 minutes. After centrifugation, remove the supernatant and then resuspend the cells in PBS. The cells should be washed twice using sterile PBS and centrifuged at 1200 rpm for 5 minutes. Cells were analyzed using flow cytometry, following the instructions provided by Shanghai BioCloud Biotechnology Co., Ltd. for the Annexin V-FITC Apoptosis Detection Kit [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eOur research uses R language, version 4.2.1. The compilation of R language is performed using the integrated development environment RStudio. The current version of RStudio is 2022.12.0-353. For file processing, we utilized Perl version 5.30.0. In addition, we used GraphPad Prism software, version 8.0.\u003c/p\u003e \u003cp\u003ePerforming statistical analysis using SPSS 17.0 software. Once the normality of the data has been tested, multiple comparisons will be conducted using analysis of variance (ANOVA). Inter-group comparisons are performed using t-tests. Data is represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. A p-value below 0.05 indicates statistical significance [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eA global database reveals the essential genes for cervical cancer through a comprehensive analysis of GEO, TCGA, and GTEx\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAccording to data from the World Health Organization (WHO) on global cancer incidence and mortality rates and global statistical analysis results from the Global Cancer Observatory database, cervical cancer (CC) ranks as the fourth most prevalent cancer in women, making it a substantial public health concern. Cervical cancer is one of the most prevalent cancers among middle-aged women in most countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Hence, developing novel treatment strategies for cervical cancer is vital for improving the overall prognosis of patients.\u003c/p\u003e \u003cp\u003eTo investigate the crucial genes involved in cervical cancer, we retrieved data from the GTEx database. We obtained transcriptome data for 31 diverse tissue types across genders (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Through differential analysis, we identified 1804 genes that exhibited differential expression in cervical cancer compared to the adjacent normal cervical tissues. This analysis used transcriptomic data from TCGA and GTEx (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Furthermore, by utilizing the GSE63514 dataset, we obtained 510 genes that displayed differential expression in cervical cancer compared to normal cervical tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFurthermore, we identified 1702 genes that exhibited a differential expression in cervical cancer compared to normal cervical tissue, using data from the GSE192804 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The results demonstrate our successful acquisition of transcriptome data from the TCGA and GTEx databases and the retrieval of two microarray datasets from the GEO database. Furthermore, differential analysis has allowed us to individually identify distinct genes for each dataset.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWGCNA uncovers genes characteristic of cervical cancer: A thorough investigation of modules associated with the disease\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo effectively identify disease-related gene characteristics closely associated with CC, we performed a weighted gene co-expression network analysis (WGCNA) using TCGA and GTEx databases. We obtained nine gene modules: black, blue, brown, green, grey, magenta, red, turquoise, and yellow. Among these modules, the turquoise module exhibited the highest proportion of gene importance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The correlation analysis results between the module genes and CC revealed a negative correlation between the turquoise module genes and CC. This result suggests that the turquoise module genes may exert inhibitory effects on cervical cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To identify potential overlap, we conducted an intersection analysis between 988 disease-associated genes extracted from the turquoise module and the differentially expressed genes identified from GEO, TCGA, and GTEx databases. This analysis led to the identification of six genes that intersected across these datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on six overlapping genes. GO enrichment analysis showed that the differentially expressed genes in the CC samples were primarily enriched in biological processes, including tissue development, extracellular structure organization, and wound healing (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The KEGG enrichment analysis revealed that the differentially expressed genes in the CC samples were primarily enriched in signaling pathways, including ECM-receptor interaction, Human papillomavirus infection, and Focal adhesion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The results above indicate that we identified six genes closely associated with cervical cancer through WGCNA co-expression and differential analyses.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMachine learning is vital in disease gene screening, specifically utilizing LASSO and random forest algorithms\u003c/h2\u003e \u003cp\u003eSubsequently, the expression levels of the six genes in the GSE192804 dataset were extracted, and a multivariate Cox study with LASSO regression was conducted. This analysis identified five disease-associated genes, namely ACOX2, IL-24, SPP1, CRYAB, and ANKRD22 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). In addition, we utilized the random forest algorithm to assess gene importance, identifying IL24 as one disease-associated gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Finally, by performing the intersection, we identified a single gene associated with cervical cancer: IL-24 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Based on the results above, we have successfully identified the genes associated with cervical cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ehIL-24: A comprehensive study of the newly discovered multi-functional anti-cancer protein and its interaction with Siha cells\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further elucidate the impact of human IL-24 (hIL-24) on cervical cancer, an overexpression plasmid of hIL-24 (designated as pcDNA3.1 (+)-hIL-24) was constructed and subsequently transfected into Siha cervical cancer cells to evaluate its effects on their biological functions. MTT analysis revealed that Siha cells' average optical density (OD) value in the pcDNA3.1 (+)-hIL-24 plasmid group decreased to 1.0127 compared to the transfection reagent and empty vector groups. This finding highlights the crucial role hIL-24 in inhibiting the growth of Siha cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA; Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, cell migration experiments demonstrated an inhibitory effect of hIL-24 on the migration ability of Siha cells. The average number of migrated cells in the pcDNA3.1 (+)-hIL-24 plasmid group was 105, markedly lower than the other groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, and Table\u0026nbsp;2). Further invasive experiments confirmed that overexpression of hIL-24 substantially reduced the invasiveness of Siha cells. The invasive ability of cells transfected with pcDNA3.1(+)-hIL-24 plasmid was the lowest, with an average of 90.5 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eFinally, flow cytometry analysis revealed an increase in the apoptotic rate of Siha cells upon treatment with hIL-24. The pcDNA3.1 (+)-hIL-24 plasmid group showed an apoptotic rate of 12.81% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC; Table\u0026nbsp;4). These comprehensive data strongly suggest that human interleukin-24 (hIL-24) inhibits the growth, migration, and invasion ability of Siha cells and promotes apoptosis in these cells. This result demonstrates the tremendous potential of hIL-24 in the treatment of cervical cancer.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary objective of this study was to comprehensively investigate the impact of hIL-24 on Siha cells, a subtype of cervical cancer. We focused on assessing its effects on cell growth inhibition, migration reduction, invasion attenuation, and apoptosis enhancement. The insights gained from this study significantly contribute to our understanding of cervical cancer at the molecular level and pave the way for innovative therapeutic approaches. Considering the ongoing global threat posed by cervical cancer to women's health, there is an urgent need for novel treatment modalities [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMTT assays confirmed that hIL-24 notably inhibits the proliferation of Siha cells, in line with existing literature documenting its tumor-suppressive properties [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Furthermore, our study provides insights into the signaling pathways and molecular mechanisms underpinning hIL-24's tumor-suppressive actions, laying the foundation for future investigations.\u003c/p\u003e \u003cp\u003eWe also demonstrated that hIL-24 significantly reduces the migratory and invasive capabilities of Siha cells, which are crucial for the management and prevention of tumor metastasis [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Compared to other studies, our experimental protocol was highly stringent, enhancing the robustness of our findings and supporting the potential of hIL-24 in inhibiting metastasis. In addition, flow cytometry analyses validated hIL-24's role in promoting apoptosis in Siha cells, further underscoring its therapeutic promise [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study's experimental design is rigorous, and our results are substantiated by stringent statistical tests, ensuring high reliability. Nonetheless, the limited sample size may necessitate further validation within a broader cohort.\u003c/p\u003e \u003cp\u003eThe study's limitations encompass the small cohort size and the absence of multicentric and diverse cell line validation. To comprehensively evaluate hIL-24's therapeutic potential in cervical cancer, future research should address these gaps.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings elucidate the role of hIL-24 in modulating the growth, migration, invasion, and apoptosis of Siha cervical cancer cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), enriching our understanding of the molecular mechanisms underlying cervical cancer. These findings introduce new perspectives and potential targets for clinical interventions. Given the current scarcity of effective cervical cancer treatments, hIL-24 emerges as a molecule of significant scientific and clinical interest. Nevertheless, further investigations are imperative to confirm these preliminary results and explore the practical applications of hIL-24 in cervical cancer therapies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProjects of Medical and Health Technology Development Program in Shandong Province (No. 202202080776).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo need.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCohen PA, Jhingran A, Oaknin A, Denny L. Cervical cancer. Lancet. 2019;393(10167):169-182. doi:10.1016/S0140-6736(18)32470-X\u003c/li\u003e\n\u003cli\u003eArbyn M, Weiderpass E, Bruni L, et al. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis [published correction appears in Lancet Glob Health. 2022 Jan;10(1):e41]. Lancet Glob Health. 2020;8(2):e191-e203. doi:10.1016/S2214-109X(19)30482-6\u003c/li\u003e\n\u003cli\u003eFerrall L, Lin KY, Roden RBS, Hung CF, Wu TC. Cervical Cancer Immunotherapy: Facts and Hopes. Clin Cancer Res. 2021;27(18):4953-4973. doi:10.1158/1078-0432.CCR-20-2833\u003c/li\u003e\n\u003cli\u003eSrivastava S, Koay EJ, Borowsky AD, et al. Cancer overdiagnosis: a biological challenge and clinical dilemma. Nat Rev Cancer. 2019;19(6):349-358. doi:10.1038/s41568-019-0142-8\u003c/li\u003e\n\u003cli\u003eArbyn M, Redman CWE, Verdoodt F, et al. Incomplete excision of cervical precancer as a predictor of treatment failure: a systematic review and meta-analysis. Lancet Oncol. 2017;18(12):1665-1679. doi:10.1016/S1470-2045(17)30700-3\u003c/li\u003e\n\u003cli\u003eChargari C, Deutsch E, Blanchard P, et al. Brachytherapy: An overview for clinicians. CA Cancer J Clin. 2019;69(5):386-401. doi:10.3322/caac.21578\u003c/li\u003e\n\u003cli\u003eOjesina AI, Lichtenstein L, Freeman SS, et al. Landscape of genomic alterations in cervical carcinomas. Nature. 2014;506(7488):371-375. doi:10.1038/nature12881\u003c/li\u003e\n\u003cli\u003eSchmid MP, Lindegaard JC, Mahantshetty U, et al. Risk Factors for Local Failure Following Chemoradiation and Magnetic Resonance Image-Guided Brachytherapy in Locally Advanced Cervical Cancer: Results From the EMBRACE-I Study. J Clin Oncol. 2023;41(10):1933-1942. doi:10.1200/JCO.22.01096\u003c/li\u003e\n\u003cli\u003eGhaem-Maghami S, Sagi S, Majeed G, Soutter WP. Incomplete excision of cervical intraepithelial neoplasia and risk of treatment failure: a meta-analysis. Lancet Oncol. 2007;8(11):985-993. doi:10.1016/S1470-2045(07)70283-8\u003c/li\u003e\n\u003cli\u003eCancer Genome Atlas Research Network; Albert Einstein College of Medicine; Analytical Biological Services; Integrated genomic and molecular characterization of cervical cancer. Nature. 2017;543(7645):378-384. doi:10.1038/nature21386\u003c/li\u003e\n\u003cli\u003eNorberg SM, Hinrichs CS. Engineered T cell therapy for viral and non-viral epithelial cancers. Cancer Cell. 2023;41(1):58-69. doi:10.1016/j.ccell.2022.10.016\u003c/li\u003e\n\u003cli\u003eJeannot E, Latouche A, Bonneau C, et al. Circulating HPV DNA as a Marker for Early Detection of Relapse in Patients with Cervical Cancer. Clin Cancer Res. 2021;27(21):5869-5877. doi:10.1158/1078-0432.CCR-21-0625\u003c/li\u003e\n\u003cli\u003eMaarof G, Bouchet-Delbos L, Gary-Gouy H, Durand-Gasselin I, Krzysiek R, Dalloul A. Interleukin-24 inhibits the plasma cell differentiation program in human germinal center B cells. Blood. 2010;115(9):1718-1726. doi:10.1182/blood-2009-05-220251\u003c/li\u003e\n\u003cli\u003eLiu S, Hur YH, Cai X, et al. A tissue injury sensing and repair pathway distinct from host pathogen defense. Cell. 2023;186(10):2127-2143.e22. doi:10.1016/j.cell.2023.03.031\u003c/li\u003e\n\u003cli\u003eBordon Y. Hypoxia and IL-24 drive a sterile wound healing pathway. Nat Rev Immunol. 2023;23(6):344. doi:10.1038/s41577-023-00888-4\u003c/li\u003e\n\u003cli\u003eEmdad L, Bhoopathi P, Talukdar S, et al. Recent insights into apoptosis and toxic autophagy: The roles of MDA-7/IL-24, a multidimensional anti-cancer therapeutic. Semin Cancer Biol. 2020;66:140-154. doi:10.1016/j.semcancer.2019.07.013\u003c/li\u003e\n\u003cli\u003eRasoolian M, Kheirollahi M, Hosseini SY. MDA-7/interleukin 24 (IL-24) in tumor gene therapy: application of tumor penetrating/homing peptides for improvement of the effects. Expert Opin Biol Ther. 2019;19(3):211-223. doi:10.1080/14712598.2019.1566453\u003c/li\u003e\n\u003cli\u003eMiri SM, Pourhossein B, Hosseini SY, et al. Enhanced synergistic antitumor effect of a DNA vaccine with anticancer cytokine, MDA-7/IL-24, and immune checkpoint blockade. Virol J. 2022;19(1):106. Published 2022 Jun 25. doi:10.1186/s12985-022-01842-x\u003c/li\u003e\n\u003cli\u003eHur J, Park I, Lim KM, Doh J, Cho SG, Chung AJ. Microfluidic Cell Stretching for Highly Effective Gene Delivery into Hard-to-Transfect Primary Cells. ACS Nano. 2020;14(11):15094-15106. doi:10.1021/acsnano.0c05169\u003c/li\u003e\n\u003cli\u003eHelmink BA, Reddy SM, Gao J, et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature. 2020;577(7791):549-555. doi:10.1038/s41586-019-1922-8\u003c/li\u003e\n\u003cli\u003eXie Q, Li Z, Luo X, et al. piRNA-14633 promotes cervical cancer cell malignancy in a METTL14-dependent m6A RNA methylation manner. J Transl Med. 2022;20(1):51. Published 2022 Jan 29. doi:10.1186/s12967-022-03257-2\u003c/li\u003e\n\u003cli\u003eZhong G, Zhao Q, Chen Z, Yao T. TGF-\u0026beta; signaling promotes cervical cancer metastasis via CDR1as. Mol Cancer. 2023;22(1):66. Published 2023 Mar 31. doi:10.1186/s12943-023-01743-9\u003c/li\u003e\n\u003cli\u003eWei Z, Gan J, Feng X, et al. APOBEC3B is overexpressed in cervical cancer and promotes the proliferation of cervical cancer cells through apoptosis, cell cycle, and p53 pathway. Front Oncol. 2022;12:864889. Published 2022 Sep 29. doi:10.3389/fonc.2022.864889\u003c/li\u003e\n\u003cli\u003eCao G, Yue J, Ruan Y, et al. Single-cell dissection of cervical cancer reveals key subsets of the tumor immune microenvironment. EMBO J. 2023;42(16):e110757. doi:10.15252/embj.2022110757\u003c/li\u003e\n\u003cli\u003eButler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411-420. doi:10.1038/nbt.4096\u003c/li\u003e\n\u003cli\u003eTrapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381-386. doi:10.1038/nbt.2859\u003c/li\u003e\n\u003cli\u003eLangfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. Published 2008 Dec 29. doi:10.1186/1471-2105-9-559\u003c/li\u003e\n\u003cli\u003eTrubetskoy V, Pardi\u0026ntilde;as AF, Qi T, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604(7906):502-508. doi:10.1038/s41586-022-04434-5\u003c/li\u003e\n\u003cli\u003eWilliams JA, Burgess S, Suckling J, et al. Inflammation and Brain Structure in Schizophrenia and Other Neuropsychiatric Disorders: A Mendelian Randomization Study. JAMA Psychiatry. 2022;79(5):498-507. doi:10.1001/jamapsychiatry.2022.0407\u003c/li\u003e\n\u003cli\u003eYang SL, Tan HX, Niu TT, et al. The IFN-\u0026gamma;-IDO1-kynureine pathway-induced autophagy in cervical cancer cell promotes phagocytosis of macrophage. Int J Biol Sci. 2021;17(1):339-352. Published 2021 Jan 1. doi:10.7150/ijbs.51241\u003c/li\u003e\n\u003cli\u003eHuang L, Chen Y, Liu R, et al. P-Glycoprotein Aggravates Blood Brain Barrier Dysfunction in Experimental Ischemic Stroke by Inhibiting Endothelial Autophagy. Aging Dis. 2022;13(5):1546-1561. Published 2022 Oct 1. doi:10.14336/AD.2022.0225\u003c/li\u003e\n\u003cli\u003eXiong J, Nie M, Fu C, et al. Hypoxia Enhances HIF1\u0026alpha; Transcription Activity by Upregulating KDM4A and Mediating H3K9me3, Thus Inducing Ferroptosis Resistance in Cervical Cancer Cells. Stem Cells Int. 2022;2022:1608806. Published 2022 Mar 5. doi:10.1155/2022/1608806\u003c/li\u003e\n\u003cli\u003eZhang K, Zhang Y, Chen C, et al. miR-139-5p mediates the palmitate-induced inhibition of insulin secretion by targeting neuronal pentraxin 1 in INS-1 cells. Acta Biochim Biophys Sin (Shanghai). 2021;53(8):1017-1026. doi:10.1093/abbs/gmab082\u003c/li\u003e\n\u003cli\u003eKumar P, Nagarajan A, Uchil PD. Analysis of Cell Viability by the MTT Assay. Cold Spring Harb Protoc. 2018;2018(6):10.1101/pdb.prot095505. Published 2018 Jun 1. doi:10.1101/pdb.prot095505\u003c/li\u003e\n\u003cli\u003eLiu Y. Understanding the biological activity of amyloid proteins in vitro: from inhibited cellular MTT reduction to altered cellular cholesterol homeostatis. Prog Neuropsychopharmacol Biol Psychiatry. 1999;23(3):377-395. doi:10.1016/s0278-5846(99)00003-2\u003c/li\u003e\n\u003cli\u003eKan L, Capuano E, Fogliano V, et al. Inhibition of \u0026alpha;-glucosidases by tea polyphenols in rat intestinal extract and Caco-2 cells grown on Transwell [published correction appears in Food Chem. 2022 Jan 1;366:130649]. Food Chem. 2021;361:130047. doi:10.1016/j.foodchem.2021.130047\u003c/li\u003e\n\u003cli\u003eDing L, Chakrabarti J, Sheriff S, et al. Toll-like Receptor 9 Pathway Mediates Schlafen\u003csup\u003e+\u003c/sup\u003e-MDSC Polarization During Helicobacter-induced Gastric Metaplasias. Gastroenterology. 2022;163(2):411-425.e4. doi:10.1053/j.gastro.2022.04.031\u003c/li\u003e\n\u003cli\u003eChen Q, Wang H, Li Z, et al. Circular RNA ACTN4 promotes intrahepatic cholangiocarcinoma progression by recruiting YBX1 to initiate FZD7 transcription. J Hepatol. 2022;76(1):135-147. doi:10.1016/j.jhep.2021.08.027\u003c/li\u003e\n\u003cli\u003ePijuan J, Barcel\u0026oacute; C, Moreno DF, et al. In vitro Cell Migration, Invasion, and Adhesion Assays: From Cell Imaging to Data Analysis. Front Cell Dev Biol. 2019;7:107. Published 2019 Jun 14. doi:10.3389/fcell.2019.00107\u003c/li\u003e\n\u003cli\u003eHuang XY, Huang ZL, Huang J, et al. Exosomal circRNA-100338 promotes hepatocellular carcinoma metastasis via enhancing invasiveness and angiogenesis. J Exp Clin Cancer Res. 2020;39(1):20. Published 2020 Jan 23. doi:10.1186/s13046-020-1529-9\u003c/li\u003e\n\u003cli\u003eYu-Ju Wu C, Chen CH, Lin CY, et al. CCL5 of glioma-associated microglia/macrophages regulates glioma migration and invasion via calcium-dependent matrix metalloproteinase 2. Neuro Oncol. 2020;22(2):253-266. doi:10.1093/neuonc/noz189\u003c/li\u003e\n\u003cli\u003eSymons RA, Colella F, Collins FL, et al. Targeting the IL-6-Yap-Snail signalling axis in synovial fibroblasts ameliorates inflammatory arthritis. Ann Rheum Dis. 2022;81(2):214-224. doi:10.1136/annrheumdis-2021-220875\u003c/li\u003e\n\u003cli\u003eGu Y, Wang Z, Wei C, et al. Photonic hyperthermia of malignant peripheral nerve sheath tumors at the third near-infrared biowindow. Elife. 2022;11:e75473. Published 2022 Sep 16. doi:10.7554/eLife.75473\u003c/li\u003e\n\u003cli\u003eKumar R, Saneja A, Panda AK. An Annexin V-FITC-Propidium Iodide-Based Method for Detecting Apoptosis in a Non-Small Cell Lung Cancer Cell Line. Methods Mol Biol. 2021;2279:213-223. doi:10.1007/978-1-0716-1278-1_17\u003c/li\u003e\n\u003cli\u003eMing J, Liu W, Wu H, et al. The active ingredients and mechanisms of Longchai Jiangxue Formula in treating PV, based on UPLC/Q-TOF-MS/MS, systematic pharmacology, and molecular biology validation. Biomed Pharmacother. 2021;140:111767. doi:10.1016/j.biopha.2021.111767\u003c/li\u003e\n\u003cli\u003eAbdel-Fattah M, Mostafa A, Familusi A, Ramsay I, N\u0026apos;dow J. Prospective randomised controlled trial of transobturator tapes in management of urodynamic stress incontinence in women: 3-year outcomes from the Evaluation of Transobturator Tapes study [published correction appears in Eur Urol. 2019 Apr;75(4):e119]. Eur Urol. 2012;62(5):843-851. doi:10.1016/j.eururo.2012.04.021\u003c/li\u003e\n\u003cli\u003eZhang Y, Su D, Chen Y, Tan M, Chen X. Effect of socioeconomic status on the physical and mental health of the elderly: the mediating effect of social participation. BMC Public Health. 2022;22(1):605. Published 2022 Mar 29. doi:10.1186/s12889-022-13062-7\u003c/li\u003e\n\u003cli\u003eKurmyshkina OV, Dobrynin PV, Kovchur PI, Volkova TO. Sequencing-based transcriptome analysis reveals diversification of immune response- and angiogenesis-related expression patterns of early-stage cervical carcinoma as compared with high-grade CIN. Front Immunol. 2023;14:1215607. Published 2023 Sep 4. doi:10.3389/fimmu.2023.1215607\u003c/li\u003e\n\u003cli\u003eFanouriakis A, Tziolos N, Bertsias G, Boumpas DT. Update \u0026omicron;n the diagnosis and management of systemic lupus erythematosus. Ann Rheum Dis. 2021;80(1):14-25. doi:10.1136/annrheumdis-2020-218272\u003c/li\u003e\n\u003cli\u003eSawaya GF, Smith-McCune K, Kuppermann M. Cervical Cancer Screening: More Choices in 2019. JAMA. 2019;321(20):2018-2019. doi:10.1001/jama.2019.4595\u003c/li\u003e\n\u003cli\u003eBruni L, Serrano B, Roura E, et al. Cervical cancer screening programmes and age-specific coverage estimates for 202 countries and territories worldwide: a review and synthetic analysis [published correction appears in Lancet Glob Health. 2023 Jul;11(7):e1011]. Lancet Glob Health. 2022;10(8):e1115-e1127. doi:10.1016/S2214-109X(22)00241-8\u003c/li\u003e\n\u003cli\u003eShamseddine AA, Burman B, Lee NY, Zamarin D, Riaz N. Tumor Immunity and Immunotherapy for HPV-Related Cancers. Cancer Discov. 2021;11(8):1896-1912. doi:10.1158/2159-8290.CD-20-1760\u003c/li\u003e\n\u003cli\u003eLiao S, Yang Y, Chen S, et al. IL-24 inhibits endometrial cancer cell proliferation by promoting apoptosis through the mitochondrial intrinsic signaling pathway. Biomed Pharmacother. 2020;124:109831. doi:10.1016/j.biopha.2020.109831\u003c/li\u003e\n\u003cli\u003eQu J, Wang W, Feng Y, et al. Cationic Antheraea pernyi Silk Fibroin-Modified Adenovirus-Mediated ING4 and IL-24 Dual Gene Coexpression Vector Suppresses the Growth of Hepatoma Carcinoma Cells. Int J Nanomedicine. 2019;14:9745-9761. Published 2019 Dec 10. doi:10.2147/IJN.S230693\u003c/li\u003e\n\u003cli\u003ePradhan AK, Bhoopathi P, Talukdar S, et al. MDA-7/IL-24 regulates the miRNA processing enzyme DICER through downregulation of MITF. Proc Natl Acad Sci U S A. 2019;116(12):5687-5692. doi:10.1073/pnas.1819869116\u003c/li\u003e\n\u003cli\u003eModi J, Roy A, Pradhan AK, et al. Insights into the Mechanisms of Action of MDA-7/IL-24: A Ubiquitous Cancer-Suppressing Protein. Int J Mol Sci. 2021;23(1):72. Published 2021 Dec 22. doi:10.3390/ijms23010072\u003c/li\u003e\n\u003cli\u003eKroemer G, Pouyssegur J. Tumor cell metabolism: cancer\u0026apos;s Achilles\u0026apos; heel. Cancer Cell. 2008;13(6):472-482. doi:10.1016/j.ccr.2008.05.005\u003c/li\u003e\n\u003cli\u003eMassagu\u0026eacute; J, Ganesh K. Metastasis-Initiating Cells and Ecosystems. Cancer Discov. 2021;11(4):971-994. doi:10.1158/2159-8290.CD-21-0010\u003c/li\u003e\n\u003cli\u003eKlein CA. Cancer progression and the invisible phase of metastatic colonization. Nat Rev Cancer. 2020;20(11):681-694. doi:10.1038/s41568-020-00300-6\u003c/li\u003e\n\u003cli\u003eBhoopathi P, Lee N, Pradhan AK, et al. mda-7/IL-24 Induces Cell Death in Neuroblastoma through a Novel Mechanism Involving AIF and ATM. Cancer Res. 2016;76(12):3572-3582. doi:10.1158/0008-5472.CAN-15-2959\u003c/li\u003e\n\u003cli\u003eZhang J, Zhang K, Ren Y, Wei D. The expression, purification, and functional evaluation of the novel tumor suppressor fusion protein IL-24-CN. Appl Microbiol Biotechnol. 2021;105(20):7889-7898. doi:10.1007/s00253-021-11558-7\u003c/li\u003e\n\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":"WGCNA, Machine learning, Cervical cancer, Differentially expressed genes (DEGs), hIL-24, Gene expression data","lastPublishedDoi":"10.21203/rs.3.rs-3560710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3560710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eThis study aimed to identify potential therapeutic targets for cervical cancer by analyzing global gene expression data to pinpoint key differentially expressed genes (DEGs) associated with the disease.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eGene expression datasets from GEO, TCGA, and GTEx databases were analyzed to identify DEGs in cervical cancer. Weighted Gene Co-expression Network Analysis (WGCNA) was used to uncover disease-specific genes, and machine learning techniques, including LASSO regression and random forest, were employed to refine the search for pivotal genes.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe study successfully identified DEGs related to cervical cancer, with human IL-24 (hIL-24) emerging as a critical gene through WGCNA and machine learning predictions. Experimental validation demonstrated that hIL-24 suppressed Siha cervical cancer cell proliferation, migration, and invasion, and induced apoptosis, underscoring its potential as a therapeutic target.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThe comprehensive analysis of global gene expression data highlighted hIL-24 as a key gene in cervical cancer, suggesting its potential as a viable therapeutic target. These findings provide valuable insights into the role of hIL-24 in cervical cancer pathogenesis and have the potential to guide the development of novel treatment strategies in the field of oncology.\u003c/p\u003e","manuscriptTitle":"hIL-24: A Promising Therapeutic Target for Cervical Cancer Running Title: Targeting hIL-24 in Cervical Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-08 15:58:13","doi":"10.21203/rs.3.rs-3560710/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":"c060dd90-3b9f-4b84-af0e-eda0999b3324","owner":[],"postedDate":"November 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-18T01:15:18+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-08 15:58:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3560710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3560710","identity":"rs-3560710","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}