Bioinformatics-based Investigation of Autophagy-Related Biomarkers in Heritable Ovarian Carcinoma

Bioinformatics-based Investigation of Autophagy-Related Biomarkers in Heritable Ovarian Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bioinformatics-based Investigation of Autophagy-Related Biomarkers in Heritable Ovarian Carcinoma Zhi-min Wang, Jia Ning Liu, Nan-xiang Sun, Xiao-yu Han, Xin Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3820181/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 To investigate the role of autophagy-related genes (ARGs) in Heritable Ovarian Carcinoma (HOC) and identify potential biomarkers and therapeutic targets. Methods We conducted a comprehensive bioinformatics-based analysis of gene expression patterns in 420 HOC samples and 7 normal tissues. Differential expression of 17 ARGs out of 232 candidate genes was identified. Functional annotation and pathway enrichment analyses were performed to explore the biological functions of these ARGs. A prognostic model based on 11 survival-associated ARGs was established and validated. Results Our analysis revealed differentially expressed ARGs in HOC and normal tissues, suggesting their potential as diagnostic biomarkers. GO and KEGG analyses indicated the involvement of these genes in critical biological processes and signaling pathways. The prognostic model demonstrated promising predictive capabilities for patient outcomes in HOC. Conclusion Our findings shed light on the significance of ARGs in HOC and provide potential biomarkers and therapeutic targets for improved patient outcomes in this heritable ovarian carcinoma. Heritable Ovarian Carcinoma (HOC) autophagy-related genes (ARGs) gene expression analysis prognostic model biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Heritable Ovarian Carcinoma (HOC), a cancer with a heritable nature, is a formidable challenge in women's health, being the fifth leading cause of death among gynecological malignancies in developed countries. Despite advancements in surgical techniques and therapeutic options, the overall 5-year survival rate for ovarian cancer remains disappointingly low, at approximately 30–40% [ 1 , 2 ]. Consequently, there is an urgent need to explore novel avenues for the prevention and treatment of this heritable form of ovarian cancer. Research on HOC has revealed its heritable nature, with genetic factors playing a significant role in disease development and progression. Understanding the underlying genetic mechanisms of HOC may provide critical insights for targeted therapies and personalized treatment strategies[ 3 – 5 ]. In recent years, autophagy has emerged as a key cellular process implicated in various diseases, including cancer. Autophagy is an evolutionarily conserved self-digestion pathway that plays a vital role in maintaining cellular homeostasis by recycling intracellular components, such as macromolecules and organelles, through lysosome-dependent mechanisms [ 6 , 7 ]. Dysregulation of autophagy has been observed in numerous cancer types, suggesting its potential involvement in tumorigenesis and progression[ 8 – 10 ]. Notably, the connection between autophagy and ovarian carcinoma has gained increasing attention, offering potential therapeutic targets for this aggressive cancer [ 11 ]. In previous studies, monoallelic deletions in autophagy genes suppressed ovarian cancer cells' ability to handle proteotoxic stress, leading to selective cell death without programmed cell death. Homozygous autophagy gene deletions can prevent tumor formation, but the reason for tumor development despite autophagy allele losses is unclear. Dysregulation of this homeostatic pathway may induce genomic instability. Heterogeneity in Serous Ovarian Cancer (SOC) correlates with poor patient outcomes, displaying significant intra-tumor clonal heterogeneity in pan-cancer analysis [ 12 – 14 ]. However, the specific role of autophagy-related genes (ARGs) in HOC remains poorly understood. To bridge this knowledge gap and explore the potential clinical implications, our study aims to investigate the differential expression of ARGs in HOC and normal tissues.This study aims to investigate autophagy-related genes (ARGs) in HOC using a comprehensive workflow. It involves analyzing gene expression patterns in HOC and normal samples, identifying differentially expressed ARGs, and exploring their molecular mechanisms. Additionally, a prognostic model based on survival-associated ARGs will be established and validated. The research aims to identify potential biomarkers and therapeutic targets for ovarian carcinoma, contributing to improved patient outcomes in HOC. 2. Materials and Methods 2.1. Ethics statement This study was approved by the ethical committee of the Affiliated Hospital of Inner Mongolia Medical University. 2.2. Data Acquisition and Preprocessing A total of 232 autophagy-related genes (ARGs) were obtained from the Human Autophagy Portal ( http://www.autophagy.lu/index HTML), covering all ARGs in the human genome. RNA sequencing data and clinical records of 420 tumor samples and 7 healthy samples were extracted from the TCGA database. GO enrichment analysis utilized data from the Cistrome project ( http://www.cistrome.org/ ), while KEGG enrichment analysis used data from the KEGG project ( http://www.kegg.jp/ ). 2.3. Identification of Differentially Expressed ARGs in HOC The expression levels of 232 ARGs in 420 tumor samples and 7 normal samples were averaged, and the limma package in R language was utilized to analyze the differential expression of each gene between tumor and normal samples. The threshold for differential expression was set at ∣log2 fold change (FC)∣ > 1 and adjusted P value < 0.05. Subsequently, we integrated the differentially expressed ARGs data from tumor and normal ovarian tissues with their corresponding clinical information for further analysis. 2.4. Enrichment Analysis of ARGs To explore the potential tumor-associated molecular mechanisms of autophagy-related genes (ARGs), we conducted GO functional annotation and KEGG pathway enrichment analysis using the R language. The R packages Goplot, DOSE, ggplot2, enrichplot, clusterProfiler, and BiocManager were utilized with a significance level set at P < 0.05. The Z-score was calculated as Z-score = (U - D) / p, where U represents upregulated genes enriched in GO, D represents downregulated genes enriched in GO, and T represents the total number of genes enriched in the pathway. 2.5. Construction of the Prognostic Model In the construction of the prognostic model for HOC, we analyzed the expression levels of 17 autophagy-related genes (ARGs) in HOC tissues and normal tissues using univariate Cox regression. Among them, 11 ARGs were found to be associated with prognosis (P < 0.05). The risk score was calculated based on the expression levels and regression coefficients of these selected genes. This model serves as a valuable tool for predicting the prognosis of HOC patients. 2.6. Model Validation and Accuracy Assessment Using the median risk score as a threshold, we divided the patients into low-risk and high-risk groups. The Kaplan-Meier (K-M) survival curve was employed to assess the statistical divergence between these two groups, thereby validating the accuracy of the model. Additionally, we utilized ROC curves to evaluate the model's fitting performance. This rigorous validation process ensures the reliability of the prognostic model for HOC. 2.7. Statistical Analysis Statistical analysis and graphic rendering were mainly conducted using R version 4.3.1 ( https://www.r-project.org/ ). Kaplan-Meier (K-M) curves were generated to visualize the differences in survival rates between the high-risk and low-risk groups. The model's fitting performance was assessed by calculating the area under the curve (AUC) of the multi-ROC curve. Additionally, SPSS version 26.0 was employed to create statistical charts of the model risk score and clinical parameters. These rigorous statistical analyses ensure the robustness and accuracy of the study's findings. 3. Results 3.1. Differential Expression of ARGs in HOC and Normal Tissues To further investigate the expression of autophagy-related genes (ARGs) in HOC, we designed an experimental workflow to select ARGs as predictive factors (Fig. 1 a). We conducted gene expression analysis on 427 HOC samples and 7 normal samples using the Wilcoxon Signed-Rank Test. Subsequently, we identified 17 differentially expressed ARGs out of 232 ARGs, based on the criteria of ∣ log2FC ∣ > 1 and adjusted P value < 0.05 (Fig. 1 b). Among them, 11 ARGs (WDFY3, ARNT, BID, BNIP1, CCL2, DIRAS3, DNAJB9, GABARAPL2, GRID2, IL24, and ITGB1) showed low expression levels in tumor tissues, while 7 ARGs (KIF5B, NAMPT, PTK6, RAB33B, SERPINA1, and SQSTM1) exhibited high expression levels in tumor tissues. The expression profiles of the 17 ARGs in tumor and normal tissues are presented in Fig. 1 c. 3.2. GO and KEGG Analyses of ARGs We investigated the molecular mechanisms of autophagy-related genes (ARGs) in HOC through GO functional annotation and KEGG pathway enrichment analysis. The GO analysis revealed that ARGs were primarily associated with positive regulation of protein localization to the plasma membrane, cell periphery, and membrane, as well as modulation of chemical synaptic transmission and trans-synaptic signaling in Biological Process (BP). In Cellular Component (CC), ARGs were predominantly related to autophagosome, autophagosome membrane, excitatory synapse, inclusion body, and PML body. In Molecular Function (MF), ARGs showed involvement in cytokine activity, ubiquitin protein ligase binding, ubiquitin-like protein ligase binding, protein tyrosine kinase binding, protease binding, and receptor ligand activity (Figs. 2 a). Additionally, KEGG enrichment analysis highlighted the significant enrichment of ARGs in pathways such as Autophagy - animal, NOD-like receptor signaling pathway, Mitophagy - animal, Viral protein interaction with cytokine and cytokine receptor, and the Amyotrophic lateral sclerosis pathway (Fig. 2 b). These findings provide valuable insights into the potential role of ARGs in the oncogenesis of HOC. 3.3. Prognostic Model Based on Survival-Associated ARGs in HOC Through univariate Cox regression analysis, we identified 11 autophagy-related genes (ARGs) associated with the survival rate of Heritable Ovarian Tumor (HOC) patients (Fig. 3 a). These genes (BNIP1, DNAJB9, GRID2, IL24, ITGB1, KIF5B, NAMPT, PTK6, RAB33B, SERPINA1, WDFY3) showed a positive correlation with the risk score (P < 0.05). Using the 11 candidate ARGs, we constructed a risk score model for HOC patients based on their gene expression values (GEV). The results indicated that patients in the low-risk group have longer survival compared to the high-risk group. The scatterplot also illustrated that patients in the low-risk group exhibited better survival outcomes (Fig. 3 b). 3.4. Model Performance Evaluation To assess the model's fitting degree, we plotted the Kaplan-Meier (K-M) curve to examine the survival of two risk groups. The results demonstrated that high-risk patients had poorer outcomes (Fig. 4 a). Additionally, we generated ROC curves for the 11 genes, and their respective AUC values were as follows: ARNT (AUC = 0.534), DNAJB9 (AUC = 0.608), GRID2 (AUC = 0.360), IL24 (AUC = 0.639), ITGB1 (AUC = 0.532), KIF5B (AUC = 0.542), NAMPT (AUC = 0.569), PTK6 (AUC = 0.691), RAB33B (AUC = 0.538), SERPINA1 (AUC = 0.639), and WDFY3 (AUC = 0.567) (Fig. 4 b). 3.5. The Correlation between Model Elements and Clinical Parameters of HOC Figures 5 a depict the relationship between the riskscores of the model and clinical parameters of HOC. Three genes with high AUC values obtained from the ROC curves were selected as high-risk factors. Further investigations revealed statistical differences in the expression of IL24 and its association with ovarian tumor subtypes (Figs. 5 b). Additionally, the expression of PTK6 and SERPINA1 showed correlations with FIGO stages in HOC (Figs. 5 c and 5 d). 4. Discussion In this study, we conducted a comprehensive bioinformatics-based investigation of autophagy-related biomarkers in HOC. We identified differentially expressed ARGs in HOC and established a prognostic model based on survival-associated ARGs. The results provided valuable insights into the potential role of autophagy in HOC and its implications for patient outcomes. The experimental design of this study was well-justified. By analyzing gene expression patterns in a large cohort of HOC and normal samples, we were able to identify 17 differentially expressed ARGs using stringent criteria. The subsequent functional annotation and pathway enrichment analysis helped us understand the molecular mechanisms of these ARGs in HOC. The establishment of a prognostic model based on survival-associated ARGs further validated the potential clinical utility of these biomarkers in predicting patient outcomes. Furthermore, our findings are consistent with previous research that highlights the importance of autophagy in cancer, including ovarian carcinoma. Among the three selected genes, IL24, also known as Interleukin 24, has been associated with various cancers, and its dysregulation has been linked to tumor development and progression [ 15 , 16 ]. Studies have reported the potential of IL24 as a diagnostic marker in ovarian tumor subtypes, supporting its relevance in subtype-specific diagnosis [ 17 , 18 ]. PTK6, also known as Protein Tyrosine Kinase 6, is another gene of interest that has been implicated in cancer pathogenesis [ 19 – 21 ]. Previous investigations have demonstrated the correlation between PTK6 expression and cancer stages in ovarian carcinoma, suggesting its potential as a staging biomarker for disease prognosis and management [ 22 , 23 ]. SERPINA1, or Serpin Family A Member 1, has been extensively studied in various cancers and is known to play a crucial role in regulating tumor growth and metastasis [ 24 , 25 ]. However, there are certain limitations in our study that need to be acknowledged. The investigation is solely based on bioinformatics analysis, and further experimental validations, such as in vitro and in vivo studies, are necessary to confirm the functional roles of the identified ARGs in HOC. Additionally, the study is retrospective in nature, and a prospective study with a larger patient cohort will be valuable to validate the prognostic model's accuracy and reliability. Looking ahead, our study paves the way for future research on autophagy-related biomarkers in HOC with a focus on its heritable aspects. Further elucidation of the underlying molecular mechanisms and functional roles of the identified genes may lead to the development of targeted therapies for this aggressive ovarian cancer with heritable factors. Additionally, exploring the potential of the prognostic model in clinical settings may contribute to personalized treatment strategies and improved patient outcomes in HOC, especially for individuals with a genetic predisposition to the disease. The integration of genetic information into clinical practice could provide valuable insights for early detection, risk assessment, and tailored interventions in managing HOC cases with heritable components. 5. Conclusion The bioinformatics investigation of autophagy-related biomarkers in Heritable Ovarian Carcinoma (HOC) provided valuable insights. Differentially expressed ARGs were identified, suggesting their potential as HOC biomarkers. Pathway analysis revealed their involvement in cancer progression. The established prognostic model based on survival-associated ARGs showed promising predictive capabilities. These findings highlight autophagy-related mechanisms in HOC, offering potential therapeutic targets for improved patient outcomes. Further experimental validation is needed to fully understand these biomarkers and their clinical applications. Declarations Author Contribution Wang. Sha.and Dai were responsible for writing the manuscript,Han.Liu.Sun and Chen were responsible for creating icons in the article.All authors reviewed the manuscript. References Wang J, Wu GS. Role of autophagy in cisplatin resistance in ovarian cancer cells. J Biol Chem. 2014;289(24):17163–73. Zhan L, et al. Autophagy as an emerging therapy target for ovarian carcinoma. Oncotarget. 2016;7(50):83476–87. Kumariya S, et al. Autophagy in ovary and polycystic ovary syndrome: role, dispute and future perspective. Autophagy. 2021;17(10):2706–33. Peracchio C, et al. Involvement of autophagy in ovarian cancer: a working hypothesis. J Ovarian Res. 2012;5(1):22. Pinto MT et al. Molecular Biology of Pediatric and Adult Ovarian Germ Cell Tumors: A Review . Cancers (Basel), 2023. 15(11). Zhang X, et al. Autophagy-related genes contribute to malignant progression and have a clinical prognostic impact in colon adenocarcinoma. Exp Ther Med. 2021;22(3):932. Zhou J, Peng X, Mei S. Autophagy in Ovarian Follicular Development and Atresia. Int J Biol Sci. 2019;15(4):726–37. Delaney JR, et al. Autophagy gene haploinsufficiency drives chromosome instability, increases migration, and promotes early ovarian tumors. PLoS Genet. 2020;16(1):e1008558. Hou M, Ma J, Ma J. Study on the Expression Profile of Autophagy-Related Genes in Colon Adenocarcinoma. Comput Math Methods Med, 2022. 2022: p. 7525048. Kotnik EN, et al. Genetic characterization of primary and metastatic high-grade serous ovarian cancer tumors reveals distinct features associated with survival. Commun Biol. 2023;6(1):688. De A, et al. Emblica officinalis extract induces autophagy and inhibits human ovarian cancer cell proliferation, angiogenesis, growth of mouse xenograft tumors. PLoS ONE. 2013;8(8):e72748. Delaney JR, et al. A strategy to combine pathway-targeted low toxicity drugs in ovarian cancer. Oncotarget. 2015;6(31):31104–18. Andor N, et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat Med. 2016;22(1):105–13. Raynaud F, et al. Pan-cancer inference of intra-tumor heterogeneity reveals associations with different forms of genomic instability. PLoS Genet. 2018;14(9):e1007669. Ma C, et al. Lentivirus–mediated MDA7/IL24 expression inhibits the proliferation of hepatocellular carcinoma cells. Mol Med Rep. 2018;17(4):5764–73. Wang L, et al. Bifidobacterium breve as a delivery vector of IL-24 gene therapy for head and neck squamous cell carcinoma in vivo. Gene Ther. 2017;24(11):699–705. Zhang M, Zhao H, Gao H. Interleukin-24 Limits Tumor-Infiltrating T Helper 17 Cell Response in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma. Viral Immunol. 2022;35(3):212–22. Panneerselvam J, et al. IL-24 inhibits lung cancer cell migration and invasion by disrupting the SDF-1/CXCR4 signaling axis. PLoS ONE. 2015;10(3):e0122439. Chen X, et al. PTK6 promotes hepatocellular carcinoma cell proliferation and invasion. Am J Transl Res. 2016;8(10):4354–61. Correa I, et al. Protocol for evaluating autophagy using LysoTracker staining in the epithelial follicle stem cells of the Drosophila ovary. STAR Protoc. 2021;2(2):100592. Hsieh YP et al. Epigenetic Deregulation of Protein Tyrosine Kinase 6 Promotes Carcinogenesis of Oral Squamous Cell Carcinoma . Int J Mol Sci, 2022. 23(9). Liu Z, et al. Prognosis-related autophagy genes in female lung adenocarcinoma. Med (Baltim). 2022;101(1):e28500. Wu D, et al. Decreased expression of protein tyrosine kinase 6 contributes to tumor progression and metastasis in laryngeal squamous cell carcinoma. Biochem Biophys Res Commun. 2018;503(3):1378–84. Zhang LY, et al. SERPINA1 Methylation Levels are Associated with Lung Cancer Development in Male Patients with Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis. 2022;17:2117–25. Zhang Y, et al. Identification of SERPINA1 promoting better prognosis in papillary thyroid carcinoma along with Hashimoto's thyroiditis through WGCNA analysis. Front Endocrinol (Lausanne). 2023;14:1131078. Additional Declarations No competing interests reported. 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(a) Overview of the experimental workflow. (b) Volcano plots illustrating the expression of 232 ARGs. Genes in red indicate significantly higher expression in tumor tissues, while genes in blue show lower expression levels. (c) Heat map visualizing the expression differences of ARGs between HOC and normal specimens. Red indicates elevated expression of ARGs in tissues, while blue represents lower expression levels.\u003c/p\u003e","description":"","filename":"Figure103.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3820181/v1/67bd8f90614766a101460f85.jpg"},{"id":49135313,"identity":"d89148a3-fe22-43c6-b145-f68cd4e14c20","added_by":"auto","created_at":"2024-01-03 17:04:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":470231,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG pathway enrichment analysis of autophagy-related genes (ARGs). (a) The number of genes enriched in each GO category is shown from top to bottom, representing the results of Biological Process, Cellular Component, and Molecular Function analyses. (b) KEGG pathway enrichment analysis of ARGs.\u003c/p\u003e","description":"","filename":"Figure201.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3820181/v1/6753eba21dbc68131e77fbb3.jpg"},{"id":49135788,"identity":"e3cbc13c-bc61-45db-a6cd-5b42960e9080","added_by":"auto","created_at":"2024-01-03 17:12:27","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":385071,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the Prognostic Model for HOC. (a) The forest plot illustrates the hazard ratio of 11 autophagy-related genes (ARGs) associated with patients' survival. (b) Divided into three sections from top to bottom. The top section represents the distribution of the prognostic model for HOC patients. The middle section depicts HOC patients' survival in the TCGA dataset. The bottom section shows the expression of ARGs in the model in two groups.\u003c/p\u003e","description":"","filename":"Figure301.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3820181/v1/58149ebfb185f50f937b72c4.jpg"},{"id":49135312,"identity":"38faf44f-a94e-4c9e-b4e1-c06318e18c2a","added_by":"auto","created_at":"2024-01-03 17:04:27","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":286068,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the model.(a) Survival curve generated by the model.(b) Multi-ROC analysis of each risk factor, including ARNT, DNAJB9, GRID2, IL24, ITGB1, KIF5B, NAMPT, PTK6, RAB33B, SERPINA1, and WDFY3.\u003c/p\u003e","description":"","filename":"Figure401.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3820181/v1/e650a1e519b718df4e17175d.jpg"},{"id":49135309,"identity":"86716929-ddba-4c33-96cb-2e3499bbf468","added_by":"auto","created_at":"2024-01-03 17:04:26","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":197809,"visible":true,"origin":"","legend":"\u003cp\u003eApplication of the model and risk factors in clinical practice for HOC.(a) Correlation between riskscore and HOC status.(b) Correlation between IL24 expression and HOC stages.(c) Correlation between PTK6 expression and HOC stages.(d) Correlation between SERPINA1 expression and HOC stages. (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure501.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3820181/v1/03c2ec0429a76921f087d8a7.jpg"},{"id":53892335,"identity":"c0c468b5-f928-4d5e-9a10-c310565181c5","added_by":"auto","created_at":"2024-04-01 22:07:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":720645,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3820181/v1/563feddd-0734-4002-9275-bd16b29f695c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatics-based Investigation of Autophagy-Related Biomarkers in Heritable Ovarian Carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHeritable Ovarian Carcinoma (HOC), a cancer with a heritable nature, is a formidable challenge in women's health, being the fifth leading cause of death among gynecological malignancies in developed countries. Despite advancements in surgical techniques and therapeutic options, the overall 5-year survival rate for ovarian cancer remains disappointingly low, at approximately 30\u0026ndash;40% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consequently, there is an urgent need to explore novel avenues for the prevention and treatment of this heritable form of ovarian cancer. Research on HOC has revealed its heritable nature, with genetic factors playing a significant role in disease development and progression. Understanding the underlying genetic mechanisms of HOC may provide critical insights for targeted therapies and personalized treatment strategies[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, autophagy has emerged as a key cellular process implicated in various diseases, including cancer. Autophagy is an evolutionarily conserved self-digestion pathway that plays a vital role in maintaining cellular homeostasis by recycling intracellular components, such as macromolecules and organelles, through lysosome-dependent mechanisms [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Dysregulation of autophagy has been observed in numerous cancer types, suggesting its potential involvement in tumorigenesis and progression[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, the connection between autophagy and ovarian carcinoma has gained increasing attention, offering potential therapeutic targets for this aggressive cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In previous studies, monoallelic deletions in autophagy genes suppressed ovarian cancer cells' ability to handle proteotoxic stress, leading to selective cell death without programmed cell death. Homozygous autophagy gene deletions can prevent tumor formation, but the reason for tumor development despite autophagy allele losses is unclear. Dysregulation of this homeostatic pathway may induce genomic instability. Heterogeneity in Serous Ovarian Cancer (SOC) correlates with poor patient outcomes, displaying significant intra-tumor clonal heterogeneity in pan-cancer analysis [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the specific role of autophagy-related genes (ARGs) in HOC remains poorly understood.\u003c/p\u003e \u003cp\u003eTo bridge this knowledge gap and explore the potential clinical implications, our study aims to investigate the differential expression of ARGs in HOC and normal tissues.This study aims to investigate autophagy-related genes (ARGs) in HOC using a comprehensive workflow. It involves analyzing gene expression patterns in HOC and normal samples, identifying differentially expressed ARGs, and exploring their molecular mechanisms. Additionally, a prognostic model based on survival-associated ARGs will be established and validated. The research aims to identify potential biomarkers and therapeutic targets for ovarian carcinoma, contributing to improved patient outcomes in HOC.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Ethics statement\u003c/h2\u003e \u003cp\u003eThis study was approved by the ethical committee of the Affiliated Hospital of Inner Mongolia Medical University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Acquisition and Preprocessing\u003c/h2\u003e \u003cp\u003eA total of 232 autophagy-related genes (ARGs) were obtained from the Human Autophagy Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.autophagy.lu/index\u003c/span\u003e\u003cspan address=\"http://www.autophagy.lu/index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e HTML), covering all ARGs in the human genome. RNA sequencing data and clinical records of 420 tumor samples and 7 healthy samples were extracted from the TCGA database. GO enrichment analysis utilized data from the Cistrome project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://www.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while KEGG enrichment analysis used data from the KEGG project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kegg.jp/\u003c/span\u003e\u003cspan address=\"http://www.kegg.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Identification of Differentially Expressed ARGs in HOC\u003c/h2\u003e \u003cp\u003eThe expression levels of 232 ARGs in 420 tumor samples and 7 normal samples were averaged, and the limma package in R language was utilized to analyze the differential expression of each gene between tumor and normal samples. The threshold for differential expression was set at ∣log2 fold change (FC)∣ \u0026gt; 1 and adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Subsequently, we integrated the differentially expressed ARGs data from tumor and normal ovarian tissues with their corresponding clinical information for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Enrichment Analysis of ARGs\u003c/h2\u003e \u003cp\u003eTo explore the potential tumor-associated molecular mechanisms of autophagy-related genes (ARGs), we conducted GO functional annotation and KEGG pathway enrichment analysis using the R language. The R packages Goplot, DOSE, ggplot2, enrichplot, clusterProfiler, and BiocManager were utilized with a significance level set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The Z-score was calculated as Z-score = (U - D) / p, where U represents upregulated genes enriched in GO, D represents downregulated genes enriched in GO, and T represents the total number of genes enriched in the pathway.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Construction of the Prognostic Model\u003c/h2\u003e \u003cp\u003eIn the construction of the prognostic model for HOC, we analyzed the expression levels of 17 autophagy-related genes (ARGs) in HOC tissues and normal tissues using univariate Cox regression. Among them, 11 ARGs were found to be associated with prognosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The risk score was calculated based on the expression levels and regression coefficients of these selected genes. This model serves as a valuable tool for predicting the prognosis of HOC patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Model Validation and Accuracy Assessment\u003c/h2\u003e \u003cp\u003eUsing the median risk score as a threshold, we divided the patients into low-risk and high-risk groups. The Kaplan-Meier (K-M) survival curve was employed to assess the statistical divergence between these two groups, thereby validating the accuracy of the model. Additionally, we utilized ROC curves to evaluate the model's fitting performance. This rigorous validation process ensures the reliability of the prognostic model for HOC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis and graphic rendering were mainly conducted using R version 4.3.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Kaplan-Meier (K-M) curves were generated to visualize the differences in survival rates between the high-risk and low-risk groups. The model's fitting performance was assessed by calculating the area under the curve (AUC) of the multi-ROC curve. Additionally, SPSS version 26.0 was employed to create statistical charts of the model risk score and clinical parameters. These rigorous statistical analyses ensure the robustness and accuracy of the study's findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Differential Expression of ARGs in HOC and Normal Tissues\u003c/h2\u003e \u003cp\u003eTo further investigate the expression of autophagy-related genes (ARGs) in HOC, we designed an experimental workflow to select ARGs as predictive factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). We conducted gene expression analysis on 427 HOC samples and 7 normal samples using the Wilcoxon Signed-Rank Test. Subsequently, we identified 17 differentially expressed ARGs out of 232 ARGs, based on the criteria of ∣ log2FC ∣ \u0026gt; 1 and adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Among them, 11 ARGs (WDFY3, ARNT, BID, BNIP1, CCL2, DIRAS3, DNAJB9, GABARAPL2, GRID2, IL24, and ITGB1) showed low expression levels in tumor tissues, while 7 ARGs (KIF5B, NAMPT, PTK6, RAB33B, SERPINA1, and SQSTM1) exhibited high expression levels in tumor tissues. The expression profiles of the 17 ARGs in tumor and normal tissues are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. GO and KEGG Analyses of ARGs\u003c/h2\u003e \u003cp\u003eWe investigated the molecular mechanisms of autophagy-related genes (ARGs) in HOC through GO functional annotation and KEGG pathway enrichment analysis. The GO analysis revealed that ARGs were primarily associated with positive regulation of protein localization to the plasma membrane, cell periphery, and membrane, as well as modulation of chemical synaptic transmission and trans-synaptic signaling in Biological Process (BP). In Cellular Component (CC), ARGs were predominantly related to autophagosome, autophagosome membrane, excitatory synapse, inclusion body, and PML body. In Molecular Function (MF), ARGs showed involvement in cytokine activity, ubiquitin protein ligase binding, ubiquitin-like protein ligase binding, protein tyrosine kinase binding, protease binding, and receptor ligand activity (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Additionally, KEGG enrichment analysis highlighted the significant enrichment of ARGs in pathways such as Autophagy - animal, NOD-like receptor signaling pathway, Mitophagy - animal, Viral protein interaction with cytokine and cytokine receptor, and the Amyotrophic lateral sclerosis pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). These findings provide valuable insights into the potential role of ARGs in the oncogenesis of HOC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Prognostic Model Based on Survival-Associated ARGs in HOC\u003c/h2\u003e \u003cp\u003eThrough univariate Cox regression analysis, we identified 11 autophagy-related genes (ARGs) associated with the survival rate of Heritable Ovarian Tumor (HOC) patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). These genes (BNIP1, DNAJB9, GRID2, IL24, ITGB1, KIF5B, NAMPT, PTK6, RAB33B, SERPINA1, WDFY3) showed a positive correlation with the risk score (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Using the 11 candidate ARGs, we constructed a risk score model for HOC patients based on their gene expression values (GEV). The results indicated that patients in the low-risk group have longer survival compared to the high-risk group. The scatterplot also illustrated that patients in the low-risk group exhibited better survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Model Performance Evaluation\u003c/h2\u003e \u003cp\u003eTo assess the model's fitting degree, we plotted the Kaplan-Meier (K-M) curve to examine the survival of two risk groups. The results demonstrated that high-risk patients had poorer outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Additionally, we generated ROC curves for the 11 genes, and their respective AUC values were as follows: ARNT (AUC\u0026thinsp;=\u0026thinsp;0.534), DNAJB9 (AUC\u0026thinsp;=\u0026thinsp;0.608), GRID2 (AUC\u0026thinsp;=\u0026thinsp;0.360), IL24 (AUC\u0026thinsp;=\u0026thinsp;0.639), ITGB1 (AUC\u0026thinsp;=\u0026thinsp;0.532), KIF5B (AUC\u0026thinsp;=\u0026thinsp;0.542), NAMPT (AUC\u0026thinsp;=\u0026thinsp;0.569), PTK6 (AUC\u0026thinsp;=\u0026thinsp;0.691), RAB33B (AUC\u0026thinsp;=\u0026thinsp;0.538), SERPINA1 (AUC\u0026thinsp;=\u0026thinsp;0.639), and WDFY3 (AUC\u0026thinsp;=\u0026thinsp;0.567) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. The Correlation between Model Elements and Clinical Parameters of HOC\u003c/h2\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea depict the relationship between the riskscores of the model and clinical parameters of HOC. Three genes with high AUC values obtained from the ROC curves were selected as high-risk factors. Further investigations revealed statistical differences in the expression of IL24 and its association with ovarian tumor subtypes (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Additionally, the expression of PTK6 and SERPINA1 showed correlations with FIGO stages in HOC (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we conducted a comprehensive bioinformatics-based investigation of autophagy-related biomarkers in HOC. We identified differentially expressed ARGs in HOC and established a prognostic model based on survival-associated ARGs. The results provided valuable insights into the potential role of autophagy in HOC and its implications for patient outcomes. The experimental design of this study was well-justified. By analyzing gene expression patterns in a large cohort of HOC and normal samples, we were able to identify 17 differentially expressed ARGs using stringent criteria. The subsequent functional annotation and pathway enrichment analysis helped us understand the molecular mechanisms of these ARGs in HOC. The establishment of a prognostic model based on survival-associated ARGs further validated the potential clinical utility of these biomarkers in predicting patient outcomes.\u003c/p\u003e \u003cp\u003eFurthermore, our findings are consistent with previous research that highlights the importance of autophagy in cancer, including ovarian carcinoma. Among the three selected genes, IL24, also known as Interleukin 24, has been associated with various cancers, and its dysregulation has been linked to tumor development and progression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies have reported the potential of IL24 as a diagnostic marker in ovarian tumor subtypes, supporting its relevance in subtype-specific diagnosis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. PTK6, also known as Protein Tyrosine Kinase 6, is another gene of interest that has been implicated in cancer pathogenesis [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Previous investigations have demonstrated the correlation between PTK6 expression and cancer stages in ovarian carcinoma, suggesting its potential as a staging biomarker for disease prognosis and management [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. SERPINA1, or Serpin Family A Member 1, has been extensively studied in various cancers and is known to play a crucial role in regulating tumor growth and metastasis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, there are certain limitations in our study that need to be acknowledged. The investigation is solely based on bioinformatics analysis, and further experimental validations, such as in vitro and in vivo studies, are necessary to confirm the functional roles of the identified ARGs in HOC. Additionally, the study is retrospective in nature, and a prospective study with a larger patient cohort will be valuable to validate the prognostic model's accuracy and reliability.\u003c/p\u003e \u003cp\u003eLooking ahead, our study paves the way for future research on autophagy-related biomarkers in HOC with a focus on its heritable aspects. Further elucidation of the underlying molecular mechanisms and functional roles of the identified genes may lead to the development of targeted therapies for this aggressive ovarian cancer with heritable factors. Additionally, exploring the potential of the prognostic model in clinical settings may contribute to personalized treatment strategies and improved patient outcomes in HOC, especially for individuals with a genetic predisposition to the disease. The integration of genetic information into clinical practice could provide valuable insights for early detection, risk assessment, and tailored interventions in managing HOC cases with heritable components.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe bioinformatics investigation of autophagy-related biomarkers in Heritable Ovarian Carcinoma (HOC) provided valuable insights. Differentially expressed ARGs were identified, suggesting their potential as HOC biomarkers. Pathway analysis revealed their involvement in cancer progression. The established prognostic model based on survival-associated ARGs showed promising predictive capabilities. These findings highlight autophagy-related mechanisms in HOC, offering potential therapeutic targets for improved patient outcomes. Further experimental validation is needed to fully understand these biomarkers and their clinical applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWang. Sha.and Dai were responsible for writing the manuscript,Han.Liu.Sun and Chen were responsible for creating icons in the article.All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang J, Wu GS. Role of autophagy in cisplatin resistance in ovarian cancer cells. 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Med (Baltim). 2022;101(1):e28500.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu D, et al. Decreased expression of protein tyrosine kinase 6 contributes to tumor progression and metastasis in laryngeal squamous cell carcinoma. Biochem Biophys Res Commun. 2018;503(3):1378\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang LY, et al. SERPINA1 Methylation Levels are Associated with Lung Cancer Development in Male Patients with Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis. 2022;17:2117\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, et al. Identification of SERPINA1 promoting better prognosis in papillary thyroid carcinoma along with Hashimoto's thyroiditis through WGCNA analysis. Front Endocrinol (Lausanne). 2023;14:1131078.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Heritable Ovarian Carcinoma (HOC), autophagy-related genes (ARGs), gene expression analysis, prognostic model, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-3820181/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3820181/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the role of autophagy-related genes (ARGs) in Heritable Ovarian Carcinoma (HOC) and identify potential biomarkers and therapeutic targets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive bioinformatics-based analysis of gene expression patterns in 420 HOC samples and 7 normal tissues. Differential expression of 17 ARGs out of 232 candidate genes was identified. Functional annotation and pathway enrichment analyses were performed to explore the biological functions of these ARGs. A prognostic model based on 11 survival-associated ARGs was established and validated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur analysis revealed differentially expressed ARGs in HOC and normal tissues, suggesting their potential as diagnostic biomarkers. GO and KEGG analyses indicated the involvement of these genes in critical biological processes and signaling pathways. The prognostic model demonstrated promising predictive capabilities for patient outcomes in HOC.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings shed light on the significance of ARGs in HOC and provide potential biomarkers and therapeutic targets for improved patient outcomes in this heritable ovarian carcinoma.\u003c/p\u003e","manuscriptTitle":"Bioinformatics-based Investigation of Autophagy-Related Biomarkers in Heritable Ovarian Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 17:04:22","doi":"10.21203/rs.3.rs-3820181/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":"bd993286-eeb8-46af-916f-7958a0849832","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-01T21:59:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-03 17:04:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3820181","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3820181","identity":"rs-3820181","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}