Proteomics profiling for the global and acetylated proteins of High-Grade Serous Ovarian Carcinoma

Proteomics profiling for the global and acetylated proteins of High-Grade Serous 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Proteomics profiling for the global and acetylated proteins of High-Grade Serous Ovarian Carcinoma Lifan Shen, Xiuzhen Wang, Genhai Zhu, Haocheng Gao, Xiaohang Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6885266/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 Background High-grade serous ovarian cancer (HGSOC) is the main type of ovarian cancer with a poor prognosis. Although protein omics is widely used in HGSOC, the general situation of acetylated proteins in HGSOC is still uncertain, which is helpful to understanding the carcinogenesis mechanism and identifying useful biomarkers of HGSOC. Methods Six female patients with pathologically diagnosed HGSOC were included in the study. After six mixed extracts of whole protein and acetylated protein were prepared, 4D Label-free mass spectrometry was applied to the determination of global protein and acetylated protein. Bioinformatics analysis was carried out, including KEGG, gene ontology (GO), clustering and protein interaction. Finally, the meaningful biomarkers were screened out by multi-omics joint analysis. Results Compared with the normal tissues near the lesion, 356 proteins identified in tumor tissues were considered as differentially expressed proteins (DEPs) in global protein histology, of which 124 were up-regulated and 232 were down-regulated, and 57 were differentially expressed acetylated proteins (DEAPs) in acetylated protein histology, including 29 up-regulated and 2 down-regulated, respectively. DEPs protein in cytosol accounts for the highest proportion, and CTF/NFI is the largest transcription factor family in DEPs. Joint analysis showed that differential proteins and their acetylation were mainly related to metabolic pathways, which were up-regulated in tumors. Conclusions This study will combine global protein omics with acetylated protein omics, which will provide a broader perspective for protein to change its view on carcinogenesis, as well as provide a new direction for selecting biomarkers for diagnosing HGSOC. high grade serous ovarian cancer proteomics༛ acetylated proteins༛ 4D Label-free༛ Functional enrichment analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Ovarian cancer (OC) is the leading cause of death among all gynecological malignancies worldwide, with high mortality and low 5-year survival. Due to the complexity of clinical symptoms and biological and molecular characteristics, OC is often not diagnosed until it has developed to an advanced stage [ 1 , 2 ] . High-grade serous ovarian cancers (HGSOC) are the most common and fatal type of ovarian cancer [ 2 ] . The lack of specific symptoms leads to late diagnosis, making HGSOC one of the gynecologic cancers with the worst prognosis. The cellular origin of HGSOC and the role of reproductive hormones, genetic features (e.g., alterations in P53 and DNA repair mechanisms), chromosomal instability, or dysregulation of key signaling pathways have been found to be associated with patient prognosis and treatment response [ 3 ] . However, carbohydrate antigen 125 (CA125) detection [ 4 ] and transvaginal ultrasound [ 5 ] are still the common clinical detection methods for HGSOC. Biomarkers that can be used as exact indicators for the diagnosis, prognosis prediction and treatment monitoring of HGSOC patients have not been found. Advances in proteomics have introduced new techniques for screening biomarkers and brought the diagnosis of different types of cancer to a new level. Several biomarkers have been identified for the diagnosis of HGSOC, including the calcium phospholipid binding protein annexin A2 (ANXA2) [ 6 ] , WT1-associated protein (WTAP) [ 7 ] , α1-antitrypsin (AAT) [ 8 ] , nuclear factor-κB (NFKB) and mevalonate phosphate kinase (PMVK) [ 9 ] . However, most of these biomarkers lack specificity or have poor positive predictive value to some extent. Therefore, understanding the proteome and the complexity of post-translational acetylation modification in high-grade serous ovarian cancer is critical for clinical guidance of patient management. Acetylation is a widespread and dynamic post-translational modification (PTM) of proteins, which affects gene expression without changing the DNA sequence and plays a crucial role in many cellular physiological and pathological processes [ 10 ] . It can regulate many protein-related processes such as apoptosis, subcellular localization, DNA-protein interaction, DNA replication and repair, DNA transcription activity, and protein stability [ 10 , 11 ] . Therefore, dysregulation of acetylation patterns can lead to abnormal gene expression and affect cancer initiation and progression. Moreover, acetylation patterns on specific proteins can be used as biomarkers for cancer diagnosis, prognosis, and treatment response [ 12 ] . Protein acetylation is mainly dependent on lysine acetyltransferase and lysine deacetylase. Studies have shown that lysine acetylation and the enzymes that regulate its occurrence are closely related to the progress of OC, such as lysine acetyltransferase 6A (KAT6A) [ 13 ] . As an intersection of proteomics and epigenetics, acetyl proteomics has attracted increasing attention. In this study, we aimed to investigate the global protein profiles of patients with HGSOC and identify the associated acetylated proteins to understand the molecular mechanism of HGSOC pathogenesis. 2. Patients and Methods 2.1 Sample Collection and Preparation In this study, samples of surgically removed tumor tissues and normal tissues without abnormal pathological signs near the lesion site of 6 patients with HGSOC were collected for proteomic and acetylated proteomic analysis (Table 1 ). These cases were summarized for age, lymph node status, TNM stage, and treatment regimen. 4D label-free quantitative proteomics and acetylation analysis were performed in Genechem Biotechnology Co., Ltd. (Shanghai, China). The protocol was approved by committee/IRB of Hainan General Hospital, and all subjects signed informed consent (Fig. 1 ). Table 1 Clinical baseline characteristics of the HGSOC patients. Age/year Laterality FIGO stage lymph node metastasis TNM stage Treatment 62 Left ⅣB No T3cN0M1b TC 35 Both IVB Yes T3cN1bM1b TC plus bevacizumab 54 Right ⅢC No T3cN0M0 TC 69 Both unknown No T3bN0M0 TC 66 unknown ⅢB No T3bN0M0 TC 35 Both IIIC unknown T3cN0M0 TC TC: paclitaxel-carboplatin 2.2 RNA and Quantitative real-time RT-PCR For the total RNA was extracted from thawed materials using the Maxwell RSC RNA Kit. RNA quality and integrity were confirmed by using the Agilent 2100 RNA Nano 6000 Assay Kit (Agilent Tech., USA) according to the manufacturer’s protocol. The specific primers for the target genes of qPCR, which interest were designed using Primer3 software ( http://bioinfo.ut.ee/primer3/ ) and synthesized by Integrated DNA Technologies. The qPCR data were analyzed using the QuantStudio Real-Time PCR Software (Applied Biosystems). 2.3 Mass spectrometry analysis Samples were analyzed on a nanoElute (Bruker, Bremen, Germany) coupled to a timsTOF Pro (Bruker, Bremen, Germany) with a CaptiveSpray source. Peptides were separated on a 25 cm × 75 µm column packed with 1.6 µm C18 beads (packed emitter tip; IonOpticks, Australia) at 50°C (integrated oven, Sonation GmbH, Germany). The column was equilibrated with 4 volumes of 100% buffer A (99.9% Milli-Q water, 0.1% FA) before loading (both at 800 bar). Separation used a linear gradient at 300 nl/min. The timsTOF Pro operated in PASEF mode: mass range 100–1700 m/z; 1/K0 0.75–1.4 V⋅s/cm² (ramp 100 ms); duty cycle 100%; capillary voltage 1500 V; dry gas 3 l/min (180°C). PASEF settings: 10 MS/MS scans (cycle 1.16 sec); charge range 0–5; active exclusion 0.5 min; target intensity 10,000; threshold 2500; CID energy 20–59 eV. 2.4 MS data and differential expression analysis Mass spectrometry (MS) data were analyzed using MaxQuant software (v1.6.17.0) with searches performed against the UniProt human reference database (uniprot_homo_20230312_20423_9606_swiss_prot; http://www.uniprot.org ). An initial precursor mass window of 6 ppm was applied, with subsequent processing parameters including: trypsin/P digestion with up to 2 missed cleavages; fragment ion mass tolerance of 20 ppm; fixed modification (cysteine carbamidomethylation); and variable modifications (protein N-terminal acetylation, methionine oxidation). The global false discovery rate (FDR) for peptide and protein identification was set to 1%. Protein abundance was quantified using normalized spectral intensity (LFQ intensity). Differentially expressed proteins were defined as those with fold change > 2 or < 0.5 and Student’s t-test p-value < 0.05. Prior to cluster analysis, quantitative data of these proteins were normalized. Hierarchical clustering heatmaps were generated using Python’s matplotlib package, with Euclidean distance algorithm and Ward’s linkage for both sample and protein expression dimensions. For protein acetylation modification detection, acetylated peptides were first enriched via immunoprecipitation using anti-acetylated lysine (Kac) antibodies. Detailed procedures for high-precision mass spectrometric detection and analysis followed the manufacturer’s protocol (Acetyl-Lysine Motif [Ac-K] Kit; Cell Signaling Technology, 13416S). 2.5 Gene Ontology (GO) function and KEGG pathway analysis Using Blast2GO (V1.4.4) [ 14 ] was carried out on the set of differentially expressed proteins Gene Ontology (GO) functional annotation KEGG pathways annotation: KOALA (KEGG Orthology And Links Annotation, V2.3) software [ 15 ] was used to compare the KEGG gene database, and the differentially expressed protein sequences were KO classified. According to the KO classification, the pathway information involved in the differentially expressed protein sequence was automatically obtained. Based on Fisher's Exact Test, the distribution of each GO entry or KEGG pathway in the differentially expressed protein set and the overall protein set was compared to evaluate the significance level of enrichment. 2.6 Domain annotation and subcellular localization analysis Interpro database collection the family classification, the structure of protein sequence domain and special site prediction, and other functions, we use this database to differentially be expressed protein domain annotation of function structure is analyzed. Fisher's Exact Test was used to compare the distribution of differentially expressed proteins in the total protein set to evaluate the significance level of enrichment of a functional domain. Using the WoLF PSORT [ 16 ] ( https://wolfpsort.hgc.jp/ ) software of differentially expressed protein subcellular localization prediction analysis. 2.7 Transcription factor analysis and protein-protein interactions PlantTFDB5.0 (Plant Transcription Factor Database) was used for transcription factor prediction. In a String ( https://www.string-db.org/ ) database to find the direct or indirect interaction network with the differentially expressed proteins, and the interaction network analysis results were generated by AnyChart software (V8.11.0.1934). The protein samples are prepared by lysing the tissues using a suitable lysis buffer containing detergents, protease inhibitors, and phosphatase inhibitors. And then, the samples are mixed with the gel loading buffer, heated to denature the proteins, and loaded onto the SDS-PAGE gel wells. The membrane is blocked by incubating it in the blocking buffer and antibodies of candidate proteins, as well as the bands corresponding to the target protein are identified based on their molecular weight compared to the molecular weight markers. 2.8 Western blot analysis SDT buffer was added to the samples, which were then transferred to 2 ml tubes containing an appropriate amount of quartz sand. The lysate was homogenized using an MP Fastprep-24 Automated Homogenizer (6.0 m/s, 30 s, twice). After homogenization, the mixture was sonicated and subsequently boiled for 10 min. Following centrifugation at 14,000 × g for 15 min, the supernatant was filtered through a 0.22 µm filter. Protein concentration in the filtrate was determined using the BCA Protein Assay Kit (P0012, Beyotime), and the samples were stored at -80°C. For SDS-PAGE analysis, 20 µg of protein from each sample was mixed with 6× loading buffer and boiled for 5 min. Proteins were separated on a 12% SDS-PAGE gel, and bands were visualized via Coomassie Brilliant Blue R-250 staining. For protein digestion, 100 µg of protein from each sample was reduced with 100 mM DTT at 100°C for 5 min. Detergents, DTT, and other low-molecular-weight components were removed by repeated ultrafiltration (30 kDa cutoff, Sartorius) using UA buffer (8 M urea, 150 mM Tris-HCl, pH 8.5). Then, 100 µl of 100 mM iodoacetamide (dissolved in UA buffer) was added to block reduced cysteine residues, and the samples were incubated in the dark for 30 min. The filters were washed three times with 100 µl UA buffer and twice with 100 µl 50 mM NH₄HCO₃ buffer. Finally, the protein suspension was digested with 4 µg trypsin (Promega) in 40 µl 50 mM NH₄HCO₃ buffer overnight at 37°C, and the resulting peptides were collected as filtrate. Peptides were desalted using a C18 column, and their concentration was estimated by UV absorbance at 280 nm, with an extinction coefficient of 1.1 for a 0.1% (g/l) solution. Antibodies used in this experiment were as follows: anti-LRP1/CD91 (1:3000, AffinitY, China), anti-IDH1 (1:2000, AffinitY, China), anti-CAV1 (1:2000, AffinitY, China), anti-EPXH2 (1:2000, AffinitY, China), anti-PARP1 (1:1000, AffinitY, China), and anti-GAPDH (1:50,000, Proteintech, China) as the loading control. 3. Results 3.1 Global protoplasmic of HGSOC We used 4D label-free mass spectrometry to explore differentially expressed Global proteins (DGPs). Figure 2 shows the quantitative statistics of the global protein map and the general pattern of DGPs. Using MS Platform, we identified 68,711 unique peptides corresponding to 4,581 proteins with FDR < 1%. When the 1.2-fold variation criterion was used, 356 proteins in HGSOC were considered DGPs (P < 0.05), with 124 DGPs up-regulated and 232 DGPs down-regulated (histogram). The results of GO analysis showed that DGPs were mainly enriched on organelles, neural structures and connections, extracellular matrix and cytoskeleton, and involved molecular functions such as isocitrate dehydrogenase (NAD+) binding and activity, and participated in isocitrate dehydrogenase metabolism, neuronal axons and cell proliferation (Fig. 3 A). The enrichment analysis of KEGG pathway showed that DGPs were related to carbon metabolism and energy metabolism pathway (Fig. 3 B). The results of subcellular localization showed that DGPs mainly existed in cytoplasm (28.4%) and nucleus (26.1%) (Fig. 3 C). The function of a protein is largely determined by a specific domain in the sequence. To assess the most regulated domains of HGSOC generation, protein domain enrichment analyses were performed (Fig. 3 D and 3 F). It was mainly enriched in Isopropylmalate dehydrogenase-like domain and socitrate dehydrogenase. We also found that 11 proteins in DGPs act as transcription factors, of which 3 DGPs belong to CTF/NFI transcription factors and 2 DGPs belong to CTF/NFI transcription factors (Fig. 3 F). Network diagram showing the interaction between DGPs (Fig. 3 G). 3.2 Acetyl proteomics of HGSOC We also used 4D label-free mass spectrometry to explore differentially expressed acetylation proteins (DAPs). Mass spectrometry identified 31 differentially acetylated proteins, of which 29 were up-regulated and 2 were down-regulated (LMNA and H2BC18) (Fig. 4 ). As shown in Fig. 5 A and 5 B, GO and KEGG enrichment results show that many differentially acetylated modified proteins are significantly enriched in nucleosome, Barr body, nucleoplasm and other cellular components. It involves molecular functions such as STAT family protein binding, protein heterodimerization activity and DNA binding. Involved in biological processes such as innate immune response in mucosa, antibacterial humoral response and killing of cells of other organisms. DAPs are mainly associated with viral carcinogenesis and metabolic pathways. Subcellular localization in Fig. 5 C shows that 60% of DAPs are found in the nucleus, 28% in the cytoplasm, and 12% in the mitochondria. DAPs are mainly enriched in the Histone-fold, Histone H2AVH2B/H3 and Histone H2B domains (Fig. 5 D). 3.3 Combined analysis of global proteomics and acetyl proteomics In this study, a total of 30 proteins with simultaneous global and acetylated differential expression (DGAPs) were identified. Among them, 28 cases were up-regulated (H3-3A, HMGCL, H2BC14, etc.) and 2 cases were down-regulated (LMNA and H2BC18) (Figs. 6 A and 6 B). GO and KEGG enrichment analysis showed that differentially acetylated modified proteins were significantly enriched in ATP biosynthesis, cytochrome c oxidase activity, apoptosis, ATPase activity, lipid metabolism, glycolysis/gluconeogenesis, iron apoptosis/cholesterol metabolism, oxidative phosphorylation, and butyric acid metabolism related pathways, etc. (Fig. 6 D and 6 E, Table 2 ). More than half of DGAPs is in nucleus location (58.3%), 29.3% in the cytoplasm, and the remaining 12.5% in the mitochondria (Fig. 6 F). Domain enrichment analysis showed that most DGAPs were related to Histone H2B, Histone-fold, and Histone H2A/H2B/H3 (Fig. 6 G). Network diagram showing the interaction between DGPs showed that HTS-protein family were enriched (Fig. 6 H). Moreover, the real-time quantitative PCR detected the expression of genes, including PLEK , LRP1 , IDH1/2 , CAV1 , ETHE1 , SQOR , SMAD4 , EPHX2 , GLUL , PARP1 (Table 3 ). Our results found that the expression of LRP1 , CAV1 and PARP1 were significantly different between the two groups (Fig. 6 I). At the same time, the differentially expressed protein, such as LPR1 and PARP1, the same verified based on the Western Blot (Fig. 6 J). The original data of WB results are attached in supplement Fig. 1 (Fig. S1 ). Table 2 GO and KEGG functional enrichment analysis of global and acetylation proteins GO ID GO term KEGG ID Map name Gene Regulation GO:0006754 ATP biosynthetic process ko00010 Glycolysis/Gluconeogenesis ALDO Up GO:0004129 cytochrome-c oxidase ko00190 Oxidative phosphorylation COX4 Up GO:0006915 apoptotic process ko04216 /ko04979 Ferroptosis/Cholesterol metabolism VDAC3 Up GO:0016887 ATPase activity ko00190 Oxidative phosphorylation ATP5H Up GO:0006629 lipid metabolic process ko00650 Butanoate metabolism HMGCL Up Table 3 List primers of identified proteins by real-time PCR Primer Sequence(5'-3') Product(bp) PLEK-F2 TGTTTACTGAAGCAGGGGCAT PLEK-R2 CACCACACAGCCTCTCAAGT 144 LRP1-F1 AACTCTACAACCCCAAGGGC LRP1-R1 GGTTCTGCCCATCCATGTCA 111 IDH1-F1 ATATTCTGGGTGGCACGGTC IDH1-R1 CCCCATAAGCATGACGACCT 108 IDH2-F2 AGATGGCAGTGGTGTCAAGG IDH2-R2 GCGCAAAACCTGAGATGGAC 101 CAV1-F TGTCTGCCCTCTTTGGCATC CAV1-R GACGGTGTGGACGTAGATGG 158 ETHE1-F1 TGTCATCTCCCGCCTTAGTG ETHE1-R1 CAACAGGGCATCTCCAGTGA 172 SQOR-F2 GCACCAACCTTCCTACGTCA SQOR-R2 CGGTCACCAGTGGACATGAT 141 SMAD4-F2 CTTTGAGGGACAGCCATCGT SMAD4-R2 GATGGGGCTAACAGAGCTGG 117 EPHX2-F2 CCGTGACTTGGGAATGGTCA EPHX2-R2 GCTCCACAAAATGCAGACGG 185 GLUL-F2 GCTGCCATACCAACTTCAGC GLUL-R2 TGGGATCATAGGCACGGATG 120 PARP1-F1 GAATGCCAGCGTTACAAGCC PARP1-R1 GTTGGCACTCTTGGAGACCA 189 hACTB-F1 AGACCTGTACGCCAACACAG hACTB-R1 CCAGGGCAGTGATCTCCTTC 89 4. Discussion In the present study, for the first time, we conducted a joint analysis of HGSOC proteomics and acetylated modified proteomics, providing an effective dataset for reference for ovarian cancer research. Most of the differentially acetylated proteins of HGSOC were detected in global proteomics and DGPs and DAPs showed the same direction of change. At the same time, most of the identified DAPs were upregulated in HGSOC compared to normal tissues. This suggests that acetylation plays an important role in the pathogenesis of HGSOC and is related to viral carcinogenesis, energy metabolism and oxidative phosphorylation. Subcellular localization showed that DEPs, DEAPs and DGAPs were mainly located in the nucleus. Ovarian cancer (OC) accounts for a large proportion of gynecological cancers and has a high mortality rate [ 17 ] . This is largely due to the late diagnosis of high-grade serous ovarian cancer (HGSOC), the most common and aggressive tissue type. This effort is focused on early detection of the disease so that more effective treatment strategies can be implemented at the treatment stage. However, there is no effective screening method to date. In recent years, with the development of proteomics and mass spectrometry technology, the early diagnosis and screening of cancer have made great progress [ 18 ] . One study found that OC has a unique proteomic signature [ 19 ] . Clear cell ovarian carcinoma was found to differ significantly in its proteomic features from other epithelial ovarian cancer subtypes, including alterations in lipid and purine metabolic pathways. A recent study also confirmed the use of blood glycoproteomics analysis for diagnosis and staging of epithelial ovarian cancer [ 20 ] . Dai et al. found that gene characteristics based on histone acetylation have a good predictive effect on the prognosis of OC, and may be applied to clinical treatment [ 21 ] . Unlike proteomic detection of cancer, little is known about the state of protein acetylation in cancer. OC, as a highly malignant tumor, has also been observed to have abnormal acetylation of histones [ 22 ] . For the first time, we applied Label-free proteomics to study the acetyl proteomics of HGSOC and discovered the potential regulatory mechanism of acetylation modification on HGSOC. Due to the small acetylation group, the use of isotope labeling techniques may be limited. However, Label-free does not depend on isotope labeling, so it is more suitable for the study of acetylated modified proteomics. It enables comprehensive scanning of acetylation modification sites in a sample and provides accurate estimates of occupancy and heterogeneity. It is helpful to provide technical reference for the follow-up study of acetylation. However, our study inevitably had some limitations. The first is that the sample size is small, and the second is that the acetylated protein map of HGSOC is not fully revealed, and the data analysis methods are not rich enough. Finally, validation of the identified differential and acetylated proteins was lacking in clinical samples. In summary, this study analyzed the global proteins and acetylation modifications associated with HGSOC through 4D label-free proteomics analysis technology, thereby revealing the interactions with dysregulated proteins and the acetylation regulatory network, providing a new perspective for understanding the pathogenesis of HGSOC. It is of great significance for diagnosis and treatment of HGSOC. Taken together, the combination of these two proteomics will give us a broader understanding of how protein alterations occur on HGSOC. Declarations Acknowledgments and authors contribution The authors want to thank all the patients that took part in this study. Lifan Shen and Xiuzhen Wang mainly responsible for the study design; Genhai Zhu, Haocheng Gao, Xiaohang Liu, and Lang Zheng mainly responsible for the data collection and data analysis; Jun Liu and Lan Hong mainly responsible for the paper written and revised the study. Funding This work was supported by Clinical Translational Innovation Cultivating Fund 550 Project of Hainan General Hospital (2021CXZH03), and the Joint Program on Health Science & Technology Innovation of Hainan Province (WSJK2024MS125). Data availability The raw data generated in this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate The protocol was approved by committee/IRB of Hainan General Hospital in accordance with the Declaration of Helsinki (2013), and all subjects signed informed consent (No. Med-Eth-Re [2025] 608). Consent to participate Informed consent to participate in the study was obtained from all participants prior to enrollment. 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Bioorganic & medicinal chemistry; 2021. p. 116512. Additional Declarations No competing interests reported. Supplementary Files Fig.S1.pdf The original data of WB results 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-6885266","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516793149,"identity":"6bbf535c-7277-4bff-9b5e-689554c55c13","order_by":0,"name":"Lifan Shen","email":"","orcid":"","institution":"Hainan Affiliated Hospital of Hainan Medical University（Hainan General Hospital）","correspondingAuthor":false,"prefix":"","firstName":"Lifan","middleName":"","lastName":"Shen","suffix":""},{"id":516793150,"identity":"91cf982f-e992-4e8a-841b-46d6386d1ebd","order_by":1,"name":"Xiuzhen Wang","email":"","orcid":"","institution":"Hainan Affiliated 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02:47:49","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90212,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6885266/v1/62cf997e16e6bbd78341a85e.html"},{"id":91934260,"identity":"dfee8949-1580-449b-b136-6cc55c44943e","added_by":"auto","created_at":"2025-09-23 02:39:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of this study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6885266/v1/dad5336ca59fe40136181c70.png"},{"id":91931669,"identity":"860784ba-6701-4ef4-8042-346f1ab9fd5e","added_by":"auto","created_at":"2025-09-23 02:31:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression analysis of identified proteins.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Histogram shows Statistics of Proteomic Identification; B. Histograms of up-regulated and down-regulated differential protein quantities in HGSOC tissues; C. Volcano map of differentially expressed proteins. Gray indicates that the protein has no significant abundance change, green indicates the significantly differentially abundant proteins down-regulated in HGSOC tissues, and blue indicates the differentially up-regulated proteins; D. Heatmaps of protein expression showing the differences between HGSOC and non-HGSOC tissues.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6885266/v1/7617d3b33e26e793b8cdced0.png"},{"id":91931670,"identity":"3cac148c-f94e-4afe-913f-622430cbcc37","added_by":"auto","created_at":"2025-09-23 02:31:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100365,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential regulatory patterns of HGSOC by differential global proteins.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. GO Enrichment Histogram of differential proteins showed the first ten pathway processes (BP: biological process; MF: molecular function; CC: cellular component); B. Bobble diagram shows the first 20 pathway processes for KEGG enrichment analysis of differential proteins; C. Pie chart shows the Subcellular Location of differential proteins. D. String diagram of domain functional enrichment; E. Enrichment Histogram of the domain; F. The histogram shows the number of transcription factor families in the identified differential proteins; G. Map of the identified global protein interaction network.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6885266/v1/17ed6ab4f15c3f4a6d04a707.png"},{"id":91931673,"identity":"a693e62b-af0f-4f02-aebc-df0f44b92035","added_by":"auto","created_at":"2025-09-23 02:31:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential regulatory patterns of differential acetylproteins on HGSOC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Box diagram for sample uniformity evaluation; B. Histogram shows Statistics of Proteomic Identification; C. Distribution map of the number of acetylation modification sites in proteins; D. Histograms of the number of up-regulated and down-regulated differentially acetylated proteins in HGSOC tissues. E. Volcano map of differential expression of acetylated modified proteins. F. Heatmaps of acetylated protein expression.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6885266/v1/8b92050431fb70aa83ef7d39.png"},{"id":91936374,"identity":"f34dd8d5-ff68-4e06-8c6e-daec0b38cd22","added_by":"auto","created_at":"2025-09-23 02:47:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":143440,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential regulatory models of HGSOC by differential acetylation modification.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The bubble diagram shows the GO Enrichment analysis of differential proteins, which shows the first ten pathway processes; B. Bubble diagram shows the first 20 pathway processes for KEGG enrichment analysis of differential proteins; C. Pie chart shows the subcellular location of differential proteins. D. String diagram of domain functional enrichment; E. Enrichment Histogram of the domain; F. The histogram shows the number of transcription factor families in the identified differential proteins.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6885266/v1/eb54a7a2df79fa70dd05ae0b.png"},{"id":91936373,"identity":"1a7df4a7-8e6a-4e46-b213-7181e1d1d9de","added_by":"auto","created_at":"2025-09-23 02:47:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":195597,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCombined analysis of proteomics and acetylated proteomics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Histogram shows Statistics of global and acetylation proteomic Identification; B. Histograms of global and acetylation proteins with differentially up-regulated and down-regulated HGSOC tissues; C. Heatmaps of global and acetylation protein expression showing the differences between HGSOC and non-HGSOC tissues; D.\u0026nbsp; The first ten pathway processes were demonstrated by the GO enrichment histogram of differential proteins; E. Bubble diagram shows the first 20 pathway processes for KEGG enrichment analysis of differential proteins; F. Pie chart shows the Subcellular Location of differential proteins; G. Bubble diagram shows the first 20 pathway processes for KEGG enrichment analysis of domain proteins; H. Map of the interaction network of identified global and acetylation proteins. I. Real-time quantitative PCR was used to verify the expression of differential genes, including \u003cem\u003ePLEK\u003c/em\u003e, \u003cem\u003eLRP1\u003c/em\u003e, \u003cem\u003eIDH1/2\u003c/em\u003e, \u003cem\u003eCAV1\u003c/em\u003e, \u003cem\u003eETHE1\u003c/em\u003e, \u003cem\u003eSQOR\u003c/em\u003e, \u003cem\u003eSMAD4\u003c/em\u003e, \u003cem\u003eEPHX2\u003c/em\u003e, \u003cem\u003eGLUL\u003c/em\u003e, \u003cem\u003ePARP1\u003c/em\u003e. J. The protein expression was preformed using Western Blot. (\u003csub\u003e*\u003c/sub\u003e represent p \u0026lt; 0.05;\u003csub\u003e **\u003c/sub\u003e represent p \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6885266/v1/de04ecaef4d19f2d25ece2e0.png"},{"id":94094032,"identity":"f25b758a-5e68-4127-8eee-6bb9fe0f4d1c","added_by":"auto","created_at":"2025-10-22 09:24:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1470734,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6885266/v1/08caf246-559d-4dbc-88b7-208298f45adb.pdf"},{"id":91931674,"identity":"41e9f5ae-22c5-4263-8001-85be0df19636","added_by":"auto","created_at":"2025-09-23 02:31:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":239653,"visible":true,"origin":"","legend":"\u003cp\u003eThe original data of WB results\u003c/p\u003e","description":"","filename":"Fig.S1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6885266/v1/d8276c7e35f046358f0a0943.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Proteomics profiling for the global and acetylated proteins of High-Grade Serous Ovarian Carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOvarian cancer (OC) is the leading cause of death among all gynecological malignancies worldwide, with high mortality and low 5-year survival. Due to the complexity of clinical symptoms and biological and molecular characteristics, OC is often not diagnosed until it has developed to an advanced stage\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. High-grade serous ovarian cancers (HGSOC) are the most common and fatal type of ovarian cancer\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The lack of specific symptoms leads to late diagnosis, making HGSOC one of the gynecologic cancers with the worst prognosis. The cellular origin of HGSOC and the role of reproductive hormones, genetic features (e.g., alterations in P53 and DNA repair mechanisms), chromosomal instability, or dysregulation of key signaling pathways have been found to be associated with patient prognosis and treatment response\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. However, carbohydrate antigen 125 (CA125) detection\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e and transvaginal ultrasound\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e are still the common clinical detection methods for HGSOC. Biomarkers that can be used as exact indicators for the diagnosis, prognosis prediction and treatment monitoring of HGSOC patients have not been found. Advances in proteomics have introduced new techniques for screening biomarkers and brought the diagnosis of different types of cancer to a new level. Several biomarkers have been identified for the diagnosis of HGSOC, including the calcium phospholipid binding protein annexin A2 (ANXA2)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, WT1-associated protein (WTAP)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, α1-antitrypsin (AAT)\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, nuclear factor-κB (NFKB) and mevalonate phosphate kinase (PMVK)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, most of these biomarkers lack specificity or have poor positive predictive value to some extent. Therefore, understanding the proteome and the complexity of post-translational acetylation modification in high-grade serous ovarian cancer is critical for clinical guidance of patient management.\u003c/p\u003e\u003cp\u003eAcetylation is a widespread and dynamic post-translational modification (PTM) of proteins, which affects gene expression without changing the DNA sequence and plays a crucial role in many cellular physiological and pathological processes\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. It can regulate many protein-related processes such as apoptosis, subcellular localization, DNA-protein interaction, DNA replication and repair, DNA transcription activity, and protein stability\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Therefore, dysregulation of acetylation patterns can lead to abnormal gene expression and affect cancer initiation and progression. Moreover, acetylation patterns on specific proteins can be used as biomarkers for cancer diagnosis, prognosis, and treatment response\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Protein acetylation is mainly dependent on lysine acetyltransferase and lysine deacetylase. Studies have shown that lysine acetylation and the enzymes that regulate its occurrence are closely related to the progress of OC, such as lysine acetyltransferase 6A (KAT6A)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. As an intersection of proteomics and epigenetics, acetyl proteomics has attracted increasing attention.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to investigate the global protein profiles of patients with HGSOC and identify the associated acetylated proteins to understand the molecular mechanism of HGSOC pathogenesis.\u003c/p\u003e"},{"header":"2. Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Sample Collection and Preparation\u003c/h2\u003e\u003cp\u003eIn this study, samples of surgically removed tumor tissues and normal tissues without abnormal pathological signs near the lesion site of 6 patients with HGSOC were collected for proteomic and acetylated proteomic analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These cases were summarized for age, lymph node status, TNM stage, and treatment regimen. 4D label-free quantitative proteomics and acetylation analysis were performed in Genechem Biotechnology Co., Ltd. (Shanghai, China). The protocol was approved by committee/IRB of Hainan General Hospital, and all subjects signed informed consent (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical baseline characteristics of the HGSOC patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge/year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLaterality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFIGO stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003elymph node metastasis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTNM stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅣB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT3cN0M1b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBoth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIVB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT3cN1bM1b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTC plus bevacizumab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅢC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT3cN0M0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBoth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eunknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT3bN0M0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eunknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅢB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT3bN0M0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBoth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIIIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eunknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT3cN0M0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eTC: paclitaxel-carboplatin\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 RNA and Quantitative real-time RT-PCR\u003c/h2\u003e\u003cp\u003eFor the total RNA was extracted from thawed materials using the Maxwell RSC RNA Kit. RNA quality and integrity were confirmed by using the Agilent 2100 RNA Nano 6000 Assay Kit (Agilent Tech., USA) according to the manufacturer\u0026rsquo;s protocol. The specific primers for the target genes of qPCR, which interest were designed using Primer3 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.ut.ee/primer3/\u003c/span\u003e\u003cspan address=\"http://bioinfo.ut.ee/primer3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and synthesized by Integrated DNA Technologies. The qPCR data were analyzed using the QuantStudio Real-Time PCR Software (Applied Biosystems).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Mass spectrometry analysis\u003c/h2\u003e\u003cp\u003eSamples were analyzed on a nanoElute (Bruker, Bremen, Germany) coupled to a timsTOF Pro (Bruker, Bremen, Germany) with a CaptiveSpray source. Peptides were separated on a 25 cm \u0026times; 75 \u0026micro;m column packed with 1.6 \u0026micro;m C18 beads (packed emitter tip; IonOpticks, Australia) at 50\u0026deg;C (integrated oven, Sonation GmbH, Germany). The column was equilibrated with 4 volumes of 100% buffer A (99.9% Milli-Q water, 0.1% FA) before loading (both at 800 bar). Separation used a linear gradient at 300 nl/min. The timsTOF Pro operated in PASEF mode: mass range 100\u0026ndash;1700 m/z; 1/K0 0.75\u0026ndash;1.4 V\u0026sdot;s/cm\u0026sup2; (ramp 100 ms); duty cycle 100%; capillary voltage 1500 V; dry gas 3 l/min (180\u0026deg;C). PASEF settings: 10 MS/MS scans (cycle 1.16 sec); charge range 0\u0026ndash;5; active exclusion 0.5 min; target intensity 10,000; threshold 2500; CID energy 20\u0026ndash;59 eV.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 MS data and differential expression analysis\u003c/h2\u003e\u003cp\u003eMass spectrometry (MS) data were analyzed using MaxQuant software (v1.6.17.0) with searches performed against the UniProt human reference database (uniprot_homo_20230312_20423_9606_swiss_prot; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.uniprot.org\u003c/span\u003e\u003cspan address=\"http://www.uniprot.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). An initial precursor mass window of 6 ppm was applied, with subsequent processing parameters including: trypsin/P digestion with up to 2 missed cleavages; fragment ion mass tolerance of 20 ppm; fixed modification (cysteine carbamidomethylation); and variable modifications (protein N-terminal acetylation, methionine oxidation). The global false discovery rate (FDR) for peptide and protein identification was set to 1%. Protein abundance was quantified using normalized spectral intensity (LFQ intensity). Differentially expressed proteins were defined as those with fold change\u0026thinsp;\u0026gt;\u0026thinsp;2 or \u0026lt;\u0026thinsp;0.5 and Student\u0026rsquo;s t-test p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Prior to cluster analysis, quantitative data of these proteins were normalized. Hierarchical clustering heatmaps were generated using Python\u0026rsquo;s matplotlib package, with Euclidean distance algorithm and Ward\u0026rsquo;s linkage for both sample and protein expression dimensions.\u003c/p\u003e\u003cp\u003eFor protein acetylation modification detection, acetylated peptides were first enriched via immunoprecipitation using anti-acetylated lysine (Kac) antibodies. Detailed procedures for high-precision mass spectrometric detection and analysis followed the manufacturer\u0026rsquo;s protocol (Acetyl-Lysine Motif [Ac-K] Kit; Cell Signaling Technology, 13416S).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Gene Ontology (GO) function and KEGG pathway analysis\u003c/h2\u003e\u003cp\u003eUsing Blast2GO (V1.4.4)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e was carried out on the set of differentially expressed proteins Gene Ontology (GO) functional annotation KEGG pathways annotation: KOALA (KEGG Orthology And Links Annotation, V2.3) software\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e was used to compare the KEGG gene database, and the differentially expressed protein sequences were KO classified. According to the KO classification, the pathway information involved in the differentially expressed protein sequence was automatically obtained. Based on Fisher's Exact Test, the distribution of each GO entry or KEGG pathway in the differentially expressed protein set and the overall protein set was compared to evaluate the significance level of enrichment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Domain annotation and subcellular localization analysis\u003c/h2\u003e\u003cp\u003eInterpro database collection the family classification, the structure of protein sequence domain and special site prediction, and other functions, we use this database to differentially be expressed protein domain annotation of function structure is analyzed. Fisher's Exact Test was used to compare the distribution of differentially expressed proteins in the total protein set to evaluate the significance level of enrichment of a functional domain. Using the WoLF PSORT\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wolfpsort.hgc.jp/\u003c/span\u003e\u003cspan address=\"https://wolfpsort.hgc.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) software of differentially expressed protein subcellular localization prediction analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Transcription factor analysis and protein-protein interactions\u003c/h2\u003e\u003cp\u003ePlantTFDB5.0 (Plant Transcription Factor Database) was used for transcription factor prediction. In a String (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.string-db.org/\u003c/span\u003e\u003cspan address=\"https://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database to find the direct or indirect interaction network with the differentially expressed proteins, and the interaction network analysis results were generated by AnyChart software (V8.11.0.1934).\u003c/p\u003e\u003cp\u003eThe protein samples are prepared by lysing the tissues using a suitable lysis buffer containing detergents, protease inhibitors, and phosphatase inhibitors. And then, the samples are mixed with the gel loading buffer, heated to denature the proteins, and loaded onto the SDS-PAGE gel wells. The membrane is blocked by incubating it in the blocking buffer and antibodies of candidate proteins, as well as the bands corresponding to the target protein are identified based on their molecular weight compared to the molecular weight markers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Western blot analysis\u003c/h2\u003e\u003cp\u003eSDT buffer was added to the samples, which were then transferred to 2 ml tubes containing an appropriate amount of quartz sand. The lysate was homogenized using an MP Fastprep-24 Automated Homogenizer (6.0 m/s, 30 s, twice). After homogenization, the mixture was sonicated and subsequently boiled for 10 min. Following centrifugation at 14,000 \u0026times; g for 15 min, the supernatant was filtered through a 0.22 \u0026micro;m filter. Protein concentration in the filtrate was determined using the BCA Protein Assay Kit (P0012, Beyotime), and the samples were stored at -80\u0026deg;C.\u003c/p\u003e\u003cp\u003eFor SDS-PAGE analysis, 20 \u0026micro;g of protein from each sample was mixed with 6\u0026times; loading buffer and boiled for 5 min. Proteins were separated on a 12% SDS-PAGE gel, and bands were visualized via Coomassie Brilliant Blue R-250 staining.\u003c/p\u003e\u003cp\u003eFor protein digestion, 100 \u0026micro;g of protein from each sample was reduced with 100 mM DTT at 100\u0026deg;C for 5 min. Detergents, DTT, and other low-molecular-weight components were removed by repeated ultrafiltration (30 kDa cutoff, Sartorius) using UA buffer (8 M urea, 150 mM Tris-HCl, pH 8.5). Then, 100 \u0026micro;l of 100 mM iodoacetamide (dissolved in UA buffer) was added to block reduced cysteine residues, and the samples were incubated in the dark for 30 min. The filters were washed three times with 100 \u0026micro;l UA buffer and twice with 100 \u0026micro;l 50 mM NH₄HCO₃ buffer. Finally, the protein suspension was digested with 4 \u0026micro;g trypsin (Promega) in 40 \u0026micro;l 50 mM NH₄HCO₃ buffer overnight at 37\u0026deg;C, and the resulting peptides were collected as filtrate. Peptides were desalted using a C18 column, and their concentration was estimated by UV absorbance at 280 nm, with an extinction coefficient of 1.1 for a 0.1% (g/l) solution.\u003c/p\u003e\u003cp\u003eAntibodies used in this experiment were as follows: anti-LRP1/CD91 (1:3000, AffinitY, China), anti-IDH1 (1:2000, AffinitY, China), anti-CAV1 (1:2000, AffinitY, China), anti-EPXH2 (1:2000, AffinitY, China), anti-PARP1 (1:1000, AffinitY, China), and anti-GAPDH (1:50,000, Proteintech, China) as the loading control.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Global protoplasmic of HGSOC\u003c/h2\u003e\u003cp\u003eWe used 4D label-free mass spectrometry to explore differentially expressed Global proteins (DGPs). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the quantitative statistics of the global protein map and the general pattern of DGPs. Using MS Platform, we identified 68,711 unique peptides corresponding to 4,581 proteins with FDR\u0026thinsp;\u0026lt;\u0026thinsp;1%. When the 1.2-fold variation criterion was used, 356 proteins in HGSOC were considered DGPs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with 124 DGPs up-regulated and 232 DGPs down-regulated (histogram). The results of GO analysis showed that DGPs were mainly enriched on organelles, neural structures and connections, extracellular matrix and cytoskeleton, and involved molecular functions such as isocitrate dehydrogenase (NAD+) binding and activity, and participated in isocitrate dehydrogenase metabolism, neuronal axons and cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The enrichment analysis of KEGG pathway showed that DGPs were related to carbon metabolism and energy metabolism pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The results of subcellular localization showed that DGPs mainly existed in cytoplasm (28.4%) and nucleus (26.1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The function of a protein is largely determined by a specific domain in the sequence. To assess the most regulated domains of HGSOC generation, protein domain enrichment analyses were performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). It was mainly enriched in Isopropylmalate dehydrogenase-like domain and socitrate dehydrogenase. We also found that 11 proteins in DGPs act as transcription factors, of which 3 DGPs belong to CTF/NFI transcription factors and 2 DGPs belong to CTF/NFI transcription factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Network diagram showing the interaction between DGPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 \u003cb\u003eAcetyl proteomics of HGSOC\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eWe also used 4D label-free mass spectrometry to explore differentially expressed acetylation proteins (DAPs). Mass spectrometry identified 31 differentially acetylated proteins, of which 29 were up-regulated and 2 were down-regulated (LMNA and H2BC18) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, GO and KEGG enrichment results show that many differentially acetylated modified proteins are significantly enriched in nucleosome, Barr body, nucleoplasm and other cellular components. It involves molecular functions such as STAT family protein binding, protein heterodimerization activity and DNA binding. Involved in biological processes such as innate immune response in mucosa, antibacterial humoral response and killing of cells of other organisms. DAPs are mainly associated with viral carcinogenesis and metabolic pathways. Subcellular localization in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC shows that 60% of DAPs are found in the nucleus, 28% in the cytoplasm, and 12% in the mitochondria. DAPs are mainly enriched in the Histone-fold, Histone H2AVH2B/H3 and Histone H2B domains (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Combined analysis of global proteomics and acetyl proteomics\u003c/h2\u003e\u003cp\u003eIn this study, a total of 30 proteins with simultaneous global and acetylated differential expression (DGAPs) were identified. Among them, 28 cases were up-regulated (H3-3A, HMGCL, H2BC14, etc.) and 2 cases were down-regulated (LMNA and H2BC18) (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). GO and KEGG enrichment analysis showed that differentially acetylated modified proteins were significantly enriched in ATP biosynthesis, cytochrome c oxidase activity, apoptosis, ATPase activity, lipid metabolism, glycolysis/gluconeogenesis, iron apoptosis/cholesterol metabolism, oxidative phosphorylation, and butyric acid metabolism related pathways, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). More than half of DGAPs is in nucleus location (58.3%), 29.3% in the cytoplasm, and the remaining 12.5% in the mitochondria (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Domain enrichment analysis showed that most DGAPs were related to Histone H2B, Histone-fold, and Histone H2A/H2B/H3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). Network diagram showing the interaction between DGPs showed that HTS-protein family were enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). Moreover, the real-time quantitative PCR detected the expression of genes, including \u003cem\u003ePLEK\u003c/em\u003e, \u003cem\u003eLRP1\u003c/em\u003e, \u003cem\u003eIDH1/2\u003c/em\u003e, \u003cem\u003eCAV1\u003c/em\u003e, \u003cem\u003eETHE1\u003c/em\u003e, \u003cem\u003eSQOR\u003c/em\u003e, \u003cem\u003eSMAD4\u003c/em\u003e, \u003cem\u003eEPHX2\u003c/em\u003e, \u003cem\u003eGLUL\u003c/em\u003e, \u003cem\u003ePARP1\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Our results found that the expression of \u003cem\u003eLRP1\u003c/em\u003e, \u003cem\u003eCAV1\u003c/em\u003e and \u003cem\u003ePARP1\u003c/em\u003e were significantly different between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). At the same time, the differentially expressed protein, such as LPR1 and PARP1, the same verified based on the Western Blot (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ). The original data of WB results are attached in supplement Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGO and KEGG functional enrichment analysis of global and acetylation proteins\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGO ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGO term\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKEGG ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMap name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRegulation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGO:0006754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATP biosynthetic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eko00010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGlycolysis/Gluconeogenesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eALDO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGO:0004129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecytochrome-c oxidase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eko00190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOxidative phosphorylation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCOX4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGO:0006915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eapoptotic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eko04216 /ko04979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFerroptosis/Cholesterol metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVDAC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGO:0016887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATPase activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eko00190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOxidative phosphorylation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eATP5H\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGO:0006629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elipid metabolic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eko00650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eButanoate metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHMGCL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList primers of identified proteins by real-time PCR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSequence(5'-3')\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProduct(bp)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLEK-F2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGTTTACTGAAGCAGGGGCAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLEK-R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCACCACACAGCCTCTCAAGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLRP1-F1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAACTCTACAACCCCAAGGGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLRP1-R1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGTTCTGCCCATCCATGTCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDH1-F1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATATTCTGGGTGGCACGGTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDH1-R1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCCCCATAAGCATGACGACCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDH2-F2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGATGGCAGTGGTGTCAAGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDH2-R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCGCAAAACCTGAGATGGAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAV1-F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGTCTGCCCTCTTTGGCATC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAV1-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGACGGTGTGGACGTAGATGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETHE1-F1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGTCATCTCCCGCCTTAGTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETHE1-R1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCAACAGGGCATCTCCAGTGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSQOR-F2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCACCAACCTTCCTACGTCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSQOR-R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCGGTCACCAGTGGACATGAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMAD4-F2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTTTGAGGGACAGCCATCGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSMAD4-R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGATGGGGCTAACAGAGCTGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEPHX2-F2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCCGTGACTTGGGAATGGTCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEPHX2-R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCTCCACAAAATGCAGACGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGLUL-F2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCTGCCATACCAACTTCAGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGLUL-R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTGGGATCATAGGCACGGATG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePARP1-F1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGAATGCCAGCGTTACAAGCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePARP1-R1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGTTGGCACTCTTGGAGACCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehACTB-F1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGACCTGTACGCCAACACAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehACTB-R1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCCAGGGCAGTGATCTCCTTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the present study, for the first time, we conducted a joint analysis of HGSOC proteomics and acetylated modified proteomics, providing an effective dataset for reference for ovarian cancer research. Most of the differentially acetylated proteins of HGSOC were detected in global proteomics and DGPs and DAPs showed the same direction of change. At the same time, most of the identified DAPs were upregulated in HGSOC compared to normal tissues. This suggests that acetylation plays an important role in the pathogenesis of HGSOC and is related to viral carcinogenesis, energy metabolism and oxidative phosphorylation. Subcellular localization showed that DEPs, DEAPs and DGAPs were mainly located in the nucleus.\u003c/p\u003e\u003cp\u003eOvarian cancer (OC) accounts for a large proportion of gynecological cancers and has a high mortality rate\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. This is largely due to the late diagnosis of high-grade serous ovarian cancer (HGSOC), the most common and aggressive tissue type. This effort is focused on early detection of the disease so that more effective treatment strategies can be implemented at the treatment stage. However, there is no effective screening method to date. In recent years, with the development of proteomics and mass spectrometry technology, the early diagnosis and screening of cancer have made great progress\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. One study found that OC has a unique proteomic signature\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Clear cell ovarian carcinoma was found to differ significantly in its proteomic features from other epithelial ovarian cancer subtypes, including alterations in lipid and purine metabolic pathways. A recent study also confirmed the use of blood glycoproteomics analysis for diagnosis and staging of epithelial ovarian cancer\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Dai et al. found that gene characteristics based on histone acetylation have a good predictive effect on the prognosis of OC, and may be applied to clinical treatment\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUnlike proteomic detection of cancer, little is known about the state of protein acetylation in cancer. OC, as a highly malignant tumor, has also been observed to have abnormal acetylation of histones\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. For the first time, we applied Label-free proteomics to study the acetyl proteomics of HGSOC and discovered the potential regulatory mechanism of acetylation modification on HGSOC. Due to the small acetylation group, the use of isotope labeling techniques may be limited. However, Label-free does not depend on isotope labeling, so it is more suitable for the study of acetylated modified proteomics. It enables comprehensive scanning of acetylation modification sites in a sample and provides accurate estimates of occupancy and heterogeneity. It is helpful to provide technical reference for the follow-up study of acetylation. However, our study inevitably had some limitations. The first is that the sample size is small, and the second is that the acetylated protein map of HGSOC is not fully revealed, and the data analysis methods are not rich enough. Finally, validation of the identified differential and acetylated proteins was lacking in clinical samples.\u003c/p\u003e\u003cp\u003eIn summary, this study analyzed the global proteins and acetylation modifications associated with HGSOC through 4D label-free proteomics analysis technology, thereby revealing the interactions with dysregulated proteins and the acetylation regulatory network, providing a new perspective for understanding the pathogenesis of HGSOC. It is of great significance for diagnosis and treatment of HGSOC. Taken together, the combination of these two proteomics will give us a broader understanding of how protein alterations occur on HGSOC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments and authors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors want to thank all the patients that took part in this study. Lifan Shen and Xiuzhen Wang mainly responsible for the study design; Genhai Zhu, Haocheng Gao, Xiaohang Liu, and Lang Zheng mainly responsible for the data collection and data analysis; Jun Liu and Lan Hong mainly responsible for the paper written and revised the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Clinical Translational Innovation Cultivating Fund 550 Project of Hainan General Hospital (2021CXZH03), and the Joint Program on Health Science \u0026amp; Technology Innovation of Hainan Province (WSJK2024MS125).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data generated in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol was approved by committee/IRB of Hainan General Hospital in accordance with the Declaration of Helsinki (2013), and all subjects signed informed consent (No. Med-Eth-Re [2025] 608).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent to participate in the study was obtained from all participants prior to enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent authorizing the publication of the study findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHOU Y, ZHAO X. NIE X. Enhancing the therapeutic efficacy of NK cells in the treatment of ovarian cancer (Review) [J]. Oncol Rep, 2024, 51(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePUNZ\u0026oacute;N-JIM\u0026eacute;NEZ P, LAGO V. DOMINGO S, Molecular Management of High-Grade Serous Ovarian Carcinoma [J]. Int J Mol Sci, 2022, 23(22).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRISO PL, VILLA C E, GASPARONI G et al. The developmental origins of high grade serous ovarian cancer [J]. 2019, 37(15_suppl): e17063\u0026ndash;e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eALSOMAIRI A, HIMAYDA S, ALTELMESANI A, et al. Prognostic value of HE4 in advanced-stage, high-grade serous ovarian cancer: Analysis of HE4 kinetics during NACT, predicting surgical outcome and recurrence in comparison to CA125 [J]. Gynecol Oncol. 2024;181:155\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMOORE E, CHANDRANANDA D. PO-483 Improved sensitivity for non-invasive diagnosis of high-grade serous ovarian cancer [J]. ESMO Open. 2018;3:A419.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLOKMAN NA, RICCIARDELLI C, STEPHENS A N et al. Diagnostic Value of Plasma Annexin A2 in Early-Stage High-Grade Serous Ovarian Cancer [J]. Diagnostics (Basel Switzerland), 2021, 11(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYU HL, MA X D, TONG J F, et al. WTAP is a prognostic marker of high-grade serous ovarian cancer and regulates the progression of ovarian cancer cells [J]. OncoTargets therapy. 2019;12:6191\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKIM S I, JUNG M. DAN K, Proteomic Discovery of Biomarkers to Predict Prognosis of High-Grade Serous Ovarian Carcinoma [J]. Cancers, 2020, 12(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDUTT M, HARTEL G, RICHARDS RS, et al. Discovery and validation of serum glycoprotein biomarkers for high grade serous ovarian cancer [J]. Proteom Clin Appl. 2023;17(4):e2200114.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXIA C, TAO Y, LI M, et al. Protein acetylation and deacetylation: An important regulatory modification in gene transcription (Review) [J]. Experimental therapeutic Med. 2020;20(4):2923\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSHVEDUNOVA M. Modulation of cellular processes by histone and non-histone protein acetylation [J]. Nat Rev Mol Cell Biol. 2022;23(5):329\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLI S, SHI B, LIU X, et al. Acetylation and Deacetylation of DNA Repair Proteins in Cancers [J]. Front Oncol. 2020;10:573502.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLIU W, ZHAN Z, ZHANG M, et al. KAT6A, a novel regulator of β-catenin, promotes tumorigenicity and chemoresistance in ovarian cancer by acetylating COP1 [J]. Theranostics. 2021;11(13):6278\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026ouml;TZ S, GARC\u0026iacute;A-G\u0026oacute;MEZ JM, TEROL J, et al. High-throughput functional annotation and data mining with the Blast2GO suite [J]. Nucleic Acids Res. 2008;36(10):3420\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKANEHISA M, SATO Y. BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences [J]. J Mol Biol. 2016;428(4):726\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHORTON P, PARK K J, OBAYASHI T et al. WoLF PSORT: protein localization predictor [J]. Nucleic Acids Res, 2007, 35(Web Server issue): W585\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHU X Q, ZHANG X C, LI S T, et al. Construction and validation of a histone acetylation-related lncRNA prognosis signature for ovarian cancer [J]. Front Genet. 2022;13:934246.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWENK D, ZUO C, KISLINGER T, et al. Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers [J]. Clin Proteomics. 2024;21(1):6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJI J X, COCHRANE D R, NEGRI G L, et al. The proteome of clear cell ovarian carcinoma [J]. J Pathol. 2022;258(4):325\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDHAR C, RAMACHANDRAN P, XU G, et al. Diagnosing and staging epithelial ovarian cancer by serum glycoproteomic profiling [J]. Br J Cancer. 2024;130(10):1716\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDAI Q. Development and Validation of a Novel Histone Acetylation-Related Gene Signature for Predicting the Prognosis of Ovarian Cancer [J]. Front cell Dev biology. 2022;10:793425.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDING H, PEI Y, LI Y, et al. Design, synthesis and biological evaluation of a novel spiro oxazolidinedione as potent p300/CBP HAT inhibitor for the treatment of ovarian cancer [J]. Volume 52. Bioorganic \u0026amp; medicinal chemistry; 2021. p. 116512.\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":"high grade serous ovarian cancer, proteomics༛ acetylated proteins༛ 4D Label-free༛ Functional enrichment analysis","lastPublishedDoi":"10.21203/rs.3.rs-6885266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6885266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHigh-grade serous ovarian cancer (HGSOC) is the main type of ovarian cancer with a poor prognosis. Although protein omics is widely used in HGSOC, the general situation of acetylated proteins in HGSOC is still uncertain, which is helpful to understanding the carcinogenesis mechanism and identifying useful biomarkers of HGSOC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eSix female patients with pathologically diagnosed HGSOC were included in the study. After six mixed extracts of whole protein and acetylated protein were prepared, 4D Label-free mass spectrometry was applied to the determination of global protein and acetylated protein. Bioinformatics analysis was carried out, including KEGG, gene ontology (GO), clustering and protein interaction. Finally, the meaningful biomarkers were screened out by multi-omics joint analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCompared with the normal tissues near the lesion, 356 proteins identified in tumor tissues were considered as differentially expressed proteins (DEPs) in global protein histology, of which 124 were up-regulated and 232 were down-regulated, and 57 were differentially expressed acetylated proteins (DEAPs) in acetylated protein histology, including 29 up-regulated and 2 down-regulated, respectively. DEPs protein in cytosol accounts for the highest proportion, and CTF/NFI is the largest transcription factor family in DEPs. Joint analysis showed that differential proteins and their acetylation were mainly related to metabolic pathways, which were up-regulated in tumors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study will combine global protein omics with acetylated protein omics, which will provide a broader perspective for protein to change its view on carcinogenesis, as well as provide a new direction for selecting biomarkers for diagnosing HGSOC.\u003c/p\u003e","manuscriptTitle":"Proteomics profiling for the global and acetylated proteins of High-Grade Serous Ovarian Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 02:31:43","doi":"10.21203/rs.3.rs-6885266/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":"4efba58e-d60f-45be-aefb-b3a3b5fb4ee9","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T09:23:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 02:31:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6885266","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6885266","identity":"rs-6885266","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}