High expression of Fibroblast activation protein (FAP) predicts poor outcome in high-grade serous ovarian cancer

High expression of Fibroblast activation protein (FAP) predicts poor outcome in high-grade serous ovarian cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Help Center Sign In Submit a Preprint Cite Share Download PDF Research article High expression of Fibroblast activation protein (FAP) predicts poor outcome in high-grade serous ovarian cancer Min Li, Xue Cheng, Rong Rong, Yan Gao, Xiuwu Tang, Youguo Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-28242/v3 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Oct, 2020 Read the published version in BMC Cancer → Version 3 posted 4 You are reading this latest preprint version Show more versions Abstract Background: High-grade serous ovarian cancer (HGSOC) is a fatal form of ovarian cancer. Previous studies indicated some potential biomarkers for clinical evaluation of HGSOC prognosis. However, there is a lack of systematic analysis of different expression genes (DEGs) to screen and detect significant biomarkers of HGSOC. Methods: TCGA database was conducted to analyze relevant genes expression in HGSOC. Outcomes of candidate genes expression, including overall survival (OS) and progression-free survival (PFS), were calculated by Cox regression analysis for hazard rates (HR). Histopathological investigation of the identified genes was carried out in 151 Chinese HGSOC patients to validate gene expression in different stages of HGSOC. Results: Of all 57,331 genes that were analyzed, FAP was identified as the only novel gene that significantly contributed to both OS and PFS of HGSOC. In addition, FAP had a consistent expression profile between carcinoma-paracarcinoma and early-advanced stages of HGSOC. Immunological tests in paraffin section also confirmed that up-regulation of FAP was present in advanced stage HGSOC patients. Prediction of FAP network association suggested that FN1 could be a potential downstream gene which further influenced HGSOC survival. Conclusions: High-level expression of FAP was associated with poor prognosis of HGSOC via FN1 pathway. Cancer Biology Oncology Fibroblast activation protein (FAP) The Cancer Genome Atlas Program (TCGA) high-grade serous ovarian cancer (HGSOC) survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Ovarian cancer is one of the major causes of death in females globally. According to 2018 global cancer statistics, 295,414 new cases and 184,799 deaths were reported (1). In gynecological oncology, ovarian cancer is less prevalent than breast cancer and cervix cancer, however the death rate of OC is the highest (1). The most recent 2020 cancer statistics in United States also confirmed that ovarian cancer is the fifth cause of deaths of females (13,940 patients, 5% of total cancer-related death), only trailing by lung & bronchus cancer, breast cancer, colon & rectum cancer, and pancreas cancer (2). According to the NIH Surveillance, Epidemiology, and End Results program (SEER) survival statistics (2009-2015), 5-year survivorship of ovarian cancer is only 47.6% (3), which remained virtually unchanged since the last decade (4). Based on the immunohistological variation, serous ovarian cancer is the most common subtype of ovarian cancer, which could be further categorized as high-grade and low-grade neoplasm according to tumor Federation International of Gynecology and Obstetrics (FIGO) grade (5, 6). High-grade serous ovarian cancer (HGSOC) is the most common, aggressive, and fatal type of ovarian cancer. Almost 30% of patients died within five years of diagnosis (7), mainly because of lack of disease-specific symptoms, prominent biomarkers, and effective therapy or targeted drugs (8-10). Despite sharing some similar histological characters and terminology, high- and low- grade SOCs are now acknowledged as two different neoplasms (11). In 2011, the Cancer Genome Atlas (TCGA) program published the genomic and transcriptomic data of ovarian serous carcinoma, which summarized specific features of HGSOC such as TP53 mutation, extensive DNA copy variation, BRCA1/BRCA2 inactive mutation, CCNE1 aberrations, and other survival-related preliminary transcriptional signatures (12-15). Fibroblast activation protein (FAP), a cell-surface serine protease, emerges as an imperative factor in cancer-associated fibroblasts (CAFs), especially relevant to tumor occurrence and progression. Structurally, FAP consists of a cytoplasmic tail, a single transmembrane domain, and an extracellular domain (16). FAP is rarely expressed in healthy adult tissues. However, FAP is usually highly upregulated during tissue remodeling events, including cancers or cancer-associated fibroblasts (CAFs) (17-20). In addition, FAP is considered as a potential biomarker in certain tumor diagnosis and progression due to its protumorigenic specificity in both enzymatic and non-enzymatic manners (21-24). In this study, we aim to identify potential biomarkers of HGSOC survival from TCGA ovarian cancer cohort bioinformatics data. We analyzed gene expression, clinical and/or demographic information, and targeted strategies for potential HGSOC biomarkers. In addition, we validated our findings by immunohistological investigation of tissues in a group of Chinese HGSOC patients. Results suggest that FAP expression could be an effective biomarker for HGSOC survival, which warrants further investigation as potential intervention of HGSOC. Methods Dynamic protein analysis of TCGA database Gene expression data (379 cases, Workflow Type: HTSeq-Counts) and clinical information were downloaded by the TCGAbiolink package in R 3.6.0 (https://www.R-project.org/) from the official TCGA website. After screening the clinical database, 320 patients were included for G3 histologic grade indicating HGSOC. Then, 2 of these patients were excluded because of lack of relevant information. Finally, a total of 318 participants were recruited in our study. For gene expression analysis, we downloaded the entire 57,331-gene data in serous cystadenocarcinoma from the TCGA RNAseq database. After data cleaning, 19 of 57,331 genes were excluded because of their insufficient expression in total gene expression (expressions of these excluded genes were < 1 copy in all participants). Then RNA expression data were transformed to z-scores in survival analysis. Participants recruitment This study followed the Declaration of Helsinki and was approved by the Institution of Research Ethics Committee of the First Hospital affiliated to Soochow University. All participants were fully aware of all protocols of this study, and signed up written consent forms to authorize the utilization of their tissues and relevant information. For the validation set of the findings in HGSOC TCGA database, 151 Chinese Han patients were diagnosed and recruited by the First Hospital affiliated to Soochow University, the Nanjing Maternity and Child Health Care Hospital, and the First Hospital affiliated to the Nanjing Medical University, from January 2013 to May 2019. After surgery, patients’ tumor tissues were prepared into the paraffin sections. Their demographic and clinical characteristics were collected as well. Immunohistochemistry staining (IHC) and IHC score IHC was performed on paraffin sections of ovarian cancer tissues to characterize target gene (FAP) expression profile. Detailed steps can be found in our previous study (25). FAP antibody (#66562) was purchased from CST company to incubate the preparing sections overnight at 4°C for further staining. Immunohistochemical staining was performed by using the Boster SABC (rabbit IgG)-POD kit (Wuhan, China) with the recommendation of manufacturer. The above-mentioned FAP antibody was used to incubate the preparing sections overnight at 4°C, and 3, 3’- diaminobenzidine was taken to dye for scoring, which was evaluated by two independent and qualified pathologists who were blinded to actual clinical outcomes. IHC scoring was then established as follows. Percentage of positive cells and intensity of staining of FAP antibody were first calculated and then divided into these three major categories: ≤3, negative or weak; >3 and ≤6, Moderate; >6, strong. Association Network prediction of targeting gene Prediction of functional protein association networks of candidate genes was performed by STRING version 11.0 ( https://string-db.org/ ) and Genecard version 4.13 ( www.genecards.org ). In order to increase prediction accuracy, only common predictions present in both websites were included. GO and KEGG pathway analysis of host gene The gene ontology (GO) functional annotation and KEGG pathway analysis of host genes of polymorphisms were carried out by using the package ‘clusterProfiler’ in R (version 4.0.1). Statistical analysis All statistical analyses were calculated in R 3.6.0. Overall survival (OS) and progression-free survival (PFS) analyses in SOC patients were conducted by Cox regression and the Kaplan-Meier method. Multivariate Cox analysis was applied to identify potential influence of FAP expression on OS and PFS at different clinical stages of HGSOC. Other relevant demographic and clinical characteristics were compared by Student’s t test or Wilcoxon test whenever appropriate between the two groups. For immunohistochemical testing in HGSOC patients, the FAP expression score between Stage I+II and Stage III+IV were compared by Student’s t . P value < 0.05 was considered as statistical significance in this study. Results Workflow of gene identification in TCGA database As shown in Figure 1, there were 304 and 237 genes with different expressions in OS and FPS analysis, respectively. By comparing early stage (Stage I+II) with advanced stage (Stage III+IV) HGSOC, we identified 544 stage-related aberrantly expressed genes in HGSOC patients. After cross-checking OS, FPS and stage-related genes, only FAP and SSC5D were still present. And, FAP was finally included because of its characteristics of typical enzyme-catalyzed activity in uniprot database ( www.uniprot.org/ ) and its potential role in HGSOC patient’s survival (Figure 1). Associations of FAP expression with overall survival (OS) and progression-free survival (PFS) Cox regression analysis based on FAP expression was performed to generate survival curves in OS and PFS. As shown in figure 2A, low FAP expression group showed a significant protective effect on HGSOC prognosis in OS ( P = 0.005). Longitudinally, low FAP expression group had 91.1% survival rate in a period of 12 months, compared with 84.4% in high FAP expression group. Survival rate in 50 months decreased to 31.9% in low FAP group and 21.4% in high group, respectively (Table 1). Results of PFS also showed similar patterns to OS between high and low FAP expression groups ( P = 0.008, figure 2B). Expression of FAP in HGSOC patients Sections of 151 HGSOC patients’ tumor tissues in different stages were stained with FAP-antibody. We identified an increasing trend of FAP expression with respect to severity of cancer stages (figure 3). In addition, strong positive FAP-staining generally showed in membranous and cytosolic compartments in HGSOC tissues. Figure 3b revealed the total difference scores between early stage HGSOC patients and advanced patients ( P = 0.016). Additionally, negative FAP was observed in 33 (21.85%) patients. Prediction of network influenced by FAP Based on STRING and Genecard, prediction of co-influence genes with FAP and their potential regulating effects was shown in figure 4. Specifically, fibronetin-1 (FN1), collagen family genes-COL1A1, COL1A2, COL3A1, COL5A2, Thy-1 cell surface antigen (THY1), and insulin (INS) were identified as co-influence genes. FN1 was the only significant gene based on Cox regression with its hazardous influence on HGSOC ( P = 0.018, as shown in table 1). FN1 is demonstrated to over-express in ovarian cancer, which could eventually influence the formation of multicellular aggregate of ovarian cancer cells, migration and invasion of cancer cells, and aggravating platinum-resistance to deactivate chemotherapy. Additionally, epithelial-mesenchymal transition of ovarian cancer is shown to relate to aberrant expression of FN1. Therefore, FN1 and its regulatory factors, including genes, non-coding RNAs and epigenetic regulations, might be valuable candidates for ovarian cancer studies. Considering the similar function of collagen (COL) family, we extended the search of collagen encoding genes. Additional COL genes that could influence HGSOC included COL16A1 (HR = 2.50, P = 0.001 for Cox regression), COL5A1 (HR = 2.43, P = 0.002), COL8A1 (HR = 2.16, P = 0.006), and COL4A1 (HR = 1.83, P = 0.035) (Table 1). Bioinformation of FAP in GO and KEGG analysis For gene ontology (GO) analysis of FAP, the top three enriched GO annotations were listed as “regulation of fibrinolysis”, “negative regulation of extracellular matrix organization”, and positive regulation of execution phase of apoptosis” three in Biological Process, while as “dipeptidyl-peptidase activity”, “aminopeptidase activity”, and “metalloendopeptidase activity” in Molecular Function (figure 5). On the other hand, however, for KEGG analysis, there is no related available record for FAP in corresponding database. Discussion In this study we extensively searched the TCGA database for HGSOC patients to identify significant biomarkers for HGSOC survival. We constructed a comprehensive summary of differentially expressed genes (DEGs) of HGSOC, especially with regard to patients’ survival, in order to evaluate the effects of gene expression on HGSOC survival through data mining. FAP is a typical plasma membrane-bound serine protease, which is implicated in matrix digestion and invasion (26, 27). Overexpression of FAP has been investigated and believed to be associated with prognosis in many diseases, especially in cancers (20, 22, 28). Some studies have demonstrated that high FAP expression is a negative prognostic factor for epithelial ovarian cancer (29). Increased expression of FAP is believed to cause recurrence of epithelial ovarian cancer after chemotherapy (29, 30). Considering that HGSOC is a common histological type of epithelial ovarian cancer, our findings in TCGA database on HGSOC support this finding from a new perspective. Based on the location of FAP in HGSOC tissue, we suggest that FAP could be a strong positive cell-surface receptor. FAP has high expression in HGSOC patients in both Chinese population (over 60% in our study) and other ethnics (over 50%) (30). In addition, performance of therapeutic targeting at FAP also suggests its effectiveness for cancers (31). Therefore, FAP could be a potential biomarker for drug delivery or even direct therapy for HGSOC. Cancer-associated fibroblasts (CAFs), including FAP, usually participate in extracellular matrix structure remodeling and tumor microarray reconstruction. In this study, we also retrospectively explored the TCGA database and investigated the effects of other CAF members on HGSOC prognosis, such as ACTA2, PDGFRα/β, S100A4, and αSMA. These CAF members are widely adopted as biomarkers of HGSOC. Nevertheless, none of these genes showed significant association with HGSOC survival. This result may be partly due to negative expression of αSMA (30). We identified FN1 as the only potential gene regulated by FAP in HGSOC. Currently, there is no direct evidence to establish the causal relationship between FAP and FN1 . Nevertheless, in this study, we demonstrated similar eventual clinical outcomes induced by FAP and FN1 , positive correlation between their expression, significance of their role in overall survival of HGSOC, and direct regulating relationship predicted by STRING and Genecard. All these novel findings suggested FAP as a novel trigger for FN1 , at least for HGSOC survival. For THY1 gene, though it was reported as a putative tumor suppressor of ovarian cancer, our study did not produce the same results as previous ones (32), despite THY1 ’s presence in our prediction of potential FAP association networks. It is possible that THY1 cooperates with FAP during HGSOC occurrence but not in prognostic period. Conclusion After extensive data mining of TCGA database, we identified FAP as a significant biomarker for HGSOC survival. FAP overexpression led to worse outcome of HGSOC patients, especially in the advanced clinical stage. FN1 expression is potentially down regulated by FAP and further influences HGSOC survival. Declarations Ethics approval and consent to participate This study followed the Declaration of Helsinki and was approved by the Institution of Research Ethics Committee of the First Hospital affiliated to Soochow University (No.20180912). All participants were fully aware of all protocols of this study, and signed up written consent forms to authorize the utilization of their tissues and relevant information. Consent for publication Not Applicable. This manuscript does not contain any individual person’s data in any form Availability of data and materials The main datasets for screening DEGs and analysis during the current study are available from TCGA database. And the corresponding validation IHC data are available from the corresponding authors upon reasonable request. Competing Interests The authors have no conflicts of interest to declare. Funding This study was supported by Suzhou industrial technology innovation projection (201900180051, SYS2019041), and the Jiangsu Provincial Medical Youth Talent (QNRC2016231). Authors' contributions XWT and YGC made substantial contribution to design the entire research. ML made substantial acquisition and analysis of corresponding data. XC, RR and YG collected the paraffin-sample of OV patients and performed the IHC for validation. XWT wrote the article. All authors revised the manuscript and approved the final manuscript for publication Acknowledgements We thank the study participants in our study. We also thank the help of Dr. Yan Gao in Suzhou Center for Disease Prevention and Control. And finally, we appreciate the technical support on statistics from Target-Gene biotechnology co. LTD in Nanjing. Abbreviations HGSOC: High-grade serous ovarian cancer; DEGs:Different expression genes; OS: overall survival; PFS: progression-free survival; HR: hazard rate; FAP: fibroblast activation protein alpha; FN1: fibronectin 1; TCGA: The Cancer Genome Atlas Program; SEER: Surveillance, Epidemiology, and End Results program; FIGO: Federation International of Gynecology and Obstetrics; CAFs: cancer-associated fibroblasts; IHC: Immunohistochemistry staining; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; THY1: Thy-1 cell surface antigen; INS: insulin. References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2018: 68(6): 394. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA: a cancer journal for clinicians. 2020: 70(1): 7. Surveilllance, Epidemiology, and End Result Program. 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Table Table 1 Significant difference of Predicted gene influenced by FAP in HSOC overall survival COX analysis Ensembl ID Gene β-coefficient Hazard Rate (HR) Standard error of coefficient Z-value P -value ENSG00000078098 FAP 0.740663 2.097325 0.271298 2.730071964 0.006332 ENSG00000115414 FN1 0.663511 1.941597 0.28008 2.369006093 0.017836 ENSG00000084636 COL16A1 0.916471 2.500451 0.279922 3.274028048 0.00106 ENSG00000130635 COL5A1 0.887919 2.430066 0.289441 3.067700678 0.002157 ENSG00000144810 COL8A1 0.768551 2.15664 0.279769 2.747088883 0.006013 ENSG00000187498 COL4A1 0.608281 1.837271 0.289627 2.100225766 0.035709 Cite Share Download PDF Status: Published Journal Publication published 27 Oct, 2020 Read the published version in BMC Cancer → Version 3 posted Editorial decision: Accept 16 Oct, 2020 Editor assigned by journal 08 Oct, 2020 Submission checks completed at journal 07 Oct, 2020 Editor invited by journal 07 Oct, 2020 You are reading this latest preprint version Show more versions 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. 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Also discoverable on Platform About In Review Editorial Policies 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-28242","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":3339419,"identity":"0f12122d-165e-406f-8b5b-cf610becee7a","order_by":0,"name":"Min Li","email":"","orcid":"","institution":"the first affiliated hospital of soochow university","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Li","suffix":""},{"id":3339420,"identity":"6d1e5c17-0e63-4c1c-8111-a69adf706516","order_by":1,"name":"Xue Cheng","email":"","orcid":"","institution":"Obstetrics and Gynecology hosptial affiliated to nanjing medical university","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Cheng","suffix":""},{"id":3339421,"identity":"633f494d-60ae-4af8-93f7-105633fcb55d","order_by":2,"name":"Rong Rong","email":"","orcid":"","institution":"First affiliated hospital with nanjing medical university","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Rong","suffix":""},{"id":3657931,"identity":"75d74533-4774-41f0-9152-4912c5ec530a","order_by":3,"name":"Yan Gao","email":"","orcid":"","institution":"Suzhou Center for Disease Prevention and Control","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Gao","suffix":""},{"id":3339422,"identity":"d1fad682-4f88-46df-9b57-30b203d7cb07","order_by":4,"name":"Xiuwu Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYNACAyBmZj744EMFkCFBQDEPXAs7W7LhjDMwLQmEtIAAP4+ZNG8bEVrs2c8efs1TcDhxw2EeYwPeeYfl+Gc3sD34+AOPLTx5aZYzDEBa2AofSG47bCxx5wC74Qy8DssxM/hgcDh3w2HmzQaG2w4nNtxIYJPmwaeF/42ZQQJYC4OZROKcw/XzQVr+4NMikWP8AGILi5nEwYbDCQYgLXhD7MYbM8YZBun1Mw8DA7nhWLrhxhuJbZI9abi1sPfnGH/m+WNtzHf+8MHHf2qs5eVuJB+T+GGDWwsQsCFHdzMQMzbgVQ8EzB+QOHWEVI+CUTAKRsEIBAAyEFXzydH8kgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3202-7377","institution":"first affiliated hospital of Soochow University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiuwu","middleName":"","lastName":"Tang","suffix":""},{"id":3339423,"identity":"032d347f-5243-4455-a905-106518ae70b5","order_by":5,"name":"Youguo Chen","email":"","orcid":"","institution":"first affiliated hospital of soochow university","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Youguo","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2020-05-11 03:55:48","currentVersionCode":3,"declarations":"","doi":"10.21203/rs.3.rs-28242/v3","doiUrl":"https://doi.org/10.21203/rs.3.rs-28242/v3","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-020-07541-6","type":"published","date":"2020-10-27T15:02:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":3066819,"identity":"3b24a195-1b5e-43ea-b9af-9d9300ee3839","added_by":"auto","created_at":"2020-10-19 19:05:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":879827,"visible":true,"origin":"","legend":"Venn diagram of target genes selection principle. ","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-28242/v3/0f43134e712c39328206f8fe.jpg"},{"id":3066820,"identity":"f4159d21-be9b-4a6b-858f-d4e14483cbe1","added_by":"auto","created_at":"2020-10-19 19:05:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":975804,"visible":true,"origin":"","legend":"Impact of FAP expression on survival in HGSOC patients in TCGA cohort. A. Overall Survival, OS; B. Progression Free Survival, PFS。","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-28242/v3/002b1459e9603aacc506da08.jpg"},{"id":3066821,"identity":"42f85ad6-a213-4b9f-87f0-9f6770434d2e","added_by":"auto","created_at":"2020-10-19 19:05:14","extension":"tif","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":919508,"visible":true,"origin":"","legend":"Expression of FAP in HGSOC tissues. A. Expression of FAP in early-stage patients’ tissues. B. Expression of FAP in advanced-stage patients’ tissues. C. Total expression of FAP staining in patients with different stages of HGSOC.","description":"","filename":"figure3re.tif","url":"https://assets-eu.researchsquare.com/files/rs-28242/v3/d591a8bef4709a672090a1c2.tif"},{"id":3066822,"identity":"ef5f568c-900a-4954-a611-07230765dcd6","added_by":"auto","created_at":"2020-10-19 19:05:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1171600,"visible":true,"origin":"","legend":"Prediction of FAP influence in gene regulation. ","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-28242/v3/40cfb875589a449daa58cdec.jpg"},{"id":3066823,"identity":"6da0ef6f-5415-4f18-bef7-cc14db019b4d","added_by":"auto","created_at":"2020-10-19 19:05:14","extension":"tif","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":129222,"visible":true,"origin":"","legend":"GO annotations of FAP. A. Biological Process. B. Molecular Function.","description":"","filename":"figure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-28242/v3/416ce8d164c31a306996e106.tif"},{"id":13604888,"identity":"18014e83-9d04-4828-b656-0b8505e81a8f","added_by":"auto","created_at":"2021-09-17 06:01:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1871592,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-28242/v3/969ac5a1-e07b-42e2-ae1e-efb43fea314f.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eHigh expression of Fibroblast activation protein (FAP) predicts poor outcome in high-grade serous ovarian cancer\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eOvarian cancer is one of the major causes of death in females globally. According to 2018 global cancer statistics, 295,414 new cases and 184,799 deaths were reported (1). In gynecological oncology, ovarian cancer is less prevalent than breast cancer and cervix cancer, however the death rate of OC is the highest (1). The most recent 2020 cancer statistics in United States also confirmed that ovarian cancer is the fifth cause of deaths of females (13,940 patients, 5% of total cancer-related death), only trailing by lung \u0026amp; bronchus cancer, breast cancer, colon \u0026amp; rectum cancer, and pancreas cancer (2). According to the NIH Surveillance, Epidemiology, and End Results program (SEER) survival statistics (2009-2015), 5-year survivorship of ovarian cancer is only 47.6% (3), which remained virtually unchanged since the last decade (4).\u003c/p\u003e\n\u003cp\u003eBased on the immunohistological variation, serous ovarian cancer is the most common subtype of ovarian cancer, which could be further categorized as high-grade and low-grade neoplasm according to tumor Federation International of Gynecology and Obstetrics (FIGO) grade (5, 6). High-grade serous ovarian cancer (HGSOC) is the most common, aggressive, and fatal type of ovarian cancer. Almost 30% of patients died within five years of diagnosis (7), mainly because of lack of disease-specific symptoms, prominent biomarkers, and effective therapy or targeted drugs (8-10). Despite sharing some similar histological characters and terminology, high- and low- grade SOCs are now acknowledged as two different neoplasms (11). In 2011, the Cancer Genome Atlas (TCGA) program published the genomic and transcriptomic data of ovarian serous carcinoma, which summarized specific features of HGSOC such as TP53 mutation, extensive DNA copy variation, BRCA1/BRCA2 inactive mutation, CCNE1 aberrations, and other survival-related preliminary transcriptional signatures (12-15).\u003c/p\u003e\n\u003cp\u003eFibroblast activation protein (FAP), a cell-surface serine protease, emerges as an imperative factor in cancer-associated fibroblasts (CAFs), especially relevant to tumor occurrence and progression. Structurally, FAP consists of a cytoplasmic tail, a single transmembrane domain, and an extracellular domain (16). FAP is rarely expressed in healthy adult tissues. However, FAP is usually highly upregulated during tissue remodeling events, including cancers or cancer-associated fibroblasts (CAFs) (17-20). In addition, FAP is considered as a potential biomarker in certain tumor diagnosis and progression due to its protumorigenic specificity in both enzymatic and non-enzymatic manners (21-24).\u003c/p\u003e\n\u003cp\u003eIn this study, we aim to identify potential biomarkers of HGSOC survival from TCGA ovarian cancer cohort bioinformatics data. We analyzed gene expression, clinical and/or demographic information, and targeted strategies for potential HGSOC biomarkers. In addition, we validated our findings by immunohistological investigation of tissues in a group of Chinese HGSOC patients. Results suggest that FAP expression could be an effective biomarker for HGSOC survival, which warrants further investigation as potential intervention of HGSOC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eDynamic protein analysis of TCGA database\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eGene expression data (379 cases, Workflow Type: HTSeq-Counts) and clinical information were downloaded by the \u003cem\u003eTCGAbiolink\u003c/em\u003e package in \u003cem\u003eR\u003c/em\u003e 3.6.0 (https://www.R-project.org/) from the official TCGA website. After screening the clinical database, 320 patients were included for G3 histologic grade indicating HGSOC. Then, 2 of these patients were excluded because of lack of relevant information. Finally, a total of 318 participants were recruited in our study.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eFor gene expression analysis, we downloaded the entire 57,331-gene data in serous cystadenocarcinoma from the TCGA RNAseq database. After data cleaning, 19 of 57,331 genes were excluded because of their insufficient expression in total gene expression (expressions of these excluded genes were \u0026lt; 1 copy in all participants). Then RNA expression data were transformed to z-scores in survival analysis.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eParticipants recruitment\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThis study followed the Declaration of Helsinki and was approved by the Institution of Research Ethics Committee of the First Hospital affiliated to Soochow University. All participants were fully aware of all protocols of this study, and signed up written consent forms to authorize the utilization of their tissues and relevant information.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eFor the validation set of the findings in HGSOC TCGA database, 151 Chinese Han patients were diagnosed and recruited by the First Hospital affiliated to Soochow University, the Nanjing Maternity and Child Health Care Hospital, and the First Hospital affiliated to the Nanjing Medical University, from January 2013 to May 2019. After surgery, patients\u0026rsquo; tumor tissues were prepared into the paraffin sections. Their demographic and clinical characteristics were collected as well.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eImmunohistochemistry staining (IHC) \u003c/strong\u003e\u003cstrong\u003eand IHC score\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eIHC was performed on paraffin sections of ovarian cancer tissues to characterize target gene (FAP) expression profile. Detailed steps can be found in our previous study (25). FAP antibody (#66562) was purchased from CST company to incubate the preparing sections overnight at 4\u0026deg;C for further staining.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eImmunohistochemical staining was performed by using the Boster SABC (rabbit IgG)-POD kit (Wuhan, China) with the recommendation of manufacturer. The above-mentioned FAP antibody was used to incubate the preparing sections overnight at 4\u0026deg;C, and 3, 3\u0026rsquo;- diaminobenzidine was taken to dye for scoring, which was evaluated by two independent and qualified pathologists who were blinded to actual clinical outcomes. IHC scoring was then established as follows. Percentage of positive cells and intensity of staining of FAP antibody were first calculated and then divided into these three major categories: \u0026le;3, negative or weak; \u0026gt;3 and \u0026le;6, Moderate; \u0026gt;6, strong.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eAssociation Network prediction of targeting gene\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003ePrediction of functional protein association networks of candidate genes was performed by STRING version 11.0 (\u003ca style=\"color: #000000;\" href=\"https://string-db.org/\"\u003ehttps://string-db.org/\u003c/a\u003e) and Genecard version 4.13 (\u003ca style=\"color: #000000;\" href=\"http://www.genecards.org\"\u003ewww.genecards.org\u003c/a\u003e). In order to increase prediction accuracy, only common predictions present in both websites were included.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eGO and KEGG pathway analysis of host gene\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe gene ontology (GO) functional annotation and KEGG pathway analysis of host genes of polymorphisms were carried out by using the package \u0026lsquo;clusterProfiler\u0026rsquo; in R (version 4.0.1).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eAll statistical analyses were calculated in \u003cem\u003eR\u003c/em\u003e 3.6.0. Overall survival (OS) and progression-free survival (PFS) analyses in SOC patients were conducted by Cox regression and the Kaplan-Meier method. Multivariate Cox analysis was applied to identify potential influence of FAP expression on OS and PFS at different clinical stages of HGSOC. Other relevant demographic and clinical characteristics were compared by Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test or Wilcoxon test whenever appropriate between the two groups. For immunohistochemical testing in HGSOC patients, the FAP expression score between Stage I+II and Stage III+IV were compared by Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e. \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered as statistical significance in this study.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eWorkflow of gene identification in TCGA database\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eAs shown in Figure 1, there were 304 and 237 genes with different expressions in OS and FPS analysis, respectively. By comparing early stage (Stage I+II) with advanced stage (Stage III+IV) HGSOC, we identified 544 stage-related aberrantly expressed genes in HGSOC patients. After cross-checking OS, FPS and stage-related genes, only \u003cem\u003eFAP\u003c/em\u003e and \u003cem\u003eSSC5D\u003c/em\u003e were still present. And, \u003cem\u003eFAP\u003c/em\u003e was finally included because of its characteristics of typical enzyme-catalyzed activity in uniprot database (\u003ca style=\"color: #000000;\" href=\"http://www.uniprot.org/\"\u003ewww.uniprot.org/\u003c/a\u003e) and its potential role in HGSOC patient\u0026rsquo;s survival (Figure 1).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eAssociations of FAP expression with overall survival (OS) and progression-free survival (PFS)\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eCox regression analysis based on FAP expression was performed to generate survival curves in OS and PFS. As shown in figure 2A, low FAP expression group showed a significant protective effect on HGSOC prognosis in OS (\u003cem\u003eP\u003c/em\u003e = 0.005). Longitudinally, low FAP expression group had 91.1% survival rate in a period of 12 months, compared with 84.4% in high FAP expression group. Survival rate in 50 months decreased to 31.9% in low FAP group and 21.4% in high group, respectively (Table 1). Results of PFS also showed similar patterns to OS between high and low FAP expression groups (\u003cem\u003eP \u003c/em\u003e= 0.008, figure 2B).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eExpression of FAP in HGSOC patients\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eSections of 151 HGSOC patients\u0026rsquo; tumor tissues in different stages were stained with FAP-antibody. We identified an increasing trend of FAP expression with respect to severity of cancer stages (figure 3). In addition, strong positive FAP-staining generally showed in membranous and cytosolic compartments in HGSOC tissues. Figure 3b revealed the total difference scores between early stage HGSOC patients and advanced patients (\u003cem\u003eP \u003c/em\u003e= 0.016). Additionally, negative FAP was observed in 33 (21.85%) patients.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003ePrediction of network influenced by FAP\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eBased on STRING and Genecard, prediction of co-influence genes with FAP and their potential regulating effects was shown in figure 4. Specifically, fibronetin-1 (FN1), collagen family genes-COL1A1, COL1A2, COL3A1, COL5A2, Thy-1 cell surface antigen (THY1), and insulin (INS) were identified as co-influence genes.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eFN1 was the only significant gene based on Cox regression with its hazardous influence on HGSOC (\u003cem\u003eP\u003c/em\u003e = 0.018, as shown in table 1). FN1 is demonstrated to over-express in ovarian cancer, which could eventually influence the formation of multicellular aggregate of ovarian cancer cells, migration and invasion of cancer cells, and aggravating platinum-resistance to deactivate chemotherapy. Additionally, epithelial-mesenchymal transition of ovarian cancer is shown to relate to aberrant expression of FN1. Therefore, FN1 and its regulatory factors, including genes, non-coding RNAs and epigenetic regulations, might be valuable candidates for ovarian cancer studies.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eConsidering the similar function of collagen (COL) family, we extended the search of collagen encoding genes. Additional COL genes that could influence HGSOC included COL16A1 (HR = 2.50, \u003cem\u003eP \u003c/em\u003e= 0.001 for Cox regression), COL5A1 (HR = 2.43, \u003cem\u003eP \u003c/em\u003e= 0.002), COL8A1 (HR = 2.16, \u003cem\u003eP \u003c/em\u003e= 0.006), and COL4A1 (HR = 1.83, P = 0.035) (Table 1).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eBioinformation of FAP in GO and KEGG analysis\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eFor gene ontology (GO) analysis of FAP, the top three enriched GO annotations were listed as \u0026ldquo;regulation of fibrinolysis\u0026rdquo;, \u0026ldquo;negative regulation of extracellular matrix organization\u0026rdquo;, and positive regulation of execution phase of apoptosis\u0026rdquo; three in Biological Process, while as \u0026ldquo;dipeptidyl-peptidase activity\u0026rdquo;, \u0026ldquo;aminopeptidase activity\u0026rdquo;, and \u0026ldquo;metalloendopeptidase activity\u0026rdquo; in Molecular Function (figure 5). On the other hand, however, for KEGG analysis, there is no related available record for FAP in corresponding database.\u003c/span\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we extensively searched the TCGA database for HGSOC patients to identify significant biomarkers for HGSOC survival. We constructed a comprehensive summary of differentially expressed genes (DEGs) of HGSOC, especially with regard to patients\u0026rsquo; survival, in order to evaluate the effects of gene expression on HGSOC survival through data mining.\u003c/p\u003e\n\u003cp\u003eFAP is a typical plasma membrane-bound serine protease, which is implicated in matrix digestion and invasion (26, 27). Overexpression of FAP has been investigated and believed to be associated with prognosis in many diseases, especially in cancers (20, 22, 28). Some studies have demonstrated that high FAP expression is a negative prognostic factor for epithelial ovarian cancer (29). Increased expression of FAP is believed to cause recurrence of epithelial ovarian cancer after chemotherapy (29, 30). Considering that HGSOC is a common histological type of epithelial ovarian cancer, our findings in TCGA database on HGSOC support this finding from a new perspective.\u003c/p\u003e\n\u003cp\u003eBased on the location of FAP in HGSOC tissue, we suggest that FAP could be a strong positive cell-surface receptor. FAP has high expression in HGSOC patients in both Chinese population (over 60% in our study) and other ethnics (over 50%) (30). In addition, performance of therapeutic targeting at FAP also suggests its effectiveness for cancers (31). Therefore, FAP could be a potential biomarker for drug delivery or even direct therapy for HGSOC.\u003c/p\u003e\n\u003cp\u003eCancer-associated fibroblasts (CAFs), including FAP, usually participate in extracellular matrix structure remodeling and tumor microarray reconstruction. In this study, we also retrospectively explored the TCGA database and investigated the effects of other CAF members on HGSOC prognosis, such as ACTA2, PDGFR\u0026alpha;/\u0026beta;, S100A4, and \u0026alpha;SMA. These CAF members are widely adopted as biomarkers of HGSOC. Nevertheless, none of these genes showed significant association with HGSOC survival. This result may be partly due to negative expression of \u0026alpha;SMA (30).\u003c/p\u003e\n\u003cp\u003eWe identified \u003cem\u003eFN1\u003c/em\u003e as the only potential gene regulated by \u003cem\u003eFAP\u003c/em\u003e in HGSOC. Currently, there is no direct evidence to establish the causal relationship between \u003cem\u003eFAP\u003c/em\u003e and\u003cem\u003e FN1\u003c/em\u003e. Nevertheless, in this study, we demonstrated similar eventual clinical outcomes induced by \u003cem\u003eFAP\u003c/em\u003e and \u003cem\u003eFN1\u003c/em\u003e, positive correlation between their expression, significance of their role in overall survival of HGSOC, and direct regulating relationship predicted by STRING and Genecard. All these novel findings suggested \u003cem\u003eFAP\u003c/em\u003e as a novel trigger for \u003cem\u003eFN1\u003c/em\u003e, at least for HGSOC survival. For\u003cem\u003e THY1\u003c/em\u003e gene, though it was reported as a putative tumor suppressor of ovarian cancer, our study did not produce the same results as previous ones (32), despite \u003cem\u003eTHY1\u003c/em\u003e\u0026rsquo;s presence in our prediction of potential \u003cem\u003eFAP \u003c/em\u003eassociation networks. It is possible that\u003cem\u003e THY1\u003c/em\u003e cooperates with\u003cem\u003e FAP\u003c/em\u003e during HGSOC occurrence but not in prognostic period.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAfter extensive data mining of TCGA database, we identified FAP as a significant biomarker for HGSOC survival. FAP overexpression led to worse outcome of HGSOC patients, especially in the advanced clinical stage. \u003cem\u003eFN1\u003c/em\u003e expression is potentially down regulated by\u003cem\u003e FAP\u003c/em\u003e and further influences HGSOC survival.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study followed the Declaration of Helsinki and was approved by the Institution of Research Ethics Committee of the First Hospital affiliated to Soochow University (No.20180912). All participants were fully aware of all protocols of this study, and signed up written consent forms to authorize the utilization of their tissues and relevant information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003eThis manuscript does not contain any individual person\u0026rsquo;s data in any form\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main datasets for screening DEGs and analysis during the current study are available from TCGA database. And the corresponding validation IHC data are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Suzhou industrial technology innovation projection (201900180051, SYS2019041), and the Jiangsu Provincial Medical Youth Talent (QNRC2016231).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXWT and YGC made substantial contribution to design the entire research. ML made substantial acquisition and analysis of corresponding data. XC, RR and YG collected the paraffin-sample of OV patients and performed the IHC for validation. XWT wrote the article. All authors revised the manuscript and approved the final manuscript for publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the study participants in our study. We also thank the help of Dr. Yan Gao in Suzhou Center for Disease Prevention and Control. And finally, we appreciate the technical support on statistics from Target-Gene biotechnology co. LTD in Nanjing.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHGSOC: High-grade serous ovarian cancer; DEGs:Different expression genes; OS: overall survival; PFS: progression-free survival; HR: hazard rate; FAP: fibroblast activation protein alpha; FN1: fibronectin 1; TCGA: The Cancer Genome Atlas Program; SEER: Surveillance, Epidemiology, and End Results program; FIGO: Federation International of Gynecology and Obstetrics; CAFs: cancer-associated fibroblasts; IHC: Immunohistochemistry staining; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; THY1: Thy-1 cell surface antigen; INS: insulin.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Cancer genetics and cytogenetics. 2003: 143(2): 125.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cdiv class=\"gridtable\" style='box-sizing: inherit; color: rgb(74, 74, 74); font-family: Roboto, BlinkMacSystemFont, -apple-system, \"Segoe UI\", Oxygen, Ubuntu, Cantarell, \"Fira Sans\", \"Droid Sans\", \"Helvetica Neue\", Helvetica, Arial, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration-style: initial; text-decoration-color: initial;'\u003e\n \u003ctable border=\"1\" style=\"box-sizing: inherit; border-collapse: collapse; border-spacing: 0px;\"\u003e\n \u003ccaption language=\"En\" style=\"box-sizing: inherit;\"\u003e\n \u003cdiv class=\"CaptionNumber\" style=\"box-sizing: inherit;\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\" style=\"box-sizing: inherit;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003eSignificant difference of Predicted gene influenced by FAP in HSOC overall survival COX analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead style=\"box-sizing: inherit;\"\u003e\n \u003ctr style=\"box-sizing: inherit;\"\u003e\n \u003cth align=\"left\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top; color: rgb(54, 54, 54);\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003eEnsembl ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top; color: rgb(54, 54, 54);\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top; color: rgb(54, 54, 54);\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003e\u0026beta;-coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top; color: rgb(54, 54, 54);\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003eHazard Rate (HR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top; color: rgb(54, 54, 54);\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003eStandard error of coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top; color: rgb(54, 54, 54);\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003eZ-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top; color: rgb(54, 54, 54);\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003e\u003cem style=\"box-sizing: inherit;\"\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody style=\"box-sizing: inherit;\"\u003e\n \u003ctr style=\"box-sizing: inherit;\"\u003e\n \u003ctd align=\"left\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003eENSG00000078098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003eFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003e0.740663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003e2.097325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003e0.271298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003e2.730071964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003e0.006332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"box-sizing: inherit;\"\u003e\n \u003ctd align=\"left\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003eENSG00000115414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003eFN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003e0.663511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003e1.941597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; padding: 0px 0px 10px; font-weight: 400; font-size: 0.925rem; letter-spacing: 0.35px; line-height: 1.65rem; color: rgb(36, 44, 53);\"\u003e0.28008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"box-sizing: inherit; padding: 0px; vertical-align: top;\"\u003e\n \u003cp style=\"box-sizing: inherit; margin: 0px; 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Previous studies indicated some potential biomarkers for clinical evaluation of HGSOC prognosis. However, there is a lack of systematic analysis of different expression genes (DEGs) to screen and detect significant biomarkers of HGSOC.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eTCGA database was conducted to analyze relevant genes expression in HGSOC. Outcomes of candidate genes expression, including overall survival (OS) and progression-free survival (PFS), were calculated by Cox regression analysis for hazard rates (HR). Histopathological investigation of the identified genes was carried out in 151 Chinese HGSOC patients to validate gene expression in different stages of HGSOC.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Of all 57,331 genes that were analyzed, \u003cem\u003eFAP\u003c/em\u003e was identified as the only novel gene that significantly contributed to both OS and PFS of HGSOC. In addition, \u003cem\u003eFAP\u003c/em\u003e had a consistent expression profile between carcinoma-paracarcinoma and early-advanced stages of HGSOC. Immunological tests in paraffin section also confirmed that up-regulation of FAP was present in advanced stage HGSOC patients. Prediction of \u003cem\u003eFAP \u003c/em\u003enetwork association suggested that FN1 could be a potential downstream gene which further influenced HGSOC survival.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eHigh-level expression of FAP was associated with poor prognosis of HGSOC via FN1 pathway.\u003c/p\u003e","manuscriptTitle":"High expression of Fibroblast activation protein (FAP) predicts poor outcome in high-grade serous ovarian cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2020-10-19 18:57:51","doi":"10.21203/rs.3.rs-28242/v3","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2020-10-16T12:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-10-08T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-10-07T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-10-07T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":2,"date":"2020-07-02 13:44:17","doi":"10.21203/rs.3.rs-28242/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2020-09-13T12:00:00+00:00","index":2,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nThe authors identified high expression of FAP as predictive marker associated with PFS and OS in HGSOC by mining TCGA results and including 318 cases in their analysis. This result was the verified in a cohort of 32 patients via histopathology. High RNA expression of FAP was associated with poor prognosis in HGSOC. Prediction of FAP network association suggested that FN1 could be downstream gene associated with HGSOC\nsurvival. The FAP results were validated by immunocytochemistry study using FAP antibody in 32 Chinese Han cases wof HGSOC.\n\nThe authors grouped stage I+II vs stage III+IV in comparison groups. Do their conclusion re FAP holds is Stage I is compared to StageI+II+III. this is relevant because stage I HGSOC has distincltly superior prognosis in comparison to each of stages II, III, and IV.\n\nThe authors also state in the Discussion that this is the first study of its kind. But several other studies are published such as Yang L et al in 2018 and others, it is important to revise this and present the complete info.\n\n\n\n\nOther comments\n1. Please consider designation HGSOC throughout the manuscript\n2. Line 48, Figure 1 (instead of figure 1) (Immunocytochemistry section)\n\n\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I have no competing interests of any kind.**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"decision","content":"Minor revision","date":"2020-09-13T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-09-09T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nThe authors analyze high-grade serous ovarian carcinoma in the TCGA dataset and validate FAP immunohistochemically. In the method section immunohistochemical Evaluation should be described in more Detail and also statistic Evaluation should be clarified.\nThe work is interesting as a Pilot study but 32 patients is a small cohort even for a seldom desease like high-grade serous ovarian Cancer.* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I dedeclare that I have no competing interests**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. 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