Diagnostic and Prognostic Significance of Fallopian Tube Ciliated Epithelial Cell Markers in Serous Ovarian Cancer

Diagnostic and Prognostic Significance of Fallopian Tube Ciliated Epithelial Cell Markers in 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 Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diagnostic and Prognostic Significance of Fallopian Tube Ciliated Epithelial Cell Markers in Serous Ovarian Cancer Jingchen Zhao, Mengyan He, Ying Zhang, Zhiyan Li, Dong Xu, Shurui Kang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8695930/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 Serous ovarian cancer may originate from the ciliated epithelial cells of the fimbriated end of fallopian tube. The aim of this study was to explore the diagnostic and prognostic value of fallopian tube ciliated epithelial cell markers in serous ovarian cancer, and to preliminarily explore the potential mechanisms. Marker genes of fallopian tube ciliated epithelial cells were obtained from the human single-cell database CZ CELLxGENE. GEPIA, HPA, and Kaplan-Meier Plotter databases were applied to analyze their expression in serous ovarian cancer and correlation with clinical prognosis. The diagnostic potential of candidate biomarkers was evaluated using the GEO datasets. GeneMANIA, TISCH2, and TISIDB databases were implemented to explore the regulatory networks and related pathways of these biomarkers. The fallopian tube ciliated epithelial cell markers SCGB2A1, FOXJ1, CFAP126, and DYDC2 were found to be upregulated in serous ovarian tumor tissues, high expression levels of these markers were correlated with longer survival in patients. Gene interaction network and functional enrichment analysis indicated that these genes were involved in immune regulation, cell adhesion and signal transduction pathways. SCGB2A1, FOXJ1, CFAP126, and DYDC2 were associated with the occurrence and progression of serous ovarian cancer and could serve as markers for its diagnosis and prognosis. serous ovarian cancer diagnosis prognosis fallopian tube ciliated epithelial cells biomarkers database mining Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Ovarian cancer is one of the most lethal gynecological malignancies. According to the latest statistical data from the International Agency for Research on Cancer (IARC), there were 324,398 new cases and 206,839 deaths worldwide in 2022 [ 1 ]. Epithelial Ovarian Cancer (EOC) accounts for more than 90% of all ovarian malignancies and can be histologically classified into several subtypes, including serous, endometrioid, mucinous, clear cell, and other rare histological subtypes, serous ovarian cancer (SOC) accounts for more than 75% of epithelial ovarian cancers [ 2 , 3 ]. Due to the lack of specific early symptoms and effective screening methods, most patients are diagnosed at an advanced stage, which severely affects patient prognosis. Therefore, in-depth exploration of the pathogenesis of ovarian cancer and identification of new diagnostic and prognostic markers are particularly important. Previously, it was believed that epithelial ovarian cancer originated from ovarian surface epithelial cells. However, with in-depth research, it has been found that different subtypes of EOC may originate from different cell types. In recent years, increasing evidence has indicated that the occurrence and development of serous ovarian cancer are closely related to genes expressed in ciliated epithelial cells [ 4 , 5 ]. The distinctive histological architecture of this region, characterized by a high density of ciliated epithelial cells, enables efficient ovum capture through precisely coordinated ciliary movement. A recent study has identified transitional preciliated cells in the mouse fallopian tube as a cancer-prone cell state [ 6 ]. Ciliated cell markers possibly serve as valuable indicators for disease progression and patient outcomes of serous ovarian cancer. Using immunohistochemistry and tissue microarray analysis, Richardson MT et al. observed that higher tumor grade in serous ovarian carcinoma correlates with a decreased proportion of cells expressing ciliated cell markers [ 7 ]. Weir A et al. found that the expression of the tubal ciliated cell marker FOXJ1 was strongly correlated with survival in patients with HGSOC, while patients with high expression tended to have longer overall survival [ 8 ]. Thus, fallopian tube ciliated cell-specific markers demonstrate significant potential as diagnostic and prognostic biomarkers for serous ovarian cancer. The specific mechanism of the action of fallopian tube ciliated epithelial cell markers in serous ovarian cancer remains to be further explored. Therefore, we screened out the markers of fallopian tube ciliated epithelial cells based on the single-cell database CZ CELLxGENE, examined their expression levels in normal ovarian and serous cancer tissues using the GEPIA database, and further validated their protein expression levels through the HPA database. We focused on the SCGB2A1, FOXJ1, CFAP126, and DYDC2 genes, which exhibited significantly elevated expression in serous ovarian cancer tissues and demonstrated high cancer specificity. Kaplan-Meier analysis revealed that increased expression of these genes was significantly associated with prolonged progression-free survival (PFS) and overall survival (OS) in patients with serous cancer. Through integrated analysis utilizing TISCH2, TISIDB, and Metascape databases, we evaluated the expression levels of these genes across distinct molecular subtypes of ovarian cancer, and performed functional enrichment analysis in the tumor microenvironment, to initially explore the roles of the above genes in tumorigenesis and progression. These findings contribute to a deeper understanding of the pathogenic mechanisms of serous ovarian cancer, offering new targets and ideas for its early diagnosis and treatment. 2. Materials and Methods 2.1. Preliminary Screening of Candidate Biomarkers In the “Gene Expression” module of single-cell database CZ CELLxGENE ( https://cellxgene.cziscience.com ) [ 9 ], we chose the cell type “ciliated epithelial cell” in tissue “fallopian tube” to obtain the top 100 specifically expressed marker genes in normal fallopian tube ciliated epithelial cells. The module “Expression Analysis” of the GEPIA (Gene Expression Profiling Interactive Analysis, http://gepia2.cancer-pku.cn/#index/ ) [ 10 ] database was used to explore the expression level of candidate genes in tumor and normal tissues at pancancer level. The expression levels of candidate biomarkers in serous ovarian cancer tissues and normal tissues were validated using the HPA (the Human Protein Atlas) database ( https://www.proteinatlas.org ) [ 11 ]. The candidate genes was searched respectively to access protein expression data. 2.2. Assessment of the diagnostic performance of candidate markers based on GEO datasets Based on the GEO (Gene Expression Omnibus) database ( https://www.ncbi.nlm.nih.gov/geo/ ) of the National Center for Biotechnology Information (NCBI) [ 12 ], the expression microarray datasets of primary ovarian tumors and normal tissues were retrieved. Using the keywords "ovarian cancer" and "expression" with the search condition set to Homo sapiens, three datasets (GSE26712 [ 13 ], GSE6008 [ 14 ], and GSE14407 [ 15 ]) were retrieved from the GEO database. ROC curves of candidate biomarkers were plotted in each dataset using the pROC package (version 1.18.5) [ 16 ] in R language (version 4.4.3) [ 17 ]. The area under the ROC curve (AUC) was calculated to evaluate the diagnostic performance of the candidate biomarkers. 2.3. The interaction network and prognostic significance of candidate biomarkers in serous ovarian cancer patients Utilizing the Kaplan-Meier Plotter database ( https://kmplot.com/analysis/ ) [ 18 ], we assessed the association between candidate biomarkers and clinical outcomes of serous ovarian cancer patients. The search criteria were defined as “histology: serous”. The gene interaction network of candidate biomarkers was constructed using the GeneMANIA (Gene Multiple Association Network Integration Algorithm) database ( http://genemania.org/ ) [ 19 ]. 2.4. Expression patterns of biomarkers in distinct molecular subtypes of serous ovarian cancer We investigated the expression profiles of candidate biomarkers across different subtypes utilizing the TISIDB (Tumor Immune System Interaction Database) database ( http://cis.hku.hk/TISIDB/ ) [ 20 ]. Inter-subtype differential expression of these biomarkers was assessed using the Kruskal-Wallis test, with statistical significance defined as P < 0.05. 2.5. Tumor microenvironment and functional enrichment analysis We searched for ovarian cancer-related databases in the "Dataset" module of the TISCH2 (Tumor Immune Single-cell Hub 2) database ( http://tisch.compbio.cn/home/ ) [ 21 ]. According to the different expression levels of genes in malignant cells, they were divided into different clusters. Then the cluster with high expression of fallopian tube ciliated epithelial cell markers was selected for analysis, and the differentially expressed gene data of this cluster was downloaded from the TISCH2 database. The online database Metascape ( http://metascape.org ) [ 22 ] was used to perform Gene Ontology (GO) analysis on the differentially expressed genes, as well as enrichment analysis and functional annotation of these differential genes. After the gene list was input, the analysis objective was set as “GO Molecular Functions”. The “Functional Annotation” module of the DAVID (Database for Annotation, Visualization and Integrated Discovery) database ( https://davidbioinformatics.nih.gov/ ) [ 23 ] was applied to analyze the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways of the differential genes, and the functional annotation chart was downloaded. R package ggplot2 [ 24 ] was used for the visualization of the analysis results, we plotted bubble charts of the top 10 most significant pathways. 3. Results 3.1. Preliminary screening of diagnostic markers for serous ovarian cancer One hundred highly specific genes expressed in human fallopian tube ciliated epithelial cells were obtained from the CZ CELLxGENE database [ 25 – 27 ]. By analyzing mRNA levels in 426 serous ovarian tumor tissues and 88 corresponding normal ovarian tissues in the GEPIA database, 27 genes with significantly increased expression in tumor tissues (fold change of TPM[Transcripts Per Million] > 3, two-tailed P < 0.001) were selected. Analysis of the HPA database showed that proteins encoded by 8 of these candidate genes were significantly upregulated in serous ovarian cancer, namely SMIM22 (Small Integral Membrane Protein 22), CD24, SLPI (Secretory Leukocyte Peptidase Inhibitor), LDLRAD1 (Low Density Lipoprotein Receptor Class A Domain Containing 1), SCGB2A1 (Secretoglobin Family 2A Member 1), CFAP126 (Cilia And Flagella Associated Protein 126), FOXJ1 (Forkhead Box J1), and DYDC2 (DPY30 Domain Containing 2). Among these, 4 genes showed higher specificity for serous ovarian cancer, namely SCGB2A1, CFAP126, FOXJ1, and DYDC2. SCGB2A1 was highly expressed only in ovarian cancer and UCEC, with low levels in other tumors and normal tissues. CFAP126 was most highly expressed in normal testicular germ cells and upregulated in tumor tissues of GBM, LGG, and UCEC. FOXJ1 was most highly expressed in normal testicular germ cells and UCEC tumor tissues, followed by ovarian tumor tissues, and also upregulated in GBM, LUAD, and UCS. In addition to ovarian cancer, DYDC2 was upregulated in UCEC and UCS tumor tissues (Fig. 1 ). In summary, the genes SCGB2A1, CFAP126, FOXJ1, and DYDC2 were included in subsequent studies. ACC: adrenocortical carcinoma, BLCA: Bladder Urothelial Carcinoma, BRCA: Breast Invasive Carcinoma, CESC: Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma, CHOL: Cholangiocarcinoma, COAD: Colon Adenocarcinoma, ESCA: Esophageal Carcinoma, HNSC: head and neck squamous cell carcinoma, LIHC: liver hepatocellular carcinoma, LUAD: Lung Adenocarcinoma, LUSC: lung squamous cell carcinoma, PAAD: Pancreatic Adenocarcinoma, PRAD: Prostate Adenocarcinoma, READ: Rectal Adenocarcinoma, STAD: Stomach Adenocarcinoma, UCEC: Uterine Corpus Endometrial Carcinoma, KICH: kidney chromophobe, KIRC: Kidney Renal Clear Cell Carcinoma, KIRP: Kidney Renal Papillary Cell Carcinoma, PCPG: Pheochromocytoma and Paraganglioma, THCA: Thyroid Carcinoma, UCS: Uterine Carcinosarcoma 3.2. Further evaluation of diagnostic value of candidate markers using clinical samples from GEO database Expression data from three datasets (GSE26712 [ 13 ], GSE6008 [ 14 ], and GSE14407 [ 15 ]) were retrieved from the GEO database. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of the four markers (SCGB2A1, CFAP126, FOXJ1, and DYDC2) in different datasets, and the Area under the ROC curve (AUC) was calculated (Table 1 ). SCGB2A1 had the most prominent diagnostic ability, with AUC higher than 0.9 in GSE26712 and GSE6008, but an AUC of 0.743 in GSE14407, showing moderate discriminative ability. FOXJ1 was detected in all three datasets, with AUC values of 0.897, 0.921, and 0.736. CFAP126 and DYDC2 were only detected in the GSE14407 dataset, with AUC values of 0.611 and 0.618, respectively, indicating weak diagnostic ability, and no expression data were available in the other two datasets. Table 1 Area under the ROC curve of SCGB2A1, CFAP126, FOXJ1, and DYDC2 in different datasets datasets GSE26712 GSE6008 GSE14407 Patients and controls SOC = 185 HC = 10 SOC = 41 HC = 4 SOC = 12 HC = 12 SCGB2A1 0.940 0.994 0.743 FOXJ1 0.897 0.921 0.736 CFAP126 N/A N/A 0.611 DYDC2 N/A N/A 0.618 3.3. Expression of SCGB2A1 and FOXJ1 in serous ovarian cancer in the HPA database Six serous ovarian cancer samples with SCGB2A1 immunohistochemical (IHC) staining results (antibody: HPA034584) were retrieved from the HPA database. Among the 6 samples, 3 were scored as "not detected" and 3 as "low," with staining localized to the cytoplasm/membrane. SCGB2A1 IHC staining in normal ovarian tissues was negative (Fig. 2 a). Seven serous ovarian cancer samples with FOXJ1 IHC staining results (antibody: HPA005714) were retrieved from the HPA database. Among the 7 samples, 4 were scored as "not detected," 1 as "low," and 2 as "medium," with staining localized to the nucleus. FOXJ1 IHC staining in normal ovarian tissues was negative (Fig. 2 b). Five cases of IHC staining results for CFAP126 in serous ovarian cancer (antibody: HPA045904) were retrieved from the HPA database. Among these, only one case showed a "low" staining result, while the rest were not detected. For DYDC2 (antibody: HPA038006), IHC staining results were obtained from seven cases of serous ovarian carcinoma. One case demonstrated "weak" staining, with no detectable staining observed in the remaining cases. The staining results of DYDC2 and CFAP126 in normal ovarian tissues were negative (Fig. 2 c,d). 3.4. Gene interaction network of SCGB2A1, FOXJ1, CFAP126, DYDC2 To better understand the interaction functions of SCGB2A1, FOXJ1, CFAP126, and DYDC2, the GeneMANIA online database was used to analyze their interaction network. Results showed that secretoglobin family members (SCGB1D1, SCGB2B2, SCGB2A2, SCGB3A2, SCGB1D2, SCGB1A1, SCGB1C1, SCGB1C2, SCGB1D4); adenylate kinase family member AK7; ciliary motility-related genes (EFCAB1, NME5, DYDC1, DNAH9); core component of the SET1/MLL histone methyltransferase complex DPY30; transcription factor NF1 family member NFIA; RFX transcription factor family member RFX2; testis-specific gene TSGA13; and potential liver cancer suppressor gene CCDC158 were closely related to SCGB2A1, FOXJ1, CFAP126, and DYDC2. The top 5 most correlated genes included SCGB1D1, NME5, DYDC1, DPY30, and RFX2. These genes are involved in functions such as lipid binding or transport, regulation of cellular energy metabolism, ciliary motility, histone methylation regulation, and transcription initiation regulation (Fig. 3 a). 3.5. Expression of SCGB2A1, FOXJ1, CFAP126, DYDC2 in different molecular subtypes of serous ovarian cancer Studies based on TCGA database have classified serous ovarian tumors into four molecular subtypes: differentiated, immunoreactive, mesenchymal, and proliferative subtype [ 28 ]. The differentiated subtype exhibits high expression of epithelial differentiation-related genes such as MUC16 and KRT7. The immunereactive subtype highly expresses immune-related genes accompanied by significant immune cell infiltration. The mesenchymal subtype shows upregulation of epithelial-mesenchymal transition (EMT) and angiogenesis-related genes, with a tumor microenvironment enriched in fibroblasts. The proliferative subtype highly expresses cell cycle and proliferation-related genes (e.g., MYC, cyclins) and may derive the greatest benefit from bevacizumab treatment [ 29 ]. Analysis using the TISIDB database showed that the 4 genes (SCGB2A1, FOXJ1, CFAP126, and DYDC2) had significantly different expression patterns among the four molecular subtypes, showing obvious heterogeneity ( P < 0.01), with higher expression levels in the differentiated and proliferative subtypes, especially SCGB2A1 and FOXJ1. This suggests that SCGB2A1 and FOXJ1 may be involved in maintaining cell differentiation status, promoting tumor cell differentiation in specific directions, and their increased expression may reflect relatively low invasiveness of tumor cells. CFAP126 and DYDC2 were significantly upregulated in the proliferative subtype, suggesting their potential involvement in key processes such as cell cycle regulation, DNA replication, and mitosis, with high expression possibly closely related to rapid proliferation of tumor cells (Fig. 3 b). 3.6. Correlation between expression of SCGB2A1, FOXJ1, CFAP126, DYDC2 and prognosis of serous ovarian cancer patients The Kaplan-Meier Plotter database was used to study the correlation between the expression of SCGB2A1, FOXJ1, CFAP126, DYDC2 and clinical outcomes in serous ovarian cancer patients. Results showed that high expression of these genes was associated with longer progression-free survival and overall survival in serous ovarian cancer patients (Fig. 4 ). The high expression of CFAP126 was associated with longer progression-free survival (PFS) (HR = 0.66, P < 0.001). Patients with high CFAP126 expression showed a trend toward longer overall survival (OS), but this difference was not statistically significant ( P = 0.057). The expression of FOXJ1 was associated with longer OS (HR = 0.77, P < 0.001). Patients with high FOXJ1 expression exhibited a trend toward longer PFS, yet the statistical difference was not significant ( P = 0.056). The high expressions of DYDC2 and SCGB2A1 predicted longer OS and PFS in patients, with DYDC2 showing a more prominent effect. 3.7. Profiling the Tumor Microenvironment and Functional Enrichment Analysis of SCGB2A1, FOXJ1, CFAP126, and DYDC2 The GSE130000 dataset [ 30 ] from the TISCH2 database was used to evaluate the expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2 in malignant cells. The cells in the dataset were divided into 5 main types: CD8Tex, fibroblasts, malignant, mono/macro and myofibroblasts, with malignant cells being the most numerous (n = 8876). All cells in the dataset were further divided into 17 clusters based on the expression levels of marker genes (Fig. 5 a). The cluster 0 consisted of malignant cells from serous ovarian cancer patients, in which SCGB2A1, FOXJ1, CFAP126, and DYDC2 were all highly expressed (Fig. 5 b). To understand the cellular biological functions and related pathways associated with high expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2, functional enrichment analysis of differentially expressed genes in cluster 0 was performed using the Metascape database. The most significant GO terms included antimicrobial humoral immune response mediated by antimicrobial peptides, positive regulation of cell motility, response to bacteria, cellular oxidant detoxification, response to nutrients, regulation of immune effector processes, negative regulation of intracellular signal transduction, regulation of leukocyte migration, and negative regulation of gliogenesis and cell development. Functional enrichment results revealed the potential roles of differentially expressed genes in cluster 0 in physiological and pathological processes, suggesting that these genes may play important roles in immune surveillance and response, as well as malignant progression (e.g., invasion, metastasis, microenvironmental adaptation, and potential immune regulation/escape) (Fig. 5 c). Subsequently, the online analysis function of the DAVID database was used to analyze KEGG pathways enriched in differentially expressed genes in cluster 0, exploring enriched biological processes in malignant cells with high expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2 in serous ovarian cancer. The main biological processes involved viral myocarditis, p53 signaling pathway, amebiasis, AGE-RAGE signaling pathway in diabetic complications, leukocyte transendothelial migration, focal adhesion, tight junctions, proteoglycans in cancer, viral carcinogenesis, and human papillomavirus infection pathways. Most of these pathways are related to immune response, cell adhesion, and signal transduction, indicating that high expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2 may be associated with enhanced immune regulation, cell migration and invasion ability in serous ovarian cancer, and these markers may be involved in the occurrence and development of ovarian cancer. In particular, the involvement of the p53 signaling pathway suggests that these genes may regulate ovarian cancer progression by affecting tumor cell apoptosis and proliferation (Fig. 5 d). 4. Discussion In this study, we explored the expression of fallopian tube ciliated epithelial cell markers in serous ovarian tumors and their potential as diagnostic and prognostic markers for serous ovarian cancer. Through a series of bioinformatics analyses, we found that some genes specifically expressed in normal fallopian tube ciliated epithelial cells were upregulated in serous ovarian tumors. We focused on four genes: SCGB2A1, FOXJ1, CFAP126, and DYDC2. These genes were expressed at low levels in normal ovarian tissues but significantly increased in serous ovarian cancer. These results are consistent with previous studies, indicating that the aforementioned genes played a role in the pathogenesis of serous ovarian cancer, and their upregulation in tumor tissues supported their potential as diagnostic biomarkers [ 31 – 33 ]. The ROC curve analysis showed that SCGB2A1 and FOXJ1 can well distinguish between healthy controls and tumor patients, with AUC higher than 0.7 in three datasets. The diagnostic performance of CFAP126 and DYDC2 was slightly lower than that of SCGB2A1 and FOXJ1, and they were not detected in datasets GSE26712 and GSE6008. These discrepancies may be attributed to tumor heterogeneity or differences in detection platforms. Further validation of the diagnostic efficacy of these biomarkers should be conducted in large, independent clinical patient cohorts. The immunohistochemical (IHC) staining results from the HPA database confirmed the high expression of SCGB2A1 and FOXJ1 in serous ovarian tumors. Future studies should include more serous ovarian tumor tissues to validate the expression of CFAP126 and DYDC2. Survival analysis using the Kaplan-Meier Plotter database found that the high expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2 was associated with favorable prognosis in patients. Prior investigations established the prognostic value of SCGB2A1 and FOXJ1 for serous ovarian cancer, the prognostic predictive value of CFAP126 and DYDC2 remained further study [ 8 , 34 ]. Notably, SCGB2A1, FOXJ1, CFAP126, and DYDC2 showed significant differences in expression levels among different molecular subtypes of serous ovarian cancer, providing a new perspective for understanding ovarian cancer heterogeneity. The high expression of SCGB2A1 and FOXJ1 in the differentiated subtype may indicate that tumor cells retain differentiated characteristics and a relatively stable phenotype, consistent with the generally lower invasiveness and metastatic potential of the differentiated subtype [ 35 ]. Thus, SCGB2A1 and FOXJ1 may serve as auxiliary biomarkers to distinguish the low-invasive differentiated subtype, helping to identify patient groups with relatively favorable prognosis. CFAP126 and DYDC2 were significantly upregulated in the proliferative subtype, suggesting their potential involvement in key processes such as cell cycle regulation, DNA replication, and mitosis, with high expression possibly closely related to rapid proliferation of tumor cells. Gene interaction network analysis revealed close connections between SCGB2A1, FOXJ1, CFAP126, DYDC2 and other functionally related genes. The functions of these genes (e.g., lipid binding or transport, regulation of cellular energy metabolism, ciliary motility, and histone methylation regulation) played important roles in tumor progression. SCGB2A1 gene (Secretoglobin Family 2A Member 1), also known as Mammaglobin 1, belongs to the secretoglobin superfamily. SCGB2A1 is one of the breast cancer-related biomarkers [ 36 ] and a marker of chemotherapy and radiotherapy resistance in colorectal cancer [ 37 ]. In ovarian cancer, SCGB2A1 is a novel tumor-associated antigen that can be detected in serous ovarian cancer and other histological subtypes, with significant differences in expression levels among subtypes [ 38 ]. FOXJ1 gene (Forkhead Box Protein J1) belongs to the forkhead box transcription factor family and plays a key role in cilia formation, immune response regulation, and normal development of various tissues. The role of FOXJ1 in different tumors is complex: studies have shown that overexpression of FOXJ1 weakens the proliferation, migration, and invasion of gastric cancer cells [ 39 ], while other studies indicate that FOXJ1 promotes the progression of laryngeal squamous cell carcinoma [ 40 ], which may be related to tumor type and microenvironment. The current literature on CFAP126 and DYDC2 remains limited. CFAP126 gene (Cilia-Associated Protein 126) is closely related to ciliary structure and function. Its encoded protein is mainly localized in the axoneme of cilia and may be involved in axoneme assembly or stability maintenance [ 41 ]. DYDC2 gene (DPY30 Domain Containing 2) encodes the member of the protein family containing the DPY30 domain. The high expression of DYDC2 in endometrial cancer is associated with improved patient survival [ 42 ]. Analysis of single-cell transcriptomes in the tumor microenvironment found that SCGB2A1, FOXJ1, CFAP126, and DYDC2 were upregulated in specific malignant cell clusters, further illustrating the heterogeneity of the serous ovarian cancer microenvironment. GO and KEGG enrichment analyses of expressed genes in this malignant cell cluster showed that these cells may be involved in regulating the body's immune response, thereby affecting the recognition and killing of tumor cells by immune cells [ 43 ]. Enriched KEGG pathways are mostly related to signal transduction and immune response, suggesting that these pathways play important roles in tumor cell proliferation, migration, invasion, angiogenesis, and immune escape [ 44 ]. Enrichment of pathways seemingly weakly related to ovarian cancer (e.g., viral myocarditis, amebiasis) may reflect shared molecular mechanisms between these pathways and malignant cell behaviors [ 45 ]. More in vitro experiments are required to elucidate the specific mechanisms of action of these genes in the aforementioned biological processes and pathways. This study had several limitations. First, although several biomarkers and pathways potentially associated with ovarian cancer were identified through bioinformatics analyses, these findings were primarily derived from existing public datasets and were needed to be validated in independent clinical samples. Second, the specific roles and regulatory networks of these biomarkers in biological processes and pathways required further experimental investigation to elucidate. Additionally, the present study mainly focused on genomic alterations, while changes at the protein or epigenetic levels were less extensively explored, which represents a potential direction for future research. This study comprehensively analyzed multiple databases to screen four molecules from fallopian tube ciliated epithelial cell markers that can serve as diagnostic and prognostic markers for serous ovarian cancer, verified their diagnostic function in independent datasets, and preliminarily explored their expression characteristics and potential involvement in biological processes and signaling pathways in microenvironment. Their expression patterns in serous ovarian cancer and interactions with other genes provide new insights into understanding the pathogenesis of ovarian cancer. 5. Conclusions The fallopian tube ciliated epithelial cell markers SCGB2A1, FOXJ1, CFAP126, and DYDC2 can be used for the diagnosis of serous ovarian cancer, and their high expression in tumor tissues is associated with longer survival in patients. These four markers may be implicated in the occurrence and development of serous ovarian cancer. Declarations Funding This work was supported by the National Natural Science Foundation of China (Grant No. 82472358), the Natural Science Foundation of Beijing-Daxing Innovation Joint Fund, China (Grant No. L256079) and National High Level Hospital Clinical Research, Science and Technology Achievement Transformation Incubation Guiding Fund, Peking University First Hospital (Grant No. 2024CX13). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contribution Jingchen Zhao analyzed the data, prepared figures and/or tables, authored and reviewed manuscript of the article. Mengyan He and Ying Zhang analyzed the data, prepared figures and/or tables. Zhiyan Li provided guidance to the manuscript preparation, and revised the manuscript. Dong Xu and Shurui Kang revised the manuscript. Haixia Li provided guidance to the manuscript preparation, reviewed manuscript of the article. Each author had read the complete manuscript, and all authors approved submission of the paper. Data Availability The datasets utilized for this study are publicly available and archived in the following online repositories. Direct web links to datasets:CZ CELLxGENE: https://cellxgene.cziscience.com/GEPIA: http://gepia2.cancer-pku.cn/#index/HPA: https://www.proteinatlas.orgKaplan–Meier Plotter: https://kmplot.com/analysis/GEO: https://www.ncbi.nlm.nih.gov/geo/GeneMANIA: http://genemania.org/TISCH2: http://tisch.compbio.cn/home/TISIDB: http://cis.hku.hk/TISIDB/Metascape: http://metascape.orgDAVID: https://davidbioinformatics.nih.gov/ References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229–263. doi: 10.3322/caac.21834. Epub 2024 Apr 4. PMID: 38572751. Lheureux S, Gourley C, Vergote I, Oza AM. 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Mammaglobin B (SCGB2A1) is a novel tumour antigen highly differentially expressed in all major histological types of ovarian cancer: implications for ovarian cancer immunotherapy. Br J Cancer. 2013;109(2):462–71. Epub 2013 Jun 27. PMID: 23807163; PMCID: PMC3721400. Wang J, Cai X, Xia L, Zhou J, Xin J, Liu M, et al. Decreased expression of FOXJ1 is a potential prognostic predictor for progression and poor survival of gastric cancer. Ann Surg Oncol. 2015;22(2):685–92. 10.1245/s10434-014-3742-2 . Epub 2014 May 9. PMID: 24809300. Liu L, Zhang P, Shao Y, Quan F, Li H. Knockdown of FOXJ1 inhibits the proliferation, migration, invasion, and glycolysis in laryngeal squamous cell carcinoma cells. J Cell Biochem. 2019;120(9):15874–82. 10.1002/jcb.28858 . Epub 2019 May 6. PMID: 31062413. Gegg M, Böttcher A, Burtscher I, Hasenoeder S, Van Campenhout C, Aichler M, et al. Flattop regulates basal body docking and positioning in mono- and multiciliated cells. Elife. 2014;3:e03842. 10.7554/eLife.03842 . PMID: 25296022; PMCID: PMC4221739. Cochrane DR, Campbell KR, Greening K, Ho GC, Hopkins J, Bui M, et al. Single cell transcriptomes of normal endometrial derived organoids uncover novel cell type markers and cryptic differentiation of primary tumours. J Pathol. 2020;252(2):201–14. 10.1002/path.5511 . Epub 2020 Aug 22. PMID: 32686114. Agudo J, Miao Y. Stemness in solid malignancies: coping with immune attack. Nat Rev Cancer. 2025;25(1):27–40. 10.1038/s41568-024-00760-0 . Epub 2024 Oct 25. PMID: 39455862. Azambuja JH, Ludwig N, Braganhol E, Whiteside TL. Inhibition of the Adenosinergic Pathway in Cancer Rejuvenates Innate and Adaptive Immunity. Int J Mol Sci. 2019;20(22):5698. 10.3390/ijms20225698 . PMID: 31739402; PMCID: PMC6888217. Hazari V, Samali SA, Izadpanahi P, Mollaei H, Sadri F, Rezaei Z. MicroRNA-98: the multifaceted regulator in human cancer progression and therapy. Cancer Cell Int. 2024;24(1):209. 10.1186/s12935-024-03386-2 . PMID: 38872210; PMCID: PMC11177407. Additional Declarations No competing interests reported. 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-8695930","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599172445,"identity":"bf73adb2-9729-40d0-b5fb-6a2fdf914db5","order_by":0,"name":"Jingchen Zhao","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingchen","middleName":"","lastName":"Zhao","suffix":""},{"id":599172446,"identity":"1c33aef2-7d11-478e-8646-e1d467050359","order_by":1,"name":"Mengyan He","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mengyan","middleName":"","lastName":"He","suffix":""},{"id":599172447,"identity":"38a84b0c-ff74-4727-b8c7-0bece026f723","order_by":2,"name":"Ying Zhang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhang","suffix":""},{"id":599172449,"identity":"7cc53431-d33d-4fdb-be39-d02482b86b24","order_by":3,"name":"Zhiyan Li","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiyan","middleName":"","lastName":"Li","suffix":""},{"id":599172451,"identity":"fa07ccad-8629-4e71-976e-d9fef8c534d8","order_by":4,"name":"Dong Xu","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Xu","suffix":""},{"id":599172453,"identity":"6d8bd4fc-6bed-41cb-857f-4be52b28a29e","order_by":5,"name":"Shurui Kang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shurui","middleName":"","lastName":"Kang","suffix":""},{"id":599172455,"identity":"79d37aa4-c93b-4e52-bc41-c8510a0740ad","order_by":6,"name":"Haixia Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYBACA4YEBoaHDQxybOzNB0jQktjAYMzHcyyBNC2J8yRyFIjTYs6eYyaRuONwehtDDgPDj4pthLVY9rwBajmTltvGcPYAY8+Z20Q47AbIljab3DbGvgRmxjbitUikszHzGJCkxSaBjY1YLZY9z4otEtvSDNt42BIOEuUXc/bkjTc+th2Wl5//+OCDHxVEaGFg4DCRgDEPEKMeCNgffyBS5SgYBaNgFIxUAADyNTyfm4bwWwAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":true,"prefix":"","firstName":"Haixia","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-01-26 03:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8695930/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8695930/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104175612,"identity":"fe6eddbb-7881-405d-b9f8-184964d8ad3f","added_by":"auto","created_at":"2026-03-08 16:31:04","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148126,"visible":true,"origin":"","legend":"\u003cp\u003eExpression level of candidate biomarkers at pan-cancer level. \u003cstrong\u003e(a)\u003c/strong\u003e SMIM22 \u003cstrong\u003e(b)\u003c/strong\u003e CD24 \u003cstrong\u003e\u0026nbsp;(c)\u003c/strong\u003e SLPI \u003cstrong\u003e(d)\u003c/strong\u003e LDLRAD1 \u003cstrong\u003e(e) \u003c/strong\u003eSCGB2A1 \u003cstrong\u003e(f)\u003c/strong\u003e CFAP126 \u003cstrong\u003e(g) \u003c/strong\u003eFOXJ1 \u003cstrong\u003e(h) \u003c/strong\u003eDYDC2\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8695930/v1/4ea24b15942b2dd4adcd5cff.jpeg"},{"id":104175614,"identity":"abe89cd5-a479-4455-976f-5a50532bbeae","added_by":"auto","created_at":"2026-03-08 16:31:04","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116939,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Expression of SCGB2A1 in normal ovarian tissues and serous ovarian cancer. (b) Expression of FOXJ1 in normal ovarian tissues and serous ovarian cancer. (c) Expression of CFAP126 in normal ovarian tissues and serous ovarian cancer. (d) Expression of DYDC2 in normal ovarian tissues and serous ovarian cancer\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8695930/v1/304d4a432a489c0bbb20497b.jpeg"},{"id":104403748,"identity":"e08ee3c9-a519-4665-a873-901380991bcc","added_by":"auto","created_at":"2026-03-11 12:18:58","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99508,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Gene interaction network of SCGB2A1, FOXJ1, CFAP126, and DYDC2. (b) Expression levels of SCGB2A1, FOXJ1, CFAP126, and DYDC2 across different molecular subtypes of serous ovarian cancer\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8695930/v1/ec7dee1ce7973ab03c45db57.jpeg"},{"id":104175615,"identity":"634b8716-69f2-4bae-8d9d-dfe77172148b","added_by":"auto","created_at":"2026-03-08 16:31:04","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":101258,"visible":true,"origin":"","legend":"\u003cp\u003eHigh expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2 is associated with longer progression-free survival and overall survival in patients with serous ovarian cancer\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8695930/v1/557df351a20bc1a7b0e75554.jpeg"},{"id":104175617,"identity":"9ac06488-b6a7-46b1-a596-7aa885671676","added_by":"auto","created_at":"2026-03-08 16:31:04","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":202503,"visible":true,"origin":"","legend":"\u003cp\u003eExploring the mechanism of SCGB2A1, FOXJ1, CFAP126, and DYDC2 in serous ovarian cancer by scRNA-seq analysis. (a) Cell type distribution in GSE130000 dataset. 17 clusters were represented visually through UMAP algorithm. (b) Single-cell expression levels of SCGB2A1, FOXJ1, CFAP126, and DYDC2 in GSE130000 dataset. (c) Gene Ontology (GO) functional enrichment terms for differentially expressed genes in cluster 0. (d) KEGG pathways associated with high expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2 in serous ovarian cancer\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8695930/v1/f20a4f8024abd4b00f6a2519.jpeg"},{"id":105359185,"identity":"19173b39-8ee6-4de6-95ef-46af2cad7673","added_by":"auto","created_at":"2026-03-25 07:27:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1634235,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8695930/v1/978ab357-b765-47fd-97b6-6d8caa3d9f0c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic and Prognostic Significance of Fallopian Tube Ciliated Epithelial Cell Markers in Serous Ovarian Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOvarian cancer is one of the most lethal gynecological malignancies. According to the latest statistical data from the International Agency for Research on Cancer (IARC), there were 324,398 new cases and 206,839 deaths worldwide in 2022 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Epithelial Ovarian Cancer (EOC) accounts for more than 90% of all ovarian malignancies and can be histologically classified into several subtypes, including serous, endometrioid, mucinous, clear cell, and other rare histological subtypes, serous ovarian cancer (SOC) accounts for more than 75% of epithelial ovarian cancers [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Due to the lack of specific early symptoms and effective screening methods, most patients are diagnosed at an advanced stage, which severely affects patient prognosis. Therefore, in-depth exploration of the pathogenesis of ovarian cancer and identification of new diagnostic and prognostic markers are particularly important.\u003c/p\u003e \u003cp\u003ePreviously, it was believed that epithelial ovarian cancer originated from ovarian surface epithelial cells. However, with in-depth research, it has been found that different subtypes of EOC may originate from different cell types. In recent years, increasing evidence has indicated that the occurrence and development of serous ovarian cancer are closely related to genes expressed in ciliated epithelial cells [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The distinctive histological architecture of this region, characterized by a high density of ciliated epithelial cells, enables efficient ovum capture through precisely coordinated ciliary movement. A recent study has identified transitional preciliated cells in the mouse fallopian tube as a cancer-prone cell state [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCiliated cell markers possibly serve as valuable indicators for disease progression and patient outcomes of serous ovarian cancer. Using immunohistochemistry and tissue microarray analysis, Richardson MT et al. observed that higher tumor grade in serous ovarian carcinoma correlates with a decreased proportion of cells expressing ciliated cell markers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Weir A et al. found that the expression of the tubal ciliated cell marker FOXJ1 was strongly correlated with survival in patients with HGSOC, while patients with high expression tended to have longer overall survival [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thus, fallopian tube ciliated cell-specific markers demonstrate significant potential as diagnostic and prognostic biomarkers for serous ovarian cancer. The specific mechanism of the action of fallopian tube ciliated epithelial cell markers in serous ovarian cancer remains to be further explored.\u003c/p\u003e \u003cp\u003eTherefore, we screened out the markers of fallopian tube ciliated epithelial cells based on the single-cell database CZ CELLxGENE, examined their expression levels in normal ovarian and serous cancer tissues using the GEPIA database, and further validated their protein expression levels through the HPA database. We focused on the SCGB2A1, FOXJ1, CFAP126, and DYDC2 genes, which exhibited significantly elevated expression in serous ovarian cancer tissues and demonstrated high cancer specificity. Kaplan-Meier analysis revealed that increased expression of these genes was significantly associated with prolonged progression-free survival (PFS) and overall survival (OS) in patients with serous cancer. Through integrated analysis utilizing TISCH2, TISIDB, and Metascape databases, we evaluated the expression levels of these genes across distinct molecular subtypes of ovarian cancer, and performed functional enrichment analysis in the tumor microenvironment, to initially explore the roles of the above genes in tumorigenesis and progression. These findings contribute to a deeper understanding of the pathogenic mechanisms of serous ovarian cancer, offering new targets and ideas for its early diagnosis and treatment.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Preliminary Screening of Candidate Biomarkers\u003c/h2\u003e \u003cp\u003eIn the \u0026ldquo;Gene Expression\u0026rdquo; module of single-cell database CZ CELLxGENE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cellxgene.cziscience.com\u003c/span\u003e\u003cspan address=\"https://cellxgene.cziscience.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], we chose the cell type \u0026ldquo;ciliated epithelial cell\u0026rdquo; in tissue \u0026ldquo;fallopian tube\u0026rdquo; to obtain the top 100 specifically expressed marker genes in normal fallopian tube ciliated epithelial cells. The module \u0026ldquo;Expression Analysis\u0026rdquo; of the GEPIA (Gene Expression Profiling Interactive Analysis, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/#index/\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/#index/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] database was used to explore the expression level of candidate genes in tumor and normal tissues at pancancer level. The expression levels of candidate biomarkers in serous ovarian cancer tissues and normal tissues were validated using the HPA (the Human Protein Atlas) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The candidate genes was searched respectively to access protein expression data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Assessment of the diagnostic performance of candidate markers based on GEO datasets\u003c/h2\u003e \u003cp\u003eBased on the GEO (Gene Expression Omnibus) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) of the National Center for Biotechnology Information (NCBI) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the expression microarray datasets of primary ovarian tumors and normal tissues were retrieved. Using the keywords \"ovarian cancer\" and \"expression\" with the search condition set to Homo sapiens, three datasets (GSE26712 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], GSE6008 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and GSE14407 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]) were retrieved from the GEO database.\u003c/p\u003e \u003cp\u003eROC curves of candidate biomarkers were plotted in each dataset using the pROC package (version 1.18.5) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] in R language (version 4.4.3) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The area under the ROC curve (AUC) was calculated to evaluate the diagnostic performance of the candidate biomarkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. The interaction network and prognostic significance of candidate biomarkers in serous ovarian cancer patients\u003c/h2\u003e \u003cp\u003eUtilizing the Kaplan-Meier Plotter database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kmplot.com/analysis/\u003c/span\u003e\u003cspan address=\"https://kmplot.com/analysis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], we assessed the association between candidate biomarkers and clinical outcomes of serous ovarian cancer patients. The search criteria were defined as \u0026ldquo;histology: serous\u0026rdquo;. The gene interaction network of candidate biomarkers was constructed using the GeneMANIA (Gene Multiple Association Network Integration Algorithm) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org/\u003c/span\u003e\u003cspan address=\"http://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Expression patterns of biomarkers in distinct molecular subtypes of serous ovarian cancer\u003c/h2\u003e \u003cp\u003eWe investigated the expression profiles of candidate biomarkers across different subtypes utilizing the TISIDB (Tumor Immune System Interaction Database) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB/\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Inter-subtype differential expression of these biomarkers was assessed using the Kruskal-Wallis test, with statistical significance defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Tumor microenvironment and functional enrichment analysis\u003c/h2\u003e \u003cp\u003eWe searched for ovarian cancer-related databases in the \"Dataset\" module of the TISCH2 (Tumor Immune Single-cell Hub 2) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.compbio.cn/home/\u003c/span\u003e\u003cspan address=\"http://tisch.compbio.cn/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. According to the different expression levels of genes in malignant cells, they were divided into different clusters. Then the cluster with high expression of fallopian tube ciliated epithelial cell markers was selected for analysis, and the differentially expressed gene data of this cluster was downloaded from the TISCH2 database. The online database Metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metascape.org\u003c/span\u003e\u003cspan address=\"http://metascape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was used to perform Gene Ontology (GO) analysis on the differentially expressed genes, as well as enrichment analysis and functional annotation of these differential genes. After the gene list was input, the analysis objective was set as \u0026ldquo;GO Molecular Functions\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;Functional Annotation\u0026rdquo; module of the DAVID (Database for Annotation, Visualization and Integrated Discovery) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://davidbioinformatics.nih.gov/\u003c/span\u003e\u003cspan address=\"https://davidbioinformatics.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was applied to analyze the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways of the differential genes, and the functional annotation chart was downloaded. R package ggplot2 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] was used for the visualization of the analysis results, we plotted bubble charts of the top 10 most significant pathways.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Preliminary screening of diagnostic markers for serous ovarian cancer\u003c/h2\u003e \u003cp\u003eOne hundred highly specific genes expressed in human fallopian tube ciliated epithelial cells were obtained from the CZ CELLxGENE database [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. By analyzing mRNA levels in 426 serous ovarian tumor tissues and 88 corresponding normal ovarian tissues in the GEPIA database, 27 genes with significantly increased expression in tumor tissues (fold change of TPM[Transcripts Per Million]\u0026thinsp;\u0026gt;\u0026thinsp;3, two-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were selected. Analysis of the HPA database showed that proteins encoded by 8 of these candidate genes were significantly upregulated in serous ovarian cancer, namely SMIM22 (Small Integral Membrane Protein 22), CD24, SLPI (Secretory Leukocyte Peptidase Inhibitor), LDLRAD1 (Low Density Lipoprotein Receptor Class A Domain Containing 1), SCGB2A1 (Secretoglobin Family 2A Member 1), CFAP126 (Cilia And Flagella Associated Protein 126), FOXJ1 (Forkhead Box J1), and DYDC2 (DPY30 Domain Containing 2).\u003c/p\u003e \u003cp\u003eAmong these, 4 genes showed higher specificity for serous ovarian cancer, namely SCGB2A1, CFAP126, FOXJ1, and DYDC2. SCGB2A1 was highly expressed only in ovarian cancer and UCEC, with low levels in other tumors and normal tissues. CFAP126 was most highly expressed in normal testicular germ cells and upregulated in tumor tissues of GBM, LGG, and UCEC. FOXJ1 was most highly expressed in normal testicular germ cells and UCEC tumor tissues, followed by ovarian tumor tissues, and also upregulated in GBM, LUAD, and UCS. In addition to ovarian cancer, DYDC2 was upregulated in UCEC and UCS tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, the genes SCGB2A1, CFAP126, FOXJ1, and DYDC2 were included in subsequent studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eACC: adrenocortical carcinoma, BLCA: Bladder Urothelial Carcinoma, BRCA: Breast Invasive Carcinoma, CESC: Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma, CHOL: Cholangiocarcinoma, COAD: Colon Adenocarcinoma, ESCA: Esophageal Carcinoma, HNSC: head and neck squamous cell carcinoma, LIHC: liver hepatocellular carcinoma, LUAD: Lung Adenocarcinoma, LUSC: lung squamous cell carcinoma, PAAD: Pancreatic Adenocarcinoma, PRAD: Prostate Adenocarcinoma, READ: Rectal Adenocarcinoma, STAD: Stomach Adenocarcinoma, UCEC: Uterine Corpus Endometrial Carcinoma, KICH: kidney chromophobe, KIRC: Kidney Renal Clear Cell Carcinoma, KIRP: Kidney Renal Papillary Cell Carcinoma, PCPG: Pheochromocytoma and Paraganglioma, THCA: Thyroid Carcinoma, UCS: Uterine Carcinosarcoma\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Further evaluation of diagnostic value of candidate markers using clinical samples from GEO database\u003c/h2\u003e \u003cp\u003eExpression data from three datasets (GSE26712 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], GSE6008 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and GSE14407 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]) were retrieved from the GEO database. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of the four markers (SCGB2A1, CFAP126, FOXJ1, and DYDC2) in different datasets, and the Area under the ROC curve (AUC) was calculated (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSCGB2A1 had the most prominent diagnostic ability, with AUC higher than 0.9 in GSE26712 and GSE6008, but an AUC of 0.743 in GSE14407, showing moderate discriminative ability. FOXJ1 was detected in all three datasets, with AUC values of 0.897, 0.921, and 0.736. CFAP126 and DYDC2 were only detected in the GSE14407 dataset, with AUC values of 0.611 and 0.618, respectively, indicating weak diagnostic ability, and no expression data were available in the other two datasets.\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\u003eArea under the ROC curve of SCGB2A1, CFAP126, FOXJ1, and DYDC2 in different datasets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003edatasets\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE26712\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSE6008\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGSE14407\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients and controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOC\u0026thinsp;=\u0026thinsp;185\u003c/p\u003e \u003cp\u003eHC\u0026thinsp;=\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOC\u0026thinsp;=\u0026thinsp;41\u003c/p\u003e \u003cp\u003eHC\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSOC\u0026thinsp;=\u0026thinsp;12\u003c/p\u003e \u003cp\u003eHC\u0026thinsp;=\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCGB2A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOXJ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFAP126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDYDC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.618\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 \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Expression of SCGB2A1 and FOXJ1 in serous ovarian cancer in the HPA database\u003c/h2\u003e \u003cp\u003eSix serous ovarian cancer samples with SCGB2A1 immunohistochemical (IHC) staining results (antibody: HPA034584) were retrieved from the HPA database. Among the 6 samples, 3 were scored as \"not detected\" and 3 as \"low,\" with staining localized to the cytoplasm/membrane. SCGB2A1 IHC staining in normal ovarian tissues was negative (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eSeven serous ovarian cancer samples with FOXJ1 IHC staining results (antibody: HPA005714) were retrieved from the HPA database. Among the 7 samples, 4 were scored as \"not detected,\" 1 as \"low,\" and 2 as \"medium,\" with staining localized to the nucleus. FOXJ1 IHC staining in normal ovarian tissues was negative (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eFive cases of IHC staining results for CFAP126 in serous ovarian cancer (antibody: HPA045904) were retrieved from the HPA database. Among these, only one case showed a \"low\" staining result, while the rest were not detected. For DYDC2 (antibody: HPA038006), IHC staining results were obtained from seven cases of serous ovarian carcinoma. One case demonstrated \"weak\" staining, with no detectable staining observed in the remaining cases. The staining results of DYDC2 and CFAP126 in normal ovarian tissues were negative (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec,d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Gene interaction network of SCGB2A1, FOXJ1, CFAP126, DYDC2\u003c/h2\u003e \u003cp\u003eTo better understand the interaction functions of SCGB2A1, FOXJ1, CFAP126, and DYDC2, the GeneMANIA online database was used to analyze their interaction network. Results showed that secretoglobin family members (SCGB1D1, SCGB2B2, SCGB2A2, SCGB3A2, SCGB1D2, SCGB1A1, SCGB1C1, SCGB1C2, SCGB1D4); adenylate kinase family member AK7; ciliary motility-related genes (EFCAB1, NME5, DYDC1, DNAH9); core component of the SET1/MLL histone methyltransferase complex DPY30; transcription factor NF1 family member NFIA; RFX transcription factor family member RFX2; testis-specific gene TSGA13; and potential liver cancer suppressor gene CCDC158 were closely related to SCGB2A1, FOXJ1, CFAP126, and DYDC2. The top 5 most correlated genes included SCGB1D1, NME5, DYDC1, DPY30, and RFX2. These genes are involved in functions such as lipid binding or transport, regulation of cellular energy metabolism, ciliary motility, histone methylation regulation, and transcription initiation regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Expression of SCGB2A1, FOXJ1, CFAP126, DYDC2 in different molecular subtypes of serous ovarian cancer\u003c/h2\u003e \u003cp\u003eStudies based on TCGA database have classified serous ovarian tumors into four molecular subtypes: differentiated, immunoreactive, mesenchymal, and proliferative subtype [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The differentiated subtype exhibits high expression of epithelial differentiation-related genes such as MUC16 and KRT7. The immunereactive subtype highly expresses immune-related genes accompanied by significant immune cell infiltration. The mesenchymal subtype shows upregulation of epithelial-mesenchymal transition (EMT) and angiogenesis-related genes, with a tumor microenvironment enriched in fibroblasts. The proliferative subtype highly expresses cell cycle and proliferation-related genes (e.g., MYC, cyclins) and may derive the greatest benefit from bevacizumab treatment [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnalysis using the TISIDB database showed that the 4 genes (SCGB2A1, FOXJ1, CFAP126, and DYDC2) had significantly different expression patterns among the four molecular subtypes, showing obvious heterogeneity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with higher expression levels in the differentiated and proliferative subtypes, especially SCGB2A1 and FOXJ1. This suggests that SCGB2A1 and FOXJ1 may be involved in maintaining cell differentiation status, promoting tumor cell differentiation in specific directions, and their increased expression may reflect relatively low invasiveness of tumor cells. CFAP126 and DYDC2 were significantly upregulated in the proliferative subtype, suggesting their potential involvement in key processes such as cell cycle regulation, DNA replication, and mitosis, with high expression possibly closely related to rapid proliferation of tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Correlation between expression of SCGB2A1, FOXJ1, CFAP126, DYDC2 and prognosis of serous ovarian cancer patients\u003c/h2\u003e \u003cp\u003eThe Kaplan-Meier Plotter database was used to study the correlation between the expression of SCGB2A1, FOXJ1, CFAP126, DYDC2 and clinical outcomes in serous ovarian cancer patients. Results showed that high expression of these genes was associated with longer progression-free survival and overall survival in serous ovarian cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The high expression of CFAP126 was associated with longer progression-free survival (PFS) (HR\u0026thinsp;=\u0026thinsp;0.66, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with high CFAP126 expression showed a trend toward longer overall survival (OS), but this difference was not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057). The expression of FOXJ1 was associated with longer OS (HR\u0026thinsp;=\u0026thinsp;0.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with high FOXJ1 expression exhibited a trend toward longer PFS, yet the statistical difference was not significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.056). The high expressions of DYDC2 and SCGB2A1 predicted longer OS and PFS in patients, with DYDC2 showing a more prominent effect.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Profiling the Tumor Microenvironment and Functional Enrichment Analysis of SCGB2A1, FOXJ1, CFAP126, and DYDC2\u003c/h2\u003e \u003cp\u003eThe GSE130000 dataset [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] from the TISCH2 database was used to evaluate the expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2 in malignant cells. The cells in the dataset were divided into 5 main types: CD8Tex, fibroblasts, malignant, mono/macro and myofibroblasts, with malignant cells being the most numerous (n\u0026thinsp;=\u0026thinsp;8876). All cells in the dataset were further divided into 17 clusters based on the expression levels of marker genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The cluster 0 consisted of malignant cells from serous ovarian cancer patients, in which SCGB2A1, FOXJ1, CFAP126, and DYDC2 were all highly expressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eTo understand the cellular biological functions and related pathways associated with high expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2, functional enrichment analysis of differentially expressed genes in cluster 0 was performed using the Metascape database. The most significant GO terms included antimicrobial humoral immune response mediated by antimicrobial peptides, positive regulation of cell motility, response to bacteria, cellular oxidant detoxification, response to nutrients, regulation of immune effector processes, negative regulation of intracellular signal transduction, regulation of leukocyte migration, and negative regulation of gliogenesis and cell development. Functional enrichment results revealed the potential roles of differentially expressed genes in cluster 0 in physiological and pathological processes, suggesting that these genes may play important roles in immune surveillance and response, as well as malignant progression (e.g., invasion, metastasis, microenvironmental adaptation, and potential immune regulation/escape) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eSubsequently, the online analysis function of the DAVID database was used to analyze KEGG pathways enriched in differentially expressed genes in cluster 0, exploring enriched biological processes in malignant cells with high expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2 in serous ovarian cancer. The main biological processes involved viral myocarditis, p53 signaling pathway, amebiasis, AGE-RAGE signaling pathway in diabetic complications, leukocyte transendothelial migration, focal adhesion, tight junctions, proteoglycans in cancer, viral carcinogenesis, and human papillomavirus infection pathways. Most of these pathways are related to immune response, cell adhesion, and signal transduction, indicating that high expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2 may be associated with enhanced immune regulation, cell migration and invasion ability in serous ovarian cancer, and these markers may be involved in the occurrence and development of ovarian cancer. In particular, the involvement of the p53 signaling pathway suggests that these genes may regulate ovarian cancer progression by affecting tumor cell apoptosis and proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we explored the expression of fallopian tube ciliated epithelial cell markers in serous ovarian tumors and their potential as diagnostic and prognostic markers for serous ovarian cancer. Through a series of bioinformatics analyses, we found that some genes specifically expressed in normal fallopian tube ciliated epithelial cells were upregulated in serous ovarian tumors. We focused on four genes: SCGB2A1, FOXJ1, CFAP126, and DYDC2. These genes were expressed at low levels in normal ovarian tissues but significantly increased in serous ovarian cancer. These results are consistent with previous studies, indicating that the aforementioned genes played a role in the pathogenesis of serous ovarian cancer, and their upregulation in tumor tissues supported their potential as diagnostic biomarkers [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe ROC curve analysis showed that SCGB2A1 and FOXJ1 can well distinguish between healthy controls and tumor patients, with AUC higher than 0.7 in three datasets. The diagnostic performance of CFAP126 and DYDC2 was slightly lower than that of SCGB2A1 and FOXJ1, and they were not detected in datasets GSE26712 and GSE6008. These discrepancies may be attributed to tumor heterogeneity or differences in detection platforms. Further validation of the diagnostic efficacy of these biomarkers should be conducted in large, independent clinical patient cohorts. The immunohistochemical (IHC) staining results from the HPA database confirmed the high expression of SCGB2A1 and FOXJ1 in serous ovarian tumors. Future studies should include more serous ovarian tumor tissues to validate the expression of CFAP126 and DYDC2.\u003c/p\u003e \u003cp\u003eSurvival analysis using the Kaplan-Meier Plotter database found that the high expression of SCGB2A1, FOXJ1, CFAP126, and DYDC2 was associated with favorable prognosis in patients. Prior investigations established the prognostic value of SCGB2A1 and FOXJ1 for serous ovarian cancer, the prognostic predictive value of CFAP126 and DYDC2 remained further study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, SCGB2A1, FOXJ1, CFAP126, and DYDC2 showed significant differences in expression levels among different molecular subtypes of serous ovarian cancer, providing a new perspective for understanding ovarian cancer heterogeneity. The high expression of SCGB2A1 and FOXJ1 in the differentiated subtype may indicate that tumor cells retain differentiated characteristics and a relatively stable phenotype, consistent with the generally lower invasiveness and metastatic potential of the differentiated subtype [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Thus, SCGB2A1 and FOXJ1 may serve as auxiliary biomarkers to distinguish the low-invasive differentiated subtype, helping to identify patient groups with relatively favorable prognosis. CFAP126 and DYDC2 were significantly upregulated in the proliferative subtype, suggesting their potential involvement in key processes such as cell cycle regulation, DNA replication, and mitosis, with high expression possibly closely related to rapid proliferation of tumor cells. Gene interaction network analysis revealed close connections between SCGB2A1, FOXJ1, CFAP126, DYDC2 and other functionally related genes. The functions of these genes (e.g., lipid binding or transport, regulation of cellular energy metabolism, ciliary motility, and histone methylation regulation) played important roles in tumor progression.\u003c/p\u003e \u003cp\u003eSCGB2A1 gene (Secretoglobin Family 2A Member 1), also known as Mammaglobin 1, belongs to the secretoglobin superfamily. SCGB2A1 is one of the breast cancer-related biomarkers [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and a marker of chemotherapy and radiotherapy resistance in colorectal cancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In ovarian cancer, SCGB2A1 is a novel tumor-associated antigen that can be detected in serous ovarian cancer and other histological subtypes, with significant differences in expression levels among subtypes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFOXJ1 gene (Forkhead Box Protein J1) belongs to the forkhead box transcription factor family and plays a key role in cilia formation, immune response regulation, and normal development of various tissues. The role of FOXJ1 in different tumors is complex: studies have shown that overexpression of FOXJ1 weakens the proliferation, migration, and invasion of gastric cancer cells [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], while other studies indicate that FOXJ1 promotes the progression of laryngeal squamous cell carcinoma [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], which may be related to tumor type and microenvironment.\u003c/p\u003e \u003cp\u003eThe current literature on CFAP126 and DYDC2 remains limited. CFAP126 gene (Cilia-Associated Protein 126) is closely related to ciliary structure and function. Its encoded protein is mainly localized in the axoneme of cilia and may be involved in axoneme assembly or stability maintenance [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. DYDC2 gene (DPY30 Domain Containing 2) encodes the member of the protein family containing the DPY30 domain. The high expression of DYDC2 in endometrial cancer is associated with improved patient survival [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnalysis of single-cell transcriptomes in the tumor microenvironment found that SCGB2A1, FOXJ1, CFAP126, and DYDC2 were upregulated in specific malignant cell clusters, further illustrating the heterogeneity of the serous ovarian cancer microenvironment. GO and KEGG enrichment analyses of expressed genes in this malignant cell cluster showed that these cells may be involved in regulating the body's immune response, thereby affecting the recognition and killing of tumor cells by immune cells [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Enriched KEGG pathways are mostly related to signal transduction and immune response, suggesting that these pathways play important roles in tumor cell proliferation, migration, invasion, angiogenesis, and immune escape [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Enrichment of pathways seemingly weakly related to ovarian cancer (e.g., viral myocarditis, amebiasis) may reflect shared molecular mechanisms between these pathways and malignant cell behaviors [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. More in vitro experiments are required to elucidate the specific mechanisms of action of these genes in the aforementioned biological processes and pathways.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, although several biomarkers and pathways potentially associated with ovarian cancer were identified through bioinformatics analyses, these findings were primarily derived from existing public datasets and were needed to be validated in independent clinical samples. Second, the specific roles and regulatory networks of these biomarkers in biological processes and pathways required further experimental investigation to elucidate. Additionally, the present study mainly focused on genomic alterations, while changes at the protein or epigenetic levels were less extensively explored, which represents a potential direction for future research.\u003c/p\u003e \u003cp\u003eThis study comprehensively analyzed multiple databases to screen four molecules from fallopian tube ciliated epithelial cell markers that can serve as diagnostic and prognostic markers for serous ovarian cancer, verified their diagnostic function in independent datasets, and preliminarily explored their expression characteristics and potential involvement in biological processes and signaling pathways in microenvironment. Their expression patterns in serous ovarian cancer and interactions with other genes provide new insights into understanding the pathogenesis of ovarian cancer.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe fallopian tube ciliated epithelial cell markers SCGB2A1, FOXJ1, CFAP126, and DYDC2 can be used for the diagnosis of serous ovarian cancer, and their high expression in tumor tissues is associated with longer survival in patients. These four markers may be implicated in the occurrence and development of serous ovarian cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 82472358), the Natural Science Foundation of Beijing-Daxing Innovation Joint Fund, China (Grant No. L256079) and National High Level Hospital Clinical Research, Science and Technology Achievement Transformation Incubation Guiding Fund, Peking University First Hospital (Grant No. 2024CX13).\u003c/p\u003e \u003cp\u003eCompeting Interests\u003c/p\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJingchen Zhao analyzed the data, prepared figures and/or tables, authored and reviewed manuscript of the article. Mengyan He and Ying Zhang analyzed the data, prepared figures and/or tables. Zhiyan Li provided guidance to the manuscript preparation, and revised the manuscript. Dong Xu and Shurui Kang revised the manuscript. Haixia Li provided guidance to the manuscript preparation, reviewed manuscript of the article. Each author had read the complete manuscript, and all authors approved submission of the paper.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets utilized for this study are publicly available and archived in the following online repositories. Direct web links to datasets:CZ CELLxGENE: https://cellxgene.cziscience.com/GEPIA: http://gepia2.cancer-pku.cn/#index/HPA: https://www.proteinatlas.orgKaplan\u0026ndash;Meier Plotter: https://kmplot.com/analysis/GEO: https://www.ncbi.nlm.nih.gov/geo/GeneMANIA: http://genemania.org/TISCH2: http://tisch.compbio.cn/home/TISIDB: http://cis.hku.hk/TISIDB/Metascape: http://metascape.orgDAVID: https://davidbioinformatics.nih.gov/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229\u0026ndash;263. doi: 10.3322/caac.21834. Epub 2024 Apr 4. PMID: 38572751.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLheureux S, Gourley C, Vergote I, Oza AM. Epithelial ovarian cancer. 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PMID: 38872210; PMCID: PMC11177407.\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":"serous ovarian cancer, diagnosis, prognosis, fallopian tube ciliated epithelial cells, biomarkers, database mining","lastPublishedDoi":"10.21203/rs.3.rs-8695930/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8695930/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSerous ovarian cancer may originate from the ciliated epithelial cells of the fimbriated end of fallopian tube. The aim of this study was to explore the diagnostic and prognostic value of fallopian tube ciliated epithelial cell markers in serous ovarian cancer, and to preliminarily explore the potential mechanisms. Marker genes of fallopian tube ciliated epithelial cells were obtained from the human single-cell database CZ CELLxGENE. GEPIA, HPA, and Kaplan-Meier Plotter databases were applied to analyze their expression in serous ovarian cancer and correlation with clinical prognosis. The diagnostic potential of candidate biomarkers was evaluated using the GEO datasets. GeneMANIA, TISCH2, and TISIDB databases were implemented to explore the regulatory networks and related pathways of these biomarkers. The fallopian tube ciliated epithelial cell markers SCGB2A1, FOXJ1, CFAP126, and DYDC2 were found to be upregulated in serous ovarian tumor tissues, high expression levels of these markers were correlated with longer survival in patients. Gene interaction network and functional enrichment analysis indicated that these genes were involved in immune regulation, cell adhesion and signal transduction pathways. SCGB2A1, FOXJ1, CFAP126, and DYDC2 were associated with the occurrence and progression of serous ovarian cancer and could serve as markers for its diagnosis and prognosis.\u003c/p\u003e","manuscriptTitle":"Diagnostic and Prognostic Significance of Fallopian Tube Ciliated Epithelial Cell Markers in Serous Ovarian Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 16:30:59","doi":"10.21203/rs.3.rs-8695930/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":"b74406ef-1c72-4a5c-a48a-2981b8594cb5","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T07:25:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 16:30:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8695930","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8695930","identity":"rs-8695930","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}