Weighted correlation network and differential expression analyses identify prognostic lncRNA-miRNA-mRNA-ceRNA regulatory network in esophageal squamous cell carcinoma

Weighted correlation network and differential expression analyses identify prognostic lncRNA-miRNA-mRNA-ceRNA regulatory network in esophageal squamous cell carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Weighted correlation network and differential expression analyses identify prognostic lncRNA-miRNA-mRNA-ceRNA regulatory network in esophageal squamous cell carcinoma Jun-Hui Guo, Bei-Bei Liu, Jun-Hui Chen, Si-Run Du, Chang Liu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5721822/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Esophageal squamous cell carcinoma (ESCC) is one of the most common malignant tumours arose from the esophagus. ESCC is highly lethal due to the late onset of symptoms and therefore, there is an urgent need to deepen the molecular understanding of this disease and identify potential prognostic biomarkers to further guide ESCC treatment. As a type of non-coding RNAs, competing endogenous RNA (ceRNA) reveals a novel mechanism of interaction between RNAs in various cancers. However, the understanding of the ceRNA regulatory network in ESCC is still unclear. Methods In this study, RNA-seq and clinicopathological characteristics data of ESCC and normal esophageal tissues was obtained from TCGA and GTEx database, respectively. Differentially expressed genes (DEGs) between ESCC and normal esophagel tissues were identified by employing R package (edgeR). Functional enrichment analysis of these DEGs was performed through the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes-Gene Set Enrichment Analysis (KEGG-GSEA). Subsequently, multivariate cox and survival analysis were performed to evaluate these DEGs. Then the expression of these 9 DEGs was investigated through Q-PCR in normal esophageal epithelial and ESCC cells. Results Compared to normal tissues, a total of 794 mRNAs were up-regulated and 1118 mRNAs were down-regulated in ESCC. The results of GO analysis showed an enrichment of the up-regulated genes in leukocyte migration, humoral immune response, phagocytosis and complement activation. Meanwhile, the results of KEGG-GSEA analysis showed an enrichment of the up-regulated genes in cell cycle, p53 signaling pathway and extracellular matrix receptor interaction, while an enrichment of the down-regulated genes in vascular smooth muscle contraction, ribosome and oxidative phosphorylation. The survival analysis identified significant association of poor prognosis with five up-regulated genes and four down-regulated genes. Conclusion This study identified several differential expression genes with prognostic values, and these genes may provide new insights into the roles of ceRNA regulatory network in ESCC. Biological sciences/Cancer/Gastrointestinal cancer/Oesophageal cancer Biological sciences/Cancer Health sciences/Biomarkers Health sciences/Gastroenterology esophageal squamous cell carcinoma competing endogenous RNAs prognosis non-coding RNAs mRNAs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies, with estimated over 500,000 new cases each year ( 1 ) . Despite recent efforts to clarify the underlying pathogenesis, the early diagnostic biomarkers and the development of new therapies, ESCC is still highly lethal due to the late onset of symptoms, with the five-year overall survival rate of less than 10% (2, 3) . Therefore, it is of great importance to identify potential biomarkers for predicting prognosis of ESCC patients and further guiding treatment of ESCC. Competing endogenous RNAs (ceRNAs) is a type of non-coding RNAs that compete and bind to microRNAs (miRNAs), and subsequently regulate the activity of mRNAs to form a ceRNA regulatory network ( 4 – 6 ) . Non-coding RNAs includes pseudogenes, long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and ceRNAs, which have significant influences on the occurrence and development of diseases. The lncRNA-miRNA-mRNA-ceRNA regulatory network has previously been implicated in the prognosis of various diseases, including cardiovascular disease, leukemia, diabetes cataract, and gastrointestinal cancers ( 7 – 11 ) . Therefore, determining the mechanism of RNA interaction not only helps to understand the pathogenesis of these diseases, but also affects the prognosis of patients with these disease. In recent years, bioinformatics analysis is increasingly employed to identify novel biomarkers of human diseases, especially for studying the potential correlation of gene expression in malignant tumors. For instance, weighted gene co-expression network analysis (WGCNA) is used to describe gene association patterns across samples, recognize coordinated gene sets and identify candidate biomarkers ( 12 ) . In the present study, a comprehensive analysis of ESCC-related ceRNAs was conducted based on high-throughput sequencing data derived from TGCA database through WGCNA. The prognostic significance of the identified genes was explored through survival analysis in order to enrich understanding of the role of the ceRNAs regulatory network in ESCC. This research might provide insights into the molecular mechanisms related to the carcinogenesis and progression of ESCC. Methods Data collection and processing We first retrieved RNA sequencing (RNA-seq) and clinicopathological data of ESCC patients from publicly available resources: TCGA database (as of May 7, 2019, https://portal.gdc.cancer.gov ). After excluding samples with incomplete data, we obtained a total of 162 cancer and 11 normal samples. We also obtained the RNA-seq data of 653 normal subjects from the GTEx database (as of September 6, 2019, https://www.gtexportal.org/home/index.html ). The genes were re-annotated by the rtracklayer package of the R software (Version 3.6.1). The gene annotation file “Homo_sapiens.GRCh38.91.CRH.gtf” was downloaded from the Ensembl Genomes website ( http://asia.ensembl.org/index.html ). The analysis process is shown in the flowchart (Fig. 1 ). Identification of differentially expressed genes (DEGs) The ensemble ID of samples was converted by using “Homo_sapiens.GRCh38.91.CRH.gtf”. LncRNA or mRNA without ensemble ID was excluded. R package (edgeR) was used to identify significant DEGs between ESCC and normal samples. q values (false discovery rate, FDR) were used to adjust the statistical significance of multiple tests. Absolute log 2 FC ≥ 2 and FDR < 0.05 were used as the inclusion criteria. Functional enrichment analysis GO was used to analyze the functional enrichment of DEGs in terms of biological processes (BP), cellular components (CC) and molecular functions (MF) ( 13 ) . KEGG-GSEA was used to identify gene enrichment in metabolic pathways ( 14 ) , and the significance level was p < 0.05. Weighted gene co‑expression network analysis (WGCNA) WGCNA is an algorithm for gene co-expression network identification through high-throughput expression profile (mRNA or lncRNA) with different traits. Pairwise Pearson correlation analysis was used to evaluate the weighted co-expression relationship among all dataset subjects in an adjacency matrix. In this study, WGCNA was used to obtain the mRNA or lncRNA most relevant to ESCC patients. Cox regression analysis Univariate cox regression analysis was employed to determine the relationship between mRNA expression and the overall survival (OS) rate of patients, and then multivariate cox analysis was used to evaluate the contribution of the candidate genes. The analysis was conducted using the R package of survival. Cell culture and transfection Human normal esophageal epithelial cell line HET-1 and ESCC cell lines TE-1, KYSE30, KYSE180 and KYSE450 were cultured in RPMI1640 medium containing 10%FBS and placed in a 37℃, 5% CO 2 incubator. When cells grow to 70% confluence, these cells were transfected with plasmids using transfection reagent lipofectamine 2000. Real-time Q-PCR The expression differences of these 9 DEGs between normal esophageal epithelial and ESCC cells were detected through real-time Q-PCR, including JOSD1, RBM27, MOB4, TBC1D13, TNRC6B, DYRK2, YAP1, PDZD11 and SNRPB2. The correlation between their expression levels and the risk level of postoperative esophageal cancer patients was also analyzed. Results Differential expression analysis between ESCC and normal tissues denotes their distinct expression patterns RNA-seq data of 162 ESCC patients and 664 normal samples was collected from TCGA and GTEx. EdgeR was used to normalized the gene read counts to the trimmed mean of M values (TMM). As shown in volcano map, 794 mRNAs were up-regulated and 1118 mRNAs were down-regulated (Fig. 2 A). The heatmap displayed the expression of 1912 DEGs (Fig. 2 B). Gene Ontology (GO) of the up-regulated mRNAs was applied to investigate their potential functions. In biological processes (BP), the up-regulated mRNAs were enriched in the leukocyte migration, humoral immune response, phagocytosis and complement activation. The cellular component (CC) and molecular function (MF) analysis results also showed specific enrichment of up-regulated mRNA (Fig. 2 C-E). To investigate the roles of DEGs in various biological pathways, Kyoto Encyclopedia of Genes and Genomes-Gene Set Enrichment Analysis (KEGG-GSEA) was employed ( 15 ) . The results showed that up-regulated genes were enriched in cell cycle, p53 signaling pathway and extracellular matrix (ECM) receptor interaction, while down-regulated genes were enriched in vascular smooth muscle contraction, ribosome and oxidative phosphorylation (Fig. 3 ). Characteristic mRNAs or lncRNA of ESCC analyzed by WGCNA Gene modules were analyzed using the WGCNA in the top 40% mRNAs by variance comparison. As shown in Fig. 4 A, softpower 12 and the module size cut-off 25 were selected as the thresholds for identifying co-expressed gene modules, and 13 modules were determined. Then, the co-expression similarity and contiguity of the gene analysis module-trait (ESCC and normal) in the 13 color modules were continuously used. Gray60 module was highly related to ESCC, which included 3594 mRNAs (Fig. 4 B and 4 C). These 3594 mRNAs were further used for GO analysis to show the gene enrichment and their interactions in BP (Fig. 4 D). These genes were most related to DNA replication, IncRNA metabolic process and ribonucleoprotein complex biogenesis. In addition, the KEGG analysis showed that genes were highly enriched in the cell cycle, DNA replication and protein processing in endoplasmic reticulum (Fig. 4 E). To further study the co-expression network, lncRNA modules were analyzed by WGCNA through variance comparison. As shown in Fig. 5 A, choosing softpower 6 as the threshold, 6 co-expressed lncRNA modules were identified. Correlation analysis showed that the red module displayed the highest correlation with ESCC (Fig. 5 B and 5 C; r = 0.69). Then, miRcode was used to predict the 134 lncRNA-sponged miRNAs to identify the lncRNAs-miRcode-miRNAs relationship. Meanwhile, TCGA miRNA-Seq data was used to analyze the expression of miRNA. Then the overlapped miRNAs were selected between TCGA-miRNAs and the lncRNAs-miRcode-miRNAs to estimate the lncRNAs-miRNAs relationship. Furthermore, 1919 predicted target mRNAs were explored and obtained by starBase, miRDB, miRTarBase and Targetscan dataset (Fig. 5 D). Importantly, as shown in Fig. 5 E, 711 overlapping target mRNAs were selected by analyzing the predicted target mRNA, namely WGCNA-turquoise-cyan mRNAs. Cox regression analysis of characteristic genes demonstrates their relevance to the survival of ESCC patients Next, a univariate cox regression analysis was conducted to clarify the relationship between the expression of the 711 characteristic genes and overall survival (OS) of ESCC patients. A total 15 genes were obtained by the threshold of p value < 0.01. These 15 genes were used for further multivariate cox regression analysis (Table I). Then, a 3-year OS survival model with 9 genes was established through regression algorithms: JOSD1*(-3.00) + RBM27*(2.59) + MOB4*(1.68) + TBC1D13*(-2.28) + TNRC6B*(-1.52) + DYRK2*(1.56) + YAP1*(-1.40) + PDZD11*(2.64) + SNRPB2*(-1.58). The survival curves showed that the high expression of DYRK2, MOB4, RBM27, SNRPB2 or PDZD11 was significantly related to poor prognosis of patients, while the low expression of TNRC6B, TBC1D13, JOSD1 or YAP1 was associated with poor prognosis of patients (Fig. 6 ). Based on this survival model, patients from TCGA dataset were divided into low-risk group and high-risk group. The Kaplan-Meier survival curve showed that the predicted OS of high-risk patients was significantly shorter than that of low-risk patients (n = 160, p < 0.01, Fig. 7 A). A receiver operating characteristic (ROC) analysis was performed to compare the sensitivity and specificity of the survival prediction of our model. As a result, the area under ROC curve (AUC) of the 9-gene signature were 0.759 (3-years, Fig. 7 B) and 0.831 (5-years, Fig. 7 C), respectively. Furthermore, the expression heatmap of the 9-gene signature between the high-risk group and the low-risk group was shown in Fig. 7 D. Validation of DEGs between normal esophageal epithelial and ESCC cells The expression of 9 DEGs genes in normal esophageal epithelial cells and 4 types of ESCC cells (TE-1, KYSE30, KYSE180 and KYSE450) was analyzed by real-time Q-PCR. The results showed that: for those genes associated with poor prognosis, the expression of DYRK2, MOB4, RBM27, SNRPB2 or PDZD11 was significantly lower in normal esophageal epithelial cells than that in ESCC cells; (Fig. 8 -A,B,C,D,E) the expression of DYRK2, MOB4, RBM27 or SNRPB2 in ESCC was significantly higher in KYSE30 than that in other cell lines(Fig. 8 -A,B,C,D); the expression of DYRK2 in KYSE30, KYSE180 or KYSE450 cells was significantly higher than that in HET1 cells (p < 0.05)(Fig. 8 -A); the expression of MOB4 in KYSE30 cells was significantly higher than that in HTE1 cells (p < 0.05)(Fig. 8 -B); the expression of RBM27 in HET1 cells was significantly lower than that in KYSE30 or KYSE450 cells (p < 0.05)(Fig. 8 -C); the expression of SNRPB2 in HET1 cells was significantly lower than that in KYSE30, KYSE180 or KYSE450 cells (p < 0.05)(Fig. 8 -D); the expression of PDZD11 in KYSE180 cells was higher than that in HET1 cells (p < 0.05)(Fig. 8 -E). Additionally, for those genes associated with good prognosis, low expression of TNRC6B, TBC1D13, JOSD1 or YAP1 was found to be associated with poor prognosis. In normal esophageal epithelial cell line HET1, the expression level of TNRC6B or TBC1D13 was relatively high, while their expression levels slightly decreased in ESCC cell line TE1, and gradually decreased in KYSE30, KYSE180 or KYSE450 cells. The expression of JOSD1 was higher in HET1, TE1, KYSE30, KYSE180 or KYSE450 cells (no significant difference). The expression of TBC1D13 in HET1 cells was significantly higher than that in KYSE30, KYSE180 or KYSE450 cells (p < 0.05)(Fig. 8 -G); However, the expression of YAP1 in HET1 cells was lower than that in TE1 and KYSE450 cells (p < 0.05)(Fig. 8 -H). The expression of TNRC6B in HET1 cells was higher than that in TE1 and KYSE30, KYSE180 or KYSE450 cells(Fig. 8 -I). Discussion In gastrointestinal cancers, ESCC may be relatively asymptomatic, but the late-diagnosis leads to a persistently low survival rate of patients with ESCC. The identification of potential biomarkers for disease diagnosis and targeted therapy is crucial. Therefore, in the present study, we applied a systematic bio-informatic approach by using WGCNA to identify ESCC-related non-coding RNAs, established a specific ceRNA network, and identified disease-associated miRNAs and lncRNA-miRNA-mRNA ceRNA network-related genes to predict the prognosis of ESCC patients. More and more studies have shown that lncRNA-miRNA-mRNA ceRNA network acts as a prognostic predictor in various cancers since the ceRNA hypothesis proposed by Salmenta et al. in 2011. Ever since, several research groups have constructed mRNA-miRNA-lncRNA sub-network by mining the GEO, GTEx and TCGA databases, including high-grade serous ovarian cancer ( 16 ) , breast cancer ( 17 ) , pancreatic cancer ( 18 ) , acute myeloid leukemia ( 19 ) , and melanoma ( 20 ) . To our knowledge, the present study is the first to correlate the ceRNA network with the prognosis of esophageal cancer. In this study, we developed a relatively stable prognostic prediction model by using a comprehensive bioinformatics-clinical trait relationship. Our WGCNA analysis of the gene co-expression similarity and contiguity of module-trait identified 3594 mRNAs, which further enriched in DNA replication clarified by GO and KEGG analyses. It is worth noting that DNA replication errors sabotage genome integrity, accumulate genetic aberrations, and promote the pathogenesis of various diseases, including cancers ( 21 ) . On the other hand, oncogenes can further interfere with DNA replication, trigger a series of cellular responses and targeted pathways, leading to the uncontrollable cell division, thereby promoting cancer progression. Through analysis, nine genes significantly related to prognosis of ESCC patients were identified. The five highly expressed genes, DYRK2, MOB4, RBM27, SNRPB2 and PDZD11 were significantly correlated with poor overall survival. DYRK2 (dual-specificity tyrosine-regulated kinase 2) is located in the cytoplasm and accumulates in the nucleus under demonic stress, which phosphorylates p53 and mediates apoptosis ( 22 ) . Furthermore, DYRK2 also degrades the proteasome and regulates the cell cycle by combining c-Jun and c-Myc ( 23 – 25 ) . In breast cancer, patients with distinct copy-number variations (CNVs) signatures show a higher risk of relapse or metastasis. Validation by the in vitro cell experiments demonstrated that DYRK2 displayed lower expression in normal esophageal epithelial cells and may have an ability to promote tumor growth and metastasis. Among the reported CNVs, SNRPB2 is associated with low survival rates, and CRISPR/Cas9-mediated knockdown of SNRPB2 suppresses the proliferation and invasion potential of breast cancer cells ( 26 ) . MOB4 is a member of the MOB family and is phosphorylated by germinal center kinases (GCK) ( 27 ) . RBM27 (RNA binding motif protein 27) is a cytoplasmic protein. PDZD11 (PDZ domain-containing protein 11) is an interactor of PLEKHA7, which is a junctional protein that stabilizes the cadherin protein complex ( 28 ) . However, the functional implications of MOB4, RBM27 and PDZD11 remains to be further investigated. We also found that lower expression of TNRC6B, TBC1D13, JOSD1 and YAP1 was significantly associated with poorer prognosis of ESCC patients. TNRC6B (trinucleotide repeat-containing gene 6B) is required for miRNA-dependent translational repression and is reported to be one of the two core protein components of human multi-protein assemblies using microRNAs ( 29 ) . TBC1D13 is a Rab GTPase activator protein ( 30 ) , which plays an essential role in GLUT transport in adipocytes ( 31 ) . Although the function of TBC1D13 is yet to be investigated, metabolic alterations are hallmarks of cancers, making TBC1D13 an intriguing target for further investigations. However, it is contrary to a previous study, which suggested that the deubiquitinating enzyme JOSD1 inhibits mitochondrial apoptosis by stabilizing the MCL1 protein and promotes acquired chemoresistance in gynecological cancer cell lines ( 32 ) . Our prediction indicate that ESCC patients with lower JOSD1 are significantly associated with poor prognosis. Similarly, the lower expression of the candidate oncogene YAP1 (yes-associated protein 1) is significantly associated with poor prognosis. Although YAP1 amplification and overexpression are often found to be poor prognostic factors in various cancers, including breast, pancreatic and hepatic cancers, it is also widely reported as a tumor suppressor ( 33 – 35 ) . Notably, in the process of cisplatin-induced DMA injury, YAP1 promotes p73-induced apoptosis, is a mediator of c-Jun-dependent apoptosis, and can be phosphorylated by Akt, thereby inhibiting various pro-apoptotic genes ( 36 – 38 ) . Further investigations are warranted to determine the functional roles of the above mentioned genes in ESCC. Conclusion In conclusion, in this study, we developed a co-expression network of lncRNA-miRNA-mRNA ceRNA through bioinformatics analysis, and identified several DEGs with prognostic values in ESCC. Although our work has promoted a new understanding of the role of ceRNA regulatory networks in ESCC, this study has some limitations. First, this study lacks detailed analysis of correlated gene expression and clinicopathological characteristics, such as disease stage, pathological subtype, histological grade, number of lymph node metastasis and distant metastasis. Second, this study focuses on the in silico prediction of potential biomarkers in ESCC and lacks in vitro and in vivo experimental support for validation. Further experimental verification is necessary to explore the potential functions of the identified genes and ceRNA in ESCC. Declarations Ethics approval and consent to participate Not applicable. Patient consent for publication Not applicable. Competing interests The authors declare no conflict of interest. Ethical review reports Henan Hospital of Traditional Chinese Medicine(The Second Affiliated Hospital of Henan University of Traditional Chinese Medicine) Ethics No: HNSZYYWZ-20240023 Funding This study was financially supported by Youth Found of the National Natural Science Foundation of China (project code: 82204981). Author Contribution Jun-Hui Guo and Tian-Wen Xu conceived and designed the study and drafted the manuscript. Bei-Bei Liu, Jun-Hui Chen collected the data and drafted the manuscript. Si-Run Du, Chang Liu, Dong-Dong Li，Xin-Xin Wang, Xu Wang, Lu-Yuan Bai, Pei-Min Liu, Chun-Zheng Ma and Yu-Ling Zheng comment on and revised the manuscript. All authors read and approved final version.The authors declare no conflict of interest. Acknowledgements Not applicable. Data Availability The dataset generated during and analyzed during the current study are available from the corresponding author on reasonable request. References Ferlay, J. et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int. J. Cancer . 127 (12), 2893–2917 (2010). Rasouli, M. et al. Survival analysis of patients with esophageal cancer using parametric cure model. Asian Pac. J. Cancer Prev. 12 (9), 2359–2363 (2011). Ramzan, Z., Nassri, A. B. & Huerta, S. The use of imaging and biomarkers in diagnosing Barrett's esophagus and predicting the risk of neoplastic progression. Expert Rev. Mol. Diagn. 14 (5), 575–591 (2014). Salmena, L., Poliseno, L., Tay, Y., Kats, L. & Pandolfi, P. P. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA. language? Cell. 146 (3), 353–358 (2011). Qi, X. et al. ceRNA in cancer: possible functions and clinical implications. J. Med. Genet. 52 (10), 710–718 (2015). Ala, U. et al. Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments. Proc. Natl. Acad. Sci. U S A . 110 (18), 7154–7159 (2013). Greco, S., Gaetano, C. & Martelli, F. Long Noncoding Competing Endogenous RNA Networks in Age-Associated Cardiovascular Diseases. Int. J. Mol. Sci. ; 20 (12). (2019). Yang, J., Zhao, S. & Tian, F. SP1-mediated lncRNA PVT1 modulates the proliferation and apoptosis of lens epithelial cells in diabetic cataract via miR-214-3p/MMP2 axis. J. Cell. Mol. Med. 24 (1), 554–561 (2020). He, B., Bai, Y., Kang, W., Zhang, X. & Jiang, X. LncRNA SNHG5 regulates imatinib resistance in chronic myeloid leukemia via acting as a CeRNA against MiR-205-5p. Am. J. Cancer Res. 7 (8), 1704–1713 (2017). Zhou, Z. et al. Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis. Int. J. Biol. Sci. 14 (2), 124–136 (2018). Zhou, X. G. et al. Identifying miRNA and gene modules of colon cancer associated with pathological stage by weighted gene co-expression network analysis. Onco Targets Ther. 11 , 2815–2830 (2018). Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9 , 559 (2008). Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25 (1), 25–29 (2000). Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M. & Hirakawa, M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38 (Database issue), D355–D360 (2010). Hart, J. et al. Recommendations for Prehospital Airway Management in Patients with Suspected COVID-19 Infection. West. J. Emerg. Med. 21 (4), 809–812 (2020). Wu, M., Sun, Y., Wu, J. & Liu, G. Identification of Hub Genes in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis. Med. Sci. Monit. 26 , e922107 (2020). Fan, C. N., Ma, L. & Liu, N. Systematic analysis of lncRNA-miRNA-mRNA competing endogenous RNA network identifies four-lncRNA signature as a prognostic biomarker for breast cancer. J. Transl Med. 16 (1), 264 (2018). Giulietti, M., Righetti, A., Principato, G. & Piva, F. LncRNA co-expression network analysis reveals novel biomarkers for pancreatic cancer. Carcinogenesis 39 (8), 1016–1025 (2018). Wang, J. D. et al. Prediction of competing endogenous RNA coexpression network as prognostic markers in AML. Aging (Albany NY) . 11 (10), 3333–3347 (2019). Zhao, B. et al. Weighted correlation network and differential expression analyses identify candidate genes associated with BRAF gene in melanoma. BMC Med. Genet. 20 (1), 54 (2019). Boyer, A. S., Walter, D. & Sorensen, C. S. DNA replication and cancer: From dysfunctional replication origin activities to therapeutic opportunities. Semin Cancer Biol. 37–38 , 16–25 (2016). Taira, N., Nihira, K., Yamaguchi, T., Miki, Y. & Yoshida, K. DYRK2 is targeted to the nucleus and controls p53 via Ser46 phosphorylation in the apoptotic response to DNA damage. Mol. Cell. 25 (5), 725–738 (2007). Taira, N. et al. DYRK2 priming phosphorylation of c-Jun and c-Myc modulates cell cycle progression in human cancer cells. J. Clin. Invest. 122 (3), 859–872 (2012). Mimoto, R. et al. DYRK2 controls the epithelial-mesenchymal transition in breast cancer by degrading Snail. Cancer Lett. 339 (2), 214–225 (2013). Tuazon, P. T. & Traugh, J. A. Casein kinase I and II–multipotential serine protein kinases: structure, function, and regulation. Adv. Second Messenger Phosphoprot. Res. 23 , 123–164 (1991). Wang, L. et al. Novel RNA-Affinity Proteogenomics Dissects Tumor Heterogeneity for Revealing Personalized Markers in Precision Prognosis of Cancer. Cell. Chem. Biol. 25 (5), 619–633 (2018). e5. Hergovich, A. MOB control: reviewing a conserved family of kinase regulators. Cell. Signal. 23 (9), 1433–1440 (2011). Guerrera, D. et al. PLEKHA7 Recruits PDZD11 to Adherens Junctions to Stabilize Nectins. J. Biol. Chem. 291 (21), 11016–11029 (2016). Sack, L. M. et al. Profound Tissue Specificity in Proliferation Control Underlies Cancer Drivers and Aneuploidy Patterns. Cell 173 (2), 499–514 (2018). e23. Gapud, E. J., Kronbichler, A., Gauckler, P. & Geetha, D. Immunotherapy for ANCA-associated vasculitis during the COVID-19 pandemic. Eur. J. Rheumatol. (2020). Davey, J. R. et al. TBC1D13 is a RAB35 specific GAP that plays an important role in GLUT4 trafficking in adipocytes. Traffic 13 (10), 1429–1441 (2012). Wu, X. et al. JOSD1 inhibits mitochondrial apoptotic signalling to drive acquired chemoresistance in gynaecological cancer by stabilizing MCL1. Cell. Death Differ. 27 (1), 55–70 (2020). Zhao, B. et al. Inactivation of YAP oncoprotein by the Hippo pathway is involved in cell contact inhibition and tissue growth control. Genes Dev. 21 (21), 2747–2761 (2007). Zender, L. et al. Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach. Cell 125 (7), 1253–1267 (2006). Camargo, F. D. et al. YAP1 increases organ size and expands undifferentiated progenitor cells. Curr. Biol. 17 (23), 2054–2060 (2007). Strano, S. et al. The transcriptional coactivator Yes-associated protein drives p73 gene-target specificity in response to DNA Damage. Mol. Cell. 18 (4), 447–459 (2005). Danovi, S. A. et al. Yes-associated protein (YAP) is a critical mediator of c-Jun-dependent apoptosis. Cell. Death Differ. 15 (1), 217–219 (2008). Yuan, M. et al. Yes-associated protein (YAP) functions as a tumor suppressor in breast. Cell. Death Differ. 15 (11), 1752–1759 (2008). Quevedo-Ramirez, A., Al-Kassab-Cordova, A., Mendez-Guerra, C., Cornejo-Venegas, G. & Alva-Chavez, K. P. Altitude and excess mortality during COVID-19 pandemic in Peru. Respir Physiol. Neurobiol. :103512. (2020). Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 03 Jul, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers invited by journal 16 Jan, 2025 Editor assigned by journal 16 Jan, 2025 Editor invited by journal 16 Jan, 2025 Submission checks completed at journal 16 Jan, 2025 First submitted to journal 27 Dec, 2024 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-5721822","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":404003357,"identity":"9ffac206-647e-41b3-b0e2-8c3517c845cc","order_by":0,"name":"Jun-Hui Guo","email":"","orcid":"","institution":"The Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun-Hui","middleName":"","lastName":"Guo","suffix":""},{"id":404003359,"identity":"a67d7724-663c-4f0b-ae42-3bf23fcb38c8","order_by":1,"name":"Bei-Bei Liu","email":"","orcid":"","institution":"Henan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bei-Bei","middleName":"","lastName":"Liu","suffix":""},{"id":404003364,"identity":"c12bec00-188a-49f9-aad8-aa5393b00939","order_by":2,"name":"Jun-Hui Chen","email":"","orcid":"","institution":"Henan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jun-Hui","middleName":"","lastName":"Chen","suffix":""},{"id":404003366,"identity":"c8a0e01e-3cf4-417b-b508-9913af2cd916","order_by":3,"name":"Si-Run Du","email":"","orcid":"","institution":"Henan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Si-Run","middleName":"","lastName":"Du","suffix":""},{"id":404003367,"identity":"030b583e-f4ef-4539-8f54-8b7223985593","order_by":4,"name":"Chang Liu","email":"","orcid":"","institution":"Henan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Liu","suffix":""},{"id":404003369,"identity":"95435720-6765-45e1-8305-92ae1a07e8c7","order_by":5,"name":"Dong-Dong Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Henan University of Chinese Medicine, Henan Province Hospital of TCM","correspondingAuthor":false,"prefix":"","firstName":"Dong-Dong","middleName":"","lastName":"Li","suffix":""},{"id":404003371,"identity":"1972887d-acbf-4a5c-94dd-ec0ebfe29937","order_by":6,"name":"Xin-Xin Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Henan University of Chinese Medicine, Henan Province Hospital of TCM","correspondingAuthor":false,"prefix":"","firstName":"Xin-Xin","middleName":"","lastName":"Wang","suffix":""},{"id":404003372,"identity":"e816c5c9-96c2-40f8-933c-c9765feb7ed1","order_by":7,"name":"Xu Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Henan University of Chinese Medicine, Henan Province Hospital of TCM","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Wang","suffix":""},{"id":404003373,"identity":"916d1783-6f58-46b1-bd4b-24ea21e5f70b","order_by":8,"name":"Lu-Yuan Bai","email":"","orcid":"","institution":"The Second Affiliated Hospital of Henan University of Chinese Medicine, Henan Province Hospital of TCM","correspondingAuthor":false,"prefix":"","firstName":"Lu-Yuan","middleName":"","lastName":"Bai","suffix":""},{"id":404003374,"identity":"2dac108d-6f26-424d-b0c9-27b9ed6f470c","order_by":9,"name":"Pei-Min Liu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Henan University of Chinese Medicine, Henan Province Hospital of TCM","correspondingAuthor":false,"prefix":"","firstName":"Pei-Min","middleName":"","lastName":"Liu","suffix":""},{"id":404003375,"identity":"483923ac-1eae-41c2-b3dc-ae1c2cefc890","order_by":10,"name":"Chun-Zheng Ma","email":"","orcid":"","institution":"The Second Affiliated Hospital of Henan University of Chinese Medicine, Henan Province Hospital of TCM","correspondingAuthor":false,"prefix":"","firstName":"Chun-Zheng","middleName":"","lastName":"Ma","suffix":""},{"id":404003376,"identity":"a2b96fb1-3527-47e2-aa11-030472e9d10b","order_by":11,"name":"Yu-Ling Zheng","email":"","orcid":"","institution":"Henan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yu-Ling","middleName":"","lastName":"Zheng","suffix":""},{"id":404003377,"identity":"bd2bcc43-19ae-4989-ae80-02bb32222974","order_by":12,"name":"Tian-Wen Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYJCCA2BS/vjBBwkVNcRoYIZqkeBJNnhw5hhxWiBAgsFM8mELM161YGBwI//ggZ87DsuZSzekVSQ2sDHwt3cn4NUiOSOZ4WDvmcPGlnMOHruRuEOGQeLM2Q14tfBLJDMc4G27nbjhQELajcQzbAwGErn4tbABtRz823a7HqjFrCCxjZmwFpAth4G2JBjcSDBjIEqLZM9jg8Oybf8NN5w5kyyRcOYYD0G/GBxPfPzxbVuavMHx9oMff1TUyPG39+LXggF4SFM+CkbBKBgFowArAAAktU9CMYrYjQAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Tian-Wen","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-12-27 13:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5721822/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5721822/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74261956,"identity":"768a199f-4caf-4a5d-9526-bfafb31a1885","added_by":"auto","created_at":"2025-01-20 12:23:56","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe workflow of this work.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5721822/v1/1900f5a15e0e958e3dedb69e.jpeg"},{"id":74262665,"identity":"b8424b6a-65ac-4a27-ae1e-1c70de42e9c7","added_by":"auto","created_at":"2025-01-20 12:31:57","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178343,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression analysis between normal and ESCC samples denotes their distinct expression patterns.\u003c/strong\u003e (A) The volcano map showing the differentially expressed mRNAs derived from TCGA and GTEx. Red spots represent up-regulated genes, and green spots represent down-regulated genes. (B) Heatmap of the top 1912 DEGs based on the value of |logFC\u0026gt;2|. High or low expression is shown as a red or blue strip. The cancer and normal groups were labelled, respectively. (C-E) Plot of up-regulated mRNA enrichment in BP, CC and MF in GO analysis.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5721822/v1/aa10e5a65602cbe756250585.jpeg"},{"id":74261088,"identity":"076f0d5e-45eb-4b01-9384-fb1fc081083e","added_by":"auto","created_at":"2025-01-20 12:15:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":143904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe KEGG pathway enrichment analysis of DEGs. \u003c/strong\u003e(A-C) KEGG pathways enrichment for all DEGs. (D) KEGG-GSEA was applied for signaling pathway enrichment analysis.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5721822/v1/ae47baf8e3147fe450033798.jpeg"},{"id":74261084,"identity":"1bf8ff45-4631-4dc8-a812-3855ae357681","added_by":"auto","created_at":"2025-01-20 12:15:57","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":143754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWGCNA is applied to analyze gene modules. \u003c/strong\u003e(A) Cluster dendrogram of the coexpression network modules was produced based on topological overlap in the mRNAs. (B) The relation of genes in modules between ESCC and normal samples was investigated. (C) The scatter plot of gene significance (GS) versus module membership (MM) in the gray60 module. (D) GO enrichment analysis of gray60 module. (E) KEGG analysis was used to investigate the pathway enrichment in gray60 module.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5721822/v1/66f4af11c10247258891addb.jpeg"},{"id":74261082,"identity":"fabc6afd-9b3c-45e1-a667-372ee538a69e","added_by":"auto","created_at":"2025-01-20 12:15:57","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":88637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLncRNAs modules are analyzed by WGCNA.\u003c/strong\u003e (A) Cluster dendrogram of the coexpression network modules was produced based on topological overlap in the lncRNAs. (B) The relation of lncRNAs in modules between ESCC and normal samples was investigated. (C) Red module showed highest relationship with ESCC. (D) Flow chart exhibited the process of predicting target mRNAs. (E) Overlapped target mRNAs were enriched by the predicted target mRNAs and WGCNA-gray60 mRNAs.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5721822/v1/d8ecf19ca6e313a6b2ce8c85.jpeg"},{"id":74261090,"identity":"f1c3644d-eb94-4c13-8e2e-5b75a3772bc2","added_by":"auto","created_at":"2025-01-20 12:15:57","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":250082,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier analysis of DYRK2, MOB4, RBM27, SNRPB2, PDZD11, TNRC6B, TBC1D13, JOSD1 or YAP1 by comparing the higher (red) and lower (blue) expression with overall survival (OS) outcomes for patients with ESCC.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5721822/v1/01f193c54811782145a16224.jpeg"},{"id":74261080,"identity":"6fce4ffd-1b8d-4214-9c5c-a4f8c1bb8c37","added_by":"auto","created_at":"2025-01-20 12:15:57","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":93126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival analysis of the 9-gene model is conducted. \u003c/strong\u003e(A) Kaplan-Meier survival analysis of the 9-gene model was performed \u003csup\u003e(39)\u003c/sup\u003e. Time-dependent ROC curve of the 9-gene model (3-years \u0026amp; 5-years). (D) The expression heatmap of these 9 genes in high risk or low risk group was shown.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5721822/v1/6a657622ce879d674d2cc24d.jpeg"},{"id":74261077,"identity":"e58813af-d6da-47f9-a6ea-8a7096653f08","added_by":"auto","created_at":"2025-01-20 12:15:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":96826,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of real-time Q-PCR of 9 DEGs genes in normal esophageal epithelial cells and 4 types of ESCC cells.\u003c/strong\u003e (A)Relative mRNA expression of DYRK2; (B)Relative mRNA expression of MOB4; (C)Relative mRNA expression of RNM27; (D)Relative mRNA expression of SNRPB2; (E)Relative mRNA expression of PDZD11; (F)Relative mRNA expression of JOSD1; (G)Relative mRNA expression of TBC1D13; (H)Relative mRNA expression of YAP1; (I)Relative mRNA expression of TNRC6B.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5721822/v1/1a72d4391696b5c4b483f400.png"},{"id":74264117,"identity":"b4a8e00f-878b-4ca0-b9f2-42a199d90923","added_by":"auto","created_at":"2025-01-20 12:39:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2113351,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5721822/v1/988419b2-bbeb-4fc7-823e-0f83547827b1.pdf"},{"id":74261072,"identity":"8c9c393c-283b-4e81-b4da-98bdd81de6c0","added_by":"auto","created_at":"2025-01-20 12:15:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13697,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5721822/v1/703bba860a93a6373e1066ea.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Weighted correlation network and differential expression analyses identify prognostic lncRNA-miRNA-mRNA-ceRNA regulatory network in esophageal squamous cell carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEsophageal squamous cell carcinoma (ESCC) is one of the most common malignancies, with estimated over 500,000 new cases each year \u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e. Despite recent efforts to clarify the underlying pathogenesis, the early diagnostic biomarkers and the development of new therapies, ESCC is still highly lethal due to the late onset of symptoms, with the five-year overall survival rate of less than 10% \u003csup\u003e(2, 3)\u003c/sup\u003e. Therefore, it is of great importance to identify potential biomarkers for predicting prognosis of ESCC patients and further guiding treatment of ESCC.\u003c/p\u003e \u003cp\u003eCompeting endogenous RNAs (ceRNAs) is a type of non-coding RNAs that compete and bind to microRNAs (miRNAs), and subsequently regulate the activity of mRNAs to form a ceRNA regulatory network \u003csup\u003e(\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/sup\u003e. Non-coding RNAs includes pseudogenes, long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and ceRNAs, which have significant influences on the occurrence and development of diseases. The lncRNA-miRNA-mRNA-ceRNA regulatory network has previously been implicated in the prognosis of various diseases, including cardiovascular disease, leukemia, diabetes cataract, and gastrointestinal cancers \u003csup\u003e(\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e. Therefore, determining the mechanism of RNA interaction not only helps to understand the pathogenesis of these diseases, but also affects the prognosis of patients with these disease.\u003c/p\u003e \u003cp\u003eIn recent years, bioinformatics analysis is increasingly employed to identify novel biomarkers of human diseases, especially for studying the potential correlation of gene expression in malignant tumors. For instance, weighted gene co-expression network analysis (WGCNA) is used to describe gene association patterns across samples, recognize coordinated gene sets and identify candidate biomarkers \u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e. In the present study, a comprehensive analysis of ESCC-related ceRNAs was conducted based on high-throughput sequencing data derived from TGCA database through WGCNA. The prognostic significance of the identified genes was explored through survival analysis in order to enrich understanding of the role of the ceRNAs regulatory network in ESCC. This research might provide insights into the molecular mechanisms related to the carcinogenesis and progression of ESCC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection and processing\u003c/h2\u003e \u003cp\u003eWe first retrieved RNA sequencing (RNA-seq) and clinicopathological data of ESCC patients from publicly available resources: TCGA database (as of May 7, 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After excluding samples with incomplete data, we obtained a total of 162 cancer and 11 normal samples. We also obtained the RNA-seq data of 653 normal subjects from the GTEx database (as of September 6, 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/home/index.html\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/home/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The genes were re-annotated by the rtracklayer package of the R software (Version 3.6.1). The gene annotation file \u0026ldquo;Homo_sapiens.GRCh38.91.CRH.gtf\u0026rdquo; was downloaded from the Ensembl Genomes website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://asia.ensembl.org/index.html\u003c/span\u003e\u003cspan address=\"http://asia.ensembl.org/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The analysis process is shown in the flowchart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of differentially expressed genes (DEGs)\u003c/h3\u003e\n\u003cp\u003eThe ensemble ID of samples was converted by using \u0026ldquo;Homo_sapiens.GRCh38.91.CRH.gtf\u0026rdquo;. LncRNA or mRNA without ensemble ID was excluded. R package (edgeR) was used to identify significant DEGs between ESCC and normal samples. q values (false discovery rate, FDR) were used to adjust the statistical significance of multiple tests. Absolute log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026ge;\u0026thinsp;2 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used as the inclusion criteria.\u003c/p\u003e\n\u003ch3\u003eFunctional enrichment analysis\u003c/h3\u003e\n\u003cp\u003eGO was used to analyze the functional enrichment of DEGs in terms of biological processes (BP), cellular components (CC) and molecular functions (MF) \u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e. KEGG-GSEA was used to identify gene enrichment in metabolic pathways \u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e, and the significance level was \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eWeighted gene co‑expression network analysis (WGCNA)\u003c/h3\u003e\n\u003cp\u003eWGCNA is an algorithm for gene co-expression network identification through high-throughput expression profile (mRNA or lncRNA) with different traits. Pairwise \u003cem\u003ePearson\u003c/em\u003e correlation analysis was used to evaluate the weighted co-expression relationship among all dataset subjects in an adjacency matrix. In this study, WGCNA was used to obtain the mRNA or lncRNA most relevant to ESCC patients.\u003c/p\u003e\n\u003ch3\u003eCox regression analysis\u003c/h3\u003e\n\u003cp\u003eUnivariate cox regression analysis was employed to determine the relationship between mRNA expression and the overall survival (OS) rate of patients, and then multivariate cox analysis was used to evaluate the contribution of the candidate genes. The analysis was conducted using the R package of survival.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and transfection\u003c/h2\u003e \u003cp\u003eHuman normal esophageal epithelial cell line HET-1 and ESCC cell lines TE-1, KYSE30, KYSE180 and KYSE450 were cultured in RPMI1640 medium containing 10%FBS and placed in a 37℃, 5% CO\u003csub\u003e2\u003c/sub\u003e incubator. When cells grow to 70% confluence, these cells were transfected with plasmids using transfection reagent lipofectamine 2000.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eReal-time Q-PCR\u003c/h3\u003e\n\u003cp\u003eThe expression differences of these 9 DEGs between normal esophageal epithelial and ESCC cells were detected through real-time Q-PCR, including JOSD1, RBM27, MOB4, TBC1D13, TNRC6B, DYRK2, YAP1, PDZD11 and SNRPB2. The correlation between their expression levels and the risk level of postoperative esophageal cancer patients was also analyzed.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression analysis between ESCC and normal tissues denotes their distinct expression patterns\u003c/h2\u003e \u003cp\u003eRNA-seq data of 162 ESCC patients and 664 normal samples was collected from TCGA and GTEx. EdgeR was used to normalized the gene read counts to the trimmed mean of M values (TMM). As shown in volcano map, 794 mRNAs were up-regulated and 1118 mRNAs were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The heatmap displayed the expression of 1912 DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Gene Ontology (GO) of the up-regulated mRNAs was applied to investigate their potential functions. In biological processes (BP), the up-regulated mRNAs were enriched in the leukocyte migration, humoral immune response, phagocytosis and complement activation. The cellular component (CC) and molecular function (MF) analysis results also showed specific enrichment of up-regulated mRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo investigate the roles of DEGs in various biological pathways, Kyoto Encyclopedia of Genes and Genomes-Gene Set Enrichment Analysis (KEGG-GSEA) was employed \u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e. The results showed that up-regulated genes were enriched in cell cycle, p53 signaling pathway and extracellular matrix (ECM) receptor interaction, while down-regulated genes were enriched in vascular smooth muscle contraction, ribosome and oxidative phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristic mRNAs or lncRNA of ESCC analyzed by WGCNA\u003c/h2\u003e \u003cp\u003eGene modules were analyzed using the WGCNA in the top 40% mRNAs by variance comparison. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, softpower 12 and the module size cut-off 25 were selected as the thresholds for identifying co-expressed gene modules, and 13 modules were determined. Then, the co-expression similarity and contiguity of the gene analysis module-trait (ESCC and normal) in the 13 color modules were continuously used. Gray60 module was highly related to ESCC, which included 3594 mRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These 3594 mRNAs were further used for GO analysis to show the gene enrichment and their interactions in BP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). These genes were most related to DNA replication, IncRNA metabolic process and ribonucleoprotein complex biogenesis. In addition, the KEGG analysis showed that genes were highly enriched in the cell cycle, DNA replication and protein processing in endoplasmic reticulum (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). To further study the co-expression network, lncRNA modules were analyzed by WGCNA through variance comparison. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, choosing softpower 6 as the threshold, 6 co-expressed lncRNA modules were identified. Correlation analysis showed that the red module displayed the highest correlation with ESCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC; r\u0026thinsp;=\u0026thinsp;0.69). Then, miRcode was used to predict the 134 lncRNA-sponged miRNAs to identify the lncRNAs-miRcode-miRNAs relationship. Meanwhile, TCGA miRNA-Seq data was used to analyze the expression of miRNA. Then the overlapped miRNAs were selected between TCGA-miRNAs and the lncRNAs-miRcode-miRNAs to estimate the lncRNAs-miRNAs relationship. Furthermore, 1919 predicted target mRNAs were explored and obtained by starBase, miRDB, miRTarBase and Targetscan dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Importantly, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, 711 overlapping target mRNAs were selected by analyzing the predicted target mRNA, namely WGCNA-turquoise-cyan mRNAs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCox regression analysis of characteristic genes demonstrates their relevance to the survival of ESCC patients\u003c/h2\u003e \u003cp\u003eNext, a univariate cox regression analysis was conducted to clarify the relationship between the expression of the 711 characteristic genes and overall survival (OS) of ESCC patients. A total 15 genes were obtained by the threshold of \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.01. These 15 genes were used for further multivariate cox regression analysis (Table I). Then, a 3-year OS survival model with 9 genes was established through regression algorithms: JOSD1*(-3.00)\u0026thinsp;+\u0026thinsp;RBM27*(2.59)\u0026thinsp;+\u0026thinsp;MOB4*(1.68)\u0026thinsp;+\u0026thinsp;TBC1D13*(-2.28)\u0026thinsp;+\u0026thinsp;TNRC6B*(-1.52)\u0026thinsp;+\u0026thinsp;DYRK2*(1.56)\u0026thinsp;+\u0026thinsp;YAP1*(-1.40)\u0026thinsp;+\u0026thinsp;PDZD11*(2.64)\u0026thinsp;+\u0026thinsp;SNRPB2*(-1.58). The survival curves showed that the high expression of DYRK2, MOB4, RBM27, SNRPB2 or PDZD11 was significantly related to poor prognosis of patients, while the low expression of TNRC6B, TBC1D13, JOSD1 or YAP1 was associated with poor prognosis of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on this survival model, patients from TCGA dataset were divided into low-risk group and high-risk group. The Kaplan-Meier survival curve showed that the predicted OS of high-risk patients was significantly shorter than that of low-risk patients (n\u0026thinsp;=\u0026thinsp;160, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). A receiver operating characteristic (ROC) analysis was performed to compare the sensitivity and specificity of the survival prediction of our model. As a result, the area under ROC curve (AUC) of the 9-gene signature were 0.759 (3-years, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB) and 0.831 (5-years, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), respectively. Furthermore, the expression heatmap of the 9-gene signature between the high-risk group and the low-risk group was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eValidation of DEGs between normal esophageal epithelial and ESCC cells\u003c/h2\u003e \u003cp\u003eThe expression of 9 DEGs genes in normal esophageal epithelial cells and 4 types of ESCC cells (TE-1, KYSE30, KYSE180 and KYSE450) was analyzed by real-time Q-PCR. The results showed that: for those genes associated with poor prognosis, the expression of DYRK2, MOB4, RBM27, SNRPB2 or PDZD11 was significantly lower in normal esophageal epithelial cells than that in ESCC cells; (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-A,B,C,D,E) the expression of DYRK2, MOB4, RBM27 or SNRPB2 in ESCC was significantly higher in KYSE30 than that in other cell lines(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-A,B,C,D); the expression of DYRK2 in KYSE30, KYSE180 or KYSE450 cells was significantly higher than that in HET1 cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-A); the expression of MOB4 in KYSE30 cells was significantly higher than that in HTE1 cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-B); the expression of RBM27 in HET1 cells was significantly lower than that in KYSE30 or KYSE450 cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-C); the expression of SNRPB2 in HET1 cells was significantly lower than that in KYSE30, KYSE180 or KYSE450 cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-D); the expression of PDZD11 in KYSE180 cells was higher than that in HET1 cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-E). Additionally, for those genes associated with good prognosis, low expression of TNRC6B, TBC1D13, JOSD1 or YAP1 was found to be associated with poor prognosis. In normal esophageal epithelial cell line HET1, the expression level of TNRC6B or TBC1D13 was relatively high, while their expression levels slightly decreased in ESCC cell line TE1, and gradually decreased in KYSE30, KYSE180 or KYSE450 cells. The expression of JOSD1 was higher in HET1, TE1, KYSE30, KYSE180 or KYSE450 cells (no significant difference). The expression of TBC1D13 in HET1 cells was significantly higher than that in KYSE30, KYSE180 or KYSE450 cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-G); However, the expression of YAP1 in HET1 cells was lower than that in TE1 and KYSE450 cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-H). The expression of TNRC6B in HET1 cells was higher than that in TE1 and KYSE30, KYSE180 or KYSE450 cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e-I).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn gastrointestinal cancers, ESCC may be relatively asymptomatic, but the late-diagnosis leads to a persistently low survival rate of patients with ESCC. The identification of potential biomarkers for disease diagnosis and targeted therapy is crucial. Therefore, in the present study, we applied a systematic bio-informatic approach by using WGCNA to identify ESCC-related non-coding RNAs, established a specific ceRNA network, and identified disease-associated miRNAs and lncRNA-miRNA-mRNA ceRNA network-related genes to predict the prognosis of ESCC patients.\u003c/p\u003e \u003cp\u003eMore and more studies have shown that lncRNA-miRNA-mRNA ceRNA network acts as a prognostic predictor in various cancers since the ceRNA hypothesis proposed by Salmenta et al. in 2011. Ever since, several research groups have constructed mRNA-miRNA-lncRNA sub-network by mining the GEO, GTEx and TCGA databases, including high-grade serous ovarian cancer \u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e, breast cancer \u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e, pancreatic cancer \u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e, acute myeloid leukemia \u003csup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e, and melanoma \u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/sup\u003e. To our knowledge, the present study is the first to correlate the ceRNA network with the prognosis of esophageal cancer.\u003c/p\u003e \u003cp\u003eIn this study, we developed a relatively stable prognostic prediction model by using a comprehensive bioinformatics-clinical trait relationship. Our WGCNA analysis of the gene co-expression similarity and contiguity of module-trait identified 3594 mRNAs, which further enriched in DNA replication clarified by GO and KEGG analyses. It is worth noting that DNA replication errors sabotage genome integrity, accumulate genetic aberrations, and promote the pathogenesis of various diseases, including cancers \u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/sup\u003e. On the other hand, oncogenes can further interfere with DNA replication, trigger a series of cellular responses and targeted pathways, leading to the uncontrollable cell division, thereby promoting cancer progression.\u003c/p\u003e \u003cp\u003eThrough analysis, nine genes significantly related to prognosis of ESCC patients were identified. The five highly expressed genes, DYRK2, MOB4, RBM27, SNRPB2 and PDZD11 were significantly correlated with poor overall survival. DYRK2 (dual-specificity tyrosine-regulated kinase 2) is located in the cytoplasm and accumulates in the nucleus under demonic stress, which phosphorylates p53 and mediates apoptosis \u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/sup\u003e. Furthermore, DYRK2 also degrades the proteasome and regulates the cell cycle by combining c-Jun and c-Myc \u003csup\u003e(\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/sup\u003e. In breast cancer, patients with distinct copy-number variations (CNVs) signatures show a higher risk of relapse or metastasis. Validation by the in vitro cell experiments demonstrated that DYRK2 displayed lower expression in normal esophageal epithelial cells and may have an ability to promote tumor growth and metastasis. Among the reported CNVs, SNRPB2 is associated with low survival rates, and CRISPR/Cas9-mediated knockdown of SNRPB2 suppresses the proliferation and invasion potential of breast cancer cells \u003csup\u003e(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/sup\u003e. MOB4 is a member of the MOB family and is phosphorylated by germinal center kinases (GCK) \u003csup\u003e(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/sup\u003e. RBM27 (RNA binding motif protein 27) is a cytoplasmic protein. PDZD11 (PDZ domain-containing protein 11) is an interactor of PLEKHA7, which is a junctional protein that stabilizes the cadherin protein complex \u003csup\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/sup\u003e. However, the functional implications of MOB4, RBM27 and PDZD11 remains to be further investigated.\u003c/p\u003e \u003cp\u003eWe also found that lower expression of TNRC6B, TBC1D13, JOSD1 and YAP1 was significantly associated with poorer prognosis of ESCC patients. TNRC6B (trinucleotide repeat-containing gene 6B) is required for miRNA-dependent translational repression and is reported to be one of the two core protein components of human multi-protein assemblies using microRNAs \u003csup\u003e(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/sup\u003e. TBC1D13 is a Rab GTPase activator protein \u003csup\u003e(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/sup\u003e, which plays an essential role in GLUT transport in adipocytes \u003csup\u003e(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/sup\u003e. Although the function of TBC1D13 is yet to be investigated, metabolic alterations are hallmarks of cancers, making TBC1D13 an intriguing target for further investigations. However, it is contrary to a previous study, which suggested that the deubiquitinating enzyme JOSD1 inhibits mitochondrial apoptosis by stabilizing the MCL1 protein and promotes acquired chemoresistance in gynecological cancer cell lines \u003csup\u003e(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/sup\u003e. Our prediction indicate that ESCC patients with lower JOSD1 are significantly associated with poor prognosis. Similarly, the lower expression of the candidate oncogene YAP1 (yes-associated protein 1) is significantly associated with poor prognosis. Although YAP1 amplification and overexpression are often found to be poor prognostic factors in various cancers, including breast, pancreatic and hepatic cancers, it is also widely reported as a tumor suppressor \u003csup\u003e(\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/sup\u003e. Notably, in the process of cisplatin-induced DMA injury, YAP1 promotes p73-induced apoptosis, is a mediator of c-Jun-dependent apoptosis, and can be phosphorylated by Akt, thereby inhibiting various pro-apoptotic genes \u003csup\u003e(\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/sup\u003e. Further investigations are warranted to determine the functional roles of the above mentioned genes in ESCC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, in this study, we developed a co-expression network of lncRNA-miRNA-mRNA ceRNA through bioinformatics analysis, and identified several DEGs with prognostic values in ESCC. Although our work has promoted a new understanding of the role of ceRNA regulatory networks in ESCC, this study has some limitations. First, this study lacks detailed analysis of correlated gene expression and clinicopathological characteristics, such as disease stage, pathological subtype, histological grade, number of lymph node metastasis and distant metastasis. Second, this study focuses on the \u003cem\u003ein silico\u003c/em\u003e prediction of potential biomarkers in ESCC and lacks \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experimental support for validation. Further experimental verification is necessary to explore the potential functions of the identified genes and ceRNA in ESCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003ePatient consent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical review reports\u003c/h2\u003e \u003cp\u003eHenan Hospital of Traditional Chinese Medicine(The Second Affiliated Hospital of Henan University of Traditional Chinese Medicine)\u003c/p\u003e \u003cp\u003eEthics No: HNSZYYWZ-20240023\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was financially supported by Youth Found of the National Natural Science Foundation of China (project code: 82204981).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJun-Hui Guo and Tian-Wen Xu conceived and designed the study and drafted the manuscript. Bei-Bei Liu, Jun-Hui Chen collected the data and drafted the manuscript. Si-Run Du, Chang Liu, Dong-Dong Li，Xin-Xin Wang, Xu Wang, Lu-Yuan Bai, Pei-Min Liu, Chun-Zheng Ma and Yu-Ling Zheng comment on and revised the manuscript. All authors read and approved final version.The authors declare no conflict of interest.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset generated during and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFerlay, J. et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. \u003cem\u003eInt. J. Cancer\u003c/em\u003e. \u003cb\u003e127\u003c/b\u003e (12), 2893\u0026ndash;2917 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasouli, M. et al. Survival analysis of patients with esophageal cancer using parametric cure model. \u003cem\u003eAsian Pac. J. Cancer Prev.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (9), 2359\u0026ndash;2363 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamzan, Z., Nassri, A. B. \u0026amp; Huerta, S. The use of imaging and biomarkers in diagnosing Barrett's esophagus and predicting the risk of neoplastic progression. \u003cem\u003eExpert Rev. Mol. Diagn.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (5), 575\u0026ndash;591 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalmena, L., Poliseno, L., Tay, Y., Kats, L. \u0026amp; Pandolfi, P. P. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA. \u003cem\u003elanguage? Cell.\u003c/em\u003e \u003cb\u003e146\u003c/b\u003e (3), 353\u0026ndash;358 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi, X. et al. ceRNA in cancer: possible functions and clinical implications. \u003cem\u003eJ. Med. Genet.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e (10), 710\u0026ndash;718 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAla, U. et al. Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments. \u003cem\u003eProc. Natl. Acad. Sci. U S A\u003c/em\u003e. \u003cb\u003e110\u003c/b\u003e (18), 7154\u0026ndash;7159 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreco, S., Gaetano, C. \u0026amp; Martelli, F. Long Noncoding Competing Endogenous RNA Networks in Age-Associated Cardiovascular Diseases. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e ;\u003cb\u003e20\u003c/b\u003e(12). (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, J., Zhao, S. \u0026amp; Tian, F. SP1-mediated lncRNA PVT1 modulates the proliferation and apoptosis of lens epithelial cells in diabetic cataract via miR-214-3p/MMP2 axis. \u003cem\u003eJ. Cell. Mol. Med.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e (1), 554\u0026ndash;561 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, B., Bai, Y., Kang, W., Zhang, X. \u0026amp; Jiang, X. LncRNA SNHG5 regulates imatinib resistance in chronic myeloid leukemia via acting as a CeRNA against MiR-205-5p. \u003cem\u003eAm. J. Cancer Res.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (8), 1704\u0026ndash;1713 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, Z. et al. Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis. \u003cem\u003eInt. J. Biol. Sci.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (2), 124\u0026ndash;136 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, X. G. et al. Identifying miRNA and gene modules of colon cancer associated with pathological stage by weighted gene co-expression network analysis. \u003cem\u003eOnco Targets Ther.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 2815\u0026ndash;2830 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangfelder, P. \u0026amp; Horvath, S. WGCNA: an R package for weighted correlation network analysis. \u003cem\u003eBMC Bioinform.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 559 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (1), 25\u0026ndash;29 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M., Goto, S., Furumichi, M., Tanabe, M. \u0026amp; Hirakawa, M. KEGG for representation and analysis of molecular networks involving diseases and drugs. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e (Database issue), D355\u0026ndash;D360 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHart, J. et al. Recommendations for Prehospital Airway Management in Patients with Suspected COVID-19 Infection. \u003cem\u003eWest. J. Emerg. Med.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (4), 809\u0026ndash;812 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, M., Sun, Y., Wu, J. \u0026amp; Liu, G. Identification of Hub Genes in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis. \u003cem\u003eMed. Sci. Monit.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, e922107 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan, C. N., Ma, L. \u0026amp; Liu, N. Systematic analysis of lncRNA-miRNA-mRNA competing endogenous RNA network identifies four-lncRNA signature as a prognostic biomarker for breast cancer. \u003cem\u003eJ. Transl Med.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (1), 264 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiulietti, M., Righetti, A., Principato, G. \u0026amp; Piva, F. LncRNA co-expression network analysis reveals novel biomarkers for pancreatic cancer. \u003cem\u003eCarcinogenesis\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e (8), 1016\u0026ndash;1025 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J. D. et al. Prediction of competing endogenous RNA coexpression network as prognostic markers in AML. \u003cem\u003eAging (Albany NY)\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e (10), 3333\u0026ndash;3347 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, B. et al. Weighted correlation network and differential expression analyses identify candidate genes associated with BRAF gene in melanoma. \u003cem\u003eBMC Med. Genet.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (1), 54 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoyer, A. S., Walter, D. \u0026amp; Sorensen, C. S. DNA replication and cancer: From dysfunctional replication origin activities to therapeutic opportunities. \u003cem\u003eSemin Cancer Biol.\u003c/em\u003e \u003cb\u003e37\u0026ndash;38\u003c/b\u003e, 16\u0026ndash;25 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaira, N., Nihira, K., Yamaguchi, T., Miki, Y. \u0026amp; Yoshida, K. DYRK2 is targeted to the nucleus and controls p53 via Ser46 phosphorylation in the apoptotic response to DNA damage. \u003cem\u003eMol. Cell.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (5), 725\u0026ndash;738 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaira, N. et al. DYRK2 priming phosphorylation of c-Jun and c-Myc modulates cell cycle progression in human cancer cells. \u003cem\u003eJ. Clin. Invest.\u003c/em\u003e \u003cb\u003e122\u003c/b\u003e (3), 859\u0026ndash;872 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMimoto, R. et al. DYRK2 controls the epithelial-mesenchymal transition in breast cancer by degrading Snail. \u003cem\u003eCancer Lett.\u003c/em\u003e \u003cb\u003e339\u003c/b\u003e (2), 214\u0026ndash;225 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuazon, P. T. \u0026amp; Traugh, J. A. Casein kinase I and II\u0026ndash;multipotential serine protein kinases: structure, function, and regulation. \u003cem\u003eAdv. Second Messenger Phosphoprot. Res.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 123\u0026ndash;164 (1991).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, L. et al. Novel RNA-Affinity Proteogenomics Dissects Tumor Heterogeneity for Revealing Personalized Markers in Precision Prognosis of Cancer. \u003cem\u003eCell. Chem. Biol.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (5), 619\u0026ndash;633 (2018). e5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHergovich, A. MOB control: reviewing a conserved family of kinase regulators. \u003cem\u003eCell. Signal.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (9), 1433\u0026ndash;1440 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerrera, D. et al. PLEKHA7 Recruits PDZD11 to Adherens Junctions to Stabilize Nectins. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e291\u003c/b\u003e (21), 11016\u0026ndash;11029 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSack, L. M. et al. Profound Tissue Specificity in Proliferation Control Underlies Cancer Drivers and Aneuploidy Patterns. \u003cem\u003eCell\u003c/em\u003e \u003cb\u003e173\u003c/b\u003e (2), 499\u0026ndash;514 (2018). e23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGapud, E. J., Kronbichler, A., Gauckler, P. \u0026amp; Geetha, D. Immunotherapy for ANCA-associated vasculitis during the COVID-19 pandemic. \u003cem\u003eEur. J. Rheumatol.\u003c/em\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavey, J. R. et al. TBC1D13 is a RAB35 specific GAP that plays an important role in GLUT4 trafficking in adipocytes. \u003cem\u003eTraffic\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (10), 1429\u0026ndash;1441 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, X. et al. JOSD1 inhibits mitochondrial apoptotic signalling to drive acquired chemoresistance in gynaecological cancer by stabilizing MCL1. \u003cem\u003eCell. Death Differ.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (1), 55\u0026ndash;70 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, B. et al. Inactivation of YAP oncoprotein by the Hippo pathway is involved in cell contact inhibition and tissue growth control. \u003cem\u003eGenes Dev.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (21), 2747\u0026ndash;2761 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZender, L. et al. Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach. \u003cem\u003eCell\u003c/em\u003e \u003cb\u003e125\u003c/b\u003e (7), 1253\u0026ndash;1267 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamargo, F. D. et al. YAP1 increases organ size and expands undifferentiated progenitor cells. \u003cem\u003eCurr. Biol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (23), 2054\u0026ndash;2060 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrano, S. et al. The transcriptional coactivator Yes-associated protein drives p73 gene-target specificity in response to DNA Damage. \u003cem\u003eMol. Cell.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (4), 447\u0026ndash;459 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanovi, S. A. et al. Yes-associated protein (YAP) is a critical mediator of c-Jun-dependent apoptosis. \u003cem\u003eCell. Death Differ.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (1), 217\u0026ndash;219 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan, M. et al. Yes-associated protein (YAP) functions as a tumor suppressor in breast. \u003cem\u003eCell. Death Differ.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (11), 1752\u0026ndash;1759 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuevedo-Ramirez, A., Al-Kassab-Cordova, A., Mendez-Guerra, C., Cornejo-Venegas, G. \u0026amp; Alva-Chavez, K. P. Altitude and excess mortality during COVID-19 pandemic in Peru. \u003cem\u003eRespir Physiol. Neurobiol.\u003c/em\u003e :103512. (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"esophageal squamous cell carcinoma, competing endogenous RNAs, prognosis, non-coding RNAs, mRNAs","lastPublishedDoi":"10.21203/rs.3.rs-5721822/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5721822/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEsophageal squamous cell carcinoma (ESCC) is one of the most common malignant tumours arose from the esophagus. ESCC is highly lethal due to the late onset of symptoms and therefore, there is an urgent need to deepen the molecular understanding of this disease and identify potential prognostic biomarkers to further guide ESCC treatment. As a type of non-coding RNAs, competing endogenous RNA (ceRNA) reveals a novel mechanism of interaction between RNAs in various cancers. However, the understanding of the ceRNA regulatory network in ESCC is still unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, RNA-seq and clinicopathological characteristics data of ESCC and normal esophageal tissues was obtained from TCGA and GTEx database, respectively. Differentially expressed genes (DEGs) between ESCC and normal esophagel tissues were identified by employing R package (edgeR). Functional enrichment analysis of these DEGs was performed through the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes-Gene Set Enrichment Analysis (KEGG-GSEA). Subsequently, multivariate cox and survival analysis were performed to evaluate these DEGs. Then the expression of these 9 DEGs was investigated through Q-PCR in normal esophageal epithelial and ESCC cells.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to normal tissues, a total of 794 mRNAs were up-regulated and 1118 mRNAs were down-regulated in ESCC. The results of GO analysis showed an enrichment of the up-regulated genes in leukocyte migration, humoral immune response, phagocytosis and complement activation. Meanwhile, the results of KEGG-GSEA analysis showed an enrichment of the up-regulated genes in cell cycle, p53 signaling pathway and extracellular matrix receptor interaction, while an enrichment of the down-regulated genes in vascular smooth muscle contraction, ribosome and oxidative phosphorylation. The survival analysis identified significant association of poor prognosis with five up-regulated genes and four down-regulated genes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study identified several differential expression genes with prognostic values, and these genes may provide new insights into the roles of ceRNA regulatory network in ESCC.\u003c/p\u003e","manuscriptTitle":"Weighted correlation network and differential expression analyses identify prognostic lncRNA-miRNA-mRNA-ceRNA regulatory network in esophageal squamous cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 12:15:52","doi":"10.21203/rs.3.rs-5721822/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-26T01:27:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177034564267034254928747862841130487838","date":"2026-02-24T04:15:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274774110959099454322675566243386555176","date":"2025-07-03T23:55:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T01:39:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313832794857552365508611721137012884719","date":"2025-04-03T14:41:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-17T01:04:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-17T01:01:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-16T11:29:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-16T10:37:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-12-27T13:14:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d940f1ec-2baf-4b66-9d6e-ceed815eb815","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":43073252,"name":"Biological sciences/Cancer/Gastrointestinal cancer/Oesophageal cancer"},{"id":43073253,"name":"Biological sciences/Cancer"},{"id":43073254,"name":"Health sciences/Biomarkers"},{"id":43073255,"name":"Health sciences/Gastroenterology"}],"tags":[],"updatedAt":"2025-01-20T12:15:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-20 12:15:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5721822","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5721822","identity":"rs-5721822","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}