Study on DLGAP5 and MELK as potential biomarkers in esophageal squamous cell carcinoma

Study on DLGAP5 and MELK as potential biomarkers 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 Research Article Study on DLGAP5 and MELK as potential biomarkers in esophageal squamous cell carcinoma Yu Su, Jin Yang, Jie Yang, Kexiong Song, Mengmeng Li, Yuchen Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8197956/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : The objective of this research was to investigate the high expression of DLGAP5 and MELK as molecular biomarkers involved in the regulation of the occurrence in ESCC. Methods : Through bioinformatics analysis, ESCC-related datasets GSE17351 and GSE20347 were downloaded from GEO database. In addition, de-batch treatment, DEGs screening, functional enrichment analysis, GSEA, WGCNA, PPI network construction and analysis were carried out. Mapping gene expression calorimetry. Immunoinfiltration analysis and CTD analysis were conducted. TargetScan was employed to identify miRNA that regulate DEG. RT-qPCR was used to detect the relative mRNA expression levels of potential biomarkers. Results : The results showed that 1144 DEGs were identified, which were primarily focused on biological processes such as cell cycle, REDOX enzyme activity, serine hydrolase activity, cell proliferation and tissue development. KEGG analysis highlighted that these DEGs were predominantly enriched in fatty acid metabolism, IL-17 signaling pathway and p53 signaling pathway. For Metascape, there were positive regulation of DNA metabolism,cell cycle and mitotic cytoplasmic division in the enrichment project of GO. Then, DLGAP5 and MELK were identified as potential biomarkers in ESCC. We found that DLGAP5 and MELK were highly expressed in ESCC group. CTD analysis found that these biomarkers were related to precancerous lesions,esophageal diseases and pain. RT-qPCR further verified that the high expression of DLGAP5 and MELK in mRNA levels of ESCC. Conclusion : DLGAP5 and MELK were over-expressed in ESCC and play roles in its occurrence and progression, making them potential biomarkers for screening. DLGAP5 MELK ESCC Genetic marker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Background Esophageal squamous cell carcinoma (ESCC) originates in the esophageal endothelial cells, as the tumor progresses, exhibits invasive spread to adjacent tissues and organs [ 1 ] .The five-year survival rate of ESCC varies significantly by stage at diagnosis, reaching more than 50% in the early stages and less than 20% in the later stages [ 2 ] . ESCC is characterized by its highly invasive nature, with a propensity to infiltrate through the layers of the esophageal wall and, in advanced cases, to penetrate beyond the wall to invade adjacent structures or organs [ 3 , 4 ] . The histological characteristics of ESCC are primarily irregular squamous epithelial hyperplasia, accompanied by cellular atypia and variations in differentiation [ 5 ] .The treatment approaches principally include surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy, depending on the location, tumor stage and overall health status of the patient [ 6 ] . Nevertheless, the etiology of ESCC remains uncertain and may involve gene fusions,chromosomal abnormalities, genetic factors, and other molecular alterations. Consequently, uncovering the molecular mechanisms of ESCC is of considerable importance. Bioinformatics is an interdisciplinary area that combines biology, mathematics,computer science and statistics to understand vast amounts of biological data. With the advancement of high-throughput sequencing technologies and the accumulation of diverse biological experimental data, bioinformatics has become increasingly crucial in modern biological research [ 7 ] . By employing sophisticated algorithms and computational models, bioinformatics enables scientists to unravel the complexities of biological systems, thereby driving progress in fundamental biology, medicine, and biotechnology. As the scale of data continues to expand and the types of data become increasingly complex, bioinformatics faces new opportunities, offering unprecedented opportunities to understand the essence of life [ 8 , 9 ] . Currently, the roles of DLGAP5 and MELK in ESCC remain poorly understood. In this study, we employed bioinformatics approaches to screen key DEGs between ESCC and normal tissues, followed by functional enrichment analysis. Our findings revealed that DLGAP5 and MELK were significantly up-regulated in ESCC, a result that was subsequently validated using RT-qPCR. Material and methods Microarray Data Processing The two gene expression profiles related to ESCC were retrieved from GEO database (https://www.ncbi.nlm.nih.gov/geo/). Specifically, GSE17351 was based on the platforms of the GPL570,comprising 5 ESCC samples and 5 normal tissue samples. GSE20347 was based on the platforms of the GPL571,comprising 17 ESCC samples and 17 normal tissue samples. These two GEO platforms were integrated into a merged dataset using the “inSilicoMerging” R package to eliminate batch effects. Differential expression analysis Differentially expressed genes (DEGs) between normal and ESCC groups in merged dataset were screened using “limma” package with thresholds set at P-value2. The results were visualized via volcano plots. GO and KEGG Functional enrichment analysis was performed across three domains of GO: MF, CC and BP. The KEGG functions of the DEGs were analyzed via enrichment analysis using the clusterProfiler R package.With the minimum gene set size set at 5 and the maximum at 5000, a P value of <0.05 ＆ a FDR of <0.25 were deemed statistically significant thresholds. GSEA GSEA was used to find potential functions of hub genes by clusterProfiler package (adj.p < 0.05). Metascape was utilized to carry out functional enrichment analysis and subsequently export the list of DEGs. WGCNA The MAD was computed individually for each gene based on the gene expression profile, and the top 50% of genes displaying the lowest MAD values were excluded. The outlier genes and samples were removed using the goodSamplesGenes method of the R package WGCNA.Based on the TOM-based dissimilarity measure, a minimum module size of 30 genes was established. The sensitivity parameter was set to 3. To further investigate the identified modules, the differences between module eigengenes were calculated. Notably, modules with a dissimilarity distance under 0.25 were merged, while the grey module, representing genes that could not be assigned to any specific module, was not included in further analysis. PPI The STRING online database was utilized to analyze protein-protein interactions (PPI). The PPI pairs with confidence scores >0.40 were screened, which was subsequently visualized using Cytoscape software.The importance of each node was evaluated by three algorithms (MCC, MNC, and EPC) through CytoHubba. The top 10 hub nodes were selected, and the hub genes were identified as the common nodes among them. The R package heatmap was utilized to create a heat map of the expression of the core genes found by the three algorithms. Immune infiltration analysis CIBERSORT, a popular method for calculating immune cell infiltration, utilizes the LM22 gene set to delineate 22 immune cell subsets. In this study, CIBERSORT was used to analyze the differential expression of 22 immune cells in the normal and the ESCC groups. Correlations between the differentially expressed immune cells were further analyzed. Construction of regulatory networks The CTD was utilized to identify diseases related to the core genes. Importantly, predictions of miRNAs targeting the core genes were made with the help of TargetScan (www.targetscan.org). Molecular docking UniProt database served as the source for obtaining the protein sequences of the core genes. Subsequently, the protein complex structures were predicted using AlphaFold3 based on their amino acid sequences (https://alphafoldserver.com/). The protein complexes were then processed using the Protein Preparation Wizard module in the Schrodinger 2019.01 software platform, which involved adding missing hydrogen atoms and repairing missing bond information. The proteins were then subjected to energy minimization and geometric optimization using the OPLS3e force field with constraints. Finally, the conformations were analyzed for binding affinity using the MMGBSA module, with the sampling method set to Minimize, the solvation model set to VSGB, and the force field set to OPLS3e. The models were visualized and analyzed using PyMOL 2.1 software. Quantitative fluorescence quantitative PCR experiments First, RNA is extracted and then reverse-transcribed into cDNA. Primers, template nucleic acids, SYBR Green fluorescent dye, probes, reaction buffer, and Taq enzyme are mixed to prepare the reaction system. The amplification program is set using a qPCR instrument, which includes cycles of pre-denaturation, denaturation, annealing, and extension. Fluorescence signals are detected at the end of each cycle, with the fluorescence intensity being proportional to the amount of amplified product. The initial quantity of the target nucleic acid is calculated by analyzing the Ct value or using a standard curve. Results Identification and functional enrichment of the DEGs in ESCC Within GSE17351 &GSE20347, 1,144 DEGs were identified in ESCC (Figure1). GO analysis revealed that these DEGs were primarily concentrated in cell cycle, oxidoreductase activity, serine hydrolase activity, cell proliferation, and tissue development (Figure 2A, B, C). The KEGG analysis highlighted that they were primarily enriched in IL-17 signaling pathway, p53 signaling pathway, and fatty acid metabolism.Subsequently, by intersecting the results from GSEA, GO, and KEGG analyses, it was found that these DEGs were related to cell cycle, oxidoreductase activity, serine hydrolase activity, and fatty acid metabolism (Figure2E, F, G, H) (Figure3 A,3B,C, Figure4). Determination of the Key Module Genes in ESCC A co-expression gene network was established based on those DEGs through WGCNA. The optimal soft-thresholding power was determined to be β = 6 (R² > 0.85) (Figure5A, B). Then, a hierarchical clustering dendrogram was constructed (Fig.5C, D). In this study, the heat-map of module-trait correlations (Figure 6A) and the scatter plots of GS-MM correlations for the associated hub genes were presented（Figure6B-E, Figure7A-F）. Ultimately, 508 genes (|MM| > 0.85) were designated as key module genes. Identification of the key biomarkers Totally 360 candidate genes were determined by taking intersection of 1,144 DEGs and 508 key module genes（Figure 8A). To further screen hub genes, PPI network were performed（Figure8B）.Then 3 biomarkers, including DLGAP5, MELK and KIF2C were screened in ESCC(Figure9A-CB). Additionally, DLGAP5 and MELK are over-expressed in the ESCC group compared with the normal group, which may have a regulatory role in the occurrence of ESCC（Figure10A）.Our CTD analysis showed that DLGAP5 and MELK have connections with Barrett's esophagus, precancerous lesions, and pain（Figure10B,C）. Immune Infiltration Profiling and Network Analysis To investigate the immune microenvironment of ESCC, we presented the abundance of 22 immune cells across different sample groups (Figure 11A,B). Notably, the co-expression patterns between immune cell components were depicted in Figure 11C. In this study, inputting the hub gene lists into TargetScan allowed to identify the relevant miRNAs and better understand gene expression regulation. The relevant miRNA for the DLGAP5 was found to be hsa-miR-409-5p, and the relevant miRNA for MELK was hsa-miR-802. Expression validation of the biomarkers Significantly higher relative mRNA expression levels of DLGAP5 and MELK were observed in cancer cells compared to control normal cells using RT-PCR (*P<0.05) (Figure 12). Analysis of protein interaction between DLGAP5 and MELK（Table 1） The binding of DLGAP5 to MELK protein is divided into-180.36 kcal / mol, The binding sites of the DLGAP5 proteins include: ARG-110, LYS-398, ASP-507, LYS-304, GLY-121, ARG-9, GLN-516, ASP-519, ASN-251, GLN-564, ASP-257, Of amino acid residues such as GLU-17, The binding sites for MELK include GLU-272, ASP-284, LYS-219, ILE-237, ASN-259, LEU-559, GLN-181, LYS-183, ASP-227,TYR-163, LYS-225, And amino acid residues such as LYS-145. DLGAP5 Contact residues with the MELK protein are able to form multiple interactions, Such as the hydrogen bond (LYS-304:ILE-237, GLY-121:ASN-259,ARG-9:LEU-559, GLN-516:GLN-181, ASN-251:ASP-227,GLN-564:TYR-163), Salt Bridge(ARG-110:GLU-272, LYS-398:ASP-284,ASP-507:LYS-219, ASP-519:LYS-183, ASP-257:LYS-225, GLU-17: LYS-145) (table1).In addition, we can find that DLGAP5 and MELK protein surface match well, which facilitates the formation of a stable binding effect (Figure 13). Table 1 The docking results of two target protein Protein1 Protein2 Binding Energy (kcal/mol) Contact Sites (protein1) Contact Sites (protein2) Combination Type DLGAP5 MELK -180.36 ARG-110, LYS-398, ASP-507, LYS-304, GLY-121, ARG-9, GLN-516, ASP-519, ASN-251, GLN-564, ASP-257 , GLU-17 GLU-272, ASP-284, LYS-219, ILE-237, ASN-259, LEU-559, GLN-181, LYS-183, ASP-227, TYR-163, LYS-225, LYS-145 Salt bridge, Hydrogen bond, Hydrophobic interaction Discussion ESCC is marked by high morbidity and mortality rates, aggressive invasiveness, and a marked propensity for rapid dissemination via lymphatic and hematogenous system, ultimately resulting in a poor prognosis [ 10 ] . Further exploration of the molecular mechanism of ESCC can explicit the research direction for its targeted drug therapy. The principal result of this study was the identification of high expression levels of DLGAP5 and MELK in ESCC, so DLGAP5 and MELK can serve as the biomarkers involved in regulating the occurrence of ESCC. The Discs Large Associated Protein 5 (DLGAP5) situated on chromosome 14q22.3 and encodes a protein [ 11 ] , that is related to cell cycle regulation and microtubule dynamics. DLGAP5 is significantly up-regulated expressed in G2/M stage of “cell cycle” and participates in chromosome segregation and spindle formation and functional maintenance during mitosis through interactions with other cell cycle regulatory proteins and microtubule-associated proteins. DLGAP5 promotes spindle assembly and stability by binding to microtubules, ensuring proper segregation of [ 12 ] by chromosomes during cell division. Further, DLGAP5 may contribute to mitophagy, a mechanism involving the selective degradation of mitochondria through autophagy [ 13 ] . Growing evidence indicates that dysregulated mitophagy is crucial in tumorigenesis and malignant progression, potentially influencing therapeutic outcomes in cancer treatment [ 14 ] . As a cell cycle regulator implicated in carcinogenesis, DLGAP5 plays an important role in cell cycle regulation, spindle formation, and cancer progression. In this study, DLGAP5 and MELK were selected as the core targets of ESCC to determine whether they could be used as one of the biomarkers in ESCC. Several studies have shown that DLGAP5 is an important regulator of the cytoskeleton reorganization and microtubule dynamics, which are vital for the migratory of tumor cells [ 16 ] . Over-expression of DLGAP5 can enhance the stability of the cytoskeleton, thereby promoting the invasiveness and metastatic potential of tumor cells. Additionally, DLGAP5 may regulate the interaction between tumor cells and the surrounding stroma, enhancing the invasiveness of tumor cells and facilitating both local and distant metastasis [ 17 ] . A growing body of research has shown that the tumor microenvironment, with its infiltrating immune cells, is key to tumor initiation and progression, thereby influencing the prognosis of cancer patients [ 18 – 19 ] . The experimental results presented in Section 2.7 of this study reveal that differential expression genes centered around DLGAP5 and MELK were analyzed for immune cell infiltration in ESCC, providing localization analysis of the important immune cells that affect ESCC. Aberrant expression of DLGAP5 was related to tumorigenesis and metastasis, which makes it a cancer biomarker and therapeutic target [ 20 ] . Moreover, tt has been demonstrated that the up-regulation of PLK1 by DLGAP5 contributes to the growth of lung adenocarcinoma [ 21 ] . An increase in DLGAP5 mRNA expression is associated with adverse prognoses in breast cancer [ 22 ] . Pan-cancer analysis and cell line experiments have further shown that a slight up-regulation of DLGAP5 expression is closely associated with the progression of renal clear cell carcinoma [ 23 ] . The relationship between the DLGAP5 and ESCC is primarily reflected in its function as a cancer-related gene. In ESCC tissues, DLGAP5 expression is significantly higher than that in normal esophageal tissues. Specifically, the over-expression of DLGAP5 might result in unregulated cell cycle progression, which in turn stimulates the proliferation and invasion of cancer cells, ultimately fueling cancer progression and metastasis. In this study, we thought that DLGAP5 and MELK were screened as core genes in ESCC, which were highly expressed in ESCC tissue. MELK was a serine/threonine kinase that is mainly expressed during the G2/M phase of the cell cycle and in mitosis. It regulated spindle assembly, chromosome segregation, and cell division by phosphorylating target proteins, thereby affecting cell cycle progression and cell proliferation [ 24 ] . In this study, we found DLGAP5 and MELK were mainly enriched in cell cycle, oxidoreductase activity, serine hydrolase activity, cell proliferation, and tissue development. Several studies have demonstrated that MELK is a participant of the serine-threonine protein kinase family, specifically the non-sucrose fermenting/amino acid-activated protein kinase subfamily [ 25 ] . MELK is essential for embryonic development and the maintenance of adult stem cells, and it also plays significant roles in the self-renewal and differentiation of stem cells [ 26 ] . In this study, we further reveal that DLGAP5 and MELK were also associated with Barrett's esophagus, precancerous lesions, and pain. MELK exhibited abnormal over-expression across a range of cancers, with high levels of MELK correlating with cancer cell proliferation, invasion, and metastasis [ 27 ] . During mitosis, MELK was closely associated with spindle assembly, chromosome segregation, and normal progression for “cell cycle”. MELK affected mitosis by regulating cell-cycle-related proteins. Specifically, it may function by affecting the cytoskeleton, microtubule dynamics, and spindle assembly, promoting cancer cell migration and invasion, thereby driving cancer development [ 28 ] . The over-expression of MELK was associated with enhanced proliferative capacity and invasiveness of ESCC cells, likely by promoting cell cycle progression and mitosis, thereby enhancing the growth and spread of ESCC cells.The experimental results in this study show that the miRNA related to DLGAP5 regulation is hsa-miR-409-5p, while the miRNA associated with MELK regulation is hsa-miR-802. Related studies have also shown that MELK promotes tumor metastasis in ESCC by stimulating the FOXM1 signaling pathway. MELK may also promote the metastasis of ESCC by activating the NF-κB pathway [ 29 ] . In ESCC, elevated levels of MELK were strongly associated with tumor progression, increased invasiveness, and unfavorable prognosis. Although there are few studies directly investigating the interaction between DLGAP5 and MELK in ESCC, existing research has hinted at potential links between the two in certain functions and pathways. MELK may activate upstream factors that regulate DLGAP5 expression through its kinase activity, thereby enhancing tumor cell division. The high expression of both DLGAP5 and MELK may act in concert to intensified the invasiveness of tumor cells. This study, through systematic bioinformatics analysis and experimental validation, strongly demonstrated the over-expression of DLGAP5 and MELK in ESCC, offering convincing evidence to elucidate the molecular mechanisms underlying ESCC. However, this study still has some limitations, as it failed to carry out animal experiments featuring gene over-expression or knockout to further verify the molecular functions. Thus, future studies should probe into this field for a more thorough investigation. Conclusion In ESCC tissue, there is a marked elevation in the expression levels of DLGAP5 and MELK, which may participate in the regulation of expression in ESCC and may serve as potential biomarkers for ESCC screening and intervention. Intensive research and intervention on the expression regulation of DLGAP5 and MELK may be a key breakthrough point in the treatment of this disease. Declarations Acknowledgments Not applicable. Author contributions Conceived and designed the experiments: Jin Yang, Yu Su, Mengmeng Li. Performed the experiments: Jin Yang, Kexiong Song, Yuchen Wang. Statistical analysis: Yu Su, Jie Yang. Wrote the paper: Yu Su, Jin Yang. All authors have read and approved the final manuscript. Clinical trial number Not applicable. Consent to participate Not applicable. This study did not involve human participants or samples. Consent to publish Not applicable. This study did not involve individual person’s data. Conflicts of interest The authors declare that they have no competing interests. Ethical approval This study did not involve human participants, human tissue samples, or animal experiments. All data were obtained from publicly available databases. Data availability statements The microarray datasets analyzed during this study are available in GEO database of the NCBI GSE17351: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17351. GSE20347: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20347. Funding This research reported in this project was generously supported by Hebei Provincial Health Commission under grant agreement number 20230153. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. References Hsu PK, Lee YY, Chuang LC, Wu YC. Lymph Node Dissection for Esophageal Squamous Cell Carcinoma. Thorac Surg Clin. 2022. 32(4): 497-510. An L, Li M, Jia Q. Mechanisms of radiotherapy resistance and radiosensitization strategies for esophageal squamous cell carcinoma. Mol Cancer. 2023. 22(1): 140. Li Y, Li Y, Chen X. NOTCH and Esophageal Squamous Cell Carcinoma. Adv Exp Med Biol. 2021. 1287: 59-68. Lam AK. Histopathological Assessment for Esophageal Squamous Cell Carcinoma. Methods Mol Biol. 2020. 2129: 7-18. Lam AK. Introduction: Esophageal Squamous Cell Carcinoma-Current Status and Future Advances. Methods Mol Biol. 2020. 2129: 1-6. 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Maternal embryonic leucine zipper kinase (MELK): a novel regulator in cell cycle control, embryonic development, and cancer. Int J Mol Sci. 2013. 14(11): 21551-60. Ye J, Deng W, Zhong Y, et al. MELK predicts poor prognosis and promotes metastasis in esophageal squamous cell carcinoma via activating the NF‑κB pathway. Int J Oncol. 2022. 61(2). 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. 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1","display":"","copyAsset":false,"role":"figure","size":2317855,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential gene analysis. A total of 1,144 DEGs were identified.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/62f2ce39c466d44a3defd602.png"},{"id":98871198,"identity":"e59c139a-f56e-4c24-a5da-a2a3bab1ab1e","added_by":"auto","created_at":"2025-12-23 12:00:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5518186,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of the DEGs.(A) bioprocess.(B) cell component.(C) The molecular function.(D) KEGG enrichment analysis.(E-H) Gene-set enrichment analysis.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/21ad0b2ef7377293ab839593.png"},{"id":98871201,"identity":"c99af682-c5a7-4c16-9223-bdd2ace0e17b","added_by":"auto","created_at":"2025-12-23 12:00:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5085033,"visible":true,"origin":"","legend":"\u003cp\u003eMetascape enrichment analysis. (A) Enrichment bar chart of the input gene list, colored by p-value. (B) Enriched term network: colored by cluster ID, nodes sharing the same cluster ID are typically close to each other. (C) Colored by p-value, items containing more genes often have significantly higher p-values.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/3993bd55b6a43f9c5ac18b53.png"},{"id":98871203,"identity":"43b776dc-3f89-4238-8489-00b836415bce","added_by":"auto","created_at":"2025-12-23 12:00:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6890038,"visible":true,"origin":"","legend":"\u003cp\u003eMetascape enrichment analysis. (A) Protein-protein interaction network. (B) MCODE components identified in the list of genes.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/5b4fa393d3401067c08b57be.png"},{"id":98871197,"identity":"f9d14be8-1cb7-404d-a806-f7524c73cdba","added_by":"auto","created_at":"2025-12-23 12:00:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3112432,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA.(A) β= 6,0.87. (B) β= 6,54.24. (C, D) Build a hierarchical cluster tree of all genes to generate important modules.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/6ff530960bb7ae50f64b0e09.png"},{"id":99309189,"identity":"76659555-6dd1-4cd2-9fba-50d00f1879da","added_by":"auto","created_at":"2025-12-31 16:09:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1723699,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Heat map of the correlation between module and phenotype.(B – E) Scatter plot of GS and MM correlation of related hub genes.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/67779143b14b33243798bb23.png"},{"id":99308969,"identity":"337369b9-fc9f-4ea2-ab6f-2476fe09b56a","added_by":"auto","created_at":"2025-12-31 16:09:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2444794,"visible":true,"origin":"","legend":"\u003cp\u003e(A – F) Scatter plot of GS and MM correlation of related hub genes.(G) DEGs and core genes, combined using a Venn diagram\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/e5af9aec334ea0cc3b8bf498.png"},{"id":99309171,"identity":"a28b6f79-0c2a-4515-bfeb-22546f099281","added_by":"auto","created_at":"2025-12-31 16:09:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":13328682,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and analysis of protein-protein interaction (PPI) network. (A) PPI network of DEGs. (B) Core gene clusters obtained by MCODE.\u003c/p\u003e","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/b067dc2b0f4f4d521c661735.png"},{"id":99309133,"identity":"c705ddba-0839-4ee0-a590-f88714d51c0d","added_by":"auto","created_at":"2025-12-31 16:09:47","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4022295,"visible":true,"origin":"","legend":"\u003cp\u003e(A) MCC identifies core genes. (B) MNC identifies core genes. (C) EPC is used to identify core genes. (F) Union is performed using a Venn diagram.\u003c/p\u003e","description":"","filename":"figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/8509317d83faab2fe2e42884.png"},{"id":99309063,"identity":"82449336-df85-488a-913b-5ef05ba911ae","added_by":"auto","created_at":"2025-12-31 16:09:42","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2834976,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Heat-map of the gene expression.(B, C) The CTD analysis.2 core genes (DLGAP5, MELK).\u003c/p\u003e","description":"","filename":"figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/fb744072d1e78db603216d87.png"},{"id":98871259,"identity":"6088f03b-e1fc-44e2-abe2-845cc2e2aa4c","added_by":"auto","created_at":"2025-12-23 12:00:32","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":7223906,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration.(A) Proportion of immune cells resulting to whole gene-expression matrix.(B) Heat-map of the immune cell expression.(C) Co-expression patterns between the immune cell fractions.\u003c/p\u003e","description":"","filename":"figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/721d53877a8c82e0c5c3ffe4.png"},{"id":98871209,"identity":"2522e0ab-f66c-4773-b594-dddde4ccb9f4","added_by":"auto","created_at":"2025-12-23 12:00:30","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":2745424,"visible":true,"origin":"","legend":"\u003cp\u003ePCR examined the expression of DLGAP5 and MELK in esophageal squamous cell carcinoma\u003c/p\u003e","description":"","filename":"figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/342de058ab8f4379e3f12130.png"},{"id":98871204,"identity":"6b564b08-403c-4d38-9621-67b57964cd17","added_by":"auto","created_at":"2025-12-23 12:00:30","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":2052405,"visible":true,"origin":"","legend":"\u003cp\u003eBinding mode of the complex DLGAP5-MELK.(A) The skeleton of the protein is shown in tubes, DLGAP5 in green and MELK in red.(B) Surface of the complex.(C) The detailed binding mode of DLGAP5 and MELK.\u003c/p\u003e","description":"","filename":"figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/b4ec8cb6f21023cad8b12134.png"},{"id":101304322,"identity":"b0a77dd9-224c-42fb-9277-b9ac228210a5","added_by":"auto","created_at":"2026-01-28 10:02:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":58730240,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8197956/v1/35165248-a3c1-448c-9f99-4f2e79e55b14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on DLGAP5 and MELK as potential biomarkers in esophageal squamous cell carcinoma","fulltext":[{"header":"Background","content":"\u003cp\u003eEsophageal squamous cell carcinoma (ESCC) originates in the esophageal endothelial cells, as the tumor progresses, exhibits invasive spread to adjacent tissues and organs\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.The five-year survival rate of ESCC varies significantly by stage at diagnosis, reaching more than 50% in the early stages and less than 20% in the later stages\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. ESCC is characterized by its highly invasive nature, with a propensity to infiltrate through the layers of the esophageal wall and, in advanced cases, to penetrate beyond the wall to invade adjacent structures or organs\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The histological characteristics of ESCC are primarily irregular squamous epithelial hyperplasia, accompanied by cellular atypia and variations in differentiation\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.The treatment approaches principally include surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy, depending on the location, tumor stage and overall health status of the patient \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the etiology of ESCC remains uncertain and may involve gene fusions,chromosomal abnormalities, genetic factors, and other molecular alterations. Consequently, uncovering the molecular mechanisms of ESCC is of considerable importance.\u003c/p\u003e \u003cp\u003eBioinformatics is an interdisciplinary area that combines biology, mathematics,computer science and statistics to understand vast amounts of biological data. With the advancement of high-throughput sequencing technologies and the accumulation of diverse biological experimental data, bioinformatics has become increasingly crucial in modern biological research\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. By employing sophisticated algorithms and computational models, bioinformatics enables scientists to unravel the complexities of biological systems, thereby driving progress in fundamental biology, medicine, and biotechnology. As the scale of data continues to expand and the types of data become increasingly complex, bioinformatics faces new opportunities, offering unprecedented opportunities to understand the essence of life\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrently, the roles of DLGAP5 and MELK in ESCC remain poorly understood. In this study, we employed bioinformatics approaches to screen key DEGs between ESCC and normal tissues, followed by functional enrichment analysis. Our findings revealed that DLGAP5 and MELK were significantly up-regulated in ESCC, a result that was subsequently validated using RT-qPCR.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eMicroarray Data Processing\u003c/p\u003e\n\u003cp\u003eThe two gene expression profiles related to ESCC were retrieved from GEO database (https://www.ncbi.nlm.nih.gov/geo/). Specifically, GSE17351 was based on the platforms of the GPL570,comprising 5 ESCC samples and 5 normal tissue samples. GSE20347 was based on the platforms of the GPL571,comprising 17 ESCC samples and 17 normal tissue samples. These two GEO platforms were integrated into a merged dataset using the \u0026ldquo;inSilicoMerging\u0026rdquo; R package to eliminate batch effects.\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis\u003c/p\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) between normal and ESCC groups in merged dataset were screened using \u0026ldquo;limma\u0026rdquo; package with thresholds set at P-value\u0026lt;0.05\u0026nbsp;＆|log₂FC|\u0026gt;2. The results were visualized via volcano plots.\u003c/p\u003e\n\u003cp\u003eGO and KEGG\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analysis was performed across three domains of GO: MF, CC and BP. The KEGG functions of the DEGs were analyzed via enrichment analysis using the clusterProfiler R package.With the minimum gene set size set at 5 and the maximum at 5000, a P value of \u0026lt;0.05\u0026nbsp;＆\u0026nbsp;a FDR of \u0026lt;0.25 were deemed statistically significant thresholds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSEA\u003c/p\u003e\n\u003cp\u003eGSEA was used to find potential functions of hub genes by clusterProfiler package (adj.p \u0026lt; 0.05). Metascape was utilized to carry out functional enrichment analysis and subsequently export the list of DEGs.\u003c/p\u003e\n\u003cp\u003eWGCNA\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The MAD was computed individually for each gene based on the gene expression profile, and the top 50% of genes displaying the lowest MAD values were excluded. The outlier genes and samples were removed using the goodSamplesGenes method of the R package WGCNA.Based on the TOM-based dissimilarity measure, a minimum module size of 30 genes was established. The sensitivity parameter was set to 3. To further investigate the identified modules, the differences between module eigengenes were calculated. Notably, modules with a dissimilarity distance under 0.25 were merged, while the grey module, representing genes that could not be assigned to any specific module, was not included in further analysis.\u003c/p\u003e\n\u003cp\u003ePPI\u003c/p\u003e\n\u003cp\u003eThe STRING online database was utilized to analyze protein-protein interactions (PPI). The PPI pairs with confidence scores \u0026gt;0.40 were screened, which was subsequently visualized using Cytoscape software.The importance of each node was evaluated by three algorithms (MCC, MNC, and EPC) through CytoHubba. The top 10 hub nodes were selected, and the hub genes were identified as the common nodes among them. The R package heatmap was utilized to create a heat map of the expression of the core genes found by the three algorithms.\u003c/p\u003e\n\u003cp\u003eImmune infiltration analysis\u003c/p\u003e\n\u003cp\u003eCIBERSORT, a popular method for calculating immune cell infiltration, utilizes the LM22 gene set to delineate 22 immune cell subsets. In this study, CIBERSORT was used to analyze the differential expression of 22 immune cells in the normal and the ESCC groups. Correlations between the differentially expressed immune cells were further analyzed.\u003c/p\u003e\n\u003cp\u003eConstruction of regulatory networks\u003c/p\u003e\n\u003cp\u003eThe CTD was utilized to identify diseases related to the core genes. Importantly, predictions of miRNAs targeting the core genes were made with the help of TargetScan (www.targetscan.org).\u003c/p\u003e\n\u003cp\u003eMolecular docking\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUniProt database served as the source for obtaining the protein sequences of the core genes. Subsequently, the protein complex structures were predicted using AlphaFold3 based on their amino acid sequences (https://alphafoldserver.com/). The protein complexes were then processed using the Protein Preparation Wizard module in the Schrodinger 2019.01 software platform, which involved adding missing hydrogen atoms and repairing missing bond information. The proteins were then subjected to energy minimization and geometric optimization using the OPLS3e force field with constraints. Finally, the conformations were analyzed for binding affinity using the MMGBSA module, with the sampling method set to Minimize, the solvation model set to VSGB, and the force field set to OPLS3e. The models were visualized and analyzed using PyMOL 2.1 software.\u003c/p\u003e\n\u003cp\u003eQuantitative fluorescence quantitative PCR experiments\u003c/p\u003e\n\u003cp\u003eFirst, RNA is extracted and then reverse-transcribed into cDNA. Primers, template nucleic acids, SYBR Green fluorescent dye, probes, reaction buffer, and Taq enzyme are mixed to prepare the reaction system. The amplification program is set using a qPCR instrument, which includes cycles of pre-denaturation, denaturation, annealing, and extension. Fluorescence signals are detected at the end of each cycle, with the fluorescence intensity being proportional to the amount of amplified product. The initial quantity of the target nucleic acid is calculated by analyzing the Ct value or using a standard curve.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIdentification and functional enrichment of the DEGs in ESCC\u003c/p\u003e\n\u003cp\u003eWithin GSE17351 \u0026amp;GSE20347, 1,144 DEGs were identified in ESCC (Figure1). GO analysis \u0026nbsp; revealed that these DEGs were primarily concentrated in cell cycle, oxidoreductase activity, serine hydrolase activity, cell proliferation, and tissue development (Figure 2A, B, C). The KEGG analysis highlighted that they were primarily enriched in IL-17 signaling pathway, p53 signaling pathway, and fatty acid metabolism.Subsequently, by intersecting the results from GSEA, GO, and KEGG analyses, it was found that these DEGs were related to cell cycle, oxidoreductase activity, serine hydrolase activity, and fatty acid metabolism (Figure2E, F, G, H) (Figure3 A,3B,C, Figure4).\u003c/p\u003e\n\u003cp\u003eDetermination of the Key Module Genes in ESCC\u003c/p\u003e\n\u003cp\u003eA co-expression gene network was established based on those DEGs through WGCNA. The optimal soft-thresholding power was determined to be \u0026beta; = 6 (R\u0026sup2; \u0026gt; 0.85) (Figure5A, B). Then, a\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehierarchical clustering dendrogram was constructed (Fig.5C, D). In this study, the heat-map of module-trait correlations (Figure 6A) and the scatter plots of GS-MM correlations for the associated hub genes were presented（Figure6B-E, Figure7A-F）. Ultimately, 508 genes (|MM| \u0026gt; 0.85) were designated as key module genes.\u003c/p\u003e\n\u003cp\u003eIdentification of the key biomarkers\u003c/p\u003e\n\u003cp\u003eTotally 360 candidate genes were determined by taking intersection of 1,144 DEGs and 508 key module genes（Figure 8A). To further screen hub genes, PPI network were performed（Figure8B）.Then 3 biomarkers, including DLGAP5, MELK and KIF2C were screened in ESCC(Figure9A-CB). Additionally, DLGAP5 and MELK are over-expressed in the ESCC group compared with the normal group, which may have a regulatory role in the occurrence of ESCC（Figure10A）.Our CTD analysis showed that DLGAP5 and MELK have connections with Barrett\u0026apos;s esophagus, precancerous lesions, and pain（Figure10B,C）.\u003c/p\u003e\n\u003cp\u003eImmune Infiltration Profiling and Network Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate the immune microenvironment of ESCC, we presented the abundance of 22 immune cells across different sample groups (Figure 11A,B). Notably, the co-expression patterns between immune cell components were depicted in Figure 11C. In this study, inputting the hub gene lists into TargetScan allowed to identify the relevant miRNAs and better understand gene expression regulation. The relevant miRNA for the DLGAP5 was found to be hsa-miR-409-5p, and the relevant miRNA for MELK was hsa-miR-802.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpression validation of the biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificantly higher relative mRNA expression levels of DLGAP5 and MELK were observed in cancer cells compared to control normal cells using RT-PCR (*P\u0026lt;0.05) (Figure 12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis of protein interaction between DLGAP5 and MELK（Table 1）\u003c/p\u003e\n\u003cp\u003eThe binding of DLGAP5 to MELK protein is divided into-180.36 kcal / mol, The binding sites of the DLGAP5 proteins include: ARG-110, LYS-398, ASP-507, LYS-304, GLY-121, ARG-9, GLN-516, ASP-519, ASN-251, GLN-564, ASP-257, Of amino acid residues such as GLU-17, The binding sites for MELK include GLU-272, ASP-284, LYS-219, ILE-237, ASN-259, LEU-559, GLN-181, LYS-183, ASP-227,TYR-163, LYS-225, And amino acid residues such as LYS-145. DLGAP5 Contact residues with the MELK protein are able to form multiple interactions, Such as the hydrogen bond (LYS-304:ILE-237, GLY-121:ASN-259,ARG-9:LEU-559, GLN-516:GLN-181, ASN-251:ASP-227,GLN-564:TYR-163), Salt Bridge(ARG-110:GLU-272, LYS-398:ASP-284,ASP-507:LYS-219, ASP-519:LYS-183, ASP-257:LYS-225, GLU-17: LYS-145) (table1).In addition, we can find that DLGAP5 and MELK protein surface match well, which facilitates the formation of a stable binding effect (Figure 13).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 The docking results of two target protein\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eProtein1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eProtein2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eBinding Energy (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eContact Sites (protein1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eContact Sites (protein2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eCombination\u003c/p\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eDLGAP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eMELK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-180.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eARG-110, \u0026nbsp; \u0026nbsp; LYS-398, ASP-507, LYS-304, GLY-121, ARG-9, GLN-516, ASP-519, ASN-251, GLN-564, ASP-257 , GLU-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eGLU-272, ASP-284, LYS-219, ILE-237, ASN-259, LEU-559, GLN-181, LYS-183, ASP-227, TYR-163, LYS-225, LYS-145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSalt bridge,\u003c/p\u003e\n \u003cp\u003eHydrogen bond,\u003c/p\u003e\n \u003cp\u003eHydrophobic interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eESCC is marked by high morbidity and mortality rates, aggressive invasiveness, and a marked propensity for rapid dissemination via lymphatic and hematogenous system, ultimately resulting in a poor prognosis \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Further exploration of the molecular mechanism of ESCC can explicit the research direction for its targeted drug therapy. The principal result of this study was the identification of high expression levels of DLGAP5 and MELK in ESCC, so DLGAP5 and MELK can serve as the biomarkers involved in regulating the occurrence of ESCC.\u003c/p\u003e \u003cp\u003eThe Discs Large Associated Protein 5 (DLGAP5) situated on chromosome 14q22.3 and encodes a protein \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, that is related to cell cycle regulation and microtubule dynamics. DLGAP5 is significantly up-regulated expressed in G2/M stage of \u0026ldquo;cell cycle\u0026rdquo; and participates in chromosome segregation and spindle formation and functional maintenance during mitosis through interactions with other cell cycle regulatory proteins and microtubule-associated proteins. DLGAP5 promotes spindle assembly and stability by binding to microtubules, ensuring proper segregation of \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e by chromosomes during cell division. Further, DLGAP5 may contribute to mitophagy, a mechanism involving the selective degradation of mitochondria through autophagy\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Growing evidence indicates that dysregulated mitophagy is crucial in tumorigenesis and malignant progression, potentially influencing therapeutic outcomes in cancer treatment\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. As a cell cycle regulator implicated in carcinogenesis, DLGAP5 plays an important role in cell cycle regulation, spindle formation, and cancer progression. In this study, DLGAP5 and MELK were selected as the core targets of ESCC to determine whether they could be used as one of the biomarkers in ESCC.\u003c/p\u003e \u003cp\u003eSeveral studies have shown that DLGAP5 is an important regulator of the cytoskeleton reorganization and microtubule dynamics, which are vital for the migratory of tumor cells\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Over-expression of DLGAP5 can enhance the stability of the cytoskeleton, thereby promoting the invasiveness and metastatic potential of tumor cells. Additionally, DLGAP5 may regulate the interaction between tumor cells and the surrounding stroma, enhancing the invasiveness of tumor cells and facilitating both local and distant metastasis\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. A growing body of research has shown that the tumor microenvironment, with its infiltrating immune cells, is key to tumor initiation and progression, thereby influencing the prognosis of cancer patients\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The experimental results presented in Section 2.7 of this study reveal that differential expression genes centered around DLGAP5 and MELK were analyzed for immune cell infiltration in ESCC, providing localization analysis of the important immune cells that affect ESCC. Aberrant expression of DLGAP5 was related to tumorigenesis and metastasis, which makes it a cancer biomarker and therapeutic target\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Moreover, tt has been demonstrated that the up-regulation of PLK1 by DLGAP5 contributes to the growth of lung adenocarcinoma\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. An increase in DLGAP5 mRNA expression is associated with adverse prognoses in breast cancer\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Pan-cancer analysis and cell line experiments have further shown that a slight up-regulation of DLGAP5 expression is closely associated with the progression of renal clear cell carcinoma\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe relationship between the DLGAP5 and ESCC is primarily reflected in its function as a cancer-related gene. In ESCC tissues, DLGAP5 expression is significantly higher than that in normal esophageal tissues. Specifically, the over-expression of DLGAP5 might result in unregulated cell cycle progression, which in turn stimulates the proliferation and invasion of cancer cells, ultimately fueling cancer progression and metastasis. In this study, we thought that DLGAP5 and MELK were screened as core genes in ESCC, which were highly expressed in ESCC tissue. MELK was a serine/threonine kinase that is mainly expressed during the G2/M phase of the cell cycle and in mitosis. It regulated spindle assembly, chromosome segregation, and cell division by phosphorylating target proteins, thereby affecting cell cycle progression and cell proliferation\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In this study, we found DLGAP5 and MELK were mainly enriched in cell cycle, oxidoreductase activity, serine hydrolase activity, cell proliferation, and tissue development. Several studies have demonstrated that MELK is a participant of the serine-threonine protein kinase family, specifically the non-sucrose fermenting/amino acid-activated protein kinase subfamily\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. MELK is essential for embryonic development and the maintenance of adult stem cells, and it also plays significant roles in the self-renewal and differentiation of stem cells\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we further reveal that DLGAP5 and MELK were also associated with Barrett's esophagus, precancerous lesions, and pain. MELK exhibited abnormal over-expression across a range of cancers, with high levels of MELK correlating with cancer cell proliferation, invasion, and metastasis\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. During mitosis, MELK was closely associated with spindle assembly, chromosome segregation, and normal progression for \u0026ldquo;cell cycle\u0026rdquo;. MELK affected mitosis by regulating cell-cycle-related proteins. Specifically, it may function by affecting the cytoskeleton, microtubule dynamics, and spindle assembly, promoting cancer cell migration and invasion, thereby driving cancer development\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The over-expression of MELK was associated with enhanced proliferative capacity and invasiveness of ESCC cells, likely by promoting cell cycle progression and mitosis, thereby enhancing the growth and spread of ESCC cells.The experimental results in this study show that the miRNA related to DLGAP5 regulation is hsa-miR-409-5p, while the miRNA associated with MELK regulation is hsa-miR-802. Related studies have also shown that MELK promotes tumor metastasis in ESCC by stimulating the FOXM1 signaling pathway. MELK may also promote the metastasis of ESCC by activating the NF-κB pathway\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In ESCC, elevated levels of MELK were strongly associated with tumor progression, increased invasiveness, and unfavorable prognosis. Although there are few studies directly investigating the interaction between DLGAP5 and MELK in ESCC, existing research has hinted at potential links between the two in certain functions and pathways. MELK may activate upstream factors that regulate DLGAP5 expression through its kinase activity, thereby enhancing tumor cell division. The high expression of both DLGAP5 and MELK may act in concert to intensified the invasiveness of tumor cells.\u003c/p\u003e \u003cp\u003eThis study, through systematic bioinformatics analysis and experimental validation, strongly demonstrated the over-expression of DLGAP5 and MELK in ESCC, offering convincing evidence to elucidate the molecular mechanisms underlying ESCC. However, this study still has some limitations, as it failed to carry out animal experiments featuring gene over-expression or knockout to further verify the molecular functions. Thus, future studies should probe into this field for a more thorough investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn ESCC tissue, there is a marked elevation in the expression levels of DLGAP5 and MELK, which may participate in the regulation of expression in ESCC and may serve as potential biomarkers for ESCC screening and intervention. Intensive research and intervention on the expression regulation of DLGAP5 and MELK may be a key breakthrough point in the treatment of this disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceived and designed the experiments: Jin Yang, Yu Su, Mengmeng Li. Performed the experiments: Jin Yang, Kexiong Song, Yuchen Wang. Statistical analysis: Yu Su, Jie Yang. Wrote the paper: Yu Su, Jin Yang. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study did not involve human participants or samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study did not involve individual person\u0026rsquo;s data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve human participants, human tissue samples, or animal experiments. All data were obtained from publicly available databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe microarray datasets analyzed during this study are available in GEO database of the NCBI \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSE17351: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17351. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSE20347: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20347. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research reported in this project was generously supported by Hebei Provincial Health Commission under grant agreement number 20230153. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHsu PK, Lee YY, Chuang LC, Wu YC. 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Int J Oncol. 2022. 61(2).\u003c/li\u003e\n\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":"DLGAP5, MELK, ESCC, Genetic marker","lastPublishedDoi":"10.21203/rs.3.rs-8197956/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8197956/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: The objective of this research was to investigate the high expression of DLGAP5 and MELK as molecular biomarkers involved in the regulation of the occurrence in ESCC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Through bioinformatics analysis, ESCC-related datasets GSE17351 and GSE20347 were downloaded from GEO database. In addition, de-batch treatment, DEGs screening, functional enrichment analysis, GSEA, WGCNA, PPI network construction and analysis were carried out. Mapping gene expression calorimetry. Immunoinfiltration analysis and CTD analysis were conducted. TargetScan was employed to identify miRNA that regulate DEG. RT-qPCR was used to detect the relative mRNA expression levels of potential biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The results showed that 1144 DEGs were identified, which were primarily focused on biological processes such as cell cycle, REDOX enzyme activity, serine hydrolase activity, cell proliferation and tissue development. KEGG analysis highlighted that these DEGs were predominantly enriched in fatty acid metabolism, IL-17 signaling pathway and p53 signaling pathway. For Metascape, there were positive regulation of DNA metabolism,cell cycle and mitotic cytoplasmic division in the enrichment project of GO. Then, DLGAP5 and MELK were identified as potential biomarkers in ESCC. We found that DLGAP5 and MELK were highly expressed in ESCC group. CTD analysis found that these biomarkers were related to precancerous lesions,esophageal diseases and pain. RT-qPCR further verified that the high expression of DLGAP5 and MELK in mRNA levels of ESCC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: DLGAP5 and MELK were over-expressed in ESCC and play roles in its occurrence and progression, making them potential biomarkers for screening.\u003c/p\u003e","manuscriptTitle":"Study on DLGAP5 and MELK as potential biomarkers in esophageal squamous cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-23 12:00:21","doi":"10.21203/rs.3.rs-8197956/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":"a90fd9de-e1ed-4a0a-9885-cb16c86890ff","owner":[],"postedDate":"December 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T09:51:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-23 12:00:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8197956","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8197956","identity":"rs-8197956","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}