Unveiling the Critical Role of DMRTA2-Mediated JAK2-STAT3 Pathway Activation in Glioma Prognosis and Malignant Progression

Unveiling the Critical Role of DMRTA2-Mediated JAK2-STAT3 Pathway Activation in Glioma Prognosis and Malignant Progression | 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 Unveiling the Critical Role of DMRTA2-Mediated JAK2-STAT3 Pathway Activation in Glioma Prognosis and Malignant Progression Taohui Ouyang, Junyi Xiong, Jun Yang, Zesong He, Haoran Dai, Jiayi Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5887330/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 Although several studies have highlighted the significant role of DMRTA2 in several cancers, its specific function and the underlying mechanisms in glioma remain unclear. CRISPR data was leveraged to identify DMRTA2 as a key candidate. We utilized bulk-tumor, single-cell, and spatial sequencing to explore the role of DMRTA2 in glioma malignancy and its possible mechanisms. Glioma specimens were used to assess DMRTA2 expression. In vitro and in vivo experiments were performed to validate the role of DMRTA2 in glioma malignancy and its possible mechanisms. Drug prediction and molecular docking were also conducted. We found that DMRTA2 was markedly upregulated and was identified as an independent prognostic marker. Moreover, single-cell and spatial sequencing analysis demonstrated that DMRTA2 was mainly localized in glioma cells. We constructed a malignant regulatory network for DMRTA2, with the JAK-STAT pathway as a central bridge. In vitro and in vivo experiments confirmed that DMRTA2 promoted the malignant behavior of glioma cells by activating the JAK2-STAT3 pathway. Additionally, DMRTA2 was significantly correlated with genomic mutation. Drugs potentially targeting DMRTA2 were screened and docked to DMRTA2. Taken together, DMRTA2 promotes the malignant progression of gliomas by activating the JAK2-STAT3 pathway and serves as a prognostic marker. DMRTA2 Glioma JAK2 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Gliomas, encompassing low-grade glioma (LGG) and glioblastoma (GBM), are the most prevalent primary malignancies in the central nervous system. Despite advances in traditional treatments such as surgery, radiation, and chemotherapy, the prognosis for glioma remains bleak, with a 5-year survival rate below 10% 1–3 . Given the low survival rates with current glioma treatments, new treatment strategies are urgently needed. It is encouraging to note that immunotherapy, widely used in some other human cancers, has also been positively tested in clinical trials for glioma 4 . The development of glioma is a synergistic accumulation process involving multiple stages and multiple genes, and the molecular regulatory mechanisms involved are extremely complex 5 . Given this, to improve the prognosis of glioma, it is urgent to focus on the interaction and regulation of key genes, proteins, signaling pathways, and tumor microenvironment in glioma. DMRTA2 (also designated DMRT5), a member of the DMRT family, is a DM domain transcription factor that plays a role in gonadal differentiation and the development of the central nervous system. Its expression during neural tissue development, as well as its involvement in neurogenesis and brain development, has been documented in zebrafish 6 , mice 7 – 9 , and humans 10 . Studies in DMRTA2-deficient mice have shown that deletion of the DMRTA2 gene leads to hypoplasia or deletion of the medial structure of the distal cerebral cortex, suggesting that DMRTA2 plays a key role in regulating neocortex patterns and forming brain differentiation signaling centers 6 , 7 . Abnormal expression of DMRTA2 has been reported in several cancers, including head and neck squamous cell carcinoma 11 , 12 , clear cell renal cell carcinoma 13 , bladder cancer 14 , and glioblastoma 15 . However, the regulatory mechanisms of DMRTA2 in the malignant progression of glioma remain unclear. In this study, we utilized bulk-tumor, single-cell, and spatial transcription data to perform large-scale bioinformatics analyses to explore the role of DMRTA2 in glioma progression and its potential mechanisms. More importantly, through extensive experiments, including glioma specimens, in vitro, and in vivo experiments, we confirmed the regulatory role and mechanisms of DMRTA2 in the malignant behaviors of glioma. This is the first comprehensive study to systematically elucidate that DMRTA2 acts as a prognostic marker and promotes malignant progression in glioma by activating the JAK2-STAT3 signaling pathway. By thoroughly investigating the role of DMRTA2, we aim to provide a theoretical foundation for molecular targeted therapy in glioma. Materials and methods Screening genes based on CRISPR and data processing We retrieved data from 694 glioma cases in the TCGA database and 325 glioma cases from the Chinese Glioma Genome Atlas (CGGA_325) database. We screened the top prognosis-related genes for glioma from the TCGA and CGGA databases, performed a cross-analysis of the results, selected the gene to be studied, and then performed a Cox regression analysis on the selected gene. CERES is a computational method for estimating gene dependency in CRISPR screens, accounting for the simultaneous effects of copy number variations 16 . The CERES results were generated for primary glioma cell lines using the Avana sgRNA library downloaded from the DepMap database 17 . Notably, a negative gene score indicates that gene silencing impairs the survival of the cell line, whereas a positive score suggests that gene silencing promotes survival. We selected the candidate DMRTA2 gene with the most negative dependency score as the focus of our study. Subsequently, we obtained standardized pan-cancer data from TCGA and GTEx via the UCSC Xena database for pan-cancer analysis( https://xenabrowser.net/ ). Patients with DMRTA2 expression levels of 0 or overall survival(OS) time less than 30 days were excluded, and the remaining expression values underwent log2(x + 1) transformation. Single-cell RNA sequencing analyses of glioma cohorts (GSE131928_10X) were conducted using the Tumor Immune Single-cell Hub (TISCH) 18 . The spatial transcription data of glioma (SCAR_ST_000096) were downloaded from the public dataset ( http://scaratlas.com/ ). Strict adherence to the data access policies of each database was maintained throughout this study. Prognostic value of DMRTA2 According to the median expression of DMRTA2, glioma patients in TCGA and CGGA datasets were classified into low and high DMRTA2 expression subgroups. We used the Kaplan-Meier survival curve and area under the curve (AUC) analysis to evaluate the prognostic significance of DMRTA2 expression in gliomas. The independent predictive value of DMRTA2 in glioma was further assessed through univariate and multivariate Cox regression analyses. Establishment and confirmation of the clinical nomogram model According to the results of Cox regression analysis of TCGA and CGGA datasets, a nomogram model was constructed with the R software package "rms." The model combined clinical traits such as DMRTA2 expression, IDH status, and WHO grade. The calibration curves of TCGA and CGGA datasets were also generated. Functional enrichment of DMRTA2 DMRTA2-related differentially expressed genes (DEGs) in TCGA and CGGA cohorts were screened using the “DESeq2” R package. 6363 DEGs from TCGA and 2687 DEGs from CGGA were identified with the criteria of |log2[fold change]| > 1 and FDR < 0.05." The "clusterProfiler" R package was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs 19 . Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to clarify the relationship between DMRTA2 and the pathway. Gene sets related to glioma malignant behaviors were compiled from the Molecular Signatures Database ( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp ). The ssGSEA algorithm was employed to quantify these malignant biological behaviors and to examine their correlation with DMRTA2 expression. Genomic mutation and heterogeneity analysis A Circos plot was created with the "RCircos" R package to visualize differences in chromosomal copy numbers between the low DMRTA2 and high DMRTA2 subtypes. Additionally, correlation analyses were conducted between tumor mutational burden (TMB), microsatellite instability (MSI), and DMRTA2 expression levels in the TCGA dataset using the R package ggplot2. A waterfall plot was created with the R package "maftools" to illustrate the two subtypes' gene mutation frequencies and mutation types. Single-cell and spatial sequencing analysis We utilized t-SNE dimensionality reduction to visualize the distribution and heterogeneity of different cell types. Cluster-specific markers were used to identify various cell types, and MES-like malignant cells were classified into two groups based on DMRTA2 expression. The CellChat package was utilized to predict intercellular communication using scRNA-seq data 20 . The top differentially expressed genes from the MES-like/DMRTA2 high population were identified and a GO and KEGG analysis was performed. We used the Seurat R software package to process the spatial sequencing data, normalized the data by SCTransform function, and reduced the dimension of the data by RunPCA function. Cluster resolution was determined using the clusterree R package and sites with significant genetic alterations were integrated using FindClusters. The dimension is further reduced and the spots are visualized using the RunUMAP function. Weighted Gene Co-expression Network Analysis (WGCNA) To investigate the potential mechanisms linking DMRTA2 to malignant behaviors, we performed Weighted Gene Co-expression Network Analysis (WGCNA) with the “WGCNA” R package 21 . A soft threshold power of β = 7 and β = 8 was applied to construct scale-free topological networks for the TCGA and CGGA datasets, respectively. Modules most strongly associated with DMRTA2 expression and malignant biological behaviors were identified and considered as key modules involved in glioma progression. Key module genes between the TCGA and CGGA datasets were annotated with GO and KEGG enrichment analyses to identify regulatory pathways. Human glioma and peritumoral tissue specimens Tumor tissues and peritumoral brain tissue samples were collected from 6 glioma surgery patients at the Department of Neurosurgery, First Affiliated Hospital of Nanchang University. The ethics involved in this study were agreed upon by the participants and approved by our hospital Ethics Committee. Cell culture and transfection Glioma cell lines (SW1783, U87, U251, LN229) and normal human astrocytes (NHA) were sourced from the American Type Culture Collection (ATCC). The cells were grown in Dulbecco's Modified Eagle Medium (DMEM) and incubated at 37°C in a 5% CO2 environment (Thermo Scientific, Waltham, MA, USA). LN229 cells were transfected with a lentiviral vector containing DMRTA2 shRNA (5'-CUGACAAAGAAGAGGGUGATTUCAC CCUCUUCUUUGUCAGTT-3') or a negative control vector (NC). Puromycin selection was used to screen for positive cells post-transfection. Western blot analysis Proteins were extracted from tumor or non-tumorous tissues or cells using RIPA lysis buffer. Proteins were isolated using SDS-PAGE before being transferred to PVDF membranes. We then used a sealing solution for 1 hour, incubated with the primary antibody (as listed in Supplementary Table 1) at 4°C overnight, and incubated the secondary antibody for 1 hour. Finally, imaging and strip strength analysis were performed. Quantitative real-time PCR (qRT-PCR) RNA extracted from collected cells using Trizol reagent was converted to cDNA through reverse transcription, followed by qRT-PCR analysis using SYBR Green on a Real-Time PCR system. GAPDH served as the endogenous control, and comparative quantification was performed using the △CT method. The primers that were employed were: GAPDH: 5′-GGAGCGAGATCCCTCCAAAAT-3′(forward).5′-GGCTGTTGTCATACTTCTCATGG-3′ (reverse).DMRTA2:5′ACGAGGTCTTCGGTTCAGTG-3′(forward),5′-CGTCGGGTGATA AGGGCTTC-3′(reverse). CCK-8 assay 96-well plates were used to inoculate control cells and transfected cells with 2000 cells per well. 10ul CCK reagent was added to each well according to the CCK kit scheme. Absorbance at 450nm was recorded at different time points after incubation for 0h,24h, 48h, 72h, 96h. Finally, the data at these different time points are collected and analyzed. Colony formation assay Each well of the 6-well plate was inoculated with 2000 cells, incubated for 2 weeks, then fixed the cells with a concentration of 4% paraformaldehyde for 15 minutes, stained with 0.1% crystal violet for 30 minutes, and quantified using Image J software. BrdU assay The cells were placed in 24-well plates. After 24 hours, the BrdU labeling solution was incubated for 2 hours, and 4% paraformaldehyde was fixed for 15 minutes. The cells were treated with hydrochloric acid at room temperature for 30 minutes, sealed with an antibody-blocking solution, and incubated with BrdU antibody overnight. Then, the corresponding secondary antibodies were added, and the nuclei were stained with DAPI. Finally, the proportion of BrdU-positive cells was analyzed statistically. Transwell cell invasion and migration assay The bottom of the Transwell compartments is coated with or without a matrix (invasion). A serum-free medium and cells were added to the chamber, and a serum-containing medium was added outside the chamber. The medium was removed after 24 hours of culture, and then the cells were treated with 4% paraformaldehyde and 0.1% crystal violet and photographed with an inverted microscope. Immunofluorescence (IF) staining For immunofluorescence of mouse brain sections, dewaxing and hydration with xylene and ethanol were first performed, followed by high-temperature antigen repair. The peroxidase was then inactivated with hydrogen peroxide, and the primary antibody(as listed in Supplementary Table 1) was incubated overnight. Finally, the corresponding fluorescent secondary antibody(Alexa Fluor® 488 or Alexa Fluor® 594 )(Invitrogen, 1:1000 dilution) was incubated, and the nucleus was stained with DAPI. For immunofluorescence, the cells were first placed in a 24-well plate and fixed with 4% paraformaldehyde after 24 hours. Then, the antibody-blocking solution was closed, and the primary antibody was incubated overnight. Finally, the corresponding fluorescent secondary antibody was incubated, and the nucleus was also stained with DAPI. H&E and Immunohistochemical (IHC) staining For H&E staining of mouse brain sections, xylene and ethanol were first dewaxed and hydrated. The sections were then stained with hematoxylin and eosin, and subsequently dehydrated with ethanol. The volume of each brain tumor was calculated using the formula (V) = L × W 2 /2 (L represents the length and W the width). The relative number of invasive fingers per tumor was determined microscopically by counting the protruding tumor tissue fingers and areas of dissemination, following previously established methods 22 . For immunohistochemistry, the mouse brain sections were first dewaxed and treated with xylene and ethanol hydration, followed by high-temperature antigen repair. Hydrogen peroxide inactivates peroxidase, the primary antibody (see supplemental Table 1) is incubated overnight, and the corresponding fluorescent secondary antibody is incubated. Finally, DAB staining and hematoxylin staining were used. The immune response score is calculated as follows: The proportion of positive cells: 0 indicates no positive cells; 1 indicates that the proportion of positive cells is less than 10%; 2 represents 10–50% of the positive cells; 3 represents 51% ~ 80% positive cells; 4 represents more than 80% of the positive cells. Dyeing intensity: 0 indicates no color reaction; 1 indicates a weak reaction; 2 indicates moderate reaction; Three is a strong reaction. The final immune score is calculated by multiplying the proportion of immunopositive cells with the intensity of the stain. Flow cytometry for apoptosis assay Logarithmic growing cells were inoculated in 12-well plates and incubated at 37℃ for 24 hours. The cells were centrifuged, washed with pre-cooled PBS, and incubated in 100 µl cell suspension with 5µl Annexin V 633 conjugate and 5 propyl iodide solution for 15 minutes. After adding 400 µl working solution, the samples were analyzed by flow cytometry. Intracranial tumor in situ assay All experiments on nude mice with intracranial tumors were performed following the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. U251(3×10 5 ) cells containing the luciferase gene were injected into the frontal lobe of 4–6 week-old BALB/c nude mice using a stereotaxic apparatus. Six mice in each group were intraperitoneally injected with fluorescein substrates at different time points, and the growth of intracranial tumors was monitored using an in vivo imaging system (IVIS). Mice were euthanized upon reaching a moribund state or at 60 days, and survival data were analyzed using Kaplan-Meier curves. Co-immunoprecipitation (Co-IP) The cells were lysed in a Co-IP buffer containing 10 mM HEPES (pH 8.0), 100 mM NaCl, 0.1 mM EDTA, 0.2% NP-40, Halt™ phosphatase inhibitor cocktail, 20% glycerol, and protease inhibitor cocktail(Thermo Fisher). The lysate was incubated with 25 µL protein G agarose beads (Millipore) for 2 hours. The supernatant is collected and incubated with the specified primary antibody overnight to bind the antigen-antibody. Subsequently, protein G agarose beads were added to the lysate and incubated for 4 hours to capture the immune complex. Finally, the protein complex was eluted and the target protein was analyzed by western blotting. Therapeutic response and molecular docking To assess the effect of DMRTA2 on conventional treatments in gliomas, we analyzed differences in DMRTA2 expression between responders and non-responders. ROC curves for therapy-associated survival were generated and evaluated( https://www.rocplot.org/ ). Subsequently, drug sensitivity prediction data from the Cancer Therapeutics Response Portal (CTRP) and Genomics of Drug Sensitivity in Cancer(GDSC2) databases were downloaded from the OncoPredict platform ( https://osf.io/c6tfx/ ). AUC values for each sample were calculated using the "prophetic" package 23 , where lower AUCs suggested higher drug sensitivity. Drugs were selected based on negative correlation coefficients (r < -0.40) and validated through the Connectivity Map(CMap, https://clue.io/ ). Molecular docking analyses were conducted using AutoDock Tools software to explore the interactions between DMRTA2 and small-molecule drugs. Virtual screening simulations were conducted with the Vina Wizard to identify protein binding sites and molecular conformations. The three-dimensional visualization of binding pockets was carried out with PyMOL software (version 1.8). Statistics analysis Pairwise comparisons utilized the Student's t-test or Wilcoxon rank-sum test, while analyses involving more than two groups employed one-way ANOVA and the Kruskal-Wallis test. R software (version 4.1.2) was used to conduct bioinformatics analyses and GraphPad Prism 8 was used to assess statistical differences in functional validation experiments. Results Using CRISPR to screen DMRTA2 and pan-cancer analysis Through the analysis of the top prognosis-related genes for glioma in the TCGA and CGGA databases, we identified 17 candidate genes(Fig. 1 A). Then we calculated the CERES score of these 17 genes based on CRISPR data, and selected DMRTA2, which had the lowest CERES score, as our research object(Fig. 1 B, C). Immunofluorescence of U251 cells showed that DMRTA2 was localized within the cytoplasm and cell nucleus (Fig. 1 D). The expression of DMRTA2 shows significant differences between various cancers (TCGA database) and normal tissues (GTEx database). Compared to normal tissues, DMRTA2 expression is significantly elevated in 32 types of tumors, including glioma(GBMLGG)(Fig. 1 E). DMRTA2 correlated with clinical features and poor prognosis in glioma We analyzed the relationship between DMRTA2 expression levels and clinical traits in glioma. Our findings indicated that higher DMRTA2 expression correlated closely with older age, higher WHO grade, IDH wildtype status, unmethylated MGMT promoter status, and 1p/19q non-codel status in the TCGA dataset(Fig. 1 F and Supplementary Fig. 1A, B). Similarly, higher DMRTA2 expression correlated closely with higher WHO grade, IDH wildtype status, and 1p/19q non-codel status in the CGGA dataset(Fig. 1 G, Supplementary Fig. 1C, Supplementary Fig. 2A). These results confirmed significant correlations between DMRTA2 expression and clinical traits in glioma patients. Prognostic analysis confirmed that in both the TCGA (Fig. 1 H) and CGGA (Supplementary Fig. 1D) cohorts, patients in the low DMRTA2 subgroup demonstrated significantly better prognosis compared to those in the high DMRTA2 subgroup. To validate the accuracy of DMRTA2 expression in predicting OS in glioma patients, ROC curve analyses were performed on the TCGA (Fig. 1 I) and CGGA (Supplementary Fig. 1E) datasets. In the TCGA dataset, the AUC values for 1-year, 3-year, and 5-year OS were 0.815, 0.853, and 0.820, respectively. Collectively, these results suggest that DMRTA2 may represent a significant prognostic factor in glioma patients. DMRTA2 is identified as an independent prognostic marker, and the established nomogram model exhibits strong predictive accuracy in glioma To evaluate whether DMRTA2 serves as an independent prognostic biomarker for glioma patients, Cox regression analysis was conducted. In the TCGA dataset, we identified DMRTA2 expression, age, WHO grade, and IDH status as independent prognostic indicators (Supplementary Fig. 2B). Similarly, in the CGGA dataset, DMRTA2 expression along with WHO grade, 1p/19q status, and IDH status were determined to be independent prognostic indicators (Supplementary Fig. 2C). Collectively, these findings suggest that DMRTA2 is a prognostic biomarker for glioma patients. We developed a nomogram model to assess the clinical prognostic potential of DMRTA2 in glioma, using data on its expression, WHO grade, and IDH status from multivariate Cox regression analyses of both TCGA (Supplementary Fig. 2D) and CGGA datasets (Supplementary Fig. 2E). This nomogram model calculated scores to predict 1/3/5 year OS in glioma patients and used calibration plots to evaluate the accuracy of the nomogram model in prognostic estimates. The results demonstrated that the nomogram model accurately forecasted 1/3/5-year OS in both TCGA(Fig. 1 J) and CGGA datasets (Supplementary Fig. 1F), suggesting its reliability in predicting patient outcomes. These findings underscore the potential clinical utility of the established nomogram model for glioma patients. The JAK-STAT pathway is identified as the potential mechanism by which DMRTA2 regulates malignant biological behavior in glioma To investigate the molecular mechanisms linked to the differential expression of DMRTA2, we performed a GSVA analysis using the TCGA(Fig. 2 A) and CGGA(Fig. 2 B) datasets. Our analysis revealed significant associations of the high-DMRTA2 subtype with glial cell proliferation, macrophage proliferation, cell cycle, glial cell migration, epithelial-mesenchymal transition (EMT), and JAK-STAT signaling pathway. Subsequently, GO enrichment analysis showed that DMRTA2-related DEGs were mainly enriched in B cell-mediated immunity, epithelial-mesenchymal transition(EMT), and the tyrosine phosphorylation of STAT proteins in the TCGA (Fig. 2 C) dataset. KEGG enrichment analysis revealed that DMRTA2-related DEGs were notably enriched in the JAK-STAT signaling pathway(Fig. 2 D). Additionally, GSEA analysis showed that EMT and JAK-STAT signaling pathways were activated in the high DMRTA2 gliomas in both TCGA (Fig. 2 E) and CGGA (Fig. 2 F) datasets. Taken together, DMRTA2 may promote the malignant biological behavior of gliomas through the JAK-STAT signaling pathway. DMRTA2 is associated with genomic variations in glioma A substantial body of research suggests that genomic alterations may play a key role in predicting tumor prognosis 24 – 27 . To investigate genetic variations between the high-DMRTA2 and low-DMRTA2 subgroups, we performed analyses of copy number alterations (CNA) and somatic mutations. CNA analysis showed that the high-DMRTA2 subgroup had more copy number amplifications and deletions than the low-DMRTA2 subgroup (Fig. 3 A, B). Somatic mutation analysis identified IDH1 as the most common mutation in both DMRTA2 expression subtypes, with a higher mutation frequency in the low-DMRTA2 subgroup (Fig. 3 C, D). Extensive literature has confirmed that TMB and MSI are key factors influencing cancer progression and the response to immunotherapy 28 , 29 . Therefore, we conducted a comprehensive analysis of the relationship between DMRTA2 levels and TMB/MSI in glioma. We found that the level of TMB was positively connected with the DMRTA2 expression(Fig. 3 E, F). However, MSI levels were negatively connected with the DMRTA2 expression(Fig. 3 G, H). Furthermore, we conducted additional analysis to assess differential DMRTA2 expression between subgroups stratified by low and high TMB, and its association with OS in glioma patients. Our results revealed that higher levels of DMRTA2 expression and TMB were correlated with poorer OS outcomes(Fig. 3 I, J). Single-cell and spatial sequencing analyses show the localization and function of DMRTA2 in gliomas To explore the differential expression of DMRTA2 across various cell types, we conducted single-cell and spatial transcriptomic analyses. The single-cell sequencing datasets comprised a total of 13,553 cells, annotated into 8 cell types, including MES-like malignant, AC-like malignant, NPC-like malignant, OPC-like malignant, mono/macro, and others. t-SNE analysis showed that DMRTA2 was significantly upregulated in malignant cells, especially in MES-like malignant cells, suggesting that DMRTA2 was closely related to the malignant potential of tumors(Fig. 4 A, B). Then we performed an intercellular communication analysis and the results showed that MES-like/DMRTA2 high malignant cells exhibited stronger interactions with other cells, both in terms of the number and probability of interactions, compared to MES-like/DMRTA2 low malignant cells (Fig. 4 C, D). Considering the differentially expressed genes in MES-like/DMRTA2 high malignant, we performed GO and KEGG analysis, revealing that the main enrichment was cell cycle, apoptosis, glioma, and neural precursor cell proliferation. Notably, the JAK-STAT pathway was highlighted, consistent with the bulk-tumor functional enrichment results (Fig. 4 E). The UMAP visualization delineates distinct trajectories among various cell types, illustrating a complex landscape of cellular differentiation. Pseudotime analysis reveals a maturation gradient from MES-like/DMRTA2 low to MES-like/DMRTA2 high malignant cell states, highlighting key transitional phenotypes within the malignant spectrum (Fig. 4 F). The spatial sequencing dataset contained 4,691 spots with an optimal spatial resolution of 0.8. Eleven spot clusters were identified, and malignant and immune scores were calculated for each spot. It was observed that spots with high DMRTA2 expression predominantly occurred in malignant regions, particularly in clusters 4 and 11(Fig. 4 G, H). Building a DMRTA2-mediated regulatory network for malignant behaviors using WGCNA To explore the effects of DMRTA2 on the malignant biological behavior of glioma, we employed the ssGSEA algorithm for quantitative analysis. The high-DMRTA2 group displayed markedly increased cell proliferation, tumor invasiveness, cell migration, and regulation of apoptosis in both the TCGA(Fig. 5 A) and CGGA datasets(Fig. 5 B). Correspondingly, DMRTA2 levels were positively correlated with these malignant biological behaviors in both the TCGA(Fig. 5 C) and CGGA datasets(Fig. 5 D). Subsequently, WGCNA co-expression analyses were conducted to create a regulatory network, selecting power values of β = 7 and 8 as thresholds for scale-free network construction in the TCGA(Fig. 5 E) and CGGA cohorts(Supplementary Fig. 3A). In TCGA(Fig. 5 F) and CGGA(Supplementary Fig. 3B) databases, ten modules are separated from each cluster tree. DMRTA2, along with factors related to cell proliferation, cell migration, tumor invasiveness, and apoptosis regulation, clustered into the same highly correlated module (MEturquoise) in both the TCGA(Fig. 5 G) and CGGA (Fig. 5 H) datasets. We identified 26 genes common to both modules, with GO analysis indicating their involvement in cell proliferation, migration in the hindbrain, and positive regulation of the cell cycle (Fig. 5 I). KEGG pathway analysis indicated significant enrichment in glioma, cell cycle, and focal adhesion. Notably, the JAK-STAT signaling pathway was also highlighted by KEGG enrichment(Fig. 5 J). DMRTA2 is highly expressed in glioma tissues and regulates cell proliferation and apoptosis through the JAK2-STAT3 pathway To confirm the impact of DMRTA2 on proliferation, apoptosis, and its underlying mechanisms, we first observed that DMRTA2 is highly expressed in glioma surgical specimens(Fig. 6 A), with expression levels increasing with higher pathological grades(Fig. 6 B). Next, we analyzed various cell lines and found that both protein expression and mRNA levels of DMRTA2 were higher in glioma cell lines than in normal cells(Fig. 6 C, D). We then selected the LN229 cell line, which exhibits relatively high DMRTA2 expression, for knockdown experiments using specific plasmids, and the U251 cell line, which has relatively low DMRTA2 expression, for overexpression experiments. In LN229 cells, we found that DMRTA2 knockdown inhibited the expression of phosphorylated JAK2 and STAT3 without affecting their total protein levels. Similar results were observed upon addition of the JAK2-STAT3 pathway inhibitor AZD1480 to the control group (Fig. 6 E). In U251 cells, DMRTA2 overexpression promoted the expression of phosphorylated JAK2 and STAT3, and this effect was reversed by the pathway inhibitor AZD1480(Fig. 6 F). Moreover, Co-immunoprecipitation (Co-IP) assays in U251 cells demonstrated an interaction between DMRTA2 and phosphorylated JAK2 protein(Fig. 6 G). Collectively, these results indicated that DMRTA2 could activate the JAK2-STAT3 signaling pathway. In the BrdU assay, we observed that DMRTA2 knockdown or AZD1480 addition suppressed DNA replication, whereas overexpression enhanced it, and AZD1480 reversed this effect(Fig. 6 H). In the CCK8 assay, we found that both DMRTA2 knockdown and the addition of the inhibitor AZD1480 inhibited glioma cell proliferation, while DMRTA2 overexpression promoted proliferation, an effect that was reversed by AZD1480(Fig. 6 I). Similarly, in the colony formation assay, both DMRTA2 knockdown and AZD1480 inhibited colony formation, while overexpression promoted it, with the inhibitor reversing this effect(Fig. 6 J). Flow cytometry showed that DMRTA2 overexpression inhibited apoptosis, which was also reversed by AZD1480(Fig. 6 K). Overall, DMRTA2 promotes glioma cell proliferation and inhibits apoptosis by activating the JAK2-STAT3 signaling pathway. DMRTA2 regulates glioma cell invasion and migration through the JAK2-STAT3 pathway The Transwell assay demonstrated that DMRTA2 knockdown inhibited glioma cell invasion and migration, and similar results were obtained with the use of the pathway inhibitor AZD1480(Fig. 7 A). However, DMRTA2 overexpression promoted glioma cell invasion and migration. This effect was attenuated by the inhibitor AZD1480(Fig. 7 B). To explore the impact of DMRTA2 on glioma further, we conducted an orthotopic tumor formation experiment in nude mice. IVIS imaging revealed that DMRTA2 overexpression promoted intracranial tumor growth in mice, which was attenuated by the inhibitor AZD1480(Fig. 7 C). Survival curves indicated that nude mice in the DMRTA2 overexpression group had a shorter OS than the control group. This effect was reversed by the inhibitor AZD1480(Fig. 7 D). H&E staining revealed that DMRTA2 overexpression tumors displayed larger tumor volumes and more invasive margins, and this effect was also attenuated by the inhibitor AZD1480(Fig. 7 E, F). IHC and IF staining of mouse brain sections revealed that DMRTA2 overexpression increased Ki-67 expression, which was also reversed by the inhibitor AZD1480(Fig. 7 G, H). Taken together, these results suggest that DMRTA2 promotes invasion and migration of glioma cells by the JAK2-STAT3 signaling pathway. The DMRTA2-regulated network influences glioma treatment responses, along with molecular docking analyses of DMRTA2-targeting drugs To investigate the therapeutic significance of DMRTA2, we collected data from ROC curve analysis to illustrate the relationship between DMRTA2 gene expression and glioma treatment outcomes. Under chemotherapy and temozolomide treatment, DMRTA2 expression increased in non-responders, achieving AUC values of 0.636 and 0.654 for 16 months of OS, respectively (Fig. 8 A). Moreover, we leveraged gene expression data from the TCGA cohort in combination with drug sensitivity metrics derived from the CTRP and GDSC2 datasets to predict therapeutic responses. Our analysis also identified five CTRP compounds (Fluvastatin, IC.87114, TGX.221, Betulinic.acid, KU.0060648) and five GDSC2 compounds (Entospletinib, PD0325901, ZM447439, PLX.4720, Dasatinib) to which high-DMRTA2 gliomas may be particularly responsive(Fig. 8 B, C). Subsequently, both CMap score and molecular docking analyses were employed to verify the efficacy of the selected compounds, with Fluvastatin, KU.0060648, PD0325901, and PLX.4720 showing notably lower CMap scores and binding affinities(Fig. 8 D, E). Finally, we illustrated the docking sites and the intermolecular interactions formed by the four compounds with the DMRTA2 protein (Fig. 8 F, G). Discussion Increasing evidence suggests that DMRTA2 plays a critical role in various cancers 12 , 15 ; however, its specific role and mechanism in glioma progression remains unclear. This study systematically analyzed the expression patterns and effects of DMRTA2 in gliomas through comprehensive bioinformatics, in vitro, and in vivo experiments. Our findings revealed that DMRTA2 was upregulated in glioma tissues and was closely associated with clinical features, and genomic mutation. We observed that DMRTA2 was mainly localized in malignant cells (glioma cells). Furthermore, DMRTA2 promoted the malignant biological behaviors of glioma cells, including proliferation, invasion, migration, and resistance to apoptosis, primarily via the JAK2-STAT3 pathway activation. Additionally, we found that DMRTA2 was a reliable, independent prognostic marker, and the clinical nomogram model showed high accuracy in forecasting glioma outcomes. Finally, regarding treatment, drugs potentially targeting DMRTA2 were screened and docked to DMRTA2. Emerging evidence has demonstrated that DMRTA2 expression is linked to cancer survival, including in triple-negative breast cancer 30 . In our study, DMRTA2 expression was notably elevated in gliomas compared to normal tissues. This finding aligns with previous research indicating that DMRTA2 is upregulated in human bladder cancer 14 . We also observed that DMRTA2 expression was significantly higher in more aggressive glioma subtypes, including high-grade, IDH wildtype, and 1p19q non-codeletion. Elevated DMRTA2 levels were associated with poorer prognosis. Moreover, we found that DMRTA2 was highly expressed in malignant glioma cells, which is consistent with earlier research indicating that DMRTA2 is upregulated in malignant cells, while its expression is low or undetectable in non-malignant cells 15 . Additionally, our study identified DMRTA2 as a reliable independent prognostic marker for glioma, corroborating previous reports in head and neck squamous cell carcinoma 12 and clear cell renal cell carcinoma 13 . In the malignant progression of cancer, sustaining cell proliferation, activating cell invasion and metastasis, and resisting cell death are critical hallmarks 31 , 32 . Some studies have reported that DMRTA2 influences GBM cell proliferation; however, its effects on other biological behaviors, such as invasion, migration, and resistance to apoptosis, have not been elucidated 15 .In this study, we observed that DMRTA2's functional enrichment was closely linked to the processes of proliferation, apoptosis, invasion, and migration in gliomas. Both in vitro and in vivo experiments demonstrated that DMRTA2 knockdown suppresses glioma cell proliferation, invasion, and migration while enhancing apoptosis. Considering the expression characteristics of DMRTA2 in gliomas, we hypothesized that DMRTA2 was a significant oncogene in gliomas, particularly in malignant glioma cells. Subsequent analyses using GSVA, GO-BP, KEGG, GSEA, and WGCNA revealed that the JAK-STAT pathway was the central mechanism connecting DMRTA2 with the associated malignant behaviors. The JAK-STAT pathway is one of the most commonly activated signaling pathways in cancers. Numerous studies have demonstrated its critical role in the proliferation, migration, and invasion of glioblastoma 33 – 35 . The JAK family consists of four main members: JAK1, JAK2, JAK3, and TYK2, while the STAT family includes seven major groups: STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6 36, 37 . JAK and STAT proteins are closely interconnected and collectively drive processes such as growth, proliferation, survival, inflammation, invasion, angiogenesis, and tumor progression 38 . Of the STAT family members, STAT3 displays the most pronounced oncogenic and immune-suppressive effects in glioblastoma. The JAK2/STAT3 signaling axis, particularly when phosphorylated, is implicated in driving cancer cell proliferation 39 . Our findings align with these observations. Importantly, our study identified a previously unreported connection between DMRTA2 and the JAK-STAT pathway in gliomas. Overall, our results suggest that DMRTA2 influences glioma cell proliferation, apoptosis, invasion, and migration by activating the JAK2-STAT3 pathway. To our knowledge, this is the first comprehensive study to systematically elucidate that DMRTA2 regulates glioma progression by activating the JAK2-STAT3 pathway. However, this study still has some limitations. The specific sites at which DMRTA2 activates the JAK2-STAT3 pathway require further investigation. Conclusions DMRTA2 promotes the malignant progression of gliomas by activating the JAK2-STAT3 pathway and serves as an independent prognostic marker and a potential molecular therapeutic target. Declarations Acknowledgments We thank all patients who participated in this study and all the public database websites used. Funding This work was supported by grants from the Natural Science Foundation of Jiangxi Province (grant No.20242BAB25501, grant No.20242BAB20381) and the National Natural Science Foundation(grant No.82460468). Data availability statement Most datasets used in this study were sourced from the public databases. Other datasets are available from the corresponding author upon reasonable request. Supplementary Information accompanies this paper as Supplementary Tables. Competing interests The authors declare no competing interests. Ethics approval and consent to participate All animal use procedures adhered to relevant international, national, and institutional guidelines, with approval from the Animal Research Committee of Nanchang University. Human participant procedures followed the ethical standards outlined by institutional and national research committees, conforming to the 1964 Helsinki Declaration and its subsequent amendments. This study received ethical approval from the Ethics Committee of the First Affiliated Hospital of Nanchang University. Informed consent was obtained from all study participants. 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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-5887330","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":410987447,"identity":"7c4541f8-ae2f-4b3f-a242-c8f0a4f66082","order_by":0,"name":"Taohui Ouyang","email":"","orcid":"","institution":"First Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Taohui","middleName":"","lastName":"Ouyang","suffix":""},{"id":410987448,"identity":"f55bdf06-ca36-4b3e-a68f-24951fd5531f","order_by":1,"name":"Junyi Xiong","email":"","orcid":"","institution":"First Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Junyi","middleName":"","lastName":"Xiong","suffix":""},{"id":410987450,"identity":"ae910fdc-83fa-433f-95d3-595ddce85250","order_by":2,"name":"Jun Yang","email":"","orcid":"","institution":"First Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Yang","suffix":""},{"id":410987452,"identity":"9c921349-5d97-4e86-a84c-a2ba72cb1f44","order_by":3,"name":"Zesong He","email":"","orcid":"","institution":"First Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Zesong","middleName":"","lastName":"He","suffix":""},{"id":410987455,"identity":"925700c5-88b7-4c75-902b-a7f9834cf38b","order_by":4,"name":"Haoran Dai","email":"","orcid":"","institution":"First Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Haoran","middleName":"","lastName":"Dai","suffix":""},{"id":410987457,"identity":"9b44c719-3098-4800-8250-8a4c9a020b14","order_by":5,"name":"Jiayi Wang","email":"","orcid":"","institution":"Queen Mary College of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Wang","suffix":""},{"id":410987459,"identity":"a17da23b-aed1-42da-9a8f-1f361be3b7a7","order_by":6,"name":"Wei Meng","email":"","orcid":"","institution":"First Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Meng","suffix":""},{"id":410987462,"identity":"438a4e46-dd9b-4584-875f-93be8f78e73d","order_by":7,"name":"Meihua Li","email":"","orcid":"","institution":"First Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Meihua","middleName":"","lastName":"Li","suffix":""},{"id":410987465,"identity":"d1f96f7c-e275-4492-926c-9539c66851bb","order_by":8,"name":"Xiaowei Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Xiaowei","middleName":"","lastName":"Zhang","suffix":""},{"id":410987467,"identity":"82ea228a-4241-44e4-8bf7-e750f5fd6bd0","order_by":9,"name":"Na Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYFCCBIYPDAZscvzszQcgAgcIa2GcwVDBZyzZcyyBFC1n5BI33PAxIE6LOXuOYTNvm5kxww2ebw9+tjHI8d1IYPxcgEeLZc8bkJY0OcbZvdsNe9sYjCVvJDBLz8CjxeBGjvlj3rZjxswyZ7dJM7YxAF2YwMbMg18LyJb/iW0SOc9AWuqJ08Jzhi2xRyKHDaQlwYCgljPPChvnVLAZS/AcM5PsOSdhOPPMw2ZpvFqOJ29seAOMSvvjzc8kfpTZyPMdTz74GZ8WBgYOAyYkBRJAzNiAVwMDA/sDxh8ElIyCUTAKRsEIBwBR2k/YUDQG+QAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Na","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-01-23 10:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5887330/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5887330/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75607601,"identity":"8835b206-4864-4226-83b4-97a8f1aff251","added_by":"auto","created_at":"2025-02-06 09:42:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4600874,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene screening and prognosis analysis of DMRTA2. \u003c/strong\u003e(A) The top prognosis-related genes identified from the TCGA and CGGA databases were intersected. (B, C) The CERES score of these intersected genes was based on CRISPR data and DMRTA2 was selected as a significant candidate. (D) Intercellular sublocalized immunofluorescence of DMRTA2 protein in U251 cells. Scale bar=50μm. (E) The differential expression of DMRTA2 in pan-cancer tissues and corresponding normal tissues. (F, G) Association between DMRTA2 expression and the clinical characteristics of gliomas in TCGA(F) and CGGA(G) datasets. (H) Overall survival(OS) analysis of the low-DMRTA2 and high-DMRTA2 expression subtypes in TCGA. (I) The time-dependent ROC of the nomogram predicted 1/3/5-year OS in the TCGA dataset. (J)Nomogram model in forecasting 1/3/5-year OS for glioma patients in the TCGA dataset. (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5887330/v1/f60f9edd1c4564d8abb9294b.png"},{"id":75607604,"identity":"9d37fdcd-1b5a-4e9a-9103-d482117fef74","added_by":"auto","created_at":"2025-02-06 09:42:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4082277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentify the potential mechanism of the effect of DMRTA2 on the malignant biological behavior of glioma. \u003c/strong\u003e(A, B) DMRTA2-related GSVA in patients with gliomas in TCGA(A) and CGGA(B) datasets. (C) GO-BP analysis of DEGs according to DMRTA2 expression levels in patients with gliomas in the TCGA dataset. (D) KEGG analysis of DEGs according to DMRTA2 expression levels in patients with gliomas in the TCGA dataset. (E, F) GSEA analysis of DEGs according to DMRTA2 expression levels in patients with gliomas in TCGA (E) and CGGA (F) datasets.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5887330/v1/44b87cfde362dcab6dae81df.png"},{"id":75607961,"identity":"2aecd2a5-259c-4ecc-b993-4cc1b359126a","added_by":"auto","created_at":"2025-02-06 09:50:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4735367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of the gene mutation and genomic heterogeneity between the high-DMRTA2 and low-DMRTA2 subtypes. \u003c/strong\u003e(A, B) Circos plots showing the chromosomal amplifications and deletions in low-DMRTA2 and high-DMRTA2 subtypes. The boxplots show that the copy number amplification and deletion burden was lower in the low-DMRTA2 expression subtype. (C, D) Waterfall plots showing the top 15 mutated genes in the high-DMRTA2 (C) and low-DMRTA2 (D) subtypes. (E, F) Tumor Mutation Burden(TMB) levels were higher in the high-DMRTA2 expression subtype. (G, H) Microsatellite Instability(MSI) levels were lower in the high-DMRTA2 expression subtype. (I, J) The correlation between TMB levels and the prognosis of patients with gliomas (I), and the differential prognostic value in the two subgroups with distinct TMB levels (J). (*p\u0026lt; 0.05, **p\u0026lt; 0.01, ***p\u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5887330/v1/654117916466cfd666908828.png"},{"id":75607960,"identity":"a3a09a42-d899-4ced-b08c-e932db1095a9","added_by":"auto","created_at":"2025-02-06 09:50:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7110250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocalization and function of DMRTA2 in gliomas by single-cell and spatial sequencing analyses. \u003c/strong\u003e(A) tSNE was used to visualize the distribution and dissimilarity of cell types in scRNA-seq data (GSE131928_10X). (B) tSNE plot showing expression of DMRTA2. (C)The alluvial diagram displays outgoing signaling patterns from secretory cells, illustrating the relationships between the deduced latent patterns and the cell types, along with the involved signaling pathways. (D) Cell-to-cell communication analysis showed that MES-like malignant DMRTA2_High cells were suggested to have stronger interactions with other cells than MES-like malignant DMRTA2_Low cells, both in number and probabilities of interactions. (E) GO and KEGG analysis of DEGs in MES-like malignant DMRTA2_High cells. JAK-STAT signaling pathway was highlighted. (F) The trajectory of cell clusters over pseudotime is depicted by black lines illustrating the trajectory structure. (G) Spatial distribution of DMRTA2, malignant cells, and immune cells expression within the spatial dataset. (H)Spatial distribution, malignant, and immune cell expression in different spot clusters.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5887330/v1/420435edc8976bf96aa7bec1.png"},{"id":75607606,"identity":"7aa1c838-5d82-4cc1-b74a-174d41d6bf52","added_by":"auto","created_at":"2025-02-06 09:42:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5348692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of a DMRTA2-mediated regulatory network of malignant biological behaviors in gliomas.\u003c/strong\u003e (A, B) The quantified level of malignant biological\u003c/p\u003e\n\u003cp\u003ebehaviors between the high- and low-DMRTA2 groups in TCGA(A) and CGGA(B) datasets. (C, D) Correlation analysis among the DMRTA2 and malignant biological behaviors in TCGA(C) and CGGA(D) datasets. (E) A scale-free network construction in the TCGA cohort (power threshold = 7). (F) Gene dendrogram generating gene modules in the TCGA cohort. (G, H) Correlation analysis between modules and DMRTA2/malignant biological behaviors in the TCGA(G) and CGGA(H) cohorts. The DMRTA2 and malignant biological behaviors had the same high correlation modules in MEturquoise in both datasets. (I) GO enrichment analysis for the intersected genes of the MEturquoise module in TCGA and CGGA datasets. (J) KEGG enrichment analysis for the intersected genes of the MEturquoise module in TCGA and CGGA datasets. The JAK-STAT pathway was highlighted. (*p\u0026lt; 0.05, **p\u0026lt; 0.01, ***p\u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5887330/v1/40f501a4cbff446a1cb4b584.png"},{"id":75609517,"identity":"53a2e9ab-3abc-4493-9c1e-fde844fb5fe8","added_by":"auto","created_at":"2025-02-06 09:58:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7820166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDMRTA2 is highly expressed in glioma and regulates cell proliferation and apoptosis through JAK2-STAT3 pathway activation. \u003c/strong\u003e(A) Western blot analysis of DMRTA2 expression in glioma specimen and adjacent non-tumorous tissues. (B)Immunohistochemistry shows the expression of DMRTA2 in normal brain tissue and gliomas of different pathological grades. Scale bar=50μm. (C) Western blot analysis of DMRTA2 in various glioma and NHA cell lines. (D) qRT-PCR analysis of DMRTA2 mRNA level in glioma and NHA cell lines. (E)Western blot analysis of the effect of DMRTA2 knockdown or inhibitor AZD1480 on the JAK2-STAT3 pathway in LN229 cells. (F)Western blot analysis of the effect of DMRTA2 overexpression or inhibitor AZD1480 on the JAK2-STAT3 pathway in U251 cells. (G)Co-IP assay showed the interaction between DMRTA2 and p-JAK2 in U251 cells. (H) Representative images and statistical analysis of BrdU assay of the effect of DMRTA2 DMRTA2 knockdown or overexpression with inhibitor AZD1480 in LN229 or U251 cells. Scale bar=100μm. (I)CCK-8 assay was used to analyze the effect of DMRTA2 knockdown or overexpression with inhibitor AZD1480 on glioma cell viability in LN229 or U251 cells. (J)The effect of DMRTA2 knockdown or overexpression with inhibitor AZD1480 on colony formation in LN229 or U251 cells. (K) Flow cytometry assay was employed to inspect the cell apoptosis of the U251 after DMRTA2 DMRTA2 overexpression or inhibitor AZD1480. (*p\u0026lt; 0.05, **p\u0026lt; 0.01, ***p\u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5887330/v1/07d9c04719d709961699bb90.png"},{"id":75607608,"identity":"f4db340c-6291-47fb-bfbd-0532f8765d4d","added_by":"auto","created_at":"2025-02-06 09:42:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":12392141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDMRTA2 regulates glioma cell invasion and migration through the JAK2-STAT3 activation. \u003c/strong\u003e(A) Representative images and statistical results of the effects of DMRTA2 knockdown or the inhibitor AZD1480 on cell invasion and migration in LN229 cells. scale bar=50μm. (B) Representative images and statistical results of the effects of DMRTA2 overexpression or the inhibitor AZD1480 on cell invasion and migration in U251 cells. scale bar=50μm. (C) IVIS imaging and statistical analysis revealed the tumor growth ability after DMRTA2 overexpression or the inhibitor AZD1480 at different times. (D) Overall survival curves of nude mice in DMRTA2 overexpression or the inhibitor AZD1480 groups. The survival of mice was evaluated (n = 5 mice, Kaplan-Meier with two-sided log-rank test). (E) Brain sections stained with H\u0026amp;E show representative tumor xenografts. Tumor volumes were calculated in DMRTA2 overexpression or the inhibitor AZD1480 groups. Data are mean ± S.D. (F) Representative H\u0026amp;E staining showing edges of the mice brain tumors, and the relative invasive fingers per tumor were counted in DMRTA2 overexpression or the inhibitor AZD1480 groups. Scale bar = 100 μm. Data are mean ± S.D. (G) Representative immunohistochemistry images and statistical analysis of Ki-67 were shown in DMRTA2 overexpression or the inhibitor AZD1480 groups. Scale bar = 50 μm. (H) Representative tissue immunofluorescence images and statistical analysis of Ki-67 were shown in DMRTA2 overexpression or the inhibitor AZD1480 groups. Scale bar = 50 μm. (*p\u0026lt; 0.05, **p\u0026lt; 0.01, ***p\u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5887330/v1/85cac0bc0a31f4eb72a89a1e.png"},{"id":75607610,"identity":"736d5c8c-7460-434f-8045-c0c6502d1701","added_by":"auto","created_at":"2025-02-06 09:42:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5564023,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe prediction of therapeutic responses and molecule-drug docking based on the DMRTA2 in gliomas. (\u003c/strong\u003eA) DMRTA2 disparities between the responders and non-responders based on the ROC plotter. ROC presents the predictive accuracy of patient therapeutic response. (B) Correlation analysis of DMRA2 expression and drugs and differential drug response in the CTRP dataset. (C) Correlation analysis of DMRA2 expression and drugs and differential drug response in the GDSC2 dataset. (D) Identification of most promising therapeutic drugs for patients with a high DMRTA2 according to CMap analysis and molecular docking. (E) The visualization of molecular docking between the DMRTA2 protein and the most promising therapeutic drugs, including Fluvastatin, KU.0060648, PD0325901, and PLX.4720.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5887330/v1/65a2250601c3aff59932cacf.png"},{"id":88191924,"identity":"1c09829b-bce3-4cb7-b9cf-8c66c9a294ac","added_by":"auto","created_at":"2025-08-03 14:32:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":51007001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5887330/v1/01313637-d2b3-4a41-99bb-d07948e9bb60.pdf"},{"id":75607602,"identity":"27d33c0d-c9e8-421d-9f67-3490aa9c5e4c","added_by":"auto","created_at":"2025-02-06 09:42:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1585850,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5887330/v1/1c89a7f1518df30f226a4382.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling the Critical Role of DMRTA2-Mediated JAK2-STAT3 Pathway Activation in Glioma Prognosis and Malignant Progression","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGliomas, encompassing low-grade glioma (LGG) and glioblastoma (GBM), are the most prevalent primary malignancies in the central nervous system. Despite advances in traditional treatments such as surgery, radiation, and chemotherapy, the prognosis for glioma remains bleak, with a 5-year survival rate below 10%\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. Given the low survival rates with current glioma treatments, new treatment strategies are urgently needed. It is encouraging to note that immunotherapy, widely used in some other human cancers, has also been positively tested in clinical trials for glioma \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The development of glioma is a synergistic accumulation process involving multiple stages and multiple genes, and the molecular regulatory mechanisms involved are extremely complex\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Given this, to improve the prognosis of glioma, it is urgent to focus on the interaction and regulation of key genes, proteins, signaling pathways, and tumor microenvironment in glioma.\u003c/p\u003e \u003cp\u003eDMRTA2 (also designated DMRT5), a member of the DMRT family, is a DM domain transcription factor that plays a role in gonadal differentiation and the development of the central nervous system. Its expression during neural tissue development, as well as its involvement in neurogenesis and brain development, has been documented in zebrafish\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, mice\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and humans\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Studies in DMRTA2-deficient mice have shown that deletion of the DMRTA2 gene leads to hypoplasia or deletion of the medial structure of the distal cerebral cortex, suggesting that DMRTA2 plays a key role in regulating neocortex patterns and forming brain differentiation signaling centers\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Abnormal expression of DMRTA2 has been reported in several cancers, including head and neck squamous cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, clear cell renal cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, bladder cancer\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and glioblastoma\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, the regulatory mechanisms of DMRTA2 in the malignant progression of glioma remain unclear.\u003c/p\u003e \u003cp\u003eIn this study, we utilized bulk-tumor, single-cell, and spatial transcription data to perform large-scale bioinformatics analyses to explore the role of DMRTA2 in glioma progression and its potential mechanisms. More importantly, through extensive experiments, including glioma specimens, in vitro, and in vivo experiments, we confirmed the regulatory role and mechanisms of DMRTA2 in the malignant behaviors of glioma. This is the first comprehensive study to systematically elucidate that DMRTA2 acts as a prognostic marker and promotes malignant progression in glioma by activating the JAK2-STAT3 signaling pathway. By thoroughly investigating the role of DMRTA2, we aim to provide a theoretical foundation for molecular targeted therapy in glioma.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eScreening genes based on CRISPR and data processing\u003c/h2\u003e \u003cp\u003eWe retrieved data from 694 glioma cases in the TCGA database and 325 glioma cases from the Chinese Glioma Genome Atlas (CGGA_325) database. We screened the top prognosis-related genes for glioma from the TCGA and CGGA databases, performed a cross-analysis of the results, selected the gene to be studied, and then performed a Cox regression analysis on the selected gene. CERES is a computational method for estimating gene dependency in CRISPR screens, accounting for the simultaneous effects of copy number variations \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The CERES results were generated for primary glioma cell lines using the Avana sgRNA library downloaded from the DepMap database\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Notably, a negative gene score indicates that gene silencing impairs the survival of the cell line, whereas a positive score suggests that gene silencing promotes survival. We selected the candidate DMRTA2 gene with the most negative dependency score as the focus of our study. Subsequently, we obtained standardized pan-cancer data from TCGA and GTEx via the UCSC Xena database for pan-cancer analysis(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Patients with DMRTA2 expression levels of 0 or overall survival(OS) time less than 30 days were excluded, and the remaining expression values underwent log2(x\u0026thinsp;+\u0026thinsp;1) transformation. Single-cell RNA sequencing analyses of glioma cohorts (GSE131928_10X) were conducted using the Tumor Immune Single-cell Hub (TISCH) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The spatial transcription data of glioma (SCAR_ST_000096) were downloaded from the public dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://scaratlas.com/\u003c/span\u003e\u003cspan address=\"http://scaratlas.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Strict adherence to the data access policies of each database was maintained throughout this study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrognostic value of DMRTA2\u003c/h3\u003e\n\u003cp\u003eAccording to the median expression of DMRTA2, glioma patients in TCGA and CGGA datasets were classified into low and high DMRTA2 expression subgroups. We used the Kaplan-Meier survival curve and area under the curve (AUC) analysis to evaluate the prognostic significance of DMRTA2 expression in gliomas. The independent predictive value of DMRTA2 in glioma was further assessed through univariate and multivariate Cox regression analyses.\u003c/p\u003e\n\u003ch3\u003eEstablishment and confirmation of the clinical nomogram model\u003c/h3\u003e\n\u003cp\u003eAccording to the results of Cox regression analysis of TCGA and CGGA datasets, a nomogram model was constructed with the R software package \"rms.\" The model combined clinical traits such as DMRTA2 expression, IDH status, and WHO grade. The calibration curves of TCGA and CGGA datasets were also generated.\u003c/p\u003e\n\u003ch3\u003eFunctional enrichment of DMRTA2\u003c/h3\u003e\n\u003cp\u003eDMRTA2-related differentially expressed genes (DEGs) in TCGA and CGGA cohorts were screened using the \u0026ldquo;DESeq2\u0026rdquo; R package. 6363 DEGs from TCGA and 2687 DEGs from CGGA were identified with the criteria of |log2[fold change]| \u0026gt; 1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\" The \"clusterProfiler\" R package was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to clarify the relationship between DMRTA2 and the pathway. Gene sets related to glioma malignant behaviors were compiled from the Molecular Signatures Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The ssGSEA algorithm was employed to quantify these malignant biological behaviors and to examine their correlation with DMRTA2 expression.\u003c/p\u003e\n\u003ch3\u003eGenomic mutation and heterogeneity analysis\u003c/h3\u003e\n\u003cp\u003eA Circos plot was created with the \"RCircos\" R package to visualize differences in chromosomal copy numbers between the low DMRTA2 and high DMRTA2 subtypes. Additionally, correlation analyses were conducted between tumor mutational burden (TMB), microsatellite instability (MSI), and DMRTA2 expression levels in the TCGA dataset using the R package ggplot2. A waterfall plot was created with the R package \"maftools\" to illustrate the two subtypes' gene mutation frequencies and mutation types.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell and spatial sequencing analysis\u003c/h2\u003e \u003cp\u003eWe utilized t-SNE dimensionality reduction to visualize the distribution and heterogeneity of different cell types. Cluster-specific markers were used to identify various cell types, and MES-like malignant cells were classified into two groups based on DMRTA2 expression. The CellChat package was utilized to predict intercellular communication using scRNA-seq data\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The top differentially expressed genes from the MES-like/DMRTA2 high population were identified and a GO and KEGG analysis was performed. We used the Seurat R software package to process the spatial sequencing data, normalized the data by SCTransform function, and reduced the dimension of the data by RunPCA function. Cluster resolution was determined using the clusterree R package and sites with significant genetic alterations were integrated using FindClusters. The dimension is further reduced and the spots are visualized using the RunUMAP function.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWeighted Gene Co-expression Network Analysis (WGCNA)\u003c/h3\u003e\n\u003cp\u003eTo investigate the potential mechanisms linking DMRTA2 to malignant behaviors, we performed Weighted Gene Co-expression Network Analysis (WGCNA) with the \u0026ldquo;WGCNA\u0026rdquo; R package \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. A soft threshold power of β\u0026thinsp;=\u0026thinsp;7 and β\u0026thinsp;=\u0026thinsp;8 was applied to construct scale-free topological networks for the TCGA and CGGA datasets, respectively. Modules most strongly associated with DMRTA2 expression and malignant biological behaviors were identified and considered as key modules involved in glioma progression. Key module genes between the TCGA and CGGA datasets were annotated with GO and KEGG enrichment analyses to identify regulatory pathways.\u003c/p\u003e\n\u003ch3\u003eHuman glioma and peritumoral tissue specimens\u003c/h3\u003e\n\u003cp\u003eTumor tissues and peritumoral brain tissue samples were collected from 6 glioma surgery patients at the Department of Neurosurgery, First Affiliated Hospital of Nanchang University. The ethics involved in this study were agreed upon by the participants and approved by our hospital Ethics Committee.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and transfection\u003c/h2\u003e \u003cp\u003eGlioma cell lines (SW1783, U87, U251, LN229) and normal human astrocytes (NHA) were sourced from the American Type Culture Collection (ATCC). The cells were grown in Dulbecco's Modified Eagle Medium (DMEM) and incubated at 37\u0026deg;C in a 5% CO2 environment (Thermo Scientific, Waltham, MA, USA). LN229 cells were transfected with a lentiviral vector containing DMRTA2 shRNA (5'-CUGACAAAGAAGAGGGUGATTUCAC\u003c/p\u003e \u003cp\u003eCCUCUUCUUUGUCAGTT-3') or a negative control vector (NC). Puromycin selection was used to screen for positive cells post-transfection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003eProteins were extracted from tumor or non-tumorous tissues or cells using RIPA lysis buffer. Proteins were isolated using SDS-PAGE before being transferred to PVDF membranes. We then used a sealing solution for 1 hour, incubated with the primary antibody (as listed in Supplementary Table\u0026nbsp;1) at 4\u0026deg;C overnight, and incubated the secondary antibody for 1 hour. Finally, imaging and strip strength analysis were performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative real-time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eRNA extracted from collected cells using Trizol reagent was converted to cDNA through reverse transcription, followed by qRT-PCR analysis using SYBR Green on a Real-Time PCR system. GAPDH served as the endogenous control, and comparative quantification was performed using the △CT method. The primers that were employed were: GAPDH: 5\u0026prime;-GGAGCGAGATCCCTCCAAAAT-3\u0026prime;(forward).5\u0026prime;-GGCTGTTGTCATACTTCTCATGG-3\u0026prime;\u003c/p\u003e \u003cp\u003e(reverse).DMRTA2:5\u0026prime;ACGAGGTCTTCGGTTCAGTG-3\u0026prime;(forward),5\u0026prime;-CGTCGGGTGATA\u003c/p\u003e \u003cp\u003eAGGGCTTC-3\u0026prime;(reverse).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCCK-8 assay\u003c/h2\u003e \u003cp\u003e96-well plates were used to inoculate control cells and transfected cells with 2000 cells per well. 10ul CCK reagent was added to each well according to the CCK kit scheme. Absorbance at 450nm was recorded at different time points after incubation for 0h,24h, 48h, 72h, 96h. Finally, the data at these different time points are collected and analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eColony formation assay\u003c/h2\u003e \u003cp\u003eEach well of the 6-well plate was inoculated with 2000 cells, incubated for 2 weeks, then fixed the cells with a concentration of 4% paraformaldehyde for 15 minutes, stained with 0.1% crystal violet for 30 minutes, and quantified using Image J software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBrdU assay\u003c/h2\u003e \u003cp\u003eThe cells were placed in 24-well plates. After 24 hours, the BrdU labeling solution was incubated for 2 hours, and 4% paraformaldehyde was fixed for 15 minutes. The cells were treated with hydrochloric acid at room temperature for 30 minutes, sealed with an antibody-blocking solution, and incubated with BrdU antibody overnight. Then, the corresponding secondary antibodies were added, and the nuclei were stained with DAPI. Finally, the proportion of BrdU-positive cells was analyzed statistically.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTranswell cell invasion and migration assay\u003c/h2\u003e \u003cp\u003eThe bottom of the Transwell compartments is coated with or without a matrix (invasion). A serum-free medium and cells were added to the chamber, and a serum-containing medium was added outside the chamber. The medium was removed after 24 hours of culture, and then the cells were treated with 4% paraformaldehyde and 0.1% crystal violet and photographed with an inverted microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence (IF) staining\u003c/h2\u003e \u003cp\u003eFor immunofluorescence of mouse brain sections, dewaxing and hydration with xylene and ethanol were first performed, followed by high-temperature antigen repair. The peroxidase was then inactivated with hydrogen peroxide, and the primary antibody(as listed in Supplementary Table\u0026nbsp;1) was incubated overnight. Finally, the corresponding fluorescent secondary antibody(Alexa Fluor\u0026reg; 488 or Alexa Fluor\u0026reg; 594 )(Invitrogen, 1:1000 dilution) was incubated, and the nucleus was stained with DAPI.\u003c/p\u003e \u003cp\u003eFor immunofluorescence, the cells were first placed in a 24-well plate and fixed with 4% paraformaldehyde after 24 hours. Then, the antibody-blocking solution was closed, and the primary antibody was incubated overnight. Finally, the corresponding fluorescent secondary antibody was incubated, and the nucleus was also stained with DAPI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eH\u0026amp;E and Immunohistochemical (IHC) staining\u003c/h2\u003e \u003cp\u003eFor H\u0026amp;E staining of mouse brain sections, xylene and ethanol were first dewaxed and hydrated. The sections were then stained with hematoxylin and eosin, and subsequently dehydrated with ethanol. The volume of each brain tumor was calculated using the formula (V)\u0026thinsp;=\u0026thinsp;L \u0026times; W\u003csup\u003e2\u003c/sup\u003e/2 (L represents the length and W the width). The relative number of invasive fingers per tumor was determined microscopically by counting the protruding tumor tissue fingers and areas of dissemination, following previously established methods\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor immunohistochemistry, the mouse brain sections were first dewaxed and treated with xylene and ethanol hydration, followed by high-temperature antigen repair. Hydrogen peroxide inactivates peroxidase, the primary antibody (see supplemental Table\u0026nbsp;1) is incubated overnight, and the corresponding fluorescent secondary antibody is incubated. Finally, DAB staining and hematoxylin staining were used. The immune response score is calculated as follows: The proportion of positive cells: 0 indicates no positive cells; 1 indicates that the proportion of positive cells is less than 10%; 2 represents 10\u0026ndash;50% of the positive cells; 3 represents 51% ~ 80% positive cells; 4 represents more than 80% of the positive cells. Dyeing intensity: 0 indicates no color reaction; 1 indicates a weak reaction; 2 indicates moderate reaction; Three is a strong reaction. The final immune score is calculated by multiplying the proportion of immunopositive cells with the intensity of the stain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry for apoptosis assay\u003c/h2\u003e \u003cp\u003eLogarithmic growing cells were inoculated in 12-well plates and incubated at 37℃ for 24 hours. The cells were centrifuged, washed with pre-cooled PBS, and incubated in 100 \u0026micro;l cell suspension with 5\u0026micro;l Annexin V 633 conjugate and 5 propyl iodide solution for 15 minutes. After adding 400 \u0026micro;l working solution, the samples were analyzed by flow cytometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIntracranial tumor in situ assay\u003c/h2\u003e \u003cp\u003eAll experiments on nude mice with intracranial tumors were performed following the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. U251(3\u0026times;10\u003csup\u003e5\u003c/sup\u003e) cells containing the luciferase gene were injected into the frontal lobe of 4\u0026ndash;6 week-old BALB/c nude mice using a stereotaxic apparatus. Six mice in each group were intraperitoneally injected with fluorescein substrates at different time points, and the growth of intracranial tumors was monitored using an in vivo imaging system (IVIS). Mice were euthanized upon reaching a moribund state or at 60 days, and survival data were analyzed using Kaplan-Meier curves.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCo-immunoprecipitation (Co-IP)\u003c/h2\u003e \u003cp\u003eThe cells were lysed in a Co-IP buffer containing 10 mM HEPES (pH 8.0), 100 mM NaCl, 0.1 mM EDTA, 0.2% NP-40, Halt\u0026trade; phosphatase inhibitor cocktail, 20% glycerol, and protease inhibitor cocktail(Thermo Fisher). The lysate was incubated with 25 \u0026micro;L protein G agarose beads (Millipore) for 2 hours. The supernatant is collected and incubated with the specified primary antibody overnight to bind the antigen-antibody. Subsequently, protein G agarose beads were added to the lysate and incubated for 4 hours to capture the immune complex. Finally, the protein complex was eluted and the target protein was analyzed by western blotting.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eTherapeutic response and molecular docking\u003c/h2\u003e \u003cp\u003eTo assess the effect of DMRTA2 on conventional treatments in gliomas, we analyzed differences in DMRTA2 expression between responders and non-responders. ROC curves for therapy-associated survival were generated and evaluated(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rocplot.org/\u003c/span\u003e\u003cspan address=\"https://www.rocplot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Subsequently, drug sensitivity prediction data from the Cancer Therapeutics Response Portal (CTRP) and Genomics of Drug Sensitivity in Cancer(GDSC2) databases were downloaded from the OncoPredict platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/c6tfx/\u003c/span\u003e\u003cspan address=\"https://osf.io/c6tfx/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). AUC values for each sample were calculated using the \"prophetic\" package\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, where lower AUCs suggested higher drug sensitivity. Drugs were selected based on negative correlation coefficients (r \u0026lt; -0.40) and validated through the Connectivity Map(CMap, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clue.io/\u003c/span\u003e\u003cspan address=\"https://clue.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMolecular docking analyses were conducted using AutoDock Tools software to explore the interactions between DMRTA2 and small-molecule drugs. Virtual screening simulations were conducted with the Vina Wizard to identify protein binding sites and molecular conformations. The three-dimensional visualization of binding pockets was carried out with PyMOL software (version 1.8).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eStatistics analysis\u003c/h2\u003e \u003cp\u003ePairwise comparisons utilized the Student's t-test or Wilcoxon rank-sum test, while analyses involving more than two groups employed one-way ANOVA and the Kruskal-Wallis test. R software (version 4.1.2) was used to conduct bioinformatics analyses and GraphPad Prism 8 was used to assess statistical differences in functional validation experiments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eUsing CRISPR to screen DMRTA2 and pan-cancer analysis\u003c/h2\u003e \u003cp\u003eThrough the analysis of the top prognosis-related genes for glioma in the TCGA and CGGA databases, we identified 17 candidate genes(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Then we calculated the CERES score of these 17 genes based on CRISPR data, and selected DMRTA2, which had the lowest CERES score, as our research object(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C). Immunofluorescence of U251 cells showed that DMRTA2 was localized within the cytoplasm and cell nucleus (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe expression of DMRTA2 shows significant differences between various cancers (TCGA database) and normal tissues (GTEx database). Compared to normal tissues, DMRTA2 expression is significantly elevated in 32 types of tumors, including glioma(GBMLGG)(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eDMRTA2 correlated with clinical features and poor prognosis in glioma\u003c/h2\u003e \u003cp\u003eWe analyzed the relationship between DMRTA2 expression levels and clinical traits in glioma. Our findings indicated that higher DMRTA2 expression correlated closely with older age, higher WHO grade, IDH wildtype status, unmethylated MGMT promoter status, and 1p/19q non-codel status in the TCGA dataset(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF and Supplementary Fig.\u0026nbsp;1A, B). Similarly, higher DMRTA2 expression correlated closely with higher WHO grade, IDH wildtype status, and 1p/19q non-codel status in the CGGA dataset(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, Supplementary Fig.\u0026nbsp;1C, Supplementary Fig.\u0026nbsp;2A). These results confirmed significant correlations between DMRTA2 expression and clinical traits in glioma patients.\u003c/p\u003e \u003cp\u003ePrognostic analysis confirmed that in both the TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH) and CGGA (Supplementary Fig.\u0026nbsp;1D) cohorts, patients in the low DMRTA2 subgroup demonstrated significantly better prognosis compared to those in the high DMRTA2 subgroup.\u003c/p\u003e \u003cp\u003eTo validate the accuracy of DMRTA2 expression in predicting OS in glioma patients, ROC curve analyses were performed on the TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI) and CGGA (Supplementary Fig.\u0026nbsp;1E) datasets. In the TCGA dataset, the AUC values for 1-year, 3-year, and 5-year OS were 0.815, 0.853, and 0.820, respectively. Collectively, these results suggest that DMRTA2 may represent a significant prognostic factor in glioma patients.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDMRTA2 is identified as an independent prognostic marker, and the established nomogram model exhibits strong predictive accuracy in glioma\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo evaluate whether DMRTA2 serves as an independent prognostic biomarker for glioma patients, Cox regression analysis was conducted. In the TCGA dataset, we identified DMRTA2 expression, age, WHO grade, and IDH status as independent prognostic indicators (Supplementary Fig.\u0026nbsp;2B). Similarly, in the CGGA dataset, DMRTA2 expression along with WHO grade, 1p/19q status, and IDH status were determined to be independent prognostic indicators (Supplementary Fig.\u0026nbsp;2C). Collectively, these findings suggest that DMRTA2 is a prognostic biomarker for glioma patients.\u003c/p\u003e \u003cp\u003eWe developed a nomogram model to assess the clinical prognostic potential of DMRTA2 in glioma, using data on its expression, WHO grade, and IDH status from multivariate Cox regression analyses of both TCGA (Supplementary Fig.\u0026nbsp;2D) and CGGA datasets (Supplementary Fig.\u0026nbsp;2E). This nomogram model calculated scores to predict 1/3/5 year OS in glioma patients and used calibration plots to evaluate the accuracy of the nomogram model in prognostic estimates. The results demonstrated that the nomogram model accurately forecasted 1/3/5-year OS in both TCGA(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ) and CGGA datasets (Supplementary Fig.\u0026nbsp;1F), suggesting its reliability in predicting patient outcomes. These findings underscore the potential clinical utility of the established nomogram model for glioma patients.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe JAK-STAT pathway is identified as the potential mechanism by which DMRTA2 regulates malignant biological behavior in glioma\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate the molecular mechanisms linked to the differential expression of DMRTA2, we performed a GSVA analysis using the TCGA(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and CGGA(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) datasets. Our analysis revealed significant associations of the high-DMRTA2 subtype with glial cell proliferation, macrophage proliferation, cell cycle, glial cell migration, epithelial-mesenchymal transition (EMT), and JAK-STAT signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, GO enrichment analysis showed that DMRTA2-related DEGs were mainly enriched in B cell-mediated immunity, epithelial-mesenchymal transition(EMT), and the tyrosine phosphorylation of STAT proteins in the TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) dataset. KEGG enrichment analysis revealed that DMRTA2-related DEGs were notably enriched in the JAK-STAT signaling pathway(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eAdditionally, GSEA analysis showed that EMT and JAK-STAT signaling pathways were activated in the high DMRTA2 gliomas in both TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and CGGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) datasets. Taken together, DMRTA2 may promote the malignant biological behavior of gliomas through the JAK-STAT signaling pathway.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eDMRTA2 is associated with genomic variations in glioma\u003c/h2\u003e \u003cp\u003eA substantial body of research suggests that genomic alterations may play a key role in predicting tumor prognosis\u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. To investigate genetic variations between the high-DMRTA2 and low-DMRTA2 subgroups, we performed analyses of copy number alterations (CNA) and somatic mutations. CNA analysis showed that the high-DMRTA2 subgroup had more copy number amplifications and deletions than the low-DMRTA2 subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). Somatic mutation analysis identified IDH1 as the most common mutation in both DMRTA2 expression subtypes, with a higher mutation frequency in the low-DMRTA2 subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExtensive literature has confirmed that TMB and MSI are key factors influencing cancer progression and the response to immunotherapy\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Therefore, we conducted a comprehensive analysis of the relationship between DMRTA2 levels and TMB/MSI in glioma. We found that the level of TMB was positively connected with the DMRTA2 expression(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, F). However, MSI levels were negatively connected with the DMRTA2 expression(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, H). Furthermore, we conducted additional analysis to assess differential DMRTA2 expression between subgroups stratified by low and high TMB, and its association with OS in glioma patients. Our results revealed that higher levels of DMRTA2 expression and TMB were correlated with poorer OS outcomes(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI, J).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell and spatial sequencing analyses show the localization and function of DMRTA2 in gliomas\u003c/h2\u003e \u003cp\u003eTo explore the differential expression of DMRTA2 across various cell types, we conducted single-cell and spatial transcriptomic analyses. The single-cell sequencing datasets comprised a total of 13,553 cells, annotated into 8 cell types, including MES-like malignant, AC-like malignant, NPC-like malignant, OPC-like malignant, mono/macro, and others. t-SNE analysis showed that DMRTA2 was significantly upregulated in malignant cells, especially in MES-like malignant cells, suggesting that DMRTA2 was closely related to the malignant potential of tumors(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThen we performed an intercellular communication analysis and the results showed that MES-like/DMRTA2\u003csup\u003ehigh\u003c/sup\u003e malignant cells exhibited stronger interactions with other cells, both in terms of the number and probability of interactions, compared to MES-like/DMRTA2\u003csup\u003elow\u003c/sup\u003e malignant cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). Considering the differentially expressed genes in MES-like/DMRTA2\u003csup\u003ehigh\u003c/sup\u003e malignant, we performed GO and KEGG analysis, revealing that the main enrichment was cell cycle, apoptosis, glioma, and neural precursor cell proliferation. Notably, the JAK-STAT pathway was highlighted, consistent with the bulk-tumor functional enrichment results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The UMAP visualization delineates distinct trajectories among various cell types, illustrating a complex landscape of cellular differentiation. Pseudotime analysis reveals a maturation gradient from MES-like/DMRTA2\u003csup\u003elow\u003c/sup\u003e to MES-like/DMRTA2\u003csup\u003ehigh\u003c/sup\u003e malignant cell states, highlighting key transitional phenotypes within the malignant spectrum (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The spatial sequencing dataset contained 4,691 spots with an optimal spatial resolution of 0.8. Eleven spot clusters were identified, and malignant and immune scores were calculated for each spot. It was observed that spots with high DMRTA2 expression predominantly occurred in malignant regions, particularly in clusters 4 and 11(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG, H).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBuilding a DMRTA2-mediated regulatory network for malignant behaviors using WGCNA\u003c/h3\u003e\n\u003cp\u003eTo explore the effects of DMRTA2 on the malignant biological behavior of glioma, we employed the ssGSEA algorithm for quantitative analysis. The high-DMRTA2 group displayed markedly increased cell proliferation, tumor invasiveness, cell migration, and regulation of apoptosis in both the TCGA(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and CGGA datasets(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Correspondingly, DMRTA2 levels were positively correlated with these malignant biological behaviors in both the TCGA(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) and CGGA datasets(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, WGCNA co-expression analyses were conducted to create a regulatory network, selecting power values of β\u0026thinsp;=\u0026thinsp;7 and 8 as thresholds for scale-free network construction in the TCGA(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE) and CGGA cohorts(Supplementary Fig.\u0026nbsp;3A). In TCGA(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF) and CGGA(Supplementary Fig.\u0026nbsp;3B) databases, ten modules are separated from each cluster tree. DMRTA2, along with factors related to cell proliferation, cell migration, tumor invasiveness, and apoptosis regulation, clustered into the same highly correlated module (MEturquoise) in both the TCGA(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG) and CGGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH) datasets. We identified 26 genes common to both modules, with GO analysis indicating their involvement in cell proliferation, migration in the hindbrain, and positive regulation of the cell cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). KEGG pathway analysis indicated significant enrichment in glioma, cell cycle, and focal adhesion. Notably, the JAK-STAT signaling pathway was also highlighted by KEGG enrichment(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDMRTA2 is highly expressed in glioma tissues and regulates cell proliferation and apoptosis through the JAK2-STAT3 pathway\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo confirm the impact of DMRTA2 on proliferation, apoptosis, and its underlying mechanisms, we first observed that DMRTA2 is highly expressed in glioma surgical specimens(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), with expression levels increasing with higher pathological grades(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Next, we analyzed various cell lines and found that both protein expression and mRNA levels of DMRTA2 were higher in glioma cell lines than in normal cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, D). We then selected the LN229 cell line, which exhibits relatively high DMRTA2 expression, for knockdown experiments using specific plasmids, and the U251 cell line, which has relatively low DMRTA2 expression, for overexpression experiments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn LN229 cells, we found that DMRTA2 knockdown inhibited the expression of phosphorylated JAK2 and STAT3 without affecting their total protein levels. Similar results were observed upon addition of the JAK2-STAT3 pathway inhibitor AZD1480 to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). In U251 cells, DMRTA2 overexpression promoted the expression of phosphorylated JAK2 and STAT3, and this effect was reversed by the pathway inhibitor AZD1480(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Moreover, Co-immunoprecipitation (Co-IP) assays in U251 cells demonstrated an interaction between DMRTA2 and phosphorylated JAK2 protein(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). Collectively, these results indicated that DMRTA2 could activate the JAK2-STAT3 signaling pathway.\u003c/p\u003e \u003cp\u003eIn the BrdU assay, we observed that DMRTA2 knockdown or AZD1480 addition suppressed DNA replication, whereas overexpression enhanced it, and AZD1480 reversed this effect(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eIn the CCK8 assay, we found that both DMRTA2 knockdown and the addition of the inhibitor AZD1480 inhibited glioma cell proliferation, while DMRTA2 overexpression promoted proliferation, an effect that was reversed by AZD1480(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). Similarly, in the colony formation assay, both DMRTA2 knockdown and AZD1480 inhibited colony formation, while overexpression promoted it, with the inhibitor reversing this effect(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ). Flow cytometry showed that DMRTA2 overexpression inhibited apoptosis, which was also reversed by AZD1480(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK). Overall, DMRTA2 promotes glioma cell proliferation and inhibits apoptosis by activating the JAK2-STAT3 signaling pathway.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eDMRTA2 regulates glioma cell invasion and migration through the JAK2-STAT3 pathway\u003c/h2\u003e \u003cp\u003eThe Transwell assay demonstrated that DMRTA2 knockdown inhibited glioma cell invasion and migration, and similar results were obtained with the use of the pathway inhibitor AZD1480(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). However, DMRTA2 overexpression promoted glioma cell invasion and migration. This effect was attenuated by the inhibitor AZD1480(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). To explore the impact of DMRTA2 on glioma further, we conducted an orthotopic tumor formation experiment in nude mice. IVIS imaging revealed that DMRTA2 overexpression promoted intracranial tumor growth in mice, which was attenuated by the inhibitor AZD1480(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Survival curves indicated that nude mice in the DMRTA2 overexpression group had a shorter OS than the control group. This effect was reversed by the inhibitor AZD1480(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). H\u0026amp;E staining revealed that DMRTA2 overexpression tumors displayed larger tumor volumes and more invasive margins, and this effect was also attenuated by the inhibitor AZD1480(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, F). IHC and IF staining of mouse brain sections revealed that DMRTA2 overexpression increased Ki-67 expression, which was also reversed by the inhibitor AZD1480(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG, H). Taken together, these results suggest that DMRTA2 promotes invasion and migration of glioma cells by the JAK2-STAT3 signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eThe DMRTA2-regulated network influences glioma treatment responses, along with molecular docking analyses of DMRTA2-targeting drugs\u003c/h2\u003e \u003cp\u003eTo investigate the therapeutic significance of DMRTA2, we collected data from ROC curve analysis to illustrate the relationship between DMRTA2 gene expression and glioma treatment outcomes. Under chemotherapy and temozolomide treatment, DMRTA2 expression increased in non-responders, achieving AUC values of 0.636 and 0.654 for 16 months of OS, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Moreover, we leveraged gene expression data from the TCGA cohort in combination with drug sensitivity metrics derived from the CTRP and GDSC2 datasets to predict therapeutic responses. Our analysis also identified five CTRP compounds (Fluvastatin, IC.87114, TGX.221, Betulinic.acid, KU.0060648) and five GDSC2 compounds (Entospletinib, PD0325901, ZM447439, PLX.4720, Dasatinib) to which high-DMRTA2 gliomas may be particularly responsive(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB, C). Subsequently, both CMap score and molecular docking analyses were employed to verify the efficacy of the selected compounds, with Fluvastatin, KU.0060648, PD0325901, and PLX.4720 showing notably lower CMap scores and binding affinities(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD, E). Finally, we illustrated the docking sites and the intermolecular interactions formed by the four compounds with the DMRTA2 protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF, G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIncreasing evidence suggests that DMRTA2 plays a critical role in various cancers\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e; however, its specific role and mechanism in glioma progression remains unclear. This study systematically analyzed the expression patterns and effects of DMRTA2 in gliomas through comprehensive bioinformatics, in vitro, and in vivo experiments. Our findings revealed that DMRTA2 was upregulated in glioma tissues and was closely associated with clinical features, and genomic mutation. We observed that DMRTA2 was mainly localized in malignant cells (glioma cells). Furthermore, DMRTA2 promoted the malignant biological behaviors of glioma cells, including proliferation, invasion, migration, and resistance to apoptosis, primarily via the JAK2-STAT3 pathway activation. Additionally, we found that DMRTA2 was a reliable, independent prognostic marker, and the clinical nomogram model showed high accuracy in forecasting glioma outcomes. Finally, regarding treatment, drugs potentially targeting DMRTA2 were screened and docked to DMRTA2.\u003c/p\u003e \u003cp\u003eEmerging evidence has demonstrated that DMRTA2 expression is linked to cancer survival, including in triple-negative breast cancer\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In our study, DMRTA2 expression was notably elevated in gliomas compared to normal tissues. This finding aligns with previous research indicating that DMRTA2 is upregulated in human bladder cancer\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. We also observed that DMRTA2 expression was significantly higher in more aggressive glioma subtypes, including high-grade, IDH wildtype, and 1p19q non-codeletion. Elevated DMRTA2 levels were associated with poorer prognosis. Moreover, we found that DMRTA2 was highly expressed in malignant glioma cells, which is consistent with earlier research indicating that DMRTA2 is upregulated in malignant cells, while its expression is low or undetectable in non-malignant cells\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Additionally, our study identified DMRTA2 as a reliable independent prognostic marker for glioma, corroborating previous reports in head and neck squamous cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and clear cell renal cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the malignant progression of cancer, sustaining cell proliferation, activating cell invasion and metastasis, and resisting cell death are critical hallmarks\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Some studies have reported that DMRTA2 influences GBM cell proliferation; however, its effects on other biological behaviors, such as invasion, migration, and resistance to apoptosis, have not been elucidated\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.In this study, we observed that DMRTA2's functional enrichment was closely linked to the processes of proliferation, apoptosis, invasion, and migration in gliomas. Both in vitro and in vivo experiments demonstrated that DMRTA2 knockdown suppresses glioma cell proliferation, invasion, and migration while enhancing apoptosis. Considering the expression characteristics of DMRTA2 in gliomas, we hypothesized that DMRTA2 was a significant oncogene in gliomas, particularly in malignant glioma cells. Subsequent analyses using GSVA, GO-BP, KEGG, GSEA, and WGCNA revealed that the JAK-STAT pathway was the central mechanism connecting DMRTA2 with the associated malignant behaviors.\u003c/p\u003e \u003cp\u003eThe JAK-STAT pathway is one of the most commonly activated signaling pathways in cancers. Numerous studies have demonstrated its critical role in the proliferation, migration, and invasion of glioblastoma\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. The JAK family consists of four main members: JAK1, JAK2, JAK3, and TYK2, while the STAT family includes seven major groups: STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6\u003csup\u003e36, 37\u003c/sup\u003e. JAK and STAT proteins are closely interconnected and collectively drive processes such as growth, proliferation, survival, inflammation, invasion, angiogenesis, and tumor progression\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Of the STAT family members, STAT3 displays the most pronounced oncogenic and immune-suppressive effects in glioblastoma. The JAK2/STAT3 signaling axis, particularly when phosphorylated, is implicated in driving cancer cell proliferation\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Our findings align with these observations. Importantly, our study identified a previously unreported connection between DMRTA2 and the JAK-STAT pathway in gliomas. Overall, our results suggest that DMRTA2 influences glioma cell proliferation, apoptosis, invasion, and migration by activating the JAK2-STAT3 pathway.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first comprehensive study to systematically elucidate that DMRTA2 regulates glioma progression by activating the JAK2-STAT3 pathway. However, this study still has some limitations. The specific sites at which DMRTA2 activates the JAK2-STAT3 pathway require further investigation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eDMRTA2 promotes the malignant progression of gliomas by activating the JAK2-STAT3 pathway and serves as an independent prognostic marker and a potential molecular therapeutic target.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all patients who participated in this study and all the public database websites used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Natural Science Foundation of Jiangxi Province (grant No.20242BAB25501, grant No.20242BAB20381) and the National Natural Science Foundation(grant No.82460468).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost datasets used in this study were sourced from the public databases. Other datasets are available from the corresponding author upon reasonable request. Supplementary Information accompanies this paper as Supplementary Tables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal use procedures adhered to relevant international, national, and institutional guidelines, with approval from the Animal Research Committee of Nanchang University. Human participant procedures followed the ethical standards outlined by institutional and national research committees, conforming to the 1964 Helsinki Declaration and its subsequent amendments. This study received ethical approval from the Ethics Committee of the First Affiliated Hospital of Nanchang University. 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CR\u003c/em\u003e.\u003cstrong\u003e38(1)\u003c/strong\u003e,399(2019)\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":"DMRTA2, Glioma, JAK2","lastPublishedDoi":"10.21203/rs.3.rs-5887330/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5887330/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough several studies have highlighted the significant role of DMRTA2 in several cancers, its specific function and the underlying mechanisms in glioma remain unclear. CRISPR data was leveraged to identify DMRTA2 as a key candidate. We utilized bulk-tumor, single-cell, and spatial sequencing to explore the role of DMRTA2 in glioma malignancy and its possible mechanisms. Glioma specimens were used to assess DMRTA2 expression. In vitro and in vivo experiments were performed to validate the role of DMRTA2 in glioma malignancy and its possible mechanisms. Drug prediction and molecular docking were also conducted. We found that DMRTA2 was markedly upregulated and was identified as an independent prognostic marker. Moreover, single-cell and spatial sequencing analysis demonstrated that DMRTA2 was mainly localized in glioma cells. We constructed a malignant regulatory network for DMRTA2, with the JAK-STAT pathway as a central bridge. In vitro and in vivo experiments confirmed that DMRTA2 promoted the malignant behavior of glioma cells by activating the JAK2-STAT3 pathway. Additionally, DMRTA2 was significantly correlated with genomic mutation. Drugs potentially targeting DMRTA2 were screened and docked to DMRTA2. Taken together, DMRTA2 promotes the malignant progression of gliomas by activating the JAK2-STAT3 pathway and serves as a prognostic marker.\u003c/p\u003e","manuscriptTitle":"Unveiling the Critical Role of DMRTA2-Mediated JAK2-STAT3 Pathway Activation in Glioma Prognosis and Malignant Progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-06 09:42:50","doi":"10.21203/rs.3.rs-5887330/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":"689da490-4cc4-4f6c-9e3b-f866679b148c","owner":[],"postedDate":"February 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-03T14:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-06 09:42:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5887330","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5887330","identity":"rs-5887330","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}