The prognosis model of glioblastoma was constructed based on lactic acid metabolism-related genes

The prognosis model of glioblastoma was constructed based on lactic acid metabolism-related genes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The prognosis model of glioblastoma was constructed based on lactic acid metabolism-related genes Feng Lu, Xiaohang Jiang, Guangwei Zheng, Guangming Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3784359/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 Related studies have shown that lactate played a key role in immune escape and metastasis. Exploring the roles of lactic acid metabolism-related genes (LRGs) in glioblastoma (GBM) has great significance for clinic treatment of GBM.The target genes were obtained by intersecting the differentially expressed genes (DEGs) and the module genes. Biomarkers of GBM were screened out to construct the survival risk model, and the nomogram of GBM was constructed to clinically predict the survival of GBM patients. Moreover, the gene set enrichment analysis (GSEA) and the tumor micro-environment analysis were conducted to study the functions of different risk groups and the potential mechanism of GBM. Furthermore, the drug sensitivity analysis were carried out to provide theoretical support for clinical treatment of GBM.The risk score was constructed with six biomarkers, including CALN 1, CDHR1 , CRTAC1 , GNAL , SLC7A14 , and SPHKAP , and SLC7A14 was negative factors of GBM. Based on it, the prognostic model was constructed with age, IDH status, grade, and risk score. Noticeable, the clinical risk of GBM were associated with proliferation, migration, apoptosis, and immune related signaling pathways. In addition, the level of immune escape was higher in high risk group, and samples in high risk group were more sensitive to Vinorelbine_2048, Paclitaxel_1080, Docetaxel_1007, Gefitinib_1010, Erlotinib_1168, and etc. drugs. In this study, we identified six LRGs, including CALN 1, CDHR1 , CRTAC1 , GNAL , SLC7A14 , and SPHKAP . These findings might help to deepen the understanding of the regulatory mechanism of LRGs in GBM. Glioblastoma lactic acid metabolism prognosis biomarkers function immune Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Gliomas are the most common types of tumors in the brain and spinal cord, with an estimated global incidence of 6 cases per 100,000 people per year [ 1 ] . They account for approximately 80% of malignant brain tumors and 30% of central nervous system and brain tumors [ 2 ] . Gliomas are classified into several types based on histological characteristics, including astrocytoma, ependymoma, oligodendroglioma, optic nerve glioma, mixed glioma, and brainstem glioma. Among these, astrocytoma is a common type of primary brain tumor in adults [ 3 ] . According to the classification criteria of the World Health Organization, gliomas are further divided into low-grade gliomas and high-grade gliomas [ 4 ] . Low-grade gliomas have a relatively good prognosis, including diffuse low-grade and intermediate-grade gliomas (World Health Organization [WHO] grade II and III), while glioblastoma multiforme (GBM) is typically classified as a high-grade glioma (grade IV) [ 5 , 6 ] . Despite the implementation of various treatment strategies such as surgery, radiation therapy, and chemotherapy, the average survival time of GBM patients remains less than 15 months. Efficiently addressing the needs of patients with glioblastoma (GBM) and improving their survival rate and overall well-being remains a significant challenge. Therefore, it is essential to thoroughly investigate the regulatory mechanisms that contribute to the malignant progression of GBM and identify potential early biomarkers for GBM. This research is of great importance as it can aid in early diagnosis, treatment, and prognosis of GBM. For a considerable amount of time, the significance of lactate in the emergence and advancement of tumors has been underestimated. It was primarily viewed as a byproduct of glycolysis or an indicator of poor prognosi [ 7 , 8 ] . However, recent findings have unveiled the pivotal role of lactate in tumor growth, immune evasion, angiogenesis, invasion and metastasis, metabolic regulation, and interactions between cells in the tumor microenvironment (TME) [ 9 , 10 ] . For instance, cancer cells secrete lactate into the extracellular environment, stimulating the progression of cancer. The proton-coupled efflux of lactate from cancer cells or stromal cells can modulate the tumor microenvironment, influencing cell invasion, angiogenesis, survival signals, metastasis, development, and immune evasion, thereby facilitating tumor advancement [ 11 ] . The acidification of the extracellular space hampers T cell-mediated immunity, while neutralizing tumor acidity can bolster the effectiveness of immunotherapy [ 12 ] . Studies have indicated that lactate, functioning as an epigenetic metabolite, promotes the survival of glioblastoma models [ 13 ] , and lactate dehydrogenase promotes the growth and invasion of glioblastoma through metabolic symbiosis. Nevertheless [ 14 ] , there remains a dearth of bioinformatics research on genes related to lactic acid metabolism (LRGs) in GBM.。 Through the utilization of bioinformatics analysis, this study successfully identified biomarkers linked to lactate metabolism. Additionally, a pioneering prognostic model for glioblastoma was developed, accompanied by the identification of potential targets for clinical diagnosis. Furthermore, a comprehensive analysis of the functions and tumor microenvironment of both high and low-risk groups was conducted, aiming to enhance our understanding of the pathogenesis of glioblastoma. Materials and methods Data extraction The RNA sequencing data, survival and clinical information of glioma (LGG and GBM) were downloaded from the TCGA and CGGA databases. The TCGA dataset was used as the training dataset, which contains 511 LGG and 155 GBM samples. The CGGA dataset was used as the validation dataset to verify the availability of survival risk model (risk score), which contains 657 GBM samples with survival information. On the other hand, 324 lactic acid metabolism-related genes (LRGs) were obtained from the KEGG database. Screening of the target genes In this study, the differentially expressed genes (DEGs) between 155 GBM and 511 LGG samples in TCGA dataset were compared by “DESeq” R package (version 1.40.2) (|log 2 FC| > 2, adj. p .value < 0.05) [ 15 ] . Next, the LRGs score of all samples were calculated and the relevant module genes which were associated with the score of LRGs were screened by “WGCNA” R package (version 1.70.3). Based on it, the target genes were obtained by intersecting the DEGs and module genes. Construction of the survival risk model and prognostic model of GBM Based on above target genes, the biomarkers of GBM obtained by cox and LASSO analyses were screened, and the survival risk model was calculated by the algorithm: Risk score = β 1 X 1 + β 2 X 2 + ... + β n X n . The risk curve, K-M survival curve, and ROC curve were used to predict the accuracy of survival risk model. Moreover, the CGGA dataset was used to verify the applicability of this survival risk model. The clinical characteristics of GBM contains risk score, gender, age, IDH status, MGMT promoter status, and garde. Then, the significant prognostic factors that explored by cox analyses were utilized for constructing the prognostic model (nomogram). Then, the calibration curve and ROC curve of above nomogram were drawn to evaluate the validity of the prognostic model. Function and tumor micro-environment (TME) analyses On the one hand, the GSEA were performed to study the function of genes in different risk groups by “GSEA” (version 4.1.0) ( p .value < 0.05). On the other hand, the proportions of immune cells were calculated by “ssGSEA” algorithm. Secondly, the differences of eight immune checkpoints ( CD274 ( PD-L1 ), CTLA-4 , LAG-3 , LGALS9 ( GAL9 ), HAVCR2 ( TIM-3 ), PDCP1 ( PD-1 ), PDCP1LG2 ( PD-1LG2 ), and TIGHT ) between different risk groups were compared, and the correlations between immune checkpoints and risk score, between immune response and risk score were calculated by “Spearman” for assessing the immune reaction. Moreover, the ESTIMATE score was calculated for assessing the tumor purity. Furthermore, the TIDE score was calculated for assessing the immunotherapy sensitivity (immune escape level). Drug sensitivity analysis In this study, the common chemotherapy chemotherapy drugs (Vinorelbine_2048, Paclitaxel_1080, Docetaxel_1007, Crizotinib_1083, Gefitinib_1010, Erlotinib_1168, and etc.) in different risk groups were compared by “oncopredict” R package (version 0.2). Expression analysis of biomarkers On the one hand, the expressions of biomarkers between GBM and LGG samples in both TCGA and CGGA databases were compared, respectively. On the other hand, the immunohistochemistry results of biomarkers were analyzed in the HPA database. Statistical analysis All analyses were conducted using R language (version 4.2.0). Differences between two groups were compared by “wilcoxon” test. If not specified above, p < 0.05 was regarded as statistically significant. Results Totals of 37 target genes were screened for subsequent analyses There were 2,275 DEGs between 155 GBM and 511 LGG samples (Fig. 1 A). Sample clustering analysis was implemented and totals of 12 modules were obtained (Fig. 1 B&C). The turquoise module had a significantly negative correlation with the score of LRGs (cor = -0.52, p = 2e-47) (Fig. 1 D&E). Then, 4,922 genes in turquoise module were screened, and totals of 37 target genes were obtained by intersecting 2,275 DEGs and 4,922 module genes, which including CALN1 , CDHR1 , CHGB , CPLX2 , CRTAC1 , CSMD3 , ELFN2 , ETNPPL , GABRB3 , GABRG1 , GALNT13 , GFRA1 , GNAL , GRIN1 , INA , JPH3 , KCNB1 , KCNIP2 , KCNIP3 , KIAA1644 , MYT1L , PCDH15 , PTPRT , RASGRF1 , REPS2 , RTP5 , SEC61G , SHANK2 , SLC7A14 , SMOC1 , SNAP91 , SPHKAP , SVOP , TMEM151B , TNR , TRIM67 and USH1C (Fig. 1 F). CALN1 , CDHR1 , CRTAC1 , GNAL , SLC7A14 and SPHKAP were used to construct the survival risk and prognostic models of GBM Based on above 37 target genes, six biomarkers, including CALN1 , CDHR1 , CRTAC1 , GNAL , SLC7A14 , and SPHKAP were identified, among them, SLC7A14 was negative factor (Hazard Ratio > 1) and others were positive factors (Hazard Ratio < 1) of GBM (Table 1, Fig. 2 A&B). The risk curve and K-M curve showed that there were significant survival differences between these two risk groups ( p = 6.4e-42) (Fig. 2 C&D). Besides, the AUC values of 1-, 3- and 5-year were higher than 0.7 (Fig. 2 E). Moreover, the results of risk curve, K-M curve, and ROC curve in validation dataset (CGGA dataset) were consistent with the training dataset ( p = 1.8e-89, AUC > 0.8) (Fig. 2 F-H). These results indicated that this risk score could be used for constructing the prognostic model of GBM. Distributions of clinical characteristics of different risk groups were shown in Fig. 3 A. Among them, four factors (age, IDH status, grade, and risk score) associated with prognosis of GBM were screened, and all of them were negatively associated with patient survival (Hazard Ratio > 1) (Fig. 3 B&C). The prognostic model with these four prognostic factors was constructed, and the calibration curve showed that the slopes of 1-, 3- and 5-year survival rate were close to fact, and the AUC value of the nomogram was higher than 0.9, which were indicated that the nomogram could be used as an effective prognostic model of GBM (Fig. 3 D-F). The clinical risk of GBM were associated with proliferation, migration, apoptosis, and immune related signaling pathways The GSEA results showed that angiogenesis, apoptosis, EMT, IL6 JAK STAT3, IL2 STAT5, p53, PI3K AKT MTOR signaling, and etc. 36 HALLMARK pathways were significantly highly enriched in high risk group, and Wnt beta catenin, TGF beta, Notch signaling, bile acid metabolism, oxidative phosphorylation, and etc. 14 HALLMARK pathways were significantly highly enriched in low risk group ( Fig. 4 A, Supplement Table 1) . Similarly, primary immunodeficiency, ECM receptor interaction, tryptophan metabolism, p53, Nod like receptor, Toll like receptor, T cell receptor, B cell receptor signaling pathways and etc. 79 KEGG pathways were significantly highly enriched in high risk group, and ERBB, Notch, TGF beta signaling pathways, and etc. 107 KEGG pathways were significantly highly enriched in low risk group ( Fig. 4 B, Supplement Table 2) . Interestingly, these pathways were associated with proliferation, migration, apoptosis, and immune. The level of immune escape was higher in high risk group In this study, 15 immune cells (activated CD4 T cell, activated CD8 T cell, central memory CD8 T cell, and etc.) were significantly increased and 11 immune cells (activated B cells, effector memory CD4 T cell, type 17 helper T cell, and etc.) were significantly decreased in high risk group ( p < 0.05) (Fig. 5 A). Besides, all these eight immune checkpoints were significantly different between different risk group ( p < 0.05), and among them, only TIGHT was significantly lower in high risk score group, and negatively associated with rick score (Fig. 5 B). In addition, the present of immune response was higher in high rick score samples (Fig. 5 C). Moreover, the ESTIMATE and TIDE score were significantly higher in high risk group ( p < 0.05) (Fig. 5 D&E).In addition, the samples in high risk group were more sensitive to Vinorelbine_2048, Paclitaxel_1080, Docetaxel_1007, Crizotinib_1083, Gefitinib_1010, Erlotinib_1168, and etc. drugs ( p < 0.05) (Fig. 5 F). Expression analysis of biomarkers In this study, all these six biomarkers were significantly lowly expressed in GBM group in both TCGA and CGGA datasets ( p < 0.05) ( Fig. 6 A ) . Compared with SVGP12 cells, the expression of six biomarkers in BRTBG cells was decreased ( Fig. 6 B ) . Compared with normal tissues, the expression of six biomarkers in cancer tissues was significantly reduced ( Fig. 6 C ) . Besides, the immunohistochemistry results of biomarkers in GBM were showed in Fig. 6 D. Discussion As the end product of glycolysis, lactic acid metabolism has been proved to play a key role in the pathogenesis of many cancers in recent years. The accumulation of lactic acid in tumor microenvironment (TME) promotes a variety of key carcinogenic processes, including angiogenesis, tissue invasion / metastasis and drug effects [ 16 , 17 ] . Consuelo Torrini et al have revealed the important role of lactic acid in a variety of GBM. They have shown that lactic acid is active in metabolism in a manner dependent on cell respiration, and that lactic acid affects gene expression through epigenetic regulation. Therefore, we identified the expression of lactic acid-related genes in gliomas and constructed a prognostic risk model, which can provide a new research direction for clinical treatment and molecular targets of gliomas. Our study identified six lactic acid metabolism-related biomarkers ( CALN1 , CDHR1 , CRTAC1 , GNAL , SLC7A14 , and SPHKAP ) that have significant implications for glioma prognosis. The expression patterns of these biomarkers, whether upregulated or downregulated, were found to be consistent with the prognosis of glioma patients. The increased expression of CALN1 in osteosarcoma indicates poor survival and prognosis [ 18 ] . Further studies on the mechanism have found that exosome miR-675 from metastatic osteosarcoma promotes cell migration and invasion by targeting CALN1 . Another study in gliomas found that the expression of CALN1 was significantly decreased in gliomas, and its expression level was negatively correlated with tumor grade. Patients with low expression of CALN1 had a poor prognosis, and the overall survival time, disease-specific survival time and progression-free interval were significantly shortened. In this study, the expression of CALN1 is decreased, which is consistent with previous research results, so we can further explore the mechanism of targeting CALN1 in the future. CDHR1 , a member of cadherin-related family 1 ( CDHR1 ), is a photoreceptor-specific cadherin, which belongs to the cadherin superfamily [ 19 ] . The study of cadherin-related family 1 (cadherin-related family 1) in tumors is not clear. At the same time, in a glioma study, we found that the expression of CDHR1 was down-regulated in glioma tissues compared with normal brain tissue. Low expression of CDHR1 is a poor prognostic factor for gliomas. Mechanism studies have found that overexpression of CDHR1 can inhibit the growth and invasion of glioma cells. This is consistent with our results [ 20 ] . Cartilage acidic protein 1 ( CRTAC1 ) is a calcium glycosylated extracellular matrix protein. Many studies suggest that CRTAC1 is involved in the occurrence and development of tumors. For example, CRTAC1 may be involved in the progression of UC and serve as a prognostic marker of metastasis. Low expression of CRTAC1 is significantly associated with invasive UC features and poor clinical outcomes [ 21 ] . Meanwhile, there is abnormal expression of CRTAC1 in bladder cancer, and its expression is down-regulated in bladder cancer tissues and cells. Overexpression of CRTAC1 inhibits cell viability, proliferation, migration, invasion and epithelial-mesenchymal transformation (EMT) in bladder cancer. Further studies have found that CRTAC1 inhibits the malignant phenotype of bladder cancer cells by targeting YY1 to inactivate TGF- β pathway [ 22 ] . However, it has not been studied in gliomas. Therefore, CRTAC1 is likely to be a target for glioma therapy in the future. GNAL , there are few studies at present. In a previous case of bioinformatics analysis, it was found that GNAL was low-expressed in gliomas, and it could be used as a protective factor for gliomas [ 23 ] , this result is consistent with our research, and it is also one of the potential sites for future targeting research. SLC7A14 is considered to be a group of transmembrane transporters, which is essential for arginine transport in mammals [ 24 ] . However, unfortunately, this gene has not been reported in tumors, and in our study, its expression in tumor tissues is decreased, so it may be involved in the regulation of glioma occurrence and development as a protective factor. As for SPHKAP , SPHKAP mutation is associated with poor survival in esophageal squamous cell carcinoma [ 25 ] . This is similar to our results in glioma, but we have not analyzed the mutation rate of this gene in gliomas, so we need to explore the mutation of this gene in glioma tissues and further explore the mechanism to clarify the role of this gene. In short, based on the genes related to lactic acid metabolism, we constructed a six-gene model in glioma, which can well predict the survival and prognosis of patients and provide effective and relatively sensitive biomarkers for clinical patients. In order to clarify the role of lactic acid metabolism genes in the occurrence and development of gliomas, based on our risk model, we divided glioma patients into high and low risk groups. We conducted GSEA analysis to explore the functional pathways of gene enrichment in high and low risk groups. The results showed that 36 HALLMARK pathways such as angiogenesis, apoptosis, EMT, IL6/JAK/STAT3, IL2/STAT5, p53 and PI3K/AKT/MTOR signaling pathways were significantly highly enriched in high risk group. 14 HALLMARK pathways, such as Wnt β-catenin, TGF β, Notch signaling pathway, bile acid metabolism and oxidative phosphorylation, were significantly highly enriched in low risk group. 79 KEGG pathways such as p53, Nod-like receptor, Toll-like receptor, T cell receptor and B cell receptor were significantly enriched in the high risk group, while 107 KEGG pathways such as ERBB, Notch and TGF β signaling pathways were significantly higher in the low risk group. Interestingly, these pathways are associated with proliferation, migration, apoptosis, immunity and so on. Among them, angiogenesis, apoptosis and EMT participate in the occurrence and development of tumor, which is consistent with our results [ 26 ] . As for p53 and PI3K/AKT/MTOR, they are involved in the signal pathways of cell proliferation, survival, invasion, migration, apoptosis, glucose metabolism and DNA repair [ 27 ] . In gliomas the activation of PI3K/AKT/mTOR signal transduction significantly promotes glioma cell proliferation invasion migration and EMT [ 28 ] . These findings suggest that our risk model accurately reflects the biological characteristics of gliomas. Based on the above research, we found that there were differences in immune microenvironment between high and low risk groups. Therefore, we explored the immune microenvironment of patients in high and low risk groups by SSGSEA. The results showed that 15 immune cells (activated CD4T cells, activated CD8T cells, central memory CD8T cells, etc.) were significantly increased in high risk group. Eleven immune cells (activated B cells, effective memory CD4T cells, type 17 helper T cells, etc.) were significantly decreased. CD8T cells, as cytotoxic T cells, play an anti-tumor role in the immune microenvironment, and they are significantly increased in the high-risk group, indicating the existence of immune activation in the tumor microenvironment in the high-risk group. In the future, further exploration of the interaction between CD8T cells and tumor cells will help to explore the composition of immune microenvironment and the mechanism of occurrence and development of gliomas. Subsequently, the immune score ESTIMATE and TIDEscore increased significantly in the high-risk group, suggesting that a higher TIDE score means a higher possibility of immune surveillance escape and a lower success rate of immunotherapy, suggesting that the benefit rate of immunotherapy in high-risk patients will be reduced. High-risk patients are more sensitive to Vinorelbine_2048, Paclitaxel_1080, Docetaxel_1007, Crizotinib_1083, Gefitinib_1010, Erlotinib_1168 and other drugs, suggesting that patients may benefit more from the treatment of these drugs. In short, we constructed and identified a risk model composed of six genes, which can well predict the survival and prognosis of patients, the composition of immune microenvironment, and clinical immunotherapy, and provide potential molecular targets for clinical treatment of glioma. However, this study did not explore the mechanism of related molecules in the future. We will explore its important role in the occurrence and development of gliomas based on risk genes. Declarations Acknowledgments Not applicable. Author contributions LF is the first author. JX and LF design of the work; ZG and JX the acquisition analysis, LF interpretation ofdata; LF the creation of new software used in the work; LF, JX, and ZG have drafted and guided the work or substantively revised it. All authors read and approved the final manuscript. Funding This study was supported by grants from the Natural Science Foundation of Fujian Province (No.2022J01997). Data Availability All the necessary data are included within the current study. Further data will be shared by request. Ethics Approval Not applicable. Consent to Participate Not applicable. Consent for Publication Not applicable. Competing Interests The authors declare no competing interests. References Ostrom QT, Cioffi G, Gittleman H, Patil N, Waite K, Kruchko C, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012-2016. Neuro-oncology 2019; 21(Suppl 5):v1-v100.https://doi:10.1093/neuonc/noz150 Jackson CM, Choi J, Lim M. 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PI3K/AKT/mTOR Signaling Pathway in Breast Cancer: From Molecular Landscape to Clinical Aspects. International journal of molecular sciences 2020; 22(1).https://doi:10.3390/ijms22010173 Gao X, Jiang W, Ke Z, Huang Q, Chen L, Zhang G, et al. TRAM2 promotes the malignant progression of glioma through PI3K/AKT/mTOR pathway. Biochemical and biophysical research communications 2022; 586:34-41.https://doi:10.1016/j.bbrc.2021.11.061 Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementTable1GSEAHALLMARK.xlsx SupplementTable2GSEAKEGG.xlsx Table1unicox37.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-3784359","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265291467,"identity":"3611965d-0919-49cc-b303-f25a74c9cebd","order_by":0,"name":"Feng Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACPgbGB0BKgkeemfnABwmwWAJ+LWwMzAZAykLGsL0tcQYpWipsGM6cMZzBQJQWiWTGxwW/JHgYZ+R8bLDMOczAz55jwPBzB14tzMYz+yR42CVyNzZIbjvMINnzxoCx9ww+LfnHpHl7QLbkbn8A0mJwI8eAmbENry3sv0FaGG7kPATbYk+EFjZmnh9ALWfOMIK1GEgQ0sLzmFmat0GCBxjIhkAt6TwSZ54VHOzFo4WfPZnxM8+fOntgVD5sltxmLcffnrzxwU88WsAA5gxmYFTygBgHCGgAgj9QrR8IKx0Fo2AUjIIRCAAIJ0o8C4SE8gAAAABJRU5ErkJggg==","orcid":"","institution":"Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Lu","suffix":""},{"id":265291468,"identity":"b1c3367a-c86e-4e56-8798-273da5000135","order_by":1,"name":"Xiaohang Jiang","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohang","middleName":"","lastName":"Jiang","suffix":""},{"id":265291469,"identity":"80f2ed2f-c010-49d5-beae-eb5ca6bc47f0","order_by":2,"name":"Guangwei Zheng","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangwei","middleName":"","lastName":"Zheng","suffix":""},{"id":265291470,"identity":"2865d646-1224-4d4a-99a8-bdf3096693d3","order_by":3,"name":"Guangming Zeng","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangming","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2023-12-21 02:29:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3784359/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3784359/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49240302,"identity":"76da54a7-b67b-4209-a513-2506926743d8","added_by":"auto","created_at":"2024-01-05 18:21:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":281120,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of target genes. \u003cstrong\u003e(A)\u003c/strong\u003e Volcano plot of DEGs; \u003cstrong\u003e(B)\u003c/strong\u003e Determination of soft threshold; \u003cstrong\u003e(C) \u003c/strong\u003eGene cluster tree and module cluster graph; \u003cstrong\u003e(D) \u003c/strong\u003eHeatmap of module-trait correlation; (E) Module and gene correlation diagram; \u003cstrong\u003e(F) \u003c/strong\u003eVenn plot\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3784359/v1/05f8ac2ed56d92b4aac21ab7.png"},{"id":49241965,"identity":"423121bf-828c-4331-a0c5-aed8c51f7cbe","added_by":"auto","created_at":"2024-01-05 18:29:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":215174,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of GBM survival risk and prognosis model. \u003cstrong\u003e(A)\u003c/strong\u003eLASSO cross validation error plot; \u003cstrong\u003e(B)\u003c/strong\u003e Multivariate Cox regression analysis; \u003cstrong\u003e(C)\u003c/strong\u003e KM survival curve of Training set; \u003cstrong\u003e(D)\u003c/strong\u003e Prognostic heatmap of Training set; \u003cstrong\u003e(E)\u003c/strong\u003e ROC curve of Training set; \u003cstrong\u003e(F\u003c/strong\u003e) KM survival curve of Validation set ;\u003cstrong\u003e(G)\u003c/strong\u003e Prognostic heatmap of Validation set; \u003cstrong\u003e(H)\u003c/strong\u003e ROC curve of Validation set\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3784359/v1/2d240ebde0e5831182546a31.png"},{"id":49240297,"identity":"b8260147-be69-4ba6-88a2-0a5786fca861","added_by":"auto","created_at":"2024-01-05 18:21:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131770,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and validation of nomogram. \u003cstrong\u003e(A\u003c/strong\u003e) Distribution of clinical features in different risk groups; \u003cstrong\u003e(B) \u003c/strong\u003eUnivariate cox regression analysis; \u003cstrong\u003e(C) \u003c/strong\u003eMultivariate cox regression analysis; \u003cstrong\u003e(D\u003c/strong\u003e) Nomogram of risk model and clinical information;\u003cstrong\u003e (E) \u003c/strong\u003eNomogram calibration curve; \u003cstrong\u003e(F) \u003c/strong\u003eROC curve of nomogram\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3784359/v1/410612e8598a17f78530d9c6.png"},{"id":49241967,"identity":"0d3acc6f-650d-4ad1-a3b9-6ca5988508d1","added_by":"auto","created_at":"2024-01-05 18:29:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":260982,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA enrichment analysis.\u003cstrong\u003e (A) \u003c/strong\u003eGSEA_HALLMARK; \u003cstrong\u003e(B) \u003c/strong\u003eGSEA_KEGG\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3784359/v1/a0c906187f6ad0730e8c702d.png"},{"id":49240300,"identity":"ae7bcb90-d257-431b-879c-4f1cdf991f17","added_by":"auto","created_at":"2024-01-05 18:21:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":258505,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of immunoinfiltration and immune response. \u003cstrong\u003e(A)\u003c/strong\u003e Multigroup boxplot of immune infiltration;\u003cstrong\u003e (B) \u003c/strong\u003eDifferential expression of immune checkpoints; \u003cstrong\u003e(C) \u003c/strong\u003eImmune respond box; \u003cstrong\u003e(D) \u003c/strong\u003eImmuneScore, StromalScore_, and ESTIMATE scatter plots; \u003cstrong\u003e(E) \u003c/strong\u003eTide score, scatter plot and ROC curve; \u003cstrong\u003e(F) \u003c/strong\u003eDrug sensitivity analysis results\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3784359/v1/82903dae7ea2a7ea593a4d39.png"},{"id":49240304,"identity":"05fa0b1a-9229-4897-b5c0-32f7f25522b7","added_by":"auto","created_at":"2024-01-05 18:21:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":988172,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of biomarker expression.\u003cstrong\u003e(A) \u003c/strong\u003eBiomarker expression in TCGA and CGGA datasets; \u003cstrong\u003e(B) \u003c/strong\u003eDifferential expression of biomarkers in cells;\u003cstrong\u003e (C) \u003c/strong\u003eDifferential expression of biomarkers in tissues; \u003cstrong\u003e(D) \u003c/strong\u003eHPA staining results\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-3784359/v1/5239c21bcaf3c5bfb07a9419.png"},{"id":59780438,"identity":"d13ff09b-07b2-449a-af20-f879f062ebe2","added_by":"auto","created_at":"2024-07-06 17:36:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2560461,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3784359/v1/6eff0b8d-e1d4-4615-9401-054d84fa682d.pdf"},{"id":49241966,"identity":"d7e7c43a-6c73-435e-bfea-97503f96290e","added_by":"auto","created_at":"2024-01-05 18:29:05","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11860,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable1GSEAHALLMARK.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3784359/v1/e2c4a19e49be2b5501509985.xlsx"},{"id":49241964,"identity":"2ea13e70-7d5e-4958-9105-5eb7c6b47ef0","added_by":"auto","created_at":"2024-01-05 18:29:04","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20143,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable2GSEAKEGG.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3784359/v1/b51b34687ba4670cf7346655.xlsx"},{"id":49240299,"identity":"9064e28b-e492-4355-baa6-352f626efacd","added_by":"auto","created_at":"2024-01-05 18:21:04","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3291,"visible":true,"origin":"","legend":"","description":"","filename":"Table1unicox37.csv","url":"https://assets-eu.researchsquare.com/files/rs-3784359/v1/28669421e36f3161787c6563.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"The prognosis model of glioblastoma was constructed based on lactic acid metabolism-related genes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGliomas are the most common types of tumors in the brain and spinal cord, with an estimated global incidence of 6 cases per 100,000 people per year\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. They account for approximately 80% of malignant brain tumors and 30% of central nervous system and brain tumors\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Gliomas are classified into several types based on histological characteristics, including astrocytoma, ependymoma, oligodendroglioma, optic nerve glioma, mixed glioma, and brainstem glioma. Among these, astrocytoma is a common type of primary brain tumor in adults\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. According to the classification criteria of the World Health Organization, gliomas are further divided into low-grade gliomas and high-grade gliomas\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Low-grade gliomas have a relatively good prognosis, including diffuse low-grade and intermediate-grade gliomas (World Health Organization [WHO] grade II and III), while glioblastoma multiforme (GBM) is typically classified as a high-grade glioma (grade IV)\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Despite the implementation of various treatment strategies such as surgery, radiation therapy, and chemotherapy, the average survival time of GBM patients remains less than 15 months. Efficiently addressing the needs of patients with glioblastoma (GBM) and improving their survival rate and overall well-being remains a significant challenge. Therefore, it is essential to thoroughly investigate the regulatory mechanisms that contribute to the malignant progression of GBM and identify potential early biomarkers for GBM. This research is of great importance as it can aid in early diagnosis, treatment, and prognosis of GBM.\u003c/p\u003e \u003cp\u003eFor a considerable amount of time, the significance of lactate in the emergence and advancement of tumors has been underestimated. It was primarily viewed as a byproduct of glycolysis or an indicator of poor prognosi\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. However, recent findings have unveiled the pivotal role of lactate in tumor growth, immune evasion, angiogenesis, invasion and metastasis, metabolic regulation, and interactions between cells in the tumor microenvironment (TME)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. For instance, cancer cells secrete lactate into the extracellular environment, stimulating the progression of cancer. The proton-coupled efflux of lactate from cancer cells or stromal cells can modulate the tumor microenvironment, influencing cell invasion, angiogenesis, survival signals, metastasis, development, and immune evasion, thereby facilitating tumor advancement\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The acidification of the extracellular space hampers T cell-mediated immunity, while neutralizing tumor acidity can bolster the effectiveness of immunotherapy\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Studies have indicated that lactate, functioning as an epigenetic metabolite, promotes the survival of glioblastoma models\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, and lactate dehydrogenase promotes the growth and invasion of glioblastoma through metabolic symbiosis. Nevertheless\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, there remains a dearth of bioinformatics research on genes related to lactic acid metabolism (LRGs) in GBM.。\u003c/p\u003e \u003cp\u003eThrough the utilization of bioinformatics analysis, this study successfully identified biomarkers linked to lactate metabolism. Additionally, a pioneering prognostic model for glioblastoma was developed, accompanied by the identification of potential targets for clinical diagnosis. Furthermore, a comprehensive analysis of the functions and tumor microenvironment of both high and low-risk groups was conducted, aiming to enhance our understanding of the pathogenesis of glioblastoma.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData extraction\u003c/h2\u003e \u003cp\u003eThe RNA sequencing data, survival and clinical information of glioma (LGG and GBM) were downloaded from the TCGA and CGGA databases. The TCGA dataset was used as the training dataset, which contains 511 LGG and 155 GBM samples. The CGGA dataset was used as the validation dataset to verify the availability of survival risk model (risk score), which contains 657 GBM samples with survival information. On the other hand, 324 lactic acid metabolism-related genes (LRGs) were obtained from the KEGG database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eScreening of the target genes\u003c/h2\u003e \u003cp\u003eIn this study, the differentially expressed genes (DEGs) between 155 GBM and 511 LGG samples in TCGA dataset were compared by \u0026ldquo;DESeq\u0026rdquo; R package (version 1.40.2) (|log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 2, adj.\u003cem\u003ep\u003c/em\u003e.value\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Next, the LRGs score of all samples were calculated and the relevant module genes which were associated with the score of LRGs were screened by \u0026ldquo;WGCNA\u0026rdquo; R package (version 1.70.3). Based on it, the target genes were obtained by intersecting the DEGs and module genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the survival risk model and prognostic model of GBM\u003c/h2\u003e \u003cp\u003eBased on above target genes, the biomarkers of GBM obtained by cox and LASSO analyses were screened, and the survival risk model was calculated by the algorithm: Risk score\u0026thinsp;=\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e + ...\u0026thinsp;+\u0026thinsp;β\u003csub\u003en\u003c/sub\u003eX\u003csub\u003en\u003c/sub\u003e. The risk curve, K-M survival curve, and ROC curve were used to predict the accuracy of survival risk model. Moreover, the CGGA dataset was used to verify the applicability of this survival risk model.\u003c/p\u003e \u003cp\u003eThe clinical characteristics of GBM contains risk score, gender, age, IDH status, MGMT promoter status, and garde. Then, the significant prognostic factors that explored by cox analyses were utilized for constructing the prognostic model (nomogram). Then, the calibration curve and ROC curve of above nomogram were drawn to evaluate the validity of the prognostic model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunction and tumor micro-environment (TME) analyses\u003c/h2\u003e \u003cp\u003eOn the one hand, the GSEA were performed to study the function of genes in different risk groups by \u0026ldquo;GSEA\u0026rdquo; (version 4.1.0) (\u003cem\u003ep\u003c/em\u003e.value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). On the other hand, the proportions of immune cells were calculated by \u0026ldquo;ssGSEA\u0026rdquo; algorithm. Secondly, the differences of eight immune checkpoints (\u003cem\u003eCD274\u003c/em\u003e (\u003cem\u003ePD-L1\u003c/em\u003e), \u003cem\u003eCTLA-4\u003c/em\u003e, \u003cem\u003eLAG-3\u003c/em\u003e, \u003cem\u003eLGALS9\u003c/em\u003e (\u003cem\u003eGAL9\u003c/em\u003e), \u003cem\u003eHAVCR2\u003c/em\u003e (\u003cem\u003eTIM-3\u003c/em\u003e), \u003cem\u003ePDCP1\u003c/em\u003e (\u003cem\u003ePD-1\u003c/em\u003e), \u003cem\u003ePDCP1LG2\u003c/em\u003e (\u003cem\u003ePD-1LG2\u003c/em\u003e), and \u003cem\u003eTIGHT\u003c/em\u003e) between different risk groups were compared, and the correlations between immune checkpoints and risk score, between immune response and risk score were calculated by \u0026ldquo;Spearman\u0026rdquo; for assessing the immune reaction. Moreover, the ESTIMATE score was calculated for assessing the tumor purity. Furthermore, the TIDE score was calculated for assessing the immunotherapy sensitivity (immune escape level).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDrug sensitivity analysis\u003c/h2\u003e \u003cp\u003eIn this study, the common chemotherapy chemotherapy drugs (Vinorelbine_2048, Paclitaxel_1080, Docetaxel_1007, Crizotinib_1083, Gefitinib_1010, Erlotinib_1168, and etc.) in different risk groups were compared by \u0026ldquo;oncopredict\u0026rdquo; R package (version 0.2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExpression analysis of biomarkers\u003c/h2\u003e \u003cp\u003eOn the one hand, the expressions of biomarkers between GBM and LGG samples in both TCGA and CGGA databases were compared, respectively. On the other hand, the immunohistochemistry results of biomarkers were analyzed in the HPA database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted using R language (version 4.2.0). Differences between two groups were compared by \u0026ldquo;wilcoxon\u0026rdquo; test. If not specified above, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eTotals of 37 target genes were screened for subsequent analyses\u003c/h2\u003e\n\u003cp\u003eThere were 2,275 DEGs between 155 GBM and 511 LGG samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Sample clustering analysis was implemented and totals of 12 modules were obtained (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB\u0026amp;C). The turquoise module had a significantly negative correlation with the score of LRGs (cor = -0.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2e-47) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD\u0026amp;E). Then, 4,922 genes in turquoise module were screened, and totals of 37 target genes were obtained by intersecting 2,275 DEGs and 4,922 module genes, which including \u003cem\u003eCALN1\u003c/em\u003e, \u003cem\u003eCDHR1\u003c/em\u003e, \u003cem\u003eCHGB\u003c/em\u003e, \u003cem\u003eCPLX2\u003c/em\u003e, \u003cem\u003eCRTAC1\u003c/em\u003e, \u003cem\u003eCSMD3\u003c/em\u003e, \u003cem\u003eELFN2\u003c/em\u003e, \u003cem\u003eETNPPL\u003c/em\u003e, \u003cem\u003eGABRB3\u003c/em\u003e, \u003cem\u003eGABRG1\u003c/em\u003e, \u003cem\u003eGALNT13\u003c/em\u003e, \u003cem\u003eGFRA1\u003c/em\u003e, \u003cem\u003eGNAL\u003c/em\u003e, \u003cem\u003eGRIN1\u003c/em\u003e, \u003cem\u003eINA\u003c/em\u003e, \u003cem\u003eJPH3\u003c/em\u003e, \u003cem\u003eKCNB1\u003c/em\u003e, \u003cem\u003eKCNIP2\u003c/em\u003e, \u003cem\u003eKCNIP3\u003c/em\u003e, \u003cem\u003eKIAA1644\u003c/em\u003e, \u003cem\u003eMYT1L\u003c/em\u003e, \u003cem\u003ePCDH15\u003c/em\u003e, \u003cem\u003ePTPRT\u003c/em\u003e, \u003cem\u003eRASGRF1\u003c/em\u003e, \u003cem\u003eREPS2\u003c/em\u003e, \u003cem\u003eRTP5\u003c/em\u003e, \u003cem\u003eSEC61G\u003c/em\u003e, \u003cem\u003eSHANK2\u003c/em\u003e, \u003cem\u003eSLC7A14\u003c/em\u003e, \u003cem\u003eSMOC1\u003c/em\u003e, \u003cem\u003eSNAP91\u003c/em\u003e, \u003cem\u003eSPHKAP\u003c/em\u003e, \u003cem\u003eSVOP\u003c/em\u003e, \u003cem\u003eTMEM151B\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003eTRIM67\u003c/em\u003e and \u003cem\u003eUSH1C\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCALN1\u003c/strong\u003e, \u003cstrong\u003eCDHR1\u003c/strong\u003e, \u003cstrong\u003eCRTAC1\u003c/strong\u003e, \u003cstrong\u003eGNAL\u003c/strong\u003e, \u003cstrong\u003eSLC7A14\u003c/strong\u003e \u003cstrong\u003eand\u003c/strong\u003e \u003cstrong\u003eSPHKAP\u003c/strong\u003e \u003cstrong\u003ewere used to construct the survival risk and prognostic models of GBM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on above 37 target genes, six biomarkers, including \u003cem\u003eCALN1\u003c/em\u003e, \u003cem\u003eCDHR1\u003c/em\u003e, \u003cem\u003eCRTAC1\u003c/em\u003e, \u003cem\u003eGNAL\u003c/em\u003e, \u003cem\u003eSLC7A14\u003c/em\u003e, and \u003cem\u003eSPHKAP\u003c/em\u003e were identified, among them, \u003cem\u003eSLC7A14\u003c/em\u003e was negative factor (Hazard Ratio\u0026thinsp;\u0026gt;\u0026thinsp;1) and others were positive factors (Hazard Ratio\u0026thinsp;\u0026lt;\u0026thinsp;1) of GBM (Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026amp;B).\u003c/p\u003e\n\u003cp\u003eThe risk curve and K-M curve showed that there were significant survival differences between these two risk groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.4e-42) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC\u0026amp;D). Besides, the AUC values of 1-, 3- and 5-year were higher than 0.7 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). Moreover, the results of risk curve, K-M curve, and ROC curve in validation dataset (CGGA dataset) were consistent with the training dataset (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.8e-89, AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.8) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF-H). These results indicated that this risk score could be used for constructing the prognostic model of GBM.\u003c/p\u003e\n\u003cp\u003eDistributions of clinical characteristics of different risk groups were shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA. Among them, four factors (age, IDH status, grade, and risk score) associated with prognosis of GBM were screened, and all of them were negatively associated with patient survival (Hazard Ratio\u0026thinsp;\u0026gt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB\u0026amp;C). The prognostic model with these four prognostic factors was constructed, and the calibration curve showed that the slopes of 1-, 3- and 5-year survival rate were close to fact, and the AUC value of the nomogram was higher than 0.9, which were indicated that the nomogram could be used as an effective prognostic model of GBM (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD-F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe clinical risk of GBM were associated with proliferation, migration, apoptosis, and immune related signaling pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GSEA results showed that angiogenesis, apoptosis, EMT, IL6 JAK STAT3, IL2 STAT5, p53, PI3K AKT MTOR signaling, and etc. 36 HALLMARK pathways were significantly highly enriched in high risk group, and Wnt beta catenin, TGF beta, Notch signaling, bile acid metabolism, oxidative phosphorylation, and etc. 14 HALLMARK pathways were significantly highly enriched in low risk group \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cstrong\u003eSupplement Table\u0026nbsp;1)\u003c/strong\u003e. Similarly, primary immunodeficiency, ECM receptor interaction, tryptophan metabolism, p53, Nod like receptor, Toll like receptor, T cell receptor, B cell receptor signaling pathways and etc. 79 KEGG pathways were significantly highly enriched in high risk group, and ERBB, Notch, TGF beta signaling pathways, and etc. 107 KEGG pathways were significantly highly enriched in low risk group \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cstrong\u003eSupplement Table\u0026nbsp;2)\u003c/strong\u003e. Interestingly, these pathways were associated with proliferation, migration, apoptosis, and immune.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eThe level of immune escape was higher in high risk group\u003c/h2\u003e\n\u003cp\u003eIn this study, 15 immune cells (activated CD4 T cell, activated CD8 T cell, central memory CD8 T cell, and etc.) were significantly increased and 11 immune cells (activated B cells, effector memory CD4 T cell, type 17 helper T cell, and etc.) were significantly decreased in high risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). Besides, all these eight immune checkpoints were significantly different between different risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and among them, only \u003cem\u003eTIGHT\u003c/em\u003e was significantly lower in high risk score group, and negatively associated with rick score (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). In addition, the present of immune response was higher in high rick score samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). Moreover, the ESTIMATE and TIDE score were significantly higher in high risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD\u0026amp;E).In addition, the samples in high risk group were more sensitive to Vinorelbine_2048, Paclitaxel_1080, Docetaxel_1007, Crizotinib_1083, Gefitinib_1010, Erlotinib_1168, and etc. drugs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eExpression analysis of biomarkers\u003c/h2\u003e\n\u003cp\u003eIn this study, all these six biomarkers were significantly lowly expressed in GBM group in both TCGA and CGGA datasets (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. Compared with SVGP12 cells, the expression of six biomarkers in BRTBG cells was decreased\u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e. Compared with normal tissues, the expression of six biomarkers in cancer tissues was significantly reduced\u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e. Besides, the immunohistochemistry results of biomarkers in GBM were showed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs the end product of glycolysis, lactic acid metabolism has been proved to play a key role in the pathogenesis of many cancers in recent years. The accumulation of lactic acid in tumor microenvironment (TME) promotes a variety of key carcinogenic processes, including angiogenesis, tissue invasion / metastasis and drug effects\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Consuelo Torrini et al have revealed the important role of lactic acid in a variety of GBM. They have shown that lactic acid is active in metabolism in a manner dependent on cell respiration, and that lactic acid affects gene expression through epigenetic regulation. Therefore, we identified the expression of lactic acid-related genes in gliomas and constructed a prognostic risk model, which can provide a new research direction for clinical treatment and molecular targets of gliomas.\u003c/p\u003e \u003cp\u003eOur study identified six lactic acid metabolism-related biomarkers (\u003cem\u003eCALN1\u003c/em\u003e, \u003cem\u003eCDHR1\u003c/em\u003e, \u003cem\u003eCRTAC1\u003c/em\u003e, \u003cem\u003eGNAL\u003c/em\u003e, \u003cem\u003eSLC7A14\u003c/em\u003e, and \u003cem\u003eSPHKAP\u003c/em\u003e) that have significant implications for glioma prognosis. The expression patterns of these biomarkers, whether upregulated or downregulated, were found to be consistent with the prognosis of glioma patients. The increased expression of \u003cem\u003eCALN1\u003c/em\u003e in osteosarcoma indicates poor survival and prognosis\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Further studies on the mechanism have found that exosome \u003cem\u003emiR-675\u003c/em\u003e from metastatic osteosarcoma promotes cell migration and invasion by targeting \u003cem\u003eCALN1\u003c/em\u003e. Another study in gliomas found that the expression of \u003cem\u003eCALN1\u003c/em\u003e was significantly decreased in gliomas, and its expression level was negatively correlated with tumor grade. Patients with low expression of \u003cem\u003eCALN1\u003c/em\u003e had a poor prognosis, and the overall survival time, disease-specific survival time and progression-free interval were significantly shortened. In this study, the expression of \u003cem\u003eCALN1\u003c/em\u003e is decreased, which is consistent with previous research results, so we can further explore the mechanism of targeting \u003cem\u003eCALN1\u003c/em\u003e in the future. \u003cem\u003eCDHR1\u003c/em\u003e, a member of cadherin-related family 1 (\u003cem\u003eCDHR1\u003c/em\u003e), is a photoreceptor-specific cadherin, which belongs to the cadherin superfamily\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The study of cadherin-related family 1 (cadherin-related family 1) in tumors is not clear. At the same time, in a glioma study, we found that the expression of \u003cem\u003eCDHR1\u003c/em\u003e was down-regulated in glioma tissues compared with normal brain tissue. Low expression of \u003cem\u003eCDHR1\u003c/em\u003e is a poor prognostic factor for gliomas. Mechanism studies have found that overexpression of \u003cem\u003eCDHR1\u003c/em\u003e can inhibit the growth and invasion of glioma cells. This is consistent with our results\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Cartilage acidic protein 1 (\u003cem\u003eCRTAC1\u003c/em\u003e) is a calcium glycosylated extracellular matrix protein. Many studies suggest that \u003cem\u003eCRTAC1\u003c/em\u003e is involved in the occurrence and development of tumors. For example, \u003cem\u003eCRTAC1\u003c/em\u003e may be involved in the progression of UC and serve as a prognostic marker of metastasis. Low expression of \u003cem\u003eCRTAC1\u003c/em\u003e is significantly associated with invasive UC features and poor clinical outcomes\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, there is abnormal expression of \u003cem\u003eCRTAC1\u003c/em\u003e in bladder cancer, and its expression is down-regulated in bladder cancer tissues and cells. Overexpression of \u003cem\u003eCRTAC1\u003c/em\u003e inhibits cell viability, proliferation, migration, invasion and epithelial-mesenchymal transformation (EMT) in bladder cancer. Further studies have found that \u003cem\u003eCRTAC1\u003c/em\u003e inhibits the malignant phenotype of bladder cancer cells by targeting \u003cem\u003eYY1\u003c/em\u003e to inactivate TGF- β pathway\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. However, it has not been studied in gliomas. Therefore, \u003cem\u003eCRTAC1\u003c/em\u003e is likely to be a target for glioma therapy in the future. \u003cem\u003eGNAL\u003c/em\u003e, there are few studies at present. In a previous case of bioinformatics analysis, it was found that \u003cem\u003eGNAL\u003c/em\u003e was low-expressed in gliomas, and it could be used as a protective factor for gliomas\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, this result is consistent with our research, and it is also one of the potential sites for future targeting research. \u003cem\u003eSLC7A14\u003c/em\u003e is considered to be a group of transmembrane transporters, which is essential for arginine transport in mammals\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. However, unfortunately, this gene has not been reported in tumors, and in our study, its expression in tumor tissues is decreased, so it may be involved in the regulation of glioma occurrence and development as a protective factor. As for \u003cem\u003eSPHKAP\u003c/em\u003e, \u003cem\u003eSPHKAP\u003c/em\u003e mutation is associated with poor survival in esophageal squamous cell carcinoma\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. This is similar to our results in glioma, but we have not analyzed the mutation rate of this gene in gliomas, so we need to explore the mutation of this gene in glioma tissues and further explore the mechanism to clarify the role of this gene. In short, based on the genes related to lactic acid metabolism, we constructed a six-gene model in glioma, which can well predict the survival and prognosis of patients and provide effective and relatively sensitive biomarkers for clinical patients.\u003c/p\u003e \u003cp\u003eIn order to clarify the role of lactic acid metabolism genes in the occurrence and development of gliomas, based on our risk model, we divided glioma patients into high and low risk groups. We conducted GSEA analysis to explore the functional pathways of gene enrichment in high and low risk groups. The results showed that 36 HALLMARK pathways such as angiogenesis, apoptosis, EMT, IL6/JAK/STAT3, IL2/STAT5, p53 and PI3K/AKT/MTOR signaling pathways were significantly highly enriched in high risk group. 14 HALLMARK pathways, such as Wnt β-catenin, TGF β, Notch signaling pathway, bile acid metabolism and oxidative phosphorylation, were significantly highly enriched in low risk group. 79 KEGG pathways such as p53, Nod-like receptor, Toll-like receptor, T cell receptor and B cell receptor were significantly enriched in the high risk group, while 107 KEGG pathways such as ERBB, Notch and TGF β signaling pathways were significantly higher in the low risk group. Interestingly, these pathways are associated with proliferation, migration, apoptosis, immunity and so on. Among them, angiogenesis, apoptosis and EMT participate in the occurrence and development of tumor, which is consistent with our results\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. As for p53 and PI3K/AKT/MTOR, they are involved in the signal pathways of cell proliferation, survival, invasion, migration, apoptosis, glucose metabolism and DNA repair\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In gliomas the activation of PI3K/AKT/mTOR signal transduction significantly promotes glioma cell proliferation invasion migration and EMT\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that our risk model accurately reflects the biological characteristics of gliomas.\u003c/p\u003e \u003cp\u003eBased on the above research, we found that there were differences in immune microenvironment between high and low risk groups. Therefore, we explored the immune microenvironment of patients in high and low risk groups by SSGSEA. The results showed that 15 immune cells (activated CD4T cells, activated CD8T cells, central memory CD8T cells, etc.) were significantly increased in high risk group. Eleven immune cells (activated B cells, effective memory CD4T cells, type 17 helper T cells, etc.) were significantly decreased. CD8T cells, as cytotoxic T cells, play an anti-tumor role in the immune microenvironment, and they are significantly increased in the high-risk group, indicating the existence of immune activation in the tumor microenvironment in the high-risk group. In the future, further exploration of the interaction between CD8T cells and tumor cells will help to explore the composition of immune microenvironment and the mechanism of occurrence and development of gliomas. Subsequently, the immune score ESTIMATE and TIDEscore increased significantly in the high-risk group, suggesting that a higher TIDE score means a higher possibility of immune surveillance escape and a lower success rate of immunotherapy, suggesting that the benefit rate of immunotherapy in high-risk patients will be reduced. High-risk patients are more sensitive to Vinorelbine_2048, Paclitaxel_1080, Docetaxel_1007, Crizotinib_1083, Gefitinib_1010, Erlotinib_1168 and other drugs, suggesting that patients may benefit more from the treatment of these drugs.\u003c/p\u003e \u003cp\u003eIn short, we constructed and identified a risk model composed of six genes, which can well predict the survival and prognosis of patients, the composition of immune microenvironment, and clinical immunotherapy, and provide potential molecular targets for clinical treatment of glioma. However, this study did not explore the mechanism of related molecules in the future. We will explore its important role in the occurrence and development of gliomas based on risk genes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eLF is the first author. JX and LF design of the work; ZG and JX the acquisition analysis, LF interpretation ofdata; LF the creation of new software used in the work; LF, JX, and ZG have drafted and guided the work or substantively revised it. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis study was supported by grants from the Natural Science Foundation of Fujian Province (No.2022J01997).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e All the necessary data are included within the current study. Further data will be shared by request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOstrom QT, Cioffi G, Gittleman H, Patil N, Waite K, Kruchko C, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012-2016. \u003cem\u003eNeuro-oncology \u003c/em\u003e2019; 21(Suppl 5):v1-v100.https://doi:10.1093/neuonc/noz150\u003c/li\u003e\n\u003cli\u003eJackson CM, Choi J, Lim M. 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TRAM2 promotes the malignant progression of glioma through PI3K/AKT/mTOR pathway. \u003cem\u003eBiochemical and biophysical research communications \u003c/em\u003e2022; 586:34-41.https://doi:10.1016/j.bbrc.2021.11.061\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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":"Glioblastoma, lactic acid metabolism, prognosis, biomarkers, function, immune","lastPublishedDoi":"10.21203/rs.3.rs-3784359/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3784359/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRelated studies have shown that lactate played a key role in immune escape and metastasis. Exploring the roles of lactic acid metabolism-related genes (LRGs) in glioblastoma (GBM) has great significance for clinic treatment of GBM.The target genes were obtained by intersecting the differentially expressed genes (DEGs) and the module genes. Biomarkers of GBM were screened out to construct the survival risk model, and the nomogram of GBM was constructed to clinically predict the survival of GBM patients. Moreover, the gene set enrichment analysis (GSEA) and the tumor micro-environment analysis were conducted to study the functions of different risk groups and the potential mechanism of GBM. Furthermore, the drug sensitivity analysis were carried out to provide theoretical support for clinical treatment of GBM.The risk score was constructed with six biomarkers, including \u003cem\u003eCALN\u003c/em\u003e1, \u003cem\u003eCDHR1\u003c/em\u003e, \u003cem\u003eCRTAC1\u003c/em\u003e, \u003cem\u003eGNAL\u003c/em\u003e, \u003cem\u003eSLC7A14\u003c/em\u003e, and \u003cem\u003eSPHKAP\u003c/em\u003e, and \u003cem\u003eSLC7A14\u003c/em\u003e was negative factors of GBM. Based on it, the prognostic model was constructed with age, IDH status, grade, and risk score. Noticeable, the clinical risk of GBM were associated with proliferation, migration, apoptosis, and immune related signaling pathways. In addition, the level of immune escape was higher in high risk group, and samples in high risk group were more sensitive to Vinorelbine_2048, Paclitaxel_1080, Docetaxel_1007, Gefitinib_1010, Erlotinib_1168, and etc. drugs. In this study, we identified six LRGs, including \u003cem\u003eCALN\u003c/em\u003e1, \u003cem\u003eCDHR1\u003c/em\u003e, \u003cem\u003eCRTAC1\u003c/em\u003e, \u003cem\u003eGNAL\u003c/em\u003e, \u003cem\u003eSLC7A14\u003c/em\u003e, and \u003cem\u003eSPHKAP\u003c/em\u003e. These findings might help to deepen the understanding of the regulatory mechanism of LRGs in GBM.\u003c/p\u003e","manuscriptTitle":"The prognosis model of glioblastoma was constructed based on lactic acid metabolism-related genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-05 18:21:00","doi":"10.21203/rs.3.rs-3784359/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":"c49cde1d-ed74-49e4-8985-ff37e9563f74","owner":[],"postedDate":"January 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-06T17:27:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-05 18:21:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3784359","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3784359","identity":"rs-3784359","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}