Prognostic value and immunological role of Adenylate Kinase 2 in human glioma

doi:10.21203/rs.3.rs-2830047/v1

Adenylate Kinase 2 (AK2) is an enzyme responsible for catalyzing the exchange of nucleotide phosphate groups in cells, playing a crucial role in energy metabolism and transfer. Recent reports have suggested an association between AK2 and immune response in certain tumors. However, the biological function of AK2 in gliomas remains unknown. Thus, this study aims to explore the potential role of AK2 in gliomas by analyzing the relationship between AK2 gene expression and survival outcomes in glioma patients using data from the CGGA and TCGA databases and clinical samples. In addition, we assessed the correlation between AK2 expression and tumor immune score in gliomas utilizing the TIMER 2.0 tool. Furthermore, we investigated the possible biological function of AK2 in gliomas by performing GO annotation and KEGG signal pathway enrichment analyses. Our research indicates that: (1) AK2 is significantly overexpressed in gliomas and associated with poor prognosis in low-grade gliomas (LGGs); (2) AK2 expression is positively correlated with glioma grade; (3) AK2 gene knockdown induces cell apoptosis via the Caspase10/Caspase3 pathway; (4) Gene annotation and enrichment analyses reveal that AK2 function is mainly related to energy metabolism, energy transfer, and antigen presentation of glioma; (5) AK2 expression is positively correlated with the infiltration of certain immune cells. These results suggest that AK2 may serve as a promising therapeutic target and prognostic marker for LGGs.

Glioma

AK2

Biomarker

Immune Infiltration

Apoptosis

Gliomas are neurological tumors with the highest lethality and recurrence rates^[1–3]. Many evidences indicate that high grade glioma (HGG) and low grade glioma (LGG) have a complex and special tumor microenvironment (TME)^[4–7]. According to the molecular characteristics of gliomas, which will contribute patients to obtain more personal diagnosis and precise treatment in the clinic^[8]. Exploring new molecular markers is an vital and meaningful work^{[9, 10]}. AK2 is an adenylate kinase isoenzyme that plays a important role in energy metabolism and energy transfer in mitochondria^[11–14]. It is specifically expressed in the intermembrane space of mitochondria and is highly expressed in the liver and heart^[15]. Meanwhile, it has been reported that AK2 can further activate Caspase 10 to participate in the apoptosis process of mitochondria by binding to the death domain protein FADD^{[15, 16]}.

However, the role and biological function of AK2 in tumors, especially gliomas, are currently unknown. At present, it is generally accepted that the TME is one of the vital factors for tumor development and progressed^[17–21]. It is mainly composed of tumor cells, immune-realted cells, signaling molecules and extracellular matrix factors. There is evidence that TME significantly related to diagnosis and clinical treatment sensitivity of tumors, and its formation causes are diverse, complex in structure, and composed of multiple components, which determine the ultimate fate of tumor cells^[4]. In recent years, relevant immunological studies have found that immune cell infiltration plays a key role in TME to tumor formation, occurrence and development. The most concerned are the immune checkpoint protein PD-1 and its ligands PD-L1/ PDCD1LG2^[19], tumor transforming growth factor TGFB1 and its receptors TGFB1R, and CTL4. Taking the TGFB1 as an example, it can play an immunosuppressive role in the tumor progression, and further lead to immune dysfunction by inhibiting the activation and differentiation of B lymphocytes and T lymphocytes, so that tumor cells evade immune system surveillance.

The primary objective of this study is to investigate the potential role of AK2 gene in glioma using bioinformatics and experimental methods. Our database analysis revealed a positive correlation between AK2 expression and glioma grade, which was subsequently validated by clinical sample testing. In vitro experiments further demonstrated that silencing of AK2 gene expression induces apoptosis cells and enhances the expression of cleaved-caspase3 and cleaved-caspase10 in GBM cells, indicating its ability to trigger cell apoptosis through the caspase10-caspase3 pathway. Moreover, our prognostic analysis indicated a positive correlation between AK2 expression and favorable outcomes in glioma patients. The study also aimed to elucidate the relationship between AK2 expression and immune microenvironment factors, exploring the association between AK2 expression and immune cells, as well as immune inhibitory and activation factors. Collectively, our findings suggest that AK2 represents a promising novel biomarker for glioma and may serve as a potential target for immunotherapy.

1. Public Database

The samples and clinical informations used in this study were obtained from the CGGA ^[22](Chinese Glioma Patient Genome Atlas http://www.cgga.org.cn/) and the TCGA (Cancer Genome Atlas https://tcga-data.nci.nih.gov/tcga/) databases. Gene annotation and differential gene analysis are performed by the GeneMANIA ( http://genemania.org/ ) data platform, a visualization platform that gathers a large amount of annotated information and gene interactions, identifies co-expressed genes of specific genes in specific tumors, and predicts gene function and signaling pathway information from the annotated information^[23]. During the test, p＜0.05 indicates statistical significance, and p＜0.01 indicates significant statistical significance).

The analysis of AK2 gene mutations was conducted by utilizing mutation data accessed from the TCGA datasets hosted on the online platform cBioPortal. The investigation of the relationship between AK2 and the extent of tumor immune infiltration was performed using TIMER2.0, an analytical tool^[24]. TIMER2.0 is an immune profiling and evaluation tool that utilizes gene expression information from TCGA to investigate the association between various genes and different immune cell subtypes, as well as quantify the extent and purity of immune cell infiltration. LinkedOmics, an analytical tool (http://www.linkedomics.org/login.php), was employed in this study to identify differentially expressed genes linked with AK2. Pearson's correlation coefficient was utilized to test and distinguish the statistical methods employed. The TISIDB database was used to explore the relationship between the AK2 gene and immune-related factors, as well as perform GO annotation and KEGG signaling pathway enrichment analysis of the identified genes^[25]. TISIDB ( http://cis.hku.hk/TISIDB/index.php ) is is a comprehensive resource that assists in the identification of molecular targets for immunotherapy and precision medicine. It provides access to multiple types of data, including immune-related genes, pathways, and drug-target interactions.

2. AK2-related genes GSEA analysis （Gene set enrichment analysis）

In this study, the GSEA tool was used to identify co-expressed genes associated with AK2 function. Based on the expression levels of key genes, they were divided into high and low expression groups. Further analysis was conducted using GSEA to evaluate the enrichment of gene sets using the standardized enrichment fraction (NES) and assess the significance of the differences with FDR ≤ 0.25 and p＜0.05.

3. Transient transfection of siRNA gene knockdown

In this study, we used siRNA technology to knock down AK2 gene and employed flow cytometry to detect the apoptosis rates of U87-MG and U251 cells after AK2 knockdown. We selected two effective AK2 siRNA sequences: siRNA-1, 5’AAGUUUUCAGCCAAUCUGGGU 3’; siRNA-2, 5’AGAAUCAAGCUUCUCUUUCCU 3’; siRNA-3, 5’UCUUUCAUGGGCUCUUUUGGA 3’. In addition, a non-specific control siRNA sequence (5'-UUCUCCGAACGUGUCACGUTT-3') was designed and used as a negative control. U87-MG and U251 cells were seeded into 6-well plates and cultured for 24 hours to reduce their density. Then, AK2 siRNA (siRNA-1 /-2/-3) and control siRNA were separately mixed with Lipofectamine 2000 to form different treatment groups and control groups. The final concentration of siRNA in each well was 50nM, and the cells were cultured for 72 hours to ensure effective knockdown of AK2 gene.

4. Flow cytometry apoptosis assay

Apoptotic cells were assessed using Annexin V/PI staining. Specifically, U87-MG and U251 cells were exposed to siRNA transient transfection for 72 hours, followed by incubation with 5 μL of Annexin V for 20 minutes at room temperature in the dark. Then, 5 μg/μL of PI (propidium iodide) was added and cells were incubated for an additional 10 minutes prior to loading onto a flow cytometer. The resulting data were analyzed using appropriate statistical methods.

5．Western blotting

To extract total protein, frozen tumor tissues were weighed and slowly added to RIPA lysing buffer. Then, the samples were crushed in a tissue homogenizer for 2 minutes and sonicated using an ultrasonic breaker (4 cycles of 3 seconds on and 4 seconds off). After centrifugation at 12,000 g at 4°C, the supernatant was collected, and the extracted protein was quantified using the BCA kit (Thermo, USA). The protein samples were loaded onto a 10% SDS-PAGE gel and transferred onto a PVDF membrane (Millipore, USA), followed by blocking with 5% fat-free milk for 1 hour at room temperature. The membrane was then incubated overnight at 4°C with AK2 and GAPDH antibodies (purchased from Proteintech) and subsequently incubated with HRP-conjugated secondary antibodies (Sigma, USA). The WB band was visualized using ECL (NCM, China).

6. Univariate and multivariate regression analysis

In order to investigate the impact of the AK2 gene on glioma development, regression analyses were conducted to examine the association between AK2 gene expression levels and prognosis. Univariate regression analysis included two steps: RNA-seq data was normalized and analyzed using the edgeR R package, with AK2 gene expression levels transformed into log2 conversion values; univariate Cox regression model was constructed using the survival R package to calculate hazard ratios and p-values. For multivariate regression analysis, clinically relevant variables significantly correlated with AK2 gene expression levels were selected based on correlation analysis, and multiple linear regression modeling was performed. Finally, a multivariate Cox regression model was constructed using the survival R package to calculate adjusted hazard ratios and p-values.

7. Statistical analysis

In this study, the significance and correlation between expressed genes were assessed using the Spearman test as the statistical method. To divide all patients into two groups based on their expression level, a critical point of 50% was set for gene expression value. The difference in survival rate and significance between the two groups was evaluated using the Kaplan-Meier algorithm.

AK2 is upregulated in glioma

To understand the expression of the AK2 gene in gliomas and other types of cancer, we utilized the TIMER2.0 tool to analyze the transcriptome expression levels of AK2 in different tumors and normal tissues. The results revealed that AK2 was abnormally expressed in most tumor tissues compared to normal tissues. Of particular interest, AK2 was significantly upregulated in gliomas, particularly in GBM (Figure 1A). Furthly, this study analyzed four independent transcriptome datasets using Gliovis analysis tool . And the accuracy of these results was further tested using glioma transcriptome datasets from different Lab groups provided by the Gliovis platform, which showed abnormal upregulation of AK2 in TCGA, CGGA, Rembrandit, and Gravendeel datasets relative to LGG and in GBM samples(Figure 1B). To validate these findings, we collected tumor tissue samples from different grades of glioma patients and prepared protein samples for detection. The results demonstrated that AK2 expression was significantly higher in HGG compared to normal tissue and LGG (Figure 1C and D).The expression of AK2 protein in clinical specimens of glioma was also examined using the Human Protein Atlas database, which revealed higher expression in GBM than normal tissue (Figure 1E).

AK2 Gene Knockdown Induces Apoptosis in GBM Cells

To further investigate the function of AK2 in glioma cells, we designed siRNA sequences to knock down AK2 gene expression in tumor cells. Our results demonstrated that AK2 knockdown led to a certain degree of apoptosis in U87-MG and U251 cells (Figure 2A), and downregulation of AK2 significantly affected the activation and expression of cleaved-caspase10 and cleaved-caspase3(Figure 2B). These findings suggest that AK2 may be involved in apoptosis in GBM and warrant further investigation into the molecular mechanism underlying it.

We also utilized flow cytometry to detect changes in apoptotic cell numbers of U87-MG and U251 cells following siRNA-mediated AK2 gene knockdown. Our results showed that compared to the control group, there was a certain degree of cell apoptosis in the siRNA-AK2 knockdown groups, with the most significant effect observed in the siRNA-AK2-3 (Figure 2C and D). Therefore, the experimental results show that in the U87-MG cell, knockdown AK2 gene expression can activate the apoptosis procedure executive proteins Caspase3, which personally induce apoptosis. As an apoptosis-related gene, AK2 gene has been reported to correlate with patient prognosis in other tumors and generally believed that the prognosis of patients is often closely related to the clinical indicators of the sample.

To understand the coorelation between AK2 gene expression and the clinical characteristics in glioma, we performed the univariate and multivariate regression analysis for gene expression and clinical merge informations. It is worth noting that AK2 expression in TCGA database was statistically correlated with sample’s age and tumor grade. AK2 expression in CGGA database was statistically correlated with sample age , tumor grade, chemotherapy resistance , IDH mutation and 1p19q_codeletion (Table 1 and 2).

Prognostic value of AK2 expression in glioma

An analysis of patient survival provides one of the most direct methods for identifying key gene expression and determining prognosis in tumor patients. This approach has led to the identification of several important proto-oncogenes and tumor suppressor genes. In our study, we analyzed the association between AK2 gene expression and glioma patient prognosis in TCGA. Our findings demonstrate that high expression of AK2 is significantly correlated with improved overall survival (OS) and progression-free survival (DFS) in LGG patients, while no significant correlation was observed in GBM patients (Figure 3A and B). The overall prognosis of glioma patients is positively correlated with higher AK2 expression levels relative to all glioma patients. Moreover, we validated these conclusions in three independent CGGA cohorts, which provided consistent results with our previous findings regarding the overall prognosis of gliomas with high AK2 expression (Figure 3C).

Mutation Frequency and Gene Interaction Network Analysis of AK2 in Tumors

In order to gain a deeper understanding of the relationship between AK2 gene mutations and tumor clinical features, we conducted an analysis of the mutation frequency of AK2 in 33 types of cancer using the cBioPortal platform within the TCGA database. Our results indicate that AK2 has a lower mutation frequency in tumors (Figure 4A). Specifically, the mutation frequency for LGG and GBM is 0.1% and 0.6%, respectively (Figure 4B). Furthermore, based on the GeneMANIA database, our investigation into the AK2 gene-gene interaction network demonstrates that its function may be related to neuron necrosis, phosphotransferase activity, cell death due to oxidative stress, and nucleotide biosynthesis (Figure 4C).

Study on AK2 co-expression network in glioma

Gene co-expression networks is a significant area of research in tumor biology due to its close relationship with gene function. Co-expression networks are defined as the simultaneous expression of multiple genes in a single biological process, and these networks have been observed to provide insight into gene function and regulation in tumorigenesis.

By examining the correlation between the expression of multiple genes, researchers have been able to identify important regulators of tumor progression and the biochemical pathways involved. Furthermore, the analysis of gene co-expression networks has enabled a better understanding of the complexity of tumor biology, which can ultimately lead to improved treatments and therapies. To further explore the function of AK2 in gliomas, we used the LinkedOmics database to analyze the AK2-core gene co-expression network in gliomas. As shown below, red represents positively correlated genes co-expressed with AK2 and green represents negatively correlated genes co-expressed with AK2 (Figure 5A).

We selected the genes in the top 50% for further correlation analysis and visualized them by heat maps，the top ten genes with positive correlation with AK2 gene are SQOR、PCK2、IQGAP1、SUCLG2、PPA2、HMGCL、CASP1、LACTB、RUFY1 and SNX2. The top ten genes with negative correlation with AK2 gene are PODXL2、CSNK1E、MB21D2、STMN3、AGAP1、ADD1、MAGI1、MARK4、RUNDC3A and TANC2 (Figure 5B). The results of gene GO annotation and KEGG pathway enrichment analysis (GSEA) showed that AK2 and its co-expressed genes were mainly involved in the protein folding of cells, redox homeostasis maintenance of mitochondria, and DNA damage stress. (Figure 5C).

Correlation Between AK2 Expression and Immune Cell Infiltration in TME

Immune cell infiltration analysis is important for tumor research because it can provide insight into the tumor's immune environment and its potential response to immunotherapies. Next, in order to clarify the relationship between AK2 expression and immune cell infiltration in the TME, the TIMER2.0 platform was used to analyze different dimensions of immunity.

The results showed that AK2 expression was positively correlated with macrophages and CD4+ T cells, and negatively correlated with regulatory T cell (Treg cell) and myeloid-derived suppressor cells（MDSC）(Figure 6A). We further analyzed the relationship between AK2 expression and immune cell infiltration through the TISIDB database. The results showed that AK2 expression was positively correlated with MSDC, inhibitory dendritic cells, effector memory CD8+ T cells, and effector memory CD4+ T cells (Figure 6B and C).

8. Correlation between AK2 mRNA expression and immune microenvironment

The TME is a complex interactive network composed of tumor cells, immune cells and surrounding tissues, which includes the chemical substances, proteins, cytokines, and other molecules that can interact with each other. Studying the tumor TME is critically important for developing novel immune-based therapies for cancer, improving patient survival rates, and exploring new treatment modalities. In this study, the relationship between AK2 expression and the immune microenvironment was investigated using the TISIDB database.

Our findings demonstrate significant correlation between AK2 expression and immunosuppressive gene expression, particularly with the upregulation of inhibitors such as CSF1R, HAVCR2, TGFB1, IL10RB, PDCD1LG2, and LGALS9 (Figure 7A). Additionally, we observed a positive association between AK2 and genes that exhibit robust expression of HLA-DMA, HLA-DMB, HLA-DOA, HLA-DRA, and HLA-DRB (Figure 7B).

Moreover, by analyzing the relationship between AK2 expression and chemokines, we identified a substantial positive correlation with CD276, CD40, CD86, MICB, and TMEM173 (Figure 7C). We also investigated the correlation between AK2 expression and chemokine receptors, finding a positive association with the first four receptors, including CCL2, CCL5, CCL22, and CXCL16 (Figure 7D). Our results suggest that AK2 may serve as a critical immunomodulatory gene in the tumor immune microenvironment.

Based on extant literature, mammalian cells are known to possess nine AK homologous proteins, specifically, AK1, AK2, AK3, AK4, AK5, AK6, AK7, AK8, and AK9.^[26]. Although the AK homologous proteins share similar structural characteristics, their distribution and specific functions vary among different members^{[26, 27]}. For instance, AK1 is predominantly found in red blood cells and skeletal muscle cells, located mainly in the cytoplasm. Studies have demonstrated that mutations in the genes encoding AK1 lead to a nearly 40% reduction in β-phosphorylation transfer ^[28]. On the other hand, AK2 is primarily expressed in the mitochondria of liver and kidney cells, although some studies have also identified its expression in the cytoplasm^[27].

It has been reported that phosphotransferase CK is associated with the proliferation of AK pre-cancer cells^[29]. Upon knocking out AK2 in Drosophila melanogaster, the flies exhibited lethality^[30]. In bacteria, due to the genome encoding only one AK enzyme, the inactivation of this enzyme leads to cell death, underscoring AK's vital role in energy metabolism. Inhibition of AK2 kinase activity in tumor cells affects the transfer process of phosphate groups, which subsequently impairs AMP production and downstream AMPK activity, ultimately disrupting the cyclic regulation of cellular proliferation.

A recent investigation has revealed the prognostic and therapeutic potential of AK2 in lung adenocarcinoma. Notably, knockdown of AK2 in pancreatic cancer cells significantly affects various cellular processes such as proliferation, migration, and invasion^[31]. Additionally, knockout of AK2 induces both apoptosis and autophagy ^[32]. However, in some tumor cells, the AK2-FADD-mediated apoptosis pathway is missing or defective, implying that AK2 may prevent apoptosis and promote tumorigenesis. Research on Caspase10's functional study suggested that AK2 plays an essential role in Caspase-10 activation during metabolic stress induced apoptosis. Nevertheless, this apoptosis is part of the endogenous apoptotic pathway and not related to the death receptor. Moreover, loss of AK2 gene function affects nucleotide exchange between mitochondria and cytoplasm, leading to a substantial increase in reactive oxygen species production and mitochondrial polarization^{[15, 33]}. Our experimental evidence indicates that knockdown of AK2 gene expression can activate the apoptosis executive proteins Caspase8 and Caspase3, consequently inducing apoptosis in the U87-MG cell.

In a study of teratomas in mice, it was discovered that AK2 expression was significantly higher in metastatic tumor cells than in non-metastatic ones. One possible explanation for this is that the migration of tumors is closely linked to cell movement, and the transfer of phosphotransferases to regions with high energy consumption may be an important factor in tumor development. Furthermore, AK4 has been identified as a biomarker for lung cancer metastasis^[34–37], whereby its overexpression promotes metastasis by stabilizing HIF-1, thus facilitating epithelial-mesenchymal transition under hypoxic conditions.^[34]. Notably, increased expression of AK4 has also been associated with migration and invasion of bladder and breast cancers. Despite these findings, the precise function and role of AK kinases, notably AK2 kinase, in glioma remain unclear.

Through genome-wide analysis, it was discovered in this study that abnormally high expression of AK2 was linked to an increase in immune cell infiltration in LGG, but not in HGG. As per current reports and consensus, the efficacy of tumor immunotherapy is mainly contingent on the immune microenvironment of the tumor, wherein the expression of immune checkpoint genes is a key factor. Upon investigating the relationship between the AK2 gene and immune checkpoint genes, we observed a strong co-expression relationship between the AK2 gene and immune checkpoint inhibitor TGFB1 in glioma. This suggests that AK2 expression may be associated with upregulation of immune checkpoints. However, no prior studies have examined the connection between AK2 and immune checkpoints, for gliomas or other solid tumors.

By analyzing TCGA and CGGA databases, we discovered a positive correlation between high AK2 expression in glioma tissue and tumor grade. Additionally, high AK2 expression may be a contributing factor to poor prognosis for glioma patients. Through gene annotation of AK2 co-expression genes in gliomas, both GO annotation and KEGG signaling pathway enrichment analysis suggest that AK2 expression is closely linked to biological processes involving immune response and inflammation. Notably, our study also found that high AK2 expression is associated with infiltration of various immune cell types, including B cells, CD4 + T cells, MDSC, macrophages, and DCs.

Overall, our study is innovative as it presents the first investigation into the role of the AK2 gene in glioma. Our bioinformatics analysis results indicate that AK2 expression is linked to unfavorable prognosis in glioma patients, and downregulation of AK2 can induce cell apoptosis via the caspase10-caspase8 pathway. Moreover, we have explored potential mechanisms by which AK2 may impact the immune microenvironment of gliomas. Our findings suggest that AK2 could serve as a novel biomarker for glioma and a promising target for immunotherapy. As such, this work provides valuable insights into the optimization of immunotherapy strategies for glioma.

Acknowledgements

Not applicable

Funding

Natural Science Foundation of China, 82072777

Xiamen Municipal Bureau of Science and Technology, 3502Z20209005

Xiamen Science and Technology Major Project, 3502Z2020YJ05

Science and Technology Program of Fujian Provincial Health Commission, 2021ZD02006

Project of Xiamen Cell Therapy Research, Xiamen, Fujian, China,3502Z20214001

Data Availability Statement

Data available within the article or its supplementary materials.

Ethics approval and consent to participate

The present study and experimental procedures were approved by the Human Research Ethics Committee of First Affiliated Hospital of Xiamen University xiamen China Written informed consent was obtained from all patients and also from the patients families in certain cases The study was conducted according to the principles outlined in the Declaration of HelsinkiPatient consent for publication Written, Informed consent was obtained from the patients or the patients families.

Conflicts of Interest

The authors declare no conflict of interest.

Patient consent for publication

Informed consent was obtained from the patients or the patients families.

Author’s Contributions

Formal analysis, HL. Funding acquisition, ZW, BZ,WZ. Investigation,. Writing–original draft, HL. Writing–review and editing, HL. All authors contributed to the article and approved the submitted version.

Author’s information

¹The Department of Neuroscience, Institute of Neurosurgery, School of Medicine, Xiamen University, Xiamen City; No. 422, Siming South Road, Siming District, Xiamen City, Fujian Province

²Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, Xiamen City; No. 55 Zhenhai Road, Siming District, Xiamen City, Fujian Province

³Department of Neurosurgery, First Affiliated Hospital of Xiamen University; No.55 Zhenhai Road, Siming District, Xiamen City, Fujian Province

Ferlay, J., et al., Cancer statistics for the year 2020: An overview. Int J Cancer, 2021.
Siegel, R.L., et al., Cancer Statistics, 2021. CA Cancer J Clin, 2021. 71(1): p. 7-33.
Siegel, R.L., et al., Cancer statistics, 2022. CA Cancer J Clin, 2022. 72(1): p. 7-33.
Lan, X., et al., Fate mapping of human glioblastoma reveals an invariant stem cell hierarchy. Nature, 2017. 549(7671): p. 227-232.
Chen, R., et al., A hierarchy of self-renewing tumor-initiating cell types in glioblastoma. Cancer Cell, 2010. 17(4): p. 362-75.
Sottoriva, A., et al., Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci U S A, 2013. 110(10): p. 4009-14.
Fu, W., et al., Single-Cell Atlas Reveals Complexity of the Immunosuppressive Microenvironment of Initial and Recurrent Glioblastoma. Front Immunol, 2020. 11: p. 835.
Bush, N.A., S.M. Chang, and M.S. Berger, Current and future strategies for treatment of glioma. Neurosurg Rev, 2017. 40(1): p. 1-14.
Wu, L. and X. Qu, Cancer biomarker detection: recent achievements and challenges. Chem Soc Rev, 2015. 44(10): p. 2963-97.
Zou, J. and E. Wang, Cancer Biomarker Discovery for Precision Medicine: New Progress. Curr Med Chem, 2019. 26(42): p. 7655-7671.
Dzeja, P. and A. Terzic, Adenylate kinase and AMP signaling networks: metabolic monitoring, signal communication and body energy sensing. Int J Mol Sci, 2009. 10(4): p. 1729-1772.
Fujisawa, K., et al., Adenylate kinase isozyme 2 is essential for growth and development of Drosophila melanogaster. Comp Biochem Physiol B Biochem Mol Biol, 2009. 153(1): p. 29-38.
Six, E., et al., AK2 deficiency compromises the mitochondrial energy metabolism required for differentiation of human neutrophil and lymphoid lineages. Cell Death Dis, 2015. 6(8): p. e1856.
Kim, H., et al., AK2 is an AMP-sensing negative regulator of BRAF in tumorigenesis. Cell Death Dis, 2022. 13(5): p. 469.
Cai, F., et al., AK2 Promotes the Migration and Invasion of Lung Adenocarcinoma by Activating TGF-beta/Smad Pathway In vitro and In vivo. Front Pharmacol, 2021. 12: p. 714365.
Lee, H.J., et al., AK2 activates a novel apoptotic pathway through formation of a complex with FADD and caspase-10. Nat Cell Biol, 2007. 9(11): p. 1303-10.
Joyce, J.A., Therapeutic targeting of the tumor microenvironment. Cancer Cell, 2005. 7(6): p. 513-20.
Yin, Z., et al., Targeting T cell metabolism in the tumor microenvironment: an anti-cancer therapeutic strategy. J Exp Clin Cancer Res, 2019. 38(1): p. 403.
Acurcio, R.C., et al., Therapeutic targeting of PD-1/PD-L1 blockade by novel small-molecule inhibitors recruits cytotoxic T cells into solid tumor microenvironment. J Immunother Cancer, 2022. 10(7).
Bahrami, A., et al., Targeting the tumor microenvironment as a potential therapeutic approach in colorectal cancer: Rational and progress. J Cell Physiol, 2018. 233(4): p. 2928-2936.
Dzobo, K., et al., Advances in Therapeutic Targeting of Cancer Stem Cells within the Tumor Microenvironment: An Updated Review. Cells, 2020. 9(8).
Zhao, Z., et al., Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients. Genomics Proteomics Bioinformatics, 2021. 19(1): p. 1-12.
Warde-Farley, D., et al., The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res, 2010. 38(Web Server issue): p. W214-20.
Li, T., et al., TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res, 2017. 77(21): p. e108-e110.
Ru, B., et al., TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics, 2019. 35(20): p. 4200-4202.
Panayiotou, C., N. Solaroli, and A. Karlsson, The many isoforms of human adenylate kinases. Int J Biochem Cell Biol, 2014. 49: p. 75-83.
Wujak, M., et al., [Human adenylate kinases - classification, structure, physiological and pathological importance]. Postepy Hig Med Dosw (Online), 2015. 69: p. 933-45.
Bettendorff, L., et al., Thiamine triphosphate: a ubiquitous molecule in search of a physiological role. Metab Brain Dis, 2014. 29(4): p. 1069-82.
Puurand, M., et al., Intracellular Energy-Transfer Networks and High-Resolution Respirometry: A Convenient Approach for Studying Their Function. Int J Mol Sci, 2018. 19(10).
Horiguchi, T., et al., Adenylate kinase 2 deficiency limits survival and regulates various genes during larval stages of Drosophila melanogaster. J Med Invest, 2014. 61(1-2): p. 137-50.
Hansel, D.E., et al., Met proto-oncogene and insulin-like growth factor binding protein 3 overexpression correlates with metastatic ability in well-differentiated pancreatic endocrine neoplasms. Clin Cancer Res, 2004. 10(18 Pt 1): p. 6152-8.
Liu, H., et al., Prognostic and therapeutic potential of Adenylate kinase 2 in lung adenocarcinoma. Sci Rep, 2019. 9(1): p. 17757.
Zhang, S., et al., Adenylate kinase AK2 isoform integral in embryo and adult heart homeostasis. Biochem Biophys Res Commun, 2021. 546: p. 59-64.
Jan, Y.H., et al., Adenylate kinase 4 modulates oxidative stress and stabilizes HIF-1alpha to drive lung adenocarcinoma metastasis. J Hematol Oncol, 2019. 12(1): p. 12.
Jan, Y.H., et al., A co-expressed gene status of adenylate kinase 1/4 reveals prognostic gene signature associated with prognosis and sensitivity to EGFR targeted therapy in lung adenocarcinoma. Sci Rep, 2019. 9(1): p. 12329.
Jan, Y.H., et al., Adenylate kinase-4 is a marker of poor clinical outcomes that promotes metastasis of lung cancer by downregulating the transcription factor ATF3. Cancer Res, 2012. 72(19): p. 5119-29.
Wu, Z., et al., Knockdown of circ-ABCB10 promotes sensitivity of lung cancer cells to cisplatin via miR-556-3p/AK4 axis. BMC Pulm Med, 2020. 20(1): p. 10.

Table1．Cox regression analysis of the clinical variables, and overall survival in TGGA cohorts

Variables	Univariates		Multivariates
Variables	HR (95% CI for HR)	p value	HR(95% CI for HR)	p value
AK2	1.191(1.155-1.227)	<0.001*	1.074(1.030-1.121)	<0.001*
Age	1.703(1.061-1.084)	<0.001*	1.048(1.036-1.060)	<0.001*
Gender	1.124(0.858-1.471)	0.396	0.991(0.751-1.308)	0.95
Grade	4.655(3.748-5.782)	<0.001*	2.705(2.077-3.522)	<0.001*
Radio	1.087(0.742-1.591)	0.669	1.023(0.688-1.522)	0.909
Chemo	1.052(0.783-1.413)	0.737	0.805(0.586-1.105)	0.18
IDH-mutation	0.867(0.664-1.132)	0.294	0.663(0.486-0.905)	0.01*
1p19q_codeletion	1.244(0.912-1.695)	0.168	1.245(0.874-1.775)	0.225

Note: HR, hazard ratio; CI, confidence interval; Radio, Radiotherapy; Chemo, Chemotherapy; TGGA, The Cancer Genome Atlas; IDH, isocitrate dehydrogenase; “*” and Bold values indicates P-value <0.05.

Table2．Cox regression analysis of the clinical variables, and overall survival in CGGA cohorts

Variables	Univariates		Multivariates
Variables	HR(95% CI for HR)	p value	HR(95% CI for HR)	p value
AK2	0.085(1.155-1.227)	0.085	1.117(0.986-1.264)	<0.082
Age	1.641(1.346-2.001)	<0.001*	1.274(1.035-1.569)	0.023*
Gender	1.030(0.846-1.253)	0.770	1.075(0.880-1.312)	0.481
Grade	2.842(2.474-3.266)	<0.001*	2.670(1.926-3.700)	<0.001*
Radio	1.011(0.766-1.335)	0.938	0.938(0.702-1.253)	0.664
Chemo	1.660(1.322-2.084)	<0.001*	0.708(0.551-0.910)	0.007*
IDH-mutation	0.312(0.255-0.381)	<0.001*	0.575(0.453-0.731)	<0.001*
1p19q_codeletion	0.239(0.173-0.330)	<0.001*	0.378(0.268-0.533)	0.168

Note: HR, hazard ratio; CI, confidence interval; Radio, Radiotherapy; Chemo, Chemotherapy; CGGA, Chinese Glioma Patient Genome Atlas; IDH, isocitrate dehydrogenase; “*” and Bold values indicates P-value <0.05.

No competing interests reported.

Prognostic value and immunological role of Adenylate Kinase 2 in human glioma

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Abstract

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Introduction

Materials And Methods

Result

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