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- Publisher
- Hindawi Publishing Corporation
- Copyright
- Copyright © 2020 Ya-Dan Wen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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- 1687-8450
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- 1687-8469
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- 10.1155/2020/3656841
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Abstract
Hindawi Journal of Oncology Volume 2020, Article ID 3656841, 13 pages https://doi.org/10.1155/2020/3656841 Research Article Genetic Profiles Playing Opposite Roles of Pathogenesis in Schizophrenia and Glioma 1,2,3,4 5 6,7 2,4,8 2,3,4,6 Ya-Dan Wen, Zhi-Wei Xia, Dong-Jie Li, Quan Cheng, Qing Zhao, and Hui Cao Department of Psychiatry, e Second People’s Hospital of Hunan Province, e Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Rd., Changsha 410008, China Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Rd., Changsha 410008, China National Clinical Research Center for Geriatric Disorders, 87 Xiangya Rd., Changsha 410008, Hunan, China Department of Neurology, Hunan Aerospace Hospital, Changsha, Hunan 410205, China Engineering Research Center of Applied Technology of Pharmacogenetics, Ministry of Education, 110 Xiangya Rd., Changsha 410008, China Department of Geriatric Urology, Xiangya International Medical Center, Xiangya Hospital, Central South University, Changsha 410008, China Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China Correspondence should be addressed to Hui Cao; caohui717@126.com Received 25 January 2020; Accepted 27 March 2020; Published 28 May 2020 Guest Editor: Ewa Sierko Copyright © 2020 Ya-Dan Wen et al. .is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Patients diagnosed with schizophrenia were found having lower risks to develop cancers, including glioma. Based on this epidemiology, we hypothesized that there were gene profiles playing opposite roles in pathogenesis of schizophrenia and glioma. Methods. Based on GEO datasets and TCGA, key genes of schizophrenia genes on the opposite development of glioma were screened by different expressed genes (DEGs) screening, weighted gene coexpression network analysis (WGCNA), disease- specific survival (DSS), and glioma grading and verified by gene set enrichment analysis (GSEA). Results. First, 612 DEGs were screened from schizophrenia and control brain samples. Second, 134 key genes more specific to schizophrenia were left by WGCNA, with 93 key genes having annotations in TCGA. .ird, DSS of glioma helped to find 42 key gene expressions of schizophrenia oppositely associated with survival of glioma. Finally, 24 key genes showed opposite expression trends in schizophrenia and different glioma grading, i.e., the upregulated key genes in schizophrenia expressed increasingly in higher grade glioma, and vice versa. CAMK2D and MPC2 were taken as the examples and evaluated by GSEA, which indeed showed opposite trends in the same pathways of schizophrenia and glioma. Conclusion. .is workflow of selecting novel targeted genes which may have opposite roles in pathogenesis of two diseases was firstly and innovatively generated by our team. Some filtered key genes were indeed found by their potential effects in several mechanism studies, indicating our process could be effective to generate novel targeted genes. .ese 24 key genes may provide potential directions for future biochemical and pharmacotherapeutic research studies. England and Wales in 1909 [1]. A latest meta-analysis 1. Introduction recruited schizophrenia patients from 16 cohort studies .e incidence of cancers in patients with schizophrenia was found decreased overall cancer incidence (RR �0.90, 95% proposed lower than that of general population, firstly raised confidence interval (CI) 0.81–0.99), especially in lung cancer, by the Board of Control of the Commissioners in Lunacy for colorectal cancer, liver cancer, stomach cancer, and prostate 2 Journal of Oncology cancer [2]. Interestingly, the overall incidence of cancer in which was GSE35794 (platform GPL6244) from the Na- patients with schizophrenia did not parallel their cancer risk tional Center of Biotechnology Information Gene Ex- factor exposures [3]. Noteworthy, many potential con- pression Omnibus, the most acknowledged gene founding factors, including sex, ethnicity, genetic back- expression resource for scientific community submitted ground, environmental exposure, and antipsychotic data. All samples in this dataset were from cadaver with medications, influenced that the cancer prevalence among proper consent. .e data were divided into two groups (the schizophrenia patients did not decrease in all types of cancer SCZ group and control group). DEGs between SCZs and [2]. .erefore, some factors specifically related to schizo- control tissues were screened using the R software. Once phrenia may influence the tumorigenesis. DEGs were identified, functional and pathway enrichment Considering that schizophrenia was well known for its analyses were used to analyze connections of DEGs and to heritability and familial transmission, the genetic compo- determine the interaction of DEGs on the molecular level. nents of schizophrenia may weigh heavily in the develop- Meanwhile, WGCNA was used to find the key genes ment of cancers. Some studies revealed significantly positively and negatively related to schizophrenia. .en, decreased risks of cancers in persons with schizophrenia and DSS was used to find the key genes’ opposite influence of their relatives, suggesting that the familiar/genetic factors survival in glioma and GETx and TCGA were used to contributing to schizophrenia may potentially inhibit tu- evaluate the expression levels of these key genes in different morigenesis and lead to the better survival [4–6]. In cerebral glioma grading. Additionally, two genes were exhibited as cancers, Grinshpoon et al. [7] reported that the standardized examples and analyzed by GSEA to check whether the incidence ratios (SIRs) of the cancers in brain sites were common pathways of schizophrenia and glioma in the two significantly lower than 1.0 among men with schizophrenia genes showed opposite trends or not. Lastly, six key genes (SIR �0.56, 95% CI 0.32–0.81), suggesting a decreased risk of were reevaluated in another brain tumor database, CGGA cerebral carcinomas in this group of people [7]. (Chinese Glioma Genome Atlas), through gene expression Some epidemiological studies showed that persons with in different glioma grading and survival curves in patients schizophrenia may less likely to suffer from glioma [7, 8]. with high- or low-level gene expression. .e processing Gao et al. reviewed several genes involved in pathogenesis of flow is shown in Figure 1. schizophrenia play opposite roles in the development of glioma, such as neural progenitor proliferation, neurite 2.2. Microarray Data Preprocessing and DEGs Screening. outgrowth, neuronal migration, synapse formation, neu- rogenesis, and synaptic transmission and consolidation [9]. .e raw data of GSE35794 were filtered out by probes with a corresponding gene symbol, and the average value of the Not only the epidemiological information, there were some antipsychotic agents, such as pimozide, trifluoperazine, and gene symbols was calculated with multiple probes. Between the two groups, the Linear Models for Microarray Data brexpiprazole sensitizing glioblastoma or glioma stem cells, partially indicating that schizophrenia and glioma may have Analysis (limma) package was used to screen the DEGs [12]. .reshold values were set as p<0.05. crosstalk on pathological mechanisms [3, 10, 11]. Above information provides hints, and we hypothesize that key genes of schizophrenia were crosstalk and negatively asso- 2.3. Functional Analysis of the DEGs. Nowadays, the most ciated with the development of glioma. However, limited commonly trusted gene function knowledge bases are gene studies directly unveiled the association between schizo- ontology (GO) and Kyoto Encyclopedia of Genes and Ge- phrenia genes and the survival of glioma. .erefore, iden- nomes (KEGG). In this study, we used a clusterProfiler tification of key genes of crosstalk between schizophrenia package to analyze function profiles (gene ontology, GO; and glioma will be helpful to guide more insightful inves- Kyoto Encyclopedia of Genes and Genomes, KEGG) of tigations and excavate novel targets of biochemical and genes and gene clusters to identify major biological func- pharmacotherapy research of schizophrenia and glioma in tions of genes [13]. All DEGs went through KEGG pathway the future. analyses and GO analyses including the biological process In summary, our study using a special gene expression (BP) using clusterProfiler. profiles, based on GEO datasets, TCGA, and CGGA, first time, directly found 24 key genes of schizophrenia genes on the opposite development of glioma through different expressed 2.4. Gene Network Construction and Module Detection. genes (DEGs) screening, weighted gene coexpression network WGCNA was used to identify modules of coexpressed genes analysis (WGCNA), disease-specific survival (DSS), and gli- within gene expression networks [14]. To construct the oma grading, and they were verified by Gene Set Enrichment network, the absolute values of Pearson correlation coeffi- Analysis (GSEA). .ese identified key genes through this cients were calculated for all possible gene pairs. Values were workflow help to determine novel therapeutic targets for the entered into a matrix, and the data were transformed so that treatments of schizophrenia and glioma. the matrix followed an approximate scale-free topology. A dynamic tree cut algorithm was used to detect network 2. Materials and Methods modules. WGCNA R package was used to evaluate the correlation of schizophrenia and module membership by the 2.1. Analysis Overview. In this study, the dataset about ‘p. weighted’ function [15]. schizophrenia (SCZ) was downloaded and reanalyzed, Journal of Oncology 3 used for the univariate analyses where appropriate. Survival rates of the expression level (high vs. low) were estimated by NCBI National center for the Kaplan–Meier method with Rothman CIs. Survival Biotechnology information Gene expression omnibus curves were compared with the logrank test. .e HR and GSE35794 (94 samples) 95% CI associated with the expressions of h-prune were estimated through a univariable Cox regression. A p val- ue<0.05 was considered statistically significant. DEGs WGCNA screening 3. Results 612 DEGs 3.1. Data Preprocessing and DEGs Screening. Removing the bipolar and depression samples, the dataset of GSE35974 contained 94 samples of the human cerebellum from 134 key genes schizophrenia and unaffected control in total 144 samples. .e data of GSE35974 contained the clinical characteristics TCGA annotation of age and gender, but not antipsychotic drug treatment. From the 94 samples, 612 DEGs were screened out with a 93 key genes threshold of p<0.05. .e limma package was employed to filter, and 332 upregulated genes and 280 downregulated DSS genes were recognized afterward. A volcano plot and heatmap were depicted with the full picture of DEGs in all 42 key genes cases (Figures 2(a)–2(b)). Glioma grading 3.2. Functional Analysis of DEGs. By examining functions of 24 key genes DEGs, we had a better view about disease progression of Figure 1: .e workflow of screening the key genes playing opposite schizophrenia. .e GO analysis and KEGG pathway were roles in schizophrenia and glioma. employed to sort out DEGs. In GO biological processes, the most overrepresented are gene silencing by miRNA, G1/S transition of mitotic cell cycle, regulation of histone mod- 2.5. Key Genes of Schizophrenia Evaluated by DSS and Glioma ification, regulation of calcium ion transport into cytosol, Grading. After the DEGs were evaluated by WGCNA, the positive regulation of mitochondrion organization, miRNA left genes were tested by DSS from TCGA. .e upregulated mediated inhibition of translation, regulation of nucleocy- key genes were intersected with hazard ratio (HR)<1 of toplasmic transport, negative regulation of protein locali- glioma (generated from DSS in TCGA, p value<0.05), and zation to membrane, positive regulation of ATP biosynthetic downregulated key genes were intersected with HR>1 of process, and response to leucine (Figure 2(c)). In the KEGG glioma (generated from DSS in TCGA, p value<0.05) to pathway analysis, DEGs were notably enriched in Micro- find out key genes that may play opposite roles in schizo- RNAs in cancer, phospholipase D signaling pathway, neu- phrenia and glioma. rotrophin signaling pathway, mTOR signaling pathway, Following that, the left key genes were evaluated by insulin signaling pathway, synaptic vesicle cycle, insulin TCGA and the Genotype-Tissue Expression (GTEx) data- resistance, cell cycle, phosphatidylinositol signaling system, bases to observe the gene expression in different glioma and Fc gamma Rmediated phagocytosis (Figure 2(d)). grading. Expression data of glioma and normal controls were obtained from TCGA and the Genotype-Tissue Ex- pression (GTEx) databases. 3.3. Schizophrenia Genes Screened by WGCNA. Gene ex- pression network analyses are an analyzing approach for describing the interactions among groups of transcripts so 2.6. Gene Set Enrichment Analysis (GSEA). GSEA helps to that the systematic alterations in expression could be ob- determine whether distinct sets of genes have significant served. .e modules identified by WGCNA were illustrated differences using computational methods. We performed in a cluster dendrogram of modules identified by WGCNA, the GSEA analysis using the software clusterProfiler package eigengene adjacency heatmap of module expression asso- of R language. Differences were considered statistically ciations, module-trait relationship, and interesting genes in significant at |NES|>1, nominal p value<0.05, and FDR q network heatmap (Figures 3(a)–3(d)), indicating that the value<0.25. .en, the genes were used with the cluster- clinical features were specific to schizophrenia. .e module- Profiler package for analysis of the GO biological process. trait relationship >0.3 was set as the modules positively .e cutoff value for the significant enrichment was set at related to schizophrenia, which were module midnight blue, p<0.05. red, and grey, and module-trait relationship < − 0.3 was set as the modules negatively related to schizophrenia, which 2.7. Statistical Analysis. Statistical analyses and graphics were module grey 60 and brown. .erefore, WGCNA was were undertaken using R version 3.5.1. Student’s t-test was applied to evaluate gene expressions from SCZ and control 4 Journal of Oncology Group 10.0 7.5 –2 5.0 –4 –6 Group 2.5 Con Sch 0.0 –1.0 –0.5 0.0 0.5 1.0 Log fold change (a) (b) Gene silencing by miRNA MicroRNAs in cancer G1/S transition of mitotic cell cycle Phospholipase D signaling pathway p value p value Regulation of histone modification Neurotrophin signaling pathway 0.004 0.01 0.008 Regulation of calcium ion transport into cytosol mTOR signaling pathway 0.02 0.012 0.03 Positive regulation of mitochondrion organization Insulin signaling pathway 0.016 MiRNA mediated inhibition of translation Synaptic vesicle cycle Count Count Regulation of nucleocytoplasmic transport Insulin resistance Negative regulation of protein localization to membrane Cell cycle Positive regulation of ATP biosynthetic process Phosphatidylinositol signaling system Response to leucine Fc gamma R−mediated phagocytosis 0.01 0.02 0.03 0.04 0.07 0.09 0.11 0.03 0.05 Gene ratio Gene ratio (c) (d) Figure 2: Functional Analysis of DEGs of schizophrenia. (a) Volcano plot for the DEGs. A total of 612 DEGs were screened out with a threshold of p<0.05. (b) Heatmap showing the expression profiles of DEGs, with a gradual change in color from red to blue indicating high to low. (c) GO enrichment in molecular function with the 10 terms. (d) KEGG pathway enrichment analysis of common DEGs with 10 terms. ∗∗ ∗∗∗ samples in GSE 35974. .e intersections of module midnight p<0.01, p<0.001), and their survival curves of glioma red with upregulated DEGs and the intersections of grey 60 (Figures 4(d)–4(f) and 4(j)–4(l)). Other DEGs are shown in with downregulated genes were zero. Finally, there were 26 Table 1 for your reference in your advance research. upregulated key genes and 107 downregulated key genes identified by WGCNA, which found the genes close to 3.5. Key Genes Evaluated by Glioma Grading. Although 42 clinical data of schizophrenia. key genes were selected by WGCNA and DSS, the range of potential targeted genes for future biochemical and phar- 3.4. DEGs Screened by DSS in Glioma. Since schizophrenia macological research studies would be better to be com- patients had lower cancer incidence, the opposite gene pressed. .erefore, we considered that gliomas could be expressions in glioma were expected to identify among 134 divided into low-grade gliomas (grade I and II) and high- key genes filtered by WGCNA. DSS of glioma, a survival rate grade gliomas (grade III and IV) according to the World specific to glioma, was recognized as the evaluation method. Health Organization classification criteria. When the grade Among 134 key genes, 93 key genes with annotations in was higher (malignancy degree increased), the gene ex- TCGA were chosen for DSS. .ere were 6 upregulated key pression increased, meaning this gene may play a role in genes intersected with high risk in glioma and 36 down- pathogenesis of glioma. Checking the expression levels of regulated key genes intersected with low risk in glioma these genes in different glioma grading may help to observe through DSS, shown in Table 1. 6 most significant DEGs the relationship of 42 key genes and the severity of glioma. If were chosen as examples to exhibit the difference between the expression level of a gene increases in higher-grade schizophrenia and control (Figures 4(a)–4(c) and 4(g)–4(i), glioma, this gene may be closer to pathogenesis of glioma, Log P 10 Journal of Oncology 5 Cluster dendrogram Eigengene adjacency heatmap 1.0 1.0 0.9 0.8 0.6 0.8 0.4 0.2 0.7 0.6 0.5 0.8 0.4 0.6 0.3 0.4 0.2 Module colors 0 (a) (b) Module–trait relationships Network heatmap plot, selected genes 0.36 MEred (3e − 04) 0.085 MEmagenta (0.4) −0.34 MEbrown (8e − 04) 0.032 MElightgreen (0.8) 0.12 MEgreenyellow (0.3) −0.27 MEdarkred (0.008) 0.44 MEmidnightblue 0.5 (1e − 05) −0.098 MEsalmon (0.3) −0.24 MEroyalblue (0.02) −0.27 MElightcyan (0.007) −0.055 MElightyellow (0.6) −0.036 MEpink (0.7) −0.41 MEgrey60 (4e − 05) −0.16 MEblue (0.1) 0.18 MEblack (0.09) −0.12 MEdarkturquoise (0.3) −0.19 MEdarkgreen (0.07) 0.25 −0.5 MEturquoise (0.01) −0.017 MEgreen (0.9) −0.17 MEcyan (0.09) −0.052 MEpurple (0.6) −0.019 MEyellow (0.9) −0.18 MEtan −1 (0.09) 0.34 MEgrey (9e − 04) Group (c) (d) Figure 3: Modules features chosen by WGCNA. (a) Cluster dendrogram of modules identified by WGCNA. (b) Eigengene adjacency heatmap of module expression associations. (c) Module-trait relationships. (d) Network heatmap plot among selected genes. and vice versa. .erefore, the upregulated key genes in MPC2, MYL12B, PAM, and SLC35B4) of schizophrenia are schizophrenia were expected to find increased expression displayed as the expression trends in glioma grading in ∗∗ ∗∗∗ levels in higher-grade glioma, and downregulated key genes Figures 5(a)–5(f) ( p<0.01, p<0.001). CAMK2D and in schizophrenia decreased in lower-grade glioma. After MPC2 were selected as the examples and evaluated by GSEA. observing the expression trends, 24 genes, i.e., ACOT9, In the common pathways of schizophrenia and glioma, these ADA2, AP2M1, APMAP, APOO, ARPC2, CAMK2D, two genes showed opposite trends in schizophrenia and DHDDS, EIF3K, ERGIC3, EXTL2, FUNDC2, LZIC, MPC2, glioma, shown in Figures 5(j)–5(k). MYL12B, PAM, PRMT2, SLC35B4, TMEM167A, TMEM19, CGGA, another brain tumor database, stored Chinese TSPAN13, VPS35, CNKSR2, and RTN4RL1, were accorded glioma datasets over 2,000 samples with mRNA sequencing, with the above expectation. 6 key genes (CAMK2D, EIF3K, mRNA microarray, and matched clinical data to benefit the Height MEred MEmagenta MEbrown MElightgreen MEgreenyellow MEdarkred MEmidnightblue MEsalmon MEroyalblue MElightcyan MElightyellow MEcyan group MEblue MEblack MEdarkturquoise MEpurple MEyellow MEtan MEpink MEgrey60 MEdarkgreen MEturquoise MEgreen 6 Journal of Oncology Table 1: 42 genes closely related to schizophrenia and glioma (TCGA) but may play opposite roles in the two diseases. Upregulated key genes of schizophrenia intersected CNKSR2, NPFFR1, RTN4RL1, WAPL, ZNF281, and ZNF519 with high risk in glioma ACOT9, ADA2, AP2M1, APMAP, APOO, ARPC2, C19orf12, CAMK2D, CAP2, CFL1, CNR1, DHDDS, DYNLT3, EIF3K, ERGIC3, EXTL2, FDPSP2, FUNDC2, Downregulated key genes of schizophrenia GPAT3, LAMTOR5P1, LZIC, MPC2, MRAP2, MYL12B, NRN1, PAM, PGK1, intersected with low risk in glioma PRMT2, RHBG, SLC35B4, SNX10, TMEM159, TMEM167A, TMEM19, TSPAN13, and VPS35 correlation and survival analysis. CAMK2D, EIF3K, MPC2, MYL12B, PAM, and SLC35B4 from 24 genes with references MYL12B, PAM, and SLC35B4, selected from the schizo- discussing their different roles in the two cerebral diseases. phrenia dataset and TCGA, were reevaluated in CGGA CAMK2D encodes one of the subfamilies of calcium/ calmodulin-dependent kinase II (CaMK II), which regulate through gene expression in glioma grading and survival 2+ curves between patients having high level of gene expression Ca homeostasis. In mammalian cells, CaMK II is com- and those having low level of gene expression. Luckily, the posed of four different chains: α, β, c, and δ. .e encoded analyzed datum was quite in line with the above datum. protein is contributed to this kinase δ chain [16]. Calcium/ .ese six key genes expressed increasingly in a higher glioma calmodulin-dependent kinase II alpha knockouts mice ∗∗ ∗∗∗ grade, shown in Figures 6(a)–6(f) ( p<0.01, p<0.001). presented schizophrenia features that showed remarkable Additionally, the survival rate was higher in patients with elevated levels of D2 receptors in their high-affinity state low gene expression level, shown in Figures 6(j)–6(k). [17]. .is trend was consistent with our results that CAMK2D expression was significantly lower in schizo- phrenia patients. Meanwhile, the activity of calcium/cal- 4. Discussion modulin-dependent protein kinase II was upregulated in resistant glioma cells and its cDNA transfection in sensitive Schizophrenia patients were found to have reduced overall glioma cells lead to glioma cells resistance, indicating that risk of cancers compared to the general population, in- CaMK II may be involved in malignant glioma cell resistance cluding the cancers of lung, melanoma, brain, breast, corpus [18]. .is association was in line with our finding that uteri, and prostate. Additionally, there were several anti- CAMK2D expression was associated with the decreased DSS psychotic agents presenting their influence on glioma cells. in glioma patients. Although there were some studies Although the epidemiology and pharmacological evidences revealed the cell resistant by CaMK II may be through the provided the association of schizophrenia and incidence of Fas pathway, our findings provided a new vision of cancers, the gene profiles associated with underlying CAMK2D that may regulate common pathogenesis of mechanisms between schizophrenia and glioma were still schizophrenia and glioma so that the potential treatments of unclear. Gene profiling, based on our workflow, may provide schizophrenia could be found in the glioma pathway, and the most related genes that play opposite roles in the two vice versa. cerebral diseases. MPC2, encoding one subunit of the mitochondrial py- In our study, the workflow first used DEGs screening, ruvate carrier (MPC) complex, a transporter protein in the WGCNA, DSS, and expression in glioma grading to find the mitochondrial inner membrane to control pyruvate trans- key genes that were significantly and differently expressed in portation into the mitochondria, therefore plays a crucial role schizophrenia patients, closely related to clinical data of in the pathways of pyruvate metabolism and citric acid (TCA) schizophrenia, opposite influence of survival of glioma, and cycle and glucose/energy metabolism [19]. .e MPC complex opposite trends of gene expressions in glioma, respectively. contains MPC1 and MPC2, two obligate protein subunits. Finally, 24 key genes of schizophrenia were screened out, .e loss in any subunit results in the destabilization of the showing opposite influences in the survival of glioma and MPC complex and thus dysfunction of the MPC complex opposite gene expression trends in glioma grading, i.e., key [20]. Recent GWAS and meta-analysis in East Asian pop- genes upregulated in schizophrenia and low risk of glioma ulation showed MPC2 variant rs10489202 may be a risk locus and key genes down-regulated in schizophrenia but also for schizophrenia [21–23], indicating that expression of having high risk glioma. .is workflow is an innovation of MPC2 may play a role in pathogenesis of schizophrenia. gene profiling to nicely find the intersection containing the Additionally, abnormal MPC function was found in several key genes playing opposite roles in the two brain diseases. cancers and contributed proliferation of cancers [20]. A Based on our findings, several proteins expressed by glioma study found that MPC1 distinguished improved above genes were found in peers’ mechanism studies both in survival but MPC2 worsened survival in 1p19q-intact tumors schizophrenia and glioma. Interestingly, these genes indeed (p<0.01) [24]. .ese studies partially supported our finding showed totally two direction effects, i.e., the risky genes in that MPC2 expression was striking lower in schizophrenia schizophrenia showed in glioma patients with good survival, patients and associated with deteriorated survival in glioma. while genes may prevent the development of schizophrenia .erefore, MPC2 could be a novel targeting gene to inves- showed in glioma patients with worse survival. Due to the tigate new mechanisms between the two diseases. limited studies, there were CAMK2D, EIF3K, MPC2, Journal of Oncology 7 CAMK2D EIF3K MPC2 Con Con Con ∗∗ ∗∗∗ ∗∗∗ Sch Sch Sch 10.4 10.6 10.8 11.0 9.8 10.0 10.2 10.4 9.0 9.2 9.4 CAMK2D EIF3K MPC2 (a) (b) (c) CAMK2D EIF3K MPC2 1.00 1.00 1.00 Log−rank Log−rank Log−rank 0.75 0.75 0.75 p < 0.0001 p < 0.0001 p = 0.00037 0.50 0.50 0.50 0.25 0.25 0.25 0.00 0.00 0.00 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 Day Day Day Strata Strata Strata High High High Low Low Low Number at risk Number at risk Number at risk High 319 27 6 1 High 319 20 5 1 High 319 20 5 1 Low 319 29 6 0 Low 319 36 7 0 Low 319 36 7 0 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 Day Day Day (d) (e) (f) MYL12B PAM SLC35B4 Con Con Con ∗∗∗ ∗∗ ∗∗ Sch Sch Sch 9.6 9.8 10.0 10.2 10.4 9.00 9.25 9.50 9.75 9.2 9.4 9.6 MYL12B PAM SLC35B4 (g) (h) (i) Figure 4: Continued. Group Strata Survival probability Group Group Strata Survival probability Group Group Strata Group Survival probability 8 Journal of Oncology MYL12B PAM SLC35B4 1.00 1.00 1.00 Log−rank Log−rank Log−rank p < 0.0001 p < 0.0001 p = 0.0016 0.75 0.75 0.75 0.50 0.50 0.50 0.25 0.25 0.25 0.00 0.00 0.00 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 Day Day Day Strata Strata Strata High High High Low Low Low Number at risk Number at risk Number at risk High 319 20 4 1 High 319 28 6 0 High 319 29 7 1 Low 319 36 8 0 Low 319 28 6 1 Low 319 27 5 0 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 Day Day Day (j) (k) (l) Figure 4: 6 key genes as examples exhibiting the gene expression differences in schizophrenia and survival curves in glioma (TCGA). (a b, c g, h i) .e differences of gene expression of CAMK2D (a), EIF3K (b), MPC2 (c), MYL12B (g), PAM (h), and SLC35B4 (i) in schizophrenia and control. (d e, f, j, k l) .e survival curves of CAMK2D (d), EIF3K (e), MPC2 (f), MYL12B (j), PAM (k), and SLC35B4 (l) in glioma ∗∗ ∗∗∗ patients (TCGA). p<0.01, p<0.001. N N ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ G2 G2 ∗∗∗ ∗∗∗ G3 G3 G4 G4 510 15 510 15 CAMK2D EFI3K (a) (b) ∗∗∗ ∗∗∗ G2 ∗∗∗ G2 ∗∗∗ ∗∗∗ ∗∗∗ G3 G3 G4 G4 510 15 510 15 MPC2 MYL12B (c) (d) Figure 5: Continued. Strata Survival probability Grade Grade Strata Survival probability Grade Grade Strata Survival probability Journal of Oncology 9 N N ∗∗∗ ∗∗∗ G2 ∗∗∗ G2 ∗∗∗ ∗∗∗ ∗∗∗ G3 G3 G4 G4 510 15 510 15 PAM SLC35B4 (e) (f) 0.1 0.0 0.4 −0.1 0.2 −0.2 −0.3 0.0 −0.4 1.0 1.0 0.5 0.5 0.0 0.0 −0.5 5000 10000 15000 10000 20000 30000 Rank in ordered dataset Rank in ordered dataset CAMK2D CAMK2D Myeloid cell differentiation Myeloid cell differentiation Positive regulation of interferon−gamma production Positive regulation of interferon−gamma production Regulation of vasculature development Regulation of vasculature development (g) (h) 0.5 0.1 0.4 0.0 0.3 −0.1 0.2 −0.2 0.1 −0.3 0.0 −0.4 −0.1 1.0 0.5 1.0 0.0 0.5 −0.5 0.0 5000 10000 15000 −0.5 Rank in ordered dataset 10000 20000 30000 MPC2 Rank in ordered dataset MPC2 Cellular response to abiotic stimulus DNA integrity checkpoint Cellular response to abiotic stimulus Positive regulation of angiogenesis DNA integrity checkpoint Positive regulation of angiogenesis (i) (j) Figure 5: 6 key genes as examples exhibiting their expressions in different glioma grading (TCGA) and 2 genes as examples to show their pathways opposite in schizophrenia and glioma (a–f) .e trends of gene expressions of CAMK2D, EIF3K, MPC2, MYL12B, PAM, and SLC35B4 in different glioma grading (TCGA). (g–h) .e GSEA analysis of CAMK2D in schizophrenia (g) and glioma (h). (i–j) .e GSEA ∗∗ ∗∗∗ analysis of MPC2 in schizophrenia (i) and glioma (j). p<0.01, p<0.001. EIF3K encodes eukaryotic translation initiation factor-3 translation initiation, termination, ribosomal recycling, and (eIF3) subunit k, assembling with other 12 subunits to form the stimulation of stop codon readthrough [25]. Recent the largest eIF3 complex, which implicates in mRNA studies found that the changes of expression of a single eIF3 Ranked list metric Ranked list metric Running enrichment score Running enrichment score Grade Ranked list metric Running enrichment score Ranked list metric Running enrichment score Grade 10 Journal of Oncology G2 G2 G2 ∗∗∗ NS. NS. ∗∗∗ G3 G3 ∗∗∗ G3 ∗∗∗ ∗ ∗ ∗∗∗ G4 G4 G4 0 5 10 15 0 5 10 15 0 5 10 15 CAMK2D EIF3K MPC2 (a) (b) (c) G2 G2 G2 NS. NS. ∗∗∗ ∗∗∗ ∗ G3 G3 ∗∗∗ G3 ∗∗∗ NS. ∗∗∗ G4 G4 G4 0 5 10 15 0 5 10 15 05 10 MYL12B PAM SLC35B4 (d) (e) (f) CAMK2D EIF3K MPC2 1.00 1.00 1.00 0.75 0.75 0.75 0.50 0.50 0.50 Log−rank Log−rank Log−rank 0.25 0.25 0.25 p < 0.0001 p < 0.0001 p = 0.0028 0.00 0.00 0.00 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000 Day Day Day Strata Strata Strata High High High Low Low Low Number at risk Number at risk Number at risk High 490 168 72 23 3 0 High 488 184 77 16 5 0 High 488 185 92 27 6 0 Low 488 256 144 55 16 0 Low 490 240 139 62 14 0 Low 490 239 124 51 13 0 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000 Day Day Day (g) (h) (i) Figure 6: Continued. Strata Survival probability Grade Grade Grade Grade Strata Survival probability Grade Strata Survival probability Grade Journal of Oncology 11 MYL12B PAM SLC35B4 1.00 1.00 1.00 0.75 + 0.75 0.75 ++ 0.50 0.50 0.50 + ++ + + + ++ ++ + + + + Log−rank Log−rank +++++ ++ Log−rank ++ 0.25 0.25 0.25 + + + ++ p < 0.0001 + + + ++ ++ + ++ + p < 0.0001 p = 0.00015 0.00 0.00 0.00 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000 Day Day Day Strata Strata Strata High + High + High Low Low + Low Number at risk Number at risk Number at risk High High High 489 131 54 15 3 0 492 157 66 23 4 0 490 180 84 26 5 0 Low Low 489 293 162 63 16 0 Low 486 267 150 55 15 0 488 244 132 52 14 0 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000 Day Day Day (j) (k) (l) Figure 6: 6 key genes, CAMK2D, EIF3K, MPC2, MYL12B, PAM, and SLC35B4, were reevaluated by CGGA in glioma grading and survival curves. (a–f) .e trends of gene expressions of CAMK2D, EIF3K, MPC2, MYL12B, PAM, and SLC35B4 in different glioma grading (CGGA). (g–i) .e survival curves of CAMK2D (g), EIF3K (h), MPC2 (i), MYL12B (j), PAM (k), and SLC35B4 (l) in glioma patients ∗∗ ∗∗∗ (CGGA). p<0.01, p<0.001. dramatic changes of cell morphology and dynamics in NIH subunit influence other subunit expressions [26], suggesting that changes of expression of any single eIF3 subunit may 3T3 cells [34]. A GWAS study found that the susceptibility genes of schizophrenia were associated with mRNA levels of promote human disorders, including neurodegenerative disease, cancer, and infection. eIF3 was interacted with the MYL2 (p<1.0E − 4) [35]. Another study conducted on protein encoded by a candidate gene of schizophrenia, postmortem brains from schizophrenia patients observed disrupted-in-schizophrenia 1 (DISC1) gene, which was that MYL was phosphorylated in the anterior cingulate disrupted by a balanced chromosomal translocation, t (1; 11) cortex [36]. Regarding to glioma, MYL was related to glioma (q42.1; q14.3) [27]. .e translocation reduced DISC1 protein cell migration [37], which can be blocked by inhibitors of expression [28]. Overexpression of DISC1 promoted the myosin II [38]. .erefore, above studies of MYL and myosin assembly of the eIF3 complex [27], generating a hypothesis II both on schizophrenia and glioma accorded with our that lacks DISC1 protein may be hard to stimulate the findings that MYL12B was expressed lower in schizophrenia and was found decreasing DSS when glioma grade in- expressions of eIF3 subunits. .ese studies not only agreed well our finding that the expression of EIF3K was decreased creasing. Although there were only several studies investi- in schizophrenia but also supported to hypothesize that the gated on MYL12B, they could be the forerunner of exploring protein encoded by EIF3K may be involved in schizophrenia this potential intersecting gene, MYL12B, to excavate some pathogenesis. Additionally, the subunits eIF3 a, b, c, e, and f novel minerals of the two diseases’ mechanisms. have been found as the oncogene overexpressed in several PAM encodes a preproprotein, peptidylglycine α-ami- cancers, including nonsmall-cell lung cancer, breast cancer, dating monooxygenase (PAM), which is proteolyzed to cervical carcinoma, esophagus squamous-cell carcinoma, generate the mature enzyme, including two distinct catalytic gastric carcinoma, and osteosarcoma. [25]. Recently, sys- domains, a peptidylglycine α-hydroxylating monooxygenase tematically profiling found that the expression of eIF3b, (PHM) domain and a peptidylalphahydroxyglycine α-ami- eIF3i, eIF3k, and eIF3m was increased with the glioma grade dating lyase (PAL) domain [39]. .ese domains sequentially catalyze neuroendocrine peptides to active α-amidated and poorer overall survival [29]. More studies showed knockdown of EIF3B [30], decreasing EIF3C [31], and si- products and regulate complex signaling between intestinal lencing EIF3D [32] and EIF3E [33] alleviated proliferation organs, peripheral neurons, and the central neuronal system and migration of glioma cells. Our profiling results were in [39]. PAM was recognized as one of the most promising accordance with above studies that increased expression of candidate genes of schizophrenia [40]. Moreover, the ex- EIF3K was associated with decreased survival of glioma pression of PAM was found increased in glioma cells [41]. patients and increased glioma grades. Based on the peer .ere studies linked PAM to both schizophrenia and glioma, investigations of pathogenesis on both schizophrenia and supporting our gene profiling that PAM may have a close glioma, we have the reason to advise EIF3K as the potential relationship of both pathogenesis. Currently, there were targets of the mechanism study of schizophrenia and glioma, limited mechanism studies of PAM on schizophrenia and resulting new therapies for both diseases. glioma so that researchers may reveal deeper mechanisms that why an enzyme related to secreted peptides could MYL12B encodes a subunit of myosin regulatory light chain 2 (MYL2), which regulates the activity of nonmuscle conduct pathogenic or antipathogenic effects on the two myosin II [34]. Knockdown of MYL12A/12B leads to diseases. 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Journal
Journal of Oncology – Hindawi Publishing Corporation
Published: May 28, 2020