Clinical Features and Differential Gene Screening of Invasive Behaviors between Glioma and Brain Metastasis

Zeng X; Chen G; Dong M; Feng L; Chu L; Liu J

Original Article

Austin J Clin Neurol 2022; 9(1): 1159.

Clinical Features and Differential Gene Screening of Invasive Behaviors between Glioma and Brain Metastasis

Zeng X¹, Chen G¹, Dong M², Feng L², Chu L² and Liu J¹*

¹Guizhou Medical University, Guiyang, Guizhou, People’s Republic of China

²Department of Neurosurgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, People’s Republic of China

*Corresponding author: Jian Liu, Guizhou Medical University, Guiyang, Guizhou550004, China

Received: October 22, 2022; Accepted: November 18, 2022; Published: November 25, 2022

Abstract

Objective: To analyze invasive behaviors between glioma and Brain Metastasis (BM), and to screen invasive differentially expressed genes.

Methods: Patients diagnosed pathologically with glioma or BM divided into low-grade glioma groups (n = 19), high-grade glioma group (n = 18), and BM group (n = 15). The survival period was determined. The clinical characteristics were retrospectively analyzed to draw Kaplan-Meier survival curve. Glioma and BM samples were obtained for RNA sequencing. By GO, KEGG, PubMed, and GeneCard, invasive genes were theoretical selected. Correlation between invasive genes and pathological grade was performed. The expression level of invasive genes was verified.

Results: The survival curve found that the clinical invasive behavior related to short lifetime includes: size, edema range, blood supply, vascular invasion, grade, and Ki 67 expression. Theoretical analysis finally found that the expression levels of CALM3, CAMK2A, CAMK2B, and PRKCG were negatively correlated with pathological grade (P< 0.01), of which CALM3 was highly correlated with BM (P< 0.01). CALM3 mRNA was significantly down-regulated in BM (P< 0.05), and PRKCG mRNA was significantly down-regulated in both glioma and BM (P< 0.05).

Conclusion: The clinical invasive behavior between glioma and BM have significantly shortened the median survival time of patients. Down-regulation of CALM3 suggested a higher correlation with BM, meanwhile Down-regulation of PRKCG played a role in glioma and BM The detection of CALM3 and PRKCG may be helpful for invasive behavior and provide a reference for the targeted therapy in glioma and BM.

Keywords: Glioma; Brain metastasis; Bioinformatics; Neoplasm invasiveness

Introduction

Intracranial malignant tumor, characterized by its localization and local invasive growth, the disease exhibits high mortality and high disability rate. In general, intracranial malignant tumor can be divided into primary tumor and Brain Metastasis (BM).

Glioma accounts for nearly 30% of all primary brain tumors, and is the main cause of death of primary brain tumors [1]. The histological classification of gliomas is based on Bailey and Cushing’s embryonic theory and Kernohan’s anamorphology theory, which are astrocytoma, oligodendroglioma, oligoastrocytoma, ependymoma, and choroid plexus epithelioma. According to WHO’s central nervous system tumor classification in 2016, molecular typing is included in the diagnostic criteria for the first time, such as Isocitrate Dehydrogenase (IDH) wild/mutant, H3K27M mutant, 1p/19q codeletion, RELA fusion, WNT/SHH activation, TP 53 wild/mutant etc., performing individualized treatment and clinical prognosis analysis in patients [2]. However, the molecular typing and corresponding clinical significance of gliomas need to be further proved. The main reason is that clinical outcomes from current surgical outcomes, chemotherapy drugs, and radiotherapy, are not ideal [3]. The overall prognosis of patients with gliomas is still poor. The median survival time of glioblastoma is 1 year, and the 5-year mortality rate exceeds 95% [4]. With the deepening of research, most patients with gliomas have been confirmed to have polygenic mutations [5]. Therefore, it may provide guidance for improving the prognosis of glioma patients in studying the gene function network, finding a new gene mutation, using differentially expressed genes to add the molecular genetic diagnosis, and elaborating the potential regulatory mechanism.

Most neoplastic brain injuries are caused by cancers outside the central nervous system. Studies have shown that BM is the most common intracranial malignant tumor in adults. The incidence of peripheral BM is 10% -40%. The number of BM patients is about 10 times that of primary brain tumors. The median survival time of BM without treated is about 2 months, and can be extended to about 5 months after treatment [6]. The primary lesion of BM is lung cancer, followed by breast cancer, as well as malignant melanoma, gastrointestinal tumors, and kidney cancer [7]. Clinically, although BM patients have tried a variety of surgical-based treatment strategies, the results are not good [8]. By observing changed gene in BM tissues, and corresponding analysis with gliomas, it is possible to discover the underlying regulatory mechanism of BM, which has certain guiding significance for the clinical diagnosis and treatment [9].

At present, the genomic variation of gliomas is still a hot spot. The relational research can be divided into the following 7 groups [10]: telomerase reverse transcriptase (TERT, rs2736100), Epidermal growth factor receptor (EGFR), coiled-coil domain containing 26 mutant group (CCDC26, rs55705857), cyclin dependent kinase inhibitor 2B mutant group (CDKN2B, rs1412829), PH homologous domain B family member 1 mutation group (PHLDB1, rs498872), tumor protein p53 mutation group (TP53, rs78378222),and telomere extension Helicase regulator mutant group (RTEL1, rs6010620).The study of glioma genome variation not only analyzes the biological function and signaling pathway, but also guides clinical diagnosis and treatment plans. For example, the mutations of CCDC26 and PHLDB1 are associated with IDH variant gliomas, suggesting that patients have a lower relapse rate and longer survival time. The 1p/19q co-deletion is of great significance in the pathological diagnosis of oligodendroglioma, the evaluation of the efficacy of radiotherapy and chemotherapy, and the prediction of clinical prognosis, which is related to the variation of TERT. On the other hand, BM shows the characteristics of primary gene mutations, for example, nonsmall cell lung cancer (NSCLC) often takes EGFR mutation [11] and anaplastic lymphoma kinase gene (Anaplastic Lymphoma Kinase, ALK) Rearrangement. Breast cancer can be driven by human epidermal growth factor receptor 2 [12]. Melanoma often demonstrates BRAF V600E mutation.BM also illustrates intracranial malignant tumor characteristics in genes, such as TP53, NRAS, And KRAS mutations, DSC2, ST7, PIK3R1 and SMC mutations, etc. In short, genomic mutations of gliomas and BM provide ideas for new therapeutic targets.

Gene Sequencing, a high-throughput technology combining life science and microelectronics technology, has been widely studied and applied in various research fields of biology and medicine in recent years. It provides important theoretical and practical value for sequence analysis, gene expression, genome research, and hybridization signal intensity analysis with gene expression profiles. Accompanied large-scale, high-throughput information, a series of biological information in brain tumor research is integrated [13]. Zhu XP et al. [14] studied the expression characteristics of micro RNAs of different grades of gliomas through gene micro-matrix technology. Ondracek J et al. [15] discovered the full micro RNA expression profile of radio resistant glioblastoma cells. Bhawe KM et al. [16] formulated some principles of gene chip application in glioblastoma. Then, the expression profile of exons of gliomaswas found [17], and Protein- Protein Interactions (PPI) network construction and hierarchical cluster analysis were conducted [18]. It suggests the application of gene chip technology in the regulatory mechanism of gliomas.In addition, whole-gene high-throughput analysis of BM has also been reported [19]. However, there are few studies on the comparative analysis of differential genes between gliomas and BM.

This study collected samples and clinical data of glioma and BM, analyzing the clinical features of aggressive behavior, exploring the different genes between gliomas and BM by RNA sequencing (RNAseq), screening the invasive genes with bioinformatics principles, and finally verifying invasive genes by quantitative real-time polymerase chain reaction (qRT-PCR), which aimed at the molecular mechanism of intracranial malignant tumor progression.

Materials and Methods

Ethical Approval

The clinical data and tumor specimens pathologically diagnosed gliomas or metastases were collected from November 2017 to November 2019. The procedure was approved by the Ethics Committee of Guizhou Medical University and affiliated hospital (IRB-2019-152). All procedures performed in studies related to human participants were in accordance with the 1964 Declaration of Helsinki and its later amendments.

Clinical Data Recording and Grouping

52 patients were divided into low-grade glioma group (group A, n = 19), high-grade glioma group (group B, n = 18), BM group (group C, n = 15) .Gender, age, tumor location, tumor size, edema range, chief complaint, preoperative KPS, surgical approach, tumor blood supply, vascular invasion, presence or absence of cystic changes, degree of resection, tumor grade, and Ki 67 expression were recorded.

Specimen Collection and Processing

According to the operation plan, RNAwait (Solarbio, Beijing, China), cryotube, 4°C physiological saline, ice box, sterile instrument bag, sterile syringe, liquid nitrogen and liquid nitrogen were prepared 2h in advance. In the operating room environment, the tumor tissues (cancer tissues and adjacent tissues 3 cm away from the lesion center) were washed with normal saline, placed into cryotube in pieces, and put into a liquid nitrogen tank within 10 minutes. After 24 h, the tissues were transferred to a refrigerator at -80°C for future use.

RNA-seq

The RNA-seq process was supported by Shanghai Jinteda Gene Technology Co., Ltd (NO. G2016083036XM). Briefly, after conventional RNA extraction, Nanodrop 2000 (Thermo Fisher Scientific, Waltham, Massachusetts, USA) determined the nucleic acid protein ratio A260/A280. The RNA concentration was measured on the Qubit 2.0 platform (Thermo Fisher Scientific, Waltham, Massachusetts, USA). 4200 TapeStation detected (Agilent Technologies, Santa Clara, California, USA) RNA RIN value. OligodT magnetic bead method was used to establish and amplify the mRNA library.

Bioinformatics Analysis

According to the previous report, the amplified sequences were analyzed using the next-generation sequencing comparison tools HISAT2 [20] and Stringtie [21]. Differentially expressed genes were obtained after correction using Fold-Change (FC). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to enrich and annotate differentially expressed genes. After selection of invasion biological pathways involves genes, the reduced target invasion genes are confirmed by literature screening and human tissue expression.

qRT-PCR

cDNA was synthesized according to the instructions of the reverse transcription™ first strand cDNA synthesis Kit (Thermo Fisher Scientific, Waltham, Massachusetts, USA). Amplification was performed according to FastStart Universal SYBR Green Master (Roche, Basel, Switzerland). The reaction parameters set by the PCR instrument: pre-denaturation at 95°C for 2 min, denaturation at 95°C for 15 s, annealing for 20 s (Table 1) and extension at 60°C for 40 s. 40 cycles in total. after manual correction, the relative expression level were calculate by 2-ΔΔCt method. The sequence of the primers is shown in the (Table 1), synthesized by Beijing Solarbio company reference (http://asia.ensembl.org/).

Table 1: Primer information of β -actin, CALM3, CAMK2A, CAMK2B, and PRKCG. Annealing temperature with β -actin was 54°C.Annealing temperature with CALM3 was 51°C.Annealing temperature with CAMK2A was 50°C. Annealing temperature with CAMK2B was 53°C.Annealing temperature with PRKCG was 54°C.




  
    gene
    primer sequence
    temperature
  
  
    β -actin 
    sense5'- ATGTGCGACGAAGACGAGAC- 3'
    54°C
  
  
    antisense 5'-ACCAAGCAGGAGTACGACGA- 3
  
  
    CALM3 
    sense5'- ATGGCTGACCAGCTGACTGA- 3'
    51°C
  
  
    antisense5'- TGGCCAGGTCAATTATGAAG- 3'
  
  
    CAMK2A 
    sense5'- TGGCCACCATCACCTGCAC- 3'
    50°C
  
  
    antisense5'- TCGTCCACTTCCACAGATCT - 3'
  
  
    CAMK2B 
    sense5'- ATGGCCACCACGGTGACCTG - 3'
    53°C
  
  
    antisense5'- AACGTGCACTTCCACTGCTC - 3'
  
  
    PRKCG 
    sense5'- TGAGGCAGAAGGTGGTCCAC- 3'
    54°C
  
  
    antisense5'- CAGCGCTTCACCTACGTGAA - 3'



Table 1: Primer information of β -actin, CALM3, CAMK2A, CAMK2B, and
PRKCG. Annealing temperature with β -actin was 54°C.Annealing temperature
with CALM3 was 51°C.Annealing temperature with CAMK2A was 50°C.
Annealing temperature with CAMK2B was 53°C.Annealing temperature with
PRKCG was 54°C.

Follow-up

The follow-up started from the patient’s first diagnosis, by WeChat or telephone. The interval was 3 months. the deadline was 12 months, and the Overall Survival time (OS) was recorded.

Statistical Processing

IBM SPSS Statistics 20.0 was use for date processing. The measurement data adopts the mean ± standard deviation, and the count data is expressed by frequency (or percentage). Survival analysis draws Kaplan-Meier curve and performs log-rank test. Chisquare test (or Fisher’s exact test) was used for clinical data. Spearman correlation analysis was performed between invasion genes and tumor grade. Differences in the expression levels of the target genes in each group were analyzed by ANOVA. P<0.05 or P<0.01 means the difference is statistically significant.

Results

Poor Clinical Pathological Parameters Suggested Short OS

Univariate log-rank test analyzed the relationship between clinical characteristics and survival, and found that the median survival of patients who complained of non-headache was significantly longer than that of patients who complained of headache (11 months vs 7.5 months, P = 0.006).The median survival time of patients with tumor diameter <3 cm was significantly longer than that with tumor diameter ≥3 cm (11 months vs 5 months, P = 0.002). The median survival time of patients with tumor edema range <2 cm was significantly longer than that with tumor edema range ≥ 2 cm (12 months vs 3 months, P<0.01).The median survival time of patients with moderate tumor blood supply was significantly longer than that of tumor rich blood supply (11 months vs 5 months, P<0.01).

The median survival time of patients with tumors that did not invade blood vessels was significantly longer than those with tumors that invaded blood vessels (11 months vs 4 months, P<0.01).The median survival time of patients with complete tumor resection was significantly longer than that of patients with incomplete tumor resection (11 months vs 3 months, P<0.01).Patients with low-grade tumors had a significantly longer median survival than those with higher-grade tumors (12 months vs 8 months, P<0.01).The median survival time of patients with low expression of Ki 67 was significantly longer than that with high expression of Ki 67 (12 months vs 8 months, P<0.01).

Biological Enrichment of RNA-Seq Invasion Genes

After RNA-seq, the differentially expressed genes in the intersection of group A, B and C, a total of 1083 differentially expressed genes, of which 585 were up-regulated and 498 were down-regulated, were used for further screening. Through GO analysis of all upregulated differentially expressed genes, using standard enrichment methods, the selected biological pathways for invasion are: cell response to type I interferon, type I interferon signaling pathway, interferon-γ response, cell to interferon -γ response, Th1 and Th2 cell differentiation, NOD-like receptor signaling pathway, and Toll-like receptor signaling pathway. Comparatively, the biological pathways or signaling pathways for selected down-regulated differentially expressed genes are: glioma, Wnt signaling pathway, Rap1 signaling pathway. The above pathway further negative feedback to obtain the invasion genes involved.

Secondary Screening of Aggressive Differential Genes

According to the characteristics described in the above biological pathways or signaling pathways, the invasion genes can be screened into 5 group: Interferon-related genes, Th1 / Th2 balance genes, glioma genes, cell adhesion molecule genes, and pure signals pathway genes, as shown in Table 2. There are 66 candidate invasive genes screened by biological or signaling pathways, of which 48 are upregulated and 18 are down-regulated. Literatureswere searchedin PubMed database with the formula: (“Glioma” [Mesh] And “X gene” [gene]) or (“Neoplasm Metastasis” [Mesh] And “X gene” [gene]) (X represents the 66 genes). The selected literature standards are: 1) clinical specimens; 2) solid tumor samples; 3) there are clear experimental results about the X gene and invasiveness (tumor grade, lymphatic metastasis, prognosis, etc.) to illustrate the research progress of this gene. In order to explore the invasive genes that have not been studied, 32 genes were excluded, and a further 34 genes were obtained, of which 23 were up-regulated and 11 were downregulated. As shown in (Table 2).

Table 2: Statistics of aggressive differentially expressed genes. After biological pathways or signaling pathways screening, there are 66 candidate invasive genes, of which 48 are up-regulated and 18 are down-regulated. By PubMed screening, 34 genes were obtained, of which 23 were up-regulated and 11 were down-regulated. The clinical studies reported and the corresponding genes are shown in the table. The blank part indicates that it has not been reported yet.




  
    gene
    tumor type
    n
    conclusion
    literature
  
  
    CARD9
    COAD
    48
    positively related to TNM
    [22]
  
  
    CASP1
    glioma
    6
    up-regulation
    [23] 
  
  
    CCL3L3
    GBM
    8
    up-regulation
    [24] 
  
  
    CCL8
    PDAC
    10
    up-regulation
    [25] 
  
  
    CD2
    BRCA
    819
    well up-regulation
    [26] 
  
  
    CD226
    STAD
    30
    down-regulationof progression
    [27] 
  
  
    CD274
    glioma
    21
    up-regulation
    [28] 
  
  
    CD4
    NSCLC
    102
    positively related to TNM
    [29] 
  
  
    CD8A
    LUAD
    49
    up-regulation
    [30] 
  
  
    CXCL10
    CESC
    60
    down-regulationof invasion
    [31] 
  
  
    CXCL9
    SCC
    51
    up-regulationof progression
    [32] 
  
  
    CYBB
     
     
     
    
  
  
    GBP4
    
    
    
     
  
  
    HLA-DOA
    
    
    
     
  
  
    HLA-DQA1
    
    
    
     
  
  
    HLA-DQA2
    
    
    
    
  
  
    IFIT2
    GBC
    80
    down-regulation
    [33] 
  
  
    IFITM1
    GBC
    215
    positively related to TNM
    [34] 
  
  
    IFITM3
    glioma
    60
    up-regulation
    [35] 
  
  
    IFNA21
    
    
    
     
  
  
    IL12RB1
    
    
    
     
  
  
    IL4R
    
    
    
     
  
  
    IL5
    
    
    
     
  
  
    IRF5
    BRCA
    83
    positively related tograde
    [36] 
  
  
    IRF7
    NPC
    49
    positively related tometastasis
    [37] 
  
  
    LGALS9
    glioma
    1292
    negatively related toOS
    [38] 
  
  
    NFATC2
    glioma
    135
    up-regulation
    [39] 
  
  
    OAS2
    COADREAD
    130
    positively related tometastasis
    [40] 
  
  
    OAS3
    
    
    
     
  
  
    OASL
    
    
    
     
  
  
    PARP14
    
    
    
     
  
  
    PARP9
    
    
    
     
  
  
    PLCB2
    
    
    
     
  
  
    RSAD2
    
    
    
     
  
  
    SIGLEC1
    PRAD
    109
    negatively related to invasion
    [41] 
  
  
    SP100
    GBM
    19
    down-regulation
    [42] 
  
  
    SPN
    GBM
    20
    up-regulation
    [43] 
  
  
    STAT2
    
    
    
    
  
  
    TBX21
    BRCA
    242
    positively related toOS
    [44] 
  
  
    TICAM2
    
    
    
    
  
  
    TIGIT
    
    
    
    
  
  
    TLR3
    
    
    
    
  
  
    TLR7
    CESC
    126
    up-regulation
    [45] 
  
  
    TRIM21
    
    
    
    
  
  
    TRIM22
    
    
    
    
  
  
    TRPM2
    
    
    
    
  
  
    UBD
    glioma
    165
    positively related tograde
    [46] 
  
  
    XAF1
    OC
    94
    positively expression
    [47] 
  
  
    ADCY1
    
    
    
    
  
  
    AKT1
    glioma
    60
    positively related tograde
    [48] 
  
  
    CALM3
    
    
    
    
  
  
    CAMK2A
    
    
    
    
  
  
    CAMK2B
    
    
    
    
  
  
    CNR1
    glioma
    16
    down-regulation
    [49] 
  
  
    DKK2
    BRCA
    98
    down-regulation
    [50] 
  
  
    GRIN2A
    
    
    
    
  
  
    GRIN2B
    
    
    
    
  
  
    P2RY1
    
    
    
    
  
  
    PRKCG
    
    
    
    
  
  
    RGS14
    
    
    
    
  
  
    RSPO2
    SCC
    73
    positively related to grade
    [51] 
  
  
    THBS1
    glioma
    121
    positively related to grade
    [52] 
  
  
    WNT10B
    PRAD
    38
    up-regulation
    [53] 
  
  
    WNT11
    OCC
    47、127
    positively related tometastasis
    [54] 
  
  
    WNT16
    
    
    
    
  
  
    WNT9A
    
    
    
    
  
  
    COAD: Colon    Adenocarcinoma; BRCA: Breast Invasive Carcinoma; STAD: Stomach Adenocarcinoma;    LUAD: Lung Adenocarcinoma; GBC: Gallbladder Cancer; NPC: Nasopharyngeal    Carcinoma; COADREAD: Colon Adenocarcinoma/Rectum Adenocarcinoma Esophageal    Carcinoma; PRAD: Prostate Adenocarcinoma; OC: Ovarian Carcinoma; OCC: Ovarian    and Cervical Carcinoma; GBM: Glioblastoma Multiforme; PDAC: Pancreatic    Ductal  Adenocarcinoma; NSCLC:    Non-Small Cell Lung Cancer; CESC: Cervical Squamous Cell Carcinoma and    Endocervical Adenocarcinoma; SCC: Squamous Cell Carcinoma; OS: Overall    Survival



Table 2: Statistics of aggressive differentially expressed genes. After biological
pathways or signaling pathways screening, there are 66 candidate invasive
genes, of which 48 are up-regulated and 18 are down-regulated. By PubMed
screening, 34 genes were obtained, of which 23 were up-regulated and 11 were
down-regulated. The clinical studies reported and the corresponding genes are
shown in the table. The blank part indicates that it has not been reported yet.

CALM3, CAMK2A, CAMK2B, and PRKCG Were Responsible For the Invasion Genes

Searching the Gene Card database (https://www.GeneCards. org/), focusing on the distribution of 34 differentially expressed genes in human tissues and the sub cellular localization of the encoded protein, the down-regulated genes of CALM3, CAMK2A, CAMK2B, and PRKCG were verified. The four genes expression levels, mainly distributed in the brain and spinal cord, is more than twice that in other tissues or organs. Therefore, the targeted aggressive genes were theoretical confirmed.

CALM3, CAMK2A, CAMK2B, and PRKCG Shared Negative Correlation with Pathological Grade

Spearman correlation analysis of CALM3, CAMK2A, CAMK2B, PRKCG expression levels found that CALM3, CAMK2A, CAMK2B, PRKCG expression had a certain negative correlation with pathological grade. The lower the genes expressed, the higher the pathological grade was, and the stronger the aggressive behavior was. Among them, CALM3 expression level share no correlation in group B between group A, was moderately negatively correlated in group C between group B (P< 0.01, r = -0.716), and was highly negatively correlated in group C between group B (P< 0.01, r = -0.833). PRKCG expression levels were moderately negatively correlated in group B between group A (P< 0.01, r = -0.775), group C between group B (P< 0.01, r = -0.684), and in group C between B of analysis showed a high negative correlation (P< 0.01, r = -0.860).

Down-Regulation of CALM3 and PRKCG Was Responsible For Glioma and BM

Compared with group A, PRKCG mRNA expression level was significantly down-regulated (P<0.05). Compared with group A, the levels of CALM3 mRNA and PRKCG mRNA were significantly down-regulated (P<0.05). compared with group C and group B, the levels of CALM3 mRNA and PRKCG mRNA were significantly down-regulated (P<0.05) (Figure 1).

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Citation: Zeng X, Chen G, Dong M, Feng L, Chu L and Liu J. Clinical Features and Differential Gene Screening of Invasive Behaviors between Glioma and Brain Metastasis. Austin J Clin Neurol 2022; 9(1): 1159.

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