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Back to Journal »International Journal of General Medicine» Volume 14

Comprehensive analysis of mutations, miRNA and mRNA expression in glioblastoma

Authors: Wang Sheng, Zhou Hua, Zhang Ru, Zhang Yan

Published on November 16, 2021, the 2021 volume: 14 pages 8281-8292

DOI https://doi.org/10.2147/IJGM.S336421

Single anonymous peer review

Editor who approved for publication: Dr. Scott Fraser

Wang Shichao, 1 Zhou Huanmin, 1 Zhang Ruijian, 2 Zhang Yanru 1 1 School of Life Sciences, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, 010018; 2 Neurosurgery, Inner Mongolia People’s Hospital, Hohhot, Inner Mongolia, 010017, People’s Republic of China Correspondent: Zhang Yanru Tel 976 18047183314 Email [email protected] Background: Glioblastoma multiforme (GBM) is a common malignant brain tumor in adults, with a median survival time of only 15-23 months. Organisms respond to disease stress through complex mechanisms at the physiological, transcriptional and metabolic levels. However, the molecular regulatory network responsible for the occurrence, progression and recurrence of gliomas has not yet been elucidated. Method: In this study, we tried to determine the etiology of glioma by developing an RNA-seq technology that analyzes mRNA and small RNA (sRNA), with the goal of discovering the precise blocking of key signaling pathways in the occurrence, progression, and recurrence Potential method. . Explaining the mechanism leading to the formation of GBM has become a feasible and promising new treatment. Results: Based on the expression during the disease stress response, GBM-related genes were identified. Analysis of the negative correlation between microRNA (miRNA) and target mRNA revealed 43 mRNA-miRNA interactions during disease progression. Studies have found that BOC-SMO and BOC-RAS promote the malignant progression of gliomas. A total of 3088 differentially expressed genes were identified, involving various biological processes, such as amino acid metabolism, protein transport related to immune response, cell proliferation, and cell apoptosis. It was also determined that 15 miRNAs were differentially expressed in GBM and the control group. Conclusion: The results of this study provide an important basis for understanding the pathogenesis of glioma and discovering new therapeutic targets. Keywords: glioblastoma multiforme, transcriptome, miRNA

Glioblastoma multiforme (GBM) is the most common type of malignant primary central nervous system (CNS) tumor, and the prognosis of patients is usually poor. 1 According to histological classification, the World Health Organization (WHO) classifies gliomas into grades I-IV. Astrocytoma (grade I-II) is classified as low-grade glioma, and grades III and IV are considered high-grade. The differences in histopathological characteristics between high-grade and low-grade gliomas include increased atypical cell density, enlargement of necrotic areas, and abnormal vascular proliferation. 2,3 Grade I patients have the best prognosis and can be cured in clinical practice. GBM is classified as a grade IV tumor and is the most malignant tumor, accounting for 55% of intracranial tumors. Although there are currently strict treatment options for GBM, the survival rate of patients diagnosed with GBM is still very low, with a median overall survival rate of only 15-23 months. 4

MicroRNA (miRNA) is a type of small (20-24 nucleotides in length) endogenous RNA that plays a variety of important regulatory roles in cells. As an evolutionarily conserved small RNA family, miRNAs can regulate physiological and pathological processes by inhibiting the translation of downstream target mRNAs. They can bind to the untranslated region of target gene mRNA, leading to mRNA cleavage and negative regulation of target gene post-transcriptional expression. 5 The expression of comorbid genes is controlled by the up-regulation and down-regulation of miRNA, which contributes to cancer cell infection, proliferation, invasion, angiogenesis, pro-/anti-inflammatory, and metastasis of breast cancer, prostate cancer, nasopharyngeal carcinoma and glioblastoma . 6-9 Therefore, miRNA can be used as an important diagnostic and prognostic biomarker for many cancers, including glioblastoma. 10, 11

Next-generation sequencing (NGS) technology provides a comprehensive understanding of gene status and has become a precise molecular analysis tool for transcription analysis. 12 With the development of NGS, changes in the genome, transcriptome, and epigenome can be observed on a single cell. Level, allowing the occurrence and development of diseases to be monitored at the cellular level, including in tumors. Cell cycle regulation is a basic feature of cancer pathogenesis, and genes involved in cell growth and proliferation are expressed at high levels in almost all tumor microarray data sets. Sequencing methods have been used to identify the expression of cell growth and proliferation genes in gastric cancer, 13 colorectal adenomas, 14 and glioblastomas. 15 Understanding the miRNA-mediated cell cycle response network will provide a genetic basis for future research.

In this study, we investigated the expression of miRNA and mRNA in glioblastoma cells, and the negative correlation of miRNA-mRNA in human astroglioma cell lines. The technique used in this study allows to ask about BOC gene expression and its relationship with GBM. According to our research, the draft genome sequence of cancer and para-cancerous tissues from four samples identified genes that provide a description of the disease function and served as a way of visual management.

This study was approved by the local ethics committee of Hohhot First Hospital and Inner Mongolia People's Hospital, and all participants gave informed consent. This research was conducted in accordance with the Declaration of Helsinki. All experimental protocols comply with the safety requirements guidelines approved by the hospital department administrator. All information about participants is anonymous.

The research standards are strictly in line with China's diagnostic guidelines for the diagnosis and treatment of central nervous system gliomas and the WHO (2016) Central Nervous System Tumor Classification Rules16. Corresponding adjacent non-affected tissues (ANIT) were collected from four patients (35-60 years old) in stage III and IV. An ANIT sample was obtained at a certain distance from the edge of the tumor. GBM and ANIT use clinical histopathological methods for diagnosis in the pathology department, and determine the stage according to the tumor-lymph node-metastasis (TNM) classification. For decades, the TNM staging system has been introduced globally as a benchmark for cancer staging, which takes into account the weight, type, and multiple distant metastases of tumor size. 17,18 All lesions are located in the cerebral hemispheres, three of which are located in the cerebral hemispheres. One of the frontal and temporal lobes.

Samples including tumors and adjacent tissues were collected and fixed with formalin for pathological examination, and stained with hematoxylin and eosin (H&E). Distinguish and confirm adjacent cancer tissues and tumor tissues by optical microscope. Provides an enhanced MRI image of the registered patient (Figure S1). The remaining tissue samples were cut into 0.5×0.5×0.5 cm pathological sections, and immediately frozen with liquid nitrogen for subsequent sequencing experiments. See Table 1 for detailed sample information. Table 1 Grouping table of GBM tissues and corresponding adjacent tissue samples in this study

Table 1 The sample grouping schedule of GBM tissues and corresponding adjacent tissues included in this study

Grind the appropriate amount of tissue thoroughly in liquid nitrogen. Add the TRIzol reagent mix (1 mL) and mix the sample thoroughly before incubating for 10 minutes at room temperature. Add chloroform (200 μL) and shake the sample thoroughly before centrifuging at 12,000 rpm for 10 minutes at 4°C. Before centrifugation at 12,000 rpm at 4°C, an equal volume of phenol:chloroform (25:24) was used to separate the upper aqueous layer containing RNA. Then add the upper aqueous phase to an equal volume of chloroform and centrifuge at 12,000 rpm for 10 minutes at 4°C. Finally, the upper aqueous phase was mixed with an equal volume of isopropanol and centrifuged at 12,000 rpm at 4°C for 10 minutes, and then the mixture was purified with ethanol. Use 20–50 μL of RNase-free water to elute total plasma RNA and store at –80°C until subsequent analysis. Four controls and four GBM tissue samples were used for quality control by RT-q PCR, and the nanodrop method was used to determine the quality and quantity of RNA extracted from the tissue samples. RNA integrity number (RIN) and the percentage of RNA fragments> 200 nt are used as quality indicators for monitoring.

3 micrograms of total RNA per sample is used for RNA preparation. First use Epicenter Ribo-zero rRNA Removal Kit (Epicentre, USA) to remove ribosomal RNA, and then remove rRNA-free residues by ethanol precipitation. Then use rRNA depleted RNA and NEBNext Ultra Directional RNA Library Prep Kits for Illumina (NEB, USA) to generate a sequencing library according to the manufacturer's recommendations. In short, in NEBNext First Strand Synthesis Reaction Buffer (5X), divalent cations are used for fragmentation at elevated temperature. Use random hexamer primers and M-MuLV reverse transcriptase (RNaseH-) to synthesize first-strand cDNA. Use DNA polymerase I and RNase H for second-strand cDNA synthesis. In the reaction buffer, dNTP with dTTP is replaced by dUTP. Use exonuclease and polymerase activity to convert the remaining overhanging ends into blunt ends. After the 3ʹ ends of the DNA fragments are adenylated, they are connected to the EBNext adaptor with a hairpin loop structure to prepare for hybridization. Use the AMPure XP system (Beckman Coulter, Beverly, USA) to purify the cDNA fragments with a length of approximately 150–200 bp from the library. Before PCR analysis, USER enzyme (3 μL; NEB, USA) was used with the size-selected, adaptor-linked cDNA at 37°C for 15 minutes, and then at 95°C for 5 minutes. Use Phusion high-fidelity DNA polymerase, universal PCR primers, and index (X) primers for PCR. Finally, the product was purified (AMPure XP system) and the library quality was evaluated on the Agilent Bioanalyzer 2100 system.

According to the manufacturer's instructions, use TruSeq PE Cluster Kit v3-cBot-HS (Illumina) to cluster the index coded samples on the cBot Cluster Generation System. The library was then sequenced on the Illumina HiSeq 4000 platform and a 150-bp paired-end read was generated. The raw data (raw read) in FastQ format was initially processed with an internal Perl script. This step involves obtaining clean data by removing reads containing adapters, policy N, and low-quality reads (clean reads) from the original data. Calculate the Q20, Q30 and GC content in the clean data, and all subsequent analyses are based on the processed clean data.

Cuffdiff is used to evaluate the fragments of long non-coding RNA (lncRNA) and encoding gene exons per million reads mapping (FPKM) in each sample. 19 Gene FPKM is calculated by adding the transcript FPKM of each group. FPKM is numbered according to the length of the fragments and the read count mapped to the fragments.

The Ballgown suite includes interactive data exploration for transcriptome assembly, visualization of transcription structure, specific abundance of features for each locus, and post-event annotation functions from assembly features to annotation features. Transcripts with P-adjust <0.05 were determined as differentially expressed transcripts. Cuffdiff provides a statistical program for determining different expressions in digital transcripts and gene expression data using a model based on the negative binomial distribution, and designating P-adjusted transcripts as differential expression. 20

The GO enrichment analysis of differentially expressed genes or lncRNA target genes is performed using the Goseq R package with gene length deviation correction. 21 GO terms with a corrected P value of <0.05 are considered to be significantly enriched in differentially expressed genes.

The KEGG database is a widely used resource for high-level annotations and related functions for identifying biological systems. The molecular level information of cells, organisms, and ecosystems can be determined, especially when using large-scale molecular data sets generated by genome sequencing and high-throughput experimental techniques. We used KOBAS software to test the statistical enrichment of differentially expressed genes and lncRNA target genes in the KEGG pathway. twenty two

Use lentiviral transduction to use BOC gene knockdown in the U251 cell line of human glioblastoma to verify the importance of the BOC gene in glioblastoma. Establish a knockout (KD) group and a negative control (NC) group to evaluate the knockout effect. The transfection process is as follows: lentivirus (MOI=10) is inoculated into a 6-well plate, and the cell density is set to 30%. Add polybrene to the plate 10 minutes before adding the lentivirus to improve transfection efficiency. Before replacing the medium with normal medium, incubate the plate with 5% CO2 for 16 hours at 37°C. After 24 hours of incubation, 1 μg/mL puromycin was added to the medium to select stable transfected cells. After 72 hours of incubation under the above conditions, fluorescence photography was used to verify the infection effect (Table S1).

QRT-PCR was used to detect the mRNA expression levels of BOC and downstream genes HRAS, MRAS and SMO in the KD-BOC cell line and the control group. Trizol reagent was used to extract total RNA from cells according to the manufacturer's instructions. CDNA was synthesized by reverse transcription using Promega M-MLV kit, and cDNA was amplified using SYBR Premix Ex Taq RT-PCR kit. Finally, the Roche LightCycler 480 II instrument was used to detect mRNA levels by quantitative PCR. GAPDH was used as a reference gene, and the mRNA expression levels of BOC and downstream genes were calculated using the 2-ΔΔCT method. The primer information is shown in Table S2.

In the pathological sections stained with H&E, the tumor area was darker than the surrounding normal brain tissue. Necrosis of glioma cells and fences is also visible, which proves to be an indicator of glioma. Figure 1 shows long spindle and stellate tumor cells with large, round, deeply stained nuclei. Pathological mitosis is common, and binuclear cells are occasionally seen. The tumor tissue hemorrhages, necrosis, and contains a lot of capillaries. Tumor tissues are densely arranged around blood vessels with focal infiltration. Tumor cells also show the characteristics of active growth, arranged in small clusters. Other pathology reports are shown in Figure S2. Figure 1 H&E staining results. Note: The left picture is the tissue image at low magnification of 10 × 10, and the right picture is the tissue image at the selected area at 40 × 10 magnification.

Figure 1 H&E staining results.

Note: The left picture is the tissue image at low magnification of 10 × 10, and the right picture is the tissue image at the selected area at 40 × 10 magnification.

The 8 qualified libraries from the GBM disease group and the corresponding adjacent tissues were sequenced. An overview of the sequencing and assembly data is provided in Table 2. Clean up the original data by removing low-quality readings. The small RNA Sample Prep Kit was used to construct the GLAT, GLBT, GLCT and GLDT of the four samples in the Q1 group, which produced 14,491,255, 15,478,540, 15,161,494 and 14,660,621 raw readings, respectively. After removing the low-quality readings from the raw data, 14,107,719, 15,019,165, 14,746,657, and 14,205,763 clean readings were obtained. The adjacent tissues corresponding to the above samples produced 15,495,417, 13,323,123, 14,247,033 and 14,370,467 raw readings, respectively. Table 2 Summary of Small RNA (sRNA) Sequencing Data Set

Table 2 Summary of Small RNA (sRNA) Sequencing Data Set

After removing the low-quality data, there are 15,150,481, 12,710,143, 13,839,763, 13,855,917 corresponding clean reads respectively. For all eight libraries, most sRNAs are 21-24 nt in length, of which 24 nt is the most common length (Figure S3). According to the described method, a total of 1730 miRNAs and 120 novel miRNAs were identified in the eight sRNA libraries (Table S3). Detailed information about sRNA distribution is provided in Table S4.

miRNA-seq data analysis identified a total of 15 differentially expressed miRNAs (DE miRNAs) in GBM samples and their corresponding adjacent tissues, which met the criteria | log2 FC |> 0.5 and P value <0.05. Two of them were identified as new miRNAs (Table 3). Conserved (n=13) and new (n=2) miRNAs are significantly expressed in GBM, of which 8 are up-regulated and 7 are down-regulated. New miRNAs (novel_912 and new_328) are similarly regulated in GBM tissues. Table 3 List of differentially expressed microRNA (DE miRNA) in response to GBM

Table 3 List of differentially expressed microRNA (DE miRNA) in response to GBM

The 8 qualified libraries from the GBM disease group and the corresponding paracancerous tissues were sequenced. An overview of the sequencing and assembly data is shown in Table 4. Clean up the raw data from cancer tissue by removing low-quality readings, resulting in 100,911,602, 100,277,916, 112,758,002 and 82,991,704 clean readings, respectively. More than 94.28% of clean reads have a Q score at the Q30 level (error threshold <0.01%). Pearson correlation analysis was used to evaluate the reliability between Q1 and Q2 sample replicates. Figure 2 shows the results of the correlation analysis, indicating that the similarity between tumor tissues is >69%, and the similarity between adjacent tissues is >60%. Table 4 Summary of mRNA sequencing data set

Table 4 Summary of mRNA sequencing data set

Through differential expression analysis, DESeq software used |log 2 FC|> 2 and P value <0.05 as the strict criteria for screening differences, to check the differences in gene expression between glioma and cancer tissues in the process of disease progression. A total of 3088 genes were differentially expressed in response to GBM, of which 1375 were up-regulated and 1713 were down-regulated (Table S5, S6). Figure 2 Pearson correlation heat map of miRNA expression levels in samples.

Figure 2 Pearson correlation heat map of miRNA expression levels in samples.

All response genes are mapped to the KEGG database to fully understand the function of DEG. Differentially expressed mRNA transcripts were screened in tumor tissues and adjacent tissues, and 19 important pathways were enriched. Significantly enriched KEGG terms for 27 up-regulated genes and 18 down-regulated genes were identified. The abundant KEGG pathway is related to immune response, amino acid metabolism, cell proliferation, protein transport and other physiological and biochemical reactions. The enrichment analysis is shown in Figure 3, mainly involving the hedgehog (Hh) signaling pathway (hsa04340), glioma signaling pathway (hsa05214), pancreatic cancer signaling pathway (hsa05212), axon guidance (hsa04360), TNF signaling pathway (hsa04668) ),in. Figure 3 Rich scatter plot of KEGG pathway.

Figure 3 Rich scatter plot of KEGG pathway.

Cytoscape version 3.6.1 software was used to study the interaction between DE miRNA and its target. This analysis process identified miRNA-mRNA interactions, which involved DE miRNA and DE mRNA (Figure 4). A single miRNA can regulate multiple mRNAs, and a single mRNA can be targeted by multiple miRNAs. These results indicate that the miRNA and mRNA interaction network involved in disease progression is highly complex. Based on network regulation, in-depth analysis of the interaction between miRNA and mRNA to determine the location distribution and sequence correlation. Systematic and comprehensive analysis of different RNA molecules with potential cross-effects. In general, these findings have important scientific value for revealing the complex mechanisms of tumorigenesis. Figure 4 miRNA-mRNA correlation network. The circle in the network represents the target mRNA. The orange squares and blue ellipses in the network represent DE miRNA and mRNA.

Figure 4 miRNA-mRNA correlation network. The circle in the network represents the target mRNA. The orange squares and blue ellipses in the network represent DE miRNA and mRNA.

After screening with puromycin, the cells showed obvious green fluorescence, and the adherent cells showed a complete morphology, which is an index for evaluating the effect of virus transfection. Dead cells were observed in both the experimental group and the empty carrier group. Many dead cells were detected in the untreated control group. Adherent cells lacking intact cell morphology are sparse. After virus transduction, puromycin-resistant cells were successfully constructed, and the stably transfected cells were confirmed by puromycin screening (Figure 5). Figure 5 Cell pattern after puromycin selection.

Figure 5 Cell pattern after puromycin selection.

BOC is a receptor-like protein in the Ig/FNIII repeat family and can directly bind to Hh ligands. The abnormal expression of BOC can amplify the Hh signal by increasing the local Hh concentration or prolonging the binding time of Hh ligand, thereby affecting the response of the entire nervous system to Hh. Therefore, we conducted a quantitative analysis based on BOC and its downstream genes. The interaction mechanism between BOC and downstream genes of glioma cells is shown in Figure S3. The expression levels of BOC, SMO, HRAS, and MRAS in the KD-BOC group of U251 cells were significantly lower than those in the control group (P<0.05), indicating that the ideal model was successfully constructed using knockdown technology. BOC knockdown affects downstream gene expression, resulting in a decrease in expression level, thereby affecting the occurrence and development of glioblastoma (Figure 6). Figure 6 Verification of qRT-PCR results. Note: Bank of China (A); SMO (B); HRAS (C); MRAS (D). * P <0.05, ** P <0.01, *** P <0.001.

Figure 6 Verification of qRT-PCR results.

Note: Bank of China (A); SMO (B); HRAS (C); MRAS (D). * P <0.05, ** P <0.01, *** P <0.001.

Glioma is the most common primary tumor of the central nervous system, posing a major threat to human health. The accelerated development of NGS technology has enabled people to study a wide range of physiological and metabolic changes at the molecular level. In this study, we simultaneously analyzed mRNA and miRNA expression levels in GBM disease progression. NGS technology can not only provide comprehensive RNA analysis results, but also can effectively narrow the range of target genes and capture key genes through correlation multiple analysis. Using comprehensive analysis, we obtained a set of mRNA and miRNA related to the occurrence and development of GBM, and determined the interaction and potential role of these mRNA and miRNA in the process of disease progression. These data provide systematic understanding and allow in-depth exploration of related molecular mechanisms.

During the period of disease stress, humans can accumulate essential substances to act as tumors and regulate gene expression to maintain metabolic homeostasis. The 23 Hh signaling pathway was reported for the first time in common fruit flies, and it plays a vital role in normal embryonic development. When its function is disrupted, Hh is associated with a variety of tumors. 24 Through a signal cascade, Hh can cause a change in the balance between the activator and inhibitor forms of the glioma-related oncogene (GLi) transcription factor. Elements of the Hh signaling pathway related to the signaling of GLi transcription factors include hedgehog ligand, patch receptors (Ptch1, Ptch2), smooth receptors (Smo), protein kinase A (PKA), and cyclic adenosine monophosphate (cAMP). GLi activation targets it to the nucleus, where it modifies the transcription of the target gene by binding to a promoter. The main gene targets of the Hh signaling pathway contribute to many important pathways, including pathways related to PTCH1, 25 Rab23, 26 RAS, BRAF, 27 VEGF, and MAPK. 28 In fact, Rab 23 mRNA and its related proteins are highly expressed in glioma tissues. 29 Similarly, the RAS-mitogen-activated protein kinase (RAS-MAPK) pathway is a common feature of tumors. The development of rate-targeted therapy is very important. 30 In this study, compared with neighboring tissues, the mRNA expression level of RAS family proteins in tumor tissues was significantly up-regulated, which is consistent with previous studies. This includes the protein encoded by the BRAF gene, a Raf/mil serine/threonine protein kinase, which is involved in the regulation of the MAP/ERKs signaling pathway and plays an important role in cell division, differentiation and secretion. Mutations in the BRAF gene are associated with a variety of cancers, including colorectal cancer, malignant melanoma, non-small cell lung cancer, and lung adenocarcinoma. Mitogen-activated protein kinase (MAPK) is a highly conserved serine/threonine protein kinase in eukaryotes, which participates in biological processes such as cell growth, proliferation, differentiation, motility and apoptosis. In our study, BRAF and MAPK with increased abundance were found in tumor tissues (Table S5), indicating that this mRNA has a potential role in the malignant proliferation and invasion of glioma.

BOC is a transmembrane protein located upstream of the Hh signaling pathway. Compared with adjacent tissues, the expression level of BOC mRNA in tumor tissues has changed, although this change is not significant. The small number of participants participating in this study may lead to variability in the expression levels of related genes. The main reason why our research focuses on the BOC and Hh signaling pathways is the novelty and reliability of the Hh pathway. More and more studies have shown that the expression of BOC in glioma tissue is significantly higher than that in normal brain tissue. In addition, the survival time of glioma patients with high BOC levels is significantly shortened, indicating that BOC may be a potential drug target because it activates the Hh signaling pathway and promotes the progression of GBM. 31,32 The Ivy Glioblastoma Atlas Project (Ivy GAP) is an anatomical transcript of human glioblastoma, which links individual histological characteristics with genome and gene expression patterns. It allows researchers to explore the anatomical and genetic mechanisms of glioblastoma at the cellular and molecular levels. The database creates a comprehensive collection of suspected molecular variations in glioblastoma cells, which helps to understand tumors more deeply. 33 Using this tool, we will focus our research on the regulated genes of the Hh signaling pathway. As part of the research, we added cell line verification analysis to further verify the relationship between the BOC gene and tumor malignancy, invasion and migration. q-PCR technology is widely used in the quantitative analysis of BOC expression levels in glioma tumor tissues and adjacent tissues to verify the accuracy of the sequencing results of the entire transcriptome. Based on this, we selected the U251 cell line for our study and performed BOC targeted silencing using RNA interference technology to suppress BOC expression. Then, we observed the expression changes of downstream genes and pathways affected by the decrease in BOC expression to understand the molecular mechanisms that lead to the promotion of BOC-mediated glioma proliferation, invasion and migration.

A large number of recent studies have confirmed the key role of miRNA in the occurrence and development of diseases. Our research uses sRNA sequencing to identify 13 known miRNAs and 2 new miRNAs in GBM samples. Consistent with previous sRNA sequencing studies on cancer samples, our results show that the most abundant transcripts in all libraries are has-miR-4763-5P, has-miR-6848-5p, and has-miR-6860. miRNA expression is related to cancer type, stage and other clinical variables. miRNA analysis is a tool for cancer diagnosis and prognosis. miRNAs play a role in almost all stages of cancer biology, including proliferation, apoptosis, invasion/metastasis, and angiogenesis. 34-36 Our research has identified 13 known and 2 new miRNAs, which correspond to the occurrence and development of diseases. Significant regulation of the miR135 family was observed in GBM samples and tissues adjacent to GBM. The conservative miR135 family is a well-studied family that plays a vital role in both diffuse and gastrointestinal cancer subtypes. In cancer, antisense oligonucleotides (anti-miR) are transfected to inhibit the activity of specific microRNAs. In prostate cancer, transfection of antimiRs against miR-375 results in tumor cell suppression in 2D models, which further validates its regulatory role in carcinogenesis. 37 AntimiRs transfection significantly reduced the expression of hsa-miR-135b-5p, which resulted in the regulation of miR299 in 38 papillary thyroid carcinoma (PTC) and found that it is in PTC tissue compared to matched normal thyroid tissue Significant imbalance. 39 Our experimental data further confirms the importance of miR135 and miR299 in cancer development, including cell proliferation, apoptosis and migration. These results also provide valuable information about the role of miR135 and miR299 in cancer development. The other four miRNAs (hsa-miR-4763-5p, hsa-miR-6815-5p, hsa-miR-7974 and hsa-miR-6848-5p) were found to be dysregulated and became the focus of our research because they are related to the BOC gene Close ties. BOC contains potential target binding sites for hsa-miR-4763-5P, hsa-miR-6848-5P, and has-miR-6860. In addition, BOC downstream genes POU2F1 and TP53 are related to tumor gene transcription, which may explain BOC's ability to regulate downstream genes to affect tumor formation through miRNA binding. A similar effect was previously observed with hsa-miR-3157-3p, in which the downstream gene PNMA was identified as a Bax-related protein, which can induce apoptosis in cultured mammalian cells. 40

Generally, miRNAs negatively regulate gene transcription by targeting complementary RNA sequences in gene targets for cleavage. Our comprehensive analysis of miRNA and mRNA transcription levels revealed the reverse regulation of the 20 and 28 interactions in the GBM group and the control group. For example, miRNA 196 is up-regulated in GBM, mRNA HNRNPUL1 shows a high mutation frequency, and HNRNPs are involved in cancer-related pathways, including protein secretion and mitotic spindles. 41,42 However, no miRNA 196 target HNRNPs was found in our study. Learn. Recently, the role of miR128/WNT5B network in regulating depression susceptibility has been discovered in a rat model. 43 In our study, we also found that the miR-299-3P target TVF4 inhibits the regulation of cervical cell proliferation and invasion by targeting TCF4. 43 As expected, nt-miR128 targeting WNT5B showed strict negative regulation in GBM, indicating that nt-miR128 plays an important role in GBM by regulating the expression of WNT5B.

In addition, a large number of tumor-reactive miRNAs and their targets have also been discovered, such as the target-action relationship of miR-3157/PNMA3, miR-6860/FBXW11, miR-6858-3p/WNT5B, and there are separate reports on tumor development. mRNA and miRNA (Figure 6); however, the interaction of this regulatory network needs to be further studied.

All in all, sequencing analysis provides an overview of dysregulated miRNAs and mRNAs. These miRNAs and mRNAs may have the potential to diagnose and treat gliomas, and may help to discover new potential biomarkers. Further study of the functions of these RNAs will help us understand the mechanism of occurrence and development of glioblastoma.

This research was funded by the Science and Technology Project of Hohhot, Inner Mongolia (No. 2019-1-5).

The author declares that there are no competing interests.

1. Nagaraja S, Vitanza NA, Woo PJ, etc. The transcriptional dependence of diffuse intrinsic pontine glioma. cancer cell. 2017; 31:635–652.e6.

2. Shergalis A, Bankhead A 3rd, Luesakul U, etc. Current challenges and opportunities in the treatment of glioblastoma. Revised Pharmacology 2018;70:412-445.

3. Ostrom QT, Gittleman H, Liao P, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States from 2010 to 2014. Nerve tumors. 2017;19:v1–v88.

4. Lim KY, Won JK, Park CK, etc. H3 G34-mutant high-grade glioma. Brain tumor pathology. 2021; 38:4-13.

5. Inui M, Martello G, Piccolo S. MicroRNA controls signal transduction. Nat Rev Mol Cell Biology. 2010; 11:252-263.

6. Que Tao, Song Ya, Liu Zhong, etc. Reduced miRNA-637 is an unfavorable prognostic marker, which promotes the growth, migration and invasion of glioma cells by directly targeting Akt1. Oncogene. 2015;34:4952-4963.

7. Hu Nan, Zhang Jie, Cui Wei, etc. miR-520b regulates the migration of breast cancer cells by targeting hepatitis B X-interacting protein and interleukin-8. J Biochemistry. 2011; 286: 13714–13722.

8. LeBlanc VC, Morin P. Explore miRNA-related features related to the diagnosis of patients with glioblastoma multiforme and breast cancer. J Clinical Medicine. 2015; 4: 1612-1630.

9. Chiosea S, Jelezcova E, Chandran U, etc. Up-regulation of the microtome (a component of the MicroRNA mechanism) in prostate adenocarcinoma. I’m J Pathol. 2006;169:1812-1820.

10. Deng B, Wang M, Liu Z. A set of 8 miRNAs are used as new diagnostic biomarkers for pancreatic cancer. Medicine (Baltimore). 2020; 99: e22261.

11. Rezaei O, Honarmand K, Nateghinia S, etc. Features of miRNAs in glioblastoma: potential biomarkers and therapeutic targets. Exp Mol Pathol. 2020;117:104550.

12. Behjati S, Tarpey PS. What is next-generation sequencing? Arch Dis Child Educ Pract Ed 2013; 98: 236-238.

13. Manicum T, Ni F, Ye Y, et al. The prognostic value of E2F mRNA expression in human gastric cancer. Biosci Rep. 2018; 38: BSR20181264.

14. Xue VW, Cheung MT, Chan PT, etc. Non-invasive potential circulating mRNA labeling of colorectal adenomas using targeted sequencing. Science Representative 2019; 9: 12943.

15. Liu Ke, Pan Yu, Li Yu, etc. Features of lncRNA and mRNA for prognostic prediction of glioblastoma. Future tumors. 2020; 16: 837-848.

16. Jiang T, Nam DH, Ram Z, etc. Clinical practice guidelines for the management of diffuse glioma in adults. Cancer Letters. 2021; 499: 60-72.

17. Denaro N, Russi EG, Merlano MC. The pros and cons of the new TNM classification of head and neck squamous cell carcinoma. Oncology. 2018; 95: 202-210.

18. Paner GP, Stadler WM, Hansel DE, etc. The eighth edition of urinary system tumor tumor-lymph node-metastasis classification update. Auror. 2018; 73: 560-569.

19. Trapnell C, Williams BA, Pertea G, etc. RNA-Seq transcript assembly and quantification revealed unannotated transcripts and subtype transitions during cell differentiation. Nat Biotech. 2010; 28: 511-515.

20. Frazee AC, Pertea G, Jaffe AE, etc. Ballgown bridges the gap between transcriptome assembly and expression analysis. Nat Biotech. 2015; 33: 243-246.

21. Young MD, Wakefield MJ, Smyth GK, etc. RNA-seq gene ontology analysis: consider selection bias. Genomic biology. 2010; 11: R14.

22. Mao X, Cai Tao, Olyarchuk JG, etc. Automated genome annotation and pathway identification using KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics. 2005;21:3787-3793.

23. Sjodahl G, Eriksson P, Liedberg F, etc. Molecular classification of urothelial carcinoma: global mRNA classification and tumor cell phenotype classification. J Pasol. 2017; 242: 113-125.

24. Girardi D, Barrichello A, Fernandes G, etc. Targeting the hedgehog pathway in cancer: current evidence and future prospects. cell. 2019; 8:135.

25. Agren M, Kogerman P, Kleman MI, etc. The expression of the PTCH1 tumor suppressor gene is regulated by an alternative promoter and a single functional Gli binding site. Gene. 2004;330:101-114.

26. Wang M, Dong Q, Wang Y. Rab23 is overexpressed in human astrocytoma and promotes cell migration and invasion by regulating Rac1. Tumor biology. 2016;37:11049-11055.

27. Jiang Jie, Li Xu, Liu Aibi, etc. The tectorial glioma has a high incidence of KRAS G12R and accompanying changes in KRAS and BRAF. Journal of Neuropathology. 2020;139:601-602.

28. Chen X, Hao A, Li X, etc. ZDHHC17-mediated activation of JNK and p38 MAPK drives the development and malignant progression of glioblastoma multiforme. Therapeutics. 2020; 10: 998-1015.

29. Dong Jie, Liu Y, Hao Y, et al. The expression of Rab23 in human glioma cells and its correlation with pathological grade. J Biol Regul Homeost Agents. 2019;33:1885-1889.

30. Ryall S, Tabori U, Hawkins C. Pediatric low-grade glioma in the era of molecular diagnosis. Acta Neuropathol community. 2020; 8:30.

31. Nitta Y, Miyachi M, Tomida A, etc. Identification of a new BOC-PLAG1 fusion gene in a case of lipoblastoma. Biochem Biophys Res Commun. 2019; 512: 49-52.

32. Jiang C, Qu X, Ke Hua, et al. The relationship between HMGB1/TLR4 signaling pathway and clinicopathological characteristics of ovarian cancer. Mol Med Rep. 2018; 18: 3093-3098.

33. Zheng S, Tao W. Identification of novel transcriptome features as potential prognostic biomarkers for anti-angiogenic treatment of glioblastoma multiforme. Cancer (Basel). 2020; 12:2368.

34. Sun Z, Shi K, Yang S, et al. The influence of exosomal miRNA on cancer biology and clinical application. Moore's cancer. 2018; 17:147.

35. Xu Bo, Liu Jie, Xiang X, etc. The expression of miRNA-143 in pancreatic cancer and its clinical significance. Cancer Biother Radiopharm. 2018; 33: 373-379.

36. Zhang B, Pan X, Cobb GP ​​etc. microRNA acts as an oncogene and tumor suppressor gene. Development of biology. 2007;302:1-12.

37. Kunz M, Brandl M, Bhattacharya A, etc. Nanoparticles combined with antimiR for inhibiting tumor growth and metastasis in prostate cancer and melanoma. J Nano Biotechnology. 2020; 18:173.

38. Magalhaes L, Quintana LG, Lopes DCF, etc. In diffuse and gastrointestinal cancer subtypes, the APC gene is regulated by hsa-miR-135b-5p. BMC cancer. 2018; 18:1055.

39. Liu X, He Min, Hou Yan, et al. The expression profile of microRNAs and their target genes in papillary thyroid carcinoma. Oncol Rep. 2013;29:1415-1420.

40. Schuller M, Jenne D, Voltz R. Human PNMA family: a new type of neuronal protein related to paraneoplastic neurological diseases. J Neuroimmunology. 2005; 169: 172-176.

41. Li H, Liu J, Shen S, et al. Pan-cancer analysis of the heterogeneous ribonucleoprotein (hnRNPs) family of alternative splicing regulators and its prognostic potential. J Cell Molecular Medicine. 2020; 24: 11111-11119.

42. Guan Y, Mizoguchi M, Yoshimoto K, etc. miRNA-196 is up-regulated in glioblastoma, but not up-regulated in anaplastic astrocytoma, which has prognostic significance. Clinical cancer research. 2010; 16: 4289-4297.

43. Roy B, Dunbar M, Agrawal J, etc. The altered mirnome and miR-128-3p based on the amygdala transmit epigenetic contributions to the susceptibility to depression-like behavior via Wnt signaling. Int J Neuropsychopharmacol. 2020; 23:165-177.

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