Knockdown of hsa_circ_0008922 inhibits the progression of glioma

Patients and samples

From May 2018 to November 2021, glioma tissues were obtained from 40 patients with glioma who underwent surgical resection in the Department of Neurosurgery, the First Affiliated Hospital of Guangxi Medical University. All patients were confirmed to be glioma by postoperative pathology without preoperative radiotherapy or chemotherapy. At the same time, 10 non-glioma surgical brain tissue samples were collected. The brain tissue was obtained from the brain tissue needed to be removed due to the surgical approach and the focal edema tissue (normal brain tissue), and the histological characteristics were confirmed by the pathologist. All surgically resected specimens were frozen in liquid nitrogen. The specimen collection was examined and authorized by Medical Ethics Committee of Guangxi Medical University (2018087). We received written informed consent from participants of our study.

RNA sequencing

The tissues were collected as previously described in Liu et al. (2021). Specifically, to reduce the impact of individual differences, we set up each group containing five separate samples from five patients. The 10 tissues were divided into two groups: NB (normal brain tissue, n = 5) and GBM (glioblastoma, n = 5). Total RNA was extracted from glioma tissues using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol and sequenced by Aksomics Biology Technology Co. Ltd (Shanghai, China). The RNA library was constructed by using KAPA Stranded RNA-Seq Library Prep Kit (Illumina, San Diego, CA, USA). The raw sequencing data (FASTQ files generated by the Illumina sequencer) is subjected to quality control to assess whether the sequencing data can be used for subsequent analysis. The expression of circRNA was quantified by calculating the Backsplice junction reads through CIRCexplorer2. Differential expression analysis was conducted using Ballgown (https://rdrr.io/bioc/ballgown/). The circRNA with a P-value ≤ 0.05 and fold change ≥ or ≤1.5 was considered significantly differentially expressed. The statistical power of this experimental design, calculated in RNASeqPower using the web page at https://rodrigo-arcoverde.shinyapps.io/rnaseq_power_calc/ is 0.8083.

Cell culture and transfection

The human GM cell lines (A172, U251, SF763 and U87) were purchased from the Chinese Academy of Sciences (Shanghai, China). Cells were cultured in complete medium (10% fetal bovine serum (Wisent, Quebec, Canada); 1% penicillin streptomycin (Solarbio, Beijing, China); DMEM medium (Wisent, Quebec, Canada)) and placed in a 37 °C, 5% CO₂ incubator.

Based on the hsa_circ_0008922 sequence, two siRNAs (s1-hsa_circ_0008922 and s2-hsa_circ_0008922) and random siRNA (NC) fragments were ordered from Bioengineering Co., Ltd. (Shanghai, China). According to the instructions for siRNA usage, Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) was used to transfect siRNA into cells. The sequences of siRNA hsa_circ_0008922 were as follows: s 1_hsa_circ_0008922: (sense) 5′-AAG AUA AGU AAC GAU GAC U-3′, (antisense) 5′-AGU CAU CGU UAC UUA UCU U-3′; s2_hsa_circ_0008922: (sense) 5′-UAA CGA UGA CUU GAA AGU A-3′, (antisense) 5′-UAC UUU CAA GUC AUC GUU A-3′.

qRT-PCR

Total RNA was extracted by Vazyme Kit (RC101-01) (Vazyme, Nanjing, China). RNA was reverse transcribed into cDNA by HiScript III RT SuperMix 100 for qPCR (+gDNAwiper) (Vazyme, Nanjing, China). ChamQSYBR qPCR Master Mix (Vazyme Q711-02; Vazyme, Nanjing, China) was used for polymerase chain reaction in StepOne Real-time PCR System (Applied Biosystems, Waltham, MA, USA). The relative level was calculated with 2^−ΔΔCt.

RNase R exonuclease digestion experiment and electrophoresis

According to the instructions in RNase R endonuclease digestion kit (Geneseed Biotech Co., Ltd., Guangzhou, China), the total RNA was divided into digestion group (RNase R treatment) and control group, with 5 μg RNA (1 μg RNA required 8 U RNase R endonuclease digestion) in each group. the RNase R was not added in the control group. The above groups were incubated at 37 °C for 25 min. Then 1 μg RNA was reversed and detected by qRT-PCR. The qRT-PCR products amplified by the divergent primer were transcribed to Sangon Biotech (Shanghai, China) Co., Ltd. for TA cloning sequencing to determine the full length of PCR products. The PCR products of gDNA and cDNA were further detected by 1.3% agarose gel electrophoresis. PCR products were separated by 110 V electrophoresis for 35 min and detected by UV. GL DNA Marker 100 (AGbio, Beijing, China) was used as a marker of DNA size. The sequences of primers were as follows: hsa_circ_0008922 Divergent Primer: (sense) 5′-TCC ATC AGG ACC CCA GAT GTC-3′, (antisense) 5′-ACT GCA CAT GCA GAC TGT CAC-3′; hsa_circ_0008922 convergent Primer: (sense) 5′-GGG CAT CCT TCA CCC ATC TG-3′, (antisense) 5′-ATC TTG GTG TCA CAC AGG GC-3′.

Wound healing assay

After transfection with siRNA to hsa_circ_0008922 for 48 h, 100 μL of 2 × 10⁴ cells were lifted and seeded into the single hole of ibidi plug-in and cultured for 22 h. After the cells were full of ibidi plug-in, the plug-in was removed. Then the DMEM complete medium was replaced with appropriate low serum DMEM complete medium (2% FBS). Photographs were taken at 0, 6, 12 h and 0, 12, 24 h, respectively.

CCK8 assay

CCK8 detection kit (Vazyme A311-01; Vazyme, Nanjing, China) was used to detect the proliferation of glioma cells treated with siRNA. A total of 100 μL of 4 × 10³ cells were inoculated in 96-well plates, five wells in each group. A total of 10 μL of CCK8 solution was added to each well and cells were incubated at 5% CO₂ 37 °C for 2 h. The optical density (OD) values at 450 nm were measured at 0, 24, 48, 72 and 96 h, respectively.

Transwell assay

The migration and invasion ability of glioma cells were evaluated using a Transwell chamber (Corning Incorporated, Corning, NY, USA) with or without matrix glue (Absin). Forty-eight hours after transfection, a 200 μL cell suspension (2 × 10⁴ cells) was inoculated in the upper chamber of transwell chamber, and DMEM (500 μL) containing 20% FBS was added in the lower chamber of transwell chamber. They were incubated at 37 °C for 24 h. Then, 4% methanol was applied to fix the cells for 30 min followed by staining with 0.1% Crystal Violet Stain solution (Solarbio, Beijing, China) for 30 min. Stained cells were photographed by inverted microscope (Olympus, Shinjuku City, Japan) and counted manually.

Flow cytometry

Apoptosis was detected by using FITC Annexin V apoptosis detection Kit (BD Biosciences 556547; BD Biosciences, Franklin Lakes, NJ, USA). After glioma cells were transfected, the cells were completely digested with EDTA-free trypsin and washed twice with PBS buffering at 4 °C. The cells were dyed with 5 μL FITC Annexin V and 5 μL propidium iodide (PI) for 15 min at room temperature in the dark and then observed by flow cytometry (BD, Franklin Lakes, NJ, USA). FlowjoV 1.8.1 (Becton, Dickinson & Company, Franklin Lakes, NJ, USA) was used to analyze the results.

Western blot

Western Blot was used to detect apoptosis-related proteins in glioma cells. After transfecting siRNA into cells and culturing cells for 48 h, the cells were harvested and lysed with protease inhibitor and phosphatase inhibitor to extract proteins. Then, the protein was separated by SDS-PAGE and transferred to PVDF membrane. 1:1,000 diluted primary antibodies (caspase 3, rabbit, #14220S; caspase 9, mouse, #9508, Cell Signaling Technology and GAPDH, rabbit, #ab181602 Abcam, Cambridge, UK) were incubated overnight at 4 °C. After completing incubation of primary antibodies, the secondary antibodies in 1:5,000 dilution were incubated at room temperature (rabbit, #ab6721; mouse, #ab6728 Abcam Cambridge, UK).

Plate cloning assay

The clone formation rate indicated the proliferation capacity and cell population dependency (Liu et al., 2022).

3 × 10³ of siRNA treated cells were inoculated in the 6-well plates at 37 °C in 5 % CO₂ for 11 days. After culture, cells were fixed with 4% formaldehyde for 30 min and stained with 0.1% Crystal Violet Stain solution for 12 min. The photograph was recorded with a stereomicroscope (Olympus, Shinjuku City, Japan).

Bioinformatics analysis

ENCORI (https://starbase.sysu.edu.cn/agoClipRNA.php?source=circRNA) was used to predict hsa_circ_0008922 downstream miRNA. OECloud tools (https://cloud.oebiotech.cn/task/detail/array_miranda_plot/?version=old) was used to predict binding fraction and free energy. miRDB (http://mirdb.org/index.html), miRWalk (http://mirwalk.umm.uni-heidelberg.de/), Linkedomics (http://linkedomics.org/login.php) were used to predict miRNA downstream mRNA, respectively. Cytoscape was used to draw the interaction network of hsa_circ_0008922 with its downstream miRNA, and the interaction network of miRNA with its downstream mRNA (Shannon et al., 2003; Assenov et al., 2008). GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis of miRNA downstream mRNA was performed on OECloud tools (https://cloud.oebiotech.cn/task/detail/enrichment-oehw/?id=57).

Statistical analysis

Data analysis was performed using Graphpad and SPSS 22.0 statistical analysis software. Student’s t-test was used for comparison between the experimental groups. Fisher’s exact test was used for categorical variables. Quantitative data were expressed as mean ± standard deviation. P < 0.05 was considered statistically significant.

Identifying hsa_circ_0008922 in glioma cell

In order to seek circRNA aberrantly expressed in glioma, we performed RNA sequencing of glioma tissues. Here, we selected a novel circRNA, hsa_circ_0008922, from circRNA identified above because there was no report in glioma except only one study in hypopharyngeal squamous cell carcinoma (Wang et al., 2020). As shown in Fig. 1A, hsa-circ-0008922 is produced from regions of exons 3–4 of (MATR3, NM199189),with a junction of 161 nt (http://www.circbank.cn/). We designed divergent primers for hsa_circ_0008922 and convergent primers for MATR3 mRNA. As shown in Fig. 1B, hsa_circ_0008922 can be amplified by divergent primers with cDNA as template, whereas there was no amplification detected at similar sizes using gDNA as template. Contrary, using convergent primers RT-PCR could amplify MATR3 mRNA in the sample without RNase R treatment, while it failed to amplify MATR3 mRNA in the sample with RNase R treatment. Moreover, PCR products amplified by divergent primers were used for TA cloning and sequencing to confirm that hsa_circ_0008922 had the reverse splicing site (Fig. 1C). These results indicated that the divergent primers we designed can specifically amplify hsa_circ_0008922 and can be used for detection of its expression.

Up-regulation of hsa_circ_0008922 in glioma tissues

We cannot gain any information for the expression profile of hsa_circ_0008922 in glioma from five public databases of circRNA (ClRCpedia v2, http://yang-laboratory.com/circpedia/; circRNADb, circatlas.biols.ac.cn; TSCD, http://gb.whu.edu.cn/TSCD/; MiOncoCirc, https://mioncocirc.github.io/; and circRNADisease, http://cgga.org.cn:9091/circRNADisease/). Therefore, we tested our clinical samples as a primary study for hsa_circ_0008922 expression. As shown in Fig. 2A, the expression of hsa_circ_0008922 in glioma tissues (n = 40) and normal tissues (n = 10) was detected by qRT-PCR. We found that the relative expression of hsa_circ_0008922 was significantly higher in gliomas than in normal tissues (P = 0.027). It was found that in low-grade tumor and high-grade tumor the expression of hsa_circ_0008922 was significantly higher than normal tissues, respectively (Fig. 2B). Furthermore, we also detected the relative expression of hsa_circ_0008922 in different types of gliomas. We found that in astrocytoma and glioblastoma and oligodendroglioma the expression of hsa_circ_0008922 was higher than normal tissues, respectively (Fig. 2C).

Correlation between hsa_circ_0008922 expression and clinicopathological parameters of glioma patients

As hsa_circ_0008922 was up-regulated in gliomas, we further investigated the correlation between the expression of hsa_circ_0008922 and clinical data such as age, gender, tumor size, WHO grade and GFAP (glial fibrillary acidic protein). As shown in Table 1, the expression of hsa_circ_0008922 was correlated with the WHO grade, tumor size and Ki-67 of patients. Multiple linear regression showed that tumor size and WHO grade played a significant role in explaining hsa_circ_0008922 (Table 2).

Screening highly efficient siRNA for hsa_circ_0008922

Considering up-regulation of hsa_circ_0008922 correlated with some of clinicopathological parameters of glioma patients, we further conducted loss-of-function experiments to explore the biological function of has_circ_0008922 in vitro. We first detected the relative expression of hsa_circ_0008922 in a panel of glioma cells (A172, U251, U87 and SF763 cells) by qRT-PCR. Figure 3A shown all of cells expressing hsa_circ_0008922 with different levels. A172 expressed the highest hsa_circ_0008922, which was three times of SF763. Whereas U251 expressed the second higher level of hsa_circ_000892 that was 1.6 times of SF763. Therefore, A172 and U251 were selected for hsa_circ_0008922 depletion cell models.

Subsequently, we determined the siRNA fragment and optimized its transfection time in vitro. Transfecting cells with two siRNA fragments, s1-hsa_circ_0008922 and s2-hsa_circ_0008922, respectively, it was found that s2-hsa_circ_0008922 had the best effect of -hsa_circ_0008922 down-regulation in the time point of forty-eight hours (Figs. 3B and 3C). Therefore, we chose s2-hsa_circ_0008922 for hsa_circ_0008922 depletion experiments and set the 48 h after siRNA transfection to perform a series experiment for biological behavior in glioma cells.

Down-regulation of hsa-circ-0008922 inhibits migration and invasion in glioma cells

Because migration and invasion were closely related to the malignant degree of tumor (van de Merbel et al., 2018), we used wound healing assays to evaluate lateral migration of glioma cells. Through the analysis of time series data and comparison between groups, it was found that the migration rate of s2-hsa_circ _0008922 treatment group in A172 (Figs. 4A and 4C) and U251 (Figs. 4B and 4D) was lower than that of the control. Further, the transwell assays of migration and invasion showed suppression of longitudinal migration and invasion of A172 (Figs. 5A–5C) and U251 (Figs. 5D–5F) following hsa-circ-0008922 silencing. In summary, the down-regulation of hsa_circ_0008922 in glioma cells can significantly inhibit the migration and invasion ability of glioma cells.

Influence of colony formation, proliferation, and apoptosis after hsa_circ_0008922 depletion in glioma cells

After down-regulating hsa_circ_0008922 in A172 and U251, the colony formation ability of cells was detected by plate cloning. It was found that compared to the NC group, the number of cell colonies in the s2-hsa_circ_0008922 group was significantly lower than that in the NC group (Figs. 6A and 6B). CCK8 experiment was used to detect the proliferation in A172 and U251. In this experiment, we detected cell viability at five time points (0, 24, 48, 72, 96 h). It was found that the cell viability in the s2-hsa_circ_0008922 group in A172 (Fig. 6C) and U251 (Fig. 6D) was significantly different from that in the NC group. This result indicated that the down-regulation of hsa_circ_0008922 in glioma cells can significantly inhibit the cell growth in glioma cells.

Moreover, flow cytometry was used to study whether hsa_circ_0008922 affected apoptosis in glioma cells. We found that in A172 and U251, the apoptosis rate in the s2-hsa_circ_0008922 group was higher than that in the NC group, and there was significant indigenous difference between the two groups (P < 0.05) (Figs. 7A–7C). Furthermore, we also detected apoptosis proteins caspase 3 and caspase 9. It was found that in A172 (Figs. 7D and 7E) and U251 (Figs. 7D and 7F) the expression of caspase 3 and caspase 9 in the s2-hsa_circ_0008922 group was higher than those in the NC group, respectively. Taken together, hsa_circ_0008922 promoted the proliferation of glioma cells by playing an anti-apoptotic role.

Predicting hsa_circ_0008922-miRNA-mRNA networks with potential functions in glioma by bioinformatics

Generally, circRNA regulates gene transcription and expression by serving as a sponge of miRNA. circRNA-miRNA-mRNA regulatory axis has been widely accepted to illustrate the biological functions of circRNA (Chen, 2016). To understand the potential function of hsa_circ_0008922 in regulation of glioma related genes, we first analyzed miRNAs binding to hsa_circ_0008922 by ENCORI, by which a total of 14 miRNAs (hsa-let-7e-5p, hsa-miR-506-5p, hsa-let-7b-5p, hsa-let-7c-5p, hsa-let-7g-5p, hsa-mir-98-5p, hsa-let-7d-5p, hsa-let-7i-5p, hsa-miR-377-3p, hsa-mir-605-3p, hsa-let-7a-5p, hsa-let-7f-5p, hsa-miR-4458 and hsa-miR-4500) were obtained (Fig. 8A). Then, OECloud tools were used to predict the binding fraction and free energy of hsa_circ_0008922 with its downstream miRNAs. The information of these miRNAs was presented in Table 3. The miRNAs (hsa-let-7e-5p, hsa-miR-506-5p, hsa-let-7b-5p, hsa-let-7c-5p and hsa-let-7a-5p) with length greater than 22 bp, predicted score greater than 150 and predicted free energy less than −15 Kcal/Mol were selected for further bioinformatic analysis according to the OECloud tools at https://cloud.oebiotech.com/task/detail/array_miranda_plot/ (energy is less than 10, the energy is smaller, the accuracy is higher. Score is greater than 140, the score is the greater, the accuracy is the higher. In order to screen out more accurate downstream genes, the screening criteria were appropriately improved). With the combined analysis of three databases (miRDB, miRWalk and Linkedomics) we gained mRNAs that were all related to glioma. As shown in Fig. 8B, 103 mRNAs interact with hsa-let-7a-5p, 123 mRNAs interact with hsa-let-7b-5p, 99 mRNAs interact with hsa-let-7e-5p, 37 mRNAs interact with hsa-let-7c-5p, and 5 mRNAs interact with hsa-miR-506-5p. Finally, an interactive network between miRNA and mRNA was constructed (Fig. 8C).

Based on the ceRNA hypothesis (Whereas micrornas are known to cause gene silencing by binding mrnas, cernas can competitively bind micrornas to regulate gene expression (Salmena et al., 2011)), namely, mRNAs should be negatively correlated with targeted miRNAs and positively correlated with circRNA. The top 30 of mRNAs fitting this criterion were selected for further functional analysis. GO analysis showed that the functions of these mRNAs were mainly related to multiple signaling pathways, such as mitotic sister chromatid cohesion, stem cell population maintenance, negative regulation of intrinsic apoptotic signaling pathway in response to DNA damage by p53 class mediator, negative regulation of cAMP-mediated signaling, and regulation of transcription involved in G1/S transition of mitotic cell cycle (Fig. 9A and Table S1). KEGG analysis indicated that these mRNAs were involved in signaling pathways regulating pluripotency of stem cells, cell cycle, and microRNAs in cancer and glioma (Fig. 9B and Table S2).