JSUR Journal

Introduction

Esophageal Cancer (EC) is still a common disease in the world. According to the latest statistics, approximately 604,000 novel cases and 544,000 mortalities from EC are reported annually worldwide, ranking seventh in incidence, and ranking sixth in overall deaths [1]. East Asia had the highest regional incidence, partly due to the high burden in China, which recorded 278,121 new cases of EC and 257,316 deaths in 2019, accounting for more than half of the global number of cases and deaths [2]. EC has two common histologic types: Esophageal Squamous Cell Carcinoma (ESCC) and Esophageal Adenocarcinoma (EAC) [3]. Most EAC cases occur in developed countries, whereas more than 90% of ESCC cases occur in Asia and sub-Saharan Africa [1]. For advanced esophageal cancer, chemotherapy based on cisplatin (CDDP) is considered as the first-line treatment [4,5]. Although some progress has been achieved in the early diagnosis and treatment, the prognosis for patients with advanced EC remains poor. Cisplatin (CDDP) is commonly used to treat a multitude of tumors including gastric tumors, hepatocellular carcinoma, colon cancer, and esophageal tumors [6-8]. However, patients benefited during the initial 4-6 cycles of chemotherapy and the majority of patients subsequently develop cisplatin resistance [9,10], which is the main reason for chemotherapy failure and the molecular mechanisms are not completely understood [11,12].

Circular RNAs (circRNAs) are a special type of endogenous noncoding RNA that is selectively cleaved and widely expressed in eukaryotic cells. CircRNAs belong to long-chain non-coding RNA (lncRNAs) and play an important role in human diseases [13-16]. Aberrant expression of circRNAs may be associated with tumor development and progression. CircRNAs can be divided into three categories: exonic circular RNA, exon-intron circular RNA, and circular intronic RNA. The main functions of circRNAs are acting as a sponge of microRNAs, binding to RNA binding proteins, affecting the formation of homologous linear mRNAs and regulating transcription. Some circRNAs can also directly code protein, which changed the impression that circRNAs only act as noncoding RNAs [17-19]. Furthermore, studies have shown that circRNA expression profiles differ in different esophageal derived tissues and directly affect tumor cell proliferation and tumor progression [20]. In addition, it has also been reported that circRNAs regulate cisplatin chemotherapy [21]. However, the mechanisms between circRNAs and cisplatin resistance in EC remains unknown. In this study, we established a CDDP resistance model and detected the biological characteristics between the resistant strain (TE-1/CDDP cell) and the parent strain (TE-1 cell). Especifically, we evaluated whether chemotherapy-resistant EC cells show a characteristic circRNA expression pattern, and whether the expression of CDDP resistance-related targets regulated by circRNAs is altered in EC cells. Small interfering RNA technology was used to knock down the target gene (HDAC9) regulated by circRNA and the biological mechanisms related to drug resistance were investigated. Gene chip technology was used to detect the differentially expressed circRNA profiles of the two group cells, identify differentially expressed circRNAs and their regulated target genes.

Materials and Methods

Cell Culture and Establishment of Cell Lines with Acquired Resistance to Cisplatin

TE-1 cells (a human ESCC cell line) were purchased from the Chinese Academy of Sciences in Shanghai, China. All cell lines were validated by STR profiling and tested negative for mycoplasma. Cells were cultured in a humidified incubator at 37 °C and 5% CO₂. The culture medium for TE-1 cells consisted of RPMI 1640 supplemented with 10% Fetal Bovine Serum (FBS) and 1% penicillin/streptomycin (both from Gibco, Thermo Fisher Scientific, Inc., USA). The cisplatin resistant cell model was established by concentration gradient increasing method [22]. In short, the cisplatin resistant cells that grew well in a culture medium containing 0.5 µmol/ml cisplatin were obtained by exposure to cisplatin for 8 months with step-wise escalation of cisplatin concentration. Detection of cisplatin halfmaximal inhibitory concentration (IC₅₀): TE-1 or TE-1/CDDP cells (5,000 cells per well) were seeded into 96-well plates. After treating with different concentrations of cisplatin for 96 hours, the growth rate of these cells was measured using CCK8 methods. Inhibitory rates and the cisplatin resistance index (RI) of TE-1/ CDDP was calculated by Microsoft Excel, and the IC₅₀ values were calculated using GraphPad Prism 8.0 software.

RNA Extraction and Reverse Transcription‑Quantitative PCR (RT‑Qpcr)

To extract the total RNA from cell cultures, Trizol^® reagent solution (Invitrogen; Thermo Fisher Scientific, Inc) was used. cDNAs were synthesized using the GoScript Reverse Transcription System RT reagent Kit (Promega Corporation, USA). Next, qPCR was conducted using SYBR Green PCR Master Mix (Promega Corporation) on a Roche Applied Science LightCycler 480II system (Hoffman-La Roche Ltd., Basel, Switzerland). Primers for HDAC9 were designed through Primer 5.0 and synthesized by Realgene (Nanjing, China). The primer sequences for HDAC9 and GAPDH were 5′-AGTAGAGAGGCATCGCAGAGA-3′ and 5′-GGAGTGTCTTTCGTTGCTGAT-3′, 5′-GTCTCCTCTGACTTCAACAGCG-3′ and 5′-ACCACCCTGTTGCTGTAGCC AA-3′, respectively. GAPDH was used as an internal control for normalization. The PCR cycle conditions were an initial denaturation at 95℃ for 10 min. Afterwards the following thermocycling protocol was utilized for 40 cycles: 95°C for 15 sec, 60℃ for 60 sec, and a final extension at 60℃ for 1 min. GAPDH was used as the endogenous controls. The relative expression level of mRNA was calculated by using the 2^-ΔΔCq method [23].

Cell Counting Kit‑8 (CCK‑8) Assay

TE-1 cells (5x10³ cells/well) were plated into 96-well plates (Corning, Inc.). After incubation at different time intervals, 10 μl CCK-8 reagent (Beyotime Institute of Biotechnology) was added to every well and incubated at 37℃ for 2h. At the indicated time points, the optical density (OD) value was quantified at 450 nm with a microplate reader (Thermo Fisher Scientific, Inc.).

Colony Formation Assay

TE-1 cells (2x10³ cells) were seeded into 6-well plates and cultured at 37℃ for 14 days. After visible colonies were formed, the cells were fixed with 4% formaldehyde (Wuhan Servicebio Technology Co., Ltd.) at 37℃ for 20 min. Cell colonies were subsequently stained with 0.1% crystal violet (Sangon Biotech, Co., Ltd.) solution for 20 min and imaged using a light microscope (magnification, x40; Olympus CX31; Olympus Corporation, Tokyo, Japan).

Wound Healing Assay

To conduct the experiment, TE-1 cells were plated onto 6-well plates and allowed to incubate for 24 hours at a density of 2×10⁶ cells per well. When the cells reached full confluence (90-100% coverage), a sterile pipette tip was employed to generate scratches in the cell monolayer. Subsequently, the cells were cultured in a medium devoid of fetal bovine serum (FBS) at a temperature of 37℃. After various time intervals, the cells that migrated into the scratch area were observed using a light microscope (magnification, x100; Olympus CX31; Olympus Corporation). Images were captured and subjected to analysis using ImageJ software (version 1.46; National Institutes of Health, Bethesda, MD, USA).

Transwell Assay

For the migration experiment, TE-1 cells at a concentration of 5x10⁴ cells/well were resuspended in 100 μL RPMI 1640. These cells were subsequently seeded onto an 8-µm pore size chamber (Corning Inc.; Sigma-Aldrich Co.) without any prior coating of Matrigel. The bottom of the chamber was then supplemented with 600 μL of complete medium. Following a 24hour incubation period, the cells that failed to migrate through the membrane of chamber were eliminated. Conversely, the cells that successfully migrated beneath the chamber were fixed with 4% paraformaldehyde (Wuhan Servicebio Technology Co., Ltd.). After fixation, these cells were stained with 0.1% crystal violet (Sangon Biotech, Co., Ltd.) for a duration of 30 minutes at room temperature. Stained cells were counted in three fields using a microscope (magnification, x200; Olympus CorporationCX31; Olympus Corporation, Tokyo, Japan). For invasion assay, the same experimental procedure was followed, with the exception that the chambers were pre-coated with Growth Factor Reduced Matrigel (1:8, Corning, New York, USA).

Microarray and Bioinformatics Analysis

TE-1 cells and TE-1/CDDP cells were hybridized in a hybridization oven for 17 hours at 65℃. The chips were then scanned with a Sure Scan Dx scanner. To normalize the microarray data, we utilized the Linear Models for Micro Array Data package available in the R software. The analysis was conducted on the Affymetrix PrimeViewTM Human Gene Expression Array. The raw data underwent several processing steps including background correction using Robust Multichip Average (RMA), quantile normalization, and probe summarization. This processing was carried out using Expression Console software (version 1.3.1, Affymetrix). To identify differential circRNA expression, we employed ANOVA (analysis of variance) with the p-value adjusted using GeneSpring software (version 14.8, Agilent Technologies). Additionally, we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using the “clusterProfler” and “pathview” software packages[24,25]. The GO analysis can be accessed at http:// www.geneontology.org, while KEGG information can be found at https://www.kegg.jp/. GO includes three categories: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). The top 5 enrichment pathways of GO and the first 10 enrichment pathways of KEGG were selected respectively. The circRNA microarray data in this study have been made publicly available in the NCBI Gene Expression Omnibus (GEO) data base under the accession number GSE242834 (http://www.ncbi.nlm. nih.gov/geo/ query/acc.cgi?acc = GSE242834).

Identification of Differentially Expressed Circrnas

The cells were divided into two groups, with each group containing four TE-1 samples and four TE-1/CDDP samples. In datasets, the probes corresponding to multiple circRNAs were removed. Those circRNAs corresponding to multiple probes were averaged as the expression level. The raw data from the two groups were normalized and analyzed. After scale standardization and logarithmic processing of datasets, the “Limma” software package in R software was used to identify differentially expressed circRNAs under the screening criteria of |log2 Fold Change (FC)| ≥ 2 or ≤ 0.5 and p value < 0.05 (a FC ≥ 2 indicated that the gene was 2 times up-regulated, and conversely ≤ 0.5 indicated a half- down-regulated gene). Volcano map and heat map were generated using the “ggplot2” and “pheatmap” packages in R software.

Sirna and Transfection

For RNAi experiments, based on the mRNA sequence of HDAC9 from GenBank and criteria for designing small interfering RNA (siRNA), three si-RNA targeting HDAC9 (si-HDAC9_001, 002, 003) and scrambled control si-RNA (si-NC) were purchased from RiboBio Co., Ltd. (Guangzhou, China). The sequences of si-HDAC9 were as follows: si-HDAC9_001, AGCCACCCTCATGTTACTT; si-HDAC9_002, CATTAGAGGTACCCACAAA; si-HDAC9_003, GGTTTCACAGCAACGCATT. The interference effects were detected using RT-qPCR, and the siRNA with the strongest interference effect was used for subsequent experiments. In transfection experiments, the siRNA (100nM final concentration) were transfected into TE-1/CDDP cells using Lipofectamine® 2000 (Invitrogen; Thermo Fisher Scientific, Inc.) . The transfected cisplatin-resistance cells (TE-1/CDDP) were divided into 2 groups: si-HDAC9 group (si-HDAC9) and control group (si-NC). 48 hours after transfection, cell pellets were collected and subjected to cell biology experiments and western blotting analysis.

Western Blotting

Proteins were extracted from TE-1/CDDP cells as previously described [26]. Cells transfected with si-HDAC9 or si-NC were collected after 48 h and washed thrice with ice-cold PBS. For the cellular protein lysates, RIPA buffer containing PMSF was used. The lysates were then centrifuged at 12,000 rpm at 4℃ for 10 min. Protein concentration was measured by using a BCA protein assay kit (Thermo Fisher Scientific, Waltham, Ma, USA) following the manufacturer’s instructions. For western blotting assay, the supernatant was boiled for 5 min and 50 μg of protein was electrophoresed on a 10% SDS-PAGE gel and then transferred to PVDF membranes (Millipore, Sigma; USA). This was followed by blocking with 5% nonfat dry milk in Tris-buffered saline with 0.5% Tween-20 for 60 min at room temperature. The membranes were then incubated with primary antibodies. The antibodies used were as follows: HADC9 (1:1000, cat. no. ab109446, Rabbit monoclonal, Abcam), PAI-1 1:1000, cat. no. ab66705, Rabbit monoclonal, Abcam), AKT (1:1000, cat. no. ab38449, Rabbit Abcam), ERK1 (1:1000, ab184699, Rabbit monoclonal, Abcam) and β-actin (1:1000, cat. no. ab8227, Rabbit polyclonal, as loading control) at 4℃ overnight. Blots were detected with Horseradish Peroxidase (HRP)-conjugated goat anti-rabbit or rabbit anti-mouse secondary antibody (ABclonal, Wuhan, China) for 2 h at room temperature. Bands were visualized using an ECL detection system (Thermo Scientific, Waltham, MA, USA). Protein expression was analyzed by the Quantity One software (Bio-Rad, Hercules, CA, USA) and normalized to that of β-actin).

Statistical Analysis

GraphPad Prism 8.0 (GraphPad Software; Dotmatics) and SPSS 21.0 software (IBM Corp.) were used for statistical analyses. All experiments were independently repeated ≥3 times, and the results are presented as the mean ± SD. To compare differences between two groups, a two-sided paired Student’s t-test was employed; while for comparing differences among multiple groups, oneway ANOVA test was conducted followed by Tukey’s post hoc test. P<0.05 was considered to indicate a statistically significant difference.

Results

Establishment of a Cisplatin‑Resistant Cell Line (TE‑1/CDDP) and Comparison with their Cellular Biological Characteristics between TE‑1 and TE‑1/CDDP

To explore the mechanisms of CDDP-resistance in EC, treatment of TE-1 cells with conditioned CDDP increased viability of the TE-1 cells after 8 months. The IC₅₀ value of CDDP in TE-1 cells was 4.33 µmol/L and the IC₅₀ in TE-1/CDDP cells was 37.67 µmol/L. The RI of TE-1/CDDP was 8.70 (Figure1A, P < 0.05), indicating the successful cultivation and induction of drug-resistant strains. Under a phase contrast microscope, TE-1 cells display round and regular in shape and uniform in size. However, after induction with cisplatin, TE-1/CDDP cells become narrow and elongated irregular shape, with an increase in black particles and protrusion formation in the cytoplasm (Figure.1B). Furthermore, the colony formation assay indicated that TE-1/CDDP cells had less proliferation capacity (Figure1C). Moreover, Transwell and wound healing assay revealed that TE-1/CDDP cells had the enhanced migration and invasion ability, compared to TE-1 cells (Figure. 1D, E and F).

Figure 1: Comparison of cellular biological characteristics between TE-1 and TE-1/CDDP cells. A. IC₅₀ of CDDP was determined by CCK8 assay after treatment with different concentrations of cisplatin on TE-1 or TE-1/CDDP cells. B. The morphology of two group cells was observed under an inverted microscope (magnification, x200; scale bar, 50 µm). C. Cell proliferation was investigated by colony formation assays. D. The graphs display the results of a wound healing assay (magnification, ×200; scale bar, 50 µm). The measurement of wound width was performed using Image J software. E and F. Migration and invasion were assayed using the transwell method between the two group cells. (magnification, ×200) ,*P＜0.05 and **P＜0.01 in two-tailed Student’s t-test.

Screening for Differentially Expressed Circrnas between TE‑1 and TE‑1/CDDP Cells.

To investigate the effect of circRNAs on EC cells, the circRNAs expression profiles were performed based on the Affymetrix PrimeView^TM Human Gene Expression Array. Scatter plot and volcano plot were used to assess circRNAs expression variation between TE-1 and TE-1/CDDP groups (Figure. 2A-B). These microarray results showed that there were 410 differentially-expressed circRNAs, of which 124 up-regulated and 286 down-regulated. The top 12 up‐regulated and 12 down‐regulated differentially-expressed circRNAs are displayed in heat maps, in which circRNAs are sorted based on differential expression of key host genes (Figure.2C). These differentially-expressed circRNAs were related to 191 host genes (HGs), including 56 up-regulated and 135 down-regulated genes. The top 20 up‐regulated and down‐regulated HGs are displayed in Table 1. Some cisplatin resistance genes, such as LCN2, HDAC9, AREG and EPHA2 showed differential expression in our experiments (Tab.1). HDAC9 was three-fold upregulated, which was related to hsa_circ_0133911, hsa_circ_0005331, hsa_circ_0079527 and hsa_circ_0133904. All the four circRNAs showed significant differential expression and were uploaded to GEO data base (GSE242834). Among these circRNAs, hsa_circ_0133911 had a 3-fold upregulated and was shown in Figure 2C. Thereby HDAC9 was identified as a host gene of circRNAs and chosen for further experiments.

Figure 2. The circRNAs profiling in esophageal cancer cisplatin resistance cells. A. Scatter plots were generated to assess variations in circRNA expression: abscissa represents the signal value normalized to the corresponding data in TE-1. The ordinate represents the normalized signal value of the corresponding data in TE-1/CDDP. B. Volcano plot of circRNAs were constructed by fold change and P-values. Red dots represent genes with increased differential expression, green dots represent genes with decreased differential expression, and gray dots represent insignificant differences in two group cells. C. A heat map was drawn to show the differentially expressed circRNAs. Changes in gene expression levels are shown in a variety of colors, where red represents an increase, blue represents a decrease, and yellow indicates no change. The corresponding degree of change is shown by the depth of color.TE-1-A to TE-1-D are parent strains (TE-1); TE-1-5um-A to TE-1-5um-D are cisplatin resistant strains（TE-1/CDDP（.

Summary of circRNA microarray results for the cisplatin resistant cell line (TE-1/CDDP), including dysregulated host genes, fold changes, and regulation in expression, compared to parental cell line (TE-1). ↑: Upregulation of the circRNA; ↓: Downregulation of the circRNA .

Table 1: Top 20 up-regulated and down-regulated host genes

Gene Ontology (GO) enrichment analysis

Gene product annotations were divided into three categories: Biological Process (BP), Molecular Functions (MF), and Cellular Component (CC). GO enrichment analysis revealed that in cisplatin resistant cells, the most significantly BP were negative regulation of B cell activation, spindle localization, and intermediate filament cytoskeleton organization. Differential genes involved in CC were mainly manifested in mitotic spindle, stress fiber, spindle microtubule. Differential genes involved in MF were mainly in scaffold protein binding, microtubule binding, and structural constituent of cytoskeleton. According to the secondary classification annotation map, the host genes involved in biological processes are mainly distributed in the cell growth process (80.62%), single-cell biological growth process (69.63%), biological regulation (57.59%), metabolic process (56.54%) and biological process regulation (55.50%) (Figure. 3A and 3B). Table 2 showed cisplatin-resistance related pathways in the GO analysis, mainly including negative regulation of B cell activation, intermediate filament-based process, mitotic spindle, spindle microtubule, microtubule binding, microtubule motor activity. Through GO enrichment analysis, we found that DEGs play a role in cell growth process, biological regulation, and metabolic process, and differential expression of genes may cause disorders of the above biological functions.

Figure 3: GO enrichment analysis.A. GO classification annotation map: X-axis is the GO classification and Y-axis is the number of genes. B. GO analysis bubble diagram: the ordinate on the left side is the GO function, and the abscissa represents enrichment degree.

GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.

Table 2: GO (BP, CC and MF) enrichment analysis

KEGG Pathway Enrichment Analysis

In KEGG pathway enrichment analysis, carbon metabolism, p53 signaling pathway, base excision repair, TCA cycle, pyrimidine metabolism, and DNA replication were markedly enriched (Table 3). These potential molecular mechanisms provide the basis for new treatment methods of drug resistance. The enriched analysis data were then converted into visual representations using an R program containing the ggplot2 package (Figure. 4A and 4B). KEGG analysis showed that these differential host genes on target pathways were significantly correlated with DNA replication, base exception repair, p53 signaling pathway, cell cycle and pyrimidine metabolism. Among them, ATM, SERPINE1, XRCC1 and UPP1 have been reported as genes involved in drug resistance and cancer metastasis.

Table 3: KEGG pathway enrichment analysis

Figure 4. KEGG pathway enrichment analysis. A. KEGG classification annotation map of differential host genes regulated by circRNAs: ordinate indicates the specific signaling pathway and abscissa indicates the number of genes enriched within that pathway. B. Top 30 of KEGG pathway enrichment analysis: a dot in the figure represents a pathway, the X-axis indicates the magnitude of the enrichment, and the left side of the Y-axis marks the specific signaling pathway. The higher the enrichment degree of differential host genes on target pathways, the more reliable the enrichment significance.

Knockdown of HDAC9 Suppresses Cell Proliferation, Invasion and the Protein Expression of PAI‑1, AKT and ERK on TE‑1/ CDDP Cells

HDAC9 has a high differential fold in our differentially expressed gene profile and also involved in a variety of drug resistance related biological functions, we therefore selected HDAC9 for further studies. To further understand the role of HDAC9 in EC cell biological function, three siRNA targeting HDAC9 (siHDAC9_001, 002, 003) were transiently transfected into TE-1/ CDDP cells. RT-qPCR results showed that the relative mRNA expression of HDAC9 was 62%, 45% and 27%, respectively. Of which, si-HDAC9_003 showed the highest interference efficiency and was chosen as the next interference experiments. (Figure. 5A, p< 0.01). To determine whether HDAC9 could be a potential target for esophageal cancer cisplatin resistance, the tumor cell growth, migration and invasion was detected after silencing endogenous HDAC9 expression. As shown in Figure 5C, knockdown of HDAC9 markedly inhibited TE-1/CDDP cell viability. Colony formation assay also confirmed that knockdown of HDAC9 could decrease the colony formation ability on TE-1/CDDP cells (Figure. 5E, p< 0.01). Transwell and wound healing assays validated that knockdown of HDAC9 suppressed TE-1/CDDP cell invasion and migration (Fig. 5B, p< 0.05 and Fig. 5D, p< 0.01). Collectively, these results suggested that knockdown of HDAC9 inhibited cell proliferation and motility in cisplatin resistant TE-1 cells.

In this study, HDAC9 were significantly upregulated, and KEGG enrichment analyses indicated that p53 signaling pathway included ATM, SERPINE1, CCNB2, GTSE1, and RRM2 (Table.3). It is reported that PAI-1, encoded by the SERPINE1 gene, is highly expressed in various tumor tissues, and participates in cancer development [27]. To explore whether HDAC9 increased cell proliferation and invasion was related to PAI-1, the expression levels of PAI-1, AKT and ERK were examined by western blotting. Our results demonstrated that the relative expressions of PAI-1, AKT and ERK were all lower in si-HDAC9 groups in comparison with the si-NC group (p< 0.01, Figure.5F). These results showed that knockdown of HDAC9 inhibits the expression of PAI-1, AKT, and ERK in cisplatin resistant cells.

Figure 5. Knockdown of HDAC9 inhibited cell proliferation, invasion and related protein expression in TE/CDDP cells. A. TE-1/CDDP cells were transfected with si-RNA targeting HDAC9 (si-HDAC9_001, 002, 003 and control si-NC). HDAC9 mRNA expression levels were assessed by RT-qPCR. B. Transwell assay was performed to measure the invasion and migration ability of TE-1/CDDP cells. C. CCK8 assay was used to detect the proliferation of TE-1/CDDP cells at different time points D. Wound closure was measured at different time points by scratch wound-healing assay. E. Colony formation of si-NC cells and si-HDAC9 cells. F. Relative protein expression levels of PAI-1, AKT, and ERK were measured by western blotting. Protein’s relative quantity was calculated by normalizing to β-actin. *P＜0.05 and **P＜0.01 compared with si-NC group in the two-tailed Student’s t-test.