Circular RNA SPECC1 promoted tumorigenesis and osimertinib resistance in lung adenocarcinoma via a circular RNA-microRNA network

Introduction

Among tumors, lung cancer is one of the most malignant, and its morbidity and mortality are increasing, with one-fifth of cancer-related deaths being attributable to this disease (1). Lung adenocarcinoma (LUAD) is one of the most common pathological subtypes of lung cancer, and 30% patients with LUAD harbor the epidermal growth factor receptor (EGFR) mutation, including exon 19 deletion (about 45–50% of EGFR mutations) and L858R point mutation in exon 21 (about 40–45% of EGFR mutations) (2-5). Activated mutations of EGFR are considered to be clinically responsive to tyrosine kinase inhibitors (TKIs) (6-8).

The first-generation TKIs, erlotinib and gefitinib, were developed to target those tumors with EGFR mutations. Specifically, they targeted mutations of L858R and exon 19 deletion by reversibly inhibiting the autophosphorylation of EGFR (9). The second-generation TKI, afatinib, could irreversibly inhibit pan-ErbB (10). Although a portion of patients can benefit from disease relief after administration of first- and second-generation TKIs, others experience acquired resistance. Among the patients resistant to gefitinib or erlotinib, 43–50% are positive for T790M mutations (11). Third-generation TKIs, including osimertinib, were designed to target both EGFR sensitizing mutations and T790M resistance mutations (12). The reported progression-free survival of patients treated with osimertinib is 10–12 months (13,14). However, similar to patients treated with first- or second-generation EGFR TKIs, resistance eventually occurs in patients treated with osimertinib. The development of the EGFR C797S mutation is one of the most commonly reported mechanisms of resistance to third-generation EGFR TKIs (15). Other EGFR-independent mechanisms of resistance have been reported, such as amplification in MET (16-18), HER-2 (16,19), and FGFR (20); MAPK activation (20); PTEN deletion (21); and transformation to small cell lung cancer (SCLC) (22). Histone lysine demethylases influence cell signaling pathways and promote changes in the tumor microenvironment, leading to resistance to TKIs (23). Osimertinib is a targeted therapy for EGFR mutations in the treatment of non-small cell lung cancer (NSCLC). Although it demonstrates significant efficacy in the early stages of treatment, tumor cells ultimately develop resistance, leading to disease progression. Following osimertinib treatment, tumor cells may alter their growth patterns under drug pressure and potentially influence their interactions with surrounding tissues, thereby contributing to the occurrence of metastasis (24). The clinical characteristics of osimertinib resistance include: imaging studies indicating tumor enlargement or the occurrence of metastasis, tissue biopsies revealing mutations in resistance-related genes (such as C797S) or other molecular markers, blood tests detecting circulating tumor DNA (ctDNA), changes in patient symptoms (such as dyspnea and cough), as well as alterations in patient survival duration (15-22,25). However, the resistance mechanism is complex and still not fully understood.

Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules, which are generated from pre-messenger RNAs (mRNAs) through back splicing (26). It was reported that circRNA could sequester microRNA (miRNA) via miRNA response elements (MREs) and serve as miRNA sponges through a competing endogenous RNA (ceRNA) mechanism (27,28). Emerging evidence suggests that the abnormal expression of circRNAs contributes to the tumorigenesis and development of NSCLC as well as its differentiation to drug resistance (29-31). circ_0000567, a circRNA that is differentially expressed in lung cancer before and after gefitinib resistance, was reported to promote the proliferation of gefitinib-resistant cancer cells (32). Moreover, it was found that circ_0048234 may regulate the resistance of EGFR target therapy in colorectal cancer (33-35). However, the circRNAs associated with the third-generation TKIs resistance in NSCLC have not been identified. Therefore, in this study, we attempted to clarify the relationship between circRNAs and resistance to third-generation TKIs in lung cancer. We present this article in accordance with the MDAR reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2144/rc).

Methods

Clinical samples

Tumor samples and adjacent noncancerous samples were obtained from patients with LUAD from The First Affiliated Hospital of Guangzhou Medical University. All tumors and paired nontumor samples were confirmed by experienced pathologists. All samples were rinsed with diethylpyrocarbonate (DEPC)-treated water and then kept in RNAlater (cat. no. AM7020; Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) for 30 minutes (36,37). We used three paired tumor samples with background tissue from patients with postoperative TKI treatment as the discovery set and 20 paired tumor samples from patients after surgery as the validation set. The detailed clinical features are provided in Table 1. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (Nos. 2018-82 and ES-2024-K161-01) and written informed consent was taken from all the patients.

Cell culture

Normal lung bronchial epithelium cell line (BEAS-2B) (BEBM) and lung cancer cell lines (PC9, A549, H1975, H1299, H1650, H441) were purchased from the National Collection of Authenticated Cell Cultures (Salisbury, UK). The BEAS-2B cell line was cultured with the base medium for BEBM along with additives (Clonetics Corporation, Lonza, Basel, Switzerland). For lung cancer cell lines, A549 was cultured with Dulbecco’s Modified Eagle Medium (DMEM) (cat. no. C11995500B; Gibco, Thermo Fisher Scientific), and all of the other above-mentioned lung cancer cell lines were cultured with RPMI 1640 medium (cat. no. C11875500B; Gibco). All cells were cultured under conditions including 10% fetal bovine serum (FBS) at 37 ℃ in a 5% CO₂ atmosphere.

RNA interference knockdown

We used small interfering RNA (siRNA) (cat. no. A01001; GenePharma, Shanghai, China) to silence the related genes (GenePharma). The sequence of siRNAs targeting human SPECC1 circular RNA (circSPECC1) were ΔcircSPECC1 (sense, GUCUGCUGGCCAAGGGGCCT; antisense, GGCCCCUUGGCCAGCAGACTT) and ΔcircSPECC1-2 (sense, GCCAAGGGGCCUUUACAACTT; antisense, GUUGUAAAGGCCCCUUGGCTT). The siRNA was transfected into LUAD cells via Lipofectamine 3000 (cat. no. L3000015; Invitrogen) according to the manufacturer’s instructions, at least two experiments were repeated.

Cell proliferation and cell cycle assay

Cells were seeded at 1,000 cells per well in 96-well culture plates, and Cell Counting Kit-8 (CCK8) assays were performed with the corresponding kit (cat. no. K1018—5 mL; ApexBio, Houston, TX, USA) following the manufacturer’s guidelines. The absorbance value was measured at 450 nm using a Cytation Imaging Reader (BioTek Instruments, Winooski, VT, USA).

For the cell cycle assay, cells were collected, rinsed with phosphate-buffered saline (PBS), and fixed overnight via the addition of absolute ethanol at −20 ℃. Cells were then sequentially washed once in PBS, and cell pellets were resuspended in 0.5 mL of BD Pharmingen Stain Buffer (cat. no. 550825; BD Biosciences, Franklin Lakes, NJ, USA) with propidium iodide (PI) and RNase for 15 minutes at room temperature, and cells were immediately analyzed using the FACSVerse flow cytometer (BD Biosciences), at least two experiments were repeated. The data were analyzed with FlowJo software (FlowJo, LLC, Ashland, OR, USA).

Wound healing assay, invasion assay, and adhesion assay

A wound healing assay was performed when confluence reached 80% to 90%. A wound was created in the center of the cell monolayer by gently removing the attached cells with a pipette tip. A wash with PBS was applied to remove floating cells, and then medium containing 1% FBS was added. Cells were visualized and photographed under an inverted microscope after 24 and 48 hours, at least two experiments were repeated.

For the invasion assay, Transwell chambers were coated on the upper surface with Matrigel Basement Membrane Matrix (10 µg per chamber; cat. no. 356234 Corning Inc., Corning, NY, USA) for at least 2 hours before the experiment. Cells (5×10⁴) with pure medium were transferred to the upper chambers of 24-well Transwell chambers (cat. no. 3422; Corning Inc.), with 10% FBS being added to the lower chambers. After 24-hour incubation at 37 ℃, cells in the upper chambers were removed for cell fixation with 4% paraformaldehyde (PFA) and staining with 0.5% crystal violet solution, at least two experiments were repeated.

For the adhesion assay, 5 µg of Matrigel was added in 100 µL of serum-free medium to wells of a 96-well plate and dried in an oven to form an artificial basement membrane. Subsequently, 4.5×10⁴ cells were seeded onto the Matrigel basement membrane in 200 µL of normal medium and incubated for 40 minutes. Following incubation, the medium was removed and the membrane washed five times with 150 µL of PBS to remove loosely attached cells. Adherent cells were then fixed in 4% PFA for 15 minutes, and stained in 5% crystal violet solution, at least two experiments were repeated.

EdU staining

The proliferation of cells was determined with BeyoClickTM EdU Cell Proliferation Kit (cat. no. C0078S Beyotime, Nantong, China) according to the manufacturer’s protocols. Proliferating cells were washed with PBS twice and incubated with EdU working solution (10 µM) for 2 hours at 37 ℃. Next, the cells were permeabilized with 0.1% Triton-X100 for 15 minutes. Following this, the cells were incubated with Click Additive Solution for 30 minutes and 4',6-diamidino-2-phenylindole (DAPI) (cat. no. C1005; Beyotime) for 5 minutes in the dark, at least two experiments were performed multiple times. The images were captured with an Olympus IX73 microscope (Olympus, Tokyo, Japan).

Flow cytometry of cell apoptosis

H1975 and PC9 cells were cultured in six-well plates. After 24 hours of transfection, 0.24 µmol/L of osimertinib was added to cells. Subsequently, the cells were collected and stained with an Annexin V-fluorescein 5-isothiocyanate (FITC) apoptosis detection kit (cat. no. 556547; BD Biosciences) and were analyzed with a BD FACSVerse flow cytometer (BD Biosciences) according to the manufacturer’s protocol, at least two experiments were repeated.

Hoechst 33342/PI staining assay

H1975 and PC9 cells were cultured in six-well plates. After 24 hours of transfection, 0.24 µmol/L of osimertinib was added to cells. After this, the cells were fixed with 4% polyoxymethylene, washed three times with PBS, and incubated in PBS with 5 µL of Hoechst33342 and 5 µL of PI (cat. no. C0003; Beyotime) for 30 minutes at 4 ℃. Cells were washed twice with PBS and observed using an Olympus IX73 fluorescence microscope (Olympus). ImageJ software (National Institutes of Health, Bethesda, MD, USA) was used to analyze the results.

Total RNA extraction

The clinical samples were dissected into small fragments and subsequently homogenized in TRIzol reagent (cat. no. 15596018CN; Invitrogen) with a homogenizer to ensure thorough lysis of cellular components (CCs). Total RNA was extracted via using TRIzol reagent according to the manufacturer’s instructions, including phase separation with chloroform, RNA precipitation with isopropanol, and washing with ethanol to obtain purified RNA. The quality and quantity of the extracted RNA were assessed using a spectrophotometer.

Reverse transcription and quantitative real-time polymerase chain reaction (qRT-PCR)

RNA was converted to complement DNA (cDNA) via a PrimeScript RT Reagent Kit (cat. no. RR037A; Takara Bio, Kusatsu, Japan) according to the manufacturer’s instructions. qRT-PCR was performed with KAPA SYBR FAST qPCR Master Mix (cat. no. KK4601; KAPA Biosystems, Wilmington, MA, USA) on a CFX96 Real-Time PCR System (Bio-Rad Laboratories, Hercules, CA, USA) according to standard procedures. The levels of circRNAs were normalized to the levels of beta-actin (ACTB) or glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The results of qRT-PCR were evaluated, expressed as the cycle threshold (Ct) value, converted into fold change (FC), and each sample is generally subjected to three technical replicates. PCR was performed with Premix Taq (Takara Bio). All the primers are shown in Table 2.

The library construction and high-throughput sequencing

Total RNA from three pairs of LUAD patients was extracted for high-throughput sequencing. Total RNA (10 µg) was used to construct the whole transcriptome library with the TruSeq Ribo Profile Library Prep Kit (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions. In brief, ribosomal RNA was depleted with the Ribo-Zero Gold Kit (Illumina), and surplus RNA was purified for end repair and 50-adaptor ligation. Following this, reverse transcription was performed with random primers containing 3’ adaptor sequences. Finally, the cDNAs were purified and amplified with PCR reactions. The products in 300–500 lengths were purified and quantified. These libraries were used for 150-nt paired-end sequencing with an HiSeq PE150 system (Illumina). Each library has generated at a depth of ~50 million read pairs, and then adapters sequences were removed using Cutadapt to obtain clean read. For small RNA (sRNA), six sRNA libraries were generated with TruSeq small RNA (Illumina), according to the manufacturer’s instructions. The prepared libraries were used for 50-nt single-end sequencing with a HiSeq SE150 system (Illumina). Any reads shorter than 15-nt were filtered out to obtain clean reads. Our sequencing data has been deposited in a public repository (http://www.ncbi.nlm.nih.gov/bioproject/707622).

Transcriptome sequencing data analysis

The high-throughput sequencing tools, Hisat2 (38), Samtools (39), and feature counts (40) were used to map clean reads to the Homo sapiens reference genome (hg19) and to calculate the gene expression level, which was normalized to the z score. To determine the differentially expressed mRNAs, the “DEseq2” package in R software (The R Foundation of Statistical Computing) was used with the corresponding cutoff (q <0.05 and |log2 FC| >1 for mRNA). To predict the circRNAs, we identified the possible candidates with find_circ (41), and the junction reads were normalized to z score. The criteria of P<0.05 and |log2 FC| >1 were used to identify differentially expressed circRNAs in the three pairs of samples. The Cancer-Specific CircRNA database (CSCD; http://gb.whu.edu.cn/CSCD/) was referenced for the prediction of MRE and RNA binding protein (RBP) among the differentially expressed circRNAs. For sRNA, the clean reads were mapped to the human genome (hg19) and the miRNA database in miRBase with bowtie (-v 1). The differentially expressed miRNAs were determined via the R package “DEseq2” according to the cutoffs of P<0.05 and |log2 FC| >1.

Construction of the ceRNA network

For the ceRNA network of significantly dysregulated circRNAs, mRNAs, and miRNAs, the miRNA-mRNA and miRNA-circRNA interactions were determined according to a correlation coefficient parameter of an R² value >0.99. These networks were constructed using Cytoscape (https://cytoscape.org/).

Gene Ontology (GO) analysis

GO enrichment analysis of the target genes in the networks was performed with the “cluster profiler” package in R. GO analysis included three aspects—CCs, molecular functions (MFs), and biological processes (BPs)—and was conducted according to previously reported methods (42,43).

Survival analysis

The University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) online tool (http://ualcan.path.uab.edu) was used to analyze the significantly dysregulated mRNAs under default parameters.

Statistical analysis

Statistical significance was assessed using the t-test. All statistical analyses were performed using SPSS 25 (IBM Corp., Armonk, NY, USA). The values reported in the graphs in this article represent the averages of the actual number of independent experiments, with the error bars indicating the standard deviation (SD).

Results

Identification of differentially expressed circRNAs in LUAD samples

To identify LUAD-associated RNA, RNA sequencing was performed from tumor samples and paired adjacent nontumor samples from three patients with LUAD. Subsequently, the differential expression analysis of mRNAs, circRNAs, and miRNAs was performed (Figure S1A). Pearson correlation coefficient analysis demonstrated a significant correlation between tumor samples or normal samples between groups (Figure S1B). The genetic expression of these three pairs of tumor samples was markedly different from that of normal samples, and samples between each group had a high degree of similarity in a principal component analysis (PCA) model (Figure S1C).

We found that the expression level of overall circRNAs in tumor samples was lower than that of the background according to the box diagram (Figure 1A). The cumulative distribution function (CDF) showed a faster accumulation of genes in tumor samples (Figure 1B). Moreover, the number of circRNA reads with a larger spliced sequence length was relatively higher in tumor samples, which appeared to contain more circRNAs longer than 3,000 nucleotides compared to normal samples (Figure 1C,1D). Moreover, the differentially expressed circRNAs were identified, and the expression levels of these circRNAs were examined. Through volcano plot and hierarchical cluster analysis, six differentially expressed circRNAs were identified (Figure 1E,1F), with circGLIS2, circACVR2A, and circEPB41L2 being downregulated in tumor samples and circTET2, circSPECC1, and circZKSCAN1 being upregulated (Figure 1E,1F).

Figure 1 Identification of dysregulated circRNAs from RNA-seq. (A) Boxplot of all circRNA expression levels in normal and tumor tissue, respectively. (B) The cumulative distribution function for LUAD circRNA expression in normal and tumor tissue, respectively. (C,D) The distribution of circRNA spliced sequence length among normal and LUAD tissues. (E) Volcano plot of the differentially expressed circRNAs. (F) Heatmap of the expression level of differentially expressed circRNAs. (G) Detailed information and element prediction for six differentially expressed circRNAs. (H) The validation results of 6 dysregulated circRNAs from qRT-PCR. (I) Expression profile of 6 dysregulated circRNAs from a PCR assay. Data are the mean ± SD. P<0.05 for the t-test. *, P<0.05; **, P<0.01. circRNA, circular RNA; CDF, cumulative distribution function; DEGs, differentially expressed genes; MRE, miRNA response element; RBP, RNA binding protein; ORF, open reading frame; ACTB, beta-actin; LUAD, lung adenocarcinoma; qRT-PCR, quantitative real-time polymerase chain reaction; PCR, polymerase chain reaction; SD, standard deviation.

The open reading frame (ORF) of the six differentially expressed circRNAs was predicted for a potentially translatable template, MRE for potential miRNA binding capabilities, and RBP for circRNA-RBP interactions. As shown in Figure 1G, all six of these six circRNAs had MREs, which indicated that they could bind directly to the corresponding miRNAs to inhibit miRNA activity and thus regulate the expression of target genes. Except for circGLIS2, all had predicted RBP binding sites, suggesting that they might function as protein scaffolds. circGLIS2, circEPB41L2, circTET2 and circZKSCAN1 were found to have ORFs, indicating that they might be translated into peptides. Interestingly, circTET2 harbored predominantly ORFs and RBPs, rather than MREs, while circSPECC1 predominantly harbored MREs. The expression level of circRNAs was verified in the LUAD patient cohorts via qRT-PCR. circGLIS2 was significantly downregulated in tumors as compared to background tissues, while circSPECC1 was upregulated in tumors (Figure 1H). The expression profile of circRNAs was also screened in a panel of normal bronchial epithelium and LUAD cell lines. The results showed that circGLIS2 was weakly expressed in the A549, H1975, H1299, H1650, and H441 cell lines, while it was highly expressed in the BEAS-2B cell lines. circSPECC1 was highly expressed in BEAS-2B, A549, H1975, H1650, and H441 cell lines, while it was weakly expressed in H1299 cell lines (Figure 1I). Therefore, we selected circSPECC1 from six differentially expressed circRNAs for the subsequent experiments.

circSPECC1 promoted tumorigenesis and metastasis in NSCLC cells

To investigate the potential function of circSPECC1 in lung cancer cells, we selected two NSCLC cell lines, H1975 (T790M/L858R) and PC9 (exon 19 deletion), which have been commonly used in previous LUAD studies (44,45). Two rationally designed siRNA oligonucleotides, targeting the back splice junction of circSPECC1, were transfected into cell lines, and the most efficient siRNA was then selected for further investigation (Figure 2A and Figure S1D). This siRNA, as shown in Figure 2A (ΔcircSPECC1), successfully knocked down circSPECC1 (~20% reduction; P<0.05) but had no effect on the linear SPECC1 mRNA expression level in H1975 (T790M/L858R) or PC9 (exon 19 deletion) cell lines. A 20%-decrease of Edu-555 fluorescence intensity in these cell lines caused by circSPECC1 knockdown indicated that circSPECC1 promoted NSCLC cell proliferation (Figure 2B,2C). The wound healing assay demonstrated that the motility of H1975 and PC9 was inhibited by circSPECC1 knockdown. The number of invading NSCLC cells was significantly decreased after circSPECC1 knockdown (Figure 2D,2E) as was the adhesion ability of H1975 and PC9 cells (Figure 2F). In addition, cell cycle analysis further indicated the regulation of circSPECC1 in tumor cell proliferation, as shown in Figure S1E, which was associated with a decrease in G2/M and S arrest. Overall, these findings indicate that circSPECC1 could promote the tumorigenesis and metastasis of two NSCLC cell lines and affect the cell cycle.

Figure 2 circSPECC1 promoted tumorigenesis and metastasis in NSCLC. (A) qRT-PCR detection of circSPECC1 after siRNA interference as compared to NCs. (B,C) EdU staining assay showed proliferation of tumor cells after ΔcircSPECC1 for 24 hours, captured at ×10 magnification. (D) The migration of cells was assessed by wound healing assay, captured at ×20 magnification. (E) The invasion of cells was assessed by Transwell assay with Matrigel, stained by crystal violet, captured at ×20 magnification. (F) The metastasis of cells was assessed by adhesion assay, stained by crystal violet, captured at ×20 magnification. Data are the mean ± SD. P<0.05 for the t-test. ns, no significance; *, P<0.05. NC, negative control; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; circSPECC1, SPECC1 circular RNA; NSCLC, non-small cell lung cancer; qRT-PCR, quantitative real-time polymerase chain reaction; siRNA, small interfering RNA; SD, standard deviation.

circSPECC1 reduced the sensitivity of tumor cells to osimertinib

To further determine whether circSPECC1 was associated with the sensitivity and resistance to TKI treatment, a CCK8 assay was performed. It was found that the proliferation of H1975 and PC9 cells decreased after 72 hours of circSPECC1 knockdown. Under osimertinib treatment, cell proliferation was further inhibited, and the original sensitivity of H1975 and PC9 cells to TKIs was restored (Figure 3A,3B). Notably, with a 24-hour treatment of osimertinib, the number of apoptotic cells increased significantly in ΔcircSPECC1 H1975 cells and ΔcircSPECC1 PC9 cells as compared to control cells (Figure 3C,3D). As can be seen in Figure 3E,3F, Hoechst 33342 and PI assay consistently showed that in the 24 hours of osimertinib treatment, the death rate of cells rose significantly after circSPECC1 knockdown as compared to that observed in cells without osimertinib treatment. There was no significant difference in the increase in apoptosis of tumor cells upon circSPECC1 knockdown, while with the addition of osimertinib treatment, the increase in apoptosis rate of tumor cells in the knockdown group was twice as high as that in the control group. circSPECC1 demonstrated a protective effect against the cell apoptosis induced by osimertinib, which is related to the sensitivity and response of LUAD cells to TKIs.

Figure 3 circSPECC1 reduced the sensitivity of tumor cells to TKI. (A) Relative OD in H1975 cells after RNA interference and TKI treatment for 72 hours. (B) Relative OD in PC9 cells after RNA interference and TKI treatment for 72 hours. (C,D) Flow cytometry of cell apoptosis after RNA interference and TKI treatment for 24 hours in PC9 cells. (E,F) The rate of cell death was assessed via Hoechst 33342/PI staining assay in PC9 cells after RNA interference and TKI treatment for 24 hours, captured at ×10 magnification. Data are the mean ± SD. P<0.05 for the t-test. *, P<0.05. NC, negative control; circSPECC1, SPECC1 circular RNA; TKI, tyrosine kinase inhibitor; OD, optical density; FITC, fluorescein isothiocyanate; PI, propidium iodide; SD, standard deviation.

Identification of target genes of circRNAs

To identify potential target genes of circRNAs, we aimed to analyze the differentially expressed mRNAs from RNA-sequencing data in the above-mentioned LUAD samples, with 4,137 differentially expressed mRNAs ultimately being screened (Figure 4A). Among these, 2,496 (60.33%) were upregulated compared to normal samples, while 1,641 (39.67%) were downregulated (Figure 4A). Hierarchical cluster analysis was also performed to evaluate these mRNAs (Figure 4B). GO enrichment analysis was performed with three categories: BP, CC, and MF. The top 15 GO terms in terms of enrichment are listed in Figure 4C. Cell adhesion molecule binding, collagen-containing extracellular matrix, and extracellular structure organization were the most enriched, indicating that these differentially expressed mRNAs may be closely associated with molecular binding and extracellular matrix organization, which may be associated with tumorigenesis and metastasis. For survival analysis, the UALCAN online tool was used to investigate the relationship between the identified target genes and clinical observations (Figure 4D). The top 10 differentially expressed mRNAs were selected according to P value for survival curves analysis, with eight mRNAs being significantly correlated with LUAD patient survival. Among these, six (LAMA3, HMGA1, SLC2A1, PROM2, KRT80, and SFN) were negatively correlated with survival curves (patients with higher expression had shorter survival). Meanwhile, MFAP4 and PREX2 were positively correlated with LUAD patient survival (patients with higher expression had longer survival).

Figure 4 Identification of target genes of circRNAs. (A,B) Volcano plot of the differentially expressed transcripts and the hierarchical cluster heatmap of the differential expression of mRNAs in three LUAD tumor samples and their controls. (C) GO analyses and annotation of dysregulated genes in three main categories: biological processes, cellular components, and molecular functions. (D) Survival curves of identified genes were analyzed by UALCAN analysis. The P value was calculated with the log-rank test. DEGs, differentially expressed genes; mRNA, messenger RNA; UTR, untranslated regions; ER, endoplasmic reticulum; LUAD, lung adenocarcinoma; circRNA, circular RNA; GO, Gene Ontology; UALCAN, University of ALabama at Birmingham CANcer data analysis Portal.

Construction of the ceRNA regulatory network

CircRNAs functioning as miRNA sponges have been widely reported in a study (46). As previously demonstrated, circSPECC1 has an abundance of MREs but a lack of oligonucleotide repeat domains (ORDs). We screened out dysregulated miRNAs through volcano plot (Figure 5A) and constructed a ceRNA network containing differentially expressed circRNAs, miRNAs, and mRNAs to clarify the underlying mechanism of circRNAs’ effect in LUAD (Figure 5B). In this ceRNA network, 28 miRNAs and 55 mRNAs were found to have correlations with 6 circRNAs. circSPECC1 was found to sponge four miRNAs; among these, miR-598-3p, miR-33a-3p, and miR-497-5p were downregulated in clinical samples, while miR-369-3p was upregulated. These miRNAs further affected mRNAs, including TUBB1, BCHE, BNIP3, LHFPL6, and SLC39A4. We also conducted GO analysis to determine the enrichment of RNA processes, which showed that chromosomal region, cell adhesion molecule binding, extracellular structure organization, and ribosome biogenesis were the most enriched (Figure 5C).

Figure 5 Construction of the competing endogenous RNA regulatory network. (A) Volcano plot of the differentially expressed miRNAs. (B) A ceRNA network was constructed according to the interactions among circRNAs, miRNAs, and mRNAs (correlation coefficient absolute value 0.99). Boxes represent the dysregulated mRNAs, triangles represent the dysregulated miRNAs, and circles represent the dysregulated circRNAs. Red, upregulated; blue, downregulated. (C) GO analyses of the affected pathways in three main categories: cellular components, biological processes, and molecular functions. DEGs, differentially expressed genes; mRNA, messenger RNA; SRP, study reference population; miRNA, microRNA; ceRNA, competing endogenous RNA; circRNA, circular RNA; GO, Gene Ontology.

Discussion

There is a growing body research suggesting that circRNAs play crucial roles in drug resistance (5,47). We performed high-throughput sequencing on three tumors from patients with poor postoperative TKI treatment outcomes and compared them with paired background tissue. Six circRNAs (circSPECC1, circGLIS2, circACVR2A, circEPB41L2, circTET2, and circZKSCAN1) were screened according to z scores in a discovery set of tissue samples associated with poor prognosis after osimertinib treatment. In addition, we noted that there were several circRNA expression profiles (GSE158695, GSE112214, GSE101586, and GSE101684) from lung cancer tissues in the Gene Expression Omnibus (GEO) database (48-51). We therefore examined the expression of the six circRNAs we identified in these data and found that five of them (circSPECC1, circTET2, circACVR2A, circGLIS2, and circEPB41L2) were included, among which circSPECC1 and circTET2 were upregulated in tumors, while circACVR2A, circGLIS2, and circEPB41L2 were downregulated; this was consistent with our differentially expressed gene screening and validation results (Figure S2). In the lung cancer cell lines from GSE152434 dataset, circSPECC1 and circTET2 were highly expressed in A549 cells, while circACVR2A, circGLIS2, and circEPB41L2 were highly expressed in BEAS-2B cells (52).

Interestingly, circSPECC1 was found to be markedly upregulated in tumor samples, a finding which was further validated in our study (53-55). circSPECC1 was reported to promote hepatocellular and gastric carcinoma tumorigenesis via a ceRNA mechanism (29,30). However, whether circSPECC1 is involved in TKI resistance has not been extensively examined. We further investigated the biological function of circSPECC1 and its effect on the response of TKIs in NSCLC. First, we found that the proliferation, migration, and invasion ability of NSCLC cell lines were significantly decreased upon circSPECC1 knockdown. Second, the apoptotic rate of cell lines with TKI-sensitive EGFR mutation was increased after circSPECC1 was knocked down in the presence of osimertinib. Therefore, we hypothesize that circSPECC1 may reduce the sensitivity of TKI treatment by targeting EGFR-mutant cells, specifically H1975 (T790M/L858R) and PC9 (exon 19 deletion) cells. To characterize the mechanisms underlying the effect of circRNAs on TKI resistance, we further constructed a ceRNA network and performed GO analysis.

We constructed circRNA-miRNA-mRNA networks for circSPECC1 and for five other circRNAs. We further screened a group of mRNAs as potential target genes via RNA-sequencing analysis and qRT-PCR. Some of these differentially expressed genes have already been reported to be related to drug resistance in other cancers (56-60). In this network, circSPECC1 was predicted to interact with four miRNAs capable of regulating five mRNAs. TUBB1 has been implicated in the accumulation of DNA damage, which can result in genome instability (61). This process may arise from the most primitive TKI-refractory cells present in both TKI-naive and TKI-treated patients (62). Our GO analysis revealed the pathways these five genes might participate in. The target genes were significantly enriched in the chromosomal region and DNA packaging complex, which may be related to TKI resistance (63). BNIP3-related autophagy caused by gemcitabine leads to reduced lung cancer cell death, which may contribute to gemcitabine-acquired resistance (64). The pathways identified in or GO analysis were mainly related to ribosome biogenesis, mitotic nuclear division, and mRNA catabolic process, which may be related to cell proliferation.

Acquired resistance to EGFR-TKIs has been thought to represent the end product of two different but possibly concurrent evolutionary pathways, including the selection of cancer clones that contain preexisting resistance mutations and the reacquisition of these genetic defects due to random mutations (56). The mechanism of TKI resistance is complex, and its molecular mechanism has only begun to be clarified in recent years. The common resistance mutations of the new the third-generation EGFR-TKIs include L718Q (57), L844V (58), and C797S (58), all of which confer resistance to rociletinib, with C797S conferring resistance to osimertinib (61). The combination of traditional TKIs combined with chemotherapy or other targeted drugs provide a degree of benefit to patients with TKI resistance (65). With the development of the fourth-generation EGFR-TKIs (66), it is necessary to further investigate the mechanisms of drug resistance. At present, there is still a lack of broad-spectrum and inexpensive genetic testing methods in clinical practice. The reason we designed only two siRNAs in this study is that siRNAs targeting circSPECC1 must be specifically designed at the splicing junction, thus limiting our choice for circSPECC1-specific siRNAs. One of the siRNAs targeting circSPECC1 showed stable knockdown efficiency as high as 20%. CircRNA has the potential to serve as a biomarker for early cancer diagnosis and prognosis assessment, and it may also emerge as a novel therapeutic target to enhance cancer treatment outcomes through interventions in its expression or function. However, despite the significant promise of circRNA in cancer research, several challenges persist. Currently, the methods for detecting and analyzing circRNA are not standardized, which affects the comparability of results. Additionally, the diversity and complex functional mechanisms of circRNA present difficulties in research. Furthermore, the effective translation of circRNA research findings into clinical applications remains to be addressed (67). Therefore, future research should focus on further elucidating the functions and mechanisms through which circRNAs influence TKI resistance, as well as advancing clinical detection methods. Enhancing early detection of TKI resistance is essential for guiding personalized treatment strategies in clinical practice.

Conclusions

We found that circSPECC1 participates in the tumorigenesis and TKI resistance of NSCLC and may thus be a candidate for the targeted therapy of NSCLC.

Acknowledgments

We would like to thank the following institutions for providing valuable resources: the State Key Laboratory of Respiratory Disease, the National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, and the School of Basic Medical Sciences, Division of Life Science and Medicine, USTC. We also appreciate the great support from Professor Chen Liang in improving the quality of this paper.

Funding: This study was supported by the project of National Natural Science Foundation of China (No. 82273450), the Project of Guangdong Medical Research Foundation (No. B2021313), the Science and Technology Innovation Committee Joint Funding (Dengfeng Hospital) Project of Guangzhou (Nos. 2023A03J0355, 2023A03J0343 and 2023A03J0356), and the Opening Foundation of the State Key Laboratory of Respiratory Diseases (No. SKLRD-OP-202404).

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2144/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2144/dss

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Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2144/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (Nos. 2018-82 and ES-2024-K161-01). All patients provided their written informed consent.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Gray)