Open Access

Identification of potential circular RNA biomarkers in lung adenocarcinoma: A bioinformatics analysis and retrospective clinical study

  • Authors:
    • Yonggang Zhu
    • Feng Cao
    • Fei Liu
    • Sihua Liu
    • Lingjiao Meng
    • Lina Gu
    • Hanjun Zhao
    • Meixiang Sang
    • Baoen Shan
  • View Affiliations

  • Published online on: March 11, 2022     https://doi.org/10.3892/ol.2022.13264
  • Article Number: 144
  • Copyright: © Zhu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Non‑small cell lung cancer (NSCLC) is one of the leading causes of cancer‑associated mortality. Lung adenocarcinoma (LAC) is the most prevalent pathological subtype of NSCLC and accounts for ~40% of all lung cancer mortalities. There remains an urgent demand for the identification of novel biomarkers for the diagnosis and development of therapeutic strategies for LAC. In the present study, the profiles of the differentially‑expressed circular RNAs (circRNAs) in LAC tissues compared with those in their corresponding non‑cancerous tissues were obtained after analyzing the circRNA microarray dataset GSE101586. The expression pattern of the indicated circRNAs in the LAC tissues were subsequently verified using reverse transcription‑quantitative PCR (RT‑qPCR). The potential prognostic significance of these circRNAs in patients with LAC were then analyzed in a retrospective clinical study. A circRNA‑microRNA (miR or miRNA)‑mRNA regulatory network in LAC was established by using Cytoscape. In addition, a protein‑protein interaction (PPI) network was plotted using the Search Tool for the Retrieval of Interacting Genes/Proteins and visualized through Cytoscape. The prognostic value of the hub genes found was then analyzed based on the Gene Expression Profiling Interactive Analysis database. In total, four differentially‑expressed circRNAs were obtained from the GSE101586 microarray dataset, three of which (hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666) were confirmed by RT‑qPCR to be highly expressed in LAC tissues. This retrospective clinical study revealed that higher expression levels of these three circRNAs were associated with poorer prognoses in patients with LAC. In addition, siRNA‑mediated knockdown of these circRNAs was found to inhibit cell proliferation, migration and invasion in LAC cells. Following analysis of the molecular mechanism underlying these circRNAs, eight miRNAs, namely miR‑520f, miR‑1261, miR‑1270, miR‑620, miR‑188‑3p, miR‑516b, miR‑940 and miR‑661, were identified with potential binding sites for these three circRNAs. Subsequently, 232 overlapped genes from the 795 upregulated genes in the LAC samples from The Cancer Genome Atlas database and 7,829 predicted target genes of the list of eight aforementioned miRNAs were obtained. A circRNA‑miRNA‑mRNA network was then constructed. A PPI network was established, with six hub genes, namely kinesin family member (KIF) 2C, KIF18B, maternal embryonic leucine zipper kinase, baculoviral IAP repeat‑containing 5, polo‑like kinase 1 and cytoskeleton‑associated protein 2‑like, determined from this network. Higher expression levels of each of these hub genes were found to be associated with poorer prognoses of patients with LAC. To conclude, data from the present study suggested that circRNAs hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666 have the potential to be viable biomarkers and therapeutic targets for LAC.

Introduction

Despite the recent progress in the development of targeted therapies, lung cancer remains to be the most prevalent malignancy and the leading cause of cancer-associated mortality worldwide (1). Non-small cell lung cancer (NSCLC) accounts for 80–85% of all types of lung cancer, where lung adenocarcinoma (LAC) is the most prevalent pathological subtype and accounts for ~40% of all lung cancer-associated mortality (2). Although comprehensive therapeutic strategies, including surgery and targeted therapies, have improved clinical outcome over the past number of decades, the 5-year survival rate of LAC remains at only 18% (3). The reasons for this poor prognostic rate from advanced LAC include low diagnosis rates and the absence of effective therapeutic targets. Therefore, it would be of great value to identify novel specific and sensitive biomarkers and therapeutic targets for patients with LAC.

Circular RNAs (circRNAs) are a novel class of endogenously-spliced RNAs that have been previously found to regulate cancer progression (4,5). Due to their covalently closed loop without a 5′-3′poly-adenylation end, circRNAs possess the ability to resist the activities of exonucleases and remain highly stable, suggesting that they have potential to serve as cancer biomarkers and therapeutic targets (610). Accumulating evidence has demonstrated that circRNAs are involved in the development of LAC (1113). One key molecular function of circRNAs is that they can function as competing endogenous RNAs (ceRNAs) to sponge microRNAs (miRNAs), leading to the enhancement of the expression of genes normally suppressed by miRNAs. The circRNA circ-cysteine-rich transmembrane bone morphogenetic protein regulator 1 inhibits the invasion and metastasis of LAC cells by serving as the ceRNA of miR-182/miR-93 (14). In addition, another circRNA, has_circ_0001946, promotes LAC cell proliferation by sponging miR-135a-5p (15). In another study, circ-zinc finger protein 609 promotes the proliferation of LAC by targeting miR-1224-3p/ETS translocation variant 1 signaling (16). Taken together, these previous findings suggest that circRNAs serve crucial roles in regulating the physiology of LAC and have the potential to serve as biomarkers and therapeutic targets of LAC.

In the present study, by analyzing the GEO database, three circRNAs were identified to be highly expressed in LAC tissues compared with the paired-matched adjacent non-cancerous tissues. A retrospective clinical study revealed that higher expression levels of these three circRNAs indicated poorer prognoses in patients with LAC. To unravel the potential biological mechanism of these circRNAs, their target miRNAs and possible mRNAs affected downstream were screened, following which a circRNA-miRNA-mRNA regulatory network was constructed. A protein-protein interaction (PPI) network was subsequently established and six hub genes were eventually identified. A retrospective study using the Gene Expression Profiling Interactive Analysis (GEPIA) database showed that higher expression levels of each hub gene were associated with poorer overall survival in patients with LAC. Taken together, through bioinformatic analysis and retrospective clinical study, these findings revealed a number of novel circRNAs that could be important for LAC pathophysiology. In addition, they provided insights into the molecular mechanism that regulate the progression of LAC with respect to the circRNA-miRNA-mRNA network.

Materials and methods

Datasets

The circRNA expression profile of GSE101586 was obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), which included five pairs of matched LAC tissues (GSM2706428, GSM2706429, GSM2706430, GSM2706431 and GSM2706432) and adjacent non-cancerous tissues (GSM2706423, GSM2706424, GSM2706425, GSM2706426 and GSM2706427). The tissues were obtained from five female patients with LAC with no smoking history, and the physiology of the tissues were confirmed by pathologists. The mRNA data of the LAC tissues was downloaded from The Cancer Genome Atlas (https://portal.gdc.cancer.gov).

Clinical samples

In total, 60 cases of LAC tissues in the tissue bank were obtained from the patients who underwent surgery in the Fourth hospital of Hebei Medical University (Shijiazhuang, China) between January 2016 and June 2016. A total of 20 pairs of LAC and adjacent normal tissue (5-cm from the tumor tissue) were collected from the above 60 cases. The clinicopathological characteristics and survival status of the patients with LAC were obtained from the follow-up data. (The duration of follow-up was between January 2016 and June 2021). The inclusion criteria were: i) All patients were diagnosed LAC through pathology and ii) All patients did not receive any therapy before surgery. The clinicopathological data for the patients are presented in Table SI. The human tissues were obtained with written informed consent, and the present study was approved by The Clinical Research Ethics Committee of The Fourth hospital of Hebei Medical University (approval no. 2021KY157).

Differentially-expressed circRNAs and mRNAs

The limma package in R (v3.4.1; http://bioconductor.org/packages/release/bioc/html/limma.html) was used to identify the differentially expressed circRNAs between the LAC tissues and the adjacent non-cancerous tissues from the GEO101586 dataset using the criteria of P<0.05 and |Log2 fold change (FC)|>1. The ‘edger’ package (v3.52; http://bioconductor.org/packages/edgeR/) was used to screen for the differentially-expressed mRNAs using thresholds of |Log2FC|>3 and P<0.01. The miRNAs-targeted mRNAs were predicted based on the miRWalk software (v2.0; http://www.umm.uni-heidelberg.de/apps/zmf/mirwalk/).

Cell culture

LAC cell lines H1299 and H1975 were obtained from Chinese Academy of Sciences Cell Bank were cultured in RPMI-1640 media (Gibco; Thermo Fisher Scientific, Inc.) supplemented with 10% FBS (Gibco; Thermo Fisher Scientific, Inc.), 100 U/ml penicillin and 100 µg/ml streptomycin. Cells were cultured at 37°C under 5% CO2.

RNA extraction and reverse transcription-quantitative PCR (RT-qPCR)

Total RNA from 80% confluent cells and tissues were isolated by TRIzol® Reagent (Thermo Fisher Scientific, Inc.) according to the manufacturer's protocols. The cDNA was prepared from total RNA according to the GoScript Reverse Transcription System (Promega Corporation) protocol. qPCR analysis was performed using the Promega GoTaq qPCR Master Mix (Promega Corporation). GAPDH was used as the mRNA internal reference. The primer sequences for all qPCR reactions are shown in Table SII. The PCR cycling conditions was as follows: Denaturation at 95°C for 15 sec, annealing at 58°C for 30 sec and extension at 72°C for 30 sec. Three repeat wells were set. The relative expression levels were calculated using the 2−ΔΔCq method (17). The sequencing of PCR products were obtained from Sangon Biotech Co., Ltd. RNase R treatment was carried out for 15 min at 37°C using RNase R 3 U/mg.

siRNA transfection

siRNA for negative control and siRNAs targeting the junction sites of circ-0006220, circ-0072088, and circ-0001666 were designed and synthesized by Guangzhou RiboBio Co., Ltd. The sequences of these siRNAs are listed in Table SIII. H1299 cells were transfected with above siRNAs when reached 70–80% confluence by using HiPerFect Transfection Reagent (Qiagen GmbH) based on the manufacturer's instruction. The final siRNA concentrations were 50 nmol/l. Following transfection, cells were cultured at 37°C for 72 h.

Cell Counting Kit-8 (CCK-8) (MCE®) assay

H1299 cells were first seeded at a cell density of 5×103 cells/well into 96-well plates. After attachment overnight, CCK-8 assays were performed after 24, 48 and 72 h. In brief, 10 µl CCK-8 solution (cat. no. HY-K030; MedChemExpress) was added into each well. After 1 h incubation at 37°C, the absorbance readings for each well were performed at 450 nm using the microplate reader (Tecan Group, Ltd.).

Transwell migration and Matrigel invasion assays

The migration and Matrigel invasion assays were performed in 6.5-mm Transwell chambers for migration assays or Matrigel pre-coated (at 37°C for 24 h) chambers for invasion assays based on the manufacturer's protocol (BD Biosciences). The cell suspensions of different groups were added to the upper chambers at a density of 5×104 cells/well and incubated for 24 h. The migratory and invasive cell numbers were then quantified in five random fields per chamber under the inverted microscope at ×100 magnification.

Prediction of circRNA-miRNA binding sites

Prediction of interactions between circRNAs and miRNAs was performed using the CircInteractome database (https://circinteractome.irp.nia.nih.gov/). miRNAs with a context score percentile ≥98 were eventually selected.

Construction of the circRNA-miRNA-mRNA regulatory network

The ceRNA regulatory network was established according the possible interactions among the four differentially-expressed circRNAs, eight miRNAs predicted to be targeted by these circRNAs and the 232 overlapped mRNAs from the list of predicted target genes and the upregulated genes in LAC. Cytoscape 3.7.1 software (v.3.7.1; http://cytoscape.org/) was used to visualize the established ceRNA regulatory network.

Establishment of the PPI regulatory network and identification of hub genes

A PPI network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software (version 11.5; http://string-db.org/) and visualized using the Cytoscape 3.7.1 software (https://cytoscape.org/). Additionally, suspected hub genes with high predicted degrees of interactions from the PPI network were screened using the CytoHubba plugin in Cytoscape.

Verification of hub gene expression and survival using the GEPIA database

The mRNA expression levels of the hub genes and their association with survival were assessed using the web-based GEPIA database (http://gepia.cancer-pku.cn/) with the settings of P≤0.05 and |Log2FC|≥1.

Immunohistochemistry

The 5-µm, paraffin-embedded slides were deparaffinized in xylene, rehydrated using a decreasing alcohol gradient and washed with 1X PBS (pH 7.2) three times at 5 min each. The sections were then heated in a microwave oven for 5 min in 10 mmol/l Na-citrate buffer (pH 6.0) for antigen retrieval and washed with 1X PBS. The sections were immersed in 0.3% hydrogen peroxide in methanol for 20 min to suppress endogenous peroxidase activity. After further washing with 1X PBS, the sections were incubated in 10% normal goat serum (cat. no. SP-9001 or SP-9002; OriGene Technologies, Inc.) at room temperature in a humidified chamber for 30 min to prevent nonspecific immunoglobulin binding. The sections were then treated with the 1:100-diluted KIF2C antibody (cat. no. TA503320; OriGene Technologies, Inc.), KIF18B (cat. no. ab121798; Abcam), CKAP2L (cat. no. ab122617; Abcam), PLK1 antibody (cat. no. TA500383; OriGene Technologies, Inc.), MELK antibody (cat. no. ab129373; Abcam), BIRC5 antibody (cat. no. TA301427; OriGene Technologies, Inc.) at 4°C overnight. Normal IgG in place of the primary antibody served as the negative control. A streptavidin-biotinylated HRP-based detection system was used to reveal specific binding. The sections were counterstained for 2 min at room temperature with hematoxylin for light microscopic review and evaluation. The expression was ranked on the sum of intensity and area from 0 to 7: 0–2, negative expression; 3–7, positive staining (of those, 3–4, weak positive expression; and 5–7, strong positive expression). Staining intensity was graded as follows: 0 for no staining; 1 for mild staining; 2 for moderate staining; and 3 for intense staining. The staining area was scored as follows: 0 for no staining; 1 for 1–25% area; 2 for 26–50% area; 3 for 51–75% area; and 4 for 76–100% area.

Statistical analysis

Statistical analysis was performed using SPSS 22.0 software (IBM Corp). The data were presented as the mean ± standard deviation and measured using the Student's t-test (paired t-tests were used in Fig. 1C, and unpaired t-tests were used in Fig. 2C-F). ANOVA analysis followed by Dunnett's post hoc test was used for 2G-I. Chi-square test was used to analyze the association of circRNA expression with the hub protein expression (Fig. 7C). Kaplan-Meier analysis was used to evaluate the overall survival and log-rank test was performed to estimate the differences among the various groups. P<0.05 was considered to indicate a statistically significant difference.

Results

Identification of differentially expressed circRNAs

The GSE101586 dataset from GEO was analyzed using the limma package in R (P<0.05 and |Log2FC|>1). In total, 47 upregulated and 21 downregulated circRNAs were identified in LAC tissues compared with adjacent non-cancerous tissues (Table I). After tightening the parameters to P<0.01 and |Log2FC|>2, four circRNAs, hsa_circ_0043278, hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666, were identified to be significantly upregulated in LAC tissues compared with adjacent non-cancerous tissues (Fig. 1A). The basic structural profiles of these four circRNAs are shown in Fig. 1B. All four of these circRNAs are derived from exons of their parent genes. Among them, three circRNAs (hsa_circ_0006220, hsa_circ_0072088, hsa_circ_0001666) were confirmed to be upregulated in LAC tissues compared with those in the corresponding normal tissues by RT-qPCR (Fig. 1C).

Table I.

The differential expressed circRNAs between LAC tissues and the corresponding normal lung tissues.

Table I.

The differential expressed circRNAs between LAC tissues and the corresponding normal lung tissues.

CircRNA IDP-valueLog2 (fold-change)
hsa_circ_00432780.0026022.654576
hsa_circ_00062200.0072782.567267
hsa_circ_00720880.0019792.513804
hsa_circ_00009770.0235682.450922
hsa_circ_00016660.0081872.450695
hsa_circ_00223830.0053011.898136
hsa_circ_00462630.000161.669339
hsa_circ_00005140.0216381.553487
hsa_circ_00223920.0169061.491607
hsa_circ_00053970.0082141.480409
hsa_circ_00690860.0063731.466104
hsa_circ_00362870.0055991.460223
hsa_circ_00825640.01071.442822
hsa_circ_00270890.0284811.394235
hsa_circ_00652140.0057691.393836
hsa_circ_00189090.038371.389946
hsa_circ_00019980.0416251.340807
hsa_circ_00041040.0179031.324693
hsa_circ_00005190.022141.267825
hsa_circ_00038380.0395731.267251
hsa_circ_00113850.010961.251054
hsa_circ_00023600.0188031.249496
hsa_circ_00085830.0355121.17612
hsa_circ_00550330.0390731.169861
hsa_circ_00917100.0225751.161549
hsa_circ_00679340.0071011.143916
hsa_circ_00085390.0477051.143884
hsa_circ_00844430.0475431.136258
hsa_circ_00030280.033671.133201
hsa_circ_00176390.0238141.123251
hsa_circ_00844290.0210751.114951
hsa_circ_00724300.0257041.103268
hsa_circ_00039580.0248071.100155
hsa_circ_00012380.0347051.099251
hsa_circ_00035280.0341481.09902
hsa_circ_00408090.0403981.077003
hsa_circ_00679710.030121.075765
hsa_circ_00073450.0417661.075431
hsa_circ_00082740.0383721.063426
hsa_circ_00252010.0085551.062317
hsa_circ_00171090.0119751.058244
hsa_circ_00691520.0314011.051094
hsa_circ_00069480.0284761.037828
hsa_circ_00006900.0307951.033918
hsa_circ_00830540.0411981.023474
hsa_circ_00059620.0466751.002101
hsa_circ_00623890.041191.00164
hsa_circ_00053940.025027−1.12294
hsa_circ_00051390.035675−1.16601
hsa_circ_00923670.048694−1.17796
hsa_circ_00760920.028292−1.18494
hsa_circ_00006620.03011−1.23507
hsa_circ_00617490.01061−1.24893
hsa_circ_00016440.011159−1.29063
hsa_circ_00024040.021288−1.29969
hsa_circ_00019360.006073−1.32711
hsa_circ_00009790.027511−1.32785
hsa_circ_00294260.000788−1.32894
hsa_circ_00002530.015088−1.3675
hsa_circ_00305690.006596−1.37248
hsa_circ_00031620.001318−1.44889
hsa_circ_00310270.044118−1.49911
hsa_circ_00432560.029359−1.51818
hsa_circ_00193900.007575−1.58187
hsa_circ_00075180.000301−1.76043
hsa_circ_00492710.037986−1.78724
hsa_circ_00767980.000399−1.81001
hsa_circ_00152780.013596−1.81839
Survival analysis and biological functions of the four differentially-expressed circRNAs

To further explore the characteristics of three differentially-expressed circRNAs, two sets of primers for each circRNA were first designed. Divergent primers were expected to amplify the circular forms of the RNAs, whereas convergent primers were expected to amplify the linear forms of RNAs. Using cDNA and genomic DNA (gDNA) from LAC tissues as templates, all three of the differentially-expressed circRNAs were amplified by the divergent primers in cDNA, but no amplification products were observed in the gDNA samples (Fig. 2A). All three linear forms of the circRNAs were amplified in both cDNA and gDNA samples. The sequences of the PCR products from all three circRNAs were verified by sequencing (Fig. 2B). By using RT-qPCR, it was verified further that all three of these circRNAs were resistant to RNase R, whilst the levels of their corresponding linear forms were significantly reduced after RNase R treatment (Fig. 2C).

Subsequently, the expression of these three circRNAs were observed in the 60 LAC tissue samples. In addition, higher expression levels of hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666 indicated poor prognosis in patients with LAC (Fig. 2D). To further explore the biological functions of these differentially-expressed circRNAs, their expression was then measured in the LAC cell lines (Fig. 2E). siRNA-mediated knockdown of these circRNAs was found to suppress proliferation, migration and invasion in LAC cells (Fig. 2F-I). Taken together, these results suggest that hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666 can serve oncogenic roles in LAC progression and have potential as biomarkers and therapeutic targets for LAC.

Identification of miRNAs that can interact with the circRNAs

Accumulating evidence has demonstrated that several circRNAs derived from exons can exert biological functions by acting as ‘sponges’ or ‘decoys’ to sequester miRNAs. To assess if these three aforementioned circRNAs performed similar functions in LAC, miRNAs that can potentially interact with these three circRNAs were screened using the CircInteractome database. In total, eight miRNAs (miR-520f, miR-1261, miR-1270, miR-620, miR-188-3p, miR-516b, miR-940 and miR-661) with context score percentiles ≥98 were selected (Fig. 3A). The predicted binding sites between the circRNAs and miRNAs of interest are shown in Fig. 3B.

Construction of the circRNA-miRNA-mRNA regulatory network in LAC

A total of 7,829 target genes of the eight aforementioned miRNAs were obtained from the miRWalk software. Additionally, 795 upregulated genes in LAC were obtained from TCGA, according to the thresholds of false discovery rate value <0.01 and Log2|FC|>3 (Fig. 4A). By overlapping the predicted target genes and the upregulated genes in LAC, 232 target genes that may serve key roles in LAC were identified (Fig. 4B). From this, a circRNA-miRNA-mRNA network was constructed by integrating the circRNA-miRNA interactions and miRNA-mRNA interactions (Fig. 4C), which provided a preliminary insight into the links among the three differentially-expressed circRNAs (hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666), their eight suspected target miRNAs (miR-520f, miR-1261, miR-1270, miR-620, miR-188-3p, miR-516b, miR-940 and miR-661) and the 232 mRNAs.

PPI network construction and module selection

After removing the unconnected nodes, a PPI network consisting of 158 nodes and 287 edges was constructed to view the interaction among the 232 target genes (Fig. 5A). Subsequently, a pivotal module of six hub genes, kinesin family member (KIF) 2C, KIF18B, maternal embryonic leucine zipper kinase (MELK), baculoviral IAP repeat-containing 5 (BIRC5), polo-like kinase 1 (PLK1) and cytoskeleton-associated protein 2-like (CKAP2L), were identified from the PPI network using the CytoHubba plugin in Cytoscape (Fig. 5B).

Survival analysis of the six hub genes

The six hub genes in the PPI network were next evaluated for their expression levels and prognostic value using the GEPIA database. As shown in Fig. 6A-F, the expression of these six hub genes in LAC tissues was significantly higher compared with that in the normal tissues. All of these six hub genes exhibited their potential in the prediction of overall survival based on their expression. High expression levels of each hub gene were associated with poorer overall survival in patients with LAC (Fig. 6A-F).

A circRNA-miRNA-hub gene sub-network was then built to delineate the links among the differentially-expressed circRNAs, target miRNAs and hub genes (Fig. 6G). Of note, the hsa_circ_0006220/hsa-miR-520f/KIF2C, has_circ_0001666/hsa-miR-940/KIF18B, has_circ_0001666/hsamiR-940/CKAP2L, has_circ_0001666/hsa-miR-661/CKAP2L, has_circ_0001666/hsa-miR-188-3p/PLK1, has_circ_0072088/hsamiR-1270/MELK and has_circ_0072088-hsa-miR-1261-BIRC5 regulatory axes were found from this network. Taken together, these results suggest that hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666 may serve oncogenic roles by regulating the expression of these six hub genes.

Validation in clinical samples

To further validate the findings, immunohistochemistry was performed in the 60 LAC samples. The statistical results showed that high expression levels of KIF2C, KIF18B, MELK, BIRC5, PLK1 and CKAP2L were associated with the poorer survival in patients with LAC (Fig. 7A and B), which was consistent with the results of GEPIA database analysis. In addition, the expression of hsa_circ_0006220 was positively correlated with the expression of KIF2C, whereas the expression of has_circ_0001666 was positively correlated with the expression of KIF18B, MELK and BIRC5. The expression of has_circ_0072088 was positively correlated with the expression of PLK1 and CKAP2L (Fig. 7C).

Discussion

Accumulating evidence indicates that circRNAs are stable, abundant and highly conserved in eukaryotic cells, with high disease specificity (1820). In lung cancer, an increasing number of circRNAs, including circ-calcium/calmodulin dependent protein kinase IIα (21), circ-solute carrier family 25 member 16 (22), circ-SATB homeobox 2 (23), circ-ATP-binding cassette subfamily B member 10 (24), circHMCU (25) and circ-phosphatidylinositol-4-phosphate 5-kinase type 1α (26), have been reported to serve regulatory roles in disease progression. These previous findings suggest that circRNAs are suitable markers for guiding clinical diagnosis and therapy. However, a large number of potentially beneficial circRNAs remain undiscovered.

In the present study, a GEO dataset was analyzed to investigate the differentially-expressed circRNAs in LAC, which found four circRNAs (hsa_circ_0043278, hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666) to be highly expressed in LAC tissues. These candidate circRNAs may be involved in LAC pathogenesis and can serve as the biomarkers for targeted therapy. By analyzing their expression in 20 paired LAC tissues and adjacent normal lung tissues, it was found that the expression levels of hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666 were markedly higher in LAC tissues, where high expression levels of these three circRNAs indicated poorer prognosis in patients with LAC. In addition, siRNA-mediated knockdown of these three circRNAs suppressed the proliferation, migration, and invasion of LAC cell lines, suggesting their potential oncogenic functions in LAC. These results suggest that hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666 can serve oncogenic roles in LAC cells.

As conserved endogenous RNAs, circRNAs can sponge miRNAs and serve key roles in regulating the expression of the target genes of miRNAs. To explore the sponged miRNAs of these three aforementioned circRNAs, the CircInteractome online database (27,28) was used to predict the miRNAs that have the ability to bind these circRNAs. In total, eight miRNAs (miR-520f, miR-1261, miR-1270, miR-620, miR-188-3p, miR-516b, miR-940 and miR-661) were determined to have high potential to bind these circRNAs. Subsequently, potential mRNAs targeted by these miRNAs were predicted using miRWalk before a ceRNA network was constructed.

To verify the action of this ceRNA network, a PPI network was next constructed, which yielded six hub genes, namely KIF2C, KIF18B, MELK, BIRC5, PLK1 and CKAP2L. The oncogenic roles of these six genes in cancer have also been demonstrated by previous studies (2939). In the present study, the prognostic value of these hub genes was also analyzed in LAC based on the GEPIA database. High expression levels of each hub gene were associated with a poorer overall survival in patients with LAC. The in vivo experiments showed that high expression levels of KIF2C, KIF18B, MELK, BIRC5, PLK1 and CKAP2L were associated with poorer survival in patients with LAC, which was in accordance with the results from the GEPIA database. In addition, the expression of hsa_circ_0006220 was positively correlated with the expression of KIF2C, whereas the expression of has_circ_0001666 was positively correlated with the expression of KIF18B, MELK and BIRC5. The expression of has_circ_0072088 was positively correlated with the expression of PLK1 and CKAP2L. These results suggested that hsa_circ_0006220, hsa_circ_0072088 and hsa_circ_0001666 may serve oncogenic roles in LAC by regulating the expression of these hub genes. However, further studies are required to confirm that the circRNAs affect the expression of the proteins encoded via the above six miRNAs.

As there is no public circRNA data in TCGA database, the GEO database was chosen to screen for the target circRNAs in LAC, which provided guidance for the follow-up validation and experiments. The retrospective clinical study and biological experiments confirmed the oncogenic function of these selected circRNAs. The ceRNA and PPI networks were then constructed using bioinformatic analysis, using which the hub genes were revealed and partially explained the molecular mechanism underlying the aforementioned circRNAs. However, further experiments are required to validate these findings. Conclusively, through the bioinformatic analysis and retrospective clinical study, results from the present study provided novel circRNAs biomarkers and therapeutic targets for LAC. Further experiments in animals would be useful to validate these findings.

Supplementary Material

Supporting Data

Acknowledgements

The authors would like to thank Dr Qianglin Duan, a skilled English proofreader from Tongji University, for assistance with the revision of this paper.

Funding

This study was supported by the Financial Supporting Program of Hebei Province [grant nos. (2014)1257 and (2016)361006].

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

BS and MS contributed to the conception and design of the work. YZ, FC, SL, LM, LG performed the experiments. FL and HZ analyzed the data and performed the bioinformatical analysis. YZ and BS confirm the authenticity of all the raw data. All authors read and approved the final manuscript.

Ethical approval and consent to participate

The human tissues were obtained with informed consent and the present study was approved by The Clinical Research Ethics Committee of The Fourth hospital of Hebei Medical University (approval no. 2021KY157).

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China, 2015. CA Cancer J Clin. 66:115–132. 2016. View Article : Google Scholar : PubMed/NCBI

2 

Siegel RL, Miller KD and Jemal A: Cancer statistics, 2019. CA Cancer J Clin. 69:7–34. 2019. View Article : Google Scholar : PubMed/NCBI

3 

Lin JJ, Cardarella S, Lydon CA, Dahlberg SE, Jackman DM, Jänne PA and Johnson BE: Five-year survival in EGFR-mutant metastatic lung adenocarcinoma treated with EGFR-TKIs. J Thorac Oncol. 11:556–565. 2016. View Article : Google Scholar : PubMed/NCBI

4 

Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, Maier L, Mackowiak SD, Gregersen LH, Munschauer M, et al: Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 495:333–338. 2013. View Article : Google Scholar : PubMed/NCBI

5 

He J, Xie Q, Xu H, Li J and Li Y: Circular RNAs and cancer. Cancer Lett. 396:138–144. 2017. View Article : Google Scholar : PubMed/NCBI

6 

Wu G, Zhou W, Pan X, Sun Z, Sun Y, Xu H, Shi P, Li J, Gao L and Tian X: Circular RNA profiling reveals exosomal circ_0006156 as a novel biomarker in papillary thyroid cancer. Mol Ther Nucleic Acids. 19:1134–1144. 2020. View Article : Google Scholar : PubMed/NCBI

7 

Fan L, Wang J, Cao Q, Ding X and Li B: Aberrant miR-1246 expression promotes radioresistance in non-small cell lung cancer: A potential prognostic biomarker and radiotherapy sensitization target. Am J Cancer Res. 10:314–335. 2020.PubMed/NCBI

8 

Lei B, Tian Z, Fan W and Ni B: Circular RNA: A novel biomarker and therapeutic target for human cancers. Int J Med Sci. 16:292–301. 2019. View Article : Google Scholar : PubMed/NCBI

9 

Cheng X, Qiu J, Wang S, Yang Y, Guo M, Wang D, Luo Q and Xu L: Comprehensive circular RNA profiling identifies CircFAM120A as a new biomarker of hypoxic lung adenocarcinoma. Ann Transl Med. 7:4422019. View Article : Google Scholar : PubMed/NCBI

10 

Chen F, Huang C, Wu Q, Jiang L, Chen S and Chen L: Circular RNAs expression profiles in plasma exosomes from early-stage lung adenocarcinoma and the potential biomarkers. J Cell Biochem. 121:2525–2533. 2020. View Article : Google Scholar : PubMed/NCBI

11 

Wang C, Tan S, Liu WR, Lei Q, Qiao W, Wu Y, Liu X, Cheng W, Wei YQ, Peng Y and Li W: RNA-Seq profiling of circular RNA in human lung adenocarcinoma and squamous cell carcinoma. Mol Cancer. 18:1342019. View Article : Google Scholar : PubMed/NCBI

12 

Liu XX, Yang YE, Liu X, Wang X, Liu XX and Mai J: A two-circular RNA signature as a noninvasive diagnostic biomarker for lung adenocarcinoma. J Transl Med. 17:502019. View Article : Google Scholar : PubMed/NCBI

13 

Qiu M, Xia W, Chen R, Wang S, Xu Y, Ma Z, Xu W, Zhang E, Wang J, Fang T, et al: The Circular RNA circPRKCI promotes tumor growth in lung adenocarcinoma. Cancer Res. 78:2839–2851. 2018. View Article : Google Scholar : PubMed/NCBI

14 

Wang L, Liang Y, Mao Q, Xia W, Chen B, Shen H, Xu L, Jiang F and Dong G: Circular RNA circCRIM1 inhibits invasion and metastasis in lung adenocarcinoma through the microRNA (miR)-182/miR-93-leukemia inhibitory factor receptor pathway. Cancer Sci. 110:2960–2972. 2019. View Article : Google Scholar : PubMed/NCBI

15 

Yao Y, Hua Q, Zhou Y and Shen H: CircRNA has_circ_0001946 promotes cell growth in lung adenocarcinoma by regulating miR-135a-5p/SIRT1 axis and activating Wnt/β-catenin signaling pathway. Biomed Pharmacother. 111:1367–1375. 2019. View Article : Google Scholar : PubMed/NCBI

16 

Zuo Y, Shen W, Wang C, Niu N and Pu J: Circular RNA Circ-ZNF609 promotes lung adenocarcinoma proliferation by modulating miR-1224-3p/ETV1 signaling. Cancer Manag Res. 12:2471–2479. 2020. View Article : Google Scholar : PubMed/NCBI

17 

Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) method. Methods. 25:402–408. 2001. View Article : Google Scholar : PubMed/NCBI

18 

Kristensen LS, Andersen MS, Stagsted LVW, Ebbesen KK, Hansen TB and Kjems J: The biogenesis, biology and characterization of circular RNAs. Nat Rev Genet. 20:675–691. 2019. View Article : Google Scholar : PubMed/NCBI

19 

Patop IL, Wüst S and Kadener S: Past, present, and future of circRNAs. EMBO J. 38:e1008362019. View Article : Google Scholar : PubMed/NCBI

20 

Bach DH, Lee SK and Sood AK: Circular RNAs in cancer. Mol Ther Nucleic Acids. 16:118–129. 2019. View Article : Google Scholar : PubMed/NCBI

21 

Du J, Zhang G, Qiu H, Yu H and Yuan W: The Novel Circular RNA circ-CAMK2A enhances lung adenocarcinoma metastasis by regulating the miR-615-5p/fibronectin 1 pathway. Cell Mol Biol Lett. 24:722019. View Article : Google Scholar : PubMed/NCBI

22 

Shangguan H, Feng H, Lv D, Wang J, Tian T and Wang X: Circular RNA circSLC25A16 contributes to the glycolysis of non-small-cell lung cancer through epigenetic modification. Cell Death Dis. 11:4372020. View Article : Google Scholar : PubMed/NCBI

23 

Zhang N, Nan A, Chen L, Li X, Jia Y, Qiu M, Dai X, Zhou H, Zhu J, Zhang H and Jiang Y: Circular RNA circSATB2 promotes progression of non-small cell lung cancer cells. Mol Cancer. 19:1012020. View Article : Google Scholar : View Article : Google Scholar : PubMed/NCBI

24 

Ma D, Qin Y, Huang C, Chen Y, Han Z, Zhou X and Liu H: Circular RNA ABCB10 promotes non-small cell lung cancer progression by increasing E2F5 expression through sponging miR-584-5p. Cell Cycle. 19:1611–1620. 2020. View Article : Google Scholar : PubMed/NCBI

25 

Song X, Liang Y, Sang Y, Li Y, Zhang H, Chen B, Du L, Liu Y, Wang L, Zhao W, et al: circHMCU promotes proliferation and metastasis of breast cancer by sponging the let-7 family. Mol Ther Nucleic Acids. 20:518–533. 2020. View Article : Google Scholar : PubMed/NCBI

26 

Ma Y, Cong X, Zhang Y, Yin X, Zhu Z and Xue Y: CircPIP5K1A facilitates gastric cancer progression via miR-376c-3p/ZNF146 axis. Cancer Cell Int. 20:812020. View Article : Google Scholar : PubMed/NCBI

27 

Bai S, Wu Y, Yan Y, Shao S, Zhang J, Liu J, Hui B, Liu R, Ma H, Zhang X and Ren J: Construct a circRNA/miRNA/mRNA regulatory network to explore potential pathogenesis and therapy options of clear cell renal cell carcinoma. Sci Rep. 10:136592020. View Article : Google Scholar : PubMed/NCBI

28 

Liu Y, Xu Y, Xiao F, Zhang J, Wang Y, Yao Y and Yang J: Comprehensive analysis of a circRNA-miRNA-mRNA network to reveal potential inflammation-related targets for gastric adenocarcinoma. Mediators Inflamm. 2020:94356082020. View Article : Google Scholar : PubMed/NCBI

29 

Wei S, Dai M, Zhang C, Teng K, Wang F, Li H, Sun W, Feng Z, Kang T, Guan X, et al: KIF2C: A novel link between Wnt/β-catenin and mTORC1 signaling in the pathogenesis of hepatocellular carcinoma. Protein Cell. 12:788–809. 2021. View Article : Google Scholar : PubMed/NCBI

30 

Li X, Huang W, Huang W, Wei T, Zhu W, Chen G and Zhang J: Kinesin family members KIF2C/4A/10/11/14/18B/20A/23 predict poor prognosis and promote cell proliferation in hepatocellular carcinoma. Am J Transl Res. 12:1614–1639. 2020.PubMed/NCBI

31 

Gan H, Lin L, Hu N, Yang Y, Gao Y, Pei Y, Chen K and Sun B: KIF2C exerts an oncogenic role in nonsmall cell lung cancer and is negatively regulated by miR-325-3p. Cell Biochem Funct. 37:424–431. 2019. View Article : Google Scholar : PubMed/NCBI

32 

Bai Y, Xiong L, Zhu M, Yang Z, Zhao J and Tang H: Co-expression network analysis identified KIF2C in association with progression and prognosis in lung adenocarcinoma. Cancer Biomark. 24:371–382. 2019. View Article : Google Scholar : PubMed/NCBI

33 

Gao T, Yu L, Fang Z, Liu J, Bai C, Li S, Xue R, Zhang L, Tan Z and Fan Z: KIF18B promotes tumor progression in osteosarcoma by activating β-catenin. Cancer Biol Med. 17:371–386. 2020. View Article : Google Scholar : PubMed/NCBI

34 

Chung S, Suzuki H, Miyamoto T, Takamatsu N, Tatsuguchi A, Ueda K, Kijima K, Nakamura Y and Matsuo Y: Development of an orally-administrative MELK-targeting inhibitor that suppresses the growth of various types of human cancer. Oncotarget. 3:1629–1640. 2012. View Article : Google Scholar : PubMed/NCBI

35 

Cao Y, Zhu W, Chen W, Wu J, Hou G and Li Y: Prognostic value of BIRC5 in lung adenocarcinoma lacking EGFR, KRAS, and ALK mutations by integrated bioinformatics analysis. Dis Markers. 2019:54512902019. View Article : Google Scholar : PubMed/NCBI

36 

Shin SB, Jang HR, Xu R, Won JY and Yim H: Active PLK1-driven metastasis is amplified by TGF-beta signaling that forms a positive feedback loop in non-small cell lung cancer. Oncogene. 39:767–785. 2020. View Article : Google Scholar : View Article : Google Scholar : PubMed/NCBI

37 

Nieto-Jimenez C, Galan-Moya EM, Corrales-Sanchez V, Noblejas-Lopez MDM, Burgos M, Domingo B, Montero JC, Gomez-Juarez M, Picazo-Martinez MG, Esparis-Ogando A, et al: Inhibition of the mitotic kinase PLK1 overcomes therapeutic resistance to BET inhibitors in triple negative breast cancer. Cancer Lett. 491:50–59. 2020. View Article : Google Scholar : PubMed/NCBI

38 

Wang P and He X: Oncogenic and prognostic role of CKAP2L in hepatocellular carcinoma. Int J Clin Exp Pathol. 13:923–933. 2020.PubMed/NCBI

39 

Xiong G, Li L, Chen X, Song S, Zhao Y, Cai W and Peng J: Up-regulation of CKAP2L expression promotes lung adenocarcinoma invasion and is associated with poor prognosis. Onco Targets Ther. 12:1171–1180. 2019. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

May-2022
Volume 23 Issue 5

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Zhu Y, Cao F, Liu F, Liu S, Meng L, Gu L, Zhao H, Sang M and Shan B: Identification of potential circular RNA biomarkers in lung adenocarcinoma: A bioinformatics analysis and retrospective clinical study. Oncol Lett 23: 144, 2022.
APA
Zhu, Y., Cao, F., Liu, F., Liu, S., Meng, L., Gu, L. ... Shan, B. (2022). Identification of potential circular RNA biomarkers in lung adenocarcinoma: A bioinformatics analysis and retrospective clinical study. Oncology Letters, 23, 144. https://doi.org/10.3892/ol.2022.13264
MLA
Zhu, Y., Cao, F., Liu, F., Liu, S., Meng, L., Gu, L., Zhao, H., Sang, M., Shan, B."Identification of potential circular RNA biomarkers in lung adenocarcinoma: A bioinformatics analysis and retrospective clinical study". Oncology Letters 23.5 (2022): 144.
Chicago
Zhu, Y., Cao, F., Liu, F., Liu, S., Meng, L., Gu, L., Zhao, H., Sang, M., Shan, B."Identification of potential circular RNA biomarkers in lung adenocarcinoma: A bioinformatics analysis and retrospective clinical study". Oncology Letters 23, no. 5 (2022): 144. https://doi.org/10.3892/ol.2022.13264