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Early diagnosis and prognosis of hepatocellular carcinoma based on a ceRNA array

  • Li-xin Wang , Ao-ran Kong and Hui Dong ORCID logo EMAIL logo
Published/Copyright: March 15, 2023
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Oncologie
From the journal Oncologie Volume 25 Issue 3

Abstract

Objectives

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related deaths, due to high morbidity, a low early diagnosis rate, and poor prognosis. It is essential to explore competitive endogenous RNA (ceRNA) arrays for early diagnosis and prognosis of HCC.

Methods

The original gene expression profiles of differentially expressed lncRNA, miRNA and mRNA in HCC were downloaded from TCGA database. Differentially expressed lncRNA-miRNA and miRNA-mRNA interaction pairs were extracted from miRcode and starBase, a ceRNA network was constructed, and GO annotation and KEGG pathway analyses were performed. Cox regression and Kaplan-Meier survival analysis screening identified core genes in the network associated with HCC survival, centering on miRNA, which were screened using ceRNA arrays. qRT-PCR was used to detect the expression level of key genes in clinical samples. Dual luciferase reporter gene assays were used to verify the target binding relationship among lncRNA, mRNA, and miRNA. ROC curves were used to analyze the diagnostic efficacy of the ceRNA array.

Results

A total of 8 lncRNAs, 5 miRNAs, and 21 mRNAs were used to construct a ceRNA network. Functional enrichment analysis showed that mRNAs in the ceRNA network were mainly enriched in 14 signaling pathways, especially microRNAs in cancer. Survival analysis showed that lncRNA FOXD2-AS1 and miRNA miR-9-5p were related to the prognosis of HCC, and the targeted binding relationships between mRNAs. STMN1, COL15A1, and CCNE2 and miR-9-5p from the TargetScan, starBase, miRDB, and PicTar databases were reliable. qRT-PCR showed that expression levels of FOXD2-AS1, miR-9-5p, STMN1, COL15A1, and CCNE2 were upregulated in HCC tissues. Dual luciferase reporter assays showed that FOXD2-AS1 and STMN1 had a targeted binding relationship with miR-9-5p, but not with COL15A1 or CCNE2. The area under the curve of the candidate ceRNA array (FOXD2-AS1/miR-9-5p/STMN1/COL15A1/CCNE2) was higher than that of each member and ceRNA combination (FOXD2-AS1/miR-9-5p/STMN1).

Conclusions

The candidate ceRNA array formed by FOXD2-AS1/miR-9-5p/STMN1/COL15A1/CCNE2 could be a biomarker for early diagnosis and prognosis of HCC.

Keywords: biomarker; competitive endogenous RNA array; FOXD2-AS1/miR-9-5p/STMN1/COL15A1/CCNE2 ; hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) has one of the highest incidences and mortality rates worldwide. It involves the carcinogenesis of liver parenchymal cells. In 2020, global cancer data showed 910,000 new liver cancer cases and 830,000 deaths, both of which were in the top 10 of all cancers [1]. Many factors such as infection (HBV, HCV, and parasites), eating food containing aflatoxin, and heavy drinking are related to the occurrence of liver cancer. Despite the development of effective anti-viral drugs, the incidence of HCC is increasing worldwide [2]. Although surgical resection, hepatic artery embolization, radiofrequency ablation, radiotherapy, and chemotherapy have significantly increased the overall survival (OS) of patients with HCC, the prognosis remains very poor, due to a lack of early diagnoses [3, 4]. Therefore, early diagnostic markers and therapeutic targets for HCC need to be urgently explored.

Competing endogenous RNA (ceRNA) networks combine the functions of protein coding and non-coding RNAs (ncRNAs), and their mutual communication and co-regulation have important roles in human development and disease processes [5, 6]. The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and other public databases are indispensable for bioinformatics analysis, from which researchers can download tumor gene expression profiles for weighted gene co-expression analysis to construct ceRNA networks, which provide important information for tumor marker screening and pathogenic mechanisms [7]. For example, a study found that a ceRNA network composed of three lncRNAs (LINC01106, FOXD2-AS1, and AC103702.2) and three mRNAs (CCDC34, ORC6, and Sox4) was strongly associated with the prognosis of gastric cancer [8]. Three lncRNAs (Dirc3, DNAH10OS, and GK-IT1) were screened from the constructed ceRNA network and were used for evaluation of esophageal cancer risk models [9].

Based on HCC information from the TCGA database, we established a lncRNA-miRNA-mRNA ceRNA network to explore the key genes related to the diagnosis and prognosis of HCC, and verified their expression levels and the targeted binding relationship between them in clinical tissues.

Materials and methods

Access to ceRNA network genes

The gene expression profiling information of HCC was obtained from the TCGA database, and differential gene screening was completed using the DESEQ2 package in R4.0.2 software [false discovery rate, (FDR)<0.05; | Log(fold change) |>1], mainly targeting middle lncRNA, miRNA, and mRNA.

Construction of the ceRNA network

Interactions between lncRNA, miRNA, and mRNA were analyzed using miRcode [10] and starbase [11] databases (hypergeometric testing, p<0.05; correlation testing, p<0.05). Cytoscape 3.7.2 was used to establish the ceRNA network relationship.The DAVID database was used to perform Gene Ontology and KEGG analyses (p<0.01).

Identification of targeted genes of the key miRNA

To obtain more reliable targets of core miRNA, TargetScan [12], miRDB [13], and PicTar [14] were searched to identify the intersections with starBase.

Validation by qRT-PCR in clinical tissues

Based on the results of bioinformatics analysis, one lncRNA (FOXD2-AS1), one miRNA (has-miR-9-5p), and three mRNAs (STMN1, COL15A1, CCNE2) were identified as candidate genes. Clinical tissue chips were used for gene expression analysis [chip lot numbers: cDNA-HLivH30PG02 and cDNA-HLivH087Su02 (Shanghai Outdo Biotech Co., Ltd. Shanghai, China)], containing a total of 96 cancer tissue samples and 51 normal adjacent tissue samples. The tailing reaction was used for reverse-transcription of miRNA, and the primers were universal. The primers are shown in Table 1. ACTB, encoding β-actin was used for internal reference of lncRNA and mRNA, and U6 was applied for miRNA. The experiment was conducted strictly according to the instructions of the PerfectStart™ Green qPCR SuperMix (TransGen Biotech, Beijing, China) and miRcute Plus miRNA qPCR detection kit (SYBR Green) (Tiangen, Beijing, China). Primer synthesis was completed by Sangon Biotech Co., Ltd., and the primer for miR-9-5p was provided by Tiangen Co., Ltd.

Table 1:

Primers for lncRNA and mRNA.

Gene Primer sequence (5′–3′)
FOXD2-AS1 F GGCACAGCGGAACGATTG
R TGTACCGGCTGAGGCTAG
STMN1 F TGGTAGAAATACCCAACGC
R GAGGCATCCAAACAAAGCAGT
COL15A1 F TGCGACTGAAACAAACAGGTTCA
R GAAGAAAGCACCCGGAAAAGATG
CCNE2 F GCATTATGACACCACCGAAGA
R TAGGGCAATCAATCAATCACAGC

  1. F, forward; R, reverse.

Tissue cDNA samples

All HCC tissue cDNA was provided by commercial cDNA microarrays, which were purchased from Shanghai Xinchao Biotech Co., Ltd. The batch numbers of the chips were CDNA-HLIVH30PG02 and cDNA-HLivH087Su02, which contained 96 cancer tissue samples and 51 normal adjacent tissue samples.

Dual luciferase reporter assay

A wild-type 3′-UTR and its mutants were designed for FOXD2-AS1, STMN1, COL15A1, and CCNE2 binding site sequences to miR-9-5p. The 3′-UTR and its mutants were cloned downstream of the luciferase fragment of a pMiR reporter vector, and then the constructed vector was sequenced. The vector and chemically synthesized miR-9-5p fragment (NC mimics as a control group) were co-transfected into 293T cells. After 48 h, luciferase activity was detected by using a dual-luciferase system (Promega, USA). There were two miR-9-5p binding sites in the 3′-UTR sequence of FOXD2-AS1, and therefore, the base fragments of these two binding sites were connected in series to develop the 3′-UTR and mutant of FOXD2-AS1, which was cloned into a pMiR reporter vector for analysis.

Statistical analysis

The GDCRNATools, GGPLOT2, DESEQ2, survival, glmnet, and survminer modules in R4.0.2 software were used to analyze HCC expression profile data from TCGA. Single-factor Cox regression and lasso regression analyses were employed to ensure that the model was not overfitting and all significant genes were integrated into the Cox regression model. Risk survival and the ROC curves were used to evaluate the diagnostic value of the model. Kaplan-Meier survival analysis was applied in batches for each gene in the ceRNA network to assess its hazard. The relative expression of key genes in HCC tissues was calculated via qRT-PCR with the 2⁻△△Ct method, and the difference in the relative expression between the HCC cancer group and the para-cancer group was analyzed using an independent sample t-test. The ROC curve was drawn according to qRT-PCR detection of the tissue cDNA chip. The diagnostic value of all important members, ceRNA combinations and candidate ceRNA arrays was evaluated with the AUC value. The statistical analysis and plotting were performed using IBM SPSS Statistics 25.0. and GraphPad Prism 8.

Results

LncRNA-miRNA-mRNA ceRNA in HCC

In this study, HCC transcriptome data from 424 samples (adjacent: 50 cases, cancer: 374 cases), miRNA data from 425 samples (adjacent: 50 cases, cancer: 375 cases), and related clinical information were downloaded from the TCGA database. Survival analysis and univariate analysis were performed on the clinical information of the 366 HCC patients in the TCGA database, which revealed that T stage, M stage and N stage were associated with survival of HCC patients (see Table 2).

Table 2:

Clinical correlation analysis of TCGA database baseline data.

Group N Cumulative survival rate Log-rank Univariate analysis
3 years 5 years X2 p-Value HR 95% CI p-Value
Age, years 366 1.384 0.239
≤60 175 62.1% 54.6% 1
＞60 191 62.3% 47.7% 1.235 0.868–1.758 0.240
Gender 366 1.204 0.273
Female 118 58.1% 45.3% 1
Male 248 65.1% 52.9% 0.818 0.571–1.172 0.273
Grade 366 1.275 0.735
G1 55 67.8% 55.2% 1
G2 178 62.1% 50.1% 1.183 0.699–2.003 0.531
G3 121 59.2% 48.1% 1.238 0.713–2.149 0.448
G4 TNM 12 – – 1.712 0.632–4.636 0.290
T 366 39.373 ≤0.001
T1 179 77.1% 64.6% 1
T2 95 59.4% 43.2% 1.520 0.959–2.410 0.075
T3 79 44.4% 33.4% 2.715 1.775–4.151 ≤0.001
T4 13 0 0 5.505 2.742–11.053 ≤0.001
N 366 4.680 0.096
N0 250 66.4% 58.8% 1
N1 4 33.3% - 2.111 0.517–8.627 0.298
NX 112 53.8% 27.4% 1.457 1.002–2.119 0.049
M 366 10.810 0.004
M0 264 67.4% 58.8% 1
M1 4 0 0 4.263 1.340–13.566 0.014
MX 98 52,6% 31.9% 1.547 1.058–2.261 0.024
Stage 366 34.761 ≤0.001
Ⅰ 174 77.4% 65.8% 1
Ⅱ 95 57.6% 41.9% 1.660 1.045–2.639 0.032
Ⅲ 88 42.0% 31.6% 2.922 1.919–4.450 ≤0.001
Ⅳ 9 14.3% 0 4.684 2.097–10.460 ≤0.001

A total of 136 DE-lncRNAs (104 upregulated, 32 downregulated) (Figure 1A), 128 DE-miRNAs (107 upregulated, 21 downregulated) (Figure 1B), and 2028 DE-miRNAs (1222 upregulated, 806 downregulated) (Figure 1C) were obtained using gene differential expression analysis.

Figure 1: Competing endogenous RNA-related gene screening. (A) Heat map and volcano plot of differentially expressed lncRNAs. (B) Heat map and volcano plot of differentially expressed miRNAs. (C) Heat map and volcano plot of differentially expressed mRNAs.
Figure 1:

Competing endogenous RNA-related gene screening. (A) Heat map and volcano plot of differentially expressed lncRNAs. (B) Heat map and volcano plot of differentially expressed miRNAs. (C) Heat map and volcano plot of differentially expressed mRNAs.

The differentially expressed lncRNAs (n=8), miRNAs (n=5), and prognostic mRNAs (n=21) were used to construct a ceRNA network (Figure 2A) (see Table 3).

Figure 2: CeRNA network and functional enrichment analysis. (A) Competing endogenous RNA network in HCC. (B) Results for KEGG enrichment analysis of the mRNAs in the ceRNA network.
Figure 2:

CeRNA network and functional enrichment analysis. (A) Competing endogenous RNA network in HCC. (B) Results for KEGG enrichment analysis of the mRNAs in the ceRNA network.

Table 3:

LncRNA, miRNA and mRNA that constituted the ceRNA network.

RNA type Gene ID
LncRNA AC092171.2, AC239868.2, CRNDE, PVT1, FOXD2-AS1, AL445524.1, LINC00511, AC111000.4
miRNA hsa-miR-96-5p, hsa-miR-182-5p, hsa-miR-424-5p, hsa-miR-217, hsa-miR-9-5p
mRNA ANLN, KIF23, CCNE1, RBM24, GNAL, SLC6A9, ZIC2, STMN1, CTHRC1, CBX2, DTL, EZH2, CCNE2, APLN, SNCG, GPC3, LRRC1, NRCAM, IGF2BP1, TSPAN5, COL15A1

The detailed regulatory relationships of lncRNA-miRNA and miRNA-mRNA are described in Table 4. Functional enrichment analysis of GO and KEGG showed that 21 mRNAs were mainly enriched in 14 signaling pathways, of which “microRNAs in cancer pathway” was the most important (Figure 2B).

Table 4:

Regulatory relationships between genes in ceRNA network.

lncRNA Protein-coding RNA miRNA Hypergeometric test p-Value Correlation p-Value
AC092171.2 ANLN hsa-miR-96-5p 0.000233 1.85E-30
hsa-miR-424-5p
hsa-miR-182-5p
KIF23 hsa-miR-217 0.006413 2.57E-28
hsa-miR-424-5
CCNE1 hsa-miR-424-5p 0.001179 1.37E-24
RBM24 hsa-mi R-424-5p 0.00389 0.000133
GNAL hsa-miR-424-5p 0.005745 3.40E-06
SLC6A9 hsa-miR-96-5p 0.000109 1.20E-09
hsa-miR-182-5p
AC239868.2 ZIC2 hsa-miR-96-5p 0.004116 1.15E-17
STMN1 hsa-miR-9-5p 0.007418 4.86E-21
CTHRC1 hsa-miR-9-5p 0.000589 0.0000108
CBX2 hsa-miR-96-5p 0.000126 1.97E-12
CRNDE DTL hsa-miR-217 0.006307 1.69E-09
EZH2 hsa-miR-217 0.000365 1.58E-11
CCNE2 hsa-miR-9-5p 0.003376 0.000215
PVT1 APLN hsa-miR-424-5p 0.000445 0.00073
KIF23 hsa-miR-424-5p 0.000109 1.24E-06
CTHRC1 hsa-miR-9-5p 0.00137 1.23E-13
SNCG hsa-miR-424-5p 0.000489 1.41E-19
GNAL hsa-miR-424-5p 0.003684 1.15E-08
FOXD2-AS1 STMN1 hsa-miR-9-5p 0.001944 1.01E-26
CCNE1 hsa-miR-9-5p 0.007714 8.93E-29
AL445524.1 GPC3 hsa-miR-96-5p 0.001159 1.23E-11
hsa-miR-182-5p
ANLN hsa-miR-96-5p 0.001191 0.000745
hsa-miR-182-5p
hsa-miR-217
LRRC1 hsa-miR-9-5p 0.000305 0.000682
NRCAM hsa-miR-182-5p 0.004292 0.0003
LINC00511 IGF2BP1 hsa-miR-9-5p 0.000393 9.07E-17
CTHRC1 hsa-miR-9-5p 0.000168 1.84E-19
CBX2 hsa-miR-424-5p 0.00014 9.90E-16
SNCG hsa-miR-424-5p 0.001747 2.04E-08
GNAL hsa-miR-424-5p 0.001747 1.27E-23
TSPAN5 hsa-miR-424-5p 0.000883 0.004075
AC111000.4 COL15A1 hsa-miR-9-5p 0.000909 5.40E-05

Determination of the ceRNA array

Single-factor Cox regression and lasso regression were utilized to ensure that the model was not overfitting (Figure 3A and B). Multivariate Cox regression analysis showed that the risk scoring model composed of CCNE1, EZH2, CTHRC1, CBX2, and COL15A1 and miR-9-5p had the smallest Akaike information criterion (AIC) (Figure 3C). A risk survival curve revealed that the samples with a low hazard had better survival status (Figure 3D). The AUCs of the ROC curves in the model were 0.776, 0.745 and 0.789 for 1‐year, 3‐year, and 5‐year OS, respectively (Figure 3E).

Figure 3: Construction of Cox regression model and analysis of its diagnostic value. (A) Ten‐time cross‐validation figure. (B) Lasso regression coefficient profiles of 16 genes. (C) Forest plot corresponding to multivariate Cox regression model. (D) Kaplan-Meier risk survival curve of HCC patients. (E) ROC curve assessed the diagnostic efficacy of the risk scoring model.
Figure 3:

Construction of Cox regression model and analysis of its diagnostic value. (A) Ten‐time cross‐validation figure. (B) Lasso regression coefficient profiles of 16 genes. (C) Forest plot corresponding to multivariate Cox regression model. (D) Kaplan-Meier risk survival curve of HCC patients. (E) ROC curve assessed the diagnostic efficacy of the risk scoring model.

Kaplan-Meier survival analysis suggested that lncRNA FOXD2-AS1, miRNA miR-9-5p and mRNAs STMN1, COL15A1, CCNE2, CTHRC1, LRRC1 and CCNE1 were associated with the prognosis of HCC (Figure 4a–h). To accurately identify the targeted mRNAs of miR-9-5p, the intersections from starBase (seven target genes), TargetScan (1292 target genes), miRDB (1236 target genes) and PicTar (642 target genes) were predicted (Figure 4I), which showed that COL15A1, CCNE2, and STMN1 targeted miR-9-5p in all four databases (Figure 4J). In summary, the FOXD2-AS1/miR-9-5p/STMN1/COL15A1/CCNE2 regulatory axis was established as a candidate ceRNA array related to the diagnosis and prognosis of HCC.

Figure 4: Kaplan-Meier survival analysis and identification of the ceRNA array. (A–H) Kaplan-Meier survival curves of eight genes in HCC patients. (I) Venn diagram of targeted genes of hsa-miR-9-5p. (J) Screening of the ceRNA array.
Figure 4:

Kaplan-Meier survival analysis and identification of the ceRNA array. (A–H) Kaplan-Meier survival curves of eight genes in HCC patients. (I) Venn diagram of targeted genes of hsa-miR-9-5p. (J) Screening of the ceRNA array.

Verification of the candidate ceRNA array as the HCC biomarker

The FOXD2-AS1/miR-9-5p/STMN1/COL15A1/CCNE2 array selected from the TCGA database was verified via qRT-PCR. The qRT-PCR results showed that compared with adjacent tissues, FOXD2-AS1, miR-9-5p, STMN1, COL15A1 and CCNE2 were highly expressed in cancer tissues (p<0.05) (Figure 5A). The dual luciferase reporter assay indicated that there were targeted binding relationships between FOXD2-AS1 and miR-9-5p, miR-9-5p and STMN1, but not between miR-9-5p and COL15A1, miR-9-5p or CCNE2 (Figure 5B). The ROC curve demonstrated the diagnostic efficiency of the candidate ceRNA array (FOXD2-AS1/miR-9-5p/STMN1/COL15A1/CCNE2), the ceRNA combination (FOXD2-AS1/miR-9-5p/STMN1), and each member (Figure 5C). The AUC value, sensitivity, and specificity of the ceRNA array were 0.926, 84.4%, and 93.1%, respectively, which were all higher than those of the ceRNA combination, which were 0.818, 70.1%, and 79.3% (see Table 5). The ceRNA array formed by the combination of FOXD2-AS1/miR-9-5p/STMN1 combined with COL15A1/CCNE2 could be a diagnostic and prognostic marker for HCC.

Figure 5: Verification of FOXD2-AS1/miR-9-5p/STMN1/COL15A1/CCNE2 array as a HCC marker. (A) Scatter plot corresponding to the differential expression of FOXD2-AS1, miR-9-5p, STMN1, COL15A1 and CCNE2 in HCC tissues and normal tissues. (B) Dual luciferase reporter assay results between miR-9-5p and FOXD2-AS1, miR-9-5p and STMN1, miR-9-5p and COL15A1, miR-9-5p and CCNE2. (C) ROC curves of FOXD2-AS1, miR-9-5p, STMN1, COL15A1, CCNE2, ceRNA (FOXD2-AS1/miR-9-5p/STMN1) and ceRNA array (combination). One-way ANOVA was used for comparison between the two groups. *p<0.05 vs. the control group.
Figure 5:

Verification of FOXD2-AS1/miR-9-5p/STMN1/COL15A1/CCNE2 array as a HCC marker. (A) Scatter plot corresponding to the differential expression of FOXD2-AS1, miR-9-5p, STMN1, COL15A1 and CCNE2 in HCC tissues and normal tissues. (B) Dual luciferase reporter assay results between miR-9-5p and FOXD2-AS1, miR-9-5p and STMN1, miR-9-5p and COL15A1, miR-9-5p and CCNE2. (C) ROC curves of FOXD2-AS1, miR-9-5p, STMN1, COL15A1, CCNE2, ceRNA (FOXD2-AS1/miR-9-5p/STMN1) and ceRNA array (combination). One-way ANOVA was used for comparison between the two groups. *p<0.05 vs. the control group.

Table 5:

The results of ROC curve in HCC tissues.

Group AUC Std. p-Value 95% CI Cut-off Sensibility/% Specificity/%
FOXD2-AS1 0.677 0.053 0.005 0.573–0.781 10.237 62.1% 66.2%
Has-miR-9-5p 0.633 0.054 0.025 0.527–0.739 9.616 62.0% 64.7%
STMN1 0.753 0.055 ≤0.001 0.646–0.860 9.442 65.5% 80.5%
COL15A1 0.818 0.045 ≤0.001 0.730–0.905 8.931 65.5% 83.1%
CCNE2 0.835 0.046 ≤0.001 0.744–0.926 9.336 75.9% 83.1%
ceRNA 0.818 0.044 ≤0.001 0.732–0.905 0.751 70.1% 79.3%
ceRNA array 0.926 0.030 ≤0.001 0.867–0.985 0.768 84.4% 93.1%

Discussion

HCC is characterized by hidden early clinical symptoms, rapid progression and a poor prognosis, which seriously threatens the lives of cancer patients worldwide [15]. Thus, biomarkers for early diagnosis and prognosis of HCC have become a research hotspot. Numerous ceRNAs have been identified as HCC biomarkers. For example, Dan Cao et al. downloaded HCC data from the TCGA database and constructed a ceRNA network that included lncRNA, mRNA, and miRNA, and used this model to conduct new explorations of molecular mechanisms and prognosis [16]. In recent years, the use of bioinformatics databases and ceRNA theory to explore molecular biomarkers of cancer has become a promising new development. In our study, expression of key genes of a candidate ceRNA array of 96 cancer and 51 adjacent tissues, showed that FOXD2-AS1, miR-9-5p, STMN1, COL15A1, and CCNE2 were upregulated in cancer tissues. The candidate ceRNA array (FOXD2-AS1/miR-9-5p/STMN1/COL15A1/CCNE2), has not been reported in previously constructed HCC-related ceRNA networks.

FOXD2-AS1, miR-9-5p, STMN1, COL15A1, and CCNE2 have been reported as possible molecular tumor biomarkers. The transcription length of the lncRNA FOXD2-AS1 is 2527 nucleotides, and its abnormal expression in many cancers might contribute to tumor progression through carcinogenesis, cancer cell proliferation, migration and invasion [17]. FOXD2-AS1 plays an oncogenic role in HCC and, can epigenetically silence CDKN1B by recruiting EZH2 to the CDKN1B promoter region [18], and through a ceRNA mechanism with miR-206 and annexin A2 (ANXA2) [19]. FOXD2-AS1 enhanced radiosensitivity in gastric cancer by upregulating SETD1A expression with miR-1913 sponges [20]. MiR-9-5p was found to be highly expressed in HCC and affected the transmission of upstream and downstream molecular regulatory chains as an oncogene. For example, LINC00467 mediated the occurrence of HCC through a ceRNA mechanism with miR-9-5p and peroxisome proliferator-activated receptor alpha (PPARA) [21]. MiR-9-5p was shown to promote the proliferation, invasion and migration of HCC cells by targeting estrogen receptor 1(ESR1) [22]. The miR-9-5p/FOXO1/cytoplasmic polyadenylate binding protein 3(CPEB3) feedforward loop promoted the proliferation and progression of HCC [23]. STMN1, a microtubule-binding protein, regulated the proliferation and migration of HCC cells in vitro and regulated tumor growth in vivo [24]. COL15A1(collagen XV) was highly expressed in tumor tissue of HCC and showed regularity [25]. CCNA2 functions as an oncogene by regulating cell cyclin-dependent kinase (CDK) mediated cell proliferation [26]. These studies indicate that each gene in the ceRNA array model we constructed, bears an important role in tumor progression, and their upregulated expression is consistent with our findings. The scope of targeted ncRNAs was narrowed and the accuracy improved based on ceRNA network relationships to provide specific candidate molecular biomarkers for assessing HCC prognosis.

This study found that there were targeted regulatory relationships between FOXD2-AS1 and miR-9-5p, miR-9-5p and STMN1. A report by Bi et al. showed that STMN1 is the target gene of miR-9-5p [27], The findings in this study were in accordance with this report. However, there were no targeted regulatory relationships among miR-9-5p, COL15A1 and CCNE2. Instead, this study found that the ceRNA combination (FOXD2-AS1/miR-9-5p/STMN1) had higher diagnostic ability for HCC than the individual components. Given that the AUC values of COL15A1 and CCNE2 were higher than or equal to the ceRNA combination (FOXD2-AS1/miR-9-5p/STMN1), the candidate ceRNA array with COL15A1 and CCNE2 represented the highest diagnostic value. We speculated that COL15A1 and CCNE2, selected from the ceRNA network, might have some indirect or unverified regulatory relationship with miR-9-5p in addition to the binding relationship of the 3′-UTR region. In view of this, continued exploration of the ceRNA relationships must not only focus on finding direct pathways for lncRNA-miRNA-mRNA interactions, but also focus on indirect ceRNA-distributed network genes.

Recommendations

Our research was carried out using HCC tissue. In general, biological indicators in the blood are more useful; therefore, it is suggested that more markers related to ceRNA should be explored and searched in the blood of HCC patients.

Limitations

Normally, the regulatory network of ceRNAs in cells tends to be relatively balanced, and if perturbed, this balance will be upset. However, ceRNAs form a complicated network structure composed of numerous genes including lncRNA, circRNA, pseudogenes, miRNA, and mRNA. Moreover, the role of ceRNA in vivo is also constrained by a number of factors, including subcellular location and the abundance of ceRNA components, the interaction with RNA-binding proteins, and the affinity of RNA editing and ceRNA in an endogenous cell environment. Therefore, the uncovered ceRNA mechanism often found can only represent a certain time point of the cell state. As a biomarker, ceRNAs still require substantial research.

Conclusions

In summary, the candidate ceRNA array formed by FOXD2-AS1/miR-9-5p/STMN1/COL15A1/CCNE2 could be a biomarker for early diagnosis and prognosis of HCC.

Corresponding author: Hui Dong, Institute of Medical Sciences, General Hospital of Ningxia Medical University, Yinchuan, 750004, China, E-mail:

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 82002955

Acknowledgments

The authors would like to thank all study participants who were enrolled in this study. We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

  1. Research funding: The study was supported by the Ningxia Key R&D Program (2021BEG03065) and the National Natural Science Foundation of China (82002955).

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Ethical approval: This project was approved by the Ethics Committee of the Ningxia Medical University General Hospital, Ethics No. 2020663.

  4. Availability of Data and Materials: The datasets used and/or analyzed in the present study are available from the corresponding author on reasonable request.

  5. Author contribution: The authors confirm contribution to the paper as follows: study conception and design: Hui Dong. Author; data collection, analysis and interpretation of results: Ao-Ran Kong. Author, Li-xin Wang. Author; draft manuscript preparation: Li-xin Wang. Author. All authors reviewed the results and approved the final version of the manuscript.

  6. Competing interests: The authors declare that they have no conflicts of interest to report regarding the present study.

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Received: 2022-11-14
Accepted: 2023-03-02
Published Online: 2023-03-15

© 2023 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Experience of patients with metastatic breast cancer in France: results of the 2021 RÉALITÉS survey and comparison with 2015 results
  4. An evaluation of cancer aging research group (CARG) score to predict chemotherapy toxicity in older Iranian patients with cancer
  5. Anemarrhenasaponin I suppresses ovarian cancer progression via inhibition of SHH signaling pathway
  6. Early diagnosis and prognosis of hepatocellular carcinoma based on a ceRNA array
  7. Treatment with camrelizumab plus tyrosine kinase inhibitors with or without TACE for intermediate-advanced hepatocellular carcinoma: a clinical efficacy and safety study
  8. CISD2 protects against Erastin induced hepatocellular carcinoma ferroptosis by upregulating FSP1
  9. Screening and biomarker assessment of ferroptosis genes FLT3 and ALOX5 in lung adenocarcinoma
  10. Anoikis-related gene signature as novel prognostic biomarker to predict immunotherapy with bladder urothelial carcinoma
  11. Effects of stress response induced by laparoscopic colectomy and laparotomy on TLR-mediated innate immune responses in colon cancer patients
  12. CSF-1R promotes vasculogenic mimicry via epithelial-mesenchymal transition in nasopharyngeal carcinoma cells
  13. Case Report
  14. Anaplastic extramedullary plasmacytoma resistant to novel therapies: a case report
  15. Miscellaneous
  16. A summary of the second MACR international scientific conference (2nd MSC)
https://doi.org/10.1515/oncologie-2023-0088
Keywords for this article
biomarker; competitive endogenous RNA array; FOXD2-AS1/miR-9-5p/STMN1/COL15A1/CCNE2; hepatocellular carcinoma
Creative Commons
BY 4.0

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Experience of patients with metastatic breast cancer in France: results of the 2021 RÉALITÉS survey and comparison with 2015 results
  4. An evaluation of cancer aging research group (CARG) score to predict chemotherapy toxicity in older Iranian patients with cancer
  5. Anemarrhenasaponin I suppresses ovarian cancer progression via inhibition of SHH signaling pathway
  6. Early diagnosis and prognosis of hepatocellular carcinoma based on a ceRNA array
  7. Treatment with camrelizumab plus tyrosine kinase inhibitors with or without TACE for intermediate-advanced hepatocellular carcinoma: a clinical efficacy and safety study
  8. CISD2 protects against Erastin induced hepatocellular carcinoma ferroptosis by upregulating FSP1
  9. Screening and biomarker assessment of ferroptosis genes FLT3 and ALOX5 in lung adenocarcinoma
  10. Anoikis-related gene signature as novel prognostic biomarker to predict immunotherapy with bladder urothelial carcinoma
  11. Effects of stress response induced by laparoscopic colectomy and laparotomy on TLR-mediated innate immune responses in colon cancer patients
  12. CSF-1R promotes vasculogenic mimicry via epithelial-mesenchymal transition in nasopharyngeal carcinoma cells
  13. Case Report
  14. Anaplastic extramedullary plasmacytoma resistant to novel therapies: a case report
  15. Miscellaneous
  16. A summary of the second MACR international scientific conference (2nd MSC)
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