Abstract
Background
Long noncoding ribonucleic acid (lncRNA) promoter methylation is
closely related to the occurrence and development of hepatocellular
carcinoma (HCC). Thus, we aim to screen and verify the lncRNA promoter
methylation sites associated with overall survival (OS), vascular
invasion, pathological grade, and clinical stage in HCC.
Methods
Methylation-related data including clinical characteristic,
transcriptome, methylation, and messenger RNA (mRNA) expression were
taken from the Cancer Genome Atlas (TCGA) database. The OS, vascular
invasion, pathological grade, and clinical stage-related lncRNA
promoter methylation models were developed by the least absolute
shrinkage and selection operator (LASSO) algorithm based on the lncRNA
promoter methylation sites screened via R software. The Kaplan–Meier
analysis, the area under the receiver operating characteristic (ROC)
curve (AUC), the calibration curve (C-index) were performed to evaluate
the performance of these models. Finally, the methylation-specific
polymerase chain reaction (MS-PCR) was performed to verify the accuracy
of these models based on 146 HCC tissues from our hospital.
Results
A total of 10 methylation sites were included in the OS-related lncRNA
promoter methylation model that could effectively divide HCC patients
into high-risk and low-risk groups (P < 0.0001) via survival analysis.
COX univariable and multivariable regression analysis found that the
OS-related model (P < 0.001, 95% CI 1.378–2.942) and T stage
(P < 0.001, 95% CI 1.490–3.418) were independent risk factors affecting
OS in HCC patients. The vascular invasion-related model contained 8
methylation sites with its AUC value of 0.657; the pathological
grade-related model contained 22 methylation sites with its AUC value
of 0.797; the clinical stage-related model contained 13 methylation
sites with its AUC of 0.724. Target genes corresponded to vascular
invasion-related lncRNA promoter methylation sites were involved in
many kinds of biological processes in HCC such as PI3K-Akt signaling
pathway. The accuracy of the vascular invasion-related model was
consistent with our bioinformatics conclusion after being verified via
MS-PCR.
Conclusion
The lncRNA promoter methylation sites are closely correlated with the
process of HCC and can be utilized to improve the therapy and prognosis
of HCC.
Keywords: LncRNA, Hepatocellular carcinoma, Bioinformatics, Promoter
methylation, Overall survival, Vascular invasion
Background
HCC is the most common type of primary liver cancer (PLC) [[41]1].
Because of the poor diagnostic approaches and the high recurrence and
metastasis rates, the 5-year survival rate is still at a low level
[[42]2, [43]3]. Thus, it is extremely important to identify biomarkers
that are specific and sensitive for HCC [[44]4].
With the breakthrough of high-throughput sequencing technology, the
biological functions of noncoding RNAs are being discovered gradually
[[45]5], and their abnormal functioning can lead to the occurrence of
neoplasms [[46]6]. Among ncRNAs, lncRNAs play a critical role in the
occurrence and development of HCC, and they are involved in
proliferation, differentiation, metastasis, invasion, apoptosis, and
metabolism [[47]7] and have become noval field of cancer biology in
recent years [[48]8]. Some of the lncRNAs that have been reported to be
associated with HCC so far include HULC [[49]9], HOTAIR [[50]10],
MALAT1 [[51]11], H19 [[52]12], and so on. Additionally, lncRNAs found
in serum have been demonstrated to be potential blood-based noninvasive
markers for clinical and therapeutic targets of HCC [[53]13]. Xiao et
al. [[54]14] identified that lncRNAs associated with prognosis could be
used as biomarkers for predicting the OS of HCC patients.
Furthermore, the common patterns of lncRNA regulation of cellular
physiological are as follows: lncRNA regulates the target gene by
affecting gene epigenetics such as the promoter methylation [[55]15];
and lncRNA competes with miRNA for its target RNA, making the miRNA
unable to affect the function of the target mRNA [[56]16, [57]17].
Recent studies have found that the expression levels of CDKN2A, HHIP,
PTGR1, TMEM106A, MT1M, MT1E, and CPS1 in HCC tissues are significantly
reduced, and this is caused by its promoter region methylation. These
genes are involved in various processes of HCC [[58]18]. Another study
found that the expression of ZEB1-AS1 is significantly increased
because its promoter is hypomethylated, which leads to patients with
these manifestations having a poor prognosis [[59]19].
Based on the significance of promoter methylation for HCC-related
lncRNA expression regulation and molecular mechanisms, we utilized
bioinformatics methods to screen lncRNA promoter methylation sites that
were associated with the occurrence and development of HCC. Then, a
total of 146 samples of HCC tissues resected from HCC patients at our
hospital were utilized to verify the methylation degree of the
prescreened lncRNA promoter methylation sites and verify the accuracy
of the models constructed above, which was to determine the
HCC-specific lncRNA promoter methylation sites.
Materials and methods
Research object
The clinical characteristic, transcriptome, methylation, and mRNA data
were taken from the TCGA database. The exclusion criteria were as
follows: histopathological diagnosis in not HCC; and sample data are
incomplete. A total of 367 cases were eligible for this study.
Additionally, we collected 146 HCC tissues with their clinical
characteristics from the Central Laboratory Human Specimen Library from
HCC patients who underwent surgical therapy between June 2017 and April
2018 at our hospital. The collection of the 146 HCC samples was
approved by the Central Laboratory Human Specimen Library. This study
was approved by the Review of Ethics Committee in Clinical Research
(ECCR) of the First Affiliated Hospital of Wenzhou Medical University
according to the Regulations and Rules of “Ethical Reviews for
Biomedical Research Involving Human Subjects” (2016) of the National
Health Commission of PRC, “Declaration of Helsinki” of WMA, and
“International Ethical Guidelines for Human Biomedical Research” of
CIOMS.
Screening of the lncRNA promoter methylation sites
The methylation sites related to lncRNA were screened from lncRNA
promoter region located within 2 kb upstream of transcription start
site (TSS) according to the annotation of methylation sites by using a
HumanMethylation450 chip. After analyzing the beta values normalized by
the methylation chip data, the methylation sites with a significant
difference between HCC and adjacent tissues and an absolute value of
the beta difference greater than 0.1 were selected. The final
methylation sites with a significant negative correlation between the
beta value and lncRNA expression were screened for further analyzing.
Cluster analysis and heatmap production
The beta values of each lncRNA promoter methylation site screened in
the previous experiments were normalized by Z-scores. Then, R software
was utilized to construct a heatmap with the “pheatmap” package.
LASSO regression to develop the lncRNA promoter methylation models
We utilized the “glmnet algorithm” package in R software to establish
LASSO-Logistic and LASSO-COX regression classification models [[60]20].
Construction of lncRNA promoter methylation models by LASSO-COX
algorithm were used for evaluating the OS of HCC patients; construction
of lncRNA promoter methylation models for evaluating vascular invasion,
pathological grade, and clinical stage used the LASSO-Logistic
algorithm.
Evaluation and analysis of the lncRNA promoter methylation models
The ROC curves constructed by MedCalc (version 14.0) were used to
evaluate the accuracy of the different lncRNA promoter methylation
models associated with vascular invasion, pathological grade, and
clinical stage. A time-dependent ROC (tdROC) analysis performed by the
SurvivalROC program in R software was used to evaluate the accuracy of
the OS-related lncRNA promoter methylation model. The recognition
ability of the ROC curves and the tdROC curves were evaluated by AUC
values. COX regression analysis was performed to determine whether the
OS-related lncRNA promoter methylation model was a separate risk factor
for predicting the OS of HCC patients. The Kaplan–Meier survival
analysis of the OS-related lncRNA promoter methylation model was
performed to divide HCC patients with the following categories of OS,
tumor-free status, pathological grade, clinical stage, age (the
boundary of 65 years), and sex into high-risk and low-risk groups.
The functional analysis of lncRNA corresponding to vascular invasion-related
lncRNA promoter methylation sites
Co-expression analysis between lncRNA and mRNA was utilized to screen
for mRNAs positively associated with lncRNA for functional analysis.
The function of the lncRNA was analyzed by utilizing “clusterProfiler”
package in R software for KEGG pathway and GO analysis, and revealed
differentially expressed genes involved in tumor-associated signaling
pathways.
Genomic DNA extraction and bisulfite conversion assay in HCC tissues
A TIANamp Genomic DNA Kit (TIANGEN, Beijing, China) was utilized to
extract total genomic DNA from the HCC tissues. Then, an EZ DNA
Methylation-Gold Kit (ZYMO Research, USA) was utilized to perform
bisulfite conversion with a certain mass of DNA (500 ng) for subsequent
methylation-specific PCR (MSP).
Methylation-specific PCR assay
After extraction of 146 DNA samples and bisulfite conversion, real-time
quantitative PCR (qRT-PCR) experiments were performed by utilizing a
FAST qPCR Master Mix (2×) Kit (KAPA Biosystems, Wilmington,
Massachusetts, USA) to detect the methylation degree of HCC tissues.
First, a total of 146 DNA samples were divided into two groups, the
vascular invasion group (n[1] = 66) and the nonvascular invasion group
(n[2] = 80), based on their pathological results. Then, DNA samples
with its reaction system solution used one set of 96-well PCR plates
(Applied Biosystems, Thermo Fisher Scientific, USA), which were read on
a 7500 FAST Real-Time Fluorescent PCR System (Applied Biosystems,
Thermo Fisher Scientific, USA). The primers used here were designed and
synthesized by Invitrogen (Thermo Fisher Scientific, USA), and their
sequences are shown in Table [61]1. The results were analyzed by the
2^−ΔΔCt method.
Table 1.
List of the methylation-specific PCR primer sequence
CG sites Sequence
cg11201447
Forward AATTTGATATAGTTTTGTGGTTATAGC
Reverse AATTCTATATTTATCCTTCTACAACTTCC
cg16186435
Forward TTTTTAGTTTTTGGGTGGGGAC
Reverse CCACTACTACAATCACTCCATACT
cg16201808
Forward GGCGTTAGAGTGGATATTGC
Reverse ATATACCTTTTACCTTCTACCATAATC
cg20535723
Forward AGTTAGTGGGGAGTGAGGTC
Reverse CCAATCTTACAACTTTCTAAAATAACAAAT
cg03209812
Forward GTTTGTGGTAGAAAAATCGAGTTTAAGT
Reverse TATAATAACGCTTCCCCTCTCCTAA
cg14743534
Forward TAGCGGTGGGTGGGGTCG
Reverse AAACTTCATCACCAAACTCGTAAACAT
ACTB
Forward TGGTGATGGAGGAGGTTTAGTAAGT
Reverse AACCAATAAAACCTACTCCTCCCTTAA
[62]Open in a new tab
Statistical analysis
The data analysis was completed by utilizing R version 3.5.3 software
(University of Science and Technology of China). For the
methylation-specific PCR assay, we adjusted the fluorescence threshold
to 0.56 to normalize all of the samples. Additionally, a P value less
than 0.05 was considered to have statistical significance.
Results
Baselines clinical characteristics of the HCC samples
Among 367 cases of HCC patients in the TCGA database, 249 patients were
men (67.8%) and 118 patients were women (32.2%); the average age of the
patients was 59.6 ± 13.3 years old; 131 patients (35.7%) died, and 236
patients survived; and 197 patients (53.7%) currently had a tumor-free
status. The baseline of the 146 HCC patients’ clinical characteristics
were shown in Table [63]2. Among them, 50 patients were older than
65 years (34.2%), and the majority of patients were men (87.0%). A
total of 66 HCC tissues had vascular invasion according to pathological
grade, and the majority of the HCC tissues were at pathological grade
II.
Table 2.
Baseline of clinical characteristic of HCC patients
Characteristic Patients (%)
All patients (n = 146)
Age
< 65 96 (65.8)
≥ 65 50 (34.2)
Gender
Male 127 (87.0)
Female 19 (13.0)
Pathological grade
I 19 (13.0)
II 77 (52.7)
III 26 (17.8)
IV 24 (16.5)
Vascular infiltration
Positive 66 (45.2)
Negative 80 (54.8)
[64]Open in a new tab
All values are presented as quantity and percentage of cases
Development of the OS-related lncRNA promoter methylation model
There were 112 methylation sites identified with a significant negative
correlation between the beta values and the expression of lncRNA. To
visualize the results above, a heatmap was constructed and was shown in
Fig. [65]1. The LASSO-COX algorithm was performed to select the
variables, determine the coefficients, and finally derive the
OS-related lncRNA promoter methylation model (Fig. [66]2a, b). In
Fig. [67]2a, the best lambda value is at the lowest point of the red
curve (i.e., at the dotted line) with 10 lncRNA promoter methylation
sites.
Fig. 1.
[68]Fig. 1
[69]Open in a new tab
Differential expression heatmap of lncRNA promoter methylation sites
between HCC cancer tissues and adjacent tissues
Fig. 2.
[70]Fig. 2
[71]Open in a new tab
Development and evaluation of the OS-related lncRNA promoter
methylation model. a OS-related lncRNA promoter methylation sites were
screened by LASSO-COX method. b The coefficients of each methylation
sites were determined by LASSO-COX method. c The calibration curve of
predicted 1-year OS of HCC patients by OS-related lncRNA promoter
methylation model. d The calibration curve of predicted 3-year OS of
HCC patients by OS-related lncRNA promoter methylation model. e The
calibration curve of predicted 5-year OS of HCC patients by OS-related
lncRNA promoter methylation model. f ROC curve of predicted 1-, 3-, and
5-year OS of HCC patients by OS-related lncRNA promoter methylation
model. g The Kaplan–Meier analysis of OS-related lncRNA promoter
methylation model for HCC patients from the TCGA database
The OS-related lncRNA promoter methylation model was constructed as
follows:
[MATH: 0.032×E_cg
09293786+0.017×E_cg11988604-0.055×E_cg13698168
-0.001×E_cg14360448-0.074×E_cg14743534
-0.054×E_cg22131172
-0.085×E_cg22790835-0.108×E_cg23082281-0.005×E_cg26703507-0.027×E_cg27005794.E_cg=βvaluethe degree of lncRNA promoter
methylation.<
mtd columnalign="right">
:MATH]
Evaluation of the OS-related lncRNA promoter methylation model
In Fig. [72]2c–e, the calibration curves suggested that the OS-related
model possesses a certain accuracy better than random guessing, with
their C-indexes for predicted 1-, 3-, and 5-year OS were 0.675 (95% CI
0.605–0.745), 0.653 (95% CI 0.596–0.709), and 0.651 (95% CI
0.597–0.706), respectively. The results of the td-AUC values of the
td-ROC curve for the predicted 1-, 3-, and 5-year OS were 0.709 (95% CI
0.635–0.784, sensitivity: 67.40%, specificity: 63.08%, accuracy:
63.66%), 0.637 (95% CI 0.557–0.716, sensitivity: 68.35%, specificity:
56.38%, accuracy: 59.84%), and 0.679 (95% CI 0.585–0.773, sensitivity:
62.35%, specificity: 75.61%, accuracy: 72.22%), respectively, which
could derive the same conclusion for the C-index (Fig. [73]2f). In
Fig. [74]2g, this OS-related model could effectively divide HCC
patients into a high-risk group and a low-risk group, and the survival
rate of the high-risk group was significantly lower than the low-risk
group (HR: 2.096, 95% CI 1.480–2.967, P < 0.001).
In addition, further analysis suggested that this OS-related model
could also effectively divide patients into high-risk and low-risk
groups for the following categories (Fig. [75]3): clinical stage
(Fig. [76]3a, b), pathological grade (Fig. [77]3c, d), age
(Fig. [78]3e, f), gender (Fig. [79]3g, h), tumor T stage (Fig. [80]3i,
j), tumor-free survival (Fig. [81]3k). The survival rate of each
high-risk group was significantly lower than that of the low-risk
group. The clinical stage and tumor T stage shown in Fig. [82]3 is
according to the AJCC eighth edition; the pathological grade is in the
light of the Edmondson classification.
Fig. 3.
[83]Fig. 3
[84]Open in a new tab
The Kaplan–Meier analysis of grouped HCC patients by OS-related lncRNA
promoter methylation model. a The survival analysis of HCC patients
with high tumor clinical stage by OS-related lncRNA promoter
methylation model. b The survival analysis of HCC patients with low
tumor clinical stage by OS-related lncRNA promoter methylation model. c
The survival analysis of HCC patients with high pathological grade by
OS-related lncRNA promoter methylation model. d The survival analysis
of HCC patients with low pathological grade by OS-related lncRNA
promoter methylation model. e The survival analysis of HCC patients
(age ≥ 65) by OS-related lncRNA promoter methylation model. f The
survival analysis of HCC patients (age < 65) by OS-related lncRNA
promoter methylation model. g The survival analysis of HCC patients
(female) by OS-related lncRNA promoter methylation model. h The
survival analysis of HCC patients (male) by OS-related lncRNA promoter
methylation model. i The survival analysis of HCC patients with high T
stage by OS-related lncRNA promoter methylation model. j The survival
analysis of HCC patients with low T stage by OS-related lncRNA promoter
methylation model. k The survival analysis of HCC patients with
tumor-free status by OS-related lncRNA promoter methylation model
Additionally, COX univariable and multivariable regression analyses
were shown in Table [85]3, which suggested that the tumor T category of
AJCC stage (HR 2.166, 95% CI 1.490–3.148, P < 0.001) and OS-related
lncRNA model (HR 2.014, 95% CI 1.378–2.942, P < 0.001) were independent
risk factors that affected the OS of HCC patients. Thus, the OS-related
lncRNA model combined with T category of AJCC stage to evaluate the
overall survival in 1-, 3-, and 5-year (Fig. [86]4) with its AUC values
were higher than that of the OS-related model and T category alone.
Table 3.
COX regression analysis of lncRNA survival model and relationship
between clinicopathological features and OS of HCC
Variables Univariable analysis Multivariable analysis
HR (95% CI) P value HR (95% CI) P value
Gender 1.205 (0.846–1.714) 0.304
Age 1.012 (0.998–1.025) 0.097
Tumor stage (G3/4 vs G1/2) 1.044 (0.729–1.496) 0.815
T category (T3/4 vs T1/2) 2.483 (1.756–3.517) < 0.001 2.166
(1.490–3.148) < 0.001
Pathological grade (III + IV vs I + II) 2.382 (1.649–3.443) < 0.001
Vascular infiltration (with vs without) 1.358 (0.900–2.051) 0.148
lncRNA survival model (high-risk vs low-risk) 2.131 (1.500–3.028)
< 0.001 2.014 (1.378–2.942) < 0.001
[87]Open in a new tab
Fig. 4.
[88]Fig. 4
[89]Open in a new tab
ROC curve analysis of the OS-related lncRNA promoter methylation model
combined with T category of AJCC stage. a OS-related lncRNA promoter
methylation model combined with T stage to evaluate 1-year OS of HCC
patients. b OS-related lncRNA promoter methylation model combined with
T stage to evaluate 3-year OS of HCC patients. c OS-related lncRNA
promoter methylation model combined with T stage to evaluate 5-year OS
of HCC patients
Development and evaluation of the vascular invasion-related lncRNA promoter
methylation model
In this section, we utilized LASSO-Logistic to select variables and
determine the coefficients, and finally derived the vascular
invasion-related lncRNA promoter methylation model (Fig. [90]5a, b).
Fig. 5.
[91]Fig. 5
[92]Open in a new tab
Development and evaluation of the vascular infiltration, pathological
grade, and clinical stage-related lncRNA promoter methylation model. a
Vascular infiltration-related lncRNA promoter methylation sites were
screened by LASSO-COX method. b The coefficients of each methylation
sites were determined by LASSO-COX method. c ROC curve analysis of
vascular infiltration-related lncRNA promoter methylation model to
evaluate vascular infiltration in HCC patients. d Pathological
grade-related lncRNA promoter methylation sites were screened by
LASSO-COX method. e The coefficients of each methylation sites were
determined by LASSO-COX method. f ROC curve analysis of pathological
grade-related lncRNA promoter methylation model to evaluate
pathological grade in HCC patients. g Clinical stage-related lncRNA
promoter methylation sites were screened by LASSO-COX method. h The
coefficients of each methylation sites were determined by LASSO-COX
method. i ROC curve analysis of clinical stage-related lncRNA promoter
methylation model to evaluate clinical stage in HCC patients
The vascular invasion-related model was constructed as follows:
[MATH: -1.169-0.016×E_cg
03209812-0.029×E_cg
09392940-0.056×E_cg11201447-0.101×E_cg14743534
-0.280×E_cg16186435
-0.272×E_cg16201808+0.095×E_cg16458494
-0.136×E_cg20535723. :MATH]
The AUC value of the vascular invasion -related lncRNA promoter
methylation model was 0.657 (95% CI 0.601–0.710, sensitivity: 41.51%,
specificity: 88.73%, accuracy: 72.58%) and is plotted in Fig. [93]5c,
which suggested that this model possessed a certain degree of accuracy.
Development and evaluation of the pathological grade-related lncRNA promoter
methylation model
Similarly, there were 22 methylation sites associated with pathological
grade (Fig. [94]5d, e).
The pathological grade-related model was constructed as follows:
[MATH: -0.110-0.015×E_cg
00366814-0.054×E_cg
01329690+0.008×E_cg
02294055+0.199×E_cg
02607683+0.046×E_cg
05295388+0.453×E_cg
09151131-0.038×E_cg
09635053+0.626×E_cg10078898+0.007×E_cg10501210-0.222×E_cg12303981+0.042×E_cg13698168
-0.338×E_cg14360448-0.418×E_cg14743534
-0.051×E_cg1847653<
/mtext>0+0.149×E_cg18567924
-0.133×E_cg20535723-0.249×E_cg21204600-0.070×E_cg23082281-0.127×E_cg23651826
+1.399×E_cg24218935
-0.082×E_cg26703507+0.096×E_cg27005794. :MATH]
The AUC value of the pathological grade-related lncRNA promoter
methylation model was 0.797 (95% CI 0.752–0.837, sensitivity: 81.34%,
specificity: 66.52%, accuracy: 72.02%) and was plotted in Fig. [95]5f,
which suggested that this model possessed significant accuracy.
Development and evaluation of the clinical stage-related lncRNA promoter
methylation model
There were 13 methylation sites associated with clinical stage
(Fig. [96]5g, h).
The clinical stage-related model was constructed as follows:
[MATH: -1.943-0.209×E_cg
00595472-0.015×E_cg
04396850+0.348×E_cg
05239504-0.217×E_cg
09077752-0.015×E_cg11201447-0.007×E_cg12280407-0.042×E_cg14360448+0.118×E_cg1645849<
/mtext>-0.030×E_cg16650292-0.241×E_cg21546522
-0.153×E_cg23082281-0.139×E_cg24731441
-0.172×E_cg27005794. :MATH]
The AUC value of the clinical stage-related lncRNA promoter methylation
model was 0.724 (95% CI 0.673–0.770, sensitivity: 57.30%, specificity:
77.47%, accuracy: 72.22%) and was plotted in Fig. [97]5i, which
suggested that this model possessed significant accuracy.
Functional analysis of the screened vascular invasion-related lncRNA related
to promoter methylation sites
Functional analysis of vascular invasion-related lncRNA related to
promoter methylation sites was performed via GO/KEGG pathway enrichment
analysis. The mainly GO enrichment term was the biological process (BP)
(Fig. [98]6a) which suggested that the vascular invasion-related lncRNA
involving leukocyte differentiation, T cell activation, microtubule
cytoskeleton organization, organelle fission, regulation of lymphocyte
activation, regulation of cell–cell adhesion, and nuclear division. In
Fig. [99]6b suggested that the centrosome, side of the membrane,
chromosomal region were enriched GO terms related to the cellular
components (CC). In Fig. [100]6d, transcription coactivator activity
composed the cellular molecular function (MF). The results of the KEGG
pathway were plotted in Fig. [101]6e, which suggested that lncRNA
corresponding to vascular invasion-related methylation sites affected
HCC via the PI3K/Akt signaling pathway.
Fig. 6.
[102]Fig. 6
[103]Open in a new tab
The functional analysis of the lncRNAs corresponding to vascular
infiltration-related lncRNAs promoter methylation model. a GO-BP
analysis of lncRNA target gene. b GO-CC analysis of lncRNA target gene.
c GO analysis after grouping target genes of vascular
infiltration-related lncRNA. d GO-MF analysis of lncRNA target gene. e
KEGG-pathway analysis of lncRNA target gene. f Molecular functional
network of lncRNA target genes
Verification of the vascular invasion-related lncRNA promoter methylation
model
In MS-PCR, a total of 146 HCC tissues were utilized to detect the
degree of methylation of each site and to verify the accuracy of the
vascular invasion-related lncRNA promoter methylation model. The AUC
value (Fig. [104]7) of the vascular invasion-related model was 0.697
(95% CI 0.615–0.770, sensitivity: 43.94%, specificity: 96.25%,
accuracy: 72.60%), which is consistent with our previous bioinformatics
assay.
Fig. 7.
Fig. 7
[105]Open in a new tab
Verification of the accuracy of vascular infiltration-related lncRNA
promoter methylation model by using HCC tissues
Discussion
Previously, the role of protein-coding genes in the pathogenesis of HCC
has been the focus of research in the field of oncology. However, with
the advancement of high-resolution microarrays and massively parallel
sequencing technologies, ncRNAs that do not encode proteins have been
proven to possess many kinds of biological functions [[106]21,
[107]22]. The current study utilized biomedical statistical research
methods based on bioinformatics. On the basis of statistical
significance, inferring possible medical mechanisms and molecular
regulatory mechanisms provides the whole perspective for the study of a
biomedical phenomenon to design more specific molecular biological
experiments.
Although the function of HCC-related lncRNA expression regulation and
molecular mechanisms that are caused by its promoter methylation
remains unclear, some previous studies have yielded similar results.
Tang et al. [[108]23] found that lncRNA CRNDE promotes HCC cells
proliferation by affecting the PI3K/Akt/GSK3β-Wnt/β-catenin signaling
pathway, and CRNDE is closely correlated with a poor prognosis of HCC
patients. Hou et al. [[109]24] utilized 5 lncRNAs (CTD-2116N20.1,
[110]AC012074.2, RP11-538D16.2, LINC00501, and RP11-136I14.5) to
construct a prognostic model of HCC patients, with a C-index of 0.701.
Another lncRNA SRHC has been proven to have the ability to inhibit HCC
cells proliferation with low transcription levels in HCC tissues due to
promoter methylation [[111]25]. Braconi et al. [[112]26] found that
miR-29a promotes excessive transcription in HCC tissues by inhibiting
the methylation of the lncRNA MEG3 promoter methylation to stimulates
proliferation of HCC cells. In addition, another study completed by
utilizing bioinformatics methods found that 6 lncRNAs (CECR7,
LINC00346, MAPKAPK5-AS1, LOC338651, FLJ90757, LOC283663) were
significantly correlated with the OS of HCC patients [[113]27].
LINC00346 is one of them, which happens to be the lncRNA corresponding
to the methylation site cg13698168 we identified. Although this is a
coincidence, it also reflects the accuracy of the methylation sites we
screened for previously.
The angiogenesis and vascular invasion of HCC have always been research
hotspots, and anti-angiogenesis has been applied in clinical practice
and has achieved certain curative effects. However, the efficacy of
these drugs still needs to be improved. Thus, it is necessary to find
new targets of diagnosis or to improve the efficacy of existing drugs
[[114]28]. Recent studies suggested that lncRNA FEZF1-AS1 [[115]29],
the signaling axis of lncRNA n335586/miR-924/CKMT1A [[116]30], lncAKHE
[[117]31], and the signaling axis composed of PVT1/EZH2/miR-214
[[118]32] were involved in the regulation of invasion of HCC via many
kinds of signaling pathway. The methylation of a lncRNA promoter
directly affects its transcription level, which inevitably has a
decisive influence on the downstream lncRNA function. The methylation
site of the promoter associated with HCC vascular invasion in this
study lacks in-depth mechanistic research. The results of this study
can provide ideas for further research. Moreover, most of the screened
CG sites and their corresponding miRNAs have not been reported in
in-depth studies related to vascular invasion, which reflects the
innovation of this research and provides the whole perspective for
future research.
The commonality of these studies is that one of the prerequisites for
the HCC-related lncRNAs to contribute to their abnormal behavior is
through promoter methylation levels. Therefore, finding HCC-related
lncRNA promoter methylation sites is essential to explore the upstream
mechanism of lncRNA regulation of HCC, which can lead to understanding
the occurrence and development of HCC and exploring new therapeutic
targets. All of these efforts are new and significant in terms of
finding new tumor-related markers.
Conclusion
In summary, the biological phenomenon of lncRNA promoter methylation is
closely correlated with the occurrence and development of HCC in many
aspects, including overall survival, vascular invasion, pathological
grade, and clinical stage. We utilized bioinformatics methods to screen
for these lncRNA promoter methylation sites that have become new
potential targets for the diagnosis, therapy options, and the prognosis
of HCC patients.
Abbreviations
RNA
ribonucleic acid
DNA
deoxyribonucleic acid
ncRNA
noncoding RNA
lncRNA
long noncoding RNA
mRNA
messenger RNA
HCC
hepatocellular carcinoma
PLC
primary liver cancer
TCGA
the Cancer Genome Atlas
OS
overall survival
MS-PCR
methylation-specific polymerase chain reaction
ROC
receiver operating characteristic
AUC
area under the ROC curve
td-AUC
time-dependent AUC
LASSO
the least absolute shrinkage and selection operator
Authors’ contributions
KS, HS, and ZL designed the entire study, and ZL, XN, and SD
contributed equally to this work. HC, JC, BW, and JA were partly
involved in the experimental process and data analysis. XN collected
146 HCC tissues and performed the whole process of genomic DNA
extraction and subsequent MS-PCR. All authors read and approved the
final manuscript.
Funding
This study was supported by Grants from the Youth Foundation of
National Natural Science Foundation of China (No. 81700568), and the
Natural Science Foundation of Zhejiang Province (No. LY17H160057).
Availability of data and materials
Data of clinical characteristic, expression profile of lncRNA, DNA
methylation, and mRNA expression profile were taken from the Cancer
Genome Atlas (TCGA). Data with the following two conditions were
excluded: not diagnosed with HCC and loss of relevant data. A total of
367 HCC samples and 50 cancer adjacent samples were included in this
study for subsequent analysis. We collected 146 cancer tissues from HCC
patients who underwent surgical treatment at our hospital between June
2017 and April 2018.
Ethics approval and consent to participate
This study was approved by the Review of Ethics Committee in Clinical
Research (ECCR) of the First Affiliated Hospital of Wenzhou Medical
University according to the Regulations and Rules of “Ethical Reviews
for Biomedical Research Involving Human Subjects” (2016) of the
National Health Commission of PRC, “Declaration of Helsinki” of WMA,
and “International Ethical Guidelines for Human Biomedical Research” of
CIOMS. The collection of the 146 HCC samples was approved by the
Central Laboratory Human Specimen Library.
Consent for publication
This manuscript is approved by all authors for publication.
Competing interests
The authors declare that there is no conflict of interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Zhuo Lin, Xiaofeng Ni and Shengjie Dai contributed equally to this work
Contributor Information
Keqing Shi, Email: skochilly@wmu.edu.cn.
Hongwei Sun, Email: sunhongwei1@163.com.
References