Liver-specific LINC01146, a Promising Prognostic Indicator, Inhibits Malignant Phenotype of Hepatocellular Carcinoma Cells Both in Vitro and in Vivo

doi:10.21203/rs.3.rs-1088533/v1

Background

Long non-coding RNAs (lncRNAs) have been proven to be involved in the development of hepatocellular carcinoma (HCC). We aimed to investigate the function of LINC01146 in HCC.

Methods

The expression of LINC01146 in HCC tissues was explored via the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and was verified using quantitative real-time polymerase chain reaction (qRT-PCR) in our HCC cohort. Kaplan-Meier analysis was used to assess the relationship between LINC01146 and the prognosis of HCC patients. Cell Counting Kit 8, colony formation assays, transwell assays, flow cytometric assays, and tumor formation models in nude mice were conducted to reveal the effects of LINC01146 on HCC cells both in vitro and in vivo. Bioinformatic methods were used to explore the possible potential pathways of LINC01146 in HCC.

Results

LINC01146 was significantly decreased in HCC tissues compared with adjacent normal tissues and it was found to be related to the clinical presentations of malignancy and the poor prognosis of HCC patients. Overexpression of LINC01146 inhibited the proliferation, migration, and invasion, while promoting the apoptosis of HCC cells in vitro. On the contrary, downregulation of LINC01146 exerted the opposite effects on HCC cells in vitro. In addition, overexpression of LINC01146 significantly inhibited tumor growth, while downregulation of LINC01146 promoted tumor growth in vivo. Furthermore, the co-expression genes of LINC01146 were mainly involved in the “metabolic pathway” and “complement and coagulation cascade pathway”.

Conclusion

LINC01146 expression was found to be decreased in HCC tissues and associated with the prognosis of HCC patients. It may serve as a cancer suppressor and prognostic biomarker in HCC.

Epidemiology

Hepatocellular carcinoma

LINC01146

prognosis

malignant phenotype

in vitro

in vivo

Primary liver cancer is the sixth most commonly diagnosed cancer, with an estimated 905,677 new cases and 830,180 deaths worldwide in 2020 [1]. China bears a huge burden of primary liver cancer cases in the world, accounting for up to 50 percent of all patients [2]. Hepatocellular carcinoma (HCC) accounts for 85-90% of primary liver cancer incidence [3]. Despite the wide scope of research on HCC diagnosis and treatment, approximately 60-70% of HCC patients have a risk of recurrence within 5 years, giving rise to poor prognosis and poor quality of life [4, 5]. Therefore, it is urgent to discover novel prognostic biomarkers and satisfactory diagnostic and therapeutic strategies.

Long non-coding RNAs (lncRNAs) have attracted widespread attention in recent years due to increasing evidence on their important role in HCC [6-10]. Several studies have indicated that lncRNAs are involved in many crucial regulatory processes in HCC including epigenetics, transcription factors, post-transcriptional, protein regulation, and so on [11-13]. A rising number of studies have proven that lncRNAs are associated with the proliferation, migration, invasion, apoptosis, differentiation, angiogenesis, and metabolism of HCC cell lines [14-17]. For instance, LINC00205 directly interacts with miR-122-5p by acting as a competitive endogenous RNA (ceRNA) to promote the proliferation, migration, and invasion of HCC cells [18]. LncRNA RHPN1-AS1 promotes the proliferation, migration, and invasion of HCC cells by targeting miR-7-5p and activating the PI3K/Akt/mTOR pathway [19]. LncRNA HAND2-AS1 inhibits the proliferation, migration, and invasion of SNU-398 cells by mediating the downregulation of ROCK2 protein in HCC [20]. LncRNA TMPO-AS1 adsorbs miR-329-3p and stimulates the FOXK1-mediated AKT/mTOR signaling pathway, thereby promoting the proliferation, migration, and invasion of HCC cells [21]. These results suggest that abnormally expressed lncRNAs may play a cancer-promoting or cancer-inhibiting role in HCC by regulating biological characteristics such as the proliferation, migration, invasion, and apoptosis of HCC cells. These findings are helpful in exploring the role of lncRNAs in the occurrence and metastasis of HCC and establishing a new approach for identifying lncRNAs as prognostic indicators and therapeutic targets.

In recent years, tissue-specific lncRNAs, which are restrictedly expressed in certain normal tissues and show tissue-enriched features, have arouse wide interest. Several liver-specific non-coding RNAs (ncRNA) have been identified to be involved in HCC. For example, LINC01093, a novel liver-specific lncRNA, inhibits HCC cell proliferation and metastasis in vitro and in vivo by interacting with IGF2BP1 to promote GLI1 mRNA decay [22]. Liver-specific miRNA-122 may be involved in the development of HCC by affecting the apoptosis of liver cancer cells [23]. Our previous study have identified two novel liver-specific lncRNAs, FAM99B and LINC02499, that may play similar inhibitory effects on the process of HCC [24, 25]. We found that FAM99B and LINC02499 can prevent the progression of HCC by inhibiting the proliferation, migration, and invasion abilities of HCC cell lines.

LINC01146 is located on chromosome 14q31.3. Based on the quantitative RNA sequencing of major human organs and tissues from the Genotype Tissue Expression Project (GTEx) database, we found the expression of LINC01146 in normal liver tissue to be more than 3 times that in any other normal tissue, showing certain tissue specificity [26]. Based on the role of liver-specific lncRNAs in the development of HCC, we hypothesized that LINC01146 may be related to the occurrence and development of HCC. In the present study, we aim to detect the expression level of LINC01146 in HCC tissues compared with adjacent normal tissues and validated the exact effects of LINC01146 in vitro and in vivo. Furthermore, bioinformatic methods were used to explore the possible biological processes and potential pathways of LINC01146 in HCC, and to provide a scientific basis for subsequent molecular mechanism studies.

Microarray assay

Five pairs of HCC tissues and their corresponding adjacent normal tissues were used to conduct a high-throughput microarray expression profile by KangChen Bio-tech. The samples were labeled according to the ArraryStar RNA Flash Labeling Kit specifications, and Agilent SureHyb was used to conduct the hybridization experiments. The specific workflow is described in detail in the article by Guo X [27].

The Cancer Genome Atlas (TCGA) Database

RNA sequencing data and the corresponding clinical information of HCC patients were obtained from the TCGA (https://cancergenome.nih.gov/) [28] database. We used RNA expression data in the Transcripts Per Million (TPM) and converted it into the base-2 logarithm for normalization [29]. The follow-up time and survival state of clinical information were used for Kaplan-Meier analysis. Patients without LINC01146 expression, follow-up time, or survival state were ruled out.

Gene Expression Omnibus (GEO) Database

LINC01146 expression in HCC and normal control samples was searched from the GEO (http://www.ncbi.nlm.nih.gov/geo/) [30] database. The search keywords were as follows: (long non-coding RNA OR lncRNAs) AND (hepatic OR liver OR hepatocellular) AND (cancer OR carcinoma OR tumor OR neoplasm). We also set the type to “series” and the species to “human”. The inclusion criteria were as follows: 1) each chip must contain HCC tissues and normal liver tissues; 2) the expression of LINC01146 was detected in at least three cancer tissues and normal tissues; 3) the expression of LINC01146 data was directly available or calculable.

Tissue samples

We collected HCC tissues and adjacent normal tissues from the Affiliated Cancer Hospital of Guangxi Medical University from January 2016 to December 2019. We included the patients who were about to undergo surgical excision and had not previously received additional adjuvant therapy in our HCC cohort study. The clinical features and follow-up information of patients were also collected for the survival analysis. Each patient entering our cohort signed an informed consent form. Our research was approved by the Ethics Committee of Guangxi Medical University.

HCC cell lines and animals

A total of six HCC cell lines were used to detect the expression of LINC01146, including Huh7, Hep3B, HCCLM3, MHCC97H, SNU423, and SNU449. The Dulbecco’s modified Eagle’s medium (DMEM), Minimum Essential Medium (MEM), and Roswell Park Memorial Institute 1640 (RPMI-1640) (Gibco, USA) were utilized for cell culturing. All HCC cell lines were cultured in a 37 °C incubator containing 5% CO₂.

A total of 24 specific pathogen free (SPF) 4-week-old male nude mice (BALB/c) were fed in the Guangxi Medical University Laboratory Animal Center. The nude mice were randomly divided into 4 groups according to their weight, with 6 mice in each group. All experimental procedures were reviewed and approved by the Ethics Committee of Guangxi Medical University, and all animal experiments were performed in the Guangxi Medical University Laboratory Animal Center [SYXKGUI 2020-0004] in accordance with the welfare and ethical standards of animal experiments in China.

Quantitative real-time Polymerase Chain Reaction (qRT-PCR)

The total RNA of HCC tissues and cell lines was extracted by TRIzol (Invitrogen, USA). Reverse transcription of complementary deoxyribose nucleic acid (cDNA) was conducted according to the PrimeScript RT reagent Kit (Takara, Japan). qRT-PCR was performed using the TB Green TM Premix Ex Taq TM II Kit (Takara, Japan). The primers are as follows: GAPDH forward: 5’- AGCCACATCGCTCAGACAC-3’, GAPDH reverse: 5’- GCCCAATACGACCAAATCC-3’; LINC01146 forward: 5’-TTGAAGGCAGTATGCTTGGTAA-3’, LINC01146 reverse: 5’-TTCCGCAGTGTATCGTGTCC-3’.

Cell transfection

MHCC97H and Huh7 cells were selected to construct LINC01146 stably overexpression cell lines through lentiviral transfection, while Huh7 and Hep3B cells were selected to construct LINC01146 stably downregulation cell lines through lentiviral transfection (Genepharma, Shanghai, China). The Lv-NC and sh-NC groups served as the negative control groups of the overexpression and downregulation HCC cell lines, respectively. After seventy-two hours of lentivirus infection, we used the puromycin (2.5 or 3.5μg/ml) to screen successfully transfected HCC cells. Finally, qRT-PCR was performed to detect the efficiency of overexpression and downregulation of LINC01146 in HCC cell lines. The sequences of sh-NC and sh-LINC01146 are as follows: LINC01146-Homo-NC: 5’-TTCTCCGAACGTGTCACGT-3’; LINC01146-Homo-330: 5’-GGTCTCCAGCTTCGTCAATGT-3’.

Cell proliferation assays

The Cell Counting Kit-8 (CCK-8; Dojindo, Japan) was used to investigate the effect of LINC01146 on the proliferation ability of HCC cells. The cells were diluted to 10^4 cells/ml and seeded into five 96-well plates. Then we added a mixture of CCK-8 and complete medium (CCK-8: complete medium = 1:10) into each well after 24, 48, 72, 96, and 120 hours incubation. Finally, we measured the absorbance value (OD = 450 nm) of each group with a microplate reader after incubating for another 2 hours.

The colony formation assays were also conducted to investigate the effect of LINC01146 on the proliferation ability of HCC cells. The cells were diluted to a density of 250 cells/ml and cultured in a six-well plate until most clones having more than 50 cells. Afterwards, the cells were fixed with methanol and stained with 0.1% crystal violet for another half an hour. Finally, the treated cells were scanned and the number of clones in each group was calculated as the average number in three parallel wells.

Transwell assays

Transwell chambers with 8μm pore filters (Costar, Corning, NY) were utilized to explore the effect of LINC01146 on the migration and invasion abilities of HCC cells. The specific experimental procedure was described in our previous article [25]. The cells with serum-free medium were seeded into the upper chambers, and the complete media with 20% serum was added into the lower chambers. After 48 or 72 hours, the cells were fixed with methanol and stained with 0.1% crystal violet, followed by observing and counting under an inverted microscope.

Flow cytometric assays

Flow cytometry was performed to assess the cell cycle distribution in different groups. Huh7 cells were digested and washed with PBS twice when the density of cells reached 80-90% in six-well plate. Then, the cells were fixed with cold 75% ethanol overnight at 4℃. After centrifugation, the fixed cells were washed by precooled PBS and then with periodic reagents. Afterwards, the cells were incubated with PI/RNase Staining Buffer (BD Pharmingen™, USA) at room temperature for 15 minutes and the cycle distribution were measured by flow cytometry immediately.

To detect the apoptotic rate, the cells were digested by Ethylene Diamine Tetraacetic Acid (EDTA)-free trypsin and washed with pre-cooled PBS and then with 1 × Buffer. The cells were later double stained with PE Annexin V and 7-AAD according to the instructions of the PE Annexin V Apoptosis Detection Kit I (BD Pharmingen™, USA). The cells’ apoptosis rate was analyzed by flow cytometry within 1h.

Tumor formation model in nude mice

A total of 2×10⁷cells of MHCC97H and 5×10⁷cells of Huh7 cells were suspended in 2 ml mixture (1:1, v/v) of serum-free medium and Matrigel (Costar, Corning, NY). Then 0.2 ml of cell suspension was injected subcutaneously into the left axilla of the nude mice. The formed tumor was measured with a vernier caliper every 5 days, and the tumor volume was calculated as follows: V=1/2ab², where V represents the volume of the tumor, while a and b represent the longest diameter and the shortest diameter of the tumor, respectively. 30 and 25 days after the inoculation of MHCC97H and Huh7 cells respectively, the nude mice were sacrificed by cervical vertebra dislocation, followed by removing, weighing, and measuring the tumor tissues. Each tumor was then placed in 4% paraformaldehyde for fixation for 24 hours.

Hematoxylin-eosin (HE) staining and immunohistochemical (IHC) staining

For HE, after deparaffinization and rehydration, the sections were stained with hematoxylin for 8 min followed by 5 dips in 1% acid ethanol (1% HCl in 70% ethanol) and then rinsed in distilled water. Then the sections were stained with 1% eosin aqueous solution for 4 minutes followed by dehydration with graded alcohol and clearing in xylene [31]. Finally, the sections were sealed with a neutral adhesive and observed under a microscope.

For IHC, after incubating with antigen retrieval solution and 3% H₂O₂ for 15-20 minutes, the slides were rinsed with water and incubated with the primary antibody Ki-67 (Abcam, Cambridge, MA; 1:200) 1 h at 37 °C. Then the slides were incubated with the biotinylated secondary antibody and DAB followed by hematoxylin staining [32]. Finally, the slices were dried and sealed with neutral gum. The results were analyzed by Image-Pro Plus 6.0 software.

Co-expression genes related to LINC01146

GEPIA (http://gepia.cancer-pku.cn) [33], MEM (https://biit.cs.ut.ee/mem/) [34], and TANRIC (https://ibl.mdanderson.org/tanric/_design/basic/index.html) [35] were used to predict the co-expression genes of LINC01146. The Venn diagram (http://jvenn.toulouse.inra.fr/app/example.html) was performed to obtain the most promising co-expression genes, Genes with at least two intersections in the Venn diagram were selected for further analysis.

LINC01146 pathway analysis

In order to reveal the basic molecular mechanisms of LINC01146 in HCC, we conducted a pathway enrichment analysis on the co-expression genes of LINC01146. The WebGestalt (http://www.webgestalt.org/) [36] was used to conduct the Gene Ontology (GO) function annotation, and the KOBAS 3.0 (http://kobas.cbi.pku.edu.cn/kobas3) [37] was utilized to construct the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The STRING (http://string.embl.de/) [38] database was applied to construct a protein-protein interaction (PPI) network.

Statistical analysis

The median value was used as the cut-off value to distinguish between high- and low- expression groups, and the student’s t-test was performed to compare the expression levels of LINC01146 in HCC tissues and normal tissues. The chi-square test was performed to explore the relationship between LINC0146 expression and the clinical features of HCC patients. The Kaplan-Meier analysis and log-rank test was used to assess the association between LINC01146 expression and the overall survival (OS) time of HCC patients. The Cox regression models was utilized to explore the independent prognostic risk factors of HCC patients. The above statistical analyses were all conducted using SPSS 22.0 software, and P < 0.05 was considered statistically significant. The GraphPad Prism 8.0 was used to draw graphs.

Meta-analysis was conducted to integrate the GEO chips results using STATA 13.0 software. The chi-squared and I²test were used to explore the heterogeneity between studies. When I² < 50% or P > 0.1, no significant heterogeneity was existed among studies and the fixed effect model was used for meta-analysis. Otherwise, the random-effect model was selected. A forest plot was utilized to obtain the combined effect value, including the standard mean deviation (SMD) and 95% confidence interval (CI). When SMD < 0 and P < 0.05, the expression of LINC01146 was considered as downregulated in HCC tissues compared with adjacent normal tissues. Otherwise, the expression of LINC01146 was considered as upregulated or unaltered in HCC tissues compared with adjacent normal tissues. The sensitivity analysis was used to evaluate the robustness of the meta-analysis results. The funnel graph and Egger’s test were applied to evaluate for reporting bias, with P > 0.1 deemed as no apparent reporting bias existing.

LINC01146 is downregulated based on lncRNA microarray, TCGA and GEO databases

In our study, under the restriction of fold change (FC) > 2 and adjusted P < 0.05, we found 4433 differentially expressed lncRNAs in HCC, including 1708 up-regulated lncRNAs and 2725 down-regulated lncRNAs (GSE93789, Supplementary Figure 1A). The GTEx database showed that the expression of LINC01146 was restrictedly expressed in normal liver tissue than in any other normal tissue (Supplementary Figure 1B). Hence, we selected the liver-specific LINC01146 which was downregulated in HCC tissues as our research object (FC = 3.92, P = 6.93E-4, FDR = 0.027; Supplementary Table 1).

For TCGA database, the results showed that LINC01146 was decreased in HCC tissues compared with normal liver tissues (P < 0.001; Figure 1A). A total of 330 HCC patients with complete follow-up information were used for five-year OS analysis. Results indicated that low LINC01146 expression was associated with poor five-year OS of HCC patients (P = 0.030; Figure 1B).

A total of thirteen chips were involved in the meta-analysis, and the detailed information are presented in Table 1. We selected the random-effect model due to a heterogeneity existing among these studies (P = 0.007; Figure 1C). The forest plot showed that LINC01146 was decreased in HCC tissues compared with normal liver tissues (SMD = -0.97, 95%CI [-1.33, -0.60], P < 0.001; Figure 1C). Moreover, the results showed that no reporting bias was discovered in our study (P = 0.656; Supplementary Figure 1C). The sensitivity analysis also showed that the results of this meta-analysis was stable (Supplementary Figure 1D).

Decreased expression of LINC01146 is associated with aggressive clinical features and poor prognosis of HCC patients

Result of qRT-PCR indicated that LINC01146 was downregulated in HCC tissues compared with normal tissues (P < 0.001, n = 88; Figure 1D). Furthermore, the expression of LINC01146 had a distinct negative association with tumor size (P = 0.005), tumor numbers (P = 0.020), microvascular invasion (MVI) (P = 0.033), satellite nodules (P = 0.007), DNA content of HBV (P = 0.018), and Barcelona Clinic Liver Cancer (BCLC) grade (P = 0.010), as shown in Table 2.

The Kaplan-Meier analysis indicated that decreased LINC01146 was significantly associated with poor prognosis of HCC patients (P < 0.001, n = 85; Figure 1E). Moreover, the multivariate Cox regression model showed that high expression of LINC01146 was an independent protective factor for the prognosis of HCC patients (HR = 0.38, 95%CI: 0.16-0.92, P = 0.033), as shown in Table 3.

LINC01146 inhibits the proliferation of HCC cells in vitro

The expression of LINC01146 was lowest in MHCC97H and Huh7 cells, and highest in Hep3B cells by qRT-PCR (Figure 2A). Therefore, we chose MHCC97H and Huh7 cells to construct stable overexpressing HCC cell lines through lentiviral transfection, and Huh7 and Hep3B cells to construct stable knockdown HCC cell lines through lentiviral transfection. Results showed that we successfully constructed overexpression of LINC01146 in MHCC97H (P < 0.001) and Huh7 cells (P < 0.001; Figure 2B), whereas downregulation of LINC01146 in Huh7 (P < 0.001) and Hep3B cells (P < 0.001; Figure 2C).

The results of CCK-8 assays indicated that the overexpression of LINC01146 inhibited the rate of growth in MHCC97H (P < 0.001; Figure 2D) and Huh7 cells (P < 0.001; Figure 2E). On the contrary, downregulation of LINC01146 increased the growth rate of Huh7 (P < 0.001; Figure 2F) and Hep3B cells (P < 0.001; Figure 2G).

In addition, the colony formation assays also showed that the overexpression of LINC01146 reduced the number of colonies in MHCC97H (P < 0.001) and Huh7 cells (P < 0.001; Figure 3A), while downregulation of LINC01146 increased the number of colonies in Huh7 (P < 0.001) and Hep3B cells (P < 0.001; Figure 3B). All the above results showed that LINC01146 inhibited the proliferation ability of HCC cell lines.

LINC01146 inhibits the migration and invasion abilities of HCC cell lines in vitro

Transwell assays showed that overexpression of LINC01146 suppressed the migration (P < 0.001) and invasion (P < 0.001; Figure 4A) activities of MHCC97H cells, and inhibited the migration (P < 0.001) and invasion (P < 0.001; Figure 4B) activities of Huh7 cells. On the contrary, downregulation of LINC01146 promoted the migration (P < 0.001) and invasion (P = 0.001; Figure 4C) activities of Huh7 cells, and enhanced the migration (P = 0.006) and invasion (P < 0.001; Figure 4D) activities of Hep3B cells.

LINC01146 affects the cell cycle and promotes the apoptosis of HCC cells in vitro

The cell cycle results showed that overexpression of LINC01146 increased the proportion of Huh7 cells in the G0/G1 phase (64.25% vs. 54.3%, P < 0.001), while reducing the proportion of cells in the S phase (18.95% vs. 24.21%, P < 0.001, Figure 5A). In contrast, downregulation of LINC01146 reduced the proportion of Huh7 cells in the G0/G1 phase (57.59% vs. 63.99%, P < 0.001), while increasing the proportion of cells in the S phase (32.12% vs. 24.12%, P < 0.001, Figure 5B). These results suggests that LINC01146 inhibits HCC cells proliferation ability by affecting the progression of cell cycle.

Furthermore, we assessed the effect of LINC01146 on the apoptosis of HCC cells by the flow cytometric assay. The results showed that overexpression of LINC01146 increased the apoptotic rates of Huh7 cells (P < 0.001, Figure 5C), while downregulation of LINC01146 inhibited the apoptotic rates of Huh7 cells (P < 0.001, Figure 5D).

LINC01146 inhibits the tumor growth of HCC cells in vivo

The tumor formation model in nude mice was used to evaluate the effect of LINC01146 on the proliferation of HCC cells in vivo (Figures 6A, B). Results showed that the weight (P = 0.001, Figure 6C) and the volume (P < 0.001, Figure 6D) of tumors was significantly smaller in the overexpressed group, while (weight: P = 0.001, Figure 6E; volume: P < 0.001, Figure 6F) significant bigger in the downregulated group when compared with the control group.

HE staining was used to detect the effect of LINC01146 on the morphology and structure of HCC. As shown in Figure 7A (HE), the tumor cells in LINC01146 downregulation group were irregular in morphology and different in nuclear size and shape, with obvious atypia, deep staining, shrinkage of nuclear membrane, active mitosis, and pathological mitosis compared with the control group.

IHC staining was used to detect the expression levels of the cell proliferation marker protein Ki-67 between the downregulation of LINC01146 group and the control group. The results showed that the positive expression of Ki-67 protein in the downregulation of LINC01146 group was significantly higher than that in the control group (P < 0.001; Figures 7A (IHC), B), which further indicates that downregulation of LINC01146 promoted the growth of HCC cells in vivo.

Functional and pathway enrichment analysis

Three relevant websites were utilized to screen for promising co-expression genes related to LINC01146. Specifically, a total of 199, 1443, and 204 potential co-expression genes were found in the GEPIA, MEM, and TANRIC websites respectively, and subsequently 107 overlapping co-expression genes were screened out for deeper functional and pathway enrichment analyses (Figure 8A). The results of GO enrichment analysis indicated that these overlapping co-expression genes were mainly involved in “small molecule metabolism”, “carboxylic acid metabolism”, “organic acid metabolism”, “ketoacid metabolism”, “small molecule decomposition” and other biological processes (Figure 8B). The KEGG pathway enrichment analysis showed that these genes were mainly enriched in “metabolic pathway”, “complement and coagulation cascade”, “retinol metabolism”, “caffeine metabolism”, “propionic acid metabolism” and other pathways (Figure 8C).

Construction of the PPI network and identification of core genes

The STRING online website was used to construct PPI network of 107 overlapping co-expression genes to reveal their mutual associations. The results showed that the network has a total of 100 nodes and 181 edges, with an average node degree of 3.62 and an average local clustering coefficient of 0.37 (P < 1.0E-16, Figure 8D). Subsequently, we used the MCODE plug-in of the Cytoscape software to screen for core genes. The results showed that FETUB (Fetuin-B), TTR (transthyretin), LPA [lipoprotein(a)], ALDH8A1 (aldehyde dehydrogenase 8 family member), SERPINA4 (serpin family A member 4), AGXT (alanine--glyoxylate and serine--pyruvate), APOH (apolipoprotein H), SERPINF2 (Serpin family F member 2), F13B (Coagulation factor XIII B chain), ORM2 (Orosomucoid 2), KNG1 (kininogen 1), and F11 (Coagulation factor XI) were the 12 most significant core genes (Figure 8E).

In addition, we explored the expression levels of these 12 core genes in HCC tissues based on the TCGA database, and found that they were all lower in HCC tissues compared with adjacent normal tissues (all P < 0.001, Table 4). The Pearson correlation analysis was used to explore the correlation between LINC01146 and these 12 core genes. The results indicated that LINC001146 has the strongest correlation with FETUB (P < 0.001) and TTR (P < 0.001) among these 12 core genes, and the details are shown in Table 4.

In the present study, we identified an unreported liver-specific lncRNA, LINC01146, evidently decreased in HCC tissues that may serve as an independent prognostic factor for HCC patients. Furthermore, the expression of LINC01146 was negatively related to the aggressive clinical features of HCC patients. In addition, overexpression of LINC01146 inhibited the proliferation, migration, and invasion, while promoting the apoptosis of HCC cells in vitro. On the contrary, downregulation of LINC01146 exerted opposite effects. Moreover, overexpression of LINC01146 inhibited the tumor growth of HCC cells in vivo, while downregulation of LINC01146 played the opposite role in vivo. All the above results indicated that LINC01146 may play a cancer-inhibiting role in HCC progression.

As we know, HCC patients with aggressive clinical features are prone to recurrence, metastasis, and poor prognosis. Previous studies have shown that MVI indicates the metastasis of HCC, and patients with positive MVI will have a poor prognosis [39, 40]. The increase of tumor size is one of the signals of metastasis in HCC patients and is directly proportional to the risk of metastasis in HCC patients [41]. Also, the presence of satellite nodules in the pathological diagnosis of HCC patients shows invasion of cancer cells and suggests poor prognosis of HCC patients and increased proneness to recurrence symptoms [42, 43]. A great deal of evidence suggested that lncRNAs can serve as molecular targets to predict the prognosis of HCC patients, such as lncRNA DGCR5, lncRNA GAS5-AS1, lncRNA miR210Hg, and lncRNA SNHG16 [44-47]. In our study, we found for the first time that low expression of LINC01146 was associated with aggressive clinical features, including tumor size, tumor numbers, MVI, satellite nodules, DNA content of HBV, and BCLC grade. In addition, we found that low expression of LINC01146 was associated with poor prognosis of HCC patients. These results suggested that low expression of LINC01146 may affect the progression and prognosis of HCC patients, and may serve as a tumor inhibitor and a prognostic molecular marker for HCC patients.

Recently, several studies have proven that tissue-specific lncRNAs may affect the biological characteristics of HCC cells, such as LINC01193, FAM99B, and LINC02499 [22, 24, 25]. The negative correlation between the expression of LINC01146, MVI, and satellite nodules prompted us to hypothesize that LINC01146 may be involved in the proliferation, migration, invasion, and apoptosis of HCC cells. By overexpressing and knocking down LINC01146, we found that LINC01146 inhibited the proliferation, migration, and invasion abilities, and promoted the apoptotic abilities of HCC cells in vitro. Also, LINC01146 inhibited the growth of tumors in vivo. Rapid proliferation is one of the most important biological characteristics of HCC cells [48]. LncRNAs can affect the proliferation of HCC cells by targeting key regulatory factors in different pathways. For instance, lncRNA MALAT1 promoted HCC cell proliferation by regulating the expression of oncogenic transcription factor B-MYB to facilitate cell cycle progression [49]. Overexpressed lncRNA SNHG16 sponged hsa-miR-93 and inhibited the proliferation of HCC cells [50]. The upregulation of lncRNA FAM83H-AS1 promoted HCC cell proliferation through the Wnt/β-catenin pathway [51]. Previous studies also revealed that HCC patients harboring cancer cells with high migration and invasion activities often had increased aggressiveness, recurrence, and poor survival. For example, lncRNA MYLK-AS1 facilitated HCC progression and angiogenesis by promoting the invasion and metastatic abilities of HCC cells in vivo through targeting the miR-424-5p/E2F7 axis and activating the VEGFR-2 signaling pathway [52]. Silencing of LINC00240 suppressed the migration and invasion of HCC cells through promoting miR-4465 and inhibiting the HGF/c-Met signaling pathway [53].

Previous studies have reported that lncRNAs influenced the occurrence and progression of tumors by participating in a variety of pathways. We found that the co-expression genes of LINC01146 were mainly involved in “metabolic pathway”, “complement and coagulation cascade”, “retinol metabolism”, “caffeine metabolism” and other pathways. The reprogramming of cell energy metabolism, which provided an energy base for unrestricted proliferation and metastasis of cancer cells, is widely recognized as an emerging cancer marker. Metabolic reprogramming plays a key role in promoting tumor survival and proliferation to sustain the increasing metabolic demands of cancer cells [54, 55]. In recent years, complement and coagulation cascades have played crucial roles in the carcinogenesis and progression of cancer [56-58]. Complement cascades contributed to the development of the major features of carcinogenesis, including the maintenance of cell proliferation, inhibition of apoptosis, and the promotion of cell invasion [59]. In addition, many bioinformatic studies have shown that retinol metabolism played an important role in the occurrence and development of HCC [60-62]. Caffeine has also been reported to have an anti-tumor effect and can protect the liver function. Edling CE et al. found that caffeine blocked the proliferation of HCC and pancreatic cancer adenocarcinoma cells by inhibiting the PI3K/Akt pathway [63]. Moreover, Okano J et al. reported that caffeine inhibited the proliferation of HCC cells by activating the MEK/ERK/EGFR signaling pathway [64]. We also found that there was a strong correlation between the expression levels of FETUB/TTR and LINC01146 in HCC. However, we only discovered this relationship through the TCGA database and did not verify it through experimental methods, which needs further study.

In conclusion, LINC01146 is downregulated in HCC tissues and is negatively correlated with aggressive clinical features and poor prognosis of HCC patients. It can serve as a prognostic biomarker for HCC patients and a cancer suppressor by repressing the proliferation, migration, and invasion abilities of HCC cells, while promoting the apoptosis of HCC cells.

Authors’ contributions

XM, MM, and SL contributed to conception and design of the study. MM and JT organized the database and CT performed the statistical analysis. HH and BL completed the data collection. XM wrote the first draft of the manuscript. DH, XZ, and XQ made critical revisions and approved the final manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Funding

This work was supported by the National Natural Science Foundation of China (81960613 and 81660563), Natural Science Foundation of Guangxi Province (2018GXNSFBA138003), and Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Gaungxi Medical University), Ministry of Education (GKE-ZZ202001).

Acknowledgements

We acknowledge the Guangxi Colleges and Universities Key Laboratory of Prevention, the Guangxi Medical University Laboratory Animal Center, and Control of Highly Prevalent Diseases and Academician Dongxin Lin workstation.

Competing interests

The authors declare that they have no competing interest.

Consent for publication

All authors have approved the manuscript for submission.

Availability of data and materials

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

Ethics approval and consent to participate

The animal study was reviewed and approved by the Guangxi Medical University Laboratory Animal Center [SYXKGUI 2020-0004]. The studies involving human participants were reviewed and approved by the Ethics Committee of Guangxi Medical University. The patients/participants provided their written informed consent to participate in this study.

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Table 1 The basic information and expression of LINC01146 in the GEO datasets included in this study
GEO datasets	Year	Platform	Country	Tissue types	N	LINC01146 expression	t	P value
GEO datasets	Year	Platform	Country	Tissue types	N	(c ± s)	t	P value
GSE29721	2011	GPL570	Canada	HCC tissue	10	3.85 ± 1.42	2.21	0.054
				Normal tissue	10	4.88 ± 0.24
GSE33006	2011	GPL570	China	HCC tissue	3	8.51 ± 0.14	3.70	0.066
				Normal tissue	3	7.51 ± 0.41
GSE62232	2014	GPL570	France	HCC tissue	81	1.39 ± 0.37	2.90	0.020
				Normal tissue	5	1.60 ± 0.14
GSE84402	2017	GPL570	China	HCC tissue	14	1.17 ± 0.47	2.78	0.016
				Normal tissue	14	1.53 ± 0.22
GSE93789	2017	GPL16956	China	HCC tissue	5	4.69 ± 2.65	6.86	< 0.001
				Normal tissue	5	8.77 ± 0.37
GSE94660	2017	GPL16791	USA	HCC tissue	21	1.96 ± 1.08	3.10	0.003
				Normal tissue	21	2.69 ± 0.34
GSE98269	2017	GPL20712	China	HCC tissue	3	7.12 ± 0.56	3.70	0.066
				Normal tissue	3	8.37 ± 0.14
GSE101685	2017	GPL570	China	HCC tissue	24	4.75 ± 1.12	2.51	0.018
				Normal tissue	8	5.77 ± 0.38
GSE101728	2017	GPL21047	China	HCC tissue	7	7.92 ± 0.94	3.33	0.016
				Normal tissue	7	9.24 ± 0.29
GSE104310	2017	GPL16791	China	HCC tissue	12	5.88 ± 3.42	2.23	0.038
				Normal tissue	8	9.09 ± 2.68
GSE115018	2018	GPL20115	China	HCC tissue	12	1.87 ± 1.74	3.12	0.010
				Normal tissue	12	3.41 ± 0.57
GSE121248	2018	GPL570	Singapore	HCC tissue	70	5.74 ± 1.08	7.49	< 0.001
				Normal tissue	37	6.87 ± 0.46
GSE124535	2019	GPL20795	China	HCC tissue	35	3.97 ± 1.75	0.95	0.350
				Normal tissue	35	4.26 ± 0.62

Table 2 The relationship between LINC01146 expression and clinical features of HCC patients
Characteristics	Number (n = 88)	LINC01146 levels		c²	P value
Characteristics	Number (n = 88)	Low expression	High expression	c²	P value
Ages (years)				0.97	0.325
≥ 50	66	31	35
< 50	22	13	9
Sex				0.09	0.764
Male	75	37	38
Female	13	8	5
Liver cirrhosis				0.77	0.381
Negative	34	15	19
Positive	54	29	25
Alpha fetoprotein				3.14	0.076
≤400	32	12	20
>400	56	32	24
Tumor size				8.02	0.005
<5cm	35	11	24
≥5cm	53	33	20
Tumor number				5.44	0.020
Single	74	33	41
Multiple	14	11	3
Microvascular invasion				4.55	0.033
Negative	45	17	27
Positive	43	27	17
Satellite nodules				7.31	0.007
Negative	75	33	42
Positive	13	11	2
Distant metastasis				0.30	0.584
Negative	85	43	42
Positive	3	2	1
Lymphatic metastasis				0.16	0.694
Negative	81	41	40
Positive	7	3	4
DNA content of HBV				5.57	0.018
Negative	49	19	30
Positive	39	25	14
Edmondson-Steiner grade				1.25	0.265
Ⅰ+Ⅱ	31	13	18
Ⅲ+Ⅳ	57	31	26
Barcelona Clinic Liver Cancer grade (BCLC)				9.19	0.010
A	44	15	29
B	18	11	7
C	26	18	8

Table 3 Univariate and multivariate Cox’s proportional risk model for overall survival of HCC patients (n = 85)
Variables	Univariate analysis				Multivariable analysis
Variables	β	HR	95%CI	P value	β	HR	95%CI	P value
Age (≥ 50 vs.< 50)	0.03	1.03	0.99-1.06	0.135
Sex (male vs. female)	-0.11	0.89	0.31-2.63	0.837
Liver cirrhosis (positive vs. negative)	0.13	1.14	0.49-2.67	0.757
AFP (> 400 vs. ≤ 400)	0.93	2.54	0.87-7.45	0.090
Tumor size (≥ 5cm vs. < 5cm)	0.81	2.25	0.89-5.69	0.087
Tumor number (multiple vs. single)	0.12	1.13	0.39-3.31	0.822
Distant metastasis (positive vs. negative)	1.23	3.42	0.44-26.73	0.242
Lymphatic metastasis (positive vs. negative)	0.61	1.85	0.55-6.21	0.323
DNA content of HBV (positive vs. negative)	0.12	1.13	0.50-2.53	0.768
Satellite nodules (positive vs. negative)	1.35	3.86	1.59-9.40	0.003	0.92	2.51	0.78-8.08	0.124
BCLC grade (A vs. B vs. C)	0.42	1.53	0.98-2.38	0.061	0.01	1.01	0.57-1.79	0.983
MVI (positive vs. negative)	0.89	2.43	1.46-4.04	0.001	2.04	7.73	2.24-26.72	0.001
Edmondson-Steiner grade（＞Ⅱ vs. I-Ⅱ）	1.91	6.73	1.58-28.63	0.010	2.36	10.57	2.14-52.15	0.004
LINC01146 expression (high vs. low)	-1.32	0.27	0.09-0.78	0.015	-0.97	0.38	0.16-0.92	0.033

Note: vs.: versus; AFP: Alpha fetoprotein; BCLC: Barcelona Clinic Liver Cancer grade; MVI: microvascular invasion.

Table 4 The expression levels of core genes in HCC tissues and the correlation between core genes and LINC01146
Core genes	Tissue types	N	Expression	t	P value	Pearson correlation
Core genes	Tissue types	N	(c ± s)	t	P value	r	P value
FETUB	HCC tissue	371	5.45 ± 2.40	-12.00	< 0.001	0.56	< 0.001
	Normal tissue	50	7.28 ± 0.62
TTR	HCC tissue	371	10.14 ± 2.78	-11.83	< 0.001	0.53	< 0.001
	Normal tissue	50	11.93 ± 0.76
LPA	HCC tissue	371	2.69 ± 1.37	-17.29	< 0.001	0.49	< 0.001
	Normal tissue	50	4.83 ± 0.72
ALDH8A1	HCC tissue	371	5.49 ± 1.66	-18.96	< 0.001	0.46	< 0.001
	Normal tissue	50	7.33 ± 0.32
SERPINA4	HCC tissue	371	7.26 ± 1.89	-8.81	< 0.001	0.46	< 0.001
	Normal tissue	50	8.37 ± 0.57
AGXT	HCC tissue	371	8.56 ± 2.08	-11.13	< 0.001	0.45	< 0.001
	Normal tissue	50	9.88 ± 0.34
APOH	HCC tissue	371	12.29 ± 1.98	-8.09	< 0.001	0.44	< 0.001
	Normal tissue	50	13.20 ± 0.32
SERPINF2	HCC tissue	371	9.72 ± 1.62	-11.53	< 0.001	0.43	< 0.001
	Normal tissue	50	10.78 ± 0.27
F13B	HCC tissue	371	6.26 ± 1.75	-3.53	< 0.001	0.41	< 0.001
	Normal tissue	50	6.65 ± 0.45
ORM2	HCC tissue	371	10.51±1.69	-11.97	< 0.001	0.38	< 0.001
	Normal tissue	50	11.96 ± 1.69
KNG1	HCC tissue	371	10.00 ± 1.89	-4.08	< 0.001	0.37	< 0.001
	Normal tissue	50	10.44 ± 0.32
F11	HCC tissue	371	5.01 ± 1.34	-10.20	< 0.001	0.32	< 0.001
	Normal tissue	50	5.97 ± 0.45

FigureS1.tif
The supplementary materials for LINC01146. (A) Volcano map of differentially expressed lncRNAs in GSE93789 microarray dataset. (B) LINC01146 was specifically expressed in normal liver tissues. (C) No obvious publication bias was observed in this meta-analysis by funnel graph. (D) The result of this meta-analysis was stable by sensitivity analysis.
TableS1.doc

Liver-specific LINC01146, a Promising Prognostic Indicator, Inhibits Malignant Phenotype of Hepatocellular Carcinoma Cells Both in Vitro and in Vivo

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