Long non-coding RNA SREBF2-AS1 promotes cell progression by increasing SREBF2 expression in Hepatocellular carcinoma

Comprehensive analysis of the lncRNA expression prole of HCC was performed by using TCGA and Gene Expression Omnibus (GEO) database to screen the target lncRNA(s). LncRNA of SREBF2-AS1 was selected and its expression level in a cohort of 15 pairs of HCC tissues was veried by quantitative real-time PCR (qRT-PCR). Loss-of-function and gain-of-function assays were carried out to investigate the role of SREBF2-AS1 in HCC progression in vitro. Tumor formation assay was performed to verity the role of SREBF2-AS1 in HCC progression in vivo.

Accumulating evidence based on high-throughput sequencing technology suggests that noncoding RNAs (ncRNAs) constitute more than 90% of the RNAs [4], and participate in various physiological and pathological process [5]. Recently, as a novel member of ncRNA, long noncoding RNAs (lncRNAs) with more than 200 nucleotides in length have attracted attention [6,7]. LncRNAs are implicated in in uencing gene expression at transcriptional, post-transcriptional, and epigenetic levels [8]. Recently, although some lncRNAs have been reported to be implicated in the occurrence and progression of HCC [9], the expression pattern and regulatory mechanisms need further study [10].
Development of the high-throughput RNA sequencing techniques and bioinformatics methods emerge as promising and helpful tools for screening of genetic alterations in carcinogenesis and discovering new biomarkers for cancers [11]. In particular, integrating multiple microarray datasets could provide convincing results [12]. In this study, we aimed to identify novel candidate lncRNAs that contribute to the progression of HCC. We compared the lncRNAs pro les of HCC and adjacent non-tumor tissues based on TCGA RNA sequencing data and two Gene Expression Omnibus (GEO) RNA sequencing and microarray datasets (GSE GSE55191 and GSE67260).
Our results showed that SREBF2-AS1 was upregulated in HCC and the expression level of SREBF2-AS1 was associated with poor prognosis, perhaps due to the regulation of HCC cells proliferation and apoptosis through regulating sterol regulatory element-binding protein 2 (SREBF2).

Screening of DElncRNAs in HCC
To nd novel lncRNAs involved in HCC tumorigenesis, 50 paired HCC and normal tissues in TCGA and two microarray gene pro ling data (GSE55191 and GSE67260) were utilized. Results revealed that 387 DElncRNAs were dysregulated in the TCGA dataset (294 up-regulated and 93 down-regulated), 232 DElncRNAs in the GSE55191 dataset (139 up-regulated and 93 down-regulated), and 125 DElncRNAs in the GSE67260 dataset (65 up-regulated and 60 down-regulated) (Figs.1a-1f). Further intersection analysis revealed that SREBF2-AS1, PRKAR2A-AS1 and TM4SF1-AS1 were consistently up-regulated in these three datasets (Fig.1g). Thus, we pay more attention to these three lncRNAs most likely owing to their oncogenic ability.

Prognostic assessment of DElncRNAs pro les
The univariate and multivariate Cox analysis between clinical features and HCC were performed to con rm the prognostic signi cance of the clinical characteristics. As shown in table 1, clinical Neoplasm type, Pathological M stage and SREBF2-AS1 were signi cantly correlated with OS, which indicated that SREBF2-AS1 may be an important risk factor in uencing the progression of HCC.

Expression of SREBF2-AS1 in HCC and Clinical Characteristic
We observed similarly signi cant upregulation of SREBF2-AS1 in HCC specimens and cell lines, consistent with the results shown in TCGA (Figs. 2a-2c). To further explore the relationship between SREBF2-AS1 and clinical Characteristic data of HCC patients, clinicopathological characteristics were divided into different groups. Due to the lack of survival time, 340 cases were included for univariate and multivariate Cox analysis, and since the missing of clinical information, 307 cases were included for clinical characteristic analysis. As shown in table 2 and Fig. 2e, SREBF2-AS1 was correlated with histologic grade. These results revealed that SREBF2-AS1 can be used for effective risk strati cation in HCC. By Kaplan-Meier and Cox's proportional hazards regression model analysis we found that SREBF2-AS1 level was signi cantly correlated with poor OS of HCC patients (Fig. 2d).

Knockdown of SREBF2-AS1 inhibited HCC tumorigenesis in vivo
Compared with the NC group, tumor volume and weight in si-SREBF2-AS1 group were signi cantly lower (Figs. 4a-4d). By HE staining we observed typical characteristics of tumor cells, and the Ki-67 staining showed low proliferative activity of the HCC cells in si-SREBF2-AS1 group (Fig. 4e). Moreover, the expression level of SREBF2-AS1 in the si-SREBF2-AS1group were lower (Fig. 4f). Collectively, these data indicated that SREBF2-AS1 knockdown suppressed HCC growth in vivo.

Higher expression of SREBF2 in HCC
Based on the bioinformatics analysis, we found that 265 bp of SREBF2-AS1 overlapped with the rst exon 1 of SREBF2 (Fig. 5a), which may lay the structural foundation of the regulatory relationship between the two molecules. We observed similarly signi cant upregulation of SREBF2 in HCC specimens and cell lines as well as in TCGA, which was similar with the expression pattern of SREBF2-AS1 (Figs. 5b-5f).

SREBF2-AS1 regulated SREBF2 expression in HCC
To identify the association between SREBF2-AS1 and SREBF2, we rst detected SREBF2-AS1 and SREBF2 mRNA expression levels in SREBF2-AS1 and si-SREBF2-AS1 treated cells by qRT-PCR and western blotting. SREBF2 mRNA and protein levels in Huh7 and HepG2 cells transfected with SREBF2-AS1 were higher than those in NC (Figs. 6a and 6b), while SREBF2-AS1 knockdown signi cantly reduced SREBF2 mRNA and protein levels (Figs. 6c and 6d). Similar results were observed in xenograft model (Figs. 6e-6f). Importantly, the CCK8 assay and ow cytometry assays showed that the abilities of cell growth and apoptosis by SREBF2-AS1 over expression were partially counteracted by SREBF2 knockdown (Figs. 6g, 6i), as well as a decreased percentage of Huh7 and HepG2 cells in G0/G1 phase (Fig. 6h).

Discussion
HCC carcinogenesis is a multi-step process involving various genetic and environmental factors, while molecular mechanisms are still poorly understood. Recent studies have focused on lncRNAs in HCC pathogenesis [13,14]. Bioinformatic data mining of gene expression data provides a helpful tool for revealing lncRNAs alterations in tumorigenesis and progression by epigenetic regulation, transcription and posttranscription regulation [15]. Since individual data investigation often shows a bias due to insu cient numbers of specimens, integrating multiple individual data has been considered as a better approach of enhancing the reliability of results [16].
In present study, we observed that SREBF2-AS1 was upregulated and correlated with the prognosis of HCC and promoted cell progression by increasing SREBF2 expression. For lncRNA, its biological function mainly determined by the subcellular localization. Here, we found that SREBF2-AS1 was mainly located in the cytoplasmic base on FISH analysis, which implicated that SREBF2-AS1 mainly participated in posttranscriptional regulation. SREBF2-AS1 is classi ed as antisense (AS) lncRNAs that are reverse complements of endogenous sense counterparts [17], account for 50-70% of ncRNAs. Antisense lncRNAs appear to function in a locus-speci c effects on their neighboring protein-coding genes including suppression, activation, or homeostatic adjustment [18]. For example, PCNA-AS1 binds PCNA mRNA and forms a lncRNA: mRNA duplex to promote tumor growth by enhancing PCNA mRNA stability in HCC [19]. While AChE-AS exerts an anti-apoptotic effect via epigenetic modi cation of AChE promoter to repress AChE expression in HCC cells [20]. These divergent evidences suggest a potential mechanism of LncRNA: antisense lncRNAs hybridized with cognate mRNA to form a duplex for gene transcriptional regulation by promoter activation to post-transcriptional regulation by controlling mRNA stability and translatability. However, the mechanisms by which the vast majority antisense lncRNAs function, exerting any activity, remain largely unknown. There are two regulation types between an antisense lncRNA and its cognate sense mRNA: discordant or concordant. Our data suggested that SREBF2-AS1 and SREBF2 are expressed in a concordant manner. Mechanisms of concordant regulation have been explored in previous investigations, however, why the antisense lncRNAs can exert biologically effects on its sense partner without being expressed in equimolar amounts remains incompletely understood [21]. Moreover, why the RNA duplex has no impact on mRNA translation need further explored.
SREBF2 is a vital regulator of genes associated with cholesterol biosynthesis, the key component of cell membranes of proliferating cells [22]. Previous study demonstrated that SREBF2 as a regulator of HMGCR, was increased in esophageal squamous cell carcinoma and promoted the migration and invasive abilities [23]. Also, aberrant SREBF2 expression has been associated with prostatic cancer progression probably due to the regulation of cholesterol and other lipids [24]. So, SREBF2 metabolic pathway impairment is proposed as potential anti-tumor approach [25]. In addition, Chen demonstrated that the reactivation of the MAPK pathway determined an up-regulation of SREBF proteins promoting metastatic prostatic cancer [24]. In this study, we demonstrated that the expression of SREBF2 was upregulated in HCC, in accordance with Jiang's report [26]. Furthermore, we found that the inhibition of SREBF2 in HCC cells induced an anti-tumor activity.

Conclusions
In summary, our study revealed an oncogenic role for SREBF2-AS1 upregulation in HCC. Additionally, we reported for the rst time that the activity of SREBF2-AS1 is attributable to its regulation of SREBF2, which may open avenues for utilizing lncRNAs to insight into the molecular mechanism of HCC.

Datasets
Clinical information and RNA sequencing data of HCC and paired normal tissues were downloaded from TCGA data up to February 7, 2019. HCC RNA sequencing and microarray datasets GSE55191 and GSE67260 downloaded from GEO datasets was analyzed using Affymetrix Human Genome U133 Plus 2.0 Array. The differently expressed lncRNAs (DElncRNAs) between HCC tumor and adjacent normal tissues were screened using edgeR package of the R platform, with adjusted P< 0.05 and the thresholds of |log2FC| >2.0.

Survival analysis and Clinical signi cance of DElncRNAs
The association of each lncRNA expression level and overall survival (OS) was calculated by univariate Cox model. Then, the contribution of lncRNA as independent prognosis factors of OS was evaluated via multivariate Cox analysis. Kaplan-Meier curves of DElncRNA was plotted using the "survival" package in R software with P< 0.05.
The association of LncRNA with clinicopathological characteristics of HCC patients, including age (over or under 60 years), gender (male or female), clinical stage (I, II or III, IV), histologic grade (G1,G2,G3 or G4), Pathologic(T,N,M), and risk factors (alcohol consumption, hepatitis B, hepatitis C, no history of primary risk factors and non-alcoholic fatty liver disease) were analyzed with P<0.05.

Tissue collection
Fifteen paired HCC tumors and adjacent normal tissues were collected from HCC patients without any local or systemic anticancer treatment before surgical resection at the Second A liated Hospital of Nanjing Medical University. The specimens were collected during surgery, and immediately kept in RNA Later stabilization solution (Invitrogen, USA) and froze. Ethics approval was obtained from the Ethics Committee of the Second A liated Hospital of Nanjing Medical University and written informed consent was obtained from all participants.

Fluorescence in situ immunohybridization (FISH)
The probe of SREBF2-AS1 was designed and synthesized by Invitrogen (Shanghai, China), and its sequences were 5′-ACGCACCGCTTCGCTCGCCCATTG G-3′. Cells were xed in 4% formaldehyde, washed, and treated with pepsin K for 3 min. Next the cells were air-dried and incubated with FISH probe diluted in hybridization buffer. After the hybridization, the cells were washed, dehydrated, and visualized under a uorescence microscope (DMI4000B, Leica, German).

RNA isolation and detection of lncRNA and mRNA
The total RNA was isolated from cell lines or tissue samples using TRIzol® reagent (Invitrogen, USA) and reversely transcribed to cDNA using HiScript ® II Q RT SuperMix kit (Vazyme, China). Then, qRT-PCR analyses were performed using the SYBR Green PCR Master Mix (ThermoFisher, USA). The primers were Proteins were extracted from cells or tissue samples using RIPA buffer containing Phenyle methane sulfonyl uoride (Beyotime, China). Proteins were separated by SDS-PAGE and transferred onto PVDF membranes, which were then blocked with 5% non-fat milk in Tris-buffered saline with 0.05% Tween-20 (TBST) and incubated overnight with following primary antibodies: rabbit polyclonal antibody SREBF2(Abcam, USA) and mouse monoclonal antibody GAPDH (Abmart, China). The membranes were washed with TBST and then incubated with horseradish peroxidase conjugated secondary antibodies: goat anti-rabbit IgG (Biosharp, China) or goat anti-mouse IgG (Abmart). Finally, the immunoreactive protein bands on the membrane were visualized using an ECL Kit (Beyotime, China).

Cell proliferation assay
Cells proliferation was evaluated using Cell Counting Kit-8 assays (CCK8) (Dojindo Laboratories, Japan). Huh7 and HepG2 cells transfected by siRNAs after 24h were seeded into the 96-well plates at a density of 5,000 cells/well and then incubated for 0 h, 24 h, 48 h and 72 h. Subsequently, 10 µl CCK8 solution was added to each well and incubated for 2 h at 37˚C. The optical density (OD) of 450 nm was measured by a microplate reader.

Colony formation assay
Huh7 and HepG2 cells were transfected by siRNAs and after 24 h were seeded into 6-well plates with 500 cells per well. After culture for two weeks, most of the colonies contained at least 50 cells, the colonies were xed in methanol and stained with 0.1% crystal violet.

Flow-cytometric analysis for apoptosis and cell cycle
For apoptosis assay, Huh7 and HepG2 cells were harvested after transfection for 24h. Firstly, 5×10 5 cells were suspended in binding buffer and stained by Annexin V-FITC and Propidium Iodide (PI) (BD Biosciences, USA). Secondly, cells were vortexed and then incubated for 15-20 min after mixing, and immediately subjected to ow cytometry within an hour. For cell cycle assay, cells were collected and xed using 70% ethanol at -20 ˚C for 18 h, and stained with 400 ul of propidium iodide (PI) for 30 min.
The results were analyzed with FlowJo software (FlowJo LLC, USA).

Xenograft model
Nude mice (male, ve-weeks old) were subcutaneously injected with 1 × 10 7 HepG2 cells randomly and divided into two groups (n=5). When tumors grew to 50 mm 3 , siRNAs modi ed by 2OMe + 5Chol were injected into tumors in si-SREBF2-AS1 group every two days, while NC group were injected with control siRNAs. Tumor diameter was measured every two days by digital calipers. After three weeks, the mice were sacri ced and the tumor size and weight were measured. The protocol and procedures employed were ethically reviewed and approved by Animal Ethical and Welfare Committee of NJMU.

Immunohistochemistry
The tissues were xed in 4% paraformaldehyde, embedded in para n, and cut into sections (4 µm thin). After antigen retrieval, the sections were blocked with bovine serum albumin, and incubated with primary antibody at 4°C overnight. Next, the sections were incubated with horseradish peroxidase-conjugated secondary antibody at room temperature for 1 h, and staining signal was detected with Tissue Staining HRP-DAB Kit (DAKO).

Statistical analysis
Data were analyzed using GraphPad Prism 7 (GraphPad, USA). The comparison of two groups was performed using Student's t-test, and analysis between multiple groups was conducted by one-way analysis of variance (ANOVA) with the Bonferroni correction. P value< 0.05 indicated signi cant difference.