High-throughput sequencing analysis of differentially expressed microRNAs associated with SREBP1 in differentiated thyroid carcinoma

doi:10.21203/rs.3.rs-1546671/v1

MicroRNAs (miRNAs) interact with genes posttranscriptionally and play important roles in progression and prognosis in multiple cancers. Sterol regulatory element-binding protein 1 (SREBP1) is an important lipid metabolism regulatory gene. The aim of the present study was to analyze the profiles of miRNAs that associated with SREBP1 expression in differentiated thyroid carcinoma (DTC). In the present study, a high-throughput small RNA sequencing (miRNA-Seq) method was used to investigate differences in miRNA profiling with versus without interference in the expression of SREBP1 via small interfering RNA. Real-time qPCR (qRT-PCR) was performed to confirm the results. A total of 1393 conserved and 84 novel miRNAs were successfully discovered. Among them, 27 (11 upregulated and 16 downregulated) differentially expressed miRNAs were significantly altered in BCPAP cells following SREBF1 interference of two fragments compared with the control in two batch. Hsa-miR-941, hsa-miR-27a-5p, hsa-miR-29a-3p, hsa-miR-100-5p, and hsa-miR-21-3p were selected for validation by qRT-PCR. The qRT-PCR results were consistent with the sequencing data. GO enrichment showed that the predicted targets of these miRNAs were mainly involved in the regulation of system development, metabolism and protein binding cellular processes, and metabolic processes. KEGG pathways analysis showed that predicted target genes were involved in several signaling pathways, including the Ras, MAPK, insulin, thyroid hormone and metabolic pathway signaling pathways. Our findings suggest that differentially expressed miRNAs and their target genes may play an important role in the progression and prognosis of DTC that related with SREBP1 expression.

high-throughput sequencing

differentiated thyroid cancer

sterol regulatory element-binding proteins 1

microRNAs

Thyroid cancer (TC) is the most common endocrine malignancy of the endocrine gland. After more conservative diagnostic techniques were implemented in response to a rapid increase in mostly indolent tumors in recent decades, thyroid cancer incidence has begun to drop in women ,but not yet in men [1]. Papillary thyroid cancer (PTC) and follicular thyroid cancer (FTC) are classified as differentiated thyroid cancer (DTC), the most frequent type accounting for 80–90% of TC. Although patients have achieved a good therapeutic response, recurrence can reach as high as 24% in patients after initial thyroidectomy, endocrine and adjuvant radioactive iodine treatment[2]. Thus, there is an urgent need for a better understanding of the molecular mechanism of the occurrence and progression of PTC. In cancer studies, tumor cell lines are widely used as tumor models in complex biological analyses. BCPAP is the most commonly used cell model in the study of PTC, which originates from human papillary thyroid cancer.

MicroRNAs (miRNAs) are small noncoding RNAs of approximately 20–24 nucleotides in length. MiRNAs can negatively regulate the translation of their target mRNAs via specific recognition of the “seed sequence” on untranslated regions at the 3’ end of mRNA[3]. The available research implicates abnormally expressed miRNAs in various biological processes, including cell differentiation, proliferation, apoptosis, tumorigenesis and drug resistance[4, 5], as well as tumor radiosensitivity and the DNA damage response[6]. MiRNA expression profile analysis could be useful for diagnostics, therapeutics and prognosis and serve as a powerful tool for multiple cancers[7]. Many studies have implicated miRNAs in PTC[8, 9].

Sterol regulatory element-binding protein 1 (SREBP1) is a transcription factor that is encoded by SREBF1 and can regulate lipid biosynthesis and metabolism. The roles of SREBP1 have been revealed in multiple cancers, and aberrant expression of SREBP-1 via its upregulation allows it to act as an oncogene in various cancers[10], such as hepatocellular carcinoma, breast cancer, glioblastoma, and colon cancer, among others, associated with malignancy progression and a poor prognosis[11–13]. It also functions in type II diabetes, immunity, autophagy and neuroprotection[14]. In our previous study, we have demonstrated that SREBP1 is significantly upregulated in PTC and associated with an advanced TNM stage, lymph and extrathyroidal extension, and the promotion of cell proliferation, migration, invasion and apoptosis in vitro. The role of SREBP1 in diseases is based on its regulation of lipid metabolism; however, the impact of miRNA on SREBP1 remains to be fully elucidated.

In our present study, small RNA libraries were constructed from the experimental group with abrogated expression of SREBP1 and a negative control group, and high-throughput sequencing was used for the differential expression profiling analysis of miRNA in BCPAP cell lines and validated by qRT-PCR. To understand their functional differences, target genes of all miRNAs were predicted by gene functional enrichment analysis (GO and KEGG pathway).

Cell culture and transfection

The papillary thyroid carcinoma cell line BCPAP was obtained from the Stem Cell Bank, Chinese Academy of Sciences. The follicular thyroid cancer cell line WRO was obtained from the Endocrinology Department of the Affiliated Second Xiangya Hospital of Central South University. The BCPAP cells were grown in RPMI-1640 medium (HyClone, America) supplemented with 10% fetal bovine serum (Gibco, Biological Industries, Israel), 1% sodium pyruvate (Gibco, Biological Industries, Israel), 1% NEAA (Gibco, Biological Industries, Israel) and 1% glutamax (Gibco, Biological Industries, Israel) in a 37°C humidified incubator with 5% CO2. WRO cells was grown in RPMI-1640 medium supplemented with 10% fetal bovine serum. BCPAP and WRO cell were transfected with SREBP1 small interfering RNA (siSREBF1#1 and siSREBF1#2) and a negative control (control siRNA), respectively, which were purchased from RIBOBIO, Guangzhou, China. BCPAP and WRO cells were plated in 6-well culture plates for 24 h before transfection, and then the medium was discarded and replaced with fresh complete RPMI-1640 medium containing siRNA and iMAX transfection reagent for 24 h.

Sample preparation and small RNA sequencing

Novogene (Beijing, China) performed the RNA extraction, library preparation, and sequencing analyses. After that, the complete database sequencing process is carried out (Supplemental Fig. 1). At 24 h posttransfection, the cells were harvested, and total RNA was extracted using TRIzol reagent (Takara Bio Inc, Japan). RNA degradation and contamination were monitored on 1% agarose gels, and then a NanoPhotometer® spectrophotometer (IMPLEN, CA, USA), Qubit® RNA Assay Kit in Qubit® 2.0 Flurometer (Life Technologies, CA, USA) and RNA Nano 6000 Assay Kit associated with the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA) were used to detect the RNA purity and contamination, concentration and integrity of the samples, respectively.

A total amount of 3 µg total RNA per sample was used for the small RNA library. Then, the miRNA libraries were generated using the NEBNext® Multiplex Small RNA Library Prep Kit from Illumina® (NEB, USA) according to the manufacturer’s instructions. By using the particular structure at the 3’ and 5’ end of the small RNA, the adapter was added at the 3’ and 5’ ends and reverse-transcribed into cDNA, followed by PCR amplification. PCR products were purified on an 8% polyacrylamide gel, and size selection was used to generate cDNA libraries. After construction of the libraries, Qubit 2.0 was used for quantification, and the Agilent 2100 Bioanalyzer was used to test the insert size. Finally, library quality was assessed on the Agilent Bioanalyzer 2100 system using DNA High Sensitivity Chips. The Illumina Hiseq 2500/2000 platform was then carried out after the library test.

Filtering sRNA reads and miRNA identification

Raw reads in FASTQ format were processed using base-calling. Clean reads were obtained by removing low-quality reads (smaller than 18 nt and longer than 35 nt) containing poly-N, with 5’ adapter contaminants and without the 3’ adapter or insert tags, and with poly A/T/G/C. Then, sRNA reads of the specified size with a length of 18–35 nt were screened and mapped to determine the sequence for downstream analyses. Simultaneously, the Q20, Q30, and GC contents of the raw data were calculated.

The small RNA tags were mapped to the reference sequence by Bowtie without mismatches. Mapped sRNA reads were examined for known miRNAs using miRBase20.0 (http://www.mirbase.org/index.shtml) as a reference, and the modified software miRDeep2[15] and sRNA-tools-cli were used to obtain the potential miRNA. Detailed information for the sRNA of each sample, including the miRNA secondary structure, sequence and length, were obtained by contrast with the specific reads. Subsequently, the unannotated sRNA reads were further filtered by rRNA, tRNA, snRNA, and snoRNA from Rfam[16], and they were compared with the exons and introns of mRNAs to identify the degradation fragment of the mRNA and dislodge them. Finally, The remaining sRNAs reads were further analyzed by exploring the secondary structure, the Dicer cleavage site and the minimum free energy using miRDeep2 and miREvo[17] methods to predict novel miRNAs. Subsequently, summarizing all alignments and annotations obtained, base edit analysis of the miRNAs was performed by aligning all the sRNA tags to mature miRNA and miRNA families.

Differential expression analysis of miRNAs

miRNA expression levels were normalized using the TPM (transcript per million) method[18]: normalized expression = mapped read count × 10⁶/total reads. Then, the obtained read counts were used to conduct the miRNA differential expression analysis. The DESeq R package (Novogene, Beijing, China)[19] was used to analyze differentially expressed miRNA. The P-values were adjusted using the q-value. The adjusted P < 0.05 was considered significantly differentially expressed. A Volcano Plot was used to deduce the distribution of differential miRNAs. Cluster analysis was applied to determine the clustering pattern of miRNA expression under different experimental conditions. Moreover, a Venn chart was used to compare the miRNA numbers that were common and unique to each group.

Target gene prediction and GO and KEGG enrichment analysis

For differentially expressed miRNA target gene prediction, miRanda (http://www.mirandaim.org), PITA[20] and RNAhybrid were used. Furthermore, Gene Ontology (GO) enrichment analysis (http://www.feneontology.org/) was used for target gene candidates of differentially expressed miRNAs with terms of biological process (BP), cellular component (CC) and molecular function (MF). The GOseq-based Wallenius noncentral hypergeometric distribution, which can adjust for gene length bias, was implemented for GO enrichment analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG)[21] (http://www.genome.jp/kegg/) enrichment analysis was applied for predicting the biochemical metabolic pathways and signal transduction pathways of candidate target genes. KOBAS (Mao et al, 2005) software was used to test the statistical enrichment of the target gene candidates in the KEGG pathways.

Real-time qPCR validation

Quantitative real time polymerase chain reaction (qRT-PCR) was performed to further validate selected differentially expressed miRNAs that target SREBP1. The cells were collected from one 6-well culture plate, and total RNA was isolated from thyroid cancer BCPAP and WRO cell lines following the method described previously. Then, total RNA was reverse-transcribed into cDNA using random primers (for SREBP1) and specific stem-loop RT primers (for miRNA) with the PrimeScript™ RT reagent Kit with gDNA Eraser (Perfect Real Time) (Takara Bio Inc, Japan). Moreover, differentially expressed miRNAs targeting SREBP1 were also validated in thyroid cancer tissue and adjacent normal tissues. Total RNA was reverse-transcribed by polyadenylation of Oligo dT adaptor primer with the Mir-X™ miRNA Frist-Strand Synthesis Kit (Takara Bio Inc, Japan). Real-time qPCR was performed using SYBR® Premix DimerEraser™ (Perfect Real Time) (Takara Bio Inc, Japan) following the manufacturer’s instructions. The process was performed on the LightCycle 480 II (Roche) as follows: 95°C for 30 sec followed by 40 cycles of 95°C for 5 seconds, and 60°C for 30 seconds. Each sample was analyzed in triplicate. β-actin was used as an endogenous control to normalize the data, and miRNAs were normalized to nuclear RNA U6. The expression level was calculated using the 2 − ΔΔCT method. Bulge-Loop miRNA qRT-PCR primer sets (one reverse transcription primer and a pair of qPCR primers for each set) specific for the corresponding miRNAs (hsa-miR-100-3p, hsa-miR-27a-5p, hsa-miR-29a-3p, hsa-miR-21-5p and hsa-miR-941) and RNU6B (U6) were designed by and purchased from RiboBio.

Statistical analysis

All statistical analyses were performed using SPSS 18.0 software (IBM, Chicago, IL, USA) and GraphPad Prism 7.0 (GraphPad Software, La Jolla, CA, USA). Detailed information for the sRNA of each sample, including the miRNA secondary structure, sequence and length, were obtained by contrast with the specific reads. The experimental results are displayed as the mean ± SD. P < 0.05 was considered statistically significant in the comparisons.

The expression of SREBP1 in the BCPAP cell line after interference

We used qRT-PCR to determine the transfection efficiency. The integrity and quality of the sample RNA was checked by 1% agarose gel electrophoresis. The electrophoresis results showed that all the samples consisted of three bands of 28s, 18s and 5s, and the brightness of the 28s band was 2 times greater than that of 18s (Fig. 1). In contrast, after application of the siRNA, the mRNA expression level of SREBP1 was significantly reduced.

Sequencing summary of small RNAs

To identify miRNA expression changes in the BCPAP cell lines with SREBF1 interference, three small RNA libraries from the SREBF1 (siSREBF1#1 and siSREBF1#2) interference group and control group (siControl) were constructed. We obtained approximately 11,011,091, 12971724 and 13108163 raw reads from the three groups, respectively. SiControl had an error rate of 0.01%, Q20 of 97.85%, Q30 of 95.37%, and GC content of 49.10%; siSREBF1#1 had an error rate of 0.01%, Q20 of 97.57%, Q30 of 94.77%, and GC content of 48.62%. And siSREBF1#2 had an error rate of 0.01%, Q20 of 97.86%, Q30 of 95.38%, and GC content of 48.90%. Then, clean reads were obtained by removing sequences containing poly-N, 5’ adapter, absence of 3’ adapter or the insert tag, poly-A or T or G or C and low-quality reads, as well as those reads shorter than 18 nt and longer than 35 nt. A total of 9,226,515 clean reads were obtained from the siControl libraries, 11,622,510 reads obtained from the siSREBF1#1 libraries and 10,782,791 from the siSREBF1#2 libraries, respectively. Then, sRNAs ranging between 18 nt and 30 nt in length were subjected to further analysis. Length distribution analysis showed that the majority of miRNAs ranged from 22 nt to 24 nt in length. The 23-nt RNAs were the most abundant, accounting for 28.23%, 29.31% and 29.26% in the SREBF1 libraries without and with interference. The distributions of selected reads were analyzed (Supplemental Fig. 2).

We analyzed all clean reads using miRBase20.0, miREvo software, mirdeep2 and sRNA-tools-cli to search for known secondary structures to identify novel miRNAs. Total sRNAs in each sample were mapped to the reference sequence by Bowtie without mismatches. Approximately 8151154 reads, 10966080 reads and 9968089 reads were mapped in BCPAP cells without and with SREBF1 interference, respectively. Further mapping to RepeatMasker, the Rfam database or other similar data types from specified species was conducted to remove tags originating from protein-coding genes, repeat sequences, rRNA, tRNA, snRNA and snoRNA. Moreover, the characteristics of hairpin structure of the miRNA precursor were used to predict novel miRNAs. Finally, a total of 4207611, 5967909 and 5253436 reads were annotated as known miRNAs in cells with and without SREBF1 interference. The 1393 known and 84 novel miRNAs were discovered in the SREBF1 interference compared with the control groups (Supplemental Table 1).

Novel predicted miRNAs

A total of 84 novel miRNAs were identified in all cells, including the siControl and SREBF1 interference groups, in which 20 overlapping novel miRNAs were found. However, all of these novel miRNAs were expressed at low levels (less than 300 reads), except for novel-miR-63 (greater than 10,000 reads) (Supplemental Table 2 and Supplemental Fig. 3).

Nucleotide bias and sequence diversity analyses of known and novel miRNAs

MiRNAs is cleaved by Dicer during its maturation from pre-miRNAs to mature miRNAs. For the specificity of the Dicer cleavage site, a nucleotide bias was observed at the first position. Nucleotide bias analyses of the first and each position revealed a preference distribution of nucleotide utility for known and novel miRNAs. The results showed that the U nucleotide was the most frequent base at the first position of the 5′ end and each base position in both known and novel miRNAs. The other three nucleotide rates followed the order A, G and C for known miRNAs and C, G and A for novel miRNAs (Supplemental Fig. 4).

Changes in miRNA expression profiles in SREBF1 interference BCPAP cells

We used a heat map (Fig. 2) to show differentially expressed miRNAs. Terms of P value < 0.01 were used as the cut-off values. There were 82 (28 upregulated and 54 downregulated) differentially expressed miRNAs in siSREBF1#1–1 compared with the controls, in contrast to 56 (29 upregulated and 27 downregulated) differentially expressed miRNAs in siSREBF1#2 − 1 compared with the controls in the first batch. Moreover, there were 74 (21 upregulated and 53 downregulated) differentially expressed miRNAs in siSREBF1#1–2, as compared to 92 (36 upregulated and 56 downregulated) differentially expressed miRNAs in siSREBF1#2–2 compared with the controls in the second batch. In particularly, 27 (11 upregulated and 16 downregulated) differentially expressed miRNAs were obtained in all SREBF1 interference groups compared with the control group (Fig. 3). The details of 27 differentially expressed miRNAs were shown in (Table 1). In particular, hsa-miR-27a-5p, hsa-miR-21-3p, hsa-miR-100-5p and hsa-miR-941 showed the maximum fold change and significant P values. Hsa-miR-29a-3p was also selected for it has been studied in thyroid cancer, which can prove the reliability of our sequencing results.

Table 1

27 differentially expressed microRNAs after the siRNA interference in all groups
miRNA name	Log₂ (fold change)	P-value
hsa-miR-27a-5p	1.0168	9.30E-78
hsa-miR-100-5p	0.45425	0
hsa-miR-21-3p	0.70907	9.90E-40
hsa-miR-941	0.61138	4.15E-16
hsa-miR-30a-3p	0.47848	4.17E-07
hsa-miR-151a-3p	0.40892	6.59E-12
hsa-miR-221-5p	0.36953	4.80E-05
hsa-miR-99b-5p	0.3421	4.51E-06
hsa-miR-21-5p	0.204	4.73E-25
hsa-let-7i-5p	0.086295	3.63E-24
hsa-let-7g-5p	0.081014	3.71E-08
hsa-miR-29a-3p	0.072124	8.41E-37
hsa-miR-191-5p	-0.012713	1.93E-09
hsa-miR-93-5p	-0.038409	2.98E-08
hsa-miR-16-5p	-0.046925	1.12E-05
hsa-miR-103a-3p	-0.051142	6.00E-33
hsa-miR-125a-5p	-0.058383	3.13E-06
hsa-miR-103b	-0.058854	1.28E-33
hsa-miR-27a-3p	-0.0719	2.66E-21
hsa-miR-20a-5p	-0.091851	5.04E-25
hsa-miR-92a-3p	-0.114	3.51E-38
hsa-miR-26a-5p	-0.13004	7.63E-55
hsa-miR-125b-5p	-0.13725	1.16E-58
hsa-miR-17-5p	-0.14318	2.00E-20
hsa-miR-221-3p	-0.19027	8.10E-154
hsa-miR-210-3p	-0.2639	1.28E-17
hsa-miR-186-5p	-0.28127	2.45E-29

Validation of differentially expressed miRNAs by qRT-PCR

To validate the miRNA expression profiles, we determined the expression levels of 5 miRNAs by qRT-PCR for comparison with the miRNA expression profiles determined by high-throughput sequencing. The two interference fragments (siSREBF1#1 and siSREBF1#2) were selected in our previous studies. The sequences of the primers and SREBF1 siRNA for the expression measurements are shown in (Supplemental Table 3). The data demonstrated that siSREBF1#1 and siSREBF1#2 significantly inhibited SREBF1 expression in the WRO and BCPAP cell lines (Fig. 4). According to the results, hsa-miR-941, hsa-miR-27a-5p, hsa-miR-100-5p, hsa-miR-29a-3p and hsa-miR-21-3p were significantly promoted after SREBF1 interference. The qRT-PCR results were consistent with the sequencing data, indicating that the high-throughput sequencing data were reliable. However, the results showed some differences in the two thyroid cancer cell lines, BCPAP and WRO, especially hsa-miR-941 and hsa-miR-100. Moreover, the interference efficiency was higher in BCPAP than in WRO cells (Fig. 5).

Target gene prediction and GO and KEGG pathway enrichment analysis

MiRNAs regulate mRNA expression at the transcriptional and posttranscriptional level by targeting the specific region of the mRNA with a very complicated regulatory relationship. One miRNA may target many mRNAs, and one mRNA may also be targeted by many miRNAs. Subsequently, we used miRanda, PITA and RNAhybrid software to predict potential target genes of the differentially expressed miRNAs. GO and KEGG analyses were preformed to better elucidate the biological functions of the differentially expressed miRNAs and their target genes (Fig. 6). GO enrichment analysis showed that the target genes were predominantly involved in system development, metabolism and protein binding. KEGG is composed of several independent databases, one of which is the pathway database. We preformed KEGG pathway enrichment analysis, and the results showed that the target genes of the miRNAs were mainly involved in the Ras signaling pathway, glycerophospholipid metabolism, endocytosis, mitogen-activated protein kinase (MAPK) signaling pathway, insulin signaling pathway, thyroid hormone signaling pathway, metabolic pathways, the Rap1 signaling pathway and epithelial cell signaling in Helicobacter pylori, among others. However, we found that the enriched KEGG pathways of the target genes for the SREBF1 interference group miRNAs were largely different from those of the siControl group miRNAs. We also found that the ras signaling pathway was commonly enriched in both interference groups. Detailed data for the top 20 pathways are summarized (Supplemental Tables 4, 5 and 6).

MiRNAs play critical roles in controlling their targets mRNAs for regulated gene expression at transcriptional and posttranscriptional levels[22, 23]. Accumulating evidence suggests that miRNAs play important roles in many diseases and cancers through the regulation of cell proliferation, cell differentiation, metabolism, and signal transduction, among others[22, 24, 25]. It is well known that SREBP1 is a key regulatory factor in lipid and cholesterol metabolism and thus exerts vital functions in the genesis, development and prognosis of cancers[26]. Our previous study has revealed that SREBP1 also plays an important role in DTC to influence cell proliferation, invasion, metastasis and apoptosis, and it is associated with an advanced TNM stage and a poor prognosis of patients with PTC. SREBP1 is involved in the regulation of lipid metabolism, and many miRNAs have been demonstrated to be associated with its roles in lipid metabolism and tumor progression, such as miR-33; miR-27a, which functions as a regulatory hub in cholesterol and lipid metabolism and is targeted for the treatment of atherosclerosis and obesity through mediation of the expression of SREBP-1c[27]; and miR-142a-5p, which induces hepatic lipid accumulation by regulating SREBP-1c[28]. In GBM cells, epidermal growth factor receptor (EGFR) signaling enhances miR-29 expression by upregulating SCAP/SREBP1 to promote GBM cell growth, and in contrast, the miR-29 negative feedback loop inhibits SREBP1[29]. Hence, it is reasonable to believe that miRNAs are likely involved in the malignant progress caused by SREBP1 in DTC.

As high-throughput sequencing has the advantages of high-sensitivity, high-accuracy and high-throughput, an increasing number of studies have utilized this method to evaluate the differential expression profiles of genes, miRNAs and other molecules[30, 31]. This is the first study to explore the changes in miRNA expression, which was correlation with SREBP1 expression in DTC. Our results revealed a total of 27 (11 up-regulated and 16 down-regulated) differentially expressed miRNAs were obtained in all SREBF1 interferenced groups compared with control group. The qRT-PCR results confirmed the above results, which indicated that those miRNAs might be involved in the progression of thyroid carcinoma and related to SREBP1 expression. However, some differences verification results were observed in the two TC cells, potentially because BCPAP is a PTC cell line carrying the BRAF mutation and WRO are human poorly differentiated TC cells with different rates of malignancy and degrees of differentiation.

To further delineate the possible genes and pathways targeted by the differential expressed miRNAs, we predicted their target genes and performed GO enrichment and KEGG pathway analyses. A total of 17,166 target gene transcripts were identified from the differently expressed miRNAs. Go enrichment analysis indicated that the aberrant miRNAs might be involved in cell and organism process, intracellular and cytoplasm component and protein binding. Moreover, the results of the KEGG pathway analysis showed that the insulin signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, Ras signaling pathway, thyroid hormone signaling pathway, metabolic pathways, and epithelial cell signaling in Helicobacter pylori infection were likely regulated by miRNAs that might play important roles in PTC malignancy progression induced by SREBP1.

Insulin signaling was found to be activated in many types of cancers, and it is especially associated with lipid and triglyceride metabolism in the liver. Insulin increases lipid synthesis in the liver by activating the transcription of SREBP1[32]. In recent years, studies have demonstrated that hepatic insulin signaling is required for the expression of SREBP-1c in an obesity-dependent manner[33]. Morral, et al also revealed that SREBP1 silencing could decrease insulin signaling in mouse liver[34]. MiR-33 is located within the intron of SREBPs that control cholesterol homoeostasis and participate in lipid metabolism[35]. Price NL, et al[36] have shown that miR-33 knockout enhances insulin resistance and promotes obesity. Overall, similarly to the roles of SREBP-1, aberrant miRNAs obtained by high-throughput sequencing were also associated with lipid metabolism. MAPK signaling pathway has important roles in thyroid tumorigenesis, particularly in PTC[37]. MAPK signaling is also related to SREBP expression and function. Nehman, et al[38] revealed that membrane sphingomyelin might be critical for regulating SREBPs via a MAPK-dependent pathway in adipocytes. More interestingly, Li et al[39] demonstrated that insulin could suppress AMPK signaling to regulate lipid metabolism in primary cultured hepatocytes. SREBP1 is a sterol-regulatory element binding protein that participates in lipid metabolism[40]. Our KEGG analysis also revealed that the target genes of differentially expressed miRNAs were also involved in metabolic pathways. Furthermore, in our previous study, we found that SREBP1 could promote a prognosis of PTC malignancy and was associated with thyroid receptor expression, which might regulate thyroid stimulating hormone (TSH) levels in vivo in patients with PTC and affect TSH suppression treatment in patients. Similarly, our present analysis also elucidated target genes of differentially expressed miRNAs related with SREBP1 expression that played roles in the thyroid hormone signaling pathway. Membrane sphingomyelin enrichment could decrease SREBP1 expression through inhibition of Ras-ERK signaling, indicating that Ras signaling also plays roles in SREBP1-mediated lipid metabolism[38]. We also found that the Ras signaling pathway was commonly enriched for both interference groups.

In summary, this research shows differentially expressed miRNAs associated with SREBP1 expression in DTC, and miRNAs might play an important negative regulatory role toward SREBP1 in DTC progression. Current studies of the roles of miRNAs in DTC are often limited to miRNA function research. Expression profiling and the precise mechanism remain uncharacterized. Thus, well-designed follow-up studies are needed to elucidate the biological significance of these differentially expressed miRNAs, which associated with the expression of SREBP1.

SREBF1, Sterol regulatory element-binding proteins 1; miRNAs, MicroRNAs; TC, thyroid cancer; PTC papillary thyroid cancer; DTC, differentiated thyroid cancer; siRNA, small interfering RNA; qRT-PCR, Quantitative real time polymerase chain reaction; DAG, Directed Acyclic Graph; TSH, thyroid stimulating hormone; MAPK, mitogen-activated protein kinase; EGFR, epidermal growth factor receptor.

Acknowledgments

The authors thank all the volunteers for their cooperation and the involved physicians of the Affiliated Cancer Hospital of Xiangya Medical School, Central South University and Zhengzhou Central Hospital. This work was supported by the Natural Science Foundation of of Hunan, China Grant[ No. 2020JJ4888].

Funding

The present review was partially supported by the Major Project of 863 Plan [Grant Nos. 2012AA02A517 and 2012AA02A518], the Natural Science Foundation of Hunan, China [Grant No. 2020JJ4888], the Foundation for Open Creative Platform in Higher Education of Hunan, China [Grant No. 13K002] and the National Science and Technology Major Project [Grant No. 2012ZX09303013-004].

Competing interests

The authors have no conflicts of interest to declare

Authors’ Contributions

LZ, YJY and LJ made substantial contributions to conception and design, or analysis and interpretation of data; ZJT, PXW, LJ carried out Article modification; ZJT, LJQ, ZK carried out the molecular biology experiment, participated in the primer design and drafted the manuscript; LJQ participated in the acquisition of data and performed the statistical analysis. All authors read and approved the final manuscript.

Availability of data and materia

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the Ethics Committee of the Affiliated Cancer Hospital of Xiangya Medical School, Central South University. Informed consent was obtained from all subjects of this study.

Consent for publication

Not applicable.

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No competing interests reported.

SupplementaryMaterials.doc

High-throughput sequencing analysis of differentially expressed microRNAs associated with SREBP1 in differentiated thyroid carcinoma

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Version 1

Abstract

Figures

Introduction

Materials And Methods

Statistical analysis

Results

Discussion

Abbreviations

Declarations