Profile analysis of differentially expressed long non‑coding RNAs in metabolic memory induced by high glucose in human umbilical vein endothelial cells
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- Published online on: May 2, 2023 https://doi.org/10.3892/etm.2023.11987
- Article Number: 288
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Copyright: © Cheng et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Diabetes mellitus (DM) can result in cardiomyopathy, kidney failure, retinopathy and other complications of chronic hyperglycemia, severely affecting life quality and expectancy in this patient population (1,2). Hyperglycemia-induced endothelial dysfunction causes diabetes-related micro- and macrovascular complications in patients with long-term DM (3). However, endothelial dysfunction persists if hyperglycemia is not controlled in a timely manner; this phenomenon is known as metabolic memory (MM) (4). MM is an obstacle in the treatment of diabetic complications. Oxidative stress, non-enzymatic glycation of proteins, epigenetic changes and chronic inflammation are the four basic mechanisms playing vital roles in MM (5-9). Epigenetic mechanisms, including those involving non-coding RNAs, histone modifications and DNA methylation, are crucial components in the pathology of diabetic complications (7). The roles of long non-coding RNAs (lncRNAs) in diabetes-associated endothelial dysfunction and the effects of lncRNAs on MM-related mechanisms are currently being investigated (10,11).
lncRNAs, which are long transcripts containing >200 nucleotides, are structurally similar to, but functionally different from mRNAs. By acting as molecular sponges or host genes for microRNAs (miRNAs/miRs), serving as scaffolds for specific protein complexes and guiding histone-modifying complexes, lncRNAs serve key roles in the epigenetic mechanisms mediating numerous physiological and pathological processes (12-14). Several previous studies have indicated that lncRNAs are involved in diabetes-related nephropathy, retinopathy and cardiovascular diseases, and numerous lncRNAs, such as ENST00000600527, NONHSAT037576.2 and NONHSAT135706.2, have been reported to demonstrate abnormal expression in vascular endothelial and smooth muscle cells placed under high glucose (HG) culture conditions (14-17). The functions of lncRNAs in diabetic endothelial dysfunction and MM have been gathering increasing attention. In human retinal endothelial cells, lncRNA antisense non-coding RNA in the INK4 locus is highly expressed under HG conditions, causing the overexpression of vascular endothelial growth factors (VEGFs), which induces angiogenesis (18). HG-treated retinal endothelial cells express high levels of the lncRNA myocardial infarction associated transcript (MIAT), which functions as a competing endogenous RNA (ceRNA) by absorbing miR-150-5p, thereby weakening the miRNA-mediated inhibition of VEGF expression. Accordingly, knockdown of lncRNA MIAT alleviates microvascular dysfunction in vivo (19). HG levels can also upregulate the expression of lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in the retinas of rats with diabetes. Silencing MALAT1 expression ameliorates diabetic-induced retinal vasculopathy and inflammation in vivo and endothelial dysfunction in vitro (20). Furthermore, lncRNA maternally expressed gene 3 (MEG3) levels are significantly reduced in the retinas of mice with diabetes and in endothelial cells subjected to HG concentrations or oxidative stress. Knockdown of lncRNA MEG3 enhances inflammation and retinal vascular dysfunction, and promotes the proliferation and migration of retinal endothelial cells and endothelial tube formation (21). Overexpression of MEG3 alleviates diabetic retinopathy by reducing the expression of TGF-β1 and VEGFs (22).
Determining which lncRNAs are expressed in MM can help uncover their roles in the molecular mechanisms driving MM and may facilitate the therapeutic use of these lncRNAs in the treatment of patients with MM. Therefore, in the present study, RNA sequencing and bioinformatics analyses were used to identify lncRNAs with upregulated [(up)-MM-involved differentially expressed lncRNAs (MMDELs)] and downregulated expression (down-MMDELs) in MM induced by HG conditions.
Materials and methods
Cell groups and establishment of the MM model
Primary human umbilical vein endothelial cells (HUVECs) were purchased from the China Center for Type Culture Collection and were maintained in Modified Eagle's Medium (Hyclone; Thermo Fisher Scientific, Inc.) supplemented with 10% fetal bovine serum (Gibco; Thermo Fisher Scientific, Inc.), 100 U/ml penicillin (Gibco; Thermo Fisher Scientific, Inc.) and 100 µg/ml streptomycin (Gibco; Thermo Fisher Scientific, Inc.) at 37˚C in a humidified, 5% CO2 chamber. Cell culture and establishment of the MM model were performed as described previously (16,23). Briefly, HUVEC cells in the low glucose (LG) group were cultured with 5 mM glucose and 20 mM mannitol for 6 days. The cells in the HG group were cultured with 25 mM glucose for 6 days. The cells in the MM group were treated for 3 days using 25 mM glucose, followed by another 3 days of treatment using 5 mM glucose and 20 mM mannitol, to induce the state of MM in these cells.
RNA preparation and sequencing
Total RNA was isolated from HUVECs and purified using TRIzol® (cat. no. 15596026, Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions, the quantity and quality of the RNA were determined using an Agilent 4200 Bioanalyzer (Thermo, Fisher Scientific, Inc.). The RNA integrity was assessed by electrophoresis with denaturing agarose gels. Libraries were constructed using a VAHTS Total RNA-Seq(H/M/R) Library PrepKit for Illumina (cat. no. NR603-02; Vazyme Biotech Co., Ltd.) according to the manufacturer's protocol, and libraries were identified using a Qubit™ dsDNA HS Assay Kit (cat. no. Q32854; Invitrogen; Thermo Fisher Scientific, Inc.) and Agilent High Sensitivity DNA Kit (cat. no. 5067-4626; Agilent Technologies, Inc.). The qualities of the libraries were assessed using a Qubit® 2.0 Fluorometer (Thermo Fisher Scientific, Inc.) and the concentration of DNA in the libraries was analyzed using an Agilent 2100 bioanalyzer (Agilent Technologies, Inc.). Clusters were generated bycBot (Illumina, Inc.) and the libraries were diluted to 10 pm, then sequencing was performed on the Illumina HiSeq 2500 according to the manufacturer's protocol at Shanghai Ao-Ji Biotechnology Co., Ltd (2x150 bp, paired-end). Details of the experimental procedures and instruments used are described in our previous study (24).
Identification and analysis of the parental genes of MMDELs
FastQC (version 0.11.3; https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was used to ensure the quality of RNA-sequencing (RNA-seq) reads. The reads were trimmed using seqtk (https://github.com/lh3/seqtk), and then Illumina TruSeq adapter sequences, poor reads and ribosome RNA reads were removed. The trimmed reads were mapped onto the Homo sapiens genome (hg38) using Hisat2 (version 2.0.4) (25). StringTie (version 1.3.0) and gffcompare (version 0.9.8) were used to assemble and compile transcripts from the trimmed reads (26,27). These transcripts were then compared with the reference annotation databases NONCODE (version 5, http://v5.noncode.org/introduce.php) and Ensembl (http://grch37.ensembl.org/index.html). EdgeR and Venny were used to screen out the differentially expressed lncRNAs in the HG vs. LG and MM vs. LG groups (P<0.05 and fold change >2) (28,29). Non-significant differentially expressed lncRNAs were then filtered through the comparison of HG vs. MM group (P>0.05), and the intersection of these differentially expressed lncRNAs was illustrated using a Venn diagram. Gene Ontology (GO) and KEGG (https://www.kegg.jp/) analysis were used to examine the enrichment of parental genes from which the lncRNAs are transcribed and the function of MMDELs using clusterProfiler (version 3.16, https://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html) (30,31).
Construction of lncRNA-mRNA-co-expression network
The Pearson correlation coefficient (PCC) was calculated for the analysis of the correlation between the expression of lncRNAs and mRNAs. LncRNA-mRNA pairs with PCC >0.9 and P<0.01 were selected for the construction of the co-expression network, which was visualized using Cytoscape (version 2.8.3) (32). The node degree indicates the number of directly linked neighbors for each node. GO and pathway analyses were also used to estimate the highly correlated candidate coding genes using clusterProfiler (version 3.16).
Reverse transcription-quantitative PCR (RT-qPCR) validation
To minimize selection bias, 3 upregulated and 3 downregulated MMDELs were randomly selected. The expression level of these six MMDELs was quantified using qPCR on a Roche LightCycler 480 (Roche Applied Science). The sequences of the specific primers used and the lengths of the products are indicated in Table I. After the total RNA was isolated from HUVECs according to the aforementioned method. RT-qPCR was performed according to the manufacturer's protocol using a ABScript II cDNA First-Strand Synthesis Kit (cat. no. RK20400; Abclonal Biotech Co., Ltd.) and qPCR was performed using a ABScript II One Step SYBR Green RT-qPCR Kit (cat. no. RK20404; Abclonal Biotech Co., Ltd.). PCR reactions were implemented using the following temperature protocol: 95˚C for 10 min, followed by 40 cycles of 95˚C for 15 sec and 60˚C for 20 sec. Expression levels of target genes were normalized to that of GAPDH, which used as an internal reference gene. The relative expression of each lncRNA was calculated using the 2-ΔΔCq method (33).
Statistical analysis
SPSS software (version 24.0; IBM Corp.) was used for data analysis, and the results are presented as the mean ± SEM. There were three repeats per group. To analyze the RT-qPCR validation data, sample distribution was examined using Shapiro-Wilk test, and Levene's test was used to analyze the homogeneity of variance. One-way ANOVA analysis was used to test overall statistical significance, followed by the Least Significance Difference test for pairwise comparisons. P<0.05 was considered to indicate a statistically significant difference.
Results
Identification of HG-induced MMDELs
Three samples each of normal HUVECs (LG group), HG-induced HUVECs (HG group) and MM-induced HUVECs (MM group) were analyzed using RNA-seq to characterize the differences in the lncRNA expression levels. In a total of nine samples, 41,484 lncRNAs were identified and of these, 36,387 lncRNAs were shared between the three groups examined in the present study (Fig. 1A). These lncRNAs were widely distributed on all chromosomes, with higher numbers of lncRNAs revealed on chromosomes 1 and 2 (Fig. 1B). LncRNAs are classified into six types: Intergenic, exonic-antisense, exonic-sense, bidirectional, intronic-antisense and intronic-sense (34). The most numerous species of the identified lncRNAs were intergenic, followed by the exonic-sense and exonic-antisense types (Fig. 1C). These findings suggested the potential diversity and complexity of regulatory mechanisms.
Using edgeR analysis and comprehensive filtration, 308 up- and 157 downregulated MMDELs were identified. As presented in Fig. 2A, 308 upregulated MMDELs were screened from the intersection of upregulated lncRNAs in all groups; among them, 1,448 lncRNAs were upregulated in the HG vs. LG group (fold-change >2, P<0.05), 793 lncRNAs were upregulated in the MM vs. LG group (fold-change >2, P<0.05) and 37,320 lncRNAs were non-significantly differentially expressed (MM vs. HG; P>0.05). Similarly, 157 downregulated MMDELs were identified from the intersection of downregulated lncRNAs; among them, 1,732 lncRNAs were downregulated in the HG vs. LG group (fold-change <-2, P<0.05), 814 lncRNAs were downregulated in the MM vs. LG group (fold-change <-2, P<0.05) and 37,320 lncRNAs were non-significantly differentially expressed (MM vs. HG; P>0.05) (Fig. 2B). The MMDEL expression profile was presented in a heatmap (Fig. 2C).
GO and KEGG analyses of parental genes of MMDELs
To understand the characteristics and functions of lncRNAs, it is important to investigate their parental genes. In the present study, 465 MMDELs were identified; the parental genes of these 465 MMDELs were annotated using GO and pathway enrichment analysis.
The top 30 GO terms with the highest enrichment factors identified using GO analysis included ‘positive regulation of telomere maintenance’, ‘positive regulation of protein acetylation’, ‘regulation of DNA recombination’ and ‘interstrand cross-link repair’ in biological process (BP) terms, ‘striated muscle thin filament’, ‘spindle microtubule’ and ‘nuclear chromosome, telomeric region’ in cellular component (CC) terms, ‘NAD binding’, ‘protein serine/threonine phosphatase activity’, ‘transcriptional repressor activity, RNA polymerase II transcription factor binding’ and ‘dioxygenase activity’ in molecular function (MF) terms (Fig. 3A). These results indicated disturbances in cell cycle and proliferation.
KEGG pathway enrichment analysis indicated that a total of 126 pathway terms were enriched with MMDELs. The top 30 pathways with the highest enrichment factors are demonstrated in Fig. 3B; significantly enriched terms included ‘glycerophospholipid metabolism’, ‘proteasome’, ‘cardiac muscle contraction’, ‘signaling pathways regulating pluripotency of stem cells’, ‘mRNA surveillance pathway’, ‘cell cycle’, ‘peroxisome’, ‘TGF-β signaling pathway’ and ‘AGE-RAGE signaling pathway in diabetic complications’.
LncRNA-mRNA co-expression network and functional analysis of target mRNAs
To further explore the regulatory role of these lncRNAs, the differentially expressed genes in the same HUVEC samples were evaluated and Cytoscape was used to select the lncRNA-mRNA pairs with PPC (Pearson correlation coefficient) >0.9 and P<0.01 to construct and visualize the co-expression network. Consequently, 397 lncRNA nodes, 708 mRNA nodes and 8,303 edges were used to build the network (Fig. S1). The top 10 up- and downregulated MMDELs with the highest node degree are presented in Table II.
The top 30 GO terms revealed using GO analysis of target mRNAs included ‘negative regulation of telomerase activity’, ‘regulation of double-strand break repair via homologous recombination’, ‘mitotic cytokinesis’, ‘negative regulation of DNA biosynthetic process’ and ‘exit from mitosis’ in BP terms, ‘condensed nuclear chromosome’, ‘centromeric region,’ ‘nuclear nucleosome’ and ‘mitotic spindle’ in CC terms and ‘14-3-3 protein binding’ in MF terms, which were also demonstrated in our previous study (24). This may be attributed to the similar screening criteria used in the two bioinformatics analyses. In this study, we focused on the mRNA whose expression was positively correlated with MMDELs, KEGG pathway enrichment terms included ‘cell cycle’, ‘p53 signaling pathway’, ‘Notch signaling pathway’, ‘β-alanine metabolism’, ‘glutathione metabolism’, ‘oxidative phosphorylation’, ‘arginine and proline metabolism’, ‘glyoxylate and dicarboxylate metabolism’, ‘insulin secretion’ and ‘homologous recombination’ (Fig. 4).
Verification of the expression levels of MMDELs using RT-qPCR
To minimize selection bias, the expression levels of the six randomly selected MMDELs were verified using RT-qPCR. As presented in Fig. 5, the expression levels of these six MMDELs were in accordance with the results obtained using high-throughput RNA-seq. The expression levels of ENST00000621248, NONHSAT175141.1 and ENST00000530490 were significantly downregulated in the MM and HG groups compared with those of the LG group (P<0.05). However, the expression levels of ENST00000537869, ENST00000603538 and NONHSAT180590.1 were significantly increased in the MM and HG groups compared with those of the LG group (P<0.05). The expression levels of the six selected lncRNAs did not differ significantly between the MM and HG groups (P>0.05), indicating that the results of RNA-seq analysis were reliable.
Discussion
Various vascular complications, such as nephropathy, retinopathy and atherosclerosis, are responsible for the decreased quality of life and increased mortality in patients with diabetes. Increased levels of inflammation, non-enzymatic glycation of proteins and oxidative stress are common characteristics of the majority of complications observed in patients with diabetes. At present, MM is considered a major obstacle to implementing effective control of diabetes-related complications (35,36). Therefore, it is important to delineate the mechanisms underlying vascular complications and MM. In the present study, the expression profiles of lncRNAs were comprehensively analyzed in HUVECs from the LG, HG and MM groups, identifying a total of 308 up- and 157 downregulated MMDELs. GO analysis of the parental genes of these MMDELs suggested that the regulation of proteasomal ubiquitin-dependent catabolic process and DNA repair may participate in stress-caused vascular damage induced by exposure to HG in HUVECs. KEGG analysis indicated that in diabetic complications, the TGF-β and advanced glycation end products (AGE)-receptor for AGE (RAGE) signaling pathways may participate in MM-mediated pathogenic mechanisms induced by HG environments. Previous studies have demonstrated that AGE-modified proteins remain in the vessels, kidneys and hearts of patients with diabetes for extended periods of time, even after control of hyperglycemia is achieved in these patients. In addition, AGEs can induce oxidative stress and inflammation by interacting with RAGE receptors on the cellular surface. Therefore, the AGE-RAGE signaling pathway provides a clinical link between MM and diabetic complications (37-39). Additionally, the findings of the present study indicated that these pathways were enriched on the parental genes of MMDELs, which suggested that these lncRNAs may affect vascular endothelial cell function, cellular proliferation and apoptosis by altering the expression of parental genes.
In the present study, a bioinformatics analysis of target mRNAs was performed, which indicated that several DM-associated pathways, including cell cycle, p53 signaling and oxidative phosphorylation, may play a role in MM.
The present study indicated that 15 target genes were enriched in the cell cycle pathway, 14 of which had upregulated expression levels in MM compared with LG, including cyclin dependent kinase 1 (CDK1), Cyclin B1 (CCNB1) and CCNB2 (Table SI). This finding suggested that abnormal proliferation of vascular endothelial cells may lead to pathological angiogenesis, which is a crucial component of the pathological alterations observed in diabetic retinopathy (22). Furthermore, high levels of the CCNB1/CDK1 complex contribute to apoptosis by promoting cell cycle arrest at the mitotic prometaphase and induce the phosphorylation of antiapoptotic proteins, such as Mcl-1 and Bcl-xl, which can activate the subsequent intrinsic cell death pathway (40-42). CDK1 is an essential regulator of mitosis; therefore, CDK1 expression was predicted to be associated with 24 of the MMDELs identified in the present study. The present study indicated that 20 of these MMDELs had downregulated expression and were negatively correlated with CDK1 levels in MM compared with LG. The other four MMDELs, including lncRNA SNHG1 (ENST00000537869; node degree=180; up-MMDELs), had upregulated expression levels in MM compared with LG, a positive correlation with CDK1 levels and a strong positive correlation with the levels of CCNB1 and CCNB2.
The lncRNA SNHG1 is implicated in the progression of various cancers and is associated with a poor prognosis in patients with cancer. SNHG1 acts as a ceRNA to inhibit the expression of miR-140, thereby upregulating the expression of its downstream target, A disintegrin and metalloproteinase domain-containing protein 10, and promoting the proliferation and invasion of gastric cancer cells (43). Toll-like receptor 4 (TLR4) is a target of miR-140(44). TLR4 expression can also be increased by SNHG1, which activates the NF-κB signaling pathway to regulate growth and tumorigenesis in cholangiocarcinoma tissues (44). SNHG1 promotes the expression of NUAK family SNF1-like kinase 1 by downregulating miR-145-5p expression, thereby promoting the invasion of nasopharyngeal carcinoma cells via the AKT signaling pathway (45). SNHG1 knockdown inhibits cancer cell migration and proliferation in vitro and in vivo (45,46). These findings suggest that upregulation of SNHG1 expression in HG environments may lead to proliferation abnormalities in vascular endothelial cells via mechanisms similar to those found in cancers. Specifically, interference with the expression of cell cycle-related regulatory molecules may occur via adsorption of specific miRNAs and the resultant upregulated expression of their target molecules.
The p53 signaling pathway, which mediates cell cycle arrest, cellular senescence and apoptosis, can be activated by various stressors including oxidative stress and DNA damage (47). The analysis of the present study indicated that eight target genes were enriched in this pathway and seven of these genes (Table SI), including CDK1, G2 and S phase-expressed-1 (GTSE1) and DNA damage-binding protein 2 (DDB2), had upregulated expression in MM compared with the LG group. GTSE1 leads to cell cycle arrest by inhibiting the expression of CDK1/CCNB. However, several previous studies have demonstrated that GTSE1 is overexpressed in several malignant tumors and is closely associated with tumor cell migration and invasiveness (48,49). Knockdown of GTSE1 expression suppresses the proliferation, migration and invasiveness of tumor cells (48). In the present study, 28 MMDELs were predicted to be correlated with GTSE1 expression; however, only four MMDELs, including lnc-FCN2-4:1 (NONHSAT135407.2; node degree=83; up-MMDELs), were upregulated and positively correlated with GTSE1 expression (Table SI and SII). Furthermore, lnc-FCN2-4:1 was also predicted to be correlated with CCNB1, CCNB2 and cell division cycle 20 expression. The expression of DDB2, which serves a key role in the repair of DNA (50), was associated with 15 of the MMDELs examined in the present study. Among these 15 MMDELs, eight had upregulated expression and were positively correlated with related lncRNAs, such as lncRNA SNHG1. The mechanism underlying the dysregulated expression of these lncRNAs, which were observed in HUVECs of the MM group in the present study, needs to be further explored in future studies.
Compared with the LG group, a total of 11 target genes were enriched in oxidative phosphorylation (OXPHOS) and five of these genes had upregulated expression in the MM group. Ubiquinol-cytochrome c reductase core protein 1 (UQCRC1), one of the five genes with upregulated expression, is a subunit of complex III of the respiratory chain in the mitochondria (51). UQCRC1 serves a fundamental role in normal mitochondrial function and cellular metabolism. Mutations and abnormal expression of UQCRC1 are associated with Parkinson's disease and multiple malignancies (52-54). In pancreatic ductal adenocarcinoma, increased expression of UQCRC1 mRNA promotes cellular proliferation by generating excessive levels of ATP and OXPHOS via the ATP/RTK/AKT pathway (53). In the present study, UQCRC1 expression was predicted to be positively associated with four MMDELs including lncRNA SNHG7 (NONHSAT135581.2; node degree=80; up-MMDELs). SNHG7, which is highly expressed in certain neoplastic diseases, drives the occurrence and development of tumors by sponging miRNAs, such as miR-34a, miR-2682-5p and miR-449a, thereby promoting proliferation and suppressing apoptosis in cancer cells (55-57). SNHG7 is overexpressed in the areas surrounding the site of myocardial infarction. By acting as a ceRNA and targeting miR-34-5p expression, SNHG7 can promote cardiac fibrosis. Therefore, silencing SNHG7 expression can improve cardiac function (58). The present study indicated that the expression levels of MT-ND2, MT-ND3, MT-ND4L, MT-ATP8, MT-CYB and ATP6V1E1 were downregulated among 11 target genes in MM than LG group that were enriched in Oxidative phosphorylation (Table SI). The expression of MT-ND2, MT-ND3, MT-CYB and MT-ATP8 was positively associated with that of lncRNA XIST (NONHSAT137546.2; node degree=11; down-MMDELs) (Table SII). A previous study reported that knockdown of lncRNA XIST expression can inhibit proliferation, promote apoptosis, and even increase the production of reactive oxygen species (ROS) in non-small-cell lung cancer cells (59). It has been also demonstrated that mitochondrial dysfunction and fission can increase the levels of mitochondrial ROS (60,61). Excess accumulation of mitochondrial ROS in endothelial cells from diabetic patients results in cell death by damaging DNA, lipids and proteins, and leads to vasoconstriction caused by the decreased bioavailability of nitric oxide (62). These findings may partially explain the impairment of endothelial function in DM. Furthermore, based on the aforementioned findings and the results of the present study, it is proposed that the expression of OXPHOS-related mRNAs may be regulated by certain lncRNAs. These currently unknown and complex regulatory networks may be retained for extended periods of time in MM.
In summary, the present study analyzed the expression of MMDELs and the co-expression of mRNA networks in HUVECs subjected to conditions of LG, HG and induction of MM. The present study indicated that several MMDELs were abnormally expressed in experimentally-induced diabetic MM and may be involved in the regulation of mRNA expression through various mechanisms in MM-related pathways, which may play roles in the damage associated with long-term exposure to HG environments. The findings obtained in the present study contribute to improving the understanding of the molecular mechanisms underlying the pathology of MM. Further investigation into the functions of these lncRNAs may result in novel insights and treatments, which could help control MM in patients with diabetes.
Supplementary Material
lncRNA-mRNA co-expression network of the MMDELs analyzed in the present study. Circles represent mRNAs and squares represent lncRNAs. Red color indicates RNAs with upregulated expression and green color indicates RNAs with downregulated expression. The size of the nodes is positively associated with the node degree. lncRNA, long non-coding RNA; MMDEL, metabolic memory-involved differentially expressed lncRNA.
KEGG analysis of target mRNAs of MM-involved differentially expressed lncRNAs
Analysis of correlated expression of mRNAs and LncRNAs
Acknowledgements
The authors would like to acknowledge Mr. Qiang Fan (Shanghai Ao-Ji Biotechnology Co., Ltd., China) for technical assistance with RNA-seq experiments.
Funding
Funding: The present study was funded by the Key Program of Nature Science Foundation of Anhui Education Committee (grant no. KJ2019A0353), Key Project of Translational Medicine of Bengbu Medical College (grant no. BYTM2019039) and Key Project of Natural Science Foundation of Bengbu Medical College (grant no. 2020BYZD020).
Availability of data and materials
The datasets generated and/or analyzed during the current study are available in the National Center for Biotechnology Information, BioProject repository (accession no. PRJNA534362; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA534362).
Authors' contributions
JinC and JiC contributed to acquisition of data for the work, wrote the draft of the paper and contributed to the literature review of the study. JiC performed the bioinformatics analysis. AH performed formal analysis and validation of sequencing data. LY and EX collected and analyzed RT-qPCR data. XP and AH contributed to formal bioinformatics analysis and the literature review. GJ conceived and designed the study. GJ and XP confirm the authenticity of all the raw data. All authors have read and approved the final manuscript.
Ethics approval and consent to participate
Ethics approval for the use of commercially purchased primary human cells was waived by the Ethics Committee of Bengbu Medical College (Bengbu, China).
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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