Open Access

Bioinformatics analysis of a long non‑coding RNA and mRNA regulation network in rats with middle cerebral artery occlusion based on RNA sequencing

  • Authors:
    • Xianchun Duan
    • Lan Han
    • Daiyin Peng
    • Can Peng
    • Ling Xiao
    • Qiuyu Bao
    • Huasheng Peng
  • View Affiliations

  • Published online on: May 27, 2019     https://doi.org/10.3892/mmr.2019.10300
  • Pages: 417-432
  • Copyright: © Duan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Long non‑coding RNAs (lncRNAs) have been proven to be critical gene regulators of development and disease. The main aim of the present study was to elucidate the lncRNA‑mRNA regulation network in ischemic stroke induced by middle cerebral artery occlusion (MCAO) using RNA sequencing (RNA‑seq) in rats. lncRNA expression profiles were screened in brain tissues to identify a number of differentially expressed lncRNAs (DELs) and genes (DEGs) by RNA‑seq. Reverse transcription‑quantitative polymerase chain reaction was performed to further confirm the lncRNA expression data. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to mine mRNA functions, and a lncRNA‑mRNA network was constructed. Additionally, cis‑ and trans‑regulatory gene analyses of DELs were predicted. A total of 134 DELs (fold change >2, false discovery rate <0.05) and 1,006 DEGs (fold change >2 and P<0.05) were identified. Eighteen lncRNAs were predicted to regulate heme oxygenase 1, mitotic checkpoint serine/threonine kinase B, chemokine ligand 2 and DNA Topoisomerase IIα, amongst other genes. These genes are all associated with a cellular response to inorganic substances, alkaloids, estradiol, reactive oxygen species, metal ions, oxidative stress, and are associated with metabolic pathways, chemokine signaling pathways, malaria, Parkinson's disease, the cell cycle and other GO and KEGG pathway enrichments. The present study identifies novel DELs and an lncRNA‑mRNA regulatory network that may allow for an improved understanding of the molecular mechanism of ischemic stroke induced by MCAO.

Introduction

Stroke, universally acknowledged as a cerebrovascular accident, may result in lasting brain damage, long-term disability or even mortality (1,2). A multitude of biological processes are implicated in ischemic stroke, including oxygen deprivation, neuronal necrosis and an intense inflammatory response (3,4). MicroRNAs (miRNAs), long non-coding RNAs (lncRNAs) and even circular RNAs (circRNAs) contribute to RNA-mediated networks (58) that regulate notable cellular events through a variety of complicated mechanisms (9,10). These networks have been implicated in ischemic stroke in previous studies (510); however, there remain gaps in current knowledge in this regard, and novel ncRNAs need be mined in order to provide a better understanding of the precise molecular mechanisms involved in ischemic stroke.

lncRNAs have been proven to be critical gene regulators of development and disease (1113). lncRNAs may also perform functions through competitively binding to miRNAs known as competitive endogenous RNAs (14). Washietl et al (15) systematically analyzed the conservatism of human lncRNA and other six mammalian lncRNA and identified that ~54% human lncRNA loci may be mapped to that of a rat. A previous study has demonstrated that significantly differentially expressed lncRNAs (DELs) may contribute to the stabilization of mRNA expressions in stroke (7). Stroke-induced lncRNAs may also interact with chromatin-modifying proteins and modulate genes associated with ischemic brain damage (16,17). Furthermore, lncRNA BC088414 was revealed to be involved with apoptosis-associated genes following hypoxic-ischemic brain damage (8). Similarly, another study suggested that lncRNA C2dat1 may modulate calcium/calmodulin-dependent protein kinase II expression to promote neuronal survival following cerebral ischemia (10). Although a host of lncRNAs have been identified by massive parallel sequencing, to date, little is known on functional RNA molecules and RNA-mediated regulation networks in ischemic stroke.

The main aim of the present study is to elucidate the lncRNA-mRNA regulation networks in ischemic stroke induced by middle cerebral artery occlusion (MCAO) using RNA sequencing (RNA-seq) in rats.

Materials and methods

MCAO model and tissue preparation

A focal cerebral ischemia model induced by MCAO, prepared as previously described (18), was prepared using 20 7-week-old male Sprague-Dawley rats of a specific pathogen-free grade (weighing 200±20 g), purchased from the experimental animal center of Anhui Medical University (Anhui, China). The study protocol was ethically approved by the Committee on the Ethics of Animal Experiments of Anhui University of Chinese Medicine (approval no. 2012AH-036-03). In brief, the animals were fasted overnight but allowed ad libitum access to water. They were then anesthetized with chloral hydrate (350 mg/kg, intraperitoneal injection). A 4-0 silicon-coated monofilament nylon suture with a round tip was inserted through an arteriectomy in the common carotid artery just below the carotid bifurcation and then advanced into the internal carotid artery ~18 mm distal to the carotid bifurcation until a mild resistance was felt. Following 2 h of MCAO, the filament was removed to allow reperfusion. As a control, control-operated rats underwent identical surgery but did not have the suture inserted. Four days subsequent to MCAO, the left hemispheres were collected and immediately frozen in liquid nitrogen.

RNA-seq

RNA-seq was performed by Ao-Ji Bio-Tech (Shanghai, China). Briefly, total RNA was extracted using an RNeasy Mini kit (Qiagen GmbH, Hilden, Germany), according to the manufacturer's protocol. The RNA quality control was performed using Nanodrop 2000 and Agilent 2100, and mainly depended on the concentration, purity and integrity of the RNA. Ribosomal RNA was removed from total RNA using Ribo-Zero rRNA removal beads (Illumina, Inc., San Diego, CA, USA). Libraries were constructed according to the standard TruSeq protocol (19). Purified cDNA libraries were prepared for cluster generation and sequencing on an Illumina HiSeq 2500 (Illumina, Inc.) according to the manufacturer's protocol. Subsequently, data analyses were performed in silico.

lncRNA annotation

Quality control of the RNA-Seq reads was conducted using FastQC (v0.11.3) (The Babraham Institute, Cambridge, UK). Reads were trimmed using the software seqtk (github.com/lh3/seqtk) for known Illumina TruSeq adapter sequences, poor reads and ribosome RNA reads. Trimmed reads were aligned to the rat genome (Rn6) using Hisat2 (version 2.0.4) (20). Transcripts were assembled using Stringtie (v1.3.0) (20,21). Transcripts constructed from Stringtie were compiled together by gffcompare (v0.9.8) (20,21). Transcripts detected in at least five samples (half of the total number) were considered to be bona fide transcripts. Transcripts, with the exception of those with just one exon and shorter than 200 base pairs, were further analyzed for the identification of lncRNAs. Transcripts with class codes ‘i,’ ‘u,’ and ‘x,’ were considered to be potential novel long transcripts. Pfam (22), Coding Potential Calculator (CPC) (23) and Coding-Non-Coding Index (CNCI) (24) were used to estimate the coding potential of each novel transcript. Transcripts with a Pfam score <0, CNCI <0 and CPC non-significant were considered to lack coding potential. Transcripts were compared with annotation databases, including NONCODE (v4) (http://www.noncode.org) and Ensembl (25). The matched transcripts were considered to be known lncRNAs, and others were considered to be novel lncRNAs. All lncRNAs were quantified using Stringtie. According to the positional association between lncRNA and mRNA in the genome, lncRNA may be classified into six types: Bidirectional, exonic_antisense, exonic_sense, intergenic, intronic_antisense and intronic_sense (26).

The lncRNA-mRNA coexpression network

Initially, the DELs and differentially expressed genes (DEGs) were analyzed using EdgeR (27). For DEGs, log2| [fold change (FC)] |>1 and P<0.05 were used as the cutoff values. Meanwhile, log2| (FC) |>1 and false discovery rate (FDR) <0.05 were used as the threshold for DELs. Hierarchical clustering of DELs was performed based on mean signals using a Euclidean distance function. In addition, a volcano plot was generated. The Pearson's correlation coefficient (PCC) between lncRNAs and mRNAs was calculated (cutoff value, PCC>0.9, P<0.05) and the lncRNA-mRNA regulatory network was structured using Cytoscape 2.8.3 (28).

Prediction of target genes and enrichment analysis

cis- or trans-acting algorithms were used to predict the potential targets of lncRNAs. The first algorithm predicted potential target genes of cis-acting lncRNAs that were physically located within 10 kb upstream or 20 kb downstream of lncRNAs using liftOver genome browser (genome.ucsc.edu/cgi-bin/hgLiftOver). The second algorithm predicted potential target genes of trans-acting lncRNAs based on the lncRNA-mRNA complementary sequences, and predicted lncRNA-mRNA duplex energy. First, BLASTN (29) was performed to detect potential target mRNA sequences with >95% identity and E value <1×10−5 (https://blast.ncbi.nlm.nih.gov/Blast.cgi). Then, RNAplex (30) was used to calculate the complementary energy between lncRNAs and their potential trans-regulated target genes with RNAplex-10−30. Gene Ontology (GO) (31) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (32) enrichment analyses of the identified potential target genes were performed using the Database for Annotation, Visualization and Integrated Discovery (33); and P<0.05 was considered to indicate a statistically significant difference.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

Total RNA was extracted from left hemisphere samples using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA) and reverse-transcribed using a Thermo Fisher Scientific RevertAid First Strand cDNA Synthesis kit (cat. no. K1622; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol at 42°C for 60 min. To further confirm the expression data from RNA-seq, a cutoff value (FC>2, P<0.05) was randomly selected for qPCR verification. The expression levels of six randomly DELs (NONRATT027551.2, MSTRG.1836.1, MSTRG.4344.10, MSTRG.7720.11, NONRATT005132.2 and MSTRG.20633.3) were assayed using a SYBRGreen flurophore (Applied Biosystems; Thermo Fisher Scientific, Inc.) using the PikoReal real-time PCR system (Thermo Fisher Scientific, Inc.) under the following conditions: Initial denaturation at 95°C for 30 sec, followed by 40 cycles at 95°C for 30 sec and 60°C for 30 sec, and a final extension step at 4°C for 20 min. FC was determined using the 2−ΔΔCq method (34). GAPDH mRNA was used as an internal control. The primers used are listed in Table I.

Table I.

Primer sequences.

Table I.

Primer sequences.

GeneSequencePolymerase chain reaction product length (base pairs)
GAPDHF: 5′-CCTGGTATGACAACGAATTTG-3′131
R: 5′-CAGTGAGGGTCTCTCTCTTCC-3′
NONRATT027551.2F: 5′- GGACCTGGAAGGTGAACAGG-3′118
R: 5′-TGAATGGGTGACCAACAGGG-3′
MSTRG.1836.1F: 5′-CCATTGTCCTTCCATCCCCC-3′85
R: 5′-CCACCCTACCAAACTTCCCC-3′
MSTRG.4344.10F: 5′-GACTTAGGCACAGTGGGTGG-3′119
R: 5′-ATGGCAGAGAGCGAATGGAG-3′
MSTRG.7720.11F: 5′-TCCCTAGAGCAGTCCTCACC-3′97
R: 5′- ATCTCGGGTTCGCCTTTTGT-3′
NONRATT005132.2F: 5′-CCTGACTATGGCACGTCCTC-3′152
R: 5′-CTGAGTCCAGTGTGCCTGTT-3′
MSTRG.20633.3F: 5′-CTTTCACTCCGAGAACCCCC-3′117
R: 5′-GCAAGCAGGTTGGTTCCTTG-3′

[i] F, forward; R, reverse.

Statistical analysis

The comparisons between the MCAO group and the control group were determined using a Student's t-test for the RT-qPCR results by SPSS 22.0 statistical software (IBM Corp., Armonk, NY, USA). P<0.05 was considered to indicate a statistically significant difference. The PCC between lncRNAs and mRNAs was calculated using the Hmisc package in R based on the expression determined using RNA-seq (PCC>0.9, P<0.05). The correlation analysis between the RT-qPCR results and RNA-seq results was calculated in Excel 2013 (Microsoft Corporation, Redmond, WA, USA) with the function of CORR.

Results

lncRNA-sequencing data analysis

The present study characterized the lncRNA landscape and expression by performing deep RNA-seq experiments on three control and three MCAO tissue samples. Subsequent to the seqtk quality assessment of sequencing, >33 million total original reads for each sample were obtained, and the proportion of bases with quality values >20 was >94%. These results indicated that the quality of the sequencing results was acceptable (Table II). Subsequent to filtering out the adaptor sequence and low quality reads, the percentage of clean reads within the raw reads accounted for 94% of the total sequences in two groups. Hisat2 software was used to map the obtained clean reads to the Rattus norvegicus reference genome. As presented in Table II, ~97% of the trimmed reads were mapped onto the reference genome. In total, 24,304 lncRNAs were screened from six samples, and there were 23,255 shared lncRNAs detected in the MCAO and control groups (Fig. 1A). The majority of the identified lncRNAs were transcribed from protein-coding exons; others were from introns and intergenic regions (Fig. 1B). In addition, the present study analyzed the distribution of the identified lncRNAs on the rat chromosomes; 24,304 lncRNA transcripts were identified in all chromosomes, and chromosome 1 included the most lncRNAs (Fig. 1C).

Table II.

Results of the RNA sequencing.

Table II.

Results of the RNA sequencing.

Sample IDRaw readsClean readsClean ratio (%)rRNA trimmedMapped readsMapped ratio
MCAO 115519087014728290194.901472142741312904230.824026529
MCAO 214445013013693006494.791367687081203111950.812346697
MCAO 315141198614286243994.351427249111255407430.804354762
Control 116930391616054496094.831604665681422898850.819832697
Control 213653393012967289294.971296096051160720610.828973282
Control 312487837611843297194.841183492991055062860.821661742

[i] MCAO, middle cerebral artery occlusion.

Identification of DEGs and DELs

EdgeR was used to filter DEGs and DELs and differentiate their expression between the control and MCAO groups. A total of 1,007 DEGs (|FC|>2, P<0.05) were identified, including 785 upregulated genes and 222 downregulated genes. Similarly, as presented in Fig. 2, 134 DELs (|FC|>2, FDR<0.05) were identified in the MCAO group (Fig. 2A and B), including 77 upregulated and 57 downregulated DELs (Fig. 2C and D). In the present study, it was revealed that the FC values of certain DELs were equal to positive infinity and negative infinity, meaning that these lncRNAs are switched-on or off with MCAO. Essentially, positive or negative infinity indicates zero expression of the lncRNA in normal or MCAO groups. It was speculated that this may be associated with the abundance of lncRNAs and the sensitivity to RNA-seq. The top five upregulated DELs were NONRATT027551.2, MSTRG.1836.1, MSTRG.4344.10, NONRATT028102.2 and MSTRG.31500.2; the top five downregulated DELs were MSTRG.7720.11, NONRATT005132.2, MSTRG.20633.3, NONRATT020232.2 and MSTRG.1836.3.

lncRNA-mRNA network

The cutoff correlation r-values (|PCC|>0.9) and P-values (P<0.05) were selected to structure a lncRNA-mRNA co-expression network between DEGs and DELs. As Fig. 3 presents, 46 DEGs, 104 DELs and 664 edges were filtered out using Cytoscape to construct the co-expression network. The co-expression-associated top 30 GO terms and pathway terms enrichment analyses presented in Figs. 4 and 5 suggest that these DELs were associated with the cellular response to inorganic substances, alkaloids, estradiols, reactive oxygen species, metal ions and oxidative stress. In particular, the heme oxygenase 1 (HO-1) gene participates in many of these functions. A multitude of pathways were implicated, including metabolic pathways, chemokine signaling pathways, malaria, Parkinson's disease and the cell cycle. Notably, the BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) and C-C motif chemokine ligand 2 (CCL2) genes were associated with the cell cycle.

Regulatory analysis of DELs

A total of 91 cis-regulatory genes of 94 DELs, including 55 upregulated lncRNAs in the MCAO group were identified; 14 of the 91 cis-regulatory genes exhibited differential expression. A total of 13 of the DEL/cis-regulatory gene pairs had positive correlations as follows: NONRATT021925.2 (Rho GDP dissociation inhibitor β), NONRATT004791.2 (G protein subunit γ transducing 2), NONRATT015286.2 (periostin), MSTRG.30235.10 (LRR binding FLII interacting protein 1), NONRATT015403.2 (IQ motif containing GTPase activating protein 3), NONRATT008267.2 (kinesin family member 14), NONRATT009960.2 (non-SMC condensin I complex subunit G), NONRATT011312.2 (retinol binding protein 3), NONRATT005985.2 (DNA topoisomerase IIα), NONRATT016680.2 (ENSRNOG00000053081), NONRATT013960.2 (galanin receptor 1), NONRATT016022.2 (CART prepropeptide) and NONRATT024616.2 (δ like non-canonical Notch ligand 1) (Table III). Additionally, 90 trans-regulatory genes of lncRNAs were filtered by BLASTN and RNAplex, with a negative correlation identified between ENSRNOT00000092040 and Ccl9 (Table IV).

Table III.

Differentially expressed lncRNAs mechanisms involved in cis-regulatory elements.

Table III.

Differentially expressed lncRNAs mechanisms involved in cis-regulatory elements.

lncRNA IDlog2FCQ value Up/downregulatedTypeGene IDGene namelog2FCP-value Up/downregulated
NONRATT017723.2−3.680.0125Downcis ENSRNOG00000000633Rhobtb10.850.1148
NONRATT008064.2−6.730.0145Downcis ENSRNOG00000001183Hnf1a1.250.7907
NONRATT000377.2+∞0.0492Upcis ENSRNOG00000001492Slc8a20.130.9091
NONRATT006957.26.690.0000Upcis ENSRNOG00000001982Cblb0.600.1402
NONRATT009831.2−5.720.0425Downcis ENSRNOG00000002137Aasdh0.210.7048
NONRATT009718.2−5.480.0020Downcis ENSRNOG00000002146Pkd20.290.5035
NONRATT009780.2−3.940.0473Downcis ENSRNOG00000002208Shroom30.830.1301
NONRATT005026.26.140.0000Upcis ENSRNOG00000002607Sox90.250.5566
NONRATT014565.2−2.910.0492Downcis ENSRNOG00000002864Nacc10.260.5744
NONRATT005048.2-∞0.0432Downcis ENSRNOG00000003144Gprc5c0.480.5195
NONRATT024547.2-∞0.0082Downcis ENSRNOG00000003955Spata70.260.6325
NONRATT026753.23.080.0450Upcis ENSRNOG00000003993Thap20.260.7629
NONRATT005132.2−8.710.0471Downcis ENSRNOG00000004049Baiap2−0.040.8211
NONRATT026300.2+∞0.0469Upcis ENSRNOG00000004628Dazap20.130.8554
NONRATT026156.27.440.0000Upcis ENSRNOG00000005332Csdc2−0.580.1263
NONRATT005775.23.690.0453Upcis ENSRNOG00000005538Psmd11−0.030.7616
NONRATT023334.2−4.850.0448Downcis ENSRNOG00000005711Ptprd−0.210.4658
NONRATT021925.2+∞0.0000Upcis ENSRNOG00000005809Arhgdib1.810.0074Up
NONRATT004791.24.020.0218Upcis ENSRNOG00000006108Gngt24.950.0000Up
MSTRG.22811.4-∞0.0388Downcis ENSRNOG00000006966Nfia0.210.6590
NONRATT025479.2−4.140.0000Downcis ENSRNOG00000007610Gdf110.080.9402
NONRATT020232.2−7.720.0000Downcis ENSRNOG00000007804C1galt10.450.2729
NONRATT008198.24.830.0305Upcis ENSRNOG00000007887Elk40.760.2762
NONRATT022252.2+∞0.0305Upcis ENSRNOG00000008099Galnt120.850.2496
NONRATT024954.2−3.980.0471Downcis ENSRNOG00000008155Dus4l−0.170.7340
NONRATT028439.25.060.0430Upcis ENSRNOG00000008187Ubash3b−0.210.4764
NONRATT022210.2−4.240.0492Downcis ENSRNOG00000008237Unc13b0.300.6027
NONRATT027551.29.080.0119Upcis ENSRNOG00000008709Arhgap32−0.240.5090
NONRATT027576.2-∞0.0061Downcis ENSRNOG00000008757Tmem2180.160.8511
NONRATT021402.2-∞0.0291Downcis ENSRNOG00000009156Tra2a0.470.2267
NONRATT021972.2+∞0.0335Upcis ENSRNOG00000009338Kras−0.110.6201
NONRATT022345.2−5.230.0000Downcis ENSRNOG00000009795Nfib0.040.9239
MSTRG.16900.3−4.700.0248Downcis ENSRNOG00000009882Ppp3ca0.071.0000
MSTRG.19870.10+∞0.0000Upcis ENSRNOG00000010993Dpm1−0.140.5966
NONRATT008272.2−3.960.0431Downcis ENSRNOG00000011063Dennd1b0.200.7240
MSTRG.10245.26.310.0002Upcis ENSRNOG00000011704Fbxo34−0.370.2745
NONRATT001841.23.410.0430Upcis ENSRNOG00000012110Col17a1−1.520.0483Down
NONRATT002035.27.890.0003Upcis ENSRNOG00000012324Soga3−0.040.7913
MSTRG.22390.2-∞0.0000Downcis ENSRNOG00000012634Fbxo10−0.410.1989
MSTRG.22390.1+∞0.0000Upcis ENSRNOG00000012634Fbxo10−0.410.1989
NONRATT015286.25.300.0425Upcis ENSRNOG00000012660Postn3.660.0004Up
MSTRG.1836.3−7.600.0041Downcis ENSRNOG00000012716Chd20.060.9929
MSTRG.1836.19.240.0001Upcis ENSRNOG00000012716Chd20.060.9929
NONRATT016334.2+∞0.0000Upcis ENSRNOG00000012734Dcun1d1−0.200.5140
NONRATT015057.2−4.700.0049Downcis ENSRNOG00000012799Prkaa1−0.280.3193
NONRATT000212.2−6.120.0378Downcis ENSRNOG00000013194Rps6ka2−0.140.5704
NONRATT030198.25.340.0000Upcis ENSRNOG00000013213Epha40.350.5640
NONRATT010352.2−6.230.0004Downcis ENSRNOG00000013353Tmem2600.070.9952
NONRATT029471.24.910.0082Upcis ENSRNOG00000013557Lancl1−0.170.5373
NONRATT028588.26.050.0378Upcis ENSRNOG00000013829Chrna31.170.2523
NONRATT023203.2+∞0.0007Upcis ENSRNOG00000013956Rnf380.170.7841
MSTRG.29693.5+∞0.0484Upcis ENSRNOG00000013991Creg2−0.690.2339
MSTRG.12408.2-∞0.0041Downcis ENSRNOG00000014007Gfod10.040.9704
NONRATT004566.23.440.0440Upcis ENSRNOG00000015002Abhd150.760.3266
MSTRG.15067.2+∞0.0090Upcis ENSRNOG00000015334Fcho20.490.2280
NONRATT003289.27.560.0052Upcis ENSRNOG00000015717Ptpre0.210.6977
NONRATT013960.2−3.810.0248Downcis ENSRNOG00000016654Galr1−3.380.0001Down
NONRATT028604.24.710.0000Upcis ENSRNOG00000017193Lingo1−0.520.2454
NONRATT016022.2−5.740.0001Downcis ENSRNOG00000017712Cartpt−3.830.0001Down
NONRATT015604.2−5.600.0000Downcis ENSRNOG00000018166Prkab20.000.8495
NONRATT024616.2−3.790.0143Downcis ENSRNOG00000019584Dlk1−4.210.0000Down
MSTRG.30235.108.500.0275Upcis ENSRNOG00000019892Lrrfip11.140.0479Up
NONRATT026461.2+∞0.0118Upcis ENSRNOG00000020230Pias40.170.8050
NONRATT018820.2-∞0.0002Downcis ENSRNOG00000020337Sla20.520.5953
NONRATT004912.26.000.0041Upcis ENSRNOG00000020658Aarsd10.050.9927
NONRATT019712.2−6.200.0446Downcis ENSRNOG00000021262Slc23a20.090.9742
NONRATT027268.25.270.0071Upcis ENSRNOG00000022570Pus7l0.510.5096
MSTRG.12863.69+∞0.0304Upcis ENSRNOG00000023661Celf20.300.5495
NONRATT030368.25.550.0043Upcis ENSRNOG00000025527Mtcl10.160.8136
NONRATT027862.2−3.750.0304Downcis ENSRNOG00000027145Rora−0.160.5963
NONRATT015403.24.830.0409Upcis ENSRNOG00000027894Iqgap32.030.0137Up
NONRATT003576.2+∞0.0028Upcis ENSRNOG00000028017Tmem1090.360.4097
NONRATT004361.2−5.860.0409Downcis ENSRNOG00000028341Alkbh50.000.8591
NONRATT012903.2−5.260.0000Downcis ENSRNOG00000031706AABR07027388.1−0.850.1761
MSTRG.15111.2+∞0.0000Upcis ENSRNOG00000032735Srek10.180.7203
MSTRG.15111.1−7.420.0000Downcis ENSRNOG00000032735Srek10.180.7203
NONRATT028102.210.210.0000Upcis ENSRNOG00000033809Mlh10.760.1272
NONRATT019889.2−4.600.0113Downcis ENSRNOG00000034031Vstm2l−0.450.3771
NONRATT008267.2+∞0.0039Upcis ENSRNOG00000037211Kif145.800.0005Up
NONRATT009960.24.060.0430Upcis ENSRNOG00000038572Ncapg3.280.0054Up
MSTRG.21884.7+∞0.0492Upcis ENSRNOG00000042458Stau2−0.200.5104
NONRATT030464.25.300.0042Upcis ENSRNOG00000046053Nudt10−0.590.1573
MSTRG.15418.3+∞0.0448Upcis ENSRNOG00000048993Metazoa_SRP0.030.9881
MSTRG.28323.2+∞0.0492Upcis ENSRNOG00000049584AABR07070555.1−0.130.8591
MSTRG.4080.13+∞0.0002Upcis ENSRNOG00000049768Adcy9−0.040.7770
NONRATT025333.26.610.0000Upcis ENSRNOG00000051719AABR07065498.10.110.9304
NONRATT011312.2+∞0.0479Upcis ENSRNOG00000051911Rbp39.840.0000Up
NONRATT024648.2-∞0.0492Downcis ENSRNOG00000052540SNORD1130.450.6631
NONRATT005985.24.530.0490Upcis ENSRNOG00000053047Top2a5.280.0000Up
NONRATT016680.27.620.0041Upcis ENSRNOG00000053081 3.590.0011Up
NONRATT009382.28.350.0257Upcis ENSRNOG00000056826Arap20.210.6941
MSTRG.15263.2-∞0.0041Downcis ENSRNOG00000060329Emb−0.060.7939
NONRATT015639.23.250.0005Upcis ENSRNOG00000061058Csde10.020.8739
NONRATT027585.27.190.0000Upcis ENSRNOG00000061656SNORD140.170.9160

[i] lncRNA, long noncoding RNA; FC, fold change.

Table IV.

Differentially expressed lncRNA mechanisms involved in trans-regulatory elements.

Table IV.

Differentially expressed lncRNA mechanisms involved in trans-regulatory elements.

lncRNA IDlog2FCQ value Up/downregulatedTypeGene namelog2FCP-value Up/downregulated
ENSRNOT00000092040−3.350.0204DownTransCcl95.190.0017Up
ENSRNOT00000092040−3.350.0204DownTransHomer31.000.0611
ENSRNOT00000088402+∞0.0471UpTransAurkb2.350.0723
ENSRNOT00000092040−3.350.0204DownTransGalns1.030.0724
ENSRNOT00000092040−3.350.0204DownTransSlc39a10.920.0739
ENSRNOT00000092040−3.350.0204DownTransAC099384.2+∞0.1052
ENSRNOT00000092040−3.350.0204DownTransHsd3b70.830.1369
ENSRNOT00000092040−3.350.0204DownTransKlrb1b1.980.1413
ENSRNOT00000092040−3.350.0204DownTransMe20.630.1558
ENSRNOT00000092040−3.350.0204DownTransPnma2−0.510.1564
ENSRNOT00000092040−3.350.0204DownTransCass41.240.1599
ENSRNOT00000092040−3.350.0204DownTransKlhl23−0.440.2192
ENSRNOT00000092040−3.350.0204DownTransElac1−0.490.2260
ENSRNOT00000092040−3.350.0204DownTransClcf11.260.2519
ENSRNOT00000092040−3.350.0204DownTransFzd60.800.2590
NONRATT009960.24.060.0430UpTransLcorl−0.440.3199
ENSRNOT00000092040−3.350.0204DownTransGpr37l10.400.3297
ENSRNOT00000092040−3.350.0204DownTransMmab−0.350.3391
ENSRNOT00000092040−3.350.0204DownTransSpon1−0.280.3414
ENSRNOT00000092040−3.350.0204DownTransIfngr20.440.3660
ENSRNOT00000092040−3.350.0204DownTransFoxred2−0.350.3732
ENSRNOT00000092040−3.350.0204DownTransPrr22−0.670.3889
ENSRNOT00000092040−3.350.0204DownTransSppl2a0.400.3894
ENSRNOT00000092040−3.350.0204DownTransHaus50.550.4019
ENSRNOT00000092040−3.350.0204DownTransCpvl0.800.4041
ENSRNOT00000092040−3.350.0204DownTransFam120b−0.240.4061
ENSRNOT00000092040−3.350.0204DownTransFbxo3−0.210.4206
ENSRNOT00000092040−3.350.0204DownTransYpel4−0.240.4477
ENSRNOT00000092040−3.350.0204DownTransKlhl26−0.200.4480
ENSRNOT00000092040−3.350.0204DownTransSync0.430.5076
ENSRNOT00000092040−3.350.0204DownTransLhfpl5−0.310.5231
ENSRNOT00000092040−3.350.0204DownTransMrpl520.440.5306
ENSRNOT00000092040−3.350.0204DownTransCldn15−0.390.5315
ENSRNOT00000092040−3.350.0204DownTransMrps35−0.180.5443
ENSRNOT00000092040−3.350.0204DownTransZfp382−0.170.5640
ENSRNOT00000092040−3.350.0204DownTransUbe2k−0.140.5697
ENSRNOT00000092040−3.350.0204DownTransDrg1−0.120.5755
ENSRNOT00000092040−3.350.0204DownTransP2rx5−0.360.5770
ENSRNOT00000092040−3.350.0204DownTransXpnpep30.250.5933
ENSRNOT00000092040−3.350.0204DownTransRGD13113450.280.5950
ENSRNOT00000092040−3.350.0204DownTransAcer20.320.6034
ENSRNOT00000092040−3.350.0204DownTransUrgcp0.280.6182
ENSRNOT00000092040−3.350.0204DownTransMegf8−0.100.6223
ENSRNOT00000092040−3.350.0204DownTransLrtm2−0.140.6301
ENSRNOT00000092040−3.350.0204DownTransRGD15622990.260.6325
ENSRNOT00000092040−3.350.0204DownTransRpl90.380.6347
ENSRNOT00000092040−3.350.0204DownTransSlc25a44−0.100.6348
ENSRNOT00000092040−3.350.0204DownTransSsmem1−0.280.6350
ENSRNOT00000092040−3.350.0204DownTransStt3a0.240.6468
ENSRNOT00000092040−3.350.0204DownTransSpire1−0.090.6631
ENSRNOT00000092040−3.350.0204DownTransRGD1561777−0.130.6846
ENSRNOT00000092040−3.350.0204DownTransLuzp1−0.090.6941
ENSRNOT00000092040−3.350.0204DownTransAcot20.260.7000
ENSRNOT00000092040−3.350.0204DownTransFitm2−0.070.7111
ENSRNOT00000092040−3.350.0204DownTransAox40.320.7374
ENSRNOT00000092040−3.350.0204DownTransGolga1−0.040.7775
ENSRNOT00000092040−3.350.0204DownTransLefty21.230.7977
ENSRNOT00000092040−3.350.0204DownTransZdhhc24−0.040.8075
ENSRNOT00000092040−3.350.0204DownTransZfp329−0.030.8205
ENSRNOT00000092040−3.350.0204DownTransTmem101−0.030.8236
ENSRNOT00000092040−3.350.0204DownTransDhh0.280.8353
ENSRNOT00000092040−3.350.0204DownTransNcr30.570.8354
ENSRNOT00000092040−3.350.0204DownTransPpp1r15b0.140.8423
ENSRNOT00000092040−3.350.0204DownTransZfyve27−0.010.8439
ENSRNOT00000092040−3.350.0204DownTransInts70.150.8457
ENSRNOT00000092040−3.350.0204DownTransDus1l0.140.8545
ENSRNOT00000092040−3.350.0204DownTransAhsa20.150.8576
ENSRNOT00000092040−3.350.0204DownTransAif1l0.150.8579
ENSRNOT00000092040−3.350.0204DownTransRsg10.160.8645
ENSRNOT00000092040−3.350.0204DownTransFuom0.150.8883
ENSRNOT00000092040−3.350.0204DownTransCwf19l10.120.8894
ENSRNOT00000092040−3.350.0204DownTransPaqr70.010.8942
ENSRNOT00000092040−3.350.0204DownTransCoa50.020.8985
ENSRNOT00000092040−3.350.0204DownTransCdc14b0.130.8989
ENSRNOT00000092040−3.350.0204DownTransCrebl2−0.010.9081
ENSRNOT00000092040−3.350.0204DownTransRGD15645410.100.9210
ENSRNOT00000092040−3.350.0204DownTransSlc15a1−0.030.9211
ENSRNOT00000092040−3.350.0204DownTransPpm1k0.030.9299
NONRATT025479.2−4.140.0000DownCis and transGdf110.080.9402
ENSRNOT00000092040−3.350.0204DownTransFam20b0.040.9436
ENSRNOT00000092040−3.350.0204DownTransAnapc110.090.9536
ENSRNOT00000092040−3.350.0204DownTransTmem79−0.040.9595
ENSRNOT00000092040−3.350.0204DownTransCwc250.060.9692
ENSRNOT00000092040−3.350.0204DownTransBlvrb0.070.9859
ENSRNOT00000092040−3.350.0204DownTransStk40.080.9880
ENSRNOT00000092040−3.350.0204DownTransRbm200.080.9896
ENSRNOT00000092040−3.350.0204DownTransTbc1d10b0.060.9927
ENSRNOT00000092040−3.350.0204DownTransMapkbp10.070.9986
ENSRNOT00000092040−3.350.0204DownTransPsma81.001.0000
ENSRNOT00000092040−3.350.0204DownTransRbbp8nl0.371.0000

[i] lncRNA, long non-coding RNA; FC, fold change.

Validation of expression of DELs by RT-qPCR

From the data in Fig. 6, NONRATT027551.2, MSTRG.1836.1 and MSTRG.4344.10 were identified to be significantly upregulated in the MCAO group compared with the control (P<0.05), consistent with the RNA-seq data, while MSTRG.7720.11, NONRATT005132.2 and MSTRG.20633.3 were significantly downregulated in the MCAO group compared with the control (P<0.01), also consistent with the RNA-seq data. These results, revealing that the RNA-seq results were consistent with the RT-qPCR results, verified that the RNA-seq results were reliable (Fig. 6).

Discussion

A host of lncRNAs have been indicated to be involved in ischemic stroke by microarray or RNA-seq studies (35,36). Metastasis associated lung adenocarcinoma transcript 1 was identified to have a function in ischemic stroke through inhibiting endothelial cell death and inflammation (36,37). Additionally, the upregulation of H19 imprinted maternally expressed transcript may induce apoptosis and necrosis in cerebral ischemic reperfusion injury (3840). In the present study, a total of 77 upregulated and 57 downregulated DELs (|FC|>2, P<0.05) were identified through reliable RNA-seq and validated using RT-qPCR in an ischemic stroke group induced by MCAO compared with a control group.

HO-1-mediated neurogenesis has been demonstrated to be enhanced in ischemic stroke in mice (41). HO-1 has been revealed to promote angiogenesis following cerebral ischemic reperfusion injury in rats (42). GO enrichment analysis suggested that HO-1 was associated with responses to alkaloids, cellular responses to oxidative stress and responses to reactive oxygen species. BUB1B has been reported to promote tumor proliferation in glioblastoma (43,44). Similarly, BUB1B has been implicated in tumor growth and the progression of prostate cancer (45), and overexpressed BUB1B has been demonstrated to be involved in lung adenocarcinoma in humans (46). The KEGG enrichment analysis in the present study indicated that BUB1B was associated with the cell cycle. It has previously been reported that upregulated CCL2 is associated with protection from stroke induced by hypoxic preconditioning (47), and the knockdown of CCL2 was used to successfully reverse the drug resistance of tumor cells in gastric cancer (48). In the KEGG enrichment analysis performed in the present study, CCL2 was additionally associated with the cell cycle. Furthermore, based on the data presented in Fig. 3, HO-1, BUB1B and CCL2 may be regulated by a number of novel lncRNAs, including NONRATT008267.2, NONRATT015286.2, NONRATT004791.2, MSTRG.15067.2, NONRATT003289.2, NONRATT004566.2, NONRATT005985.2, NONRATT008198.2, NONRATT028439.2, NONRATT026753.2, NONRATT027268.2, MSTRG.15418.3, NONRATT016680.2, NONRATT015403.2, MSTRG.29693.5, NONRATT009960.2, MSTRG.27670.3 and NONRATT000377.2. A previous study has suggested that the knockdown of DNA topoisomerase IIα (Top2a) may suppress proliferation and invasion of colon cancer cells (49); based on the present regulatory analysis of DELs, Top2a, as a cis-regulatory gene of NONRATT005985.2, may have a vital function in ischemic stroke. Overall, the analyzed data provide novel DELs and an lncRNA-mRNA regulatory network that may provide a better understanding of ischemic stroke induced by MCAO.

Acknowledgements

The authors would like to thank Mr. Qiang Fan (Ao-Ji Bio-tech Co., Ltd., Shanghai, China) for help with data analysis.

Funding

The present study was financially supported by the National Key Research and Development Plan (grant nos. 2017YFC1701600 and 2017YFC1701601), the National Natural Science Foundation of China (grant nos. 81473387, 81503291 and 81703805), the Anhui Provincial Natural Science Foundation of China (grant no. 1508085QH191) and the Key Project of the National Science Fund of Anhui Province (grant no. KJ2013A169).

Availability of data and materials

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

Authors' contributions

XD and DP conceived and designed the study. XD and QB performed the experiments. LH, CP, LX and HP analyzed the data and drafted the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The present study was approved by The Animal Experiments Ethics Committee of The Anhui University of Chinese Medicine (Hefei, China).

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Writing Group Members, Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Després JP, et al: Heart disease and stroke statistics-2016 update: A report from the american heart association. Circulation. 133:e38–e360. 2016.PubMed/NCBI

2 

Tewari D, Majumdar D, Vallabhaneni S and Bera AK: Aspirin induces cell death by directly modulating mitochondrial voltage-dependent anion channel (VDAC). Sci Rep. 7:451842017. View Article : Google Scholar : PubMed/NCBI

3 

Mitsios N, Gaffney J, Kumar P, Krupinski J, Kumar S and Slevin M: Pathophysiology of acute ischaemic stroke: An analysis of common signalling mechanisms and identification of new molecular targets. Pathobiology. 73:159–175. 2006. View Article : Google Scholar : PubMed/NCBI

4 

Deb P, Sharma S and Hassan KM: Pathophysiologic mechanisms of acute ischemic stroke: An overview with emphasis on therapeutic significance beyond thrombolysis. Pathophysiology. 17:197–218. 2010. View Article : Google Scholar : PubMed/NCBI

5 

Dharap A, Bowen K, Place R, Li LC and Vemuganti R: Transient focal ischemia induces extensive temporal changes in rat cerebral microRNAome. J Cereb Blood Flow Metab. 29:675–687. 2009. View Article : Google Scholar : PubMed/NCBI

6 

Jeyaseelan K, Lim KY and Armugam A: MicroRNA expression in the blood and brain of rats subjected to transient focal ischemia by middle cerebral artery occlusion. Stroke. 39:959–966. 2008. View Article : Google Scholar : PubMed/NCBI

7 

Dharap A, Nakka VP and Vemuganti R: Effect of focal ischemia on long noncoding RNAs. Stroke. 43:2800–2802. 2012. View Article : Google Scholar : PubMed/NCBI

8 

Zhao F, Qu Y, Liu J, Liu H, Zhang L, Feng Y, Wang H, Gan J, Lu R and Mu D: Microarray profiling and co-expression network analysis of lncRNAs and mRNAs in neonatal rats following hypoxic-ischemic brain damage. Sci Rep. 5:138502015. View Article : Google Scholar : PubMed/NCBI

9 

Wei N, Xiao L, Xue R, Zhang D, Zhou J, Ren H, Guo S and Xu J: MicroRNA-9 mediates the cell apoptosis by targeting Bcl2l11 in ischemic stroke. Mol Neurobiol. 53:6809–6817. 2016. View Article : Google Scholar : PubMed/NCBI

10 

Xu Q, Deng F, Xing Z, Wu Z, Cen B, Xu S, Zhao Z, Nepomuceno R, Bhuiyan MI, Sun D, et al: Long non-coding RNA C2dat1 regulates CaMKIIδ expression to promote neuronal survival through the NF-κB signaling pathway following cerebral ischemia. Cell Death Dis. 7:e21732016. View Article : Google Scholar : PubMed/NCBI

11 

Qureshi IA and Mehler MF: Emerging roles of non-coding RNAs in brain evolution, development, plasticity and disease. Nat Rev Neurosci. 13:528–541. 2012. View Article : Google Scholar : PubMed/NCBI

12 

Schaukowitch K and Kim TK: Emerging epigenetic mechanisms of long non-coding RNAs. Neuroscience. 264:25–38. 2014. View Article : Google Scholar : PubMed/NCBI

13 

Briggs JA, Wolvetang EJ, Mattick JS, Rinn JL and Barry G: Mechanisms of long non-coding RNAs in mammalian nervous system development, plasticity, disease, and evolution. Neuron. 88:861–877. 2015. View Article : Google Scholar : PubMed/NCBI

14 

Salmena L, Poliseno L, Tay Y, Kats L and Pandolfi PP: A ceRNA hypothesis: The Rosetta Stone of a hidden RNA language? Cell. 146:353–358. 2011. View Article : Google Scholar : PubMed/NCBI

15 

Washietl S, Kellis M and Garber M: Evolutionary dynamics and tissue specificity of human long noncoding RNAs in six mammals. Genome Res. 24:616–628. 2014. View Article : Google Scholar : PubMed/NCBI

16 

Dharap A, Pokrzywa C and Vemuganti R: Increased binding of stroke-induced long non-coding RNAs to the transcriptional corepressors Sin3A and coREST. ASN Neuro. 5:283–289. 2013. View Article : Google Scholar : PubMed/NCBI

17 

Mehta SL, Kim T and Vemuganti R: Long noncoding RNA FosDT promotes ischemic brain injury by interacting with REST-associated chromatin-modifying proteins. J Neurosci. 35:16443–16449. 2015. View Article : Google Scholar : PubMed/NCBI

18 

Han L, Ji Z, Chen W, Yin D, Xu F, Li S, Chen F, Zhu G and Peng D: Protective effects of tao-Hong-si-wu decoction on memory impairment and hippocampal damage in animal model of vascular dementia. Evid Based Complement Alternat Med. 2015:1958352015. View Article : Google Scholar : PubMed/NCBI

19 

Duan X, Han L, Peng D, Chen W, Peng C, Xiao L and Bao Q: High throughput mRNA sequencing reveals potential therapeutic targets of Tao-Hong-Si-Wu decoction in experimental middle cerebral artery occlusion. Front Pharmacol. 9:15702019. View Article : Google Scholar : PubMed/NCBI

20 

Pertea M, Kim D, Pertea GM, Leek JT and Salzberg SL: Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 11:1650–1667. 2016. View Article : Google Scholar : PubMed/NCBI

21 

Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT and Salzberg SL: StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 33:290–295. 2015. View Article : Google Scholar : PubMed/NCBI

22 

Sun L, Zhang Z, Bailey TL, Perkins AC, Tallack MR, Xu Z and Liu H: Prediction of novel long non-coding RNAs based on RNA-Seq data of mouse Klf1 knockout study. BMC Bioinformatics. 13:3312012. View Article : Google Scholar : PubMed/NCBI

23 

Kong L, Zhang Y, Ye ZQ, Liu XQ, Zhao SQ, Wei L and Gao G: CPC: Assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res 35 (Web Server Issue). W345–D349. 2007. View Article : Google Scholar

24 

Sun L, Luo H, Bu D, Zhao G, Yu K, Zhang C, Liu Y, Chen R and Zhao Y: Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts. Nucleic Acids Res. 41:e1662013. View Article : Google Scholar : PubMed/NCBI

25 

Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, Billis K, Cummins C, Gall A, Girón CG, et al: Ensembl 2018. Nucleic Acids Res 46 D. D754–D761. 2018. View Article : Google Scholar

26 

Knauss JL and Sun T: Regulatory mechanisms of long noncoding RNAs in vertebrate central nervous system development and function. Neuroscience. 235:200–214. 2013. View Article : Google Scholar : PubMed/NCBI

27 

Nikolayeva O and Robinson MD: edgeR for differential RNA-seq and ChIP-seq analysis: An application to stem cell biology. Methods Mol Biol. 1150:45–79. 2014. View Article : Google Scholar : PubMed/NCBI

28 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI

29 

Altschul SF, Gish W, Miller W, Myers EW and Lipman DJ: Basic local alignment search tool. J Mol Biol. 215:403–410. 1990. View Article : Google Scholar : PubMed/NCBI

30 

Tafer H and Hofacker IL: RNAplex: A fast tool for RNA-RNA interaction search. Bioinformatics. 24:2657–2663. 2008. View Article : Google Scholar : PubMed/NCBI

31 

Gene Ontology Consortium, Blake JA, Dolan M, Drabkin H, Hill DP, Li N, Sitnikov D, Bridges S, Burgess S, Buza T, et al: Gene ontology annotations and resources. Nucleic Acids Res 41 (Database Issue). D530–D535. 2013.

32 

Kanehisa M and Goto S: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI

33 

Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:P32003. View Article : Google Scholar : PubMed/NCBI

34 

Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 25:402–408. 2001. View Article : Google Scholar : PubMed/NCBI

35 

Dykstra-Aiello C, Jickling GC, Ander BP, Shroff N, Zhan X, Liu D, Hull H, Orantia M, Stamova BS and Sharp FR: Altered expression of long noncoding RNAs in blood after ischemic stroke and proximity to putative stroke risk loci. Stroke. 47:2896–2903. 2016. View Article : Google Scholar : PubMed/NCBI

36 

Zhang J, Yuan L, Zhang X, Hamblin MH, Zhu T, Meng F, Li Y, Chen YE and Yin KJ: Altered long non-coding RNA transcriptomic profiles in brain microvascular endothelium after cerebral ischemia. Exp Neurol. 277:162–170. 2016. View Article : Google Scholar : PubMed/NCBI

37 

Zhang X, Tang X, Liu K, Hamblin MH and Yin KJ: Long noncoding RNA malat1 regulates cerebrovascular pathologies in ischemic stroke. J Neurosci. 37:1797–1806. 2017. View Article : Google Scholar : PubMed/NCBI

38 

Wang J, Cao B, Han D, Sun M and Feng J: Long non-coding RNA H19 induces cerebral ischemia reperfusion injury via activation of autophagy. Aging Dis. 8:71–84. 2017. View Article : Google Scholar : PubMed/NCBI

39 

Tao H, Cao W, Yang JJ, Shi KH, Zhou X, Liu LP and Li J: Long noncoding RNA H19 controls DUSP5/ERK1/2 axis in cardiac fibroblast proliferation and fibrosis. Cardiovasc Pathol. 25:381–389. 2016. View Article : Google Scholar : PubMed/NCBI

40 

Puyal J and Clarke PG: Targeting autophagy to prevent neonatal stroke damage. Autophagy. 5:1060–1061. 2009. View Article : Google Scholar : PubMed/NCBI

41 

Nada SE, Tulsulkar J and Shah ZA: Heme oxygenase 1-mediated neurogenesis is enhanced by Ginkgo biloba (EGb 761®) after permanent ischemic stroke in mice. Mol Neurobiol. 49:945–956. 2014. View Article : Google Scholar : PubMed/NCBI

42 

Dong B, Zhang Z, Xie K, Yang Y, Shi Y, Wang C and Yu Y: Hemopexin promotes angiogenesis via up-regulating HO-1 in rats after cerebral ischemia-reperfusion injury. BMC Anesthesiol. 18:22018. View Article : Google Scholar : PubMed/NCBI

43 

Ma Q, Liu Y, Shang L, Yu J and Qu Q: The FOXM1/BUB1B signaling pathway is essential for the tumorigenicity and radioresistance of glioblastoma. Oncol Rep. 38:3367–3375. 2017.PubMed/NCBI

44 

Lee E, Pain M, Wang H, Herman JA, Toledo CM, DeLuca JG, Yong RL, Paddison P and Zhu J: Sensitivity to BUB1B inhibition defines an alternative classification of glioblastoma. Cancer Res. 77:5518–5529. 2017. View Article : Google Scholar : PubMed/NCBI

45 

Fu X, Chen G, Cai ZD, Wang C, Liu ZZ, Lin ZY, Wu YD, Liang YX, Han ZD, Liu JC and Zhong WD: Overexpression of BUB1B contributes to progression of prostate cancer and predicts poor outcome in patients with prostate cancer. Onco Targets Ther. 9:2211–2220. 2016.PubMed/NCBI

46 

Chen H, Lee J, Kljavin NM, Haley B, Daemen A, Johnson L and Liang Y: Requirement for BUB1B/BUBR1 in tumor progression of lung adenocarcinoma. Genes Cancer. 6:106–118. 2015.PubMed/NCBI

47 

Stowe AM, Wacker BK, Cravens PD, Perfater JL, Li MK, Hu R, Freie AB, Stüve O and Gidday JM: CCL2 upregulation triggers hypoxic preconditioning-induced protection from stroke. J Neuroinflammation. 9:332012. View Article : Google Scholar : PubMed/NCBI

48 

Xu W, Wei Q, Han M, Zhou B, Wang H, Zhang J, Wang Q, Sun J, Feng L, Wang S, et al: CCL2-SQSTM1 positive feedback loop suppresses autophagy to promote chemoresistance in gastric cancer. Int J Biol Sci. 14:1054–1066. 2018. View Article : Google Scholar : PubMed/NCBI

49 

Zhang R, Xu J, Zhao J and Bai JH: Proliferation and invasion of colon cancer cells are suppressed by knockdown of TOP2A. J Cell Biochem. 119:7256–7263. 2018. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

July-2019
Volume 20 Issue 1

Print ISSN: 1791-2997
Online ISSN:1791-3004

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Duan X, Han L, Peng D, Peng C, Xiao L, Bao Q and Peng H: Bioinformatics analysis of a long non‑coding RNA and mRNA regulation network in rats with middle cerebral artery occlusion based on RNA sequencing. Mol Med Rep 20: 417-432, 2019.
APA
Duan, X., Han, L., Peng, D., Peng, C., Xiao, L., Bao, Q., & Peng, H. (2019). Bioinformatics analysis of a long non‑coding RNA and mRNA regulation network in rats with middle cerebral artery occlusion based on RNA sequencing. Molecular Medicine Reports, 20, 417-432. https://doi.org/10.3892/mmr.2019.10300
MLA
Duan, X., Han, L., Peng, D., Peng, C., Xiao, L., Bao, Q., Peng, H."Bioinformatics analysis of a long non‑coding RNA and mRNA regulation network in rats with middle cerebral artery occlusion based on RNA sequencing". Molecular Medicine Reports 20.1 (2019): 417-432.
Chicago
Duan, X., Han, L., Peng, D., Peng, C., Xiao, L., Bao, Q., Peng, H."Bioinformatics analysis of a long non‑coding RNA and mRNA regulation network in rats with middle cerebral artery occlusion based on RNA sequencing". Molecular Medicine Reports 20, no. 1 (2019): 417-432. https://doi.org/10.3892/mmr.2019.10300