Abstract
Background
Neurogenic bladder (NB) following spinal cord injury (SCI) is a debilitating complication characterized by bladder fibrosis and structural remodeling. Non-coding RNAs (ncRNAs) are emerging regulators of fibrotic processes, yet their roles in SCI-induced NB remain unexplored. This study aimed to delineate the dynamic ncRNA-messenger RNA (mRNA) regulatory landscape in SCI-associated NB using transcriptomic profiling, identifying potential biomarkers and therapeutic targets.
Methods
Twenty female Wistar rats underwent complete T10–T11 spinal cord transection (SCI groups, n=15) or laminectomy-only (NC group, n=5). Bladder tissues were harvested at 1, 2, and 4 weeks post-SCI. Next-generation sequencing (NGS) analyzed mRNA, long non-coding RNA (lncRNA), and microRNA (miRNA) expression. Differentially expressed genes/mRNAs/lncRNAs/miRNAs (DEGs/DEMs/DELs/DEMIs) were identified (|log2FC|>1, P<0.05), and lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed. Functional enrichment and quantitative real-time polymerase chain reaction (qPCR) validation were performed to confirm key findings.
Results
Histopathology confirmed progressive bladder fibrosis, with collagen deposition increasing from 7.3% (NC) to 34.6% at 4 weeks post-SCI (P<0.001). NGS revealed 3,255/3,449/884 DEMs, 904/870/278 DELs, and 229/77/127 DEMIs in SCI-1/2/3 vs. NC, respectively. Temporal analysis identified 420 shared DEMs and 102 DELs, with lncRNAs enriched on chromosomes 1 and 7. The ceRNA networks implicated transforming growth factor-beta (TGF-β), calcium, and interleukin-17 (IL-17) signaling pathways in fibrosis. Notably, miR-21-5p showed progressive upregulation, while miR-139-5p decreased, correlating with ROCK2-mediated detrusor overactivity (DO). Functional enrichment highlighted inflammatory response and extracellular matrix reorganization as dominant processes. qPCR validated 12 candidate DEGs, including PVT1 and miR-21-5p, aligning with sequencing data.
Conclusions
This first comprehensive transcriptomic atlas of SCI-induced NB uncovers dynamic ncRNA-mRNA networks driving fibrosis through TGF-β and inflammatory pathways. The identified dysregulated ncRNAs and their target genes provide novel biomarkers for early NB diagnosis and potential therapeutic entry points. Our findings establish a foundation for developing ncRNA-targeted strategies to mitigate bladder remodeling post-SCI, addressing a critical unmet need in neuro-urology.
Keywords: Spinal cord injury (SCI), neurogenic bladder (NB), bladder fibrosis, competing endogenous RNA, biomarkers
Highlight box.
Key findings
• This study establishes the first comprehensive transcriptomic landscape of neurogenic bladder (NB) development following spinal cord injury (SCI) in a rat model.
What is known and what is new?
• NB after SCI is known to involve fibrosis and structural remodeling, but the regulatory roles of non-coding RNAs (ncRNAs) remained unexplored.
• This study provides the first comprehensive transcriptomic atlas of SCI-induced NB, uncovering dynamic ncRNA-mRNA networks and identifying novel biomarkers (e.g., PVT1, miR-21-5p) and therapeutic targets linked to fibrotic signaling pathways.
What is the implication, and what should change now?
• The dysregulated ncRNAs and associated pathways offer potential biomarkers for early NB diagnosis and therapeutic interventions targeting fibrosis.
• Future research should prioritize validating these ncRNAs in human cohorts and developing ncRNA-based strategies to mitigate bladder remodeling post-SCI, addressing a critical unmet need in neuro-urology.
Introduction
Normal urinary storage and voiding functions of the bladder rely on precise neural regulation by the brain and spinal cord. Following spinal cord injury (SCI), over 95% of patients develop neurogenic bladder (NB) of varying severity, with fewer than 1% recovering pre-injury bladder function (1). Typically caused by traumatic events such as spinal fractures or dislocations-primarily from traffic accidents and injuries-SCI induces multisystem physiological dysfunction, including NB, disability, and potentially fatal complications (2). Acute SCI particularly disrupts lower urinary tract function, rapidly progressing to severe bladder fibrosis that profoundly impacts quality of life and longevity (3). These complications frequently result in upper urinary tract damage and substantially impaired health-related quality of life (4). Notably, NB-associated complications account for significant mortality in SCI patients, with annual treatment costs exceeding 182,033 dollars per patient, imposing substantial socioeconomic burdens (5).
Non-coding RNAs (ncRNAs), particularly competing endogenous RNAs (ceRNAs), play pivotal roles in post-transcriptional gene regulation (6). Among these, long non-coding RNAs (lncRNAs) (7) and microRNAs (miRNAs) (8,9) have been implicated in fibrosis and disease pathophysiology, including cancer and organ fibrosis. For instance, lncRNAs such as PVT1, GAS5, H19, and TILAM are associated with fibrotic processes in multiple organs (10,11). However, while over 50,000 lncRNAs have been identified in humans through next-generation sequencing (NGS), fewer than 1% have been functionally characterized (12). To date, no studies have explored the expression dynamics or roles of lncRNAs in SCI-induced NB.
Similarly, miRNAs regulate diverse physiological and pathological processes by modulating transcriptional and post-transcriptional pathways. Dysregulation of miRNAs (miRNA219a-2, miR-190b-5p and miR-21) has been linked to fibrosis in renal, hepatic, and cardiac tissues (13-15). In bladder disorders, elevated miR-221 and miR-125b levels in detrusor overactivity (DO) patients correlate with post-treatment urinary retention risks (16), while urinary miRNA (miR-10a-5p, miR-301b-3p and miR-363-3p) could discriminate between controls and patients with bladder outlet obstruction (BOO) (17). However, these studies focus on overactive bladder (OAB)/DO, leaving the miRNA landscape of SCI-induced NB and its fibrotic progression unexplored.
Despite growing interest in bladder fibrosis mechanisms, the roles of ncRNAs in SCI-associated NB remain poorly understood. To address this critical gap in knowledge, this study employed longitudinal transcriptomic NGS to profile messenger RNA (mRNA), lncRNA, and miRNA expression in rat bladders at multiple post-SCI timepoints. We aimed to: (I) identify dynamic changes in differentially expressed genes (DEGs) across these RNA classes; (II) correlate these molecular changes with the progression of bladder fibrosis; and (III) construct lncRNA-miRNA-mRNA ceRNA regulatory networks to elucidate potential mechanisms underlying ncRNA-mediated bladder remodeling following SCI. This study provides the first comprehensive exploration of ncRNA dynamics into SCI-induced fibrosis found in NB. These findings offer us insight into potential diagnostic biomarkers and therapeutic targets that could mitigate the fibrotic component of NB morbidity. We present this article in accordance with the ARRIVE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-376/rc).
Methods
Experimental animals and grouping
Twenty healthy 10-week-old female Wistar rats (body weight: 200±20 g) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. The rats were randomly divided into four groups (n=5 per group) using a random number table: a negative control group (NC) and three SCI groups (SCI-1, SCI-2, and SCI-3), which underwent bladder tissue collection at 1, 2, and 4 weeks post-SCI, respectively. The NC group received laminectomy only at the same vertebral level (T10–T11), with bladder tissue harvested at 4 weeks post-surgery. All animals were housed under standard conditions (12/12 h light/dark cycle, 24–26 ℃, 50–70% humidity) with free access to food and water. Experiments were approved by the Institutional Animal Care and Use Committee of Xuanwu Hospital Capital Medical University (No. XW-20210910-1), in compliance with the National Institute of Health Guidelines for the Care and Use of Laboratory Animals. A protocol was prepared before the study without registration.
Establishment of SCI rat model
Rats in the SCI groups were fasted for 24 h pre-surgery and received prophylactic ampicillin sodium (100 mg/kg, i.p.). Under deep anesthesia (3% isoflurane; confirmed by loss of corneal and tail pinch reflexes), a complete spinal cord transection was performed at the T10–T11 level. Briefly, a dorsal midline incision exposed the T10–T11 vertebrae. Laminectomy was performed to expose the spinal cord, which was then completely transected. Complete transection was confirmed by persistent hindlimb paralysis post-surgery. The wound was closed in layers. Postoperatively, ampicillin sodium (100 mg/kg/day) was administered for 5 days, and bladders were manually emptied every 8 h until spontaneous voiding resumed. The NC group underwent laminectomy without spinal cord transection. Inclusion criteria: rats demonstrating complete hindlimb paralysis (BBB score =0) and sustained urinary retention requiring manual emptying. Exclusion criteria: subjects exhibiting hindlimb motor recovery (BBB score >0), spontaneous urination post-injury, autophagy, or mortality prior to endpoint.
Tissue harvesting and processing
Bladder tissues were collected from SCI-1, SCI-2, and SCI-3 groups at 1, 2, and 4 weeks post-SCI, respectively, under anesthesia (1% sodium pentobarbital, 40 mg/kg, i.p.). NC group tissues were harvested at 4 weeks post-laminectomy. Each bladder was divided into three portions: (I) one-third was snap-frozen for total RNA extraction and subsequent NGS to profile mRNA, lncRNA, and miRNA expression; (II) one-third was fixed in 4% paraformaldehyde for paraffin embedding, sectioning (3–5 µm), and histological staining [hematoxylin-eosin (H&E) and Masson’s trichrome]; (III) one-third was stored at −80 ℃ for reverse transcription (RT)-quantitative real-time polymerase chain reaction (qPCR) validation of NGS results.
Histological staining
Fixed tissues were fixed and embedded in paraffin, and they underwent staining with H&E and Masson’s trichome to evaluate for the presence of fibrosis. Fibrosis was quantified as the percentage of blue-stained collagen area relative to the total tissue area in five randomly selected fields per section (200× magnification) using ImageJ software (v1.53).
Total RNA extraction and library construction
Total RNA was extracted from frozen tissue (5 mg) using TRIzol (15596018, Sigma, USA). RNA quality was assessed using Qubit (Q32856, Invitrogen, California, USA) quantification (detection range: 250 pg/µL–100 ng/µL), NanoDrop 2000 for purity (OD260/280 ≈2.0), and Agilent 2100 Bioanalyzer for integrity (RIN >7.0). High-quality RNA samples meeting criteria (total ≥5 µg, concentration ≥200 ng/µL) were used for library preparation. Long RNA (mRNA/lncRNA): Ribosomal RNA was removed using the TruSeq Stranded Total RNA with Ribo-Zero Globin kit (20020613, Illumina, California, USA). RNA was fragmented, followed by strand-specific cDNA synthesis, adapter ligation, and qPCR amplification. Small RNA (miRNA): Libraries were prepared using the TruSeq Small RNA Sample Prep Kit (RS-200-0024, Illumina, USA) involving adapter ligation, RT, qPCR amplification, and size selection (147–160 bp).
Library quality control and NGS
Constructed libraries were comprehensively assessed for quality, total concentration, and effective concentration using the Agilent 2100 Bioanalyzer. Qualified libraries were sequenced on the Illumina platform by Shanghai OE Biotech Co., Ltd., long RNA libraries via paired-end sequencing (PE150), small RNA libraries via single-end sequencing (SE50). Raw reads were processed (Trimmomatic v0.36) to remove low-quality sequences, adapters, and reads with excessive Ns. Ribosomal RNA reads were removed. Clean reads were assessed (FastQC v0.11.5). Small RNA reads were filtered for adapter contamination, no inserts, or length <18 nt. Statistical power (RNASeq Power) was 0.86.Total RNA purity and integrity were evaluated using a NanoDrop 2000 spectrophotometer (OD260/280 and OD260/230 ratios) and an Agilent 2100 Bioanalyzer (RNA Integrity Number, RIN). Samples with OD260/230 >1.8 and RIN >7.0 were deemed suitable for sequencing. As shown in Table S1, RNA from SCI-1, SCI-2, SCI-3, and NC groups exhibited OD260/280 ratios of 1.8–2.0 and OD260/230 ratios >1.8, with RIN values >7.5, confirming high-quality RNA for downstream sequencing.
Raw sequencing data from 12 samples (three biological replicates per group) were processed to remove adapter-contaminated and low-quality reads. High-quality Clean Reads were retained, with Q30 scores (base calling accuracy ≥99.9%) ranging from 92.14% to 93.97% and an average GC content of 48.16% (Table S2). Genome alignment rates reached 93.54–94.76%, and >90% of reads per sample passed quality filtering, indicating minimal sequencing bias and robust data reliability.
Screening of DEGs
Clean reads were aligned to the reference genome (Rnor_6.0) using Hisat2 (v2.0.5) with subsequent transcript assembly via StringTie2 (v1.3.3b). Differential expression analysis between SCI and NC groups was performed using DESeq2 (v1.18.0) with thresholds of |fold change (FC)| >2 and P<0.05. DEGs were visualized via hierarchical clustering and volcano plots, where log2(FC) >1 and <−1 indicated up- and downregulation, respectively. For mRNA analysis, FPKM-normalized expression identified differentially expressed mRNAs (DEMs). lncRNA identification involved filtering assembled transcripts (exons >2, length >200 bp), validating non-coding potential (PLEK/CNCI/Pfamscan), and identifying differentially expressed lncRNAs (DELs). miRNA analysis included processing small RNAs (18–36 nt), alignment to miRBase, novel miRNA prediction (Mireap), and identification of differentially expressed miRNAs (DEMIs). DEGs were visualized through hierarchical clustering, volcano plots, and temporal commonality assessed via Venn diagrams.
Construction of ceRNA networks
Commonly expressed DEMs, DELs, and DEMIs across SCI groups 1–3 were selected. Target genes of shared DEMIs were predicted using miRanda, forming the basis for lncRNA-miRNA-mRNA co-expression networks. Pearson correlation analysis (coefficient >0.8) identified positive correlations between lncRNAs and mRNAs. Pairs with shared miRNA response elements (MREs) underwent hypergeometric testing to assess common miRNA regulatory features. Two scoring metrics were applied: (I) regulatory similarity score, evaluating expression correlation between miRNA-mRNA and miRNA-lncRNA pairs, and (II) sensitivity score, calculated as the average correlation of lncRNA-miRNA-mRNA trios (threshold >0.3). Networks were visualized using Cytoscape (v3.7.2) to depict key regulatory interactions.
Functional enrichment analysis of ceRNA networks
Genes within ceRNA networks were subjected to functional enrichment via hypergeometric testing. GO analysis categorized genes into biological process (BP), cellular component (CC), and molecular function (MF) terms using the Gene Ontology database (http://geneontology.org/). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (http://www.kegg.jp/) identified metabolic and signaling pathways enriched with target genes. Term-specific gene counts were calculated, and pathway hierarchies were annotated to elucidate biological roles. This integrated approach revealed critical pathways and functional modules underlying SCI-associated ceRNA regulatory mechanisms.
Validation of DEGs by RT-qPCR
RNA (8 µg) was reverse transcribed. qPCR was performed using gene-specific primers (Table S3) and SYBR Green Master Mix on a 7500 FAST Real-Time PCR System. GAPDH and U6 served as internal controls for mRNAs/lncRNAs/circRNAs and miRNAs, respectively. Relative RNA expression was calculated using the 2−ΔΔCt method to confirm differential expression patterns identified in sequencing analyses.
Statistical analysis
All experiments were performed in three independent replicates. Normality was assessed using Shapiro-Wilk tests (P>0.05). Data were analyzed using SPSS software (version 21.0) and presented as mean ± standard deviation (SD). Differences between groups were assessed using independent-sample t-tests (for normally distributed data) or Mann-Whitney U tests (for non-normally distributed data), with P<0.05 considered significant. For transcriptome sequencing, three biological replicates per group were included. DEGs, including lncRNAs, miRNAs, and mRNAs, were identified using thresholds of |log2(FC)| >1 and P<0.05.
Results
Validation of NB following rat SCI model
Histopathological analysis via H&E and Masson’s trichrome staining (Figure 1) revealed progressive bladder remodeling post-SCI. Collagen fiber cross-sectional area increased over time, while mean muscle fiber diameter and muscle cross-sectional area decreased. In the NC group, a small amount of collagen was diffusely distributed in the submucosal layer, which was a normal physiological aggregation. SCI groups exhibited disrupted bladder wall architecture: the lamellar structure progressively disintegrated, with initial thinning followed by thickening of the bladder wall. The lamina propria was reduced, accompanied by smooth muscle hypertrophy and increased intermuscular collagen deposition (intensified Masson staining). Early post-SCI (SCI-1 and SCI-2 groups), muscle atrophy and fibrosis were not statistically significant compared to the NC group. However, by 4 weeks post-SCI (SCI-3), collagen fiber area, muscle cross-sectional area, and fiber diameter differed significantly from NC, confirming pronounced bladder fibrosis and successful NB modeling in later stages.
Figure 1.
Histopathological evaluation of rat bladder tissues via H&E and Masson’s trichrome staining. (A,B) NC group. (C,D) SCI-1 group. (E,F) SCI-2 group. (G,H) SCI-3 group. (I) Quantitative analysis of collagen fiber area. Error bars represent mean ± SD. **, P<0.01, ***, P<0.001, ****, P<0.0001 vs. NC group. H&E, hematoxylin-eosin; NC, normal control; SCI, spinal cord injury; SD, standard deviation.
Expression profiling analysis
Whole transcriptome sequencing (NGS) was performed to profile mRNA, lncRNA, and miRNA expression in NB tissues from SCI rats (1, 2, and 4 weeks post-injury) and NC group. Seven comparison groups were analyzed against the Rnor_6.0 reference genome: SCI-1 vs. NC, SCI-2 vs. NC, SCI-3 vs. NC, SCI-2 vs. SCI-1, SCI-3 vs. SCI-1, SCI-3 vs. SCI-2, and SCI 1–3 vs. NC. DEGs were identified using DESeq with thresholds of |log2(FC)| >1 and P<0.05.
mRNA expression profiles
The number of DEMs detected in the seven comparison groups in this sequencing was: 3,255, 3,449, 884, 1,056, 3,228, 3,024, and 2,256. Volcano plots (Figure 2A-2C) were used to evaluate the locations of DEMs in the SCI 1–3 groups and the NC group. Figure 2D shows that compared with NC, 3,255 (1,885 up-regulated and 1,370 down-regulated), 3,449 (2,052 up-regulated and 1,397 down-regulated), and 884 (254 up-regulated and 630 down-regulated) DEMs were detected in SCI 1–3 groups, respectively; compared with SCI-1 group, there were 1,056 (636 up-regulated and 420 down-regulated) and 3,228 (1,077 up-regulated and 2,151 down-regulated) DEMs in SCI-2 and SCI-3 groups; compared with SCI-2 group, there were 3,024 DEMs in SCI-3 group, including 937 up-regulated and 2,087 down-regulated. Hierarchical clustering heat map analysis (Figure 2E) can group genes with high expression and correlation between samples into one category. It is clearly observed that the three replicate samples of SCI 1–3 groups are clustered together, while the SCI group and the NC group show a separation trend, showing the difference in mRNA expression patterns between SCI 1–3 groups and NC groups. The Venn diagram of up-regulated and down-regulated DEMs (Figure 2F,2G) showed that there were 420 co-existences in the intersection of SCI 1–3 groups and NC groups, including 115 up-regulated and 305 down-regulated, respectively. Table 1 lists the top 10 up-regulated DEMs and top 10 down-regulated DEMs co-expressed in all SCI 1–3 groups compared with the NC group.
Figure 2.
mRNA expression profiles in SCI groups vs. NC. (A-C) Volcano plots of DEMs in SCI-1, SCI-2, and SCI-3 vs. NC. (D) Bar graph showing up-regulated (red) and down-regulated (blue) DEM counts. (E) Hierarchical clustering heatmap. (F,G) Venn diagrams of shared DEMs. DEMs, differentially expressed mRNAs; NC, normal control; SCI, spinal cord injury.
Table 1. The top 10 co-expressed up-regulated and down-regulated DEMs.
| Gene name | Log2(FC) | Regulation | P | ||
|---|---|---|---|---|---|
| SCI-1 vs. NC | SCI-2 vs. NC | SCI-3 vs. NC | |||
| Grem1 | 8.12 | 8.48 | 4.77 | Up | <0.05 |
| Bdnf | 8.10 | 6.90 | 6.16 | Up | <0.05 |
| Cyp11b2 | 7.82 | 7.02 | 3.38 | Up | <0.05 |
| Myh2 | 7.32 | 4.79 | 5.26 | Up | <0.05 |
| Myh1 | 7.24 | 5.43 | 5.07 | Up | <0.05 |
| Grpr | 6.24 | 5.01 | 4.48 | Up | <0.05 |
| Aldh1a7 | 5.60 | 6.08 | 3.93 | Up | <0.05 |
| Rasgrf1 | 5.37 | 4.41 | 3.73 | Up | <0.05 |
| Ltb | 7.32 | 5.62 | 3.70 | Up | <0.05 |
| Aoc1 | 6.29 | 6.80 | 3.54 | Up | <0.05 |
| Dcdc5 | −10.14 | −6.56 | −5.73 | Down | <0.05 |
| Cmtm2a | −8.33 | −5.73 | −4.74 | Down | <0.05 |
| Slc6a1 | −8.31 | −5.01 | −4.64 | Down | <0.05 |
| Phactr3 | −7.67 | −7.74 | −6.48 | Down | <0.05 |
| Cylc2 | −6.05 | −4.26 | −4.86 | Down | <0.05 |
| Cnpy1 | −5.24 | −7.82 | −3.91 | Down | <0.05 |
| Fam69c | −4.64 | −6.58 | −3.08 | Down | <0.05 |
| Mpped1 | −5.48 | −6.51 | −4.35 | Down | <0.05 |
| Mstn | −3.82 | −5.24 | −5.83 | Down | <0.05 |
| Cdh7 | −5.76 | −4.85 | −5.53 | Down | <0.05 |
DEMs, differentially expressed mRNAs; FC, fold change; NC, normal control; SCI, spinal cord injury.
lncRNA expression profiles
The expression profiles of lncRNAs in the SCI 1–3 group and the NC group were obtained by NGS. A total of 10,707 lncRNAs were identified, with a total length (number of bases) of 27,883,645 nucleotides and an average length of 2,604.24 nucleotides. The number of DELs in the seven groups was 904, 870, 278, 293, 883, 781, and 699, respectively. The volcano plot was used to evaluate the location of DELs in the SCI 1–3 group and the NC group (Figure 3A-3C). According to the data of the lncRNA expression profile, there were 904, 870, and 278 DELs in the SCI 1–3 groups compared with the NC group, of which 469, 453, and 99 DELs were up-regulated, and 435, 417, and 179 DELs were down-regulated, respectively. Compared with the SCI-1 group, the SCI 2–3 groups also expressed different numbers of DELs. Specifically, there were 293 and 883 DELs in the SCI-2 and SCI-3 groups, including 151 and 358 up-regulated DELs and 142 and 525 down-regulated DELs, respectively. Compared with the SCI-2 group, the SCI-3 group expressed 781 DELs, of which 288 were up-regulated and 493 were down-regulated (Figure 3D). Hierarchical clustering heat map analysis showed that the lncRNA expression patterns between the SCI 1–3 group and the NC group were different (Figure 3E). The Venn diagram of up-regulated and down-regulated DELs showed that there were 102 commonly expressed DELs (53 upregulated and 49 downregulated) in the SCI 1-3 group compared with the NC group (Figure 3F,3G). We further analyzed the molecular characteristics of DELs, focusing on their length and chromosomal distribution. Figure 3H showed that most DELs originated from chromosome 1 (11.33%; 1,213/10,707), followed by chromosome 2 (7.91%; 847/10,707) and chromosome 7 (6.86%; 735/10,707). In addition, Figure 3I showed that the proportion of DELs with a length greater than 2,000 nucleotides was 43.53% (4,661/10,707), and the proportion greater than 1,000 nucleotides was 69.73% (7,273/10,707). Table 2 lists the top 10 up-regulated DELs and top 10 down-regulated DELs co-expressed in all SCI 1–3 groups compared with the NC group.
Figure 3.
lncRNA expression profiles in SCI groups vs. NC. (A-C) Volcano plots of DELs in SCI-1, SCI-2, and SCI-3 vs. NC. (D) Bar graph showing up-regulated (red) and down-regulated (blue) DEL counts. (E) Hierarchical clustering heatmap. (F,G) Venn diagrams of shared DELs. (H) Chromosomal distribution of DELs. (I) Length distribution of DELs. DELs, differentially expressed lncRNAs; NC, normal control; SCI, spinal cord injury.
Table 2. The top 10 co-expressed up-regulated and down-regulated DELs.
| ID | Position | Log2(FC) | Length (nt) | Host gene | Regulation |
|---|---|---|---|---|---|
| LOC102552363 | Chr9:71446760-71453444+ | 4.80/4.70/3.97 | 1,514 | Fzd5 | Up |
| lncRNA PVT1 | Chr7:102648400-102871316+ | 4.63/4.07/4.28 | 4,571 | Myc | Up |
| LOC103690538 | Chr9:65236894-65249587- | 4.39/6.19/3.89 | 1,005 | Aox2 | Up |
| LOC102547940 | Chr9:80078960-80081320+ | 4.13/2.69/3.15 | 631 | Igfbp2 | Up |
| LOC103693028 | Chr8:23649346-23677422+ | 4.22/3.22/2.30 | 5,699 | Bbs9 | Up |
| LOC103693676 | Chr13:84533278-84536246+ | 4.08/2.69/1.82 | 2,734 | Ildr2 | Up |
| XLOC_003520 | Chr11:45007030-45009166+ | 4.00/2.38/1.05 | 2,107 | Col8a1 | Up |
| LOC108351018 | Chr5:132308469-132322217+ | 2.22/3.97/2.50 | 1,308 | LOC100362122 | Up |
| XLOC_003520 | Chr11:45006970-45009166+ | 3.36/1.67/1.11 | 2,169 | Col8a1 | Up |
| Mir155hg | Chr11:24175417-24177646- | 2.46/2.03/8.82 | 5,879 | Mrpl39 | Up |
| AABR07026032.3 | Chr16:59999903-60007556- | −6.68/−2.72/−1.33 | 1,664 | LOC100910088 | Down |
| LOC108352894 | Chr15:19955561-19965492+ | −6.09/−4.53/−1.51 | 2,137 | Ddhd1 | Down |
| LOC102552946 | Chr9:94832665-94841151- | −5.65/−2.75/−1.61 | 3,155 | Inpp5d | Down |
| LOC102550782 | Chr10:97553591-97556928+ | −4.97/−3.04/−3.67 | 2,333 | Rgs9 | Down |
| LOC100909777 | Chr7:22600323-22618413+ | −4.94/−3.08/−3.13 | 3,348 | LOC103692804 | Down |
| XLOC_002029 | Chr1:262635602-262640858- | −4.93/−3.53/−2.02 | 5,231 | Hpse2 | Down |
| AABR07001210.1 | Chr1:38859463-38882496- | −4.93/−2.40/−3.77 | 584 | LOC688839 | Down |
| XLOC_008204 | Chr2:60299090-60313047+ | −4.50/−4.13/−2.76 | 4,941 | Prlr | Down |
| LOC103693014 | Chr8:13492122-13498219- | −4.49/−4.77/−3.71 | 890 | LOC108348070 | Down |
| LOC102548522 | Chr6:75622774-75626216+ | −4.38/−3.21/−2.34 | 2,212 | Snx6 | Down |
DELs, differentially expressed lncRNAs; FC, fold change.
miRNA expression profiles
In this study, miRNA expression profiles were also obtained through NGS. The number of DEMIs in the seven groups sequenced this time was 229, 77, 127, 155, 138, 41, and 98, respectively. The volcano plot was used to evaluate the location of DEMIs in the SCI 1–3 group and the NC group (Figure 4A-4C). According to the miRNA expression profile data, there were 229, 77, and 127 DEMIs in the SCI 1–3 group compared with the NC group, respectively. Specifically, 133, 49, and 76 DEMIs were significantly upregulated in the three groups, and 96, 28, and 51 DEMIs were significantly downregulated. In addition, 206 new miRNAs were identified in bladder tissue. Compared with the SCI-1 group, there were 155 and 138 DEMIs in the bladder tissue of rats in the SCI-2 and SCI-3 groups, respectively, including 47 and 50 DEMIs that were significantly up-regulated and 108 and 88 DEMIs that were significantly down-regulated. Compared with the SCI-2 group, there were 41 DEMIs in the bladder of rats in the SCI-3 group, including 32 DEMIs that were significantly up-regulated and 9 DEMIs that were significantly down-regulated (Figure 4D). The results of the hierarchical clustering heat map analysis showed that the expression patterns of DEMIs between the SCI 1–3 group and the NC group were different (Figure 4E). The Venn diagram based on the up-regulated and down-regulated DEMIs showed that there were 45 commonly expressed DEMIs (32 up-regulated and 13 down-regulated) in the SCI 1–3 group compared with the NC group (Figure 4F,4G). The top 10 up-regulated and down-regulated DEMIs are listed in Table 3.
Figure 4.
miRNA expression profiles in SCI groups vs. NC. (A-C) Volcano plots of DEMIs in SCI-1, SCI-2, and SCI-3 vs. NC. (D) Bar graph showing up-regulated (red) and down-regulated (blue) DEMI counts. (E) Hierarchical clustering heatmap. (F,G) Venn diagrams of shared DEMIs. DEMIs, differentially expressed miRNAs; miRNA, microRNA; NC, normal control; SCI, spinal cord injury.
Table 3. The top 10 co-expressed up-regulated and down-regulated DEMIs.
| ID | Log2(FC) | Regulation | Sequence | ||
|---|---|---|---|---|---|
| SCI-1 vs. NC | SCI-2 vs. NC | SCI-3 vs. NC | |||
| miRNA-675-3p | 5.38 | 4.04 | 3.58 | Up | UGAGCGACUGAAAGGGCUGGCG |
| miRNA-147 | 4.72 | 4.13 | 7.14 | Up | UGUGACUGACACUUGUGAAAC |
| miRNA-466b-4-3p | 3.57 | 2.59 | 2.93 | Up | UGGGACGGGGAGAGUGUGGUUG |
| miRNA-466b-2-3p | 3.57 | 2.59 | 2.93 | Up | UUGAGACGACAGUGAAAGCUGA |
| miRNA-466c-5p | 3.18 | 1.53 | 2.42 | Up | UGAGACGACAGUGAAAGGCUGA |
| miRNA-212-3p | 3.17 | 1.68 | 1.76 | Up | UGUGACAGUAUACAGUGCAGUGA |
| miRNA-298-5p | 3.05 | 1.4 | 1.46 | Up | UGGGUGGGAGUGGGGGAUGGG |
| miRNA-20b-5p | 2.41 | 2.5 | 2.97 | Up | UGUGCAAAUCUAUGCAAUGCAU |
| miRNA-223-5p | 2.3 | 2.17 | 2.96 | Up | UGUCAGUUUGUCAAAUACCCCA |
| miRNA-21-5p | 2.22 | 1.34 | 2.37 | Up | UAGCUUAUCAGACUGAUGUUGA |
| miRNA-139-5p | −3.10 | −1.67 | −1.67 | Down | UGACCGUAUGUUGAAUCCUAUG |
| miRNA-504 | −2.29 | −1.77 | −2.33 | Down | UGGAGAGAUCUAGGACUCUGA |
| miRNA-338-5p | −2.04 | −1.49 | −1.82 | Down | UGCAUUGUUGUCCAGCGGUGA |
| miRNA-3099 | −2.88 | −1.96 | −4.99 | Down | UCCCCAACCUCUUUCUAGCC |
| miRNA-129-2-3p | −2.22 | −1.23 | −1.71 | Down | UGGAGUGUUUCCAAACUUGU |
| miRNA-129-1-3p | −2.89 | −1.60 | −2.30 | Down | UGGAGUGUUUCCAAACUUGU |
| miRNA-139-3p | −2.43 | −1.12 | −1.44 | Down | UAUUGCUUUCUUUCCUGGUUC |
| miRNA-149-5p | −2.14 | −1.52 | −1.89 | Down | UGGAGACCCUGUUGACCAGGC |
| miRNA-129-5p | −2.68 | −1.96 | −2.12 | Down | UGGAGAACGAGCAGTGTGUGU |
| miRNA-879-3p | −1.66 | −2.03 | −1.68 | Down | UGUGGUUAUCCAGUCAGUGUCC |
DEMIs, differentially expressed miRNAs; FC, fold change; miRNA, microRNA; NC, normal control; SCI, spinal cord injury.
ceRNA network construction
lncRNA-miRNA-mRNA co-expression networks
To elucidate the mechanisms underlying post-SCI NB, we constructed lncRNA-miRNA-mRNA ceRNA networks using co-differentially expressed mRNAs, lncRNAs, and miRNAs (DEMs, DELs, DEMIs) shared across SCI-1, SCI-2, SCI-3, and NC groups. Networks were built by correlating lncRNA and mRNA expression (positive correlation) with miRNA interactions (negative correlation). The analysis included 904 lncRNAs, 762 miRNAs, and 3,255 mRNAs. The top 200 lncRNA-miRNA-mRNA regulatory pairs were visualized (Figure 5), revealing potential key mediators of NB progression post-SCI. Temporally resolved lncRNA-miRNA-mRNA co-expression networks revealed progressive architectural consolidation in SCI-induced NB, evolving from decentralized inflammatory regulation at 1-week post-injury (SCI-1 vs. NC) to fibrosis-dominated interactomes by 4 weeks (SCI-3 vs. NC).
Figure 5.
lncRNA-miRNA-mRNA co-expression networks in SCI groups vs. NC. (A) SCI-1 vs. NC. (B) SCI-2 vs. NC. (C) SCI-3 vs. NC. lncRNA, long non-coding RNA; miRNA, microRNA; NC, normal control; SCI, spinal cord injury.
Functional enrichment analysis
GO and KEGG pathway analyses were performed on genes within the ceRNA networks. GO enrichment (Figure 6A-6C) highlighted significant terms across MFs, CCs, and BPs. Top MF terms included integrin binding, calcium ion binding, and voltage-gated sodium channel activity. CC terms were enriched in extracellular space, integral plasma membrane components, and membrane-anchored complexes. BP terms involved inflammatory response, collagen fibril organization, and excitatory postsynaptic potential.
Figure 6.
GO enrichment analysis of lncRNA-miRNA-mRNA networks. (A-C) The top 10 enriched terms for MF, CC, and BP in SCI-1, SCI-2, and SCI-3 groups vs. NC. BP, biological process; CC, cellular component; GO, Gene Ontology; MF, molecular function; long non-coding RNA; miRNA, microRNA; NC, normal control; SCI, spinal cord injury.
KEGG pathway analysis (Figure 7A-7C) identified 30 enriched pathways, including neuroactive ligand-receptor interaction, cytokine-cytokine receptor interaction, inflammatory response, TGF-β signaling pathway, and calcium signaling pathway. These pathways are implicated in fibrosis, neural remodeling, and inflammation, aligning with SCI-induced bladder pathology.
Figure 7.
KEGG pathway enrichment analysis of lncRNA-miRNA-mRNA networks. (A-C) Top 30 enriched pathways in SCI-1, SCI-2, and SCI-3 groups vs. NC. ECM, extracellular matrix; KEGG, Kyoto Encyclopedia of Genes and Genomes; long non-coding RNA; miRNA, microRNA; NC, normal control; SCI, spinal cord injury.
Validation of differential gene expression by qPCR
To confirm the reliability of NGS-identified DEGs, we randomly selected four top-ranked candidates each from the DEMs (Grem1, Aoc1, Slc6a1, Cdh7), DELs (LOC102552363, PVT1, LOC108352894, LOC102548522), and DEMIs (miR-675-3p, miR-21-5p, miR-139-5p, miR-149-5p) based on their expression fold changes, molecular relevance, and potential association with bladder fibrosis. qPCR analysis demonstrated consistent trends with sequencing data (Figure 8). Specifically, the expression level of LOC102548522 was gradually decreased, and that of Grem1 was gradually increased. The expression level of miRNA-139-5p was the lowest in SCI–1 group, and then gradually increased. LOC102552363 was highest in SCI–1 group and then decreased gradually. The expression level of AOC1, lncRNA PVT1, miRNA-675-3p, and miRNA-21-5p increased overall but fluctuated between the SCI 1–3 groups. These results validated the accuracy of transcriptomic profiling and reinforced the biological significance of candidate genes in SCI-induced NB.
Figure 8.
qPCR validation of DEMs, DELs, and DEMIs. Expression levels of selected genes in SCI groups vs. NC. Error bars: mean ± SD. **, P<0.01, ***, P<0.001, ****, P<0.0001 vs. NC. DELs, differentially expressed lncRNAs; DEMIs, differentially expressed miRNAs; DEMs, differentially expressed mRNAs; lncRNA, long non-coding RNA; miRNA, microRNA; NC, normal control; qPCR, quantitative real-time polymerase chain reaction; SCI, spinal cord injury; SD, standard deviation.
Discussion
The overall global incidence of SCI is reported to be 23.77 per million people, with a range of 20–45 million (18). The irreversible loss of neural regulation post-SCI drives persistent bladder remodeling, with bladder fibrosis emerging as a prominent pathological feature among other alterations such as tissue remodeling, detrusor muscle changes, and inflammatory responses (19). Fibrosis leads to bladder wall stiffening, reduced compliance, elevated intravesical pressure, and subsequent complications such as urinary incontinence, retention, hydronephrosis, and ultimately chronic renal failure, severely impacting patient survival and quality of life (20). While delaying bladder fibrosis and preserving upper urinary tract function are crucial for improving NB prognosis post-SCI, current therapeutic strategies remain unsatisfactory.
The selection of appropriate animal models proves critical in SCI research. Contusion models, considered the gold standard for replicating clinical histopathological features, demonstrate spinal tissue destruction, intervertebral disc extrusion, and bone fragmentation (21). However, their limitations in assessing neural regeneration and functional recovery led us to employ complete transection models that better address residual tissue variability. Secondary injury mechanisms, central to NB pathophysiology and therapeutic timing, peak at 7 days post-SCI with maximal tissue necrosis and apoptosis (22). This informed our decision to analyze ncRNA expression profiles in bladder tissues at 1, 2, and 4 weeks post-injury, capturing dynamic molecular changes during NB progression.
SCI-induced NB represents a complex disorder, with the biological functions and mechanistic roles of most ncRNAs remaining incompletely elucidated. This study constitutes the first comprehensive exploration of ncRNA expression profiles in post-SCI NB. Comparative analysis between SCI and NC rats revealed significant up-regulation or down-regulation of specific ncRNAs. The lncRNAs modulate gene expression through diverse mechanisms, including transcription factor sequestration, transcriptional activation, and base-pairing to sequester complementary miRNAs, thereby exerting bidirectional regulatory effects on fibrotic processes (23,24). Among the DELs identified, lncRNA PVT1 exhibited marked upregulation, suggesting its potential involvement in SCI-associated NB pathogenesis. Recent study has shown that lncRNA-PVT1 can competitively bind to miR-497-5p to promote the proliferation, activation and migration of lung fibroblasts (25). Mechanistically, PVT1 acts as a ceRNA for miR-128-3p, upregulating Sp1 expression to potentiate TGF-β1/Smad signaling (26).
Notably, lncRNA Mir155hg, another DEL identified in this study, participates in immunomodulatory and inflammatory pathways (27). Mir155hg serves as a critical regulator in physiological and pathological processes, including immune responses and organ fibrosis (28). Derived from the Mir155 host gene, Mir155hg exerts its functions via interactions with miR-155 (29), a pivotal immune modulator governing T-cell differentiation, dendritic cell maturation, B-cell proliferation, and antibody production (30,31). Mir155hg expression is activated by transcriptional regulators such as AP-1, NF-κB, and Smad4 (32,33). The Mir155hg/miR-627/HMGB1/NF-κB axis forms a regulatory loop influencing TGF-β1-induced fibroblast activation, wherein Mir155hg suppresses miR-627 expression through direct binding (34). While Mir155hg’s role in NB remains underexplored, our GO enrichment and KEGG pathway analyses revealed co-expressed mRNAs predominantly enriched in immune-related terms and pathways, suggesting Mir155hg’s potential immunomodulatory involvement in NB pathogenesis.
This study represents the first comprehensive investigation of miRNA expression profiles in SCI-associated NB using a rat model. We identified critical DEMIs targeting genes implicated in pivotal signaling cascades, including the PI3K-Akt, MAPK, TGF-β, calcium signaling, and cGMP-PKG pathways, as well as tight junction regulation and metabolic processes. It is worth noting that miR-21-5p was significantly upregulated in all SCI patient groups, which is consistent with our previous study that overexpression of miR-21-5p abnormally activates the TGF-β1 signaling pathway, induces EMT and promotes the progression of fibrosis (35). Recent studies report elevated miR-21-5p induces macrophage M2-like polarization and enhances TGF-β expression (36). Furthermore, TGF-β reciprocally stimulates miR-21-5p production in macrophages, establishing a self-reinforcing positive feedback loop that amplifies the TGF-β/Smad signaling pathway and drives macrophage-to-myofibroblast transition (37). Building on global research advancements and our preliminary findings, we postulate the existence of a TGF-β1/miR-21-5p/Smad7 regulatory axis in post-SCI NB.
In contrast, miR-139-5p emerged as the most significantly down-regulated DEMI in SCI groups. There is no study on the mechanism of miR-139-5p and bladder fibrosis. A study on the mechanism of liver fibrosis showed that in fibrotic liver tissue, lncRNA NEAT1 expression was up-regulated, while miR-139-5p expression was down-regulated. Moreover, lncRNA NEAT1 can adsorb miR-139-5p and promote hepatic stellate cell activation by directly inhibiting the expression of miR-139-5p (38). Combined with our previous studies, we believe that overexpression of miRNA-139-5p can delay the process of EMT and fibrosis through certain signaling pathways (35). Clinical corroboration arises from observations of reduced plasma miR-139-5p levels in OAB patients (39). Functional studies identify Rho-associated coiled-coil containing protein kinase 2 (ROCK2) as a direct miR-139-5p target, operating through ROCK2/myosin light chain (MLC) and cholinergic signaling pathways (40). These findings suggest that reduced miR-139-5p levels may upregulate ROCK2 expression, potentiating RhoA/ROCK signaling and contributing to DO pathogenesis.
Temporal expression patterns revealed dynamic ncRNA involvement across SCI phases. The initial “spinal shock” phase (0–10 days post-SCI) featured reduced collagen expression coinciding with miRNA-134-5p elevation (41), while later stages showed miRNA-146a-5p up-regulation (5.48-fold in SCI-3) potentially modulating TGF-β/Smad3-dependent EMT processes (42,43). Co-expression network analyses highlighted pathway convergence in IL-17, TGF-β, PI3K-Akt, and MAPK signaling, with GO terms emphasizing inflammatory/immune responses and extracellular remodeling—findings corroborated by recent transcriptomic studies showing up-regulated immune/inflammatory genes (CXCL13, CCR7, TREM2) in post-SCI bladder tissue (44).
Despite these insights, several limitations warrant consideration. Our bulk tissue analysis cannot distinguish cell-type specific ncRNA expression changes from compositional shifts—a critical caveat given varying ncRNA profiles across cell types (45). Future investigations should employ single-cell sequencing and spatial transcriptomics to resolve cellular heterogeneity. While we identified promising candidates like PVT1 and miRNA-21-5p, functional validation and longitudinal studies beyond 4 weeks are essential to establish therapeutic relevance. The relatively small sample size necessitates larger preclinical validation followed by human tissue studies. Furthermore, the interactions between miRNAs and lncRNAs/mRNAs were not investigated in detail. So, the mechanistic exploration of ncRNA-mRNA interactions and pathway crosstalk remains imperative for translating these findings into targeted therapies.
In conclusion, this first comprehensive ncRNA atlas of SCI-induced NB reveals dynamic molecular networks coordinating fibrosis and voiding dysfunction. While preliminary, these findings lay crucial groundwork for developing ncRNA-based diagnostics and therapeutics. Future research directions should focus on temporal-spatial resolution of ncRNA functions, cross-validation in human specimens, and combinatorial targeting of key pathways identified through this systems-level analysis.
Conclusions
This study provides the first comprehensive characterization of ncRNA and mRNA expression profiles in NB following SCI through NGS. We identified critical ncRNAs and mRNAs associated with NB progression and bladder fibrosis, while predicting their potential involvement in key signaling pathways. These findings advance our understanding of ncRNA-mediated mechanisms underlying SCI-induced NB and highlight promising candidates for early diagnostic biomarkers and therapeutic targets. The dynamic ncRNA-mRNA regulatory networks revealed here lay a foundation for further exploration of molecular drivers in bladder dysfunction post-SCI. Future studies should prioritize functional validation of these ncRNAs and translational efforts to bridge preclinical insights into clinical applications for improving patient outcomes.
Supplementary
The article’s supplementary files as
Acknowledgments
None.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Experiments were approved by the Institutional Animal Care and Use Committee of Xuanwu Hospital Capital Medical University (No. XW-20210910-1), in compliance with the National Institute of Health Guidelines for the Care and Use of Laboratory Animals.
Footnotes
Reporting Checklist: The authors have completed the ARRIVE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-376/rc
Funding: This work was supported by the Capital Medical University Xuanwu Hospital National Natural Science Youth Cultivation Project (No. QNPY202424), the Talent Training Program of Xuanwu Hospital of Capital Medical University (No. YC20250203), the National Natural Science Foundation of China (No. 82100819), the Capital Health Research and Development of Special Fund (No. 2020-2-2015), the Beijing Hospitals Authority Youth Program (No. QML20230808), and the Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University (No. CCMU2023ZKYXY019).
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-376/coif). The authors have no conflicts of interest to declare.
Data Sharing Statement
Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-376/dss
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