{"paper_id":"4a9e53dd-26dc-4c8f-85f7-e4b9be53a793","body_text":"Construction and Bioinformatics Analysis of the miRNA-mRNA Regulatory Network in Liver regeneration in rats | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Construction and Bioinformatics Analysis of the miRNA-mRNA Regulatory Network in Liver regeneration in rats Hanqing Hu, Xin Zheng, Guodong Tian, Yong Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5290996/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common and aggressive malignant tumors. Partial hepatectomy (PHx) is currently the primary treatment for HCC, but many patients suffer from poor liver reserve function and insufficient remaining liver volume, limiting the liver's regenerative capacity. Therefore, this study aims to explore the mechanisms of miRNA and mRNA in liver regeneration through high-throughput sequencing. Methods: A rat model of 70% hepatectomy was used, and physiological indicators related to liver regeneration were assessed on days 3, 7, and 14 post-surgery. Small RNA sequencing and transcriptome analysis were conducted to evaluate the miRNA and mRNA expression profiles at different stages of regeneration. Bioinformatics tools were used to identify differentially expressed genes, construct miRNA-mRNA regulatory networks, and protein-protein interaction (PPI) networks, to identify key regulatory molecules. Results: The rat liver regeneration model was successfully established, and the body weight and liver regeneration rate data on days 3, 7, and 14 indicated a smooth regeneration process. Small RNA sequencing and transcriptome analysis identified 395 known miRNAs and 299 precursor miRNAs. Differential expression analysis revealed dynamic expression patterns of multiple miRNAs and mRNAs during liver regeneration. The miRNA-mRNA regulatory network showed interactions between 17 differentially expressed miRNAs and 31 differentially expressed mRNAs involved in liver regeneration. Conclusion: This study, through small RNA sequencing and transcriptome analysis, revealed key regulatory roles of miRNAs in various signaling pathways during liver regeneration. The constructed miRNA-mRNA regulatory network further elucidates the molecular mechanisms of liver regeneration. The results demonstrate the complex regulatory roles of miRNAs in promoting hepatocyte proliferation, inhibiting apoptosis, and regulating multiple key signaling pathways, providing new insights into the understanding of liver regeneration mechanisms. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Hepatocellular carcinoma (HCC) is one of the most common and aggressive malignant tumors. According to the latest data from 2023, there were 410,000 new cases of liver cancer in China in 2020, making it the fifth most prevalent cancer, while liver cancer-related deaths reached 390,000, ranking second [ 1 ]. Currently, partial hepatectomy remains the primary curative treatment for HCC. However, many patients suffer from poor liver reserve function and insufficient residual liver volume, limiting the liver’s regenerative capacity. In cases of extensive liver cancer, undergoing extended liver resection may lead to liver failure and small-for-size syndrome (SFSS), which increases the risk of postoperative mortality [ 2 ].The liver’s remarkable regenerative ability is driven mainly by hepatocytes, which have an average lifespan of up to 300 days. Under normal conditions, hepatocytes divide minimally, but in response to injury, they proliferate and can restore the liver’s original cellular structure within 7 to 14 days. This process is regarded as compensatory hyperplasia rather than true regeneration [ 3 ]. Growing evidence suggests that liver regeneration is governed by a complex regulatory network involving various molecules, such as growth factors, transcription factors, mRNA, miRNA, lncRNA, circRNA, DNA methylation, RNA methylation, and histone modifications [ 4 ]. miRNAs do not function in isolation during liver regeneration but interact with multiple signaling pathways and gene regulatory networks. The TGF-β (transforming growth factor-β) signaling pathway plays a key role in terminating cell proliferation and tissue remodeling in the later stages of liver regeneration. miR-23b promotes hepatocyte proliferation and regeneration by inhibiting the expression of Smad3, thereby blocking the TGF-β signaling pathway [ 5 ]. The HGF (hepatocyte growth factor)/c-Met pathway is crucial in the early stages of liver regeneration, as it accelerates regeneration by promoting hepatocyte proliferation and survival. miR-101-3p inhibits this signaling pathway by downregulating the expression of c-Met, thereby suppressing tumor growth[ 6 ]. Additionally, miR-122 is involved in the regulation of the HGF/c-Met signaling pathway and accelerates liver regeneration by promoting hepatocyte proliferation [ 7 ]. To explore the role of the miRNA-mRNA regulatory network in liver regeneration, a study was conducted in which rats underwent liver resection surgery. Physiological indicators related to liver regeneration were assessed on postoperative days 3, 7, and 14. Small RNA sequencing and transcriptome analysis were employed to evaluate the expression profiles of miRNAs and mRNAs at various stages of regeneration. Bioinformatics tools were then used to identify and functionally analyze differentially expressed genes, aiming to uncover key miRNA-mRNA networks that regulate liver regeneration. Methods Construction of a 70% Hepatectomy Model in Rats All animal experiments were conducted in accordance with the guidelines approved by the Three Gorges University Animal Care and Use Ethics Committee. Male Sprague-Dawley rats, aged 28–42 days and weighing approximately 220g, were obtained from the Hubei Provincial Center for Experimental Animals. After a 7-day acclimation period, the rats were randomly assigned to either a sham surgery group (control) or a liver resection group (treatment), with 9 rats in each group. Prior to surgery, the rats were fasted for 12 hours, with free access to water. Following the fasting period, body weights were measured, and anesthesia was induced with an intraperitoneal injection of 10% chloral hydrate (330 µL/100 g). In the treatment group, after full anesthesia was achieved, a longitudinal incision was made using sterilized surgical scissors to expose the liver. The hepatic pedicle was ligated, and the medial and left lobes of the liver were resected along the ligature line. The excised liver tissue was weighed and recorded. After ensuring hemostasis, the incision was sutured, and the rats were allowed to recover before being returned to their cages. Additionally, 100,000 IU of penicillin was administered intraperitoneally. In the sham surgery group, only a laparotomy was performed without liver resection. Body weights were recorded pre-surgery, on the day of surgery, and on days 3, 7, and 14 post-surgery. At the same time points, the ratio of the regenerated liver weight to the residual liver weight was measured. Serum and tissue samples were collected for further analysis. Serum markers of liver function, including ALT, AST, γ-GT, TBIL, DBIL, ALP, ALB, and TBA, were measured. Hematoxylin and eosin (HE) staining was performed on liver tissues for histopathological analysis. The expression of PCNA and HSC70 in regenerating liver tissues was evaluated using immunohistochemistry. Immunofluorescence Staining The tissue was dehydrated using a gradient of alcohol solutions: 75% ethanol for 4 hours, 85% ethanol for 2 hours, 90% ethanol for 1.5 hours, 95% ethanol for 1 hour, anhydrous ethanol I for 0.5 hours, and anhydrous ethanol II for 0.5 hours. After alcohol dehydration, the tissue blocks were cleared with xylene using the following conditions: xylene (1:1) for 10 minutes, xylene I for 10 minutes, and xylene II for 10 minutes. The cleared tissue blocks were then sequentially impregnated with three baths of paraffin (60°C). All of these steps were performed in a computerized tissue dehydration machine (Wuhan Junjie JT-12J). After impregnation, the tissue blocks were embedded to ensure complete integration with the embedding paraffin. The embedded tissues were sectioned and placed on slides, followed by baking for 3 hours. Subsequently, the slides were sequentially immersed in xylene I for 20 minutes, xylene II for 20 minutes, xylene III for 20 minutes, anhydrous ethanol I for 5 minutes, anhydrous ethanol II for 5 minutes, 95% ethanol for 5 minutes, 90% ethanol for 5 minutes, 80% ethanol for 5 minutes, and 70% ethanol for 5 minutes. Then, distilled water was used for a 5-minute wash to remove the paraffin. After blocking endogenous peroxidase activity, the samples were incubated with serum for 30 minutes, followed by the addition of primary antibodies (HSC70 and PCNA at a dilution ratio of 1:100) and secondary antibodies. After adding the chromogenic substrate, the samples were stained. Mayer's hematoxylin was used for a 2-minute counterstain, followed by dehydration and mounting for subsequent microscopic examination. RNA extraction and quality control Total RNA was extracted using the RNeasy Plus Micro Kit (cat. no. 74034; Qiagen). RNA concentration and purity were measured using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA) and the Labchip GX Touch HT Nucleic Acid Analyzer (PerkinElmer, USA). Small RNA and transcriptome library construction Small RNA sequencing libraries were generated using the NEBNext® Multiplex Small RNA Library Prep Set for Illumina® (NEB, USA) following the manufacturer’s recommendations, and index codes were added to attribute sequences to each sample. In brief, libraries were prepared by ligating different adaptors to the total RNA, followed by reverse transcription, PCR amplification, and size selection using 6% polyacrylamide gels. Library quality was assessed using the Agilent Bioanalyzer 2100 system. The QIAseq Stranded RNA Library Kit for Illumina® from NEB (USA) was used to generate the libraries of transcriptome. Sequencing was performed using an Illumina Nova6000. Data analysis Raw data (raw reads) in the fastq format were first processed using in-house Perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapters and low-quality reads from raw data. At the same time, the Q20, Q30, and GC contents of the clean data were calculated. All downstream analyses were based on high-quality, clean data. The known miRNA sequences of the species were obtained using miRBase software. Information on known miRNA expression levels in samples and predictions of novel miRNAs were obtained using miRDeep2. Differential expression analysis of treatment group compared with control group was performed using DESeq2. The screening criteria for significantly different genes were corrected P values of < 0.05 and log 2 (fold change) ≥ 1. miRNA target prediction was performed using TargetScan, PicTar, microT, miRmap, RNA22, PITA, and miRanda. The target genes were functionally annotated and enriched according to the predicted results. Transcriptome sequencing data analysis involves performing statistical analysis on the standardized processed data using the DESeq2 software, with criteria of |log2(Fold change)| > 1 and P < 0.05 to select differentially expressed genes. miRNA-mRNA regulatory network construction The target gene prediction for differentially expressed miRNAs was performed using miRanda software and the starBase database (version 3.0; starbase.sysu.edu.cn/index.php). The starBase database integrates prediction results from seven miRNA databases, including TargetScan, picTar, microT, miRmap, RNA22, PITA, and miRanda. The prediction is based on the complementarity and thermal stability between the miRNA sequence and the mRNA 3'UTR sequence. The screening criteria for selecting target genes included a predicted score above 140, a binding free energy lower than − 20 kcal/mol, and prediction consensus from multiple databases. Only target genes meeting these criteria were considered.The co-expression map of miRNA predicted target genes and differential genes was mapped in Cytoscape (V3.9.1). Functional enrichment of differentially expressed genes The identified target genes were then compared with the differentially expressed genes, and the overlapping genes were selected as potential targets of the differentially expressed miRNAs. KEGG/GO pathway enrichment analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using the clusterProfiler package in R. The analysis involved utilizing the KEGG annotations of genes, with all human genes serving as the background gene set. A significance threshold of P < 0.05 was applied to identify statistically significant overrepresented annotations relative to the background.In addition to KEGG pathway analysis, Gene Ontology (GO) analysis was conducted. GO consists of three main categories: Molecular Function, Biological Process, and Cellular Component. The analysis aimed to identify enriched GO terms within these categories.In R, the package org.Rn.eg.db was used as the OrgDb database, and clusterProfiler was used to calculate the GSEA enrichment results of differential genes. swiss-model was used to conduct homology modeling, and then Verify3D, PROCHECK and ERRAT were used to evaluate the protein model with high accuracy. Construction of the protein-protein interaction (PPI) network The selected key genes were constructed by the STRING software, and Cytoscape (V3.9.1) software was used to construct the PPI network. Real-time PCR Total RNA was reverse transcribed to cDNA using a Reverse Transcription Kit (Takara Co., Ltd., Dalian, China). Primer BLAST ( https://www.ncbi.nlm.nih.gov/tools/primer-blast/ ) was used for designing primers. The designed primers were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China). cDNA was amplified using SYBR® Premix Ex Taq™ (TaKaRa, Dalian). Gene expression levels were calculated by the ΔΔCt method with GAPDH and U6 as internal controls for mRNA and miRNA, respectively. The above primer sequences are listed in Table 1 . Table 1 QPCR primer sequences for rno-miR-34a-5p, Nceh1 and internal controls Primer name Primer sequence(5'to3') miR-34a-5p-F CGGGTGGATCACGATGCAAT miR-34a-5p-R TCCTGCGGTTTACAGATGGAT Nceh1-F CCTTGTCTCTGGTGGAGTCG Nceh1-R GCACAGCAGTCACATTCTCG Results successfully constructed the rat liver regeneration models Following a 70% liver resection, body weight and the ratio of regenerated liver weight to residual liver weight were measured at 3, 7, and 14 days post-surgery (Table 2 ). The results indicated a successful liver regeneration process, with no instances of mortality in the liver resection group, suggesting the surgery was well-tolerated. To further validate the pathological changes at these time points, a series of serum tests were conducted, including ALT, AST, γ-GT, TBIL, DBIL, ALP, ALB, and TBA levels. Notably, serum albumin (ALB) levels progressively increased across the postoperative period, while the other markers showed a gradual decrease. By day 14, these indicators closely resembled those of the control group, suggesting that liver function had nearly returned to pre-resection levels within 14 days (Table 3 ). Table 2 − 1 Rat weight data recording (g) Group pre-operation postoperation 0d postoperation 3d postoperation 7d postoperation 14d Sham operation group 165 165 172 194 289 161 161 177 190 301 163 163 174 188 305 Hepatectomy group 162 155 153 157 236 161 153 156 166 252 158 150 151 154 271 Table 2 2 Weight ratio of rat regenerated liver to residual liver Group postoperation 0d postoperation 3d postoperation 7d postoperation 14d Hepatectomy group 1 1.76 0 0 1 1.8 0 0 1 1.69 0 0 1 0 2.91 0 1 0 2.72 0 1 0 2.79 0 1 0 0 3.19 1 0 0 2.98 1 0 0 3.14 Table 3 serum tests were conducted, including measurements of ALT, AST, γ-GT, TBIL, DBIL, ALP, ALB, and TBA Group γ-GT(U/L) ALP(King/100mL) T-BIL(U/mL) D-BIL(U/mL) TBA(µmol/L) ALB(g/L) ALT(U/mL) AST(U/mL) sham operation group 3days 18.762 10.935 4.462 3.874 11.611 34.577 54.318 67.936 sham operation group 7days 18.230 10.886 4.321 3.964 12.521 35.469 65.836 73.662 sham operation group 14days 18.473 11.025 4.686 4.087 11.570 35.425 59.213 69.270 Hepatectomy group 3days 78.386 45.510 22.202 15.788 45.971 17.846 224.449 257.820 Hepatectomy group 7days 35.280 17.931 10.636 7.606 20.896 23.111 107.982 155.377 Hepatectomy group 14days 19.025 11.040 4.637 4.146 11.422 31.186 68.038 70.488 Histopathological analysis of liver tissues from the regenerated liver group was performed using hematoxylin-eosin (HE) staining. At 3 days post-resection, significant abnormalities were observed in the portal areas compared to the control group, with marked cellular proliferation (stained black) and mild inflammatory infiltration (stained red) around the bile ducts and blood vessels. By day 7, there was a slight increase in both cellular proliferation and inflammatory cell infiltration in these regions. By day 14, cellular proliferation and inflammatory infiltration were minimal, indicating that liver regeneration was progressing toward normal levels (Fig. 1 ). Additionally, the expression of PCNA (proliferating cell nuclear antigen) and HSC70 (heat shock cognate 70) in the regenerated liver tissues was assessed through immunohistochemistry and Western blotting, respectively. PCNA staining, a key marker of cell proliferation, revealed significant differences between the treatment and control groups at all three time points (3, 7, and 14 days), highlighting active proliferation during liver regeneration (Fig. 1 ). small RNA sequencing and transcriptome sequencing to uncover the key regulatory miRNA-mRNA networks Small RNA sequencing generated a substantial amount of read data, with the processed results summarized in the attached table. The read counts across all samples ranged from 12.0 million to 26.5 million, with more than 90.3% of the reads successfully mapped in each sample. Based on mature miRNA sequences from miRBase, at least 395 known miRNAs and 299 known pre-miRNAs were identified in each sample library. Additionally, novel miRNAs were predicted based on the characteristic hairpin structure of miRNA precursors. Comparative analysis revealed that each sample contained at least 108 novel mature miRNAs and 95 novel pre-miRNAs (Table 4 ). Table 4 Known and unknown numbers of mature miRNAs and hairpin miRNAs Sample Known_mature_miRNA Known_hairpin_miRNA Novel_mature_miRNA Novel_hairpin_miRNA sham operation group 3days-1 417 301 121 108 sham operation group 3days-2 405 299 119 107 sham operation group 3days-3 395 301 106 95 Hepatectomy group 3days-1 419 311 134 124 Hepatectomy group 3days-2 439 311 132 116 Hepatectomy group 3days-3 459 331 165 147 sham operation group 7days-1 425 312 130 117 sham operation group 7days-2 425 310 108 96 sham operation group 7days-3 453 323 128 114 Hepatectomy group 7days-1 455 326 144 127 Hepatectomy group 7days-2 434 314 130 115 Hepatectomy group 7days-3 429 311 133 120 sham operation group 14days-1 426 316 119 104 sham operation group 14days-2 441 317 128 114 sham operation group 14days-3 466 331 124 111 Hepatectomy group 14days-1 450 327 125 114 Hepatectomy group 14days-2 437 319 139 124 Hepatectomy group 14days-3 444 325 142 128 Comparison of miRNA expression between the treatment and control groups at 3-day post-surgery revealed a total of 41 differentially expressed miRNAs (DE miRNAs), identified using the criteria of p-value < 0.05, |log2(Fold change)| > 1, and reads per million (RPM) > 5 in at least one of the libraries. Of these, 12 miRNAs were upregulated, while 29 were downregulated. In the 7-day and 14-day groups, 24 and 23 DE miRNAs were identified, respectively (Fig. 2 ). Cluster analysis of these DE miRNAs showed distinct expression patterns during liver regeneration. For example, miRNAs such as rno-miR-363-3p, rno-miR-34a-5p, rno-miR-214-3p, and rno-miR-582-3p were significantly upregulated, while rno-miR-6215, rno-miR-3068-3p, rno-miR-196b-5p, and rno-miR-484 were significantly downregulated. By day 14, the expression differences in these miRNAs became less pronounced, indicating that as liver regeneration nears completion, their expression levels tend to normalize. Additionally, using the Targetscan, miRTarBase, and miRDB databases, downstream target predictions were performed for the differentially expressed miRNAs, and 927 miRNA-mRNA interactions were detected by cross-referencing the results from the three databases. Then, the target mRNAs were intersected with the significantly differentially expressed mRNAs mentioned earlier. The final miRNA-mRNA network contains 17 differentially expressed miRNAs and 31 differentially expressed mRNAs. Cytoscape was used to construct and visualize the miRNA-mRNA regulatory network (Fig. 3 ) Differential gene expression analysis and functional enrichment analysis In the transcriptome sequencing analysis, we aligned the sequenced reads with the NCBI reference genome. Across all samples, the input reads ranged from 45.5 million to 67.2 million, with more than 96.7% of the reads successfully mapped to the reference genome (see table). Using the mapped reads, we conducted a statistical analysis to evaluate gene expression levels in all samples. This analysis identified 991, 586, and 790 differentially expressed mRNAs (DE mRNAs) at different stages of the liver regeneration experiment, applying a cutoff of p-value < 0.05 and |log2(Fold change)| > 1. Among these DE mRNAs, 640, 294, and 329 genes were upregulated at the three experimental stages (day 3 to day 14), while 351, 292, and 461 genes were downregulated, respectively (Fig. 4 ). GO analysis of the differentially expressed mRNAs (DE mRNAs) revealed enrichment in 15 biological process terms, including liver development and immune response. In terms of cellular components, 15 GO terms were also enriched, primarily involving the cell and organelle membranes. Additionally, 15 molecular function GO terms were enriched, such as iron ion binding, heme binding, and the activation of growth-related factors and hormones (Fig. 5 A, B, and C). At days 3 and 7 post-surgery, common enriched metabolic pathways included cellular responses to lipids, heat generation, insulin-activated receptor activity, locomotor rhythm, mitotic spindle formation, negative regulation of gluconeogenesis and insulin secretion (related to cellular glucose response), lipid storage regulation, pheromone binding, and the positive regulation of glucose and lipid metabolic processes, as well as protein kinase B signaling. By contrast, at day 14, significant changes were observed mainly in pathways associated with the collagen-containing extracellular matrix, estradiol 17-β-dehydrogenase activity, and the innate immune response. This suggests that the pathways enriched at days 3 and 7 are more closely related to liver regeneration, while those at day 14 reflect later-stage processes. KEGG pathway enrichment analysis of the DE genes revealed significant alterations in pathways such as Human papillomavirus infection, Hepatitis C, cytokine-cytokine receptor interaction, the cell cycle, steroid hormone biosynthesis, Staphylococcus aureus infection, natural killer cell-mediated cytotoxicity, and metabolic pathways during the early stages of liver regeneration. In the later stages, altered pathways included steroid hormone biosynthesis, Staphylococcus aureus infection, retinol metabolism, the PI3K-Akt signaling pathway, the NF-kappa B signaling pathway, metabolic pathways, Epstein-Barr virus infection, cytokine-cytokine receptor interaction, and chemical carcinogenesis (Fig. 5 D, E, and F). PPI(Protein-Protein Interaction)analysis To investigate potential protein-protein interactions (PPI) among key genes that may influence protein functionality, we conducted PPI analyses. The results revealed that, at three days post-resection, the Nceh1 gene interacts with several genes, including KNG1, C9, and Fgg, many of which are involved in the Complement and Coagulation Cascades pathway. This suggests that the regulation of liver functionality related to coagulation is particularly complex during the initial three days following surgery (Fig. 6 A). By seven days post-resection, differentially expressed genes such as Cyp7a1, Sult1c3, Cyp2c11, Cyp4a2, and Ugt2a1, which are associated with bile and cholesterol secretion, displayed significant interactions, indicating their collaborative role in regulating liver recovery after surgery (Fig. 6 B). Verification of miR-34a-5p&Nceh1 In order to validate the accurate expression profiles of miRNA and mRNA during the liver regeneration process, a key miRNA-mRNA regulatory axis was selected for qPCR detection. The results showed that, compared to the control group, the expression level of miR-34a-5p was significantly upregulated in regenerating liver tissue three days after hepatectomy, while its corresponding Nceh1 was significantly downregulated. Similarly, seven days after hepatectomy, miR-34a-5p expression remained upregulated, and Nceh1 was downregulated compared to the control group. However, by day 14 after hepatectomy, there was no significant difference in the expression levels of miR-34a-5p and Nceh1. These findings suggest that miR-34a-5p may collaborate in the regulation of Nceh1 during the liver regeneration process (Fig. 7 ) Discussion Numerous studies have demonstrated that miRNAs play a crucial regulatory role in tumor development, cell differentiation, and organogenesis by post-transcriptionally modulating the degradation or translation inhibition of target mRNAs. miRNAs are implicated in various liver-related functions, including liver cancer, liver regeneration, and liver injury. For instance, miR-122, which is highly expressed in the liver, regulates key processes such as hepatic lipid metabolism and bile acid synthesis by targeting relevant genes. Reduced expression of miR-122 in the liver has been linked to the promotion of liver cancer development [ 8 ]. miR-21 is an important regulatory factor in liver regeneration. Chen et al. found that miR-21 can target and regulate the expression of PTEN, with the two showing a negative correlation. Overexpression of miR-21 in the early stages of liver regeneration accelerates the progression of hepatocytes from the G1 to the S phase, thereby speeding up the liver regeneration process [ 9 ]. In addition, Marquez RT et al. demonstrated that miR-21 inhibits the NF-κB pathway by targeting and regulating the expression of the Pelil gene [ 10 ].miR-21 is one of the first miRNAs discovered to be closely associated with liver regeneration. Following partial hepatectomy (PHx), miR-21 levels are significantly upregulated. Studies have shown that miR-21 promotes hepatocyte proliferation and survival by inhibiting the expression of the PTEN (phosphatase and tensin homolog) gene, thereby activating the PI3K/AKT signaling pathway [ 11 ]. Additionally, miR-21 reduces hepatocyte apoptosis by regulating the expression of Bcl-2 family members [ 12 ].miR-122 is highly expressed specifically in the liver, accounting for more than 70% of the total miRNAs in this organ. Its primary function is to regulate liver metabolism, particularly lipid metabolism and cholesterol homeostasis. In the early stages of liver regeneration, miR-122 levels decrease to facilitate cell proliferation; as regeneration progresses, miR-122 levels gradually return to normal, helping restore liver metabolic functions [ 13 ]. The Wnt/β-catenin signaling pathway plays a crucial role in initiating liver regeneration, with miRNAs regulating several key members of this pathway. Research indicates that miR-214 can inhibit liver regeneration by downregulating β-catenin expression [ 4 ], while miR-375 enhances the Wnt/β-catenin signaling pathway by regulating Frizzled-8, thereby promoting hepatocyte proliferation [ 15 ]. The primary mechanism through which miRNAs function in the body is by binding to the 3'-UTR regions of target genes, thereby regulating gene expression. Identifying miRNA target genes is therefore critical for understanding their regulatory roles. In this study, sRNA sequencing and transcriptome analysis were performed at three time points following liver resection, identifying a series of miRNAs, their target genes, and differentially expressed genes. To further elucidate the regulatory mechanisms of miRNA-mRNA interactions during liver regeneration, clustering analysis was conducted on the differentially expressed target genes. Correlation analysis revealed that during rat liver regeneration, 38 differentially expressed miRNAs were associated with 927 differentially expressed target genes. These genes represent targets that exhibit differential expression in response to miRNA regulation. Among the 38 miRNAs, 22 were upregulated and 16 were downregulated. Using this data, a miRNA-mRNA interaction network was constructed, identifying 120 key differentially expressed target genes. Of these, genes such as MRVI1, ENTPD1, RGS12, and IRX1 were downregulated, while TFRC, FOSB, RNF-125, TEX14, and SYNJ2 were upregulated (Suppmental Table S2). Previous studies suggest that MRVI1 and ENTPD1 are involved in inhibiting platelet activation, while IRX1 has anti-angiogenic properties. TFRC is crucial for iron uptake in red blood cells, and genes like RGS12, FOSB, and TEX14 play roles in cell proliferation. SYNJ2 is associated with membrane transport and signal transduction. These differential genes, regulated by miRNAs, are closely linked to liver development. After liver resection, the downregulation of rno-miR-200b-3p, rno-miR-203a-3p, and novel-miR-X_37782-5p, along with the upregulation of rno-miR-34a-5p, rno-miR-466b-3p, and novel-miR-13_28438-3p, regulates the expression of these target genes, enhancing iron uptake by red blood cells and promoting liver cell proliferation, thereby supporting liver regeneration in rats. We observed significant changes in several metabolic and signaling pathways during liver regeneration, including those related to phagosome formation, Epstein-Barr virus infection, Staphylococcus aureus infection, cancer pathways, cytokine-cytokine receptor interaction, the NF-kappa B signaling pathway, the NOD-like receptor signaling pathway, and the JAK-STAT signaling pathway, among others. Gene Ontology (GO) enrichment analysis further revealed that a large number of differentially expressed genes were associated with processes such as the cytoplasm, extracellular space, identical protein binding, ATP binding, membrane structures, the extracellular region, and the perinuclear region of the cytoplasm.Many of these pathways are closely linked to metabolism, cell proliferation, organ development, and cell signaling, highlighting the central role of cell proliferation as a critical biological process in liver regeneration. Through the regulation of these pathways, the liver is able to restore its original structure and functionality. Furthermore, we observed that miR-34a-5p targets and negatively regulates the Nceh1 gene during liver regeneration. This negative regulation suggests a nuanced role of miR-34a-5p in the regeneration process. Conclusion We successfully established a rat liver regeneration model, with physiological assessments on post-operative days 3, 7, and 14 showing that the liver regeneration process proceeded smoothly. Small RNA sequencing and transcriptome analysis identified 395 known miRNAs and 299 precursor miRNAs. A miRNA-mRNA regulatory network was constructed, highlighting 17 more significantly differentially expressed miRNAs and 31 mRNAs. Additionally, a protein-protein interaction (PPI) network was used to analyze the interactions of differentially expressed genes during liver regeneration. Furthermore, qPCR validated the expression of the miR-34a-5p target gene Nceh1 in the liver regeneration process. These findings provide new insights into the molecular mechanisms underlying liver regeneration. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All relevant data and materials are available from the corresponding authors on request. Competing interests The authors declared no conflict of interest. Funding This work was supported by the National Natural Sciences Foundation of China (NO. 81701800). Authors' contributions Hanqing Hu, and Yong Tang conceived, planned the study, and wrote the manuscript. Xin Zheng and Guodong Tian acquired the data and analyzed the data, participated in the discussion and provided the comments. Acknowledgements Not applicable. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48. doi:10.3322/caac.21763 He T, Zhang L, Kong Y, et al. Long non-coding RNA CASC15 is upregulated in hepatocellular carcinoma and facilitates hepatocarcinogenesis. Int J Oncol. 2017;51(6):1722-1730. doi:10.3892/ijo.2017.4175. Huang MD, Chen WM, Qi FZ, et al. Long non-coding RNA ANRIL is upregulated in hepatocellular carcinoma and regulates cell proliferation by epigenetic silencing of KLF2 [published correction appears in J Hematol Oncol. 2017 Jul 27;10(1):143. doi: 10.1186/s13045-017-0513-0]. J Hematol Oncol. 2015;8(1):57. Published 2015 May 29. doi:10.1186/s13045-015-0153-1. Zheng Z, Zhang X, Wang J, et al. Exposure to fine airborne particulate matters induces hepatic fibrosis in murine models. J Hepatol. 2015;63(6):1397-1404. doi:10.1016/j.jhep.2015.07.020. Park NR, Cha JH, Sung PS, et al. MiR-23b-3p suppresses epithelial-mesenchymal transition, migration, and invasion of hepatocellular carcinoma cells by targeting c-MET. Heliyon. 2022;8(10):e11135. Published 2022 Oct 17. doi:10.1016/j.heliyon.2022.e11135. Liu Y, Tan J, Ou S, Chen J, Chen L. MicroRNA-101-3p suppresses proliferation and migration in hepatocellular carcinoma by targeting the HGF/c-Met pathway. Invest New Drugs. 2020;38(1):60-69. doi:10.1007/s10637-019-00766-8. Hsu SH, Wang B, Kota J, et al. Essential metabolic, anti-inflammatory, and anti-tumorigenic functions of miR-122 in liver. J Clin Invest. 2012;122(8):2871-2883. doi:10.1172/JCI63539. Sendi H, Yazdimamaghani M, Hu M, et al. Nanoparticle Delivery of miR-122 Inhibits Colorectal Cancer Liver Metastasis. Cancer Res. 2022;82(1):105-113. doi:10.1158/0008-5472.CAN-21-2269 Chen X, Song M, Chen W, et al. MicroRNA-21 Contributes to Liver Regeneration by Targeting PTEN. Med Sci Monit. 2016;22:83-91. Published 2016 Jan 8. doi:10.12659/MSM.896157. Marquez RT, Wendlandt E, Galle CS, Keck K, McCaffrey AP. MicroRNA-21 is upregulated during the proliferative phase of liver regeneration, targets Pellino-1, and inhibits NF-kappaB signaling. Am J Physiol Gastrointest Liver Physiol. 2010;298(4):G535-G541. doi:10.1152/ajpgi.00338.2009. Marquez, R. T., & McCaffrey, A. P. (2008). \"Advances in microRNAs: implications for gene therapists.\" Human Gene Therapy , 19(1), 27-38.doi.org/10.1089/hum.2007.147 Hussein AM, El-Beih NM, Swellam M, El-Hussieny EA. Pomegranate juice and punicalagin-mediated chemoprevention of hepatocellular carcinogenesis via regulating miR-21 and NF-κB-p65 in a rat model. Cancer Cell Int. 2022;22(1):333. Published 2022 Nov 2. doi:10.1186/s12935-022-02759-9. Tsai WC, Hsu PW, Lai TC, et al. MicroRNA-122, a tumor suppressor microRNA that regulates intrahepatic metastasis of hepatocellular carcinoma. Hepatology. 2009;49(5):1571-1582. doi:10.1002/hep.22806. Morishita A, Oura K, Tadokoro T, Fujita K, Tani J, Masaki T. MicroRNAs in the Pathogenesis of Hepatocellular Carcinoma: A Review. Cancers (Basel). 2021;13(3):514. Published 2021 Jan 29. doi:10.3390/cancers13030514. Dinh TA, Jewell ML, Kanke M, et al. MicroRNA-375 Suppresses the Growth and Invasion of Fibrolamellar Carcinoma. Cell Mol Gastroenterol Hepatol. 2019;7(4):803-817. doi:10.1016/j.jcmgh.2019.01.008. Additional Declarations No competing interests reported. Supplementary Files SuppmentalTableS1.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5290996\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":369142614,\"identity\":\"c015cf1c-129e-49e6-a570-37296fbf63d8\",\"order_by\":0,\"name\":\"Hanqing Hu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACAwYeECXBwCD/+OCDhIoaUrQwpCUbPDhzjGgtIJBjJvmwhZmwFnOJ3IOfC8os8uQdjqVVJDawMfC3dyfg1WI5Iy9ZesY5iWLDg83HbiTukGGQOHN2A36H3cgxkOZtk0jc2MyWdiPxDBuDgUQuQS3Gv8Fa2njMChLbmInSYga2ZT4PjxkDcVrOvDGz5jknkbhBgi1ZIuHMMR7CfjmeY3ybp6wucf4M5oMff1TUyPG39+LXAgFsQL0HIEwevApRtMg3EKl2FIyCUTAKRh4AAElwSIzHGpXsAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"The First College of Clinical Medical Sciences, China Three Gorges University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Hanqing\",\"middleName\":\"\",\"lastName\":\"Hu\",\"suffix\":\"\"},{\"id\":369142615,\"identity\":\"8253dcf9-ff91-40da-833b-7b63671ba77e\",\"order_by\":1,\"name\":\"Xin Zheng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First College of Clinical Medical Sciences, China Three Gorges University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xin\",\"middleName\":\"\",\"lastName\":\"Zheng\",\"suffix\":\"\"},{\"id\":369142616,\"identity\":\"c30fb891-e5b6-4d20-998d-f8520245f51f\",\"order_by\":2,\"name\":\"Guodong Tian\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First College of Clinical Medical Sciences, China Three Gorges University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Guodong\",\"middleName\":\"\",\"lastName\":\"Tian\",\"suffix\":\"\"},{\"id\":369142617,\"identity\":\"6a5db9c3-93bc-4250-bfb7-2504f2bfa763\",\"order_by\":3,\"name\":\"Yong Tang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Huazhong University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yong\",\"middleName\":\"\",\"lastName\":\"Tang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-10-18 17:23:06\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5290996/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5290996/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":67444619,\"identity\":\"9ccdb0e5-ccbb-4fc8-88f6-a0a127cff44a\",\"added_by\":\"auto\",\"created_at\":\"2024-10-25 06:25:57\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":8075960,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHistopathological analysis of liver tissues(at 3, 7, and 14 days of post-surgery). A: hematoxylin-eosin (HE) staining. B: the expression of HSC70. C: the expression of PCNA.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5290996/v1/d635d8496e461d5935e2327e.jpg\"},{\"id\":67442540,\"identity\":\"42f20f5b-3e2e-4381-a64d-c6e41916c879\",\"added_by\":\"auto\",\"created_at\":\"2024-10-25 06:09:58\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1821654,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComparison of miRNA expression between the treatment and control groups at 3-day, 7-day and 14-day post-surgery.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5290996/v1/0acbb6de62ad6fed54f24e15.jpg\"},{\"id\":67442489,\"identity\":\"310b066b-7a71-4d37-a656-03f9e851902d\",\"added_by\":\"auto\",\"created_at\":\"2024-10-25 06:09:57\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":124940,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003emiRNA-mRNA regulatory network analysis of 17 differentially expressed miRNAs and 31 differentially expressed mRNAs.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5290996/v1/51361a2598ab88b31ef2877e.jpg\"},{\"id\":67442502,\"identity\":\"8ea34672-3101-4e91-9f6a-a74d1531f5e8\",\"added_by\":\"auto\",\"created_at\":\"2024-10-25 06:09:57\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2136716,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComparison of mRNA expression between the treatment and control groups at 3-day, 7-day and 14-day post-surgery.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5290996/v1/b7be20f36f551da7039d8597.jpg\"},{\"id\":67443399,\"identity\":\"d59273a1-15a6-43d3-a2a1-43c6f806b195\",\"added_by\":\"auto\",\"created_at\":\"2024-10-25 06:17:57\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1064369,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEnrichment analysis of KEGG and GO pathway. A, B\\u0026amp;C: GO enrichment analysis of differentially expressed genes at 3, 7 and 14 days post-surgery; D, E\\u0026amp;F: KEGG pathway enrichment analysis of differentially expressed genes at 3, 7 and 14 days post-surgery\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5290996/v1/8020f4cf34a79ea985d3ec7b.jpg\"},{\"id\":67442541,\"identity\":\"805bb374-cc5d-4022-a279-59b9f8df338a\",\"added_by\":\"auto\",\"created_at\":\"2024-10-25 06:09:58\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":880506,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eprotein-protein interactions (PPI) analysis among key genes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5290996/v1/34dc12b0725a3791d73b0c43.jpg\"},{\"id\":67442511,\"identity\":\"a752534e-eb75-4913-ba75-e99b0f2bd6a2\",\"added_by\":\"auto\",\"created_at\":\"2024-10-25 06:09:57\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":107159,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe expression level of rno-miR-34a-5p and Nceh1 were detected using qPCR in sham operation group and Hepatectomy group tissues at 3, 7 and 14 days, and the P-value≤0.05.(NC represents the sham operation group, and test represents the Hepatectomy group) . A: On the 3 day, the expression levels of rno-miR-34a-5p in the Hepatectomy group were significantly higher than those in the sham operation group. On the seventh day, the expression differences gradually decreased, and by the fourteenth day, there was no significant difference; B: The expression level of Nceh1 on the third day was significantly lower in the Hepatectomy group compared to the sham operation group. By the seventh day, the expression difference increased, but by the fourteenth day, there was no significant difference\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure7.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5290996/v1/8d2c33827ffc38dd1e8daa87.jpg\"},{\"id\":67444620,\"identity\":\"632dc295-c680-4bbe-993f-30a985e7e742\",\"added_by\":\"auto\",\"created_at\":\"2024-10-25 06:26:06\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":14894689,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5290996/v1/8b56b145-56f6-4074-a6f6-17e25546a6f3.pdf\"},{\"id\":67442536,\"identity\":\"ee27b458-5433-4ff2-9cfe-25e3c9eb5bb9\",\"added_by\":\"auto\",\"created_at\":\"2024-10-25 06:09:57\",\"extension\":\"xlsx\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":51098,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SuppmentalTableS1.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5290996/v1/fe93aef34d91a8d55764e0df.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Construction and Bioinformatics Analysis of the miRNA-mRNA Regulatory Network in Liver regeneration in rats\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eHepatocellular carcinoma (HCC) is one of the most common and aggressive malignant tumors. According to the latest data from 2023, there were 410,000 new cases of liver cancer in China in 2020, making it the fifth most prevalent cancer, while liver cancer-related deaths reached 390,000, ranking second [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Currently, partial hepatectomy remains the primary curative treatment for HCC. However, many patients suffer from poor liver reserve function and insufficient residual liver volume, limiting the liver\\u0026rsquo;s regenerative capacity. In cases of extensive liver cancer, undergoing extended liver resection may lead to liver failure and small-for-size syndrome (SFSS), which increases the risk of postoperative mortality [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e].The liver\\u0026rsquo;s remarkable regenerative ability is driven mainly by hepatocytes, which have an average lifespan of up to 300 days. Under normal conditions, hepatocytes divide minimally, but in response to injury, they proliferate and can restore the liver\\u0026rsquo;s original cellular structure within 7 to 14 days. This process is regarded as compensatory hyperplasia rather than true regeneration [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eGrowing evidence suggests that liver regeneration is governed by a complex regulatory network involving various molecules, such as growth factors, transcription factors, mRNA, miRNA, lncRNA, circRNA, DNA methylation, RNA methylation, and histone modifications [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. miRNAs do not function in isolation during liver regeneration but interact with multiple signaling pathways and gene regulatory networks. The TGF-β (transforming growth factor-β) signaling pathway plays a key role in terminating cell proliferation and tissue remodeling in the later stages of liver regeneration. miR-23b promotes hepatocyte proliferation and regeneration by inhibiting the expression of Smad3, thereby blocking the TGF-β signaling pathway [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. The HGF (hepatocyte growth factor)/c-Met pathway is crucial in the early stages of liver regeneration, as it accelerates regeneration by promoting hepatocyte proliferation and survival. miR-101-3p inhibits this signaling pathway by downregulating the expression of c-Met, thereby suppressing tumor growth[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Additionally, miR-122 is involved in the regulation of the HGF/c-Met signaling pathway and accelerates liver regeneration by promoting hepatocyte proliferation [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTo explore the role of the miRNA-mRNA regulatory network in liver regeneration, a study was conducted in which rats underwent liver resection surgery. Physiological indicators related to liver regeneration were assessed on postoperative days 3, 7, and 14. Small RNA sequencing and transcriptome analysis were employed to evaluate the expression profiles of miRNAs and mRNAs at various stages of regeneration. Bioinformatics tools were then used to identify and functionally analyze differentially expressed genes, aiming to uncover key miRNA-mRNA networks that regulate liver regeneration.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eConstruction of a 70% Hepatectomy Model in Rats\\u003c/h2\\u003e \\u003cp\\u003e All animal experiments were conducted in accordance with the guidelines approved by the Three Gorges University Animal Care and Use Ethics Committee. Male Sprague-Dawley rats, aged 28\\u0026ndash;42 days and weighing approximately 220g, were obtained from the Hubei Provincial Center for Experimental Animals. After a 7-day acclimation period, the rats were randomly assigned to either a sham surgery group (control) or a liver resection group (treatment), with 9 rats in each group. Prior to surgery, the rats were fasted for 12 hours, with free access to water. Following the fasting period, body weights were measured, and anesthesia was induced with an intraperitoneal injection of 10% chloral hydrate (330 \\u0026micro;L/100 g).\\u003c/p\\u003e \\u003cp\\u003eIn the treatment group, after full anesthesia was achieved, a longitudinal incision was made using sterilized surgical scissors to expose the liver. The hepatic pedicle was ligated, and the medial and left lobes of the liver were resected along the ligature line. The excised liver tissue was weighed and recorded. After ensuring hemostasis, the incision was sutured, and the rats were allowed to recover before being returned to their cages. Additionally, 100,000 IU of penicillin was administered intraperitoneally. In the sham surgery group, only a laparotomy was performed without liver resection.\\u003c/p\\u003e \\u003cp\\u003eBody weights were recorded pre-surgery, on the day of surgery, and on days 3, 7, and 14 post-surgery. At the same time points, the ratio of the regenerated liver weight to the residual liver weight was measured. Serum and tissue samples were collected for further analysis. Serum markers of liver function, including ALT, AST, γ-GT, TBIL, DBIL, ALP, ALB, and TBA, were measured. Hematoxylin and eosin (HE) staining was performed on liver tissues for histopathological analysis. The expression of PCNA and HSC70 in regenerating liver tissues was evaluated using immunohistochemistry.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eImmunofluorescence Staining\\u003c/h3\\u003e\\n\\u003cp\\u003eThe tissue was dehydrated using a gradient of alcohol solutions: 75% ethanol for 4 hours, 85% ethanol for 2 hours, 90% ethanol for 1.5 hours, 95% ethanol for 1 hour, anhydrous ethanol I for 0.5 hours, and anhydrous ethanol II for 0.5 hours. After alcohol dehydration, the tissue blocks were cleared with xylene using the following conditions: xylene (1:1) for 10 minutes, xylene I for 10 minutes, and xylene II for 10 minutes. The cleared tissue blocks were then sequentially impregnated with three baths of paraffin (60\\u0026deg;C). All of these steps were performed in a computerized tissue dehydration machine (Wuhan Junjie JT-12J). After impregnation, the tissue blocks were embedded to ensure complete integration with the embedding paraffin. The embedded tissues were sectioned and placed on slides, followed by baking for 3 hours. Subsequently, the slides were sequentially immersed in xylene I for 20 minutes, xylene II for 20 minutes, xylene III for 20 minutes, anhydrous ethanol I for 5 minutes, anhydrous ethanol II for 5 minutes, 95% ethanol for 5 minutes, 90% ethanol for 5 minutes, 80% ethanol for 5 minutes, and 70% ethanol for 5 minutes. Then, distilled water was used for a 5-minute wash to remove the paraffin. After blocking endogenous peroxidase activity, the samples were incubated with serum for 30 minutes, followed by the addition of primary antibodies (HSC70 and PCNA at a dilution ratio of 1:100) and secondary antibodies. After adding the chromogenic substrate, the samples were stained. Mayer's hematoxylin was used for a 2-minute counterstain, followed by dehydration and mounting for subsequent microscopic examination.\\u003c/p\\u003e\\n\\u003ch3\\u003eRNA extraction and quality control\\u003c/h3\\u003e\\n\\u003cp\\u003eTotal RNA was extracted using the RNeasy Plus Micro Kit (cat. no. 74034; Qiagen). RNA concentration and purity were measured using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA) and the Labchip GX Touch HT Nucleic Acid Analyzer (PerkinElmer, USA).\\u003c/p\\u003e\\n\\u003ch3\\u003eSmall RNA and transcriptome library construction\\u003c/h3\\u003e\\n\\u003cp\\u003eSmall RNA sequencing libraries were generated using the NEBNext\\u0026reg; Multiplex Small RNA Library Prep Set for Illumina\\u0026reg; (NEB, USA) following the manufacturer\\u0026rsquo;s recommendations, and index codes were added to attribute sequences to each sample. In brief, libraries were prepared by ligating different adaptors to the total RNA, followed by reverse transcription, PCR amplification, and size selection using 6% polyacrylamide gels. Library quality was assessed using the Agilent Bioanalyzer 2100 system. The QIAseq Stranded RNA Library Kit for Illumina\\u0026reg; from NEB (USA) was used to generate the libraries of transcriptome. Sequencing was performed using an Illumina Nova6000.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData analysis\\u003c/h2\\u003e \\u003cp\\u003eRaw data (raw reads) in the fastq format were first processed using in-house Perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapters and low-quality reads from raw data. At the same time, the Q20, Q30, and GC contents of the clean data were calculated. All downstream analyses were based on high-quality, clean data.\\u003c/p\\u003e \\u003cp\\u003eThe known miRNA sequences of the species were obtained using miRBase software. Information on known miRNA expression levels in samples and predictions of novel miRNAs were obtained using miRDeep2. Differential expression analysis of treatment group compared with control group was performed using DESeq2. The screening criteria for significantly different genes were corrected P values of \\u0026lt;\\u0026thinsp;0.05 and log\\u003csub\\u003e2\\u003c/sub\\u003e (fold change)\\u0026thinsp;\\u0026ge;\\u0026thinsp;1. miRNA target prediction was performed using TargetScan, PicTar, microT, miRmap, RNA22, PITA, and miRanda. The target genes were functionally annotated and enriched according to the predicted results.\\u003c/p\\u003e \\u003cp\\u003eTranscriptome sequencing data analysis involves performing statistical analysis on the standardized processed data using the DESeq2 software, with criteria of |log2(Fold change)| \\u0026gt; 1 and P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 to select differentially expressed genes.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003emiRNA-mRNA regulatory network construction\\u003c/h2\\u003e \\u003cp\\u003eThe target gene prediction for differentially expressed miRNAs was performed using miRanda software and the starBase database (version 3.0; starbase.sysu.edu.cn/index.php). The starBase database integrates prediction results from seven miRNA databases, including TargetScan, picTar, microT, miRmap, RNA22, PITA, and miRanda. The prediction is based on the complementarity and thermal stability between the miRNA sequence and the mRNA 3'UTR sequence.\\u003c/p\\u003e \\u003cp\\u003eThe screening criteria for selecting target genes included a predicted score above 140, a binding free energy lower than \\u0026minus;\\u0026thinsp;20 kcal/mol, and prediction consensus from multiple databases. Only target genes meeting these criteria were considered.The co-expression map of miRNA predicted target genes and differential genes was mapped in Cytoscape (V3.9.1).\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eFunctional enrichment of differentially expressed genes\\u003c/h3\\u003e\\n\\u003cp\\u003eThe identified target genes were then compared with the differentially expressed genes, and the overlapping genes were selected as potential targets of the differentially expressed miRNAs. KEGG/GO pathway enrichment analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using the clusterProfiler package in R. The analysis involved utilizing the KEGG annotations of genes, with all human genes serving as the background gene set. A significance threshold of P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was applied to identify statistically significant overrepresented annotations relative to the background.In addition to KEGG pathway analysis, Gene Ontology (GO) analysis was conducted. GO consists of three main categories: Molecular Function, Biological Process, and Cellular Component. The analysis aimed to identify enriched GO terms within these categories.In R, the package org.Rn.eg.db was used as the OrgDb database, and clusterProfiler was used to calculate the GSEA enrichment results of differential genes. swiss-model was used to conduct homology modeling, and then Verify3D, PROCHECK and ERRAT were used to evaluate the protein model with high accuracy.\\u003c/p\\u003e\\n\\u003ch3\\u003eConstruction of the protein-protein interaction (PPI) network\\u003c/h3\\u003e\\n\\u003cp\\u003eThe selected key genes were constructed by the STRING software, and Cytoscape (V3.9.1) software was used to construct the PPI network.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eReal-time PCR\\u003c/h2\\u003e \\u003cp\\u003eTotal RNA was reverse transcribed to cDNA using a Reverse Transcription Kit (Takara Co., Ltd., Dalian, China). Primer BLAST (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ncbi.nlm.nih.gov/tools/primer-blast/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.ncbi.nlm.nih.gov/tools/primer-blast/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) was used for designing primers. The designed primers were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China). cDNA was amplified using SYBR\\u0026reg; Premix Ex Taq\\u0026trade; (TaKaRa, Dalian). Gene expression levels were calculated by the ΔΔCt method with GAPDH and U6 as internal controls for mRNA and miRNA, respectively. The above primer sequences are listed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eQPCR primer sequences for rno-miR-34a-5p, Nceh1 and internal controls\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimer name\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrimer sequence(5'to3')\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emiR-34a-5p-F\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCGGGTGGATCACGATGCAAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emiR-34a-5p-R\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTCCTGCGGTTTACAGATGGAT\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNceh1-F\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCCTTGTCTCTGGTGGAGTCG\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNceh1-R\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGCACAGCAGTCACATTCTCG\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003esuccessfully constructed the rat liver regeneration models\\u003c/h2\\u003e \\u003cp\\u003eFollowing a 70% liver resection, body weight and the ratio of regenerated liver weight to residual liver weight were measured at 3, 7, and 14 days post-surgery (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The results indicated a successful liver regeneration process, with no instances of mortality in the liver resection group, suggesting the surgery was well-tolerated. To further validate the pathological changes at these time points, a series of serum tests were conducted, including ALT, AST, γ-GT, TBIL, DBIL, ALP, ALB, and TBA levels. Notably, serum albumin (ALB) levels progressively increased across the postoperative period, while the other markers showed a gradual decrease. By day 14, these indicators closely resembled those of the control group, suggesting that liver function had nearly returned to pre-resection levels within 14 days (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e\\u0026thinsp;\\u0026minus;\\u0026thinsp;1 Rat weight data recording (g)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003epre-operation\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003epostoperation 0d\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003epostoperation 3d\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003epostoperation 7d\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003epostoperation 14d\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eSham operation group\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e165\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e165\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e172\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e194\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e289\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e161\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e161\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e177\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e190\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e301\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e163\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e163\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e174\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e188\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e305\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eHepatectomy group\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e162\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e155\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e153\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e157\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e236\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e161\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e153\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e156\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e166\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e252\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e158\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e150\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e151\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e154\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e271\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e2 Weight ratio of rat regenerated liver to residual liver\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003epostoperation 0d\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003epostoperation 3d\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003epostoperation 7d\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003epostoperation 14d\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"8\\\" rowspan=\\\"9\\\"\\u003e \\u003cp\\u003eHepatectomy group\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.69\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.72\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.98\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eserum tests were conducted, including measurements of ALT, AST, γ-GT, TBIL, DBIL, ALP, ALB, and TBA\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eγ-GT(U/L)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eALP(King/100mL)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eT-BIL(U/mL)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eD-BIL(U/mL)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eTBA(\\u0026micro;mol/L)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eALB(g/L)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eALT(U/mL)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eAST(U/mL)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 3days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18.762\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.935\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.462\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.874\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e11.611\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e34.577\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e54.318\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e67.936\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 7days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18.230\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.886\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.321\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.964\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e12.521\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e35.469\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e65.836\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e73.662\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 14days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18.473\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.025\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.686\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.087\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e11.570\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e35.425\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e59.213\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e69.270\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 3days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e78.386\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45.510\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22.202\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e15.788\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e45.971\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e17.846\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e224.449\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e257.820\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 7days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e35.280\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.931\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.636\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.606\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e20.896\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e23.111\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e107.982\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e155.377\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 14days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e19.025\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.040\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.637\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.146\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e11.422\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e31.186\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e68.038\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e70.488\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eHistopathological analysis of liver tissues from the regenerated liver group was performed using hematoxylin-eosin (HE) staining. At 3 days post-resection, significant abnormalities were observed in the portal areas compared to the control group, with marked cellular proliferation (stained black) and mild inflammatory infiltration (stained red) around the bile ducts and blood vessels. By day 7, there was a slight increase in both cellular proliferation and inflammatory cell infiltration in these regions. By day 14, cellular proliferation and inflammatory infiltration were minimal, indicating that liver regeneration was progressing toward normal levels (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAdditionally, the expression of PCNA (proliferating cell nuclear antigen) and HSC70 (heat shock cognate 70) in the regenerated liver tissues was assessed through immunohistochemistry and Western blotting, respectively. PCNA staining, a key marker of cell proliferation, revealed significant differences between the treatment and control groups at all three time points (3, 7, and 14 days), highlighting active proliferation during liver regeneration (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003esmall RNA sequencing and transcriptome sequencing to uncover the key regulatory miRNA-mRNA networks\\u003c/h2\\u003e \\u003cp\\u003eSmall RNA sequencing generated a substantial amount of read data, with the processed results summarized in the attached table. The read counts across all samples ranged from 12.0\\u0026nbsp;million to 26.5\\u0026nbsp;million, with more than 90.3% of the reads successfully mapped in each sample. Based on mature miRNA sequences from miRBase, at least 395 known miRNAs and 299 known pre-miRNAs were identified in each sample library. Additionally, novel miRNAs were predicted based on the characteristic hairpin structure of miRNA precursors. Comparative analysis revealed that each sample contained at least 108 novel mature miRNAs and 95 novel pre-miRNAs (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eKnown and unknown numbers of mature miRNAs and hairpin miRNAs\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSample\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKnown_mature_miRNA\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eKnown_hairpin_miRNA\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNovel_mature_miRNA\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNovel_hairpin_miRNA\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 3days-1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e417\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e301\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e108\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 3days-2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e405\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e299\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e119\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e107\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 3days-3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e395\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e301\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e106\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 3days-1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e419\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e311\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e134\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e124\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 3days-2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e439\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e311\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e132\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e116\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 3days-3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e459\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e331\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e165\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e147\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 7days-1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e425\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e312\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e130\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e117\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 7days-2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e425\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e310\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e108\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e96\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 7days-3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e453\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e323\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e128\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e114\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 7days-1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e455\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e326\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e144\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e127\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 7days-2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e434\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e314\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e130\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e115\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 7days-3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e429\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e311\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e133\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e120\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 14days-1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e426\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e316\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e119\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e104\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 14days-2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e441\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e317\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e128\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e114\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esham operation group 14days-3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e466\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e331\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e124\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e111\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 14days-1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e450\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e327\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e125\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e114\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 14days-2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e437\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e319\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e124\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHepatectomy group 14days-3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e444\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e325\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e142\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e128\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eComparison of miRNA expression between the treatment and control groups at 3-day post-surgery revealed a total of 41 differentially expressed miRNAs (DE miRNAs), identified using the criteria of p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, |log2(Fold change)| \\u0026gt; 1, and reads per million (RPM)\\u0026thinsp;\\u0026gt;\\u0026thinsp;5 in at least one of the libraries. Of these, 12 miRNAs were upregulated, while 29 were downregulated. In the 7-day and 14-day groups, 24 and 23 DE miRNAs were identified, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eCluster analysis of these DE miRNAs showed distinct expression patterns during liver regeneration. For example, miRNAs such as rno-miR-363-3p, rno-miR-34a-5p, rno-miR-214-3p, and rno-miR-582-3p were significantly upregulated, while rno-miR-6215, rno-miR-3068-3p, rno-miR-196b-5p, and rno-miR-484 were significantly downregulated. By day 14, the expression differences in these miRNAs became less pronounced, indicating that as liver regeneration nears completion, their expression levels tend to normalize.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAdditionally, using the Targetscan, miRTarBase, and miRDB databases, downstream target predictions were performed for the differentially expressed miRNAs, and 927 miRNA-mRNA interactions were detected by cross-referencing the results from the three databases. Then, the target mRNAs were intersected with the significantly differentially expressed mRNAs mentioned earlier. The final miRNA-mRNA network contains 17 differentially expressed miRNAs and 31 differentially expressed mRNAs. Cytoscape was used to construct and visualize the miRNA-mRNA regulatory network (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDifferential gene expression analysis and functional enrichment analysis\\u003c/h2\\u003e \\u003cp\\u003eIn the transcriptome sequencing analysis, we aligned the sequenced reads with the NCBI reference genome. Across all samples, the input reads ranged from 45.5\\u0026nbsp;million to 67.2\\u0026nbsp;million, with more than 96.7% of the reads successfully mapped to the reference genome (see table). Using the mapped reads, we conducted a statistical analysis to evaluate gene expression levels in all samples. This analysis identified 991, 586, and 790 differentially expressed mRNAs (DE mRNAs) at different stages of the liver regeneration experiment, applying a cutoff of p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 and |log2(Fold change)| \\u0026gt; 1. Among these DE mRNAs, 640, 294, and 329 genes were upregulated at the three experimental stages (day 3 to day 14), while 351, 292, and 461 genes were downregulated, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eGO analysis of the differentially expressed mRNAs (DE mRNAs) revealed enrichment in 15 biological process terms, including liver development and immune response. In terms of cellular components, 15 GO terms were also enriched, primarily involving the cell and organelle membranes. Additionally, 15 molecular function GO terms were enriched, such as iron ion binding, heme binding, and the activation of growth-related factors and hormones (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA, B, and C).\\u003c/p\\u003e \\u003cp\\u003eAt days 3 and 7 post-surgery, common enriched metabolic pathways included cellular responses to lipids, heat generation, insulin-activated receptor activity, locomotor rhythm, mitotic spindle formation, negative regulation of gluconeogenesis and insulin secretion (related to cellular glucose response), lipid storage regulation, pheromone binding, and the positive regulation of glucose and lipid metabolic processes, as well as protein kinase B signaling. By contrast, at day 14, significant changes were observed mainly in pathways associated with the collagen-containing extracellular matrix, estradiol 17-β-dehydrogenase activity, and the innate immune response. This suggests that the pathways enriched at days 3 and 7 are more closely related to liver regeneration, while those at day 14 reflect later-stage processes.\\u003c/p\\u003e \\u003cp\\u003eKEGG pathway enrichment analysis of the DE genes revealed significant alterations in pathways such as Human papillomavirus infection, Hepatitis C, cytokine-cytokine receptor interaction, the cell cycle, steroid hormone biosynthesis, Staphylococcus aureus infection, natural killer cell-mediated cytotoxicity, and metabolic pathways during the early stages of liver regeneration. In the later stages, altered pathways included steroid hormone biosynthesis, Staphylococcus aureus infection, retinol metabolism, the PI3K-Akt signaling pathway, the NF-kappa B signaling pathway, metabolic pathways, Epstein-Barr virus infection, cytokine-cytokine receptor interaction, and chemical carcinogenesis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eD, E, and F).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePPI(Protein-Protein Interaction)analysis\\u003c/h2\\u003e \\u003cp\\u003eTo investigate potential protein-protein interactions (PPI) among key genes that may influence protein functionality, we conducted PPI analyses. The results revealed that, at three days post-resection, the Nceh1 gene interacts with several genes, including KNG1, C9, and Fgg, many of which are involved in the Complement and Coagulation Cascades pathway. This suggests that the regulation of liver functionality related to coagulation is particularly complex during the initial three days following surgery (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA). By seven days post-resection, differentially expressed genes such as Cyp7a1, Sult1c3, Cyp2c11, Cyp4a2, and Ugt2a1, which are associated with bile and cholesterol secretion, displayed significant interactions, indicating their collaborative role in regulating liver recovery after surgery (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eVerification of miR-34a-5p\\u0026amp;Nceh1\\u003c/h2\\u003e \\u003cp\\u003eIn order to validate the accurate expression profiles of miRNA and mRNA during the liver regeneration process, a key miRNA-mRNA regulatory axis was selected for qPCR detection. The results showed that, compared to the control group, the expression level of miR-34a-5p was significantly upregulated in regenerating liver tissue three days after hepatectomy, while its corresponding Nceh1 was significantly downregulated. Similarly, seven days after hepatectomy, miR-34a-5p expression remained upregulated, and Nceh1 was downregulated compared to the control group. However, by day 14 after hepatectomy, there was no significant difference in the expression levels of miR-34a-5p and Nceh1. These findings suggest that miR-34a-5p may collaborate in the regulation of Nceh1 during the liver regeneration process (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eNumerous studies have demonstrated that miRNAs play a crucial regulatory role in tumor development, cell differentiation, and organogenesis by post-transcriptionally modulating the degradation or translation inhibition of target mRNAs. miRNAs are implicated in various liver-related functions, including liver cancer, liver regeneration, and liver injury. For instance, miR-122, which is highly expressed in the liver, regulates key processes such as hepatic lipid metabolism and bile acid synthesis by targeting relevant genes. Reduced expression of miR-122 in the liver has been linked to the promotion of liver cancer development [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. miR-21 is an important regulatory factor in liver regeneration. Chen et al. found that miR-21 can target and regulate the expression of PTEN, with the two showing a negative correlation. Overexpression of miR-21 in the early stages of liver regeneration accelerates the progression of hepatocytes from the G1 to the S phase, thereby speeding up the liver regeneration process [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. In addition, Marquez RT et al. demonstrated that miR-21 inhibits the NF-κB pathway by targeting and regulating the expression of the Pelil gene [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].miR-21 is one of the first miRNAs discovered to be closely associated with liver regeneration. Following partial hepatectomy (PHx), miR-21 levels are significantly upregulated. Studies have shown that miR-21 promotes hepatocyte proliferation and survival by inhibiting the expression of the PTEN (phosphatase and tensin homolog) gene, thereby activating the PI3K/AKT signaling pathway [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Additionally, miR-21 reduces hepatocyte apoptosis by regulating the expression of Bcl-2 family members [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e].miR-122 is highly expressed specifically in the liver, accounting for more than 70% of the total miRNAs in this organ. Its primary function is to regulate liver metabolism, particularly lipid metabolism and cholesterol homeostasis. In the early stages of liver regeneration, miR-122 levels decrease to facilitate cell proliferation; as regeneration progresses, miR-122 levels gradually return to normal, helping restore liver metabolic functions [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. The Wnt/β-catenin signaling pathway plays a crucial role in initiating liver regeneration, with miRNAs regulating several key members of this pathway. Research indicates that miR-214 can inhibit liver regeneration by downregulating β-catenin expression [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e], while miR-375 enhances the Wnt/β-catenin signaling pathway by regulating Frizzled-8, thereby promoting hepatocyte proliferation [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe primary mechanism through which miRNAs function in the body is by binding to the 3'-UTR regions of target genes, thereby regulating gene expression. Identifying miRNA target genes is therefore critical for understanding their regulatory roles. In this study, sRNA sequencing and transcriptome analysis were performed at three time points following liver resection, identifying a series of miRNAs, their target genes, and differentially expressed genes. To further elucidate the regulatory mechanisms of miRNA-mRNA interactions during liver regeneration, clustering analysis was conducted on the differentially expressed target genes.\\u003c/p\\u003e \\u003cp\\u003eCorrelation analysis revealed that during rat liver regeneration, 38 differentially expressed miRNAs were associated with 927 differentially expressed target genes. These genes represent targets that exhibit differential expression in response to miRNA regulation. Among the 38 miRNAs, 22 were upregulated and 16 were downregulated. Using this data, a miRNA-mRNA interaction network was constructed, identifying 120 key differentially expressed target genes. Of these, genes such as MRVI1, ENTPD1, RGS12, and IRX1 were downregulated, while TFRC, FOSB, RNF-125, TEX14, and SYNJ2 were upregulated (Suppmental Table S2).\\u003c/p\\u003e \\u003cp\\u003ePrevious studies suggest that MRVI1 and ENTPD1 are involved in inhibiting platelet activation, while IRX1 has anti-angiogenic properties. TFRC is crucial for iron uptake in red blood cells, and genes like RGS12, FOSB, and TEX14 play roles in cell proliferation. SYNJ2 is associated with membrane transport and signal transduction. These differential genes, regulated by miRNAs, are closely linked to liver development. After liver resection, the downregulation of rno-miR-200b-3p, rno-miR-203a-3p, and novel-miR-X_37782-5p, along with the upregulation of rno-miR-34a-5p, rno-miR-466b-3p, and novel-miR-13_28438-3p, regulates the expression of these target genes, enhancing iron uptake by red blood cells and promoting liver cell proliferation, thereby supporting liver regeneration in rats.\\u003c/p\\u003e \\u003cp\\u003eWe observed significant changes in several metabolic and signaling pathways during liver regeneration, including those related to phagosome formation, Epstein-Barr virus infection, Staphylococcus aureus infection, cancer pathways, cytokine-cytokine receptor interaction, the NF-kappa B signaling pathway, the NOD-like receptor signaling pathway, and the JAK-STAT signaling pathway, among others. Gene Ontology (GO) enrichment analysis further revealed that a large number of differentially expressed genes were associated with processes such as the cytoplasm, extracellular space, identical protein binding, ATP binding, membrane structures, the extracellular region, and the perinuclear region of the cytoplasm.Many of these pathways are closely linked to metabolism, cell proliferation, organ development, and cell signaling, highlighting the central role of cell proliferation as a critical biological process in liver regeneration. Through the regulation of these pathways, the liver is able to restore its original structure and functionality.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, we observed that miR-34a-5p targets and negatively regulates the Nceh1 gene during liver regeneration. This negative regulation suggests a nuanced role of miR-34a-5p in the regeneration process.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eWe successfully established a rat liver regeneration model, with physiological assessments on post-operative days 3, 7, and 14 showing that the liver regeneration process proceeded smoothly. Small RNA sequencing and transcriptome analysis identified 395 known miRNAs and 299 precursor miRNAs. A miRNA-mRNA regulatory network was constructed, highlighting 17 more significantly differentially expressed miRNAs and 31 mRNAs. Additionally, a protein-protein interaction (PPI) network was used to analyze the interactions of differentially expressed genes during liver regeneration. Furthermore, qPCR validated the expression of the miR-34a-5p target gene Nceh1 in the liver regeneration process. These findings provide new insights into the molecular mechanisms underlying liver regeneration.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eEthics approval and consent to participate\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for publication\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003eAvailability of data and materials\\u003c/p\\u003e\\n\\u003cp\\u003eAll relevant data and materials are available from the corresponding authors on request.\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting interests\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declared no conflict of interest.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by the National Natural Sciences Foundation of China (NO. 81701800).\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors\\u0026apos; contributions\\u003c/p\\u003e\\n\\u003cp\\u003eHanqing Hu, and Yong Tang conceived, planned the study, and wrote the manuscript. Xin Zheng and Guodong Tian acquired the data and analyzed the data, participated in the discussion and provided the comments.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgements\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Not applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48. doi:10.3322/caac.21763\\u003c/li\\u003e\\n\\u003cli\\u003eHe T, Zhang L, Kong Y, et al. Long non-coding RNA CASC15 is upregulated in hepatocellular carcinoma and facilitates hepatocarcinogenesis. Int J Oncol. 2017;51(6):1722-1730. doi:10.3892/ijo.2017.4175.\\u003c/li\\u003e\\n\\u003cli\\u003eHuang MD, Chen WM, Qi FZ, et al. Long non-coding RNA ANRIL is upregulated in hepatocellular carcinoma and regulates cell proliferation by epigenetic silencing of KLF2 [published correction appears in J Hematol Oncol. 2017 Jul 27;10(1):143. doi: 10.1186/s13045-017-0513-0]. J Hematol Oncol. 2015;8(1):57. Published 2015 May 29. doi:10.1186/s13045-015-0153-1.\\u003c/li\\u003e\\n\\u003cli\\u003eZheng Z, Zhang X, Wang J, et al. Exposure to fine airborne particulate matters induces hepatic fibrosis in murine models. J Hepatol. 2015;63(6):1397-1404. doi:10.1016/j.jhep.2015.07.020.\\u003c/li\\u003e\\n\\u003cli\\u003ePark NR, Cha JH, Sung PS, et al. MiR-23b-3p suppresses epithelial-mesenchymal transition, migration, and invasion of hepatocellular carcinoma cells by targeting c-MET. Heliyon. 2022;8(10):e11135. Published 2022 Oct 17. doi:10.1016/j.heliyon.2022.e11135.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu Y, Tan J, Ou S, Chen J, Chen L. MicroRNA-101-3p suppresses proliferation and migration in hepatocellular carcinoma by targeting the HGF/c-Met pathway. Invest New Drugs. 2020;38(1):60-69. doi:10.1007/s10637-019-00766-8.\\u003c/li\\u003e\\n\\u003cli\\u003eHsu SH, Wang B, Kota J, et al. Essential metabolic, anti-inflammatory, and anti-tumorigenic functions of miR-122 in liver. J Clin Invest. 2012;122(8):2871-2883. doi:10.1172/JCI63539.\\u003c/li\\u003e\\n\\u003cli\\u003eSendi H, Yazdimamaghani M, Hu M, et al. Nanoparticle Delivery of miR-122 Inhibits Colorectal Cancer Liver Metastasis. Cancer Res. 2022;82(1):105-113. doi:10.1158/0008-5472.CAN-21-2269\\u003c/li\\u003e\\n\\u003cli\\u003eChen X, Song M, Chen W, et al. MicroRNA-21 Contributes to Liver Regeneration by Targeting PTEN. Med Sci Monit. 2016;22:83-91. Published 2016 Jan 8. doi:10.12659/MSM.896157.\\u003c/li\\u003e\\n\\u003cli\\u003eMarquez RT, Wendlandt E, Galle CS, Keck K, McCaffrey AP. MicroRNA-21 is upregulated during the proliferative phase of liver regeneration, targets Pellino-1, and inhibits NF-kappaB signaling. Am J Physiol Gastrointest Liver Physiol. 2010;298(4):G535-G541. doi:10.1152/ajpgi.00338.2009.\\u003c/li\\u003e\\n\\u003cli\\u003eMarquez, R. T., \\u0026amp; McCaffrey, A. P. (2008). \\u0026quot;Advances in microRNAs: implications for gene therapists.\\u0026quot; \\u003cem\\u003eHuman Gene Therapy\\u003c/em\\u003e, 19(1), 27-38.doi.org/10.1089/hum.2007.147\\u003c/li\\u003e\\n\\u003cli\\u003eHussein AM, El-Beih NM, Swellam M, El-Hussieny EA. Pomegranate juice and punicalagin-mediated chemoprevention of hepatocellular carcinogenesis via regulating miR-21 and NF-\\u0026kappa;B-p65 in a rat model. Cancer Cell Int. 2022;22(1):333. Published 2022 Nov 2. doi:10.1186/s12935-022-02759-9.\\u003c/li\\u003e\\n\\u003cli\\u003eTsai WC, Hsu PW, Lai TC, et al. MicroRNA-122, a tumor suppressor microRNA that regulates intrahepatic metastasis of hepatocellular carcinoma. Hepatology. 2009;49(5):1571-1582. doi:10.1002/hep.22806.\\u003c/li\\u003e\\n\\u003cli\\u003eMorishita A, Oura K, Tadokoro T, Fujita K, Tani J, Masaki T. MicroRNAs in the Pathogenesis of Hepatocellular Carcinoma: A Review. Cancers (Basel). 2021;13(3):514. Published 2021 Jan 29. doi:10.3390/cancers13030514.\\u003c/li\\u003e\\n\\u003cli\\u003eDinh TA, Jewell ML, Kanke M, et al. MicroRNA-375 Suppresses the Growth and Invasion of Fibrolamellar Carcinoma. Cell Mol Gastroenterol Hepatol. 2019;7(4):803-817. doi:10.1016/j.jcmgh.2019.01.008.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5290996/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5290996/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground:\\u003c/h2\\u003e \\u003cp\\u003eHepatocellular carcinoma (HCC) is one of the most common and aggressive malignant tumors. Partial hepatectomy (PHx) is currently the primary treatment for HCC, but many patients suffer from poor liver reserve function and insufficient remaining liver volume, limiting the liver's regenerative capacity. Therefore, this study aims to explore the mechanisms of miRNA and mRNA in liver regeneration through high-throughput sequencing.\\u003c/p\\u003e\\u003ch2\\u003eMethods:\\u003c/h2\\u003e \\u003cp\\u003eA rat model of 70% hepatectomy was used, and physiological indicators related to liver regeneration were assessed on days 3, 7, and 14 post-surgery. Small RNA sequencing and transcriptome analysis were conducted to evaluate the miRNA and mRNA expression profiles at different stages of regeneration. Bioinformatics tools were used to identify differentially expressed genes, construct miRNA-mRNA regulatory networks, and protein-protein interaction (PPI) networks, to identify key regulatory molecules.\\u003c/p\\u003e\\u003ch2\\u003eResults:\\u003c/h2\\u003e \\u003cp\\u003eThe rat liver regeneration model was successfully established, and the body weight and liver regeneration rate data on days 3, 7, and 14 indicated a smooth regeneration process. Small RNA sequencing and transcriptome analysis identified 395 known miRNAs and 299 precursor miRNAs. Differential expression analysis revealed dynamic expression patterns of multiple miRNAs and mRNAs during liver regeneration. The miRNA-mRNA regulatory network showed interactions between 17 differentially expressed miRNAs and 31 differentially expressed mRNAs involved in liver regeneration.\\u003c/p\\u003e\\u003ch2\\u003eConclusion:\\u003c/h2\\u003e \\u003cp\\u003eThis study, through small RNA sequencing and transcriptome analysis, revealed key regulatory roles of miRNAs in various signaling pathways during liver regeneration. The constructed miRNA-mRNA regulatory network further elucidates the molecular mechanisms of liver regeneration. The results demonstrate the complex regulatory roles of miRNAs in promoting hepatocyte proliferation, inhibiting apoptosis, and regulating multiple key signaling pathways, providing new insights into the understanding of liver regeneration mechanisms.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Construction and Bioinformatics Analysis of the miRNA-mRNA Regulatory Network in Liver regeneration in rats\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-10-25 06:09:52\",\"doi\":\"10.21203/rs.3.rs-5290996/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"02edb431-ebf2-4943-8edc-2a2f819e46c7\",\"owner\":[],\"postedDate\":\"October 25th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-10-25T06:09:55+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-10-25 06:09:52\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5290996\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5290996\",\"identity\":\"rs-5290996\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}