Identification of the immune-related diagnostic biomarkers between Graves' disease and thyroid carcinoma based on comprehensive bioinformatics analysis and machine learning

Laryngeal squamous cell carcinoma (LSCC) is the prominent cancer in head and neck, which greatly affects life quality of patients. The pathogenesis of LSCC is not clear. Presently, the LSCC treatments include chemotherapy, surgery and radiotherapy; however, these methods have poor efficacy in patients with recurrent and persistent cancer. Therefore, the study identified the hub genes accompanied with LSCC, which may be a potential therapeutic target in the future. We extracted whole transcriptome high-throughput sequencing (HTS) LSCC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and calculate differentially expressed genes (DEGs) between LSCC and normal samples using statistical software RStudio. Through weighted gene co-expression network analysis (WGCNA), enrichment examination of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) functions, and examination of protein-protein interaction (PPI) network, we obtained network hub genes and validated the hub genes prognostic value and expression levels of protein. Through analysis of differential gene expression, from the GEO and TCGA databases 2,139 and 2,774 DEGs were obtained, respectively, 13 and 15 modules were screened from TCGA-LSCC and GSE127165 datasets by WGCNA, respectively. The most significant positive and negative correlation modules in the WGCNA and DEG lists were overlapped, and overall 36 co-expressed overlapping genes were retrieved. Through enrichment analysis of GO and KEGG, it was found that the gene functions were highly concentrated in cell junction assembly, basement membrane, extracellular matrix (ECM) structural constituent etc., and the pathways were mainly concentrated in ECM receptor interaction, focal adhesion, small cell lung cancer, and toxoplasmosis. Through analysis of PPI network analysis, 10 network hub genes (SNAI2, ITGA6, LAMB3, LAMC2, CAV1, COL7A1, GJA1, EHF, OAT, and GPT) were obtained. Finally, survival analysis and protein expression validation of these genes confirmed that low OAT expression and high CAV1 expression remarkably influenced the survival of patient's prognosis with LSCC. We recognized the hub genes and key modules nearly associated to LSCC and these genes were validated by survival analysis and the database of Human Protein Atlas (HPA), which is of high importance for unveiling the pathogenesis of LSCC and probing for new precise biological marker and potential therapeutic targets.

BackgroundThe incidence of thyroid papillary carcinoma (PTC) is increasing annually, causing both physical and psychological pressure on patients. Therefore, early recognition and specific interventions for PTC are crucial. The objective of this study is to explore novel diagnostic marker and precise intervention targets for PTC.MethodsBased on a weighted gene co-expression network analysis (WGCNA), relevant datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were collected. Enrichment analysis was performed on differentially expressed genes (DEGs) using Gene Ontology (GO), Disease Ontology (DO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Subsequently, three machine learning algorithms Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) were used to identify the core genes. Finally, receiver operating characteristic (ROC) curves were used to analyze the clinical diagnostic value of the core genes.ResultsWe found, in total, 11,194 DEGs derived the TCGA and GEO datasets, that are primarily enriched in extracellular matrix (ECM) and inflammation related pathways, such as an ECM receptor interaction, cell adhesion molecules (CAMs), Tumor necrosis factor (TNF) signaling, and nucleotide-binding oligomerization domain (NOD) like receptor signaling pathways. Further analysis of the core genes, identified by the protein–protein interaction network, using three machine learning algorithms discovered three intersecting genes GJC1, KLHL4, and NOL4. Of which, GJC1 has good clinical diagnostic ability, which was verified using both the GEO (area under the ROC curve (AUC) = .982) and TCGA databases (AUC = .840).ConclusionsGJC1 is highly expressed in PTC. Therefore, it is considered as a potential biomarker and is expected to become a new target for PTC gene therapy. However, it still needs to be supported and verified by more clinical data.

The function of glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) in neurodegenerative diseases has been widely reported; however, its role in the occurrence of glioma remains less explored. We obtained clinical data and transcriptome data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Hub gene's expression differential analysis and survival analysis were conducted by browsing the Gene Expression Profiling Interactive Analysis (GEPIA) database, Human Protein Atlas database, and LOGpc database. We conducted a variation analysis of datasets obtained from GEO and TCGA and performed a weighted gene coexpression network analysis (WGCNA) using the R programming language (3.6.3). Kaplan-Meier (KM) analysis was used to calculate the prognostic value of GRIN1. Finally, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Using STRING, we constructed a protein–protein interaction (PPI) network. Cytoscape software, a prerequisite of visualizing core genes, was installed, and CytoHubba detected the 10 most tumor-related core genes. We identified 185 differentially expressed genes (DEGs). GO and KEGG enrichment analyses illustrated that the identified DEGs are imperative in different biological functions and ascertained the potential pathways in which the DEGs may be enriched. The overall survival calculated by KM analysis showed that patients with lower expression of GRIN1 had worse prognoses than patients with higher expression of GRIN1 (p = 0.004). The GEPIA and LOGpc databases were used to verify the expression difference of GRIN1 among GBM, LGG, and normal brain tissues. Ultimately, immunohistochemical assay results showed that GRIN1 was detected in normal tissue and not in the tumor specimens. Our results highlight a potential target for glioma treatment and will further our understanding of the molecular mechanisms underlying the treatment of glioma.

Aim: To screen differentially expressed genes related to gastric cancer based on The Cancer Genome Atlas (TCGA) database and construct a gastric cancer diagnosis model by machine learning. Methods: Transcriptional data, genomic data, and clinical information of gastric cancer tissues and non-gastric cancer tissues were downloaded from the TCGA database, and differentially expressed genes of gastric cancer messenger RNA (mRNA) and long non-coding RNA (lncRNA) were screened out. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyzed the differentially expressed genes, and the protein-protein interaction (PPI) of differentially expressed genes was constructed. Core differentially expressed genes were screened by Cytoscape software’s molecular complex detection (MCODE) plug-in. The differential genes of lncRNA were analyzed by univariate Cox regression analysis and lasso regression for further dimension reduction to obtain the core genes. The core genes were screened by machine learning to construct the gastric cancer diagnosis model. The efficiency of the gastric cancer diagnosis model was verified externally by the Gene Expression Omnibus (GEO) database. Results: Finally, 10 genes including long intergenic non-protein coding RNA 1821 (LINC01821), AL138826.1, AC022164.1, adhesion G protein-coupled receptor D1-antisense RNA 1 (ADGRD1-AS1), cyclin B1 (CCNB1), kinesin family member 11 (KIF11), Aurora kinase B (AURKB), cyclin dependent kinase 1 (CDK1), nucleolar and spindle associated protein 1 (NUSAP1), and TTK protein kinase (TTK) were screened as gastric cancer diagnostic model genes. After efficiency analysis, it was found that the random forest algorithm model had the best comprehensive evaluation, with an accuracy of 92% and an area under the curve (AUC) of 0.9722, which was more suitable for building a gastric cancer diagnosis model. The GSE54129 data set was used to verify the gastric cancer diagnosis model with an AUC of 0.904, indicating that the gastric cancer diagnosis model had high accuracy. Conclusions: Machine learning can simplify the bioinformatics analysis process and improve efficiency. The core gene discovered in this study is expected to become a gene chip for the diagnosis of gastric cancer.

Background: Nasopharyngeal carcinoma (NPC) represents a highly aggressive malignant tumor. Competing endogenous RNAs (ceRNA) regulation is a common regulatory mechanism in tumors. The ceRNA network links the functions between mRNAs and ncRNAs, thus playing an important regulatory role in diseases. This study screened the potential key genes in NPC and predicted regulatory mechanisms using bioinformatics analysis.Methods: The merged microarray data of three NPC-related mRNA expression microarrays from the Gene Expression Omnibus (GEO) database and the expression data of tumor samples or normal samples from the nasopharynx and tonsil in The Cancer Genome Atlas (TCGA) database were both subjected to differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The results from two different databases were intersected with WGCNA results to obtain potential regulatory genes in NPC, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. The hub-gene in candidate genes was discerned through Protein-Protein Interaction (PPI) analysis and its upstream regulatory mechanism was predicted by miRwalk and circbank databases.Results: Totally 68 upregulated genes and 96 downregulated genes in NPC were screened through GEO and TCGA. According to WGCNA, the NPC-related modules were screened from GEO and TCGA analysis results, and the genes in the modules were obtained. After the results of differential analysis and WGCNA were intersected, 74 differentially expressed candidate genes associated with NPC were discerned. Finally, fibronectin 1 (FN1) was identified as a hub-gene in NPC. Prediction of upstream regulatory mechanisms of FN1 suggested that FN1 may be regulated by ceRNA mechanisms involving multiple circRNAs, thereby influencing NPC progression through ceRNA regulation.Conclusion: FN1 is identified as a key regulator in NPC development and is likely to be regulated by numerous circRNA-mediated ceRNA mechanisms.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumor. miR-331-3p has been reported relevant to the progression of HCC, but the molecular mechanism of its regulation is still unclear. In the study, we comprehensively studied the role of miR-331-3p in HCC through weighted gene coexpression network analysis (WGCNA) based on The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Oncomine. WGCNA was applied to build gene co-expression networks to examine the correlation between gene sets and clinical characteristics, and to identify potential biomarkers. Five hundred one target genes of miR-331-3p were obtained by overlapping differentially expressed genes (DEGs) from the TCGA database and target genes predicted by miRWalk. The critical turquoise module and its eight key genes were screened by WGCNA. Enrichment analysis was implemented based on the genes in the turquoise module. Moreover, 48 genes with a high degree of connectivity were obtained by protein–protein interaction (PPI) analysis of the genes in the turquoise module. From overlapping genes analyzed by WGCNA and PPI, two hub genes were obtained, namely coatomer protein complex subunit zeta 1 (COPZ1) and elongation factor Tu GTP binding domain containing 2 (EFTUD2). In addition, the expression of both hub genes was also significantly higher in tumor tissues compared with normal tissues, as confirmed by analysis based on TCGA and Oncomine. Both hub genes were correlated with poor prognosis based on TCGA data. Receiver operating characteristic (ROC) curve validated that both hub genes exhibited excellent diagnostic efficiency for normal and tumor tissues.

Laryngeal cancer is a highly aggressive malignancy with high incidence and mortality rates. This study aims to identify potential biomarkers and therapeutic targets for laryngeal cancer through an integrated approach combining bioinformatics and machine learning, followed by experimental validation. We obtained laryngeal cancer tissue sample data from The Cancer Genome Atlas (TCGA) database and used Weighted Gene Co-expression Network Analysis (WGCNA) to identify key gene modules. These modules were then intersected with oxidative stress-related genes from the GeneCards database to determine core genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the core genes revealed that they are primarily involved in responses to xenobiotic stimuli, oxidative stress, and multiple signaling pathways. Cox regression analysis identified GPT and EPHX2 as independent risk factors for laryngeal cancer. Through a comprehensive analysis using LASSO regression and Protein-Protein Interaction (PPI) networks, highlighted EPHX2 as a candidate gene of interest. Experimental results showed that the expression of EPHX2 in laryngeal cancer tissues was significantly lower than that in control tissues (p < 0.01). Additionally, EPHX2 expression was associated with increased infiltration of M0 macrophages (p < 0.01) and decreased infiltration of CD4 memory T cells (p < 0.01), suggest that EPHX2 expression correlates with immune-cell infiltration patterns and clinical outcomes. Western blot and immunohistochemistry corroborated the differential expression of EPHX2 between tumor and non-tumor tissues. Collectively, these findings uncover an oxidative stress-related gene signature in laryngeal cancer and suggest that EPHX2 expression correlates with immune-cell infiltration patterns and clinical outcomes. However, the present data are observational and do not establish a mechanistic or causal role for EPHX2 in laryngeal cancer progression or immune modulation; functional studies such as gene knockdown or overexpression assays are required to validate its biological role.

Investigating comorbidities of ischemic stroke (IS) enhances understanding of its intricate mechanisms. Crohn's disease (CD) is associated with an increased risk of IS, but the underlying mechanisms remain unclear. This study aims to identify shared diagnostic genes and explore the mechanisms underlying CD-IS comorbidity using bioinformatics and machine learning approaches. Gene expression data for CD and IS were obtained from the Gene Expression Omnibus. Shared genes were identified through differential expression and weighted gene co-expression network analyses (WGCNA). Functional enrichment analyses highlighted key biological pathways. Core genes were screened via machine learning algorithms and protein-protein interaction networks. Diagnostic nomograms were constructed, and single-cell RNA sequencing was used to characterize expression patterns of core genes. Immune cell infiltration was quantified using CIBERSORT, and a competing endogenous RNA network was built based on TarBase and SpongeScan databases. Mendelian randomization was performed to assess causal associations between core genes and disease risk. Candidate drugs were predicted using the Drug-Gene Interaction Database and validated through molecular docking. Twenty shared genes were identified through differential expression analysis and WGCNA. The toll-like receptor (TLR) signaling pathway was identified as a key pathway in CD-IS comorbidity. TLR2 and TLR8 were identified as core genes, with strong diagnostic performance (AUC > 0.80). The polymorphism of rs73221365 was associated with both CD and IS. Resveratrol hexanoic acid was a potential therapeutic candidate for CD-IS comorbidity. This study highlights the critical role of TLR-mediated inflammatory responses in CD-IS comorbidity. TLR2 and TLR8 may serve as promising diagnostic biomarkers. These findings advance understanding of the shared pathophysiology in CD-IS comorbidity and provide a foundation for developing precise diagnostics and targeted therapies.

A further understanding of the molecular mechanism of hepatocellular carcinoma (HCC) is necessary to predict a patient’s prognosis and develop new targeted gene drugs. This study aims to identify essential genes related to HCC. We used the Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis to analyze the gene expression profile of GSE45114 in the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas database (TCGA). A total of 37 overlapping genes were extracted from four groups of results. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were performed on the 37 overlapping genes. Then, we used the STRING database to map the protein interaction (PPI) network of 37 overlapping genes. Ten hub genes were screened according to the Maximal Clique Centrality (MCC) score using the Cytohubba plugin of Cytoscape (including FOS, EGR1, EPHA2, DUSP1, IGFBP3, SOCS2, ID1, DUSP6, MT1G, and MT1H). Most hub genes show a significant association with immune infiltration types and tumor stemness of microenvironment in HCC. According to Univariate Cox regression analysis and Kaplan-Meier survival estimation, SOCS2 was positively correlated with overall survival (OS), and IGFBP3 was negatively correlated with OS. Moreover, the expression of IGFBP3 increased with the increase of the clinical stage, while the expression of SOCS2 decreased with the increase of the clinical stage. In conclusion, our findings suggest that SOCS2 and IGFBP3 may play an essential role in the development of HCC and may serve as a potential biomarker for future diagnosis and treatment.

ObjectiveThis study aims to identify and validate key genes involved in the progression of myocardial infarction (MI) and to investigate their roles in inflammatory response, immune regulation, and myocardial remodeling. A systematic analysis will be conducted using bioinformatics and machine learning methods.MethodsGene expression data of GSE60993, GSE61144, GSE66360 and GSE48060 from four datasets were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between MI samples and normal samples were screened by the limma package. Weighted gene co-expression network analysis (WGCNA) was employed to identify genetic modules associated with MI. Core genes in key modules were screened using LASSO regression and support vector machine recursive feature elimination (SVM-RFE). These genes were then subjected to functional enrichment analysis, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA). The CIBERSORT algorithm was utilized to evaluate immune cell infiltration patterns. Finally, potential therapeutic targets were explored through drug-gene interaction analysis using the DGIdb database.ResultsAfter correcting for batch effects across datasets, we identified 687 differentially expressed genes (DEGs), including 405 upregulated and 282 downregulated genes. WGCNA analysis identified a highly correlated module with MI (turquoise module) containing 324 genes. Integrative machine learning (LASSO regression and SVM-RFE) and validation identified five key MI-associated genes: ANPEP, S100A9, MMP9, DAPK2, and FCAR. These genes were functionally enriched in inflammatory and immune-related pathways and correlated with immune cell infiltration, particularly neutrophils and macrophages. Notably, S100A9, FCAR, and MMP9 emerged as druggable targets.ConclusionThe five hub genes identified in this study (ANPEP, S100A9, MMP9, DAPK2, and FCAR) significantly contribute to MI development by modulating inflammatory responses and immune regulation. Their strong association with MI pathogenesis highlights their potential as diagnostic markers and therapeutic targets, which may lead to new clinical applications for MI management.

Background Sarcopenia is a common chronic disease characterized by age-related decline in skeletal muscle mass and function, and the lack of diagnostic biomarkers makes community-based screening problematic. Methods Three gene expression profiles related with sarcopenia were downloaded and merged by searching the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and eigengenes of a module in the merged dataset were identified by differential expression analysis and weighted gene coexpression network analysis (WGCNA), and common genes (CGs) were defined as the intersection of DEGs and eigengenes of a module. CGs were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) analysis was performed to screen the CGs for identifying the diagnostic biomarkers of sarcopenia. Based on the diagnostic biomarkers, we established a novel nomogram model of sarcopenia. At last, we validated the diagnostic biomarkers and evaluated the diagnostic performance of the nomogram model by the area under curve (AUC) value. Results We screened out 107 DEGs and 788 eigengenes in the turquoise module, and 72 genes were selected as CGs of sarcopenia by intersection. GO analysis showed that CGs were mainly involved in metal ion detoxification and mitochondrial structure, and KEGG analysis revealed that CGs were mainly enriched in the mineral absorption, glucagon signaling pathway, FoxO signaling pathway, insulin signaling pathway, AMPK signaling pathway, and estrogen signaling pathway. Then, six diagnostic biomarkers (ARHGAP36, FAM171A1, GPCPD1, MT1X, ZNF415, and RXRG) were identified by LASSO analysis. Finally, the validation AUC values indicated that the six diagnostic biomarkers had high diagnostic accuracy for sarcopenia. Conclusion We identified six diagnostic biomarkers with high diagnostic performance, providing new insights into the incidence and progression of sarcopenia in future research.

BackgroundThe proportion of patients with lung cancer complicated by atrial fibrillation (AF) is increasing. Identifying shared molecular targets between these two conditions may provide important prognostic insights for patients with comorbidities.MethodsThe GSE8569 and GSE41177 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed using the limma package in R. Weighted gene co-expression network analysis (WGCNA) was conducted to identify significant gene modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, along with gene set enrichment analysis (GSEA), were used to explore biological functions. Clinical survival data for lung cancer were obtained from The Cancer Genome Atlas (TCGA), and receiver operating characteristic (ROC) analysis was conducted using the R package ROC (version 1.17.0.1).ResultsA total of 598 differentially expressed genes (DEGs) were identified. These DEGs were primarily enriched in cell proliferation, inflammatory responses, non-small cell lung cancer, the p53 signaling pathway, and the cell cycle. Three core genes (CYBB, ITGB2, FCER1G) were identified. Notably, CYBB was downregulated in lung cancer compared to normal tissue. Patients in the low-risk group had significantly better survival outcomes. Heatmap visualization showed that expression of CYBB decreased with increasing risk scores, suggesting a protective role.ConclusionCYBB expression may influence lung cancer prognosis and contribute to the pathogenesis of AF. Further research is needed to clarify CYBB’s role in patients with both conditions.

This study aims to clarify angiogenesis mechanisms in ulcerative colitis and identify potential therapeutic targets. The Gene Expression Omnibus (GEO) database was used to obtain expression profiles and clinical data for UC and healthy colon tissues. Angiogenesis-related gene sets were acquired from GeneCards. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) identified UC-associated hub genes. The CIBERSORT algorithm assessed immune cell infiltration. Analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to determine biological mechanisms. External datasets were utilized to validate and characterize the angiogenesis-related genes in relation to biological agents. Additionally, an ulcerative colitis mouse model was constructed to verify the key genes' expression using real-time quantitative PCR. To predict potential therapeutic agents, we used the DGIdb database. Molecular docking modeled small molecule binding conformations to key gene targets. This study identified 1,247 DEGs enriched in inflammatory/immune pathways from UC and healthy colon samples. WGCNA indicated the black and light cyan modules were most relevant. Intersecting these with 89 angiogenesis genes revealed 5 UC-associated hub genes (pdgfrb, vegfc, angpt2, tnc, hgf). Validation via ROC analysis, differential expression, and a mouse model confirmed upregulation, supporting their potential as UC diagnostic biomarkers. Bioinformatics approaches like protein-protein interaction, enrichment analysis, and GSEA revealed involvement in PDGFR and PI3K-Akt signaling pathways. CIBERSORT analysis of immune cell infiltration showed positive correlations between the key genes and various immune cells, especially neutrophils, highlighting angiogenesis-inflammation interplay in UC. A ceRNA network was constructed. Drug prediction and molecular docking revealed potential UC therapies like sunitinib and imatinib targeting angiogenesis. This study identified and validated five angiogenesis-related genes (pdgfrb, vegfc, angpt2, tnc, hgf) that may serve as diagnostic biomarkers and drug targets for UC.

Based on WGCNA and machine learning studies, SMURF2 drives NSCLC malignant transformation, ferroptosis, and macrophage polarization by ubiquitinating SPP1.

Liver fibrosis is a progressive liver disease with increasing incidence, yet its underlying pathogenic mechanisms remain incompletely understood. : This study aims to explore potential therapeutic targets for liver fibrosis using weighted gene co-expression network analysis (WGCNA) and experimental validation. We retrieved the microarray data (GSE174099) from the GEO database and performed differential expression analysis and WGCNA to identify co-expression modules associated with liver fibrosis. A module with the highest correlation to liver fibrosis was selected for further analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to investigate the biological functions and signaling pathways of the identified genes. Protein-protein interaction (PPI) networks were constructed using the STRING database. The correlation between core genes and immune cells was analyzed with the CIBERSORT algorithm. Additionally, pathological and molecular biology experiments were performed to validate the expression levels of core genes in liver tissue, including HE and Masson staining, immunohistochemistry, RT-qPCR, and Western blotting. We identified a total of 86 intersecting genes from the differential expression analysis and WGCNA. GO enrichment analysis revealed that these genes were involved in processes such as cellular response to cAMP, collagen-containing extracellular matrix, and G protein-coupled receptor binding. KEGG pathway analysis highlighted the involvement of these genes in pathways like Cell Adhesion Molecules and the PI3K-Akt signaling pathway. Using Cytoscape software, we identified four core genes: Cftr, Cldn4, Map2, and Spp1. Pathological examinations showed that the experimental group exhibited significant fibrous tissue proliferation compared to the control group. Immunohistochemistry, RT-qPCR, and Western blotting analyses confirmed that these core genes were significantly upregulated in the experimental group (P< 0.05). This study identified four key genes (Cftr, Cldn4, Map2, Spp1) that are significantly associated with liver fibrosis. These genes are upregulated in liver fibrosis and could potentially as biomarkers for diagnosis and targets for therapeutic interventions.