Table 1_Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis.docx
Background<p>Glycolysis plays a crucial role in fibrosis, but the specific genes involved in glycolysis in idiopathic pulmonary fibrosis (IPF) are not well understood.</p>Methods<p>Three IPF gene expression datasets were obtained from the Gene Expression Omnibus (GEO), while glycol...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1855372480048267264 |
|---|---|
| author | Han Gao (486886) |
| author2 | Zhongyi Sun (843149) Xingxing Hu (3276786) Weiwei Song (482396) Yuan Liu (88411) Menglin Zou (11876720) Minghui Zhu (1506775) Zhenshun Cheng (9758156) |
| author2_role | author author author author author author author |
| author_facet | Han Gao (486886) Zhongyi Sun (843149) Xingxing Hu (3276786) Weiwei Song (482396) Yuan Liu (88411) Menglin Zou (11876720) Minghui Zhu (1506775) Zhenshun Cheng (9758156) |
| author_role | author |
| dc.creator.none.fl_str_mv | Han Gao (486886) Zhongyi Sun (843149) Xingxing Hu (3276786) Weiwei Song (482396) Yuan Liu (88411) Menglin Zou (11876720) Minghui Zhu (1506775) Zhenshun Cheng (9758156) |
| dc.date.none.fl_str_mv | 2025-02-28T06:52:03Z |
| dc.identifier.none.fl_str_mv | 10.3389/fphar.2025.1486357.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Table_1_Identification_of_glycolysis-related_gene_signatures_for_prognosis_and_therapeutic_targeting_in_idiopathic_pulmonary_fibrosis_docx/28511351 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Pharmacology IPF glycolysis immune microenvironment pharmacological strategies model |
| dc.title.none.fl_str_mv | Table 1_Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis.docx |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Background<p>Glycolysis plays a crucial role in fibrosis, but the specific genes involved in glycolysis in idiopathic pulmonary fibrosis (IPF) are not well understood.</p>Methods<p>Three IPF gene expression datasets were obtained from the Gene Expression Omnibus (GEO), while glycolysis-related genes were retrieved from the Molecular Signatures Database (MsigDB). Differentially expressed glycolysis-related genes (DEGRGs) were identified using the “limma” R package. Diagnostic glycolysis-related genes (GRGs) were selected through least absolute shrinkage and selection operator (LASSO) regression regression and support vector machine-recursive feature elimination (SVM-RFE). A prognostic signature was developed using LASSO regression, and time-dependent receiver operating characteristic (ROC) curves were generated to evaluate predictive performance. Single-cell RNA sequencing (scRNA-seq) data were analyzed to examine GRG expression across various cell types. Immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were performed to elucidate potential molecular mechanisms. A bleomycin (BLM)-induced pulmonary fibrosis mouse model was used for experimental validation via reverse transcription-quantitative polymerase chain reaction (RT-qPCR).</p>Results<p>14 GRGs (VCAN, MERTK, FBP2, TPBG, SDC1, AURKA, ARTN, PGP, PLOD2, PKLR, PFKM, DEPDC1, AGRN, CXCR4) were identified as diagnostic markers for IPF, with seven (ARTN, AURKA, DEPDC1, FBP2, MERTK, PFKM, SDC1) forming a prognostic model demonstrating predictive power (AUC: 0.831–0.793). scRNA-seq revealed cell-type-specific GRG expression, particularly in macrophages and fibroblasts. Immune infiltration analysis linked GRGs to imbalanced immune responses. Experimental validation in a bleomycin-induced fibrosis model confirmed the upregulation of GRGs (such as AURKA, CXCR4). Drug prediction identified inhibitors (such as Tozasertib for AURKA, Plerixafor for CXCR4) as potential therapeutic agents.</p>Conclusion<p>This study identifies GRGs as potential prognostic biomarkers for IPF and highlights their role in modulating immune responses within the fibrotic lung microenvironment. Notably, AURKA, MERTK, and CXCR4 were associated with pathways linked to fibrosis progression and represent potential therapeutic targets. Our findings provide insights into metabolic reprogramming in IPF and suggest that targeting glycolysis-related pathways may offer novel pharmacological strategies for antifibrotic therapy.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_618bb711f109fc7bb3ec8103cfc7942e |
| identifier_str_mv | 10.3389/fphar.2025.1486357.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28511351 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Table 1_Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis.docxHan Gao (486886)Zhongyi Sun (843149)Xingxing Hu (3276786)Weiwei Song (482396)Yuan Liu (88411)Menglin Zou (11876720)Minghui Zhu (1506775)Zhenshun Cheng (9758156)PharmacologyIPFglycolysisimmune microenvironmentpharmacological strategiesmodelBackground<p>Glycolysis plays a crucial role in fibrosis, but the specific genes involved in glycolysis in idiopathic pulmonary fibrosis (IPF) are not well understood.</p>Methods<p>Three IPF gene expression datasets were obtained from the Gene Expression Omnibus (GEO), while glycolysis-related genes were retrieved from the Molecular Signatures Database (MsigDB). Differentially expressed glycolysis-related genes (DEGRGs) were identified using the “limma” R package. Diagnostic glycolysis-related genes (GRGs) were selected through least absolute shrinkage and selection operator (LASSO) regression regression and support vector machine-recursive feature elimination (SVM-RFE). A prognostic signature was developed using LASSO regression, and time-dependent receiver operating characteristic (ROC) curves were generated to evaluate predictive performance. Single-cell RNA sequencing (scRNA-seq) data were analyzed to examine GRG expression across various cell types. Immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were performed to elucidate potential molecular mechanisms. A bleomycin (BLM)-induced pulmonary fibrosis mouse model was used for experimental validation via reverse transcription-quantitative polymerase chain reaction (RT-qPCR).</p>Results<p>14 GRGs (VCAN, MERTK, FBP2, TPBG, SDC1, AURKA, ARTN, PGP, PLOD2, PKLR, PFKM, DEPDC1, AGRN, CXCR4) were identified as diagnostic markers for IPF, with seven (ARTN, AURKA, DEPDC1, FBP2, MERTK, PFKM, SDC1) forming a prognostic model demonstrating predictive power (AUC: 0.831–0.793). scRNA-seq revealed cell-type-specific GRG expression, particularly in macrophages and fibroblasts. Immune infiltration analysis linked GRGs to imbalanced immune responses. Experimental validation in a bleomycin-induced fibrosis model confirmed the upregulation of GRGs (such as AURKA, CXCR4). Drug prediction identified inhibitors (such as Tozasertib for AURKA, Plerixafor for CXCR4) as potential therapeutic agents.</p>Conclusion<p>This study identifies GRGs as potential prognostic biomarkers for IPF and highlights their role in modulating immune responses within the fibrotic lung microenvironment. Notably, AURKA, MERTK, and CXCR4 were associated with pathways linked to fibrosis progression and represent potential therapeutic targets. Our findings provide insights into metabolic reprogramming in IPF and suggest that targeting glycolysis-related pathways may offer novel pharmacological strategies for antifibrotic therapy.</p>2025-02-28T06:52:03ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fphar.2025.1486357.s001https://figshare.com/articles/dataset/Table_1_Identification_of_glycolysis-related_gene_signatures_for_prognosis_and_therapeutic_targeting_in_idiopathic_pulmonary_fibrosis_docx/28511351CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/285113512025-02-28T06:52:03Z |
| spellingShingle | Table 1_Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis.docx Han Gao (486886) Pharmacology IPF glycolysis immune microenvironment pharmacological strategies model |
| status_str | publishedVersion |
| title | Table 1_Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis.docx |
| title_full | Table 1_Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis.docx |
| title_fullStr | Table 1_Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis.docx |
| title_full_unstemmed | Table 1_Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis.docx |
| title_short | Table 1_Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis.docx |
| title_sort | Table 1_Identification of glycolysis-related gene signatures for prognosis and therapeutic targeting in idiopathic pulmonary fibrosis.docx |
| topic | Pharmacology IPF glycolysis immune microenvironment pharmacological strategies model |