Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor
BackgroundWilms tumor (WT) is the most common childhood renal malignancy, with recurrence linked to poor prognosis. Identifying the molecular features of tumor phenotypes that drive recurrence and discovering novel targets are crucial for improving treatment strategies and enhancing patient outcomes...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
|
| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1539897/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850025425398398976 |
|---|---|
| author | Ran Yang Ran Yang Lulu Xie Rui Wang Yi Li Yifei Lu Baihui Liu Shuyang Dai Shan Zheng Shan Zheng Kuiran Dong Kuiran Dong Rui Dong Rui Dong |
| author_facet | Ran Yang Ran Yang Lulu Xie Rui Wang Yi Li Yifei Lu Baihui Liu Shuyang Dai Shan Zheng Shan Zheng Kuiran Dong Kuiran Dong Rui Dong Rui Dong |
| author_sort | Ran Yang |
| collection | DOAJ |
| description | BackgroundWilms tumor (WT) is the most common childhood renal malignancy, with recurrence linked to poor prognosis. Identifying the molecular features of tumor phenotypes that drive recurrence and discovering novel targets are crucial for improving treatment strategies and enhancing patient outcomes.MethodsSingle-nuclei RNA sequencing (snRNA-seq), spatial transcriptomics (ST), bulk RNA-seq, and mutation/copy number data were curated from public databases. The Seurat package was used to process snRNA-seq and ST data. Scissor analysis was applied to identify tumor subpopulations associated with poor relapse-free survival (RFS). Univariate Cox and LASSO analyses were utilized to reduce features. A prognostic ensemble machine learning model was developed. Immunohistochemistry was used to validate the expression of key features in tumor tissues. The CellChat and Commot package was utilized to infer cellular interactions. The PERCEPTION computational pipeline was used to predict the response of tumor cells to chemotherapy and targeted therapies.ResultsBy integrating snRNA-seq and bulk RNA-seq data, we identified a subtype of Scissor+ tumor cells associated with poor RFS, predominantly derived from cap mesenchyme-like blastemal and fibroblast-like tumor subgroups. These cells displayed nephron progenitor signatures and cancer stem cell markers. A prognostic ensemble machine learning model was constructed based on the Scissor+ tumor signature to accurately predict patient RFS. TGFA was identified as the most significant feature in this model and validated by immunohistochemistry. Cellular communication analysis revealed strong associations between Scissor+ tumor cells and cancer-associated fibroblasts (CAFs) through IGF, SLIT, FGF, and PDGF pathways. ST data revealed that Scissor+ tumor cells were primarily located in immune-desert niche surrounded by CAFs. Despite reduced responsiveness to conventional chemotherapy, Scissor+ tumor cells were sensitive to EGFR inhibitors, providing insights into clinical intervention strategies for WT patients at high risk of recurrence.ConclusionThis study identified a relapse-associated tumor subtype resembling nephron progenitor cells, residing in immune-desert niches through interactions with CAFs. The proposed prognostic model could accurately identify patients at high risk of relapse, offering a promising method for clinical risk stratification. Targeting these cells with EGFR inhibitors, in combination with conventional chemotherapy, may provide a potential therapeutic strategy for WT patients. |
| format | Article |
| id | doaj-art-588cd7061fb54e93811b452f0ff3286a |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-588cd7061fb54e93811b452f0ff3286a2025-08-20T03:00:50ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-03-011610.3389/fimmu.2025.15398971539897Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumorRan Yang0Ran Yang1Lulu Xie2Rui Wang3Yi Li4Yifei Lu5Baihui Liu6Shuyang Dai7Shan Zheng8Shan Zheng9Kuiran Dong10Kuiran Dong11Rui Dong12Rui Dong13Department of Pediatric Surgery, Children’s Hospital of Fudan University, Shanghai Key Laboratory of Birth Defect, Shanghai, ChinaChildren’s Hospital of Fudan University (Xiamen Branch), Xiamen Children’s Hospital, Xiamen Key Laboratory of Pediatric General Surgery Diseases, Xiamen, ChinaDepartment of Pediatric Surgery, Children’s Hospital of Fudan University, Shanghai Key Laboratory of Birth Defect, Shanghai, ChinaShanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Pediatric Surgery, Children’s Hospital of Fudan University, Shanghai Key Laboratory of Birth Defect, Shanghai, ChinaDepartment of Pediatric Surgery, Children’s Hospital of Fudan University, Shanghai Key Laboratory of Birth Defect, Shanghai, ChinaDepartment of Pediatric Surgery, Children’s Hospital of Fudan University, Shanghai Key Laboratory of Birth Defect, Shanghai, ChinaDepartment of Pediatric Surgery, Children’s Hospital of Fudan University, Shanghai Key Laboratory of Birth Defect, Shanghai, ChinaDepartment of Pediatric Surgery, Children’s Hospital of Fudan University, Shanghai Key Laboratory of Birth Defect, Shanghai, ChinaChildren’s Hospital of Fudan University (Xiamen Branch), Xiamen Children’s Hospital, Xiamen Key Laboratory of Pediatric General Surgery Diseases, Xiamen, ChinaDepartment of Pediatric Surgery, Children’s Hospital of Fudan University, Shanghai Key Laboratory of Birth Defect, Shanghai, ChinaChildren’s Hospital of Fudan University (Xiamen Branch), Xiamen Children’s Hospital, Xiamen Key Laboratory of Pediatric General Surgery Diseases, Xiamen, ChinaDepartment of Pediatric Surgery, Children’s Hospital of Fudan University, Shanghai Key Laboratory of Birth Defect, Shanghai, ChinaChildren’s Hospital of Fudan University (Xiamen Branch), Xiamen Children’s Hospital, Xiamen Key Laboratory of Pediatric General Surgery Diseases, Xiamen, ChinaBackgroundWilms tumor (WT) is the most common childhood renal malignancy, with recurrence linked to poor prognosis. Identifying the molecular features of tumor phenotypes that drive recurrence and discovering novel targets are crucial for improving treatment strategies and enhancing patient outcomes.MethodsSingle-nuclei RNA sequencing (snRNA-seq), spatial transcriptomics (ST), bulk RNA-seq, and mutation/copy number data were curated from public databases. The Seurat package was used to process snRNA-seq and ST data. Scissor analysis was applied to identify tumor subpopulations associated with poor relapse-free survival (RFS). Univariate Cox and LASSO analyses were utilized to reduce features. A prognostic ensemble machine learning model was developed. Immunohistochemistry was used to validate the expression of key features in tumor tissues. The CellChat and Commot package was utilized to infer cellular interactions. The PERCEPTION computational pipeline was used to predict the response of tumor cells to chemotherapy and targeted therapies.ResultsBy integrating snRNA-seq and bulk RNA-seq data, we identified a subtype of Scissor+ tumor cells associated with poor RFS, predominantly derived from cap mesenchyme-like blastemal and fibroblast-like tumor subgroups. These cells displayed nephron progenitor signatures and cancer stem cell markers. A prognostic ensemble machine learning model was constructed based on the Scissor+ tumor signature to accurately predict patient RFS. TGFA was identified as the most significant feature in this model and validated by immunohistochemistry. Cellular communication analysis revealed strong associations between Scissor+ tumor cells and cancer-associated fibroblasts (CAFs) through IGF, SLIT, FGF, and PDGF pathways. ST data revealed that Scissor+ tumor cells were primarily located in immune-desert niche surrounded by CAFs. Despite reduced responsiveness to conventional chemotherapy, Scissor+ tumor cells were sensitive to EGFR inhibitors, providing insights into clinical intervention strategies for WT patients at high risk of recurrence.ConclusionThis study identified a relapse-associated tumor subtype resembling nephron progenitor cells, residing in immune-desert niches through interactions with CAFs. The proposed prognostic model could accurately identify patients at high risk of relapse, offering a promising method for clinical risk stratification. Targeting these cells with EGFR inhibitors, in combination with conventional chemotherapy, may provide a potential therapeutic strategy for WT patients.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1539897/fullWilms tumorrecurrencecancer stem cellmachine learningimmune microenvironment |
| spellingShingle | Ran Yang Ran Yang Lulu Xie Rui Wang Yi Li Yifei Lu Baihui Liu Shuyang Dai Shan Zheng Shan Zheng Kuiran Dong Kuiran Dong Rui Dong Rui Dong Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor Frontiers in Immunology Wilms tumor recurrence cancer stem cell machine learning immune microenvironment |
| title | Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor |
| title_full | Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor |
| title_fullStr | Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor |
| title_full_unstemmed | Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor |
| title_short | Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor |
| title_sort | integration of single nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse free survival in wilms tumor |
| topic | Wilms tumor recurrence cancer stem cell machine learning immune microenvironment |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1539897/full |
| work_keys_str_mv | AT ranyang integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT ranyang integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT luluxie integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT ruiwang integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT yili integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT yifeilu integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT baihuiliu integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT shuyangdai integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT shanzheng integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT shanzheng integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT kuirandong integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT kuirandong integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT ruidong integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor AT ruidong integrationofsinglenucleiandspatialtranscriptomicstodeciphertumorphenotypepredictiveofrelapsefreesurvivalinwilmstumor |