Spatially resolved transcriptomics reveals gene expression characteristics in uveal melanoma
Abstract Purpose Uveal melanoma (UM) is the most common intraocular malignancy in adults. Previous studies have examined the intra-tumoral heterogeneity. However, the spatial distribution of tumor cells within the tumor microenvironment and its relationship with tumor progression still remains large...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Holistic Integrative Oncology |
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| Online Access: | https://doi.org/10.1007/s44178-024-00138-0 |
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| author | Jing-Ying Xiu Yu-Ning Chen Ya-Li Mao Jing-Ting Luo Hao-Wen Li Yang Li Wen-Bin Wei |
| author_facet | Jing-Ying Xiu Yu-Ning Chen Ya-Li Mao Jing-Ting Luo Hao-Wen Li Yang Li Wen-Bin Wei |
| author_sort | Jing-Ying Xiu |
| collection | DOAJ |
| description | Abstract Purpose Uveal melanoma (UM) is the most common intraocular malignancy in adults. Previous studies have examined the intra-tumoral heterogeneity. However, the spatial distribution of tumor cells within the tumor microenvironment and its relationship with tumor progression still remains largely unclear. Our study aimed to analyze the correlation between cell distribution patterns and the prognosis of UM. Methods In this paper, we performed spatial transcriptomics (ST) sequencing on two UM samples to describe the different cellular distribution patterns. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) functional enrichment analysis, and protein–protein interaction (PPI) network were performed to define the biological function of each cluster. Differentially expressed genes (DEGs) and survival analysis based on datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database further confirmed the correlation between cellular distribution and clinical prognosis. Results We found two different patterns of tumor cell distribution. The focal tumor cells have a distinct ribosome synthesis and rRNA pathway. In contrast, the subpopulation tented to distribute diffusely was related to fatty acids metabolism profile, presumably supporting tumor growth by providing energy. The scattered tumor cell cluster was associated with malignant biological behaviors and was involved in extensive cellular interactions, including COLLAGEN. Moreover, pseudo-time analysis showed that migration started from the basal region through cell differentiation. According to the TCGA and GEO database, genes expressed characteristically in the scattered tumor cell cluster were related to poor prognosis. Conclusions Our study drew the ST maps for UM for the first time. These findings revealed the distribution patterns of tumor cells associated with different biological functions and pointed towards specific tumor subpopulations with higher invasiveness as potential therapeutic targets. Together, our study displayed an overview of UM transcriptome and explored the intra-tumoral heterogeneity of UM at the spatial level. |
| format | Article |
| id | doaj-art-d7445e11a3174dae83877c0e5d98e856 |
| institution | Kabale University |
| issn | 2731-4529 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Holistic Integrative Oncology |
| spelling | doaj-art-d7445e11a3174dae83877c0e5d98e8562025-01-19T12:43:05ZengSpringerHolistic Integrative Oncology2731-45292025-01-014111410.1007/s44178-024-00138-0Spatially resolved transcriptomics reveals gene expression characteristics in uveal melanomaJing-Ying Xiu0Yu-Ning Chen1Ya-Li Mao2Jing-Ting Luo3Hao-Wen Li4Yang Li5Wen-Bin Wei6Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Institute of Ophthalmology, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical UniversityBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology and Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical UniversityAbstract Purpose Uveal melanoma (UM) is the most common intraocular malignancy in adults. Previous studies have examined the intra-tumoral heterogeneity. However, the spatial distribution of tumor cells within the tumor microenvironment and its relationship with tumor progression still remains largely unclear. Our study aimed to analyze the correlation between cell distribution patterns and the prognosis of UM. Methods In this paper, we performed spatial transcriptomics (ST) sequencing on two UM samples to describe the different cellular distribution patterns. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) functional enrichment analysis, and protein–protein interaction (PPI) network were performed to define the biological function of each cluster. Differentially expressed genes (DEGs) and survival analysis based on datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database further confirmed the correlation between cellular distribution and clinical prognosis. Results We found two different patterns of tumor cell distribution. The focal tumor cells have a distinct ribosome synthesis and rRNA pathway. In contrast, the subpopulation tented to distribute diffusely was related to fatty acids metabolism profile, presumably supporting tumor growth by providing energy. The scattered tumor cell cluster was associated with malignant biological behaviors and was involved in extensive cellular interactions, including COLLAGEN. Moreover, pseudo-time analysis showed that migration started from the basal region through cell differentiation. According to the TCGA and GEO database, genes expressed characteristically in the scattered tumor cell cluster were related to poor prognosis. Conclusions Our study drew the ST maps for UM for the first time. These findings revealed the distribution patterns of tumor cells associated with different biological functions and pointed towards specific tumor subpopulations with higher invasiveness as potential therapeutic targets. Together, our study displayed an overview of UM transcriptome and explored the intra-tumoral heterogeneity of UM at the spatial level.https://doi.org/10.1007/s44178-024-00138-0Uveal melanomaSpatial transcriptomicsCellular distributionTumor heterogeneityPrognosis |
| spellingShingle | Jing-Ying Xiu Yu-Ning Chen Ya-Li Mao Jing-Ting Luo Hao-Wen Li Yang Li Wen-Bin Wei Spatially resolved transcriptomics reveals gene expression characteristics in uveal melanoma Holistic Integrative Oncology Uveal melanoma Spatial transcriptomics Cellular distribution Tumor heterogeneity Prognosis |
| title | Spatially resolved transcriptomics reveals gene expression characteristics in uveal melanoma |
| title_full | Spatially resolved transcriptomics reveals gene expression characteristics in uveal melanoma |
| title_fullStr | Spatially resolved transcriptomics reveals gene expression characteristics in uveal melanoma |
| title_full_unstemmed | Spatially resolved transcriptomics reveals gene expression characteristics in uveal melanoma |
| title_short | Spatially resolved transcriptomics reveals gene expression characteristics in uveal melanoma |
| title_sort | spatially resolved transcriptomics reveals gene expression characteristics in uveal melanoma |
| topic | Uveal melanoma Spatial transcriptomics Cellular distribution Tumor heterogeneity Prognosis |
| url | https://doi.org/10.1007/s44178-024-00138-0 |
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