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学者姓名：罗上益
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Abstract ：
Although defects in Rho GTPases have been implicated in cancer, a systematic assessment of the alterations in Rho GTPases and their regulators that facilitate conformational cycling between the active GTP-bound and inactive GDP-bound Rho GTPases is lacking across human cancers. Here, we depicted a comprehensive molecular characterization of 169 genes encoding Rho GTPases and their regulators, utilizing multi-omics data of 9125 tumor samples across 33 cancer types from The Cancer Genome Atlas. Relevant findings were consolidated using mRNA expression profiles from 10,107 samples spanning seven distinct cancer types, along with data from multiple hepatocellular carcinoma (HCC) cohorts comprising 673 patients. We identified 19 candidate driver genes characterized by significant non-silent somatic mutation patterns. Rho GTPase and its regulator genes exhibited widespread dysregulation across various cancer types, mediated by diverse mechanisms, including miRNA regulation, methylation patterns, and copy number alterations, which was significantly associated with patient overall survival. Notably, we found SYDE2 was significantly associated with poorer survival outcomes in KIRC, negatively regulated by miRNA-142-5p, with both exhibiting significant differential expression. Unsupervised consensus clustering was performed to identify common Rho GTPase regulation subtypes across human cancers; six subtypes were identified, with each exhibiting different associations with patient outcomes. Using HCC as an example, we found Cluster 6-like tumors consistently exhibited aggressive characteristics, characterized by mutated TP53 and abnormal energy metabolism. Our study underscores the importance of Rho GTPases and their regulators in cancer development and establishes a foundation for the development of therapeutic targets based on Rho GTPase signaling. © 2025 UICC.
Keyword ：
hepatocellular carcinoma hepatocellular carcinoma multi-omics analysis multi-omics analysis pan-cancer pan-cancer Rho GTPase Rho GTPase Rho GTPase regulator Rho GTPase regulator
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| GB/T 7714 | Luo, S. , Qiao, S. , Liu, L. et al. Comprehensive molecular characterization of Rho GTPases and their regulators across human cancers [J]. | International Journal of Cancer , 2025 . |
| MLA | Luo, S. et al. "Comprehensive molecular characterization of Rho GTPases and their regulators across human cancers" . | International Journal of Cancer (2025) . |
| APA | Luo, S. , Qiao, S. , Liu, L. , Jia, Y. , Zhang, Y. , Zhang, X. . Comprehensive molecular characterization of Rho GTPases and their regulators across human cancers . | International Journal of Cancer , 2025 . |
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Abstract ：
Mass spectrometry imaging (MSI) provides valuable insights into metabolic heterogeneity by capturing in situ molecular profiles within organisms. One challenge of MSI heterogeneity analysis is performing an objective segmentation to differentiate the biological tissue into distinct regions with unique characteristics. However, current methods struggle due to the insufficient incorporation of biological context and high computational demand. To address these challenges, a novel deep learning-based approach is proposed, GraphMSI, which integrates metabolic profiles with spatial information to enhance MSI data analysis. Our comparative results demonstrate GraphMSI outperforms commonly used segmentation methods in both visual inspection and quantitative evaluation. Moreover, GraphMSI can incorporate partial or coarse biological contexts to improve segmentation results and enable more effective three-dimensional MSI segmentation with reduced computational requirements. These are facilitated by two optional enhanced modes: scribble-interactive and knowledge-transfer. Numerous results demonstrate the robustness of these two modes, ensuring that GraphMSI consistently retains its capability to identify biologically relevant sub-regions in complex practical applications. It is anticipated that GraphMSI will become a powerful tool for spatial heterogeneity analysis in MSI data.
Keyword ：
deep learning deep learning graph convolutional network graph convolutional network mass spectrometry imaging mass spectrometry imaging spatial heterogeneity spatial heterogeneity
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| GB/T 7714 | Guo, Lei , Xie, Peisi , Shen, Xionghui et al. Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI [J]. | ADVANCED SCIENCE , 2025 , 12 (8) . |
| MLA | Guo, Lei et al. "Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI" . | ADVANCED SCIENCE 12 . 8 (2025) . |
| APA | Guo, Lei , Xie, Peisi , Shen, Xionghui , Lam, Thomas Ka Yam , Deng, Lingli , Xie, Chengyi et al. Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI . | ADVANCED SCIENCE , 2025 , 12 (8) . |
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Abstract ：
Liver cancer manifests as a profoundly heterogeneous malignancy, posing significant challenges in terms of both therapeutic intervention and prognostic evaluation. Given that the liver is the largest metabolic organ, a prognostic risk model grounded in single-cell transcriptome analysis and a metabolic perspective can facilitate precise prevention and treatment strategies for liver cancer. Hence, we identified 11 cell types in a scRNA-seq profile comprising 105,829 cells and found that the metabolic activity of malignant cells increased significantly. Subsequently, a prognostic risk model incorporating tumor heterogeneity, cell interactions, tumor cell metabolism, and differentially expressed genes was established based on eight genes; this model can accurately distinguish the survival outcomes of liver cancer patients and predict the response to immunotherapy. Analyzing the immune status and drug sensitivity of the high- and low-risk groups identified by the model revealed that the high-risk group had more active immune cell status and greater expression of immune checkpoints, indicating potential risks associated with liver cancer-targeted drugs. In summary, this study provides direct evidence for the stratification and precise treatment of liver cancer patients, and is an important step in establishing reliable predictors of treatment efficacy in liver cancer patients.
Keyword ：
liver cancer liver cancer metabolic reprogramming metabolic reprogramming prognostic risk model prognostic risk model single-cell RNA-seq single-cell RNA-seq
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| GB/T 7714 | Xiong, Zhuang , Li, Lizhi , Wang, Guoliang et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model [J]. | GENES , 2024 , 15 (6) . |
| MLA | Xiong, Zhuang et al. "Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model" . | GENES 15 . 6 (2024) . |
| APA | Xiong, Zhuang , Li, Lizhi , Wang, Guoliang , Guo, Lei , Luo, Shangyi , Liao, Xiangwen et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model . | GENES , 2024 , 15 (6) . |
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