{"title":"UQCRB 和 LBH 与前列腺癌的格里森评分进展相关:空间转录组学和实验验证","authors":"Yongjun Quan, Hong Zhang, Mingdong Wang, Hao Ping","doi":"10.1016/j.csbj.2024.08.026","DOIUrl":null,"url":null,"abstract":"Prostate cancer (PCa) is a multifocal disease characterized by genomic and phenotypic heterogeneity within a single gland. In this study, Visium spatial transcriptomics (ST) analysis was applied to PCa tissues with different histological structures to infer the molecular events involved in Gleason score (GS) progression. The spots in tissue sections were classified into various groups using Principal Component Analysis (PCA) and Louvain clustering analysis based on transcriptome data. Anotation of the spots according to GS revealed notable similarities between transcriptomic profiles and histologically identifiable structures. The accuracy of macroscopic GS determination was bioinformatically verified through malignancy-related feature analysis, specifically inferred copy number variation (inferCNV), as well as developmental trajectory analyses, such as diffusion pseudotime (DPT) and partition-based graph abstraction (PAGA). Genes related to GS progression were identified from the differentially expressed genes (DEGs) through pairwise comparisons of groups along a GS gradient. The proteins encoded by the representative oncogenes UQCRB and LBH were found to be highly expressed in advanced-stage PCa tissues. Knockdown of their mRNAs significantly suppressed PCa cell proliferation and invasion. These findings were validated using The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset, as well as through histological and cytological experiments. The results presented here establish a foundation for ST-based evaluation of GS progression and provide valuable insights into the GS progression-related genes UQCRB and LBH.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"UQCRB and LBH are correlated with Gleason score progression in prostate cancer: Spatial transcriptomics and experimental validation\",\"authors\":\"Yongjun Quan, Hong Zhang, Mingdong Wang, Hao Ping\",\"doi\":\"10.1016/j.csbj.2024.08.026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Prostate cancer (PCa) is a multifocal disease characterized by genomic and phenotypic heterogeneity within a single gland. In this study, Visium spatial transcriptomics (ST) analysis was applied to PCa tissues with different histological structures to infer the molecular events involved in Gleason score (GS) progression. The spots in tissue sections were classified into various groups using Principal Component Analysis (PCA) and Louvain clustering analysis based on transcriptome data. Anotation of the spots according to GS revealed notable similarities between transcriptomic profiles and histologically identifiable structures. The accuracy of macroscopic GS determination was bioinformatically verified through malignancy-related feature analysis, specifically inferred copy number variation (inferCNV), as well as developmental trajectory analyses, such as diffusion pseudotime (DPT) and partition-based graph abstraction (PAGA). Genes related to GS progression were identified from the differentially expressed genes (DEGs) through pairwise comparisons of groups along a GS gradient. The proteins encoded by the representative oncogenes UQCRB and LBH were found to be highly expressed in advanced-stage PCa tissues. Knockdown of their mRNAs significantly suppressed PCa cell proliferation and invasion. These findings were validated using The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset, as well as through histological and cytological experiments. The results presented here establish a foundation for ST-based evaluation of GS progression and provide valuable insights into the GS progression-related genes UQCRB and LBH.\",\"PeriodicalId\":10715,\"journal\":{\"name\":\"Computational and structural biotechnology journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational and structural biotechnology journal\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/j.csbj.2024.08.026\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational and structural biotechnology journal","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.csbj.2024.08.026","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
UQCRB and LBH are correlated with Gleason score progression in prostate cancer: Spatial transcriptomics and experimental validation
Prostate cancer (PCa) is a multifocal disease characterized by genomic and phenotypic heterogeneity within a single gland. In this study, Visium spatial transcriptomics (ST) analysis was applied to PCa tissues with different histological structures to infer the molecular events involved in Gleason score (GS) progression. The spots in tissue sections were classified into various groups using Principal Component Analysis (PCA) and Louvain clustering analysis based on transcriptome data. Anotation of the spots according to GS revealed notable similarities between transcriptomic profiles and histologically identifiable structures. The accuracy of macroscopic GS determination was bioinformatically verified through malignancy-related feature analysis, specifically inferred copy number variation (inferCNV), as well as developmental trajectory analyses, such as diffusion pseudotime (DPT) and partition-based graph abstraction (PAGA). Genes related to GS progression were identified from the differentially expressed genes (DEGs) through pairwise comparisons of groups along a GS gradient. The proteins encoded by the representative oncogenes UQCRB and LBH were found to be highly expressed in advanced-stage PCa tissues. Knockdown of their mRNAs significantly suppressed PCa cell proliferation and invasion. These findings were validated using The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset, as well as through histological and cytological experiments. The results presented here establish a foundation for ST-based evaluation of GS progression and provide valuable insights into the GS progression-related genes UQCRB and LBH.
期刊介绍:
Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to:
Structure and function of proteins, nucleic acids and other macromolecules
Structure and function of multi-component complexes
Protein folding, processing and degradation
Enzymology
Computational and structural studies of plant systems
Microbial Informatics
Genomics
Proteomics
Metabolomics
Algorithms and Hypothesis in Bioinformatics
Mathematical and Theoretical Biology
Computational Chemistry and Drug Discovery
Microscopy and Molecular Imaging
Nanotechnology
Systems and Synthetic Biology