Hongbing Yu

Clear cell renal cell carcinoma (ccRCC), the most common RCC subtype, displays significant intratumoral heterogeneity driven by metabolic reprogramming, which complicates our understanding of disease progression and limits treatment efficacy. This study aimed to construct a comprehensive cellular and transcriptional landscape of ccRCC, with emphasis on gene expression dynamics during malignant progression. An integrated analysis of 90 scRNA-seq samples comprising 534,227 cells revealed a progressive downregulation of sodium ion transport-related genes, particularly CHP1 (calcineurin B homologous protein isoform 1), which is predominantly expressed in epithelial cells. Reduced CHP1 expression was confirmed at both mRNA and protein levels using bulk RNA-seq, CPTAC proteomics, immunohistochemistry, and ccRCC cell lines. Survival analysis showed that high CHP1 expression correlated with improved prognosis. Functional analyses, including pseudotime trajectory, Mfuzz clustering, and cell–cell communication modeling, indicated that CHP1⁺ epithelial cells engage in immune interaction via PPIA–BSG signaling. Transcriptomic profiling and molecular docking suggested that CHP1 modulates amino acid transport through SLC38A1. ZNF460 was identified as a potential transcription factor of CHP1. Virtual screening identified arbutin and imatinib mesylate as candidate CHP1-targeting compounds. These findings establish CHP1 downregulation as a novel molecular feature of ccRCC progression and support its utility as a prognostic biomarker.

The major liver cancer subtype is hepatocellular carcinoma (HCC). Studies have indicated that a better prognosis is related to the presence of tumor-infiltrating lymphocytes (TILs) in HCC. However, the molecular pathways that drive immune cell variation in the tumor microenvironment (TME) remain poorly understood. Glycosylation (GLY)-related genes have a vital function in the pathogenesis of numerous tumors, including HCC. This study aimed to develop a GLY/TME classifier based on glycosylation-related gene scores and tumor microenvironment scores to provide a novel prognostic model to improve the prediction of clinical outcomes. The reliability of the signatures was assessed using receiver operating characteristic (ROC) and survival analyses and was verified with external datasets. Furthermore, the correlation between glycosylation-related genes and other cells in the immune environment, the immune signature of the GLY/TME classifier, and the efficacy of immunotherapy were also investigated. The GLY score low/TME score high subgroup showed a favorable prognosis and therapeutic response based on significant differences in immune-related molecules and cancer cell signaling mechanisms. We evaluated the prognostic role of the GLY/TME classifier that demonstrated overall prognostic significance for prognosis and therapeutic response before treatment, which may provide new options for creating the best possible therapeutic approaches for patients.

Corrigendum: Single-cell transcriptome analysis reveals the metabolic changes and the prognostic value of malignant hepatocyte subpopulations and predict new therapeutic agents for hepatocellular carcinoma

Background: The development of HCC is often associated with extensive metabolic disturbances. Single cell RNA sequencing (scRNA-seq) provides a better understanding of cellular behavior in the context of complex tumor microenvironments by analyzing individual cell populations. Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data was employed to investigate the metabolic pathways in HCC. Principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analysis were applied to identify six cell subpopulations, namely, T/NK cells, hepatocytes, macrophages, endothelial cells, fibroblasts, and B cells. The gene set enrichment analysis (GSEA) was performed to explore the existence of pathway heterogeneity across different cell subpopulations. Univariate Cox analysis was used to screen genes differentially related to The Overall Survival in TCGA-LIHC patients based on scRNA-seq and bulk RNA-seq datasets, and LASSO analysis was used to select significant predictors for incorporation into multivariate Cox regression. Connectivity Map (CMap) was applied to analysis drug sensitivity of risk models and targeting of potential compounds in high risk groups.Results: Analysis of TCGA-LIHC survival data revealed the molecular markers associated with HCC prognosis, including MARCKSL1, SPP1, BSG, CCT3, LAGE3, KPNA2, SF3B4, GTPBP4, PON1, CFHR3, and CYP2C9. The RNA expression of 11 prognosis-related differentially expressed genes (DEGs) in normal human hepatocyte cell line MIHA and HCC cell lines HCC-LM3 and HepG2 were compared by qPCR. Higher KPNA2, LAGE3, SF3B4, CCT3 and GTPBP4 protein expression and lower CYP2C9 and PON1 protein expression in HCC tissues from Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases. The results of target compound screening of risk model showed that mercaptopurine is a potential anti-HCC drug.Conclusion: The prognostic genes associated with glucose and lipid metabolic changes in a hepatocyte subpopulation and comparison of liver malignancy cells to normal liver cells may provide insight into the metabolic characteristics of HCC and the potential prognostic biomarkers of tumor-related genes and contribute to developing new treatment strategies for individuals.

Background ZMYND11 is a reader of histone proteins and plays an important inhibition role in tumor. There is growing evidence to support its importance, but without a pan-cancer analysis, Based on multiple databases, we conducted a comprehensive analysis. Methods Tumor data from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were a clear relationship observed gene expression of ZMYND11, state of survival, induction of DNA methylation, alterations in genes, contribution to the parameter of protein phosphorylation, lymphocyte immune infiltration, and correlative cellular pathways. Results ZMYND11 was under-expressed in the great majority of carcinomas. High expression group of ZMYND11 offers a more favorable prognosis in KIRC, LGG, MESO, LUAD,PRAD,READ,THCA,BRCA, SARC and UVM. Alterations in N304Kfs*33, K345Rfs*43 and N271Tfs*18 occur in structurally important domains of ZMYND11 and was found in prostate, colorectal and breast cancers.In most tumour tissues, ZMYND11 promoter DNA methylation was lower than in normal tissues, and promoter methylation was largely negatively correlated with mRNA expression levels. We observed low levels of phosphorylation at locus 349 protein in KIRC were identified with low total ZNYND11 protein expression. High phosphorylation levels at locus 393 protein in breast cancer with high total ZNYND11 protein expression. ZMYND11 gene expression in human tumors is mostly reflected by positively relation to immune cell infiltration while it is confirmed by positive association with CD8 + T cell. There was a differential expression of ZMYND11 in different immune subtypes. The gene enrichment SOSO2, PJA2 and KIAA1109 may be molecules interacting with ZMYND11.KEGG and GO analysis involving in RNAase II promoter regulation, positive and negative regulation of the RNAase II promoter, positive and negative regulation of DNA transcription, chromatin modification. Conclusions This is a comprehensive pan-cancer analysis of the ZMYND11. It describes the role of ZMYND11 in tumour. and highlights a potential target for ZMYND11 in tumour.