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KIF18B Is Essential for Lung Adenocarcinoma Progression Through the E2F Transcriptional Network.
Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality worldwide, highlighting the urgent need to identify novel prognostic biomarkers and therapeutic targets. Kinesin Family Member 18B (KIF18B) is implicated in mitosis, yet its precise role in LUAD pathogenesis remains poorly defined. This study investigates the oncogenic and therapeutic role of KIF18B in LUAD. Integrated analysis of The Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) datasets revealed that KIF18B is significantly upregulated in LUAD tissues, with its elevated expression strongly associated with an advanced pathological stage, high grade, and poor patient survival. Single-cell sequencing data analysis further indicated that KIF18B expression in LUAD is closely linked to key malignant processes, including cell cycle progression, proliferation, migration, and epithelial-mesenchymal transition (EMT). Functional experiments demonstrated that KIF18B knockdown markedly suppressed LUAD cell proliferation, migration, and invasion in vitro and inhibited tumor growth in vivo. Mechanistically, transcriptomic and pathway analyses revealed that KIF18B depletion downregulates Early 2 Factor (E2F) target genes. Luciferase reporter assays confirmed diminished E2F reporter activity as well as E2F2 promoter activity upon KIF18B silencing, while overexpression of E2F1, E2F2, or E2F3 rescued the inhibited proliferative phenotypes induced by KIF18B loss. Collectively, our findings establish KIF18B as an essential driver of LUAD progression that acts through the E2F transcriptional network, nominating it as a promising diagnostic and therapeutic target.
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Single-cell and bulk transcriptomic analyses define an IFI27-centered interferon-response epithelial state in bladder cancer and its transcriptional response to irradiation.
BACKGROUND: Interferon-responsive tumor-cell states can simultaneously increase immune visibility and induce immune-regulatory programs, yet their epithelial-cell distribution and treatment responsiveness in bladder cancer remain incompletely defined. We sought to resolve a reproducible epithelial interferon-response state across patients, characterize its immune context, and determine whether its transcriptional components respond to ionizing radiation in vitro. METHODS: Single-cell RNA sequencing data from seven primary bladder tumors and one adjacent normal sample were reanalyzed with uniform quality control and doublet removal. After broad-lineage annotation, 29,777 identity-gated tumor epithelial cells were reclustered and analyzed by repeated non-negative matrix factorization across ranks and random seeds. An NMF-informed three-gene interferon-response score based on IFI27, ISG15, and IFI6 was used to define patient-internal upper- and lower-quartile states. Differential expression was assessed by patient-blocked pseudobulk edgeR models, followed by Hallmark, Gene Ontology, and Reactome enrichment, leave-one-patient-out analyses, immune-feature scoring, balanced CellChat analysis, and TCGA-BLCA corroboration. T24 bladder cancer cells were exposed to 0, 2, 4, or 8 Gy, and selected transcripts were quantified by qRT-PCR 24 h later. RESULTS: The final atlas comprised 39,224 cells assigned to six broad lineages. Repeated NMF reproducibly identified an IFI27-centered epithelial program, and the corresponding score showed marked interpatient variation while remaining detectable across all seven tumors. Patient-blocked analysis identified 179 genes with false-discovery rate <0.05 and absolute log2FC ≥ 0.5; IFIT1, CXCL10, IFI44L, IFITM1, HLA-B, B2M, CD74, IFI6, and ISG15 were representative upregulated genes. IFN-response-score-high epithelial cells showed strong enrichment of interferon-gamma response, interferon-alpha response, inflammatory response, IL6-JAK-STAT3 signaling, antigen processing and presentation, and chemokine-response programs. MHC-I, MHC-II, CXCL9/10/11-related, and immune-regulatory ligand scores increased in all seven patient-paired comparisons, whereas CD274 itself remained sparsely detected and was not stably associated with the continuous score. Balanced pooled CellChat analyses prioritized relatively enriched MDK-SDC1, MDK-SDC2, and MDK-ITGA6/ITGB1 candidate interactions. TCGA-BLCA analyses corroborated the broader interferon and antigen-presentation context at gene and pathway levels. Irradiation induced dose-associated increases in IFI27, ISG15, IFI6, IFIT1, CXCL10, and IFITM1 transcripts, accompanied by increases in CD274, VIM, and SNAI1 and a decrease in CDH1. CONCLUSION: Bladder tumors contain a reproducible IFI27-centered epithelial interferon-response state with coordinated antigen-presentation, chemokine-related, and selected immune-regulatory transcriptional features. Its molecular context is supported across patients and in bulk transcriptomes, while irradiation-associated qRT-PCR changes suggest that components of the program are treatment responsive. These findings define a hypothesis-generating epithelial immune state and candidate communication axes for future spatial, protein-level, and functional validation.
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Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer.
BACKGROUND: Cancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear. METHODS: In this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub genes was validated using real-time quantitative PCR and single-cell RNA sequencing data. RESULTS: We identified 13 NRGs as signature genes for predicting the prognosis of patients with GC. The high-risk group, characterized by higher NRG scores, demonstrated a shorter overall survival rate, increased immunosuppressive cell infiltration, and immune dysfunction. Moreover, associations were observed between the NRG signature and chemotherapeutic drug responsiveness, as well as immunotherapy effectiveness in GC patients. Furthermore, the deep learning model effectively stratified GC patients based on the NRG signature by leveraging morphological variances, showing promising results for the classification of GC patients. Validation experiments demonstrated that the expression level of SERPINE1 was significantly upregulated in GC, while the expression levels of GPX3 and APOD were significantly downregulated. CONCLUSION: The NRG signature and its deep learning model have significant clinical implications, highlighting the importance of individualized treatment strategies based on GC subtyping. These findings provide valuable insights for guiding clinical decision-making and treatment approaches for GC.
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Mesenchymal stem cell-derived extracellular vesicles attenuate liver transplantation-induced ischemia/reperfusion injury by suppressing hepatocellular complement C5 expression.
BACKGROUND & AIMS: Hepatic ischemia-reperfusion injury (HIRI) is an unavoidable complication of liver transplantation (LT) that drives allograft dysfunction and mortality. Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) show therapeutic promise, yet their actions within the hepatic network remain incompletely defined. We investigated how MSC-EVs protect against LT-induced HIRI, focusing on intercellular communication and complement-mediated inflammation. METHODS: Single-cell RNA sequencing, single-nucleus ATAC-seq, and spatial transcriptomics were performed in rat LT and mouse HIRI livers with or without MSC-EV treatment. Cell migration assays, AAV-mediated overexpression, and PTPN13-knockdown MSC-EVs were used to prove cellular function and mechanisms. RESULTS: MSC-EVs attenuated HIRI, improving liver function and reducing histologic injury and hepatocyte apoptosis. Single-cell analyses showed selective curtailment of mononuclear phagocyte and neutrophil recruitment to injured liver. Mechanistically, HIRI induced hepatocyte C5 and C5a, which engaged C5aR1 on mononuclear phagocytes and neutrophils to drive chemotaxis. Consistent with this pathway, MSC-EVs delivered PTPN13 to hepatocytes, lowering C5 and promoting ERK1/2 dephosphorylation and enhanced C/EBPα activity. Elevated C/EBPα activity directly suppressed C5 transcription, whereas PTPN13-depleted MSC-EVs showed diminished efficacy. CONCLUSIONS: We present a single-cell multi-omics atlas of MSC-EV therapy across rat LT and mouse HIRI, supported by clinical data. MSC-EVs mitigated HIRI by lowering hepatocyte C5 and reducing mononuclear phagocyte and neutrophil recruitment. MSC-EV delivery of PTPN13 dephosphorylated ERK1/2 and enhanced C/EBPα, repressing hepatocyte C5 and dampening C5a-C5aR1 signaling. These findings position MSC-EVs as a translational nanotherapy to improve graft outcomes after LT.
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ZNF460 inhibits HMGCL and promotes PI3K pathway in colon cancer.
BACKGROUND: Zinc finger protein 460 (ZNF460) is highly expressed in colon cancer, but its specific mechanism of action and downstream targets have not yet been clarified. Therefore, this study aimed to investigate the expression pattern, prognostic value, and biological function of ZNF460 in colorectal cancer (CRC), and to elucidate its underlying molecular mechanisms. METHODS: Cell Counting Kit-8 (CCK-8) assay, 5-ethynyl-2'-deoxyuridine (EdU) assay, and Western blot assays were used to investigate the biological function of ZNF460 in CRC. Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to assess ZNF460 expression, prognostic value, and clinical relevance. Functional enrichment, nomogram construction, single-cell sequencing, cell communication, and pseudotime analyses were conducted using R packages including seurat, cluster profiler, cell chat, and monocle 2. Statistical significance was set at P<0.05, with appropriate tests applied based on data type. RESULTS: ZNF460 expression was upregulated in colon cancer. High expression of ZNF460 was associated with poor clinical stage and prognosis of patients. Cell experiments revealed the role of ZNF460 in promoting colon cancer cell proliferation. Single-cell sequencing revealed that ZNF460 had close interactions with malignant, plasma, endothelial, myofibroblasts and other cells. Bioinformatics analysis and in vitro experiments found that ZNF460 inhibited the expression of hydroxymethyl glutaryl-CoA lyase (HMGCL) and increased the phosphorylation level of phosphatidylinositol-3-kinase (PI3K). CONCLUSIONS: ZNF460 is highly expressed in CRC and is associated with poor prognosis. ZNF460 promotes cancer cell proliferation and participates in tumor cell communication. Mechanistically, ZNF460 activates the PI3K pathway by inhibiting HMGCL expression, thereby promoting the progression of CRC, suggesting that it is a potential therapeutic target.
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Determination of Breast Metabolic Phenotypes and Their Associations With Immunotherapy and Drug-Targeted Therapy: Analysis of Single-Cell and Bulk Sequences.
Breast cancer is highly prevalent and fatal worldwide. Currently, breast cancer classification is based on the presence of estrogen, progesterone, and human epidermal growth factor 2. Because cancer and metabolism are closely related, we established a breast cancer classification system based on the metabolic gene expression profile. We performed typing of metabolism-related genes using The Cancer Genome Atlas-Breast Cancer and 2010 (YAU). We included 2,752 metabolic genes reported in previous literature, and the genes were further identified according to statistically significant variance and univariate Cox analyses. These prognostic metabolic genes were used for non-negative matrix factorization (NMF) clustering. Then, we identified characteristic genes in each metabolic subtype using differential analysis. The top 30 characteristic genes in each subtype were selected for signature construction based on statistical parameters. We attempted to identify standard metabolic signatures that could be used for other cohorts for metabolic typing. Subsequently, to demonstrate the effectiveness of the 90 Signature, NTP and NMF dimensional-reduction clustering were used to analyze these results. The reliability of the 90 Signature was verified by comparing the results of the two-dimensionality reduction clusters. Finally, the submap method was used to determine that the C1 metabolic subtype group was sensitive to immunotherapy and more sensitive to the targeted drug sunitinib. This study provides a theoretical basis for diagnosing and treating breast cancer.
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Comprehensive Analysis Reveals the Potential Roles of Transcription Factor Dp-1 in Lung Adenocarcinoma.
BACKGROUND: Transcription factor Dp-1 (TFDP1) was overexpressed and interacted with other genes to impact multiple signaling pathways in various human cancers. However, there is less research about the TFDP1 specific roles in lung adenocarcinoma (LUAD). METHODS: We first explored TFDP1 expression levels and relative diseases from a pan-cancer perspective using the ONCOMINE, TIMER, and Open Targets Platform databases. Then, we used UALCAN, GEPIA 2, TCGA-LUAD data, and Kaplan-Meier plotter to examine TFDP1 clinicopathological features and prognosis in LUAD patients. Genomic alterations and DNA methylation analysis were performed by cBioPortal and MethSurv, respectively. Then, we used a cancer single-cell state atlas (CancerSEA) to find TFDP1 functions at a single-cell resolution. LinkedOmics was used to find TFDP1 coexpressed genes, biological processes, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Then, Gene Set Cancer Analysis (GSCA) was used to examine the drug resistence of TFDP1 in LUAD. RESULTS: We found that TFDP1 was overexpressed in most human cancers and related to various diseases, including LUAD. Moreover, LUAD patients with high TFDP1 expression levels might be significantly associated with individual cancer stages and have a poor prognosis. Multivariate analysis revealed that the American Joint Committee on Cancer (AJCC) pathologic stage, AJCC stage T, and AJCC stage N were the independent prognostic factors. LUAD patients with TFDP1 alterations suggested poor overall survival (OS), and disease-free survival (DFS), while hypermethylation might lead to a good prognosis. TFDP1 and its coexpressed genes were enriched in multiple signaling pathways and biological processes involved in the cell cycle, spliceosome, and DNA replication. Furthermore, TFDP1 was strongly positively related to the half-maximal inhibitory concentration (IC50) values of multiple drugs. CONCLUSIONS: In summary, TFDP1 was a possible biomarker and potential therapeutic target for LUAD patients.
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Clinical and genomic landscape of hepatocellular carcinoma subtypes with various proportions of nonleukocyte stromal cells.
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignancies and inflicts high mortality worldwide. The effect of tumor microenvironment components on HCC oncogenesis remains unknown. In particular, the nonleukocyte portion of the stromal fraction (SF) is poorly understood. METHODS: We comprehensively evaluated the proportional cell counts and gene expression data from The Cancer Genome Atlas (TCGA) to examine the contributions of cell components to the tumor microenvironment. Single-cell sequencing data from the Gene Expression Omnibus (GEO) were also analyzed to verify the association between the nonleukocyte SF and genes. We classified HCC using a hierarchical clustering method based on diversity of nonleukocyte SF-related gene expression among different components, and we used an appropriate GEO dataset to verify the clusters with a support vector machine (SVM) model. The prognosis of subtypes and their relationship with tumor microenvironmental cell proportions, clinicopathogenesis factors, and other indicators were evaluated. RESULTS: Based on linear regression, 711 genes related to nonleukocyte SF were selected from the TCGA dataset. We classified HCC into three subtypes using genes related to the nonleukocyte SF. Additionally, the GEO single-cell sequencing data confirmed the relationship between genes and the nonleukocyte SF. The tumor microenvironment of Type 2 contained the most significant mutually reinforcing interaction between the nonleukocyte SF and tumor cells. Meanwhile, Type 2 patients had the poorest prognosis and the most severe tumor-node-metastasis (TNM) stages, histological grades, etc. The analysis based on the GEO dataset verified the classification results with an SVM model. Type 2 was associated with worse clinicopathological characteristics, including tumor grading and staging, than the other types. In addition, the pathway analysis revealed that signals related to the SF and cell proliferation were significantly enhanced in Type 2 compared to the other group, which consisted of Types 1 and 3. CONCLUSION: The nonleukocyte SF in the tumor microenvironment contributed greatly to HCC oncogenesis. We can use these HCC classification criteria to stratify patients into subtypes for personalized treatment.
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Comprehensive bioinformatic analysis constructs a CXCL model for predicting survival and immunotherapy effectiveness in ovarian cancer.
Background: Immunotherapy has limited effectiveness in ovarian cancer (OC) patients, highlighting the need for reliable biomarkers to predict the effectiveness of these treatments. The C-X-C motif chemokine ligands (CXCLs) have been shown to be associated with survival outcomes and immunotherapy efficacy in cancer patients. In this study, we aimed to evaluate the predictive value of 16 CXCLs in OC patients. Methods: We analyzed RNA-seq data from The Cancer Genome Atlas, Gene Expression Omnibus, and UCSC Xena database and conducted survival analysis. Consensus cluster analysis was used to group patients into distinct clusters based on their expression patterns. Biological pathway alterations and immune infiltration patterns were examined across these clusters using gene set variation analysis and single-sample gene set enrichment analysis. We also developed a CXCL scoring model using principal component analysis and evaluated its effectiveness in predicting immunotherapy response by assessing tumor microenvironment cell infiltration, tumor mutational burden estimation, PD-L1/CTLA4 expression, and immunophenoscore analysis (IPS). Results: Most CXCL family genes were overexpressed in OC tissues compared to normal ovarian tissues. Patients were grouped into three distinct CXCL clusters based on their CXCL expression pattern. Additionally, using differentially expressed genes among the CXCL clusters, patients could also be grouped into three gene clusters. The CXCL and gene subtypes effectively predicted survival and immune cell infiltration levels for OC patients. Furthermore, patients with high CXCL scores had significantly better survival outcomes, higher levels of immune cell infiltration, higher IPS, and higher expression of PD-L1/CTLA4 than those with low CXCL scores. Conclusion: The CXCL score has the potential to be a promising biomarker to guide immunotherapy in individual OC patients and predict their clinical outcomes and immunotherapy responses.
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Single-cell decoding of human islet cell type-specific alterations in type 2 diabetes reveals converging genetic- and state-driven β -cell gene expression defects.
Pancreatic islets maintain glucose homeostasis through coordinated action of their constituent endocrine and affiliate cell types and are central to type 2 diabetes (T2D) genetics and pathophysiology. Our understanding of robust human islet cell type-specific alterations in T2D remains limited. Here, we report comprehensive single cell transcriptome profiling of 245,878 human islet cells from a 48-donor cohort spanning non-diabetic (ND), pre-diabetic (PD), and T2D states, identifying 14 distinct cell types detected in every donor from each glycemic state. Cohort analysis reveals ~25-30% loss of functional beta cell mass in T2D vs. ND or PD donors resulting from (1) reduced total beta cell numbers/proportions and (2) reciprocal loss of 'high function' and gain of senescent β -cell subpopulations. We identify in T2D β -cells 511 differentially expressed genes (DEGs), including new (66.5%) and validated genes (e.g., FXYD2, SLC2A2, SYT1), and significant neuronal transmission and vitamin A metabolism pathway alterations. Importantly, we demonstrate newly identified DEG roles in human β -cell viability and/or insulin secretion and link 47 DEGs to diabetes-relevant phenotypes in knockout mice, implicating them as potential causal islet dysfunction genes. Additionally, we nominate as candidate T2D causal genes and therapeutic targets 27 DEGs for which T2D genetic risk variants (GWAS SNPs) and pathophysiology (T2D vs. ND) exert concordant expression effects. We provide this freely accessible atlas for data exploration, analysis, and hypothesis testing. Together, this study provides new genomic resources for and insights into T2D pathophysiology and human islet dysfunction.