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FBXO17 promotes cell proliferation through activation of Akt in lung adenocarcinoma cells.
BACKGROUND: The ubiquitin-proteasome pathway, mediated in part, by ubiquitin E3 ligases, is critical in regulating cellular processes such as cell proliferation, apoptosis, and migration. FBXO17 was recently identified as an F-box protein that targets glycogen synthase kinase-3β to the E3 ubiquitin ligase protein complex for polyubiquitination and proteasomal degradation. Here, we identified that in several lung adenocarcinoma cell lines, FBXO17 cellular protein was detected at relatively high levels, as was expression in a subset of lung cancers. Hence, we investigated the effects of FBXO17 on cell proliferation. METHODS: Single cell RNA sequencing analysis was performed on a resection of a non-small cell lung carcinoma tumor to examine FBXO17 expression. Multiple lung cancer cell lines were immunoblotted, and The Cancer Genome Atlas was analyzed to determine if FBXO17 expression was amplified in a subset of lung cancers. A549 cells were transfected with empty vector or FBXO17-V5 plasmid and immunoblotted for Akt pathway mediators including PDK1, ERK1/2, ribosomal protein S6, and CREB. Cell proliferation and viability were analyzed by trypan blue exclusion, BrdU incorporation and an MTS-based fluorometric assay. Studies were also performed after transfecting with sifbxo17. Samples were used in an RNA microarray analysis to evaluate pathways affected by reduced FBXO17 gene expression. RESULTS: We observed that overexpression of FBXO17 increased A549 cell proliferation coupled with Akt activation. Ectopically expressed FBXO17 also increased ERK1/2 kinase activation and increased phosphorylation of RPS6, a downstream target of mTOR. We also observed an increased number of cells in S-phase and increased metabolic activity of lung epithelial cells expressing FBXO17. FBXO17 knockdown reduced Akt Ser 473 phosphorylation approaching statistical significance with no effect on Thr 308. However, ERK1/2 phosphorylation, cellular metabolic activity, and overall cell numbers were reduced. When we analyzed RNA profiles of A549 cells with reduced FBXO17 expression, we observed downregulation of several genes associated with cell proliferation and metabolism. CONCLUSIONS: These data support a role for FBXO17 abundance, when left unchecked, in regulating cell proliferation and survival through modulation of Akt and ERK kinase activation. The data raise a potential role for the F-box subunit in modulating tumorigenesis.
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Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, and chemosensitivity in colorectal cancer patients.
BACKGROUND: Coagulation is critically involved in the tumor microenvironment, cancer progression, and prognosis assessment. Nevertheless, the roles of coagulation-related long noncoding RNAs (CRLs) in colorectal cancer (CRC) remain unclear. In this study, an integrated computational framework was constructed to develop a novel coagulation-related lncRNA signature (CRLncSig) to stratify the prognosis of CRC patients, predict response to immunotherapy and chemotherapy in CRC, and explore the potential molecular mechanism. METHODS: CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the substantial bulk or single-cell RNA transcriptomics from Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR (RT-qPCR) data from CRC cell lines and paired frozen tissues were used for validation. We performed unsupervised consensus clustering of CRLs to classify patients into distinct molecular subtypes. We then used stepwise regression to establish the CRLncSig risk model, which stratified patients into high- and low-risk groups. Subsequently, diversified bioinformatics algorithms were used to explore prognosis, biological pathway alteration, immune microenvironment, immunotherapy response, and drug sensitivity across patient subgroups. In addition, weighted gene coexpression network analysis was used to construct an lncRNA-miRNA-mRNA competitive endogenous network. Expression levels of CRLncSig, immune checkpoints, and immunosuppressors were determined using RT-qPCR. RESULTS: We identified two coagulation subclusters and constructed a risk score model using CRLncSig in CRC, where the patients in cluster 2 and the low-risk group had a better prognosis. The cluster and CRLncSig were confirmed as the independent risk factors, and a CRLncSig-based nomogram exhibited a robust prognostic performance. Notably, the cluster and CRLncSig were identified as the indicators of immune cell infiltration, immunoreactivity phenotype, and immunotherapy efficiency. In addition, we identified a new endogenous network of competing CRLs with microRNA/mRNA, which will provide a foundation for future mechanistic studies of CRLs in the malignant progression of CRC. Moreover, CRLncSig strongly correlated with drug susceptibility. CONCLUSION: We developed a reliable CRLncSig to predict the prognosis, immune landscape, immunotherapy response, and drug sensitivity in patients with CRC, which might facilitate optimizing risk stratification, guiding the applications of immunotherapy, and individualized treatments for CRC.
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Exploration of bacterial lipopolysaccharide-related genes signature based on T cells for predicting prognosis in colorectal cancer.
PURPOSE: The intratumoral microorganisms participates in the progression and immunotherapy of colorectal cancer (CRC). However, due to technical limitations, the impact of microorganisms on CRC has not been fully understood. Therefore, we conducted a systematic analysis of relationship between bacterial lipopolysaccharide (LPS)-associated genes and immune cells to explore new biomarkers for predicting the prognosis of CRC. METHODS: The single-cell RNA sequencing data and the Comparative Toxicogenomics Database were used to screen T cells-associated LPS-related genes (TALRGs). Then, we established and validated the TALRGs risk signature in The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) cohort and GSE39582 cohort. Besides, we compared the differences in tumor-infiltrating immune cell types, immunotherapeutic response, somatic mutation profiles, and tumor mutation burden (TMB) between high-risk group and low-risk group. In addition, the immunotherapeutic cohort (Imvigor210) treated with an anti-PD-L1 agent was performed to explore the potential value of the TALRGs signature on immunotherapy. RESULTS: Five prognostic TALRGs were identified and selected to build the prognostic model. The high-risk group had poor prognosis in both TCGA-COAD cohort (P < 0.0001) and GSE39582 cohort (P = 0.00019). The areas under the curves (AUCs) of TALRGs signature were calculated (TCGA-COAD cohort: 0.624 at 1 years, 0.639 at 3 years, 0.648 at 5 years; anti-PD-L1 cohort was 0.59). The high-risk group had advanced pathological stages and higher TMN stages in both TCGA-COAD cohort and GSE39582 cohort. The high-risk group had the higher infiltration of immunosuppressive cells, the expressions of immune checkpoint molecules, the IC50 values of chemotherapy drugs, and TP53 mutation rate (P < 0.05). In addition, patients with high TMB had worse prognosis (P < 0.05). Furthermore, the Imvigor210 also showed patients with high-risk scores had poor prognosis (platinum-treated cohort: P = 0.0032; non-platinum-treated cohort: P = 0.00017). CONCLUSIONS: Microorganisms are closely related to the tumor microenvironment to influence the progression and immune response of CRC via stimulating T cells through LPS-related genes. The TALRGs signature contributed to predict the prognosis and immunotherapy of CRC, and became new therapeutic targets and biomarkers of CRC.
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Characteristics of B lymphocyte infiltration in HPV+ head and neck squamous cell carcinoma.
Human papillomavirus (HPV) is an important etiological factor of head and neck squamous cell carcinoma (HNSCC). HPV+ HNSCC patients usually have a better prognosis, which probably results from the higher infiltration of B lymphocytes. This study was purposed to detect the infiltration of B lymphocyte subsets and the correlation between B lymphocyte subsets and the prognosis in HPV-related HNSCC. In this study, 124 HPV+ and 513 HPV- HNSCC samples were obtained from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database for transcriptomic analysis. Infiltration of B lymphocytes subsets was detected with 7 HPV+ HNSCC and 13 HPV- HNSCC tissues through immunohistochemistry and immunofluorescence. One HPV- HNSCC sample was detected with single-cell sequencing for chemokine analysis. In the results, the infiltration of plasma cells (CD19+ CD38+ ) and memory B cells (MS4A1+ CD27+ ) was higher in HPV+ HNSCC samples. High infiltration of plasma cells and memory B cells was related to a better prognosis. High density of B lymphocytes was positively correlated with high CXCL13 production mainly from CD4+ T lymphocytes in HNSCC. These results indicated that a high density of plasma cells and memory B cells could predict excellent prognosis. CD4+ T lymphocytes might affect B lymphocytes and their subsets through the CXCL13/CXCR5 axis in HNSCC.
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Pan-cancer screening and integrative multi-omics and deep learning reveal the prognostic significance of an IBD-CRC shared host-microbe signature in bladder urothelial carcinoma.
BACKGROUND: The prognostic relevance of inflammatory bowel disease (IBD)-colorectal cancer (CRC) shared host-microbe signatures in non-intestinal epithelial malignancies remains unclear. This study aimed to evaluate the prognostic and biological significance of an IBD-CRC shared host-microbe interactome signature in bladder urothelial carcinoma (BLCA). METHODS: Gene set variation analysis (GSVA) was used to assess the activity of the IBD-CRC shared signature across The Cancer Genome Atlas (TCGA) pan-cancer solid tumor cohorts, including lung, liver, colorectal, and urinary system tumors. In BLCA, weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO)-Cox regression were applied to construct a prognostic risk model, which was validated in independent transcriptomic cohorts. An attention-based multiple instance learning (MIL) model was developed to predict the LASSO-derived high- or low-risk group from H&E whole-slide images (WSIs), using TCGA cases for training and internal validation and an independent institutional cohort of 39 BLCA patients for external validation. Molecular subtype, immune infiltration, immunohistochemistry (IHC), machine learning, single nucleotide variation/copy number variation (SNV/CNV), single-cell/spatial transcriptomics, and WSI-based deep learning analyses were integrated to characterize the biological relevance of the signature. RESULTS: High GSVA scores were significantly associated with poor prognosis in BLCA. The LASSO-derived high-risk group was enriched in basal/squamous molecular features and exhibited an immune-infiltrated but immunosuppressive tumor microenvironment, characterized by increased immunosuppressive cell infiltration and elevated immune checkpoint expression. Conventional IHC markers supported distinct subtype-related protein phenotypes between risk groups. Single-cell and spatial transcriptomic analyses revealed that malignant cells with high signature activity were enriched in Wnt, Hippo, and cell adhesion pathways. The WSI-based MIL model achieved an area under the curve (AUC) of 0.852 in the internal validation cohort. Machine learning and SNV/CNV analyses further characterized key molecular features associated with the LASSO risk score, including AKR1B1, LY6E, MEST, and others. Pan-cancer characterization of AKR1B1 across multiple malignancies, including lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LIHC), and kidney renal clear cell carcinoma (KIRC), revealed cancer-type-specific associations with immunosuppressive microenvironmental features and tumor stemness. CONCLUSION: The IBD-CRC shared host-microbe signature has significant prognostic value in BLCA and is associated with basal/squamous differentiation, immunosuppressive microenvironmental features, genomic alteration patterns, and malignant cell functional heterogeneity. The integrated multi-omics framework and externally validated pathology AI model provide potential tools for BLCA risk stratification and biological interpretation.
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Spatially-resolved transcriptomics reveal macrophage heterogeneity and prognostic significance in diffuse large B-cell lymphoma.
Macrophages are abundant immune cells in the microenvironment of diffuse large B-cell lymphoma (DLBCL). Macrophage estimation by immunohistochemistry shows varying prognostic significance across studies in DLBCL, and does not provide a comprehensive analysis of macrophage subtypes. Here, using digital spatial profiling with whole transcriptome analysis of CD68+ cells, we characterize macrophages in distinct spatial niches of reactive lymphoid tissues (RLTs) and DLBCL. We reveal transcriptomic differences between macrophages within RLTs (light zone /dark zone, germinal center/ interfollicular), and between disease states (RLTs/ DLBCL), which we then use to generate six spatially-derived macrophage signatures (MacroSigs). We proceed to interrogate these MacroSigs in macrophage and DLBCL single-cell RNA-sequencing datasets, and in gene-expression data from multiple DLBCL cohorts. We show that specific MacroSigs are associated with cell-of-origin subtypes and overall survival in DLBCL. This study provides a spatially-resolved whole-transcriptome atlas of macrophages in reactive and malignant lymphoid tissues, showing biological and clinical significance.
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Cell atlas of aqueous humor outflow pathways in eyes of humans and four model species provides insight into glaucoma pathogenesis.
Increased intraocular pressure (IOP) represents a major risk factor for glaucoma, a prevalent eye disease characterized by death of retinal ganglion cells; lowering IOP is the only proven treatment strategy to delay disease progression. The main determinant of IOP is the equilibrium between production and drainage of aqueous humor, with compromised drainage generally viewed as the primary contributor to dangerous IOP elevations. Drainage occurs through two pathways in the anterior segment of the eye called conventional and uveoscleral. To gain insights into the cell types that comprise these pathways, we used high-throughput single-cell RNA sequencing (scRNAseq). From ∼24,000 single-cell transcriptomes, we identified 19 cell types with molecular markers for each and used histological methods to localize each type. We then performed similar analyses on four organisms used for experimental studies of IOP dynamics and glaucoma: cynomolgus macaque (Macaca fascicularis), rhesus macaque (Macaca mulatta), pig (Sus scrofa), and mouse (Mus musculus). Many human cell types had counterparts in these models, but differences in cell types and gene expression were evident. Finally, we identified the cell types that express genes implicated in glaucoma in all five species. Together, our results provide foundations for investigating the pathogenesis of glaucoma and for using model systems to assess mechanisms and potential interventions.
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Immunodynamic axis of fibroblast-driven neutrophil infiltration in acute pancreatitis: NF-κB-HIF-1α-CXCL1.
BACKGROUND: Acute pancreatitis (AP) is a sterile inflammation, and 10-20% of cases can progress to severe acute pancreatitis (SAP), which seriously threatens human life and health. Neutrophils and their extracellular traps (NETs) play an important role in the progression of AP. However, the immunodynamic factors between the excessive infiltration of neutrophils during the occurrence of AP have not been fully elucidated. METHODS: Adult male C57BL/6 J mice were selected. An AP model was induced by cerulein, and a control group was set up. Single-cell sequencing technology was used to reveal the cell atlas of AP pancreatitis tissue. In vivo, the model mice were treated with anti-Ly6G antibody, DNase I, SC75741, PX-478, and SRT3109 respectively. In vitro, human pancreatic stellate cells were treated with hypoxia, H2O2, NAC, and JSH-2, and co-cultured with neutrophils in Transwell chambers. The severity of inflammation was evaluated, and the molecular mechanism by which fibroblasts exacerbate AP was revealed through techniques such as cell colony formation assay, cell migration assay, cell transfection, immunofluorescence, flow cytometry, Western blot, reverse-transcription quantitative polymerase chain reaction (RT-qPCR), and co-immunoprecipitation (co-IP). RESULTS: The study showed that the elimination of neutrophils and NETs could significantly improve AP. Single-cell RNA sequencing (scRNA-seq) indicated that both neutrophils and fibroblasts in pancreatic tissue exhibited heterogeneity during AP. Among them, neutrophils highly expressed CXCR2, and fibroblasts highly expressed CXCL1. Further experimental results demonstrated that the infiltration of neutrophils in the early stage of AP was related to the activation of fibroblasts. The activation of fibroblasts depended on the nuclear factor kappa B (NF-κB) signaling pathway induced by hypoxia. NF-κB enhanced the activation of pancreatic stellate cells (PSCs) and the secretion of CXCL1 by directly promoting the transcription of HIF-1α and indirectly inhibiting PHD2, resulting in the accumulation of HIF-1α protein. The NF-κB-HIF-1α signal promoted the secretion of CXCL1 by fibroblasts through glycolysis and induced the infiltration of neutrophils. Finally, blocking the NF-κB-HIF-1α-CXCL1 signaling axis in vivo reduced the infiltration of neutrophils and improved AP. CONCLUSIONS: This study, for the first time, demonstrated that activation of fibroblasts is one of the immunological driving factors for neutrophil infiltration and elucidated that glycolysis driven by the NF-κB-HIF-1α pathway is the intrinsic molecular mechanism by which fibroblasts secrete CXCL1 to chemotactically attract neutrophils. This finding provides a highly promising target for the treatment of AP.
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The Dynamically Evolving Cell States and Ecosystem from Benign Nevi to Melanoma.
PURPOSE: Approximately 30% of nonchronically sun-damaged melanomas originate from nevi, yet the dynamic changes and crucial mechanisms driving the transition from benign nevi to melanoma remain elusive. EXPERIMENTAL DESIGN: In this study, we performed single-cell transcriptome sequencing on multiple paired tissue sites from five patients diagnosed with melanoma arising in congenital melanocytic nevi, identifying four distinct states of melanocyte subpopulations during the progression from nevi to melanoma, characterized by dynamic changes in their functions and regulatory pathways. RESULTS: In the nevi state, IFN regulatory factor 1 was specifically upregulated in melanocytes, fibroblasts, and endothelial cells, potentially activating immune surveillance in the microenvironment. Conversely, the critical inhibitory checkpoint HLA-E for NK cells exhibited high expression in a cluster of malignant melanocytes and fibroblasts enriched in melanoma. This interaction with ligands expressed in NK cells could potentially serve as a key factor, leading to immune evasion. In malignant melanoma samples, we detected high expression of midkine in melanocytes. It is a pivotal factor that facilitates melanoma invasion and malignant transformation, potentially through interaction with endothelial cells to stimulate angiogenesis. The targets identified in our study are crucial factors in detecting the malignant transformation of nevi. Ultimately, we developed a malignant progression model capable of predicting patient prognosis and malignant progression status using bulk RNA sequencing data. CONCLUSIONS: Our study provides a high-resolution atlas of the malignant transformation of melanoma from nevi and highlights potential targets for further investigation.
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Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.
BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes. METHODS: We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. RESULTS: The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. CONCLUSIONS: The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.