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UCHL1-mediated deubiquitination of PARK7 suppresses ferroptosis and enhances tumor progression in triple-negative breast cancer.
Triple-negative breast cancer (TNBC) has a poor prognosis due to the lack of targeted treatment. Previous studies have shown that the deubiquitinase UCHL1 is significantly upregulated in TNBC tissues and positively correlated with shorter overall survival in patients, suggesting that UCHL1 may drive TNBC progression. The latest research suggests that ferroptosis deficiency can promote tumor metastasis, but it is still unclear whether UCHL1 affects TNBC by regulating ferroptosis. Based on the potential role of UCHL1 in ferroptosis, we propose the hypothesis that UCHL1 enhances the survival and invasion ability of TNBC cells by inhibiting ferroptosis. We downloaded the single-cell RNA sequencing dataset for breast cancer (GSE176078) from the Gene Expression Omnibus database and integrated it with The Cancer Genome Atlas data to conduct bioinformatics analysis, focusing on identifying key genes related to TNBC. We performed gene set enrichment analysis (GSEA) to explore the pathways associated with UCHL1. After that, we validated the expression of UCHL1 in TNBC tissues and cell lines and examined its influence on cell proliferation, migration, and invasion through functional experiments. Finally, we explored the downstream targets of UCHL1, utilizing co-immunoprecipitation, western blot, immunofluorescence colocalization, and establishing TNBC xenograft models in nude mice to elucidate its mechanisms in TNBC progression. Public database analysis revealed high levels of UCHL1 in TNBC. Additional research confirmed its overexpression in TNBC tissues and cells, along with significantly increased TNBC cell proliferation, migration, and invasion associated with high UCHL1 expression. GSEA further identified UCHL1 as being predominantly enriched in pathways related to ferroptosis. UCHL1 interacted with Parkinson's disease-related glycosylation enzyme PARK7 and reduced the degradation of PARK7 protein, which was mediated through the ubiquitin-proteasome pathway. Lastly, both in vitro and in vivo studies revealed that the UCHL1-PARK7 axis fostered tumor progression in TNBC by inhibiting ferroptosis. UCHL1 deubiquitinates and stabilizes PARK7, thereby inhibiting ferroptosis and boosting tumor progression in TNBC, indicating the potential of UCHL1 and PARK7 as therapeutic targets for TNBC patients.
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Identification of cell surface markers for acute myeloid leukemia prognosis based on multi-model analysis.
Given the extremely high inter-patient heterogeneity of acute myeloid leukemia (AML), the identification of biomarkers for prognostic assessment and therapeutic guidance is critical. Cell surface markers (CSMs) have been shown to play an important role in AML leukemogenesis and progression. In the current study, we evaluated the prognostic potential of all human CSMs in 130 AML patients from The Cancer Genome Atlas (TCGA) based on differential gene expression analysis and univariable Cox proportional hazards regression analysis. By using multi-model analysis, including Adaptive LASSO regression, LASSO regression, and Elastic Net, we constructed a 9-CSMs prognostic model for risk stratification of the AML patients. The predictive value of the 9-CSMs risk score was further validated at the transcriptome and proteome levels. Multivariable Cox regression analysis showed that the risk score was an independent prognostic factor for the AML patients. The AML patients with high 9-CSMs risk scores had a shorter overall and event-free survival time than those with low scores. Notably, single-cell RNA-sequencing analysis indicated that patients with high 9-CSMs risk scores exhibited chemotherapy resistance. Furthermore, PI3K inhibitors were identified as potential treatments for these high-risk patients. In conclusion, we constructed a 9-CSMs prognostic model that served as an independent prognostic factor for the survival of AML patients and held the potential for guiding drug therapy.
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Single-cell RNA sequencing reveals cellular and molecular immune profile in a Pembrolizumab-responsive PD-L1-negative lung cancer patient.
High expression of PD-L1 predicts PD-1/PD-L1 inhibitor benefit, meanwhile a few PD-L1-negative patients still benefit from these drugs. In this study, we aimed to explore the underlying cellular and molecular characteristics via single-cell sequencing. Before and after treatment with Pembrolizumab, peripheral blood mononuclear cells (PBMCs) were isolated via Ficoll gradient. Thereafter, single-cell RNA sequencing was performed, and clinical significance was validated with The Cancer Genome Atlas (TCGA) cohort. All 3423 cells of 16 clusters were classified into eight cell types, including NKG7+ T, NKG7+ NK, Naïve T, CDC1C+ dendritic cells, CD8+ T cells, B cells, macrophages and erythrocytes. Cell proportion, the clinical significance of differentially expressed genes and significant pathways of NKG7+ T, NKG7+ NK, Naïve T and CD8+ T cells were analyzed. Ubiquitin-mediated proteolysis/cell cycle/natural killer cell-mediated cytotoxicity were identified as PD-1 blockage-responsive pathways in NKG7+ NK cells. Apoptosis/Th1 and Th2 cell differentiation were proposed as Pembrolizumab-affected pathways in NKT cells. In gene level, ID2, PIK3CD, UQCR10, MATK, MZB1, IL7R and TRGC2 showed a significant correlation with PD-1 expression after TCGA dataset validation, which could possess potential as predictive markers for patients with PD-L1-negative lung squamous cell carcinoma who can benefit from Pembrolizumab.
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NR3C1 promotes group 4 medulloblastoma invasion via activating VCAN.
Approximately 30% of group 4 medulloblastomas (G4-MBs) present with metastasis at diagnosis, yet the molecular mechanisms remain unclear. To elucidate differences of genes' expression between primary tumors of metastatic (M+) and non-metastatic (M0) G4-MB, we performed multi-omics profiling, including RNA sequencing, proteomics, single-nucleus RNA sequencing (snRNA-seq), and spatial transcriptomics on tumor samples. Integrative analyses identified VCAN, a chondroitin sulfate proteoglycan, as the most significantly up-regulated gene in primary tumors of M+ G4-MB. High expression of this gene was correlated with poor patient prognosis. Functional assays demonstrated that VCAN promotes proliferation and invasion while inhibiting apoptosis. In addition, NR3C1 was predicted as the key activator of VCAN by Single-Cell Regulatory Network Inference and Clustering (SCENIC). High-definition spatial transcriptomics revealed that NR3C1 and VCAN are highly co-expressed within the same spatial domains. Multiplex immunofluorescence confirmed the co-localization, providing spatial evidence of their regulatory interaction. ChIP-qPCR subsequently confirmed direct binding of NR3C1 to the VCAN promoter. Knockdown of NR3C1 or VCAN suppressed invasion and proliferation and induced apoptosis of the tumor cells, which were partially reversed by VCAN overexpression. Together, these findings revealed that the NR3C1-VCAN axis played a pivotal role in the metastatic progression of G4-MB, highlighting a potential therapeutic target for high-risk patients.
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A comprehensively prognostic and immunological analysis of actin-related protein 2/3 complex subunit 5 in pan-cancer and identification in hepatocellular carcinoma.
BACKGROUND: Actin-related protein 2/3 complex subunit 5 (ARPC5) is one of the members of actin-related protein 2/3 complex and plays an important role in cell migration and invasion. However, little is known about the expression pattern, prognosis value, and biological function of ARPC5 in pan-cancer. Thus, we focus on ARPC5 as cut point to explore a novel prognostic and immunological biomarker for cancers. METHODS: The public databases, including TCGA, GTEx, and UCEC, were used to analyze ARPC5 expression in pan-cancer. The Human Protein Atlas website was applied to obtain the expression of ARPC5 in different tissues, cell lines, and single-cell types. Univariate Cox regression analysis and Kaplan-Meier analysis were used to explore the prognosis value of ARPC5 in various cancers. Spearman's correlation analysis was performed to investigate the association between ARPC5 expression and tumor microenvironment scores, immune cell infiltration, immune-related genes, TMB, MSI, RNA modification genes, DNA methyltransferases, and tumor stemness. Moreover, qPCR, Western blot, and immunohistochemistry were carried out to examine the differential expression of ARPC5 in HCC tissues and cell lines. CCK8, EdU, flow cytometry, wound-healing assays, and transwell assays were conducted to explore its role in tumor proliferation, apoptosis, migration, and invasion among HCC cells. RESULTS: ARPC5 expression was upregulated in most cancer types and significantly associated with worse prognosis in KIRC, KIRP, LGG, and LIHC. mRNA expression of ARPC5 showed low tissue and cell specificity in normal tissues, cell lines, and single-cell types. ARPC5 expression was positively correlated with the tumor microenvironment scores, immune infiltrating cells, immune checkpoint-related genes in most cancers. ARPC5 in STAD and BRCA was positively associated with TMB, MSI, and neoantigens. We also discovered that ARPC5 was correlated with the expression of m1A-related genes, m5C-related genes, m6A-related genes, and DNA methyltransferases. In experiment analyses, we found that ARPC5 was significantly highly expressed in HCC tissues and HCC cells. Functionally, silencing ARPC5 dramatically decreased proliferation, migration, and invasion ability of HCC cells. CONCLUSIONS: ARPC5 expression affects the prognosis of multiple tumors and is closely correlated to tumor immune infiltration and immunotherapy. Furthermore, ARPC5 may function as an oncogene and promote tumor progression in HCC.
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In silico analysis of the immune microenvironment in bladder cancer.
BACKGROUND: Infiltrating immune and stromal cells are vital components of the bladder cancer (BC) microenvironment, which can significantly affect BC progression and outcome. However, the contribution of each subset of tumour-infiltrating immune cells is unclear. The objective of this study was to perform cell phenotyping and transcriptional profiling of the tumour immune microenvironment and analyse the association of distinct cell subsets and genes with BC prognosis. METHODS: Clinical data of 412 patients with BC and 433 transcription files for normal and cancer tissues were downloaded from The Cancer Genome Atlas. The CIBERSORT algorithm was used to determine the relative abundance of 22 immune cell types in each sample and the ESTIMATE algorithm was used to identify differentially expressed genes within the tumour microenvironment of BC, which were subjected to functional enrichment and protein-protein interaction (PPI) analyses. The association of cell subsets and differentially expressed genes with patient survival and clinical parameters was examined by Cox regression analysis and the Kaplan-Meier method. RESULTS: Resting natural killer cells and activated memory CD4+ and CD8+ T cells were associated with favourable patient outcome, whereas resting memory CD4+ T cells were associated with poor outcome. Differential expression analysis revealed 1334 genes influencing both immune and stromal cell scores; of them, 97 were predictive of overall survival in patients with BC. Among the top 10 statistically significant hub genes in the PPI network, CXCL12, FN1, LCK, and CXCR4 were found to be associated with BC prognosis. CONCLUSION: Tumour-infiltrating immune cells and cancer microenvironment-related genes can affect the outcomes of patients and are likely to be important determinants of both prognosis and response to immunotherapy in BC.
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B-cell-specific signatures reveal novel immunophenotyping and therapeutic targets for hepatocellular carcinoma.
BACKGROUND: Immunotherapy presents both promises and challenges in treating hepatocellular carcinoma (HCC) due to its complex immunological microenvironment. The role of B cells, a key part of the immune system, remains uncertain in HCC. AIM: To identify B-cell-specific signatures and reveal novel immunophenotyping and therapeutic targets for HCC. METHODS: Using the Tumor Immune Single-cell Hub 2 database, we identified B-cell-related genes (BRGs) in HCC. Gene enrichment analysis was performed to explore the possible collaboration between B cells and T cells in HCC. We conducted univariate Cox regression analysis using The Cancer Genome Atlas liver HCC collection dataset to find BRGs linked to HCC prognosis. Subsequently, least absolute shrinkage and selection operator regression was utilized to develop a prognostic model with 11 BRGs. The model was validated using the International Cancer Genome Consortium dataset and GSE76427. RESULTS: The risk score derived from the prognostic model emerged as an independent prognostic factor for HCC. Analysis of the immune microenvironment and cell infiltration revealed the immune status of various risk groups, supporting the cooperation of B and T cells in suppressing HCC. The BRGs model identified new molecular subtypes of HCC, each with distinct immune characteristics. Drug sensitivity analysis identified targeted drugs effective for each HCC subtype, enabling precision therapy and guiding clinical decisions. CONCLUSION: We clarified the role of B cells in HCC and propose that the BRGs model offers promising targets for personalized immunotherapy.
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Transcriptomic Plasticity Is a Hallmark of Metastatic Pancreatic Cancer.
UNLABELLED: Metastasis is the leading cause of cancer deaths. To develop strategies for intercepting metastatic progression, a better understanding of how tumor cells adapt to vastly different organ contexts is needed. To investigate this question, a single-cell transcriptomic atlas of primary tumors and diverse metastatic samples (liver, omentum, peritoneum, stomach wall, lymph node, and diaphragm) from a patient with pancreatic ductal adenocarcinoma who underwent rapid autopsy was generated. Using unsupervised archetype analysis, both shared and site-specific gene programs were identified, including lipid metabolism and gastrointestinal programs prevalent in peritoneal and stomach wall lesions, respectively. We developed phylogenetic inference from copy-number alterations in single-cell sequencing observations (PICASSO) as a probabilistic approach for inferring clonal phylogeny from single-cell and matched whole-exome sequencing data. Comparison of PICASSO-generated clonal structure with phenotypic signatures revealed that pancreatic cancer cells adapted to local environments with minimal contribution from clonal genotype. Our results suggest a paradigm whereby strong environmental effects are imposed on highly plastic cancer cells during metastatic dissemination. SIGNIFICANCE: Single-cell transcriptional profiling of primary tumor and metastases from rapid autopsy samples of an individual with pancreatic cancer, combined with probabilistic clonal inference by PICASSO, reveals substantial transcriptomic plasticity in metastatic cells. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .
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Epigallocatechin gallate is associated with PDGFRB downregulation and altered PI3K-AKT signaling in gastric cancer.
BACKGROUND: Gastric cancer (GC) remains a major cause of cancer-related mortality worldwide. Epigallocatechin gallate (EGCG), a natural polyphenol derived from green tea, exhibits anticancer properties; however, its molecular targets and regulatory mechanisms in GC are not fully elucidated. This study aimed to identify candidate EGCG-associated genes in GC and generate a hypothesis for future mechanistic investigation. METHODS: Differentially expressed genes (DEGs) in GC were identified and intersected with EGCG-associated targets retrieved from The Cancer Genome Atlas (TCGA) and GeneCards public databases. Least absolute shrinkage and selection operator (LASSO) regression and Cox proportional hazards analyses were performed to screen prognostically relevant genes. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves. Functional enrichment analysis was conducted to explore biological significance. Public single-cell RNA sequencing datasets were analyzed to determine the cellular localization of platelet-derived growth factor receptor beta (PDGFRB), while DepMap transcriptomic data were used to assess its expression across GC cell lines. In vitro assays, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), Transwell migration, and Western blotting, were performed to evaluate the biological effects of EGCG on GC-associated signaling pathways. RESULTS: Thirty-eight EGCG-associated DEGs were identified. Enrichment analysis revealed these genes were involved in cancer-associated pathways. LASSO-Cox modelling identified four candidate genes. Among them, PDGFRB was selected for further investigation based on its prognostic relevance and favorable diagnostic performance. PDGFRB expression was significantly higher in the TCGA genomically stable (GS) subtype than in the other molecular subtypes and was predominantly localized to cancer-associated fibroblasts (CAFs) and pericytes in single-cell RNA sequencing analysis. DepMap data demonstrated heterogeneous PDGFRB expression across GC cell lines. In vitro experiments showed that EGCG inhibited proliferation, migration, and invasion, reduced PDGFRB protein expression, and was associated with apoptosis-related protein changes and altered PI3K-AKT signaling. CONCLUSIONS: Our findings suggest that EGCG treatment was associated with reduced PDGFRB expression and altered PI3K-AKT signaling in GC cells. These findings identify PDGFRB as a candidate EGCG-associated gene and provide a hypothesis for future mechanistic investigation.
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The Medicago sativa gene index 1.2: a web-accessible gene expression atlas for investigating expression differences between Medicago sativa subspecies.
BACKGROUND: Alfalfa (Medicago sativa L.) is the primary forage legume crop species in the United States and plays essential economic and ecological roles in agricultural systems across the country. Modern alfalfa is the result of hybridization between tetraploid M. sativa ssp. sativa and M. sativa ssp. falcata. Due to its large and complex genome, there are few genomic resources available for alfalfa improvement. RESULTS: A de novo transcriptome assembly from two alfalfa subspecies, M. sativa ssp. sativa (B47) and M. sativa ssp. falcata (F56) was developed using Illumina RNA-seq technology. Transcripts from roots, nitrogen-fixing root nodules, leaves, flowers, elongating stem internodes, and post-elongation stem internodes were assembled into the Medicago sativa Gene Index 1.2 (MSGI 1.2) representing 112,626 unique transcript sequences. Nodule-specific and transcripts involved in cell wall biosynthesis were identified. Statistical analyses identified 20,447 transcripts differentially expressed between the two subspecies. Pair-wise comparisons of each tissue combination identified 58,932 sequences differentially expressed in B47 and 69,143 sequences differentially expressed in F56. Comparing transcript abundance in floral tissues of B47 and F56 identified expression differences in sequences involved in anthocyanin and carotenoid synthesis, which determine flower pigmentation. Single nucleotide polymorphisms (SNPs) unique to each M. sativa subspecies (110,241) were identified. CONCLUSIONS: The Medicago sativa Gene Index 1.2 increases the expressed sequence data available for alfalfa by ninefold and can be expanded as additional experiments are performed. The MSGI 1.2 transcriptome sequences, annotations, expression profiles, and SNPs were assembled into the Alfalfa Gene Index and Expression Database (AGED) at http://plantgrn.noble.org/AGED/ , a publicly available genomic resource for alfalfa improvement and legume research.