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At least 217 records · Page 12Linked to original sources
PubMed2026

BAP1 Loss in Pleural Mesothelioma Is Associated With Reduced Soluble CCL2 in Patient Effusion, Abrogated CCL2-Mediated Monocyte Recruitment In Vitro.

OBJECTIVES: Pleural mesothelioma is an incurable cancer of the cell layer lining the chest wall and lung. Patients frequently present with pleural effusion, which is often drained for symptom relief and enables minimally invasive sampling of the tumour environment, including immune cells and related soluble factors. Most of the mesothelioma tumours exhibit loss of BRCA1-associated protein 1 (BAP1), a multifunctional tumour suppressor protein. Here, we aim to elucidate the effect of BAP1 loss on the mesothelioma microenvironment through profiling soluble factors within pleural effusion. METHODS: A custom panel of 22 soluble factors was measured by Luminex assay and enzyme-linked immunosorbent assay in an initial cohort of 40 patients with known BAP1 status. Validation was performed by enzyme-linked immunosorbent assay in an independent cohort of 100 cases. Secretion of soluble factors and chemoattraction of monocytes were characterised using a CRISPR-mediated BAP1 deletion model in a mesothelioma and a lung cancer cell line. Immune cell infiltration, estimated by CIBERSORT, was further explored in the Cancer Genome Atlas -MESO cohort. RESULTS: Soluble C-C motif chemokine ligand 2 (CCL2) was approximately 55% to 60% lower in pleural effusion from BAP1-loss cases in both independent cohorts. Deletion of BAP1 reduced CCL2 secretion in vitro and abolished CCL2-mediated chemoattraction of monocytes in both mesothelioma and lung cancer cell lines. In the Cancer Genome Atlas -MESO cohort, BAP1-mutant tumours exhibited a reduction in estimated macrophage content. CONCLUSION: Loss of BAP1 impairs CCL2 secretion into pleural effusions, potentially influencing monocyte recruitment into the tumour microenvironment.

BAP1↗
PubMed1999

Functional genomics in cancer research: identification of target genes of the Epstein-Barr virus nuclear antigen 2 by subtractive cDNA cloning and high-throughput differential screening using high-density agarose gels.

In the past, the identification and isolation of phenotype-associated genes was a difficult and time-consuming task. However, recent improvements of methods that are designed to isolate differentially expressed genes have remarkably speeded up the process of target gene isolation. The ultimate goal of functional genomics is to apply these technologies to clone phenotype-associated genes irrespective of the availability of probes (e.g., antibodies) and an intimate knowledge of biological background. We demonstrate the use of a novel subtractive cDNA cloning approach for the isolation and characterization of target genes of the Epstein-Barr virus nuclear antigen 2 (EBNA2). Two different subtractive cDNA libraries specific for two different time periods following activation of a conditional estrogen receptor/EBNA2 (ER/EBNA2) fusion protein were generated. Comparison of the two libraries by cross-hybridization experiments allowed the differentiation between direct and indirect target genes of EBNA2 and led to the identification of a novel direct target gene of EBNA2.

Cloning, Molecular↗
PubMed2025

Defining APOBEC-induced mutation signatures and modifying activities in yeast.

APOBEC cytidine deaminases guard cells in a variety of organisms from invading viruses and foreign nucleic acids. Recently, several human APOBECs have been implicated in mutating evolving cancer genomes. Expression of APOBEC3A and APOBEC3B in yeast allowed experimental derivation of the substitution patterns they cause in dividing cells, which provided critical links to these enzymes in the etiology of the COSMIC single base substitution (SBS) signatures 2 and 13 in human tumors. Additionally, the ability to scale yeast experiments to high-throughput screens allows use of this system to also investigate cellular pathways impacting the frequency of APOBEC-induced mutation. Here, we present validated methods utilizing yeast to determine APOBEC mutation signatures, genetic interactors, and chromosomal substrate preferences. These methods can be employed to assess the potential of other human APOBECs and APOBEC orthologs in different species to contribute to cancer genome evolution as well as define the pathways that protect the nuclear genome from inadvertent APOBEC activity during viral restriction.

Humans↗
PubMed2025

Deep Learning on Histologic Slides Accurately Predicts Consensus Molecular Subtypes and Spatial Heterogeneity in Colon Cancer.

Colon cancer (CC) is the third most prevalent cancer type. It is highly heterogeneous, particularly in terms of molecular profiles, which have both prognostic and predictive impacts on the treatment efficacy. However, CC treatment in adjuvant situations is currently guided solely by T and N staging. In this context, consensus molecular subtypes (CMSs) were introduced to stratify patients with CC based on molecular profiles. Recent studies have shown that CMS can be heterogeneous in CC, leading to a worse prognosis. This study focused on predicting CMS and its heterogeneity in CC using deep learning on digitized hematoxylin and eosin ± saffron-stained whole-slide images. Data and whole-slide images of 1996 patients from the PETACC-8, The Cancer Genome Atlas-COAD, and PRODIGE-13 cohorts were used. The model is trained to predict a 4-dimensional CMS vector, reflecting intratumor heterogeneity (ITH). It comprises a self-supervised model for embedding image patches into vectors and a weakly supervised model predicting CMS calls. Ground-truth CMS scores are obtained with the CMSclassifier package. Interpretability analyses are performed at the slide and patch levels. For homogeneous tumors, the model trained on PETACC-8 achieves 93.0% (±1.4%) macroaverage area under the curve in internal cross-validation and 94.4% macroaverage area under the curve in external validation over PRODIGE-13, whereas the The Cancer Genome Atlas-COAD model reaches 85.4% (±3.0%) in cross-validation and 92.4% over PRODIGE-13. The trained models also provide spatial distributions of CMS across tumor slides and associate specific histologic features with each CMS. Finally, the models are able to predict ITH. The results show that a deep learning model trained on routine histology slides is capable of providing an efficient and robust method for predicting CMS and characterizing a patient's ITH, paving the way for the routine consideration of CMS/ITH in clinical decision making in the adjuvant setting.

Humans↗
PubMed2006

Werner syndrome: molecular insights into the relationships between defective DNA metabolism, genomic instability, cancer and aging.

Werner syndrome is a segmental progeroid disease characterized by increased cancer and acceleration of specific age-related phenotypes, due to loss of a protein known as WRN. Extensive research over the last decade has revealed much about WRN biochemistry and the etiology of Werner syndrome. WRN possesses multiple DNA-dependent enzymatic activities (ATPase, helicase, exonuclease, and strand annealing) and interacts with factors having established roles in DNA metabolic pathways. Although the exact functions of WRN remain unclear, accumulating evidence points to roles in proper resolution of replication blockage and in telomere maintenance. If WRN function is lost (as exemplified in cells from Werner patients), problems with replication and DNA damage processing arise, probably resulting in an increased number or persistence of strand breaks. In turn, these events lead to chromosomal and telomeric abnormalities or activate checkpoints that bring about early senescence or increased apoptosis. Thus, elevated cancer incidence associated with Werner syndrome is due to increased chromosomal changes, while the accelerated aging characteristics probably stem from telomere dysfunction leading to accumulation of non-functional senescent cells or excessive apoptotic cell death over time. More research is needed to determine whether these specific DNA-dependent mechanisms contribute to development of aging characteristics in normal individuals.

Adenosine Triphosphatases↗
PubMed2025

Oral Lachnoanaerobaculum Levels and Survival in Patients With Head and Neck Cancer.

IMPORTANCE: The oral microbiome plays a critical role in cancer treatment responses, yet its influence on outcomes in patients with head and neck squamous cell carcinoma (HNSCC) undergoing (chemo)radiotherapy remains poorly understood. Identifying specific microbiome signatures associated with treatment effectiveness could provide novel prognostic biomarkers and therapeutic targets. OBJECTIVE: To investigate the association between salivary Lachnoanaerobaculum spp abundance and treatment outcomes in patients with HNSCC undergoing (chemo)radiotherapy and to explore potential mechanisms. DESIGN, SETTING, AND PARTICIPANTS: This prognostic study analyzed saliva samples from patients with HNSCC who were enrolled in 2 independent prospective biomarker studies (SALIVA and ZissTrans) and underwent definitive (chemo)radiotherapy. Oral microbiome composition was assessed using 16S rRNA gene sequencing. Tumor-infiltrating lymphocytes (TILs) were evaluated via immunohistochemistry in patients with available data. Findings were further assessed using data from The Cancer Microbiome Atlas and The Cancer Genome Atlas. Sample collection occurred from 2008 to 2011 (SALIVA) and from 2017 to 2022 (ZissTrans), and the data for this study were analyzed from July to December 2024. EXPOSURE: Definitive (chemo)radiotherapy. MAIN OUTCOMES AND MEASURES: The primary outcome was locoregional recurrence-free survival (LRFS) and a secondary outcome was overall survival (OS). Additional secondary analyses evaluated the association between Lachnoanaerobaculum spp levels and TIL levels, and the incidence of severe radiation-induced oral mucositis. RESULTS: The analysis included 92 patients with HNSCC (mean [SD] age, 61.1 [7.9] years; 15 female [16.3%] 77 male [83.7%] individuals) and found that higher Lachnoanaerobaculum spp abundance was associated with substantially improved LRFS (median, 69 vs 11 months; hazard ratio [HR], 0.50; 95% CI, 0.29-0.86) and OS (median, 75 vs 27 months; HR, 0.54; 95% CI, 0.30-0.98). This finding was confirmed by multivariable Cox regression (LRFS: HR, 0.50; 95% CI, 0.25-1.00; OS: HR, 0.37; 95% CI, 0.16-0.85). TILs were evaluated in 76 patients (82.2%) and showed that increased Lachnoanaerobaculum spp levels were associated with higher CD4-positive and CD8-positive TIL counts. Lachnoanaerobaculum spp abundance showed no meaningful association with severe radiation-induced oral mucositis. Data from The Cancer Microbiome Atlas (n = 157) indicated that higher intratumoral Lachnoanaerobaculum spp levels were associated with improved OS (HR, 0.62; 95% CI, 0.39-0.98). Transcriptomic analyses in The Cancer Genome Atlas cohort further supported an immune-stimulated tumor microenvironment in Lachnoanaerobaculum-high tumors. CONCLUSIONS AND RELEVANCE: This prognostic study found that higher salivary Lachnoanaerobaculum spp abundance was associated with improved tumor control and survival in patients with HNSCC undergoing (chemo)radiotherapy. These findings support further investigation into microbiome-targeted interventions to improve HNSCC treatment effectiveness.

Humans↗
PubMed2001

Shortcuts in genome-scale cancer pharmacology research from multivariate analysis of the National Cancer Institute gene expression database.

Application of a soft multivariate statistical procedure, called PLS, partial least squares modelling in latent variables or projections to latent structures, allows extensive exploitation of the enormous amount of information embedded in the National Cancer Institute gene expression and antitumour screen databases. Interpretation of the statistical results provides new significant biological insights such as classification of human tumour cell lines based on their gene expression patterns, evaluation of the influence of gene transcripts on drug efficacy and assessment of their selectivity for classes of compounds which act by the same mechanism, and identification of uncharacterized gene expression targets involved in cancer chemotherapy. Among them, the transcripts GC11121, GC17689, and GC18564 (unknown gene products extremely selective for RNA/DNA antimetabolites) are indicated by the present work as deserving high priority in future molecular studies.

Databases, Factual↗
PubMed2002

C/EBP-beta, C/EBP-delta, PU.1, AML1 genes: mutational analysis in 381 samples of hematopoietic and solid malignancies.

Mutations of transcription factors are associated with the pathogenesis of cancer. Genomic DNA from 381 cancers and cell lines representing leukemias, lymphomas and a variety of solid tumors were examined for mutations of genes coding for the C/EBP-beta, C/EBP-alpha, PU.1, and AML1 transcription factors using single strand conformation polymorphism (SSCP) and direct DNA sequencing. Mutation of C/EBP-beta (a chronic myelogenous leukemia cell line, Kcl22) and C/EBP-delta (a Burkitt's lymphoma cell line, Raji) were found. Interestingly, the sample with a C/EBP-beta alterations had two missense (P236L and G252A) and two silent mutations in a highly conserved region of the gene. The C/EBP-delta alteration in Raji was a missense mutation (A177G). These findings suggest that mutations of the C/EBP-beta, C/EBP-delta, PU.1, and AML1 rarely contribute to the development of hematopoietic or solid cancers.

CCAAT-Enhancer-Binding Protein-beta↗
PubMed2002

An anatomy of normal and malignant gene expression.

A gene's expression pattern provides clues to its role in normal physiology and disease. To provide quantitative expression levels on a genome-wide scale, the Cancer Genome Anatomy Project (CGAP) uses serial analysis of gene expression (SAGE). Over 5 million transcript tags from more than 100 human cell types have been assembled. To enhance the utility of this data, the CGAP SAGE project created SAGE Genie, a web site for the analysis and presentation of SAGE data (http://cgap.nci.nih.gov/SAGE). SAGE Genie provides an automatic link between gene names and SAGE transcript levels, accounting for alternative transcription and many potential errors. These informatics advances provide a rapid and intuitive view of transcript expression in the human body or brain, displayed on the SAGE Anatomic Viewer. We report here an easily accessible view of nearly any gene's expression in a wide variety of malignant and normal tissues.

DNA, Mitochondrial↗
PubMed2005

WEBSAGE: a web tool for visual analysis of differentially expressed human SAGE tags.

The serial analysis of gene expression (SAGE) is a powerful method to compare gene expression of mRNA populations. To provide quantitative expression levels on a genome-wide scale, the Cancer Genome Anatomy Project (CGAP) uses SAGE. Over 7 million SAGE tags, from 171 human cell types have been assembled. The growing number of laboratories involved in SAGE research necessitates the use of software that provides statistical analysis of raw data, allowing the rapid visualization and interpretation of results. We have created the first simple tool that performs statistical analysis on SAGE data, identifies the tags differentially expressed and shows the results in a scatter plot. It is freely available and accessible at http://bioserv.rpbs.jussieu.fr/websage/index.php.

Cell Line, Tumor↗
PubMed2025

CAFs activated by YAP1 upregulate cancer matrix stiffness to mediate hepatocellular carcinoma progression.

BACKGROUND: The stiffness of the matrix is closely related to the progression of hepatocellular carcinoma (HCC). Although direct targeting of stromal rigidity in HCC remains a clinical challenge, cancer-associated fibroblasts (CAFs) are considered key contributors to this process. Given the heterogeneity of CAFs, this study explored the relationship between specific CAF subsets and liver cancer matrix stiffness, aiming to identify novel therapeutic targets for HCC patients. METHODS: Single-cell sequencing datasets were leveraged to identify cell types within liver cancer and characterize the transcriptomic profiles of CAFs. Prognostic analysis, utilizing the Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) liver cancer datasets, assessed the correlation between matrix stiffness-related genes and HCC patient outcomes. Pseudo-time analysis was applied to trace the developmental trajectories of CAFs. By calculating intercellular communication probabilities and analyzing transcription factor activity, the functions and interactions of different CAF subsets were elucidated. Gene Ontology (GO) analysis was used to explore the functional roles of CAFs in distinct Yes-associated protein 1 (YAP1) groups. Finally, cellular experiments and animal experiments were further conducted to validate the hypotheses of this study. RESULTS: This study identified CAF subpopulations based on single-cell sequencing data and analyzed transcriptional changes within these subpopulations. Key findings include the identification of collagen type I alpha 1 (COL1A1), collagen type III alpha 1 (COL3A1), and lysyloxidase (LOX) as pivotal node genes during CAF development. Moreover, the expression of matrix stiffness-related genes was inversely correlated with the prognosis of HCC patients. Notably, the YAP1-positive CAF subpopulation emerged as the primary contributor to matrix stiffness in liver cancer. This subpopulation upregulates the expression of matrix stiffness-related genes and promotes tumor progression by activating signaling pathways such as autophagy and GTPase activity regulation. Cellular experiments and animal studies further validated this conclusion. CONCLUSION: This single-cell analysis uncovered the functional roles of CAFs in liver cancer. The YAP1-positive CAF subpopulation, in particular, was shown to contribute to matrix stiffness by upregulating the expression of relevant genes and promoting tumor progression through the activation of specific signaling pathways.

Carcinoma, Hepatocellular↗
PubMed2026

Prognostic significance of DNA damage response-related markers in esophageal squamous cell carcinoma using machine learning approaches.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) lacks reliable prognostic biomarkers. Homologous recombination deficiency (HRD) has been implicated in genomic instability across multiple cancers, but its prognostic significance in ESCC remains unexplored. This study aimed to evaluate HRD score as a prognostic biomarker and develop a machine learning-based predictive model for ESCC. METHODS: Transcriptomic and clinical data from 78 ESCC patients were obtained from The Cancer Genome Atlas (TCGA) and randomly split into training (70%) and test (30%) cohorts. Prognostic models were constructed using 112 machine learning algorithm combinations based on DNA damage response (DDR)-related genes. Gene set enrichment analysis (GSEA), somatic mutation profiling, and immune cell infiltration estimation via CIBERSORT were performed to characterize HRD-associated molecular features. RESULTS: High HRD scores were significantly associated with poorer overall survival (P<0.05). Among 112 algorithm combinations, the survival support vector machine (Survival-SVM) model demonstrated optimal performance [training concordance index (C-index): 0.741; test C-index: 0.708], identifying six hub genes: PARP1, MBD4, TELO2, NSMCE3, SMUG1, and BABAM1. A nomogram incorporating risk score (RS) and clinical variables achieved strong predictive accuracy for 1- to 3-year survival [area under the curve (AUC) >0.7]. High-HRD tumors exhibited distinct mutational patterns (TP53 and TTN) and enriched glutathione metabolism and cytochrome P450 pathways. Immune infiltration analysis revealed significant differences in plasma cell and neutrophil infiltration between risk groups (P<0.05), suggesting HRD-associated immune microenvironment remodeling. CONCLUSIONS: We developed a novel HRD-based prognostic model incorporating six DDR-related genes that demonstrates robust predictive performance in ESCC. HRD score is identified as an independent prognostic factor associated with genomic instability, immune microenvironment alterations, and clinical outcomes. These findings provide a theoretical basis for personalized treatment strategies, including potential applications of PARP inhibitors and immunotherapy in ESCC.

Esophageal squamous cell carcinoma (ESCC)↗
PubMed2025

PKC&#x3b6;, CTNNBIP1 and ALDH1A3 Expression in Luminal B Breast Cancer Indicates Decreased Hormone Therapy Effectiveness.

BACKGROUND/AIM: The role of catenin &#x3b2; interacting protein 1 (CTNNBIP1), a negative regulator of the canonical Wnt/&#x3b2;-catenin signaling pathway, in luminal A and B breast cancer stem cells treated with hormone therapy is unknown. This study investigated the relationship between CTNNBIP1 and aldehyde dehydrogenase 1 family member A3 (ALDH1A3) expression and its impact on disease-specific survival in luminal A and B breast cancer. Given that high protein kinase &#x3b6; (PKC&#x3b6;) expression, together with elevated CTNNBIP1 or ALDH1A3, is linked to poor prognosis in luminal B tumors, we also examined their combined influence. MATERIALS AND METHODS: Gene expression and clinical data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC; n=2,509) were analyzed using Kaplan-Meier and Cox proportional hazards models. Findings were validated with The Cancer Genome Atlas Pan-Cancer Atlas (TCGA; n=1,084). RESULTS: CTNNBIP1 high ALDH1A3 high indicated a poor prognosis in patients with luminal B breast cancer treated with hormone therapy in the METABRIC dataset and aromatase inhibitors as hormone therapy in the TCGA data set, suggesting that high CTNNBIP1 and ALDH1A3 expression contributed to decreased effectiveness of hormone therapy in patients with luminal B breast cancer. PKC &#x3b6; high CTNNBIP1 high ALDH1A3 high was associated with a poor prognosis in patients with luminal B breast cancer treated with hormone therapy and aromatase inhibitors, suggesting that high PKC &#x3b6; , CTNNBIP1 and ALDH1A3 expression contributed to decreased effectiveness of hormone therapy in patients with luminal B breast cancer. CONCLUSION: PKC &#x3b6; and CTNNBIP1 may be involved in the progression of ALDH1A3-positive luminal B breast cancer. In luminal B breast cancer, PKC &#x3b6; , CTNNBIP1 and ALDH1A3 could serve as molecular drug targets and prognostic biomarkers to predict the effectiveness of hormone therapy.

ALDH1A3↗