Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Cancer genomics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources
PubMed2026

Profiling Dectin-2-Positive Tumor-Associated Macrophages Across Human Cancers by Immunohistochemistry.

PURPOSE: To characterize the prevalence and distribution of Dectin-2-positive macrophages across human tumors and develop a research immunohistochemistry (IHC) assay to assess Dectin-2 in cancer tissues. MATERIALS AND METHODS: C-type lectin domain family 6 member A (CLEC6A), the gene encoding Dectin-2, was evaluated across 38 tumor types using The Cancer Genome Atlas. A fit-for-purpose Dectin-2 IHC assay was developed using a monoclonal antibody selected from screening 11 anti-Dectin-2 antibodies. Assay performance was supported by Dectin-2-expressing and parental cell line controls, macrophage-associated staining patterns, and comparison with an orthogonal CLEC6A in situ hybridization method using RNAscope. Dectin-2 expression was assessed in tissue microarrays (n = 553 samples) across 6 cancer types and whole tissue sections (n = 137) across 7 cancer types. RESULTS: The Cancer Genome Atlas analysis identified enriched CLEC6A expression in several tumor types, including non-small cell lung cancer (NSCLC), triple-negative breast cancer (TNBC), and subsets of head and neck cancer (HNC) and colorectal cancer (CRC). By IHC, Dectin-2-positive macrophages were detected across tumor types, with notable heterogeneity within and across cancer types. In tissue microarrays, NSCLC showed the highest frequency of Dectin-2-positive macrophage infiltration, with 38% of cases with staining ≥1% of tumor area. Whole tissue section analysis confirmed and expanded these findings, with ≥50% of NSCLC, melanoma, HNC, TNBC, and CRC samples showing Dectin-2-positive macrophages in ≥1% tumor area. CONCLUSIONS: Dectin-2 expression was observed in subsets of tumor-associated macrophages across multiple human cancers, with relatively enriched expression in NSCLC, melanoma, HNC, TNBC, and CRC. To our knowledge, this study represents the first broad protein-level characterization of Dectin-2 across multiple human tumor types, identifies cancers with relatively enriched Dectin-2-positive macrophage infiltration, and provides a foundation for future translational studies of Dectin-2-targeted therapies.

Humans↗
PubMed2025

GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.

MOTIVATION: Gene-environment (G-E) interaction analysis is crucial in cancer research, offering insights into how genetic and environmental factors jointly influence cancer outcomes. Most existing G-E interaction methods are regression-based, which may lack flexibility to capture complex data patterns. Recent advances have investigated deep neural network-based G-E models. However, these methods may be more vulnerable to information deficiency due to challenges such as limited sample size and high dimensionality. Apart from genetic and environmental data, pathological images have emerged as a widely accessible and informative resource for cancer modeling, presenting its potential to enhance G-E modeling. RESULTS: We propose the pathological imaging-assisted neural additive model for G-E analysis (GE-IA-NAM). The flexible and interpretable additive network architecture is adopted to account for individualized effects associated with genetic factors, environmental factors, and their interactions. To improve G-E modeling, an assisted-learning strategy is investigated, which adopts a joint analysis to integrate information from pathological images. Simulations and the analysis of lung and skin cancer datasets from The Cancer Genome Atlas demonstrate the competitive performance of the proposed method. AVAILABILITY AND IMPLEMENTATION: Python code implementing the proposed method is available at https://github.com/Mr-maoge/NAM-IA-GE. The data that support the findings in this article are openly available in TCGA (The Cancer Genome Atlas) at https://portal.gdc.cancer.gov/.

Gene-Environment Interaction↗
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↗
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↗
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↗
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↗
PubMed2026

Co-expression of the Mammaglobin (SCGB2A2) Gene With hsa-miR-184 and hsa-miR-190b Indicates Its Possible Role in Oncogenic Pathways in Breast Cancer.

BACKGROUND/AIM: Breast cancer is the most common cancer in women worldwide, and early detection remains a significant challenge. Recent studies have identified increased expression of Mammaglobin A (Q13296, Gene: SCGB2A2) mRNA in breast cancer, suggesting its potential as a disease marker, although its function is not fully understood. To elucidate Mammaglobin's role, this study sought to identify co-expressed miRNAs and analyze the biological pathways they regulate. MATERIALS AND METHODS: Using TCGAbiolinks and Firebrowse, miRNA and gene expression data were collected from 86 patients, including tumor and normal tissue samples from the Cancer Genome Atlas (TCGA) Breast Cancer cohort. Transcriptomic data were analyzed with DESeq2, and a Spearman correlation was calculated for significant p-values, which were further explored using enrichment tools and target gene databases. RESULTS: DESeq2 was used to identify differential expression of miRNAs between normal and tumor breast tissues. Out of 782 miRNAs differentially expressed in breast cancer, hsa-mir-184 and hsa-mir-190b showed a significant positive correlation with SCGB2A expression. These markers were also upregulated in breast cancer tissues compared to normal tissues. Bioinformatics analysis revealed that hsa-mir-184 and hsa-mir-190b play important roles in cancer and cellular proliferation. These miRNAs target a wide range of genes, including sorting nexin 9 (SNX9) and annexin 6 (ANXA6), which are involved in membrane stability, vesicular trafficking, and cell mobility, and they contribute to cancer metastasis. CONCLUSION: The positive correlation among the expression of hsa-miR-184, hsa-miR-190b, and SCGB2A2 suggests that they may participate in shared biological pathways. These pathways govern critical cellular processes, such as membrane trafficking and cell signaling, which are frequently disrupted in cancer. Consequently, these findings enable a better understanding of the role of Mammaglobin in breast cancer signaling.

MicroRNAs (miRNAs)↗
PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗
PubMed2026

Beyond genes and hotspots: Protein-informed interpretation of cancer mutations.

Interpreting the biological and clinical significance of somatic mutations in cancer genomes remains challenging. In this issue of Cancer Cell, Hyeon et al. identify and map significantly mutated regions to protein domains and structural features, establishing a proteo-genomics framework for interpreting cancer mutations beyond genes and hotspots.

Journal Article↗
PubMed2025

Asymmetric integration of various cancer datasets for identifying risk-associated variants and genes.

MOTIVATION: Cancer genomic research provides an opportunity to identify cancer risk-associated genes, but often suffers from undesirable low statistical power due to a limited sample size. Integrated analysis with different cancers has the potential to enhance statistical power for identifying pan-cancer risk genes. However, substantial heterogeneity across various cancers makes this challenging. RESULTS: Recently, a novel asymmetric integration method was developed that can deal with data heterogeneity and exclude unhelpful datasets from the analysis. We adapted and applied this method to integrate genotype datasets with matched case and control individuals from the Michigan Genomics Initiative, using each cancer as the primary dataset of interest and the other cancers as auxiliary datasets, respectively. Conditional logistic regression models were coupled with the asymmetric integrated framework to handle the matched case-control study design and permutation tests were performed to control for false discovery rates (FDRs). At the same FDR level, the integrated analysis found more potential genetic variants and genes that are associated with the risks of various cancers, showcasing the promise of the proposed approach for integrated analysis of cancer datasets. AVAILABILITY AND IMPLEMENTATION: Our method is available as source code at https://github.com/rxxwang/integrate_cancer.

Journal Article↗
PubMed2026

CancerOmicsStudio (CoS): a web server for integrative and interpretable analysis of multi-omics cancer data.

MOTIVATION: Large-scale omics resources, including The Cancer Genome Atlas, Genomics of Drug Sensitivity in Cancer, and the Cancer Dependency Map, have become essential for cancer research. However, these datasets are distributed across different platforms, formats and analysis frameworks, which limits their practical use by researchers without extensive computational expertise. RESULTS: We developed CancerOmicsStudio (CoS), a web server for integrative and interpretable analysis of multi-omics cancer data across 33 cancer types. CoS provides five major modules: CosAI, Traditional Analysis, Drug Sensitivity, CRISPR Dependency and Single-Cell Tumor Microenvironment. The Traditional Analysis module supports expression comparison, diagnostic evaluation, survival analysis, enrichment analysis and gene correlation. The Drug Sensitivity and CRISPR Dependency modules enable systematic evaluation of gene-drug response associations and gene essentiality in cancer cell lines. The Single-Cell Tumor Microenvironment module supports tumor microenvironment analysis at single-cell resolution. In total, approximately 1.23 million results have been precomputed to enable rapid retrieval. CosAI further allows users to submit natural-language queries and obtain results through a Real-time Analysis as Retrieval framework, with responses summarized by a lightweight language model. AVAILABILITY AND IMPLEMENTATION: CancerOmicsStudio is freely available at Zenodo (doi: 10.5281/zenodo.18744990) and https://cos.wanglab.bio.

Humans↗
PubMed2025

Ancestry and somatic profile predict acral melanoma origin and prognosis.

Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, we characterise the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower median number of structural variants. The tumours with activating BRAF mutations have a transcriptional profile more similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and quadruple wild-type samples (non BRAF/NRAS/NF1/KIT) differed from mutated samples in their structural genomic profile and overall and recurrence-free survival patterns. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. We highlight potential novel low-frequency drivers, such as PTPRJ, NF2 and RDH5. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

Journal Article↗
PubMed2024

Construction and Analysis of a Mitochondrial Metabolism-Related Prognostic Model for Breast Cancer to Evaluate Survival and Immunotherapy.

As one of the most prevalent malignancies among women, breast cancer (BC) is tightly linked to metabolic dysfunction. However, the correlation between mitochondrial metabolism-related genes (MMRGs) and BC remains unclear. The training and validation datasets for BC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. MMRG-related data were obtained from the Molecular Signatures Database. A risk score prognostic model incorporating MMRGs was established based on univariate, LASSO, and multivariate Cox regression analyses. Independent factors affecting BC prognosis were identified through regression analysis and presented in a nomogram. Single-sample gene set enrichment analysis was employed to assess the immune levels of high-risk (HR) and low-risk (LR) groups. The sensitivity of BC patients in the two groups to common anti-tumor drugs was evaluated by utilizing the Genomics of Drug Sensitivity in Cancer database. 12 MMRGs significantly associated with survival were selected from 1234 MMRGs. A 12-gene risk score prognostic model was built. In the multivariate regression analysis incorporating classical clinical factors, the MMRG-related risk score remained an independent prognostic factor. As revealed by tumor immune microenvironment analysis, the LR group with higher survival rates had elevated immune levels. The drug sensitivity results unmasked that the LR group demonstrated higher sensitivity to Irinotecan, Nilotinib, and Oxaliplatin, while the HR group demonstrated higher sensitivity to Lapatinib. The development of MMRG characteristics provides a comprehensive understanding of mitochondrial metabolism in BC, aiding in the prediction of prognosis and tumor microenvironment, and offering promising therapeutic choices for BC patients with different MMRG risk scores.

Humans↗
PubMed2026

Stratifying lung adenocarcinoma: a novel prognostic model based on mitochondrial outer membrane permeabilization activity.

UNLABELLED: Mitochondrial outer membrane permeabilization (MOMP) is a core apoptotic regulatory event that dictates mitochondrial integrity, where full activation drives cell death and sublethal dysregulation contributes to tumor genomic instability. We used the Cancer Genome Atlas lung adenocarcinoma cohort (TCGA-LUAD) as the training cohort and the Gene Expression Omnibus dataset GSE42127 as the validation cohort to identify prognostic genes related to MOMP activity in lung adenocarcinoma (LUAD) and to evaluate their potential biological significance. By intersecting MOMP-related genes with differentially expressed genes, combined with survival analysis, Mendelian randomization analysis, and 101 machine-learning algorithm combinations, seven prognostic genes, namely BIRC5, PSMD11, TNFRSF13C, YWHAZ, YWHAG, CYCS, and LTB, were identified. Next, an optimal prognostic model was constructed based on the gradient boosting machine (GBM) algorithm. Based on the risk score, LUAD patients were stratified into high- and low-risk groups, and patients in the high-risk group exhibited poorer overall survival in both the training and validation cohorts. Furthermore, a nomogram integrating the risk score and clinicopathological factors was developed and showed favorable predictive performance for 1-, 3-, and 5-year survival. Meanwhile, functional and immune analyses revealed that the high-risk group was enriched in DNA replication-related pathways and demonstrated a higher tumor mutation burden (TMB). Correlation analysis indicated that TNFRSF13C was positively correlated with activated B cells, whereas BIRC5 was negatively correlated with eosinophils, suggesting that MOMP-related genes might be involved in remodeling the immune microenvironment of LUAD. Drug sensitivity analysis showed differences in predicted half-maximal inhibitory concentration (IC50) values between the risk groups, suggesting the potential value of this model in assisting therapeutic stratification. Single-cell RNA sequencing (scRNA-seq) further identified T lymphocytes as a key cell type, with numerous prognostic genes exhibiting differential expression in T cells or dynamic changes during differentiation. We suggest that the MOMP-related signature established in this study may provide a reference for prognostic stratification in LUAD and offers candidate prognostic genes for subsequent experimental and clinical validation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13205-026-05058-6.

Lung adenocarcinoma↗
PubMed2026

FZD5 drives macrophage-mediated immunomodulation and predicts prognosis in glioma: evidence from single-cell sequencing.

BACKGROUND: Gliomas are highly malignant brain tumors characterized by an immunosuppressive microenvironment, which limits therapeutic efficacy and contributes to poor clinical outcomes. The WNT/&#x3b2;-catenin signaling pathway is critically involved in tumor progression, and FZD5, a key receptor within this pathway, may participate in immune regulation. However, its specific role and underlying mechanisms in glioma remain unclear. METHODS: RNA-seq and microarray datasets from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA), together with single-cell RNA sequencing (scRNA-seq) datasets from GEO, were comprehensively analyzed. The Seurat package was used to identify macrophage-related clusters and mitophagy-associated pathways. Cox and LASSO regression analyses, along with a prognostic nomogram, were applied to evaluate the prognostic significance of FZD5. Immune infiltration, functional enrichment, and immunotherapy response analyses were conducted, followed by validation using spatial transcriptomics, immunohistochemistry, and in vitro assays. RESULTS: In bulk glioma transcriptomes, FZD5 emerged as an independent predictor of poor prognosis. Crucially, single-cell and spatial analyses revealed that the biologically significant FZD5 signal originated predominantly within tumor-associated macrophages (TAMs), where it colocalized with the M2 marker CD163. Consistently, elevated FZD5 levels correlated with increased myeloid infiltration and an immunosuppressive tumor microenvironment. Functionally, macrophage-expressed FZD5 was associated with mitophagy-related programs and promoted an M2-skewed phenotype, thereby enhancing glioma cell proliferation, migration, and invasion via macrophage-glioma crosstalk. CONCLUSION: FZD5 is a TAM-enriched marker in glioma tissues and a potential regulator of macrophage-associated immunosuppressive programs, supporting its utility as a prognostic biomarker and a candidate target for microenvironment-oriented interventions in glioma.

Humans↗