Search Results (175)

Objective: This study aims to identify clinically relevant lactylation-related biomarkers in chronic obstructive pulmonary disease (COPD) and investigate their potential mechanistic roles in COPD pathogenesis. Methods: Differentially expressed genes (DEGs) were identified from the GSE21359 dataset, followed by weighted gene co-expression network analysis (WGCNA) to detect COPD-associated modules. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine–recursive feature elimination (SVM–RFE) algorithms were applied to screen lactylation-related biomarkers, with diagnostic performance evaluated through the ROC curve. Candidates were validated in the GSE76925 dataset for expression and diagnostic robustness. Immune cell infiltration patterns were exhibited using EPIC deconvolution. Single-cell transcriptomics (from GSE173896) were processed via the ‘Seurat’ package encompassing quality control, dimensionality reduction, and cell type annotation. Cell-type-specific markers and intercellular communication networks were delineated using the ‘FindAllMarkers’ package and the ‘CellChat’ R package, respectively. In vitro validation was conducted using a cigarette smoke extract (CSE)-induced COPD model. Results: Integrated transcriptomic approaches and multi-algorithm screening (LASSO/Boruta/SVM–RFE) revealed carbonyl reductase 1 (CBR1) and peroxiredoxin 1 (PRDX1) as core COPD biomarkers enriched in oxidation–reduction and inflammatory pathways, with high diagnostic accuracy (AUC > 0.85). Immune profiling and scRNA-seq delineated macrophage and cancer-associated fibroblasts (CAFs) infiltration with oxidative-redox transcriptional dominance in COPD. CBR1 was significantly upregulated in T cells, neutrophils, and mast cells; and PRDX1 showed significant upregulation in endothelial, macrophage, and ciliated cells. Experimental validation in CSE-induced models confirmed significant upregulation of both biomarkers via transcription PCR (qRT-PCR) and immunofluorescence. Conclusions: CBR1 and PRDX1 are lactylation-associated diagnostic markers, with lactylation-driven redox imbalance implicated in COPD progression. Full article

Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. Full article

Background: Breast cancer, the most prevalent malignancy among women worldwide, exhibits significant heterogeneity, particularly in the tumor microenvironment (TME), which poses challenges for treatment. Spatial transcriptomics (ST) has emerged as a transformative technology, enabling gene expression analysis while preserving tissue spatial architecture. This provides unprecedented insights into tumor heterogeneity, cellular interactions, and disease mechanisms, offering a powerful tool for advancing breast cancer research and therapy. This review aims to synthesize the applications of ST in breast cancer research, focusing on its role in decoding tumor heterogeneity, characterizing the TME, elucidating progression and metastasis dynamics, and predicting therapeutic responses. We also explore how ST can bridge molecular profiling with clinical translation to enhance precision therapy. The key scientific concepts of review included the following: We summarize the technological advancements in ST, including imaging-based and sequencing-based methods, and their applications in breast cancer. Key findings highlight how ST resolves spatial heterogeneity across molecular subtypes and histological variants. ST reveals the dynamic interplay between tumor cells, immune cells, and stromal components, uncovering mechanisms of immune evasion, metabolic reprogramming, and therapeutic resistance. Additionally, ST identifies spatial prognostic markers and predicts responses to chemotherapy, targeted therapy, and immunotherapy. We propose that ST serves as a hub for integrating multi-omics data, offering a roadmap for precision oncology and personalized treatment strategies in breast cancer. Full article

Background: Boiling histotripsy (BH) uses high-amplitude, short-pulse focused ultrasound to disrupt tissue mechanically. Oncolytic virotherapy using reovirus has shown modest clinical benefit in pancreatic cancer patients. Here, reovirus and BH were used to treat pancreatic tumours, and their effects on the immune transcriptome of these tumours were characterised. Methods: Orthotopic syngeneic murine pancreatic KPC tumours grown in immune-competent subjects, were allocated to control, reovirus, BH and combined BH and reovirus treatment groups. Acoustic cavitation was monitored using a passive broadband cavitation sensor. Treatment effects were assessed histologically with hematoxylin and eosin staining. Single-cell multi-omics combining whole-transcriptome analysis with the expression of surface-expressed immune proteins was used to assess the effects of treatments on tumoural leukocytes. Results: Acoustic cavitation was detected in all subjects exposed to BH, causing cellular disruption in tumours 6 h after treatment. Distinct cell clusters were identified in the pancreatic tumours 24 h post-treatment. These included neutrophils and cytotoxic T cells overexpressing genes associated with an N2-like and an exhaustion phenotype, respectively. Reovirus decreased macrophages, and BH decreased regulatory T cells compared to controls. The combined treatments increased neutrophils and the ratio of various immune cells to Treg. All treatments overexpressed genes associated with an innate immune response, while ultrasound treatments downregulated genes associated with the transporter associated with antigen processing (TAP) complex. Conclusions: Our results show that the combined BH and reovirus treatments maximise the overexpression of genes associated with the innate immune response compared to that seen with each individual treatment, and illustrate the anti-immune phenotype of key immune cells in the pancreatic tumour microenvironment. Full article

Background: The prognosis management of thyroid cancer remains a significant challenge. This study highlights the critical role of T cells in the tumor microenvironment and aims to improve prognostic precision by integrating bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data, providing a more comprehensive view of tumor biology at the single-cell level. Method: 15 thyroid cancer scRNA-seq samples were analyzed from GEO and 489 patients from TCGA. A multi-level attention graph neural network (MLA-GNN) model was applied to integrate T-cell-related differentially expressed genes (DEGs) for predicting disease-free survival (DFS). Patients were divided into training and validation cohorts in an 8:2 ratio. Result: We systematically characterized the immune microenvironment of metastatic thyroid cancer by using single-cell transcriptomics and identified the important role of T-cell subtypes in the development of thyroid cancer. T-cell-based DEGS between tumor tissues and normal tissues were also identified. Subsequently, T-cell-based risk signatures were selected for establishing a risk model using MLA-GNN. Finally, our MLA-GNN-based model demonstrated an excellent ability to predict the DFS of thyroid cancer patients (1-year AUC: 0.965, 3-years AUC: 0.979, and 5-years AUC: 0.949 in training groups, and 1-year AUC: 0.879, 3-years AUC: 0.804, and 5-years AUC: 0.804 in validation groups). Conclusions: Risk features based on T-cell genes have demonstrated the effectiveness in predicting the prognosis of thyroid cancer. By conducting a comprehensive characterization of T-cell features, we aim to enhance our understanding of the tumor’s response to immunotherapy and uncover new strategies for the treatment of cancer. Full article

Background/Objectives: Cell–cell communication (CCC) is a critical process within the tumor microenvironment, governing regulatory interactions between cancer cells and other cellular subpopulations. Aiming to improve the accuracy and completeness of intercellular gene-regulatory network inference, we constructed a novel spatial-resolved gene-regulatory network framework (spGRN). Methods: Firstly, the spatial multi-omics data of colorectal cancer (CRC) patients were analyzed. We precisely located the tumor boundaries and then systematically constructed the spGRN framework to study the network regulation. Subsequently, the key signaling molecules obtained by the spGRN were identified and further validated by the spatial-proteomics dataset. Results: Through the constructed spatial gene regulatory network, we found that in the communication with malignant cells, the highly expressed ligands LIF and LGALS3BP and receptors IL6ST and ITGB1 in fibroblasts can promote tumor proliferation, and the highly expressed ligands S100A8/S100A9 in plasma cells play an important role in regulating inflammatory responses. Further, validation of the key signaling molecules by the spatial-proteomics dataset highlighted the role of these genes in mediating the regulation of boundary-related cells. Furthermore, we applied the spGRN to publicly available single-cell and spatial-transcriptomics datasets from three other cancer types. The results demonstrate that ITGB1 and its target genes FOS/JUN were commonly expressed in all four cancer types, indicating their potential as pan-cancer therapeutic targets. Conclusion: the spGRN was proven to be a useful tool to select signal molecules as potential biomarkers or valuable therapeutic targets. Full article

Colorectal cancer (CRC) is one of the most prevalent and lethal oncological diseases worldwide, with a concerning rise in incidence, particularly in developing countries. Recent advances in genetic sequencing have revealed that the gut microbiome plays a crucial role in CRC development. Mechanisms such as chronic inflammation, metabolic alterations, and oncogenic pathways have demonstrated that dysbiosis, a disruption of the gut microbiome, is linked to CRC. Associations have been found between tumor progression, treatment resistance, and pathogenic microbes such as Fusobacterium nucleatum and Escherichia coli. A promising approach for CRC prevention and treatment is microbiome manipulation through interventions such as probiotics, prebiotics, fecal microbiota transplantation, and selective antibiotics. This article explores how gut microbiome alterations influence CRC pathogenesis and examines microbiome modulation strategies currently used as adjuncts to traditional treatments. Advances in artificial intelligence, single-cell and spatial transcriptomics, and large-scale initiatives such as the ONCOBIOME Project are paving the way for the identification of microbiome-derived biomarkers for early CRC detection and personalized treatment. Despite promising progress, challenges such as interindividual variability, causal inference, and regulatory hurdles must be addressed. Future integration of microbiome analysis into multi-omics frameworks holds great potential to revolutionize precision oncology in CRC management. Full article

Background/Objectives: RAF pathway aberrations are one of the hallmarks of lung cancer. Sorafenib is a multi-kinase inhibitor targeting the RAF pathway and is FDA-approved for several cancers, yet its efficacy in lung cancer is controversial. Previous clinical research showed that a ARAF p.S214C mutation exhibited exceptional responsiveness to sorafenib in lung adenocarcinoma. Methods: Considering this promising clinical potential, the oncogenic potential and sorafenib response of the ARAF p.S214C mutation were investigated using lung cancer models. ARAF p.S214C mutant, ARAF wild-type (WT), and EGFP control genes were ectopically expressed in lung adenocarcinoma cell lines retroviral transduction. In vitro and in vivo sorafenib sensitivity studies were performed, followed by transcriptomics and proteomics analyses. Results: Compared to the ARAF-WT and EGFP-engineered cells, the ARAF p.S214C-engineered cells activated Raf-MEK-ERK signaling and exhibited enhanced oncogenic potential in terms of in vitro cell proliferation, colony and spheroid formation, migration, and invasion abilities, as well as in vivo tumorigenicity. The ARAF p.S214C-engineered cells also displayed heightened sensitivity to sorafenib in vitro and in vivo. RNA sequencing and reverse-phase protein array analyses demonstrated elevated expression of genes and proteins associated with tumor aggressiveness in the ARAF p.S214C mutants, and its sorafenib sensitivity was likely moderated through inhibition of the cell cycle and DNA replication. The ERK and PI3K signaling pathways were also significantly deregulated in the ARAF p.S214C mutants regardless of sorafenib treatment. Conclusions: This study demonstrates the oncogenicity and sorafenib sensitivity of the ARAF p.S214C mutation in lung cancer cells, which may serve as a biomarker for predicting the sorafenib response in lung cancer patients. Importantly, investigating the gene–drug sensitivity pairs in clinically exceptional responders may guide and accelerate personalized cancer therapies based on specific tumor mutations. Full article

Breast cancer (BC) poses a significant clinical challenge due to late metastatic recurrence, driven by dormant disseminated tumor cells (DTCs). This review emphasizes the urgency of addressing tumor dormancy to reduce metastatic relapse, a major contributor to BC mortality. DTCs evade conventional therapies and immune surveillance, reactivating unpredictably, thus necessitating targeted strategies. Current research is fragmented, with conflicting data, inadequate models, and a lack of biomarkers hindering progress. This review synthesizes these gaps and proposes actionable priorities, advocating for integrated, standardized approaches. It highlights the roles of single-cell multi-omics, spatial transcriptomics, and humanized long-term models in unraveling dormancy mechanisms. The review also emphasizes macrophage-targeted therapies, dormancy-specific trials, and biomarker validation, offering paths to clinical translation. Ultimately, this work emphasizes the urgent need for integrated multi-omics approaches, including single-cell and spatial transcriptomics, combined with advanced computational analysis. Moreover, this review critically analyzes the existing research landscape, meticulously identifying key gaps, and proposing concrete, forward-looking directions for both fundamental research and clinical translation in the challenging field of BC dormancy. Full article

Background: Gastric cancer is a leading cause of cancer-related mortality worldwide, with approximately one million new cases diagnosed annually. While Helicobacter pylori infection remains a primary etiological factor, mounting evidence implicates obesity and lipid metabolic dysregulation, particularly in hypercholesterolemia, as emerging drivers of gastric tumorigenesis. This study investigates the molecular intersections between gastric cancer, obesity, and hypercholesterolemia through a comprehensive multi-omics and systems biology approach. Methods: We conducted integrative transcriptomic analysis of gastric adenocarcinoma using The Cancer Genome Atlas (TCGA) RNA-sequencing dataset (n = 623, 8863 genes), matched with standardized clinical metadata (n = 413). Differential gene expression between survival groups was assessed using Welch’s t-test with Benjamini–Hochberg correction (FDR < 0.05, |log₂FC| ≥ 1). High-confidence gene sets for obesity (n = 128) and hypercholesterolemia (n = 97) were curated from the OMIM, STRING (confidence ≥ 0.7), and KEGG databases using hierarchical evidence-based prioritization. Overlapping gene signatures were identified, followed by pathway enrichment via Enrichr (KEGG 2021 Human) and protein–protein interaction (PPI) analysis using STRING v11.5 and Cytoscape v3.9.0. CD36’s prognostic value was evaluated via Kaplan–Meier and log-rank testing alongside clinicopathological correlations. Results: We identified 36 genes shared between obesity and gastric cancer, and 31 genes shared between hypercholesterolemia and gastric cancer. CD36 emerged as the only gene intersecting all three conditions, marking it as a unique molecular integrator. Enrichment analyses implicated dysregulated fatty acid uptake, adipocytokine signaling, cholesterol metabolism, and NF-κB-mediated inflammation as key pathways. Elevated CD36 expression was significantly correlated with higher tumor stage (p = 0.016), reduced overall survival (p = 0.001), and race-specific expression differences (p = 0.007). No sex-based differences in CD36 expression or survival were observed. Conclusions: CD36 is a central metabolic–oncogenic node linking obesity, hypercholesterolemia, and gastric cancer. It functions as both a mechanistic driver of tumor progression and a clinically actionable biomarker, particularly in metabolically comorbid patients. These findings provide a rationale for targeting CD36-driven pathways as part of a precision oncology strategy and highlight the need to incorporate metabolic profiling into gastric cancer risk assessment and treatment paradigms. Full article

The TRIM CIV subfamily, distinguished by its C-terminal PRY-SPRY domains, constitutes nearly half of the human TRIM family and plays pivotal roles in cancer progression through ubiquitination. Identifying TRIM CIV substrates and interactors has emerged as a critical approach for elucidating tumorigenesis. Current protein–protein interaction (PPI) prediction models face challenges, including an inherent deficiency of negative datasets, biased feature integration, and the absence of a cancer-specific interaction context. To achieve the precise identification of TRIMCIV targets, we developed TRIMCIVtargeter with predictive models that systematically integrates multi-dimensional PPI features—expression differences and correlations in specific cancer, comparable protein-docking scores, and cancer-specific context. Learning from the functional and structural interaction features between 718 experimentally validated TRIM–target pairs, two types of SVM-based binary models were independently trained using proteomic and transcriptomic data. Our models achieved robust prediction performance in cancers utilizing a fair feature space and circumventing hypothetical non-interacting pairs. TRIMCIVtargeter not only provides a cancer-related resource for studying TRIMCIV-mediated regulatory mechanisms but also offers a new perspective for family-specific PPI prediction, holding significant implications for biomarker discovery and therapeutic targeting in oncology. The online platform of TRIMCIVtargeter is now available. Full article

Background: BCR-ABL inhibitors such as imatinib and nilotinib exhibit multi-kinase activity that extends beyond oncology, offering significant potential for drug repurposing. Objectives: This study aims to systematically evaluate and prioritize the repurposing potential of BCR-ABL inhibitors, particularly imatinib and nilotinib. Methods: An integrated pharmacoinformatics framework was applied to analyze seven BCR-ABL inhibitors. Structural clustering, cheminformatics analysis, and transcriptomic profiling using the Connectivity Map were employed to evaluate structural relationships, target profiles, and gene expression signatures associated with non-oncology indications. Results: Structurally, imatinib and nilotinib clustered closely, while HY-11007 exhibited distinct features. Nilotinib’s high selectivity correlated with strong transcriptional effects in neurodegeneration-related pathways (e.g., HSP90 and LYN), whereas imatinib’s broader kinase profile (PDGFR and c-KIT) was linked to fibrosis and metabolic regulation. Connectivity Map analysis identified more than 30 non-cancer indications, including known off-target uses (e.g., imatinib for pulmonary hypertension) and novel hypotheses (e.g., nilotinib for Alzheimer’s via HSPA5 modulation). A substantial portion of these predictions aligned with the existing literature, underscoring the translational relevance of the approach. Conclusions: These findings highlight the importance of integrating structure–activity relationships and transcriptomic signatures to guide rational repurposing. We propose prioritizing nilotinib for CNS disorders and imatinib for systemic fibrotic diseases, supporting their advancement into preclinical and clinical evaluation. More broadly, this framework offers a versatile platform for uncovering hidden therapeutic potential across other drug classes with complex polypharmacology. Full article

The integration of artificial intelligence (AI) into next-generation sequencing (NGS) has revolutionized genomics, offering unprecedented advancements in data analysis, accuracy, and scalability. This review explores the synergistic relationship between AI and NGS, highlighting its transformative impact across genomic research and clinical applications. AI-driven tools, including machine learning and deep learning, enhance every aspect of NGS workflows—from experimental design and wet-lab automation to bioinformatics analysis of the generated raw data. Key applications of AI integration in NGS include variant calling, epigenomic profiling, transcriptomics, and single-cell sequencing, where AI models such as CNNs, RNNs, and hybrid architectures outperform traditional methods. In cancer research, AI enables precise tumor subtyping, biomarker discovery, and personalized therapy prediction, while in drug discovery, it accelerates target identification and repurposing. Despite these advancements, challenges persist, including data heterogeneity, model interpretability, and ethical concerns. This review also discusses the emerging role of AI in third-generation sequencing (TGS), addressing long-read-specific challenges, like fast and accurate basecalling, as well as epigenetic modification detection. Future directions should focus on implementing federated learning to address data privacy, advancing interpretable AI to improve clinical trust and developing unified frameworks for seamless integration of multi-modal omics data. By fostering interdisciplinary collaboration, AI promises to unlock new frontiers in precision medicine, making genomic insights more actionable and scalable. Full article

The IgLON family of tumor suppressor genes (TSG) impact a variety of cellular processes involved in cancer and non-cancer biology. OPCML is a member of this family and its inactivation is an important control point in oncogenesis and tumor growth. Here, we analyze RNA-Seq expression ratios in ovarian cancers from The Cancer Genome Atlas (TCGA) (189 subjects at Stage III) to identify genes that exhibit a cooperative survival impact (via Kaplan–Meier survival curves) with OPCML expression. Using enrichment analyses, we reconstruct functional pathway impacts revealing interactions of OPCML, and then validate these in independent cohorts of ovarian cancer. These results emphasize the role of OPCML’s regulation of receptor tyrosine kinase (RTK) signaling pathways (PI3K/AKT and MEK/ERK) while identifying three new potential RTK transcriptomic linkages to KIT, TEK, and ROS1 in ovarian cancer. We show that other known extracellular signaling receptor ligands are also transcriptionally linked to OPCML. Several key genes were validated in GEO datasets, including KIT and TEK. Considering the range of OPCML impacts evident in our analyses on both external membrane interactions and cytosolic signal transduction, we expand the understanding of OPCML’s broad cellular influences, demonstrating a multi-functional, pleiotropic, tumor suppressor, in keeping with prior published studies of OPCML function. Full article

Cancer progression is a complex, multi-stage development process characterized by dynamic changes at the molecular level. Understanding these changes may provide new insights into tumorigenesis and potential therapeutic targets. This study focuses on the evolutionary transcriptomics of cancer, specifically analyzing the Transcriptome Age Index (TAI) across different pathological stages. By examining various cancers at four distinct pathological stages, we identify a significant «hourglass» pattern in TAI indices of ductal carcinoma of the breast, bladder carcinoma, and liver carcinoma, suggesting a conserved evolutionary trajectory during tumor development. The results reveal that early and late stages of these cancers exhibit higher TAI values, indicative of more novel gene expression, while intermediate stages show a dip in TAI, reflecting a more ancient evolutionary origin of expressed genes. This «hourglass» pattern underscores the evolutionary constraints and innovations at play during tumor progression. Our findings contribute to the growing body of evidence that evolutionary principles are deeply embedded in cancer biology, offering new perspectives on the dynamics of gene expression in tumors. Full article