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PubMed2026

Development of a Computational Histology Artificial Intelligence-Powered Prognostic Biomarker in Colorectal Cancer in The Cancer Genome Atlas.

BACKGROUND: Risk stratification in colorectal cancer (CRC) plays an important role in treatment decision-making. As such, prognostic biomarkers that can augment risk stratification have clinical value. Quantitative histologic features from routine hematoxylin and eosin (H&E)-stained whole slide images (WSIs) provide a novel avenue for biomarker discovery. In this study, we explored the potential for a computational histology artificial intelligence (CHAI) platform to develop and validate a prognostic biomarker in CRC. METHODS: The Cancer Genome Atlas Colorectal Adenocarcinoma project was utilized for this study, with inclusion of all subjects (stage I-IV) with available digitized H&E specimens. The cohort was split into development and validation cohorts by a stratified random split. The previously developed CHAI platform was applied in the development cohort to construct a continuous risk score from histologic features associated with progression-free interval (PFI) that was dichotomized based on an optimized cutpoint for distinguishing PFI into a high risk CHAI (+) and lower risk CHAI (-). PFI was compared between CHAI (+) and CHAI (-) patients in the validation cohort in multivariable Cox proportional hazards models. Time-dependent area under the curve (tdAUC) and C-indices were also calculated for PFI. RESULTS: A total of 583 participants were included in the study, with 409 assigned to the validation cohort. The CHAI biomarker classified 229 participants (56%) as CHAI (+) and 180 (44%) as CHAI (-) in the validation set. CHAI (+) participants had worse PFI in a multivariable analysis adjusting for available clinicopathologic variables (hazard ratio (HR) = 2.65; 95% confidence interval (CI), 1.63-4.30). TdAUC for the CHAI biomarker was 0.60 (95% CI, 0.53-0.67) at 12 months, 0.62 (0.55-0.69) at 36 months, and 0.67 (0.55-0.79) at 60 months; the C-index was 0.62 (95% CI, 0.58-0.67). CONCLUSIONS: The CHAI platform was used to develop a prognostic digital pathology biomarker in CRC. This demonstrates the feasibility and potential to apply this artificial intelligence-based digital pathology biomarker platform for risk stratification in CRC and supports its further study.

Artificial intelligence↗

PubMed2026

OLFML2B promotes hepatocellular carcinoma malignancy via the PI3K/AKT-EMT axis and correlates with an immunosuppressive tumor microenvironment.

INTRODUCTION: Hepatocellular carcinoma (HCC) is a leading cause of global cancer-related mortality, highlighting the need for novel biomarkers and therapeutic targets. METHODS: The role of Olfactomedin-like 2B (OLFML2B) in HCC was investigated through multi-database analyses (The Cancer Genome Atlas, International Cancer Genome Consortium, Gene Expression Omnibus) and experimental validation. RESULTS: OLFML2B was significantly upregulated in HCC tissues, correlated with poor overall and disease-specific survival, clinicopathological features (tumor grade, stage, age, gender), and robust diagnostic performance (AUC > 0.7 across 14/15 datasets). Transcriptomic and single-cell analyses further revealed that high OLFML2B expression was associated with an immunosuppressive tumor microenvironment, characterized by increased infiltration of M2 macrophages, cancer-associated fibroblasts (CAFs), and regulatory T cells (Tregs), as well as reduced abundance of cytotoxic T cells and NK cells. Knockdown of OLFML2B suppressed malignant phenotypes, including cell proliferation, migration, invasion, and angiogenesis, attenuated PI3K/AKT-EMT signaling, and enhanced sensitivity to sorafenib, cabozantinib, and regorafenib in Huh7 and HepG2 cells. Additionally, OLFML2B knockdown suppressed tumor growth and metastasis in zebrafish xenografts. DISCUSSION: Collectively, these findings indicate that OLFML2B is required for HCC progression and represents a prognostic biomarker and potential therapeutic target.

Humans↗

PubMed2025

Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-Based Tool.

Accurate recurrence risk stratification is crucial for optimizing treatment plans for breast cancer patients. Current prognostic tools like Oncotype DX offer valuable genomic insights into hormone receptor-positive and human epidermal growth factor receptor-negative patients but are limited by cost and accessibility, particularly in underserved populations. In this study, we present Deep-Breast-Cancer-Recurrence (BCR)-Auto, a deep learning-based computational pathology approach that predicts breast cancer recurrence risk from routine hematoxylin and eosin-stained whole slide images. Our methodology was validated on 2 independent cohorts: The Cancer Genome Atlas Program breast cancer data set and an in-house data set from The Ohio State University. Deep-BCR-Auto demonstrated robust performance in stratifying patients into low- and high-recurrence risk categories. On The Cancer Genome Atlas Program breast cancer data set, the model achieved an area under the receiver operating characteristic curve of 0.827, significantly outperforming the existing weakly supervised models (P = .041). In the independent The Ohio State University data set, Deep-BCR-Auto maintained strong generalizability, achieving an area under the receiver operating characteristic curve of 0.832, along with 82.0% accuracy, 85.0% specificity, and 67.7% sensitivity. These findings highlight the potential of computational pathology as a cost-effective alternative for recurrence risk assessment, broadening access to personalized treatment strategies. This study underscores the clinical utility of integrating deep learning-based computational pathology into routine pathological assessment for breast cancer prognosis across diverse clinical settings.

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

Serum, Cell-Free, HPV-Human DNA Junction Detection and HPV Typing for Predicting and Monitoring Cervical Cancer Recurrence.

Almost all cervical cancers are caused by human papillomaviruses (HPVs). In most cases, HPV DNA is integrated into the human genome. We found that tumor-specific, HPV-human DNA junctions are detectable in serum cell-free DNA of a fraction of cervical cancer patients at the time of initial treatment and/or at six months following treatment. Retrospective analysis revealed these junctions were more frequently detectable in women in whom the cancer later recurred. We also found that cervical cancers caused by HPV types outside of phylogenetic clade α9 had a higher recurrence frequency than those caused by α9 types in both our study and The Cancer Genome Atlas cervical cancer database, despite the higher prevalence of α9 types including HPV16 in cervical cancer. Thus, HPV-human DNA junction detection in serum cell-free DNA and HPV type determination in tumor tissue may help predict recurrence risk. Screening serum cell-free DNA for junctions may also offer an unambiguous, non-invasive means to monitor absence of recurrence following treatment.

DNA integration↗

PubMed2023

Intratumor childhood vaccine-specific CD4+ T-cell recall coordinates antitumor CD8+ T cells and eosinophils.

BACKGROUND: Antitumor mechanisms of CD4+ T cells remain crudely defined, and means to effectively harness CD4+ T-cell help for cancer immunotherapy are lacking. Pre-existing memory CD4+ T cells hold potential to be leveraged for this purpose. Moreover, the role of pre-existing immunity in virotherapy, particularly recombinant poliovirus immunotherapy where childhood polio vaccine specific immunity is ubiquitous, remains unclear. Here we tested the hypothesis that childhood vaccine-specific memory T cells mediate antitumor immunotherapy and contribute to the antitumor efficacy of polio virotherapy. METHODS: The impact of polio immunization on polio virotherapy, and the antitumor effects of polio and tetanus recall were tested in syngeneic murine melanoma and breast cancer models. CD8+ T-cell and B-cell knockout, CD4+ T-cell depletion, CD4+ T-cell adoptive transfer, CD40L blockade, assessments of antitumor T-cell immunity, and eosinophil depletion defined antitumor mechanisms of recall antigens. Pan-cancer transcriptome data sets and polio virotherapy clinical trial correlates were used to assess the relevance of these findings in humans. RESULTS: Prior vaccination against poliovirus substantially bolstered the antitumor efficacy of polio virotherapy in mice, and intratumor recall of poliovirus or tetanus immunity delayed tumor growth. Intratumor recall antigens augmented antitumor T-cell function, caused marked tumor infiltration of type 2 innate lymphoid cells and eosinophils, and decreased proportions of regulatory T cells (Tregs). Antitumor effects of recall antigens were mediated by CD4+ T cells, limited by B cells, independent of CD40L, and dependent on eosinophils and CD8+ T cells. An inverse relationship between eosinophil and Treg signatures was observed across The Cancer Genome Atlas (TCGA) cancer types, and eosinophil depletion prevented Treg reductions after polio recall. Pretreatment polio neutralizing antibody titers were higher in patients living longer, and eosinophil levels increased in the majority of patients, after polio virotherapy. CONCLUSION: Pre-existing anti-polio immunity contributes to the antitumor efficacy of polio virotherapy. This work defines cancer immunotherapy potential of childhood vaccines, reveals their utility to engage CD4+ T-cell help for antitumor CD8+ T cells, and implicates eosinophils as antitumor effectors of CD4+ T cells.

Mice↗

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/β-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↗

PubMed2026

Senescent fibroblasts drive CD8+ T cell dysfunction in colorectal cancer via CD36-mediated lipid transfer and peroxidation.

BACKGROUND: Functional exhaustion of tumor-infiltrating CD8+ T cells represents a hallmark of colorectal cancer (CRC) immunosuppression, though its mechanistic drivers remain elusive. Given the established correlation between CRC progression and stromal senescence characterized by pathological lipid accumulation and impaired immunity, we investigated whether and how senescent fibroblasts actively regulate CD8+ T cell dysfunction. METHODS: Single-cell RNA sequencing (scRNA-seq) analysis was conducted to unveil the diverse fibroblast populations and the significant lipid metabolism changes between senescent fibroblasts and non-senescent fibroblasts in human CRC specimens and adjacent normal mucosa. Machine-learning identified senescent fibroblasts with a distinct gene signature. Cell-cell communication analysis was used to evaluate the interactions between senescent fibroblasts and CD8+ T cells in colorectal cancer. Co-culture experiments were conducted among senescent fibroblasts, CD8+ T cells and patient-derived organoids of CRC (CRC-PDOs), with the results evaluated with high-content imaging and propidium iodide/Hoechst 33,342 staining. Flow cytometry, ELISA and lipid pulse-chase with BODIPY FL C16 were performed to detect the alterations of CD8+ T cell cytotoxic function and metabolic status. AOM/DSS-induced CRC mouse model was used to conduct in vivo validation to evaluate whether senolytics could suppress CRC progression. Patients from the Cancer Genome Atlas colorectal cancer cohort were stratified into CD36-high and CD36-low groups by median expression, and drug sensitivity for GDSC2 compounds was predicted computationally using the oncoPredict R package. RESULTS: ScRNA-seq demonstrated the specific cell population presence and divergence of senescent fibroblasts between neoplastic and histologically normal adjacent cell clusters in CRC. Random Forest was employed for cell senescence classification. Feature importance analysis identified five genes as key contributors to the model’s decision process. Cell-cell communication analysis revealed enhanced interactions between senescent fibroblasts and CD8+ T cells in CRC. Co-culture of senescent fibroblasts significantly impaired the cytotoxic functions of CD8+ T cells on CRC-PDOs, which was reflected by the declined proportions of granzyme B (GZMB) + and interferon gamma (IFNγ) + CD8+ T cells and enhanced viability of CRC-PDOs. Mechanistically, the co-culture with senescent fibroblasts promoted the lipid shuttling into CD8+ T cells to induce lipid peroxidation and downstream impairment of cytotoxicity. Furthermore, the inhibition of CD36, the specific scavenger receptor for lipid uptake of CD8+ T cells, effectively suppressed lipid transfer and peroxidation thereby preserving the effector functions of CD8+ T cells and ultimately promoting tumor apoptosis. Complementarily, in vivo senolytic treatment significantly suppressed CRC progression in AOM-DSS CRC mouse models. Top 12 therapeutic agents were identified significantly enhanced predicted efficacy in CD36-high tumors. CONCLUSIONS: Our study identified a substantial population of senescent fibroblasts in human CRC through single cell transcriptomics, machine-learning and clinical biopsies. These senescent fibroblasts impair CD8+ T cell-mediated killing of CRC-PDOs via CD36-dependent lipid transfer, suggesting senolytic targeting of stromal cells as a promising immunotherapeutic strategy for CRC.

Colorectal Neoplasms↗

PubMed2026

NPLOC4 Constructs Tumor Immunosuppressive Microenvironment in Pan-cancer and Hepatocellular Carcinoma.

INTRODUCTION: NPLOC4 (nuclear protein localization 4 homolog) is mainly involved in DNA damage, cell cycle, and ubiquitination promotion. Nonetheless, the role of NPLOC4 in the tumor immune microenvironment (TIME) and its potential as a promising tumor therapeutic target remains unclear. METHODS: Therefore, analyses of NPLOC4 mRNA and protein expression, RNA subcellular localization, and patient prognosis associated with NPLOC4 expression were conducted across multiple tumor types. Additionally, the correlations between NPLOC4 and immune cells, non-immune cells, and immune molecules within the tumor immune microenvironment (TIME) were investigated. These analyses utilized data from various public resources, including the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), The Human Protein Atlas (HPA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), TIMER2.0, KM-Plotter, The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN), and Tumor Immune Single-cell Hub 2 (TISCH2). Subsequently, we utilized hepatocellular carcinoma (HCC) patients' cancer and adjacent tissues plus tumor cell lines to verify the differential RNA and protein expression of NPLOC4 via qRT-PCR and immunohistochemistry (IHC). Then, the relationship of NPLOC4 expression level with immune infiltration score, infiltration of effector immune cells, suppressive immune cells, and several vital immune checkpoints was analyzed in HCC immune microenvironment. Furthermore, the distribution of expression of NPLOC4 in various cells in the HCC microenvironment was determined through single-cell sequencing analysis. RESULTS: We discovered that NPLOC4 was up-regulated in a variety of tumors and was correlated with poor prognosis. NPLOC4 not only had the potential as a tumor prognostic marker and therapeutic target but also was strongly linked to immune cells, immune checkpoints, and immune-related molecules and pathways in HCC immune microenvironment. CONCLUSION: In summary, NPLOC4 may serve as a promising target for immunotherapy.

Humans↗

PubMed2026

Serum, cell-free, HPV-human DNA junction detection and HPV typing for predicting and monitoring cervical cancer recurrence.

Almost all cervical cancers are caused by human papillomaviruses (HPVs). In most cases, HPV DNA is integrated into the human genome. We found that tumor-specific, HPV-human DNA junctions are detectable in serum cell-free DNA of a fraction of cervical cancer patients at the time of initial treatment and/or at 6 months following treatment. Retrospective analysis revealed these junctions were more frequently detectable in women in whom the cancer later recurred. We also found that cervical cancers caused by HPV types outside of phylogenetic clade α9 had a higher recurrence frequency than those caused by α9 types in both our study and The Cancer Genome Atlas cervical cancer database, despite the higher prevalence ofα9 types, including HPV16, in cervical cancer. Thus, HPV-human DNA junction detection in serum cell-free DNA and HPV type determination in tumor tissue may help predict recurrence risk. Screening serum cell-free DNA for junctions may also offer an unambiguous non-invasive means to monitor absence of recurrence following treatment.

Humans↗

PubMed2026

Prognostic value and immune landscape implications of using a novel homologous recombination repair pathway signature in prostate cancer: A retrospective cohort study.

ObjectiveAlthough the homologous recombination repair (HRR) pathway plays a critical role in the treatment of prostate cancer, its prognostic value remains incompletely understood. This study aimed to identify HRR pathway-related biomarkers with clinical utility for prognosis prediction and treatment guidance.MethodsWe analyzed genomic data from The Cancer Genome Atlas and Chinese patients with prostate cancer in a retrospective cohort study using a comprehensive multiomics approach to characterize a novel HRR-related prognostic signature and its immune implications.ResultsIn the Chinese cohort, 25.6% of the patients exhibited homologous recombination deficiency scores >42, whereas 27.3% carried ≥1 HRR gene mutation. We established a prognostic HRR signature (homologous recombination deficiency score >32, HRR gene mutations, and Signature 3) associated with poor outcomes. Compared with The Cancer Genome Atlas data, the Chinese cohort demonstrated a higher prevalence of HRR signature. Patients with HRR signatures demonstrated significantly increased genomic instability markers, including segment number, alteration burden, aneuploidy score, and intratumor heterogeneity. The HRR signature was associated with higher neoantigen load but reduced T cell receptor (TCR) evenness. Immunologically, HRR-positive tumors were associated with computationally inferred immune profiles suggestive of reduced immune activity, characterized by depletion of T-helper 17 cell; downregulation of TLR4/PDCD1LG2 expression; and upregulation of ARG1, IFNG, KIR2DL3, and CXCL9. However, these findings are descriptive and require experimental validation.ConclusionOur findings identify a clinically relevant HRR signature that warrants investigation as a potential predictive biomarker for prostate cancer prognosis and treatment response. This biomarker provides new insights for personalized therapy and may help optimize patient outcomes.

Humans↗

PubMed2026

Alternative End Joining Dependency Imposed by miR-21-5p Defines Radiation Resistance and a Targetable Vulnerability in Oral Squamous Cell Carcinoma.

PURPOSE: Clinical control of oral squamous cell carcinoma (OSCC) is constrained by heterogeneous radiosensitivity driven by divergent DNA damage response programs. The architecture and functional contribution of alternative end joining (Alt-EJ), an error-prone DNA double-strand break (DSB) repair pathway frequently upregulated in cancer, to radiation resistance remains poorly defined. METHODS AND MATERIALS: We profiled microRNAs in radioresistant OSCC clones and performed multiomic integration across an institutional OSCC cohort, an external OSCC cohort from the Gene Expression Omnibus, The Cancer Genome Atlas pan-cancer tumors, and cell lines characterized by Sanger Genomics of Drug Sensitivity in Cancer to infer DNA damage response characteristics, genomic scar features, drug sensitivity, and radiation therapy outcomes. DSB repair capacity and pathway usage were validated using functional assays, including Alt-EJ reporters and droplet digital PCR quantification of microhomology-mediated repair events. Core Alt-EJ effectors such as PARP1 and POLQ were perturbed genetically and pharmacologically. Therapeutic efficacy of PARP or POLQ inhibition with or without irradiation was tested in a syngeneic OSCC model, followed by bulk tumor transcriptomics to assess pathway engagement. RESULTS: Upregulation of miR-21-5p was not only selectively detected in radioresistant OSCC, but also modulated radiosensitivity in vitro and in vivo, and was associated with inferior postradiation therapy survival. A calibrated miR-21-5p target-gene signature tracked Alt-EJ activity across patient and mouse tumors and cancer cell lines, correlated with microhomology-mediated indels and broader genomic scarring, and predicted sensitivity to clinically available PARP inhibitors. Functionally, enforced miR-21-5p expression increased Alt-EJ usage and accelerated DSB repair, whereas inhibition or depletion of key Alt-EJ effectors reduced repair efficiency and restored radiosensitivity. In vivo, Alt-EJ targeting with PARP or POLQ inhibitor abrogated miR-21-5p-driven radiation resistance; transcriptomic profiling supported suppression of Alt-EJ programs as the operative mechanism. CONCLUSIONS: These findings establish a mechanistic link between miR-21-5p activity and Alt-EJ dependence, provide a clinically deployable signature to identify Alt-EJ-dependent OSCC, and support rational combinations of Alt-EJ targeting agents with radiation therapy to overcome treatment failure and advance precision radiation oncology.

MicroRNAs↗

PubMed2026

Kynurenine metabolism-related gene signature for prognostic stratification in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a major global health burden with high mortality rates and limited therapeutic options. The identification of reliable biomarkers for early diagnosis and prognosis prediction is urgently needed. Kynurenine metabolism, a critical pathway in immune regulation and tumor progression, has been implicated in various cancers. However, its prognostic value in HCC has not been fully elucidated. This study aimed to develop a prognostic risk model based on kynurenine metabolism-related genes (KMRGs) for HCC patients. METHODS: Transcriptomic and clinical data of HCC patients were retrieved from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Survival analysis and functional enrichment analysis were conducted to validate the predictive performance of the model and to investigate the underlying mechanisms. ALDH8A1 was ultimately identified as a target gene based on survival analysis, and its impact on tumor cell migration was assessed using the HCC cell line. RESULTS: A prognostic model based on seven KMRGs was established. The high-risk group exhibited significantly worse overall survival compared to the low-risk group. Functional enrichment analysis in high-risk patients highlighted significant enrichment in core biological processes, including spliceosome assembly and ribonucleoprotein complex biogenesis. Furthermore, a nomogram integrating the risk score and clinical pathological features was developed, demonstrating moderate predictive performance for HCC prognosis. CONCLUSIONS: This study successfully constructed a prognostic risk model based on seven KMRGs, providing a valuable tool for predicting clinical outcomes in HCC patients. These findings highlight the potential role of kynurenine metabolism in HCC progression and offer new insights for future therapeutic strategies.

ALDH8A1↗

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↗

PubMed2025

PKCζ, CTNNBIP1 and ALDH1A3 Expression in Luminal B Breast Cancer Indicates Decreased Hormone Therapy Effectiveness.

BACKGROUND/AIM: The role of catenin β interacting protein 1 (CTNNBIP1), a negative regulator of the canonical Wnt/β-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 ζ (PKCζ) 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 ζ 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 ζ , CTNNBIP1 and ALDH1A3 expression contributed to decreased effectiveness of hormone therapy in patients with luminal B breast cancer. CONCLUSION: PKC ζ and CTNNBIP1 may be involved in the progression of ALDH1A3-positive luminal B breast cancer. In luminal B breast cancer, PKC ζ , 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

Deep learning-based cross-attention fusion of multimodal MRI for survival prediction and risk stratification in IDH-wildtype glioblastoma: a multicenter study.

BACKGROUND: Glioblastoma (GBM) exhibits profound molecular and spatial heterogeneity, complicating prognostic evaluations. While multiparametric MRI provides crucial multidimensional biological information, conventional end-to-end deep learning integration strategies, such as early or late fusion, often fail to capture complex nonlinear cross-modal interactions. We aimed to systematically evaluate a cross-attention fusion (CAF) architecture for GBM survival prediction and quantify its incremental prognostic value relative to existing clinical tools. METHODS: In this multicenter retrospective study, 386 adults with IDH-wildtype, WHO grade 4 GBM were assembled from an institutional cohort (n = 226), the Chinese Glioma Genome Atlas (CGGA, n = 62), and The Cancer Genome Atlas (TCGA, n = 98). Using a unified 3D ResNet-18 backbone, we compared single-modality models, early fusion, late fusion, and CAF on preoperative T1-weighted, contrast-enhanced T1-weighted (T1CE), and T2-weighted MRI, and integrated the resulting deep learning risk score with routine clinical variables through multivariable Cox regression. Performance was assessed using Harrell's C-index, time-dependent AUC, and decision curve analysis. RESULTS: CAF showed numerically higher, more consistent C-index trends than early fusion, late fusion, and single-modality models (pooled C-index 0.629, 95% CI 0.594-0.664), although pairwise differences in time-dependent AUC were not statistically significant. Integrating clinical variables raised the pooled C-index to 0.691 (95% CI 0.660-0.721) in the treatment-era model, with comparable performance across the three cohorts (Local 0.688; CGGA 0.716; TCGA 0.689); a pre-treatment configuration excluding adjuvant therapy yielded a pooled C-index of 0.642. Under leave-one-cohort-out external validation, the combined model retained significant risk stratification in all held-out cohorts (C-index 0.63-0.71; all log-rank P < 0.01), albeit with attenuated discrimination. The deep learning risk score remained independent after multivariable adjustment (HR 1.41 per SD, 95% CI 1.26-1.57; P < 0.001). Kaplan-Meier analysis confirmed significant high- versus low-risk separation in all cohorts, and decision curve analysis showed greater net benefit than clinical-only and deep-learning-only models. CONCLUSION: The CAF-derived risk score offers prognostic information complementary to routine clinical variables, representing a promising noninvasive tool for individualized risk stratification when molecular profiling is incomplete or unavailable; these findings warrant prospective external validation before clinical use.

cross-attention fusion↗