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PubMed2026

Resveratrol Attenuates Gemcitabine Resistance in Hepatocellular Carcinoma Cells by Inhibiting Thymidylate Synthase.

BACKGROUND: Hepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide. Gemcitabine (Gem) is a commonly used drug against HCC, but its efficacy is limited by the development of resistance. Resveratrol (Res), a natural polyphenol with antitumor activity, may reverse Gem resistance in HCC, although the mechanism remains unclear. METHODS: The effects of Res on the proliferation, apoptosis, cell cycle, and invasion of Hep3B and HuH-7 cells were assessed via cell counting kit-8 (CCK-8), clonogenic, flow cytometry, and Transwell assays, respectively. Potential Res targets were predicted by network pharmacology, and markers of HCC prognosis were identified from the cancer genome atlas (TCGA) data. The interaction between Res and thymidylate synthase (TYMS) was validated by molecular docking and dynamics simulation. A Gem-resistant HuH-7 cell line (HuH-7/GR) was established, and when these cells were treated with Res combined with Gem, the effect on Gem sensitivity was detected by CCK-8 assay, clonogenic assay, and flow cytometry. Finally, a subcutaneous nude mouse model of HCC was used to evaluate the in vivo effects of Res combined with Gem. RESULTS: Res inhibited HCC cell proliferation, induced apoptosis and G2/M arrest, and suppressed invasion in a concentration-dependent manner. Network pharmacology and TCGA analysis identified TYMS as an important target gene for Res. TYMS was highly expressed in HCC tissues and correlated with poor prognosis. Res treatment reduced TYMS expression, while molecular docking and simulation showed stable binding of Res to TYMS. TYMS levels were elevated in HuH-7/GR resistant cells. Res combined with Gem was found to reverse drug resistance, inhibit proliferation and colony formation, and induce apoptosis. The Res + Gem combination group showed the smallest tumor volume in the in vivo model. CONCLUSION: By attenuating Gem resistance through TYMS inhibition, Res holds promise as a clinically viable adjunct to Gem-based chemotherapy, offering a potential strategy to improve outcomes in HCC patients.

Resveratrol↗

PubMed2026

Integrative single-cell and genomic analysis reveals NMB as a driver of metastatic adaptation in esophageal squamous cell carcinoma via metabolic rewiring and immune evasion.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) has high mortality, and metastasis is the leading cause of patient death. Neuromedin B (NMB) promotes tumor development in various cancers, yet its role in ESCC metastasis remains unclear. METHODS: We integrated single-cell transcriptomic data from matched primary and metastatic ESCC lesions (GSE309392) with bulk transcriptomic cohorts from TCGA and GSE53624. In silico gene perturbation, ligand-receptor communication analysis, and single-cell prognostic model construction were performed, followed by functional validation through siRNA-mediated NMB knockdown in TE-1 and KYSE30 cell lines. RESULTS: NMB was identified as a key gene enriched in metastatic ESCC lesions, and its high expression was associated with coordinated upregulation of oxidative phosphorylation pathway genes and aldo-keto reductase family antioxidant enzymes (AKR1C1, AKR1C2, AKR1B10). Genomic analysis revealed that NMB-high tumors carried a higher clonal mutation burden and a markedly increased frequency of NFE2L2 activating mutations (23% vs. 8%, P = 0.04). In silico knockout and correlation analysis identified AKR1C1 as a downstream effector of NMB. NMB expression was negatively correlated with CD8+ T cell and activated NK cell infiltration. CellChat analysis revealed communication between NMB-positive cells and monocytes via the TGM2-ADGRG1 axis, and specifically detected IFNG signaling. In the single-cell prognostic model, NMB-positive cells accounted for 50% of the high-risk group but only 20% of the low-risk group. TCGA-based survival analysis demonstrated that high NMB expression was associated with shorter overall survival (HR = 2.98, P = 0.03). In vitro NMB-targeted RNA interference markedly inhibited proliferation, colony formation, and migration in TE-1 and KYSE30 cells. CMap screening identified the endothelin-PDE5-cGMP axis as a potential therapeutic target. CONCLUSION: NMB serves as a key driver of metastatic adaptation in ESCC, conferring a survival advantage to tumor cells during metastatic colonization through genomic evolution and immune remodeling, with metabolic adaptation as a downstream consequence of genomic alterations.

NMB↗

PubMed2026

Lymphangiogenesis-related gene signature-based risk model for prognostic assessment of cervical cancer: immune-metabolic characterization and molecular subtype analysis.

BACKGROUND: Lymphangiogenesis promotes tumor dissemination and may shape the immune contexture of cervical cancer, yet lymphangiogenesis-related prognostic stratification and its immunometabolic implications remain insufficiently defined in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). METHODS: TCGA-CESC transcriptomes and clinical data were obtained from UCSC Xena and integrated with normal cervix tissues from the Genotype-Tissue Expression Project after batch correction. Prognostic LYMRGs were first identified from the differentially expressed set using univariable Cox proportional hazards analysis. Candidate genes were then reduced using an L1-regularized Cox model (Least Absolute Shrinkage and Selection Operator), and the remaining markers were entered into a multivariable Cox regression to obtain the final coefficients and compute an individualized risk score. The model's prognostic value was further assessed in an independent Gene Expression Omnibus dataset. In addition, expression patterns of the signature genes were leveraged for molecular subtyping of TCGA samples via non-negative matrix factorization (NMF). Immune infiltration and immunotherapy-associated characteristics were interrogated through a multi-algorithm strategy (single-sample gene set enrichment analysis, CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore . Additional analyses included pathway enrichment (GSEA/GO/KEGG), drug sensitivity prediction (pRRophetic/CellMiner), and ceRNA network analysis. RESULTS: A six-gene LYMRG signature robustly stratified survival. High-risk patients had significantly worse overall survival in The Cancer Genome Atlas with AUCs of 0.819/0.801/0.801 at 1/3/5 years, and in GSE52903 (P = 0.001) with AUCs of 0.733/0.719/0.725. NMF identified two subtypes with distinct prognosis (P = 0.01) and divergent immune landscapes. Risk groups and subtypes exhibited consistent differences in immune infiltration, checkpoint expression, TIDE/IPS patterns, and pathway enrichment. Predicted chemosensitivity differed by risk group, and the ceRNA network suggested candidate upstream lncRNA regulators of the signature. CONCLUSION: A lymphangiogenesis-related six-gene model enables clinically meaningful prognostic stratification of CESC and links lymphangiogenesis programs to distinct tumor immune phenotypes and therapeutic vulnerabilities.

cancer↗

PubMed2026

Integrative subtyping by bile acid metabolism identifies CLCA1/UGT2A3/ZG16 as markers of immune dysfunction and poor prognosis in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is the primary driver of cancer-related death and illness across the world. Despite the full-scale shift of the treatment approach for some colorectal cancer patients due to the use of immune checkpoint inhibitors (ICIs), primary resistance still poses a huge challenge to clinicians. Bile acid metabolism is involved in the pathogenesis of CRC. However, its particular function in shaping the tumor immune microenvironment (TIME) and its effect on prognosis and immune treatment response remain unclear. METHODS: Based on the transcriptome and clinical data from The Cancer Genome Atlas-Colon Adenocarcinoma (TCGA-COAD) cohort, we performed unsupervised consensus clustering and classified patients into different molecular subtypes according to bile acid metabolism. We subsequently compared overall survival (OS), immune cell infiltration levels, and differentially expressed genes among the subtypes. In addition, protein-protein interaction (PPI) network and Cox proportional hazards regression were used to identify key hub genes. Finally, the expression of these crucial hub genes was validated in the Gene Expression Omnibus (GEO) cohort and independent clinical patients. RESULTS: The bile-low group showed a significant reduction in OS time (p = 0.0049). The infiltration levels of CD8+ T cells (p < 0.05) and M1 macrophages (p < 0.01) were significantly higher in the bile-low group than in the bile-high group. We identified three key genes-CLCA1, UGT2A3, and ZG16-and found that they all were downregulated in tumor tissues across the TCGA-COAD and GEO datasets, as well as in independent clinical samples. Survival analysis showed that high CLCA1 expression was significantly associated with favorable overall survival (p < 0.001), whereas UGT2A3 (p = 0.23) and ZG16 (p = 0.17) did not reach statistical significance. The three hub genes were negatively correlated with the (TIDE) score (CLCA1: R = - 0.24, p < 0.001; UGT2A3: R = - 0.15, p = 0.0022; ZG16: R = - 0.14, p = 0.0039). CONCLUSION: Our findings suggest that bile acid metabolism could shape the TIME via key genes CLCA1, UGT2A3, and ZG16, and subsequently modify CRC prognosis and immunotherapy responses. These genes may serve as potential prognostic indicators and mechanistic mediators linking bile acid metabolism to T-cell dysfunction, offering insights for future combination strategies targeting the metabolism-barrier-immunity axis.

CLCA1↗

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↗

PubMed2026

Integrated multi-omics analysis and functional experiments reveals PPAP2C as a potential prognostic biomarker and therapeutic target in breast cancer.

BACKGROUND: This study aims to systematically elucidate the clinical significance and biological function of the phospholipid phosphatase (PLPP) family member (PPAP2C) phosphatidic acid phosphatase type 2C in breast cancer, and to evaluate its potential as a prognostic biomarker and therapeutic target. METHODS: Gene expression data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases were integrated to characterize the expression profile of PLPP family members, focusing on PPAP2C in breast cancer. The prognostic value of PPAP2C, initially identified at the mRNA level (TCGA, (METABRIC) Molecular Taxonomy of Breast Cancer International Consortium, Gene Expression Omnibus (GEO)), was confirmed at the protein level by immunohistochemistry (IHC) on tissue microarrays (TMA). The oncogenic functions of PPAP2C were investigated in triple-negative breast cancer (TNBC) cells through CRISPR-Cas9-mediated knockout and ectopic overexpression, with assessment of key phenotypes including proliferation, colony formation, migration, and invasion. In vivo validation was subsequently performed using an MDA-MB-231 xenograft model. RESULTS: PPAP2C exhibits the most significant overexpression pattern across 33 cancer types (upregulated in 16 cancers, downregulated in only 3). Compared with normal tissues, PPAP2C showed specific overexpression in breast cancer tissues and was significantly associated with advanced clinical stages and aggressive subtypes (HER2+ and TNBC). Survival analysis demonstrated that high PPAP2C expression correlated with significantly shorter overall survival and disease-free survival, which was further validated in METABRIC and GEO cohorts. Tissue microarray analysis confirmed higher PPAP2C protein positivity in tumor tissues (94.7%) than in adjacent normal tissues (59.7%), with worse OS and RFS in high-expression groups. Multivariate analysis identified PPAP2C as an independent prognostic factor for OS. Functional experiments revealed that PPAP2C knockout (via 5-bp/1-bp frameshift mutations) suppressed TNBC cell proliferation, colony formation, migration, and invasion, while overexpression enhanced these phenotypes. In vivo studies further demonstrated complete tumor regression in MDA-MB-231 xenografts upon PPAP2C knockout. CONCLUSION: This study identifies PPAP2C as a key oncogenic driver and a robust independent prognostic biomarker in breast cancer. The findings provide compelling evidence that PPAP2C represents a promising therapeutic target, offering a new strategic avenue for precision therapy, particularly for aggressive breast cancer subtypes.

PLPP2↗

PubMed2026

Amplification-Driven S100A11 Overexpression in Hepatocellular Carcinoma Is Associated with Metabolic Reprogramming, ECM Remodelling, and Immune Evasion: A Pan-Cancer Genomic Study.

BACKGROUND: S100A11, a calcium-binding S100 family protein, is increasingly implicated in carcinogenesis, yet its molecular regulation and clinical relevance across cancers remain unclear. Hepatocellular carcinoma (HCC) carries a dismal prognosis, in part due to a lack of reliable biomarkers for risk stratification of established disease. METHODS: We conducted a pan-cancer analysis of S100A11 genomic alterations across 31 studies (10,767 samples) obtained from TCGA, encompassing copy number alterations, somatic mutations, and DNA methylation. HCC-specific analyses evaluated S100A11 expression, its potential as a diagnostic/prognostic marker, co-expression networks, and pathway enrichment using TCGA-LIHC data, with univariate and multivariate Cox regression to assess survival associations. RESULTS: S100A11 alterations were predominantly driven by copy number amplification, with the highest frequencies in hepatobiliary cancers, lung and breast cancers. Copy number amplification showed a consistent inverse relationship with promoter methylation, indicating amplification-driven transcriptional activation. In HCC, S100A11 was markedly overexpressed compared with normal liver tissue, with strong diagnostic discriminatory capacity. High S100A11 expression was significantly associated with inferior overall survival (log-rank p = 0.032; HR = 1.46, 95% CI 1.03-2.06) and remained an independent predictor of overall survival after adjustment for age, sex, and AJCC pathologic stage (HR = 1.27, 95% CI 1.01-1.60, p = 0.038). Co-expression and pathway analyses demonstrated an association between S100A11 and metabolic reprogramming, extracellular matrix remodelling, and immune dysregulation. CONCLUSIONS: These findings identify S100A11 as a candidate diagnostic and prognostic biomarker in HCC whose overexpression is associated with metabolic reprogramming, ECM remodelling, and immune dysregulation, warranting experimental validation of a mechanistic role.

ECM↗

PubMed2026

AI-Driven Multi-Omics Integration of Synthetic Colon Adenocarcinoma for Cluster-Guided PROTAC Candidate Design Targeting KRASG12D.

Colorectal cancer is a leading cause of cancer death, yet its molecular heterogeneity remains poorly translated into individualized treatment. We present a reproducible artificial intelligence (AI) framework that integrates multi-omics benchmarking, sample-level drug prioritization, E3 ubiquitin ligase selection, and shape-anchored Proteolysis Targeting Chimera (PROTAC) design for KRASG12D in colon adenocarcinoma (COAD). A controlled synthetic benchmark comprising 425 tumor and 41 simulated normal profiles, parameterized to match The Cancer Genome Atlas (TCGA) distributions, was used for pipeline verification. Among sixteen methods, the Balanced Latent Integration with Stability Selection (BLISS) model achieved the highest silhouette width (0.86) and competitive agreement (Adjusted Rand Index, ARI, 0.90). The pipeline was validated on real data: a TCGA COAD cohort (186 tumors) with independent Consensus Molecular Subtype (CMS) labels and a CPTAC cohort (104 tumors). Integration modestly recovered CMS (ARI 0.28), and stage, not molecular cluster, drove survival (log-rank p = 0.005 versus 0.81). Sample-level prioritization differed from cluster-level ranking in 82.6% of profiles, below chance (p < 0.0001), without indicating efficacy. Candidate NOVEL00489 showed a good MM-GBSA estimate, matching the reference ASP3082. Compounds are computational candidates requiring experimental validation. This establishes a transparent benchmark for in silico degrader generation in precision oncology.

Humans↗

PubMed2026

High-fat diet-responsive DNM1 promotes hepatocellular carcinoma progression and predicts poor prognosis in viral-associated patients.

Hepatocellular carcinoma (HCC) arises from diverse etiologies, among which metabolic dysfunction-associated liver disease and chronic viral hepatitis are the two major drivers worldwide. However, the molecular mechanisms linking metabolic stress to HCC progression remain incompletely understood. Dynamin-1 (DNM1), primarily known for its role in vesicular trafficking, has emerged as a potential oncogene, yet its prognostic and functional significance in HCC remains largely unexplored. Here, we investigated the role of DNM1 in high-fat diet (HFD)-associated hepatocarcinogenesis. Transcriptomic profiling was conducted to identify differentially expressed genes between normal and high-fat diet murine models, with human orthologs mapped. Clinical relevance was validated using The Cancer Genome Atlas (TCGA-LIHC) dataset. Survival analysis, GSEA (Gene Set Enrichment Analysis), and subgroup stratifications based on viral hepatitis status were performed. In vitro, loss-of-function assays (shRNA knockdown) were executed in HepG2 and SK-Hep1 cell lines to assess cell viability and migration. DNM1 was significantly upregulated in high-fat diet models. In the TCGA-LIHC cohort, high DNM1 expression was an independent risk factor for poor overall survival (HR=1.44, P=0.039) and correlated with advanced tumor stages (Stage III+IV, P=0.010). In vitro knockdown of DNM1 profoundly impaired cell proliferation and migration in HCC cell lines. Strikingly, DNM1 expression was further elevated in patients with concurrent viral hepatitis (P=0.009). GSEA revealed that high DNM1 expression was positively associated with viral infection pathways and negatively correlated with critical immune responses, including interferon-alpha/gamma responses and host immune cytolysis. Survival analysis stratified by four subgroups demonstrated that patients with both viral infection and high DNM1 expression exhibited the worst prognosis (Overall Log-rank P < 0.001). Our findings identify DNM1 as a high-fat diet-responsive regulator that links metabolic stress to hepatocellular carcinoma progression. Elevated DNM1 expression promotes malignant phenotypes in HCC and identifies a subgroup of viral-associated patients with particularly poor prognosis, highlighting DNM1 as a potential prognostic biomarker and therapeutic target.

Hepatocellular carcinoma (HCC)↗

PubMed1990

Nucleotide sequence of the gene coding for the BanIII DNA methyltransferase in Bacillus aneurinolyticus.

The gene coding for the ATCGAT specific BanIII DNA methyltransferase (M-BanIII) of Bacillus aneurinolyticus was cloned and its nucleotides sequenced. The coding region was assigned on the nucleotide sequence on the basis of the N-terminal amino acid sequence and molecular weight of the enzyme. The M-BanIII gene coded for a protein of 580 amino acid residues (MW 66,344). Comparison with other methylases indicated that the M-BanIII sequence contained a segment of tetra-amino acids, NPPY, characteristic of N6-adenine methylases. In addition, some homologous regions were found in the sequences of type II adenine methylases PaeR7I(CTCGAG), TaqI(TCGA) and PstI(CTGCAG), containing TCGA within the recognition sequences.

Amino Acid Sequence↗

PubMed1992

DNA recognition of base analogue and chemically modified substrates by the TaqI restriction endonuclease.

It has been proposed that protein-DNA recognition is mediated via specific hydrogen bond, hydrophobic, and/or electrostatic interactions between the protein and DNA surfaces. We have attempted to map and quantitate the energies of these interactions for the TaqI endonuclease by constructing substrates substituted with base or phosphate analogues that either remove or sterically obstruct particular functional groups in the canonical TCGA sequence. The DNA backbone was also modified using a chemical approach (phosphate ethylation) which identified several phosphates in the recognition sequence essential for cleavage. The base analogues, N6-methyl-A, N7-deaza-A, N7-deaza-G, inosine, N4-methyl-C, 5-methyl-C, uracil, 5-bromo-U, and the phosphate analogues, alpha-thio-A, alpha-thio-G, alpha-thio-T, alpha-thio-A, were substituted for their corresponding unmodified counterpart in one strand of the TCGA duplex. The effects of these analogues were monitored by measuring the steady state (Km, kcat) and single-turnover (kst) kinetic constants. Only the N6-methyl-A-substituted DNA, which mimics in vivo methylation, was unreactive while the remaining analogue substitutions exhibited Michaelis-Menten kinetics. In general, the Km was either unchanged or lowered by the analogue substitutions. In contrast, many of the analogues severely reduced kcat, suggesting the modified functional groups served mainly to destabilize the transition state. Single-turnover measurements paralleled the kcat results, pointing to the N7 and N6 of A, the N7 of G, and one of the nonbridging oxygens 3' to T as putative contacts made in achieving the transition state. Substrates with double substitutions displayed simple additivity of delta delta G" implying that these changes behaved independently. The unmodified strand in 10 out of 12 hemisubstituted substrates had a normal kst value suggesting that a particular cleavage center is controlled predominantly by recognition of determinants on the same strand as the scissile bond. These results are discussed in relation to base analogue work from the EcoRI, RsrI, and EcoRV restriction endonucleases.

Base Sequence↗

PubMed2025

Anoikis classification of lung squamous cell carcinoma reveals correlation with clinical prognosis and immune characteristics.

BACKGROUND: Anoikis is a new mode of cell death that has been shown to correlate significantly with tumors. However, the clinical prognostic significance of anoikis in lung squamous cell carcinoma (LUSC) remains poorly studied. METHODS: The differentially expressed ARGs and candidate genes were selected by the differential analysis to construct a predictive model. Independent prognostic gene was determined by Cox and LASSO analysis and we used the HCC95 and NCI H520 cell line to verify the gene function. We used the data from TCGA, GEO, GeneCards, and Harmonizome databases to analyze the immune microenvironment, functional enrichment, and drug sensitivity analysis. RESULTS: We identified 717 differentially expressed and selected 3 ARGs (FADD, SNAI1, and BAG4) to construct a predictive model. We found that SNAI1 is an independent prognostic gene and confirmed that knocking out the SNAI1 inhibited the HCC95/NCI H520 cell proliferation. We used single-sample gene-set enrichment analysis (ssGSEA) to evaluate the immune infiltration based on the 3 ARG expression levels. We constructed a risk score and provided a visual representation of the prophetic implications of the ARGs-based signature through a nomogram. We found 15 susceptible drugs in the high-risk group and 15 sensitive drugs in the low-risk group by the drug sensitivity analysis. CONCLUSION: We used ARGs to construct a prognosis model for LUSC that can accurately predict the prognosis of LUSC patients. ARGs, especially SNAI1, play an essential role in developing LUSC. These findings could provide individualized treatment plans and new research ideas for LUSC patients.

Humans↗

PubMed2026

Tumoral switch in NUMB splicing changes essential transcription pathways and induces malignant properties in tumour cells.

BACKGROUND: Emerging evidence indicates that cancer is associated with widespread splicing alterations that generate tumour-specific isoforms. One example is NUMB, an evolutionarily conserved adaptor protein, which produces four isoforms (p72, p71, p66, and p65) through alternative splicing of exons 3 and 9. Although traditionally considered as a tumour suppressor, NUMB has also been reported as an oncogene. We propose that this dual role reflects isoform-specific expression. RESULTS: Using public databases, we identify a tumour-associated switch in NUMB isoform expression: p72 and p71 are upregulated in tumours, whereas p66 and p65 are more highly expressed in non-tumour tissues. These isoforms show distinct associations with key cellular processes. NUMBL, a NUMB homolog, displays expression patterns similar to p65. We further identify two transcriptional clusters: one characterised by high expression of p72 and p71, and the other by enhanced p66/p65/NUMBL expression. These clusters exhibit differential associations with Notch, WNT/β-catenin, Hedgehog, and Hippo signalling pathways, suggesting isoform-specific regulatory roles. In breast cancer cell lines, we develop a NUMB-score based on isoform expression, which classifies cell lines into biologically distinct groups. The p72/p71-enriched group shows distinct signatures, pathway activity, and drug sensitivity. Application of this score to TCGA-BRCA samples reveals a significant link between high NUMB-score and poor survival, as confirmed by Kaplan-Meier analysis. CONCLUSIONS: We find that NUMB emerges as a potential oncogenic contributor and biomarker in the context of splicing-based precision oncology, highlighting Isoform-specific expression as a clinical determinant of tumour behaviour, pathway activity, and therapeutic response.

Humans↗

PubMed2025

Q RadFusion: Hybrid Quantum Classical Radiogenomic Framework for Breast Cancer Diagnosis.

BACKGROUND AND PURPOSE: Breast cancer remains the most common cancer in women worldwide, with early and accurate diagnosis critical for patient survival. Radiogenomics integrates imaging phenotypes with genomic profiles, offering a pathway to precision diagnostics. However, existing classical machine learning models often struggle with the high dimensionality and heterogeneity of multimodal data, leading to issues in calibration and reproducibility. This study presents Q RadFusion, a hybrid quantum-classical framework designed to enhance breast cancer diagnosis by fusing mammography and genomics data. METHODS: Q RadFusion was implemented on two publicly available datasets: CBIS-DDSM (2,600 curated mammography cases, TCIA) and TCGA-BRCA (1,000 genomic profiles, GDC). Imaging preprocessing included bias-field correction, segmentation, and harmonization, while genomic data underwent normalization and imputation. Feature selection was performed using the Quantum Approximate Optimization Algorithm (QAOA), and features were mapped into a quantum Hilbert space using Variational Quantum Circuits (VQC). For multimodal fusion, ResNet encoded mammography features, and a Transformer encoded genomic features. Patient-level and site-held-out splits were used for evaluation. RESULTS: Q RadFusion achieved an AUC of 0.96 and accuracy of 94%, outperforming baselines including CNN-LSTM, ResNet + XGBoost, and multimodal Transformers. Ablation studies confirmed the contribution of quantum components, with optimal performance observed at circuit depth, qubits, and QAOA layers. The model also demonstrated improved calibration and ~ 80% fewer parameters compared to deep fusion networks. CONCLUSION: Q RadFusion demonstrates that hybrid quantum-classical radiogenomic integration can deliver accurate, reproducible, and clinically meaningful diagnostic support for breast cancer, with strong potential for future clinical translation.

Breast Cancer↗

PubMed2025

Identification of Prognostic Gene Signatures for Survival of Patients With Phaeochromocytoma, Paraganglioma, and Other Tumor Types.

BACKGROUND/AIM: Tumor treatments remain unsatisfactory, as many patients continue to die despite therapy. There is an urgent need for novel drug targets, particularly for rare tumors. In this study, we sought to identify genes with prognostic significance for survival in patients with phaeochromocytoma or paraganglioma. We also examined whether these genes are relevant in other tumor entities. PATIENTS AND METHODS: We mined the TCGA-based KM Plotter and studied 186 risk genes for phaeochromocytoma and paraganglioma. RESULTS: Using Kaplan-Meier statistics, we performed 3,163 calculations based on 7,489 tumor biopsies and identified a 2-gene signature for phaeochromocytoma/paraganglioma (AQP4, FAM84H). Since the 186 risk genes are not exclusively related to the development of phaeochromocytoma/paraganglioma alone, we also investigated their prognostic relevance in 17 other tumor types. A clustered 12-gene signature has been found common in four other tumor entities (liver hepatocellular carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, lung adenocarcinoma). This signature consisted of BUB1, BUB1B, CDK1, CENPA, CKAP2L, IQGAP3, MKI67, NDC80, PBK, RRM2, TOP2A, and TTK. CONCLUSION: Our analysis provides a basis for the development of a novel prognostic test to predict the survival time of patients.

Kaplan-Meier analysis↗

PubMed2026

PGR expression as a pharmacogenomic companion biomarker to GENE70-derived genomic risk in ER-positive/HER2-negative breast cancer.

BACKGROUND: The biology of the estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancers is heterogeneous even when they are categorized by their risk via genomics. Transcriptomic PGR expression reflects endocrine pathway activity and may provide complementary biological information within established GENE70-derived genomic-risk categories. Whether this molecular marker improves the biological interpretation of genomic-risk stratification beyond conventional clinicopathological assessment remains uncertain. OBJECTIVES: The aim of this study was to determine whether transcriptomic PGR expression provides complementary biological and prognostic information within reconstructed GENE70-derived genomic-risk categories and refines the characterization of endocrine-related tumour biology in ER-positive/HER2-negative breast cancer. METHODS: This study analysed publicly available transcriptomic and clinical data from three cohorts: METABRIC (discovery cohort), GSE96058/SCAN-B cohort (validation cohort) and TCGA-BRCA cohort (molecular validation cohort). The GENE70-derived genomic-risk score was reconstructed for each cohort using matched genes. Cox regression, Kaplan-Meier analysis and subgroup comparisons were used to assess relationships between PGR expression, clinicopathologic variables, molecular features and survival outcomes. RESULTS: Across the three independent cohorts, low transcriptomic PGR expression was consistently associated with higher GENE70-derived genomic risk, increased MKI67 expression, reduced ESR1 expression and enrichment of the Luminal B subtype. Survival findings differed between cohorts. In the discovery METABRIC cohort, transcriptomic PGR expression showed heterogeneous associations with survival, particularly within GENE70-derived high-risk subgroups, whereas the external GSE96058/SCAN-B validation cohort demonstrated consistent associations between low PGR expression and poorer overall survival in both the overall ER-positive/HER2-negative population and GENE70-derived high-risk subgroups. CONCLUSION: These findings suggest that transcriptomic PGR provides complementary biological and prognostic information within GENE70-derived genomic-risk categories. However, because treatment response was not evaluated in the present study, the findings should not be interpreted as evidence of predictive or pharmacogenomic utility and prospective studies incorporating treatment-response analyses are required before such applications can be established.

Humans↗

PubMed2026

Histopathological evaluation of RPL5 expression in triple-negative breast cancer: an integrated immunohistochemical and transcriptomic study.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by high invasiveness, limited therapeutic options, and unfavorable clinical outcomes. Ribosomal protein L5 (RPL5), a component of the large ribosomal subunit, has been implicated in ribosome biogenesis, translational regulation, and p53-associated cellular processes. This study investigated the immunohistochemical expression pattern of RPL5 in TNBC tissues and explored its potential biological significance through integrated transcriptomic analyses. Tumor tissues from 37 patients with TNBC and 7 adjacent non-tumorous breast tissues were collected from the Affiliated Tumor Hospital of Xinjiang Medical University between December 2017 and December 2023. RPL5 protein expression was evaluated by immunohistochemistry, and its association with clinicopathological characteristics was analyzed. Public transcriptomic datasets from TCGA-BRCA and GEO were further used to validate RPL5 expression patterns in TNBC. Co-expression analysis and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate potential biological functions and signaling pathways associated with RPL5. Immunohistochemical analysis demonstrated significantly lower RPL5 protein expression in TNBC tissues compared with adjacent normal breast tissues (p=0.001). In contrast, transcriptomic analyses revealed significantly higher RPL5 expression in TNBC compared with non-TNBC breast cancer subtypes (p<0.001). No significant associations were observed between RPL5 expression and clinicopathological parameters, including age, tumor size, menopausal status, TNM stage, histological grade, or lymph node metastasis (all p>0.05). Survival analysis showed no significant difference in overall survival between patients with high and low RPL5 expression. Functional enrichment analyses indicated that RPL5-related genes were predominantly involved in ribosome biogenesis, translational regulation, and p53-related signaling pathways. These findings suggest that abnormal RPL5 expression may be associated with TNBC biology through ribosome-related programs, although causal roles require functional validation. RPL5 may represent a potential histopathological and molecular indicator associated with TNBC biology, although its precise functional role requires further experimental validation.

Humans↗

PubMed2026

Epigenetic Reactivation of TNFRSF19 Suppresses Mitophagy and Sensitizes Triple-Negative Breast Cancer to Doxorubicin.

Doxorubicin remains an important component of chemotherapy for triple-negative breast cancer (TNBC), yet chemoresistance severely limits its clinical efficacy. Here, we identify Tumor necrosis factor receptor superfamily member 19 (TNFRSF19) as an epigenetically silenced gene that critically regulates doxorubicin response. Integrative analyses of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and clinical cohorts reveal that high TNFRSF19 expression predicts superior pathological complete response and improved survival in doxorubicin-treated TNBC patients. Mechanistically, TNFRSF19 binds the kinase domain of TGFBR1 via its intracellular domain, disrupting TGFBR1-SMAD3 complex formation and thereby inhibiting SMAD3 phosphorylation, nuclear translocation, and transcriptional activation of PTEN-induced putative kinase 1 (PINK1). This suppresses PINK1/Parkin-mediated mitophagy, contributing to mitochondrial dysfunction, reactive oxygen species (ROS) accumulation, and amplified DNA damage upon doxorubicin treatment. Notably, TNFRSF19 is downregulated in TNBC due to DNA hypermethylation, and decitabine restores its expression via promoter demethylation, thereby enhancing the therapeutic efficacy of doxorubicin in vitro and in vivo. Collectively, these findings establish TNFRSF19 as a critical epigenetic regulator of mitophagy, highlighting its potential as a predictive biomarker for doxorubicin response and a therapeutic target for sensitizing TNBC to doxorubicin.

DNA methylation↗