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PubMed2026

Translational Gap in Biomarker Discovery: Tumor Surface Markers Rarely Mirror Circulating Levels.

BACKGROUND: Tumor-associated cell surface proteins are frequently proposed as circulating biomarkers for colorectal cancer (CRC) based on their high tumor expression. However, many candidates identified through tissue-based analyses fail to translate into clinically useful biomarkers. We investigated the translational gap between tissue-level expression and circulating detectability in CRC, focusing on molecular subtypes defined by caudal-type homeobox 2 (CDX2) expression. METHODS: Transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed to identify cell surface markers differentially expressed between CDX2-Low and CDX2-High CRCs. A clinical cohort of right-sided CRC patients was evaluated using paired tumor tissue and preoperative plasma samples. CDX2 expression was assessed by immunohistochemistry, and circulating concentrations of selected cell surface proteins were quantified using a multiplex ELISA platform. RESULTS: Several tumor-associated cell surface markers exhibited marked CDX2-dependent differences in tissue expression. However, for most markers, circulating plasma levels did not mirror tissue-level patterns. CEACAM1 was the sole marker demonstrating concordant CDX2-dependent differences in both tumor tissue and plasma, with significantly lower levels in CDX2-Low CRCs. In contrast, CEACAM5 showed a dissociation between tissue expression and circulating levels, despite analytical validation against serum carcinoembryonic antigen (CEA). CONCLUSIONS: Our findings demonstrate that tumor overexpression of cell surface markers does not necessarily translate into detectable circulating biomarkers. This translational disconnect underscores limitations of biomarker selection strategies based solely on tissue expression and highlights the importance of integrating systemic biology into biomarker development. While some tumor-associated proteins may lack utility as circulating biomarkers, they may still represent viable therapeutic targets in CRC.

CDX2↗
PubMed2026

Immune-Like Malignant Epithelial Programs Shape Tumor-Immune Interactions and Inform Prognostic Stratification in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is characterized by marked cellular heterogeneity, yet how malignant epithelial states contribute to immune regulation and clinical outcomes remains incompletely defined. We integrated single-cell RNA-sequencing data to map the cellular landscape of LUAD and identify malignant epithelial cells based on inferred copy-number alterations. Epithelial states were further examined through trajectory inference, transcription factor analysis, and cell-cell communication profiling. Single-cell-derived genes were subsequently integrated with TCGA and independent GEO cohorts to construct and validate a machine learning-based prognostic signature. Malignant epithelial cells displayed distinct functional programs, including an immune-like state associated with genomic instability, immune-related transcriptional activity, tumor-immune communication, and patient outcomes. The resulting immune-like malignant epithelial cell signature (IMEC-Sig) consistently stratified survival across multiple cohorts. Low IMEC-Sig scores were accompanied by greater immune infiltration, higher immune checkpoint expression, and increased immunophenoscore, whereas high scores were linked to a comparatively immunosuppressive phenotype. Pan-cancer analyses further identified KRT8 as a gene associated with unfavorable prognosis, and functional experiments showed that KRT8 silencing suppressed proliferation, migration, invasion, and colony formation in LUAD cells. Together, these findings connect malignant epithelial heterogeneity with the immune context and clinical outcomes, support IMEC-Sig as a biologically informed prognostic tool, and nominate KRT8 as a potential therapeutic target in LUAD.

Humans↗
PubMed2005

M.TaqI facilitates the base flipping via an unusual DNA backbone conformation.

MD simulations have been carried out to understand the dynamical behavior of the DNA substrate of the Thermus aquaticus DNA methyltransferase (M.TaqI) in the methylation process at N6 of adenine. As starting structures, an x-ray structure of M.TaqI in complex with DNA and cofactor analogue (PDB code: 1G 38) and free decamer d(GTTCGATGTC)(2) were taken. The x-ray structure shows two consecutive BII substates that are not observed in the free decamer. These consecutive BII substates are also observed during our simulation. Additionally, their facing backbones adopt the same conformations. These double facing BII substates are stable during the last 9 ns of the trajectories and result in a stretched DNA structure. On the other hand, protein-DNA contacts on 5' and 3' phosphodiester groups of the partner thymine of flipped adenine have changed. The sugar and phosphate parts of thymine have moved further into the empty space left by the flipping base without the influence of protein. Furthermore, readily high populated BII substates at the GpA step of palindromic tetrad TCGA rather than CpG step are observed in the free decamer. On the contrary, the BI substate at the GpA step is observed on the flipped adenine strand. A restrained MD simulation, reproducing the BI/BII pattern in the complex, demonstrated the influence of the unusual backbone conformation on the dynamical behavior of the target base. This finding along with the increased nearby interstrand phosphate distance is supportive to the N6-methylation mechanism.

Adenine↗
PubMed2026

POFUT1 Serves as an Independent Prognostic Factor and Therapeutic Target by Activating the PI3K/AKT Pathway in Glioma.

OBJECTIVE: Protein O-fucosyltransferase 1 (POFUT1) has been implicated in several malignancies, but its functional and prognostic significance in glioma remains insufficiently defined. This study evaluated whether POFUT1 expression is associated with glioma progression, patient outcome, and PI3K/AKT pathway activity. METHODS: Public glioma transcriptome datasets from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) were analyzed and compared with clinical samples collected from 123 glioma patients. POFUT1 protein levels in clinical specimens were determined by immunohistochemical staining, and its association with patient outcome was analyzed using survival curves. In vitro, glioma cell growth, motility, and invasiveness were examined using MTT and Transwell assays. The effect of POFUT1 on tumor formation was further tested in a subcutaneous xenograft model. RNA sequencing, KEGG pathway enrichment, and pharmacological inhibition were then used to explore the mechanism linking POFUT1 to PI3K/AKT signaling. RESULTS: POFUT1 expression was higher in glioma than in normal brain tissue and increased with tumor grade. Patients with high POFUT1 levels had shorter overall survival, and multivariate Cox analyses supported POFUT1 as an independent prognostic indicator. Incorporating POFUT1 into a nomogram improved prediction of 1-, 3-, and 5-year survival. Functionally, POFUT1 knockdown reduced glioma cell growth, motility, invasion, and xenograft expansion, whereas POFUT1 overexpression produced the opposite phenotype. Transcriptomic and protein analyses indicated that POFUT1 enhanced PI3K/AKT signaling. The PI3K inhibitor LY294002 weakened the tumor-promoting effects caused by POFUT1 overexpression. CONCLUSION: POFUT1 as a key driver of glioma malignancy, predominantly through activating the PI3K-AKT signaling pathway. These findings highlight POFUT1 as a promising novel therapeutic target for aggressive glioma.

Glioma↗
PubMed2026

STK11 Mutations and Deletions Define an Aggressive Molecular Subgroup of Cervical Adenocarcinoma.

Cervical adenocarcinoma accounts for 15%-20% of cervical cancers and is associated with poorer survival and reduced response to screening and immunotherapy compared with squamous cell carcinoma (SCC). The genomic drivers underlying this molecular subgroup remain incompletely characterized. Whole-exome sequencing was performed on 302 invasive cervical cancers from Guatemala and Venezuela. Structural variation analysis was conducted using SNP-array and whole-genome sequencing data. Findings were replicated in more than 4600 additional cervical cancer samples from TCGA, AACR Project GENIE, MSKCC, and Caris datasets. TP53 mutations were more frequent in adenocarcinoma than SCC, particularly in HPV-negative tumors. STK11 alterations, including mutations and focal deletions, were significantly enriched in HPV-positive adenocarcinomas compared with SCC and affected 23% of adenocarcinomas overall. Whole-genome analyses identified recurrent focal deletions, inversions, chromosomal rearrangements, and breakage-fusion-bridge events involving chromosome 19p and STK11 that were not detected by exome sequencing alone. STK11 alterations were associated with younger age at diagnosis, poorer overall survival, and inferior outcomes following immune checkpoint inhibitor (ICI) therapy. STK11 alterations significantly co-occurred with YAP1 amplification but were largely mutually exclusive with PIK3CA mutation. Cervical adenocarcinomas also demonstrated significantly lower CD274 (PD-L1) expression than SCC. STK11 alterations define a distinct molecular subgroup of cervical adenocarcinoma characterized by structural disruption of chromosome 19p, younger age at onset, and poorer clinical outcomes. These findings have implications for molecular classification and future targeted therapeutic approaches in cervical cancer.

Humans↗
PubMed2026

Age-Associated Four-Gene Prognostic Signature in Breast Cancer.

BACKGROUND: Young-onset breast cancer is associated with inferior disease-free survival (DFS), but the contribution of additional molecular heterogeneity remains unclear. AIMS: To identify an exploratory age-associated gene expression signature linked to recurrence-related outcomes and evaluate its prognostic association. METHODS AND RESULTS: We analyzed clinicopathological and RNA-sequencing data from 821 patients with Stages I-III invasive ductal or lobular carcinoma in The Cancer Genome Atlas, including 142 patients aged ≤ 45 years. Genes associated with both age and DFS were screened, followed by LASSO-Cox and stepwise multivariable Cox regression. A four-gene signature (Sig4: C4orf14 [NOA1], LINC01124, ZNF704, and AGFG2) was identified. Young patients had significantly worse DFS than older patients, whereas overall and disease-specific survival did not differ significantly. After adjustment for clinicopathological factors, young age remained associated with worse DFS. Following inclusion of the continuous Sig4 score, the age association was attenuated and no longer statistically significant, while Sig4 remained independently associated with worse DFS. Sig4-high tumors were enriched for proliferation, cell-cycle, DNA-repair, metabolic, and stress-response pathways. In METABRIC, the fixed TCGA-derived Sig4 score was associated with worse relapse-free survival in the overall cohort but not in patients aged ≤ 45 years. CONCLUSION: Sig4 is an exploratory age-associated four-gene signature with potential general prognostic relevance in breast cancer. Its utility for risk stratification specifically in young-onset breast cancer was not externally validated and requires confirmation in independent prospective cohorts enriched for young patients.

Humans↗
PubMed2026

Multi-Omics Integration Identifies a Five-Gene Metabolic Signature With Experimental Validation in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is hallmarked by profound metabolic reprogramming; however, its intricate crosstalk with the tumor immune microenvironment (TIME) and its clinical ramifications remain inadequately elucidated. This study aims to systematically decipher the metabolic-immune interplay in ccRCC through multi-omics integration, with the goal of identifying robust prognostic biomarkers and actionable therapeutic vulnerabilities. AIMS: This study aims to systematically decipher the metabolic-immune interplay in clear cell renal cell carcinoma (ccRCC) through multi‑omics integration, and to identify robust prognostic biomarkers and actionable therapeutic vulnerabilities that can inform precision risk stratification and individualized treatment strategies. METHODS: We integrated bulk transcriptomic, genomic, and clinical data from multiple ccRCC cohorts. Differential expression and functional enrichment analyses were performed to characterize metabolic pathway alterations. Mendelian randomization (MR) was employed to infer causal relationships between metabolic disorders and ccRCC risk. A machine learning-based prognostic framework, incorporating SHAP (SHapley Additive exPlanations) for feature interpretability, was constructed and rigorously validated. TIME heterogeneity was dissected using deconvolution algorithms, while drug sensitivity, tumor mutation burden (TMB), and TIDE scores were utilized to assess therapeutic responses and immune evasion. Candidate gene function was evaluated through in vitro gain- and loss-of-function assays, with expression validated via TCGA, HPA, western blot, and qRT-PCR. RESULTS: Enrichment analysis identified coordinated dysregulation in lipid metabolism, energy homeostasis, and hypoxia response pathways. MR analysis confirmed lipid metabolism disorders as a causal risk factor for ccRCC. Our machine-learning model, centered on five core SHAP-identified features (SUCLA2, ACAT1, PC, SUCLG1, and HMGCS2), demonstrated superior predictive accuracy over conventional clinical staging. Immune profiling unveiled dichotomous TIME states: the low-risk group retained active immune surveillance, whereas the high-risk group was enriched with immunosuppressive subsets. Drug sensitivity screening pinpointed LY2109761 and carmustine as high-risk-specific candidate agents. Furthermore, TMB and TIDE analyses stratified high-risk patients displaying genomic instability and immune evasion phenotypes. Functionally, SUCLA2 knockdown significantly enhanced ccRCC cell proliferation and invasion, while its overexpression suppressed these malignant phenotypes, corroborating its tumor-suppressive role. Expression patterns of the hub genes were consistently validated across multi-level datasets and experimental assays. CONCLUSION: This study establishes a precision oncology framework for ccRCC by functionally linking metabolic biomarkers, immunophenotypes, and stratified therapeutic strategies. Importantly, we identify SUCLA2 as a potential functional tumor suppressor and a promising target for further mechanistic and translational investigation.

Humans↗
PubMed2026

Dataset Readiness Assessment With Large Language Model (DRAFT-LLM): A Multi-Axis Audit Guided by LLM.

This article details the Dataset Readiness Assessment for Training (DRAFT), a systematic method for determining whether a high-dimensional biological dataset is suitable for developing reliable, equitable (i.e., the extent to which model performance, error patterns, and potential benefits or harms are evaluated and found to be acceptably distributed across relevant demographic, biological, clinical, and contextual subgroups), and scientifically meaningful machine-learning models, and DRAFT Large Language Model (DRAFT-LLM), its optional human-in-the-loop extension for calibrating study-specific audits through structured, critically reviewed LLM guidance. Standard model validation often fails to detect when apparent performance is driven by spurious correlations, technical artifacts, or hidden stratification, leading to irreproducible and inequitable findings. DRAFT-LLM addresses this gap by shifting the focus from model tuning to structured dataset auditing, organized around Support Protocols 1 to 4 that capture the scientific intent, data structure, and governance constraints of a given study. These Support Protocols: (1) elicit and formalize investigator input into a study intake and dataset card; (2) compute standardized dataset statistics and structural summaries suitable for downstream analysis and LLM context; (3) configure the language model using form-based responses, safety guardrails, and governance rules; and (4) generate personalized instructions, prompts, and code templates for running DRAFT audits. Basic Protocols 1 to 3 are instantiated from this support layer for generalization, equity, and stability: they are reusable execution patterns whose concrete behavior is determined by the cards, statistics, and configurations defined in the Support Protocols. DRAFT-LLM and DRAFT are demonstrated in this article through an end-to-end case study on The Cancer Genome Atlas (TCGA). © 2026 Wiley Periodicals LLC. Support Protocol 1: Study intake and dataset card construction Support Protocol 2: Dataset structure and advanced summary statistics for LLM context Support Protocol 3: LLM configuration using structured form responses Support Protocol 4: Generation of personalized instructions for DRAFT audits Basic Protocol 1: Generalization audit Basic Protocol 2: Equity audit Basic Protocol 3: Stability audit.

Large Language Models↗
PubMed2026

Integrative Multi-Omics Mendelian Randomization Analysis Identifies NIT2 as a Potential Metabolic Risk Gene in Hepatocellular Carcinoma.

BACKGROUND: Metabolic pathways are crucial in hepatocellular carcinoma (HCC) pathogenesis, but causal metabolic genes remain unclear. This study used Summary data-based Mendelian Randomization (SMR) and colocalization to identify metabolism-related genetic loci influencing HCC risk. METHODS: Differentially expressed genes in hepatic malignancy phenotype versus normal tissues from TCGA and GTEx were analyzed. Metabolism-related candidates were examined via SMR and colocalization using multi-omics data: methylation (mQTL), expression (eQTL), and protein (pQTL) quantitative trait loci. RESULTS: Multi-omics integration identified NIT2 as a key metabolic regulator for HCC. The cg13016775 locus of NIT2 was associated with elevated HCC risk at gene (OR = 1.618, 95% CI: 1.199-2.182) and protein (OR = 4.432, 95% CI: 1.783-11.018) levels. Colocalization supported a shared causal variant (PPH4 > 0.6), linking NIT2 to hepatocarcinogenesis via metabolic regulation. CONCLUSIONS: This study provides multi-omics evidence for NIT2 as a potential causal gene in HCC, enhancing understanding of metabolic contributions to HCC pathogenesis and highlighting integrative genomics for uncovering causal relationships.

Carcinoma, Hepatocellular↗
PubMed2026

ZNF695 Promotes Colorectal Cancer Progression Through Transcriptional Activation of CBX8 and Subsequent Wnt/β-Catenin Signaling Activation.

In this investigation, we examined the functional mechanism of the transcription factor zinc finger protein 695 (ZNF695) and its target gene chromobox protein homolog 8 (CBX8) in colorectal cancer (CRC) migration and invasion. HCT-116 and LOVO cell lines were used to establish cell models with knocked-down ZNF695 and knocked-down or over-expressed CBX8. To comprehensively evaluate the functional contributions of ZNF695 and CBX8 to cellular phenotypes, we employed CCK-8, wound-healing, and Transwell assays to evaluate cell proliferation, migration, and invasion, respectively. To assess the impact of ZNF695 on tumor progression, we generated a xenograft model utilizing nude mice. A FLAG-ZNF695 expression plasmid was constructed, and ChIP-seq experiments were performed. By integrating mRNA sequencing data following ZNF695 knockdown with highly expressed genes in CRC from the TCGA database, CBX8 was identified as a putative downstream target of ZNF695. We employed a dual-luciferase reporter assay to validate the specific binding affinity of ZNF695 toward the CBX8 promoter region. To elucidate the specific biological cascades modulated by ZNF695 and CBX8, we conducted a comprehensive pathway enrichment analysis. Rescue experiments were conducted to determine whether the ZNF695/CBX8 regulatory axis upregulates the expression of the Wnt signaling pathway downstream targets, AXIN2 and CCND1. Both in vitro assays and in vivo models confirmed that silencing ZNF695 dramatically suppresses CRC cell proliferation, migration, and invasion, while concurrently impeding tumor progression. ChIP-seq coupled with dual-luciferase reporter assays substantiated the direct binding of ZNF695 to the CBX8 promoter. Furthermore, CBX8 depletion significantly attenuated the migratory and invasive phenotypes of CRC cells. Restoring CBX8 expression effectively rescued the migratory and invasive deficits in CRC cells induced by ZNF695 silencing. Re-expression of CBX8 in ZNF695-silenced cells restored Wnt/β-catenin signaling activity, accompanied by increased expression of AXIN2 and CCND1. ZNF695 promotes CRC progression by transcriptionally activating CBX8 and subsequently enhancing Wnt/β-catenin signaling, thereby promoting tumor cell proliferation, migration, and invasion.

Humans↗
PubMed2026

Epitranscriptomic Regulation of ALDOA by SHMT2-Mediated m6A Modification Drives Gastric Cancer Malignancy.

Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, with limited therapeutic advancements despite progress in early detection. Serine hydroxymethyltransferase 2 (SHMT2), a key metabolic enzyme, and fructose-1,6-bisphosphate aldolase A (ALDOA), a glycolytic enzyme, are implicated in tumor progression. However, the molecular mechanisms linking SHMT2 and ALDOA in GC remain unclear. This study investigates how SHMT2 regulates ALDOA expression via m6A RNA modification to drive GC malignancy. Bioinformatic analyses (TCGA, LinkedOmics, and SRAMP) were used to assess SHMT2 expression in GC patients and identify its correlated genes. In vitro experiments (CCK-8, EdU, Transwell, and wound healing) evaluated the effects of SHMT2 overexpression or knockdown on GC cell proliferation, migration, invasion, and glycolysis. m6A modification of ALDOA was analyzed via MeRIP-PCR and dual-luciferase assays, while RNA stability was assessed using actinomycin D treatment. Xenograft models validated SHMT2's role in vivo. SHMT2 was upregulated in GC tissues and cell lines, correlating with advanced tumor stages and poor prognosis. SHMT2 knockdown suppressed GC cell viability, migration, invasion, and glycolysis, while overexpression enhanced these traits. Mechanistically, SHMT2 increased S-adenosylmethionine levels, promoting ALDOA m6A modification, likely mediated through the predicted site 1 (position 291). This modification stabilized ALDOA mRNA via IGF2BP1 recognition, an m6A reader. ALDOA overexpression reversed the tumor-suppressive effects of SHMT2 knockdown. In vivo, SHMT2 depletion reduced tumor growth and Ki67 expression in xenograft models. In conclusion, SHMT2 drives GC progression by enhancing ALDOA expression through m6A modification and IGF2BP1-mediated stabilization. Targeting the SHMT2-ALDOA axis represents a promising therapeutic strategy for gastric cancer.

Humans↗
PubMed2026

Hypoxia-Induced ADAM23 Drives Neuron-Tumor Crosstalk and Therapeutic Resistance in Hepatocellular Carcinoma.

Hypoxia and nutrient deprivation are fundamental drivers of tumor aggressiveness and therapeutic resistance in hepatocellular carcinoma (HCC). While the involvement of neural components in the tumor microenvironment (TME) is increasingly recognized, the molecular transducers linking metabolic stress to neuron-tumor crosstalk remain elusive. Here, we identify ADAM23 (A disintegrin and metalloproteinase 23) as a hypoxia-responsive mediator that mediates communication between HCC cells and neuronal cells. ADAM23 expression was markedly upregulated in HCC cells under both chemical (CoCl2) and physical hypoxia (1% O2), a process further amplified by glucose deprivation and directly modulated by HIF-1α. Functional assays revealed that ADAM23 overexpression promotes epithelial-mesenchymal transition (EMT) and enhances cell viability under metabolic stress. Notably, sorafenib-resistant HCC cells (Huh7SR) exhibited high levels of ADAM23 secretion, which triggered proliferative and metabolic activation in neuronal SH-SY5Y cells. In 3D co-culture spheroid models, Huh7SR cells mixed with SH-SY5Y cells displayed significantly larger spheroid volumes and enhanced neuronal fluorescence compared with parental controls, suggesting that ADAM23-mediated interactions facilitate a supportive neural niche. Analysis of The Cancer Genome Atlas (TCGA) datasets and patient microarrays confirmed that ADAM23 is significantly overexpressed in HCC and positively correlates with HIF-1α expression. Moreover, elevated expression of ADAM23 was significantly correlated with poor overall survival. Collectively, our findings underscore ADAM23 as a critical metabolic-neural linker that promotes HCC progression and drug resistance. These findings suggest that the ADAM23-mediated neuron-tumor axis may represent a potential therapeutic target in aggressive HCC.

ADAM23↗
PubMed2026

UBE2D4 Upregulation Promotes Cuproptosis Sensitivity in Colorectal Cancer.

BACKGROUND: Cuproptosis, a copper-dependent form of regulated cell death, represents a potential therapeutic vulnerability in colorectal cancer (CRC). However, the regulatory mechanisms governing cuproptosis in CRC remain largely unknown. METHODS: UBE2D4 expression was analyzed in the TCGA-COAD cohort and validated in CRC cell lines (HCT116, HT29) and normal colon epithelial cells (FHC) by qRT-PCR and western blot. Paired CRC and adjacent normal tissues (n = 5) were also examined by western blot. UBE2D4-knockdown HT29 cells were generated by transient siRNA transfection to assess cell viability (CCK-8), migration (wound healing assay), and expression of cuproptosis-related genes (DLAT, HSP70, LIAS) under copper overload conditions (elesclomol+CuSO4). RESULTS: UBE2D4 was significantly upregulated in CRC tissues and cell lines compared to normal controls. In paired clinical samples, western blot confirmed that UBE2D4 protein expression was elevated in tumor tissues, accompanied by increased DLAT, HSP70 and LIAS. Copper overload induced typical cuproptotic mitochondrial morphology and triggered a marked upregulation of UBE2D4, DLAT, and HSP70, alongside downregulation of LIAS. UBE2D4 silencing had no effect on baseline cell viability or migration but significantly rescued cells from copper-induced cytotoxicity. Genetically, UBE2D4 knockdown specifically attenuated the copper-induced elevation of DLAT, while restoring HSP70 and LIAS to near-baseline levels. CONCLUSION: These findings identify UBE2D4 as a genetically upregulated and functionally significant gene in colorectal cancer. Its upregulation correlates with altered expression of cuproptosis-related genes, particularly DLAT, suggesting that UBE2D4 expression status may represent a genetic determinant of cuproptosis sensitivity in CRC. This study provides a genetic basis for stratifying CRC patients who might benefit from copper-based therapeutic strategies.

Humans↗
PubMed2026

MRI-based radiomics model for predicting VEGFA expression and prognosis in lower-grade glioma.

BACKGROUND: Gliomas are the most common primary tumors of the central nervous system. Their treatment remains highly challenging, with high rates of associated disability and mortality. Conventional prognostic indicators no longer adequately satisfy the clinical demands of precision medicine. Therefore, it is essential to further explore novel prognostic biomarkers to enable accurate risk stratification and to provide new reference indicators for personalized precision therapy. PURPOSES: This study aimed to investigate the prognostic significance of vascular endothelial growth factor A (VEGFA) in patients diag nosed with lower-grade gliomas (LGGs) using an MRI based radiomics model. METHODS: Data regarding VEGFA expression and clinical records of LGG patients were retrieved from The Cancer Genome Atlas (TCGA). Corresponding preoperative MRI data were obtained from The Cancer Imaging Archive (TCIA) for radiomic feature extraction. Patients were stratified into high- and low- VEGFA expression groups based on survival information from the current cohort using the survminer package. The overall survival (OS) was assessed using Kaplan-Meier analysis and Cox proportional hazards regression. Predictive models were developed using logistic regression (LR), and model performance was evaluated via receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) values reported. An optimized model incorporating the Akaike information criterion (AIC) was also constructed (AIC-LR). RESULTS: VEGFA expression was significantly associated with OS (P = 0.002). Multivariate Cox regression confirmed VEGFA as an independent prognostic factor (hazard ratio [HR] = 2.545, 95% confidence interval: 1.422-4.555). Furthermore, VEGFA expression correlated with immune infiltration levels, particularly of M1 and M2 macrophages and T follicular helper cells, and was associated with enrichment in Wnt signaling and B cell receptor signaling pathways. The LR and AIC-LR models demonstrated acceptable predictive performance, with AUCs of 0.728 (95% CI: 0.612-0.843) and 0.725(95% CI: 0.612-0.839) in the training cohort, and 0.704 (95% CI: 0.562-0.847) and 0.718(95% CI: 0.576-0.861) in the validation cohort, respectively. CONCLUSIONS: The MRI based radiomics model showed potential for noninvasive assessment of VEGFA expression and may provide auxiliary information for prognostic evaluation in LGG. Further validation in larger samples and independent external cohorts is required before clinical application.

Radiomics↗
PubMed2026

Differences between mitotically old and young endometrial tumors.

Human tumors likely differ in their mitotic ages, reflecting how many divisions elapse between the final tumor progenitor cell and surgical removal. We used a rapidly fluctuating CpG (fCpG) methylation clock to infer relative endometrial adenocarcinomas (EAC) mitotic ages. Experimentally, young tumors initiated from single cells show low-diversity, high-variance fCpG distributions with trimodal peaks near 0%, 50%, and 100%, reflecting inherited progenitor methylation states. fCpG methylation becomes polymorphic with divisions, and older tumors exhibit higher diversity, lower variance, and unimodal distributions centered around 50%. Mitotic ages varied across EAC samples. Synchronous hyperplasia and invasive regions generally shared similar ages, and primary-metastatic EAC pairs showed both synchronous and stepwise progression. The Cancer Genome Atlas (TCGA) EACs also showed variable mitotic ages: younger tumors were enriched for proliferation pathways, whereas older tumors showed more immune infiltration, immune-pathway activation, and evidence of T-cell exhaustion. These results show that human tumors can be ranked by mitotic age and suggest that the growth of older cancers is restrained by immune surveillance. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Humans↗
PubMed2026

Icaritin Sensitizes Hepatocellular Carcinoma to PD-L1 Therapy by NQO1-Dependent Ferroptosis Induction.

Hepatocellular carcinoma (HCC) remains challenging with limited immunotherapy response. Despite its clinical promise in advanced HCC, the mechanisms of icaritin, especially concerning ferroptosis induction and immune modulation, remain elusive. This study aims to determine if the antitumor effect of icaritin involves the induction of ferroptosis via NAD(P)H quinone oxidoreductase 1 (NQO1) and if it can augment the efficacy of programmed cell death 1 ligand 1 (PD-L1) therapy by potentiating natural killer (NK) cell activity. Using human HCC cell lines (Huh7, Hep3B, PLC/PRF/5, SNU-449, and MHCC97-H) and two synergistic mouse models (Hepa1-6 and SgPten/c-Met), we examined icaritin's inhibition of tumor growth and induction of ferroptosis via the NQO1 pathway, monitoring key markers (reactive oxygen species [ROS], glutathione peroxidase 4 [GPX4], ferritin heavy chain 1 [FTH1]). The NQO1 inhibitor dicoumarol was employed to validate the pathway. Tumor microenvironment (TME) remodeling was assessed through cancer-associated fibroblasts (CAFs) markers and immune cell profiling, focusing on NK cell infiltration. Combination therapy with anti-PD-L1 was tested in vivo. Icaritin significantly inhibited HCC growth in vitro and in vivo. Its antitumor effect was mediated by NQO1-mediated ferroptosis, via elevated ROS, diminished mitochondrial membrane potential, and downregulated GPX4 and FTH1. Analysis of The Cancer Genome Atlas (TCGA) data revealed that NQO1 is overexpressed in human HCC tissues. Icaritin enhanced NK cell infiltration while reducing CAF abundance and suppressing recombinant focal adhesion kinase (FAK) and discoidin domain receptor 1 (DDR1) signaling. Notably, icaritin synergized with anti-PD-L1 therapy to enhance tumor suppression without increasing toxicity, correlating with potentiated NK cell immunity. Our findings demonstrate that icaritin triggered NQO1-mediated ferroptosis and remodeled TME to enhance NK cell recruitment and PD-L1 therapy efficacy. This provides rationale for evaluating icaritin-based combination immunotherapy in HCC through dual action on ferroptosis and NK cell activation.

Ferroptosis↗
PubMed2025

Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.

In biomedical studies, gene-environment (G-E) interactions have been demonstrated to have important implications for analyzing disease outcomes beyond the main G and main E effects. Many approaches have been developed for G-E interaction analysis, yielding important findings. However, hierarchical multi-label classification, which provides insightful information on disease outcomes, remains unexplored in G-E analysis literature. Moreover, unlabeled data are commonly observed in practical settings but omitted by many existing methods of hierarchical multi-label classification. In this study, we consider a semi-supervised scenario and develop a novel approach for the two-layer hierarchical response with G-E interactions. A two-step penalized estimation is then proposed using an efficient expectation-maximization (EM) algorithm. Simulation shows that it has superior performance in classification and feature selection. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer demonstrates the practical utility of the proposed method. Overall, this study can fill the important knowledge gap in G-E interaction analysis by providing a widely applicable framework for hierarchical multi-label classification of complex disease outcomes.

Humans↗
PubMed2025

Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data.

With the increasing maturity of genetic profiling, an essential and routine task in cancer research is to model disease outcomes/phenotypes using genetic variables. Many methods have been successfully developed. However, oftentimes, empirical performance is unsatisfactory because of a "lack of information." In cancer research and clinical practice, a source of information that is broadly available and highly cost-effective comes from pathological images, which are routinely collected for definitive diagnosis and staging. In this article, we consider a Bayesian approach for selecting relevant genetic variables and modeling their relationships with a cancer outcome/phenotype. We propose borrowing information from (manually curated, low-dimensional) pathological imaging features via reinforcing the same selection results for the cancer outcome and imaging features. We further develop a weighting strategy to accommodate the scenario where information borrowing may not be equally effective for all subjects. Computation is carefully examined. Simulations demonstrate competitive performance of the proposed approach. We analyze TCGA (The Cancer Genome Atlas) LUAD (lung adenocarcinoma) data, with overall survival and gene expressions being the outcome and genetic variables, respectively. Findings different from the alternatives and with sound properties are made.

Humans↗