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PubMed2026

Casein Kinase 1 Alpha 1 Is Over-expressed in Pancreatic Adenocarcinoma Tissues and Correlates With Shorter Patient Survival.

BACKGROUND/AIM: A quarter of a century has passed since the start of the 21st century, and cancer, once considered an incurable disease, has become manageable thanks to the development of various treatments. However, pancreatic adenocarcinoma (PAAD) remains one of the deadliest cancers in the world, with over 95% of patients dying within five years. This is because the anatomical location of the pancreas makes it very difficult to detect with imaging, and symptoms often do not appear until the cancer invades the nerve plexus in its terminal stages, meaning that by the time it is diagnosed, it is often too late. Therefore, the development of prognostic markers is an urgent task. Casein kinase 1 alpha 1 (CSNK1A1) is a serine/threonine protein kinase deeply involved in Wnt signaling and the tumor suppressor mechanisms of p53. While the association between increased or decreased CSNK1A1 expression and prognosis has been reported in many types of cancer tissue, its association in PAAD is not yet fully understood. This study investigated the potential of CSNK1A1 as a prognostic marker for PAAD. MATERIALS AND METHODS: We used Gene Expression Profiling Interactive Analysis (GEPIA) and the University of Alabama Birmingham Cancer Data Analysis Portal (UALCAN) bioinformatics platforms to analyze CSNK1A1 mRNA expression, protein levels, and survival rates of patients with PAAD obtained from The Cancer Genome Atlas (TCGA) database. RESULTS: CSNK1A1 mRNA and protein levels were significantly higher in PAAD tissue compared to normal pancreatic tissue, and this increase was associated with a poor prognosis in patients with PAAD. CONCLUSION: In PAAD tissue, increased expression of CSNK1A1 mRNA and protein was observed compared to normal pancreatic tissue, and this increased expression correlated with poor patient prognosis. Therefore, CSNK1A1 is considered a promising prognostic biomarker in PAAD.

CSNK1A1↗
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

Multi-omics Investigations of Immune Microenvironment of Human Colorectal Cancer.

BACKGROUND/AIM: Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide. Although immunotherapy has improved outcomes for a subset of patients, its limited efficacy in many cases highlights the need for a more comprehensive understanding of the CRC immune microenvironment. This study aimed to characterize the molecular landscape of the CRC immune microenvironment using an integrated multi-omics approach and to identify candidate regulatory molecules associated with immune remodelling. MATERIALS AND METHODS: We integrated structural variation, DNA methylation, chromatin accessibility, proteomic, and phosphoproteomic data generated from an in-house CRC cohort with transcriptomic data from The Cancer Genome Atlas (TCGA). Analyses focused on 1,539 immune-related genes (IRGs) associated with CD4+ T cells, B cells, and natural killer (NK) cells. Multi-layered genomic and proteomic analyses were performed to identify altered immune-related pathways, hub genes, candidate transcription factors, and upstream kinases. RESULTS: Higher infiltration of CD4+ T cells, B cells, and NK cells was associated with CRC. IRGs exhibited widespread alterations across genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic levels. IL10, LEP, ITGAM, and EGFR emerged as candidate hub genes. EGFR phosphorylation at S991 and T693 was significantly decreased in CRC. STAT2 and HSF1 were identified as candidate upstream transcription factors, while CDK2 emerged as a candidate upstream kinase associated with immune infiltration and immune checkpoint expression. CONCLUSION: This study provides a systematic multi-omics characterization of immune microenvironment remodelling in CRC and identifies candidate molecular regulators that may serve as potential targets for future immunotherapy research.

Humans↗
PubMed2026

CARS1 as a Prognostic Biomarker and Candidate Therapeutic Vulnerability in Hepatocellular Carcinoma: Insights Into Tumor Progression and the Immune Microenvironment.

BACKGROUND: Cysteinyl-tRNA synthetase 1 (CARS1) has been included in ferroptosis-related prognostic signatures, but its clinicopathological relevance, cellular functions, and relationship with the immune microenvironment in hepatocellular carcinoma (HCC) remain incompletely characterized. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset were integrated with corresponding data from an institutional HCC tissue cohort of 60 patients. CARS1 expression was evaluated by immunohistochemistry, and immune infiltration was examined using single-sample gene-set enrichment analysis (ssGSEA) and multiplex immunofluorescence, as well as by analyzing public single-cell datasets. The effects of CARS1 depletion were evaluated in MHCC97H and Hep3B cells using Cell Counting Kit-8 (CCK-8) assays, cell-cycle profiling, wound-healing assays, Transwell migration assays, western blotting, and erlotinib-sensitivity assays. RESULTS: CARS1 expression was elevated in HCC and was associated with adverse clinicopathological features and poor overall survival. Quantitative immunohistochemistry confirmed elevated CARS1 protein expression in tumor tissues. CARS1 depletion inhibited cell proliferation, altered cell-cycle distribution, impaired migration, and enhanced in vitro sensitivity to erlotinib. High CARS1 expression was also associated with increased infiltration of Th2-like immune cells. CONCLUSIONS: Elevated CARS1 expression is associated with an adverse biological and immune phenotype in HCC. These clinical, histopathological, and loss-of-function findings support further investigation of CARS1 as a prognostic marker and candidate therapeutic target in HCC, although additional mechanistic and in vivo validation is required.

Humans↗
PubMed2026

Meta-Merging the Transcriptomes of Gastric Tumors Redefines the Connections among Molecular and Clinical Subtypes.

INTRODUCTION: The availability of a large number of cancer expression profiles presents an excellent opportunity to re-investigate various biological and clinical questions. While several expression profiles have been established for different cancers, merging them may provide a more powerful platform for extensively extrapolating molecular and clinical features across multiple cohorts. MATERIALS AND METHODS: In this study, five gastric tumor expression profiles from the Gene Expression Omnibus [GEO] and one in-house cohort comprising a total of 1,060 samples were merged. The batch effect was removed using non-parametric ComBat analysis, and the seamless merging of datasets was confirmed through various parameters. RESULTS: Extrapolation of ACRG [Asian Cancer Research Group] and TCGA [The Cancer Genome Atlas] molecular subtypes in the merged cohort of 1,060 gastric tumors revealed nine distinct clusters. Notably, the following patterns were observed: [i] mutual exclusivity between Epithelial to Mesenchymal Transition [EMT] and Microsatellite Instability [MSI] subtypes in 90% of tumors; [ii] overlapping occurrence of EMT and MSI subtypes in the remaining tumors; [iii] overlap between MSI and Epstein-Barr Virus [EBV] subtype tumors; [iv] both commonalities and differences between EMT and Genomically Stable [GS] subtypes; and [v] an association between EBV positivity and PI3K mutation. CONCLUSION: The current study demonstrates that compiling a larger expression profile is valuable for revisiting the molecular features and epidemiology associated with molecular subtypes, thereby aiding in the development of novel diagnostics and targeted therapeutics.

Humans↗
PubMed2026

GICPIdb: an archival repository of multimodal data focusing on pathological images for gastrointestinal cancers.

INTRODUCTION: Deep learning (DL) shows great potential for predicting biomarkers from routine histopathological slides of gastrointestinal (GI) cancers. Yet most existing models are validated on limited patient cohorts, while pathological image annotation and molecular marker standardization demand substantial professional expertise. To address these gaps, we constructed the Gastrointestinal Cancer Pathological Image Archive (GICPIdb, gicpidb.shubuzuo.top), a dedicated database and web platform covering seven major GI cancer types. METHODS: High-quality hematoxylin and eosin (H&E)-stained whole-slide images were collected from multiple sources and uniformly processed. Image annotations were performed by board-certified pathologists following standardized protocols. GICPIdb offers five interactive web modules for data uploading, quality control, feature extraction, online annotation and AI-based prediction. Its intuitive interface supports data browsing, retrieval, visualization and downloading. RESULTS: The database houses 2,863 pathologist-annotated, uniformly processed, high-quality H&E stained images collected from 2,655 patients. Of these, 1,699 patients were sourced from The Cancer Genome Atlas (TCGA), 182 from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and 424 from China-Japan Friendship Hospital and 350 from Chifeng Municipal Hospital in Inner Mongolia, China. It also integrates data on over 50 key molecular markers (e.g., MSI, TMB) and prognostic labels related to survival, recurrence and metastasis. DISCUSSION: GICPIdb aims to promote the development of DL-driven AI tools for cancer research and clinical translation. The multi-institutional data collection and standardized annotation pipeline are expected to enhance the generalizability and reproducibility of AI-based prediction models across diverse patient populations.

deep learning↗
PubMed2024

Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment.

INSTRUCTION: Colorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients with CRC to identify how these interactions impact the prognosis and effectiveness of immunotherapy, focusing on patient outcomes and the potential for predicting treatment response. METHODS: Using datasets from multiple cohorts, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we conducted an in-depth multi-omics study to uncover the relationship between ferroptosis regulators and fatty acid metabolism in CRC. Through unsupervised clustering, we discovered unique patterns that link ferroptosis and fatty acid metabolism, and further investigated them in the context of immune cell infiltration and pathway analysis. We developed the FeFAMscore, a prognostic model created using a combination of machine learning algorithms, and assessed its predictive power for patient outcomes and responsiveness to treatment. The FeFAMscore signature expression level was confirmed using RT-PCR, and ACAA2 progression in cancer was further verified. RESULTS: This study revealed significant correlations between ferroptosis regulators and fatty acid metabolism-related genes with respect to tumor progression. Three distinct patient clusters with varied prognoses and immune cell infiltration were identified. The FeFAMscore demonstrated superior prognostic accuracy over existing models, with a C-index of 0.689 in the training cohort and values ranging from 0.648 to 0.720 in four independent validation cohorts. It also responses to immunotherapy and chemotherapy, indicating a sensitive response of special therapies (e.g., anti-PD-1, anti-CTLA4, osimertinib) in high FeFAMscore patients. CONCLUSION: Ferroptosis regulators and fatty acid metabolism-related genes not only enhance immune activation, but also contribute to immune escape. Thus, the FeFAMscore, a novel prognostic tool, is promising for predicting both the prognosis and efficacy of immunotherapeutic strategies in patients with CRC.

Ferroptosis↗
PubMed2024

A comprehensive meta-analysis of tissue resident memory T cells and their roles in shaping immune microenvironment and patient prognosis in non-small cell lung cancer.

Tissue-resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of TRM-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we inferred the abundance of TRM in NSCLC. We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, TRM signatures are strongly associated with immune checkpoint and stimulatory genes and the prognosis of NSCLC patients. A TRM-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the TRM signature revealed low TRM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Altogether, this study provides valuable insights into the complex interactions between TRM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification.

Humans↗
PubMed2026

Multi-omics identification and functional validation of signal regulatory protein gamma as a prognostic biomarker and immune regulator in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) comprises biologically diverse tumors, and durable responses to immune-checkpoint blockade are achieved by only a subset of patients. There remains a need for markers that connect clinical outcome with malignant-cell phenotypes and tissue-level immune organization. METHODS: We integrated The Cancer Genome Atlas HNSCC cohort (TCGA-HNSC), five Gene Expression Omnibus (GEO) validation cohorts, single-cell RNA sequencing, Visium spatial transcriptomics, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)-informed protein-potential inference, pharmacogenomic screening, genetic-risk analysis and experimental validation. A reconstructed 296-pipeline survival modelling framework was used to prioritize prognostic hub genes across validation-cohort-specific analyses. RESULTS: SIRPG was repeatedly ranked among the top ten selected genes in all five validation cohorts. At single-cell resolution, SIRPG-high tumor cells showed stronger malignant-cell features, immune-inhibitory and metabolic programs, Scissor-positive risk association, CLCA2/P53-related perturbation signals and inferred SIRPG-CD47/signal regulatory protein (SIRP) communication. Spatial analyses placed this axis within an immune-checkpoint-coupled niche, supported by Maxspin/multiview intercellular spatial modelling (MISTy) spatial coupling, communication analysis by optimal transport (COMMOT)-inferred CD47-SIRPG communication and scProTrans-inferred CD47/SIRPG protein-potential overlap. Functionally, SIRPG knockdown reduced HNSCC cell viability and increased apoptosis, whereas re-expression of short hairpin RNA (shRNA)-resistant SIRPG restored the CLCA2-BAX/BCL2 protein response. CONCLUSION: Together, these findings identify SIRPG as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.

Humans↗
PubMed2026

Efferocytosis related KCTD12 is a clinico-immune target in lung adenocarcinoma.

BACKGROUND: Efferocytosis, the clearance of apoptotic cells by phagocytes, contributes to immune homeostasis but may also promote tumor immune tolerance. However, its transcriptional landscape and clinical relevance in lung adenocarcinoma (LUAD) remain incompletely understood. METHODS: We systematically analyzed efferocytosis-associated genes across TCGA and multiple GEO datasets to classify LUAD subtypes and construct a prognostic risk model. The prognostic and immunological relevance of this model was validated in four independent cohorts and further assessed through immune infiltration, genomic, and immunotherapy datasets. Functional and pharmacogenomic analyses were performed to identify potential therapeutic vulnerabilities, and the efferocytosis-associated signature gene KCTD12 was subsequently validated in vitro. RESULTS: Unsupervised clustering identified two efferocytosis-based LUAD subtypes with distinct prognostic and immune-metabolic characteristics. The derived risk model robustly predicted overall survival across validation cohorts. Among the model genes, KCTD12 emerged as an efferocytosis-associated candidate linked to an immune-active tumor microenvironment. Across the analyzed single-cell, spatial transcriptomic, and immunotherapy-treated cohorts, higher KCTD12 expression was associated with enhanced cytotoxic T-cell activity and more favorable treatment outcomes. Functional experiments confirmed that KCTD12 suppresses tumor cell proliferation, reduces colony formation, and enhances OT-1 CD8+ T-cell activation and cytotoxicity. CONCLUSIONS: Our study identifies an efferocytosis-associated transcriptional program linked to immune heterogeneity and prognosis in LUAD. The efferocytosis-related risk signature provides a framework for prognostic and immune stratification, while KCTD12 represents a candidate biomarker associated with immune activation and clinical outcomes in immunotherapy-treated cohorts. Its treatment-specific predictive value requires prospective validation in appropriately controlled studies.

KCTD12↗
PubMed2025

Large Language Model and Knowledge Graph-Driven AJCC Staging of Prostate Cancer Using Pathology Reports.

Background/Objectives: To develop an automated American Joint Committee on Cancer (AJCC) staging system for radical prostatectomy pathology reports using large language model-based information extraction and knowledge graph validation. Methods: Pathology reports from 152 radical prostatectomy patients were used. Five additional parameters (Prostate-specific antigen (PSA) level, metastasis stage (M-stage), extraprostatic extension, seminal vesicle invasion, and perineural invasion) were extracted using GPT-4.1 with zero-shot prompting. A knowledge graph was constructed to model pathological relationships and implement rule-based AJCC staging with consistency validation. Information extraction performance was evaluated using a local open-source large language model (LLM) (Mistral-Small-3.2-24B-Instruct) across 16 parameters. The LLM-extracted information was integrated into the knowledge graph for automated AJCC staging classification and data consistency validation. The developed system was further validated using pathology reports from 88 radical prostatectomy patients in The Cancer Genome Atlas (TCGA) dataset. Results: Information extraction achieved an accuracy of 0.973 and an F1-score of 0.986 on the internal dataset, and 0.938 and 0.968, respectively, on external validation. AJCC staging classification showed macro-averaged F1-scores of 0.930 and 0.833 for the internal and external datasets, respectively. Knowledge graph-based validation detected data inconsistencies in 5 of 150 cases (3.3%). Conclusions: This study demonstrates the feasibility of automated AJCC staging through the integration of large language model information extraction and knowledge graph-based validation. The resulting system enables privacy-protected clinical decision support for cancer staging applications with extensibility to broader oncologic domains.

artificial intelligence↗
PubMed2026

Exploring the Role of HSD17B2 in Colorectal Cancer Through Bioinformatic Analysis: Preliminary Insights for Prognostic Evaluation.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer-related mortality worldwide. Although screening has reduced CRC in older adults, cases in younger individuals are rising, highlighting the need for early biomarkers. Emerging research highlights the role of estrogen metabolism in CRC progression, with enzymes such as hydroxysteroid (17-beta) dehydrogenase (HSD17B) being increasingly implicated. In this study, we performed a bioinformatics analysis using publicly available datasets, including The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) cohort and two independent Gene Expression Omnibus (GEO) cohorts (GSE40967 and GSE41258), to investigate the role of HSD17B enzymes in CRC. Our results suggest that HSD17B2 is frequently downregulated in precancerous lesions and early-stage CRC, which may contribute to elevated estradiol levels and a tumor-promoting microenvironment. In advanced stages, higher HSD17B2 expression levels are associated with poorer survival outcomes in retrospective cohorts. Other HSD17B enzymes also exhibit significant expression changes, further complicating the hormonal landscape of CRC. In addition, estrone, traditionally considered a weaker estrogen, emerges as a potential driver of CRC progression. Our in-silico analyses indicate that HSD17B2 and HSD17B11 warrant further investigation as candidate biomarkers for distinguishing CRC from benign and precancerous conditions, with the combination showing strong discriminatory power in Receiver Operating Characteristic (ROC) analyses. Overall, these findings highlight the potential role of estrogen metabolism in CRC and suggest that HSD17B enzymes may hold value as candidate prognostic and diagnostic indicators, though their clinical utility remains hypothetical at this stage. Experimental and clinical validation is strictly required to confirm these in silico observations and to clarify their mechanisms in CRC.

Humans↗
PubMed2026

Methylation-Associated Differentiation Features Define Biological and Prognostic Heterogeneity in CMS4 Colorectal Cancer.

Consensus molecular subtype 4 (CMS4) colorectal cancer (CRC) is associated with an aggressive clinical course and poor survival, yet the biological basis of heterogeneity within this subtype remains incompletely understood. DNA methylation is an epigenetic mechanism involved in transcriptional regulation, cellular differentiation, and colorectal tumorigenesis. Here, we integrated single-cell RNA sequencing (scRNA-seq), bulk data, and promoter DNA methylation data to characterize CMS4-associated cancer cell states and methylation-related features. Using the scAB algorithm, we integrated scRNA-seq with bulk CMS4 data and identified CMS4-related cells distributed across multiple patients. Single-cell analyses of cell-cell communication and transcriptional regulation revealed a CMS4-related cancer cell population characterized by macrophage migration inhibitory factor (MIF)-centered intercellular communication, enhanced caudal type homeobox 1 (CDX1) and Kruppel-like factor 5 (KLF5) regulon activity, and gene modules enriched in differentiation-related pathways. CytoTRACE analysis further stratified CMS4 cancer cells into poorly and well-differentiated states, yielding 802 differentially expressed genes (DEGs). Linking these differentiation-associated DEGs with bulk expression and promoter methylation data identified 218 methylation-associated DEGs showing significant inverse methylation expression correlations, suggesting a link between differentiation-related heterogeneity and promoter methylation. Univariable Cox regression followed by LASSO regression further prioritized eight genes for construction of the methylation and differentiation-related prognostic model (MeDiff-PM). MeDiff-PM consistently stratified overall survival in the TCGA CMS4 cohort and two independent validation cohorts, with cutoff-independent continuous Cox analyses further supporting its prognostic association across cohorts. And MeDiff-PM remained prognostically significant after adjustment for available clinical variables. High MeDiff-PM risk scores were associated with activation of P53, WNT, and ubiquitin-mediated proteolysis pathways and with consistent predicted drug response differences for compounds across three CMS4 cohorts. While individual in silico knockout analysis suggested links between MeDiff-PM genes and metallothionein-related and immune-associated transcriptional responses. Collectively, these findings indicate that methylation-associated differentiation features represent a molecular dimension of intra-CMS4 heterogeneity and provide a biologically informed framework for prognostic stratification within CMS4 CRC.

Humans↗
PubMed2026

Primary Tumor Epigenetic and Transcriptomic Alterations Associated with Nodal Burden and Metastatic Risk in ER+/HER2- Breast Cancer.

De-escalation of axillary surgery has resulted in the loss of pathologic nodal information, yet the extent of lymph node involvement remains an important determinant of treatment decisions in estrogen receptor-positive (ER+)/HER2- disease. We examined whether primary tumors differed molecularly according to the extent of this regional dissemination. Genome-wide DNA methylation profiling of primary ER+/HER2- tumors from 47 patients with pN1 (n = 29) vs. >pN1 (n = 18) disease showed differences concentrated at promoters of developmental and cell-adhesion genes. By integrating methylomes with transcriptomes from the TCGA-BRCA cohort (n = 148) and clinical outcomes from KM Plotter (RFS, n = 1154; OS, n = 442; DMFS, n = 423), we identified four genes (ARL10, RIC3, CXCL14, KCNH2) showing concordant molecular and clinical associations, from which we derived the Lymph-node Involvement Outcome Numerator (LION) score. Lower LION scores were observed in metastatic lesions from the AURORA US cohort (n = 45). In SCAN-B (n = 3969), lower scores were associated with shorter distant recurrence-free intervals (HR = 0.38; 95% CI 0.23-0.62); this association persisted after adjustment for age, nodal and tumor category but was lost after adjustment for histological grade (HR = 0.83; 95% CI 0.48-1.44), indicating that the score and grade capture overlapping biology. These findings suggest that primary tumors already display coordinated epigenetic and transcriptional alterations associated with the extent of metastatic dissemination.

Humans↗
PubMed2026

Machine learning prognostic model and drug survival analysis for lung adenocarcinoma in the context of radiotherapy.

BACKGROUND: Patients with lung adenocarcinoma (LUAD) receiving radiotherapy represent an important but underexplored clinical subgroup. These patients often undergo concomitant pharmacologic treatments, yet the prognostic impact and underlying determinants of such combined regimens remain poorly understood. OBJECTIVE: This retrospective observational study aimed to develop and validate a radiotherapy-specific machine learning prognostic model for LUAD and to compare survival across concomitant pharmacologic regimens. METHODS: In this retrospective observational study, using genomic and clinical data from TCGA, a radiotherapy-specific prognostic model for LUAD was developed and validated through ten machine learning algorithms. Survival analyses were conducted across distinct concomitant pharmacologic strategies, followed by functional enrichment to elucidate molecular mechanisms underlying differential outcomes. RESULTS: Demonstrating robust prognostic abilities, the model efficiently sorted patients into high- and low-risk categories. Both treatment type and risk score independently predicted overall survival, with significant interaction effects. Low-risk patients receiving targeted or combination therapy-mainly erlotinib, gefitinib, or bevacizumab-exhibited substantially improved survival compared with those receiving conventional chemotherapy. Enrichment of "Exogenous peptide presentation," "MHC class II assembly," "Peptide-MHC II assembly," and "Symbiotic interaction" pathways indicated immune modulation and host-tumor crosstalk as key mediators of treatment efficacy. CONCLUSION: This study establishes a radiotherapy-specific prognostic model for lung adenocarcinoma, demonstrating distinct molecular and therapeutic heterogeneity and highlighting the superior survival benefit of targeted combination therapy in low-risk patients.

Humans↗
PubMed2026

[Laryngeal squamous cell carcinoma-derived exosomes promote neuronal axonal growth by remodeling the neural microenvironment].

Objective: Perineural invasion (PNI) is a critical determinant of poor prognosis in laryngeal squamous cell carcinoma (LSCC), but its underlying mechanisms remain unclear. This study aimed to investigate whether LSCC-derived exosomes induce axonal growth by delivering neuroactive molecules, thereby contributing to tumor perineural invasion. Methods: Clinical data from the laryngeal cancer cohort of The Cancer Genome Atlas Head and Neck Squamous Cell Carcinoma (TCGA-HNSC) dataset were analyzed. Propensity score matching (PSM) and Cox regression were used to evaluate the prognostic value of nerve density, and these findings were validated using 35 pairs of laryngeal cancer and adjacent normal tissue specimens collected at Yantai Yuhuangding Hospital between 2022 and 2026 to assess neural morphological changes. Exosomes were isolated from the human LSCC cell line AMC-HN-8, characterized by quality-control assays, and co-cultured with PC12 cells. A rescue experiment using GW4869, a specific inhibitor of neutral sphingomyelinase, was performed to confirm the exosome-dependent effect. Neurite outgrowth was evaluated by immunofluorescence, and the expression of axonal growth-related genes was measured by RT-qPCR. Targeted metabolomics was employed for the absolute quantification of neuroactive metabolites within the vesicles and for pathway enrichment analysis. Results: After PSM adjustment, high nerve density was identified as an independent poor prognostic factor in LSCC patients (HR=2.10, P=0.035), with particularly pronounced prognostic value in the early-stage node-negative (N0) subgroup (HR=4.07, P=0.001). Pathological sections showed high expression of the neural markers &#x3b2;III-tubulin and PGP9.5 in LSCC tissues (&#x3b2;III-tubulin: t=2.234, P<0.05; PGP9.5: t=2.575, P<0.05). Exosomes were successfully isolated from AMC-HN-8 cells and passed quality control. In vitro assays showed that LSCC-derived exosomes significantly promoted neurite extension and branching in PC12 cells (t=4.147, P<0.000 1) and upregulated core axonal growth genes, including GAP-43, NEFL, and NEFM (GAP-43: t=3.698, P<0.05; NEFL: t=5.113, P<0.01; NEFM: t=5.263, P<0.01); this effect was completely reversed by the exosome-release inhibitor GW4869 (t=3.535, P<0.001). Targeted metabolomics revealed a specific enrichment of 12 neurotransmitters and metabolites within LSCC exosomes, centered on glutamine (83.411 &#x3bc;mol/L, FC=1.88) and glutamate (18.461 &#x3bc;mol/L, FC=1.21), which were significantly enriched in signaling pathways such as "central carbon metabolism in cancer" and "glutamatergic synapse". Conclusion: Nerve density is a potential adverse prognostic factor in patients with LSCC. LSCC-derived exosomes can directly induce axonal growth in neuron-like cells, suggesting that tumor cells actively remodel the neural microenvironment and drive axonal growth through exosome-mediated long-range signaling.

Exosomes↗
PubMed2026

Oxidative Stress Associated LncRNAs as Potential Biomarkers for Prognosis and Immune Responses in Lung Squamous Cell Carcinoma Patients.

Long-chain non-coding RNA (lncRNA) significantly influences lung squamous cell carcinoma's (LUSC) prognostic value and immune infiltration. This study aimed to demonstrate how oxidative stress-related lncRNAs impact lung squamous cell carcinoma (SCC). The Cancer Genome Atlas (TCGA) dataset gathered transcriptome information and related clinical data for LUSC. To build a prognostic model, 10 prognostic-related genes were identified using a series of bioinformatics analyses that compared the OS gene's aberrant expression in tumor and healthy tissues, as well as its association with malignancy. Subjects were stratified into high- and low-risk groups based on the median risk score derived from the 10-gene signature. While the mathematical risk model demonstrated limited independent predictive performance in the validation cohort (AUC ~ 0.5), functional and immunological evaluations revealed significant differences in the tumor microenvironment (TME) across risk strata. Specifically, high-risk patients exhibited distinct immune infiltration profiles and altered immunological scores relative to their low-risk counterparts. Therefore, rather than serving as a direct clinical prediction tool, this oxidative stress-related lncRNA signature provides valuable biological insights into the immune landscape of LUSC and highlights potential therapeutic targets for further mechanistic investigation.

Humans↗
PubMed2026

Association of MPO Expression with the Immune Microenvironment in Breast Cancer: Insights from Bioinformatics and Single-Cell Analyses.

Breast cancer remains a major cause of cancer-related mortality, and exploratory computational workflows can help prioritize immune-associated markers for further investigation. Here, we used the cancer genome atlas breast invasive carcinoma (TCGA-BRCA) bulk transcriptomic data and the public single-cell dataset GSE161529 to examine associations between myeloperoxidase (MPO) expression, clinical outcomes, immune infiltration, methylation, upstream-regulator annotations, single-cell expression patterns, virtual knockdown sensitivity outputs, drug-gene interaction retrieval, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) annotation. MPO expression was lower in breast cancer tissues than in adjacent non-tumor tissues. Higher MPO expression was associated with a longer progression-free interval, whereas its associations with overall survival and disease-specific survival were not statistically significant. Receiver operating characteristic (ROC) analysis suggested tumor-normal separation within the analyzed public dataset, but this should not be interpreted as clinical diagnostic validation. Immune deconvolution and enrichment analyses indicated that MPO expression mainly tracked with immune- and myeloid-related transcriptional features, rather than establishing tumor-intrinsic regulation of the immune microenvironment. At single-cell resolution, the MPO signal was sparse, with only 85 MPO-positive cells detected before k-nearest neighbor (KNN)-based neighborhood expansion. Detectable MPO signal and MPO-associated scores were interpreted cautiously because they may be influenced by sparse expression, cell-type annotation uncertainty, dropout, doublets, or ambient RNA. In silico virtual knockdown suggested candidate immune- and inflammatory-related transcriptional changes, but these results were considered exploratory and require validation. Drug-gene interaction database (DGIdb)-based drug-gene retrieval and ADMET annotation were used only as preliminary chemical annotations and were not interpreted as therapeutic evidence. Overall, this study provides a reproducible in silico workflow for generating hypotheses about MPO-associated immune/myeloid features in breast cancer, which require external cohort validation and experimental confirmation.

Humans↗