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PubMed2025

Comprehensive Analysis of Clinical and Molecular Features in Cancer Patients Associated With Major Human Oncoviruses.

Viral infections contribute to a higher incidence of cancer than any other individual risk factor. This study aimed to compare the clinical and molecular features of four viral-associated cancers: stomach adenocarcinoma (STAD), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), and cervical squamous cell carcinoma (CESC). Patients were categorized based on viral infection status, as provided in the clinical data, into virus-associated and non-virus-associated groups, followed by a comprehensive comparison of clinical and molecular features. Our analysis disclosed that viral infections confer unique clinical and molecular signatures to their associated tumors. Specifically, human papillomavirus-associated (HPV+) HNSC and hepatitis B virus-associated (HBV+) LIHC patients were predominantly male, younger, and exhibited better clinical prognoses. Virus-associated tumors displayed enhanced immune microenvironments and high DNA damage response scores, while non-virus-associated tumors were enriched in stromal signatures. HPV+ HNSC and Epstein-Barr virus-associated (EBV+) STAD showed similarities across multi-omics features, including better responses to immunotherapy, lower TP53 mutation rates, tumor mutation burden (TMB), and copy number alteration (CNA). Conversely, HBV+, Hepatitis C virus-associated (HCV+) LIHCs and HPV+ CESC were more genomically unstable due to high TP53 mutation rates, TMB, and CNA. At the protein level, Caspase-7 and Syk were upregulated in HPV+ HNSC and EBV+ STAD, and positively correlated with the enrichment levels of CD8 + T cell, PD-L1, and cytolytic activity. Patient stratification based on infection status has significant clinical implications, particularly for patient prognosis and drug response.

Humans↗

PubMed2026

Gastric carcinoma classification in the WHO 6th edition (2026): Updated framework and emerging entities.

The sixth edition of the WHO Classification of Digestive System Tumours (2026) represents an important step in the continuing evolution of gastric carcinoma classification. While preserving morphology as the foundation of diagnosis, it incorporates advances in molecular pathology, genotype-phenotype correlations, tumour evolution, and predictive biomarker assessment. This review summarizes the development of the WHO classification from the third edition (2000) to the sixth edition (2026) and highlights its relationship with other major classification systems, including those of Laurén, Nakamura, and the Japanese Gastric Carcinoma Association (JGCA). Major histological categories remain largely unchanged; however, several important conceptual and diagnostic refinements have been introduced. These include recognition of crawling-type adenocarcinoma as a distinctive variant of tubular adenocarcinoma, subclassification of poorly cohesive carcinoma into signet-ring cell and non-signet-ring cell subtypes, introduction of the concept of pure signet-ring cell carcinoma, and increased emphasis on tumour evolution. The sixth edition also expands and refines the spectrum of uncommon gastric carcinoma subtypes, including gastric carcinoma with lymphoid stroma, AFP-producing carcinoma, micropapillary adenocarcinoma, gastric adenocarcinoma of fundic-gland type, and gastric sarcomatoid carcinoma. Crucially, molecular subgroups originally proposed by The Cancer Genome Atlas (TCGA) and actionable biomarkers-including HER2 (ERBB2), Claudin 18.2, mismatch repair deficiency/microsatellite instability (dMMR/MSI), and programmed death-ligand 1 (PD-L1)-have transitioned from research-based categories into essential tools for precision oncology. Rather than providing exhaustive diagnostic criteria, this review offers a conceptual framework and encourages consultation of the original WHO text for full details. These advances illustrate the transition of gastric carcinoma classification from a predominantly morphology-based system toward an integrated histomolecular framework that more closely links pathological diagnosis with tumour biology, prognostication, and therapeutic stratification.

Crawling-type adenocarcinoma↗

PubMed2026

Screening and identification of key genes related to the immune microenvironment of rectal cancer influenced by radiotherapy based on bioinformatics methods.

OBJECTIVE: Radiotherapy (RT) plays a crucial role in the comprehensive treatment of rectal cancer. However, the impact of radiotherapy on the tumor microenvironment (TME), especially its effect on immune cell infiltration and immune-related gene expression, has not been fully studied. This study aims to screen and analyze key genes related to the immune microenvironment of rectal cancer influenced by radiotherapy based on bioinformatics methods for the purpose of identifying potential biomarkers and providing new insights for the personalized therapy of rectal cancer. METHODS: Using data from the Public Gene Expression Database (GEO) and the Cancer Genomics Database (TCGA), the impact of radiotherapy on the immune microenvironment of rectal cancer was explored using bioinformatics tools. Through screening differentially expressed genes (DEGs), correlation analysis, TIMER database analysis, immune infiltration score, and correlation analysis between key genes and prognosis, the effects of radiotherapy on the immune microenvironment of rectal cancer were investigated. RESULTS: Totally 7 upregulated and 4 downregulated differentially expressed genes were identified, among which MASP1, LTK, SLC9A3R2 were negatively correlated with myeloid suppressor cell infiltration (MDSCs), while ZP2 was positively correlated. The expression of MASP1 and SLC9A3R2 was closely related to the level of immune cell infiltration and played significant roles in the immune microenvironment. High expression of MASP1 was significantly correlated with survival benefits from immune checkpoint inhibitor therapy, while SLC9A3R2 was closely related to the efficacy of PD-L1 inhibitors and CTLA4 inhibitors. CONCLUSIONS: MASP1 and SLC9A3R2, as two key genes that may be related to the immune microenvironment of rectal cancer radiotherapy, deserve further exploration of their roles in the mechanism. The combination of radiotherapy and immunotherapy holds promising prospects in the treatment of rectal cancer, and exploration of related mechanisms will provide new strategies and targets for the treatment of various tumors and rectal cancer.

Bioinformatics↗

PubMed2026

Respiratory viral infections prime accelerated lung cancer growth.

The COVID-19 pandemic has highlighted the long-term consequences of viral pneumonia, yet its impact on cancer development remains unclear. Here, we show that patients previously hospitalized with severe COVID-19 have an increased risk of subsequent lung cancer. Across multiple murine models, severe respiratory viral infections accelerated lung cancer growth, whereas vaccination mitigated infection-enhanced tumor progression. Mechanistically, prior viral pneumonia reprogrammed the lung into a pro-tumor microenvironment marked by the sustained accumulation of tumor-associated neutrophils and heightened immunosuppression. We observed persistent chromatin remodeling at key cytokine loci in immune and structural cells, linking inflammatory memory to tumor-promoting signals. Therapeutically, combined blockade of neutrophil recruitment and programmed death-ligand 1 (PD-L1) restored CD8+ T cell function and suppressed tumor growth. Together, these findings establish a causal link between prior viral pneumonia and lung tumorigenesis, underscoring the need for enhanced surveillance and targeted interventions to reduce post-COVID cancer risk.

Animals↗

PubMed2026

Comprehensive molecular profiling of advanced NSCLC in Greek patients:A prospective HeCOG study.

BACKGROUND: We report for the first time the molecular landscape and outcome associations from the prospective CLIMEDIN trial in Greece. METHODS: Two hundred patients with newly diagnosed advanced NSCLC (March 2022-October 2023) were enrolled and randomized to standard-of-care education versus additional automated, adverse-event-targeted digital interventions. Within this study baseline testing (EGFR, ALK, PD-L1) was performed in all; 165 tumors underwent comprehensive NGS (Oncomine Comprehensive Assay v3). Primary endpoint was improvement in AEs/QoL; secondary endpoints included ORR, PFS and OS. Associations between genomic alterations and outcomes were explored. RESULTS: Median age was 68 years; 75% male; 52% current smokers; adenocarcinoma 68.5%. Most received chemo-immunotherapy (66%). At data cut-off (December 2025; reverse-Kaplan-Meier median follow-up 36.3 months), median PFS was 9.6 months and median OS was 15.2 months. Across 200 tumors, 495 pathogenic variants (PVs) were identified in 83 genes. Exploratory outcome analyses showed longer OS in EGFR-mutant disease (preserved under parsimonious multivariable adjustment) and a formal KRAS × smoking interaction for OS (interaction P = 0.011). CONCLUSIONS: In this cohort, the molecular profile mirrors other Caucasian series, with clinically relevant enrichment patterns for EGFR and KRAS. ECOG performance status and first line treatment were the dominant prognostic factors in this cohort. A novel KRAS and smoking interaction for overall survival warrants prospective validation.

Aged↗

PubMed2026

Identification and characterization of ectopic chromosomal amplifications in acute myeloid leukemia cell limes using high-throughput chromosome conformation capture screening.

Despite advanced molecular diagnostics, improving outcomes for refractory acute myeloid leukemia (AML) remains challenging. Although many cancer-related genes are identified, their molecular mechanisms are not fully elucidated. Amplification is a mechanism of cancer-associated gene activation, and ectopic gene amplification may have particularly high pathological significance. However, research on ectopically amplified cancer-associated genes in leukemia remains limited. Here, we evaluated the usefulness of high-throughput chromosomal conformation capture (Hi-C) as a screening method for ectopic gene amplification and assessed whether ectopic amplification of cancer-associated genes may represent a general phenomenon in AML. We screened the U-937 and NB-4 cell lines using in situ Hi-C. Regions appearing as "high-intensity bands" in Hi-C contact maps were identified and validated using fluorescence in situ hybridization (FISH). Additionally, copy number variation analysis was performed using whole-genome sequencing (WGS) to extract cancer-associated genes with ectopic amplification. In the U-937, three genomic regions showing "high-intensity bands" were identified and confirmed as ectopic amplifications-including PDCD1LG2 (PD-L2), CD274 (PD-L1), and JAK2; that is, four copies were detected by WGS, and amplification signals were observed by FISH. In the NB-4, four such regions were detected, including MYC and KRAS, with expression level of 498 transcripts per million (TPM) and 34 TPM, respectively. Copy number variation analysis further identified multiple cancer-associated genes with ectopic amplification. Overall, these findings demonstrate the presence of ectopic amplification of cancer-associated genes in AML cell lines and support the usefulness of Hi-C as a screening method for detecting such genomic alterations.

Acute myeloid leukemia↗

PubMed2024

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer.

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Humans↗

PubMed2026

Low-Grade Myofibroblastic Sarcoma Represents an Epigenetically Distinct Myofibroblastic Tumor With USP6 Upregulation and Stable Genome.

Low-grade myofibroblastic sarcoma (LGMS) is a rare, indolent mesenchymal neoplasm exhibiting myofibroblastic differentiation, with a propensity for local recurrence. The molecular basis of LGMS and its precise relationship with other histological mimics have remained largely undefined. To address this gap, we conducted the first comprehensive multiomics analysis of 6 LGMS cases, integrating whole-exome sequencing, RNA sequencing, and Illumina Methylation EPICv2 array profiling with comparative analysis against public sarcoma methylation cohorts and related fibroblastic tumors. Clinically, patients (median age 35.5 years) presented with small tumors (median size 1.45 cm), predominantly located in the head and neck, displaying classic histological features of diffusely infiltrative spindle cell fascicles with patchy mononuclear inflammation. Two of the 5 patients with follow-up developed local recurrence, and none metastasized (median follow-up duration 92.5 months). Genomically, all LGMS exhibited a low tumor mutational burden (median 2.31 mut/Mb) and a minimal fraction of genome altered, with TP53 and TSC2 deletions and NTRK1 and ERBB3 amplifications found in a subset of cases. No pathogenic fusions were detected. Transcriptomic profiling revealed a distinct signature featuring prominent USP6 overexpression and upregulation of inflammatory and immune-related genes, including CD274 (PD-L1), and enrichment of inflammatory and interferon-gamma response signatures. Epigenetically, LGMS formed a unique methylation cluster closest to inflammatory myofibroblastic tumor, with numerous differentially methylated regions and higher immune infiltration, particularly monocytes, compared with other fibroblastic tumors. These findings establish LGMS as a genomically stable, epigenetically distinct myofibroblastic sarcoma driven by USP6 overexpression and an inflammation-enriched transcriptome. They support its recognition as a standalone entity, facilitate integration into methylation-based sarcoma classifiers for improved diagnostic precision, and nominate USP6-associated pathways and immune checkpoint blockade as promising therapeutic strategies for recurrent or unresectable disease.

Humans↗

PubMed2026

Decoding tumor immune microenvironment heterogeneity by single-cell and spatial multi-omics: From immunotherapy resistance to translational biomarkers.

Immune checkpoint blockade has transformed cancer therapy, yet primary and acquired resistance remain major clinical challenges. Increasing evidence indicates that immunotherapy resistance cannot be fully explained by tumor-intrinsic alterations or conventional biomarkers such as PD-L1 expression, tumor mutational burden, or microsatellite instability. Instead, therapeutic response is shaped by the tumor immune microenvironment (TIME) as a heterogeneous, spatially organized, and dynamically evolving ecosystem. Single-cell omics has revealed diverse immune and stromal cell states, including progenitor and terminally exhausted T cells, suppressive myeloid programs, B-cell/TLS-associated immune-reactive states, and CAF-mediated exclusion phenotypes. Spatial transcriptomics, spatial proteomics, and imaging-based approaches further demonstrate that these cell states assemble into distinct immune niches, including immune-inflamed, T-cell-excluded, myeloid-suppressive, metabolic/hypoxic, and TLS-associated niches. These spatial ecosystems determine whether antitumor immune cells can access malignant cells, receive antigen-presenting support, or become restrained by stromal, vascular, metabolic, and myeloid barriers. In this review, we summarize how single-cell and spatial multi-omics redefine TIME heterogeneity in immunotherapy resistance, highlight ligand-receptor communication networks linking cell states to spatial immune dysfunction, and discuss emerging translational biomarkers for patient stratification. We further propose that future immunotherapy biomarkers should evolve from static single-marker assays toward longitudinal, spatially resolved, and interpretable multi-omics models that guide precision combination immunotherapy.

Humans↗

PubMed2026

Radiomics as a spatial context for treatment decision-making in head and neck cancer.

Radiomics has been widely explored as a non-invasive biomarker in head and neck squamous cell carcinoma (HNSCC), yet its clinical role remains unclear. Tissue-based biomarkers differ in their susceptibility to spatial sampling. Biomarkers such as PD-L1 expression, immune-cell infiltration, necrosis, and immune exclusion may exhibit substantial spatial heterogeneity, whereas HPV/p16 status and some genomic alterations are generally more stable across the tumor. Nevertheless, localized sampling may incompletely capture heterogeneity in selected clinical contexts. This mismatch becomes clinically relevant when treatment decisions, particularly for chemoradiotherapy, immunotherapy, or de-escalation, are based on potentially non-representative biopsy findings. In this narrative review, we argue that the role of radiomics is not to outperform established biomarkers, but to contextualize them by capturing spatial heterogeneity related to hypoxia, necrosis, stromal architecture, and immune exclusion. We synthesize current evidence linking radiomic features to these biological processes and map them to specific clinical decision points, including larynx preservation, immunotherapy stratification, and recurrence assessment. Rather than serving as a standalone predictor, radiomics may provide complementary spatial information that helps identify situations in which biopsy-derived biomarkers should be interpreted with caution. Although current evidence is largely retrospective, radiomics offers a pragmatic framework for integrating spatial information into biomarker-guided clinical workflows.

Journal Article↗

PubMed2026

Clinical translation of senescence-related pan-cancer multi-omics: tools for assessment and immunotherapy prediction.

Cellular senescence (CS) exerts dual roles in tumorigenesis, yet its pan-cancer molecular characteristics and clinical value remain unclear, hindering its translation to oncology and personalized therapy. To address the lack of specific and universal tools for senescence assessment and immunotherapy response prediction, this study systematically analyzed 1259 CS-related genes from the CellAge database across 31 cancer types by integrating multi-omics data, including bulk RNA-seq, single-cell/spatial transcriptomics, and CRISPR screening. We developed a rank-based algorithm SenScoreR (publicly available at https://gxhub.shinyapps.io/SenScoreR/ ) for senescence quantification, validated with 10 independent datasets, and constructed a machine learning-based predictive model CS.Sig for immunotherapy response. Results showed that tumors had significantly lower Rank-based Senescence Score (RSS) than normal tissues across 31 cancers (average diagnostic AUC = 0.895), with low RSS linked to poor survival; high RSS correlated with reduced genomic instability, enriched CD8⁺ T/NK cell/macrophage infiltration, upregulated PD-L1 expression, and elevated immune cytolytic activity. CS.Sig demonstrated robust performance in predicting ICI response (AUC = 0.716 across 10 cohorts), outperforming 13 existing signatures, while CRISPR screening identified 17 senescence-related targets (e.g., CEP55, PPP1CC) whose knockout enhanced anti-tumor immunity. Our findings clarify CS's role in maintaining tumor genomic stability and shaping immune microenvironments, and the developed SenScoreR, CS.Sig, and identified targets bridge basic CS research with clinical oncology, providing a translational resource and hypothesis basis for future experimental and clinical validation.

Journal Article↗

PubMed2026

Predictive Biomarkers for Immune Checkpoint Inhibitor Efficacy: Challenges, Innovations, and a Pathway to Precision Medicine in the Era of Cancer Immunotherapy.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed oncology practice. However, treatment response remains heterogeneous, rendering predictive biomarkers critical for optimal patient care. The 3 established biomarkers, programmed death-ligand 1, tumor mutational burden (TMB), and microsatellite instability-high/deficient mismatch repair, are approved and clinically validated but are modest predictors of benefit. As a result, multiple novel predictive biomarkers remain under investigation. CONTENT: This review highlights established and investigational predictive ICI efficacy biomarkers. For established biomarkers, we describe biology, assay modalities, approved companion diagnostics, landmark studies, and notable limitations. Due to the multisystem nature of antitumor immune effects, investigational biomarkers span multiple domains, including tumor genomic biomarkers (e.g., mutational signatures, TMB, neoantigen clonality), tumor microenvironment (e.g., tumor-infiltrating lymphocytes [TILs], tertiary lymphoid structures), systemic immune biomarkers (e.g., cytokines, autoantibodies, glycoproteins, peripheral blood mononuclear cells), and the microbiome (e.g., gastrointestinal microbial diversity, responder-enriched taxa). SUMMARY: The established biomarkers PD-L1, TMB, and microsatellite instability-high/deficient mismatch repair inform ICI use in clinical practice but have important limitations. Multiple investigational biomarkers show promise in refining patient selection and optimizing therapy. Moving forward, increased assay harmonization, prospective validation, and standardized parameters may improve performance. Composite models integrating complementary signals across domains may further individualize treatment and lead to an era of personalized cancer immunotherapy.

Humans↗

PubMed2026

Matched targeted therapy use after broad genomic profiling in advanced Non-Small cell lung cancer.

INTRODUCTION: While broad genomic profiling is increasingly used in advanced NSCLC (aNSCLC), the impact of test results on subsequent guideline-concordant targeted therapy selection remains incompletely understood. METHODS: Using a merged dataset of two large, nationwide, patient-level databases, we identified patients who were diagnosed with aNSCLC 2017-2023, had potentially actionable genomic profiling findings, and initiated systemic therapy. Patients were categorized into actionability subgroups based on contemporaneous regulatory approvals and NCCN guideline recommendations. Within each subgroup, we assessed receipt of guideline-concordant targeted therapy within 24 months, including potential underuse (non-receipt of recommended treatment) and overuse (receipt of non-recommended treatment). RESULTS: Among 6620 patients (67.4% ≥65 years, 54.6% female, 68.9% White), guideline-concordant targeted therapy use varied substantially by actionability category: 2313 (89.6%) of 2582 patients with available 1st-line on-label options received them (10.4% underuse), while 212 (67.3%) of 315 patients with available later-line on-label options received them after 1st-line (32.7% underuse). Among 441 patients with available guideline-concordant off-label options, only 122 (27.7%) received them (72.3% underuse). Conversely, 238 (8.6%) of 3282 patients received matched but guideline-discordant off-label options, representing overuse of ineffective or unestablished therapies. Smoking history, squamous histology, and high PD-L1 expression were associated with lower targeted therapy receipt. CONCLUSIONS: In this cohort study of aNSCLC care, the guideline concordance of targeted therapy use varied by clinical actionability of molecular testing results. Underuse was more common in patients with later-line and off-label targeted therapy options. Patients with classical smoking-related risk profiles were substantially less likely to receive targeted therapy even when actionable alterations were identified.

Journal Article↗

PubMed2026

A Randomized Phase II Study of Combination Atezolizumab and Varlilumab (CDX-1127) with or without Cobimetinib in Previously Treated Unresectable Biliary Tract Cancer.

PURPOSE: The addition of MEK inhibition (MEKi) to programmed cell death ligand 1 (PD-L1) blockade improves progression-free survival (PFS) in patients with advanced biliary tract cancer. Although MEK inhibitors may increase tumor cell immunogenicity, they can impair T-cell priming/effector function, limiting combination efficacy. We hypothesized that the addition of a CD27 agonist could restore T-cell function and enhance antitumor immunity in this combination. PATIENTS AND METHODS: We conducted a randomized, phase II trial evaluating atezolizumab (840 mg, intravenously, days 1 and 15) in combination with the CD27 costimulatory monoclonal antibody [CDX-1127/varlilumab (3 mg/kg, intravenously, days 1 and 15)], with/without the addition of an MEK inhibitor [cobimetinib (60 mg, orally, daily, days 1-21, off days 22-28)] in unresectable biliary tract cancer following at least one metastatic therapy. Overall response rate (ORR) and PFS were coprimary endpoints. Treatment-related changes in CD8+ tumor-infiltrating lymphocytes (TIL) were the primary correlative outcomes. RESULTS: The trial was closed early following interim preplanned ORR analysis. At closure, 57 patients had been enrolled [n = 29 in the cobimetinib + atezolizumab + varlilumab (CAV) arm; n = 28 in the atezolizumab + varlilumab (AV) arm]. A majority (67%) had intrahepatic cholangiocarcinoma, and 32% were immunotherapy experienced. Both regimens were well tolerated without new safety signals. Objective responses were rare [0% (CAV); 3.8% (AV)]. The median PFS (mPFS) was 2.40 (CAV) and 1.84 (AV) months [hazard ratio (HR), 0.67; 95% confidence interval (CI), 0.38-1.18]. Among immunotherapy-experienced patients, the mPFS was 3.62 (CAV) and 1.84 (AV) months (HR, 0.54; 95% CI, 0.18-1.62). Treatment with CAV increased intratumoral CD8+ T-cell density compared with treatment with AV. CONCLUSIONS: The combinations of atezolizumab and varlilumab with/without cobimetinib were safe, but neither meaningfully improved outcomes in biliary tract cancer treated in the later lines. Correlative tissue studies validated preclinical work that MEKi increases CD8+ TILs.

Humans↗

PubMed2025

m6A regulator-based molecular classification and hub genes associated with immune infiltration characteristics and clinical outcomes in diffuse gliomas.

BACKGROUND: m6A methylation modification is a new regulatory mechanism involved in tumorigenesis and tumor-immunity interaction. However, its impact on glioma immune microenvironment and clinical outcomes remains unclear. METHODS: Comprehensive expression profiles of 18 m6A regulators were used to identify molecular subtypes exhibiting distinct m6A modification patterns in 1673 glioma samples sourced from public datasets. A multi-genes signature was constructed for predicting clinical outcomes and response to immunotherapy in glioma patients. Immunohistochemistry and cellular experiments were performed for validation. RESULTS: Two m6A subtypes of gliomas were identified. The m6A-low-risk subtype was characterized by paucity of immune infiltrates; While the m6A-high-risk subtype had higher abundances of multiple immune cells including lymphocyte and macrophage as well as increased expression of PD-L1, corresponding to an immunosuppressive phenotype. The m6A-high-risk subtype had poorer survival than the m6A-low-risk subtype in both the glioblastoma and lower grade gliomas cohorts. Eight m6A-related hub genes of high prognostic significances were identified and selected for developing a scoring signature termed as m6Ascore. Elevated m6Ascore indicated worse survival for glioma patients under standard care, but showed enhanced response to immunotherapy. Moreover, we demonstrated that overexpression of FTO, a m6A demethylase, inhibited the expressions of m6A-related hub genes (PTX3, SPAG4), impaired glioma cell viability and reduced macrophage chemotaxis. CONCLUSION: This work develops an immune- and clinical-relevant m6A subtyping and a scoring model, which enhances our understanding of the role of m6A modification in regulating immune infiltration microenvironment in gliomas and helps to identify patients who are more likely to benefit from immunotherapy.

Humans↗

PubMed2025

Malaria: Factors affecting disease severity, immune evasion mechanisms, and reversal of immune inhibition to enhance vaccine efficacy.

Malaria is a complex parasitic disease caused by species of Plasmodium parasites. Infection with the parasites can lead to a spectrum of symptoms and disease severity, influenced by various parasite, host, and environmental factors. There have been some successes in developing vaccines against the disease recently, but the vaccine efficacies require improvement. Some issues associated with the difficulties in developing a sterile vaccine include high antigenic diversity, switching expression of the immune targets, and inhibition of immune pathways. Current vaccine research focuses on identifying conserved and protective epitopes, developing multivalent vaccines (including the whole parasite), and using more powerful adjuvants. However, overcoming the systematic immune inhibition and immune cell dysfunction/exhaustion may be required before high titers of protective antibodies can be achieved. Increased expression of surface molecules such as CD86 and MHC II on antigen-presenting cells and blocking immune checkpoint pathways (interactions of PD-1 and PD-L1; CTLA-4 and CD80) using small molecules could be a promising approach for enhancing vaccine efficacy. This assay reviews the factors affecting the disease severity, the genetics of host-parasite interaction, immune evasion mechanisms, and approaches potentially to improve host immune response for vaccine development.

Humans↗

PubMed2026

Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.

Humans↗

PubMed2026

RPN1 at the crossroads of glycosylation, tumor immunity, and disulfidptosis.

Ribophorin I (RPN1), a core component of the oligosaccharyltransferase complex, is traditionally known for its role in endoplasmic reticulum-associated N-glycosylation. Recent studies have identified RPN1 as an emerging regulator of tumor progression and immunity. Aberrant RPN1 overexpression has been reported in multiple malignancies, including glioma, hepatocellular carcinoma, sarcoma, and triple-negative breast cancer, where it is frequently associated with aggressive clinicopathological features and poor prognosis. RPN1 promotes tumor immune evasion by promoting N-glycosylation and stabilization of programmed death-ligand 1 (PD-L1), thereby enhancing immune checkpoint signaling and directly inhibiting anti-tumor T-cell responses. Consequently, elevated RPN1 expression is consistently associated with an immunosuppressive tumor microenvironment rich in M2 macrophages and poor in CD8+ T cells. More importantly, multiple omics signature analyses indicate RPN1 is integrated into several disulfidptosis-related risk models; however, direct experimental evidence confirming the causal linkage between RPN1 and disulfidptosis remains limited. Correlative database data also show potential associations between RPN1 upregulation and genomic instability and treatment resistance. Based on tiered classification of existing evidence (biochemical functional validation vs. multi-omics correlation), this review systematically summarizes the biological roles of RPN1 in cancer, its functions in tumor immunity and disulfidptosis-associated pathways and finally evaluates its potential as a therapeutic target in precision oncology.

PDL1↗