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PubMed2026

Identification of a novel human gut microbes and microbial metabolites related genes signature for prognostic implication in head and neck squamous carcinomas.

BACKGROUND: The gut microbiota acts as a critical driver influencing the pathogenesis, therapeutic response, and clinical outcomes across various cancer types. This study aimed to investigate the prognostic value of human gut microbes and microbial metabolites related genes (HGMMMRGs) in head and neck squamous cell carcinoma (HNSCC). METHODS: We constructed a prognostic risk model comprising 19 core HGMMMRGs using LASSO penalized regression and a multivariate Cox proportional hazards model. The predictive performance of the model was evaluated through Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, nomograms, and concordance index. In addition, functional enrichment analysis was performed on the differentially expressed risk genes. Furthermore, the relationship between the immune microenvironment of HNSCC and the risk diagnostic model was examined. Western blot analysis was used to assess the expression levels of IL10 in both HNSCC tissues and adjacent normal tissues. Finally, the correlation between IL10 and the gut microbiota was explored. RESULTS: This study developed a risk score model integrating 19 HGMMMRG genes, which can serve as a tool to guide prognosis and immune microenvironment assessment in HNSCC patients. Survival analysis showed that patients in the high-risk group had significantly worse outcomes (P&#x2009;<&#x2009;0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed significant enrichment of differentially expressed genes (DRLs) and immune-related pathways. Western blot analysis further confirmed that IL10 was highly expressed in HNSCC, and the abundance of Faecalibacterium prausnitzii and Enterococcus durans colonies was correlated with IL10 expression. CONCLUSION: We developed a prognostic model for HGMMMRGs that can be effectively used to predict OS in patients with HNSCC. Second, Faecalibacterium prausnitzii and Enterococcus durans can influence the prognosis of patients with HNSCC by mediating the expression IL10 and thereby affecting the prognosis of HNSCC patients. Thus, human gut microbes and microbial metabolite-related genes may be another promising strategy for the treatment of patients with HNSCC.

HNSCC↗
PubMed2024

Integrated ubiquitomics characterization of hepatocellular carcinomas.

BACKGROUND AND AIMS: Patients with aggressive HCC have limited therapeutic options. Therefore, a better understanding of HCC pathogenesis is needed to improve treatment. Genomic studies of HCC have improved our understanding of cancer biology. However, the ubiquitomic characteristics of HCC remain poorly understood. We aimed to reveal the ubiquitomic characteristics of HCC and provide clinical feature biomarkers of the aggressive HCC that may be used for diagnosis or therapy in the clinic. APPROACH AND RESULTS: The comprehensive proteomic, phosphoproteomic, and ubiquitomic analyses were performed on tumors and adjacent normal liver tissues from 85 patients with HCC. HCCs displayed overexpression of drugable targets CBR1-S151 and CPNE1-S55. COL4A1, LAMC1, and LAMA4 were highly expressed in the disease free survival-poor patients. Phosphoproteomic and ubiquitomic features of HCC revealed cross talk in metabolism and metastasis. Ubiquitomics predicted diverse prognosis and clarified HCC subtype-specific proteomic signatures. Expression of biomarkers TUBA1A, BHMT2, BHMT, and ACY1 exhibited differential ubiquitination levels and displayed high prognostic risk scores, suggesting that targeting these proteins or their modified forms may be beneficial for future clinical treatment. We validated that TUBA1A K370 deubiquitination drove severe HCC and labeled an aggressive subtype of HCCs. TUBA1A K370 deubiquitination was at least partly attributed to protein kinase B-mediated USP14 activation in HCC. Notably, targeting AKT-USP14-TUBA1A complex promoted TUBA1A degradation and blocked liver tumorigenesis in vivo. CONCLUSIONS: This study expands our knowledge of ubiquitomic signatures, biomarkers, and potential therapeutic targets in HCC.

Humans↗
PubMed2024

Comprehensive Proteomic Analysis Reveals Distinct Features and a Diagnostic Biomarker Panel for Early Pregnancy Loss in Histological Subtypes.

Early pregnancy loss (EPL) is a common event in human reproduction and is classified into histological subtypes such as hydropic abortion (HA) and hydatidiform moles, including complete hydatidiform moles (CHMs) and partial hydatidiform moles (PHMs). However, accurate diagnosis and improved patient management remain challenging due to high rates of misdiagnosis and diverse prognostic risks. Therefore, diagnostic biomarkers for EPL are urgently needed. Our study aimed to identify biomarkers for EPL through comprehensive proteomic analysis. Ten CHMs, six PHMs, ten HAs, and 10 normal control products of conception were used to obtain a proteomic portrait. Parallel reaction monitoring-targeted proteomic and regression analyses were used to verify and select the diagnostic signatures. Finally, 14 proteins were selected and a panel of diagnostic classifiers (DLK1, SPTB/COL21A1, and SAR1A) was built to represent the CHM, PHM, and normal control groups (area under the receiver operating characteristic curve = 0.900, 0.804/0.885, and 0.991, respectively). This high diagnostic power was further validated in another independent cohort (n&#xa0;=&#xa0;148) by immunohistochemistry (n&#xa0;=&#xa0;120) and Western blot analyses (n&#xa0;=&#xa0;28). The protein SPTB was selected for further biological behavior experiments in&#xa0;vitro. Our data suggest that SPTB maintains trophoblast cell proliferation, angiogenesis, cell motility, and the cytoskeleton network. This study provides a comprehensive proteomic portrait and identifies potential diagnostic biomarkers. These findings enhance our understanding of EPL pathogenesis and offer novel targets for diagnosis and therapeutic interventions.

Humans↗
PubMed2026

A multicenter kidney transplantation study identifies hierarchy and directionality of HLA-DQ alloimmune responses.

HLA-incompatibility between recipient and donor is conventionally assessed by counting serologic mismatches. More recently, tools quantifying differences at the amino acid level are advocated to prognosticate risk for generation of de-novo donor-specific antibodies (dnDSA). Using hierarchical clustering based on HLA evolutionary divergence and amino acid physiochemical unique qualities to evaluate HLA-DQ &#x3b1;&#x3b2;-heterodimers, we demonstrate that HLA-DQ alleles segregate into two main evolutionary clades, and further subdivide into six functional sub-categories. Applying this biologically grounded immune risk-stratification approach to a cohort of 3003 kidney allograft recipients from five transplant centers shows that transplantation across the two evolutionary clades is associated with hierarchy and directionality of humoral immune responses, despite similar values when applying quantitative metrics. Our three-tier risk stratification model identifies patients at higher risk to develop dnDSA (HR:6.77; 95%CI:4.64-9.90), and antibody mediated rejection (HR:4.41; 95%CI:1.44-13.53). This work provides a thoughtful immunologic framework, that outperforms quantitative metrics, for alloimmune risk stratification.

Humans↗
PubMed1978

Snake bite in cats.

In 6 years snake bite was diagnosed in 41 cats, with an average age of 20 months. The commonest presenting signs were dilated pupils, absence of the pupillary light reflex, depression and generalised muscle weakness. Other frequent findings were vomiting, dyspnoea, hindlimb ataxia and complete flaccid paralysis. Thirty-seven cases (90%) occurred in the 6 warmer months of the year. Tiger snakes were positively identified in 7 cases. A recovery rate of 89% was obtained in cases receiving 3000 units Tiger snake antivenene, fluid therapy and nursing. Cases presenting with a complete flaccid paralysis and sub-normal temperatures were poor prognostic risks.

Animals↗
PubMed2025

Multi&#x2011;omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes. METHODS: We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. RESULTS: The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. CONCLUSIONS: The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Humans↗
PubMed1979

Correlation of the FAB morphologic criteria and prognosis in acute lymphocytic leukemia of childhood.

The bone marrows at the time of initial diagnosis of 101 children with acute lymphocytic leukemia diagnosed from April 1969 to December 1974 were evaluated employing the FAB morphologic classification. The group with L1 morphology and a significantly improved duration of first remission and survival when compared to the L2--L3 morphologic groups. Both groups were similar in age and initial level of WBC, indicating that the morphologic classification has prognostic significance in childhood ALL when age and initial WBC were considered together as prognostic risk factors. The L1 morphologic groups survival for the low- and moderate-risk groups was significantly better than for the L1 high-risk group and for the L2--L3 groups.

Adolescent↗
PubMed1976

Incidence of ischaemic heart disease and prognostic importance of risk factors in agricultural and industrial populations.

A longitudinal epidemiological study of ischaemic heart disease (IHD) in men aged 40-59 years showed that the five-year incidence of the manifest form was 7.8% in agricultural workers and 9.7% in industrial employees. The corresponding incidence of the latent form was 9.0% and 12.2%, respectively. The differences in the incidence of both forms between the population samples studied was at the border of statistical significance. The importance of risk factors was evaluated on the basis of the score represented by the difference between the respective five-year incidence of IHD in subjects with and without the risk factors concerned. The highest relative scores were found with systolic and diastolic hypertension, positive family history and obesity. For prognostic purposes, the total relative risk, represented by the sum of the scores of the risk factors proved to be useful. Autopsies confirmed that a total relative risk exceeding 40% was a sensitive indicator of morphological substrate of coronary heart disease.

Adult↗
PubMed2026

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

BACKGROUND: Lymphangiogenesis promotes tumor dissemination and may shape the immune contexture of cervical cancer, yet lymphangiogenesis-related prognostic stratification and its immunometabolic implications remain insufficiently defined in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). METHODS: TCGA-CESC transcriptomes and clinical data were obtained from UCSC Xena and integrated with normal cervix tissues from the Genotype-Tissue Expression Project after batch correction. Prognostic LYMRGs were first identified from the differentially expressed set using univariable Cox proportional hazards analysis. Candidate genes were then reduced using an L1-regularized Cox model (Least Absolute Shrinkage and Selection Operator), and the remaining markers were entered into a multivariable Cox regression to obtain the final coefficients and compute an individualized risk score. The model's prognostic value was further assessed in an independent Gene Expression Omnibus dataset. In addition, expression patterns of the signature genes were leveraged for molecular subtyping of TCGA samples via non-negative matrix factorization (NMF). Immune infiltration and immunotherapy-associated characteristics were interrogated through a multi-algorithm strategy (single-sample gene set enrichment analysis, CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore . Additional analyses included pathway enrichment (GSEA/GO/KEGG), drug sensitivity prediction (pRRophetic/CellMiner), and ceRNA network analysis. RESULTS: A six-gene LYMRG signature robustly stratified survival. High-risk patients had significantly worse overall survival in The Cancer Genome Atlas with AUCs of 0.819/0.801/0.801&#xa0;at 1/3/5 years, and in GSE52903 (P = 0.001) with AUCs of 0.733/0.719/0.725. NMF identified two subtypes with distinct prognosis (P = 0.01) and divergent immune landscapes. Risk groups and subtypes exhibited consistent differences in immune infiltration, checkpoint expression, TIDE/IPS patterns, and pathway enrichment. Predicted chemosensitivity differed by risk group, and the ceRNA network suggested candidate upstream lncRNA regulators of the signature. CONCLUSION: A lymphangiogenesis-related six-gene model enables clinically meaningful prognostic stratification of CESC and links lymphangiogenesis programs to distinct tumor immune phenotypes and therapeutic vulnerabilities.

cancer↗
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↗
PubMed2024

Construction and Analysis of a Mitochondrial Metabolism-Related Prognostic Model for Breast Cancer to Evaluate Survival and Immunotherapy.

As one of the most prevalent malignancies among women, breast cancer (BC) is tightly linked to metabolic dysfunction. However, the correlation between mitochondrial metabolism-related genes (MMRGs) and BC remains unclear. The training and validation datasets for BC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. MMRG-related data were obtained from the Molecular Signatures Database. A risk score prognostic model incorporating MMRGs was established based on univariate, LASSO, and multivariate Cox regression analyses. Independent factors affecting BC prognosis were identified through regression analysis and presented in a nomogram. Single-sample gene set enrichment analysis was employed to assess the immune levels of high-risk (HR) and low-risk (LR) groups. The sensitivity of BC patients in the two groups to common anti-tumor drugs was evaluated by utilizing the Genomics of Drug Sensitivity in Cancer database. 12 MMRGs significantly associated with survival were selected from 1234 MMRGs. A 12-gene risk score prognostic model was built. In the multivariate regression analysis incorporating classical clinical factors, the MMRG-related risk score remained an independent prognostic factor. As revealed by tumor immune microenvironment analysis, the LR group with higher survival rates had elevated immune levels. The drug sensitivity results unmasked that the LR group demonstrated higher sensitivity to Irinotecan, Nilotinib, and Oxaliplatin, while the HR group demonstrated higher sensitivity to Lapatinib. The development of MMRG characteristics provides a comprehensive understanding of mitochondrial metabolism in BC, aiding in the prediction of prognosis and tumor microenvironment, and offering promising therapeutic choices for BC patients with different MMRG risk scores.

Humans↗
PubMed2026

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan&#x2013;Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans↗
PubMed1977

[Study of survival in angiographically determined ischemic cardiopathies: Apropos of 345 medically treated patients].

A study of 345 patients with ischaemic heart disease due to coronary arteriosclerosis which had been demonstrated by coronary arteriography, and seen between November 1967 and December 1974, was directed towards finding out what had happened to the patients so that, by an actuarial study of their survival, the prognostic significance of the arteriographic and ventriculographic studies could be determined. The prognostic value of the clinical findings has shown the importance:--of the presence of clinical left ventricular failure;--of the presence of coronary insufficiency with frequent attacks; of the presence of sequelae of infarction as seen on the ECG at rest;--on the length of the symptomatic history before arteriography. The assessment of the prognostic potential of the arteriographic findings has emphasised:--the fundamental importance of diffusion of stenoses and occlusions of several main coronary trunks;--the high risk if the lesion affects the left coronary trunk; the high risk of prognostic significance of lesions on the IVA. The prognostic significance of ventricular lesions as demonstrated on ventriculography in the right anterior oblique incidence has shown the sinister significance of extensive lesions of more than two or three of the seven ventriculographic segments and of lesions with dilatation, whereas very localised static plaques hardly influence the prognosis at all.

Adult↗
PubMed2025

Foundations of Artificial Intelligence in Hepatology: What a Clinician Needs to Know.

This review focuses on foundational knowledge about artificial intelligence (AI) in hepatology, exploring how AI, including machine learning and deep learning, leverages large-scale clinical data to transform the diagnosis, risk assessment, prognostication, and management of liver diseases. Online resources are described to offer fundamental AI knowledge and essential technical skills and to facilitate clinician participation across the entire AI lifecycle, ensuring they contribute not only as end users but also in development and deployment. Unlike traditional statistical approaches that prioritize interpretable parameters and clinical insight, AI focuses on maximizing predictive accuracy by identifying complex, often non-linear patterns using high-dimensional data, albeit often at the cost of model interpretability. AI is demonstrating clinical utility in liver histopathology and radiological imaging, significantly improving detection accuracy for cirrhosis, clinically significant portal hypertension, and hepatocellular carcinoma. Beyond diagnostics, AI-driven prediction models are emerging to provide personalized risk stratification for the development of liver-related complications and treatment guidance, based on complex data including longitudinal laboratory results, comorbidities, and co-medication use to monitor disease progression and therapy response. The field is rapidly expanding into novel areas such as analyzing patient-reported outcomes, genomic data, and real-time liver function monitoring, offering deeper mechanistic insights alongside clinical tools. Despite the potential to revolutionize hepatology practice and research, successful integration into routine care faces challenges. These include seamless workflow integration with existing electronic health records, establishing clear liability frameworks, and guaranteeing protection of patient privacy. Addressing these hurdles requires collaborative efforts from clinicians, researchers, and regulators to develop best practices and governance. Understanding the transformative capabilities, current applications, emerging frontiers, and essential implementation considerations is crucial for clinicians navigating the evolving AI landscape and responsibly utilizing its power for improved patient outcomes.

PROBAST+AI↗
PubMed2026

Recent Advances in Surveillance Strategies for Nasopharyngeal Carcinoma.

PURPOSE OF REVIEW: Nasopharyngeal carcinoma (NPC) is a malignant tumor characterized by a distinct geographical distribution. Effective surveillance is crucial for the early detection of recurrence or metastasis and for improving patient prognosis.This review systematically examines current NPC follow-up protocols and recent developments to inform individualized precision surveillance. RECENT FINDINGS: This review focuses on two main aspects. 1) We compare and analyze current major NPC follow-up guidelines, with key discussions covering follow-up frequency, imaging modalities (including magnetic resonance imaging [MRI] and positron emission tomography [PET]), plasma Epstein-Barr virus DNA (EBV-DNA) monitoring, and functional assessments. 2)We elaborate on the application prospects and research progress of genomics, radiomics, and artificial intelligence in NPC surveillance. Studies suggest that risk-stratified, individualized follow-up strategies, such as those based on conditional survival models, can enhance the cost-effectiveness of surveillance. Additionally, emerging technologies, including radiomics and artificial intelligence, show promise for improving recurrence risk assessment, prognostic stratification, and individualized surveillance in NPC. Concurrently, advances in genomics and radiomics offer new opportunities for predicting complications and guiding treatment adjustments. Future efforts should focus on integrating multidisciplinary expertise to develop dynamic monitoring systems that enable precise follow-up and ultimately improve patient survival outcomes.

Humans↗
PubMed2026

Further Support of Autosomal Recessive CSF3-Related Severe Congenital Neutropenia.

Severe congenital neutropenia (SCN) is a genetically heterogeneous condition. Reaching a molecular diagnosis is important for management, prognostication, and risk assessment. We present the first confirmatory report of a previously proposed SCN due to a homozygous pathogenic loss-of-function variant in CSF3. Our report supports this new gene-disease relationship in humans.

Humans↗