Biomarker Research最新文献

Remodeling of the tumor microenvironment (TME) under therapeutic pressure is a critical determinant of treatment response and resistance in hepatocellular carcinoma (HCC). Triple-combination therapy integrating targeted agents, immune checkpoint inhibitors, and radiotherapy (T+I+R) has shown potential synergistic effects in intermediate to advanced HCC, particularly in patients with portal vein tumor thrombus (PVTT), yet the spatial and cellular mechanisms underlying its efficacy remain largely unknown. In this retrospective clinical cohort study, we compared T+I+R with targeted therapy plus radiotherapy (T+R) in advanced HCC, and further employed single-cell spatial transcriptomics and spatial proteomics to generate an integrated multi-omics atlas mapping tumor and stromal compartments, cellular compositions, and intercellular interactions with spatial resolution. Clinically, T+I+R achieved superior tumor shrinkage and disease control compared with T+R. Spatial multi-omics revealed marked region-specific remodeling, with myofibroblastic cancer-associated fibroblasts, angiogenic tip endothelial cells, and conventional dendritic cells enriched at the invasive margin and associated with therapeutic resistance, while CD8⁺ effector T cells were redistributed away from immunosuppressive niches, a spatial configuration correlating with enhanced response. These findings identify spatial segregation between cytotoxic and suppressive immune elements as a potential hallmark of effective therapy, providing a high-resolution spatial framework for understanding T+I+R induced TME remodeling and offering mechanistic insights to guide biomarker discovery and the optimization of combination strategies in advanced HCC.

The mechanisms by which prostate cancer (PCa) evades anti-tumor immunity and the immune checkpoint blockade (ICB) response are poorly understood. Yes-associated protein 1 (YAP1) activation is a common feature in PCa. However, to date, there is no direct evidence regarding the effect of YAP1 activity on anti-tumor immunity in PCa patients. In this study, we discovered that YAP1 expression is usually abundant in PCa tissues. Transcriptome analysis revealed that PD-L1 and the immune costimulatory molecule CD70 were consistently upregulated in YAP1-activated PCa cells. Meanwhile, CD70 is also abundantly exhibited in ICB non-responder patients, but absent in ICB responders, who usually show high cytotoxic T cell infiltration. More importantly, CD70 inhibition restores the sensitivity to anti-PD-1 immunotherapy in YAP1-activated PCa cells. Mechanistically, YAP1 directly regulates the transcription of CD70 through cooperation with DNA-binding factor RUNX1. The upregulation of CD70 thus suppresses immune cell infiltration into malignant lesions and promotes the exhaustion of CD8 + T cells to facilitate evasion from immunosurveillance. Taken together, our findings define the YAP1-CD70 signaling axis as a novel immunosuppressive mechanism in PCa, which provides new insights into the potential of targeting the CD70 pathway to help further subdivide the population of PCa patients who can benefit from immunotherapy.

Long noncoding RNAs (lncRNAs) are promising biomarkers, but their accurate quantification by qPCR requires stable endogenous controls. In the present study, we aimed to identify suitable reference lncRNAs for normalization in whole-blood samples. We profiled the expression of 84 lncRNAs and eight commonly used mRNA reference genes by RT-qPCR in samples from 182 individuals. Transcript stability was assessed using three widely applied algorithms: geNorm, NormFinder and BestKeeper. Twenty-nine lncRNAs met predefined expression criteria: consistent detection across all samples, a maximum quantification cycle (Cq) below 33 and a median Cq below 30. Among these, TUG1 consistently ranked as the most stable candidate across all algorithms. FGD5-AS1 and ZFAS1 were also selected based on high stability rankings. We next evaluated the effectiveness of different normalization strategies, comparing them to the gold-standard method, i.e. mean-centering. We included both the selected lncRNAs and eight commonly used mRNA reference genes. TUG1 alone achieved a reduction in expression variability comparable to mean-centering and superior to all other tested strategies, including combinations of multiple reference lncRNAs and mRNAs. Moreover, TUG1 expression showed a minimal association with clinical variables and outcomes. TUG1 was consistently identified among the lncRNAs with the highest expression levels and lowest variability across four independent external RNA-seq datasets. Our findings identify TUG1 as the most stable candidate in this cohort and suggest that it may represent a promising and cost-effective endogenous control for lncRNA quantification in whole-blood studies.

Sepsis is a major cause of acute kidney injury, progressing to sepsis-associated acute kidney disease (SA-AKD). This study explores SA-AKD prediction by combining immune cell profiling. Peripheral immune cell expression and phenotypes were analyzed in sepsis patients without (n = 97) and with (n = 41) SA-AKD, admitted to a hospital (2020-2022). Blood urea nitrogen and creatinine levels were measured, and a decision tree (DT)-based model was used to evaluate their predictive power in the training (n = 106) and validation (n = 32) cohorts. The DT model, incorporating naïve Treg and CD56^dim NK cells along with clinical parameters, showed high accuracy in predicting SA-AKD. The model using blood urea nitrogen as the first node reached 89.62% accuracy (sensitivity: 94.4% and specificity: 87.14%; area under the curve = 0.91). The model starting with creatinine showed 89.62% accuracy. Validation results confirmed an 81.25% accuracy. Profiling specific immune cells may enable pre-evaluation of SA-AKD.