Aug 2026· Cancer Genomics & Proteomics· Vol 23, pp. 996 - 1020· 0 citations· 64 references
Medicine
TL;DR
MZT1 is a proliferation-associated marker that integrates clinical risk, transcriptional programs, cellular heterogeneity, and predictive modeling in LUAD, providing a potential framework for biomarker development and risk stratification.
Abstract
Abstract Background/Aim: Lung adenocarcinoma (LUAD) exhibits substantial molecular heterogeneity and variable clinical outcomes, highlighting the need for biomarkers that reflect core tumor biological processes. Centrosome-associated proteins regulate mitotic fidelity and genome stability, yet their roles in LUAD remain incompletely defined. In this study, we systematically characterized mitotic spindle organizing protein 1 (MOZART1; MZT1) and related family members in LUAD. Materials and Methods: We performed integrated analyses combining bulk transcriptomic datasets, survival modeling, gene set enrichment, immune deconvolution, machine-learning based prognostic modeling, and single-cell RNA sequencing. Expression patterns and clinical associations of MZT family genes were evaluated across pan-cancer and LUAD cohorts. Results: MZT family genes were consistently upregulated in tumor tissues, with MZT1 showing the most robust expression pattern. Elevated MZT1 expression was significantly associated with reduced overall survival. Functional analyses revealed coordinated activation of proliferative and genome maintenance pathways, including G2/M checkpoint regulation, E2F and MYC signaling, and DNA repair. A multivariable analysis indicated that the prognostic association of MZT1 was reduced after adjusting for canonical proliferation markers, suggesting partial overlap with established proliferation signals. The LASSO-based Cox model demonstrated stable time-dependent predictive performance at 1-, 3-, and 5-year survival. Immune analyses indicated associations between MZT1 expression and tumor microenvironmental features. Single-cell analysis showed that MZT1 expression was predominantly enriched in malignant epithelial cells and associated with proliferative cellular states. Protein-level validation supported concordance with transcriptomic findings. Conclusion: MZT1 is a proliferation-associated marker that integrates clinical risk, transcriptional programs, cellular heterogeneity, and predictive modeling in LUAD, providing a potential framework for biomarker development and risk stratification.
Collectively, m6A-related genes are dysregulated in breast cancer and correlate with patient outcomes, highlighting their biomarker potential, with YTHDF3 warranting in-depth investigation.
Pu Jin, Yue-Tsz Fan· Journal of Visualized Experi...· 0 citations
Glioblastoma (GBM) is the most common primary intracranial malignancy in adults, characterized by poor survival and high mortality. Emerging evidence suggests that macrophage-associated programmed cell death (MacPCD) plays a critical role in GBM pathogenesis. However, the underlying mechanisms remain poorly understood. This study aimed to identify MacPCD-related prognostic genes in GBM and explore their functional roles.
Transcriptomic data from the GSE68848 dataset were integrated with Macrophage-associated programmed cell death-related genes (MacPCD-RGs) to identify differentially expressed genes (DEGs). Univariate Cox and LASSO regression analyses were performed using the TCGA-GBM training set to construct a prognostic risk model. Beyond prognostic stratification, we conducted a comprehensive multi-omic landscape analysis, including gene set enrichment analysis (GSEA), tumor microenvironment (TME) characterization, tumor mutational burden (TMB) assessment, immunotherapy response prediction, and drug sensitivity prediction. Finally, single-cell RNA sequencing (scRNA-seq) was employed to resolve microenvironmental heterogeneity, identify key cell types and elucidate intercellular communication and developmental trajectories.
Analysis of GSE68848 identified 902 DEGs, of which five intersected with MacPCD-RGs.
FN1
and
TIMP1
were subsequently identified as core prognostic markers. The risk model demonstrated superior predictive performance across the CGGA-325 and GSE83300 validation cohorts. Functional analysis linked the risk score to specific signaling pathways,
PTEN
mutations, infiltration of immune cell subsets (e.g. NKT cells) and sensitivity to Trametinib. Tumor-associated macrophages (TAMs) were identified as the key cell type, exhibiting intense interaction with pericytes and enrichment in fructose/mannose metabolism. Furthermore, pseudotime analysis revealed that
FN1
and
TIMP1
expression peaked during the initial stages of TAM differentiation.
This study identified
FN1
and
TIMP1
as pivotal MacPCD-related prognostic genes in GBM. The risk model based on these markers exhibits moderate predictive performance, offering a reliable tool for clinical prognosis and paving the way for personalized immunotherapy strategies.
Single‐cell transcriptomics and machine learning methods are increasingly used to identify immune‐related biomarkers in solid tumors, yet their combined application to microenvironment‐related drivers of therapeutic resistance in clear cell renal cell carcinoma (ccRCC) is still limited. Here, we investigated the biological and clinical significance of myeloid‐derived growth factor (MYDGF) through an integrative strategy spanning single‐cell profiling, bulk multiomics, and functional validation. Analysis of scRNA‐seq data (GSE156632) revealed that MYDGF is preferentially detected in malignant epithelial subpopulations and associated with the composition of myeloid and lymphoid compartments. Integration with TCGA‐KIRC transcriptomic and clinical datasets demonstrated strong associations between MYDGF expression and immune‐checkpoint activation, immune dysfunction signatures, and PI3K/AKT–MAPK pathway activity. Tumors with high MYDGF expression exhibited an immune‐infiltrated yet functionally impaired microenvironment and were predicted to show reduced responsiveness to immune checkpoint blockade. Differential expression and enrichment analyses further highlighted MYDGF‐associated genes involved in inflammatory, extracellular, and receptor‐binding functions. A machine learning pipeline using LASSO Cox regression identified a preliminary 19‐gene MYDGF‐related prognostic gene set that requires further validation. Functional experiments confirmed that MYDGF knockdown suppressed proliferation, migration, and invasion in ccRCC cells. Overall, our analyses characterize MYDGF as a microenvironment‐related biomarker linked to immune‐associated features, signaling‐associated alterations, and adverse prognosis in ccRCC. These results nominate MYDGF as a candidate prognostic biomarker and show the value of pairing single‐cell resolution with computational modeling for biomarker discovery in renal cancer.
Ying-Kun Xu, Guan-Du Li, Xin-Xiu Ren et al.· Human Mutation· 0 citations
Non-histone acetylation regulates protein stability, transcription, and immune signaling, yet its substrate-specific roles in colon adenocarcinoma (COAD) remain unclear. We performed an integrative multi-omics analysis to characterize acetylation substrate-related transcriptional signatures and their clinical relevance. Expression patterns and prognostic impacts of 33 acetylation-related enzymes were evaluated pan-cancer, and substrate-related networks were computationally inferred. A 15-gene substrate expression-based acetylation substrate-related imbalance score (AIScore) stratified COAD patients into prognostically distinct groups. Single-cell RNA sequencing and spatial transcriptomics revealed AIScore-high malignant cells preferentially interacting with SPP1-positive macrophages, forming immunosuppressive niches with reduced predicted immunotherapy response. Exploratory deep learning-based pathomics of whole-slide images, including ResNet-50 feature extraction, PCA, clustering, and Grad-CAM visualization, highlighted AIScore-associated morphologic hotspots at tumor–immune interfaces. These findings indicate that non-histone acetylation substrate-related transcriptional programs in COAD are linked to poor prognosis and spatial immune heterogeneity, while pathomics provides supportive morphology-linked evidence requiring further validation.
SIRPG is identified as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.
Jiaqi Tang, Yu-Lun He, Yu-Qi Wang et al.· Frontiers in Immunology· 0 citations
A robust 17-gene ASIG-based prognostic signature that effectively stratified BRCA patients into high- and low-risk groups and served as an independent prognostic predictor is established, providing a robust tool for patient risk stratification and offering biological insights into senescence-driven microenvironmental remodeling.
Peng-Cheng Chen, Yindan Lin, Jingjia Li et al.· Genes· 0 citations
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