Researching the role of ubiquitination and macrophage activation-related genes in hepatocellular carcinoma through a systematic study based on gene expression profiling.
The findings lay the groundwork for precision oncology paradigms, facilitating the translational trajectory of these targets to optimize clinical prognosis, and underscores the potential of UMARGs in advancing HCC treatment strategies and improving patient outcomes.
Abstract
Hepatocellular carcinoma (HCC) remains a formidable global health challenge, particularly in developing nations, due to its asymptomatic nature in early stages and consequent late diagnosis. This study aims to identify novel biomarkers and therapeutic targets by focusing on Ubiquitin and Macrophage Activation-Related Genes (UMARGs), which play a crucial role in modulating the tumor microenvironment. Utilizing a comprehensive bioinformatics framework, we integrated transcriptomic data from TCGA and GEO databases with gene signatures from GeneCards. Our methodology included differential gene expression analysis, Cox regression modeling, and protein interaction networking to identify key prognostic determinants and their heterogeneity across HCC molecular subtypes. The analysis revealed two distinct HCC subtypes, Cluster A and Cluster B, each with unique gene expression profiles. Immune microenvironment heterogeneity was highlighted through CIBERSORT deconvolution, showing diverse distributions across 12 cellular subsets, notably activated T cells and macrophages.Survival analysis showed that high expression of HPX was associated with better Overall survival, while high expression of MMP9 was associated with worse Overall survival, and these trends were validated in an external GEO cohort. HPX and MMP9 may serve as candidate prognostic biomarkers for Hepatocellular carcinoma, but their Biological function and clinical application value still require further experimental investigation. Despite limitations, including the lack of experimental validation and reliance on retrospective data, this study provides valuable insights into gene expression dynamics and immune characteristics associated with HCC. These findings lay the groundwork for precision oncology paradigms, facilitating the translational trajectory of these targets to optimize clinical prognosis. Future research should focus on experimental validation and the development of targeted therapies based on these molecular insights. This study underscores the potential of UMARGs in advancing HCC treatment strategies and improving patient outcomes.
AIM
Hepatocellular carcinoma (HCC) is the most common primary liver cancer in adults, with increasing incidence. It is helpful to establish a prognostic risk prediction model related to macrophage polarization and mitochondrial dysfunction associated with HCC.
METHODS
Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression, Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA), protein-protein interaction (PPI) network.
RESULTS
A total of 7 model genes (EZH2, G6PD, HMGA2, MAGEB2, PYCR1, SLC7A11, and SPP1) were identified. And the LASSO-Cox model showed preliminary prognostic predictive performance in the TCGA-LIHC cohort (0.9 > AUC > 0.7), with decision curve analysis (DCA) showing clinical potential, particularly in the third year. GSEA revealed that genes associated with Liver hepatocellular carcinoma (LIHC) exhibited enrichment in functions and pathways, notably including FCGR3A Mediated IL10 Synthesis, etc. GSVA indicated multiple pathways were significant in both Low-Risk and High-Risk groups, such as the biocatamcm pathway (p < 0.05). The PPI Network shows connections among G6PD, SLC7A11, HMGA2, and EZH2, with GeneMANIA predicting their interactions with similar function genes.
CONCLUSION
Our study screened candidate prognostic genes associated with macrophage polarization and mitochondrial dysfunction based on bioinformatics analysis and established a prognostic model.
Bin-Bin Cheng, Wei-Ping Tu, Xiao-Yu Tu et al.· Discover Oncology· 0 citations
This study aims to construct a prognostic model based on neutrophil heterogeneity by integrating single-cell and bulk transcriptomic data. Additionally, it seeks to explore the features of the immune microenvironment across different risk subgroups to facilitate precise prognostic assessment and individualized immunotherapy for colorectal cancer.
Colorectal cancer transcriptomic datasets from TCGA and GEO were utilized. Single-cell data underwent quality control, clustering, and cell annotation. Cell communication analysis characterized neutrophil signaling interactions. Candidate genes were identified through differential expression analysis and WGCNA, leading to the establishment and validation of a CCL15/CCDC154 dual-gene prognostic model. Differences in immune infiltration, immune checkpoint expression, and immune escape between high- and low-risk groups were analyzed, followed by molecular subtype clustering.
Significant remodeling of the tumor microenvironment was observed, with neutrophils extensively participating in multicellular signaling networks. The dual-gene model demonstrated strong predictive performance and functioned as an independent prognostic indicator. The high-risk group exhibited a pronounced immunosuppressive phenotype, characterized by significant immune escape and upregulated inhibitory immune checkpoints. Notably, distinct immune and prognostic differences were identified among molecular subtypes.
The dual-gene model based on neutrophil heterogeneity facilitates accurate prognostic stratification. The neutrophil-mediated immune microenvironment is closely linked to clinical outcomes, providing a theoretical basis for personalized treatment strategies in colorectal cancer.
Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide and exhibits considerable biological heterogeneity in both molecular and clinical characteristics. The diverse molecular alterations and clinical manifestations of HCC indicate substantial heterogeneity across patient subgroups. This study aimed to identify novel therapeutic targets and predictive biomarkers associated with HCC using an integrative bioinformatics approach. High-throughput genomic datasets were obtained from the UCSC Xena browser to retrieve mRNA HTSeq-count data from the TCGA-HCC cohort. Gene co-expression network (GCN), protein–protein interaction network (PPIN), and enrichment analyses were performed to identify key dysregulated genes and their biological significance. Integrated network analyses identified three dysregulated hub genes, namely CXCR2, TLR2, and TLR4. Genomic alterations in these genes were further evaluated across tumor samples in the TCGA-HCC cohort. Kaplan–Meier (KM) survival analysis demonstrated that lower CXCR2 mRNA expression was significantly associated with poorer overall survival (OS) and recurrence-free survival (RFS). Furthermore, TIMER and UALCAN analyses revealed significant associations between CXCR2 expression and tumor purity, as well as immune cell infiltration levels, including T cells, macrophages, dendritic cells (DCs), and neutrophils. These findings suggest that CXCR2 is significantly associated with the immune microenvironment of HCC and represents a potential prognostic biomarker whose biological role warrants further mechanistic investigation.
S. Almatroodi, Tarique Sarwar, A. Rahmani· International Journal of Mol...· 0 citations
Background Oral squamous cell carcinoma (OSCC) represents a common malignancy characterized by significant morbidity and mortality rates, highlighting the critical necessity for novel therapeutic approaches. Consequently, investigating differentially expressed genes linked to inflammation and pyroptosis may identify potential prognostic biomarkers and therapeutic targets. Methods To address this research gap, our study utilized an extensive bioinformatics approach by analyzing the Cancer Genome Atlas Head and Neck Squamous Cell Carcinoma (TCGA-HNSC) dataset through differential expression analysis to identify genomic features associated with OSCC. Subsequent analyses included Gene Ontology and pathway enrichment assessments, along with survival analyses using Cox regression models, to evaluate the prognostic significance of the identified differentially expressed genes. Furthermore, immune infiltration analysis and somatic mutation assessments were conducted to elucidate the relationship between immune cell types and key prognostic genes. Additionally, copy number variation analysis was performed to highlight genomic alterations associated with immune-related and prognostic-related differentially expressed genes(DEGs). Results Our analysis identified a total of 3,495 differentially expressed genes, among which 53 immune-related and prognosis-related differentially expressed genes demonstrated a significant correlation with the prognosis of OSCC. Immune infiltration analysis further revealed the presence of 28 immune cell types within OSCC samples, with a notable prevalence of activated CD8 T cells and regulatory T cells, underscoring their association with critical prognostic genes. Additionally, pathway analysis highlighted the activation of cytokine signaling pathways and their associations with processes relevant to systemic lupus erythematosus. A prognostic risk model derived from these findings effectively stratified patients based on overall survival, identifying four key genes—CTSG,HKDC1,PTX3 and SPP1—as crucial prognostic indicators. This analysis may uncover potential prognostic biomarkers and therapeutic targets. Conclusion This study established a novel OSCC prognostic risk model based on inflammation- and pyroptosis-related interactive genes. CTSG, HKDC1, PTX3 and SPP1 were validated as independent prognostic biomarkers, and the risk score was closely associated with tumor microenvironment features, metabolic activity, and therapeutic sensitivity. This work may provides substantial references for the exploration of novel biomarkers for OSCC treatment and facilitates clinical decision-making.
Junfu Wu, Jie Fan, Shengli Shao et al.· Frontiers in Immunology· 0 citations
This study provides a refined list of genes and miRNAs with high relevance for LUAD, alongside key signaling pathways central to disease progression, to establish a foundation for future studies and support the development of targeted therapeutic strategies aimed at improving clinical outcomes in LUAD.
M. Navaei, Fatemeh Karami, A. Jahanimoghadam et al.· Cancer Investigation· 0 citations
A robust prognostic model based on four RhoGTPase-related prognostic genes was established, effectively stratifying LUAD patients and provides valuable insights into the heterogeneity of LUAD and has the potential to inform personalized therapeutic strategies.
Tianchuan Li, Danhong Wu, A. Yang et al.· Scientific Reports· 0 citations
