Establishing a Prognostic Model Correlates to Inflammatory Response Pathways for Prostate Cancer via Multiomic Analysis of Lactylation‐Related Genes

International Journal of Genomics, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Prostate cancer (PCa) continues to pose substantial clinical challenges, with molecular heterogeneity significantly impacting therapeutic decision‐making and disease trajectories. Emerging evidence implicates protein lactylation—a novel epigenetic regulatory mechanism—in oncogenic processes, though its prognostic relevance in PCa remains underexplored. Through integrative bioinformatics interrogation of lactylation‐associated signatures, we established correlations using multivariable feature selection methodologies. Initial screening via differential expression analysis (limma package) coupled Cox proportional hazards modeling revealed 11 survival‐favorable regulators 16 hazard‐associated elements linked biochemical recurrence. To enhance predictive precision, ensemble machine learning frameworks were implemented, culminating a 10‐gene lactylation signature demonstrating robust discriminative capacity (concordance index = 0.738) across both primary (TCGA‐PRAD) external validation cohorts (DKFZ). Multivariable regression confirmed the score’s independence, exhibiting prominent associations clinicopathological parameters including tumor staging metastatic potential. The developed clinical‐molecular nomogram achieved superior accuracy (C − > 0.7) through synergistic integration biological covariates. Tumor microenvironment deconvolution uncovered distinct immunological landscapes, high‐risk stratification correlating enriched stromal infiltration immunosuppressive phenotypes. Pathway enrichment analyses implicated chromatin remodeling processes cytokine‐mediated inflammatory cascades as potential mechanistic drivers divergence. Therapeutic vulnerability profiling demonstrated response patterns: low‐risk patients exhibited enhanced immune checkpoint inhibitor responsiveness, whereas subgroups showed selective chemosensitivity docetaxel mitoxantrone. Functional PC‐3 models AK5 silencing induced proapoptotic effects, suppressed migration invasion, modulated regulation CD276 coexpression. These multimodal findings position dynamics, particularly AK5‐mediated pathways, promising targets biomarkers management.

Language: Английский

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Metabolic reprogramming in hepatocellular carcinoma: mechanisms of immune evasion and therapeutic implications

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: April 30, 2025

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, with limited treatment options for advanced stages. Metabolic reprogramming hallmark cancer, enabling tumor cells to adapt the harsh microenvironment (TME) and evade immune surveillance. This review involves role metabolic in HCC, focusing on dysregulation glucose, lipid, amino acid metabolism, its impact evasion. Key pathways, such as Warburg effect, fatty synthesis, glutaminolysis, are discussed, along their influence tumor-associated macrophages (TAMs) cell function. Targeting these alterations presents promising therapeutic approach enhance immunotherapy efficacy improve HCC patient outcomes.

Language: Английский

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Efficient enrichment and specific detection of α-amanitin based on aptamer in mushroom food

Microchemical Journal, Journal Year: 2025, Volume and Issue: unknown, P. 114025 - 114025

Published: May 1, 2025

Language: Английский

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Multiple machine learning algorithms, validation of external clinical cohort and assessments of model gain effects will better serve cancer research on bioinformatic models

Cancer Cell International, Journal Year: 2024, Volume and Issue: 24(1)

Published: Dec. 23, 2024

Bioinformatics models greatly contribute to individualized assessments of cancer patients. However, considerable research neglected some critical technological points, including comparisons multiple modeling algorithms, evaluating gain effects constructed model, comprehensive bioinformatics analyses and validation clinical cohort. These issues are worthy emphasizing, which will better serve future research.

Language: Английский

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IFDlong: an isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 14, 2024

Advancements in long-read transcriptome sequencing (long-RNA-seq) technology have revolutionized the study of isoform diversity. These full-length transcripts enhance detection various structural variations, including novel isoforms, alternative splicing events, and fusion transcripts. By shifting open reading frame or altering gene expressions, studies proved that these transcript alterations can serve as crucial biomarkers for disease diagnosis therapeutic targets. In this project, we proposed IFDlong, a bioinformatics biostatistics tool to detect using bulk single-cell long-RNA-seq data. Specifically, software performed annotation each long-read, defined quantified expression by expectation-maximization algorithm, profiled For evaluation, IFDlong pipeline achieved overall best performance when compared with several existing tools large-scale simulation studies. both quantification, is able reach more than 0.8 Spearman's correlation truth, 0.9 cosine similarity distinguishing multiple events. simulation, successfully balance sensitivity (higher 90%) specificity 90%). Furthermore, has its accuracy robustness diverse in-house public datasets on healthy tissues, cell lines types diseases. Besides long-RNA-seq, compatibility This new may hold promise significant impact analysis. The available at https://github.com/wenjiaking/IFDlong.

Language: Английский

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Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer

Human Genomics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Nov. 25, 2024

Language: Английский

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