This study establishes a novel MM-based prognostic model for CC and based on in-silico predictions suggests potential therapeutic targets through comprehensive transcriptomic and experimental validation.
Abstract
Background Cervical cancer (CC) remains one of the most prevalent malignancies in the female reproductive system. Methionine metabolism (MM) plays a pivotal role in various biological processes and has been implicated in cancer progression. However, its mechanisms in CC remain unclear. Methods Transcriptomic data from 305 patients in The Cancer Genome Atlas (TCGA) (training cohort) and 299 patients from the Gene Expression Omnibus (GEO) (GSE44001) (external validation cohort) were analyzed for differentially expressed MM-related genes (MM-RGs). Prognostic MM-RGs were identified using Cox regression, proportional hazards testing, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. A risk model was constructed and validated. Functional enrichment (Gene Set Enrichment Analysis/Gene Set Variation Analysis (GSEA/GSVA)), in-silico immune infiltration (CIBERSORT) and drug sensitivity (oncoPredict) analyses were performed. Five genes were validated by Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) on clinical samples. Results Eight MM-RGs (MTHFD1, SMYD2, MSRB3, MTR, ENOPH1, DNMT3B, SLC38A7, PEMT) were identified as prognostic genes. A robust risk score model was developed, stratifying patients into high- and low-risk groups with significant differences in survival outcomes. Functional enrichment revealed pathways such as ECM–receptor interaction and focal adhesion. Immune analysis indicated altered infiltration of Tregs and mast cells. In-silico drug sensitivity analysis predicted 57 agents with significantly different IC50 values between the high- and low-risk groups (p < 0.05). Notably, agents such as Cediranib and BI-2536 exhibited markedly lower IC50 values in the high-risk cohort, suggesting their potential efficacy for advanced-stage treatment. qRT-PCR preliminarily indicated the overexpression of four genes in CC tissues within a small clinical cohort. Conclusion This study establishes a novel MM-based prognostic model for CC and based on in-silico predictions suggests potential therapeutic targets through comprehensive transcriptomic and experimental validation.
Cervical squamous cell carcinoma (CSCC) remains a leading cause of cancer-related mortality in women, and few effective prognostic biomarkers have been identified. Although manganese metabolism (MAM) has been implicated in tumorigenesis and immune regulation, its prognostic relevance in CSCC has not been systematically explored, and existing prognostic models for CSCC have largely ignored MAM-related genes.
CSCC transcriptomic and clinical datasets were obtained from public databases. MAM-related differentially expressed genes (DEGs) were identified by intersecting DEGs, weighted gene coexpression network analysis key module genes, and a curated MAM gene set. Prognostic genes were screened using univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox regression analyses to construct a risk model. The model’s performance was assessed
via
Kaplan–Meier survival analysis, time-dependent receiver operating characteristic curves, and external validation. Associations with immune infiltration, drug sensitivity, and clinical features were further investigated. Experimental validation was performed using real-time quantitative polymerase chain reaction, western blotting, and immunofluorescence staining in CSCC and normal cervical cells.
A four-gene prognostic signature (
PARP1
,
SLC20A1
,
GALNTL6
, and
TAGLN
) was established. Patients were stratified into high- and low-risk groups, and survival was significantly worse in the high-risk group (p = 0.0034). The risk model demonstrated good predictive performance (1-/2-/3-year area under the curve = 0.72/0.76/0.83), and its performance was externally validated in an independent cohort. Notably, risk stratification was associated with distinct immune infiltration patterns (including natural killer cells and regulatory T cells), focal adhesion pathway enrichment, and differential drug sensitivity. A nomogram incorporating the risk score, race, and lymphovascular invasion exhibited improved prognostic stratification.
This study presents the first MAM-related prognostic signature specifically tailored to CSCC, filling a gap in existing prognostic models that did not consider the role of MAM. The four-gene risk model offers a robust tool for patient stratification, with potential implications for guiding personalized immunotherapy and targeted therapy. These findings provide a novel framework for incorporating metabolic perspectives into CSCC prognosis and warrant further clinical validation.
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The proposed glutathione metabolism-related signature demonstrated prognostic value in both training and validation cohorts and was associated with immune characteristics, pathway enrichment patterns, genomic alterations, and tumor mutation burden in LUAD.
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Bladder cancer (BCa) is a common urinary malignancy characterized by high recurrence and mortality rates. Metabolic reprogramming, including dysregulation of nucleotide metabolism, contributes to tumor cell proliferation and disease progression. PAICS (phosphoribosylaminoimidazole succinocarboxamide synthetase) is a key enzyme involved in de novo purine biosynthesis; however, its expression pattern, clinical significance, and potential biological relevance in BCa remain incompletely elucidated. This study integrated high-throughput sequencing data from the TCGA, GEO, and GTEx databases. Differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning algorithms, including LASSO regression and Random Forest, were used to screen candidate genes and identify PAICS as a key gene for further analysis. Survival analysis, immune infiltration profiling, Gene Set Enrichment Analysis (GSEA), ssGSEA, somatic mutation analysis, single-cell transcriptomic analysis, drug sensitivity correlation analysis, molecular docking, and molecular dynamics simulations were further performed to evaluate the clinical value, biological relevance, and potential drug-binding characteristics of PAICS in BCa. PAICS was significantly upregulated in BCa tissues and showed predominant expression in tumor epithelial cells. PAICS demonstrated favorable diagnostic performance and was included in a PAICS/S100A6-based prognostic model associated with patient survival. Bioinformatics analyses suggested that PAICS overexpression may be associated with metabolic and biosynthetic programs, including oxidative phosphorylation, ribosome, proteasome, aerobic respiration, cellular respiration, protein–RNA complex assembly, and rRNA processing. PAICS expression was also associated with altered immune infiltration patterns, including positive correlations with Macrophages M0 and resting mast cells and negative correlations with activated mast cells, follicular helper T cells, monocytes, CD8 + T cells, and regulatory T cells. In addition, in silico drug sensitivity analysis, molecular docking, and molecular dynamics simulations suggested that YM201636 may represent a candidate PAICS-binding compound with favorable predicted binding affinity and structural stability. This study identifies PAICS as a potential diagnostic and prognostic biomarker for BCa. The findings suggest that PAICS may be associated with metabolic/biosynthetic activity, immune microenvironment remodeling, and tumor epithelial cell biology in BCa. YM201636 was identified as a candidate PAICS-binding compound through in silico analyses. Further in vitro and in vivo studies are required to validate the biological function and therapeutic relevance of PAICS in bladder cancer.
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A seven-gene immune-related prognostic signature that, combined with clinicopathological parameters, provides a robust tool for individualized survival prediction and may guide precision management in CRC patients is developed and validated.
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