Aug 2026· Frontiers in Molecular Biosciences· Vol 13· 0 citations· 61 references
Medicine
TL;DR
Findings indicate telomere maintenance-related gene signature could serve as a preliminary auxiliary risk stratification tool for postoperative CRC patients, and PDE1B may also serve as a potential epithelial tumor-suppressor target for future preclinical studies.
Abstract
Background Telomere maintenance-related genes (TMRGs) are implicated in Colorectal cancer (CRC) development, but their prognostic value and clinical relevance remain insufficiently explored. This study aims to develop a TMRG-based prognostic model and elucidate its clinical utility in CRC management. Methods The Cancer Genome Atlas database was utilized to download RNA-seq data from 638 CRC and 51 control samples. Differential expressed genes were screened and intersected with 2086 TMRGs, resulting in the identification of 976 TMRGs. Through univariate and multivariate Cox regression analysis, a prognostic model comprising three telomere maintenance-related biomarkers (PDE1B, TFAP2B, and HSPA1A) was developed and validated using an external dataset. By integrating the model risk score with clinical features, a nomogram was constructed to predict the survival outcomes of CRC patients. Additionally, an in-depth investigation of the immuno-infiltration, functional variation and drug sensitivity analysis were performed in two risk subgroups defined by the prognostic model. Finally, the functional significance of PDE1B in CRC cell lines was investigated through MTT assays, cell colony formation assays, transwell assays and flow cytometry. Results A total of 976 DE-TMRGs were enriched in telomere/DNA replication pathways. A three-gene signature (PDE1B, TFAP2B, and HSPA1A) stratified patients into high- and low-risk groups with divergent survival (AUC >0.60, validated externally). High-risk patients had advanced N/M stages, elevated M0/M2 macrophages, reduced CD4+ memory T cells, and upregulated immune checkpoints. Nomogram integrating risk score, age, and N/M stage accurately predicted 1-/3-/5-year survival. Low-risk patients showed greater 5-fluorouracil sensitivity. PDE1B expression was significantly reduced in CRC tissues and correlated with advanced stages. Functional assays confirmed PDE1B overexpression suppressed proliferation, migration, invasion, and induced apoptosis in CRC cells. Conclusion This study identifies a moderately predictive telomere maintenance-related gene signature as an independent prognostic predictor in CRC. The risk stratification model effectively discriminates patients with distinct survival patterns, tumor microenvironments, and therapeutic responses, while the integrated nomogram offers additional reference information for survival analysis, albeit with only moderate predictive accuracy. These findings indicate telomere maintenance-related gene signature could serve as a preliminary auxiliary risk stratification tool for postoperative CRC patients, PDE1B may also serve as a potential epithelial tumor-suppressor target for future preclinical studies.
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.
OBJECTIVE
Lipid metabolism-related genes (LMRGs) are crucial in head and neck squamous cell carcinoma (HNSCC) progression. This study aimed to construct and validate a prognostic model for HNSCC based on LMRGs.
METHODS
Prognostic LMRGs were screened by analyzing RNA sequencing and clinical data from TCGA and GEO databases, and a prognostic model was constructed. Patients were divided into high- and low-risk groups according to the risk score. Further enrichment analysis, tumor microenvironment (TME) analysis, drug sensitivity analysis, and in vitro validation were performed.
RESULTS
Eleven LMRGs (PER2, CTLA4, EPHX3, ABCB1, ANO1, TRIB3, PTX3, OLR1, DKK1, FABP4, and CDKN2A) were identified. Patients in the high-risk group had worse survival, and the risk score was an independent prognostic factor. A nomogram combining risk score and clinical characteristics accurately predicted survival. TME analysis revealed immune dysregulation in the high-risk group. Drug sensitivity analysis showed that the high-risk group was more sensitive to lovastatin. In vitro, lovastatin suppressed malignant behaviors and induced a transcriptomic shift toward a low-risk signature.
CONCLUSION
The results validate the biological significance of the model, highlight potential associations between lipid metabolism and the immune microenvironment in HNSCC, and provide a theoretical basis for further investigating metabolism-related therapeutic strategies.
Ting-Zhi Xie, Xin-Yi Sun, Jing Wang et al.· Oral Diseases· 0 citations
A ferroptosis- and lipid metabolism-related prognostic signature is developed that accurately predicts survival outcomes and immune characteristics in CRC and CRY2 was identified as a critical regulator of tumor growth.
Yu Guo, Yong-Bo Zou, Min Wang· Annals medicus· 0 citations
The developed risk model offers valuable insights for clinical prognostic prediction and immunotherapy in KIRC and indicates an increased likelihood of immune escape in the high-risk group.
Shi-Bin Guo, Shuangqin Xu, Peng Song et al.· Medicine· 0 citations
Functional enrichment analysis revealed that the prognostic model was markedly linked with the modulation of the immune microenvironment and tumor progression in breast cancer.
Zi-Ran Zhang, Xing-Xia Yang, Jie Tang et al.· Medicine· 0 citations
Background Cholangiocarcinoma (CHOL) is a highly aggressive biliary malignancy with poor clinical outcomes and limited effective prognostic biomarkers. Mitochondrial dysfunction participates in multiple oncological processes of CHOL, yet the prognostic roles of mitochondria‑related genes (MRGs) remain poorly understood. This study aimed to characterize MRGs expression in CHOL and develop a molecular prognostic model for predicting patient survival and guiding clinical management. Methods RNA sequencing (RNA-seq) and clinical data of CHOL were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) (GSE89748) databases. Differentially expressed MRGs were identified, and 10 machine learning algorithms were used to construct prognostic models. The optimal model (highest average C-index) was selected to establish a mitochondria-related risk score (MRRS), which was validated internally and externally. A nomogram integrating clinical factors and MRRS was developed, and biological mechanisms were explored via functional and immune analyses. Results A 3-MRG (MAP3K1, MRPL18, PYGB) prognostic signature was constructed, stratifying patients into high- and low-risk groups with significantly different overall survival. The model showed high predictive accuracy, with an area under the curve (AUC) up to 0.845, and MRRS was an independent prognostic factor. The signature was associated with mitochondrial pathways, and the high-risk group had distinct immune infiltration and mutation profiles. Conclusions A validated MRG prognostic model effectively stratifies CHOL patients and has potential clinical value for prognosis prediction. Further validation in larger cohorts is needed to confirm its applicability.
Shen-Jie Li, Wei Xiang, Wei Wei et al.· Translational Cancer Researc...· 0 citations
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