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
LINC01615, a long noncoding RNA, plays a pivotal role in the progression of kidney renal clear cell carcinoma (KIRC). This study aimed to assess the prognostic value of LINC01615-associated genes in KIRC by developing a risk model. Differential expression analysis in the The Cancer Genome Atlas-KIRC dataset identified differentially expressed genes between high and low LINC01615 expression groups, as well as between KIRC and control groups. Signature genes were subsequently selected through protein-protein interaction (PPI) network analysis, while prognostic genes were identified via Cox regression. The risk model was then constructed and validated using the E-MTAB-1980 dataset. Furthermore, an independent prognostic analysis identified key risk factors, and a nomogram was created for clinical application. Additional analyses, including enrichment analysis, immune-related analysis, drug sensitivity evaluation, and regulatory network construction, were performed to explore the underlying mechanisms in high and low-risk groups. The GSE40435 dataset was employed for the validation of prognostic gene expression. Reverse transcription-quantitative PCR (RT-qPCR) was conducted to confirm the expression levels of prognostic genes and LINC01615 in clinical samples. LINC01615 expression was found to differ significantly between KIRC and control groups, with notable survival differences observed between high and low expression groups. A total of 757 candidate genes were identified. Among these, COL4A4, COL5A1, and COL15A1 were screened as prognostic genes, and a risk model with better accuracy was constructed. Age and risk score were recognized as independent risk factors, and the nomogram demonstrated enhanced predictive accuracy. Twelve drugs showed a significant negative correlation with risk scores. Additionally, the high-risk group exhibited an increased likelihood of immune escape. A regulatory relationship between hsa-miR-3163 and COL4A4/LINC01615 was identified. In both The Cancer Genome Atlas-KIRC and GSE40435 datasets, COL5A1 and COL15A1 were overexpressed in the KIRC group. RT-qPCR results for COL5A1 and COL4A4 were consistent with the above findings, while COL15A1 showed no significant differences in clinical samples, possibly due to the small sample size. COL4A4, COL5A1, and COL15A1 were identified as prognostic biomarkers through bioinformatics analysis. The developed risk model offers valuable insights for clinical prognostic prediction and immunotherapy in KIRC.
Shi-Bin Guo, Shuangqin Xu, Peng Song et al.· Medicine· 0 citations
This study aims to identify programmed cell death (PCD)-associated genes linked to breast cancer prognosis for the construction of a prognostic model. Transcriptomic and clinical information was imported from the The Cancer Genome Atlas (TCGA) and GEO databases. Modulatory genes related to 18 types of PCD were evaluated. Furthermore, the TCGA and GEO datasets were employed as the training and validation datasets, respectively. A risk score prognostic model based on PCD-related genes was generated via univariate, LASSO, and multivariate Cox regression analyses. Further, differences in drug sensitivity, tumor mutation burden (TMB), immune-related pathways and cell infiltration, and immune checkpoints were compared between high-risk and low-risk cohorts to elucidate the clinical applicability of the model. Lastly, the model gene’s expression was verified by RT-PCR. The data revealed 1480 PCD-related genes from the TCGA breast cancer gene expression matrix. Differential expression analysis identified 186 differentially expressed PCD genes. Furthermore, a prognostic model was generated according to the risk scores using multivariate Cox regression. The model comprised 8 PCD-related genes (BRSK2, CD24, IFNG, LAMB3, PDX1, PMAIP1, SLC7A11, and TRIML2). Moreover, breast cancer cases were divided into high-risk and low-risk cohorts per the median risk score. The results indicated that low-risk patients had better prognoses, and the model showed good predictive performance in the GEO validation cohort. The area under the curve values were 0.819, 0.731, and 0.674 for the nomogram’s 3, 5, and 8 years overall survival, respectively. Functional enrichment analysis revealed that the prognostic model was markedly linked with the modulation of the immune microenvironment and tumor progression in breast cancer. Immune infiltration assessment revealed that low-risk patients had increased activity in immune-related pathways and infiltration of immune cells. In addition, the low-risk cohort had lower TMB and elevated immune checkpoint-related levels. Correlation analysis between immune checkpoints and risk scores indicated that low-risk patients were more responsive to immunotherapy. Drug sensitivity analysis showed variations in the IC50 values between the risk cohorts, suggesting potential variations in drug efficacy across different risk cohorts. Gene expression was verified by RT-PCR.A prognostic risk model for breast cancer based on 8 PCD-related genes was constructed and its predictive value was validated. The established model may provide novel biomarkers and effective therapeutic targets for breast cancer diagnosis and treatment.
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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