Aug 2026· Medicine· Vol 105, pp. e50176· 0 citations· 92 references
Medicine
TL;DR
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.
Abstract
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.
BACKGROUND
While mitochondrial dysfunction and immune cell dysregulation have been established as important contributors to the pathogenesis of osteosarcoma (OS), the prognostic value of mitochondria-related genes (MRGs) and immune-related genes (IRGs) in OS remains poorly understood.
METHODS
Data were obtained from public databases. Differentially expressed genes (DEGs) and key module genes were identified viadifferential expression analysis and weighted gene co-expression network analysis, respectively. Candidate genes of interest were identified by intersecting key module genes, DEGs, MRGs, and IRGs. Then, candidate genes were screened using regression analysis and proportional hazards assumption testing to identify prognostic genes. A risk model was then constructed in the TARGET-OS dataset and validated in GSE16091. Independent prognostic, immune infiltration, and gene expression analyses were conducted. In addition, single-cell analysis was performed to identify key cell populations, and pseudotime analysis as well as cell communication network construction were carried out. Finally, the expression patterns of selected prognostic genes were additionally validated via reverse transcription quantitative polymerase chain reaction (RT-qPCR), and the functional role of protein kinase Cα (PRKCA) in osteosarcoma cell proliferation, migration, and invasion was evaluated in vitro.
RESULTS
Glutathione S-transferase Pi-1 (GSTP1), catalase (CAT), TNFSF10, and PRKCA were identified as mitochondria- and immunity-related prognostic genes in OS and were used to construct a risk model. The risk model was able to effectively predict the survival of patients with OS. A nomogram based on the identified prognostic genes additionally exhibited excellent performance when predicting OS patient survival. Significant differences in the abundance of 14 immune cell types and 8 immune checkpoint molecules were noted between the high-risk and low-risk groups. Single-cell analyses were then conducted to annotate 10 cell types, and M1 macrophages were identified as a potentially relevant cell population in the OS microenvironment. During M1 macrophage differentiation, initial reductions in CAT and PRKCA expression were noted, followed by subsequent increases and final decreases. In contrast, the expression of GSTP1 and TNFSF10 in these cells first rose and then declined. Furthermore, PRKCA was selected for further in vitro analyses, which revealed that it can enhance the invasion, migration, and proliferation of OS cells.
CONCLUSION
This study identified GSTP1, CAT, TNFSF10, and PRKCA as prognostic genes associated with mitochondrial and immune function in OS, with in vitro evidence providing direct support for the potential functional role of PRKCA in OS progression. These findings highlight a new avenue for predicting clinical prognosis in OS and may provide a basis for future studies of putative therapeutic targets.
J. Ai, Zhidong Peng, Peichuan Xu et al.· Frontiers in Bioscience· 0 citations
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.
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.
Wen-Jie Fang, Yi-Fan Zhang, Jie Zhang et al.· Frontiers in Oncology· 0 citations
Hepatocellular carcinoma (HCC) is one of the malignant tumors with high incidence and mortality rates worldwide. Given the poor prognosis of patients with HCC, it is crucial to explore the molecular mechanisms underlying HCC development and to evaluate prognostic markers. Differential expression analysis followed by univariate Cox, LASSO, and multivariate Cox regression identified four genes (EPO, SOCS2, IL18RAP, and KPNA2), and a Cox-based risk score was evaluated in the TCGA-LIHC cohort and externally in GSE14520 using Kaplan–Meier and time-dependent ROC analyses. Bulk, single-cell, and protein resources provided convergent expression context. Survival machine-learning analysis using observed overall-survival time and censoring status identified Cox–Ridge as the best-performing model in TCGA-LIHC, with more modest performance in GSE14520, and immune profiling revealed risk-group-associated differences in estimated immune and stromal components, immune-cell composition, and immune-checkpoint expression. The oncoPredict/GDSC2 screen highlighted five potential drug candidates for experimental prioritization. Because the drug screen is based on computationally predicted sensitivities, these findings should be regarded as hypothesis-generating and require validation in prospective cohorts and experimental systems before clinical translation.
Yu-Xian Liu, Xing-Jie Chen, Junyuan Zhang et al.· International Journal of Mol...· 0 citations
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.
Cong-Lin Du, Yong-An Li, Qian Wang et al.· Discover Oncology· 0 citations
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.
Feng-Chan Huang, Jung-Yin Fong, C. Ng et al.· Frontiers in Molecular Biosc...· 0 citations
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