Findings indicate that prognostic gene expression signatures derived from NanoString data showed limited reproducibility across independent cohorts and provided little additional predictive value beyond established clinical factors, and suggest that robust clinical variables remain the most reliable predictors of recurrence risk in this setting.
Abstract
Gene expression-based prognostic models have shown promise for predicting recurrence in colorectal cancer (CRC), but their clinical implementation remains limited. The NanoString nCounter platform provides a practical alternative to RNA sequencing and microarrays through standardized, cost-effective gene expression profiling that is compatible with routine clinical samples. In this study, we evaluated whether NanoString nCounter gene expression data improve prediction of recurrence following curative CRC surgery. Gene expression profiles from the NanoString PanCancer IO 360 panel were analyzed in two independent CRC cohorts (cohort A, n = 189; cohort B, n = 131). Differential gene expression analyses and Cox proportional hazards models were used to assess the prognostic value of gene expression alone and in combination with established clinical risk factors. Model performance was evaluated by five-fold cross-validation and external validation between cohorts using the concordance index (C-index) and Kaplan-Meier risk stratification. The two cohorts differed significantly in recurrence-free survival, and differential expression analysis demonstrated marked cohort-specific transcriptional patterns. Ninety-one recurrence-associated genes were identified in cohort A, whereas no significant genes were detected in cohort B, with poor agreement in gene-level differential expression between cohorts (Pearson r = 0.128). Across all prediction models, external performance was modest, and inclusion of gene expression data did not improve prediction beyond clinical variables. The clinical baseline model, incorporating age, UICC stage, and tumor site, consistently achieved the highest cross-cohort performance, with UICC stage emerging as the strongest predictor of recurrence. Although overall discrimination was moderate, the baseline model successfully stratified patients into significantly different high- and low-risk groups across cohorts. These findings indicate that prognostic gene expression signatures derived from NanoString data showed limited reproducibility across independent cohorts and provided little additional predictive value beyond established clinical factors. The results highlight the importance of external validation and suggest that robust clinical variables remain the most reliable predictors of recurrence risk in this setting.
A READ-derived response-associated gene signature for recurrence stratification and exploratory cross-cohort evaluation in additional colorectal cancer cohorts, while further exploring its association with treatment-response phenotypes.
Shuai Li, Jingxian Li, Xianyue Bu et al.· Human Cell· 0 citations
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.
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.
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.
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.
Unknown authors· Frontiers in Molecular Biosc...· 0 citations
: Backgrounds: Colorectal cancer (CRC) prognosis remains difficult due to molecular heterogeneity and interaction between tumor cells and the immune microenvironment. This study aimed to identify transcriptomic and immune-cell patterns associated with overall survival (OS) and to develop an integrated prognostic model to improve risk stratification. Methods: RNA-sequencing was performed on 131 primary CRC samples and matched normal tissues. Differentially expressed genes (DEGs) were identified and functionally characterized through gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein–protein interaction (PPI) network analysis. Immune-cell composition was estimated using CIBERSORTx deconvolution and evaluated for its association with OS. Prognostic DEGs were screened using univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk score. The model was validated in the Cancer Genome Atlas Database (TCGA)-COAD (colon cancer) and READ (rectal cancer) cohorts. A nomogram integrating molecular and clinicopathological variables were generated. Results: A total of 5589 DEGs were identified between CRC and normal tissues, enriched in pathways related to cell cycle, Tumor Protein P53 (TP53), WNT Family Member (WNT), Janus kinase/signal transducer and activator of transcription 3 (JAK/STAT), calcium signaling, metabolism, and immune regulation. PPI analysis highlighted ten upregulated hub genes involved in mitotic spindle formation and chromosomal stability. Immune infiltration analysys indicated that higher proportions of plasma cells ( p = 6.9 × 10 − 4 ), naïve B cells ( p = 0.019), resting CD4 + memory T cells ( p = 0.02), M0 macrophages ( p = 0.0077), and activated dendritic cells ( p = 1.89 × 10 − 5 ) were associated with improved OS, whereas monocytes ( p = 0.012), neutrophils ( p = 0.041), activated mast cells ( p = 0.0066), and M2 macrophages ( p = 0.014) were linked to poorer survival. A seven-gene signature including aspartate beta-hydroxylase (ASPH), bradykinin receptor B1 (BDKRB1), calcium voltage-gated channel auxiliary subunit beta 1 (CACNB1), C-C motif chemokine receptor 8 (CCR8), cyclic nucleotide gated channel subunit alpha 3 (CNGA3), microtubule associated protein 1A (MAP1A) and oxytocin receptor (OXTR) stratified patients into high-and low-risk groups with significant OS differences. The model demonstrated strong predictive performance (AUC: 0.84 at 1 year) and was validated in TCGA cohorts. Multivariate analysis confirmed the risk score as an independent prognostic factor. The integrated nomogram accurately predicted 1, 3-, and 5-year survival (C-index = 0.757; 95% CI 0.724–0.791). Conclusions: We developed and validated 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.
The 36-gene signature developed in this study demonstrates favorable predictive performance and stability across both the training and independent Chinese cohorts, and underscores the critical roles of energy metabolism and ECM remodeling in NMIBC recurrence.
Liyuan Dong, Yun Peng, Yuxuan Song et al.· Beijing da xue xue bao. Yi x...· 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
The main objective of this study is to investigate the dysregulated expression, clinical relevance, and underlying mechanisms of COG8 in lung adenocarcinoma (LUAD) and other human malignancies. We assembled multi-cohort microarray and RNA sequencing data for tumor and non-tumor controls from global repositories including ArrayExpress, Gene Expression Omnibus, The Cancer Genome Atlas, and Genotype-Tissue Expression. COG8 protein expression in LUAD was evaluated using immunohistochemical staining. Differential expression was assessed using standardized mean difference (SMD) for pooled effects and Wilcoxon rank-sum tests for individual datasets. Clinical relevance of COG8 was examined using receiver operating characteristic analyses and Cox/Kaplan–Meier survival analyses. Correlation between COG8 expression and tumor mutation burden, microsatellite instability, neoantigen count, and TIMER scores was evaluated using Spearman’s rank correlation. Gene set enrichment analysis was conducted to explore the potential signaling pathways associated with COG8 across tumor types. COG8 mRNA was higher in LUAD compared to the control group (SMD = 0.29, 95% CI: 0.12–0.46, p < 0.05), consistent with COG8 protein levels by immunohistochemistry (p < 0.05). Elevated mRNA expression was associated with longer overall survival in LUAD (p < 0.05) and showed moderate discrimination between LUAD and controls (AUC = 0.72). COG8 mRNA was also overexpressed in 11 additional cancer types (p < 0.05). Higher COG8 expression was linked to worse prognosis in seven cancers (HR > 1; p < 0.05) and to improved overall and/or disease-specific survival in four cancers (HR < 1; p < 0.05). In certain cancers, COG8 expression correlated with tumor mutation burden, microsatellite instability, neoantigen count, and immune infiltration levels (p < 0.05). COG8 may impact signaling pathways such as cytokine-cytokine receptor interaction. COG8 exhibits aberrant expression in LUAD and eleven other human malignancies, holding clinical significance as a predictive and prognostic biomarker.
Fei Tan, Chun-Yan Zhao, Guo-Sheng Li et al.· Discover Oncology· 0 citations
