Prognostic risk modeling based on integrated multi-omics analysis identifies CRY2 as a key regulator in tumor immunity and patient survival in colorectal cancer
Aug 2026· Annals medicus· Vol 58· 0 citations· 41 references
Medicine
TL;DR
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.
Abstract
Abstract Background Colorectal cancer (CRC) exhibits substantial metabolic heterogeneity. This study developed a robust prognostic signature integrating ferroptosis- and lipid metabolism-related genes to investigate the role of CRY2 in CRC progression. Methods Transcriptomic and clinical data from the TCGA-COAD and GSE39582 cohorts were analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to identify disease-associated gene modules. A machine learning framework was subsequently applied to construct and optimize the prognostic model, with the combination of forward stepwise Cox regression (StepCox) and Random Survival Forest demonstrating the best predictive performance. The tumor immune microenvironment was characterized using CIBERSORT and TIDE. The biological function of CRY2 was validated through siRNA-mediated knockdown, functional assays, and a murine xenograft model. Results The ferroptosis- and lipid metabolism-related RiskScore was identified as an independent predictor of overall survival (HR > 1.1, p < 0.001). Patients in the high-risk group showed poorer overall survival and an immunosuppressive tumor microenvironment (TME) characterized by increased regulatory T cells (Tregs) and Th2 cells, reduced CD4+ T-cell infiltration, and higher TIDE scores, suggesting immunotherapy resistance. Among the signature genes, CRY2 was identified as a key regulator associated with CRC progression. In vitro, CRY2 knockdown inhibited CRC cell proliferation and migration while inducing G1-phase cell-cycle arrest. In vivo, silencing CRY2 significantly suppressed xenograft tumor growth and reduced Ki-67 expression. Conclusion This study developed and validated a ferroptosis- and lipid metabolism-related prognostic signature that accurately predicts survival outcomes and immune characteristics in CRC. Furthermore, CRY2 was identified as a critical regulator of tumor growth.
: 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.
Purpose
Breast cancer (BRCA) is a heterogeneous disease with a complex etiology. The prognostic value of genes related to migrasomes and the tumor microenvironment (MTMERGs) in BRCA is unclear.
Materials and Methods
A prognostic risk model was constructed using six core signature genes identified from MTMERGs via differential expression analysis and Cox regression. Its reliability was validated in an independent cohort using Kaplan-Meier and time-dependent ROC curves. A nomogram was developed and assessed via Decision Curve Analysis (DCA). Biological functions and immune infiltration were evaluated with GSEA, CIBERSORT, and ssGSEA. Immunotherapy sensitivity was predicted using TIDE/IPS scores and the IMvigor210 cohort. Tumor Mutation Burden (TMB) analysis and the pRRophetic algorithm were used for further clinical correlation and drug sensitivity prediction.
Results
The six-MTMERG model effectively stratified patients into high- and low-risk groups with distinct survival outcomes. The high-risk group was associated with a predicted immunosuppressive microenvironment (estimated enrichment of M0/M2 macrophages), higher TMB, and poorer prognosis. In contrast, the low-risk group was estimated to possess an immunologically active profile and showed a better predicted response to immune checkpoint inhibitors. Predicted differential sensitivities to conventional chemotherapy were also computationally evaluated between the subgroups.
Conclusion
We developed a computationally derived and externally validated prognostic model for BRCA based on migrasome and tumor microenvironment features. It successfully stratifies patients into groups with divergent clinical outcomes, immune profiles, and therapeutic responses, providing insights into BRCA heterogeneity and prognosis. Further prospective and experimental validation is warranted before clinical application.
Jingbo Du, Lin Zhu· Cancer research and treatmen...· 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
BACKGROUND
Ferroptosis is an iron-dependent programmed cell death, which plays a complex role in pancreatic ductal adenocarcinoma (PDAC), regulating both tumor development and immune interaction. However, the clinical significance and potential molecular mechanism of ferroptosis in PDAC have not been fully clarified, which limits its application in treatment.
METHODS
In order to identify ferroptosis-related genes (FRGs), we integrated transcriptome data from TCGA-PDAC and GTEx databases, as well as supplementary data from three GEO datasets (GSE62452, GSE78229 and GSE183795). We constructed a prognostic risk model by sequential analysis, which included univariate Cox regression, LASSO regression and multivariate Cox regression. Through Kaplan-Meier survival curve, time-specific ROC curve analysis and correlation study with clinicopathological features, we strictly evaluated the prediction accuracy and clinical relevance of this model. Multi-omics analyses included GO/KEGG/GSEA/GSVA pathway enrichment, CIBERSORT immune deconvolution, ESTIMATE scores, TIDE/ICB response prediction, tumor mutational burden (TMB) profiling, oncoPredict drug sensitivity estimation, single-cell RNA-seq (GSE212966), spatial transcriptome (GSM8452850), pseudotime trajectory, and HPA immunohistochemistry validation.
RESULTS
A robust five-gene ferroptosis-related prognostic signature (BCAR3, GSK3B, STAT1, MAGED2, MYEOV) was established. Patients categorized into the high-risk group demonstrated a markedly inferior overall survival compared to those in the low-risk group across both the TCGA cohort (5-year AUC = 0.87) and three external validation cohorts (5-year AUC 0.804-0.833). High-risk tumors showed enrichment in mitotic spindle, PI3K-AKT-mTOR, G2M checkpoint, glycolysis, and p53 pathways, markedly elevated KRAS/TP53 mutation rates, higher TMB, an "inflammatory yet immunosuppressive" microenvironment (increased resting NK cells/plasma cells, decreased activated NK cells, upregulated checkpoints including CD44/HHLA2/LGALS9), and differential chemotherapy sensitivity (greater sensitivity to gemcitabine, erlotinib, gefitinib, trametinib in high-risk group). Single-cell and spatial analyses confirmed predominant expression in malignant ductal cells, with dynamic pseudotime-dependent patterns (especially GSK3B upregulation in late-stage cells) and enhanced MIF signaling-mediated crosstalk in high-score subpopulations. Protein-level heterogeneity was verified by HPA-IHC.
CONCLUSION
In this study, a concise and externally validated five-gene ferroptosis-related prognostic signature was developed, which effectively stratifies survival outcomes in PDAC patients and reflects ferroptosis-associated alterations in tumor proliferation, metabolic reprogramming, immune evasion, and drug response. This model provides a framework for mechanism-informed precision treatment strategies, such as ferroptosis induction combined with immunotherapy or risk-adapted chemotherapy, and may offer new insights into improving outcomes in this highly lethal malignancy.
Wei Liu, Yifen Shen, Li Rao et al.· Discover Oncology· 0 citations
Lung adenocarcinoma (LUAD) is the most common lung cancer histological subtype. Although the unfolded protein response (UPR) has been linked to various human diseases, its role in LUAD remains unclear. To identify UPR-related genes, we applied various methods, including weighted gene co-expression network analysis, differential expression analysis, and multivariate Cox regression. Ten machine learning algorithms were used to construct a UPR-related signature (UPRRS), which was validated using multiple public LUAD datasets. The UPRRS was integrated into a nomogram used in clinical practice for prognosis prediction. We also evaluated predicted drug sensitivity patterns across different risk subgroups. We identified 33 UPR-associated hub genes. A UPRRS was developed through systematic evaluation of 101 machine-learning combinations, exhibiting stable prognostic performance across multiple cohorts. Integration of the UPRRS into a nomogram facilitated the construction of a quantitative prognostic model. Significant differences in biological processes and tumor microenvironment immune cell infiltration were observed between the high- and low-risk UPRRS groups. All five UPRRS genes (ALDH2, FKBP4, KLF4, LAIR1, SIDT2) were validated at the protein level in LUAD cell lines, and FKBP4 was further confirmed by IHC in clinical tissues. Functional experiments showed that FKBP4 knockdown inhibited proliferation, migration, and invasion of A549 and H1975 cells, supporting a potential role for FKBP4 in LUAD progression. Our UPRRS provides a promising tool for prognostic stratification and may offer additional insights into tumor immune microenvironment characterization and therapeutic response prediction in LUAD.
Rui Jiao, Chengyang Wu, Tao Zhang et al.· BMC Cancer· 0 citations
Molecular heterogeneity of colorectal cancer (CRC) remains a major barrier to precision therapy. In this study, we integrated eight public aging-related resources to construct an aging-related gene set and performed network perturbation analysis on the TCGA CRC cohort using a Reactome-based reference network. Unsupervised consensus clustering identified three molecular subtypes (C1, C2, C3). Nearest Template Prediction (NTP) validation across five independent GEO cohorts confirmed cross-cohort stability. Multi-dimensional biological characterization revealed that C1 is enriched for EMT activation, stromal infiltration, and immune exclusion; C2 for metabolic reprogramming dominated by lipid and glycerophospholipid handling; and C3 for an active cell cycle, high tumor mutational burden, and the lowest predicted immune dysfunction and exclusion. Survival analysis demonstrated that both C1 and C2 exhibit poor overall prognosis, while C3 has the most favorable outcome, with statistically significant inter-subtype differences. Through multi-cohort Cox regression cross-screening, we identified MAP1B as a core candidate driver gene of the C1 subtype. Its high expression was significantly associated with distant metastasis, lymph node metastasis, and poor prognosis, and was further validated at the protein level by immunohistochemistry. Functional experiments confirmed that MAP1B silencing significantly inhibited CRC cell proliferation, invasion, and migration, reversed EMT-related protein expression patterns, and suppressed tumor growth in nude mouse xenograft models. GDSC2 drug sensitivity analysis suggested that MAP1B-high CRC is more sensitive to dasatinib, and combining MAP1B silencing with dasatinib produced the largest reduction in invasion, migration and xenograft growth, although the added effect over MAP1B silencing alone did not reach significance in vivo. This study establishes a CRC subtyping framework based on aging-related gene network perturbation and provides preclinical evidence for MAP1B as an intervention target in EMT-activated CRC.
Wu Ning, Shuaixi Yang, Nan Qiao et al.· Translational Oncology· 0 citations