Transcriptome and clinical data from patients diagnosed with colon cancer (CC) were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Analyzing this extensive dataset enabled us to gain a comprehensive understanding of the genetic landscape, identify new biomarkers, and develop screening for therapeutic strategies. Additionally, these analyses helped predict cancer prognosis and suggested further avenues for experimental validation[25].
Cuproptosis is a newly discovered form of regulated cell death (RCD) that is distinct from other forms of RCD[1]. However, its role in predicting tumor prognosis remains unclear. Studies have demonstrated that several genes can regulate cuproptosis[24, 26]. In our current study on colon cancer, we first assessed the expression variation of cuproptosis-related genes (CRGs) and found that 7 out of 19 CRGs were upregulated, while 6 out of 19 genes were downregulated compared to normal tissues. Notably, the highly expressed regulatory genes in tumor tissues generally act as negative regulators. In our analysis, highly expressed regulatory genes in tumor tissues predominantly acted as negative regulators. Conversely, the downregulated genes were mostly positively correlated with cuproptosis, suggesting that the tumor group may exhibit lower levels of cuproptosis compared to the normal group. Subsequently, CC patients were classified into two CRGclusters based on their expression patterns. Our analysis revealed that patients with higher expression of positively regulated CRGs experienced longer survival periods than those with lower expression of these genes. Thus, we speculate that cuproptosis-related gene signatures could serve as potential indicators of colon cancer prognosis. Focusing on CRGclusters A and B, we explored their pathway activity and immune infiltration. Gene Set Variation Analysis (GSVA) using GO and KEGG databases indicated that CRGcluster A was significantly enriched in mitochondrial activity and TCA cycle metabolism, suggesting a potentially higher level of cuproptosis in this cluster. Interestingly, comparisons of immune infiltration showed that CRGcluster B had higher immune cell infiltration and immune scores, yet this was associated with unfavorable survival outcomes, contrary to what has been observed in previous studies[27, 28].
We also identified two gene subtypes based on differentially expressed genes (DEGs) related to CRGclusters. Our findings revealed significant differences in survival curves, clinicopathological features, and CRG expression between the two gene subtypes, suggesting that CRGs may serve as predictors for evaluating clinical outcomes and as auxiliary forecasters for the immunotherapy response in CC patients. Consequently, we developed an effective and reliable prognostic risk scoring system, and its predictive ability was confirmed. To enhance the feasibility and practicality of this risk score, we created a quantitative nomogram that integrates the risk score with clinical features.
Prognostic models are crucial for predicting patient stratification in colon cancer, enhancing our understanding of the molecular mechanisms of CC, and offering new insights for targeted therapy. To elucidate the specific role of CRGs in risk stratification for these objectives, we conducted comprehensive analyses of clinicopathological characteristics, prognosis, mutations, tumor mutational burden (TMB), immune checkpoints, microsatellite instability (MSI), and drug susceptibility across patients with low and high risk scores. Typically, better prognosis in CC is associated with higher levels of tumor-infiltrating lymphocytes, tumor neoantigens, and immune checkpoints[29, 30].
Contrary to typical findings, our study revealed that within both CRGclusters and risk levels, groups with higher immune infiltration and ESTIMATE scores exhibited poorer overall survival (OS) rates. While CD8 + tumor-infiltrating lymphocytes are generally associated with chemosensitive responses, better prognosis, and negative PD-L1 expression[31]. previous studies have noted a link between PD-L1 expression and cytotoxic CD8 + TILs in cancer patients[32]. Our analysis across two risk levels revealed no significant differences in PD-1 and PD-L1 expression. Additionally, activated CD8 + T cells did not show a significant correlation with the risk score. In contrast, gamma delta T cells, T helper cells, and T follicular helper cells all demonstrated positive correlations with risk scores. Gamma delta (γδ) T cells are known for their dual role in colorectal cancer (CRC). They can effectively recognize and eliminate CRC cells, thereby inhibiting tumor progression through various mechanisms. However, they also have the potential to promote tumor growth by secreting interleukin-17 (IL-17), highlighting their complex role in the tumor microenvironment[33]. γδ T cells are recognized as potential immunotherapeutic agents in anti-tumor treatments due to their potent cytotoxic activity[34]. Furthermore, in colorectal cancer (CRC) patients, a higher expression level of follicular helper T cell (Tfh)-associated genes correlates with improved prognosis [35]. Tfh cells from CRC patients at advanced stages exhibit a diminished capacity to stimulate CD8 + T cells and to produce IL-21. Additionally, treatment with autologous PD-L1-expressing tumor cells further reduces IL-21 production by PD-1 + Tfh cells [36]. Moreover, the gut microbiota may also bolster Tfh cell-mediated anti-tumor immunity in colorectal cancer[37]. There is substantial evidence indicating that some CRGs positively regulate immune activities in various solid tumors, leading to a better prognosis[38–40].
Unfortunately, this study had several limitations that warrant further improvement. Primarily, all analyses were based solely on public databases, and all samples in our study were collected retrospectively. As a result, an inevitable selection bias might have influenced the outcomes. Furthermore, although the prognostic model we developed was tested using our dataset, the content of the public databases was insufficient to support high-quality independent external validation. Therefore, large-scale prospective studies, as well as in vitro and in vivo experiments, are still required for further verification.