Colorectal cancer (CRC) is one of the most common cancers worldwide, with high incidence and mortality rates. It is reported that nearly 1.4 million new cases of CRC and 700,000 CRC-related deaths occur globally each year[19]. Brenner et al. found that patients with early-diagnosed colorectal cancer have a 5-year survival rate exceeding 90%[20]. However, due to inadequate diagnostic methods, colorectal cancer is often diagnosed at advanced stages[21]. Despite significant improvements in diagnosis and treatment, the 5-year survival rate for patients diagnosed with metastatic colorectal cancer remains low, at approximately 12%[22]. Therefore, there is an urgent need to elucidate the molecular mechanisms of colorectal cancer development and to identify novel biomarkers for early detection and prognosis assessment to improve survival outcomes.
Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool for transcriptomic profiling of various cancer cell types, crucial for identifying potential therapeutic targets. In this study, we utilized colorectal cancer scRNA-seq data from the GEO database to define cellular subpopulations within tumors and characterize their contributions to the disease based on cell numbers and gene expression changes. We then selected marker genes with the highest disease relevance from these subpopulations as a candidate gene set for further analysis. This led to the construction of a prognostic risk model with favorable prognostic efficiency, which serves as a biomarker for predicting immunotherapy response. Similar viewpoints were also proposed by Juan et al.[23]. They applied scRNA-seq to analyze the heterogeneity of tumor immune cells, developing a 3-gene biomarker (including CLTA, TALDO1, and CSTB) based on tumor immune microenvironment (TIME) heterogeneity to predict survival outcomes and immunotherapy responses. Zheng et al.[24] selected 6 prognosis-related HUB genes from GEO esophageal squamous cell carcinoma (ESCC) and TCGA esophageal cancer datasets, showing significantly increased expression of HUB genes in normal tissues and cells based on scRNA-seq. Further Kaplan-Meier survival analysis and immune infiltration analysis indicated that HUB genes are promising biomarkers for ESCC diagnosis and prognosis. Additionally, studies utilizing scRNA-seq technology have elucidated intercellular interactions in gliomas, identifying autocrine ligand-receptor signaling that significantly impacts prognosis in glioma patients[25]. Collectively, these findings demonstrate that scRNA-seq technology can effectively dissect and identify potential prognostic biomarkers, which are crucial for pinpointing therapeutic targets and improving patient survival outcomes.
In our study, the prognostic signature composed of nine marker genes (S100P, PIGR, RAB11FIP1, USP53, CDH1, LGALS4, ATP10B, SLC12A2, and LAMB3) may provide valuable insights into the molecular mechanisms of colorectal cancer (CRC). For instance, S100P, a 95-amino acid protein and member of the S100 family, plays a crucial role in regulating cell differentiation, proliferation, migration, apoptosis, and other biological functions by interacting with various signaling proteins such as P53, β-catenin, and nuclear factor-κB (NF-κB). Through these interactions, S100P is involved in tumorigenesis and tumor progression. Research has shown that SIX3 can downregulate S100P via the Wnt/β-catenin signaling pathway, thereby inhibiting cell migration and proliferation[26]. Another study identified that S100P mRNA levels correlate with the activation status of the PI3K/AKT pathway, a classical pathway involved in promoting cancer migration, invasion, proliferation, and drug resistance[27].
The performance of the prognostic model based on the nine marker genes was validated in both the test and GEO cohorts, yielding consistent results across the two cohorts, indicating good effectiveness and reproducibility of the model. Various validation methods, including univariate, multivariate, and clinical indicator analyses, demonstrated that the nomogram model has high predictive accuracy. Therefore, the nomogram can guide the establishment of personalized examination procedures for CRC patients, promoting the effective utilization of medical resources.
Given that the tumor microenvironment (TME) plays a crucial role in anti-tumor responses and significantly influences tumor diagnosis, survival outcomes, and clinical treatment sensitivity[28], we investigated the relationship between risk score and tumor immune infiltration. Firstly, we observed significant decreases in activated dendritic cells, plasma cells, and resting CD4 memory T cells in the high-risk group, suggesting that these patients may be in a relatively immunosuppressive state. Secondly, the study results showed significant positive correlations between the risk score and M0 macrophages, CD8 T cells, and M2 macrophages, and significant negative correlations with resting CD4 memory T cells, activated dendritic cells, eosinophils, and plasma cells. This indicates that the TME of the high-risk group may function to reduce inflammation, promote tumor growth, and suppress immunity.
To better guide CRC treatment, we conducted drug sensitivity analyses on different risk groups, studying six common chemotherapy drugs for colorectal cancer. The results indicated that the low-risk group is sensitive to five anticancer drugs, while the high-risk group is sensitive to one anticancer drug. These findings provide a reference for the clinical selection of chemotherapy drugs. In future studies, we will further explore the clinical significance of these drugs for LUSC patients.
Inevitably, our study has some inherent limitations. Firstly, all cohort studies are retrospective and require further validation in prospective cohort studies. Secondly, further mechanistic studies are needed to reveal the exact role of each gene, and drug sensitivity needs further confirmation through cellular experiments. Thirdly, the number and volume of scRNA-seq samples available in public databases are limited, resulting in an incomplete analysis of clinical and pathological parameters, which may lead to potential biases. Therefore, it is necessary to conduct multicenter, large-sample, prospective double-blind trials for further verification in the future.