Prognostic Prediction Model for Esophageal Squamous Cell Carcinoma Based on Single-Cell RNA Sequencing and Bulk RNA Sequencing

Research Background

Esophageal Squamous Cell Carcinoma (ESCC) is one of the most common malignant tumors globally, particularly prevalent in East Asia. Despite existing treatment methods such as surgery, endoscopic resection, and chemoradiotherapy, the prognosis for patients remains poor, with a 5-year survival rate of only 21%. Novel therapies like immune checkpoint inhibitors are effective in only 20%-30% of patients, indicating that our understanding of the molecular mechanisms of ESCC, especially the tumor microenvironment (TME), is still insufficient. Abnormal glucose metabolism is a critical feature in the early stages of ESCC. Tumor cells undergo glycolysis even in the presence of sufficient oxygen, producing large amounts of lactic acid, which affects the acidity of the microenvironment, thereby promoting tumor growth, angiogenesis, and immune suppression. Therefore, in-depth research into the relationship between glucose metabolism and ESCC, particularly its impact on the tumor microenvironment and immunotherapy, holds significant clinical importance.

Source of the Study

This study was conducted by Jiaqi Zhang, Shunzhe Song, Yuqing Li, and Aixia Gong from the First Affiliated Hospital of Dalian Medical University and published in Cancer Medicine in 2025. The study was approved by the Ethics Committee of the First Affiliated Hospital of Dalian Medical University (Approval No.: PJ-KS-KY-2024-406).

Research Process and Results

1. Data Sources and Processing

The study utilized data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and the Memorial Sloan Kettering Cancer Center (MSKCC), including samples from 8, 99, and 140 ESCC patients, respectively. The study first analyzed 51,134 cells using single-cell RNA sequencing (scRNA-seq) and identified 13 cell types, with squamous epithelial cells showing the highest glucose metabolism score. Subsequently, 558 differentially expressed genes (DEGs) were screened from squamous epithelial cells, and the metabolic score of each cell was calculated using Single-Sample Gene Set Enrichment Analysis (ssGSEA).

2. Construction and Validation of the Prognostic Model

Through univariate Cox regression analysis, 17 genes significantly associated with overall survival (OS) were identified. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, four key genes (SERP1, CTSC, RAP2B, and SSR4) were selected to construct the prognostic model. Based on the risk score (RS), patients were divided into high-risk (HRG) and low-risk (LRG) groups. Kaplan-Meier survival curves showed that the survival rate of HRG patients was significantly lower than that of LRG patients. Additionally, the model’s accuracy was validated using an external cohort (MSKCC dataset), which confirmed that HRG patients had significantly reduced survival rates.

3. Relationship Between Risk Score and Clinical Features

The researchers further analyzed the relationship between the risk score and clinical features, finding significant differences in survival status (p < 0.001) but no significant differences in gender, T stage, N stage, M stage, or clinical stage. Multivariate Cox regression analysis indicated that the risk score was an independent predictor of prognosis in ESCC patients.

4. Immune Infiltration and Drug Sensitivity Analysis

Using the CIBERSORT algorithm, the researchers analyzed the distribution of 22 immune cell types in ESCC patients and found significant differences in the content of eosinophils and M2 macrophages between HRG and LRG. Furthermore, drug sensitivity analysis revealed that LRG patients exhibited higher sensitivity to multiple chemotherapy drugs (e.g., Axitinib, AG.014699, ABT.263), suggesting these drugs as potential therapeutic targets for ESCC patients.

5. Gene Set Variation Analysis and Transcription Factor Prediction

Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) showed significant differences in multiple signaling pathways between HRG and LRG, including the reactive oxygen species pathway, mTORC1 signaling pathway, and p53 pathway. Additionally, transcription factor prediction analysis identified CISBP_M0340 as the primary transcription factor regulating key genes.

6. Immunohistochemical Validation

The researchers validated the expression of key genes in ESCC tissues, high-grade intraepithelial neoplasia (HGIEN), low-grade intraepithelial neoplasia (LGIEN), and adjacent normal tissues using immunohistochemistry (IHC). The results showed that the expression of SERP1, CTSC, RAP2B, and SSR4 was significantly higher in ESCC tissues than in normal tissues.

Research Conclusions and Significance

By integrating single-cell RNA sequencing and bulk RNA sequencing data, this study successfully constructed a prognostic prediction model for ESCC based on glucose metabolism-related genes. The model not only effectively predicts patient survival but also provides new insights into the molecular mechanisms of ESCC. The findings indicate that glucose metabolism plays a crucial role in the development and progression of ESCC, particularly by influencing the tumor microenvironment and immune cell infiltration. Additionally, the study identified multiple potential therapeutic targets, offering a theoretical basis for personalized treatment of ESCC.

Research Highlights

1. Innovative Methodology: The study is the first to combine single-cell RNA sequencing with bulk RNA sequencing, providing an in-depth analysis of cellular heterogeneity and glucose metabolism characteristics in ESCC.
2. Discovery of Key Genes: The study identified four key genes (SERP1, CTSC, RAP2B, and SSR4) related to glucose metabolism and validated their significant role in ESCC prognosis prediction.
3. Clinical Application Value: The prognostic model developed in the study has high predictive accuracy and provides important references for personalized treatment of ESCC patients.

Other Valuable Information

The study also validated the expression of key genes in ESCC tissues using immunohistochemistry, further confirming their important role in the development and progression of ESCC. Additionally, the study identified multiple potential therapeutic targets, offering new directions for personalized treatment of ESCC.