Screening, Validation, and Machine Learning-Based Evaluation of Serum Protein Biomarkers for Esophageal Squamous Cell Carcinoma Based on Single-Cell Subtype-Specific Genes

Cellular
heterogeneity of epithelial cells and fibroblasts
is critical
in esophageal squamous cell carcinoma development (ESCC). Identifying
dysregulated subtype-specific genes in these cells is essential for
early diagnosis and treatment. In this study, our pipeline integrated
scRNA-seq, proteomics, and ELISA to screen biomarkers: scRNA-seq defined
epithelial and fibroblast subtypes and their markers, while proteomics
and secretory profiling identified dysregulated secretory proteins.
Serum levels of five selected proteins were measured in 344 ESCC patients,
46 HGIN cases, and 390 normal controls. Machine learning was employed
to construct diagnostic models. An interactive web tool was implemented
in R Shiny. Six epithelial and four fibroblast subtypes, proportionally
distinct between ESCC and normal tissues, were identified. Four validated
dysregulated proteins were used to build diagnostic models; among
12 algorithms, the Support Vector Machine (SVM) achieved the best
performance with AUCs of 0.829 and 0.767 in the training and validation
sets, respectively (p > 0.05). The model effectively
distinguished early- and late-stage ESCC and HGIN from normal controls.
The web-based diagnostic tool is publicly available at https://zhangxz.shinyapps.io/P4_Pred/. The identified serum biomarkers may enhance early ESCC detection
and diagnosis. Our pipeline, leveraging heterogeneity-related genes
in fibroblasts and epithelial cells, is readily adaptable to other
tumors.