Integrative spatial and single-cell transcriptomics elucidate programmed cell death-driven tumor microenvironment...

Area of Science:

• Oncology
• Molecular Biology
• Immunology

Background:

• Programmed cell death (PCD) mechanisms are critical in cancer progression and response to therapy.
• Understanding PCD's role in hepatocellular carcinoma (HCC) is vital for improving patient outcomes.
• Tumor microenvironment heterogeneity influences HCC development and treatment efficacy.

Purpose of the Study:

• To develop a novel prediction model for programmed cell death (PCD) scores in hepatocellular carcinoma (HCC).
• To evaluate the prognostic value of PCD scores for HCC patient survival.
• To investigate differences in the tumor microenvironment associated with varying PCD scores.

Main Methods:

• Transcriptomic data from TCGA and GEO databases were analyzed to build the PCD prediction model.
• Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) were employed to study the tumor microenvironment.
• In vitro experiments were conducted to explore the oncogenic role of UBE2E1 in HCC.

Main Results:

• Seventeen PCD-related genes formed a robust prognostic prediction model for HCC.
• High PCD scores correlated with poorer overall survival (OS) and indicated increased tumor cell proliferation and malignancy.
• scRNA-seq and ST-seq revealed distinct immune microenvironments in high-PCD tumors, characterized by T-cell exhaustion.

Conclusions:

• The developed PCD prediction model accurately predicts HCC prognosis and offers insights into tumor microenvironment dynamics.
• UBE2E1 was identified as a key oncogenic gene associated with the PCD model in HCC.
• PCD-related biomarkers hold promise for guiding personalized treatment strategies in HCC.