Tumor Immune Microenvironment States for Hepatocellular Carcinoma Prognosis

A computational framework stratifying prognosis in hepatocellular carcinoma based on the composition of six TLS-associated immune cell components. The model was developed by Chihyuan Cheng using a single-cell atlas and bulk transcriptome deconvolution, with validation across multiple cohorts including TCGA-LIHC and ICGC LIRI-JP. The dataset was last updated on 2026-05-28.

Use Cases

• Predicting patient overall survival in hepatocellular carcinoma based on inferred TLS-associated immune cell fractions.
• Associating tumor immune microenvironment states with response to PD-1 blockade immunotherapy.
• Validating prognostic models using multiplex immunofluorescence data from formalin-fixed paraffin-embedded tissues.

Strengths

• Model validated across three independent cohorts: TCGA-LIHC, ICGC LIRI-JP, and an anti-PD-1-treated HCC cohort.
• Framework is translatable, with a multiplex immunofluorescence implementation reproducing stratification without model refitting.
• Published under a permissive CC-BY-4.0 license.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 3.2 MB file size suggests a small dataset, likely containing model outputs or summary statistics rather than raw data.

Provenance

Source

figshare, author Chihyuan Cheng.

Collection Method

Derived from integrating an HCC single-cell atlas with literature-curated TLS gene sets and applying BayesPrism deconvolution to bulk tumor transcriptomes.

Freshness

Last updated 2026-05-28 05:58:19; freshness should be verified.