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Health·G Space·Evidence-backed gain·Published 2026-08-24

Integrative single-cell and machine-learning analysis identifies LGALS1 as a macrophage-associated diagnostic and prognostic biomarker in hepatocellular carcinoma

Abstract: Background Hepatocellular carcinoma (HCC) is characterized by marked heterogeneity and an immunosuppressive microenvironment in which tumor-associated macrophages contribute to disease progression. This study aimed to identify macrophage-associated biomarkers with diagnostic, prognostic, and translational relevance in HCC. Methods Single-cell RNA-sequencing datasets were integrated with bulk transcriptomic and clinical data from TCGA-LIHC, GEO, and ICGC cohorts. Macrophage markers were intersected with tumor-ass…

TRV-2026-0865Peer-reviewedPermanent record — cite & verify
Integrative single-cell and machine-learning analysis identifies LGALS1 as a macrophage-associated diagnostic and prognostic biomarker in hepatocellular carcinoma

Downtown Nassau - 2025 - Doctors Hospital (2) by Bluerasberry. CC BY 4.0 · https://creativecommons.org/licenses/by/4.0

The quick read

On August 22, 2026, researchers reported an integrative analysis combining single-cell RNA-sequencing with bulk transcriptomic and clinical data from TCGA-LIHC, GEO, and ICGC to find macrophage-associated biomarkers in hepatocellular carcinoma. They derived a 16-gene signature that stratified tumors by survival and clinicopathological features, with ridge regression showing AUC 0.988 in TCGA-LIHC and 0.895-0.924 externally, and XGBoost SHAP identifying LGALS1 as highly informative.

The work matters because HCC has marked heterogeneity and an immunosuppressive microenvironment where tumor-associated macrophages drive progression, and LGALS1 was linked to adverse survival, advanced disease, sorafenib non-response, and macrophage infiltration, with silencing reducing malignant behaviors in vitro. Uncertainty remains because associations with immunotherapy outcomes were exploratory in non-HCC cohorts and the authors note need for prospective HCC-specific validation and macrophage-focused mechanistic investigation.

Main points
  • Single-cell RNA-sequencing integrated with bulk data from TCGA-LIHC, GEO, and ICGC cohorts to derive macrophage markers.
  • 16-gene signature stratified HCC into subtypes with distinct overall survival, clinicopathological features, and mutation patterns.
  • Ridge regression achieved AUC 0.988 in TCGA-LIHC and 0.895-0.924 in three external cohorts.
  • LGALS1 enriched in monocyte/macrophage and fibroblast populations and associated with greater macrophage infiltration and sorafenib non-response.
Gain

A 16-gene macrophage-associated signature developed with ridge regression and XGBoost stratified HCC patients and achieved high diagnostic discrimination, with LGALS1 emerging as a top feature linked to survival and treatment response.

The rundown

Researchers integrated single-cell RNA-sequencing with bulk transcriptomic and clinical data from TCGA-LIHC, GEO, and ICGC, intersecting macrophage markers with differentially expressed and survival-associated genes, then applying consensus clustering, immune and mutation analyses, and multiple machine-learning algorithms.

Evaluation highlighted ridge regression for diagnosis and XGBoost SHAP for feature importance, pointing to LGALS1, which was further examined for survival, sorafenib response, immune infiltration, single-cell localization, virtual-knockout, and in vitro loss-of-function showing reduced proliferation, colony formation, migration, and invasion.

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