Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0865
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-24T06:06:19.772401Z
status: published
lens: g_space
sector: health
headline: Integrative single-cell and machine-learning analysis identifies LGALS1 as a macrophage-associated diagnostic and prognostic biomarker in hepatocellular carcinoma
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: XGBoost-based SHAP analysis identified LGALS1 as a highly informative feature | LGALS1 silencing reduced HCC-cell proliferation, colony formation, migration, and invasion
problem_reading: (none)
problem_evidence: (none)
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.
limitation: Findings are based on retrospective multi-cohort and in vitro analyses and require prospective HCC-specific validation and mechanistic work before clinical use.
tag: Evidence-backed gain
key_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.
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.
sources:
- peer_reviewed | Translational Oncology | https://doi.org/10.1016/j.tranon.2026.102980 | 2026-08-22
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

b067960adb1649347636ce4e26dd4cd075a30d5c6919e5a092c3e1530f8fab71

previous

0000000000000000000000000000000000000000000000000000000000000000