image: (A) Clustering of LD-associated proteins based on their biological roles. Proteins are grouped into functional modules based on Gene Ontology (GO) analysis and UniProt functional annotations. The solid lines represent physical interactions, as annotated in the BioGRID database. The intensity of the blue color in each node corresponds to the confidence score for the interaction. The nodes outlined in red represent proteins for which LD localization has been experimentally validated, whereas the nodes outlined in black represent proteins for which LD localization has not been validated. (B) Heatmap displaying the expression profiles of the 122 LDAGs across several HCC datasets. The color intensity represents the differential expression of each gene in the HCC and control samples within the dataset. (C) Consensus matrix heatmap based on the gene expression data of 122 LDAGs in the TCGA-LIHC cohort. The color coding represents the three distinct HCC subclasses: Cluster 1 (C1), Cluster 2 (C2), and Cluster 3 (C3). (D) Heatmap showing the expression patterns of LDAGs across the three identified HCC subgroups. (E) Clinical characteristics of the LDAG-associated HCC subgroups in the TCGA-LIHC cohort. (F) Kaplan–Meier survival analysis of the three subgroups in the TCGA-LIHC dataset. (G) Comparisons of the LDAG subtypes with eight previously reported classifications based on the TCGA data set. Statistical significance is indicated as “ns” (no significant difference); ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, and ∗∗∗∗P < 0.0001.
Credit: Huiying Gu, Qiumin Wu, Haibei Zhao, JingLong Du, Zhenzhen Zhang, Juan Chen
Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and remains one of the leading causes of cancer-related deaths worldwide. Despite advances in targeted therapies and immunotherapy, substantial molecular heterogeneity continues to limit accurate prognostic prediction and effective treatment selection. While metabolic reprogramming is increasingly recognized as a hallmark of HCC, the contribution of lipid droplet-associated genes (LDAGs) to tumor classification and disease progression has remained poorly defined.
In a recent study published in Genes & Diseases, researchers from Chongqing Medical University developed a comprehensive molecular classification framework based on LDAGs to better understand the metabolic diversity of HCC. By integrating transcriptomic and clinical data from 1,834 patients across multiple independent cohorts, the investigators identified biologically distinct HCC subtypes with unique molecular features, clinical outcomes, and therapeutic vulnerabilities.
Using unsupervised consensus clustering of 122 LDAGs, the researchers classified HCC into three molecular subtypes (C1–C3). The C1 subtype represented the most aggressive form of disease, exhibiting advanced tumor stage, increased vascular invasion, enhanced inflammatory infiltration, and significantly poorer overall survival than the other subtypes. Genomic analyses further demonstrated subtype-specific mutation profiles, with TP53 mutations enriched in C1 and CTNNB1 mutations predominating in C3, reinforcing the relationship between lipid metabolism and established oncogenic pathways.
Functional enrichment and pathway analyses revealed distinct metabolic and signaling programs across the three subtypes, highlighting the biological significance of LDAG expression patterns. Drug sensitivity analyses further suggested that tumors belonging to the aggressive C1 subtype may exhibit increased responsiveness to sorafenib, indicating that LDAG-based stratification could help guide precision treatment strategies for patients with advanced HCC.
To identify molecular drivers underlying tumor aggressiveness, the researchers pinpointed five hub genes—PLIN3, SET, CKAP4, RAP1B, and PISD—with PLIN3 emerging as the strongest prognostic biomarker. Experimental validation demonstrated that silencing PLIN3 reduced intracellular lipid accumulation, inhibited HCC cell proliferation and migration, and suppressed tumor growth. Conversely, PLIN3 overexpression promoted aggressive tumor behavior, establishing its critical role in metabolic reprogramming and HCC progression.
Overall, this study establishes LDAG signatures as a robust framework for classifying HCC into clinically meaningful metabolic subtypes. By identifying PLIN3 as both a prognostic biomarker and a potential therapeutic target, the findings provide valuable insights into the metabolic mechanisms driving HCC heterogeneity and offer a foundation for developing more personalized prognostic and therapeutic strategies for liver cancer.
Reference
Title of Original Paper: Lipid droplet-associated gene signatures classify metabolic subtypes and identify PLIN3 as a key driver in hepatocellular carcinoma
Journal: Genes & Diseases
Genes & Diseases is a journal for molecular and translational medicine. The journal primarily focuses on publishing investigations on the molecular bases and experimental therapeutics of human diseases. Publication formats include full length research article, review article, short communication, correspondence, perspectives, commentary, views on news, and research watch.
DOI: https://doi.org/10.1016/j.gendis.2026.102067
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