Integrated multi-omic and functional profiling reveals a ZDHHC16-associated palmitoylation-proteostasis state in hepatocellular carcinoma

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Lihua Zhang · Xiaolei Zhang · Caiqi Liu · Xiaofei Xin · Xinchi Luan · Jialin Song · Wensheng Qiu

15 September 2026 at 18:00

Discov Oncol. 2026 Aug 1;17(1):1333. doi: 10.1007/s12672-026-05700-y.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) remains biologically heterogeneous, and molecular states linking tumor-cell intrinsic programs with post-translational regulation, immune contexture and drug-specific vulnerability remain incompletely defined. ZDHHC16 is a DHHC-family palmitoyl acyltransferase, but its clinical relevance and biological context in HCC remain unclear.

METHODS: Public transcriptomic, clinical, single-cell, proteomic, palmitoylome, immune-related and pharmacogenomic datasets were integrated to characterize ZDHHC16 in HCC. ZDHHC16 expression, exploratory survival separation, cellular localization, pathway activity, palmitoylation-associated candidates, immune microenvironment features and predicted drug response were evaluated. siRNA-mediated knockdown, MTT assays and colony formation assays were performed in HepG2 and Huh7 cells.

RESULTS: ZDHHC16 was upregulated in HCC. In exploratory Kaplan-Meier analyses restricted to primary tumors and using endpoint-specific data-derived cutoffs, the curves showed expression-group separation for overall survival, disease-free interval and progression-free interval (unadjusted log-rank P = 0.019, 0.019 and 0.011, respectively). These analyses were not adjusted for clinical covariates and do not establish independent prognostic value. Single-cell analysis localized ZDHHC16 mainly to malignant epithelial-related compartments. ZDHHC16 knockdown reduced MTT-based cell viability and clonogenic growth in HepG2 and Huh7 cells. ZDHHC16-high tumors were enriched for cell-cycle progression, DNA replication, DNA repair, RNA processing, ubiquitin-mediated proteolysis and proteasome-related programs. After deduplication at the gene-symbol level, palmitoylome-guided integration nominated 28 transcriptionally correlated palmitoylation-associated candidates, including EZH2, PI4K2A and ZDHHC6; the screen did not establish direct ZDHHC16 substrates. ZDHHC16-high tumors also showed immune-remodeled features and drug-specific predicted IC50 patterns.

CONCLUSIONS: Integrated data support ZDHHC16 as a marker of a malignant epithelial, growth-associated HCC state accompanied by palmitoylation- and proteostasis-related programs, altered immune contexture and drug-specific predicted IC50 patterns. Direct ZDHHC16-dependent palmitoylation, independent prognostic value and therapeutic utility require biochemical and prospective clinical validation.

PMID:42742876 | PMC:PMC13578202 | DOI:10.1007/s12672-026-05700-y

G9a-mediated cholesterol metabolism triggers cuproptosis to promote alcohol-related liver disease

Cell Death Discovery nature.com science feeds

By: Rong Dong · Jing Yuan · Jingzhou Chen · Yi Zhu · Yurong Zhao · Chi Luo · Jinghao Sheng · Yan Zha

7 September 2026 at 08:00

Cell Death Discovery, Published online: 07 September 2026; doi:10.1038/s41420-026-03299-1

G9a-mediated cholesterol metabolism triggers cuproptosis to promote alcohol-related liver disease

RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment

cs.AI, q-bio.NC updates on arXiv.org

By: Yuecheng Li · Hengwei Ju · Zeyu Song · Wei Yang · Chi Lu · Peng Jiang · Kun Gai

26 May 2026 at 12:00

arXiv:2602.00682v2 Announce Type: replace-cross Abstract: Integrating large language model (LLM) representations into multimodal recommendation has shown promise, yet a fundamental challenge remains largely overlooked: the semantic heterogeneity between generative LM representations and the ID-based collaborative signals that recommendation systems rely on. Naively injecting LM features without alignment degrades recommendation performance rather than improving it. To resolve this, we propose RecGOAT, a dual-granularity semantic alignment framework built on graph neural networks and optimal transport theory. RecGOAT first enriches collaborative semantics through multimodal attentive graphs that capture item-item, user-item, and user-user relationships, initializing user representations via LLM-inferred behavioral preferences. It then aligns LM-derived modality representations with recommendation IDs at two complementary granularities: (1) instance-level alignment via cross-modal contrastive learning (CMCL), which produces discriminative per-sample representations; and (2) distribution-level alignment via optimal adaptive transport (OAT), which minimizes the 1-Wasserstein distance between ID distributions and LLM semantics to produce a unified, consistently aligned feature space. Theoretically, we prove that the unified representation achieves strictly lower target error than any single-modality representation, with the gap bounded by the Wasserstein distance and the InfoNCE loss, providing rigorous guarantees for both alignment consistency and fusion comprehensiveness. Extensive experiments on three public benchmarks demonstrate state-of-the-art performance. Deployment on a large-scale online advertising platform further validates RecGOAT's industrial scalability. Our code is available at https://github.com/6lyc/RecGOAT-LLM4Rec.

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