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

Front Immunol. 2026 Apr 14;17:1767512. doi: 10.3389/fimmu.2026.1767512. eCollection 2026.

ABSTRACT

Gastric cancer (GC) is plagued by profound intratumoral heterogeneity and a complex tumor microenvironment (TME), which are the core obstacles to precise diagnosis and treatment. Conventional bulk multi-omics technologies average molecular signals across tissues, thus masking cellular heterogeneity; single-cell multi-omics resolves cellular diversity but dissociates cells from their native spatial context, leading to the loss of critical information on intercellular crosstalk and molecular spatial distribution. These limitations result in an incomplete understanding of GC pathogenesis and TME regulatory networks. Spatial multi-omics technologies, integrating genomics, transcriptomics, proteomics, and metabolomics with high-resolution spatial localization, address these key scientific problems by preserving the native tissue architecture and elucidating the spatiotemporal dynamics of molecular and cellular events in GC. This review systematically synthesizes the latest advances in the application of four major spatial multi-omics modalities in GC research over the past 15 years, with a critical evaluation of the technical performance, methodological shortcomings, and clinical translation potential of existing studies. Unlike previous reviews that only summarize research findings, this work uniquely integrates technical principles, mechanistic discoveries, and clinical translation of spatial multi-omics in GC, deeply analyzes the practical barriers to clinical application, and systematically elaborates the integration of spatial multi-omics with artificial intelligence (AI). We also identify unresolved challenges in the field and propose future development directions, providing a comprehensive and in-depth reference for the advancement of GC precision medicine based on spatial multi-omics.

PMID:42058209 | PMC:PMC13120937 | DOI:10.3389/fimmu.2026.1767512