Innovation (Camb). 2025 Aug 6;7(1):101076. doi: 10.1016/j.xinn.2025.101076. eCollection 2026 Jan 5.

ABSTRACT

Although circulating cell-free DNA (cfDNA) methylation has emerged as the mainstream approach in multi-cancer detection blood tests (MCDBTs), the potential of integrating proteins and mutations, to enhance its performance remains unclear. The PROMISE study (NCT04972201) was conducted to investigate the feasibility of a multi-omics integration strategy in MCDBTs across nine types of cancers in head and neck (excluding nasopharynx), esophagus, lung, stomach, liver, biliary tract, pancreas, colorectum, and ovary. Blood samples were prospectively collected from 1,706 participants (840 non-cancer; 866 cancer) and then randomly divided into training and validation sets. The complementarity between various omics were investigated, and specific omics features were carefully selected for further multimodal model construction. The methylation-based classifier outperformed both the mutation-based and protein-based classifiers. As 95.0% of cancer cases detected by the mutation-based classifier were simultaneously identified by the methylation-based classifier, while 14.0% of the protein-positive samples were missed, protein markers may provide complementary value to the methylation-based classifier. Compared with the methylation-based classifier, the multimodal classifier combining methylation and protein features exhibited an improved sensitivity of 75.1% (95% confidence interval [CI], 69.3%-80.3%) at the same specificity of 98.8% with the accuracy of top predicted origin (TPO1) of 73.1% (95% CI, 66.2%-79.2%). Notably, the TPO1 accuracy reached 100% in liver and ovarian cancers with negative results of the methylation-based classifier. Collectively, these data suggest that the integration of protein markers in the multimodal classifier can offer additional benefits to the methylation-based classifier, particularly in identifying liver and ovarian cancers.

PMID:41737326 | PMC:PMC12925926 | DOI:10.1016/j.xinn.2025.101076

Drug Des Devel Ther. 2026 Feb 16;20:579427. doi: 10.2147/DDDT.S579427. eCollection 2026.

ABSTRACT

BACKGROUND: Lung cancer is one of the leading causes of cancer-related deaths, among which NSCLC accounts for approximately 80-85% of all lung cancer cases. Paclitaxel (TAX) is a commonly used chemotherapeutic drug, but it is easy to cause drug resistance. Fluvastatin has anti-cancer potential, but the mechanism of its reversal of drug resistance is unclear.

METHODS: The study was divided into four groups: the A549 control group, the A549/Tax control group, the A549 Fluvastatin-treated group, and the A549/Tax Fluvastatin-treated group. CCK-8, Transwell, and flow cytometry assays were used to detect fluvastatin's effects on cell proliferation, migration, invasion, and apoptosis. Transcriptomics, proteomics, and acetylomics were combined to explore the potential molecular mechanisms.

RESULTS: The preliminary results showed that fluvastatin inhibited the proliferation, migration and invasion of A549 and A549/Tax cells and promoted their apoptosis. Multi-omics analysis revealed that a large number of differentially expressed molecules were detected in both the A549-Fluvastatin vs A549-NC group and the A549/Tax-Fluvastatin vs A549/Tax-NC group, and these molecules were significantly enriched in multiple biological processes and signaling pathways. This suggests that fluvastatin may exert its effects through the synergistic regulation of multiple molecules and pathways. Integrated multi-omics analysis identified several key molecules (for example, HMGCR, RDH11, HSPB1) and acetylated protein-target gene pairs (for example, P09874-BCL2, P42224-PTGS2, P04150-CCND3), which may mediate the antitumor mechanism of fluvastatin.

CONCLUSION: This study indicates that fluvastatin has the potential to reverse TAX resistance in lung cancer, and the results of multi-omics analysis provide a theoretical basis for the exploration of potential therapeutic targets in the future.

PMID:41728357 | PMC:PMC12922964 | DOI:10.2147/DDDT.S579427

J Hepatocell Carcinoma. 2025 Jul 17;12:1453-1469. doi: 10.2147/JHC.S493907. eCollection 2025.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) necessitates novel immunotherapeutic targets. PBK, a cancer/testis antigen (CTA), was identified as a pivotal hub gene influencing prognosis, tumor mutation burden (TMB), and immune microenvironment remodeling.

METHODS: PBK was prioritized using weighted gene co-expression network analysis (WGCNA) and differential expression screening in the TCGA-LIHC cohort, intersected with curated CTAs. Analyses assessed correlations with clinicopathological features (TNM stage, survival), genomic characterization (mutation frequencies), and functional validation via siRNA-mediated PBK knockdown in Huh7 cells (migration assay). Single-cell RNA sequencing (scRNA-seq) profiled of the tumor immune microenvironment.

RESULTS: PBK overexpression was significantly correlated with advanced TNM stage (P < 0.05) and poor survival (log-rank P = 0.003). Genomic analysis revealed distinct mutation profiles: high-PBK tumors exhibited increased TP53 mutation frequency (39% vs 17%) but decreased CTNNB1 mutations (20% vs 31%). Patients exhibiting with combined PBK overexpression and high TMB demonstrated the poorest prognosis. Functional validation confirmed that PBK knockdown significantly inhibited Huh7 cell migration capacity (P < 0.05). scRNA-seq analysis showed PBK-enriched tumors contained elevated proportions of immunosuppressive SPP1(+) macrophages (22.33% vs 6.6%, FDR corrected P < 0.001) and CD8(+) SLC4A10(+) MAIT cells (9.82% vs 4.7%, FDR corrected P < 0.001).

CONCLUSION: PBK synergistically drives HCC progression through three synergistic mechanisms: (1) promoting oncogenic mutation accumulation (eg, TP53), (2) increasing metastatic potential, and (3) reprogramming an immune-suppressive microenvironment enriched for SPP1(+) macrophages and CD8(+)SLC4A10(+) MAIT cells. This establishes PBK as a dual-purpose biomarker for prognostic stratification and immunotherapy resistance prediction, providing a mechanistic rationale for developing PBK-targeted therapies in HCC.

PMID:40697330 | PMC:PMC12279550 | DOI:10.2147/JHC.S493907