Biochim Biophys Acta Rev Cancer. 2025 Oct 23:189484. doi: 10.1016/j.bbcan.2025.189484. Online ahead of print.

ABSTRACT

The five-year survival rate for Pancreatic Ductal Adenocarcinoma (PDAC) remains below 10 %, primarily due to the limited efficacy of conventional chemotherapy and immune checkpoint inhibitors against its triple-immune-sequestered, low-TMB tumor microenvironment(TME). This situation has been furter exacerbated by the stagnation of traditional vaccine development, driven by inefficient antigen screening and high tumor heterogeneity. Artificial intelligence (AI) exhibits remarkable advantages in the design of pancreatic ductal adenocarcinoma (PDAC) vaccines. It can integrate multi - omics data to efficiently unearth cryptic neoantigens from low - tumor mutation burden (TMB) samples, significantly enhancing the screening efficiency. Through dynamic modeling, AI can rationally plan the timing of combined vaccine therapies, effectively reducing the degree of T - cell exhaustion. By leveraging the digital twin model, AI can remarkably improve the matching accuracy between antigens and human leukocyte antigen (HLA). Additionally, it can construct a monitoring system to provide early warnings of antigen loss risks, thus gaining adjustment time for clinical treatments.This review aims to accomplish three primary objectives: demonstrate AI's potential in breaking the therapeutic impasse to overcome manufacturing-related treatment delays for 25-30 % of patients; further delineates the logical progression of AI from concept to clinical application; thereby provides a translational framework to bridge the gap between research and patient benefit.

PMID:41138796 | DOI:10.1016/j.bbcan.2025.189484

Biomolecules. 2025 Jul 26;15(8):1081. doi: 10.3390/biom15081081.

ABSTRACT

Early diagnosis of lung cancer is crucial for improving patient prognosis. In this study, we developed a diagnostic model for lung cancer based on serum proteomic data from the GSE168198 dataset using four machine learning algorithms (nnet, glmnet, svm, and XGBoost). The model's performance was validated on datasets that included normal controls, disease controls, and lung cancer data containing both. Furthermore, the model's diagnostic capability was further validated on an independent external dataset. Our analysis identified SLC16A4 as a key protein in the model, which was significantly downregulated in lung cancer serum samples compared to normal controls. The expression of SLC16A4 was closely associated with clinical pathological features such as gender, tumor stage, lymph node metastasis, and smoking history. Functional assays revealed that overexpression of SLC16A4 significantly inhibited lung cancer cell proliferation and induced cellular senescence, suggesting its potential role in lung cancer development. Additionally, correlation analyses showed that SLC16A4 expression was linked to immune cell infiltration and the expression of immune checkpoint genes, indicating its potential involvement in immune escape mechanisms. Based on multi-omics data from the TCGA database, we further discovered that the low expression of SLC16A4 in lung cancer may be regulated by DNA copy number variations and DNA methylation. In conclusion, this study not only established an efficient diagnostic model for lung cancer but also identified SLC16A4 as a promising biomarker with potential applications in early diagnosis and immunotherapy.

PMID:40867526 | PMC:PMC12383841 | DOI:10.3390/biom15081081