Integration of pathomics and single-cell omics in pancreatic ductal adenocarcinoma: Applications and clinical translation prospects

Table 2 Key advances in pancreatic ductal adenocarcinoma research using single-cell RNA sequencing in recent years

Research direction	Key findings	Key techniques	Ref.
Precursor lesions and early detection	High transcriptional similarity between pancreatic intraepithelial neoplasia and pancreatic ductal adenocarcinoma; NKX6-2 drives gastric-like differentiation in intraductal papillary mucinous neoplasm	Spatial transcriptomics	[7,13]
Tumor heterogeneity	Identification of distinct ductal cell subtypes (classical, basal-like, normal-like, cycling); basal-like subtype proportion ≥ 22% is associated with poor prognosis	Consensus non-negative matrix factorization, copy number variation analysis, pseudotime trajectory inference	[66-68]
Tumor microenvironment	Discovery of CAF subtypes (myofibroblastic CAFs, inflammatory CAFs, antigen-presenting CAFs); characterization of immunosuppressive roles of Tregs and M0/M2 macrophages	Cell-cell communication analysis (CellPhoneDB), spatial transcriptomic integration	[69-72]
Metastasis and evolution	Increased clonal homogeneity in liver metastases, accompanied by KRAS/ETV1 amplification and SMAD2/MAP2K4 deletion; identification of metastasis-related transcriptional modules	Single-cell copy number variation analysis, non-negative matrix factorization-based module detection	[66,73,74]
Therapeutic resistance	Upregulation of calcium signaling in gemcitabineresistant cells; HIF-1α-mediated angiogenesis in hypoxic niches; TAM polarization associated with immunotherapy resistance	Differential gene expression analysis, pathway enrichment, validation in animal models	[63,75,76]

CAF: Cancer-associated fibroblast; HIF-1α: Hypoxia-inducible factor-1α; TAM: Tumor-associated macrophage; MAP2K4: Mitogen-activated protein kinase kinase 4.