# Spatial transcriptomics reveals the mechanistic role of lactate metabolism in the pancreatic ductal adenocarcinoma microenvironment

## TL;DR

This study explores how lactate metabolism affects pancreatic cancer, identifying key genes that could help diagnose and treat the disease more effectively.

## Contribution

The study introduces a machine learning-based prognostic model for PDAC using lactate metabolism-related genes.

## Key findings

- Lactate metabolism significantly impacts PDAC patient survival.
- Hyperactive lactate metabolism was observed in 521 malignant cells from PDAC samples.
- LYZ and polymeric immunoglobulin receptor are key prognostic genes associated with patient survival.

## Abstract

Pancreatic ductal adenocarcinoma (PDAC), an aggressive cancer with poor prognosis, poses major challenges owing to late diagnosis and limited response to current therapies. However, the identification of candidate drugs through multi-omics analyses and therapeutic peptides targeting key molecular pathways may provide improved outcomes. Although lactate metabolism is a critical factor in tumor progression, affecting cell proliferation, metastasis, and immune evasion, its role in PDAC—particularly within the tumor microenvironment, remains underexplored.

This study investigated lactate metabolism in PDAC using high-throughput transcriptomic sequencing and single-cell transcriptomic analysis.

Lactate metabolism–related gene expression was analyzed in tumor cells and their microenvironment, and correlations with patient prognosis were determined. Additionally, a machine learning–based prognostic model was established to identify lactate metabolism biomarkers for early diagnosis and personalized therapy.

Lactate metabolism significantly impacted the survival of patients with PDAC (n = 92; log-rank test, p < 0.05). Single-cell RNA and spatial transcriptomics analyses of 50, 795 cells from 8 PDAC samples revealed that 521 malignant cells exhibited hyperactive lactate metabolism (AUCell score comparison, p < 0.001). A prognostic model constructed from lactate metabolism–related genes using ensemble machine learning (StepCox + Enet, α = 0.5) effectively stratified patients into high- and low-risk groups across multiple cohorts (ICGC: n = 92; GSE28735: n = 45; GSE62452: n = 69; GSE183795: n = 139; all log-rank p < 0.05). Key prognostic genes identified included lysozyme (LYZ) and polymeric immunoglobulin receptor, which were significantly associated with patient survival (univariate Cox regression, p < 0.05). These genes may serve as clinical biomarkers of PDAC.

This study provides insights into PDAC metabolic features and highlights lactate metabolism as a potential therapeutic target. The identified biomarkers could facilitate early diagnosis and improve treatment strategies, ultimately enhancing patient outcomes.