Abstract

Background

   Pancreatic adenocarcinoma (PDAC) is the most fatal malignant tumor that
   focuses on men and the elderly (40–85 years) and is aggressive. Its
   surgical resection rate is only 10-44%, and the rate of local
   recurrence in the retroperitoneum 1 year after surgery is as high as
   about 60%. The main reason for local recurrence in the majority of
   patients is that PDAC is perineural invasion (PNI) and the cancer cells
   infiltrate and grow along the peripancreatic nerve bundles. The
   identification of biomarkers associated with the diagnosis of PDAC may
   help to improve the current difficulty in early diagnosis of pancreatic
   cancer and guide clinical treatment. We constructed a co-culture model
   system of Schwann and PDCA cells to determined that Stearoyl Coenzyme A
   Desaturase (SCD) is a key gene driving the progress of PDAC.

Methods

   Single-cell data files for PDAC were analyzed to compare cellular
   composition and subpopulation-specific gene expression between control
   (n = 4) and pancreatic cancer (n = 6). Among 36,277 cells, we obtained
   a total of 16 subpopulations, including a Neurons subpopulation, by
   UMAP analysis. Further screening by Mendelian randomization analysis
   yielded three pairs of key genes corresponding to eQTL-positive outcome
   causally, the corresponding genes were, in order: the three genes
   COL18A1, RASSF4, and SCD. Among them, SCD was significantly positively
   correlated with with the malignant progression of pancreatic cancer,
   and enriched in signaling pathways such as MTORC1_SIGNALING and
   P53-PATHWAY. In this study, We further applied CRISPR-Cas9 technology
   to knock out SCD expression in Schwann cells under co culture system to
   detect the growth status of PDAC cells.

Results

   Three genes (COL18A1, RASSF4, SCD) showed significant correlation with
   PDAC. The identified SCD genes were positively correlated with the
   development of PDAC. We further demonstrated through experiments that
   SCD is overexpressed in PDAC tissues, and knocking down SCD in neuronal
   cells reduces the PDAC cells growth rate and migration ability.

Conclusion

   In this article, we demonstrated that the upregulation of SCD
   expression level in neuronal cells is related to the PDAC, and SCD may
   be a promising candidate for PDAC therapy.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s12935-025-03682-5.

   Keywords: Pancreatic ductal adenocarcinoma, Mendelian randomization,
   Peripheral nerves

Introduction

   PDAC is a malignant tumor originating from the epithelium and
   follicular cells of the pancreatic ducts [[30]1]. Several studies have
   identified elderly age, tobacco use and a long-history chronic
   pancreatitis as clear risk factors, both diabetes and obesity also
   appear to increase risk of cancer [[31]1–[32]3]. Increased risk in
   relatives of patients with PDAC has been documented, with an estimated
   10% of PDAC cases having a genetic susceptibility based on familial
   clustering [[33]4, [34]5]. Accordingly, germline mutations were
   associated with familial PDAC, including mutations targeting the
   oncogenes INK4A, BRCA2, and LKB1, the cationic trypsinogen gene PRSS1,
   and the DNA mismatch repair gene MLH1 [[35]6, [36]7]. BRCA1 mutations
   appear to increase susceptibility to PDAC, although the associated risk
   is lower than BRCA2 [[37]8]. Cancer Data 2022 statistics show that PDAC
   mortality ranks as the fourth leading cause of tumor-related death
   [[38]9]. Its mortality to incidence M/I ratio (MIR) is about 0.94,
   which is the first among all common tumor types, reflecting its
   extremely high degree of malignancy and poor prognosis, and posing a
   more serious challenge to basic translational and clinical research in
   PDAC [[39]10]. Therefore, the identification of PDAC predisposing
   factors and biomarkers affecting the progression of PDAC is necessary,
   in order to select the best treatments for patients with PDAC, thus
   providing the best hope for a cure or prolonged life expectancy.

   Prominent perineural alterations such as an increase in the size of
   neural hypertrophy and some intrapancreatic nerves, neural density, and
   neural remodeling were observed during PDAC progression [[40]11,
   [41]12]. Growing evidence suggests active interactions between tumor
   and nerve cells. ADRB2-signaling pathway promotes the secretion of
   brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF)
   in PDAC, thereby increasing nerve density [[42]13]. Schwann cell is the
   main cell type in peripheral nerves [[43]14]. Remarkably, this type of
   cells are frequently detectable around pancreatic intraepithelial
   neoplasia (PanIN) lesions both in humans and mice [[44]15]. In this
   study, we used Schwann cells and PDAC cells (PANC-1 and MIA PaCa-2) to
   establish a co-culture model to investigate the effect of SCD
   expression in neuronal cells on PDAC progression.

   As early as 1970, Paulien Hogeweg of Utrecht University in the
   Netherlands coined the term “bioinformatics”. She defined it as “the
   study of information processes in biological systems” [[45]16]. With
   the development of large-scale high-throughput multi-omics
   technologies, such as genomics, transcriptomics, proteomics, and
   metabolomics, the generation of biomedical data has been greatly
   accumulated, further advancing the development of bioinformatics.
   Mendelian randomization (False Discovery Rate, FDR) is a statistical
   method commonly used in bioinformatics, a design which improving causal
   inferences from observational data by using genetic variation as a
   natural experiment [[46]17, [47]18]. In the last decade, genome-wide
   association analyzes (GWAS, Genome-wide association studies) of almost
   all common malignancies have been completed and more than 450 genetic
   variants associated with increased risk have been identified, and these
   findings have enabled the FDR design. Recently, Mendelian randomization
   has been increasingly used in the identification of susceptibility
   genes. Bioinformatics can help to comprehensively study tumorigenesis
   in depth, screen possible core targets, and provide references for