Expression of stearoyl coenzyme A desaturase in neuronal cells facilitates pancreatic cancer progression

doi:10.21203/rs.3.rs-5030407/v1

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Expression of stearoyl coenzyme A desaturase in neuronal cells facilitates pancreatic cancer progression

https://doi.org/10.21203/rs.3.rs-5030407/v1

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Background:

Pancreatic adenocarcinoma (PDAC) is a 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 of Schwann cells and PDCA cells, and 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 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 Macrophages.M0 and so on, and significantly negatively correlated with Mast cells resting and so on. In this study, we further co-cultured Schwann cells and PDAC cells in a co-culture model, and knocked down the SCD of neuronal cells using CRISPR-Cas9 technology to detect the proliferation and migration ability 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 experimentally that SCD was overexpressed in PDAC tissues and that knockdown of SCD in neuronal cells reduced the proliferation and migration of PDAC cells.

Conclusion:

This study demonstrated that the upregulation of SCD expression level in neuronal cells is associated with the development of PDAC, and SCD may be a potential target for PDAC therapy.

Pancreatic ductal adenocarcinoma

Mendelian randomization

Immune infiltration

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive solid malignant tumors with the worst prognosis. Perineural Invasion (PNI) is the unique biological feature of pancreatic cancer. Pancreatic cancer development is co-regulated by tumor cell intrinsic and tumor microenvironment. In this study, the relationship between neural markers and PDAC risk was investigated. Mendelian randomization analysis of subpopulations of neural cells sorted from a single-cell database determined that SCD may be associated with a high risk of PDAC. To assess the potential impact of SCD, a co-culture model of neural cells with pancreatic cancer cells was constructed. The results indicated that upregulation of SCD expression levels in neuronal cells was associated with the development of pancreatic cancer and that SCD may be a potential target for pancreatic cancer therapy.

PDAC is a malignant tumor originating from the epithelium and follicular cells of the pancreatic ducts(1). Multiple studies have identified advanced age, smoking and long-term chronic pancreatitis as clear risk factors, and diabetes and obesity also appear to increase cancer risk (1–3)。Increased risk has also been documented in relatives of PDAC patients, with an estimated 10% of PDAC cases having a genetic susceptibility based on familial clustering (4, 5). Accordingly, germline mutations were associated with familial PDAC, including mutations targeting the oncogenes INK4A, BRCA2, and LKB1, the DNA mismatch repair gene MLH1, and the cationic trypsinogen gene PRSS1 (6, 7). BRCA1 mutations appear to increase susceptibility to PDAC, although the associated risk is lower than BRCA2(8). Cancer Data 2022 statistics show that PDAC mortality ranks as the fourth leading cause of tumor-related death(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(10). Therefore, there is an urgent need to develop accurate diagnostic and prognostic biomarkers 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 were observed during PDAC progression, such as an increase in the size of intrapancreatic nerves (neural hypertrophy), neural density, and neural remodeling(11, 12). Growing evidence suggests a positive interaction between tumor cells and nerves. ADRB2-signaling pathway promotes the secretion of nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF) in PDAC cells, thereby increasing nerve density(13). Schwann cell, the predominant cell type in peripheral nerves(14). Remarkably, Schwann cells are detectable around pancreatic intraepithelial neoplasia (PanIN) lesions both in humans and mice(15). In this study, we used Schwann cells and PDAC cells (PCNA-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”(16). With the development of large-scale high-throughput multi-omics technologies, such as genomics, 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 that uses genetic variation as a natural experiment to improve causal inference from observational data(17, 18). In the last decade, genome-wide association analyses (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. In recent years, Mendelian randomization has been increasingly used for the identification of susceptibility genes. Bioinformatics can help to comprehensively study tumorigenesis in depth, screen possible core targets, and provide references for clinical diagnosis and disease treatment.

In this study, we analyzed a single-cell database of PDAC and further sorted the neuronal cell taxa therein, applied Mendelian randomization for genetic evolution, and screened for marker genes that can predict PDAC development. Finally, we utilized experiments to validate the association of SCD genes with the proliferative and invasive ability of pancreatic cells.

2.1.Quality control

The expression profile was first read in via the Seurat package, where we filtered cells based on the total number of UMIs per cell, the number of genes expressed, and the percentage of mitochondrial reads per cell and the percentage of ribosome reads per cell. Among them, outliers are defined as three median absolute deviations (MAD) from the median, and cells with less than 500 captured genes will be filtered, filtering formula: (nFeature_RNA > 500 & percent.mt < = 3MAD & nFeature_RNA < = 3MAD & nCount_RNA < = 3MAD & percent.ribo < = 3MAD), where nFeature_RNA represents the number of genes, nCount_RNA represents the total number of UMIs in the cell, percent.mt represents the mitochondrial reading percentage, and percent.ribo represents the ribosome reading percentage. The DoubletFinder package was then used to filter the double cells, and a total of 36,277 cells were retained (sup Fig. 1AB). We displayed the 10 genes with the highest standard deviations (sup FigC). Then the data were processed by standardization, homogenization, PCA, and harmony analysis in sequence (sup FigD-F), and finally a total of 16 subgroups were obtained through UMAP analysis (Fig. 1A).

2.2. Cell annotation and ligand-receptor interaction analysis

This study further annotated each subtype, which was annotated to 11 cell categories: CD8⁺ T cells, Fibroblasts, B cells, Epithelial cells, Macrophages, CD4⁺ T cells, Endothelial cells, Neutrophils, Mast cells, NK cells, and Neurons ( Fig. 1B). In addition, we show the bubble chart of the classic markers of these 11 cells (Fig. 1C) and the histogram of cell proportions corresponding to the groups (Fig. 1D). We used the software package cellchat to analyze the ligand-receptor relationship of features in single cell expression profiles. Finally, we found that there are complex interactions between these cell subtypes (Fig. 1EF).

2.3. Functional analysis of GO and KEGG

We further conducted pathway analysis on the marker genes of Neurons cells. GO enrichment analysis showed that the genes were mainly enriched in pathways such as glial cell differentiation and gliogenesis (Fig. 2A). KEGG enrichment analysis showed that the genes were mainly enriched in Cell. adhesion molecules, Cytoskeleton in muscle cells and other pathways (Fig. 2B).

2.4.Mendelian randomization analysis

In order to find out the key genes that affect PDAC, we used the marker genes obtained in the previous step and obtained the outcome ID ebi-a-GCST90018893 through the summary statistics of 476,245 samples related to PDAC (Controls: 475,049; Cases: 1,196). Using extract_instruments and extract_outcome_data in sequence, 191 pairs of causal relationships between genes and outcomes were extracted. Further, through Mendelian randomization analysis, the causal relationship of 3 pairs of genes corresponding to the positive eQTL outcome was obtained (Fig. 3A-C) (IVW pval < 0.05). The corresponding genes are: COL18A1, RASSF4, and SCD. Among them, COL18A1 (0.879; 0.790 − 0.977; p = 0.017); RASSF4 (0.819; 0.688 − 0.974; p = 0.024) may be associated with a low risk of PDAC. And SCD (1.319; 1.017 − 1.712; p = 0.037) may be associated with a higher risk of PDAC. We further conducted a sensitivity analysis on the causal relationships of the three genes to determine their reliability using the leave-one-out method. The results showed that excluding any SNP had no obvious impact on the overall error bar, which showed that the three pairs of causal relationships we selected were robust (Fig. 4A-C).

2.5.GSEA pathway enrichment analysis

Next, we studied the specific signaling pathways enriched in three key genes to explore the potential molecular mechanisms by which key genes affect the progression of PDAC. The GSEA results showed that the pathways enriched by COL18A1 include IL-17 signaling pathway and NF-κB signaling pathway. and other pathways (Fig. 5A); pathways enriched by RASSF4 include B cell receptor signaling pathway, Chemokine signaling pathway and other pathways (Fig. 5B); pathways enriched by SCD include Glucagon signaling pathway, mRNA surveillance pathway and other pathways (Fig. 5C).

2.6. SCD is upregulated in human pancreatic tumors

To further investigate the correlation between SCD and PDAC, immunohistochemical (IHC) analysis of the SCD protein was performed on paraformaldehyde-fixed tissue sections from 7 patient-derived pancreatic tomor samples (Fig. 6AB). Abundant SCD staining was detected in the cytoplasm of pancreatic tomor tissue, but much less in the adjacent tissue (Fig. 6C, representative results shown). The qpcr level of the patient's tissue samples showed that the transcription level of SCD in tumor tissue was up-regulated (Fig. 6D) but there was no significant difference in protein expression (Fig. 6E), which may be because SCD was only expressed in the nerve cells of PDAC

2.7. Depletion of SCD in Schwann cells suppresses malignant phenotypes of pancreatic tomor cells in vitro

To investigate the in vitro function of SCD in PDAC, we stably introduced Cas9 vectors containing three distinct sgRNAs specifically targeting SCD or nontargeting control sgRNAs into the human Schwann cell lines (designated sgSCD-1, sgSCD-2, sgSCD-3, and sgScamble, respectively), and the knockdown efficiency was confirmed by immunoblotting (Fig. 7A) and real-time fluorescence quantitative PCR (q-RTpcr) assay (Fig. 7B). We then constructed a co culture model of Schwann cells with two different PDAC cell lines (Fig. 7C), and performed a set of cell-based assays, including CCK-8 and Wound healing migration assay, to dissect the biological functions of SCD(Fig. 7D), depletion of SCD significantly decreased cell proliferation in sgSCD-1 and SCD-2 cells compared to their corresponding control cells. Moreover, Wound healing migration assays showed that sgSCD-1, sgSCD-2 cells demonstrated a significantly reduced capacity to invade compared to the control cells (Fig. 7EF).

Most patients with PDAC are diagnosed with locally advanced or metastatic tumors in the later stages of the disease. Despite ongoing efforts to improve PDAC treatment, patient survival remains dismal, with an overall global 5-year survival rate of less than 10%(10, 19)。These facts emphasize the urgent need for a better understanding of pancreatic tumorigenesis. In this study, the analysis of neuronal cells in PDAC monocytic cells screened by Mendelian randomization yielded three pairs of genes associated with the risk of PDAC development, of which COL18A1 and RASSF4 may be associated with a low risk of PDAC. SCD, on the other hand, may be associated with a high risk of PDAC.

Human SCD, also known as ∆9-fatty acyl-CoA desaturase, is an endoplasmic reticulum-associated enzyme that catalyzes the introduction of a double bond in the cis-∆9 position of saturated fatty acyl-CoAs(20). The primary substrates of SCD are palmitoyl-CoA and stearoyl-CoA, which produce palmitoleoyl-CoA and oleoyl-CoA, respectively(21). Further studies demonstrated that the stable knockdown of SCD in SV40-transformed human lung SV40-WI38 fibroblasts decreased MUFA and phospholipid synthesis, decreased the rate of cell proliferation, and induced apoptosis(22). Similarly, the inhibition of SCD activity led to cancer cell death through the depletion of MUFAs(23, 24).

In this study, we performed a functional analysis of the SCD gene and found that knockdown of SCD gene expression in neuronal Schwann cells significantly reduced the proliferation and migration of PDAC cells. SCD1 expression has been reported to be associated with poor prognosis in several cancer types(25). pharmacological inhibition of SCD1 as a monotherapy and in combination with chemotherapeutic agents showed promising anti-tumor potential in preclinical models(26–28). We hypothesized that elevated SCD expression in neuronal cells may lead to elevated levels of phospholipid synthesis, and neural infiltration occurs, a process that promotes PDAC progression, but evidence that SCD promotes neural infiltration in PDAC is lacking in this study.

In recent years, PNI of PDAC has become a hot research topic in academia and clinics, and its detailed mechanism has not been elucidated. In patients with PDAC, the proportion of nerve invasion can be as high as 70%~100%(29), PNI is an independent prognostic factor for overall survival in PDAC and is a common recurrence of PDAC after surgery [18]. A study using a mouse model of pancreatic ductal adenocarcinoma found that cancer cells can produce significant structural changes to the neural microenvironment in which they invade, thus favoring their roaming infiltration(30). Before infiltrating the peripheral nerves, PDAC cells express a large number of neurotrophic factors, such as nerve sphingomyelin (Artemin), nerve growth factor (NGF), etc., which can produce a strong affinity and sensitivity to the adjacent nerve fiber endings[20]. These neurotrophic factors act as a “glue”, causing a phenomenon similar to “mutual attraction” between the two[21]. Neuroglial cells, also known as Schwann cells, are the most common cell type in the injury and repair of peripheral neuropathy [22]. Therefore, in this study, we used a co-culture model of Schwann cells and PDAC cells to mimic the PNI status of PDAC.

Over the past decade, many studies have identified the ways in which SCD promotes cancer progression by affecting lipid metabolism, cell proliferation, migration, invasion, and metastasis(31), and despite strong preclinical findings supporting SCD as a therapeutic target in cancer, the development of highly potent and specific SCD inhibitors is not a major therapeutic focus on the existing SCD inhibitors have clinical applications limited to the treatment of type 2 diabetes(32). Our study identifies SCD as an important gene present in PDAC neuronal cells that promotes PDAC progression. Further studies designed and tested to knock down SCD with inhibitory effects on PDAC proliferation represent a promising strategy to ameliorate this dilemma of limited therapeutic options.

This study is based on the PDAC single-cell database combined with Mendelian randomization analysis to find key factors associated with immune infiltration in PDAC neuromarkers, which will be useful for the development of new immunotherapeutic strategies for PDAC and the identification of concomitant biomarkers for immunotherapy.

4.1. Data acquisition:

1) The GEO database (https://www.ncbi.nlm.nih.gov/geo/info/datasets.html), the full name of GENE EXPRESSION OMNIBUS, is a gene expression database created and maintained by the National Center for Biotechnology Information, NCBI. Download the single cell data file of GSE212966 from the NCBI GEO public database and include 10 sample data with complete single cell expression profiles for single cell analysis. There were 4 cases of control and 6 cases of disease.

2) TCGA database (https://portal.gdc.cancer.gov/) is currently the largest cancer gene information database, including gene expression data, miRNA expression data, lncRNA expression data, copy number variation, DNA methylation, SNP and other data. We downloaded the processed original expression data of PDAC, including normal group (n = 4) and tumor group (n = 179).

3) Exposed data: eQTL data comes from the eQTLGen consortium (https://www.eqtlgen.org) database. The eQTLGen consortium aims to study the genetic structure of blood gene expression and understand the genetic basis of complex traits. The large-scale eQTLGen project is currently in its second phase and is focused on conducting large-scale genome-wide meta-analyses in blood.

4) Outcome data: Participants in the outcome-related GWAS studies selected in this study were mainly people of European ancestry. Outcome summary data are all sourced from the EBI database (ebi-a-GCST90018893). As of now, the GWAS Catalog contains publications, top associations and complete summary statistics. GWAS Catalog data are currently mapped to Genome Assembly and dbSNP Build. Of these, there were 1,196 cases and 475,049 controls in PDAC.

4.2. Quality control:

Expression profiles were first read in via the Seurat package, where we filtered cells based on the total number of UMIs per cell, the number of genes expressed, and the percentage of mitochondrial reads per cell and the percentage of ribosomal reads per cell. Where outliers are defined as three MAD from the median. It is generally believed that cells with too high total number of UMI and expressed genes are double cells, and cells with too high mitochondrial reading percentage and ribosome reading percentage are poor quality cells that are on the verge of apoptosis or have become cell debris. After completing the above steps, use DoubletFinder (V2.0.4) to filter the double cells of each sample respectively, thus completing cell quality control.

4.3. Data standardization and cell annotation:

Use the NormalizeData function to normalize the data, use CellCycleScoring to calculate the cell cycle score, FindVariableFeatures to find hypervariable genes, ScaleData to normalize the data and eliminate the impact of mitochondrial genes, ribosomal genes, and cell cycle on subsequent analysis, and RunPCA to perform expression matrix Perform linear dimensionality reduction, select principal components for subsequent analysis, use Harmony to remove batch effects, it iteratively clusters similar cells in different batches in PCA space while maintaining the diversity of batches within each cluster, and use RunUMAP to unify Manifold approximation and projection (UMAP) performs nonlinear dimensionality reduction, FindNeighbors finds neighbor points of cells, and FindClusters divides cells into different cell clusters. By querying CellMarker and PanglaoDB databases and literature, and supplemented by automated annotation with SingleR software, we find the cell types and corresponding marker genes present in the corresponding tissue for cell annotation.

4.4. Ligand-receptor interaction analysis (Cellchat):

CellChat is a tool capable of quantitatively inferring and analyzing intercellular communication networks from single-cell data. CellChat uses network analysis and pattern recognition methods to predict the main signal inputs and outputs of cells and how these cells and signals coordinate function. In this analysis, we used the standardized single cell expression profile as the input data, and the cell subtypes obtained from the single cell analysis as the cell information. We analyzed the cell-related interactions and used the interaction strengths (weigths) between cells and The number of times (count) is used to quantify the closeness of the interaction, so as to observe the activity and influence of each type of cell in the disease.

4.5. Functional analysis of GO and KEGG:

Important genes were functionally annotated using the R package “ClusterProfiler” to comprehensively explore the functional correlation of these important genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to evaluate relevant functional categories. GO and KEGG enriched pathways with both p-value and q-value less than 0.05 were considered as significant categories.

4.6.Mendelian randomization analysis:

The EBI database contains extensive summary statistics from hundreds of GWAS studies. The outcome IDs filtered through the EBI database are extracted from the GWAS summary data (https://gwas.mrcieu.ac.uk/) and the relevant causal relationships in eQTL are selected to match the significance threshold (P < 1e-8) related SNPs as potential IVs (Instrumental variables), calculate the LD (linkage disequilibrium) between SNPs, among the SNPs with R2 < 0.001 (clumping window size = 10,000kb), only keep p2 < 5e -8 snp. In turn, it passes through Inverse variance weighted (IVW, using meta-analysis method combined with the Wald estimate of each SNP), MR Egger (based on the assumption that the instrument strength is independent of direct effects (InSIDE)), Weighted median (the weighted median method allows up to Correctly estimate causality in 50% of cases where IVs are invalid), Weighted mode (weighted model estimation has greater ability to detect causal effects, smaller bias, and lower Type I error rate than MR-Egger regression). A statistical method (if there is only one statistical method for the SNP in the causal relationship, only the Wald ratio is used) to evaluate the reliability of the causal relationship to obtain an overall estimate of the impact of all cis- and some cross-region gene expression in whole blood on PDAC.

4.7. Sensitivity analysis:

We used Mendelian Randomization (MR) leave-one-out sensitivity analysis to evaluate the impact of specific genetic variants on PDAC risk. This method identifies and eliminates variants that disproportionately affect the overall estimate by systematically excluding each SNP and recalculating the pooled effect size of the remaining SNPs. The removal of each SNP produces a new point estimate and its 95% confidence interval to assess the SNP's unique contribution and robustness to the overall results. This chart summarizes the estimates after individual SNPs are removed, as well as the overall estimate when all SNPs are included. By comparing these estimates, we can observe the impact of removing any single SNP on the overall results to determine the robustness of our analysis.

4.8.GSEA pathway enrichment analysis:

GSEA was used to further analyze the differences in signaling pathways between high and low expression groups. The background gene set is the version 7.0 annotated gene set downloaded from the MsigDB database. As an annotated gene set for subtype pathways, differential expression analysis of pathways between subtypes is performed, and significantly enriched gene sets are analyzed based on the consistency score (adjusted p value less than 0.05) to sort. GSEA analysis is often used in studies that closely combine disease classification and biological significance.

4.9. Collection of the clinical specimens

The clinical specimens involved in this study were obtained from PDAC patients who underwent pancreas at Liaoning Cancer Hospital. All patients had been pathologically confirmed and diagnosed with PDAC. Cancer tissues and matched paracancerous tissue were frozen immediately in liquid nitrogen after resection and then stored at − 80°C prior to use. The study was approved by the Ethics Committee of the Liaoning Cance Hospital and conducted following the Declaration of Helsinki.

4.10. Histology and IHC

Histology and IHC FFPE organotypic matrices and tumor tissues were processed on a Leica Peloris following standard tissue processing protocols. FFPE specimens were sectioned at 4 µm using a Leica RM2235 microtome. Sections were placed on a plain glass slide for H&E staining or a positively charged slide for IHC, which was allowed to incubate for 2 hours in a 60°C oven for maximum adhesion. Sections were deparaffinized and stained following standard H&E procedures on the Leica ST5010 Autostainer XL with hematoxylin, Australian Biostain and eosin.

4.11. Detection of mRNA expression by quantitative fluorescence polymerase chain reaction (qRT-PCR)

PCR reactions were performed with 100 ng of cDNA, using a Rotor-Gene®-Q real-time PCR cycler (Roche LightCycler 96) and TaqMan Universal PCR Master Mix (Applied Biosystems). Cycling conditions were: 10 min of denaturation at 95℃ and 40 cycles at 95℃ for 15 s and at 60℃ for 1 min. The expression levels were normalized to the expression of GAPDH, which served as the internal control. The primer sequences used for qRT-PCR are listed below:

SDC	5′-GCACATCAACTTCACCACATTCTTC-3′
SDC	3′-CAGCCACTCTTGTAGTTTCCATCTC-5′
GAPDH	5′-TGTGGGCATCAATGGATTTGG-3′
GAPDH	3′-ACACCATGTATTCCGGGTCAAT-5′

15. Immunoblotting assay

Total proteins were extracted using RIPA whole-cell lysis solution. The proteins were separated through 10% SDS-PAGE electrophoresis, measured, and semi-dry transferred to PVDF membranes (Millipore, Billerica, MA, USA). The PVDF membranes were blocked with TBS + Tween (TBST) solution containing 5% skim milk powder for 1 h, washed, and then incubated with a primary antibody (ABclonal, A16429, 1:1000) overnight at 4℃. The membranes were subsequently washed and exposed to a secondary antibody labeled with horseradish peroxidase for 1 h. Following the washing of the PVDF membrane, the chemiluminescent substrate was applied, and the grayscale values were measured using a gel imaging system.

4.12 Cell culture and transfection

Human Schwann cell line, human PDAC cell lines PCNA-1 and MIA PaCa-2 were obtained from the American Type Culture Collection (ATCC). All cell lines were maintained at 37°C in a humidified incubator with 5% CO₂. In vitro growing cell lines were treated with small guide RNA (sgRNA) against SCD (ABM, China) genes and sgScramble (ABM, China). After Schwann cells were completely attached, si-RFPL4B was transfected with a final concentration of 100 nM., and incubated for 24 and 48 hours. The sgRNA sequences were listed below:

Name	Target	Sequence
sgSCD-1	1–11	5′-CACATCGTCCTGCAGCAAGT-3′
sgSCD-2	2-118	5′-ATGTCGTCTTCCAAGTAGAG-3′
sgSCD-3	3-159	5′-TATATATGACCCCACCTACA-3′

4.13 Co-culture of Schwann cells and PDAC cells in vitro

Transwell-based co-cultures, 2 × 10⁵ PDAC cells were seeded in the lower compartment of six-well plates and 2 × 10⁵ Schwann cells were seeded on top of the Transwell membrane (Corning, 0.4 µm). After 48 h of co-culture, PDAC cells were washed by PBS three times, and then an equal number of PDAC cells were cultured individually in fresh medium for 24–48 h, then cells were harvested for further study.

4.14 Wound healing migration assay

After PDAC cells have been individually cultured for 24-48h in 6-well plate, the cells were scribed with a 200 µL tip, and the horizontal and vertical lines were scribed three times in each well. Make sure the force is uniform and the tip of the gun is perpendicular. Wash out the detached cells with preheated PBS, add 2 mL of culture medium (0–3% FBS) into each well and continue to incubate. Record at 0 hour and 48 hours respectively, and take pictures of the scratches. Image J software was used to measure the area of cell scratches, and the wound healing rate was used to reflect the cell migration ability.

4.15 Cell Proliferation Assay

After PDAC cells were cultured alone for 24h, transfected cells were plated in 96-well plates at a density of 2×10³ cells/well. From the first to the third day, the serum-free medium of 10% CCK-8 (APExBIO, USA) reaction solution was used to replace the original medium and incubated for 1h. The light absorbance was quantified at 450nm (OD-450).

4.16 Statistical analysis

Reliable MR analysis is based on three premise assumptions: (1) correlation assumption (instrumental variables are closely related to exposure, but not directly related to outcomes), (2) independence assumption (instrumental variables cannot be related to confounding factors), (3) Exclusivity hypothesis (instrumental variables can only affect outcomes through exposure. When IV can affect outcomes through other pathways, it is determined that gene pleiotropy exists). In this analysis, R language (version 4.3.0) was used. All statistical tests were two-sided, and p < 0.05 was considered statistically significant. All data in this experiment were performed by GraphPad Prism 7 software. Double-tailed t-test or one-way ANOVA were used to analyzed the difference between data. The results were shown as mean ± standard error. Significant difference: *p < 0.05, **p < 0.01, ***p < 0.001.

PDAC peripheral nerve cells are able to influencing the development of PDAC, and the high expression of SCD in nerve cells has a promoting effect on the development of PDAC.

BDNF, brain-derived neurotrophic factor ; GWAS, genome-wide association analyses; IHC, immunohistochemical; Instrumental variables; IVs, linkage disequilibrium; KEGG, Kyoto Encyclopedia of Genes and Genomes; LD, MR, Mendelian Randomization; MAD, median absolute deviations; MIR, M/I ratio; NGF , nerve growth factor; NGF, nerve growth factor; PDAC, Pancreatic ductal adenocarcinoma; PanIN, pancreatic intraepithelial neoplasia; PNI, perineural invasion; q-RTpcr, real-time fluorescence quantitative PCR; SCD, Stearoyl Coenzyme A Desaturase; UMAP, unify Manifold approximation and projection

DATA AVAILABILITY

The original datasets analyzed in this study are publicly available through the following repositories: GEO database (https://www.ncbi.nlm.nih.gov/geo/info/datasets.html), TCGA database (https://portal.gdc.cancer.gov/), eQTLGen consortium (https://www.eqtlgen.org) database, EBI database (ebi-a-GCST90018893). Further inquiries can be directed to the corresponding authors.

CONFLICT OF INTEREST STATEMENT

All participants gave written informed consent prior to participation. Personal information was anonymized and deidentified to protect participant privacy. Our study was completed in accordance with the Declaration of Helsinki. All experiments were performed in accordance with relevant guidelines and regulations. The datasets generated during the current study are available from the corresponding author on reasonable request. The authors declare no conflicts of interest.

ETHICS STATEMENT

The study was approved by the Ethics Committee of the Liaoning Cance Hospital and conducted following the Declaration of Helsinki.

COMPETING INTERESTS

The authors declare that they have no competing interests.

FUNDING

This study was financially supported by the Science and Technology Program of Liaoning Province (2024-BS-322).

AUTHOR CONTRIBUTIONS

Xue Zhang and Ling-Xiao Zhao wrote the manuscript, Si-Qi Cheng revised the manuscript. Ye-Fu Liu approved and revised the final manuscript. All authors have read and approved the final manuscript.

ACKNOWLEDGMENTS

We thank the Science and Technology Program of Liaoning Province (2023021040-JH2/1017). for their support of this study. The funders had no role in study design, data collection, data analysis, interpretation, and writing of the report

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Expression of stearoyl coenzyme A desaturase in neuronal cells facilitates pancreatic cancer progression

Status:

Version 1

Abstract

Background:

Methods:

Results:

Conclusion:

Figures

What’s New?

1. INTRODUCTION

2. RESULTS

2.1.Quality control

2.2. Cell annotation and ligand-receptor interaction analysis

2.3. Functional analysis of GO and KEGG

2.4.Mendelian randomization analysis

2.5.GSEA pathway enrichment analysis

2.6. SCD is upregulated in human pancreatic tumors

3. DISCUSSION

4. MATERIALS AND METHODS

4.1. Data acquisition:

4.2. Quality control:

4.3. Data standardization and cell annotation:

4.4. Ligand-receptor interaction analysis (Cellchat):

4.5. Functional analysis of GO and KEGG:

4.6.Mendelian randomization analysis:

4.7. Sensitivity analysis:

4.8.GSEA pathway enrichment analysis:

4.9. Collection of the clinical specimens

4.10. Histology and IHC

4.11. Detection of mRNA expression by quantitative fluorescence polymerase chain reaction (qRT-PCR)

15. Immunoblotting assay

4.12 Cell culture and transfection

4.13 Co-culture of Schwann cells and PDAC cells in vitro

4.14 Wound healing migration assay

4.15 Cell Proliferation Assay

4.16 Statistical analysis

Conclusion

Abbreviations

Declarations

References

Supplementary Figures

Additional Declarations

Supplementary Files

Status:

Version 1