One-carbon metabolic reprogramming and its relationship with tumor-infiltrating lymphocytes and Immune checkpoint in Pancreatic cancer

One-carbon metabolic reprogramming and its relationship with tumor-infiltrating lymphocytes and Immune checkpoint in Pancreatic cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article One-carbon metabolic reprogramming and its relationship with tumor-infiltrating lymphocytes and Immune checkpoint in Pancreatic cancer Dawei Deng, Song Wei, Qihang Yuan, Chao Zhang, Surong Ma, Zhihui Shu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6404670/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND The role of oncogene-driven metabolic reprogramming in pancreatic cancer (PC) remains unclear. This study explored the interplay between one-carbon metabolism (OCM), driver genes, and the tumor microenvironment (TME) in PC. METHODS Targeted metabolomics analyzed 136 PC serum samples. Transcriptomic and OCM gene data from 930 PC patients were obtained from public databases. Non-negative matrix factorization (NMF) clustering classified metabolic subtypes. Single-cell analysis deciphered OCM features in the TME. Immunohistochemistry assessed MTHFD1L expression, cancer-associated fibroblast (CAF) markers (FAP, α-SMA), immune cells (CD8+/Foxp3+ TILs, CD206+ TAMs), and PD-1/PD-L1 in 138 tissue samples. RESULTS Targeted metabolomics identified altered amino acid metabolism (73 metabolites). NMF clustering stratified patients into C1/C2 subtypes with distinct prognoses and TME characteristics (p<0.05). Single-cell analysis revealed OCM dysregulation in cancer cells, macrophages, and fibroblasts. MTHFD1L emerged as a core driver of metabolic reprogramming, correlating with poor overall survival (OS, p=0.005) and disease-free survival (DFS, p=0.006). High MTHFD1L expression was linked to lymph node metastasis and positively associated with FAP in CAFs (p<0.05), CD206+ TAMs (p<0.001), and Foxp3+ TIL infiltration (p<0.05). Multivariate analysis confirmed MTHFD1L as an independent prognostic factor (p=0.022). CONCLUSION OCM reprogramming is a hallmark of PC. MTHFD1L drives oncogenic metabolism and influences prognosis by modulating CAFs, TAMs, and Tregs. Targeting OCM or MTHFD1L may offer therapeutic potential. Pancreatic cancer multiomics Metabolic reprogramming one-carbon metabolism tumor microenvironment MTHFD1L Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Pancreatic cancer (PC) is a highly fatal and refractory malignant tumor of the digestive system 1 . The annual incidence of PC is steadily increasing, and the unsatisfactory treatment outcomes have generated significant interest in understanding and addressing this disease 2 . Most patients present with advanced-stage disease or distant metastases, thereby losing the opportunity for radical resection 3 . In addition, the high postoperative recurrence rate and the limited efficacy of adjuvant chemotherapy contribute to a poor prognosis, with a five-year survival rate of less than 5% 4 . Accordingly, there is an urgent need to identify innovative prognostic biomarkers and potential molecular targets for PC. PC is characterized by a prominent desmoplastic stromal reaction, which significantly hinders the efficacy of chemotherapy, radiotherapy, and immune therapies 5 . The tumor microenvironment (TME), a complex network of cellular and stromal components that supports tumor cells, plays a critical role in regulating tumor progression and treatment resistance 6 . Recent studies have highlighted the high heterogeneity within PC TME, which includes tumor-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), and tumor-infiltrating immune cells 7 – 9 . These components can exert either pro- or anti-tumorigenic effects, depending on the cell type and context 10 – 12 . Despite these insights, the immunosuppressive nature of the PC tumor microenvironment remains a major bottleneck, limiting improvements in therapeutic outcomes 13 . Therefore, a deeper understanding of the regulatory mechanisms governing the TME in PC is urgently needed to advance treatment strategies. Metabolic networks, with metabolic enzymes and metabolites as their core components, serve as the material basis for cellular activities 14 . Oncogene-driven metabolic adaptations enable cancer cells to survive and thrive within the TME 15 . The importance of altered metabolic requirements and the accompanying secretion of metabolites in shaping a supportive TME is increasingly being recognized 16 . One-carbon metabolism (OCM) lies at the center of cellular function, facilitating the activation of one-carbon units through the one-carbon cycle for nucleotide and amino acid biosynthesis, as well as epigenetic regulation 17 – 19 . Studies have shown that reprogramming of OCM is associated with various diseases, including non-alcoholic liver disease, cancer development, and immune regulation within the tumor microenvironment 20 – 23 . Immunotherapy utilizing drugs that target metabolic pathways may be synergistically enhanced by modulating the metabolism of the tumor immune microenvironment 24 . These insights shed new light on patient stratification for frontline therapies in PC. To the best of our knowledge, this is the first study to integrate multi-omics data to comprehensively explore the characteristics of one-carbon metabolic reprogramming in PC across multiple dimensions. We identified key tumor-driving metabolic genes and investigated their relationship with PC prognosis, tumor-infiltrating immune cells, and immune checkpoint expression. 2 Materials and methods 2.1 Patients and specimens A total of 136 fasting serum samples were collected on the first morning after admission from PC patients, benign pancreatic lesion (BPL) patients, and healthy participants (Con) at The Affiliated Hospital of North Sichuan Medical College between May 2023 and October 2024. The PC patients were pathologically diagnosed via biopsy and stratified into subgroups according to the 8th edition of the American Joint Committee on Cancer (AJCC) staging system 25 . The age and gender distribution of the Con group were matched with those of the PC group. Patients in the PC group were excluded if they had other forms of cancer, liver or renal insufficiency, severe cardiopulmonary diseases, metabolic disorders, or other mental or physical conditions. All 17 cases of Benign lesions of pancreas(BLP) were confirmed by pathological biopsy, primarily including solid pseudopapillary neoplasms of the pancreas and pancreatic pseudocysts. All participants provided written informed consent, and the study was approved by the Ethics Committee of The Affiliated Hospital of North Sichuan Medical College (Approval No. 2024006). PC tissue samples were obtained from 138 patients who underwent surgical resection at The Affiliated Hospital of North Sichuan Medical College between May 2017 and July 2023. All patients were histologically confirmed to have PC. None of the participants had received preoperative radiotherapy, chemotherapy, immunotherapy, or targeted therapy, and complete clinical data were available for all cases. Tumor-node-metastasis (TNM) staging of PC was determined according to the 8th edition of the AJCC staging system, while pathological grading was performed in accordance with the 2019 World Health Organization (WHO) classification. Postoperatively, clinical blood tests and contrast-enhanced computed tomography (CT) scans were conducted every three months. Disease-free survival (DFS) was defined as the time interval from surgery to tumor recurrence or death, while overall survival (OS) was defined as the duration from surgery to death. The study protocol was approved by the Ethics Committee of The Affiliated Hospital of North Sichuan Medical College and was granted ethical exemption.The study adhered to the ethical standards established by local and national ethics committees for human research, as well as the principles outlined in the 1964 Declaration of Helsinki and its subsequent amendments. Formalin-fixed paraffin-embedded (FFPE) samples were used for immunohistochemical (IHC) analysis. 2.2 Metabolomic analysis and Data processing Amino acids were quantified using a Shimadzu LC-20ADXR system (Shimadzu, Japan) coupled with a Sciex 4500MD triple quadrupole mass spectrometer (AB Sciex, Singapore). Chromatographic separation was performed using an Acquity UPLC BEH Amide column (1.7 µm, 2.1 mm × 100 mm, Waters). To ensure accurate quantification of metabolites, a calibration curve was constructed using ten standard points generated through serial dilution of mixed stock solutions with methanol. Quality control (QC) samples were prepared by thoroughly pooling all residual test samples. A volume of 50 µL of serum samples, QCs, or standards at varying concentrations was precisely transferred to a 750 µL 96-well sample collection plate. Subsequently, 10 µL of mixed internal standard (M1-IS) and 190 µL of methanol were added to each serum sample, followed by vortexing at 1500 rpm for 3 minutes. The mixture was then centrifuged at 5300 rpm for 20 minutes at 4°C, and 180 µL of the supernatant was collected. Finally, 2 µL of the supernatant was injected for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis 26 , 27 . The mass chromatogram peaks were extracted and integrated using the in-house software MultiQuant (AB Sciex, Singapore). Each peak was automatically identified based on its retention time and multiple reaction monitoring (MRM) parameters of the standards, followed by manual verification. Quantitative results for each metabolite were obtained by correcting with isotopically labeled internal standards and applying the standard curve for each analyte. Metabolites with missing values exceeding 50% of the total sample size or a coefficient of variation (CV) greater than 30% in quality control (QC) samples were excluded from the analysis to ensure analytical accuracy. 2.3 Transcriptomic Data Acquisition and Preprocessing Transcriptomic data and clinical features of PC patients were obtained from The Cancer Genome Atlas (TCGA), ArrayExpress, Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases. Only patients with complete follow-up data were included. For the TCGA-PAAD cohort, RNA sequencing data (expressed as fragments per kilobase of transcript per million mapped reads [FPKM]) were converted to transcripts per kilobase million (TPM) to ensure comparability with microarray results. To minimize batch effects, data from different platforms were harmonized using the "ComBat" function from the "SVA" package. A total of 930 PC patients were included, encompassing datasets from TCGA-PAAD, GSE62452, GSE28735, GSE57495, ICGC-AU, ICGC-CA, and MTAB-6134. OCM genes were derived from the following gene sets: WP_ONE_CARBON_METABOLISM ( http://www.gsea-msigdb.org/gsea/msigdb/cards/WP_ONE_CARBON_METABOLISM ) and WP_ONE_CARBON_METABOLISM_AND_RELATED_PATHWAYS ( http://www.gsea-msigdb.org/gsea/msigdb/cards/WP_ONE_CARBON_METABOLISM_AND_RELATED_PATHWAYS ). By integrating these two datasets, we identified a total of 75 genes associated with OCM. 2.4 Cluster Analysis and Gene set variation analysis The non-negative matrix factorization (NMF) algorithm aims to identify potential gene expression patterns by decomposing the original matrix into two non-negative matrices. Based on the expression profiles of OCM genes, we used the "NMF" R package to classify 930 PC patients into C1 and C2 metabolism-related subtypes. The "Survival" R package was employed to perform Kaplan-Meier (K-M) survival analysis between the C1 and C2 subtypes. Furthermore, the "GSVA" R package was utilized to calculate individual OCM pathway scores. These scores served as indicators to compare differences in OCM and 50 hallmark pathways between the two patient subgroups using the "wilcox.test" function in R. 2.5 Analyzing tumor immune microenvironments between OCM-related subtypes The "ESTIMATE" package was used to assess the immune and stromal composition of each PC sample. Pan-immune cell prediction methods were employed to assess the immune microenvironment between the C1 and C2 subtypes, including TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCP-COUNTER, XCELL, and EPIC. Moreover, the expression of immune checkpoint genes was compared between the C1 and C2 groups, referencing previous literature reports. To identify key genes, the Random Forest (RF) algorithm was utilized to predict the weight of each gene associated with OCM. 2.6 Single-cell analysis and single sample gene set enrichment analysis (ssGSEA) We obtained single-cell RNA sequencing (scRNA-seq) data from 24 PC samples and 11 normal pancreatic tissues from the CRA001160 cohort. The CreateSeuratObject function was used to process the scRNA-seq data. Data quality control was performed using the following parameters: min.cells = 3, min.features = 200, nCount_RNA ≥ 1000, nFeature_RNA ≥ 500, nFeature_RNA ≤ 6000, and percent.mt ≤20. The LogNormalize method was applied to standardize the single-cell data, followed by the FindVariableFeatures function to identify highly variable genes. The ScaleData function was used for normalized data processing, and the top 2000 highly variable genes were selected for Principal Component Analysis (PCA) dimensionality reduction. Batch effects were removed using the RunHarmony function. Cell annotation was performed based on cell-specific markers. The one-carbon metabolic activity of each cell was predicted using the ssGSEA and singscore methods, respectively. The ssGSEA package was used to predict the abundance of immune cell infiltration in the TCGA-PAAD cohort. Spearman correlation analysis was performed to evaluate the relationship between MTHFD1L expression and immune cell infiltration. 2.7 Immunohistochemistry and evaluation of TILs, CAFs, TAMs and immune checkpoints IHC analysis was performed on paraffin-embedded tissue sections obtained from PC patients. The sections were deparaffinized using an eco-friendly clearing agent (H-H0101; Wuhan Hongzhi Biotechnology Co., China) and ethanol, followed by antigen retrieval at 95°C for 20 minutes in citrate buffer (MVS-0100; Maxin Biotech Co., China). After cooling, the sections were washed with phosphate-buffered saline (PBS; BL601A; Biosharp, China). IHC staining was conducted using an ultra-sensitive SP reagent kit (KIT-9710; Maxin Biotech, China). Endogenous peroxidase activity was blocked with an inhibitor (Reagent 1) for 10 minutes, and non-specific binding was minimized using Reagent 2. Primary antibodies targeting Tumor Infiltrating Lymphocytes (TILs) and CAF markers, including CD8, FOXP3, fibroblast activation protein (FAP), alpha-smooth muscle actin (α-SMA), CD206, programmed cell death protein 1 (PD-1), and programmed death-ligand 1 (PD-L1) (dilution details provided), were applied. Sections were incubated overnight at 4°C, followed by incubation with a biotinylated secondary antibody (Reagent 3) and horseradish peroxidase (HRP)-conjugated streptavidin (Reagent 4) for signal amplification. A 3,3'-diaminobenzidine (DAB) chromogenic solution (DAB-0031; Maixin Biotech, China) was applied for 3 minutes, and counterstaining was performed using hematoxylin (BL702B; Biosharp, China) for 30 seconds. The excess stain was removed with an acidic ethanol differentiation solution (G1861; Solarbio, China). Tissue sections were examined by two independent, blinded researchers using a ×200 optical microscope. For CD8, FOXP3, and CD206, the region with the highest cellular density was selected for analysis. The number of positively stained cells within a 1 mm² area was counted across three distinct fields, and the average value was recorded. The expression of PD-1, PD-L1, and cancer-associated fibroblast (CAF) markers (FAP and α-SMA) was evaluated using a two-dimensional scoring system based on staining intensity (ranging from 0 to 3) and the proportion of positively stained cells ( 30%). A final score of ≥ 2 was defined as positive for the expression of FAP, α-SMA, PD-1, and PD-L1. 2.8 Immunohistochemistry and evaluation of MTHFD1L positivity The paraffin-embedded PC tissue sections underwent deparaffinization, antigen retrieval, blocking of endogenous peroxidase activity, and treatment with a non-specific staining inhibitor, as described in the aforementioned protocol. Subsequently, the sections were incubated overnight at 4°C in a humidified chamber with a rabbit polyclonal antibody against MTHFD1L (1:100 dilution; ab229708; Abcam, UK). Secondary antibody binding was achieved using Reagents 3 and 4. Target protein localization was visualized using a DAB chromogenic solution, followed by hematoxylin counterstaining to enhance nuclear contrast. All MTHFD1L IHC staining results were independently evaluated by two researchers who were blinded to the clinical information of the patients. The scoring system was applied as follows: staining intensity was categorized as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong), and the proportion of positive cells was graded as 0 ( 30%). The final expression score was calculated by multiplying the staining intensity score by the proportion score. MTHFD1L expression was classified as negative (< 2) or positive (≥ 2). 2.9 Statistical analyses Data analysis and visualization were performed using R software (version 4.1.2), SPSS software (IBM Corp., Armonk, NY, USA, version 27.0), and GraphPad Prism 9. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed using SIMCA-P software (Umetrics, Sweden) with permutation-based validation. The Student's t-test was adjusted using the Benjamini-Hochberg false discovery rate (FDR) correction on the MetaboAnalyst platform. Receiver operating characteristic (ROC) curves, bar plots, and scatter plots were generated using GraphPad Prism 9.0 (GraphPad Software Inc., USA). Continuous variables were expressed as mean ± standard deviation (SD) for normally distributed data and as median (interquartile range, IQR) for non-normally distributed data. Differences between groups were assessed using the Student’s t-test or Mann–Whitney U test, as appropriate. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Prognostic factors were identified through Cox proportional hazards regression, with clinical-pathological variables exhibiting p -values < 0.05 in univariate analysis and further evaluated in multivariate analysis. OS and relapse-free survival (RFS) were estimated using the Kaplan–Meier method, and survival curves were compared using the log-rank test. Correlations were analyzed using the Wilcoxon rank-sum test and chi-square test. A p -value < 0.05 was considered statistically significant. 3 Results 3.1 Characteristics of OCM in PC OPLS-DA was performed on all detected amino acids in the samples, and a score plot was generated to provide an overview. In the OPLS-DA score plot, green points represent the healthy control group, while red points indicatedenote the PC group. The plot demonstrates a distinct separation between the Con and PC (Fig. 1 A). Furthermore, a permutation test with 999 iterations revealed a slope greater than 0 and a Q2 intercept less than 0, confirming the stability of the model and the absence of overfitting (SupFigure 1). Subsequently, we characterized the OCM in PC (Fig. 1 B). Among the OCM that exhibited significant differences, five amino acids—glutamic acid, methionine sulfoxide, citrulline, 1-methylhistidine, and threonine—were selected using binary logistic regression to establish a diagnostic model (Fig. 1 C), which was subsequently validated using an independent dataset (Fig. 1 D). The diagnostic model exhibited high sensitivity and specificity for stage1PC (Fig. 1 E). To evaluate the generalizability of this diagnostic model, we further validated its performance in distinguishing benign pancreatic lesions from PC (Fig. 1 F). Finally, when combined with the commonly used clinical marker carbohydrate antigen 19 − 9 (CA19-9) for joint diagnosis, we observed that in the second quadrant, where both tests yielded positive results, the sensitivity reached 100% (Fig. 1 G). 3.2 Identification and analysis of molecular subtypes related to OCM We classified the 930 patients into two distinct molecular subtypes, C1 and C2, using non-NMF clustering (Fig. 2 A). The results showed that patients in subgroup C2 exhibited higher OCM scores(Fig. 2 B). Survival analysis revealed a significantly worse prognosis for patients in cluster C2 compared to cluster C1 (Fig. 2 C), indicating that the OCM pathway was closely associated with the prognosis of PC. Figure 2 D illustrates the differences in the expression of OCM-related genes between the two subtypes. Furthermore, ssGSEA indicated that pathway activation was higher in the C2 subtype compared to the C1 subtype, including HALLMARK_MYC_TARGETS_V2, HALLMARK_MTORC1_SIGNALING, and HALLMARK_G2M_CHECKPOINT, which are associated with cell proliferation (Fig. 2 E). 3.3 Analyzing tumor immune microenvironments between OCM-related subtypes Cancer inflammation is closely associated with immune cell infiltration. In this study, we evaluated the immune properties of the C1 and C2 subtypes using transcriptome data analyzed with the "ESTIMATE" package. The results revealed that the C2 subtype displayed elevated levels of ImmuneScore (Fig. 3 A), StromalScore (Fig. 3 B), and ESTIMATEScore (Fig. 3 C), while tumor purity was reduced (Fig. 3 D). Next, we employed multiple algorithms to determine the proportion of immune cells infiltrating both subtypes. The results demonstrated significant differences in tumor-infiltrating immune cells between the two subtypes. Specifically, the C2 subtype showed a higher proportion of tumor-infiltrating immune cells, including CAFs, regulatory T cells (Tregs), and M2 macrophages (Fig. 3 E). Moreover, the C2 subtype displayed increased expression of immune checkpoints (ICs) (Fig. 3 F), which play a key role in regulating immune cell function. 3.4 Single-cell analysis of OCM in PC and normal tissue To further analyze the molecular features of OCM in PC at single-cell resolution, we downloaded the CRA001160 dataset from the Genome Sequence Archive (GSA) database, which included 24 PC samples and 11 normal pancreatic tissues. After data quality control and preprocessing, Uniform Manifold Approximation and Projection (UMAP) was applied to distinguish different cell subpopulations in the normal and PC groups (Fig. 4 A). These subpopulations were annotated based on the information provided by the original authors. Subsequently, we employed two algorithms to validate the distribution of OCM across different cellular subtypes in PC and normal pancreatic tissues. The results revealed that OCM predominantly exhibited significant differences in three cellular subtypes: endothelial cells, macrophages, and fibroblasts (Fig. 4 B, 4 C). The CopyKat algorithm was used to predict benign and malignant ductal cells. We then compared the differences in OCM activity between benign and malignant ductal cells, finding that tumor cells exhibited more intense OCM activity (Fig. 4 D, 4 E). Using RF analysis, we predicted the weight of genes involved in OCM and ranked them from highest to lowest. This analysis identified MTHFD1L as a potential key regulator of OCM in PC (SupFigure 2). In addition, MTHFD1L was closely associated with tumor-infiltrating immune cells (SupFigure 3–4). 3.5 MTHFD1L and immune marker expression in PC tissues In this study, we included 138 patients who underwent surgical resection for PC. Among them, 98 patients (71%) succumbed to the disease, and 89 patients (64.5%) experienced recurrence, with a median follow-up time of 2.4 years. We compared the expression of MTHFD1L in cancerous tissues and adjacent non-cancerous tissues (Fig. 5 A). In cancerous tissues, 59 patients were negative for MTHFD1L, while 79 were positive.. The expression of FAP (Fig. 5 B) and α-SMA (Fig. 5 C) in CAFs was assessed, with 60 patients showing negative expression and 78 patients showing positive expression. Moreover, we quantified CD8 + TILs (Fig. 5 D), FOXP3 + TILs (Fig. 5 E) and CD206-positive macrophages (Fig. 5 F). We also evaluated the expression of PD-1 on T cells (Fig. 5 G) and PD-L1 on tumor cells (Fig. 5 H). Of these, 46 patients were PD-1 negative, and 92 patients were PD-L1 positive.Patients were categorized into high and low-expression groups based on the median count of these markers. 3.6 MTHFD1L expression was an independent prognostic factor for patients with PC Table 1 provides a detailed summary of the clinicopathological characteristics of the MTHFD1L-positive and MTHFD1L-negative groups. No significant differences were observed between the two groups in terms of age, gender, tumor size, tumor location, degree of differentiation, CA19-9 levels, bilirubin levels, pT stage, neural invasion, vascular invasion, duodenal invasion, or bile duct invasion. However, the MTHFD1L-positive group was significantly associated with a higher incidence of lymph node metastasis ( p = 0.032). Table 1 Clinicopathological features based on MTHFD1L expression. Characteristic MTHFD1L expression p value Negative(n = 59) Positive(n = 79) Age(y) 66(56–72) 66(56–71) 0.453 Sex 0.088 Male 44(74.6%) 48(60.8%) Female 15(25.4%) 31(39.2%) Tumor size (cm) 3(2-4.1) 3(2.3-4) 0.355 CA19-9 (ng/mL) 63.5(14.8-514.3) 156.4(26.5-428.5) 0.236 Total bilirubin(mmol/L) 25.5(12-159.3) 62(14.6-164.3) 0.100 Location 0.352 Head 39(66.1%) 58(73.4%) Neck,body,and tail 20(33.9%) 21(26.6%) pT 0.415 1 30(50.8%) 32(40.5%) 2 26(44.1%) 38(48.1%) 3 0(0%) 2(2.5%) 4 3(5.1%) 7(8.9%) pN 0.032 N0 44(74.6%) 45(57%) N1,2 15(25.4%) 34(43%) pM 0.308 M0 56(94.9%) 72(91.1%) M1 3(5.1%) 7(8.9%) Duodenal invasion 0.435 du0 43(72.9%) 52(66.7%) du1 16(27.1%) 26(33.3%) Nerve invasion 0.857 ne0 23(39.0%) 32(40.5%) ne1,2,3 36(61.0%) 47(59.5%) Biliary invasion 0.182 bi0 55(93.2%) 68(86.1%) bi1 4(6.8%) 11(13.9%) Vascular invasion 0.454 va0 48(81.4%) 68(86.1%) va1 11(18.6%) 11(13.9%) Differentiation 0.288 Poor 14(24.6%) 29(36.7%) Moderate 32(56.1%) 35(44.3%) Well 11(19.3%) 15(19.0%) Kaplan-Meier analysis based on MTHFD1L expression (positive vs. negative) revealed that MTHFD1L-positive patients experienced a significantly poorer prognosis compared to MTHFD1L-negative patients, both in terms of OS (Log-rank, p = 0.005; Fig. 6 A) and RFS (Log-rank, p = 0.006; Fig. 6 B). Furthermore, TCGA data analysis revealed higher MTHFD1L expression was similarly associated with poorer DFS (Fig. 6 D), although no significant correlation with OS was observed (Fig. 6 C). Univariate analysis showed that positive expression of FAP in CAFs, a high abundance of CD206 + M2 macrophages, and positive expression of MTHFD1L were associated with poorer OS. In the subsequent multivariate analysis, MTHFD1L positivity (hazard ratio [HR], 1.811; 95% confidence interval [CI], 1.088–3.015; p = 0.022) emerged as an independent prognostic factor for OS (Table 2 ). Table 2 Univariate and multivariate analysis of overall survival according to MTHFD1L expression in patients with pancreatic cancer. Variables Univariate analysis Multivariate analysis HR (95% CI ) P HR (95% CI ) P MTHFD1L1 positive 1.908(1.256–2.898) 0.002 1.811(1.088–3.015) 0.022 Female 1.184(0.773–1.812) 0.437 Neck,body,and tail 0.827(0.549–1.247) 0.366 pN1 1.290(0.860–1.936) 0.218 pM1 1.552(0.775–3.107) 0.215 Age>65 years 0.963(0.643–1.440) 0.853 Tumor size >30 mm 1.375(0.914–2.068) 0.126 CA19-9>37 U/ml 1.070(0.708–1.618) 0.747 Total bilirubin>34.2 umol/L 0.868(0.580–1.298) 0.491 FAP positive in CAFs 1.652(1.077–2.533) 0.021 α-SMA negative in CAFs 0.733(0.493–1.091) 0.126 PD-1 positive 0.713(0.473–1.074) 0.106 PDL-1 positive 0.817(0.545–1.224) 0.327 CD8 low 0.690(0.460–1.036) 0.074 FOXP3 high 1.216(0.815–1.814) 0.338 CD206 high 1.506(1.001–2.267) 0.05 3.7 The Association between MTHFD1L expression and the abundance of tumor infiltrates immune cells and immune checkpoint markers MTHFD1L positivity was significantly correlated with FAP-positive expression in CAFs ( p < 0.05; Fig. 7 A). However, no significant correlation was observed between MTHFD1L expression and α-SMA expression in CAFs ( p = 0.861; Fig. 7 B). No significant association was observed between MTHFD1L expression and the abundance of CD8 + TILs ( p = 0.787; Fig. 7 C) However, in the MTHFD1L-positive group, the abundance of Foxp3 + TILs was significantly higher compared to the MTHFD1L-negative group, indicating a significant correlation between MTHFD1L expression and Foxp3 + TIL count ( p < 0.05; Fig. 7 D). Elevated MTHFD1L expression showed a significant correlation with the high abundance group of CD206 + M2 macrophages ( p < 0.001; Fig. 7 E). We next investigated the correlation between MTHFD1L expression and the expression levels of PD-1, and PD-L1. Furthermore, the expression of PD-1 in immune cells ( p = 0.808; Fig. 7 F) and PD-L1 in cancer cells ( p = 0.362; Fig. 7 G) did not show a significant correlation with MTHFD1L expression. 4 Discussion Metabolic reprogramming is a hallmark of cancer, enabling tumors to meet the substrate and energy demands of rapid growth by altering the efficiency and utilization of nutrients such as glucose, amino acids, and lipids 28 , 29 . Insights into meaningful subtypes, metabolic signaling, and immune characteristics have provided valuable perspectives for cancer immunotherapy. In this study, we examined 136 subjects using a targeted metabolomics platform to validate metabolic disturbances in PC. The results revealed significant one-carbon metabolic reprogramming in PC patients. The metabolic landscape encompasses genes, proteins, metabolites, and their interactions. To comprehensively map the metabolic landscape of carbon metabolism in PC, we analyzed 930 PC patients with complete follow-up and transcriptomic data from four public databases. These patients were classified into two metabolism-related subtypes, C1 and C2, which exhibited significant differences in prognosis and tumor microenvironment. Single-cell analysis further highlighted differences in carbon metabolism among normal cells, cancer cells, and the tumor microenvironment. These metabolic changes are primarily driven by the abnormal expression and activation of key metabolic enzymes. We identified MTHFD1L as the core gene driving metabolic reprogramming in PC. Multi-omics analysis across multiple cohorts confirmed MTHFD1L as an independent poor prognostic factor in PC, closely associated with tumor-infiltrating immune cells. In PC, MTHFD1L may serve as a key tumor-driven metabolic gene, regulating metabolic reprogramming and reshaping the tumor microenvironment. The molecular phenotype accurately reflects the pathological features of a disease 30 . In exploring the molecular phenotype of PC, we identified significant disruptions in carbon metabolism. OCM encompasses the folate cycle, methionine cycle, and transsulfuration pathway 31 . Through these pathways, one-carbon units generate and utilize molecules such as pyrimidine, thymidylate, S-adenosylmethionine, and glutathione, which regulate tumor growth and proliferation 32 . This study found that serum glutamate and glutamine levels were significantly elevated in PC patients. High expression of glutamine synthetase in tumor cells drives metabolic reprogramming, promotes glutamine synthesis, and enhances nucleotide synthesis, thereby facilitating efficient DNA damage repair 33 , 34 . Glutamine metabolism in the tumor microenvironment not only supports tumor cell growth but also impairs the anti-tumor activity of immune cells. Early detection of cancer can significantly improve the prognosis of PC 35 . In this study, a diagnostic model based on serum carbon metabolites demonstrated strong diagnostic performance and the ability to identify early-stage PC. However, as this was a single-center study, the reproducibility and scalability of the diagnostic model need to be further validated in multi-center, large-sample cohorts. In the present study, transcriptomic data were used to characterize the carbon metabolic landscape in PC. Based on the expression of carbon metabolism genes, 930 patients were divided into two carbon metabolism-related subtypes. The prognosis of these subtypes differed significantly. Interestingly, OCM genes were highly expressed in both subtypes, but the OCM score was higher in the C2 subtype, which also had a worse prognosis. Transcriptomic data confirmed that reprogramming of carbon metabolism is closely associated with the prognosis of PC. By establishing metabolic subtypes linked to clinical outcomes based on metabolic heterogeneity, this study provides insights into targeting unique metabolic vulnerabilities in tumors. We further analyzed the relationship between carbon metabolic reprogramming and the tumor microenvironment. The results showed that the C2 subtype exhibited elevated levels of ImmuneScore, StromalScore, and ESTIMATEScore. Tumor-infiltrating immune cells, such as CAFs and TAMs, were more abundant in the C2 subtype, which also showed stronger associations with immune checkpoints. These findings highlight the critical role of carbon metabolic reprogramming in reshaping the immune microenvironment in PC. Next, we explored differences in OCM across different cell types at single-cell resolution. The results revealed that one-carbon metabolic activity was significantly higher in tumor cells and tumor stromal cells compared to normal tissue cells, particularly in endothelial cells, macrophages, and fibroblasts. Since PC is predominantly composed of pancreatic ductal adenocarcinoma (PDAC), we used the CopyKat algorithm to separately analyze ductal cells, enabling the prediction of benign and malignant ductal populations. By comparing carbon metabolism activity between benign and malignant ductal cells, we underscored the importance of OCM in the development and progression of PC. The use of drugs targeting one-carbon metabolic pathways, such as methotrexate and 5-fluorouracil, has significantly improved tumor prognosis 36 , 37 . The metabolic landscape is driven by key tumor-related metabolic genes. Recently, targeting one-carbon metabolizing enzymes has emerged as a novel anti-tumor therapeutic strategy 38 – 40 . Transcriptomic data analysis revealed that MTHFD1L plays a significant role in one-carbon metabolic pathways, is associated with poor prognosis, and is closely linked to tumor-infiltrating lymphocytes. MTHFD1L may be a key gene driving carbon metabolic reprogramming in PC 41 . MTHFD1L is reportedly involved in the synthesis of tetrahydrofolate (THF) in mitochondria, which is critical for the de novo synthesis of purines and thymidylate, as well as the regeneration of methionine from homocysteine 42 . MTHFD1L regulates macrophage polarization by mediating mitochondrial autophagy 43 . Although MTHFD1L has been less commonly reported in cancer, it has been shown to promote tumor invasion and metastasis in esophageal squamous cell carcinoma by activating the ERK5 signaling pathway 44 . To further investigate the role of MTHFD1L in PC, we examined its protein expression in 138 PC tissue samples. The results confirmed that MTHFD1L is an independent poor prognostic factor for PC. The abundance of CAFs, TAMs and Tregs was elevated in the MTHFD1L high-expression group. FAP and α-SMA are markers of myofibroblastic CAFs (myCAFs) and inflammatory CAFs (iCAFs), respectively. Depletion of FAP + CAFs has been shown to improve survival in PC 45 , as FAP + CAFs regulate cancer-associated pathways and promote the accumulation of Tregs 46 . Tumor-associated macrophages are highly infiltrated in malignant solid tumors, where they promote tumor progression and shape the immune microenvironment 47 . Macrophages are positively correlated with the incidence of cachexia in PC, and their inhibition can delay weight loss and cachexia development in mouse models 48 . Targeting TAMs can transform "cold" tumors into "hot" tumors by enhancing T cell function and synergistically improving the efficacy of PD-1 inhibitors 49 , 50 . Foxp3-positive TILs are Tregs with immunosuppressive functions that inhibit the proliferation and activation of CD8-positive TILs, which are responsible for killing cancer cells through apoptosis 51 . However, MTHFD1L was not strongly associated with immune checkpoints. ICIs are currently the cornerstone of tumor immunotherapy, but their efficacy in most PC patients remains unsatisfactory 52 . Reversing the immunosuppressive state and reshaping the immune microenvironment may be key to improving immunotherapy outcomes in PC 53 . Therefore, identifying targets that regulate both PC cells and the TME holds promising therapeutic potential. This study has several strengths: it is the first to use multi-omics analysis to explore the one-carbon metabolic landscape and the immune microenvironment in PC, identifying MTHFD1L as a key tumor-driving metabolic gene and investigating its regulatory role in the tumor microenvironment. Besides, a diagnostic panel was developed, offering a theoretical foundation for early PC screening. However, there are limitations: as a single-center study, the findings require further validation in larger, multicenter cohorts. Moreover, the regulatory relationship between MTHFD1L and the immune microenvironment in PC needs to be further confirmed through cell-based experiments. In summary, PC undergoes reprogramming of OCM. MTHFD1L may function as an oncogene-driven metabolic gene in carbon metabolism, influencing the prognosis of PC by regulating CAFs and TAMs. Targeting the one-carbon metabolic pathway or MTHFD1L represents a promising therapeutic strategy. Declarations Ethics statement All research procedures involving human participants in this study were conducted in accordance with the principles outlined in the Declaration of Helsinki. For the collection of human blood samples, written informed consent was obtained from all patients, and the study protocol was approved by the Ethics Review Committee of the Affiliated Hospital of North Sichuan Medical College (Approval No. 2024006). The human samples utilized in this study primarily originated from previously isolated specimens obtained in prior research, which had already received ethical approval. In compliance with national laws and institutional requirements, participants or their legal guardians/close relatives were not required to provide written informed consent. Author contributions As part of the author contributions: DD,SW and YQ conducted the experiments, statistical analysis, and visualization, and wrote the initial draft of the manuscript. CZ contributed to the review and revision of the manuscript. SM organized the data and reviewed and revised the manuscript. ZS, SL, JL, and LY organized the data and reviewed and revised the manuscript. PY conceived and designed the study and reviewed and revised the manuscript. All authors participated in writing the manuscript and provided final approval of the version to be submitted and published. Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (No. 82300737),the Nanchong City Science and Technology Bureau and School Strategic Cooperation Project (grant no. 22SXQT0110) and the Doctoral Start-up Fund of The Affiliated Hospital of North Sichuan Medical College (grant no.2023GC010). Data availability statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Conflict of interest The authors declare that they have no competing interests. References Halbrook CJ, Lyssiotis CA, Pasca di Magliano M, Maitra A. Pancreatic cancer: Advances and challenges. 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Immune checkpoint inhibition for pancreatic ductal adenocarcinoma: limitations and prospects: a systematic review. Cell Commun Signal Nov. 2021;24(1):117. 10.1186/s12964-021-00789-w . Supplementary Files SupFigure.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6404670","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448134915,"identity":"12d9899a-9234-4bcf-9595-6f4c31342090","order_by":0,"name":"Dawei Deng","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Dawei","middleName":"","lastName":"Deng","suffix":""},{"id":448134916,"identity":"43857269-c255-44a5-87cc-24eb5a554e5e","order_by":1,"name":"Song Wei","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Wei","suffix":""},{"id":448134917,"identity":"bfb7a8a6-b7ab-401a-acec-cfc39aa93a04","order_by":2,"name":"Qihang Yuan","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qihang","middleName":"","lastName":"Yuan","suffix":""},{"id":448134918,"identity":"b36dada9-3c2d-4332-95b1-44216a5ea1c6","order_by":3,"name":"Chao Zhang","email":"","orcid":"","institution":"Deprtment of West China Hospital,Sichuan University-Yingshan Hospital,Nanchong,China","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Zhang","suffix":""},{"id":448134919,"identity":"996f7cbc-e1d4-49b1-9062-125f4e3225da","order_by":4,"name":"Surong Ma","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Surong","middleName":"","lastName":"Ma","suffix":""},{"id":448134920,"identity":"0f120292-3671-40e8-b795-f1f672e9bdd5","order_by":5,"name":"Zhihui Shu","email":"","orcid":"","institution":"Department of Surgery,Wusheng County Second People's Hospital,Guangan,China","correspondingAuthor":false,"prefix":"","firstName":"Zhihui","middleName":"","lastName":"Shu","suffix":""},{"id":448134921,"identity":"06114580-c2f0-4762-abb7-a5005b7fc9fd","order_by":6,"name":"Suxi Li","email":"","orcid":"","institution":"North Sichuan Medical College [Search North Sichuan Medical College]: North Sichuan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Suxi","middleName":"","lastName":"Li","suffix":""},{"id":448134922,"identity":"f6ea9348-3953-4c65-87d0-a273c3927af7","order_by":7,"name":"Junning Liu","email":"","orcid":"","institution":"Nanchong Central Hospital Second Clinical School of North Sichuan Medical College: Nanchong Central Hospital Affiliated to North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Junning","middleName":"","lastName":"Liu","suffix":""},{"id":448134923,"identity":"d3f68349-ae7e-4989-b982-ec502e4ee1e3","order_by":8,"name":"Linfeng Yang","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Linfeng","middleName":"","lastName":"Yang","suffix":""},{"id":448134924,"identity":"6fa46333-39d7-46ee-a530-c947931b9643","order_by":9,"name":"Peng Sheng Yi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3PMWrDMBTG8WdUNKn1+kxCfAWBQVMOI2Hw1EKn4iFQD0Ee2uLVvUWn0lFGoEndM8b0As7WKaR7gu1uGfSbvz+8BxAEV4im1pihxGed/nR7WW6mkzukqm/9OmqgyPneu+lkhSzLbnURvVf3Ium3ZMZhi0okLbWEGy9KVVGI6xc5nixNgQOzlHf6aae+loD++2M8AemSFi3jlnzulKfA8WEqUXrxt0fuQDwqTWYkmJOMyYInrzcC5iXMRX1r1jJGmqP0jk3+ktbNYA5HlBRJd/gtN6u4fhtPzrD/zYMgCIKLTtHETABt9t9yAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5240-1127","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"Sheng","lastName":"Yi","suffix":""}],"badges":[],"createdAt":"2025-04-08 15:18:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6404670/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6404670/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82123707,"identity":"d4819965-adc9-4779-9e85-e6a5dce1b9ea","added_by":"auto","created_at":"2025-05-07 03:35:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256471,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of One-carbon metabolism in PC. (A)OPLS-DA score plots of Amino acid metabolism. (B) Key metabolic alteration involved in One-carbon metabolic reprogramming of PC. The ROC curve of 5-AAs diagnostic panel between the PC and Con group, which was constructed using the discovery set (C) and the validation set (D). ROC curve of 5-AAs diagnostic panel between early PC and BGL group (E). ROC curve of 5-AAs diagnostic panel between EPC and PGL group(F). Scatter plot for comparing the 5-AAs diagnostic panel and CA-199 (G).\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404670/v1/b9da67b6cdb3102332aee183.jpg"},{"id":82125414,"identity":"68687807-3b0c-43fb-9a45-4cf935f1c4f4","added_by":"auto","created_at":"2025-05-07 03:51:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":258990,"visible":true,"origin":"","legend":"\u003cp\u003eOne-carbon metabolism scores-based clustering analysis. (A) Identification of Molecular Subtype Related to One-carbon metabolism by NMF algorithm. (B) Enrichment scores for two clusters (C1 and C2). (C) Kaplan-Meier survival curves depict OS in subgroup. (D) Differentially expressed One-carbon metabolism genes in the C1 and C2 subgroups. (E) The classical cancer-related pathways between C1 and C2.\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404670/v1/ecd82e9482327c03431ff43d.jpg"},{"id":82123711,"identity":"bd4f6782-239e-4ea2-b45f-2c8d0f6f6f8f","added_by":"auto","created_at":"2025-05-07 03:35:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":877039,"visible":true,"origin":"","legend":"\u003cp\u003eTumor immune microenvironment in two molecular subtypes. (A-D) Boxplots showing EstimateScore, ImmuneScore, StromalScore and tumor purity in two subtypes. (E) Comparison of immune cell infiltration percentages in C1 and C2. (F) Comparison of immune checkpoint genes expression in C1 and C2.\u003c/p\u003e","description":"","filename":"image3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404670/v1/ff9bd1d24258a057c431e8af.jpg"},{"id":82124931,"identity":"4fc88295-37de-44cf-bb86-5c66b6d53fe9","added_by":"auto","created_at":"2025-05-07 03:43:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":595464,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution characteristics of One-carbon metabolism in Single-cell analysis. (A) UMAP were used to classify and label the cell subsets. Distribution differences of One-carbon metabolism in different cell subtypes: (B, singscore) and (C, ssgsea). Distribution of One-carbon metabolism in the ductal cells and PC (D, singscore) and (E, ssgsea).\u003c/p\u003e","description":"","filename":"image4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404670/v1/23d1f97515946a995094ffeb.jpg"},{"id":82123710,"identity":"a1797298-6c11-4972-9315-4d9749278e95","added_by":"auto","created_at":"2025-05-07 03:35:32","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":378132,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images of immunohistochemical staining in resected pancreatic cancer specimens:(A) Expression of MTHFD1L in both cancerous and adjacent non-cancerous tissues.(B) Expression of FAP in cancer-associated fibroblasts (CAFs).(C) Expression of α-SMA in cancer-associated fibroblasts (CAFs).(D) Expression of CD8 in tumor-infiltrating lymphocytes (TILs).(E)Expression of FOXP3 in tumor-infiltrating lymphocytes (TILs).(F) Expression of CD206 in tumor-associated macrophages (TAMs).(G) Expression of PD-1 on T cells.(H) Expression of PD-L1 in cancer cells.\u003c/p\u003e","description":"","filename":"image5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404670/v1/8f8a456018268b7b19b8f665.jpg"},{"id":82123717,"identity":"075d1375-288f-4070-8062-c60af90b5b59","added_by":"auto","created_at":"2025-05-07 03:35:32","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":214429,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival analysis in pancreatic cancer patients based on MTHFD1L expression:A. The relationship between overall survival (OS) and MTHFD1L expression.B. The relationship between recurrence-free survival (RFS) and MTHFD1L expression.C. The relationship between overall survival (OS) and MTHFD1L expression based on data from the TCGA database.D. The relationship between recurrence-free survival (RFS) and MTHFD1L expression based on data from the TCGA database.\u003c/p\u003e","description":"","filename":"image6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404670/v1/e5b580d2f08e6edb7b05ea87.jpg"},{"id":82124932,"identity":"715ca8e2-c5aa-4068-b002-781db7fe674a","added_by":"auto","created_at":"2025-05-07 03:43:32","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":198983,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between MTHFD1L expression and the expression of CD206, CD8+ TILs, Foxp3+ TILs, FAP, α-SMA, PD-1, and PD-L1: A. The relationship between MTHFD1L expression and FAP expression;B. The relationship between MTHFD1L expression and α-SMA expression;C. The relationship between MTHFD1L expression and CD8+ TILs;D. The relationship between MTHFD1L expression and Foxp3+ TILs;E. The relationship between MTHFD1L expression and CD206 expression;F. The relationship between MTHFD1L expression and PD-1 expression;G. The relationship between MTHFD1L expression and PD-L1 expression.\u003c/p\u003e","description":"","filename":"image7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6404670/v1/7f3a22a3899c28d95d7d2d82.jpg"},{"id":82555556,"identity":"7583815e-0faf-4441-b41e-90b5eea77ccd","added_by":"auto","created_at":"2025-05-12 23:12:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3889990,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6404670/v1/7a8630c6-cd86-40fc-a61d-c300222a4373.pdf"},{"id":82125416,"identity":"85d94ce3-f4bb-4018-be5a-ba40743f999b","added_by":"auto","created_at":"2025-05-07 03:51:33","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":527975,"visible":true,"origin":"","legend":"","description":"","filename":"SupFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-6404670/v1/e2e84a5e5c4be05681cf6672.docx"}],"financialInterests":"","formattedTitle":"One-carbon metabolic reprogramming and its relationship with tumor-infiltrating lymphocytes and Immune checkpoint in Pancreatic cancer","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePancreatic cancer (PC) is a highly fatal and refractory malignant tumor of the digestive system\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The annual incidence of PC is steadily increasing, and the unsatisfactory treatment outcomes have generated significant interest in understanding and addressing this disease\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Most patients present with advanced-stage disease or distant metastases, thereby losing the opportunity for radical resection\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In addition, the high postoperative recurrence rate and the limited efficacy of adjuvant chemotherapy contribute to a poor prognosis, with a five-year survival rate of less than 5%\u003csup\u003e4\u003c/sup\u003e. Accordingly, there is an urgent need to identify innovative prognostic biomarkers and potential molecular targets for PC.\u003c/p\u003e \u003cp\u003ePC is characterized by a prominent desmoplastic stromal reaction, which significantly hinders the efficacy of chemotherapy, radiotherapy, and immune therapies\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The tumor microenvironment (TME), a complex network of cellular and stromal components that supports tumor cells, plays a critical role in regulating tumor progression and treatment resistance\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Recent studies have highlighted the high heterogeneity within PC TME, which includes tumor-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), and tumor-infiltrating immune cells\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These components can exert either pro- or anti-tumorigenic effects, depending on the cell type and context\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Despite these insights, the immunosuppressive nature of the PC tumor microenvironment remains a major bottleneck, limiting improvements in therapeutic outcomes\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Therefore, a deeper understanding of the regulatory mechanisms governing the TME in PC is urgently needed to advance treatment strategies.\u003c/p\u003e \u003cp\u003eMetabolic networks, with metabolic enzymes and metabolites as their core components, serve as the material basis for cellular activities\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Oncogene-driven metabolic adaptations enable cancer cells to survive and thrive within the TME\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The importance of altered metabolic requirements and the accompanying secretion of metabolites in shaping a supportive TME is increasingly being recognized\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. One-carbon metabolism (OCM) lies at the center of cellular function, facilitating the activation of one-carbon units through the one-carbon cycle for nucleotide and amino acid biosynthesis, as well as epigenetic regulation\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Studies have shown that reprogramming of OCM is associated with various diseases, including non-alcoholic liver disease, cancer development, and immune regulation within the tumor microenvironment\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Immunotherapy utilizing drugs that target metabolic pathways may be synergistically enhanced by modulating the metabolism of the tumor immune microenvironment\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These insights shed new light on patient stratification for frontline therapies in PC.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to integrate multi-omics data to comprehensively explore the characteristics of one-carbon metabolic reprogramming in PC across multiple dimensions. We identified key tumor-driving metabolic genes and investigated their relationship with PC prognosis, tumor-infiltrating immune cells, and immune checkpoint expression.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients and specimens\u003c/h2\u003e \u003cp\u003e A total of 136 fasting serum samples were collected on the first morning after admission from PC patients, benign pancreatic lesion (BPL) patients, and healthy participants (Con) at The Affiliated Hospital of North Sichuan Medical College between May 2023 and October 2024. The PC patients were pathologically diagnosed via biopsy and stratified into subgroups according to the 8th edition of the American Joint Committee on Cancer (AJCC) staging system\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The age and gender distribution of the Con group were matched with those of the PC group. Patients in the PC group were excluded if they had other forms of cancer, liver or renal insufficiency, severe cardiopulmonary diseases, metabolic disorders, or other mental or physical conditions. All 17 cases of Benign lesions of pancreas(BLP) were confirmed by pathological biopsy, primarily including solid pseudopapillary neoplasms of the pancreas and pancreatic pseudocysts. All participants provided written informed consent, and the study was approved by the Ethics Committee of The Affiliated Hospital of North Sichuan Medical College (Approval No. 2024006).\u003c/p\u003e \u003cp\u003ePC tissue samples were obtained from 138 patients who underwent surgical resection at The Affiliated Hospital of North Sichuan Medical College between May 2017 and July 2023. All patients were histologically confirmed to have PC. None of the participants had received preoperative radiotherapy, chemotherapy, immunotherapy, or targeted therapy, and complete clinical data were available for all cases. Tumor-node-metastasis (TNM) staging of PC was determined according to the 8th edition of the AJCC staging system, while pathological grading was performed in accordance with the 2019 World Health Organization (WHO) classification.\u003c/p\u003e \u003cp\u003ePostoperatively, clinical blood tests and contrast-enhanced computed tomography (CT) scans were conducted every three months. Disease-free survival (DFS) was defined as the time interval from surgery to tumor recurrence or death, while overall survival (OS) was defined as the duration from surgery to death.\u003c/p\u003e \u003cp\u003e The study protocol was approved by the Ethics Committee of The Affiliated Hospital of North Sichuan Medical College and was granted ethical exemption.The study adhered to the ethical standards established by local and national ethics committees for human research, as well as the principles outlined in the 1964 Declaration of Helsinki and its subsequent amendments. Formalin-fixed paraffin-embedded (FFPE) samples were used for immunohistochemical (IHC) analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Metabolomic analysis and Data processing\u003c/h2\u003e \u003cp\u003eAmino acids were quantified using a Shimadzu LC-20ADXR system (Shimadzu, Japan) coupled with a Sciex 4500MD triple quadrupole mass spectrometer (AB Sciex, Singapore). Chromatographic separation was performed using an Acquity UPLC BEH Amide column (1.7 \u0026micro;m, 2.1 mm \u0026times; 100 mm, Waters). To ensure accurate quantification of metabolites, a calibration curve was constructed using ten standard points generated through serial dilution of mixed stock solutions with methanol. Quality control (QC) samples were prepared by thoroughly pooling all residual test samples. A volume of 50 \u0026micro;L of serum samples, QCs, or standards at varying concentrations was precisely transferred to a 750 \u0026micro;L 96-well sample collection plate. Subsequently, 10 \u0026micro;L of mixed internal standard (M1-IS) and 190 \u0026micro;L of methanol were added to each serum sample, followed by vortexing at 1500 rpm for 3 minutes. The mixture was then centrifuged at 5300 rpm for 20 minutes at 4\u0026deg;C, and 180 \u0026micro;L of the supernatant was collected. Finally, 2 \u0026micro;L of the supernatant was injected for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe mass chromatogram peaks were extracted and integrated using the in-house software MultiQuant (AB Sciex, Singapore). Each peak was automatically identified based on its retention time and multiple reaction monitoring (MRM) parameters of the standards, followed by manual verification. Quantitative results for each metabolite were obtained by correcting with isotopically labeled internal standards and applying the standard curve for each analyte. Metabolites with missing values exceeding 50% of the total sample size or a coefficient of variation (CV) greater than 30% in quality control (QC) samples were excluded from the analysis to ensure analytical accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Transcriptomic Data Acquisition and Preprocessing\u003c/h2\u003e \u003cp\u003eTranscriptomic data and clinical features of PC patients were obtained from The Cancer Genome Atlas (TCGA), ArrayExpress, Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases. Only patients with complete follow-up data were included. For the TCGA-PAAD cohort, RNA sequencing data (expressed as fragments per kilobase of transcript per million mapped reads [FPKM]) were converted to transcripts per kilobase million (TPM) to ensure comparability with microarray results. To minimize batch effects, data from different platforms were harmonized using the \"ComBat\" function from the \"SVA\" package. A total of 930 PC patients were included, encompassing datasets from TCGA-PAAD, GSE62452, GSE28735, GSE57495, ICGC-AU, ICGC-CA, and MTAB-6134.\u003c/p\u003e \u003cp\u003eOCM genes were derived from the following gene sets: WP_ONE_CARBON_METABOLISM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gsea-msigdb.org/gsea/msigdb/cards/WP_ONE_CARBON_METABOLISM\u003c/span\u003e\u003cspan address=\"http://www.gsea-msigdb.org/gsea/msigdb/cards/WP_ONE_CARBON_METABOLISM\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and WP_ONE_CARBON_METABOLISM_AND_RELATED_PATHWAYS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gsea-msigdb.org/gsea/msigdb/cards/WP_ONE_CARBON_METABOLISM_AND_RELATED_PATHWAYS\u003c/span\u003e\u003cspan address=\"http://www.gsea-msigdb.org/gsea/msigdb/cards/WP_ONE_CARBON_METABOLISM_AND_RELATED_PATHWAYS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). By integrating these two datasets, we identified a total of 75 genes associated with OCM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Cluster Analysis and Gene set variation analysis\u003c/h2\u003e \u003cp\u003eThe non-negative matrix factorization (NMF) algorithm aims to identify potential gene expression patterns by decomposing the original matrix into two non-negative matrices. Based on the expression profiles of OCM genes, we used the \"NMF\" R package to classify 930 PC patients into C1 and C2 metabolism-related subtypes. The \"Survival\" R package was employed to perform Kaplan-Meier (K-M) survival analysis between the C1 and C2 subtypes. Furthermore, the \"GSVA\" R package was utilized to calculate individual OCM pathway scores. These scores served as indicators to compare differences in OCM and 50 hallmark pathways between the two patient subgroups using the \"wilcox.test\" function in R.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Analyzing tumor immune microenvironments between OCM-related subtypes\u003c/h2\u003e \u003cp\u003eThe \"ESTIMATE\" package was used to assess the immune and stromal composition of each PC sample. Pan-immune cell prediction methods were employed to assess the immune microenvironment between the C1 and C2 subtypes, including TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCP-COUNTER, XCELL, and EPIC. Moreover, the expression of immune checkpoint genes was compared between the C1 and C2 groups, referencing previous literature reports. To identify key genes, the Random Forest (RF) algorithm was utilized to predict the weight of each gene associated with OCM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Single-cell analysis and single sample gene set enrichment analysis (ssGSEA)\u003c/h2\u003e \u003cp\u003eWe obtained single-cell RNA sequencing (scRNA-seq) data from 24 PC samples and 11 normal pancreatic tissues from the CRA001160 cohort. The CreateSeuratObject function was used to process the scRNA-seq data. Data quality control was performed using the following parameters: min.cells\u0026thinsp;=\u0026thinsp;3, min.features\u0026thinsp;=\u0026thinsp;200, nCount_RNA \u0026ge; 1000, nFeature_RNA \u0026ge; 500, nFeature_RNA \u0026le; 6000, and percent.mt \u0026le;20. The LogNormalize method was applied to standardize the single-cell data, followed by the FindVariableFeatures function to identify highly variable genes. The ScaleData function was used for normalized data processing, and the top 2000 highly variable genes were selected for Principal Component Analysis (PCA) dimensionality reduction. Batch effects were removed using the RunHarmony function. Cell annotation was performed based on cell-specific markers. The one-carbon metabolic activity of each cell was predicted using the ssGSEA and singscore methods, respectively.\u003c/p\u003e \u003cp\u003eThe ssGSEA package was used to predict the abundance of immune cell infiltration in the TCGA-PAAD cohort. Spearman correlation analysis was performed to evaluate the relationship between MTHFD1L expression and immune cell infiltration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Immunohistochemistry and evaluation of TILs, CAFs, TAMs and immune checkpoints\u003c/h2\u003e \u003cp\u003eIHC analysis was performed on paraffin-embedded tissue sections obtained from PC patients. The sections were deparaffinized using an eco-friendly clearing agent (H-H0101; Wuhan Hongzhi Biotechnology Co., China) and ethanol, followed by antigen retrieval at 95\u0026deg;C for 20 minutes in citrate buffer (MVS-0100; Maxin Biotech Co., China). After cooling, the sections were washed with phosphate-buffered saline (PBS; BL601A; Biosharp, China). IHC staining was conducted using an ultra-sensitive SP reagent kit (KIT-9710; Maxin Biotech, China). Endogenous peroxidase activity was blocked with an inhibitor (Reagent 1) for 10 minutes, and non-specific binding was minimized using Reagent 2.\u003c/p\u003e \u003cp\u003ePrimary antibodies targeting Tumor Infiltrating Lymphocytes (TILs) and CAF markers, including CD8, FOXP3, fibroblast activation protein (FAP), alpha-smooth muscle actin (α-SMA), CD206, programmed cell death protein 1 (PD-1), and programmed death-ligand 1 (PD-L1) (dilution details provided), were applied. Sections were incubated overnight at 4\u0026deg;C, followed by incubation with a biotinylated secondary antibody (Reagent 3) and horseradish peroxidase (HRP)-conjugated streptavidin (Reagent 4) for signal amplification. A 3,3'-diaminobenzidine (DAB) chromogenic solution (DAB-0031; Maixin Biotech, China) was applied for 3 minutes, and counterstaining was performed using hematoxylin (BL702B; Biosharp, China) for 30 seconds. The excess stain was removed with an acidic ethanol differentiation solution (G1861; Solarbio, China).\u003c/p\u003e \u003cp\u003eTissue sections were examined by two independent, blinded researchers using a \u0026times;200 optical microscope. For CD8, FOXP3, and CD206, the region with the highest cellular density was selected for analysis. The number of positively stained cells within a 1 mm\u0026sup2; area was counted across three distinct fields, and the average value was recorded. The expression of PD-1, PD-L1, and cancer-associated fibroblast (CAF) markers (FAP and α-SMA) was evaluated using a two-dimensional scoring system based on staining intensity (ranging from 0 to 3) and the proportion of positively stained cells (\u0026lt;\u0026thinsp;10%, 10\u0026ndash;30%, \u0026gt;\u0026thinsp;30%). A final score of \u0026ge;\u0026thinsp;2 was defined as positive for the expression of FAP, α-SMA, PD-1, and PD-L1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Immunohistochemistry and evaluation of MTHFD1L positivity\u003c/h2\u003e \u003cp\u003eThe paraffin-embedded PC tissue sections underwent deparaffinization, antigen retrieval, blocking of endogenous peroxidase activity, and treatment with a non-specific staining inhibitor, as described in the aforementioned protocol. Subsequently, the sections were incubated overnight at 4\u0026deg;C in a humidified chamber with a rabbit polyclonal antibody against MTHFD1L (1:100 dilution; ab229708; Abcam, UK). Secondary antibody binding was achieved using Reagents 3 and 4. Target protein localization was visualized using a DAB chromogenic solution, followed by hematoxylin counterstaining to enhance nuclear contrast. All MTHFD1L IHC staining results were independently evaluated by two researchers who were blinded to the clinical information of the patients. The scoring system was applied as follows: staining intensity was categorized as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong), and the proportion of positive cells was graded as 0 (\u0026lt;\u0026thinsp;10%), 1 (10\u0026ndash;30%), or 2 (\u0026gt;\u0026thinsp;30%). The final expression score was calculated by multiplying the staining intensity score by the proportion score. MTHFD1L expression was classified as negative (\u0026lt;\u0026thinsp;2) or positive (\u0026ge;\u0026thinsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analyses\u003c/h2\u003e \u003cp\u003eData analysis and visualization were performed using R software (version 4.1.2), SPSS software (IBM Corp., Armonk, NY, USA, version 27.0), and GraphPad Prism 9. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed using SIMCA-P software (Umetrics, Sweden) with permutation-based validation. The Student's t-test was adjusted using the Benjamini-Hochberg false discovery rate (FDR) correction on the MetaboAnalyst platform. Receiver operating characteristic (ROC) curves, bar plots, and scatter plots were generated using GraphPad Prism 9.0 (GraphPad Software Inc., USA). Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed data and as median (interquartile range, IQR) for non-normally distributed data. Differences between groups were assessed using the Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test, as appropriate. Categorical variables were analyzed using the chi-square test or Fisher\u0026rsquo;s exact test. Prognostic factors were identified through Cox proportional hazards regression, with clinical-pathological variables exhibiting \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis and further evaluated in multivariate analysis. OS and relapse-free survival (RFS) were estimated using the Kaplan\u0026ndash;Meier method, and survival curves were compared using the log-rank test. Correlations were analyzed using the Wilcoxon rank-sum test and chi-square test. A \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of OCM in PC\u003c/h2\u003e \u003cp\u003eOPLS-DA was performed on all detected amino acids in the samples, and a score plot was generated to provide an overview. In the OPLS-DA score plot, green points represent the healthy control group, while red points indicatedenote the PC group. The plot demonstrates a distinct separation between the Con and PC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Furthermore, a permutation test with 999 iterations revealed a slope greater than 0 and a Q2 intercept less than 0, confirming the stability of the model and the absence of overfitting (SupFigure 1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, we characterized the OCM in PC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Among the OCM that exhibited significant differences, five amino acids\u0026mdash;glutamic acid, methionine sulfoxide, citrulline, 1-methylhistidine, and threonine\u0026mdash;were selected using binary logistic regression to establish a diagnostic model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), which was subsequently validated using an independent dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eThe diagnostic model exhibited high sensitivity and specificity for stage1PC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). To evaluate the generalizability of this diagnostic model, we further validated its performance in distinguishing benign pancreatic lesions from PC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Finally, when combined with the commonly used clinical marker carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA19-9) for joint diagnosis, we observed that in the second quadrant, where both tests yielded positive results, the sensitivity reached 100% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification and analysis of molecular subtypes related to OCM\u003c/h2\u003e \u003cp\u003eWe classified the 930 patients into two distinct molecular subtypes, C1 and C2, using non-NMF clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The results showed that patients in subgroup C2 exhibited higher OCM scores(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Survival analysis revealed a significantly worse prognosis for patients in cluster C2 compared to cluster C1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), indicating that the OCM pathway was closely associated with the prognosis of PC. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD illustrates the differences in the expression of OCM-related genes between the two subtypes. Furthermore, ssGSEA indicated that pathway activation was higher in the C2 subtype compared to the C1 subtype, including HALLMARK_MYC_TARGETS_V2, HALLMARK_MTORC1_SIGNALING, and HALLMARK_G2M_CHECKPOINT, which are associated with cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analyzing tumor immune microenvironments between OCM-related subtypes\u003c/h2\u003e \u003cp\u003eCancer inflammation is closely associated with immune cell infiltration. In this study, we evaluated the immune properties of the C1 and C2 subtypes using transcriptome data analyzed with the \"ESTIMATE\" package. The results revealed that the C2 subtype displayed elevated levels of ImmuneScore (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), StromalScore (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), and ESTIMATEScore (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), while tumor purity was reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Next, we employed multiple algorithms to determine the proportion of immune cells infiltrating both subtypes. The results demonstrated significant differences in tumor-infiltrating immune cells between the two subtypes. Specifically, the C2 subtype showed a higher proportion of tumor-infiltrating immune cells, including CAFs, regulatory T cells (Tregs), and M2 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Moreover, the C2 subtype displayed increased expression of immune checkpoints (ICs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), which play a key role in regulating immune cell function.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Single-cell analysis of OCM in PC and normal tissue\u003c/h2\u003e \u003cp\u003eTo further analyze the molecular features of OCM in PC at single-cell resolution, we downloaded the CRA001160 dataset from the Genome Sequence Archive (GSA) database, which included 24 PC samples and 11 normal pancreatic tissues. After data quality control and preprocessing, Uniform Manifold Approximation and Projection (UMAP) was applied to distinguish different cell subpopulations in the normal and PC groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). These subpopulations were annotated based on the information provided by the original authors. Subsequently, we employed two algorithms to validate the distribution of OCM across different cellular subtypes in PC and normal pancreatic tissues. The results revealed that OCM predominantly exhibited significant differences in three cellular subtypes: endothelial cells, macrophages, and fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB,\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The CopyKat algorithm was used to predict benign and malignant ductal cells. We then compared the differences in OCM activity between benign and malignant ductal cells, finding that tumor cells exhibited more intense OCM activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD,\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Using RF analysis, we predicted the weight of genes involved in OCM and ranked them from highest to lowest. This analysis identified \u003cem\u003eMTHFD1L\u003c/em\u003e as a potential key regulator of OCM in PC (SupFigure 2). In addition, \u003cem\u003eMTHFD1L\u003c/em\u003e was closely associated with tumor-infiltrating immune cells (SupFigure 3\u0026ndash;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 MTHFD1L and immune marker expression in PC tissues\u003c/h2\u003e \u003cp\u003eIn this study, we included 138 patients who underwent surgical resection for PC. Among them, 98 patients (71%) succumbed to the disease, and 89 patients (64.5%) experienced recurrence, with a median follow-up time of 2.4 years. We compared the expression of MTHFD1L in cancerous tissues and adjacent non-cancerous tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In cancerous tissues, 59 patients were negative for MTHFD1L, while 79 were positive.. The expression of FAP (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and α-SMA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) in CAFs was assessed, with 60 patients showing negative expression and 78 patients showing positive expression. Moreover, we quantified CD8\u0026thinsp;+\u0026thinsp;TILs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), FOXP3\u0026thinsp;+\u0026thinsp;TILs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE) and CD206-positive macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). We also evaluated the expression of PD-1 on T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG) and PD-L1 on tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). Of these, 46 patients were PD-1 negative, and 92 patients were PD-L1 positive.Patients were categorized into high and low-expression groups based on the median count of these markers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 MTHFD1L expression was an independent prognostic factor for patients with PC\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a detailed summary of the clinicopathological characteristics of the MTHFD1L-positive and MTHFD1L-negative groups. No significant differences were observed between the two groups in terms of age, gender, tumor size, tumor location, degree of differentiation, CA19-9 levels, bilirubin levels, pT stage, neural invasion, vascular invasion, duodenal invasion, or bile duct invasion. However, the MTHFD1L-positive group was significantly associated with a higher incidence of lymph node metastasis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological features based on MTHFD1L expression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMTHFD1L expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative(n\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive(n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(56\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(56\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(74.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(60.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(25.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(39.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2-4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2.3-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9 (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.5(14.8-514.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156.4(26.5-428.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.5(12-159.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(14.6-164.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(73.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeck,body,and tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(44.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(74.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(25.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(94.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72(91.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuodenal invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edu0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(72.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edu1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(27.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNerve invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ene0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ene1,2,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(61.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiliary invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebi0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(93.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(86.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebi1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eva0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(81.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(86.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eva1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(56.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(44.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKaplan-Meier analysis based on MTHFD1L expression (positive vs. negative) revealed that MTHFD1L-positive patients experienced a significantly poorer prognosis compared to MTHFD1L-negative patients, both in terms of OS (Log-rank, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and RFS (Log-rank, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Furthermore, TCGA data analysis revealed higher MTHFD1L expression was similarly associated with poorer DFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), although no significant correlation with OS was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnivariate analysis showed that positive expression of FAP in CAFs, a high abundance of CD206\u0026thinsp;+\u0026thinsp;M2 macrophages, and positive expression of MTHFD1L were associated with poorer OS. In the subsequent multivariate analysis, MTHFD1L positivity (hazard ratio [HR], 1.811; 95% confidence interval [CI], 1.088\u0026ndash;3.015; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) emerged as an independent prognostic factor for OS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of overall survival according to MTHFD1L expression in patients with pancreatic cancer.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTHFD1L1 positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.908(1.256\u0026ndash;2.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.811(1.088\u0026ndash;3.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.184(0.773\u0026ndash;1.812)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeck,body,and tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.827(0.549\u0026ndash;1.247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.290(0.860\u0026ndash;1.936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.552(0.775\u0026ndash;3.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026gt;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.963(0.643\u0026ndash;1.440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size \u0026gt;30 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.375(0.914\u0026ndash;2.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9\u0026gt;37 U/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.070(0.708\u0026ndash;1.618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin\u0026gt;34.2 umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.868(0.580\u0026ndash;1.298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAP positive in CAFs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.652(1.077\u0026ndash;2.533)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-SMA negative in CAFs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.733(0.493\u0026ndash;1.091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-1 positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.713(0.473\u0026ndash;1.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDL-1 positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.817(0.545\u0026ndash;1.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8 low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.690(0.460\u0026ndash;1.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOXP3 high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.216(0.815\u0026ndash;1.814)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD206 high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.506(1.001\u0026ndash;2.267)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3.7 The Association between MTHFD1L expression and the abundance of tumor infiltrates immune cells and immune checkpoint markers\u003c/p\u003e \u003cp\u003eMTHFD1L positivity was significantly correlated with FAP-positive expression in CAFs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). However, no significant correlation was observed between MTHFD1L expression and α-SMA expression in CAFs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.861; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). No significant association was observed between MTHFD1L expression and the abundance of CD8\u0026thinsp;+\u0026thinsp;TILs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.787; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) However, in the MTHFD1L-positive group, the abundance of Foxp3\u0026thinsp;+\u0026thinsp;TILs was significantly higher compared to the MTHFD1L-negative group, indicating a significant correlation between MTHFD1L expression and Foxp3\u0026thinsp;+\u0026thinsp;TIL count (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Elevated MTHFD1L expression showed a significant correlation with the high abundance group of CD206\u0026thinsp;+\u0026thinsp;M2 macrophages (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next investigated the correlation between MTHFD1L expression and the expression levels of PD-1, and PD-L1. Furthermore, the expression of PD-1 in immune cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.808; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF) and PD-L1 in cancer cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.362; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG) did not show a significant correlation with MTHFD1L expression.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eMetabolic reprogramming is a hallmark of cancer, enabling tumors to meet the substrate and energy demands of rapid growth by altering the efficiency and utilization of nutrients such as glucose, amino acids, and lipids\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Insights into meaningful subtypes, metabolic signaling, and immune characteristics have provided valuable perspectives for cancer immunotherapy. In this study, we examined 136 subjects using a targeted metabolomics platform to validate metabolic disturbances in PC. The results revealed significant one-carbon metabolic reprogramming in PC patients. The metabolic landscape encompasses genes, proteins, metabolites, and their interactions. To comprehensively map the metabolic landscape of carbon metabolism in PC, we analyzed 930 PC patients with complete follow-up and transcriptomic data from four public databases. These patients were classified into two metabolism-related subtypes, C1 and C2, which exhibited significant differences in prognosis and tumor microenvironment. Single-cell analysis further highlighted differences in carbon metabolism among normal cells, cancer cells, and the tumor microenvironment. These metabolic changes are primarily driven by the abnormal expression and activation of key metabolic enzymes. We identified \u003cem\u003eMTHFD1L\u003c/em\u003e as the core gene driving metabolic reprogramming in PC. Multi-omics analysis across multiple cohorts confirmed \u003cem\u003eMTHFD1L\u003c/em\u003e as an independent poor prognostic factor in PC, closely associated with tumor-infiltrating immune cells. In PC, \u003cem\u003eMTHFD1L\u003c/em\u003e may serve as a key tumor-driven metabolic gene, regulating metabolic reprogramming and reshaping the tumor microenvironment.\u003c/p\u003e \u003cp\u003eThe molecular phenotype accurately reflects the pathological features of a disease\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In exploring the molecular phenotype of PC, we identified significant disruptions in carbon metabolism. OCM encompasses the folate cycle, methionine cycle, and transsulfuration pathway\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Through these pathways, one-carbon units generate and utilize molecules such as pyrimidine, thymidylate, S-adenosylmethionine, and glutathione, which regulate tumor growth and proliferation\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This study found that serum glutamate and glutamine levels were significantly elevated in PC patients. High expression of glutamine synthetase in tumor cells drives metabolic reprogramming, promotes glutamine synthesis, and enhances nucleotide synthesis, thereby facilitating efficient DNA damage repair\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Glutamine metabolism in the tumor microenvironment not only supports tumor cell growth but also impairs the anti-tumor activity of immune cells. Early detection of cancer can significantly improve the prognosis of PC\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In this study, a diagnostic model based on serum carbon metabolites demonstrated strong diagnostic performance and the ability to identify early-stage PC. However, as this was a single-center study, the reproducibility and scalability of the diagnostic model need to be further validated in multi-center, large-sample cohorts.\u003c/p\u003e \u003cp\u003eIn the present study, transcriptomic data were used to characterize the carbon metabolic landscape in PC. Based on the expression of carbon metabolism genes, 930 patients were divided into two carbon metabolism-related subtypes. The prognosis of these subtypes differed significantly. Interestingly, OCM genes were highly expressed in both subtypes, but the OCM score was higher in the C2 subtype, which also had a worse prognosis. Transcriptomic data confirmed that reprogramming of carbon metabolism is closely associated with the prognosis of PC. By establishing metabolic subtypes linked to clinical outcomes based on metabolic heterogeneity, this study provides insights into targeting unique metabolic vulnerabilities in tumors. We further analyzed the relationship between carbon metabolic reprogramming and the tumor microenvironment. The results showed that the C2 subtype exhibited elevated levels of ImmuneScore, StromalScore, and ESTIMATEScore. Tumor-infiltrating immune cells, such as CAFs and TAMs, were more abundant in the C2 subtype, which also showed stronger associations with immune checkpoints. These findings highlight the critical role of carbon metabolic reprogramming in reshaping the immune microenvironment in PC. Next, we explored differences in OCM across different cell types at single-cell resolution. The results revealed that one-carbon metabolic activity was significantly higher in tumor cells and tumor stromal cells compared to normal tissue cells, particularly in endothelial cells, macrophages, and fibroblasts. Since PC is predominantly composed of pancreatic ductal adenocarcinoma (PDAC), we used the CopyKat algorithm to separately analyze ductal cells, enabling the prediction of benign and malignant ductal populations. By comparing carbon metabolism activity between benign and malignant ductal cells, we underscored the importance of OCM in the development and progression of PC.\u003c/p\u003e \u003cp\u003eThe use of drugs targeting one-carbon metabolic pathways, such as methotrexate and 5-fluorouracil, has significantly improved tumor prognosis\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The metabolic landscape is driven by key tumor-related metabolic genes. Recently, targeting one-carbon metabolizing enzymes has emerged as a novel anti-tumor therapeutic strategy\u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Transcriptomic data analysis revealed that \u003cem\u003eMTHFD1L\u003c/em\u003e plays a significant role in one-carbon metabolic pathways, is associated with poor prognosis, and is closely linked to tumor-infiltrating lymphocytes. \u003cem\u003eMTHFD1L\u003c/em\u003e may be a key gene driving carbon metabolic reprogramming in PC\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMTHFD1L\u003c/em\u003e is reportedly involved in the synthesis of tetrahydrofolate (THF) in mitochondria, which is critical for the \u003cem\u003ede novo\u003c/em\u003e synthesis of purines and thymidylate, as well as the regeneration of methionine from homocysteine\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMTHFD1L\u003c/em\u003e regulates macrophage polarization by mediating mitochondrial autophagy\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Although \u003cem\u003eMTHFD1L\u003c/em\u003e has been less commonly reported in cancer, it has been shown to promote tumor invasion and metastasis in esophageal squamous cell carcinoma by activating the ERK5 signaling pathway\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. To further investigate the role of \u003cem\u003eMTHFD1L\u003c/em\u003e in PC, we examined its protein expression in 138 PC tissue samples. The results confirmed that \u003cem\u003eMTHFD1L\u003c/em\u003e is an independent poor prognostic factor for PC. The abundance of CAFs, TAMs and Tregs was elevated in the MTHFD1L high-expression group. FAP and α-SMA are markers of myofibroblastic CAFs (myCAFs) and inflammatory CAFs (iCAFs), respectively. Depletion of FAP\u0026thinsp;+\u0026thinsp;CAFs has been shown to improve survival in PC\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, as FAP\u0026thinsp;+\u0026thinsp;CAFs regulate cancer-associated pathways and promote the accumulation of Tregs\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Tumor-associated macrophages are highly infiltrated in malignant solid tumors, where they promote tumor progression and shape the immune microenvironment\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Macrophages are positively correlated with the incidence of cachexia in PC, and their inhibition can delay weight loss and cachexia development in mouse models\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Targeting TAMs can transform \"cold\" tumors into \"hot\" tumors by enhancing T cell function and synergistically improving the efficacy of PD-1 inhibitors\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Foxp3-positive TILs are Tregs with immunosuppressive functions that inhibit the proliferation and activation of CD8-positive TILs, which are responsible for killing cancer cells through apoptosis\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. However, \u003cem\u003eMTHFD1L\u003c/em\u003e was not strongly associated with immune checkpoints. ICIs are currently the cornerstone of tumor immunotherapy, but their efficacy in most PC patients remains unsatisfactory\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Reversing the immunosuppressive state and reshaping the immune microenvironment may be key to improving immunotherapy outcomes in PC\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Therefore, identifying targets that regulate both PC cells and the TME holds promising therapeutic potential.\u003c/p\u003e \u003cp\u003eThis study has several strengths: it is the first to use multi-omics analysis to explore the one-carbon metabolic landscape and the immune microenvironment in PC, identifying \u003cem\u003eMTHFD1L\u003c/em\u003e as a key tumor-driving metabolic gene and investigating its regulatory role in the tumor microenvironment. Besides, a diagnostic panel was developed, offering a theoretical foundation for early PC screening. However, there are limitations: as a single-center study, the findings require further validation in larger, multicenter cohorts. Moreover, the regulatory relationship between \u003cem\u003eMTHFD1L\u003c/em\u003e and the immune microenvironment in PC needs to be further confirmed through cell-based experiments.\u003c/p\u003e \u003cp\u003eIn summary, PC undergoes reprogramming of OCM. \u003cem\u003eMTHFD1L\u003c/em\u003e may function as an oncogene-driven metabolic gene in carbon metabolism, influencing the prognosis of PC by regulating CAFs and TAMs. Targeting the one-carbon metabolic pathway or MTHFD1L represents a promising therapeutic strategy.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eEthics statement\u003c/p\u003e\n\u003cp\u003eAll research procedures involving human participants in this study were conducted in accordance with the principles outlined in the Declaration of Helsinki. For the collection of human blood samples, written informed consent was obtained from all patients, and the study protocol was approved by the Ethics Review Committee of the Affiliated Hospital of North Sichuan Medical College (Approval No. 2024006). The human samples utilized in this study primarily originated from previously isolated specimens obtained in prior research, which had already received ethical approval. In compliance with national laws and institutional requirements, participants or their legal guardians/close relatives were not required to provide written informed consent.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eAs part of the author contributions: DD,SW and YQ conducted the experiments, statistical analysis, and visualization, and wrote the initial draft of the manuscript. CZ contributed to the review and revision of the manuscript. SM organized the data and reviewed and revised the manuscript. ZS, SL, JL, and LY organized the data and reviewed and revised the manuscript. PY conceived and designed the study and reviewed and revised the manuscript. All authors participated in writing the manuscript and provided final approval of the version to be submitted and published.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (No. 82300737),the Nanchong City Science and Technology Bureau and School Strategic Cooperation Project (grant no. 22SXQT0110) and the Doctoral Start-up Fund of The Affiliated Hospital of North Sichuan Medical College (grant no.2023GC010).\u003c/p\u003e\n\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHalbrook CJ, Lyssiotis CA, Pasca di Magliano M, Maitra A. Pancreatic cancer: Advances and challenges. 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Cell Commun Signal Nov. 2021;24(1):117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12964-021-00789-w\u003c/span\u003e\u003cspan address=\"10.1186/s12964-021-00789-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pancreatic cancer, multiomics, Metabolic reprogramming, one-carbon metabolism, tumor microenvironment, MTHFD1L","lastPublishedDoi":"10.21203/rs.3.rs-6404670/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6404670/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND \u003c/strong\u003eThe role of oncogene-driven metabolic reprogramming in pancreatic cancer (PC) remains unclear. This study explored the interplay between one-carbon metabolism (OCM), driver genes, and the tumor microenvironment (TME) in PC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS \u003c/strong\u003eTargeted metabolomics analyzed 136 PC serum samples. Transcriptomic and OCM gene data from 930 PC patients were obtained from public databases. Non-negative matrix factorization (NMF) clustering classified metabolic subtypes. Single-cell analysis deciphered OCM features in the TME. Immunohistochemistry assessed MTHFD1L expression, cancer-associated fibroblast (CAF) markers (FAP, α-SMA), immune cells (CD8+/Foxp3+ TILs, CD206+ TAMs), and PD-1/PD-L1 in 138 tissue samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS \u003c/strong\u003eTargeted metabolomics identified altered amino acid metabolism (73 metabolites). NMF clustering stratified patients into C1/C2 subtypes with distinct prognoses and TME characteristics (p\u0026lt;0.05). Single-cell analysis revealed OCM dysregulation in cancer cells, macrophages, and fibroblasts. MTHFD1L emerged as a core driver of metabolic reprogramming, correlating with poor overall survival (OS, p=0.005) and disease-free survival (DFS, p=0.006). High MTHFD1L expression was linked to lymph node metastasis and positively associated with FAP in CAFs (p\u0026lt;0.05), CD206+ TAMs (p\u0026lt;0.001), and Foxp3+ TIL infiltration (p\u0026lt;0.05). Multivariate analysis confirmed MTHFD1L as an independent prognostic factor (p=0.022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSION \u003c/strong\u003eOCM reprogramming is a hallmark of PC. MTHFD1L drives oncogenic metabolism and influences prognosis by modulating CAFs, TAMs, and Tregs. Targeting OCM or MTHFD1L may offer therapeutic potential.\u003c/p\u003e","manuscriptTitle":"One-carbon metabolic reprogramming and its relationship with tumor-infiltrating lymphocytes and Immune checkpoint in Pancreatic cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:35:28","doi":"10.21203/rs.3.rs-6404670/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a517cbdf-9583-43e8-bac1-e9094466c817","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T23:04:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 03:35:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6404670","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6404670","identity":"rs-6404670","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}