{"paper_id":"0f5441c9-4219-4789-a65e-305f3e2d6d85","body_text":"The Regulatory Role of Brown Adipocyte - Related Gene CALU in the Progression, Immune Microenvironment and Treatment Response of 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 The Regulatory Role of Brown Adipocyte - Related Gene CALU in the Progression, Immune Microenvironment and Treatment Response of Pancreatic Cancer Fang Wu, Xufan Cai, Zhenyuan Qian, Guangyuan Song, Xiao Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6709366/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 Purpose Pancreatic Ductal Adenocarcinoma (PDAC) remains a lethal malignancy with limited therapeutic options. This study aimed to identify brown adipocyte-related genes (BARGs) influencing PDAC prognosis and explore their roles in the tumor microenvironment (TME) and immunotherapy response. Methods Transcriptomic and proteomic data from TCGA, GEO, ICGC, and CPTAC databases were analyzed to screen prognostic BARGs. Immune infiltration, immunotherapy prediction (via TIDE, IRnet, and TCIA), and drug sensitivity analyses were conducted. Single-cell RNA sequencing (CRA001160 dataset) and experimental validation (qPCR in pancreatic cancer cell lines) were performed to validate findings. Results CALU emerged as a core prognostic gene, significantly overexpressed in PDAC tissues and correlated with advanced tumor grade. High CALU expression was linked to stromal cell activation (e.g., cancer-associated fibroblasts, M1 macrophages) and suppressed T-cell infiltration, indicating immunosuppressive TME remodeling. CALU predicted resistance to CTLA4 inhibitors but showed no significant association with PD1 blockade. Drug sensitivity analysis revealed correlations between CALU and chemotherapeutic agents (e.g., TAK-715, LGK974). Single-cell analysis localized CALU to malignant and stromal cells, highlighting its role in PERIOSTIN-mediated fibroblast-malignant cell communication. Experimental validation confirmed elevated CALU expression in pancreatic cancer cell lines compared to normal cells. Conclusion CALU is a critical regulator of PDAC progression, influencing stromal-TME interactions and immune evasion. It serves as a potential prognostic biomarker and therapeutic target, offering insights into combination strategies targeting stromal-immune crosstalk in PDAC. Pancreatic ductal adenocarcinoma (PDAC) CALU Tumor microenvironment Immunotherapy Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Pancreatic Ductal Adenocarcinoma (PDAC) is the most common type of pancreatic cancer and among the most aggressive and deadly cancers. Its incidence has been increasing in recent years, and it is expected to rank as the second leading cause of cancer-related deaths by 2030[ 1 ]. Despite significant advances in cancer treatment, the five-year survival rate for PDAC remains extremely low, primarily due to late-stage diagnosis and resistance to current therapies[ 2 ]. The early symptoms of PDAC are often non-specific, resulting in most patients being diagnosed at an advanced stage, when the optimal window for surgical resection has already passed[ 3 ]. Therefore, early diagnosis and screening are critical to improving the prognosis of PDAC. In recent years, with in-depth research into the biological characteristics of PDAC, new biomarkers and early detection technologies are being developed[ 4 ]. In terms of treatment, although chemotherapy and radiotherapy remain the standard therapeutic options, PDAC exhibits a low response rate to these therapies[ 2 ]. Additionally, the tumor microenvironment (TME) of PDAC plays a significant role in disease progression and treatment resistance, particularly due to the heterogeneity and functional diversity of cancer-associated fibroblasts (CAFs) and pancreatic stellate cells (PSCs)[ 1 , 5 ]. These cells promote tumor aggressiveness and drug resistance by regulating immune suppression and metabolic reprogramming[ 6 , 7 ]. In summary, the high lethality and therapeutic challenges of PDAC underscore the importance of early diagnosis and screening. Future research should focus on developing more effective early detection technologies, gaining a deeper understanding of the complexity of the tumor microenvironment, and exploring novel therapeutic strategies to improve patient survival rates and quality of life[ 2 – 4 , 8 ]. The relationship between obesity and cancer has garnered significant attention in recent research. Obesity is not only a major risk factor for metabolic diseases such as type 2 diabetes and cardiovascular disorders but is also closely linked to the development and progression of various cancers[ 9 , 10 ]. In obesity, the excessive accumulation and dysfunction of white adipose tissue (WAT) disrupt systemic energy metabolism, thereby promoting tumor cell proliferation and invasion[ 9 , 10 ]. In contrast, brown adipose tissue (BAT), a key energy-expending tissue, regulates systemic metabolism through non-shivering thermogenesis, and its activity is inversely correlated with tumor growth[ 11 ]. In recent years, the role of the tumor microenvironment (TME) in the progression and treatment of pancreatic cancer has garnered significant attention. The TME comprises diverse cell types, including immune cells, fibroblasts, vascular endothelial cells, and adipocytes, which collectively regulate tumor growth, invasion, and metastasis through complex interactions[ 12 , 13 ]. Brown adipose tissue (BAT), a specialized type of adipose tissue, primarily functions to maintain body temperature via non-shivering thermogenesis. Unlike white adipose tissue (WAT), BAT is rich in mitochondria and expresses high levels of uncoupling protein 1 (UCP1), enabling the release of energy as heat[ 14 ]. Activation of BAT through cold exposure significantly reduces blood glucose levels, thereby suppressing glycolytic metabolism in pancreatic cancer cells and inhibiting tumor growth[ 14 ]. Additionally, BAT activation limits glucose availability in the TME, restricting energy acquisition by cancer cells and further impairing their proliferative and invasive capabilities[ 15 ]. Recent studies have revealed that BAT not only plays a critical role in energy metabolism but may also significantly influence the tumor microenvironment. Upon cold exposure, BAT can also improve inflammatory states by modulating macrophage phenotypes, such as shifting from pro-inflammatory M1 to anti-inflammatory M2 types[ 14 ]. Additionally, BAT regulates the function of immune cells (e.g., T cells, B cells, and macrophages) within adipose tissue through the secretion of various factors, such as adiponectin and leptin, thereby influencing systemic inflammation[ 16 , 17 ]. These findings provide novel insights into pancreatic cancer treatment, suggesting that BAT activation to remodel the TME may represent a promising therapeutic strategy. CALU (Calumenin) is a calcium-binding protein belonging to the CREC (Cab45, Reticulocalbin, ERC-45, Calumenin) family, widely expressed in various cell types, particularly in the endoplasmic reticulum (ER) and Golgi apparatus[ 18 ]. Its primary functions include regulating calcium ion homeostasis, facilitating protein folding and secretion, and modulating cell signaling pathways[ 19 , 20 ]. The CALU gene exhibits significant upregulation in various cancers and is closely associated with the regulation of the tumor microenvironment (TME). Studies have shown that CALU is highly expressed in multiple tumor types, including breast cancer (BRCA), kidney renal papillary cell carcinoma (KIRP), hepatocellular carcinoma (LIHC), head and neck squamous cell carcinoma (HNSC), and lower-grade glioma (LGG), with its high expression correlating with poor prognosis[ 21 ]. CALU promotes tumor cell migration and invasion by regulating the epithelial-mesenchymal transition (EMT) process[ 21 ]. Additionally, CALU expression positively correlates with the infiltration of cancer-associated fibroblasts (CAFs), suggesting its critical role in the interaction between CAFs and malignant cells[ 21 ]. In lung cancer, CALU promotes tumor progression by mediating CAF differentiation through miR-21 [ 22 ]. These findings highlight the pivotal role of CALU in the tumor microenvironment, indicating its potential as a therapeutic target in cancer treatment. In this study, we integrated brown adipose tissue-related genes with pancreatic cancer by collecting 101 brown adipocyte-related genes (BARGs) to identify potential genes influencing pancreatic cancer prognosis. Furthermore, we explored the impact of core genes on the pancreatic cancer immune microenvironment and their implications for immunotherapy response. Materials and Methods Data collection and preprocessing We obtained pancreatic cancer transcripts per kilobase million (TPM) data from the UCSC database ( https://xenabrowser.net/datapages/ ), including 347 samples (178 tumor and 169 normal) from TCGA and GTEx. To strengthen our analysis, we also acquired datasets from E_MTAB_6134 ( https://www.omicsdi.org/dataset/biostudies-arrayexpress/E-MTAB-6134 ), GSE21501, GSE28735, GSE57495, GSE62452, GSE71729, GSE78229, GSE79668, GSE85916, ICGC_PAAD_AU_array ( https://dcc.icgc.org/ ), ICGC_PAAD_AU_seq( https://dcc.icgc.org/ ), and ICGC_PAAD_CA_seq( https://dcc.icgc.org/ ). Additionally, we incorporated proteomic data from PAAD_CPTAC ( https://proteomic.datacommons.cancer.gov/pdc/study/PDC000270 ) to complement transcriptomic findings at the protein level. For single-cell analysis, we utilized the CRA001160 dataset ( https://ngdc.cncb.ac.cn/gsa/browse/CRA001160 ), comprising 35 samples (11 normal and 24 tumor). We obtained immunohistochemical images and subcellular localization information for CALU in pancreatic cancer from the Human Protein Atlas (HPA) database ( https://www.proteinatlas.org/ ), specifically focusing on CALU-positive and medium-expression samples. Additionally, we retrieved CALU expression levels across 46 pancreatic cancer cell lines from the HPA database. To address batch effects and minimize downstream analysis errors, we merged GSE28735, GSE62452, and GSE57495 into a combined dataset named \"3GEO\" using the \"sva\" package. From prior studies, we obtained 101 brown adipocyte-related genes (BARGs)[ 23 , 24 ]. Differential analysis was performed using the \"limma\" package[ 25 ] to screen candidate core genes and explore their potential downstream mechanisms. Public datasets do not require Ethical Review Committee approval or informed consent. Survival Analysis and Random Survival Forest Construction We employed the coxph function from the survival package to conduct univariate survival analysis and the ggsurvplot() function from the survminer package to visualize survival curves. Additionally, the randomForestSRC package was used to perform survival random forest analysis, and the top 10 genes ranked by Variable Importance were selected. Immune infiltration and immunotherapy prediction Based on the median expression of CALU across multiple datasets (TCGA-PAAD, E_MTAB_6134, GSE21501, GSE28735, GSE57495, GSE62452, GSE71729, GSE78229, GSE79668, GSE85916, ICGC_PAAD_AU_array, ICGC_PAAD_AU_seq, and ICGC_PAAD_CA_seq), samples were divided into high- and low-expression groups. Immune infiltration levels were assessed using seven algorithms (CIBERSORT, TIMER, xCell, MCPcounter, ESTIMATE, EPIC, and quantTIseq), and results were ranked by CALU expression levels. Cell types showing significant expression differences were identified via the Wilcoxon test, and their correlation coefficients with CALU expression were calculated along with statistical significance. EaSIeR is a systematic tool that integrates bulk RNA-seq data with multi-source prior knowledge to predict immune response characteristics and therapeutic outcomes in the tumor microenvironment through multi-task learning[ 26 ]. Its immune signature scores include cytolytic activity (CYT), chemokine signature (chemokines), T cell-inflamed signature (T cell_inflamed), tertiary lymphoid structure signature (TLS), and IFNy signature (IFNy). We applied its algorithm to predict CALU-related immune features and performed t-tests to assess statistical significance. The Cancer Immunome Atlas (TCIA, https://tcia.at/home ) provides comprehensive data on T-cell receptor (TCR) and B-cell receptor (BCR) sequencing, as well as immune cell types, phenotypes, functions, and interactions, supporting the development of innovative cancer immunotherapy strategies[ 27 ]. We extracted immunophenotype scores (IPS) related to pancreatic cancer from TCIA and compared the results using the median expression levels of CALU as the threshold. The Tumor Immune Dysfunction and Exclusion (TIDE, http://tide.dfci.harvard.edu/ ) database specializes in studying tumor immune dysfunction and exclusion. Its TIDE algorithm predicts responses to immune checkpoint inhibitors (e.g., PD-1/PD-L1 inhibitors) by evaluating immune cell infiltration and tumor immune escape mechanisms in the tumor microenvironment[ 28 ]. We normalized TCGA RNA-seq data (TPM format) from tumor samples and uploaded it to TIDE. The association between the target gene and immune prediction was analyzed using chi-square tests and t-tests. IRnet is a deep learning framework based on graph neural networks (GNNs), specifically designed to predict patient responses to immune checkpoint inhibitor (ICI) therapy. Its core feature involves transforming gene features into pathway features using biological knowledge, enhancing prediction robustness and reducing noise interference. We applied IRnet to predict treatment outcomes for CTLA4 and PD1 inhibitors and performed chi-square analysis to assess the significance of differences between high- and low-CALU expression groups. Functional gene set enrichment We performed differential expression analysis using the limma package[ 25 ] by comparing the top 30% and bottom 30% of samples based on CALU expression levels, ranking the results by log2 Fold Change (log2FC). Subsequently, gene set enrichment analysis (GSEA) was conducted using the GSEA function from the clusterProfiler package[ 29 ], leveraging hallmark gene sets and KEGG metabolic pathways. The Normalized Enrichment Score (NES) and statistical significance of each gene set were visualized, enabling the identification of biological processes or metabolic pathways significantly enriched between high- and low-CALU expression groups. To identify and visualize potential genes associated with CALU, we performed differential expression analysis between high- and low-CALU expression groups after z-score normalization across multiple datasets. We identified significantly upregulated and downregulated genes in each dataset and calculated their frequency of occurrence. The hplot1 function from the fromto package was used to generate a heatmap, highlighting high-frequency genes functionally related to CALU. CancerSEA (Cancer Single Cell Expression Atlas) is a comprehensive database providing single-cell expression data across 14 functional states (e.g., stemness, invasion, metastasis, proliferation, EMT, angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation, and quiescence) and their associated genes[ 30 ]. Using the GSVA R package, we computed combined z-scores for these gene sets and standardized the results with the scale function. Pearson correlation analysis was then performed to assess the relationship between CALU and the scores of each functional state. Drug sensitivity prediction In this study, we utilized the Genomics of Drug Sensitivity in Cancer (GDSC) database, which includes two versions: GDSC V1, encompassing 987 cell lines and 367 compounds, and GDSC V2, comprising 809 cell lines and 198 compounds. We employed the pRRophetic R package to predict the half-maximal inhibitory concentration (IC50) as an indicator of chemotherapy response[ 31 ]. Spearman correlation analysis was performed using the cor.test function to evaluate the relationship between IC50 values and gene expression levels. Singlecell RNAseq analysis TISCH2 is a database focused on single-cell transcriptomic data of the tumor microenvironment (TME), designed to analyze and explore gene expression within the TME[ 32 ]. We obtained the pancreatic cancer single-cell dataset (CRA001160) from this database. Following quality control (using the PercentageFeatureSet function), data normalization (NormalizeData), and clustering analysis (FindNeighbors and FindClusters), we applied Uniform Manifold Approximation and Projection (UMAP) to visualize gene expression and cell clustering. This process identified 12 distinct cell types: Acinar, B, CD8Tex, DC, Ductal, Endocrine, Endothelial, Fibroblasts, Malignant, Mono/Macro, Plasma, and Stellate. Additionally, the CellChat package[ 33 ] was employed to analyze cell-cell communication. We classified malignant cells into CALU + Malignant and CALU- Malignant based on CALU expression. The computeCommunProb function was then used to calculate communication probabilities between cell types. After filtering out low-probability interactions, the netVisual_circle and netVisual_heatmap functions were applied to visualize the communication networks. Cell culture The human pancreatic cancer cell lines HPDE6-C7, SW1990, and AsPC-1, along with the human normal pancreatic cell line MIA PaCa-2, were obtained from ATCC. These cells were cultured in DMEM supplemented with 10% fetal bovine serum (FBS) and maintained in a humidified environment at 37°C with 5% CO2. RNA extraction and Quantitative Real-Time PCR (qRT-PCR) Total RNA was extracted using the FastPure Cell/Tissue Total RNA Isolation Kit V2 (Vazyme Biotech, Nanjing, China), and cDNA synthesis was performed with the HiScriptIII RT SuperMix for qPCR (+ gDNA wiper) (Vazyme Biotech, Nanjing, China). Quantitative RT-PCR was conducted using SYBR Green Mix (Vazyme Biotech, Nanjing, China) in a 20 µl reaction volume on an Applied Biosystems® 7500 Real-Time PCR System. Relative gene expression was calculated using the 2-ΔΔCT method. Primers were designed and synthesized by Repobio (Hangzhou, China): CALU-F: AATAGACGCGGATAAAGATGGGT; CALU-R: GCCATTGGTTTTCAACATTGTCA Statistic analysis GraphPad prism 9.0 was adopted for analyzing the data, which were stated as mean ± SD, by t-test or oneway ANOVA. A P value less than 0.05 was defined there is a significant difference. We utilized Python 3.8.18 to execute the IRnet algorithm and imported the results into R version 4.3 for subsequent analysis. Differential expression analysis, immune prediction, and pathway analysis were also performed using R version 4.3. Results Prognostic Significance of Brown Adipocyte-Related Genes (BARGs) in TCGA and GEO Datasets To evaluate the prognostic value of brown adipocyte-related genes (BARGs), we performed univariate Cox analysis to identify BARGs associated with pancreatic cancer prognosis (Fig. 1 A-D). A Venn diagram was used to visualize overlapping prognostic genes across four datasets, revealing 29 genes significantly correlated with overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) (Fig. 1 E). These genes include SFN, ACTB, CD59, PPP1R1A, CTSC, DAG1, LTBP1, ENO3, IDE, ERP29, SDCBP, HEBP1, CALU, SLURP1, SERPINB5, IL1RN, OAF, LAMB2, EFNB1, MCFD2, TFPI, KLK7, GBP2, A2ML1, SRPX2, SERPINB2, TFRC, C1RL, and LY6D. Subsequently, we integrated three GEO datasets (GSE28735, GSE62452, and GSE57495) after batch effect removal and conducted univariate Cox analysis and random survival forest analysis to further screen prognostic genes (Fig. 1 F, I and Supplement 1A). CALU and SRPX2 consistently demonstrated significant prognostic value across these datasets. Log-rank survival analysis in TCGA-PAAD confirmed their robust prognostic significance (Fig. 1 H and Supplement 1B, C). Notably, CALU and SRPX2 also exhibited a strong correlation with each other (Fig. 1 J) in 3GEO (R = 0.68, p < 2.2e-16) and TCGA-PAAD (R = 0.77, p < 2.2e-16). CALU is highly expressed in tumor tissues and is associated with Grade staging in pancreatic cancer CALU primarily localizes to the endoplasmic reticulum in pancreatic cancer, as demonstrated by immunohistochemical data from the Human Protein Atlas (HPA) (Fig. 2 A-C). CALU expression was markedly higher in tumor tissues than in normal tissues across multiple datasets, including TCGA & GTEx (Fig. 2 D), GSE28735 (Fig. 2 E), GSE62452 (Fig. 2 F), and GSE71729 (Fig. 2 G). Moreover, the expression of CALU demonstrated a progressive increase with advancing tumor grades in pancreatic adenocarcinoma (PAAD) across multiple datasets. A significant increase in CALU expression was observed with increasing tumor grades in TCGA-PAAD (Fig. 2 H), ICGC_PAAD_AU (Fig. 2 I), GSE78229 (Fig. 2 J) and GSE62452 (Fig. 2 K). Furthermore, at the proteomic level (PAAD_OPTAC dataset), CALU expression aligns with its mRNA levels, with CALU protein significantly higher in tumor tissues compared to normal tissues (Fig. 2 L). The schematic diagram illustrates the subcellular localization of CALU, primarily highlighting its endoplasmic reticulum distribution. (D-E) The raincloud plot demonstrates the expression of CALU in multiple datasets (TCGA, GTEx, GSE28735, GSE62452, and GSE71729). (H-I) The boxplot illustrates the relationship between CALU expression levels and tumor Grade in the TCGA-PAAD, ICGC_PAAD_AU, GSE78229, and GSE62452 datasets. (L) CALU expression levels in the proteomic dataset PAAD_CPTAC. Correlation of CALU expression with immune cell infiltrates To enhance the reliability of our predictions, we employed multiple datasets and various immune infiltration algorithms to calculate and compare immune infiltration levels. Two heatmaps were generated to illustrate the correlation between CALU expression and immune cell infiltration, as well as expression differences across cell types (Fig. 3 A, B). As shown in Fig. 3 A, CALU exhibits strong correlations with several immune cell types, including dendritic cells, endothelial cells, macrophages (MO), macrophages, fibroblasts, and neutrophils. Most of these cell types belong to stromal cell populations. Correspondingly, CALU also shows a significant correlation with StromalScore, suggesting a potential link between CALU and stromal cells within the immune microenvironment. In Fig. 3 C, we further visualized all cell types with correlation coefficients greater than 0.3 and p-values less than 0.001. Among the 25 cell types or immune scores analyzed, monocyte-derived macrophages accounted for nine. The cell types with the highest positive correlation coefficients were cancer-associated fibroblasts (EPIC: R = 0.76, p < 2.2e-16; MCPCOUNTER: R = 0.76, p < 2.2e-16) and M1 macrophages (QUANTISEQ: R = 0.56, p < 3.6e-16) ( Fig. 3 D). Conversely, the cell types with the strongest negative correlations were T cell CD4 + central memory (XCELL: R = -0.46, p = 3e-09), T cell CD8+ (EPIC: R = -0.35, p = 2.1e-06), and T cell CD4 + Th1 (XCELL: R = -0.37, p = 4.4e-07) (Fig. 3 D). Based on these findings, we hypothesize that CALU may promote stromal cell activity while inhibiting T cell infiltration, thereby suppressing anti-tumor immune responses. CALU expression levels are associated with immunotherapy outcomes Using IRnet, we predicted immunotherapy outcomes for TCGA-PAAD samples and found that the high CALU expression group exhibited a higher resistance rate to CTLA4 inhibitors compared to the low-expression group, with a chi-square p-value of 0.003(Fig. 4 A). In contrast, the prediction for PD1 inhibitors did not reach statistical significance ( p = 0.051) (Fig. 4 B). In the TIGER immunotherapy prediction results, the high CALU expression group also showed a higher resistance rate ( p < 0.001, Fig. 4 C). Additionally, TIGER provides predictions for several immune parameters, revealing that samples with high CALU expression exhibit elevated levels of CAF, CD274, Exclusion, IFNG, Merck18, MSI.Expr.Sig, and TIDE scores(Fig. 4 D). These results are consistent with findings from the 3GEO dataset (Supplement E). The TICA database also supports these findings, showing that the high CALU expression group has significantly higher scores in the ips_ctla4_pos_pd1_neg category compared to the control group. This further suggests that high CALU expression is associated with resistance to CTLA4-based therapy. In the EaSIeR analysis, the high CALU expression group exhibited higher IFNy, chemokines, T cell_inflamed, and CYT scores, while no significant difference was observed in TLS scores between the two groups(Fig. 4 F-J). Predicting potential chemotherapeutic or targeted-therapeutic drugs sensitive to hnRNPA3 Using the pRRophetic R package, we predicted the half-maximal inhibitory concentration (IC50) for each sample in TCGA-PAAD based on drug sensitivity data from GDSC1 and GDSC2, followed by correlation analysis with CALU expression (Fig. 5 A, B). In GDSC1, TAK-715 showed the highest positive correlation with CALU (R = 0.55, p = 3.9e-15) (Fig. 5 A). In GDSC2, LGK974 exhibited the strongest positive correlation (R = 0.58, p < 2.2e-16) (Fig. 5 B). To visually represent the drug molecules associated with CALU in GDSC1 and GDSC2, we constructed a network graph and annotated the signaling pathways potentially targeted by these molecules (Fig. 5 C). The pathways primarily focused on kinases, DNA replication, chromatin histone acetylation, and the cell cycle. Additionally, we validated the correlation between IC50 values of TAK-715 and LGK974 with CALU expression in external datasets, including GSE21501, GSE85916, GSE79668, GSE78229, GSE71729, GSE62452, GSE57495, and GSE28735 (Fig. 5 D, E). The results consistently demonstrated strong correlations, aligning with the findings from TCGA. Functional enrichment analysis of CALU in pancreatic cancer To explore the pathways potentially associated with CALU, we performed Gene Set Enrichment Analysis (GSEA) to compare high- and low-CALU expression groups. As shown in Figure A, the high-expression group was primarily enriched in pathways related to Cellular Processes, Environmental Information Processing, Genetic Information Processing, Human Diseases, and Organismal Systems, while the low-expression group was mainly associated with Metabolism. Additionally, to identify molecules linked to CALU, we conducted differential expression analysis between high- and low-CALU groups across 13 datasets (E_MTAB_6134, GSE21501, GSE28735, GSE57495, GSE62452, GSE71729, GSE78229, GSE79668, GSE85916, ICGC_PAAD_AU_array, ICGC_PAAD_AU_seq, ICGC_PAAD_CA_seq, and TCGA) (Fig. 6 B). We identified genes consistently upregulated (e.g., COL5A2, COL8A1, HTRA1, ITGA5, LGALS1, MMP14, PXDN, RAB23, SPOCK1, TGFBI) or downregulated (e.g., ECHDC2, ACSS1, ECHDC3, EPB41L4B, MST1, NR0B2, SGK2, SLC39A5) across multiple datasets. KEGG and GO analyses of these differentially expressed genes revealed that CALU may be involved in pathways such as external encapsulating structure organization, extracellular matrix organization, basement membrane, collagen-containing extracellular matrix, and integrin binding (GO) (Fig. 6 C), as well as Proteoglycans in cancer, PI3K-Akt signaling pathway, Focal adhesion, and ECM-receptor interaction (KEGG) (Fig. 6 D). Furthermore, GSVA analysis using CancerSEA gene sets for 14 functional states in cancer cells indicated strong correlations between CALU and Angiogenesis (R = 0.60, p < 2.2e-16), EMT (R = 0.74, p < 2.2e-16), Hypoxia (R = 0.49, p = 5.3e-12), Invasion (R = 0.76, p < 2.2e-16), and Metastasis (R = 0.61, p < 2.2e-16), suggesting a critical role for CALU in pancreatic cancer development and progression(Fig. 6 E). Expression and Role of CALU in Single-Cell Analysis and Cell-Cell Communication In the UMAP visualization, the single-cell dataset was classified into 12 cell types: Acinar, B, CD8Tex, DC, Ductal, Endocrine, Endothelial, Fibroblasts, Malignant, Mono/Macro, Plasma, and Stellate (Fig. 7 A). CALU expression was predominantly localized to Malignant, Endothelial, and Fibroblast cells (Fig. 7 B, C). Further analysis confirmed that CALU is mainly expressed in stromal cells and malignant cells, consistent with the UMAP results (Fig. 7 D-E). Notably, the proportions of Endothelial and Malignant cells were significantly higher in CALU-positive cells compared to CALU-negative cells, while Endothelial cells showed no significant difference (Fig. 7 F). To investigate the role of CALU in cell-cell communication, we performed cell-cell interaction analysis. Figure 7 G illustrates the interaction strengths between different cell types, revealing that CALU + Malignant cells exhibit stronger incoming and outgoing interaction intensities than CALU- Malignant cells (Fig. 7 H). Additionally, we visualized the interaction networks involving Fibroblasts, CALU + Malignant, and CALU- Malignant cells (Fig. 7 I-K). Figure L highlights the relative strengths of various molecular signaling pathways in outgoing and incoming signaling patterns. Interestingly, the PERIOSTIN pathway primarily mediates communication between Malignant and Fibroblast cells, with significant differences between CALU + and CALU- Malignant cells (Fig. 7 M, N). In this pathway, Malignant cells predominantly act as signal receivers, accepting signals from Fibroblasts (Fig. 7 O). This suggests that the PERIOSTIN pathway may play a key role in mediating interactions between CALU + Malignant cells and Fibroblasts within the tumor microenvironment. Expression of CALU in Pancreatic Cancer Cell Lines and Experimental Validation CALU is expressed in multiple pancreatic cancer cell lines, with the highest mRNA levels observed in the PK-45P cell line(Fig. 8 A). Subsequent qPCR analysis of CALU expression in normal pancreatic cells (MIA PaCa-2) and pancreatic cancer cell lines (AsPC-1, HPDE6-C7, and SW1990) revealed that CALU expression was significantly lower in MIA PaCa-2 compared to the other three cancer cell lines, which exhibited markedly higher CALU levels(Fig. 8 B). Discussion Cancer cells preferentially utilize glycolysis over oxidative phosphorylation for energy production, even under aerobic conditions, a phenomenon known as the Warburg effect. Brown adipose tissue (BAT), with its high metabolic activity, consumes substantial amounts of glucose, thereby influencing tumor glycolytic metabolism[ 17 ]. Studies have shown that cold exposure activates BAT, leading to reduced blood glucose levels and suppressed glycolytic metabolism in tumor cells, significantly inhibiting the growth of various solid tumors, including pancreatic cancer[ 11 ]. In terms of microenvironmental interactions, tumor-secreted factors such as ZAG promote the browning of white adipose tissue, increasing energy expenditure and exacerbating cancer-associated cachexia[ 34 ]. Poulia et al. highlighted the close relationship between cachexia in pancreatic cancer patients and metabolic dysregulation in adipose tissue, underscoring the potential role of BAT in cancer-related metabolic disorders[ 35 ]. Additionally, BAT influences cancer progression by modulating the immune microenvironment. Research has demonstrated that brown adipocyte-related genes (BARGs) are closely associated with immune cell infiltration in clear cell renal cell carcinoma, suggesting that BAT may regulate immune responses within the tumor microenvironment to impact cancer progression[ 24 ]. These studies collectively suggest that brown adipose tissue (BAT) may play a critical role in cancer development and progression. Therefore, this study focuses on the prognostic value of the BAT-related gene CALU in pancreatic cancer and explores its potential immune landscape, aiming to identify novel diagnostic and therapeutic targets for pancreatic cancer. In the tumor microenvironment, cancer-associated fibroblasts (CAFs) play a crucial role. CAFs secrete various factors, including growth factors, extracellular matrix components, and cytokines, which modulate immune responses and angiogenesis in the tumor microenvironment, thereby promoting tumor progression, and also facilitate tumor cell migration and invasion by inducing the epithelial-mesenchymal transition (EMT) process[ 21 , 22 ]. Furthermore, CALU regulates CAF-secreted factors, such as GDF-15, to enhance tumor cell migration and invasion[ 36 ]. These findings suggest that CALU's role in CAFs extends beyond calcium binding and intracellular transport, encompassing the regulation of the tumor microenvironment and the promotion of tumor progression. In this study, we observed that CALU expression levels are associated with tumor progression across multiple pancreatic cancer datasets, suggesting its potential involvement in the epithelial-mesenchymal transition (EMT) process. Immune infiltration analysis revealed a strong correlation between CALU and EMT (R = 0.74, p < 2.2e-16), supporting our hypothesis. Additionally, immune microenvironment analysis using EPIC and MCPCOUNTER algorithms indicated a robust association between CALU and cancer-associated fibroblasts (CAFs) (EPIC: R = 0.76, p < 2.2e-16; MCPCOUNTER: R = 0.76, p < 2.2e-16), suggesting that CALU may regulate EMT through CAFs. Beyond EMT regulation, CAFs have been reported to influence immunotherapy outcomes by secreting factors such as TGF-β, which inhibit T cell activation and function, thereby promoting immune evasion[ 22 ]. CAFs also upregulate immune checkpoint molecules like PD-L1, further suppressing anti-tumor immune responses[ 37 ]. By modulating the infiltration of immunosuppressive cells (e.g., Tregs and MDSCs) in the tumor microenvironment, CAFs reduce the efficacy of immunotherapy[ 37 ]. In our study, high CALU expression was associated with increased immunotherapy resistance in both IRnet and TIGER databases, indicating that CAFs may play a significant role in this process. In our single-cell analysis, we observed that CALU expression in UMAP visualization closely aligns with fibroblast clusters. Additionally, the proportion of fibroblasts was significantly higher in the CALU-positive group compared to the CALU-negative group, consistent with previous immune infiltration results. In terms of cell-cell communication, we found that the PERIOSTIN signaling pathway primarily mediates interactions between malignant cells and fibroblasts, with minimal connections to other cell types. Notably, CALU-positive malignant cells predominantly act as signal receivers in the PERIOSTIN pathway, exhibiting stronger interaction intensities than CALU-negative malignant cells. These findings suggest that the PERIOSTIN signaling pathway may serve as a critical bridge between CALU-positive malignant cells and fibroblasts. Clinical evidence indicates that periostin is upregulated in various cancers, including breast, lung, colon, pancreatic, and ovarian cancers, and promotes tumor progression by facilitating epithelial-mesenchymal transition (EMT)[ 38 ]. Periostin binds to cell surface receptors, such as integrins, activating signaling pathways like PI3K/Akt to enhance cancer cell survival, invasion, and metastasis[ 38 , 39 ]. Additionally, periostin upregulates VEGF expression, promoting tumor angiogenesis and providing nutritional support for tumor cell migration and invasion[ 40 ]. Therefore, periostin may play a pivotal role in CALU-mediated EMT in pancreatic cancer. However, our study has several limitations that need to be acknowledged. Further validation through cell-based and animal experiments is required to elucidate the interaction between CALU and CAFs, as well as the role of the Periostin pathway in this context. These limitations highlight the need for future research to expand the depth of our investigation and enhance the understanding of CALU's role in cancer biology. Conclusion In conclusion, our study investigated the BAT - related gene CALU in pancreatic cancer. We found CALU expression associated with tumor progression, EMT, and CAFs. The PERIOSTIN pathway likely links CALU - positive malignant cells to fibroblasts. We predicted drug sensitivities related to CALU, identifying TAK − 715 and LGK974 with strong correlations, and validated these in external datasets. CALU expression also varied among pancreatic cancer cell lines. Our work offers new insights into CALU as a potential diagnostic and therapeutic target in pancreatic cancer, guiding future research directions. Declarations Data availability All data generated or analysed during this study are included in this published article. Ethics, Consent to Participate, and Consent to Publish declarations Not applicable. Competing interests The authors declare no competing interests. Author contribution Conceived and designed the experiments: WL W, F W Analyzed the data: XF C,GY S Wrote and revised the paper: F W, ZY Q, WL X Draw figures: X Z, T D Finish experiments: XF C, T D Ackownledgement Not applicable. Funding This project was supported by The Medicine and Health Research Foundation of Zhejiang Province(2024KY770) and General Scientific Research Project of Zhejiang Provincial Department of Education(Y202352479) References Carpenter ES, Vendramini-Costa DB, Hasselluhn MC, et al. Pancreatic Cancer-Associated Fibroblasts: Where Do We Go from Here? Cancer Res. 2024;84:3505–8. 10.1158/0008-5472.CAN-24-2860 . Espona-Fiedler M, Patthey C, Lindblad S, et al. 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Nutrients. 2020;12:1543. 10.3390/nu12061543 . Feng H, Chen L, Wang Q, et al. Calumenin-15 facilitates filopodia formation by promoting TGF-β superfamily cytokine GDF-15 transcription. Cell Death Dis. 2013;4:e870. 10.1038/cddis.2013.403 . Ye J, Tian W, Zheng B, et al. Identification of cancer-associated fibroblasts signature for predicting the prognosis and immunotherapy response in hepatocellular carcinoma. Med (Baltim). 2023;102:e35938. 10.1097/MD.0000000000035938 . Ruan K, Bao S, Ouyang G. The multifaceted role of periostin in tumorigenesis. Cell Mol Life Sci. 2009;66:2219–30. 10.1007/s00018-009-0013-7 . L M, H M. Periostin expression and epithelial-mesenchymal transition in cancer: a review and an update. Virchows Arch 2011; 459. 10.1007/s00428-011-1151-5 K R-W AW. The Role of Periostin in Angiogenesis and Lymphangiogenesis in Tumors. Cancers. 2022;14. 10.3390/cancers14174225 . Additional Declarations No competing interests reported. Supplementary Files supplement.png 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-6709366\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":476582856,\"identity\":\"e21f94ab-d3cb-4966-8a4f-6c5c9caee084\",\"order_by\":0,\"name\":\"Fang Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Zhejiang University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fang\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":476582857,\"identity\":\"ae96998d-f91e-4e77-bb3b-8e761e24626c\",\"order_by\":1,\"name\":\"Xufan 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15:38:26\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6709366/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6709366/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":85689907,\"identity\":\"6f3b81fe-6f85-4b14-bd7e-35a9c881ec73\",\"added_by\":\"auto\",\"created_at\":\"2025-06-30 16:40:41\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1262015,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePrognostic Significance of Brown Adipocyte-Related Genes (BARGs) in Pancreatic Cancer\\u003c/strong\\u003e. \\u003cstrong\\u003e(A-D)\\u003c/strong\\u003e Univariate Cox analysis results showing BARGs associated with pancreatic cancer prognosis across overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) in TCGA-PAAD. \\u003cstrong\\u003e(E)\\u003c/strong\\u003e Venn diagram illustrating the overlap of prognostic genes identified across four datasets, pinpointing 29 BARGs significantly associated with patient outcomes. \\u003cstrong\\u003e(F)\\u003c/strong\\u003e Integration of three GEO datasets (GSE28735, GSE62452, and GSE57495) post batch effect removal, followed by univariate Cox analysis for further screening of prognostic genes.\\u003cstrong\\u003e (I) \\u003c/strong\\u003eThe curve plot illustrates the relationship between the number of trees and the error rate in the random survival forest analysis, while the bar chart ranks the importance of features based on their contribution to the model. \\u003cstrong\\u003e(G)\\u003c/strong\\u003e The Venn diagram highlights that CALU and SRPX2 are the overlapping genes between the intersection of four survival periods in 3GEO and TCGA datasets and the top 10 feature genes identified by random survival forest analysis.\\u003cstrong\\u003e (H)\\u003c/strong\\u003e The heatmap displays the results of both Cox and Log-rank survival analyses for CALU and SRPX2 in the TCGA-PAAD dataset. \\u003cstrong\\u003e(J)\\u003c/strong\\u003e Correlation analysis between CALU and SRPX2 in both 3GEO and TCGA-PAAD datasets, highlighting their strong interrelation and impact on prognosis.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6709366/v1/3d2991a75722b743d4de7fc9.png\"},{\"id\":85690499,\"identity\":\"02871147-43a5-4289-8c8a-76b68526fc79\",\"added_by\":\"auto\",\"created_at\":\"2025-06-30 16:48:41\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3380816,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSubcellular Localization and Clinical Characteristics of CALU. (A, B)\\u003c/strong\\u003e Immunohistochemical images of CALU from the Human Protein Atlas (HPA) database demonstrate its expression patterns in pancreatic cancer tissues. \\u003cstrong\\u003e(C)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe schematic diagram illustrates the subcellular localization of CALU, primarily highlighting its endoplasmic reticulum distribution.\\u003cstrong\\u003e (D-E)\\u003c/strong\\u003e The raincloud plot demonstrates the expression of CALU in multiple datasets (TCGA, GTEx, GSE28735, GSE62452, and GSE71729).\\u003cstrong\\u003e (H-I)\\u003c/strong\\u003e The boxplot illustrates the relationship between CALU expression levels and tumor Grade in the TCGA-PAAD, ICGC_PAAD_AU, GSE78229, and GSE62452 datasets.\\u003cstrong\\u003e (L)\\u003c/strong\\u003e CALU expression levels in the proteomic dataset PAAD_CPTAC.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6709366/v1/b25d37a6d98cf9f140f2a02f.png\"},{\"id\":85690498,\"identity\":\"6d1ce0ef-24df-41e7-8b09-81dcd8e4b9a4\",\"added_by\":\"auto\",\"created_at\":\"2025-06-30 16:48:41\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1992416,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCharacteristics of CALU in the Pancreatic Cancer Tumor Microenvironment. (A) \\u003c/strong\\u003eHeatmap depicting the correlation between CALU and various immune cells in TCGA-PAAD, E_MTAB_6134, GSE21501, GSE28735, GSE57495, GSE62452, GSE71729, GSE78229, GSE79668, GSE85916, ICGC_PAAD_AU_array, ICGC_PAAD_AU_seq, and ICGC_PAAD_CA_seq datasets. The color of the squares represents the correlation coefficient (p \\u0026lt; 0.05). Red: closer to 1 (positive correlation); blue: closer to -1 (negative correlation). A cross (×): p ≥ 0.05. \\u003cstrong\\u003e(B)\\u003c/strong\\u003e The relationship between HNRNPA3 and the outcomes of seven immunological algorithms (CIBERSORT, TIMER, xCell, MCPcounter, ESITMATE, EPIC, quantTIseq). \\u003cstrong\\u003e(C) \\u003c/strong\\u003eThe summary plot illustrates the correlation between CALU and immune cells with a correlation coefficient (R) greater than 0.3 and a p-value less than 0.001. \\u003cstrong\\u003e(D)\\u003c/strong\\u003e The dot plot highlights the top three positively and negatively correlated immune cell types with CALU, based on correlation coefficients.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6709366/v1/d25cf669c99aec4af8c19de1.png\"},{\"id\":85690500,\"identity\":\"2f06924c-bdd1-44e2-a805-e54018f91f7b\",\"added_by\":\"auto\",\"created_at\":\"2025-06-30 16:48:41\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":699413,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRelationship Between CALU and Immunotherapy Outcomes. (A-C) \\u003c/strong\\u003eThe bar plot illustrates the proportions of CTLA4, PD1, and TIDE prediction outcomes in high- and low-CALU expression groups. \\u003cstrong\\u003e(D) \\u003c/strong\\u003eThe boxplot demonstrates the differences in TIDE, IFNG, MSI Expr Sig, Merck18, CD274, CD8, Dysfunction, Exclusion, MDSC, CAF, and TAM M2 scores between high- and low-CALU expression groups. \\u003cstrong\\u003e(E) \\u003c/strong\\u003eThe boxplot highlights the differences in ips_ctla4_neg_pd1_neg, ips_ctla4_neg_pd1_pos, ips_ctla4_pos_pd1_neg, and ips_ctla4_pos_pd1_pos scores between high- and low-CALU expression groups.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6709366/v1/75f546a245b080aca9895490.png\"},{\"id\":85689905,\"identity\":\"d85c4efa-fa06-49fb-86d2-ddc591ebcf89\",\"added_by\":\"auto\",\"created_at\":\"2025-06-30 16:40:41\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2232819,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe relationship between drug sensitivity prediction and CALU.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e(A, B) \\u003c/strong\\u003eThe lollipop plot highlights drug molecules in GDSC1 and GDSC2 that exhibit strong correlations with CALU expression. \\u003cstrong\\u003e(C) \\u003c/strong\\u003eThe network graph illustrates molecules from GDSC1 and GDSC2 associated with CALU and their corresponding signaling pathways.\\u003cstrong\\u003e (D, E)\\u003c/strong\\u003e The correlation plot demonstrates the relationship between CALU and TAK-715 or LGK974 across multiple datasets, including GSE21501, GSE85916, GSE79668, GSE78229, GSE71729, GSE62452, GSE57495, and GSE28735.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6709366/v1/652af83fcdf848bd2db53804.png\"},{\"id\":85691563,\"identity\":\"50ba4706-7674-48ba-9ac7-3003adddddac\",\"added_by\":\"auto\",\"created_at\":\"2025-06-30 17:04:41\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2651316,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eBiological pathways analyses of CALU in pancreatic cancer.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A) The GSEA results illustrate the pathways and Normalized Enrichment Scores (NES) associated with high- (red) and low- (blue) CALU expression groups. (B) The heatmap displays the differential expression patterns of the top 25 upregulated and top 25 downregulated genes across 13 datasets, ranked by their frequency of occurrence. (C) GO analysis results for all genes with a frequency of occurrence greater than 10. (D) KEGG analysis results for all genes with a frequency of occurrence greater than 10. (E) The correlation plot illustrates the relationship between CALU and the GSVA scores of 14 functional state gene sets in pancreatic cancer.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6709366/v1/85f30e838168a0dc8a9e3620.png\"},{\"id\":85689915,\"identity\":\"52a9b9b1-9811-466c-a966-cafa2449d993\",\"added_by\":\"auto\",\"created_at\":\"2025-06-30 16:40:41\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4131308,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eExpression and Role of CALU in Single Cell Analysis and Cell - Cell Communication\\u003c/strong\\u003e. (A) UMAP visualization of the single cell dataset, classifying cells into 12 types including Acinar, B, CD8Tex, DC, Ductal, Endocrine, Endothelial, Fibroblasts, Malignant, Mono/Macro, Plasma, and Stellate. (B, C) UMAP plot showing the distribution of CALU expression across cells. (D, E) Box plots comparing CALU expression levels among different cell types, with a p - value (\\u0026lt;0.001) indicating significant differences. (F) Box plots comparing the proportions of specific cell types in CALU - positive and CALU - negative cell groups. (G) A circular plot representing the interaction strengths between different cell types, with lines indicating the connections and their thickness corresponding to interaction intensity. (H) The dot plot illustrates the incoming and outgoing interaction intensities between various cell types. (I-K) Visualization of interaction networks involving Fibroblasts, CALU+ Malignant, and CALU - Malignant cells, showing the communication relationships between these cell types. (L) Heatmaps displaying the relative strengths of outgoing and incoming molecular signaling patterns for various cell types. (M, N) Diagrams and plots related to the PERIOSTIN signaling pathway, showing its role in cell - cell communication. (O) Heatmap representing the PERIOSTIN signaling pathway network, indicating the roles of cells as signal senders, receivers, mediators, and influencers.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6709366/v1/bc033a14534ed8d342a628a3.png\"},{\"id\":85689924,\"identity\":\"d9f21347-9850-46e5-968e-301b2cc67f91\",\"added_by\":\"auto\",\"created_at\":\"2025-06-30 16:40:41\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":613146,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eExpression of CALU in Pancreatic Cancer Cell Lines.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A) mRNA Expression of CALU in 48 Pancreatic Cancer Cell Lines. (B) RT-PCR analysis was performed to validate CALU mRNA expression levels in the normal pancreatic cell line MIA PaCa-2 and pancreatic cancer cell lines AsPC-1, HPDE6-C7, and SW1990.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6709366/v1/25ff32f646906b891d1a42b0.png\"},{\"id\":90965750,\"identity\":\"9e7cc460-65ed-4091-b2a2-40b74b78744f\",\"added_by\":\"auto\",\"created_at\":\"2025-09-10 06:32:37\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":17169295,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6709366/v1/8d994278-2ac5-4511-b84d-b78a6ed9a179.pdf\"},{\"id\":85689914,\"identity\":\"116b3fb5-a5f7-4d5f-8982-f208b826f7bd\",\"added_by\":\"auto\",\"created_at\":\"2025-06-30 16:40:41\",\"extension\":\"png\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":938266,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"supplement.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6709366/v1/3cebd33b7f480357bd205512.png\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The Regulatory Role of Brown Adipocyte - Related Gene CALU in the Progression, Immune Microenvironment and Treatment Response of Pancreatic Cancer\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003ePancreatic Ductal Adenocarcinoma (PDAC) is the most common type of pancreatic cancer and among the most aggressive and deadly cancers. Its incidence has been increasing in recent years, and it is expected to rank as the second leading cause of cancer-related deaths by 2030[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Despite significant advances in cancer treatment, the five-year survival rate for PDAC remains extremely low, primarily due to late-stage diagnosis and resistance to current therapies[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. The early symptoms of PDAC are often non-specific, resulting in most patients being diagnosed at an advanced stage, when the optimal window for surgical resection has already passed[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Therefore, early diagnosis and screening are critical to improving the prognosis of PDAC. In recent years, with in-depth research into the biological characteristics of PDAC, new biomarkers and early detection technologies are being developed[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. In terms of treatment, although chemotherapy and radiotherapy remain the standard therapeutic options, PDAC exhibits a low response rate to these therapies[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Additionally, the tumor microenvironment (TME) of PDAC plays a significant role in disease progression and treatment resistance, particularly due to the heterogeneity and functional diversity of cancer-associated fibroblasts (CAFs) and pancreatic stellate cells (PSCs)[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. These cells promote tumor aggressiveness and drug resistance by regulating immune suppression and metabolic reprogramming[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. In summary, the high lethality and therapeutic challenges of PDAC underscore the importance of early diagnosis and screening. Future research should focus on developing more effective early detection technologies, gaining a deeper understanding of the complexity of the tumor microenvironment, and exploring novel therapeutic strategies to improve patient survival rates and quality of life[\\u003cspan additionalcitationids=\\\"CR3\\\" citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe relationship between obesity and cancer has garnered significant attention in recent research. Obesity is not only a major risk factor for metabolic diseases such as type 2 diabetes and cardiovascular disorders but is also closely linked to the development and progression of various cancers[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. In obesity, the excessive accumulation and dysfunction of white adipose tissue (WAT) disrupt systemic energy metabolism, thereby promoting tumor cell proliferation and invasion[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. In contrast, brown adipose tissue (BAT), a key energy-expending tissue, regulates systemic metabolism through non-shivering thermogenesis, and its activity is inversely correlated with tumor growth[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn recent years, the role of the tumor microenvironment (TME) in the progression and treatment of pancreatic cancer has garnered significant attention. The TME comprises diverse cell types, including immune cells, fibroblasts, vascular endothelial cells, and adipocytes, which collectively regulate tumor growth, invasion, and metastasis through complex interactions[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Brown adipose tissue (BAT), a specialized type of adipose tissue, primarily functions to maintain body temperature via non-shivering thermogenesis. Unlike white adipose tissue (WAT), BAT is rich in mitochondria and expresses high levels of uncoupling protein 1 (UCP1), enabling the release of energy as heat[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Activation of BAT through cold exposure significantly reduces blood glucose levels, thereby suppressing glycolytic metabolism in pancreatic cancer cells and inhibiting tumor growth[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Additionally, BAT activation limits glucose availability in the TME, restricting energy acquisition by cancer cells and further impairing their proliferative and invasive capabilities[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Recent studies have revealed that BAT not only plays a critical role in energy metabolism but may also significantly influence the tumor microenvironment. Upon cold exposure, BAT can also improve inflammatory states by modulating macrophage phenotypes, such as shifting from pro-inflammatory M1 to anti-inflammatory M2 types[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Additionally, BAT regulates the function of immune cells (e.g., T cells, B cells, and macrophages) within adipose tissue through the secretion of various factors, such as adiponectin and leptin, thereby influencing systemic inflammation[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. These findings provide novel insights into pancreatic cancer treatment, suggesting that BAT activation to remodel the TME may represent a promising therapeutic strategy.\\u003c/p\\u003e \\u003cp\\u003eCALU (Calumenin) is a calcium-binding protein belonging to the CREC (Cab45, Reticulocalbin, ERC-45, Calumenin) family, widely expressed in various cell types, particularly in the endoplasmic reticulum (ER) and Golgi apparatus[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Its primary functions include regulating calcium ion homeostasis, facilitating protein folding and secretion, and modulating cell signaling pathways[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. The CALU gene exhibits significant upregulation in various cancers and is closely associated with the regulation of the tumor microenvironment (TME). Studies have shown that CALU is highly expressed in multiple tumor types, including breast cancer (BRCA), kidney renal papillary cell carcinoma (KIRP), hepatocellular carcinoma (LIHC), head and neck squamous cell carcinoma (HNSC), and lower-grade glioma (LGG), with its high expression correlating with poor prognosis[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. CALU promotes tumor cell migration and invasion by regulating the epithelial-mesenchymal transition (EMT) process[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Additionally, CALU expression positively correlates with the infiltration of cancer-associated fibroblasts (CAFs), suggesting its critical role in the interaction between CAFs and malignant cells[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. In lung cancer, CALU promotes tumor progression by mediating CAF differentiation through miR-21 [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. These findings highlight the pivotal role of CALU in the tumor microenvironment, indicating its potential as a therapeutic target in cancer treatment.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we integrated brown adipose tissue-related genes with pancreatic cancer by collecting 101 brown adipocyte-related genes (BARGs) to identify potential genes influencing pancreatic cancer prognosis. Furthermore, we explored the impact of core genes on the pancreatic cancer immune microenvironment and their implications for immunotherapy response.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cp\\u003eData collection and preprocessing\\u003c/p\\u003e \\u003cp\\u003eWe obtained pancreatic cancer transcripts per kilobase million (TPM) data from the UCSC database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://xenabrowser.net/datapages/\\u003c/span\\u003e\\u003cspan address=\\\"https://xenabrowser.net/datapages/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), including 347 samples (178 tumor and 169 normal) from TCGA and GTEx. To strengthen our analysis, we also acquired datasets from E_MTAB_6134 (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.omicsdi.org/dataset/biostudies-arrayexpress/E-MTAB-6134\\u003c/span\\u003e\\u003cspan address=\\\"https://www.omicsdi.org/dataset/biostudies-arrayexpress/E-MTAB-6134\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), GSE21501, GSE28735, GSE57495, GSE62452, GSE71729, GSE78229, GSE79668, GSE85916, ICGC_PAAD_AU_array (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://dcc.icgc.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://dcc.icgc.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), ICGC_PAAD_AU_seq(\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://dcc.icgc.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://dcc.icgc.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), and ICGC_PAAD_CA_seq(\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://dcc.icgc.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://dcc.icgc.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). Additionally, we incorporated proteomic data from PAAD_CPTAC (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://proteomic.datacommons.cancer.gov/pdc/study/PDC000270\\u003c/span\\u003e\\u003cspan address=\\\"https://proteomic.datacommons.cancer.gov/pdc/study/PDC000270\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) to complement transcriptomic findings at the protein level. For single-cell analysis, we utilized the CRA001160 dataset (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://ngdc.cncb.ac.cn/gsa/browse/CRA001160\\u003c/span\\u003e\\u003cspan address=\\\"https://ngdc.cncb.ac.cn/gsa/browse/CRA001160\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), comprising 35 samples (11 normal and 24 tumor). We obtained immunohistochemical images and subcellular localization information for CALU in pancreatic cancer from the Human Protein Atlas (HPA) database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.proteinatlas.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.proteinatlas.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), specifically focusing on CALU-positive and medium-expression samples. Additionally, we retrieved CALU expression levels across 46 pancreatic cancer cell lines from the HPA database. To address batch effects and minimize downstream analysis errors, we merged GSE28735, GSE62452, and GSE57495 into a combined dataset named \\\"3GEO\\\" using the \\\"sva\\\" package. From prior studies, we obtained 101 brown adipocyte-related genes (BARGs)[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Differential analysis was performed using the \\\"limma\\\" package[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e] to screen candidate core genes and explore their potential downstream mechanisms. Public datasets do not require Ethical Review Committee approval or informed consent.\\u003c/p\\u003e \\u003cp\\u003eSurvival Analysis and Random Survival Forest Construction\\u003c/p\\u003e \\u003cp\\u003eWe employed the coxph function from the survival package to conduct univariate survival analysis and the ggsurvplot() function from the survminer package to visualize survival curves. Additionally, the randomForestSRC package was used to perform survival random forest analysis, and the top 10 genes ranked by Variable Importance were selected.\\u003c/p\\u003e \\u003cp\\u003eImmune infiltration and immunotherapy prediction\\u003c/p\\u003e \\u003cp\\u003eBased on the median expression of CALU across multiple datasets (TCGA-PAAD, E_MTAB_6134, GSE21501, GSE28735, GSE57495, GSE62452, GSE71729, GSE78229, GSE79668, GSE85916, ICGC_PAAD_AU_array, ICGC_PAAD_AU_seq, and ICGC_PAAD_CA_seq), samples were divided into high- and low-expression groups. Immune infiltration levels were assessed using seven algorithms (CIBERSORT, TIMER, xCell, MCPcounter, ESTIMATE, EPIC, and quantTIseq), and results were ranked by CALU expression levels. Cell types showing significant expression differences were identified via the Wilcoxon test, and their correlation coefficients with CALU expression were calculated along with statistical significance.\\u003c/p\\u003e \\u003cp\\u003eEaSIeR is a systematic tool that integrates bulk RNA-seq data with multi-source prior knowledge to predict immune response characteristics and therapeutic outcomes in the tumor microenvironment through multi-task learning[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Its immune signature scores include cytolytic activity (CYT), chemokine signature (chemokines), T cell-inflamed signature (T cell_inflamed), tertiary lymphoid structure signature (TLS), and IFNy signature (IFNy). We applied its algorithm to predict CALU-related immune features and performed t-tests to assess statistical significance.\\u003c/p\\u003e \\u003cp\\u003eThe Cancer Immunome Atlas (TCIA, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://tcia.at/home\\u003c/span\\u003e\\u003cspan address=\\\"https://tcia.at/home\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) provides comprehensive data on T-cell receptor (TCR) and B-cell receptor (BCR) sequencing, as well as immune cell types, phenotypes, functions, and interactions, supporting the development of innovative cancer immunotherapy strategies[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. We extracted immunophenotype scores (IPS) related to pancreatic cancer from TCIA and compared the results using the median expression levels of CALU as the threshold.\\u003c/p\\u003e \\u003cp\\u003eThe Tumor Immune Dysfunction and Exclusion (TIDE, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://tide.dfci.harvard.edu/\\u003c/span\\u003e\\u003cspan address=\\\"http://tide.dfci.harvard.edu/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) database specializes in studying tumor immune dysfunction and exclusion. Its TIDE algorithm predicts responses to immune checkpoint inhibitors (e.g., PD-1/PD-L1 inhibitors) by evaluating immune cell infiltration and tumor immune escape mechanisms in the tumor microenvironment[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. We normalized TCGA RNA-seq data (TPM format) from tumor samples and uploaded it to TIDE. The association between the target gene and immune prediction was analyzed using chi-square tests and t-tests.\\u003c/p\\u003e \\u003cp\\u003eIRnet is a deep learning framework based on graph neural networks (GNNs), specifically designed to predict patient responses to immune checkpoint inhibitor (ICI) therapy. Its core feature involves transforming gene features into pathway features using biological knowledge, enhancing prediction robustness and reducing noise interference. We applied IRnet to predict treatment outcomes for CTLA4 and PD1 inhibitors and performed chi-square analysis to assess the significance of differences between high- and low-CALU expression groups.\\u003c/p\\u003e \\u003cp\\u003eFunctional gene set enrichment\\u003c/p\\u003e \\u003cp\\u003eWe performed differential expression analysis using the limma package[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e] by comparing the top 30% and bottom 30% of samples based on CALU expression levels, ranking the results by log2 Fold Change (log2FC). Subsequently, gene set enrichment analysis (GSEA) was conducted using the GSEA function from the clusterProfiler package[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e], leveraging hallmark gene sets and KEGG metabolic pathways. The Normalized Enrichment Score (NES) and statistical significance of each gene set were visualized, enabling the identification of biological processes or metabolic pathways significantly enriched between high- and low-CALU expression groups.\\u003c/p\\u003e \\u003cp\\u003eTo identify and visualize potential genes associated with CALU, we performed differential expression analysis between high- and low-CALU expression groups after z-score normalization across multiple datasets. We identified significantly upregulated and downregulated genes in each dataset and calculated their frequency of occurrence. The hplot1 function from the fromto package was used to generate a heatmap, highlighting high-frequency genes functionally related to CALU.\\u003c/p\\u003e \\u003cp\\u003eCancerSEA (Cancer Single Cell Expression Atlas) is a comprehensive database providing single-cell expression data across 14 functional states (e.g., stemness, invasion, metastasis, proliferation, EMT, angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation, and quiescence) and their associated genes[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Using the GSVA R package, we computed combined z-scores for these gene sets and standardized the results with the scale function. Pearson correlation analysis was then performed to assess the relationship between CALU and the scores of each functional state.\\u003c/p\\u003e \\u003cp\\u003eDrug sensitivity prediction\\u003c/p\\u003e \\u003cp\\u003eIn this study, we utilized the Genomics of Drug Sensitivity in Cancer (GDSC) database, which includes two versions: GDSC V1, encompassing 987 cell lines and 367 compounds, and GDSC V2, comprising 809 cell lines and 198 compounds. We employed the pRRophetic R package to predict the half-maximal inhibitory concentration (IC50) as an indicator of chemotherapy response[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. Spearman correlation analysis was performed using the cor.test function to evaluate the relationship between IC50 values and gene expression levels.\\u003c/p\\u003e \\u003cp\\u003eSinglecell RNAseq analysis\\u003c/p\\u003e \\u003cp\\u003eTISCH2 is a database focused on single-cell transcriptomic data of the tumor microenvironment (TME), designed to analyze and explore gene expression within the TME[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. We obtained the pancreatic cancer single-cell dataset (CRA001160) from this database. Following quality control (using the PercentageFeatureSet function), data normalization (NormalizeData), and clustering analysis (FindNeighbors and FindClusters), we applied Uniform Manifold Approximation and Projection (UMAP) to visualize gene expression and cell clustering. This process identified 12 distinct cell types: Acinar, B, CD8Tex, DC, Ductal, Endocrine, Endothelial, Fibroblasts, Malignant, Mono/Macro, Plasma, and Stellate. Additionally, the CellChat package[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e] was employed to analyze cell-cell communication. We classified malignant cells into CALU\\u0026thinsp;+\\u0026thinsp;Malignant and CALU- Malignant based on CALU expression. The computeCommunProb function was then used to calculate communication probabilities between cell types. After filtering out low-probability interactions, the netVisual_circle and netVisual_heatmap functions were applied to visualize the communication networks.\\u003c/p\\u003e \\u003cp\\u003eCell culture\\u003c/p\\u003e \\u003cp\\u003eThe human pancreatic cancer cell lines HPDE6-C7, SW1990, and AsPC-1, along with the human normal pancreatic cell line MIA PaCa-2, were obtained from ATCC. These cells were cultured in DMEM supplemented with 10% fetal bovine serum (FBS) and maintained in a humidified environment at 37\\u0026deg;C with 5% CO2.\\u003c/p\\u003e \\u003cp\\u003eRNA extraction and Quantitative Real-Time PCR (qRT-PCR)\\u003c/p\\u003e \\u003cp\\u003eTotal RNA was extracted using the FastPure Cell/Tissue Total RNA Isolation Kit V2 (Vazyme Biotech, Nanjing, China), and cDNA synthesis was performed with the HiScriptIII RT SuperMix for qPCR (+\\u0026thinsp;gDNA wiper) (Vazyme Biotech, Nanjing, China). Quantitative RT-PCR was conducted using SYBR Green Mix (Vazyme Biotech, Nanjing, China) in a 20 \\u0026micro;l reaction volume on an Applied Biosystems\\u0026reg; 7500 Real-Time PCR System. Relative gene expression was calculated using the 2-ΔΔCT method. Primers were designed and synthesized by Repobio (Hangzhou, China): CALU-F: AATAGACGCGGATAAAGATGGGT; CALU-R: GCCATTGGTTTTCAACATTGTCA\\u003c/p\\u003e \\u003cp\\u003eStatistic analysis\\u003c/p\\u003e \\u003cp\\u003eGraphPad prism 9.0 was adopted for analyzing the data, which were stated as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD, by t-test or oneway ANOVA. A P value less than 0.05 was defined there is a significant difference. We utilized Python 3.8.18 to execute the IRnet algorithm and imported the results into R version 4.3 for subsequent analysis. Differential expression analysis, immune prediction, and pathway analysis were also performed using R version 4.3.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003ePrognostic Significance of Brown Adipocyte-Related Genes (BARGs) in TCGA and GEO Datasets\\u003c/h2\\u003e\\n\\u003cp\\u003eTo evaluate the prognostic value of brown adipocyte-related genes (BARGs), we performed univariate Cox analysis to identify BARGs associated with pancreatic cancer prognosis (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA-D). A Venn diagram was used to visualize overlapping prognostic genes across four datasets, revealing 29 genes significantly correlated with overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eE). These genes include SFN, ACTB, CD59, PPP1R1A, CTSC, DAG1, LTBP1, ENO3, IDE, ERP29, SDCBP, HEBP1, CALU, SLURP1, SERPINB5, IL1RN, OAF, LAMB2, EFNB1, MCFD2, TFPI, KLK7, GBP2, A2ML1, SRPX2, SERPINB2, TFRC, C1RL, and LY6D. Subsequently, we integrated three GEO datasets (GSE28735, GSE62452, and GSE57495) after batch effect removal and conducted univariate Cox analysis and random survival forest analysis to further screen prognostic genes (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eF, I and Supplement 1A). CALU and SRPX2 consistently demonstrated significant prognostic value across these datasets. Log-rank survival analysis in TCGA-PAAD confirmed their robust prognostic significance (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eH and Supplement 1B, C). Notably, CALU and SRPX2 also exhibited a strong correlation with each other (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eJ) in 3GEO (R\\u0026thinsp;=\\u0026thinsp;0.68, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16) and TCGA-PAAD (R\\u0026thinsp;=\\u0026thinsp;0.77, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCALU is highly expressed in tumor tissues and is associated with Grade staging in pancreatic cancer\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCALU primarily localizes to the endoplasmic reticulum in pancreatic cancer, as demonstrated by immunohistochemical data from the Human Protein Atlas (HPA) (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA-C). CALU expression was markedly higher in tumor tissues than in normal tissues across multiple datasets, including TCGA \\u0026amp; GTEx (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD), GSE28735 (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE), GSE62452 (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eF), and GSE71729 (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eG). Moreover, the expression of CALU demonstrated a progressive increase with advancing tumor grades in pancreatic adenocarcinoma (PAAD) across multiple datasets. A significant increase in CALU expression was observed with increasing tumor grades in TCGA-PAAD (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eH), ICGC_PAAD_AU (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eI), GSE78229 (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eJ) and GSE62452 (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eK). Furthermore, at the proteomic level (PAAD_OPTAC dataset), CALU expression aligns with its mRNA levels, with CALU protein significantly higher in tumor tissues compared to normal tissues (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eL).\\u003c/p\\u003e\\n\\u003cp\\u003eThe schematic diagram illustrates the subcellular localization of CALU, primarily highlighting its endoplasmic reticulum distribution. \\u003cstrong\\u003e(D-E)\\u003c/strong\\u003e The raincloud plot demonstrates the expression of CALU in multiple datasets (TCGA, GTEx, GSE28735, GSE62452, and GSE71729). \\u003cstrong\\u003e(H-I)\\u003c/strong\\u003e The boxplot illustrates the relationship between CALU expression levels and tumor Grade in the TCGA-PAAD, ICGC_PAAD_AU, GSE78229, and GSE62452 datasets. \\u003cstrong\\u003e(L)\\u003c/strong\\u003e CALU expression levels in the proteomic dataset PAAD_CPTAC.\\u003c/p\\u003e\\n\\u003cp\\u003eCorrelation of CALU expression with immune cell infiltrates\\u003c/p\\u003e\\n\\u003cp\\u003eTo enhance the reliability of our predictions, we employed multiple datasets and various immune infiltration algorithms to calculate and compare immune infiltration levels. Two heatmaps were generated to illustrate the correlation between CALU expression and immune cell infiltration, as well as expression differences across cell types (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA, B). As shown in Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA, CALU exhibits strong correlations with several immune cell types, including dendritic cells, endothelial cells, macrophages (MO), macrophages, fibroblasts, and neutrophils. Most of these cell types belong to stromal cell populations. Correspondingly, CALU also shows a significant correlation with StromalScore, suggesting a potential link between CALU and stromal cells within the immune microenvironment.\\u003c/p\\u003e\\n\\u003cp\\u003eIn Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC, we further visualized all cell types with correlation coefficients greater than 0.3 and p-values less than 0.001. Among the 25 cell types or immune scores analyzed, monocyte-derived macrophages accounted for nine. The cell types with the highest positive correlation coefficients were cancer-associated fibroblasts (EPIC: R\\u0026thinsp;=\\u0026thinsp;0.76, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16; MCPCOUNTER: R\\u0026thinsp;=\\u0026thinsp;0.76, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16) and M1 macrophages (QUANTISEQ: R\\u0026thinsp;=\\u0026thinsp;0.56, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;3.6e-16) ( Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD). Conversely, the cell types with the strongest negative correlations were T cell CD4\\u0026thinsp;+\\u0026thinsp;central memory (XCELL: R = -0.46, p\\u0026thinsp;=\\u0026thinsp;3e-09), T cell CD8+ (EPIC: R = -0.35, p\\u0026thinsp;=\\u0026thinsp;2.1e-06), and T cell CD4\\u0026thinsp;+\\u0026thinsp;Th1 (XCELL: R = -0.37, p\\u0026thinsp;=\\u0026thinsp;4.4e-07) (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD). Based on these findings, we hypothesize that CALU may promote stromal cell activity while inhibiting T cell infiltration, thereby suppressing anti-tumor immune responses.\\u003c/p\\u003e\\n\\u003cp\\u003eCALU expression levels are associated with immunotherapy outcomes\\u003c/p\\u003e\\n\\u003cp\\u003eUsing IRnet, we predicted immunotherapy outcomes for TCGA-PAAD samples and found that the high CALU expression group exhibited a higher resistance rate to CTLA4 inhibitors compared to the low-expression group, with a chi-square p-value of 0.003(Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). In contrast, the prediction for PD1 inhibitors did not reach statistical significance (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.051) (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB). In the TIGER immunotherapy prediction results, the high CALU expression group also showed a higher resistance rate (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC). Additionally, TIGER provides predictions for several immune parameters, revealing that samples with high CALU expression exhibit elevated levels of CAF, CD274, Exclusion, IFNG, Merck18, MSI.Expr.Sig, and TIDE scores(Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD). These results are consistent with findings from the 3GEO dataset (Supplement E). The TICA database also supports these findings, showing that the high CALU expression group has significantly higher scores in the ips_ctla4_pos_pd1_neg category compared to the control group. This further suggests that high CALU expression is associated with resistance to CTLA4-based therapy. In the EaSIeR analysis, the high CALU expression group exhibited higher IFNy, chemokines, T cell_inflamed, and CYT scores, while no significant difference was observed in TLS scores between the two groups(Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eF-J).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003ch3\\u003ePredicting potential chemotherapeutic or targeted-therapeutic drugs sensitive to hnRNPA3\\u003c/h3\\u003e\\n\\u003cp\\u003eUsing the pRRophetic R package, we predicted the half-maximal inhibitory concentration (IC50) for each sample in TCGA-PAAD based on drug sensitivity data from GDSC1 and GDSC2, followed by correlation analysis with CALU expression (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA, B). In GDSC1, TAK-715 showed the highest positive correlation with CALU (R\\u0026thinsp;=\\u0026thinsp;0.55, p\\u0026thinsp;=\\u0026thinsp;3.9e-15) (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). In GDSC2, LGK974 exhibited the strongest positive correlation (R\\u0026thinsp;=\\u0026thinsp;0.58, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16) (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB). To visually represent the drug molecules associated with CALU in GDSC1 and GDSC2, we constructed a network graph and annotated the signaling pathways potentially targeted by these molecules (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC). The pathways primarily focused on kinases, DNA replication, chromatin histone acetylation, and the cell cycle. Additionally, we validated the correlation between IC50 values of TAK-715 and LGK974 with CALU expression in external datasets, including GSE21501, GSE85916, GSE79668, GSE78229, GSE71729, GSE62452, GSE57495, and GSE28735 (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eD, E). The results consistently demonstrated strong correlations, aligning with the findings from TCGA.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ch3\\u003eFunctional enrichment analysis of CALU in pancreatic cancer\\u003c/h3\\u003e\\n\\u003cp\\u003eTo explore the pathways potentially associated with CALU, we performed Gene Set Enrichment Analysis (GSEA) to compare high- and low-CALU expression groups. As shown in Figure A, the high-expression group was primarily enriched in pathways related to Cellular Processes, Environmental Information Processing, Genetic Information Processing, Human Diseases, and Organismal Systems, while the low-expression group was mainly associated with Metabolism. Additionally, to identify molecules linked to CALU, we conducted differential expression analysis between high- and low-CALU groups across 13 datasets (E_MTAB_6134, GSE21501, GSE28735, GSE57495, GSE62452, GSE71729, GSE78229, GSE79668, GSE85916, ICGC_PAAD_AU_array, ICGC_PAAD_AU_seq, ICGC_PAAD_CA_seq, and TCGA) (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB). We identified genes consistently upregulated (e.g., COL5A2, COL8A1, HTRA1, ITGA5, LGALS1, MMP14, PXDN, RAB23, SPOCK1, TGFBI) or downregulated (e.g., ECHDC2, ACSS1, ECHDC3, EPB41L4B, MST1, NR0B2, SGK2, SLC39A5) across multiple datasets. KEGG and GO analyses of these differentially expressed genes revealed that CALU may be involved in pathways such as external encapsulating structure organization, extracellular matrix organization, basement membrane, collagen-containing extracellular matrix, and integrin binding (GO) (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC), as well as Proteoglycans in cancer, PI3K-Akt signaling pathway, Focal adhesion, and ECM-receptor interaction (KEGG) (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eD). Furthermore, GSVA analysis using CancerSEA gene sets for 14 functional states in cancer cells indicated strong correlations between CALU and Angiogenesis (R\\u0026thinsp;=\\u0026thinsp;0.60, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16), EMT (R\\u0026thinsp;=\\u0026thinsp;0.74, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16), Hypoxia (R\\u0026thinsp;=\\u0026thinsp;0.49, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;5.3e-12), Invasion (R\\u0026thinsp;=\\u0026thinsp;0.76, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16), and Metastasis (R\\u0026thinsp;=\\u0026thinsp;0.61, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16), suggesting a critical role for CALU in pancreatic cancer development and progression(Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eE).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ch3\\u003eExpression and Role of CALU in Single-Cell Analysis and Cell-Cell Communication\\u003c/h3\\u003e\\n\\u003cp\\u003eIn the UMAP visualization, the single-cell dataset was classified into 12 cell types: Acinar, B, CD8Tex, DC, Ductal, Endocrine, Endothelial, Fibroblasts, Malignant, Mono/Macro, Plasma, and Stellate (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA). CALU expression was predominantly localized to Malignant, Endothelial, and Fibroblast cells (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB, C). Further analysis confirmed that CALU is mainly expressed in stromal cells and malignant cells, consistent with the UMAP results (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eD-E). Notably, the proportions of Endothelial and Malignant cells were significantly higher in CALU-positive cells compared to CALU-negative cells, while Endothelial cells showed no significant difference (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eF). To investigate the role of CALU in cell-cell communication, we performed cell-cell interaction analysis. Figure\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eG illustrates the interaction strengths between different cell types, revealing that CALU\\u0026thinsp;+\\u0026thinsp;Malignant cells exhibit stronger incoming and outgoing interaction intensities than CALU- Malignant cells (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eH). Additionally, we visualized the interaction networks involving Fibroblasts, CALU\\u0026thinsp;+\\u0026thinsp;Malignant, and CALU- Malignant cells (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eI-K). Figure L highlights the relative strengths of various molecular signaling pathways in outgoing and incoming signaling patterns. Interestingly, the PERIOSTIN pathway primarily mediates communication between Malignant and Fibroblast cells, with significant differences between CALU\\u0026thinsp;+\\u0026thinsp;and CALU- Malignant cells (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eM, N). In this pathway, Malignant cells predominantly act as signal receivers, accepting signals from Fibroblasts (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eO). This suggests that the PERIOSTIN pathway may play a key role in mediating interactions between CALU\\u0026thinsp;+\\u0026thinsp;Malignant cells and Fibroblasts within the tumor microenvironment.\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eExpression of CALU in Pancreatic Cancer Cell Lines and Experimental Validation\\u003c/h2\\u003e\\n\\u003cp\\u003eCALU is expressed in multiple pancreatic cancer cell lines, with the highest mRNA levels observed in the PK-45P cell line(Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eA). Subsequent qPCR analysis of CALU expression in normal pancreatic cells (MIA PaCa-2) and pancreatic cancer cell lines (AsPC-1, HPDE6-C7, and SW1990) revealed that CALU expression was significantly lower in MIA PaCa-2 compared to the other three cancer cell lines, which exhibited markedly higher CALU levels(Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eB).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eCancer cells preferentially utilize glycolysis over oxidative phosphorylation\\u003c/p\\u003e \\u003cp\\u003efor energy production, even under aerobic conditions, a phenomenon known as the Warburg effect. Brown adipose tissue (BAT), with its high metabolic activity, consumes substantial amounts of glucose, thereby influencing tumor glycolytic metabolism[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Studies have shown that cold exposure activates BAT, leading to reduced blood glucose levels and suppressed glycolytic metabolism in tumor cells, significantly inhibiting the growth of various solid tumors, including pancreatic cancer[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. In terms of microenvironmental interactions, tumor-secreted factors such as ZAG promote the browning of white adipose tissue, increasing energy expenditure and exacerbating cancer-associated cachexia[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. Poulia et al. highlighted the close relationship between cachexia in pancreatic cancer patients and metabolic dysregulation in adipose tissue, underscoring the potential role of BAT in cancer-related metabolic disorders[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. Additionally, BAT influences cancer progression by modulating the immune microenvironment. Research has demonstrated that brown adipocyte-related genes (BARGs) are closely associated with immune cell infiltration in clear cell renal cell carcinoma, suggesting that BAT may regulate immune responses within the tumor microenvironment to impact cancer progression[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. These studies collectively suggest that brown adipose tissue (BAT) may play a critical role in cancer development and progression. Therefore, this study focuses on the prognostic value of the BAT-related gene CALU in pancreatic cancer and explores its potential immune landscape, aiming to identify novel diagnostic and therapeutic targets for pancreatic cancer.\\u003c/p\\u003e \\u003cp\\u003eIn the tumor microenvironment, cancer-associated fibroblasts (CAFs) play a crucial role. CAFs secrete various factors, including growth factors, extracellular matrix components, and cytokines, which modulate immune responses and angiogenesis in the tumor microenvironment, thereby promoting tumor progression, and also facilitate tumor cell migration and invasion by inducing the epithelial-mesenchymal transition (EMT) process[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Furthermore, CALU regulates CAF-secreted factors, such as GDF-15, to enhance tumor cell migration and invasion[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. These findings suggest that CALU's role in CAFs extends beyond calcium binding and intracellular transport, encompassing the regulation of the tumor microenvironment and the promotion of tumor progression. In this study, we observed that CALU expression levels are associated with tumor progression across multiple pancreatic cancer datasets, suggesting its potential involvement in the epithelial-mesenchymal transition (EMT) process. Immune infiltration analysis revealed a strong correlation between CALU and EMT (R\\u0026thinsp;=\\u0026thinsp;0.74, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16), supporting our hypothesis. Additionally, immune microenvironment analysis using EPIC and MCPCOUNTER algorithms indicated a robust association between CALU and cancer-associated fibroblasts (CAFs) (EPIC: R\\u0026thinsp;=\\u0026thinsp;0.76, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16; MCPCOUNTER: R\\u0026thinsp;=\\u0026thinsp;0.76, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.2e-16), suggesting that CALU may regulate EMT through CAFs. Beyond EMT regulation, CAFs have been reported to influence immunotherapy outcomes by secreting factors such as TGF-β, which inhibit T cell activation and function, thereby promoting immune evasion[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. CAFs also upregulate immune checkpoint molecules like PD-L1, further suppressing anti-tumor immune responses[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. By modulating the infiltration of immunosuppressive cells (e.g., Tregs and MDSCs) in the tumor microenvironment, CAFs reduce the efficacy of immunotherapy[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. In our study, high CALU expression was associated with increased immunotherapy resistance in both IRnet and TIGER databases, indicating that CAFs may play a significant role in this process.\\u003c/p\\u003e \\u003cp\\u003eIn our single-cell analysis, we observed that CALU expression in UMAP visualization closely aligns with fibroblast clusters. Additionally, the proportion of fibroblasts was significantly higher in the CALU-positive group compared to the CALU-negative group, consistent with previous immune infiltration results. In terms of cell-cell communication, we found that the PERIOSTIN signaling pathway primarily mediates interactions between malignant cells and fibroblasts, with minimal connections to other cell types. Notably, CALU-positive malignant cells predominantly act as signal receivers in the PERIOSTIN pathway, exhibiting stronger interaction intensities than CALU-negative malignant cells. These findings suggest that the PERIOSTIN signaling pathway may serve as a critical bridge between CALU-positive malignant cells and fibroblasts. Clinical evidence indicates that periostin is upregulated in various cancers, including breast, lung, colon, pancreatic, and ovarian cancers, and promotes tumor progression by facilitating epithelial-mesenchymal transition (EMT)[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Periostin binds to cell surface receptors, such as integrins, activating signaling pathways like PI3K/Akt to enhance cancer cell survival, invasion, and metastasis[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. Additionally, periostin upregulates VEGF expression, promoting tumor angiogenesis and providing nutritional support for tumor cell migration and invasion[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Therefore, periostin may play a pivotal role in CALU-mediated EMT in pancreatic cancer.\\u003c/p\\u003e \\u003cp\\u003eHowever, our study has several limitations that need to be acknowledged. Further validation through cell-based and animal experiments is required to elucidate the interaction between CALU and CAFs, as well as the role of the Periostin pathway in this context. These limitations highlight the need for future research to expand the depth of our investigation and enhance the understanding of CALU's role in cancer biology.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn conclusion, our study investigated the BAT - related gene CALU in pancreatic cancer. We found CALU expression associated with tumor progression, EMT, and CAFs. The PERIOSTIN pathway likely links CALU - positive malignant cells to fibroblasts. We predicted drug sensitivities related to CALU, identifying TAK \\u0026minus;\\u0026thinsp;715 and LGK974 with strong correlations, and validated these in external datasets. CALU expression also varied among pancreatic cancer cell lines. Our work offers new insights into CALU as a potential diagnostic and therapeutic target in pancreatic cancer, guiding future research directions.\\u003c/p\\u003e \"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data generated or analysed during this study are included in this published article.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics, Consent to Participate, and Consent to Publish declarations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contribution\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConceived and designed the experiments: \\u0026nbsp;WL W, F W\\u003c/p\\u003e\\n\\u003cp\\u003eAnalyzed the data: \\u0026nbsp;XF C,GY S\\u003c/p\\u003e\\n\\u003cp\\u003eWrote and revised the paper: F W, ZY Q, WL X\\u003c/p\\u003e\\n\\u003cp\\u003eDraw figures: X Z, T D\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eFinish experiments: XF C, T D\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAckownledgement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis project was supported by The Medicine and Health Research Foundation of Zhejiang Province(2024KY770) and General Scientific Research Project of Zhejiang Provincial Department of Education(Y202352479)\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eCarpenter ES, Vendramini-Costa DB, Hasselluhn MC, et al. 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Cancers. 2022;14. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3390/cancers14174225\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/cancers14174225\\\" 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\":\"info@researchsquare.com\",\"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 ductal adenocarcinoma (PDAC), CALU, Tumor microenvironment, Immunotherapy, Prognosis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6709366/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6709366/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003ePurpose\\u003c/h2\\u003e \\u003cp\\u003ePancreatic Ductal Adenocarcinoma (PDAC) remains a lethal malignancy with limited therapeutic options. This study aimed to identify brown adipocyte-related genes (BARGs) influencing PDAC prognosis and explore their roles in the tumor microenvironment (TME) and immunotherapy response.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eTranscriptomic and proteomic data from TCGA, GEO, ICGC, and CPTAC databases were analyzed to screen prognostic BARGs. Immune infiltration, immunotherapy prediction (via TIDE, IRnet, and TCIA), and drug sensitivity analyses were conducted. Single-cell RNA sequencing (CRA001160 dataset) and experimental validation (qPCR in pancreatic cancer cell lines) were performed to validate findings.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eCALU emerged as a core prognostic gene, significantly overexpressed in PDAC tissues and correlated with advanced tumor grade. High CALU expression was linked to stromal cell activation (e.g., cancer-associated fibroblasts, M1 macrophages) and suppressed T-cell infiltration, indicating immunosuppressive TME remodeling. CALU predicted resistance to CTLA4 inhibitors but showed no significant association with PD1 blockade. Drug sensitivity analysis revealed correlations between CALU and chemotherapeutic agents (e.g., TAK-715, LGK974). Single-cell analysis localized CALU to malignant and stromal cells, highlighting its role in PERIOSTIN-mediated fibroblast-malignant cell communication. Experimental validation confirmed elevated CALU expression in pancreatic cancer cell lines compared to normal cells.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eCALU is a critical regulator of PDAC progression, influencing stromal-TME interactions and immune evasion. It serves as a potential prognostic biomarker and therapeutic target, offering insights into combination strategies targeting stromal-immune crosstalk in PDAC.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The Regulatory Role of Brown Adipocyte - Related Gene CALU in the Progression, Immune Microenvironment and Treatment Response of Pancreatic Cancer\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-06-30 16:40:36\",\"doi\":\"10.21203/rs.3.rs-6709366/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"80a3aa8d-a153-4e82-8519-cd18ee54956e\",\"owner\":[],\"postedDate\":\"June 30th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-09-10T06:24:04+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-06-30 16:40:36\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6709366\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6709366\",\"identity\":\"rs-6709366\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}