Lactate-associated gene MCU promotes the proliferation, migration, and invasion of pancreatic adenocarcinoma

Lactate-associated gene MCU promotes the proliferation, migration, and invasion of pancreatic adenocarcinoma | 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 Lactate-associated gene MCU promotes the proliferation, migration, and invasion of pancreatic adenocarcinoma Yuhang Chen, Fenglin Zhang, Suoyi Dai, Jiangang Zhao, Wenxun Cai, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5899024/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 May, 2025 Read the published version in BMC Cancer → Version 1 posted 4 You are reading this latest preprint version Abstract This study aims to evaluate the predictive value and therapeutic significance of lactate-related genes (LRGs) in pancreatic ductal adenocarcinoma (PDAC). By analyzing PDAC data from the TCGA and GEO databases and employing WGCNA and consensus clustering, we identified two lactate subtypes with distinct gene expression profiles and clinical outcomes, and extracted differentially expressed genes. Functional enrichment and GSEA analyses were conducted to explore related pathways, and a lactate-associated risk signature based on four LRGs was constructed, which demonstrated significant accuracy in predicting survival. In vitro experimental results showed that MCU gene knockdown reduced the proliferation, migration, invasion, and stemness of PDAC cells, confirming its role in the malignancy of PDAC. This study underscores the importance of LRGs in PDAC, providing new biomarkers and therapeutic targets for the prognostic assessment and treatment of PDAC. Background The metabolism of lactate and lactylation of proteins are believed to influence tumor development through their effects on the tumor microenvironment and immune escape mechanisms. Nevertheless, its significance in pancreatic ductal adenocarcinoma (PDAC) has yet to be fully understood. This investigation sought to assess the predictive value and treatment implications of lactate-related genes (LRGs) in PDAC. Methods We analyzed PDAC data from TCGA and GEO, identifying LRGs. Using WGCNA and consensus clustering, we delineated lactate subtypes and extracted differentially expressed genes. Functional enrichment and GSEA analyses were conducted to explore pathways. A lactate-linked risk signature was constructed using Lasso-Cox regression, and its prognostic value was validated. In vitro experiments were executed to examine the function of MCU in PDAC cells. Results Two lactate subtypes were identified, with distinct gene expression profiles and clinical outcomes. The risk signature, comprising four LRGs, predicted survival with significant accuracy. In vitro, MCU knockdown reduced cell proliferation, migration, invasion, and stemness, confirming its role in PDAC malignancy. Conclusion Our investigation underscores the importance of LRGs in PDAC, providing a novel prognostic signature and therapeutic target. Lactate Pancreatic adenocarcinoma Tumor microenvironment Prognosis MCU Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Pancreatic ductal adenocarcinoma (PDAC) constitutes an exceptionally deadly type of malignancy [ 1 ] . It is anticipated that by the year 2030, PDAC will ascend to become the second most prevalent cause of cancer mortality in the United States [ 2 ] . The occurrence of PDAC is on the rise, but the 5-year survival rate for PDAC remains approximately 10%, despite the use of the most advanced systemic treatments available, such as the best possible surgical interventions, radiation therapy, immunotherapy, and targeted therapy [ 3 , 4 ] . Although there is extensive knowledge about its biology and pathophysiology, applying this understanding in clinical settings to enhance patient outcomes remains a significant challenge [ 5 ] . Hence, identifying key genes that could potentially govern the initiation and progression of PDAC is of paramount importance, as they may offer new avenues for therapeutic intervention. Lactate, once regarded merely as a metabolic byproduct of anaerobic glycolysis, has emerged as an essential factor in the development of malignant tumors, including PDAC. The Warburg effect, characterized by a preference for glycolysis over oxidative phosphorylation, results in elevated lactate production within the tumor microenvironment (TME) [ 6 ] . The buildup of this metabolite leads to TME acidification, which has profound implications for tumor biology. The resulting acidic conditions diminish immune cell effectiveness, particularly T lymphocytes and natural killer cells, through suppression of their cytotoxic activities and growth [ 7 ] . Moreover, investigations have demonstrated that lactic acid drives macrophage transformation toward an immunosuppressive M2 state, thereby suppressing anti-tumor immune responses [ 8 ] . Lactate also acts as a signaling molecule, influencing tumor cell behavior by regulating various signaling pathways, such as the GPR81 pathway, which modulates immune evasion and tumor growth [ 9 ] . In PDAC, lactate serves a crucial function in metabolic reprogramming, supporting tumor cell survival and proliferation by providing essential carbon skeletons for biosynthesis [ 10 ] . Furthermore, lactate can affect tumor angiogenesis by stabilizing hypoxia-inducible factors (HIFs) and promoting the expression of angiogenic factors [ 11 ] . The multifaceted effects of lactate on the TME highlight its significance as a target for therapeutic intervention in pancreatic cancer, offering potential avenues for improving treatment outcomes [ 12 ] . The aim is to elucidate the intricate relationship between lactate and PDAC, examining the lactate on tumor progression and the TME. By investigating the molecular and cellular basis of lactate in PDAC, this study aims to find the potential of interventions targeting lactate in PDAC to improve patient prognosis. Materials and methods Datasets: The Cancer Genome Atlas (TCGA) is a public project cataloging key genomic changes in cancer. We analyzed PDAC data (https://portal.gdc.cancer.gov/) from TCGA, excluding cases with 0 survival time, resulting in 176 tumors and 4 normal tissue samples. A prognostic validation dataset, GSE62452, was procured from GEO (https://www.ncbi.nlm.nih.gov/geo/). From GeneCards (http://www.genecards.org), we identified 2208 Lactate-related genes (LRGs). A set of 206 LRGs were retrieved from the prior literature [13] . After removing the duplicated genes, we got a total of 2346 LRGs, detailed in Supplementary Table 1. WGCNA And Pinpointing the Differentially Expressed Genes (DEGs): The gene co-expression network within the TCGA-PAAD dataset was generated utilizing the “WGCNA” package. The essential module was identified as having both the maximum Pearson coefficient and the strongest link to clinical characteristics. Statistical analysis of mRNA expression across diverse cohorts was executed utilizing the t.test function in R software. Subsequently, the p.adjust function was employed to determine a marked False Discovery Rate (FDR) for individual genes, which facilitated the extraction of differential expression profiles. The mRNAs were screened for differential expression based on the following criteria: an adjusted P-value below 0.05 and an absolute fold change exceeding 1.5. Consensus Clustering: This study utilized the ConsensusClusterPlus package in R to perform consensus clustering, aiming to uncover molecular subgroups associated with Lactate. To validate the findings, the optimal cluster count was evaluated for values of k ranging from 2 to 10, with the entire procedure iterated 1000 times for robustness. Cluster visualizations were produced utilizing the Pheatmap package in R. Functional Enrichment Analysis of the data: To identify enriched signaling pathways and their biological implications, we utilized KEGG and GO databases. The KEGG REST API was employed for pathway analysis (https://www.kegg.jp/kegg/rest/keggapi.html). We conducted an enrichment evaluation on recent KEGG pathway gene classifications employing the R package clusterProfiler. Gene collections were screened with a lower threshold of 5 genes and an upper limit of 5000. Statistical relevance was ascertained by a p -value less than 0.05 and an FDR below 0.1. For gene collection enrichment, we implemented the GO annotations from the R package org.Hs.eg.db and aligned genes to background collections using clusterProfiler for enrichment examination. Gene Set Enrichment Analysis (GSEA) of the data: GSEA scores were sourced from the Broad Institute's database (http://www.gsea-msigdb.org/gsea/downloads.jsp). We combined the low and high Immunosenescence cohorts and applied GSEA v3.0 to the C2.cp.kegg.v7.4 gene set. We used GMT subset analysis with parameters set for 5000 gene set size, 1000 permutations, and a minimum of 5 genes. Significance was established at P<0.05 and FDR<0.1. Survival Analysis: Cluster associations with overall survival (OS) were examined utilizing the R package 'survival'. Outcomes were visualized with heat maps from 'pheatmap' and Kaplan-Meier (KM) curves from 'survminer'. Constructing the Lactate -Linked Risk Signature: In this investigation, we combined survival outcomes, duration, and gene expression data using the glmnet R package. The Lasso-Cox method was applied for regression analysis, and the model was optimized through 10-fold cross-validation. Examination of Immune Landscape Between 2 Lactate Subgroups: Each sample's immune cell score was computed utilizing the R package IOBR, which applies the Cibersort and Estimate algorithms to expression data. IOBR serves as a standard computational tool for analyzing immune-tumor interactions. Expression verification of LRG s : We examined the mRNA expression differences in PDAC tissues utilizing the GEPIA2 platform (http://gepia2.cancerpku.cn/#index), which consolidates information from both TCGA and GTEx databases. Single cell analysis: The LRG expressions within the TME were examined at the single-cell level using the Tumor Immune Single Cell Center (TISCH) database (http://tisch.comp-genomics. org/). Cells and Treatments The pancreatic cancer cell lines Bxpc3 and Capan1 were procured from the American Type Culture Collection (ATCC, USA). For Capan1 cells, cultivation was performed in Dulbecco’s modified Eagle’s medium (DMEM, Cat. C11995500BT, Gibco, USA) enriched with fetal bovine serum (FBS, Cat. 10099-141 C, Gibco, USA, 10%), penicillin (100 U ml −1 ), and streptomycin (100 mg ml −1 ) (Cat. 15140-122, Gibco, USA). For Bxpc3 cells, cultivation was conducted in RPMI-1640 (Roswell Park Memorial Institute medium 1640, Cat. 11875093, Gibco, USA) enriched with FBS (Cat. 10099-141 C, Gibco, USA, 10%), penicillin (100 U ml −1 ), and streptomycin (100 mg ml −1 ) (Cat. 15140-122, Gibco, USA). Cell cultivation was sustained in a moisture-controlled setting at 37 °C with 5% CO2 utilizing a Thermo Scientific HERACELL 240i CO2 Incubator (240i, Thermo Scientific, USA). qRT-PCR 1 mL of RNAiso Plus reagent (Takara,9109, China) was introduced to the cells. Following complete mixing and centrifugation, the upper liquid phase was removed. The total RNA precipitate underwent purification with 75% ethanol solution, subsequently yielding RNAs for reverse transcription. The detailed protocol was executed in accordance with the guidelines of PrimeScript™ RT Master Mix (Takara, RR036A China) and TB Green® Premix Ex Taq™ II (Takara, RR820A, China). The real-time PCR analysis was conducted following the operational protocols of the 7500 Real-Time PCR System (Takara, RR820A China). The quantification of gene expression at the mRNA level was determined using the 2 −ΔΔCt methodology, with final values standardized against β-actin expression. The MCU primer sequences were: AGGATCGGGGAATTGACAGAG (F), GTGTGGTGTATAGTTGCTGGAC (R); The sequence of β-actin primers：CGTGCGTGACATTAAGGAGAA (F)，AGGAAGGAAGGCTGGAAGAG (R)； Cell transfection Cells were placed on 6-well plates at 0.8×10 6 cells/well and maintained overnight in a 37°C incubator containing 5% CO2 until reaching approximately 70% confluence for DNA transfection. Per the supplier’s protocols, 2.5 µg/well plasmid was introduced into the cells using Lipofectamine 3000 (Thermo Fisher Scientific, L3000015; USA). Western Blot The immunoblotting assay was executed per the standard protocols outlined in earlier studies. In brief, cells underwent lysis utilizing Radio Immunoprecipitation Assay Lysis buffer (Cat. 87787, Thermo Scientific, USA, RIPA) comprising protease (Cat. 04693124001, Roche, Switzerland) and phosphatase inhibitors (Cat. B15001-A, Bimake, USA) for 30 min on ice. After centrifugation (12500 rpm for 15 min at 4 °C), we obtained the supernatant. Equal amounts of total proteins were then separated through 10% SDS-polyacrylamide gel electrophoresis. The protein components were subsequently transferred to a polyvinylidene difluoride membrane (0.45 μm, Millipore, Billerica, MA). Following blockage with 5% BSA (Cat. SLBN9354V, Sigma-Aldrich, USA), the membrane underwent incubation with primary antibodies targeting these proteins at specified dilutions: MCU(1:2000, A22525, Abclonal, China), β-actin(1:2000, 4967S, Cell Signaling Technology (CST), USA). The samples were maintained at 4 °C overnight, followed by treatment with a horseradish peroxidase-conjugated goat anti-rabbit IgG (H + L) antibody. The membranes were then exposed to HRP Substrate (Millipore Corporation, Billerica, MA, USA) for visualization, and signal detection was accomplished utilizing a Bio-Rad ChemiDoc MP System (ChemiDoc MP, Bio-Rad, USA). Cell proliferation assays in vitro For the CCK-8 analysis, cells were distributed into 96-well plates with 2 × 10 3 cells in each well. After that, the Cell Counting Kit-8 (CCK8) solution (DOJINDO,CK04, Japan) was introduced, and the microplates were kept in darkness for 2 hours. The absorbance readings were then obtained at 450 nm wavelength. Regarding the colony-formation experiment, cells were initially plated at 500 cells per well and cultured in a 6-well cell culture plate (Corning) for 15 days. The cells were then stabilized with 4% paraformaldehyde and colored utilizing 0.2% crystal violet (Sigma). Using a light microscope, we counted colonies containing over 50 cells. Sphere formation assay Cells were grown in DMEM medium containing insulin (4 ng/mL; Sigma), basic fibroblast growth factor (10 ng/mL; Sigma), EGF (100 ng/mL; Sigma), and B-27 (2%; Invitrogen) was undertaken in an ultra-low attachment 6-well plate (Costar). Renewal of the medium was conducted every 2–3 days. Ten days later, spheroids were visualized using a microscope (Nikon). Migration assay To evaluate the cell invasion potential, cells in culture were diluted to 2 × 10 5 cells/mL within the serum‐free medium, and 200 μL of this cellular preparation was introduced to the upper portion of the Transwell chamber (8.0 μm pore size, No. 3422; Corning, USA). The lower compartment received 800μL of medium containing 10% FBS. Following a twenty‐four hour incubation period, cells remaining on the upper surface were carefully eliminated using a cotton swab moistened with ice‐cold PBS. Subsequently, cells that had migrated to the basolateral membrane were stabilized using 4% PFA (Sigma‐Aldrich) for 30 minutes, succeeded by crystal violet (Sigma‐Aldrich) staining conducted over 2 hours at ambient temperature. Invasion assay Using serum-free medium on ice, the matrix glue was diluted at a ratio of about 1:8, added to the upper chamber at a rate of 100μL/ well, spread over the bottom of the chamber, and incubated in the cell incubator for 2 hours. The excess matrix glue liquid in the upper chamber was gently sucked out, 200μL of cell suspension with serum-free medium was added, and 800μL of complete medium was introduced to the lower compartment. When returning to the chamber, be careful not to generate bubbles. After twenty-four hours of cell placement, the cells on the upper compartment surface were delicately eliminated utilizing a cotton applicator with ice-cold PBS. Cells adhering to the basolateral membrane of the compartment insert were subsequently stabilized with 4% PFA (Sigma-Aldrich) for 30 minutes, succeeded by crystal violet (Sigma-Aldrich) staining for 2 hours at ambient temperature. Statistical Analyses Statistical analysis was executed utilizing R (v4.4.1), implementing KM survival assessment and the log-rank method for survival comparisons. A significance level of P < 0.05 was adopted for statistical computations. GraphPad Prism v. 9.01 (GraphPad Software) was employed for data analysis. Categorical parameters were assessed through the χ 2 test or Fisher’s exact test, whereas continuous parameters were examined utilizing Student’s t-test for paired samples. The findings are denoted as means ± SEM derived from three autonomous experiments, each conducted in duplicate. RESULTS Consensus Clustering for Lactate Subtype Identification and DEGs Pathway Analysis The "limma" program was utilized to detect 1740 DEGs between PDAC and normal tissues. Subsequently, the top 30 DEGs were pinpointed (Figure 1A-B). To obtain hub genes in individuals with PDAC, we examined candidates by WGCNA analysis. Utilizing the hierarchical clustering approach, the co-expressed genes were categorized into distinct modules and assigned color codes (Figure 1C). We then investigated the link between modules and individuals with PDAC and generated the module-trait heatmap using Spearman’s correlation analysis (Figure 1D). We then found 24 LRGs that co-expressed (Figure 1E). The expression levels of these 24 LRGs exhibited significant variation between the normal tissues and PDAC samples, as illustrated in Figure 1F. Subsequently, employing consensus clustering, two clusters of PDAC associated with lactate were delineated. Utilizing the TCGA dataset, these two clusters demonstrated distinct lactate gene expression profiles following k-means clustering analysis, as depicted in Figure 2A-B. The expression of LRGs was low in cluster C1 and high in cluster C2 (Figure 2C). Furthermore, survival analysis suggested that these lactate-derived subgroups exhibited markedly distinct clinical outcomes. Notably, the C2 subgroup exhibited a better survival rate compared to the C1 subgroup, as illustrated in Figure 2D. Identifying the DEGs and Signal Pathways in the Various Lactate Subcategories Within this investigation, essential DEGs and crucial signaling pathways from both classifications were examined to elucidate the molecular mechanisms influencing prognosis across the two lactate subgroups. Analysis revealed 2055 differentially expressed genes (Figure 3A). These genes were found to be enriched in functions of the immune system, encompassing cell adhesion molecules (CAMs), chemokine signaling pathways, hematopoietic cell lineage, Pancreatic secretion, Th17 cell differentiation, extracellular region, immune system process, immune response, cell motility, and regulation of immune system process, as represented in Figure 3B-C. The observations suggested that genes associated with Lactate demonstrated connections to the immune microenvironment. Comparative analysis between C1 and C2 subcategories utilized the GSEA methodology. Furthermore, autoimmune thyroid disease, chemokine signaling pathway, calcium signaling pathway, cyclic nucleotide biosynthetic process, phagolysosome assembly, and G protein coupled receptor signaling pathway all displayed distinct enrichment (Figure 3D-E). Somatic Mutations and the TME in Diverse Immunosenescence Risk Cohorts The analysis uncovered that unique somatic mutation patterns existed between the two subtypes (Figure 4A). The genes exhibiting the highest mutation frequencies included KRAS, TP53, SMAD4, CDKN2A, TTN, and MUC16. In this analysis, we explored the disparities in the TME across two distinct patient cohorts. Figure 4B presents a comprehensive overview of the immune cell infiltration patterns among 176 PDAC patients, as documented in the TCGA dataset. Employing the Cibersort algorithm and the lm22 gene signature matrix, we assessed the presence of 22 distinct immune cell types in each cohort. Our findings revealed that the C2 cohort demonstrated superior immune activity, as evidenced by higher ImmuneScore, StromalScore, and EstimateScore, indicating a more robust immune response within the TME individuals(Figure 4C). Furthermore, the C1 cohort displayed a notable reduction in the prevalence of naive B cells, memory B cells, CD8+ T cells, activated CD4+ T cell memory, and activated NK cells, as opposed to those in the C2 cohort. Conversely, the C1 cohort demonstrated increased levels of plasma cells, M0 macrophages, monocytes and eosinophils (Figure 4D). These observations highlight the complex interactions among immune cell dynamics and the risk stratification of PDAC patients. Establishment and Validation of the Lactate Risk Signature In this study, we have developed an innovative prognostic model predicated on the LRGs. The Lasso regression analysis was employed to assess these three LRGs, and they were consequently selected to form the predictive model, as illustrated in Figure 5A-B. By setting the lambda value at 0.06676, we were able to pinpoint three key genes. The predictive model is encapsulated in the following formula: Riskscore = (0.00199 * NQO1) - (0.09731 * STAT4) + (0.15009 * KCNK1) + (0.05063 * MCU). Utilizing the median risk score, we categorized our study population into a high-risk cohort (H) and a low-risk cohort (L). We utilized the TCGA dataset as our training cohort and the GSE62452 dataset as the validation cohort. Furthermore, the connection between the two cohorts and OS status was evaluated. The findings revealed that a markedly lower proportion of patients in the H cohort survived in the TCGA dataset, with a more pronounced effect in the H cohort (Figure 5C). The KM survival assessment corroborated that individuals in the H cohort experienced shorter survival times compared to those in the L cohort, as evidenced in both two cohorts (Figure 5D and 5F). In the training cohort, the receiver operating characteristic (ROC) curves were developed with area under the curve (AUC) values of 0.56 for the 1-year survival, 0.58 for the 3-year survival, and 0.64 for the 5-year survival (Figure 5E). Similarly, in the validation cohort, the ROC curves were generated with AUC values of 0.64 for the 1-year survival, 0.65 for the 3-year survival, and 0.84 for the 5-year survival (Figure 5G). Single cell analysis of LRGs In our current research, We identified four LRGs associated with PDAC (KCNK1, MCU, NQO1 and STAT4). To delve deeper into the interplay between these LRG expressions and the tumor immune landscape, we utilized the TISCH. We analyzed 2 datasets that were PDAC_CRA001160 dataset and the PDAC_GSE162708 dataset (Figure 6A-B). Within the PDAC_CRA001160 dataset, elevated levels of KCNK1 and MCU were observed in Malignant cells, while NQO1 showed high expression in both Malignant and Endothelial cells, and STAT4 exhibited strong expression in Plasma cells (Figure 6C-F). Correspondingly, in the PDAC_GSE162708 dataset, KCNK1 demonstrated notable expression in Malignant cells, MCU displayed high levels in both Malignant and Endothelial cells, NQO1 displayed elevated expression in Endothelial cells, and STAT4 exhibited marked expression in NK cells and CD8 T cells as depicted in Figure 6G-J. Expression and prognosis of LRGs To find the expression of these four LRGs in PDAC, we performed a search using GEPIA2.0. It was found that KCNK1, MCU, and NQO1 showed high expression in tumor tissues. Moreover, elevated MCU levels were notably linked to unfavorable clinical outcomes (Figure 7A-H). Multilevel expression validation and in vitro functional investigation of MCU Given the high expression of MCU in pancreatic cancer patients and its prognostic relevance, we selected MCU for subsequent biological experiments. In order to verify the biological function of MCU, we conducted qRT-PCR (Figure 8A) and WB (Figure 8B) in whole pancreatic cancer cell lines and normal pancreatic epithelial cells and found that MCU was generally highly expressed in pancreatic cancer cell lines, especially in Bxpc3 and Capan1 cells. By verifying the knockdown efficiency of plasmids, it was found that sh1 and sh4 had the best knockdown effect (Figure 8C-D). Therefore, we chose to use sh1 and sh4 plasmosomes to knock down MCU genes in Bxpc3 and Capan1 cells to observe whether altering MCU expression would affect the malignant potential of tumor cells. Colony-formation assay (Figure 8E-F) and CCK8 experiments (Figure 8G) revealed that MCU gene silencing resulted in decreased proliferation rates of pancreatic cancer cells, suggesting the MCU gene's role in enhancing pancreatic cancer cell proliferation. The impact of MCU suppression on cellular motility (Figure 8H-I) and invasiveness (Figure 8J-K) was evaluated using transwell assays, demonstrating reduced migration and invasion capabilities. Furthermore, the sphere-formation assay (Figure 8L) examined how MCU downregulation influenced pancreatic cancer stemness, revealing diminished stem cell properties following reduced MCU expression. These experimental observations provide substantial evidence that MCU enhances pancreatic cancer cell proliferation, motility, invasiveness, and stemness characteristics, thereby contributing to the aggressive nature of pancreatic cancer. Discussion PDAC is recognized as one of the most aggressive malignancies. It is characterized by early metastasis and late diagnosis, often presenting at advanced stages with limited treatment options [ 1 ] . Despite advancements in therapeutic strategies, including surgical resection, chemotherapy, and targeted therapies, the aggregate 5-year survival outcome remains unfavorable, primarily due to the high degree of chemoresistance and early metastatic spread inherent to PDAC [ 3 , 4 ] . A significant bottleneck in enhancing patient results is the absence of effective predictive biomarkers that can guide personalized treatment strategies and prognostication. Identifying such biomarkers is imperative, as they could potentially stratify patients for more tailored therapies, predict treatment response, and provide a valuable understanding of the intricate interactions between the TME and PDAC progression [ 14 ] . The advancement of computational biology software and the amalgamation of diverse -omics datasets have paved the way for identifying innovative biological indicators [ 15 ] . These advances are critical in the quest to understand the molecular underpinnings of PDAC and to identify signatures that can predict treatment efficacy and patient survival [ 16 ] . The complexity of PDAC is further compounded by its intricate relationship with the immune system, where the TME can modulate immune responses to facilitate tumor growth and evade therapeutic interventions [ 17 ] . Lactate once considered a mere byproduct of glycolysis, is now recognized as a key mediator in cancer biology, particularly in PDAC, where it contributes to an acidic TME that impairs immune cell function and promotes macrophage polarization towards an immunosuppressive phenotype. Additionally, lactate acts as a signaling molecule, influencing pathways like GPR81 to modulate immune evasion and tumor growth, and it affects tumor angiogenesis by stabilizing HIFs [ 6 – 10 ] . The study of lactate in PDAC is of significant value as it provides a deeper understanding of the metabolic reprogramming that occurs in cancer cells. By elucidating the mechanisms through which lactate contributes to tumor progression, researchers can identify potential therapeutic targets that disrupt these processes. For instance, targeting lactate production or its signaling pathways could enhance the efficacy of existing cancer therapies by restoring immune surveillance and inhibiting tumor growth [ 18 ] . Moreover, elucidating the function of lactate in PDAC may contribute to establishing enhanced diagnostic methods and predictive indicators, enabling timely identification and improved treatment strategies for this condition [ 19 ] . Our study identifies two distinct lactate-associated subtypes of PDAC, characterized by unique gene expression profiles and divergent clinical outcomes. The creation of a risk signature model utilizing LRGs, particularly highlighting the involvement of KCNK1, NQO1, STAT4 and MCU, offers a novel prognostic tool for PDAC patients. The high-risk cohort, as determined by the risk score, is linked to poorer survival rates, indicating the potential utility of this model in stratifying patients for more personalized treatment approaches. Prior studies have highlighted that KCNK1 is instrumental in driving the malignancy of tumor cells, particularly in terms of influencing the cell cycle and the aggressive growth and spread of cancer cells. KCNK1 is highly expressed in thyroid cancer, breast cancer (BRCA), non-functioning pituitary adenoma, PDAC and bladder cancer [ 20 – 24 ] . NQO1 is a two-electron reductase that is highly active in the pro-oxidative milieu of human malignancies, which makes it a selective marker for tumors [ 25 ] . The STAT4 gene, belonging to the STAT family, exhibits significant genetic variations that profoundly impact immune reactions and the progression of illnesses, particularly in the realms of cancer and autoimmune disorders. However, research outcomes display variability when examined across different studies and demographic groups [ 26 – 29 ] . MCU serves a critical function in cancer progression. It's upregulated in metastatic tumors, enhancing cellular movement and invasion through a ROS/HIF-1α molecular cascade. However, its effects vary across different cancer types and cell lines [ 30 – 32 ] .In BRCA, particularly the triple-negative subtype, MCU expression links to tumor size and lymph node infiltration, and its downregulation reduces tumor size and cell motility in xenograft models [ 30 ] . In melanoma, MCU silencing suppresses cell proliferation while enhancing migratory and invasive capabilities, diminishing responsiveness to immune-based therapies [ 31 ] . In pancreatic cancer, MCU inhibition reduces HINT2-dependent apoptosis [ 33 ] . In colon cancer, miRNA-25 overexpression decreases mtCa2 + uptake and shows increased expression in human colon malignancies where MCU levels are diminished [ 34 ] . In hepatocellular carcinoma, MCU upregulation is associated with poor survival and metastasis, and its increase promotes ROS production and cell motility [ 35 ] . In ovarian cancer, MCU silencing diminishes cell proliferation and migration, which is linked to decreased ROS production [ 36 ] . In renal cell carcinoma, MCU expression reduction leads to decreased cell migration and metastatic potential [ 37 ] . These studies highlight the diverse effects of MCU modulation in different cancers. In this study, we have validated that MCU promotes the proliferation, migration, invasion, and stemness of tumor cells in pancreatic cancer, thereby confirming the malignant potential of the MCU gene in this disease. This indicates that MCU might serve as a crucial factor in the immune regulation and malignant advancement of PDAC. The clinical application value of MCU in PDAC lies in its potential as a therapeutic target and a biomarker for prognosis and response to therapy. The use of data from TCGA and GEO databases provided a robust foundation for our analysis. The application of WGCNA and consensus clustering allowed us to delineate distinct lactate subtypes, each with unique gene expression profiles and clinical implications. This methodological approach enabled us to identify differentially expressed genes and construct a lactate-linked risk signature with significant prognostic value. However, it is important to acknowledge the limitations of our study. The reliance on existing databases may introduce biases inherent to the data collection and annotation processes. Additionally, while our in vitro experiments provided valuable insights into the function of MCU in PDAC cells, further in vivo studies are necessary to fully validate these findings and their clinical relevance. The identification of two lactate subtypes with distinct clinical outcomes highlights the potential for personalized treatment strategies in PDAC. The risk signature we developed, comprising four LRGs, offers a novel tool for prognostic assessment. This signature could be used to stratify patients into different risk groups, potentially guiding treatment decisions and improving patient outcomes. The role of MCU in PDAC malignancy, confirmed through our in vitro experiments, presents a promising therapeutic target. Future research should focus on validating these findings in larger cohorts and exploring the potential of targeting MCU and other LRGs in clinical trials. Additionally, investigating the interplay between lactate metabolism, protein lactylation, and the immune response in PDAC could reveal new therapeutic avenues and biomarkers for early detection and treatment. Conclusion The research conducted on lactate-related genes (LRGs) in pancreatic ductal adenocarcinoma (PDAC) has shed light on their potential impact on tumor development, particularly through their influence on the tumor microenvironment and immune evasion mechanisms. The findings presented in this study have revealed the existence of distinct lactate subtypes in PDAC, each associated with unique gene expression patterns and clinical outcomes. Through the development of a predictive risk signature comprised of four LRGs, this investigation has not only provided valuable insights into the prognostic implications of lactate metabolism in PDAC but has also identified potential therapeutic targets for intervention. Additionally, the functional validation of the mitochondrial calcium uniporter (MCU) in PDAC cells has further emphasized the significance of LRGs in driving the malignancy of this aggressive cancer type. Overall, this study underscores the critical role of LRGs in PDAC pathogenesis and highlights the potential for novel prognostic tools and targeted therapies in the management of pancreatic cancer. Declarations Data Availability Statement The data examined in this research were derived from openly accessible databases: https://portal.gdc.cancer.gov/ (TCGA-PAAD) and the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). Ethics Statement Not applicable. All data in this study are publicly available. Funding This study was supported by the National Natural Science Foundation of China (82174169) and Fudan University DIGAOJIAN Project (No.DGF601020-1). Competing Interests The authors declare no competing interests. Author Contributions: Yuhang Chen : Writing–review&editing, Experiment, Methodology. Fenglin Zhang : Writing – original draft, Experiment. Suoyi Dai : Software. Jiangang Zhao and Wenxun Cai : Experiment. Ke Zhang : Writing – original draft, Data curation, Formal analysis. Xinghe Liao : Writing – review & editing, Validation. Lianyu Chen : Validation, Supervision. Acknowledgements Not applicable. References Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12-49. Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74(11):2913-2921. Yu S, Zhang C, Xie KP. Therapeutic resistance of pancreatic cancer: Roadmap to its reversal. Biochim Biophys Acta Rev Cancer. 2021;1875(1):188461. Grossberg AJ, Chu LC, Deig CR, et al. Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma. CA Cancer J Clin. 2020;70(5):375-403. Hu ZI, O'Reilly EM. Therapeutic developments in pancreatic cancer. Nat Rev Gastroenterol Hepatol. 2024;21(1):7-24. Warburg O, Wind F, Negelein E. The metabolism of tumors in the body. J Gen Physiol. 1927;8(6):519-530. Quinn WJ 3rd, Jiao J, TeSlaa T, et al. Lactate Limits T Cell Proliferation via the NAD(H) Redox State. Cell Rep. 2020;33(11):108500. Colegio OR, Chu NQ, Szabo AL, et al. Functional polarization of tumour-associated macrophages by tumour-derived lactic acid. Nature. 2014;513(7519):559-563. Brown TP, Ganapathy V. Lactate/GPR81 signaling and proton motive force in cancer: Role in angiogenesis, immune escape, nutrition, and Warburg phenomenon. Pharmacol Ther. 2020;206:107451. Hui S, Ghergurovich JM, Morscher RJ, et al. Glucose feeds the TCA cycle via circulating lactate. Nature. 2017;551(7678):115-118. Semenza GL. Hypoxia-inducible factors in physiology and medicine. Cell. 2012;148(3):399-408. Zhang D, Tang Z, Huang H, et al. Metabolic regulation of gene expression by histone lactylation. Nature. 2019;574(7779):575-580. Jiang K, Zhu L, Huang H, Zheng L, Wang Z, Kang X. Lactate score classification of hepatocellular carcinoma helps identify patients with tumors that respond to immune checkpoint blockade therapy. Cell Oncol (Dordr). 2024;47(1):175-188. Bailey P, Chang DK, Nones K, et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature. 2016;531(7592):47-52. Hoadley KA, Yau C, Wolf DM, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 2014;158(4):929-944. Collisson EA, Sadanandam A, Olson P, et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med. 2011;17(4):500-503. Hu ZI, O'Reilly EM. Therapeutic developments in pancreatic cancer. Nat Rev Gastroenterol Hepatol. 2024;21(1):7-24. Ippolito L, Morandi A, Giannoni E, Chiarugi P. Lactate: A Metabolic Driver in the Tumour Landscape. Trends Biochem Sci. 2019;44(2):153-166. Li X, Yang Y, Zhang B, et al. Lactate metabolism in human health and disease [published correction appears in Signal Transduct Target Ther. 2022 Oct 31;7(1):372. Lin X, Wu JF, Wang DM, Zhang J, Zhang WJ, Xue G. The correlation and role analysis of KCNK2/4/5/15 in Human Papillary Thyroid Carcinoma microenvironment. J Cancer. 2020;11(17):5162-5176. Huang X, Feng Y, Ma D, et al. The molecular, immune features, and risk score construction of intraductal papillary mucinous neoplasm patients. Front Mol Biosci. 2022;9:887887. Karatug Kacar A, Bulutay P, Aylar D, Celikten M, Bolkent S. Characterization and comparison of insulinoma tumor model and pancreatic damage caused by the tumor, and identification of possible markers. Mol Biol Rep. 2024;51(1):109. Xiong F, Wu GH, Wang B, Chen YJ. Plastin-3 is a diagnostic and prognostic marker for pancreatic adenocarcinoma and distinguishes from diffuse large B-cell lymphoma. Cancer Cell Int. 2021;21(1):411. Zhang W, Chen XS, Wei Y, et al. Overexpressed KCNK1 regulates potassium channels affecting molecular mechanisms and biological pathways in bladder cancer Eur J Med Res. 2024;29(1):257. Khan AEMA, Arutla V, Srivenugopal KS. Human NQO1 as a Selective Target for Anticancer Therapeutics and Tumor Imaging. Cells. 2024;13(15):1272. Wang C, Gao N, Yang L, et al. Stat4 rs7574865 polymorphism promotes the occurrence and progression of hepatocellular carcinoma via the Stat4/CYP2E1/FGL2 pathway. Cell Death Dis. 2022;13(2):130. Ma Y, Zhou Y, Zhang H, Su X. Immune Response-Related Genes - STAT4, IL8RA and CCR7 Polymorphisms in Lung Cancer: A Case-Control Study in China. Pharmgenomics Pers Med. 2020;13:511-519. Cotterchio M, Lowcock E, Bider-Canfield Z, et al. Association between Variants in Atopy-Related Immunologic Candidate Genes and Pancreatic Cancer Risk. PLoS One. 2015;10(5):e0125273. Núñez-Marrero A, Arroyo N, Godoy L, Rahman MZ, Matta JL, Dutil J. SNPs in the interleukin-12 signaling pathway are associated with breast cancer risk in Puerto Rican women. Oncotarget. 2020;11(37):3420-3431. Tosatto A, Sommaggio R, Kummerow C, et al. The mitochondrial calcium uniporter regulates breast cancer progression via HIF-1α. EMBO Mol Med. 2016;8(5):569-585. Stejerean-Todoran I, Zimmermann K, Gibhardt CS, et al. MCU controls melanoma progression through a redox-controlled phenotype switch. EMBO Rep. 2022;23(11):e54746. Vultur A, Gibhardt CS, Stanisz H, Bogeski I. The role of the mitochondrial calcium uniporter (MCU) complex in cancer. Pflugers Arch. 2018;470(8):1149-1163. Chen L, Sun Q, Zhou D, et al. HINT2 triggers mitochondrial Ca2+ influx by regulating the mitochondrial Ca2+ uniporter (MCU) complex and enhances gemcitabine apoptotic effect in pancreatic cancer. Cancer Lett. 2017;411:106-116. Marchi S, Lupini L, Patergnani S, et al. Downregulation of the mitochondrial calcium uniporter by cancer-related miR-25. Curr Biol. 2013;23(1):58-63. Ren T, Zhang H, Wang J, et al. MCU-dependent mitochondrial Ca2+ inhibits NAD+/SIRT3/SOD2 pathway to promote ROS production and metastasis of HCC cells. Oncogene. 2017;36(42):5897-5909. Zhao L, Jiang M, Tian T, et al. Effects of MCU-mediated Ca2+ Homeostasis on Ovarian Cancer Cell SKOV3 Proliferation, Migration and Transformation. Curr Mol Med. 2023;23(8):774-783. Meng K, Hu Y, Wang D, et al. EFHD1, a novel mitochondrial regulator of tumor metastasis in clear cell renal cell carcinoma. Cancer Sci. 2023;114(5):2029-2040. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx supplementaryfilerawmaterials.zip Cite Share Download PDF Status: Published Journal Publication published 21 May, 2025 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 31 Jan, 2025 Editor assigned by journal 29 Jan, 2025 Submission checks completed at journal 27 Jan, 2025 First submitted to journal 24 Jan, 2025 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-5899024","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407642969,"identity":"88c2eba0-4f00-4c74-83be-d14f4ad1d320","order_by":0,"name":"Yuhang Chen","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Yuhang","middleName":"","lastName":"Chen","suffix":""},{"id":407642970,"identity":"8836bc58-13e3-42fb-b48c-c79093f2e9d8","order_by":1,"name":"Fenglin Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fenglin","middleName":"","lastName":"Zhang","suffix":""},{"id":407642971,"identity":"08b16d56-7d6d-4750-b670-66e91cd8fe49","order_by":2,"name":"Suoyi Dai","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Suoyi","middleName":"","lastName":"Dai","suffix":""},{"id":407642972,"identity":"5a9b5f0a-5742-4bfd-a780-845b983d30c3","order_by":3,"name":"Jiangang Zhao","email":"","orcid":"","institution":"Shaoxing Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiangang","middleName":"","lastName":"Zhao","suffix":""},{"id":407642973,"identity":"d73409ab-2ad7-4be9-a239-d7d4d1e886e3","order_by":4,"name":"Wenxun Cai","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Wenxun","middleName":"","lastName":"Cai","suffix":""},{"id":407642974,"identity":"23a784e9-ef62-4da8-8cf2-3d222716c294","order_by":5,"name":"Ke Zhang","email":"","orcid":"","institution":"Shanghai Traditional Chinese Medicine Integrated Hospital, Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Zhang","suffix":""},{"id":407642975,"identity":"8eb3b2b4-d59e-458e-be42-49c68694216e","order_by":6,"name":"Xinghe Liao","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xinghe","middleName":"","lastName":"Liao","suffix":""},{"id":407642976,"identity":"0754b881-447f-4459-ac8e-d6eaf33683a8","order_by":7,"name":"Lianyu Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACPmYow4CBgfExmMXM3IBXCxuSFmZjCMVIQAuMAVTLJg2mGAhpYWd+9pi3jUHenL33WHVBxZ9o/naglh8V2/A4jM3cGKjFcGfPubTbM84Y5M44zNjA2HPmNj6/mEnztv1PMLiRY3abt80gtwGohZmxDZ8W9m9ALQwJBvffmBWDtMwnrIXHDKLlBo8ZM0jLBiK0lEnOOcdguOFMjrE0zxnj3I1ALQfx+YWf//g2iTdlDPIGx88YfuapkMudd/7wwQc/KnBrAQEmHnSRA3jVAwHjD0IqRsEoGAWjYGQDABW7S/gqPcX0AAAAAElFTkSuQmCC","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Lianyu","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-01-25 03:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5899024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5899024/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-025-14319-1","type":"published","date":"2025-05-21T15:58:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74981879,"identity":"0771de53-e1b8-40ef-b170-0ba7d75b4c6f","added_by":"auto","created_at":"2025-01-29 05:03:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3230933,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis and visualization of differentially expressed genes. (A) Expression patterns of DEGs comparing PDAC versus normal tissues; (B) Leading 30 DEGs identified between PDAC and control samples. Selection of central genes through WGCNA; (C) Hierarchical clustering tree; (D) Heat visualization of module-trait associations; (E) Overlapping analysis depicting shared genes among LRGs, module-specific genes and DEGs; (F) Expression profile matrix showing 24 LRGs across PDAC and adjacent normal specimens from the TCGA dataset.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5899024/v1/1aac4ba980380c2f482c3f37.png"},{"id":74981880,"identity":"bdc90ffb-10d3-45c6-9c30-39c928941c43","added_by":"auto","created_at":"2025-01-29 05:03:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1147093,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of Lactate-associated subgroups through consensus clustering. (A)The delta area curve for consensus clustering illustrates the comparative differences in the area beneath the cumulative distribution function (CDF) curve for k values ranging from 2 to 10; (B) The consensus pattern of the clustering examination utilizing k-means clustering (k = 2); (C) The expression patterns of 24 LRGs are displayed in the heatmap visualization; (D) KM plots showing patient OS between the C1 and C2 subgroups.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5899024/v1/6ed4352827062dfc65007b07.png"},{"id":74981869,"identity":"e20a7df1-7556-4d5b-a416-416630733ecd","added_by":"auto","created_at":"2025-01-29 05:03:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1903128,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DEGs and related signaling pathways. (A) DEG distribution between C1 and C2 subgroups within TCGA dataset; (B-C) Pathway enrichment analyses using KEGG and GO. (D-E) GSEA identifies key signal pathways present in both subcategories.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5899024/v1/3ffffac30eece2f9134f43c6.png"},{"id":74981901,"identity":"5f0105dc-6df5-4e81-a7f4-e9ee75f54968","added_by":"auto","created_at":"2025-01-29 05:03:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2714598,"visible":true,"origin":"","legend":"\u003cp\u003eImmunological characteristics of distinct Lactate subcategories. (A) Assessment of somatic mutation patterns among Lactate classifications. (B) Comparative distribution of immune cell infiltration; (C) Stromal, Immune, and Estimate scores for two subcategories; (D) Notable disparities in immune cell infiltration between subcategories. (* P＜0.05;** P＜0.01;*** P＜0.001;****P＜0.0001).\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5899024/v1/698dcfbcb287356d0e6da4f5.png"},{"id":74983508,"identity":"c0ccb96f-dba2-43a9-86b1-66d9dbc88edb","added_by":"auto","created_at":"2025-01-29 05:28:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1557239,"visible":true,"origin":"","legend":"\u003cp\u003eConstructing and confirming the Lactate risk profile. (A-B) Lasso Cox regression evaluation; (C) Score allocation of SFRGs and patient outcome patterns in the TCGA dataset. (D-E) KM analysis and temporal ROC curve evaluation for risk stratification in the TCGA dataset. (F-G) Kaplan-Meier analysis and temporal ROC curve evaluation for risk stratification in the GSE62452 dataset.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5899024/v1/223bc396c67faf1d4d430b93.png"},{"id":74983515,"identity":"1921a009-b388-4fd6-a211-0cee295ef652","added_by":"auto","created_at":"2025-01-29 05:28:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1927493,"visible":true,"origin":"","legend":"\u003cp\u003eSingle cell analysis of LRGs in PDAC. (A-J) Cellular-level mapping unveiled the allocation of KCNK1, MCU, NQO1 and STAT4 across diverse immune cell populations in PDAC_CRA001160 and PDAC_GSE162708.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5899024/v1/814ab0362e4a3a3600937314.png"},{"id":74981866,"identity":"cbe5a694-42b4-4894-90df-5e6f7ff61fb9","added_by":"auto","created_at":"2025-01-29 05:03:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":985049,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic value and expression of LRGs. (A-H) Differential analysis of mRNA expression of KCNK1, MCU, NQO1 and STAT4 in PDAC in GEPIA2.0 database. Kaplan–Meier curve for OS between the high and low expression cohort in PDAC in TCGA database.(* P＜0.05).\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5899024/v1/332f78c84b886231136e536e.png"},{"id":74981875,"identity":"c364be17-7cbe-4287-814a-5319facbc7c2","added_by":"auto","created_at":"2025-01-29 05:03:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":19418504,"visible":true,"origin":"","legend":"\u003cp\u003eVerification of MCU biological function. (A) qRT-PCR: Examining MCU expression patterns between pancreatic cancer cell lines and normal pancreatic epithelial cells; (B) WB: To verify the expression level of MCU and the quantitation of WB in pancreatic cancer cell lines and normal pancreatic epithelial cells; (C) WB: Knock-down efficiency of plasmid in Capan1 and Bxpc3 cells; (D) Knock-down quantification of WB to verify inefficiency; (E) Colony-formation assay after reducing MCU level in Capan1 and Bxpc3 cells; (F) Colony-formation assay quantification; (G) CCK8 experiments after decreasing MCU level in Capan1 and Bxpc3 cells; (H) Transwell migration assay after decreasing MCU level in Capan1 and Bxpc3 cells; (I) Quantification of migration assay (J) Transwell invasion assay after decreasing MCU expression in Capan1 and Bxpc3 cells; (K) Quantification of invasion assay; (L) Sphere-formation assay after reducing MCU expression in Capan1 and Bxpc3 cells.\u003c/p\u003e","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5899024/v1/8a27424b807a31197c2ced0d.png"},{"id":83460641,"identity":"ffb50182-22df-440d-98fe-1108680f3b85","added_by":"auto","created_at":"2025-05-26 16:13:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":30256998,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5899024/v1/31b425a8-93c2-42f5-a4fe-dbe41306c282.pdf"},{"id":74983503,"identity":"fa8ddc86-0699-4096-998d-5bc829b48e39","added_by":"auto","created_at":"2025-01-29 05:27:42","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":55207,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5899024/v1/cb5a6206b40db3f6480f0786.xlsx"},{"id":74981868,"identity":"3e1c5fef-aa0c-4963-81a4-907ad8d5957d","added_by":"auto","created_at":"2025-01-29 05:03:41","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1175037,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfilerawmaterials.zip","url":"https://assets-eu.researchsquare.com/files/rs-5899024/v1/4bbbfcc479ea1c00bf3401a2.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lactate-associated gene MCU promotes the proliferation, migration, and invasion of pancreatic adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) constitutes an exceptionally deadly type of malignancy\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. It is anticipated that by the year 2030, PDAC will ascend to become the second most prevalent cause of cancer mortality in the United States\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The occurrence of PDAC is on the rise, but the 5-year survival rate for PDAC remains approximately 10%, despite the use of the most advanced systemic treatments available, such as the best possible surgical interventions, radiation therapy, immunotherapy, and targeted therapy\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Although there is extensive knowledge about its biology and pathophysiology, applying this understanding in clinical settings to enhance patient outcomes remains a significant challenge\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Hence, identifying key genes that could potentially govern the initiation and progression of PDAC is of paramount importance, as they may offer new avenues for therapeutic intervention.\u003c/p\u003e \u003cp\u003eLactate, once regarded merely as a metabolic byproduct of anaerobic glycolysis, has emerged as an essential factor in the development of malignant tumors, including PDAC. The Warburg effect, characterized by a preference for glycolysis over oxidative phosphorylation, results in elevated lactate production within the tumor microenvironment (TME)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The buildup of this metabolite leads to TME acidification, which has profound implications for tumor biology. The resulting acidic conditions diminish immune cell effectiveness, particularly T lymphocytes and natural killer cells, through suppression of their cytotoxic activities and growth\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Moreover, investigations have demonstrated that lactic acid drives macrophage transformation toward an immunosuppressive M2 state, thereby suppressing anti-tumor immune responses\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Lactate also acts as a signaling molecule, influencing tumor cell behavior by regulating various signaling pathways, such as the GPR81 pathway, which modulates immune evasion and tumor growth\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In PDAC, lactate serves a crucial function in metabolic reprogramming, supporting tumor cell survival and proliferation by providing essential carbon skeletons for biosynthesis\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Furthermore, lactate can affect tumor angiogenesis by stabilizing hypoxia-inducible factors (HIFs) and promoting the expression of angiogenic factors\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The multifaceted effects of lactate on the TME highlight its significance as a target for therapeutic intervention in pancreatic cancer, offering potential avenues for improving treatment outcomes\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe aim is to elucidate the intricate relationship between lactate and PDAC, examining the lactate on tumor progression and the TME. By investigating the molecular and cellular basis of lactate in PDAC, this study aims to find the potential of interventions targeting lactate in PDAC to improve patient prognosis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eDatasets:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Cancer Genome Atlas (TCGA) is a public project cataloging key genomic changes in cancer. We analyzed PDAC data (https://portal.gdc.cancer.gov/) from TCGA, excluding cases with 0 survival time, resulting in 176 tumors and 4 normal tissue samples. A prognostic validation dataset, GSE62452, was procured from GEO (https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e\n\u003cp\u003eFrom GeneCards (http://www.genecards.org), we identified 2208 Lactate-related genes (LRGs). A set of 206 LRGs were retrieved from the prior literature\u003csup\u003e[13]\u003c/sup\u003e. After removing the duplicated genes, we got a total of 2346 LRGs, detailed in Supplementary Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGCNA And Pinpointing the Differentially Expressed\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Genes (DEGs):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gene co-expression network within the TCGA-PAAD dataset was generated utilizing the \u0026ldquo;WGCNA\u0026rdquo; package. The essential module was identified as having both the maximum Pearson coefficient and the strongest link to clinical characteristics. Statistical analysis of mRNA expression across diverse cohorts was executed utilizing the t.test function in R software. Subsequently, the p.adjust function was employed to determine a marked False Discovery Rate (FDR) for individual genes, which facilitated the extraction of differential expression profiles. The mRNAs were screened for differential expression based on the following criteria: an adjusted P-value below 0.05 and an absolute fold change exceeding 1.5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsensus Clustering:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized the ConsensusClusterPlus package in R to perform consensus clustering, aiming to uncover molecular subgroups associated with Lactate. To validate the findings, the optimal cluster count was evaluated for values of k ranging from 2 to 10, with the entire procedure iterated 1000 times for robustness. Cluster visualizations were produced utilizing the Pheatmap package in R.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Enrichment Analysis of the data:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify enriched signaling pathways and their biological implications, we utilized KEGG and GO databases. The KEGG REST API was employed for pathway analysis (https://www.kegg.jp/kegg/rest/keggapi.html). We conducted an enrichment evaluation on recent KEGG pathway gene classifications employing the R package clusterProfiler. Gene collections were screened with a lower threshold of 5 genes and an upper limit of 5000. Statistical relevance was ascertained by a\u003cem\u003e\u0026nbsp;p\u003c/em\u003e-value less than 0.05 and an FDR below 0.1. For gene collection enrichment, we implemented the GO annotations from the R package org.Hs.eg.db and aligned genes to background collections using clusterProfiler for enrichment examination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis (GSEA) of the data:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSEA scores were sourced from the Broad Institute\u0026apos;s database (http://www.gsea-msigdb.org/gsea/downloads.jsp). We combined the low and high Immunosenescence cohorts and applied GSEA v3.0 to the C2.cp.kegg.v7.4 gene set. We used GMT subset analysis with parameters set for 5000 gene set size, 1000 permutations, and a minimum of 5 genes. Significance was established at P\u0026lt;0.05 and FDR\u0026lt;0.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCluster associations with overall survival (OS) were examined utilizing the R package \u0026apos;survival\u0026apos;. Outcomes were visualized with heat maps from \u0026apos;pheatmap\u0026apos; and Kaplan-Meier (KM) curves from \u0026apos;survminer\u0026apos;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstructing the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eLactate\u003c/strong\u003e\u003cstrong\u003e-Linked Risk\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Signature:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this investigation, we combined survival outcomes, duration, and gene expression data using the glmnet R package. The Lasso-Cox method was applied for regression analysis, and the model was optimized through 10-fold cross-validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExamination of Immune Landscape\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Between 2 Lactate Subgroups:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach sample\u0026apos;s immune cell score was computed utilizing the R package IOBR, which applies the Cibersort and Estimate algorithms to expression data. IOBR serves as a standard computational tool for analyzing immune-tumor interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpression verification of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eLRG\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the mRNA expression differences in PDAC tissues utilizing the GEPIA2 platform (http://gepia2.cancerpku.cn/#index), which consolidates information from both TCGA and GTEx databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle cell analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LRG expressions within the TME were examined at the single-cell level using the Tumor Immune Single Cell Center (TISCH) database (http://tisch.comp-genomics. org/).\u003c/p\u003e\n\u003ch3\u003eCells and Treatments\u003c/h3\u003e\n\u003cp\u003eThe pancreatic cancer cell lines Bxpc3 and Capan1 were procured from the American Type Culture Collection (ATCC, USA). For Capan1 cells, cultivation was performed in Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium (DMEM, Cat. C11995500BT, Gibco, USA) enriched with fetal bovine serum (FBS, Cat. 10099-141 C, Gibco, USA, 10%), penicillin (100 U ml\u003csup\u003e\u0026minus;1\u003c/sup\u003e), and streptomycin (100 mg ml\u003csup\u003e\u0026minus;1\u003c/sup\u003e) (Cat. 15140-122, Gibco, USA). For Bxpc3 cells, cultivation was conducted in RPMI-1640 (Roswell Park Memorial Institute medium 1640, Cat. 11875093, Gibco, USA) enriched with FBS (Cat. 10099-141 C, Gibco, USA, 10%), penicillin (100 U ml\u003csup\u003e\u0026minus;1\u003c/sup\u003e), and streptomycin (100 mg ml\u003csup\u003e\u0026minus;1\u003c/sup\u003e) (Cat. 15140-122, Gibco, USA). Cell cultivation was sustained in a moisture-controlled setting at 37 \u0026deg;C with 5% CO2 utilizing a Thermo Scientific HERACELL 240i CO2 Incubator (240i, Thermo Scientific, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eqRT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1 mL of RNAiso Plus reagent (Takara,9109, China) was introduced to the cells. Following complete mixing and centrifugation, the upper liquid phase was removed. The total RNA precipitate underwent purification with 75% ethanol solution, subsequently yielding RNAs for reverse transcription. The detailed protocol was executed in accordance with the guidelines of PrimeScript\u0026trade; RT Master Mix (Takara, RR036A China) and TB Green\u0026reg; Premix Ex Taq\u0026trade; II (Takara, RR820A, China). The real-time PCR analysis was conducted following the operational protocols of the 7500 Real-Time PCR System (Takara, RR820A China). The quantification of gene expression at the mRNA level was determined using the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e methodology, with final values standardized against \u0026beta;-actin expression. The MCU primer sequences were: AGGATCGGGGAATTGACAGAG (F), GTGTGGTGTATAGTTGCTGGAC (R); The sequence of \u0026beta;-actin primers：CGTGCGTGACATTAAGGAGAA (F)，AGGAAGGAAGGCTGGAAGAG (R)；\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell transfection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were placed on 6-well plates at 0.8\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/well and maintained overnight in a 37\u0026deg;C incubator containing 5% CO2 until reaching approximately 70% confluence for DNA transfection. Per the supplier\u0026rsquo;s protocols, 2.5 \u0026micro;g/well plasmid was introduced into the cells using Lipofectamine 3000 (Thermo Fisher Scientific, L3000015; USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern Blot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe immunoblotting assay was executed per the standard protocols outlined in earlier studies. In brief, cells underwent lysis utilizing Radio Immunoprecipitation Assay Lysis buffer (Cat. 87787, Thermo Scientific, USA, RIPA) comprising protease (Cat. 04693124001, Roche, Switzerland) and phosphatase inhibitors (Cat. B15001-A, Bimake, USA) for 30 min on ice. After centrifugation (12500 rpm for 15 min at 4 \u0026deg;C), we obtained the supernatant. Equal amounts of total proteins were then separated through 10% SDS-polyacrylamide gel electrophoresis. The protein components were subsequently transferred to a polyvinylidene difluoride membrane (0.45 \u0026mu;m, Millipore, Billerica, MA). Following blockage with 5% BSA (Cat. SLBN9354V, Sigma-Aldrich, USA), the membrane underwent incubation with primary antibodies targeting these proteins at specified dilutions: MCU(1:2000, A22525, Abclonal, China), \u0026beta;-actin(1:2000, 4967S, Cell Signaling Technology (CST), USA). The samples were maintained at 4 \u0026deg;C overnight, followed by treatment with a horseradish peroxidase-conjugated goat anti-rabbit IgG (H + L) antibody. The membranes were then exposed to HRP Substrate (Millipore Corporation, Billerica, MA, USA) for visualization, and signal detection was accomplished utilizing a Bio-Rad ChemiDoc MP System (ChemiDoc MP, Bio-Rad, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell proliferation assays in vitro\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the CCK-8 analysis, cells were distributed into 96-well plates with 2 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells in each well. After that, the Cell Counting Kit-8 (CCK8) solution (DOJINDO,CK04, Japan) was introduced, and the microplates were kept in darkness for 2 hours. The absorbance readings were then obtained at 450 nm wavelength. Regarding the colony-formation experiment, cells were initially plated at 500 cells per well and cultured in a 6-well cell culture plate (Corning) for 15 days. The cells were then stabilized with 4% paraformaldehyde and colored utilizing 0.2% crystal violet (Sigma). Using a light microscope, we counted colonies containing over 50 cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSphere formation assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were grown in DMEM medium containing insulin (4\u0026thinsp;ng/mL; Sigma), basic fibroblast growth factor (10\u0026thinsp;ng/mL; Sigma), EGF (100\u0026thinsp;ng/mL; Sigma), and B-27 (2%; Invitrogen) was undertaken in an ultra-low attachment 6-well plate (Costar). Renewal of the medium was conducted every 2\u0026ndash;3\u0026thinsp;days. Ten days later, spheroids were visualized using a microscope (Nikon).\u003c/p\u003e\n\u003ch3\u003eMigration assay\u003c/h3\u003e\n\u003cp\u003eTo evaluate the cell invasion potential, cells in culture were diluted to 2 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/mL within the serum‐free medium, and 200 \u0026mu;L of this cellular preparation was introduced to the upper portion of the Transwell chamber (8.0 \u0026mu;m pore size, No. 3422; Corning, USA). The lower compartment received 800\u0026mu;L of medium containing 10% FBS. Following a twenty‐four hour incubation period, cells remaining on the upper surface were carefully eliminated using a cotton swab moistened with ice‐cold PBS. Subsequently, cells that had migrated to the basolateral membrane were stabilized using 4% PFA (Sigma‐Aldrich) for 30 minutes, succeeded by crystal violet (Sigma‐Aldrich) staining conducted over 2 hours at ambient temperature.\u003c/p\u003e\n\u003ch3\u003eInvasion assay\u003c/h3\u003e\n\u003cp\u003eUsing serum-free medium on ice, the matrix glue was diluted at a ratio of about 1:8, added to the upper chamber at a rate of 100\u0026mu;L/ well, spread over the bottom of the chamber, and incubated in the cell incubator for 2 hours. The excess matrix glue liquid in the upper chamber was gently sucked out, 200\u0026mu;L of cell suspension with serum-free medium was added, and 800\u0026mu;L of complete medium was introduced to the lower compartment. When returning to the chamber, be careful not to generate bubbles. After twenty-four hours of cell placement, the cells on the upper compartment surface were delicately eliminated utilizing a cotton applicator with ice-cold PBS. Cells adhering to the basolateral membrane of the compartment insert were subsequently stabilized with 4% PFA (Sigma-Aldrich) for 30 minutes, succeeded by crystal violet (Sigma-Aldrich) staining for 2 hours at ambient temperature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was executed utilizing R (v4.4.1), implementing KM survival assessment and the log-rank method for survival comparisons. A significance level of P \u0026lt; 0.05 was adopted for statistical computations. GraphPad Prism v. 9.01 (GraphPad Software) was employed for data analysis. Categorical parameters were assessed through the \u0026chi;\u003csup\u003e2\u003c/sup\u003e test or Fisher\u0026rsquo;s exact test, whereas continuous parameters were examined utilizing Student\u0026rsquo;s t-test for paired samples. The findings are denoted as means \u0026plusmn; SEM derived from three autonomous experiments, each conducted in duplicate.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eConsensus Clustering for Lactate Subtype Identification and DEGs Pathway Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;limma\u0026quot; program was utilized to detect 1740 DEGs between PDAC and normal tissues. Subsequently, the top 30 DEGs were pinpointed (Figure 1A-B). To obtain hub genes in individuals with PDAC, we examined candidates by WGCNA analysis. Utilizing the hierarchical clustering approach, the co-expressed genes were categorized into distinct modules and assigned color codes (Figure 1C). We then investigated the link between modules and individuals with PDAC and generated the module-trait heatmap using Spearman\u0026rsquo;s correlation analysis (Figure 1D). We then found 24 LRGs that co-expressed (Figure 1E). The expression levels of these 24 LRGs exhibited significant variation between the normal tissues and PDAC samples, as illustrated in Figure 1F.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubsequently, employing consensus clustering, two clusters of PDAC associated with lactate were delineated. Utilizing the TCGA dataset, these two clusters demonstrated distinct lactate gene expression profiles following k-means clustering analysis, as depicted in Figure 2A-B. The expression of LRGs was low in cluster C1 and high in cluster C2 (Figure 2C). Furthermore, survival analysis suggested that these lactate-derived subgroups exhibited markedly distinct clinical outcomes. Notably, the C2 subgroup exhibited a better survival rate compared to the C1 subgroup, as illustrated in Figure 2D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentifying the DEGs and Signal Pathways in\u0026nbsp;the Various Lactate\u0026nbsp;Subcategories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin this investigation, essential DEGs and crucial signaling pathways from both classifications were examined to elucidate the molecular mechanisms influencing prognosis across the two lactate subgroups. Analysis revealed 2055 differentially expressed genes (Figure 3A). These genes were found to be enriched in functions of the immune system, encompassing cell adhesion molecules (CAMs), chemokine signaling pathways, hematopoietic cell lineage, Pancreatic secretion, Th17 cell differentiation, extracellular region, immune system process, immune response, cell motility, and regulation of immune system process, as represented in Figure 3B-C. The observations suggested that genes associated with Lactate demonstrated connections to the immune microenvironment. Comparative analysis between C1 and C2 subcategories utilized the GSEA methodology. Furthermore, autoimmune thyroid disease, chemokine signaling pathway, calcium signaling pathway, cyclic nucleotide biosynthetic process, phagolysosome assembly, and G protein coupled receptor signaling pathway all displayed distinct enrichment (Figure 3D-E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSomatic Mutations and the TME in Diverse Immunosenescence Risk Cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis uncovered that unique somatic mutation patterns existed between the two subtypes (Figure 4A). The genes exhibiting the highest mutation frequencies included KRAS, TP53, SMAD4, CDKN2A, TTN, and MUC16. In this analysis, we explored the disparities in the TME across two distinct patient cohorts. Figure 4B presents a comprehensive overview of the immune cell infiltration patterns among 176 PDAC patients, as documented in the TCGA dataset. Employing the Cibersort algorithm and the lm22 gene signature matrix, we assessed the presence of 22 distinct immune cell types in each cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings revealed that the C2 cohort demonstrated superior immune activity, as evidenced by higher ImmuneScore, StromalScore, and EstimateScore, indicating a more robust immune response within the TME individuals(Figure 4C). Furthermore, the C1 cohort displayed a notable reduction in the prevalence of naive B cells, memory B cells, CD8+ T cells, activated CD4+ T cell memory, and activated NK cells, as opposed to those in the C2 cohort. Conversely, the C1 cohort demonstrated increased levels of plasma cells, M0 macrophages, monocytes and eosinophils (Figure 4D). These observations highlight the complex interactions among immune cell dynamics and the risk stratification of PDAC patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstablishment and Validation of the Lactate Risk Signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we have developed an innovative prognostic model predicated on the LRGs. The Lasso regression analysis was employed to assess these three LRGs, and they were consequently selected to form the predictive model, as illustrated in Figure 5A-B. By setting the lambda value at 0.06676, we were able to pinpoint three key genes. The predictive model is encapsulated in the following formula: Riskscore = (0.00199 * NQO1) - (0.09731 * STAT4) + (0.15009 * KCNK1) + (0.05063 * MCU).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUtilizing the median risk score, we categorized our study population into a high-risk cohort (H) and a low-risk cohort (L). We utilized the TCGA dataset as our training cohort and the GSE62452 dataset as the validation cohort. Furthermore, the connection between the two cohorts and OS status was evaluated. The findings revealed that a markedly lower proportion of patients in the H cohort survived in the TCGA dataset, with a more pronounced effect in the H cohort (Figure 5C). The KM survival assessment corroborated that individuals in the H cohort experienced shorter survival times compared to those in the L cohort, as evidenced in both two cohorts (Figure 5D and 5F). In the training cohort, the receiver operating characteristic (ROC) curves were developed with area under the curve (AUC) values of 0.56 for the 1-year survival, 0.58 for the 3-year survival, and 0.64 for the 5-year survival (Figure 5E). Similarly, in the validation cohort, the ROC curves were generated with AUC values of 0.64 for the 1-year survival, 0.65 for the 3-year survival, and 0.84 for the 5-year survival (Figure 5G).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle cell analysis of LRGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our current research, We identified four LRGs associated with PDAC (KCNK1, MCU, NQO1 and STAT4). To delve deeper into the interplay between these LRG expressions and the tumor immune landscape, we utilized the TISCH. We analyzed 2 datasets that were PDAC_CRA001160 dataset and the PDAC_GSE162708 dataset (Figure 6A-B). Within the PDAC_CRA001160 dataset, elevated levels of KCNK1 and MCU were observed in Malignant cells, while NQO1 showed high expression in both Malignant and Endothelial cells, and STAT4 exhibited strong expression in Plasma cells (Figure 6C-F). Correspondingly, in the PDAC_GSE162708 dataset, KCNK1 demonstrated notable expression in Malignant cells, MCU displayed high levels in both Malignant and Endothelial cells, NQO1 displayed elevated expression in Endothelial cells, and STAT4 exhibited marked expression in NK cells and CD8 T cells as depicted in Figure 6G-J.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpression and prognosis of LRGs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo find the expression of these four LRGs in PDAC, we performed a search using GEPIA2.0. It was found that KCNK1, MCU, and NQO1 showed high expression in tumor tissues. Moreover, elevated MCU levels were notably linked to unfavorable clinical outcomes (Figure 7A-H).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultilevel expression validation and in vitro functional investigation of MCU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the high expression of MCU in pancreatic cancer patients and its prognostic relevance, we selected MCU for subsequent biological experiments. In order to verify the biological function of MCU, we conducted qRT-PCR (Figure 8A) and WB (Figure 8B) in whole pancreatic cancer cell lines and normal pancreatic epithelial cells and found that MCU was generally highly expressed in pancreatic cancer cell lines, especially in Bxpc3 and Capan1 cells. By verifying the knockdown efficiency of plasmids, it was found that sh1 and sh4 had the best knockdown effect (Figure 8C-D). Therefore, we chose to use sh1 and sh4 plasmosomes to knock down MCU genes in Bxpc3 and Capan1 cells to observe whether altering MCU expression would affect the malignant potential of tumor cells. Colony-formation assay (Figure 8E-F) and CCK8 experiments (Figure 8G) revealed that MCU gene silencing resulted in decreased proliferation rates of pancreatic cancer cells, suggesting the MCU gene\u0026apos;s role in enhancing pancreatic cancer cell proliferation. The impact of MCU suppression on cellular motility (Figure 8H-I) and invasiveness (Figure 8J-K) was evaluated using transwell assays, demonstrating reduced migration and invasion capabilities. Furthermore, the sphere-formation assay (Figure 8L) examined how MCU downregulation influenced pancreatic cancer stemness, revealing diminished stem cell properties following reduced MCU expression. These experimental observations provide substantial evidence that MCU enhances pancreatic cancer cell proliferation, motility, invasiveness, and stemness characteristics, thereby contributing to the aggressive nature of pancreatic cancer.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePDAC is recognized as one of the most aggressive malignancies. It is characterized by early metastasis and late diagnosis, often presenting at advanced stages with limited treatment options\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Despite advancements in therapeutic strategies, including surgical resection, chemotherapy, and targeted therapies, the aggregate 5-year survival outcome remains unfavorable, primarily due to the high degree of chemoresistance and early metastatic spread inherent to PDAC\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. A significant bottleneck in enhancing patient results is the absence of effective predictive biomarkers that can guide personalized treatment strategies and prognostication. Identifying such biomarkers is imperative, as they could potentially stratify patients for more tailored therapies, predict treatment response, and provide a valuable understanding of the intricate interactions between the TME and PDAC progression\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The advancement of computational biology software and the amalgamation of diverse -omics datasets have paved the way for identifying innovative biological indicators\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. These advances are critical in the quest to understand the molecular underpinnings of PDAC and to identify signatures that can predict treatment efficacy and patient survival\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe complexity of PDAC is further compounded by its intricate relationship with the immune system, where the TME can modulate immune responses to facilitate tumor growth and evade therapeutic interventions\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Lactate once considered a mere byproduct of glycolysis, is now recognized as a key mediator in cancer biology, particularly in PDAC, where it contributes to an acidic TME that impairs immune cell function and promotes macrophage polarization towards an immunosuppressive phenotype. Additionally, lactate acts as a signaling molecule, influencing pathways like GPR81 to modulate immune evasion and tumor growth, and it affects tumor angiogenesis by stabilizing HIFs\u003csup\u003e[\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The study of lactate in PDAC is of significant value as it provides a deeper understanding of the metabolic reprogramming that occurs in cancer cells. By elucidating the mechanisms through which lactate contributes to tumor progression, researchers can identify potential therapeutic targets that disrupt these processes. For instance, targeting lactate production or its signaling pathways could enhance the efficacy of existing cancer therapies by restoring immune surveillance and inhibiting tumor growth\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Moreover, elucidating the function of lactate in PDAC may contribute to establishing enhanced diagnostic methods and predictive indicators, enabling timely identification and improved treatment strategies for this condition\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study identifies two distinct lactate-associated subtypes of PDAC, characterized by unique gene expression profiles and divergent clinical outcomes. The creation of a risk signature model utilizing LRGs, particularly highlighting the involvement of KCNK1, NQO1, STAT4 and MCU, offers a novel prognostic tool for PDAC patients. The high-risk cohort, as determined by the risk score, is linked to poorer survival rates, indicating the potential utility of this model in stratifying patients for more personalized treatment approaches.\u003c/p\u003e \u003cp\u003ePrior studies have highlighted that KCNK1 is instrumental in driving the malignancy of tumor cells, particularly in terms of influencing the cell cycle and the aggressive growth and spread of cancer cells. KCNK1 is highly expressed in thyroid cancer, breast cancer (BRCA), non-functioning pituitary adenoma, PDAC and bladder cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. NQO1 is a two-electron reductase that is highly active in the pro-oxidative milieu of human malignancies, which makes it a selective marker for tumors\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The STAT4 gene, belonging to the STAT family, exhibits significant genetic variations that profoundly impact immune reactions and the progression of illnesses, particularly in the realms of cancer and autoimmune disorders. However, research outcomes display variability when examined across different studies and demographic groups\u003csup\u003e[\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. MCU serves a critical function in cancer progression. It's upregulated in metastatic tumors, enhancing cellular movement and invasion through a ROS/HIF-1α molecular cascade. However, its effects vary across different cancer types and cell lines\u003csup\u003e[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.In BRCA, particularly the triple-negative subtype, MCU expression links to tumor size and lymph node infiltration, and its downregulation reduces tumor size and cell motility in xenograft models\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In melanoma, MCU silencing suppresses cell proliferation while enhancing migratory and invasive capabilities, diminishing responsiveness to immune-based therapies\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. In pancreatic cancer, MCU inhibition reduces HINT2-dependent apoptosis\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In colon cancer, miRNA-25 overexpression decreases mtCa2\u0026thinsp;+\u0026thinsp;uptake and shows increased expression in human colon malignancies where MCU levels are diminished\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. In hepatocellular carcinoma, MCU upregulation is associated with poor survival and metastasis, and its increase promotes ROS production and cell motility\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. In ovarian cancer, MCU silencing diminishes cell proliferation and migration, which is linked to decreased ROS production\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In renal cell carcinoma, MCU expression reduction leads to decreased cell migration and metastatic potential\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. These studies highlight the diverse effects of MCU modulation in different cancers. In this study, we have validated that MCU promotes the proliferation, migration, invasion, and stemness of tumor cells in pancreatic cancer, thereby confirming the malignant potential of the MCU gene in this disease. This indicates that MCU might serve as a crucial factor in the immune regulation and malignant advancement of PDAC. The clinical application value of MCU in PDAC lies in its potential as a therapeutic target and a biomarker for prognosis and response to therapy.\u003c/p\u003e \u003cp\u003eThe use of data from TCGA and GEO databases provided a robust foundation for our analysis. The application of WGCNA and consensus clustering allowed us to delineate distinct lactate subtypes, each with unique gene expression profiles and clinical implications. This methodological approach enabled us to identify differentially expressed genes and construct a lactate-linked risk signature with significant prognostic value. However, it is important to acknowledge the limitations of our study. The reliance on existing databases may introduce biases inherent to the data collection and annotation processes. Additionally, while our in vitro experiments provided valuable insights into the function of MCU in PDAC cells, further in vivo studies are necessary to fully validate these findings and their clinical relevance.\u003c/p\u003e \u003cp\u003eThe identification of two lactate subtypes with distinct clinical outcomes highlights the potential for personalized treatment strategies in PDAC. The risk signature we developed, comprising four LRGs, offers a novel tool for prognostic assessment. This signature could be used to stratify patients into different risk groups, potentially guiding treatment decisions and improving patient outcomes. The role of MCU in PDAC malignancy, confirmed through our in vitro experiments, presents a promising therapeutic target. Future research should focus on validating these findings in larger cohorts and exploring the potential of targeting MCU and other LRGs in clinical trials. Additionally, investigating the interplay between lactate metabolism, protein lactylation, and the immune response in PDAC could reveal new therapeutic avenues and biomarkers for early detection and treatment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe research conducted on lactate-related genes (LRGs) in pancreatic ductal adenocarcinoma (PDAC) has shed light on their potential impact on tumor development, particularly through their influence on the tumor microenvironment and immune evasion mechanisms. The findings presented in this study have revealed the existence of distinct lactate subtypes in PDAC, each associated with unique gene expression patterns and clinical outcomes. Through the development of a predictive risk signature comprised of four LRGs, this investigation has not only provided valuable insights into the prognostic implications of lactate metabolism in PDAC but has also identified potential therapeutic targets for intervention. Additionally, the functional validation of the mitochondrial calcium uniporter (MCU) in PDAC cells has further emphasized the significance of LRGs in driving the malignancy of this aggressive cancer type. Overall, this study underscores the critical role of LRGs in PDAC pathogenesis and highlights the potential for novel prognostic tools and targeted therapies in the management of pancreatic cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data examined in this research were derived from openly accessible databases: https://portal.gdc.cancer.gov/ (TCGA-PAAD) and the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. All data in this study are publicly available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (82174169) and Fudan University DIGAOJIAN Project (No.DGF601020-1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYuhang Chen\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Writing\u0026ndash;review\u0026amp;editing, Experiment, Methodology. Fenglin Zhang\u003cstrong\u003e:\u003c/strong\u003e Writing \u0026ndash; original draft, Experiment. \u003cstrong\u003eSuoyi Dai\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Software. Jiangang Zhao and Wenxun Cai\u003cstrong\u003e:\u003c/strong\u003e Experiment. Ke Zhang\u003cstrong\u003e:\u003c/strong\u003e Writing \u0026ndash; original draft, Data curation, Formal analysis. Xinghe Liao\u003cstrong\u003e:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Validation. Lianyu Chen\u003cstrong\u003e:\u003c/strong\u003e Validation, Supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12-49.\u003c/li\u003e\n\u003cli\u003eRahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74(11):2913-2921.\u003c/li\u003e\n\u003cli\u003eYu S, Zhang C, Xie KP. Therapeutic resistance of pancreatic cancer: Roadmap to its reversal. Biochim Biophys Acta Rev Cancer. 2021;1875(1):188461.\u003c/li\u003e\n\u003cli\u003eGrossberg AJ, Chu LC, Deig CR, et al. Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma. CA Cancer J Clin. 2020;70(5):375-403.\u003c/li\u003e\n\u003cli\u003eHu ZI, O\u0026apos;Reilly EM. Therapeutic developments in pancreatic cancer. Nat Rev Gastroenterol Hepatol. 2024;21(1):7-24.\u003c/li\u003e\n\u003cli\u003eWarburg O, Wind F, Negelein E. The metabolism of tumors in the body. J Gen Physiol. 1927;8(6):519-530.\u003c/li\u003e\n\u003cli\u003eQuinn WJ 3rd, Jiao J, TeSlaa T, et al. Lactate Limits T Cell Proliferation via the NAD(H) Redox State. Cell Rep. 2020;33(11):108500.\u003c/li\u003e\n\u003cli\u003eColegio OR, Chu NQ, Szabo AL, et al. Functional polarization of tumour-associated macrophages by tumour-derived lactic acid. Nature. 2014;513(7519):559-563.\u003c/li\u003e\n\u003cli\u003eBrown TP, Ganapathy V. Lactate/GPR81 signaling and proton motive force in cancer: Role in angiogenesis, immune escape, nutrition, and Warburg phenomenon. Pharmacol Ther. 2020;206:107451.\u003c/li\u003e\n\u003cli\u003eHui S, Ghergurovich JM, Morscher RJ, et al. Glucose feeds the TCA cycle via circulating lactate. Nature. 2017;551(7678):115-118.\u003c/li\u003e\n\u003cli\u003eSemenza GL. Hypoxia-inducible factors in physiology and medicine. Cell. 2012;148(3):399-408. \u003c/li\u003e\n\u003cli\u003eZhang D, Tang Z, Huang H, et al. Metabolic regulation of gene expression by histone lactylation. Nature. 2019;574(7779):575-580.\u003c/li\u003e\n\u003cli\u003eJiang K, Zhu L, Huang H, Zheng L, Wang Z, Kang X. Lactate score classification of hepatocellular carcinoma helps identify patients with tumors that respond to immune checkpoint blockade therapy. Cell Oncol (Dordr). 2024;47(1):175-188.\u003c/li\u003e\n\u003cli\u003eBailey P, Chang DK, Nones K, et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature. 2016;531(7592):47-52.\u003c/li\u003e\n\u003cli\u003eHoadley KA, Yau C, Wolf DM, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 2014;158(4):929-944.\u003c/li\u003e\n\u003cli\u003eCollisson EA, Sadanandam A, Olson P, et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med. 2011;17(4):500-503.\u003c/li\u003e\n\u003cli\u003eHu ZI, O\u0026apos;Reilly EM. Therapeutic developments in pancreatic cancer. Nat Rev Gastroenterol Hepatol. 2024;21(1):7-24.\u003c/li\u003e\n\u003cli\u003eIppolito L, Morandi A, Giannoni E, Chiarugi P. Lactate: A Metabolic Driver in the Tumour Landscape. Trends Biochem Sci. 2019;44(2):153-166.\u003c/li\u003e\n\u003cli\u003eLi X, Yang Y, Zhang B, et al. Lactate metabolism in human health and disease [published correction appears in Signal Transduct Target Ther. 2022 Oct 31;7(1):372. \u003c/li\u003e\n\u003cli\u003eLin X, Wu JF, Wang DM, Zhang J, Zhang WJ, Xue G. The correlation and role analysis of KCNK2/4/5/15 in Human Papillary Thyroid Carcinoma microenvironment. J Cancer. 2020;11(17):5162-5176. \u003c/li\u003e\n\u003cli\u003eHuang X, Feng Y, Ma D, et al. The molecular, immune features, and risk score construction of intraductal papillary mucinous neoplasm patients. Front Mol Biosci. 2022;9:887887.\u003c/li\u003e\n\u003cli\u003eKaratug Kacar A, Bulutay P, Aylar D, Celikten M, Bolkent S. Characterization and comparison of insulinoma tumor model and pancreatic damage caused by the tumor, and identification of possible markers. Mol Biol Rep. 2024;51(1):109.\u003c/li\u003e\n\u003cli\u003eXiong F, Wu GH, Wang B, Chen YJ. Plastin-3 is a diagnostic and prognostic marker for pancreatic adenocarcinoma and distinguishes from diffuse large B-cell lymphoma. Cancer Cell Int. 2021;21(1):411.\u003c/li\u003e\n\u003cli\u003eZhang W, Chen XS, Wei Y, et al. Overexpressed KCNK1 regulates potassium channels affecting molecular mechanisms and biological pathways in bladder cancer Eur J Med Res. 2024;29(1):257. \u003c/li\u003e\n\u003cli\u003eKhan AEMA, Arutla V, Srivenugopal KS. Human NQO1 as a Selective Target for Anticancer Therapeutics and Tumor Imaging. Cells. 2024;13(15):1272.\u003c/li\u003e\n\u003cli\u003eWang C, Gao N, Yang L, et al. Stat4 rs7574865 polymorphism promotes the occurrence and progression of hepatocellular carcinoma via the Stat4/CYP2E1/FGL2 pathway. Cell Death Dis. 2022;13(2):130.\u003c/li\u003e\n\u003cli\u003eMa Y, Zhou Y, Zhang H, Su X. Immune Response-Related Genes - STAT4, IL8RA and CCR7 Polymorphisms in Lung Cancer: A Case-Control Study in China. Pharmgenomics Pers Med. 2020;13:511-519.\u003c/li\u003e\n\u003cli\u003eCotterchio M, Lowcock E, Bider-Canfield Z, et al. Association between Variants in Atopy-Related Immunologic Candidate Genes and Pancreatic Cancer Risk. PLoS One. 2015;10(5):e0125273.\u003c/li\u003e\n\u003cli\u003eN\u0026uacute;\u0026ntilde;ez-Marrero A, Arroyo N, Godoy L, Rahman MZ, Matta JL, Dutil J. SNPs in the interleukin-12 signaling pathway are associated with breast cancer risk in Puerto Rican women. Oncotarget. 2020;11(37):3420-3431.\u003c/li\u003e\n\u003cli\u003eTosatto A, Sommaggio R, Kummerow C, et al. The mitochondrial calcium uniporter regulates breast cancer progression via HIF-1\u0026alpha;. EMBO Mol Med. 2016;8(5):569-585. \u003c/li\u003e\n\u003cli\u003eStejerean-Todoran I, Zimmermann K, Gibhardt CS, et al. MCU controls melanoma progression through a redox-controlled phenotype switch. EMBO Rep. 2022;23(11):e54746. \u003c/li\u003e\n\u003cli\u003eVultur A, Gibhardt CS, Stanisz H, Bogeski I. The role of the mitochondrial calcium uniporter (MCU) complex in cancer. Pflugers Arch. 2018;470(8):1149-1163. \u003c/li\u003e\n\u003cli\u003eChen L, Sun Q, Zhou D, et al. HINT2 triggers mitochondrial Ca2+ influx by regulating the mitochondrial Ca2+ uniporter (MCU) complex and enhances gemcitabine apoptotic effect in pancreatic cancer. Cancer Lett. 2017;411:106-116.\u003c/li\u003e\n\u003cli\u003eMarchi S, Lupini L, Patergnani S, et al. Downregulation of the mitochondrial calcium uniporter by cancer-related miR-25. Curr Biol. 2013;23(1):58-63.\u003c/li\u003e\n\u003cli\u003eRen T, Zhang H, Wang J, et al. MCU-dependent mitochondrial Ca2+ inhibits NAD+/SIRT3/SOD2 pathway to promote ROS production and metastasis of HCC cells. Oncogene. 2017;36(42):5897-5909. \u003c/li\u003e\n\u003cli\u003eZhao L, Jiang M, Tian T, et al. Effects of MCU-mediated Ca2+ Homeostasis on Ovarian Cancer Cell SKOV3 Proliferation, Migration and Transformation. Curr Mol Med. 2023;23(8):774-783. \u003c/li\u003e\n\u003cli\u003eMeng K, Hu Y, Wang D, et al. EFHD1, a novel mitochondrial regulator of tumor metastasis in clear cell renal cell carcinoma. Cancer Sci. 2023;114(5):2029-2040. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lactate, Pancreatic adenocarcinoma, Tumor microenvironment, Prognosis, MCU","lastPublishedDoi":"10.21203/rs.3.rs-5899024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5899024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to evaluate the predictive value and therapeutic significance of lactate-related genes (LRGs) in pancreatic ductal adenocarcinoma (PDAC). By analyzing PDAC data from the TCGA and GEO databases and employing WGCNA and consensus clustering, we identified two lactate subtypes with distinct gene expression profiles and clinical outcomes, and extracted differentially expressed genes. Functional enrichment and GSEA analyses were conducted to explore related pathways, and a lactate-associated risk signature based on four LRGs was constructed, which demonstrated significant accuracy in predicting survival. In vitro experimental results showed that MCU gene knockdown reduced the proliferation, migration, invasion, and stemness of PDAC cells, confirming its role in the malignancy of PDAC. This study underscores the importance of LRGs in PDAC, providing new biomarkers and therapeutic targets for the prognostic assessment and treatment of PDAC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe metabolism of lactate and lactylation of proteins are believed to influence tumor development through their effects on the tumor microenvironment and immune escape mechanisms. Nevertheless, its significance in pancreatic ductal adenocarcinoma (PDAC) has yet to be fully understood. This investigation sought to assess the predictive value and treatment implications of lactate-related genes (LRGs) in PDAC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed PDAC data from TCGA and GEO, identifying LRGs. Using WGCNA and consensus clustering, we delineated lactate subtypes and extracted differentially expressed genes. Functional enrichment and GSEA analyses were conducted to explore pathways. A lactate-linked risk signature was constructed using Lasso-Cox regression, and its prognostic value was validated. In vitro experiments were executed to examine the function of MCU in PDAC cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo lactate subtypes were identified, with distinct gene expression profiles and clinical outcomes. The risk signature, comprising four LRGs, predicted survival with significant accuracy. In vitro, MCU knockdown reduced cell proliferation, migration, invasion, and stemness, confirming its role in PDAC malignancy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur investigation underscores the importance of LRGs in PDAC, providing a novel prognostic signature and therapeutic target.\u003c/p\u003e","manuscriptTitle":"Lactate-associated gene MCU promotes the proliferation, migration, and invasion of pancreatic adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-29 05:03:35","doi":"10.21203/rs.3.rs-5899024/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-31T06:15:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-29T06:13:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-27T11:56:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-01-25T03:11:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f9e9f77-afd4-454e-8238-b7d12a43ca11","owner":[],"postedDate":"January 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:08:13+00:00","versionOfRecord":{"articleIdentity":"rs-5899024","link":"https://doi.org/10.1186/s12885-025-14319-1","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-05-21 15:58:33","publishedOnDateReadable":"May 21st, 2025"},"versionCreatedAt":"2025-01-29 05:03:35","video":"","vorDoi":"10.1186/s12885-025-14319-1","vorDoiUrl":"https://doi.org/10.1186/s12885-025-14319-1","workflowStages":[]},"version":"v1","identity":"rs-5899024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5899024","identity":"rs-5899024","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}