The glycolytic PKM gene is associated with immune infiltrates and therapeutic response of pancreatic cancer

The glycolytic PKM gene is associated with immune infiltrates and therapeutic response of pancreatic cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The glycolytic PKM gene is associated with immune infiltrates and therapeutic response of pancreatic cancer Shihang Ru, Wenshuai Li, Yingguang Liu, Bugao Chao, Yang Li, Di Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7676660/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cancer cells undergo metabolic reprogramming to sustain proliferation and metastasis, with the glycolytic pathway playing a key role. This study investigates the prognostic significance, immune infiltration, and treatment response of the glycolysis-related gene Pyruvate Kinase M (PKM) in pancreatic cancer. Methods Pancreatic cancer samples were obtained from GEO and TCGA databases. Glycolysis-related genes were identified using WGCNA and Msigdb, and PKM was selected via PPI network analysis and univariate Cox regression. Gene set enrichment analysis (GSEA), immune infiltration analysis (ESTIMATE), immune checkpoint inhibitor (ICI) response prediction (TIDE), and chemotherapeutic sensitivity analysis (pRRophetic) were conducted. Results PKM was identified as a key glycolysis-related gene. GSEA indicated that high PKM expression was associated with glycolytic processes and ERBB signaling. PKM was overexpressed in pancreatic cancer tissues, and patients with high PKM expression had significantly worse prognosis (P < 0.05). ESTIMATE analysis revealed a higher infiltration level of M0 macrophages in the high PKM expression group (P < 0.05). Additionally, high PKM expression correlated with reduced efficacy of ICI therapy (P < 0.05) and lower sensitivity to irinotecan and oxaliplatin (P < 0.05). Conclusion PKM plays a crucial role in glycolysis, immune regulation, and therapeutic response in pancreatic cancer. It may serve as a prognostic biomarker and a potential therapeutic target. PKM pancreatic cancer glycolysis immune infiltrates ICIs therapeutic response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Pancreatic cancer is a highly aggressive malignancy characterized by an insidious onset, rapid progression, poor response to treatment, and an overall dismal prognosis. Due to the lack of early symptoms and effective screening methods, most patients are diagnosed at an advanced stage. In recent years, the incidence and mortality rates of pancreatic cancer have been rising. In 1990, the global incidence was approximately 196,000 cases; however, by 2017, this number had surged to 441,000 cases [ 1 ], reflecting a significant increase over the past two decades. According to recent projections based on epidemiological data from the United States, pancreatic cancer is expected to surpass colorectal cancer as the second leading cause of cancer-related deaths by 2026 and may become the second most common cause of cancer mortality worldwide by 2040 [ 2 ]. Numerous studies have underscored the pivotal role of metabolic reprogramming, particularly the glycolytic pathway, in the initiation and progression of malignant tumors [ 3 – 5 ]. Cancer cells undergo metabolic adaptations to accommodate their high energy demands and sustain rapid proliferation. One of the most well-characterized metabolic alterations is the Warburg effect, first described by Otto Warburg in the 1920s. He observed that, even in the presence of oxygen, cancer cells preferentially rely on glycolysis rather than oxidative phosphorylation to generate ATP [ 6 ]. This metabolic shift allows cancer cells to produce ATP at a significantly higher rate—approximately 100 times faster than oxidative phosphorylation—facilitating their survival and proliferation [ 7 , 8 ]. Moreover, beyond ATP production, glycolysis supplies essential metabolic intermediates required for biosynthetic processes that sustain tumor growth. For instance, glycolytic intermediates fuel the pentose phosphate pathway (PPP), leading to the generation of ribulose-5-phosphate and nicotinamide adenine dinucleotide phosphate (NADPH), both of which are essential for lipid and nucleic acid biosynthesis [ 9 ]. Furthermore, NADPH plays a crucial role in maintaining intracellular redox homeostasis by sustaining reduced glutathione (GSH) levels, thereby enhancing resistance to chemotherapy-induced oxidative stress and drug cytotoxicity [ 10 , 11 ]. The glycolytic pathway is tightly regulated by several transcription factors, including hypoxia-inducible factor-1 (HIF-1) and c-Myc, which coordinate metabolic adaptations in cancer cells. Additionally, glycolysis activates key oncogenic signaling pathways, such as the phosphatidylinositol 3-kinase/protein kinase B (PI3K-AKT) and Wnt pathways, further driving cancer cell proliferation and survival. Given its central role in tumor metabolism and progression, glycolysis represents a promising target for therapeutic intervention in pancreatic cancer. Pyruvate kinase (PK), a rate-limiting enzyme in the glycolytic pathway, catalyzes the conversion of phosphoenolpyruvate and adenosine diphosphate (ADP) into pyruvate and adenosine triphosphate (ATP). Pyruvate kinase muscle isoform (PKM) is a key isoenzyme of PK that plays a crucial role in tumor metabolism. In this study, we investigated the expression and prognostic significance of the mitochondria-related gene PKM in pancreatic cancer using publicly available datasets. Additionally, we explored its involvement in immune infiltration and its potential response to immunotherapy and pharmacological treatments. Our findings aim to identify novel prognostic biomarkers and potential therapeutic targets for pancreatic cancer. Methods Data collection and preprocessing Two microarray datasets, GSE28735 and GSE62452, were retrieved from the GEO database. These datasets, based on the Affymetrix GPL6244 platform, contain both gene expression and clinical data. The gene expression data from these datasets were normalized and used as the experimental groups in this study. Additionally, glycolysis-related genes were obtained from the Molecular Signatures Database (Msigdb) (https://www.gsea-msigdb.org/gsea/Msigdb/human/search.jsp). To validate our findings, gene expression and clinical data for pancreatic cancer were extracted from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). The TCGA gene expression data were standardized and normalized before analysis. Since all datasets were obtained from publicly available sources, ethical approval was not required for this study. Weighted gene co-expression network analysis WGCNA is an algorithm used to identify co-expression gene modules with biological significance, facilitating the exploration of gene networks and their associations with diseases [12]. In this study, WGCNA was applied to identify gene networks and modules associated with pancreatic cancer. Initially, the top 25% of genes with the highest variance were selected for WGCNA analysis. Hierarchical clustering was performed to exclude outlier samples, followed by the selection of an optimal soft threshold (ranging from 1 to 20) to ensure that the network met scale-free topology criteria. A neighbor-joining matrix was then constructed based on the soft threshold value (β) and the correlation matrix for all gene pairs. This matrix was transformed into a Topological Overlap Matrix (TOM) and a corresponding dissimilarity matrix (1-TOM). Subsequently, hierarchical clustering dendrograms were generated, and co-expression modules were identified. The expression profiles of each module were summarized using Module Eigengenes (ME), and their correlation with pancreatic cancer was assessed. Genes from modules positively correlated with pancreatic cancer were selected for further analysis. Pancreatic cancer glycolysis-related genes and enrichment analyses Genes positively associated with pancreatic cancer were intersected with glycolysis-related genes to identify glycolysis-associated genes in pancreatic cancer, which were visualized using Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/). To explore the biological functions of these genes, Gene Ontology (GO) enrichment analysis was conducted, including analyses of biological processes (BP), cellular components (CC), and molecular functions (MF) [13]. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed to examine biological pathways associated with these genes [14]. Functional annotation of glycolysis-related genes was conducted using DAVID (https://david.ncifcrf.gov/), a freely accessible bioinformatics resource [15]. Protein-protein interaction network analysis and cox regression analysis for Hub Gene Determination Protein-Protein Interaction (PPI) networks were constructed to analyze the interactions among pancreatic cancer glycolysis-related genes [16]. The STRING database (https://cn.string-db.org) was used to build PPI networks from the GSE28735 dataset, with a confidence score of ≥0.4 considered significant. The network was visualized using Cytoscape (v3.9.1). Key genes were identified based on Maximal Clique Centrality (MCC) and Maximum Neighborhood Component (MNC) scores, calculated using the CytoHubba plugin. The top 20 genes were selected for visualization. Subsequently, Cox univariate regression analysis was performed using the GSE62452 dataset to identify prognostic genes. The hub genes were determined by intersecting the results of MCC, MNC, and Cox regression analyses, and they were visualized using Venny 2.1.0. Gene Set Enrichment Analysis (GSEA) of target gene The target gene for this study was selected from the identified hub genes, and GSEA was performed to investigate its biological function. GSEA assesses the association between the target gene and predefined gene sets, facilitating functional prediction [17]. Tumor samples were divided into high- and low-expression groups based on the mean expression value of the target gene. GSEA enrichment analysis was conducted using the c5.go.v2024.1.Hs.symbols.gmt and c2.cp.kegg_medicus.v2024.1.Hs.symbols.gmt datasets in GSEA software (v4.3.3). A P-value < 0.05 was considered statistically significant. Target gene expression and prognostic relevance Differential expression analysis of the target gene between pancreatic cancer and normal tissues was conducted. The results were validated using the GEPIA database (http://gepia2.cancer-pku.cn/#index). Kaplan-Meier survival analysis was performed, with patients categorized into high- and low-expression groups based on the median expression value. Correlation of target genes with immune cell infiltration The CIBERSORT algorithm was used to estimate the relative abundance of 22 immune cell subtypes in pancreatic cancer, including B cells, T cells, myeloid cells, NK cells, and plasma cells [18]. The relationship between target gene expression and immune cell infiltration was analyzed by comparing high- and low-expression groups and computing correlation coefficients. Evaluation of immunotherapy response The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict the response of pancreatic cancer patients to immune checkpoint inhibitors (ICIs) [19]. Tumor gene expression data were normalized, and the TIDE online platform (http://tide.dfci.harvard.edu/) was used to calculate ICI response predictions. Patients were categorized into high- and low-expression groups based on the median expression value of the target gene, and their respective immunotherapy responses were analyzed. Drug sensitivity analysis Drug sensitivity analysis was conducted to predict the response of pancreatic cancer patients to chemotherapy based on cell line expression data and drug response models [20]. Training set data were downloaded from https://osf.io/c6tfx/files/osfstorage. Patients were divided into high- and low-expression groups based on the mean expression value of the target gene, and drug response outcomes were compared between groups. Statistical analysis All data processing and statistical analyses were performed using R software (v4.4.1). The GEOquery package was used to download GEO datasets. Correlation analyses were performed using the corPvalueStudent function from the WGCNA package. Fisher’s exact test was used for KEGG and GO enrichment analyses. Cox univariate regression analysis was conducted using the Survival package. The Wilcoxon signed-rank test was applied for differential gene expression and drug sensitivity analysis. Kaplan-Meier survival analysis was performed using the Survival and Survminer packages. Immune infiltration analysis was conducted using the CIBERSORT R package, with correlations calculated using Pearson’s method. Differences in immune cell infiltration and immunotherapy response were assessed using the test function. Drug susceptibility analysis was conducted using the oncoPredict R package. A P-value < 0.05 was considered statistically significant. Results Data Information Two pancreatic cancer datasets, GSE28735 and GSE62452, which include both clinical and gene expression data, were retrieved from the GEO database for analysis. Additionally, clinical and gene expression data for pancreatic cancer were obtained from the TCGA database. The GSE28735 and GSE62452 datasets were designated as the experimental group, while the TCGA data served as the validation group. A total of 326 glycolysis-related genes were included in this study (Additional File 1: Table S1 ). A summary of the datasets is presented in Table 1. Weighted Gene Co-Expression Network Analysis (WGCNA) WGCNA was employed to identify gene modules associated with pancreatic cancer. Outlier samples were removed based on hierarchical cluster analysis, specifically GSM711957 in the GSE28735 dataset (Fig. 1 A). A soft threshold of 16 was selected to achieve a scale-free network topology (Fig. 1 B). Hierarchical clustering dendrograms and heatmaps of module-trait relationships were generated to visualize the correlation between each module and pancreatic cancer (Fig. 1 C, D). Three modules, MEyellow (r = 0.7, p = 1e-15), MEblue (r = 0.49, p = 9e-07), and MEgrey (r = 0.38, p = 3e-04), were significantly and positively correlated with pancreatic cancer and were selected for further analysis, comprising 3016 genes (Additional File 1: Table S2 ). Similarly, in the GSE62452 dataset, outlier sample GSM1527158 was excluded (Fig. 1 E), and a soft threshold of 14 was determined (Fig. 1 F). The hierarchical clustering dendrograms and heatmaps of module-trait relationships were generated (Fig. 1 G, H). The MEBrown (r = 0.67, p = 5e-18) and MECyan (r = 0.37, p = 1e-05) modules, which were significantly correlated with pancreatic cancer, were selected, comprising 1679 genes (Additional File 1: Table S3 ). Identification of Pancreatic Cancer Glycolysis-Related Genes and Enrichment Analysis The genes identified in both experimental groups that were positively associated with pancreatic cancer were intersected with the 326 glycolysis-related genes, resulting in 93 and 46 pancreatic cancer glycolysis-related genes, respectively (Fig. 2 A, B) (Additional File 1: Table S4 , S5). To elucidate the molecular mechanisms underlying these genes, Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. GO analysis revealed that these genes were predominantly involved in glycolytic processes, canonical glycolysis, and response to hypoxia (Fig. 2 C, G). Cellular component analysis indicated their enrichment in extracellular exosomes and cytosol (Fig. 2 D, H). Molecular function analysis highlighted D-glucose binding as the primary molecular function (Fig. 2 E, I). KEGG pathway analysis further demonstrated enrichment in glycolysis/gluconeogenesis, the HIF-1 signaling pathway, fructose and mannose metabolism, and central carbon metabolism in cancer (Fig. 2 F, J) (Additional File 1: Table S6 , S7). PPI Network and Hub Gene Identification A protein-protein interaction (PPI) network of 93 glycolysis-related genes from the GSE28735 dataset was constructed using the STRING database, yielding 92 nodes and 426 edges (Fig. 3 A) (Additional File 1: Table S8 ). The CytoHubba plugin in Cytoscape software was used to analyze the network, applying Maximal Clique Centrality (MCC) and Maximum Neighborhood Component (MNC) algorithms to score each node. The top 20 genes with the highest scores were selected (Fig. 3 B, C). Cox univariate regression analysis was then performed on the GSE62452 dataset, identifying 17 prognostic genes (Fig. 3 D). The intersection of these 17 genes with the top 20 genes from the MCC and MNC analyses yielded 7 hub genes: PKM, TPI1, ENO1, GAPDH, PYGL, SLC2A1, and HK1 (Fig. 3 E). Gene Set Enrichment Analysis (GSEA) of the Target Gene Among the identified hub genes, PKM was selected as the target gene for further investigation. GSEA analysis of PKM in the GSE28735, GSE62452, and TCGA datasets revealed significant enrichment in glycolytic processes, ERBB signaling pathway, and glucose metabolism-related pathways (Fig. 4 A-C). Expression and Prognostic Analysis of PKM Analysis of gene expression levels showed that PKM was significantly upregulated in pancreatic cancer tissues compared to normal pancreatic tissues in both GSE28735 and GSE62452 datasets (Fig. 5 A, B). This was further validated using GEPIA, based on TCGA and GTEx data, confirming the upregulation of PKM in pancreatic cancer (Fig. 5 C). Kaplan-Meier survival analysis indicated that high PKM expression was associated with worse prognosis and shorter survival time in both experimental and validation groups (Fig. 5 D-F). Correlation Between PKM and Immune Cell Infiltration Using the CIBERSORT algorithm, the relationship between PKM expression and immune cell infiltration was analyzed. In the GSE28735 dataset, the M0 macrophage infiltration level was significantly higher in the high PKM expression group (Fig. 6 A). In the GSE62452 dataset, the high PKM expression group had significantly higher M0 macrophage and M1 macrophage infiltration, while monocyte and resting mast cell infiltration was higher in the low PKM expression group (Fig. 6 B). In the TCGA dataset, the high PKM expression group exhibited increased M0 macrophages and resting NK cells, whereas the low PKM expression group had higher naïve B cells, plasma cells, CD8 + T cells, and resting mast cells (Fig. 6 C). In the GSE28735 dataset, PKM expression was positively correlated with macrophages M0, and negatively correlated with monocytes and CD8 + T cells (Fig. 7 A, D-F). In the GSE62452 dataset, PKM expression was positively correlated with M0 macrophages and resting dendritic cells, but negatively correlated with resting mast cells and monocytes (Fig. 7 B, G-J). In the TCGA data, PKM expression was positively correlated with M0 macrophages and resting NK cells, while negatively correlated with resting mast cells, monocytes, CD8 + T cells, plasma cells, and naive B cells (Fig. 7 C, K-Q). Impact of PKM on Immunotherapy Response The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to evaluate the impact of PKM expression on immune checkpoint inhibitor (ICI) response. Across all three datasets, TIDE scores were significantly higher in the high PKM expression group (Fig. 8 A-C), indicating a greater potential for immune escape. Patients with high PKM expression exhibited reduced responsiveness to ICI therapy (Fig. 8 D-F), suggesting that PKM may contribute to immune evasion in pancreatic cancer. Drug Sensitivity Analysis of PKM The sensitivity of PKM expression to seven chemotherapeutic agents was analyzed. In the GSE28735 dataset, the IC50 values of irinotecan, oxaliplatin, gemcitabine, and cisplatin were significantly higher in the high PKM expression group, indicating reduced sensitivity to these drugs (Fig. 9 A-D). In the GSE62452 dataset, irinotecan and oxaliplatin also exhibited higher IC50 values in the high PKM expression group, confirming lower drug efficacy (Fig. 9 E-H). Similarly, in the TCGA dataset, the IC50 values for irinotecan, oxaliplatin, gemcitabine, and cisplatin were significantly elevated in the high PKM expression group, suggesting poorer chemotherapy outcomes (Fig. 9 I-L) (Additional File 2: Fig. 4 ). Discussion Pancreatic cancer is a highly malignant disease with poor treatment outcomes, resulting in a five-year survival rate of less than 6% [ 21 ]. Metabolic reprogramming plays a pivotal role in cancer progression by providing essential energy sources and key metabolites that sustain tumor initiation and proliferation. A hallmark of this metabolic shift is aerobic glycolysis, a process in which cancer cells preferentially utilize glycolysis even in the presence of oxygen. The PKM gene plays a central role in regulating this glycolytic process. This study investigated the enrichment patterns and pathways of PKM in pancreatic cancer, its expression profile, prognostic significance, immune infiltration characteristics, and treatment response. Pyruvate kinase, a rate-limiting enzyme in glycolysis, exists in L-type and M-type isoforms, with the M-type further divided into PKM1 and PKM2. PKM1 enhances tumor growth by facilitating glucose-to-lactate conversion and activating central carbon metabolism [ 22 ]. Additionally, PKM1 promotes autophagy, particularly mitochondrial autophagy, and contributes to the tumor microenvironment by increasing lactate production in cancer-associated fibroblasts, further stimulating aerobic glycolysis in cancer cells [ 23 ]. In contrast, PKM2 exists in both tetrameric and dimeric forms. The tetrameric form exhibits high catalytic activity, efficiently converting phosphoenolpyruvate (PEP) to pyruvate and generating ATP, while the dimeric form has lower catalytic activity and favors macromolecule synthesis via the pentose phosphate pathway (PPP) [ 24 , 25 ]. The dimeric form of PKM2 also functions as a protein kinase, activating STAT3, which in turn promotes cancer cell proliferation. Specifically, PKM2-mediated phosphorylation of STAT3 at tyrosine 705 leads to the upregulation of N-cadherin, MMP-2, and MMP-9, facilitating cancer progression, particularly in colorectal cancer [ 26 ]. Moreover, PKM2 interacts with multiple co-activators to regulate glycolysis and tumor development [ 27 , 28 ]. Notably, it plays a key role in the PI3K/Akt/mTOR signaling pathway by interacting with HIF-1α and c-Myc, thereby modulating cell proliferation and metastasis [ 29 , 30 ]. Our findings indicate that PKM expression upregulates glucose-6-phosphate and fructose-6-phosphate, thereby enhancing glycolysis and the PPP. These intermediates provide the necessary energy and molecular building blocks for pancreatic cancer cell proliferation and metastasis [ 31 ]. While glycolysis produces pyruvate, which is converted into lactate, the PPP serves as a metabolic bypass that generates NADPH and ribose phosphate, both of which are critical for cancer cell survival. NADPH maintains redox homeostasis by supporting antioxidant systems and is essential for the biosynthesis of lipids, amino acids, nucleotides, and steroids, all of which contribute to tumor growth [ 32 ]. Ribose phosphate, on the other hand, is a precursor for nucleotide synthesis, which is essential for DNA replication and cell division in cancer cells [ 33 ]. Additionally, our study demonstrates that PKM is involved in upregulating the ERBB signaling pathway, a crucial oncogenic cascade mediated by EGFR (ERBB1), HER2 (ERBB2), HER3 (ERBB3), and HER4 (ERBB4). Activation of these receptors by extracellular growth factors enhances cancer cell proliferation [ 34 ]. Consistent with previous studies showing elevated PKM1 and PKM2 expression in multiple malignancies [ 22 , 35 , 36 ], our results confirm that PKM is highly expressed in pancreatic cancer tissues and correlates with poor prognosis. The tumor microenvironment plays a crucial role in pancreatic cancer progression, with tumor-associated macrophages (TAMs) being the most abundant immune cells in the tumor stroma, comprising over 50% of the immune cell population [ 37 ]. TAMs originate from M0 macrophages, which can differentiate into M1 and M2 phenotypes. While M1 TAMs exhibit pro-inflammatory and anti-tumor activity by secreting IL-1β, TNF-α, and reactive oxygen species (ROS), they gradually transition into the M2 phenotype during tumor progression, contributing to immune evasion and tumor promotion [ 38 , 39 ]. M2 TAMs secrete matrix metalloproteinases (MMPs) and serine proteases, which degrade the extracellular matrix and facilitate cancer cell invasion and metastasis [ 40 ]. They also produce VEGF, PDGF, COX-2, and IL-10, which drive angiogenesis and lymphangiogenesis in tumor tissues [ 41 ]. Moreover, M2 TAMs interact with myeloid-derived suppressor cells (MDSCs) to suppress T-cell-mediated anti-tumor responses and express PD-L1, which inhibits T-cell activation through the PD-1/PD-L1 axis, ultimately promoting immune escape [ 42 ]. Previous studies have shown that microvesicle-associated PKM2 functions as a transcriptional coactivator and protein kinase, influencing macrophage differentiation and promoting tumor progression in liver cancer [ 43 ]. Our study revealed a significant positive correlation between PKM expression and M0 macrophages in pancreatic cancer, suggesting that PKM may play a role in TAM-mediated tumor proliferation and immune evasion. In addition to its role in immune modulation, PKM contributes to chemotherapy resistance. Both PKM1 and PKM2 have been implicated in resistance to multiple chemotherapeutic agents across various cancers [ 44 , 45 ]. Our study confirms that high PKM expression correlates with reduced sensitivity to irinotecan, oxaliplatin, gemcitabine, and cisplatin in pancreatic cancer, indicating a potential role in therapy resistance. Despite these findings, this study has several limitations. First, while PKM exists as two isoforms, PKM1 and PKM2, public datasets do not differentiate their individual expression levels. Further research is required to explore the distinct contributions of each isoform to pancreatic cancer progression. Second, although we established a correlation between PKM expression, the ERBB pathway, and immune infiltration, the underlying molecular mechanisms remain unclear. Future studies should incorporate experimental validation to elucidate these mechanisms. Conclusion Through bioinformatics analysis, this study identified key glycolysis-related hub genes associated with pancreatic cancer. Among them, PKM was recognized as a central regulator of cancer metabolism, immune modulation, and treatment resistance. Its high expression correlated with poor prognosis, increased immune evasion, and decreased sensitivity to chemotherapy. These findings suggest that PKM serves as a prognostic biomarker and a potential therapeutic target for pancreatic cancer. Future research should focus on mechanistic studies and targeted therapies aimed at modulating PKM expression and function to improve clinical outcomes in pancreatic cancer patients. Declarations Authors’ contributions Shihang Ru, Di Wu and Chunlei Liu contributed to the conception and design of the study. Shihang Ru, Wenshuai Li and Bugao Chao organized the database. Shihang Ru and Wenshuai Li performed the statistical analysis. Shihang Ru and Wenshuai Li wrote the frst draft of the manuscript. Yingguang Liu, Bugao Chao and YangLi wrote sections of the manuscript. All authors contributed to manuscript revision and read and approved the submitted version. Funding This research was supported by the funding from the Inner Mongolia Medical University Joint Project, China (NO. YKD2023LH005). Data availability statement The datasets used in this study are publicly available in online repositories. The gene expression and clinical data for pancreatic cancer were retrieved from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) with accession numbers GSE28735 and GSE62452, and the The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) under the TCGA-Pancreatic Adenocarcinoma (TCGA-PAAD) cohort for validation. Glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB) (https://www.gsea-msigdb.org/gsea/Msigdb/human/search.jsp). Functional annotation and protein-protein interaction analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/), Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://cn.string-db.org), and Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia2.cancer-pku.cn/#index). Immunotherapy response prediction was conducted via the Tumor Immune Dysfunction and Exclusion (TIDE) online platform (http://tide.dfci.harvard.edu/). Data for chemotherapeutic sensitivity analysis were downloaded from the Open Science Framework (OSF) platform (https://osf.io/c6tfx/files/osfstorage). Gene intersection visualization was performed using Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/). All datasets are fully released, and the provided accession numbers and database links allow direct access to the original data in their final form. Competing interests The authors declare no conflicts of interest. Consent to publish: Not applicable. Clinical trial register number: Not applicable. Ethical approval: Not applicable. Limitations This study, a bioinformatics analysis focusing on PKM’s role in pancreatic cancer, has the following limitations to ensure result interpretation transparency: 1. Dataset Size and Representativeness Constraints: Core analyses relied on GSE28735, GSE62452 (moderate sample sizes, Table 1), and TCGA pancreatic cancer datasets. While these are widely used, their sample scales are limited—TCGA also primarily covers Western populations, lacking ethnic diversity. Additionally, datasets lack detailed clinical covariates (e.g., comorbidities, specific treatments, long-term follow-up), restricting generalizability to broader patient subgroups. 2. Inability to Distinguish PKM Isoforms: PKM has functionally distinct PKM1 and PKM2 isoforms, but public datasets (GEO, TCGA, GEPIA) only quantify total PKM mRNA. This prevents identifying whether PKM1, PKM2, or their combination drives observed associations (e.g., with poor prognosis), limiting mechanistic inference precision. 3. No Experimental Validation: All conclusions stem from public dataset bioinformatics analyses; no in vitro (e.g., cell line assays) or in vivo (e.g., animal models) experiments were conducted. Analyses reflect correlations, not causality—e.g., PKM’s correlation with M0 macrophage infiltration does not confirm direct regulation, and predicted treatment resistance needs experimental verification. 4. Limitations in Immune and Therapeutic Predictions: Immune infiltration was estimated via CIBERSORT (bulk RNA-seq-based, unable to capture immune cell spatial/functional states). ICI response predictions (TIDE) ignored TMB, MSI, etc., while chemotherapy sensitivity (oncoPredict) relied on cell line IC50 models, failing to recapitulate in vivo tumor microenvironment effects on treatment response. References GBD 2017 Pancreatic Cancer Collaborators. The global, regional, and national burden of pancreatic cancer and its attributable risk factors in 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol. Hepatol. 4, 934-947. Rahib, L. et al. Estimated projection of US cancer incidence and death to 2040. JAMA Netw. Open. 4, e214708 (2021). Ganapathy-Kanniappan S, Geschwind JF. Tumor glycolysis as a target for cancer therapy: progress and prospects. Mol Cancer. 2013 Dec 3; 12:152. Abdel-Wahab AF, Mahmoud W, Al-Harizy RM. Targeting glucose metabolism to suppress cancer progression: prospective of anti-glycolytic cancer therapy. Pharmacol Res. 2019 Dec; 150:104511. Chelakkot C, Chelakkot VS, Shin Y, Song K. Modulating Glycolysis to Improve Cancer Therapy. Int J Mol Sci. 2023 Jan 30;24(3):2606. Warburg O: On the origin of cancer cells. Science 1956, 123(3191):309-314.0036-8075 (Print). de Souza AC, Justo GZ, de Araujo DR, Cavagis AD: Defining the molecular basis of tumor metabolism: a continuing challenge since Warburg's discovery. Cell Physiol Biochem 2011, 28(5):771-792. J.W. Locasale, L.C. Cantley, Altered metabolism in cancer, BMC Biol. 8 (2010) 88. Gill KS, Fernandes P, O'Donovan TR, McKenna SL, Doddakula KK, Power DG, Soden DM, Forde PF. Glycolysis inhibition as a cancer treatment and its role in an anti-tumour immune response. Biochim Biophys Acta. 2016 Aug;1866(1):87-105. D.S. Backos, C.C. Franklin, P. Reigan, The role of glutathione in brain tumor drug resistance, Biochem. Pharmacol. 83 (2012) 1005-1012. N. Traverso, R. Ricciarelli, M. Nitti, B. Marengo, A.L. Furfaro, M.A. Pronzato, U.M. Marinari, C. Domenicotti, Role of glutathione in cancer progression and chemoresistance, Oxidative Med. Cell. Longev. 2013 (2013) 972913. Langfelder P, Horvath S. WGCNA: An R Package for Weighted Correlation Network Analysis. BMC Bioinf (2008) 9:559. doi: 10.1186/1471-2105-9-559. Consortium GO. The Gene Ontology (GO) project in 2006. Nucleic Acids Res 2006;34: D322-6. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes.Nucleic Acids Res 2000;28:27–30. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009; 4:44-57. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, Jensen LJ, von Mering C. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021 Jan 8;49(D1): D605-D612. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015 May;12(5):453-7. PMID: 25822800; PMCID: PMC4739640. TIDE: Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018 Oct;24(10):1550-1558. Maeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021 Nov 5;22(6): bbab260. Siegel R, Ma J, Zou Z, Jemal A (2014) Cancer statistics, 2014. CA Cancer J Clin 64(1):9–29. doi:10.3322/caac.21208. Morita M, Sato T, Nomura M, Sakamoto Y, Watanabe T, Soga T, Shima H, Maemondo M, Tanuma N, et al. PKM1 Confers Metabolic Advantages and Promotes Cell-Autonomous Tumor Cell Growth. Cancer Cell. 2018 Mar 12;33(3):355-367.e7. Chiavarina B, Whitaker-Menezes D, Martinez-Outschoorn UE, Witkiewicz AK, Birbe R, Howell A, Pestell RG, Smith J, Daniel R, Sotgia F, Lisanti MP. Pyruvate kinase expression (PKM1 and PKM2) in cancer-associated fibroblasts drives stromal nutrient production and tumor growth. Cancer Biol Ther. 2011 Dec 15;12(12):1101-13. Anastasiou D, Yu Y, Israelsen WJ, Jiang JK, Boxer MB, Hong BS, Tempel W, Dimov S, Shen M, Jha A, Yang H, Mattaini KR, Metallo CM, et al. Pyruvate kinase M2 activators promote tetramer formation and suppress tumorigenesis. Nat Chem Biol. 2012; 8:839–47. Zahra K, Dey T, Ashish, Mishra SP, Pandey U. Pyruvate Kinase M2 and Cancer: The Role of PKM2 in Promoting Tumorigenesis. Front Oncol. 2020 Mar 2; 10:159. Yang P, Li Z, Fu R, Wu H, Li Z (2014) Pyruvate kinase M2 facilitates colon cancer cell migration via the modulation of STAT3 signaling. Cell Signal 26(9):1853–1862. Hasan D, Gamen E, Abu Tarboush N, Ismail Y, Pak O, Azab B. PKM2 and HIF-1α regulation in prostate cancer cell lines. PLoS One. 2018 Sep 14;13(9): e0203745. Méndez-Lucas A, Li X, Hu J, Che L, Song X, Jia J, Wang J, Xie C, Driscoll PC, Tschaharganeh DF, Calvisi DF, Yuneva M, Chen X. Glucose Catabolism in Liver Tumors Induced by c-MYC Can Be Sustained by Various PKM1/PKM2 Ratios and Pyruvate Kinase Activities. Cancer Res. 2017 Aug 15;77(16):4355-4364. Chen S, Fisher RC, Signs S, Molina LA, Shenoy AK, Lopez MC, Baker HV, Koomen JM, Chen Y, Gittleman H, Barnholtz-Sloan J, Berg A, Appelman HD, Huang EH. Inhibition of PI3K/Akt/mTOR signaling in PI3KR2-overexpressing colon cancer stem cells reduces tumor growth due to apoptosis. Oncotarget. 2016 Jun 8;8(31):50476-50488. Ying J, Xu Q, Liu B, Zhang G, Chen L, Pan H. The expression of the PI3K/AKT/mTOR pathway in gastric cancer and its role in gastric cancer prognosis. Onco Targets Ther. 2015 Sep 1; 8:2427-33. Kugler W., Lakomek M. (2000). Glucose-6-phosphate isomerase deficiency. Bailliere’s Best. Pract. Res. Clin. Haematol. 13 89–101. 10.1053/beha.1999.0059 Ju HQ, Lin JF, Tian T, Xie D, Xu RH. NADPH homeostasis in cancer: functions, mechanisms and therapeutic implications. Signal Transduct Target Ther. 2020 Oct 7;5(1):231. Mullen NJ, Singh PK. Nucleotide metabolism: a pan-cancer metabolic dependency. Nat Rev Cancer. 2023 May;23(5):275-294. Carlos L, Jeffrey A. ERBB Receptors: From Oncogene Discovery to Basic Science to Mechanism-Based Cancer Therapeutics. Cancer Cell, 2014, 25(3); 282-303. Ma C, Zu X, Liu K, Bode AM, Dong Z, Liu Z, Kim DJ. Knockdown of Pyruvate Kinase M Inhibits Cell Growth and Migration by Reducing NF-kB Activity in Triple-Negative Breast Cancer Cells. Mol Cells. 2019 Sep 30;42(9):628-636. Shiroki T, Yokoyama M, Tanuma N, Shimosegawa T, Satoh K, et al. Enhanced expression of the M2 isoform of pyruvate kinase is involved in gastric cancer development by regulating cancer-specific metabolism. Cancer Sci. 2017 May;108(5):931-940. Yang L, Zhang Y. Tumor-associated macrophages: from basic research to clinical application. J Hematol Oncol, 2017, 10(1): 1-12. Bernsmeier C, van der Merwe S, Périanin A. Innate immune cells incirrhosis[J]. J Hepatol, 2020, 73(1): 186-201. Chen P, Bonaldo P. Role of macrophage polarization in tumor angiogenesis and vessel normalization: implications for new anticancer therapies. Int Rev Cel Mol Bio, 2013, 301: 1-35. Qian B Z, Pollard J W. Macrophage diversity enhances tumor progression and metastasis[J]. Cell, 2010, 141(1): 39-51. Zhong W Q, Chen G, Zhang W, et al. M2-polarized macrophages in keratocystic odontogenic tumor: relation to tumor angiogenesis. Sci Rep, 2015, 5(1): 15586. Cui X, Morales R T T, Qian W, et al. Hacking macrophage-associated immunosuppression for regulating glioblastoma angiogenesis. Biomaterials, 2018, 161: 164-178. Hou PP, Luo LJ, Chen HZ, Chen QT, Bian XL, Wu SF, Zhou JX, Zhao WX, Liu JM, Wang XM, Zhang ZY, Yao LM, Chen Q, Zhou D, Wu Q. Ectosomal PKM2 Promotes HCC by Inducing Macrophage Differentiation and Remodeling the Tumor Microenvironment. Mol Cell. 2020 Jun 18;78(6):1192-1206.e10. Taniguchi K, Sakai M, Sugito N, Kuranaga Y, Kumazaki M, Shinohara H, Ueda H, Futamura M, Yoshida K, Uchiyama K, Akao Y. PKM1 is involved in resistance to anti-cancer drugs. Biochem Biophys Res Commun. 2016 Apr 22;473(1):174-180. Wang Y, Zhao H, Zhao P, Wang X. Targeting PKM2 promotes chemosensitivity of breast cancer cells in vitro and in vivo. Cancer Biomark. 2021;32(2):221-230. Table 1 Table 1 is not available with this version. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx TableS8.tsv FigS1.pdf FigS2.pdf FigS3.pdf FigS4.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7676660","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556348941,"identity":"268d0948-5779-4656-b948-f43841c025c1","order_by":0,"name":"Shihang Ru","email":"","orcid":"","institution":"Chifeng City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shihang","middleName":"","lastName":"Ru","suffix":""},{"id":556348942,"identity":"1d9411af-ef0f-41e6-aefd-82a1884211ff","order_by":1,"name":"Wenshuai Li","email":"","orcid":"","institution":"Chifeng City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenshuai","middleName":"","lastName":"Li","suffix":""},{"id":556348943,"identity":"a34cf980-bf63-43a1-9497-be1c9ef7d9fc","order_by":2,"name":"Yingguang Liu","email":"","orcid":"","institution":"Chifeng City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingguang","middleName":"","lastName":"Liu","suffix":""},{"id":556348944,"identity":"b7742100-c60a-4019-b510-6fd05f96e775","order_by":3,"name":"Bugao Chao","email":"","orcid":"","institution":"Chifeng City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bugao","middleName":"","lastName":"Chao","suffix":""},{"id":556348947,"identity":"ef637597-f0f3-482e-a2dd-1ae18675bf23","order_by":4,"name":"Yang Li","email":"","orcid":"","institution":"Chifeng City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":556348949,"identity":"d2e5b2ad-bac7-490a-af7b-2056b72289da","order_by":5,"name":"Di Wu","email":"","orcid":"","institution":"Chifeng City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wu","suffix":""},{"id":556348952,"identity":"d4c17ef5-345b-4bc9-88ca-2321db9f3fbd","order_by":6,"name":"Chunlei Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACPmYg8cBAQs7+eGPjgw/EaGEDaUkosDFmOHO42XAGUVpARMKHtMSGG+lt0hxEaWHnMfyQYHCYsXHmwwZpBgY7Od0Ggg7jMZYAamFmlk5sMC5gSDY2O0BYixkDUAsbG1BL8gyGA4nbiNXCwyN5sOEwDwla0iQkJBgbm4nUwlYM9IuNgQFPYjPjDAMi/MLPf3jjhw9/JOo3sB9//uNDhZ0cQS1owIA05aNgFIyCUTAKcAAAM9M7HyGYMggAAAAASUVORK5CYII=","orcid":"","institution":"Chifeng City Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chunlei","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-09-22 13:24:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7676660/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7676660/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98283345,"identity":"0f7cfc6a-134a-4286-b846-57f5d563a18c","added_by":"auto","created_at":"2025-12-16 06:11:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42891,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/4807817f1257404aaabded20.docx"},{"id":98283375,"identity":"6c9eb177-54ba-4531-804f-f4fdc2a6ef58","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"json","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9598,"visible":true,"origin":"","legend":"","description":"","filename":"3b6e8cb774794e94b6b295cc043d2cfe.json","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/b2bcdec2d17d2598bf70903f.json"},{"id":98283391,"identity":"ce946c5d-5c86-42dd-bd8f-1cad57274a4a","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":107935,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/fcb3d3ebda76d8311a548b58.pdf"},{"id":98283373,"identity":"7d2b5b82-fa63-4ac7-b5ad-b381dd0a8a4b","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138462,"visible":true,"origin":"","legend":"","description":"","filename":"FigS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/c2687acec2102cbbff4949b7.pdf"},{"id":98436363,"identity":"cc606a96-22c6-465b-a69f-ea94804567aa","added_by":"auto","created_at":"2025-12-17 16:55:30","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":270666,"visible":true,"origin":"","legend":"","description":"","filename":"FigS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/0296fd284f43bc7016406e09.pdf"},{"id":98436236,"identity":"cd38458a-aaf0-4376-b9eb-5bc48e2407a5","added_by":"auto","created_at":"2025-12-17 16:55:11","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178901,"visible":true,"origin":"","legend":"","description":"","filename":"FigS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/c1a74cc64a160e65ceb998a9.pdf"},{"id":98434063,"identity":"f2ddd3ed-6a62-4f09-b376-9cf3e6654e65","added_by":"auto","created_at":"2025-12-17 16:51:28","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13787,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/576ba5af84072ff008f56613.xlsx"},{"id":98436359,"identity":"e85b77e8-a3ff-4f39-9e59-37159537668c","added_by":"auto","created_at":"2025-12-17 16:55:30","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49586,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/93ea83b87d95fa096bacb45b.xlsx"},{"id":98434581,"identity":"ccdd5065-f368-431e-aa95-0ee543df5518","added_by":"auto","created_at":"2025-12-17 16:52:22","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31378,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/d414828fe8781277c377cd58.xlsx"},{"id":98435829,"identity":"0de37b89-7f7d-4066-a4f9-f2cb7dc01cfa","added_by":"auto","created_at":"2025-12-17 16:54:28","extension":"xlsx","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9639,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/e6045607e880459c0fe48069.xlsx"},{"id":98434265,"identity":"5681a8e7-4d8d-4eb3-9121-26b95b62f908","added_by":"auto","created_at":"2025-12-17 16:51:47","extension":"xlsx","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9089,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/fc19e9e4c3ec002ba05c3e23.xlsx"},{"id":98283386,"identity":"d3ead1f1-4a56-4200-a85f-89058b1fa8f2","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"xlsx","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14358,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/33dd4cd9ddc31ab3ee0adfbc.xlsx"},{"id":98434206,"identity":"971c6ea0-5b9a-4500-b7e0-93d36803bcfc","added_by":"auto","created_at":"2025-12-17 16:51:41","extension":"xlsx","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12823,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/d18a7c443080193366f0959c.xlsx"},{"id":98435436,"identity":"5d350a9e-0b82-404b-9fef-ba2d682c96ab","added_by":"auto","created_at":"2025-12-17 16:53:47","extension":"tsv","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37238,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8.tsv","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/1ca7a13568664f2631ce9443.tsv"},{"id":98434226,"identity":"199cea52-6d9a-470e-aa20-22c69057e9ec","added_by":"auto","created_at":"2025-12-17 16:51:44","extension":"xml","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118470,"visible":true,"origin":"","legend":"","description":"","filename":"3b6e8cb774794e94b6b295cc043d2cfe1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/4aad80ed4406f3f402472bdb.xml"},{"id":98283388,"identity":"908b2ead-ecfa-494c-a632-f2d62f18faa8","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"jpg","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148710,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/1ce5f21a0382ac27f3ae3a36.jpg"},{"id":98436009,"identity":"2f465a43-5c60-456e-921f-3643b125956f","added_by":"auto","created_at":"2025-12-17 16:54:45","extension":"jpg","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155135,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/a687144a5a45e991867deaff.jpg"},{"id":98436384,"identity":"0a51a1f1-8a07-4a8c-9034-0b0873172858","added_by":"auto","created_at":"2025-12-17 16:55:34","extension":"jpg","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":206013,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/e7f009bd4d50e9c3db0290cd.jpg"},{"id":98283389,"identity":"4a5d9208-56f9-4324-82c2-1ccb63d46621","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"jpg","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196074,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/3d8dcecf047867d73cf76a92.jpg"},{"id":98436396,"identity":"7234ebc3-c88f-46fb-86e4-0595e9008666","added_by":"auto","created_at":"2025-12-17 16:55:37","extension":"jpg","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171986,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/a9117f5a30b73748f9625ec7.jpg"},{"id":98436135,"identity":"8ab02155-0132-426e-a046-5bcb9730df74","added_by":"auto","created_at":"2025-12-17 16:54:58","extension":"jpg","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147309,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/cf97c3f2d7592d7171903310.jpg"},{"id":98436180,"identity":"a96233bf-fa43-423b-8808-cc7f714d677c","added_by":"auto","created_at":"2025-12-17 16:55:02","extension":"jpg","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185285,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/b550ce2941e7978668e44498.jpg"},{"id":98283369,"identity":"7bf4b213-42c1-4112-83e3-d28c83b61140","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"jpg","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46579,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/4793a092edaa90ad3090c02b.jpg"},{"id":98283402,"identity":"74b01663-ae3d-4248-ad6d-47381057ce58","added_by":"auto","created_at":"2025-12-16 06:11:03","extension":"jpg","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82145,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/ee0f38346571a31d4b3eb738.jpg"},{"id":98435421,"identity":"d0e43f5e-15fb-4b2b-a122-4f63ebe7f8af","added_by":"auto","created_at":"2025-12-17 16:53:44","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98677,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/f41c5a7ccfc014fdb896d863.png"},{"id":98436175,"identity":"759303f6-1b5f-4867-b5d7-2f487c385b5c","added_by":"auto","created_at":"2025-12-17 16:55:01","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89305,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/7eb89805bb555e0afa2432cd.png"},{"id":98283406,"identity":"533fdf0e-2b20-4127-998c-908b078a93f3","added_by":"auto","created_at":"2025-12-16 06:11:03","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194687,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/8e9798650130c0d30e003058.png"},{"id":98283409,"identity":"29a4f1da-c8b4-492b-a831-62c835f90997","added_by":"auto","created_at":"2025-12-16 06:11:03","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110546,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/4c42f8da25fef29fdda7549b.png"},{"id":98283414,"identity":"4c09e593-e427-4bf8-bf78-521edc8cda57","added_by":"auto","created_at":"2025-12-16 06:11:03","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80086,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/55783d62169a491212b65e07.png"},{"id":98283395,"identity":"a7553b33-3f5a-4dce-885c-e544f6e0ec79","added_by":"auto","created_at":"2025-12-16 06:11:03","extension":"png","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76820,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure.6.png","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/ea6c1a9f90f601897f0bc8ca.png"},{"id":98434243,"identity":"9f61022c-5769-422a-b3ca-ffef10d4f40f","added_by":"auto","created_at":"2025-12-17 16:51:46","extension":"png","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109599,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure.7.png","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/ac72e155a11321f353e378dd.png"},{"id":98283408,"identity":"3dd81bca-66f3-4e51-be13-bfe32a716681","added_by":"auto","created_at":"2025-12-16 06:11:03","extension":"png","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31488,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure.8.png","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/3d3f89c2a107eb771b206eb2.png"},{"id":98283412,"identity":"82bb800c-4bad-42d3-bcab-b77de6e1d3c3","added_by":"auto","created_at":"2025-12-16 06:11:03","extension":"png","order_by":41,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38858,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure.9.png","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/96efceb32bffaa8332717404.png"},{"id":98283410,"identity":"92c11944-4642-4ef9-940e-908ad0e47081","added_by":"auto","created_at":"2025-12-16 06:11:03","extension":"xml","order_by":42,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117527,"visible":true,"origin":"","legend":"","description":"","filename":"3b6e8cb774794e94b6b295cc043d2cfe1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/79e5f6ffc4dcf67451713eae.xml"},{"id":98283393,"identity":"8826c721-e0aa-490e-8412-160304ba93dc","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"html","order_by":43,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127961,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/bd1e89dc3a3222cb7acfa669.html"},{"id":98283352,"identity":"973d7fdb-b493-4ce6-988a-55015f52ba8e","added_by":"auto","created_at":"2025-12-16 06:11:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148710,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted Gene Co-expression Network Analysis (WGCNA) was performed on the GSE28735 and GSE62452 datasets. \u003cstrong\u003eA\u003c/strong\u003e Exclude outlier samples in the GSE28735 dataset using hierarchical clustering analysis. \u003cstrong\u003eB\u003c/strong\u003e Select the appropriate soft threshold for constructing a scale-free network in the GSE28735 dataset. \u003cstrong\u003eC\u003c/strong\u003eConstruct a hierarchical clustering dendrogram for the GSE28735 dataset. \u003cstrong\u003eD\u003c/strong\u003ePlot a heatmap of module-trait relationships in the GSE28735 dataset, showing the correlation with pancreatic cancer. \u003cstrong\u003eE\u003c/strong\u003e Exclude outlier samples in the GSE62452 dataset using hierarchical clustering analysis. \u003cstrong\u003eF\u003c/strong\u003e Select the appropriate soft threshold for constructing a scale-free network in the GSE62452 dataset. \u003cstrong\u003eG\u003c/strong\u003e Construct a hierarchical clustering dendrogram for the GSE62452 dataset. \u003cstrong\u003eH\u003c/strong\u003e Plot a heatmap of module-trait relationships in the GSE62452 dataset, showing the correlation with pancreatic cancer.\u003c/p\u003e","description":"","filename":"Figure.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/00c338d7c1fa2f157e049281.jpg"},{"id":98436080,"identity":"e1c35dbf-66ae-4598-ba8f-816e66ce66c3","added_by":"auto","created_at":"2025-12-17 16:54:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":155135,"visible":true,"origin":"","legend":"\u003cp\u003ePancreatic cancer glycolysis-related genes and their GO and KEGG analyses. \u003cstrong\u003eA-B\u003c/strong\u003e Pancreatic cancer positively related genes from GSE28735 and GSE62452 datasets were intersected with 326 glycolysis-related genes to obtain pancreatic cancer glycolysis-related genes. \u003cstrong\u003eC-F\u003c/strong\u003e The 93 pancreatic cancer glycolysis-related genes obtained from GSE28735 data were analyzed in biological processes (BP), cellular components (CC) and molecular function (MF) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. \u003cstrong\u003eG-J\u003c/strong\u003e The 46 pancreatic cancer glycolysis-related genes obtained from GSE62452 data were analyzed in biological processes (BP), cellular components (CC) and molecular function (MF) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.\u003c/p\u003e","description":"","filename":"Figure.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/c6cf20583ef0d993cb32d010.jpg"},{"id":98283355,"identity":"34e05255-dbb9-48cc-803e-f18cffd5d48e","added_by":"auto","created_at":"2025-12-16 06:11:01","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":206013,"visible":true,"origin":"","legend":"\u003cp\u003eThe PPI network of pancreatic cancer glycolysis-related genes and its gene modules of MCC and MNC algorithms in CytoHubba plugin and Cox univariate regression analysis and the identification of Hub genes. \u003cstrong\u003eA\u003c/strong\u003e The PPI network of glycolysis-related genes in pancreatic cancer from the GSE28735 dataset was visualized using Cytoscape. \u003cstrong\u003eB-C\u003c/strong\u003e The MCC and MNC algorithms from the CytoHubba plugin in Cytoscape were applied to identify the top 20 genes with the highest scores. \u003cstrong\u003eD\u003c/strong\u003eCox univariate regression analysis was conducted on glycolysis-related genes in pancreatic cancer from the GSE62452 dataset.\u003cstrong\u003e E\u003c/strong\u003e The intersection of the top 20 genes identified by the MCC and MNC algorithms and the prognostic genes from Cox analysis revealed the hub genes.\u003c/p\u003e","description":"","filename":"Figure.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/8c1f5e929aef5f28da9c5260.jpg"},{"id":98283364,"identity":"b73fe997-925a-4ebb-affb-34e99fb6da89","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":196074,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA of the highly expressed PKM gene in the GSE28735, GSE62452, and TCGA datasets. \u003cstrong\u003eA\u003c/strong\u003eEnrichment analysis of the highly expressed PKM gene in Gene Ontology (GO) and KEGG pathways for the GSE28735 dataset. \u003cstrong\u003eB\u003c/strong\u003e Enrichment analysis of the highly expressed PKM gene in GO and KEGG pathways for the GSE62452 dataset. \u003cstrong\u003eC\u003c/strong\u003eEnrichment analysis of the highly expressed PKM gene in GO and KEGG pathways for the TCGA dataset.\u003c/p\u003e","description":"","filename":"Figure.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/1a5e0ca5e7fe39a4c9d2dba1.jpg"},{"id":98283368,"identity":"6eeaac5d-1814-4a46-92c1-205482838b11","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":171986,"visible":true,"origin":"","legend":"\u003cp\u003ePKM gene expression in pancreatic cancer tissues versus normal pancreatic tissues and prognostic analysis of pancreatic cancer patients with high and low PKM gene expression. \u003cstrong\u003eA-C\u003c/strong\u003ePKM gene expression in pancreatic cancer and normal tissues was analyzed in the GSE28735, GSE62452, and GEPIA datasets. \u003cstrong\u003eD-F\u003c/strong\u003e Prognostic analysis of pancreatic cancer patients with high and low PKM gene expression, based on the GSE28735, GSE62452, and GEPIA datasets.\u003c/p\u003e","description":"","filename":"Figure.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/eeff38bcff8a46e775648d75.jpg"},{"id":98283400,"identity":"ec41ca65-d1a1-4987-86c8-81362f58b5eb","added_by":"auto","created_at":"2025-12-16 06:11:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":147309,"visible":true,"origin":"","legend":"\u003cp\u003eImmune cell infiltration analysis of high and low PKM gene expression \u003cstrong\u003eA\u003c/strong\u003e Immune cell infiltration analysis of high and low PKM expression in GSE28735 data. \u003cstrong\u003eB\u003c/strong\u003e Immune cell infiltration analysis of high and low PKM expression in GSE62452 data.\u003cstrong\u003e C\u003c/strong\u003eImmune cell infiltration analysis of high and low PKM expression in TCGA data.\u003c/p\u003e","description":"","filename":"Figure.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/e05da55c388cfa041070c147.jpg"},{"id":98283384,"identity":"c8ee8099-36bd-402c-b69c-58b12df8c1fc","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":185285,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of PKM Gene Expression with Immune Cell Infiltration. \u003cstrong\u003eA-C\u003c/strong\u003e Bar graph showing the correlation of PKM gene expression with immune cell infiltration in the GSE28735 dataset, GSE62452 dataset and TCGA dataset.\u003cstrong\u003e D-F\u003c/strong\u003e Scatter plots of positive and negative correlations between PKM gene expression and immune cell infiltration in the GSE28735 dataset. \u003cstrong\u003eG-J\u003c/strong\u003e Scatter plots of positive and negative correlations between PKM gene expression and immune cell infiltration in the GSE62452 dataset.\u003cstrong\u003e K-Q\u003c/strong\u003e Scatter plots of positive and negative correlations between PKM gene expression and immune cell infiltration in the TCGA dataset.\u003c/p\u003e","description":"","filename":"Figure.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/83ac2cf3cf01883e6d2d5b1d.jpg"},{"id":98283357,"identity":"876996b0-edb4-4423-8575-41ac79a8e8ad","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":46579,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of PKM Response to Treatment with Immune Checkpoint Inhibitors. \u003cstrong\u003eA-C\u003c/strong\u003e TIDE scores for high and low expression of the PKM gene in the GSE28735 dataset, GSE62452 dataset and TCGA dataset. \u003cstrong\u003eD-F\u003c/strong\u003e PKM gene expression (high vs. low) in response to immunosuppressants in the GSE28735 dataset, GSE62452 dataset and TCGA dataset. P values were shown as: ***P \u0026lt; 0.001, **\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure.8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/e9592cfdcb17fc428f6ced3e.jpg"},{"id":98435776,"identity":"c4bb5828-6783-4174-9b95-569d37c9537c","added_by":"auto","created_at":"2025-12-17 16:54:24","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":82145,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential chemotherapeutic response based on IC50 between the high and low PKM groups. \u003cstrong\u003eA-D\u003c/strong\u003eThe half-maximal inhibitory concentrations (IC50) of 4 chemotherapeutic agents (irinotecan, oxaliplatin, gemcitabine, and cisplatin) in the GSE28735 data. \u003cstrong\u003eE-H\u003c/strong\u003eThe half-maximal inhibitory concentrations (IC50) of 4 chemotherapeutic agents (irinotecan, oxaliplatin, gemcitabine, and cisplatin) in the GS62452 data. \u003cstrong\u003eI-L\u003c/strong\u003eThe half-maximal inhibitory concentrations (IC50) of 4 chemotherapeutic agents (irinotecan, oxaliplatin, gemcitabine, and cisplatin) in the TCGA data.\u003c/p\u003e","description":"","filename":"Figure.9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/3eaa82c707f37fc8c70c06f2.jpg"},{"id":102415540,"identity":"81bb82a7-ab70-4b34-84ac-f07d767258a8","added_by":"auto","created_at":"2026-02-11 12:44:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2412304,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/fc7a68dd-d653-4c1b-8c19-f4e3cdbd0a24.pdf"},{"id":98435418,"identity":"a810fc1d-edcd-4113-935a-1d5f11077f06","added_by":"auto","created_at":"2025-12-17 16:53:44","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13787,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/54e2f2db0f8304cb57d6db7a.xlsx"},{"id":98283346,"identity":"ddce0fe4-79b8-47d3-a551-f33e71a6fa13","added_by":"auto","created_at":"2025-12-16 06:11:01","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":49586,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/5f2a72a1e5564f3bb2c4f340.xlsx"},{"id":98434574,"identity":"c76f7efe-22b3-48f1-b2a5-35f2c84d2b75","added_by":"auto","created_at":"2025-12-17 16:52:21","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":31378,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/691a019e56de67e7145d4821.xlsx"},{"id":98283362,"identity":"119781ee-6b6c-490b-84b9-5825539fef81","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":9639,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/12f31aa89f203a8b045d4204.xlsx"},{"id":98436190,"identity":"a9356b8a-99bb-4000-a712-64ce673b8017","added_by":"auto","created_at":"2025-12-17 16:55:03","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":9089,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/56fa0635fde9d1f80ad836ac.xlsx"},{"id":98283380,"identity":"4ac065a7-0528-42fb-8fbe-64fa461e4908","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14358,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/e3bc396628cdaa961ef9d452.xlsx"},{"id":98283356,"identity":"258ea8c6-3a4b-4d5a-8a6d-abf5278aed10","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":12823,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/46456d2069dde308b79c3b0b.xlsx"},{"id":98435387,"identity":"d7c99ce0-ad33-4151-8cbb-d6fd50f01b98","added_by":"auto","created_at":"2025-12-17 16:53:38","extension":"tsv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":37238,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8.tsv","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/ca0d13f15d3dee9670f6f63b.tsv"},{"id":98436366,"identity":"120d6732-c626-42ca-ac6e-ee6618f9163d","added_by":"auto","created_at":"2025-12-17 16:55:30","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":107935,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/4f4408782ffde2d86b9cff0b.pdf"},{"id":98436228,"identity":"dfb464c8-69fc-4a8d-8825-156c36b938e5","added_by":"auto","created_at":"2025-12-17 16:55:08","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":138462,"visible":true,"origin":"","legend":"","description":"","filename":"FigS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/6d24dd2b0002c3f474fc2107.pdf"},{"id":98283360,"identity":"b849e29f-15dc-4f43-b844-83be7dd45629","added_by":"auto","created_at":"2025-12-16 06:11:02","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":270666,"visible":true,"origin":"","legend":"","description":"","filename":"FigS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/ffbfbe2128fc1ce9f18bb7ac.pdf"},{"id":98436035,"identity":"634f1714-1db7-4291-a0fa-5b29bb3d41ab","added_by":"auto","created_at":"2025-12-17 16:54:46","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":178901,"visible":true,"origin":"","legend":"","description":"","filename":"FigS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7676660/v1/7d4b7f4bb39949cf9e3b1afc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The glycolytic PKM gene is associated with immune infiltrates and therapeutic response of pancreatic cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic cancer is a highly aggressive malignancy characterized by an insidious onset, rapid progression, poor response to treatment, and an overall dismal prognosis. Due to the lack of early symptoms and effective screening methods, most patients are diagnosed at an advanced stage. In recent years, the incidence and mortality rates of pancreatic cancer have been rising. In 1990, the global incidence was approximately 196,000 cases; however, by 2017, this number had surged to 441,000 cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], reflecting a significant increase over the past two decades. According to recent projections based on epidemiological data from the United States, pancreatic cancer is expected to surpass colorectal cancer as the second leading cause of cancer-related deaths by 2026 and may become the second most common cause of cancer mortality worldwide by 2040 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNumerous studies have underscored the pivotal role of metabolic reprogramming, particularly the glycolytic pathway, in the initiation and progression of malignant tumors [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Cancer cells undergo metabolic adaptations to accommodate their high energy demands and sustain rapid proliferation. One of the most well-characterized metabolic alterations is the Warburg effect, first described by Otto Warburg in the 1920s. He observed that, even in the presence of oxygen, cancer cells preferentially rely on glycolysis rather than oxidative phosphorylation to generate ATP [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This metabolic shift allows cancer cells to produce ATP at a significantly higher rate\u0026mdash;approximately 100 times faster than oxidative phosphorylation\u0026mdash;facilitating their survival and proliferation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, beyond ATP production, glycolysis supplies essential metabolic intermediates required for biosynthetic processes that sustain tumor growth. For instance, glycolytic intermediates fuel the pentose phosphate pathway (PPP), leading to the generation of ribulose-5-phosphate and nicotinamide adenine dinucleotide phosphate (NADPH), both of which are essential for lipid and nucleic acid biosynthesis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, NADPH plays a crucial role in maintaining intracellular redox homeostasis by sustaining reduced glutathione (GSH) levels, thereby enhancing resistance to chemotherapy-induced oxidative stress and drug cytotoxicity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe glycolytic pathway is tightly regulated by several transcription factors, including hypoxia-inducible factor-1 (HIF-1) and c-Myc, which coordinate metabolic adaptations in cancer cells. Additionally, glycolysis activates key oncogenic signaling pathways, such as the phosphatidylinositol 3-kinase/protein kinase B (PI3K-AKT) and Wnt pathways, further driving cancer cell proliferation and survival. Given its central role in tumor metabolism and progression, glycolysis represents a promising target for therapeutic intervention in pancreatic cancer.\u003c/p\u003e\u003cp\u003ePyruvate kinase (PK), a rate-limiting enzyme in the glycolytic pathway, catalyzes the conversion of phosphoenolpyruvate and adenosine diphosphate (ADP) into pyruvate and adenosine triphosphate (ATP). Pyruvate kinase muscle isoform (PKM) is a key isoenzyme of PK that plays a crucial role in tumor metabolism. In this study, we investigated the expression and prognostic significance of the mitochondria-related gene PKM in pancreatic cancer using publicly available datasets. Additionally, we explored its involvement in immune infiltration and its potential response to immunotherapy and pharmacological treatments. Our findings aim to identify novel prognostic biomarkers and potential therapeutic targets for pancreatic cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData collection and preprocessing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo microarray datasets, GSE28735 and GSE62452, were retrieved from the GEO database. These datasets, based on the Affymetrix GPL6244 platform, contain both gene expression and clinical data. The gene expression data from these datasets were normalized and used as the experimental groups in this study. Additionally, glycolysis-related genes were obtained from the Molecular Signatures Database (Msigdb) (https://www.gsea-msigdb.org/gsea/Msigdb/human/search.jsp). To validate our findings, gene expression and clinical data for pancreatic cancer were extracted from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). The TCGA gene expression data were standardized and normalized before analysis. Since all datasets were obtained from publicly available sources, ethical approval was not required for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWeighted gene co-expression network analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWGCNA is an algorithm used to identify co-expression gene modules with biological significance, facilitating the exploration of gene networks and their associations with diseases [12]. In this study, WGCNA was applied to identify gene networks and modules associated with pancreatic cancer. Initially, the top 25% of genes with the highest variance were selected for WGCNA analysis. Hierarchical clustering was performed to exclude outlier samples, followed by the selection of an optimal soft threshold (ranging from 1 to 20) to ensure that the network met scale-free topology criteria. A neighbor-joining matrix was then constructed based on the soft threshold value (\u0026beta;) and the correlation matrix for all gene pairs. This matrix was transformed into a Topological Overlap Matrix (TOM) and a corresponding dissimilarity matrix (1-TOM). Subsequently, hierarchical clustering dendrograms were generated, and co-expression modules were identified. The expression profiles of each module were summarized using Module Eigengenes (ME), and their correlation with pancreatic cancer was assessed. Genes from modules positively correlated with pancreatic cancer were selected for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePancreatic cancer glycolysis-related genes and enrichment analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenes positively associated with pancreatic cancer were intersected with \u003cstrong\u003eglycolysis-related genes\u003c/strong\u003e to identify glycolysis-associated genes in pancreatic cancer, which were visualized using \u003cstrong\u003eVenny 2.1.0\u003c/strong\u003e (https://bioinfogp.cnb.csic.es/tools/venny/). To explore the biological functions of these genes, \u003cstrong\u003eGene Ontology (GO) enrichment analysis\u003c/strong\u003e was conducted, including analyses of \u003cstrong\u003ebiological processes (BP), cellular components (CC), and molecular functions (MF)\u003c/strong\u003e [13]. Additionally, \u003cstrong\u003eKyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis\u003c/strong\u003e was performed to examine biological pathways associated with these genes [14]. Functional annotation of glycolysis-related genes was conducted using \u003cstrong\u003eDAVID\u003c/strong\u003e (https://david.ncifcrf.gov/), a freely accessible bioinformatics resource [15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProtein-protein interaction network analysis and cox regression analysis for Hub Gene Determination\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein-Protein Interaction (PPI) networks were constructed to analyze the interactions among pancreatic cancer glycolysis-related genes [16]. The STRING database (https://cn.string-db.org) was used to build PPI networks from the GSE28735 dataset, with a confidence score of \u0026ge;0.4 considered significant. The network was visualized using Cytoscape (v3.9.1). Key genes were identified based on Maximal Clique Centrality (MCC) and Maximum Neighborhood Component (MNC) scores, calculated using the CytoHubba plugin. The top 20 genes were selected for visualization.\u003c/p\u003e\n\u003cp\u003eSubsequently, Cox univariate regression analysis was performed using the GSE62452 dataset to identify prognostic genes. The hub genes were determined by intersecting the results of MCC, MNC, and Cox regression analyses, and they were visualized using Venny 2.1.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGene Set Enrichment Analysis (GSEA) of target gene\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe target gene for this study was selected from the identified hub genes, and GSEA was performed to investigate its biological function. GSEA assesses the association between the target gene and predefined gene sets, facilitating functional prediction [17]. Tumor samples were divided into high- and low-expression groups based on the mean expression value of the target gene. GSEA enrichment analysis was conducted using the c5.go.v2024.1.Hs.symbols.gmt and c2.cp.kegg_medicus.v2024.1.Hs.symbols.gmt datasets in GSEA software (v4.3.3). A P-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTarget gene expression and prognostic relevance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis of the target gene between pancreatic cancer and normal tissues was conducted. The results were validated using the GEPIA database (http://gepia2.cancer-pku.cn/#index). Kaplan-Meier survival analysis was performed, with patients categorized into high- and low-expression groups based on the median expression value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorrelation of target genes with immune cell infiltration\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CIBERSORT algorithm was used to estimate the relative abundance of 22 immune cell subtypes in pancreatic cancer, including B cells, T cells, myeloid cells, NK cells, and plasma cells [18]. The relationship between target gene expression and immune cell infiltration was analyzed by comparing high- and low-expression groups and computing correlation coefficients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEvaluation of immunotherapy response\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict the response of pancreatic cancer patients to immune checkpoint inhibitors (ICIs) [19]. Tumor gene expression data were normalized, and the TIDE online platform (http://tide.dfci.harvard.edu/) was used to calculate ICI response predictions. Patients were categorized into high- and low-expression groups based on the median expression value of the target gene, and their respective immunotherapy responses were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDrug sensitivity analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrug sensitivity analysis was conducted to predict the response of pancreatic cancer patients to chemotherapy based on cell line expression data and drug response models [20]. Training set data were downloaded from https://osf.io/c6tfx/files/osfstorage. Patients were divided into high- and low-expression groups based on the mean expression value of the target gene, and drug response outcomes were compared between groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data processing and statistical analyses were performed using R software (v4.4.1). The GEOquery package was used to download GEO datasets. Correlation analyses were performed using the corPvalueStudent function from the WGCNA package. Fisher\u0026rsquo;s exact test was used for KEGG and GO enrichment analyses. Cox univariate regression analysis was conducted using the Survival package. The Wilcoxon signed-rank test was applied for differential gene expression and drug sensitivity analysis. Kaplan-Meier survival analysis was performed using the Survival and Survminer packages. Immune infiltration analysis was conducted using the CIBERSORT R package, with correlations calculated using Pearson\u0026rsquo;s method. Differences in immune cell infiltration and immunotherapy response were assessed using the test function. Drug susceptibility analysis was conducted using the oncoPredict R package. A P-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eData Information\u003c/h2\u003e\u003cp\u003eTwo pancreatic cancer datasets, GSE28735 and GSE62452, which include both clinical and gene expression data, were retrieved from the GEO database for analysis. Additionally, clinical and gene expression data for pancreatic cancer were obtained from the TCGA database. The GSE28735 and GSE62452 datasets were designated as the experimental group, while the TCGA data served as the validation group. A total of 326 glycolysis-related genes were included in this study (Additional File 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A summary of the datasets is presented in Table\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eWeighted Gene Co-Expression Network Analysis (WGCNA)\u003c/h2\u003e\u003cp\u003eWGCNA was employed to identify gene modules associated with pancreatic cancer. Outlier samples were removed based on hierarchical cluster analysis, specifically GSM711957 in the GSE28735 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). A soft threshold of 16 was selected to achieve a scale-free network topology (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Hierarchical clustering dendrograms and heatmaps of module-trait relationships were generated to visualize the correlation between each module and pancreatic cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D). Three modules, MEyellow (r\u0026thinsp;=\u0026thinsp;0.7, p\u0026thinsp;=\u0026thinsp;1e-15), MEblue (r\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;=\u0026thinsp;9e-07), and MEgrey (r\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;3e-04), were significantly and positively correlated with pancreatic cancer and were selected for further analysis, comprising 3016 genes (Additional File 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Similarly, in the GSE62452 dataset, outlier sample GSM1527158 was excluded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), and a soft threshold of 14 was determined (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). The hierarchical clustering dendrograms and heatmaps of module-trait relationships were generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, H). The MEBrown (r\u0026thinsp;=\u0026thinsp;0.67, p\u0026thinsp;=\u0026thinsp;5e-18) and MECyan (r\u0026thinsp;=\u0026thinsp;0.37, p\u0026thinsp;=\u0026thinsp;1e-05) modules, which were significantly correlated with pancreatic cancer, were selected, comprising 1679 genes (Additional File 1: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of Pancreatic Cancer Glycolysis-Related Genes and Enrichment Analysis\u003c/h2\u003e\u003cp\u003eThe genes identified in both experimental groups that were positively associated with pancreatic cancer were intersected with the 326 glycolysis-related genes, resulting in 93 and 46 pancreatic cancer glycolysis-related genes, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B) (Additional File 1: Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e, S5). To elucidate the molecular mechanisms underlying these genes, Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. GO analysis revealed that these genes were predominantly involved in glycolytic processes, canonical glycolysis, and response to hypoxia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, G). Cellular component analysis indicated their enrichment in extracellular exosomes and cytosol (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, H). Molecular function analysis highlighted D-glucose binding as the primary molecular function (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, I). KEGG pathway analysis further demonstrated enrichment in glycolysis/gluconeogenesis, the HIF-1 signaling pathway, fructose and mannose metabolism, and central carbon metabolism in cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, J) (Additional File 1: Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e, S7).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePPI Network and Hub Gene Identification\u003c/h2\u003e\u003cp\u003eA protein-protein interaction (PPI) network of 93 glycolysis-related genes from the GSE28735 dataset was constructed using the STRING database, yielding 92 nodes and 426 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) (Additional File 1: Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). The CytoHubba plugin in Cytoscape software was used to analyze the network, applying Maximal Clique Centrality (MCC) and Maximum Neighborhood Component (MNC) algorithms to score each node. The top 20 genes with the highest scores were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C). Cox univariate regression analysis was then performed on the GSE62452 dataset, identifying 17 prognostic genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The intersection of these 17 genes with the top 20 genes from the MCC and MNC analyses yielded 7 hub genes: PKM, TPI1, ENO1, GAPDH, PYGL, SLC2A1, and HK1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eGene Set Enrichment Analysis (GSEA) of the Target Gene\u003c/h2\u003e\u003cp\u003eAmong the identified hub genes, PKM was selected as the target gene for further investigation. GSEA analysis of PKM in the GSE28735, GSE62452, and TCGA datasets revealed significant enrichment in glycolytic processes, ERBB signaling pathway, and glucose metabolism-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eExpression and Prognostic Analysis of PKM\u003c/h2\u003e\u003cp\u003eAnalysis of gene expression levels showed that PKM was significantly upregulated in pancreatic cancer tissues compared to normal pancreatic tissues in both GSE28735 and GSE62452 datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). This was further validated using GEPIA, based on TCGA and GTEx data, confirming the upregulation of PKM in pancreatic cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Kaplan-Meier survival analysis indicated that high PKM expression was associated with worse prognosis and shorter survival time in both experimental and validation groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation Between PKM and Immune Cell Infiltration\u003c/h2\u003e\u003cp\u003eUsing the CIBERSORT algorithm, the relationship between PKM expression and immune cell infiltration was analyzed. In the GSE28735 dataset, the M0 macrophage infiltration level was significantly higher in the high PKM expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In the GSE62452 dataset, the high PKM expression group had significantly higher M0 macrophage and M1 macrophage infiltration, while monocyte and resting mast cell infiltration was higher in the low PKM expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In the TCGA dataset, the high PKM expression group exhibited increased M0 macrophages and resting NK cells, whereas the low PKM expression group had higher na\u0026iuml;ve B cells, plasma cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and resting mast cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). In the GSE28735 dataset, PKM expression was positively correlated with macrophages M0, and negatively correlated with monocytes and CD8\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, D-F). In the GSE62452 dataset, PKM expression was positively correlated with M0 macrophages and resting dendritic cells, but negatively correlated with resting mast cells and monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, G-J). In the TCGA data, PKM expression was positively correlated with M0 macrophages and resting NK cells, while negatively correlated with resting mast cells, monocytes, CD8\u0026thinsp;+\u0026thinsp;T cells, plasma cells, and naive B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, K-Q).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eImpact of PKM on Immunotherapy Response\u003c/h2\u003e\u003cp\u003eThe Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to evaluate the impact of PKM expression on immune checkpoint inhibitor (ICI) response. Across all three datasets, TIDE scores were significantly higher in the high PKM expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-C), indicating a greater potential for immune escape. Patients with high PKM expression exhibited reduced responsiveness to ICI therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD-F), suggesting that PKM may contribute to immune evasion in pancreatic cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eDrug Sensitivity Analysis of PKM\u003c/h2\u003e\u003cp\u003eThe sensitivity of PKM expression to seven chemotherapeutic agents was analyzed. In the GSE28735 dataset, the IC50 values of irinotecan, oxaliplatin, gemcitabine, and cisplatin were significantly higher in the high PKM expression group, indicating reduced sensitivity to these drugs (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-D). In the GSE62452 dataset, irinotecan and oxaliplatin also exhibited higher IC50 values in the high PKM expression group, confirming lower drug efficacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE-H). Similarly, in the TCGA dataset, the IC50 values for irinotecan, oxaliplatin, gemcitabine, and cisplatin were significantly elevated in the high PKM expression group, suggesting poorer chemotherapy outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eI-L) (Additional File 2: Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePancreatic cancer is a highly malignant disease with poor treatment outcomes, resulting in a five-year survival rate of less than 6% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Metabolic reprogramming plays a pivotal role in cancer progression by providing essential energy sources and key metabolites that sustain tumor initiation and proliferation. A hallmark of this metabolic shift is aerobic glycolysis, a process in which cancer cells preferentially utilize glycolysis even in the presence of oxygen. The PKM gene plays a central role in regulating this glycolytic process. This study investigated the enrichment patterns and pathways of PKM in pancreatic cancer, its expression profile, prognostic significance, immune infiltration characteristics, and treatment response.\u003c/p\u003e\u003cp\u003ePyruvate kinase, a rate-limiting enzyme in glycolysis, exists in L-type and M-type isoforms, with the M-type further divided into PKM1 and PKM2. PKM1 enhances tumor growth by facilitating glucose-to-lactate conversion and activating central carbon metabolism [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, PKM1 promotes autophagy, particularly mitochondrial autophagy, and contributes to the tumor microenvironment by increasing lactate production in cancer-associated fibroblasts, further stimulating aerobic glycolysis in cancer cells [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In contrast, PKM2 exists in both tetrameric and dimeric forms. The tetrameric form exhibits high catalytic activity, efficiently converting phosphoenolpyruvate (PEP) to pyruvate and generating ATP, while the dimeric form has lower catalytic activity and favors macromolecule synthesis via the pentose phosphate pathway (PPP) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The dimeric form of PKM2 also functions as a protein kinase, activating STAT3, which in turn promotes cancer cell proliferation. Specifically, PKM2-mediated phosphorylation of STAT3 at tyrosine 705 leads to the upregulation of N-cadherin, MMP-2, and MMP-9, facilitating cancer progression, particularly in colorectal cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, PKM2 interacts with multiple co-activators to regulate glycolysis and tumor development [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Notably, it plays a key role in the PI3K/Akt/mTOR signaling pathway by interacting with HIF-1α and c-Myc, thereby modulating cell proliferation and metastasis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur findings indicate that PKM expression upregulates glucose-6-phosphate and fructose-6-phosphate, thereby enhancing glycolysis and the PPP. These intermediates provide the necessary energy and molecular building blocks for pancreatic cancer cell proliferation and metastasis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. While glycolysis produces pyruvate, which is converted into lactate, the PPP serves as a metabolic bypass that generates NADPH and ribose phosphate, both of which are critical for cancer cell survival. NADPH maintains redox homeostasis by supporting antioxidant systems and is essential for the biosynthesis of lipids, amino acids, nucleotides, and steroids, all of which contribute to tumor growth [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Ribose phosphate, on the other hand, is a precursor for nucleotide synthesis, which is essential for DNA replication and cell division in cancer cells [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, our study demonstrates that PKM is involved in upregulating the ERBB signaling pathway, a crucial oncogenic cascade mediated by EGFR (ERBB1), HER2 (ERBB2), HER3 (ERBB3), and HER4 (ERBB4). Activation of these receptors by extracellular growth factors enhances cancer cell proliferation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Consistent with previous studies showing elevated PKM1 and PKM2 expression in multiple malignancies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], our results confirm that PKM is highly expressed in pancreatic cancer tissues and correlates with poor prognosis.\u003c/p\u003e\u003cp\u003eThe tumor microenvironment plays a crucial role in pancreatic cancer progression, with tumor-associated macrophages (TAMs) being the most abundant immune cells in the tumor stroma, comprising over 50% of the immune cell population [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. TAMs originate from M0 macrophages, which can differentiate into M1 and M2 phenotypes. While M1 TAMs exhibit pro-inflammatory and anti-tumor activity by secreting IL-1β, TNF-α, and reactive oxygen species (ROS), they gradually transition into the M2 phenotype during tumor progression, contributing to immune evasion and tumor promotion [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. M2 TAMs secrete matrix metalloproteinases (MMPs) and serine proteases, which degrade the extracellular matrix and facilitate cancer cell invasion and metastasis [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. They also produce VEGF, PDGF, COX-2, and IL-10, which drive angiogenesis and lymphangiogenesis in tumor tissues [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Moreover, M2 TAMs interact with myeloid-derived suppressor cells (MDSCs) to suppress T-cell-mediated anti-tumor responses and express PD-L1, which inhibits T-cell activation through the PD-1/PD-L1 axis, ultimately promoting immune escape [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Previous studies have shown that microvesicle-associated PKM2 functions as a transcriptional coactivator and protein kinase, influencing macrophage differentiation and promoting tumor progression in liver cancer [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our study revealed a significant positive correlation between PKM expression and M0 macrophages in pancreatic cancer, suggesting that PKM may play a role in TAM-mediated tumor proliferation and immune evasion.\u003c/p\u003e\u003cp\u003eIn addition to its role in immune modulation, PKM contributes to chemotherapy resistance. Both PKM1 and PKM2 have been implicated in resistance to multiple chemotherapeutic agents across various cancers [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Our study confirms that high PKM expression correlates with reduced sensitivity to irinotecan, oxaliplatin, gemcitabine, and cisplatin in pancreatic cancer, indicating a potential role in therapy resistance.\u003c/p\u003e\u003cp\u003eDespite these findings, this study has several limitations. First, while PKM exists as two isoforms, PKM1 and PKM2, public datasets do not differentiate their individual expression levels. Further research is required to explore the distinct contributions of each isoform to pancreatic cancer progression. Second, although we established a correlation between PKM expression, the ERBB pathway, and immune infiltration, the underlying molecular mechanisms remain unclear. Future studies should incorporate experimental validation to elucidate these mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough bioinformatics analysis, this study identified key glycolysis-related hub genes associated with pancreatic cancer. Among them, PKM was recognized as a central regulator of cancer metabolism, immune modulation, and treatment resistance. Its high expression correlated with poor prognosis, increased immune evasion, and decreased sensitivity to chemotherapy. These findings suggest that PKM serves as a prognostic biomarker and a potential therapeutic target for pancreatic cancer. Future research should focus on mechanistic studies and targeted therapies aimed at modulating PKM expression and function to improve clinical outcomes in pancreatic cancer patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShihang Ru, Di Wu and Chunlei Liu contributed to the conception and design of the study. Shihang Ru, Wenshuai Li and Bugao Chao organized the database. Shihang Ru and Wenshuai Li performed the statistical analysis. Shihang Ru and Wenshuai Li wrote the frst draft of the manuscript. Yingguang Liu, Bugao Chao and YangLi wrote sections of the manuscript. All authors contributed to manuscript revision and read and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the funding from the Inner Mongolia Medical University Joint Project, China (NO. YKD2023LH005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are publicly available in online repositories. The gene expression and clinical data for pancreatic cancer were retrieved from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) with accession numbers GSE28735 and GSE62452, and the The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) under the TCGA-Pancreatic Adenocarcinoma (TCGA-PAAD) cohort for validation. Glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB) (https://www.gsea-msigdb.org/gsea/Msigdb/human/search.jsp). Functional annotation and protein-protein interaction analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/), Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://cn.string-db.org), and Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia2.cancer-pku.cn/#index). Immunotherapy response prediction was conducted via the Tumor Immune Dysfunction and Exclusion (TIDE) online platform (http://tide.dfci.harvard.edu/). Data for chemotherapeutic sensitivity analysis were downloaded from the Open Science Framework (OSF) platform (https://osf.io/c6tfx/files/osfstorage). Gene intersection visualization was performed using Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/). All datasets are fully released, and the provided accession numbers and database links allow direct access to the original data in their final form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial register number:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study, a bioinformatics analysis focusing on PKM’s role in pancreatic cancer, has the following limitations to ensure result interpretation transparency: 1. Dataset Size and Representativeness Constraints: Core analyses relied on GSE28735, GSE62452 (moderate sample sizes, Table 1), and TCGA pancreatic cancer datasets. While these are widely used, their sample scales are limited—TCGA also primarily covers Western populations, lacking ethnic diversity. Additionally, datasets lack detailed clinical covariates (e.g., comorbidities, specific treatments, long-term follow-up), restricting generalizability to broader patient subgroups. 2. Inability to Distinguish PKM Isoforms: PKM has functionally distinct PKM1 and PKM2 isoforms, but public datasets (GEO, TCGA, GEPIA) only quantify total PKM mRNA. This prevents identifying whether PKM1, PKM2, or their combination drives observed associations (e.g., with poor prognosis), limiting mechanistic inference precision. 3. No Experimental Validation: All conclusions stem from public dataset bioinformatics analyses; no in vitro (e.g., cell line assays) or in vivo (e.g., animal models) experiments were conducted. Analyses reflect correlations, not causality—e.g., PKM’s correlation with M0 macrophage infiltration does not confirm direct regulation, and predicted treatment resistance needs experimental verification. 4. Limitations in Immune and Therapeutic Predictions: Immune infiltration was estimated via CIBERSORT (bulk RNA-seq-based, unable to capture immune cell spatial/functional states). ICI response predictions (TIDE) ignored TMB, MSI, etc., while chemotherapy sensitivity (oncoPredict) relied on cell line IC50 models, failing to recapitulate in vivo tumor microenvironment effects on treatment response.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGBD 2017 Pancreatic Cancer Collaborators. The global, regional, and national burden of pancreatic cancer and its attributable risk factors in 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol. Hepatol. 4, 934-947.\u003c/li\u003e\n \u003cli\u003eRahib, L. et al. Estimated projection of US cancer incidence and death to 2040. JAMA Netw. Open. 4, e214708 (2021).\u003c/li\u003e\n \u003cli\u003eGanapathy-Kanniappan S, Geschwind JF. Tumor glycolysis as a target for cancer therapy: progress and prospects. Mol Cancer. 2013 Dec 3; 12:152.\u003c/li\u003e\n \u003cli\u003eAbdel-Wahab AF, Mahmoud W, Al-Harizy RM. Targeting glucose metabolism to suppress cancer progression: prospective of anti-glycolytic cancer therapy. Pharmacol Res. 2019 Dec; 150:104511.\u003c/li\u003e\n \u003cli\u003eChelakkot C, Chelakkot VS, Shin Y, Song K. Modulating Glycolysis to Improve Cancer Therapy. Int J Mol Sci. 2023 Jan 30;24(3):2606.\u003c/li\u003e\n \u003cli\u003eWarburg O: On the origin of cancer cells. Science 1956, 123(3191):309-314.0036-8075 (Print).\u003c/li\u003e\n \u003cli\u003ede Souza AC, Justo GZ, de Araujo DR, Cavagis AD: Defining the molecular basis of tumor metabolism: a continuing challenge since Warburg\u0026apos;s discovery. Cell Physiol Biochem 2011, 28(5):771-792.\u003c/li\u003e\n \u003cli\u003eJ.W. Locasale, L.C. Cantley, Altered metabolism in cancer, BMC Biol. 8 (2010) 88.\u003c/li\u003e\n \u003cli\u003eGill KS, Fernandes P, O\u0026apos;Donovan TR, McKenna SL, Doddakula KK, Power DG, Soden DM, Forde PF. Glycolysis inhibition as a cancer treatment and its role in an anti-tumour immune response. Biochim Biophys Acta. 2016 Aug;1866(1):87-105.\u003c/li\u003e\n \u003cli\u003eD.S. Backos, C.C. Franklin, P. Reigan, The role of glutathione in brain tumor drug resistance, Biochem. Pharmacol. 83 (2012) 1005-1012.\u003c/li\u003e\n \u003cli\u003eN. Traverso, R. Ricciarelli, M. Nitti, B. Marengo, A.L. Furfaro, M.A. Pronzato, U.M. Marinari, C. Domenicotti, Role of glutathione in cancer progression and chemoresistance, Oxidative Med. Cell. Longev. 2013 (2013) 972913.\u003c/li\u003e\n \u003cli\u003eLangfelder P, Horvath S. WGCNA: An R Package for Weighted Correlation Network Analysis. BMC Bioinf (2008) 9:559. doi: 10.1186/1471-2105-9-559.\u003c/li\u003e\n \u003cli\u003eConsortium GO. The Gene Ontology (GO) project in 2006. Nucleic Acids Res 2006;34: D322-6.\u003c/li\u003e\n \u003cli\u003eKanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes.Nucleic Acids Res 2000;28:27\u0026ndash;30.\u003c/li\u003e\n \u003cli\u003eHuang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009; 4:44-57.\u003c/li\u003e\n \u003cli\u003eSzklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, Jensen LJ, von Mering C. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021 Jan 8;49(D1): D605-D612.\u003c/li\u003e\n \u003cli\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50.\u003c/li\u003e\n \u003cli\u003eRobust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015 May;12(5):453-7. PMID: 25822800; PMCID: PMC4739640.\u003c/li\u003e\n \u003cli\u003eTIDE: Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018 Oct;24(10):1550-1558.\u003c/li\u003e\n \u003cli\u003eMaeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021 Nov 5;22(6): bbab260.\u003c/li\u003e\n \u003cli\u003eSiegel R, Ma J, Zou Z, Jemal A (2014) Cancer statistics, 2014. CA Cancer J Clin 64(1):9\u0026ndash;29. doi:10.3322/caac.21208.\u003c/li\u003e\n \u003cli\u003eMorita M, Sato T, Nomura M, Sakamoto Y, Watanabe T, Soga T, Shima H, Maemondo M, Tanuma N, et al. PKM1 Confers Metabolic Advantages and Promotes Cell-Autonomous Tumor Cell Growth. Cancer Cell. 2018 Mar 12;33(3):355-367.e7.\u003c/li\u003e\n \u003cli\u003eChiavarina B, Whitaker-Menezes D, Martinez-Outschoorn UE, Witkiewicz AK, Birbe R, Howell A, Pestell RG, Smith J, Daniel R, Sotgia F, Lisanti MP. Pyruvate kinase expression (PKM1 and PKM2) in cancer-associated fibroblasts drives stromal nutrient production and tumor growth. Cancer Biol Ther. 2011 Dec 15;12(12):1101-13.\u003c/li\u003e\n \u003cli\u003eAnastasiou D, Yu Y, Israelsen WJ, Jiang JK, Boxer MB, Hong BS, Tempel W, Dimov S, Shen M, Jha A, Yang H, Mattaini KR, Metallo CM, et al. Pyruvate kinase M2 activators promote tetramer formation and suppress tumorigenesis. Nat Chem Biol. 2012; 8:839\u0026ndash;47.\u003c/li\u003e\n \u003cli\u003eZahra K, Dey T, Ashish, Mishra SP, Pandey U. Pyruvate Kinase M2 and Cancer: The Role of PKM2 in Promoting Tumorigenesis. Front Oncol. 2020 Mar 2; 10:159.\u003c/li\u003e\n \u003cli\u003eYang P, Li Z, Fu R, Wu H, Li Z (2014) Pyruvate kinase M2 facilitates colon cancer cell migration via the modulation of STAT3 signaling. Cell Signal 26(9):1853\u0026ndash;1862.\u003c/li\u003e\n \u003cli\u003eHasan D, Gamen E, Abu Tarboush N, Ismail Y, Pak O, Azab B. PKM2 and HIF-1\u0026alpha; regulation in prostate cancer cell lines. PLoS One. 2018 Sep 14;13(9): e0203745.\u003c/li\u003e\n \u003cli\u003eM\u0026eacute;ndez-Lucas A, Li X, Hu J, Che L, Song X, Jia J, Wang J, Xie C, Driscoll PC, Tschaharganeh DF, Calvisi DF, Yuneva M, Chen X. Glucose Catabolism in Liver Tumors Induced by c-MYC Can Be Sustained by Various PKM1/PKM2 Ratios and Pyruvate Kinase Activities. Cancer Res. 2017 Aug 15;77(16):4355-4364.\u003c/li\u003e\n \u003cli\u003eChen S, Fisher RC, Signs S, Molina LA, Shenoy AK, Lopez MC, Baker HV, Koomen JM, Chen Y, Gittleman H, Barnholtz-Sloan J, Berg A, Appelman HD, Huang EH. Inhibition of PI3K/Akt/mTOR signaling in PI3KR2-overexpressing colon cancer stem cells reduces tumor growth due to apoptosis. Oncotarget. 2016 Jun 8;8(31):50476-50488.\u003c/li\u003e\n \u003cli\u003eYing J, Xu Q, Liu B, Zhang G, Chen L, Pan H. The expression of the PI3K/AKT/mTOR pathway in gastric cancer and its role in gastric cancer prognosis. Onco Targets Ther. 2015 Sep 1; 8:2427-33.\u003c/li\u003e\n \u003cli\u003eKugler W., Lakomek M. (2000). Glucose-6-phosphate isomerase deficiency. Bailliere\u0026rsquo;s Best. Pract. Res. Clin. Haematol. 13 89\u0026ndash;101. 10.1053/beha.1999.0059\u003c/li\u003e\n \u003cli\u003eJu HQ, Lin JF, Tian T, Xie D, Xu RH. NADPH homeostasis in cancer: functions, mechanisms and therapeutic implications. Signal Transduct Target Ther. 2020 Oct 7;5(1):231.\u003c/li\u003e\n \u003cli\u003eMullen NJ, Singh PK. Nucleotide metabolism: a pan-cancer metabolic dependency. Nat Rev Cancer. 2023 May;23(5):275-294.\u003c/li\u003e\n \u003cli\u003eCarlos L, Jeffrey A. ERBB Receptors: From Oncogene Discovery to Basic Science to Mechanism-Based Cancer Therapeutics. Cancer Cell, 2014, 25(3); 282-303.\u003c/li\u003e\n \u003cli\u003eMa C, Zu X, Liu K, Bode AM, Dong Z, Liu Z, Kim DJ. Knockdown of Pyruvate Kinase M Inhibits Cell Growth and Migration by Reducing NF-kB Activity in Triple-Negative Breast Cancer Cells. Mol Cells. 2019 Sep 30;42(9):628-636.\u003c/li\u003e\n \u003cli\u003eShiroki T, Yokoyama M, Tanuma N, Shimosegawa T, Satoh K, et al. Enhanced expression of the M2 isoform of pyruvate kinase is involved in gastric cancer development by regulating cancer-specific metabolism. Cancer Sci. 2017 May;108(5):931-940.\u003c/li\u003e\n \u003cli\u003eYang L, Zhang Y. Tumor-associated macrophages: from basic research to clinical application. J Hematol Oncol, 2017, 10(1): 1-12.\u003c/li\u003e\n \u003cli\u003eBernsmeier C, van der Merwe S, P\u0026eacute;rianin A. Innate immune cells incirrhosis[J]. J Hepatol, 2020, 73(1): 186-201.\u003c/li\u003e\n \u003cli\u003eChen P, Bonaldo P. Role of macrophage polarization in tumor angiogenesis and vessel normalization: implications for new anticancer therapies. Int Rev Cel Mol Bio, 2013, 301: 1-35.\u003c/li\u003e\n \u003cli\u003eQian B Z, Pollard J W. Macrophage diversity enhances tumor progression and metastasis[J]. Cell, 2010, 141(1): 39-51.\u003c/li\u003e\n \u003cli\u003eZhong W Q, Chen G, Zhang W, et al. M2-polarized macrophages in keratocystic odontogenic tumor: relation to tumor angiogenesis. Sci Rep, 2015, 5(1): 15586.\u003c/li\u003e\n \u003cli\u003eCui X, Morales R T T, Qian W, et al. Hacking macrophage-associated immunosuppression for regulating glioblastoma angiogenesis. Biomaterials, 2018, 161: 164-178.\u003c/li\u003e\n \u003cli\u003eHou PP, Luo LJ, Chen HZ, Chen QT, Bian XL, Wu SF, Zhou JX, Zhao WX, Liu JM, Wang XM, Zhang ZY, Yao LM, Chen Q, Zhou D, Wu Q. Ectosomal PKM2 Promotes HCC by Inducing Macrophage Differentiation and Remodeling the Tumor Microenvironment. Mol Cell. 2020 Jun 18;78(6):1192-1206.e10.\u003c/li\u003e\n \u003cli\u003eTaniguchi K, Sakai M, Sugito N, Kuranaga Y, Kumazaki M, Shinohara H, Ueda H, Futamura M, Yoshida K, Uchiyama K, Akao Y. PKM1 is involved in resistance to anti-cancer drugs. Biochem Biophys Res Commun. 2016 Apr 22;473(1):174-180.\u003c/li\u003e\n \u003cli\u003eWang Y, Zhao H, Zhao P, Wang X. Targeting PKM2 promotes chemosensitivity of breast cancer cells in vitro and in vivo. Cancer Biomark. 2021;32(2):221-230.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PKM, pancreatic cancer, glycolysis, immune infiltrates, ICIs, therapeutic response","lastPublishedDoi":"10.21203/rs.3.rs-7676660/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7676660/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCancer cells undergo metabolic reprogramming to sustain proliferation and metastasis, with the glycolytic pathway playing a key role. This study investigates the prognostic significance, immune infiltration, and treatment response of the glycolysis-related gene Pyruvate Kinase M (PKM) in pancreatic cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003ePancreatic cancer samples were obtained from GEO and TCGA databases. Glycolysis-related genes were identified using WGCNA and Msigdb, and PKM was selected via PPI network analysis and univariate Cox regression. Gene set enrichment analysis (GSEA), immune infiltration analysis (ESTIMATE), immune checkpoint inhibitor (ICI) response prediction (TIDE), and chemotherapeutic sensitivity analysis (pRRophetic) were conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePKM was identified as a key glycolysis-related gene. GSEA indicated that high PKM expression was associated with glycolytic processes and ERBB signaling. PKM was overexpressed in pancreatic cancer tissues, and patients with high PKM expression had significantly worse prognosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ESTIMATE analysis revealed a higher infiltration level of M0 macrophages in the high PKM expression group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, high PKM expression correlated with reduced efficacy of ICI therapy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and lower sensitivity to irinotecan and oxaliplatin (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePKM plays a crucial role in glycolysis, immune regulation, and therapeutic response in pancreatic cancer. It may serve as a prognostic biomarker and a potential therapeutic target.\u003c/p\u003e","manuscriptTitle":"The glycolytic PKM gene is associated with immune infiltrates and therapeutic response of pancreatic cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 06:10:52","doi":"10.21203/rs.3.rs-7676660/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30ef5dba-8d70-420e-be8d-e648735f8250","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T12:43:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 06:10:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7676660","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7676660","identity":"rs-7676660","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}