A Comprehensive Prognostic and Immunological Implications of PFKP in Pan-Cancer

A Comprehensive Prognostic and Immunological Implications of PFKP in Pan-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 A Comprehensive Prognostic and Immunological Implications of PFKP in Pan-Cancer Xiaodong Ling, Luquan Zhang, Chengyuan Fang, Hao Liang, Jianqun Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4521835/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Sep, 2024 Read the published version in Cancer Cell International → Version 1 posted 11 You are reading this latest preprint version Abstract Background As a pivotal rate-limiting enzyme in glycolysis, Phosphofructokinase P (PFKP) plays a pivotal role in multiple pathophysiological processes. However, the critical role of PFKP in most tumors remains unexplored. The present research was primarily designed to provide the expression of PFKP in Pan-cancer and its clinical relevance, and study the correlations between PFKP expression and immune infiltration characteristics in tumor microenvironment. Methods Raw data in regard to PFKP expression were obtained from TCGA and GEO databases. We examined the expression patterns and prognostic values of PFKP in pan-cancer utilizing multiple databases, and investigated the relationship of PFKP expression with immune infiltration and tumor immune microenvironment. Besides, the biological function of PFKP was explored via in vitro verification. Results PFKP is highly expressed and is a prognostic risk factor in most tumors. Increased expression of PFKP was detrimental to the clinical prognoses, especially LUAD. Also, ROC curve analysis demonstrated that PFKP showed high accuracy in distinguishing cancerous tissues from normal ones. There were significant correlations between PFKP expression and TMB, MSI, immune scores, and immune cell infiltrations. In vitro studies demonstrated that the overexpression of PFKP accelerated the proliferation and migration of lung cancer cells, whereas PFKP depletion showed the opposite effects on them. Conclusion In conclusion, PFKP participates in the carcinogenic progression, and may contribute to the immune infiltration in tumor microenvironment. Our study suggests that PFKP can serve as a potential biomarker for predicting different tumor prognoses and tumor immunogenicity, especially LUAD. PFKP pan-cancer LUAD prognosis prediction immune analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Lung cancer stands as one of the most prevalent respiratory neoplasms, exhibiting a persistently high incidence and mortality rate. Among the subtypes, non-small cell lung cancer (NSCLC) constitutes the most frequent variant within the realm of respiratory tumors, accounting for approximately two-thirds of all diagnosed cases of lung cancer[ 1 ]. Alarmingly, at least half of the patients are already diagnosed with advanced-stage disease at the time of initial assessment. While multi-modal therapies have achieved some significant milestones in the treatment of NSCLC, the efficacy of such interventions still falls short of expectations[ 2 ]. Targeted therapy emerges as the frontline approach for patients harboring oncogenic gene mutations. Nevertheless, an increasing body of research suggests that the combination therapy targeting multiple points yields superior results compared to monotherapy[ 3 ]. Hence, the discovery of additional effective molecular targets assumes paramount importance in optimizing combination treatment strategies and enhancing cancer management. The PFKP gene is situated on the diminutive extremity of chromosome 10 within the human genome[ 4 ]. PFKP exhibits 8 diverse transcript variants, and isoform 1 emerges as the most protracted of these, spanning a length of 784 amino acids. The regulation of PFKP gene expression is intricately entwined with the process of glycolysis. Phosphofructokinase (PFK) is a pivotal rate-limiting enzyme in glycolysis, which accelerates the consumption of glucose[ 5 ]. PFK encompasses three subtypes: PFKP (platelet), PFKM (muscle), and PFKL (liver). Moreover, all subtypes are found in other tissues as well[ 6 ]. PFKP exhibits expression in various cell types, gaining recognition for its crucial role in different types of cancers, including non-small cell lung cancer and breast cancer[ 7 , 8 ]. Research indicates that the downregulation of PFKP reduces glucose uptake rate, thereby inhibiting the growth of lung cancer cells. Additionally, PFKP regulates non-glycolytic functions related to metabolic stress induced by glucose deprivation in NSCLC cells through AMPK-mediated regulation of long-chain fatty acid oxidation. Thus, PFKP may serve as a prognostic indicator for lung cancer, and its regulatory role in glycolysis could be a potential therapeutic target for the disease. Gao et al. reported the involvement of Nuclear PFKP in promoting CXCR4-dependent infiltration by T-cell acute lymphoblastic leukemia. However, the critical role of PFKP in most tumors remains unexplored. The advent of next-generation sequencing (NGS) technology and in-depth exploration of cancer genome atlas (TCGA) datasets gradually reveal genomic and transcriptomic data of common tumors to researchers. Therefore, this is an optimal approach for scrutinizing and revealing the prospective prognostic and predictive significance of biomarkers through Pan-cancer analysis in the realm of precision medicine. In this study, we identified significant differences in the expression of PFKP by analyzing differentially expressed genes between tumor cells of epithelial origin and normal cells. Furthermore, we explored the expression of PFKP in Pan-cancer and its clinical relevance. Additionally, we analyzed the relationship between PFKP expression and tumor mutation burden, microsatellite instability, and immune phenotype scores. Lastly, we determined the status of immune infiltration and immunotherapy sensitivity in different subgroups. METHODS Data Acquisition and Processing We downloaded the RNA-Seq expression data of 33 different tumors from the TCGA database. We determined the expression levels of PFKP in 33 types of tumors. Additionally, we used a web-based analysis tool, the Gene Expression Profiling Interactive Analysis (GEPIA)[ 9 ], to visualize the PFKP expression levels in different stages of all tumors. Then, we explored the protein expression level of PFKP between normal and tumor tissues through the UALCAN portal[ 10 ]. To verify the expression of PFKP between the tumor and normal cells in LUAD, the single-cell RNA transcriptome data of GSE117570 were downloaded from the GEO database. Survival Analysis Patients were divided into high- and low-expression groups using the minimum P -value method. Cox regression analysis for TCGA datasets was performed using RStudio software with the “survival” and “forestplot” packages to investigate the correlation between PFKP expression and cancer prognosis, including overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). We calculated the log-rank P -value and hazard ratio (HR) with 95% confidence intervals (95% CI) via the “survival” package and utilized the “forestplot” package to visualize the survival analysis[ 11 ]. We also implemented the Kaplan-Meier survival analysis to estimate the clinical outcomes of patients with PFKP low or PFKP high tumors by executing the “survival” and “survminer” R packages. Correlation analysis of PFKP with TMB, MSI, immunophenotype scores, and tumor immune microenvironment The genome alterations of PFKP included copy number amplification, deep or shallow deletion, missense mutation with uncertain significance and mRNA upregulation. Tumor mutation burden (TMB) is calculated as total somatic nonsynonymous mutation counts in coding regions. Numerous studies have explored the significance of using TMB as a biomarker for identifying patients sensitive to checkpoint inhibitors[ 12 ]. Microsatellite instability (MSI) is featured by the widespread length polymorphisms of microsatellite sequences resulting from DNA polymerase slippage[ 13 ]. The TMB and MSI scores were obtained from TCGA database and Spearman’s rank method was used to determine the correlation of PFKP with TMB and MSI. The correlation results for TMB and MSI were visualized in radar maps. We also retrieved the immunophenoscore (IPS) for LUAD patients from The Cancer Immunome Atlas. The IPS is calculated based on four significant categories of tumor immunogenicity determinants, including effector cells, immunosuppressive cells, MHC molecules, and checkpoints/immunomodulators[ 14 ]. We made comparisons of IPS for the PFKP low and PFKP high groups and subsequently evaluated their responses to anti-PD1/PDL1/PDL2 and anti-CLA4 treatments. The stromal score and immune score were compared among different groups by the ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) method[ 15 ]. Comprehensive analysis of single cell datasets and cell cluster annotation Single-cell transcriptomic profiles of 4 NSCLC and 4 adjacent normal tissues were obtained from GSE117570[ 16 ]. Samples with unknown microsatellite stabilities were excluded from the study. We analyzed the scRNA-seq data using the R package Seurat[ 17 ]. The data were normalized using the SCTransform method and integrated using the IntegrateData function. T-distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) were applied to reduce the dimensions. The FindNeighbors and FindClusters functions were used for cell clustering analysis. Cell Culture and transfection The six human NSCLC cell lines (H1915, A549, PC9, H460, H1650, and H1299) were obtained from Laboratory of Medical Genetics (Department of Biology, Harbin Medical University, Harbin, China). Cells were cultured in RPMI-1640 (Invitrogen Corporation, Carlsbad CA, USA) supplemented with 10% FBS (PAN-Biotech, Aidenbach Germany). Cell cultures were kept in a humidified incubator at 37℃ with 5% CO 2 . The H1299 cells were transfected with PFKP siRNA to knock down the PFKP expression according to the manufacturer’s protocols. The duplex sequences of the three siRNA targeting PFKP were as follows: First, sense, 5′- GGAGCAAUUGAUACCCAAATT-3′, and antisense, 5′-UUUGGGUAUCAAUUGCUCCTT-3′; Second, sense, 5′-GCAACGUAGCUGUCAUCAATT-3′, and antisense, 5′-UUGAUGACAGCUACGUUGCTT-3′; Third, sense, 5′- CCCUCUCCAUUUGAUAGAATT-3′, and antisense, 5′-UUCUAUCAAAUGGAGAGGGTT-3′. Western blot Cells were lysed using RIPA buffer (Solarbio, Beijing, China). Protein samples were electrophoresed in 15% SDS-PAGE and transferred to PVDF membranes. After blocking with PBST containing 5% nonfat milk, membranes were incubated with primary antibodies against PFKP (Abcam, UK) and GAPDH (Cell signaling technology, USA) at 4°C overnight. Then, the membranes were incubated with a secondary antibody (Beijing Zhongshan Golden Bridge Biotechnology Co. Ltd., Beijing, China) for 1 h at room temperature. Finally, the ECL (Tanon, Shanghai) detection system was used to visualize the protein bands. Cell proliferation assay Cells with a density of 3 × 10 3 /ml were seeded in a 96-well plate, and cultured for 24, 48, and 72 h. the OD values were measured using Cell Counting Kit 8 (CCK-8, Elabscience, China). Subsequently, the OD values were measured at 450 nm with a microplate reader. Colony formation assay Cells were plated in a 12-well plate at a density of 500 cells per well. Cells grew for ten days in a 37°C incubator until more than 50 cells were available for most clones. Then, cells were fixed with 4% paraformaldehyde for 20 min, followed by staining with 0.2% crystalline violet solution for half an hour at room temperature and washing with PBS three times. Wound-Healing Assay For wound healing assay, a wound was generated in a 6-well plate by scratching the surface with a 1 mL pipette tip. Scratch width change was measured under a light microscope (Nikon, Tokyo, Japan) after 24 h of culture in serum-free 1640 medium. Statistical analysis All bioinformatic analyses were carried out with the R software version 4.1.3. The survival curve was plotted by Kaplan-Meier survival curve. The Wilcoxon test was applied in analyzing differences between clinical feature distribution, immune infiltration, and therapy response. Double-tailed p < 0.050 was considered statistically significant. RESULTS ScRNA-Seq Data Analysis and Identification of the PFKP The flowchart of this study was shown in Fig. 1 . A total of 11485 cells were obtained from the scRNA-seq data after initial screening. After log-normalization and dimensionality reduction, all cells were clustered into 20 clusters and further annotated into three cell types based on their expression of immune, epithelial, and stromal cell markers (Fig. 2 A-C). Next, we screened out differentially expressed genes (DEGs) between the tumor and normal cells in epithelial cell clusters. As shown in Fig. 2 D, we found the gene “PFKP” among the up-regulated DEGs. The heatmap of DEGs showed the expression of PFKP between tumor and normal tissues (Fig. 2 F). Expression Levels of PFKP in Pan-Cancer We first compared PFKP expression between tumor and normal tissues from the TCGA database. The results showed that the expression level of PFKP in the tumor tissues of CHOL, COAD, ESCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PCPG, STAD, and UCEC is much higher than the corresponding normal tissues ( Fig. 3 A ) . In paired samples, PFKP expression was significantly elevated in multiple cancer types, including CHOL, COAD, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, and STAD (Fig. 3 B). Furthermore, PFKP expression levels increased with tumor progression in ACC, KIRC, KIRP, LIHC, LUAD, LUSC, TGCT, and UVM (Fig. 3 C). The UALCAN online tool confirmed that PFKP protein levels were significantly upregulated in RCC, HNSCC, LUAD, PAAC, and GBM (Fig. 3 D). Correlation between PFKP Expression and Prognostic Implications The Kaplan-Meier analysis was used to evaluate the association between PFKP expression and OS, DSS, DFI, PFI in pan-cancer ( Supplementary Fig. 1–4 ). Additionally, we further compared the survival contribution of PFKP in multiple cancer types, estimated using univariate Cox regression analyses (Fig. 4 A–D). For example, the results showed that high PFKP as a risk factor for OS in ACC, CESC, HNSC, LAML, LIHC, LUAD, MESO, and UVM. Results for DSS, DFI, and PFI are shown in Fig. 4 B–D. It is worth noting that PFKP was significantly associated with the survival of LUAD (all P < 0.05). These results suggested that PFKP expression had a strong prognostic power in different tumors, and the relevance of PFKP to clinical relevance may shed new light on the underlying pathogenesis of tumors. Diagnostic Value of PFKP for Pan-Cancer Given the significant differences in the expression levels of PFKP in different tumor tissues and corresponding normal tissues, we used the receiver operating characteristic (ROC) curve to initially investigate the diagnostic value of PFKP mRNA expression levels for discriminating between tumor and normal tissues (Fig. 5 ). The area under curve (AUC) values suggested that PFKP had a strong ability to discriminate, especially UCEC (AUC = 0.665), THCA (AUC = 0.605), PAAD (AUC = 0.730), PCPG (AUC = 0.927), PRAD (AUC = 0.676), STAD (AUC = 0.686), CESC (AUC = 0.642), KIRP (AUC = 0.803), LUSC (AUC = 0.965), LUAD (AUC = 0.923), LIHC (AUC = 0.710), KIRC (AUC = 0.937), KICH (AUC = 0.858), HNSC (AUC = 0.696), ESCA (AUC = 0.875), and GBM (AUC = 0.982). It can be seen that the PFKP mRNA expression level has a high diagnostic value for distinguishing tumors from normal tissues. Association of PFKP Expression with Microsatellite Instability, Tumor Mutation Burden, and Immunophenotype Scores Both microsatellite instability (MSI) and tumor mutation burden (TMB) are pivotal characteristics of tumors, and affect response to immunotherapy in cancers[ 18 , 19 ], we next performed association analyses of PFKP expression with MSI and TMB. As shown in Fig. 6 A, PFKP expression was positively correlated with MSI in BRCA, COAD, LGG, LUSC, STAD, and THYM, while negatively correlated with MSI in DLBC (all P < 0.05). PFKP expression was also positively correlated with TMB of ACC, BRCA, COAD, HNSC, LUAD, PAAD, SKCM, STAD, THCA, THYM, UCEC, and UCS, but negatively correlated with that of LAML, LGG, LIHC, and PRAD (all P < 0.05) (Fig. 6 B). Then, we used immunophenoscore (IPS) for LUAD patients from The Cancer Immunome Atlas to determine the sensitivity to immune checkpoint inhibitors for the PFKP (Fig. 6 C-F). Our results showed that the high expression group possessed significantly higher immunophenotype scores (IPS) than the low expression group in PRAD and THCA. Moreover, the high expression group was more likely to gain benefits from anti-CTLA4 therapy in BRCA, COAD, PRAD, THCA, and respond to anti-PD1/PDL1/PDL2 therapies in BLCA, BRCA, COAD, LIHC, PRAD, READ, and THCA. Finally, the high expression group tended to respond to both anti-PD1/PDL1/PDL2 and anti-CTLA4 antibodies in BLCA, BRCA, COAD, LIHC, PRAD, READ, and THCA. The Association between PFKP and Tumor Immune Microenvironment Although our above investigations have demonstrated the prognostic ability of PFKP in pan-cancer, its potential role warranted further research. The development of malignant tumors is closely related to the tumor immune microenvironment (TIME) in which the tumor cells are located. TIME contains not only tumor cells but also immune cells, fibroblasts, and many other cells and extracellular matrix, which is the basis for the survival and development of tumor cells[ 20 , 21 ]. It is unclear whether PFKP impacts the recruitment of immune cells. With several algorithms including EPIC, TIMER, QUANTISEQ, XCELL, and CIBERSORT, we evaluated the correlation between the immune cell infiltration and PFKP expression in pan-cancer. The score of six immune cell types, including CD4 + T cells, B cells, monocytes, NK cells, dendritic cells, and CD8 + T cells, were calculated ( Supplementary Fig. 5–6 ). Results indicated that PFKP was significantly associated with immune cell subsets in BRCA, HNSC, KIRC, LUAD, TGCT, and SKCM. In contrast, no correlation was found between PFKP expression and immune cell infiltration in ACC, CHOL, DLBC, KICH, MESO, and UCS. PFKP exhibited positive associations with dendritic cells, CD4 + T helper 2 cells, and negative associations with B cells, CD8 + T cells, native CD4 + T cells in the majority of tumors. Next, by adopting the ESTIMATE method, we computed the immune and stromal scores of cancer tissues. As Fig. 7 indicated, PFKP was positively correlated with the immune scores in BLCA, BRCA, COAD, LIHC, PRAD, and UVM, but negatively correlated with the immune score in ACC, CESC, GBM, HNSC, KIRP, LGG, LUSC, PAAD, TGCT, and THYM. For the analysis of the stromal score, we have obtained similar results ( Supplementary Fig. 7 ). PFKP promoted proliferation and migration in LUAD cells We first examined the expression levels of PFKP in six lung cancer cell lines. The results showed that PFKP expression was elevated in the majority of lung cancer cell lines (Fig. 8 A). In the follow-up study, we conducted a knockdown analysis on H1299 cells with high expression of PFKP and the overexpression analysis in A549 cells (Fig. 8 B). Through CCK-8 and colony formation experiments, we found that PFKP depletion obviously inhibited lung cancer cells proliferation, while overexpression of PFKP promoted cell proliferation (Fig. 8 C-F). Besides, wound-healing assay demonstrated enhanced migration in cells overexpressing PFKP and weakened migration in cells knockdown PFKP (Fig. 8 G-H). Discussion Our study has unveiled, for the first time, the critical role of PFKP in Pan-cancer. Through multidimensional analysis, we discovered that PFKP is consistently upregulated in the majority of tumors, with its expression increasing along with tumor stage in various cancers such as LUAD, LUSC, KIRC, HNSC, COAD, among others. Furthermore, high expression of PFKP is associated with poorer prognosis in most cancer patients. Negative correlations were observed between PFKP expression and overall survival rates in several tumors, including KICH, HNSC, LUAD, and others. Importantly, consistent results were obtained when analyzing different datasets, and published studies have also demonstrated the tumorigenic role of PFKP[ 7 , 8 , 22 ]. These findings suggest that PFKP possesses widespread oncogenic characteristics in cancer, holding significant prospects in the field of cancer research. The tumor microenvironment (TME) is a highly structured ecosystem that encompasses cancer cells surrounded by various non-malignant cell types, all embedded within a dynamic, vascularized extracellular matrix[ 23 ]. The TME comprises a diverse array of immune cells, cancer-associated fibroblasts (CAFs), endothelial cells (ECs), stromal cells, and other cell types[ 24 ]. These cells engage in intercellular communication, either promoting or inhibiting tumor progression. The TME plays a pivotal role in determining the fate of tumor cells, regulating not only their proliferation, invasion, and metastasis but also influencing therapeutic outcomes[ 25 ]. Therefore, based on RNA sequencing data, we inferred the correlation between TME and PFKP. We described the stromal and immune scores of Pan-cancer samples, revealing differential associations with PFKP across different cancer types. Given the critical role of PFKP in the TME, we further explored the correlation between PFKP and immune therapy response. Microsatellite instability (MSI), tumor mutation burden (TMB), and the expression levels of various immune checkpoint markers are closely associated with the response to immune therapy, serving as important indicators for clinicians to identify patients who may benefit from immunotherapy[ 26 ]. Our analysis revealed a significant correlation between PFKP and TMB in 16 types of tumors, including LUAD, BRCA, and HNSC. TMB effectively assesses tumor neoantigen burden, and in most cancers, high TMB is associated with better overall survival (OS) and indicates a greater likelihood of response to PD-1 or CTLA-4 inhibitors. Previous studies have observed a response to immune checkpoint inhibitors (ICIs) in solid tumors with high TMB, including breast cancer, lung cancer, and gastric cancer[ 27 ]. Mechanistically, TMB-mediated neoantigens and tumor immunogenicity are associated with the recognition of immune therapy sensitivity or resistance[ 28 ]. Extensive research has demonstrated that microsatellite instability (MSI) is closely related to tumor development and is caused by defects in mismatch repair (MMR) genes. Clinically, MSI has been used as an important molecular marker for the prognosis and formulation of adjuvant treatment strategies in colorectal cancer and other solid tumors, as well as for assisting Lynch syndrome screening[ 29 ]. Our Pan-cancer analysis revealed a positive correlation between PFKP and MSI in six types of tumors, including LUSC, BRCA, and COAD. Immune checkpoint inhibitors work by reversing the "cold" tumor state and killing tumor cells, thereby significantly advancing multimodal tumor therapy. The expression of PD-1 and CTLA-4 has also shown correlations with PFKP in various tumors. These analyses highlight the potentially crucial role of PFKP in tumor immunotherapy. Lastly, we validated the role of PFKP in lung cancer through in vitro experiments. Compared to normal bronchial epithelial cells, PFKP exhibited higher expression in lung cancer-associated tumor cell lines. Depletion of PFKP suppressed the proliferation and migration of H1299 cells. Conversely, overexpression of PFKP promoted the malignant phenotype of lung cancer cell lines. Conclusions Here, we have determined the crucial role of PFKP based on mining public databases and conducting in vitro experiments. With the release of a large volume of high-throughput data, researchers in this field have gained rich information and enlightening insights into the understanding of the tumor microenvironment and the development of targets. However, it is important to note that results derived from public data and statistical algorithms may inevitably be influenced by significant heterogeneity and possible biases. The findings of bioinformatics analysis should be interpreted cautiously and cannot be directly translated into a clinical setting. In conclusion, we have identified PFKP as a potential biomarker for predicting different tumor prognoses and tumor immunogenicity, providing new avenues for the development of potential therapeutic targets in a clinical context. Declarations Competing interests Not applicable. Acknowledgements Not applicable. Author contributions All authors contributed to the study conception and design. XDL and LQZ drafted the manuscript. XDL and CYF performed the analyses and interpreted all the data. XDL, and HL prepared the figures and tables. XDL performed the experiments. JQM reviewed and revised the manuscript. All authors approved the final manuscript. Funding This study was supported by National Natural Science Foundation of China (No. 82172786 to Jianqun Ma), the Haiyan Foundation of Harbin Medical University Cancer Hospital (No. JJMS2023-04 to Xiaodong Ling) and Beijing Medical Award Foundation (No. YXJL-2023-0091-0054 to Xiaodong Ling). Availability of data and materials The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding authors. Ethical approval and consent to participate Not applicable. Consent for publication Not applicable Declarations of interest The authors declare that they have no conflict of interest. References Remon J, Hendriks LEL, Mountzios G, Garcia-Campelo R, Saw SPL, Uprety D, Recondo G, Villacampa G, Reck M. MET alterations in NSCLC-Current Perspectives and Future Challenges. J Thorac Oncol. 2023;18(4):419–35. Ozcan G, Singh M, Vredenburgh JJ. 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Supplementary Files Supplementaryfigure1.jpg Supplementaryfigure2.jpg Supplementaryfigure3.jpg Supplementaryfigure4.jpg Supplementaryfigure5.jpg Supplementaryfigure6.jpg Supplementaryfigure7.jpg Cite Share Download PDF Status: Published Journal Publication published 09 Sep, 2024 Read the published version in Cancer Cell International → Version 1 posted Editorial decision: Revision requested 16 Aug, 2024 Reviews received at journal 16 Aug, 2024 Reviews received at journal 14 Aug, 2024 Reviewers agreed at journal 08 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviews received at journal 14 Jul, 2024 Reviewers agreed at journal 06 Jul, 2024 Reviewers invited by journal 06 Jul, 2024 Submission checks completed at journal 04 Jun, 2024 Editor assigned by journal 04 Jun, 2024 First submitted to journal 03 Jun, 2024 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. 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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-4521835","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313510862,"identity":"71dd8188-f907-437e-8b51-a3580dc33c9c","order_by":0,"name":"Xiaodong Ling","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaodong","middleName":"","lastName":"Ling","suffix":""},{"id":313510863,"identity":"bc97add0-beb9-4a5f-b4ac-3a952bba44c3","order_by":1,"name":"Luquan Zhang","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Luquan","middleName":"","lastName":"Zhang","suffix":""},{"id":313510864,"identity":"46b9fa28-78a1-46c1-b08a-ae57615d1a86","order_by":2,"name":"Chengyuan Fang","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chengyuan","middleName":"","lastName":"Fang","suffix":""},{"id":313510865,"identity":"1b6b457c-0dd6-48db-bec3-778f067c21ef","order_by":3,"name":"Hao Liang","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Liang","suffix":""},{"id":313510868,"identity":"eb6a6568-44ba-42da-bb3a-a038891e587e","order_by":4,"name":"Jianqun Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACxmYYi72HgRnCSiBWC88ZIrUggEQOkVqY25mfPXjbdieaf+bbY58Lcw4z8LPnGDD83IHPYWzmhnPbnuXOuJ2XPHvmtsMMkj1vDBh7z+DTwmAmzdt2OLfhdo4xMy9Qi8GNHANmxjZ8Wti/gbXMv3kGosWesBYeiC0bbvBAbZEgrKVMcs65w7kbzwAdNnNbOo/EmWcFB3vxaDHsP75N4k3Z4dx5x4EOK9xmLcffnrzxwU98WhqABA+SAJh9ALcGBgZ5uLJRMApGwSgYBbgAAIN2T12ImarUAAAAAElFTkSuQmCC","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jianqun","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-06-03 12:44:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4521835/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4521835/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12935-024-03497-w","type":"published","date":"2024-09-09T15:58:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58597053,"identity":"232b6a83-4248-4ce7-9c59-5883a0c09d91","added_by":"auto","created_at":"2024-06-18 16:54:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":908142,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of our study.\u003c/p\u003e","description":"","filename":"FIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/6bef951d19801c339bed7c53.png"},{"id":58597054,"identity":"cee424ea-33b9-4a97-b9f9-aa6f15e39e01","added_by":"auto","created_at":"2024-06-18 16:54:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":356604,"visible":true,"origin":"","legend":"\u003cp\u003eScRNA-Seq data analysis and identification of the PFKP. (A). T-SNE and UMAP plots showing the identified cell types. (B). Top marker genes of immune, epithelial, and stromal cells in all clusters. (C). Dot plot of the 6 marker genes expression of each cluster. (D). Volcano plot showing DEGs between the tumor and normal cells. (E). The heatmap showing the expression of DEGs.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/4c4051e7d462f9d333ba06a7.jpg"},{"id":58597060,"identity":"4a9bee7e-0579-4f9c-9982-e7207a51dda3","added_by":"auto","created_at":"2024-06-18 16:54:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":368483,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of PFKP in pan-cancer. (A). Differential expression of PFKP in normal and tumor samples of 33 tumors in TCGA database. (B). The differential expression of PFKP in paired samples. (C). Expression of PFKP in different pathological stages of indicated tumors. (D). PFKP protein expression levels between cancers and normal tissues. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003e P\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/65f6a8f39959cb1a0c207468.jpg"},{"id":58597061,"identity":"90f90a89-40f5-4c29-8011-941f62dea06e","added_by":"auto","created_at":"2024-06-18 16:54:34","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":493142,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic Potential of PFKP in Pan-Cancer. (A–D). The forest plots of univariate Cox regression analysis for OS (A), DSS (B), DFI (C), and PFI (D).\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/28f35ac39c69d2efaf7c5d50.jpg"},{"id":58597835,"identity":"0eac8b64-0865-4bf0-bb5a-33df2da827c3","added_by":"auto","created_at":"2024-06-18 17:02:35","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":218958,"visible":true,"origin":"","legend":"\u003cp\u003eAccuracy of PFKP in discriminating tumor from normal tissue in pan-cancer.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/33e890795f462e6e72ba3bfb.jpg"},{"id":58597058,"identity":"0e56a570-85a0-4b7d-8c9e-7dbb274f32f0","added_by":"auto","created_at":"2024-06-18 16:54:34","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":508363,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the expression of PFKP with MSI, TMB, and immune infiltration in pan-cancer. (A-B). Radar maps of correlations between PFKP expression and MSI (A) or TMB (B). (C-F). The difference of IPS between PFKP\u003csup\u003ehigh\u003c/sup\u003e and PFKP\u003csup\u003elow\u003c/sup\u003e expression groups. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, and ***\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/b2fca41f6e3b7f35f4873806.jpg"},{"id":58597067,"identity":"769cbf41-78bb-40c7-bd30-0200495f32ae","added_by":"auto","created_at":"2024-06-18 16:54:35","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":385429,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the expression of PFKP with immune scores in pan-cancer. (A-P). The scatter plots of correlation between PFKP expression and immune scores in multiple cancers. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, and *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/73224ec67936ee22a76a1ba5.jpg"},{"id":58597055,"identity":"f6fc8950-5ce4-4e0e-a8f9-7889e5003317","added_by":"auto","created_at":"2024-06-18 16:54:34","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1431067,"visible":true,"origin":"","legend":"\u003cp\u003eThe function of PFKP in lung cancer cells. (A). Differential expression of PFKP in lung cancer cells. (B). Examination of PFKP expression in A549 and H1299 cells stably infected lentiviral vectors overexpressing PFKP and lentiviral vectors carrying shPFKP, respectively. Knockdown of PFKP inhibits the proliferation of H1299 cells by CCK-8 assay (C), colony formation (E), and wound healing test (G). The overexpression of PFKP promotes the proliferation of A549 cells by CCK-8 assay (D), colony formation (F), and wound healing test (H). * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and *** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/15de0ed39ef46119bff692a7.png"},{"id":64619216,"identity":"6181852a-5947-4913-9cea-f4bd536cd503","added_by":"auto","created_at":"2024-09-16 16:12:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5173651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/05a36cb9-2599-49db-80b0-b3d8289d2a19.pdf"},{"id":58597057,"identity":"2f30bfcc-932b-4439-97a2-ca94639bd79f","added_by":"auto","created_at":"2024-06-18 16:54:34","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":312400,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/3d74d0ab09763b655c2cf890.jpg"},{"id":58597056,"identity":"c5deffd8-85d6-4796-a162-a5abb7d901a8","added_by":"auto","created_at":"2024-06-18 16:54:34","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":253165,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/fc8fecd966eed73a3dddc4c8.jpg"},{"id":58599136,"identity":"9bb7bd09-33d2-45ed-82b7-f0c7f6b71270","added_by":"auto","created_at":"2024-06-18 17:10:38","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":245642,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/643f9af36332930b458d309a.jpg"},{"id":58597834,"identity":"7163dfbc-3231-4a5c-879d-ce7b6f2c93d8","added_by":"auto","created_at":"2024-06-18 17:02:34","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":209542,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/7b6aca46d4808294b5b675ab.jpg"},{"id":58597066,"identity":"a482832f-8ffd-4784-ad2f-7c36d9711527","added_by":"auto","created_at":"2024-06-18 16:54:35","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":337435,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/5401e078a891a9abc539b853.jpg"},{"id":58597064,"identity":"8ad9183d-da4a-415b-8692-94699f314b76","added_by":"auto","created_at":"2024-06-18 16:54:35","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":325239,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/ee09d425d6ff8f3aa0d5391b.jpg"},{"id":58597833,"identity":"c65fccfe-9d07-479d-b438-14926fe13c34","added_by":"auto","created_at":"2024-06-18 17:02:34","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":354217,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4521835/v1/55d106b7eece71c6cadb54c3.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Comprehensive Prognostic and Immunological Implications of PFKP in Pan-Cancer","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer stands as one of the most prevalent respiratory neoplasms, exhibiting a persistently high incidence and mortality rate. Among the subtypes, non-small cell lung cancer (NSCLC) constitutes the most frequent variant within the realm of respiratory tumors, accounting for approximately two-thirds of all diagnosed cases of lung cancer[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Alarmingly, at least half of the patients are already diagnosed with advanced-stage disease at the time of initial assessment. While multi-modal therapies have achieved some significant milestones in the treatment of NSCLC, the efficacy of such interventions still falls short of expectations[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Targeted therapy emerges as the frontline approach for patients harboring oncogenic gene mutations. Nevertheless, an increasing body of research suggests that the combination therapy targeting multiple points yields superior results compared to monotherapy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Hence, the discovery of additional effective molecular targets assumes paramount importance in optimizing combination treatment strategies and enhancing cancer management.\u003c/p\u003e \u003cp\u003eThe PFKP gene is situated on the diminutive extremity of chromosome 10 within the human genome[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. PFKP exhibits 8 diverse transcript variants, and isoform 1 emerges as the most protracted of these, spanning a length of 784 amino acids. The regulation of PFKP gene expression is intricately entwined with the process of glycolysis. Phosphofructokinase (PFK) is a pivotal rate-limiting enzyme in glycolysis, which accelerates the consumption of glucose[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. PFK encompasses three subtypes: PFKP (platelet), PFKM (muscle), and PFKL (liver). Moreover, all subtypes are found in other tissues as well[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. PFKP exhibits expression in various cell types, gaining recognition for its crucial role in different types of cancers, including non-small cell lung cancer and breast cancer[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Research indicates that the downregulation of PFKP reduces glucose uptake rate, thereby inhibiting the growth of lung cancer cells. Additionally, PFKP regulates non-glycolytic functions related to metabolic stress induced by glucose deprivation in NSCLC cells through AMPK-mediated regulation of long-chain fatty acid oxidation. Thus, PFKP may serve as a prognostic indicator for lung cancer, and its regulatory role in glycolysis could be a potential therapeutic target for the disease. Gao et al. reported the involvement of Nuclear PFKP in promoting CXCR4-dependent infiltration by T-cell acute lymphoblastic leukemia. However, the critical role of PFKP in most tumors remains unexplored. The advent of next-generation sequencing (NGS) technology and in-depth exploration of cancer genome atlas (TCGA) datasets gradually reveal genomic and transcriptomic data of common tumors to researchers. Therefore, this is an optimal approach for scrutinizing and revealing the prospective prognostic and predictive significance of biomarkers through Pan-cancer analysis in the realm of precision medicine.\u003c/p\u003e \u003cp\u003eIn this study, we identified significant differences in the expression of PFKP by analyzing differentially expressed genes between tumor cells of epithelial origin and normal cells. Furthermore, we explored the expression of PFKP in Pan-cancer and its clinical relevance. Additionally, we analyzed the relationship between PFKP expression and tumor mutation burden, microsatellite instability, and immune phenotype scores. Lastly, we determined the status of immune infiltration and immunotherapy sensitivity in different subgroups.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Acquisition and Processing\u003c/h2\u003e \u003cp\u003eWe downloaded the RNA-Seq expression data of 33 different tumors from the TCGA database. We determined the expression levels of PFKP in 33 types of tumors. Additionally, we used a web-based analysis tool, the Gene Expression Profiling Interactive Analysis (GEPIA)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], to visualize the PFKP expression levels in different stages of all tumors. Then, we explored the protein expression level of PFKP between normal and tumor tissues through the UALCAN portal[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To verify the expression of PFKP between the tumor and normal cells in LUAD, the single-cell RNA transcriptome data of GSE117570 were downloaded from the GEO database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis\u003c/h2\u003e \u003cp\u003ePatients were divided into high- and low-expression groups using the minimum \u003cem\u003eP\u003c/em\u003e-value method. Cox regression analysis for TCGA datasets was performed using RStudio software with the \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;forestplot\u0026rdquo; packages to investigate the correlation between PFKP expression and cancer prognosis, including overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). We calculated the log-rank \u003cem\u003eP\u003c/em\u003e-value and hazard ratio (HR) with 95% confidence intervals (95% CI) via the \u0026ldquo;survival\u0026rdquo; package and utilized the \u0026ldquo;forestplot\u0026rdquo; package to visualize the survival analysis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We also implemented the Kaplan-Meier survival analysis to estimate the clinical outcomes of patients with PFKP\u003csup\u003elow\u003c/sup\u003e or PFKP\u003csup\u003ehigh\u003c/sup\u003e tumors by executing the \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer\u0026rdquo; R packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of PFKP with TMB, MSI, immunophenotype scores, and tumor immune microenvironment\u003c/h2\u003e \u003cp\u003eThe genome alterations of PFKP included copy number amplification, deep or shallow deletion, missense mutation with uncertain significance and mRNA upregulation. Tumor mutation burden (TMB) is calculated as total somatic nonsynonymous mutation counts in coding regions. Numerous studies have explored the significance of using TMB as a biomarker for identifying patients sensitive to checkpoint inhibitors[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Microsatellite instability (MSI) is featured by the widespread length polymorphisms of microsatellite sequences resulting from DNA polymerase slippage[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The TMB and MSI scores were obtained from TCGA database and Spearman\u0026rsquo;s rank method was used to determine the correlation of PFKP with TMB and MSI. The correlation results for TMB and MSI were visualized in radar maps. We also retrieved the immunophenoscore (IPS) for LUAD patients from The Cancer Immunome Atlas. The IPS is calculated based on four significant categories of tumor immunogenicity determinants, including effector cells, immunosuppressive cells, MHC molecules, and checkpoints/immunomodulators[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe made comparisons of IPS for the PFKP\u003csup\u003elow\u003c/sup\u003e and PFKP\u003csup\u003ehigh\u003c/sup\u003e groups and subsequently evaluated their responses to anti-PD1/PDL1/PDL2 and anti-CLA4 treatments. The stromal score and immune score were compared among different groups by the ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) method[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive analysis of single cell datasets and cell cluster annotation\u003c/h2\u003e \u003cp\u003eSingle-cell transcriptomic profiles of 4 NSCLC and 4 adjacent normal tissues were obtained from GSE117570[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Samples with unknown microsatellite stabilities were excluded from the study. We analyzed the scRNA-seq data using the R package Seurat[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The data were normalized using the SCTransform method and integrated using the IntegrateData function. T-distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) were applied to reduce the dimensions. The FindNeighbors and FindClusters functions were used for cell clustering analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCell Culture and transfection\u003c/h2\u003e \u003cp\u003eThe six human NSCLC cell lines (H1915, A549, PC9, H460, H1650, and H1299) were obtained from Laboratory of Medical Genetics (Department of Biology, Harbin Medical University, Harbin, China). Cells were cultured in RPMI-1640 (Invitrogen Corporation, Carlsbad CA, USA) supplemented with 10% FBS (PAN-Biotech, Aidenbach Germany). Cell cultures were kept in a humidified incubator at 37℃ with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eThe H1299 cells were transfected with PFKP siRNA to knock down the PFKP expression according to the manufacturer\u0026rsquo;s protocols. The duplex sequences of the three siRNA targeting PFKP were as follows: First, sense, 5\u0026prime;- GGAGCAAUUGAUACCCAAATT-3\u0026prime;, and antisense, 5\u0026prime;-UUUGGGUAUCAAUUGCUCCTT-3\u0026prime;; Second, sense, 5\u0026prime;-GCAACGUAGCUGUCAUCAATT-3\u0026prime;, and antisense, 5\u0026prime;-UUGAUGACAGCUACGUUGCTT-3\u0026prime;; Third, sense, 5\u0026prime;- CCCUCUCCAUUUGAUAGAATT-3\u0026prime;, and antisense, 5\u0026prime;-UUCUAUCAAAUGGAGAGGGTT-3\u0026prime;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot\u003c/h2\u003e \u003cp\u003eCells were lysed using RIPA buffer (Solarbio, Beijing, China). Protein samples were electrophoresed in 15% SDS-PAGE and transferred to PVDF membranes. After blocking with PBST containing 5% nonfat milk, membranes were incubated with primary antibodies against PFKP (Abcam, UK) and GAPDH (Cell signaling technology, USA) at 4\u0026deg;C overnight. Then, the membranes were incubated with a secondary antibody (Beijing Zhongshan Golden Bridge Biotechnology Co. Ltd., Beijing, China) for 1 h at room temperature. Finally, the ECL (Tanon, Shanghai) detection system was used to visualize the protein bands.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCell proliferation assay\u003c/h2\u003e \u003cp\u003eCells with a density of 3 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e/ml were seeded in a 96-well plate, and cultured for 24, 48, and 72 h. the OD values were measured using Cell Counting Kit 8 (CCK-8, Elabscience, China). Subsequently, the OD values were measured at 450 nm with a microplate reader.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eColony formation assay\u003c/h2\u003e \u003cp\u003eCells were plated in a 12-well plate at a density of 500 cells per well. Cells grew for ten days in a 37\u0026deg;C incubator until more than 50 cells were available for most clones. Then, cells were fixed with 4% paraformaldehyde for 20 min, followed by staining with 0.2% crystalline violet solution for half an hour at room temperature and washing with PBS three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWound-Healing Assay\u003c/h2\u003e \u003cp\u003eFor wound healing assay, a wound was generated in a 6-well plate by scratching the surface with a 1 mL pipette tip. Scratch width change was measured under a light microscope (Nikon, Tokyo, Japan) after 24 h of culture in serum-free 1640 medium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll bioinformatic analyses were carried out with the R software version 4.1.3. The survival curve was plotted by Kaplan-Meier survival curve. The Wilcoxon test was applied in analyzing differences between clinical feature distribution, immune infiltration, and therapy response. Double-tailed \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eScRNA-Seq Data Analysis and Identification of the PFKP\u003c/h2\u003e \u003cp\u003eThe flowchart of this study was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 11485 cells were obtained from the scRNA-seq data after initial screening. After log-normalization and dimensionality reduction, all cells were clustered into 20 clusters and further annotated into three cell types based on their expression of immune, epithelial, and stromal cell markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). Next, we screened out differentially expressed genes (DEGs) between the tumor and normal cells in epithelial cell clusters. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, we found the gene \u0026ldquo;PFKP\u0026rdquo; among the up-regulated DEGs. The heatmap of DEGs showed the expression of PFKP between tumor and normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eExpression Levels of PFKP in Pan-Cancer\u003c/h2\u003e \u003cp\u003eWe first compared PFKP expression between tumor and normal tissues from the TCGA database. The results showed that the expression level of PFKP in the tumor tissues of CHOL, COAD, ESCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PCPG, STAD, and UCEC is much higher than the corresponding normal tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. In paired samples, PFKP expression was significantly elevated in multiple cancer types, including CHOL, COAD, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, and STAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Furthermore, PFKP expression levels increased with tumor progression in ACC, KIRC, KIRP, LIHC, LUAD, LUSC, TGCT, and UVM (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The UALCAN online tool confirmed that PFKP protein levels were significantly upregulated in RCC, HNSCC, LUAD, PAAC, and GBM (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between PFKP Expression and Prognostic Implications\u003c/h2\u003e \u003cp\u003eThe Kaplan-Meier analysis was used to evaluate the association between PFKP expression and OS, DSS, DFI, PFI in pan-cancer (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u0026ndash;4\u003c/b\u003e). Additionally, we further compared the survival contribution of PFKP in multiple cancer types, estimated using univariate Cox regression analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;D). For example, the results showed that high PFKP as a risk factor for OS in ACC, CESC, HNSC, LAML, LIHC, LUAD, MESO, and UVM. Results for DSS, DFI, and PFI are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u0026ndash;D. It is worth noting that PFKP was significantly associated with the survival of LUAD (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These results suggested that PFKP expression had a strong prognostic power in different tumors, and the relevance of PFKP to clinical relevance may shed new light on the underlying pathogenesis of tumors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Value of PFKP for Pan-Cancer\u003c/h2\u003e \u003cp\u003eGiven the significant differences in the expression levels of PFKP in different tumor tissues and corresponding normal tissues, we used the receiver operating characteristic (ROC) curve to initially investigate the diagnostic value of PFKP mRNA expression levels for discriminating between tumor and normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The area under curve (AUC) values suggested that PFKP had a strong ability to discriminate, especially UCEC (AUC\u0026thinsp;=\u0026thinsp;0.665), THCA (AUC\u0026thinsp;=\u0026thinsp;0.605), PAAD (AUC\u0026thinsp;=\u0026thinsp;0.730), PCPG (AUC\u0026thinsp;=\u0026thinsp;0.927), PRAD (AUC\u0026thinsp;=\u0026thinsp;0.676), STAD (AUC\u0026thinsp;=\u0026thinsp;0.686), CESC (AUC\u0026thinsp;=\u0026thinsp;0.642), KIRP (AUC\u0026thinsp;=\u0026thinsp;0.803), LUSC (AUC\u0026thinsp;=\u0026thinsp;0.965), LUAD (AUC\u0026thinsp;=\u0026thinsp;0.923), LIHC (AUC\u0026thinsp;=\u0026thinsp;0.710), KIRC (AUC\u0026thinsp;=\u0026thinsp;0.937), KICH (AUC\u0026thinsp;=\u0026thinsp;0.858), HNSC (AUC\u0026thinsp;=\u0026thinsp;0.696), ESCA (AUC\u0026thinsp;=\u0026thinsp;0.875), and GBM (AUC\u0026thinsp;=\u0026thinsp;0.982). It can be seen that the PFKP mRNA expression level has a high diagnostic value for distinguishing tumors from normal tissues.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of PFKP Expression with Microsatellite Instability, Tumor Mutation Burden, and Immunophenotype Scores\u003c/h2\u003e \u003cp\u003eBoth microsatellite instability (MSI) and tumor mutation burden (TMB) are pivotal characteristics of tumors, and affect response to immunotherapy in cancers[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], we next performed association analyses of PFKP expression with MSI and TMB. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, PFKP expression was positively correlated with MSI in BRCA, COAD, LGG, LUSC, STAD, and THYM, while negatively correlated with MSI in DLBC (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). PFKP expression was also positively correlated with TMB of ACC, BRCA, COAD, HNSC, LUAD, PAAD, SKCM, STAD, THCA, THYM, UCEC, and UCS, but negatively correlated with that of LAML, LGG, LIHC, and PRAD (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Then, we used immunophenoscore (IPS) for LUAD patients from The Cancer Immunome Atlas to determine the sensitivity to immune checkpoint inhibitors for the PFKP (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-F). Our results showed that the high expression group possessed significantly higher immunophenotype scores (IPS) than the low expression group in PRAD and THCA. Moreover, the high expression group was more likely to gain benefits from anti-CTLA4 therapy in BRCA, COAD, PRAD, THCA, and respond to anti-PD1/PDL1/PDL2 therapies in BLCA, BRCA, COAD, LIHC, PRAD, READ, and THCA. Finally, the high expression group tended to respond to both anti-PD1/PDL1/PDL2 and anti-CTLA4 antibodies in BLCA, BRCA, COAD, LIHC, PRAD, READ, and THCA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe Association between PFKP and Tumor Immune Microenvironment\u003c/h2\u003e \u003cp\u003eAlthough our above investigations have demonstrated the prognostic ability of PFKP in pan-cancer, its potential role warranted further research. The development of malignant tumors is closely related to the tumor immune microenvironment (TIME) in which the tumor cells are located. TIME contains not only tumor cells but also immune cells, fibroblasts, and many other cells and extracellular matrix, which is the basis for the survival and development of tumor cells[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It is unclear whether PFKP impacts the recruitment of immune cells. With several algorithms including EPIC, TIMER, QUANTISEQ, XCELL, and CIBERSORT, we evaluated the correlation between the immune cell infiltration and PFKP expression in pan-cancer. The score of six immune cell types, including CD4\u003csup\u003e+\u003c/sup\u003e T cells, B cells, monocytes, NK cells, dendritic cells, and CD8\u003csup\u003e+\u003c/sup\u003e T cells, were calculated (\u003cb\u003eSupplementary Fig.\u0026nbsp;5\u0026ndash;6\u003c/b\u003e). Results indicated that PFKP was significantly associated with immune cell subsets in BRCA, HNSC, KIRC, LUAD, TGCT, and SKCM. In contrast, no correlation was found between PFKP expression and immune cell infiltration in ACC, CHOL, DLBC, KICH, MESO, and UCS. PFKP exhibited positive associations with dendritic cells, CD4\u003csup\u003e+\u003c/sup\u003e T helper 2 cells, and negative associations with B cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, native CD4\u003csup\u003e+\u003c/sup\u003e T cells in the majority of tumors. Next, by adopting the ESTIMATE method, we computed the immune and stromal scores of cancer tissues. As Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e indicated, PFKP was positively correlated with the immune scores in BLCA, BRCA, COAD, LIHC, PRAD, and UVM, but negatively correlated with the immune score in ACC, CESC, GBM, HNSC, KIRP, LGG, LUSC, PAAD, TGCT, and THYM. For the analysis of the stromal score, we have obtained similar results (\u003cb\u003eSupplementary Fig.\u0026nbsp;7\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePFKP promoted proliferation and migration in LUAD cells\u003c/h2\u003e \u003cp\u003eWe first examined the expression levels of PFKP in six lung cancer cell lines. The results showed that PFKP expression was elevated in the majority of lung cancer cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). In the follow-up study, we conducted a knockdown analysis on H1299 cells with high expression of PFKP and the overexpression analysis in A549 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Through CCK-8 and colony formation experiments, we found that PFKP depletion obviously inhibited lung cancer cells proliferation, while overexpression of PFKP promoted cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-F). Besides, wound-healing assay demonstrated enhanced migration in cells overexpressing PFKP and weakened migration in cells knockdown PFKP (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG-H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study has unveiled, for the first time, the critical role of PFKP in Pan-cancer. Through multidimensional analysis, we discovered that PFKP is consistently upregulated in the majority of tumors, with its expression increasing along with tumor stage in various cancers such as LUAD, LUSC, KIRC, HNSC, COAD, among others. Furthermore, high expression of PFKP is associated with poorer prognosis in most cancer patients. Negative correlations were observed between PFKP expression and overall survival rates in several tumors, including KICH, HNSC, LUAD, and others. Importantly, consistent results were obtained when analyzing different datasets, and published studies have also demonstrated the tumorigenic role of PFKP[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings suggest that PFKP possesses widespread oncogenic characteristics in cancer, holding significant prospects in the field of cancer research.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment (TME) is a highly structured ecosystem that encompasses cancer cells surrounded by various non-malignant cell types, all embedded within a dynamic, vascularized extracellular matrix[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The TME comprises a diverse array of immune cells, cancer-associated fibroblasts (CAFs), endothelial cells (ECs), stromal cells, and other cell types[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These cells engage in intercellular communication, either promoting or inhibiting tumor progression. The TME plays a pivotal role in determining the fate of tumor cells, regulating not only their proliferation, invasion, and metastasis but also influencing therapeutic outcomes[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, based on RNA sequencing data, we inferred the correlation between TME and PFKP. We described the stromal and immune scores of Pan-cancer samples, revealing differential associations with PFKP across different cancer types.\u003c/p\u003e \u003cp\u003eGiven the critical role of PFKP in the TME, we further explored the correlation between PFKP and immune therapy response. Microsatellite instability (MSI), tumor mutation burden (TMB), and the expression levels of various immune checkpoint markers are closely associated with the response to immune therapy, serving as important indicators for clinicians to identify patients who may benefit from immunotherapy[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our analysis revealed a significant correlation between PFKP and TMB in 16 types of tumors, including LUAD, BRCA, and HNSC. TMB effectively assesses tumor neoantigen burden, and in most cancers, high TMB is associated with better overall survival (OS) and indicates a greater likelihood of response to PD-1 or CTLA-4 inhibitors. Previous studies have observed a response to immune checkpoint inhibitors (ICIs) in solid tumors with high TMB, including breast cancer, lung cancer, and gastric cancer[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Mechanistically, TMB-mediated neoantigens and tumor immunogenicity are associated with the recognition of immune therapy sensitivity or resistance[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Extensive research has demonstrated that microsatellite instability (MSI) is closely related to tumor development and is caused by defects in mismatch repair (MMR) genes. Clinically, MSI has been used as an important molecular marker for the prognosis and formulation of adjuvant treatment strategies in colorectal cancer and other solid tumors, as well as for assisting Lynch syndrome screening[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our Pan-cancer analysis revealed a positive correlation between PFKP and MSI in six types of tumors, including LUSC, BRCA, and COAD. Immune checkpoint inhibitors work by reversing the \"cold\" tumor state and killing tumor cells, thereby significantly advancing multimodal tumor therapy. The expression of PD-1 and CTLA-4 has also shown correlations with PFKP in various tumors. These analyses highlight the potentially crucial role of PFKP in tumor immunotherapy.\u003c/p\u003e \u003cp\u003eLastly, we validated the role of PFKP in lung cancer through in vitro experiments. Compared to normal bronchial epithelial cells, PFKP exhibited higher expression in lung cancer-associated tumor cell lines. Depletion of PFKP suppressed the proliferation and migration of H1299 cells. Conversely, overexpression of PFKP promoted the malignant phenotype of lung cancer cell lines.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHere, we have determined the crucial role of PFKP based on mining public databases and conducting in vitro experiments. With the release of a large volume of high-throughput data, researchers in this field have gained rich information and enlightening insights into the understanding of the tumor microenvironment and the development of targets. However, it is important to note that results derived from public data and statistical algorithms may inevitably be influenced by significant heterogeneity and possible biases. The findings of bioinformatics analysis should be interpreted cautiously and cannot be directly translated into a clinical setting. In conclusion, we have identified PFKP as a potential biomarker for predicting different tumor prognoses and tumor immunogenicity, providing new avenues for the development of potential therapeutic targets in a clinical context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. XDL and LQZ drafted the manuscript. XDL and CYF performed the analyses and interpreted all the data. XDL, and HL prepared the figures and tables. XDL performed the experiments. JQM reviewed and revised the manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Natural Science Foundation of China (No. 82172786 to Jianqun Ma), the Haiyan Foundation of Harbin Medical University Cancer Hospital (No. JJMS2023-04 to Xiaodong Ling) and Beijing Medical Award Foundation (No. YXJL-2023-0091-0054 to Xiaodong Ling).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRemon J, Hendriks LEL, Mountzios G, Garcia-Campelo R, Saw SPL, Uprety D, Recondo G, Villacampa G, Reck M. MET alterations in NSCLC-Current Perspectives and Future Challenges. J Thorac Oncol. 2023;18(4):419\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzcan G, Singh M, Vredenburgh JJ. 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Front Immunol. 2021;12:757804.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PFKP, pan-cancer, LUAD, prognosis prediction, immune analysis","lastPublishedDoi":"10.21203/rs.3.rs-4521835/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4521835/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs a pivotal rate-limiting enzyme in glycolysis, Phosphofructokinase P (PFKP) plays a pivotal role in multiple pathophysiological processes. However, the critical role of PFKP in most tumors remains unexplored. The present research was primarily designed to provide the expression of PFKP in Pan-cancer and its clinical relevance, and study the correlations between PFKP expression and immune infiltration characteristics in tumor microenvironment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRaw data in regard to PFKP expression were obtained from TCGA and GEO databases. We examined the expression patterns and prognostic values of PFKP in pan-cancer utilizing multiple databases, and investigated the relationship of PFKP expression with immune infiltration and tumor immune microenvironment. Besides, the biological function of PFKP was explored via in vitro verification.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePFKP is highly expressed and is a prognostic risk factor in most tumors. Increased expression of PFKP was detrimental to the clinical prognoses, especially LUAD. Also, ROC curve analysis demonstrated that PFKP showed high accuracy in distinguishing cancerous tissues from normal ones. There were significant correlations between PFKP expression and TMB, MSI, immune scores, and immune cell infiltrations. In vitro studies demonstrated that the overexpression of PFKP accelerated the proliferation and migration of lung cancer cells, whereas PFKP depletion showed the opposite effects on them.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, PFKP participates in the carcinogenic progression, and may contribute to the immune infiltration in tumor microenvironment. Our study suggests that PFKP can serve as a potential biomarker for predicting different tumor prognoses and tumor immunogenicity, especially LUAD.\u003c/p\u003e","manuscriptTitle":"A Comprehensive Prognostic and Immunological Implications of PFKP in Pan-Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-18 16:54:28","doi":"10.21203/rs.3.rs-4521835/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-16T13:37:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-16T08:33:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-14T04:06:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291239759295729079460229256589302191805","date":"2024-08-09T02:25:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244122816408207474769958281305151295118","date":"2024-08-07T04:43:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-14T15:06:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273370478249099728570941337780456412308","date":"2024-07-06T13:23:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-06T08:45:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-04T09:09:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-04T09:09:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Cell International","date":"2024-06-03T12:43:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38369df7-c016-4374-a481-ca75cabb23e6","owner":[],"postedDate":"June 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:05:05+00:00","versionOfRecord":{"articleIdentity":"rs-4521835","link":"https://doi.org/10.1186/s12935-024-03497-w","journal":{"identity":"cancer-cell-international","isVorOnly":false,"title":"Cancer Cell International"},"publishedOn":"2024-09-09 15:58:10","publishedOnDateReadable":"September 9th, 2024"},"versionCreatedAt":"2024-06-18 16:54:28","video":"","vorDoi":"10.1186/s12935-024-03497-w","vorDoiUrl":"https://doi.org/10.1186/s12935-024-03497-w","workflowStages":[]},"version":"v1","identity":"rs-4521835","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4521835","identity":"rs-4521835","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}