Systematic pan-cancer analysis identifies 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) as a biomarker of tumor invasion and metastasis, immunity, and prognosis

doi:10.21203/rs.3.rs-2455661/v1

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Systematic pan-cancer analysis identifies 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) as a biomarker of tumor invasion and metastasis, immunity, and prognosis

https://doi.org/10.21203/rs.3.rs-2455661/v1

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6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) is a potent regulator of glycolysis in tumor cells, and high PFKFB3 expression is significantly associated with the invasion and metastasis of several tumors. However, there are no comprehensive reports on whether PFKFB3 promotes tumor invasion and its mechanism in different cancer types. In addition, there are no systematic reports on the effect of PFKFB3 on the stemness and immune infiltration ability of different tumors and on the survival rate of patients. Herein, we conducted a pan-cancer analysis of PFKFB3 with the aim of exploring the key cellular and molecular mechanisms regulating the pathogenesis and progression of human cancers, and propose potential strategies for the prevention and treatment of cancer by targeting PFKFB3. Using bioinformatics analysis and integrative exploration from the Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases, the differential expression of PFKFB3 or phosphorylated PFKFB3 and its correlation with tumor staging and stemness, survival prognosis, and immune invasion were extensively analyzed. The analysis showed differential expression of PFKFB3 in normal tissues and in various cancers. Increased PFKFB3 expression is positively correlated with the invasive ability and immune infiltration of 31 cancers and significantly affects the staging, stemness, prognosis, and survival rate of several cancers. Alterations in phosphorylated PFKFB3 and RNA modifications are also involved in the development and progression of various cancers. PFKFB3 is involved in multiple protein interactions and has complex molecular functions, such as ATP/ADP metabolic and glycolytic processes. Furthermore, PFKFB3 has a high mutation frequency, especially amplification, in multiple tumors. These findings highlight the significance of PFKFB3 in cancer progression, which might serve as a surrogate pan-cancer biomarker to predict the progression and outcome of cancers, as well as the invasion and immune infiltration of different cancers.

Ethical compliance: This study did not involve any patient or animal samples and was approved by the academic committee of Tongji University.

PFKFB3

pan-cancer

clinical prognosis

tumor invasion

immune infiltration

gene ontology

KEGG pathway

Glycolysis converts glucose to pyruvate and is a key feature of cancer metabolism (1). Tumor cells largely depend on aerobic glycolysis even in the presence of oxygen, a metabolism known as the “Warburg effect,” which favors proliferation, invasiveness, and even resistance to apoptosis of tumor cells (2). Phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) is a bifunctional enzyme with both kinase and phosphatase activities. PFKFB3 mainly modulates and maintains intracellular levels of fructose-2,6-bisphosphate (F-2,6-BP), a potent allosteric activator of the pivotal enzyme 6-phosphofructokinase 1, which essentially regulates the rate of glycolysis (3). There are four genes, PFKFB1 to PFKFB4, which encode PFKFB, in which PFKFB3 expression is significantly altered in human tumors in situ (4). As a bifunctional enzyme, the activity ratio of kinase to phosphatase is the highest (740:1) for PFKFB3, which plays a critical role in inducing F-2,6-BP production and enhancing glycolysis (5, 6). PFKFB3 has been reported to play a critical role in a variety of tumor cells and in various cells in the tumor microenvironment (7, 8). For example, several studies have shown that upregulation or phosphorylation of PFKFB3 effectively promotes oncogenesis, tumor cell proliferation, migration, and metastasis (1). In addition, PFKFB3 can induce vascular sprouting by activating Notch signaling (9), regulating the expression of downstream transcription factors in immune cells, and participating in the progression of cancers (10, 11). Inhibitors of PFKFB3 have been shown to exhibit potent antitumor activity in several human cancer xenograft models (12–14).

Although the role of PFKFB3 has been widely studied in various tumors, most focus on its role of PFKFB3 in certain types of cancers, such as promoting tumor invasion and metastasis, regulating cancer stemness, and disturbing vascular normalization (8, 15). To fully understand the multifaceted roles of PFKFB3 in various cancers, we performed a pan-cancer analysis of PFKFB3 using an online database and R software package. In this study, the similarities and differences of PFKFB3 in different tumors were analyzed and reported to provide an instructive basis for the diagnosis, prevention, treatment, and prognosis of various cancers by evaluating PFKFB3.

2.1 Data collection:

The data used in this study are publicly available and downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.

2.2 Differential gene expression analysis

TIMER2.0, and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) were conducted to determine the differential expression of PFKFB3 in various cancers and normal tissues using data retrieved from TCGA database (Fig. 1). Moreover, the scatter plots (Fig. 3A) and violin plots (Fig. 3B) of PFKFB3 expression in various cancer pathological stages were obtained based on “ggplot2” package from R software (version 3.6.3) and the GEPIA2 dataset.

The phosphorylation levels of PFKFB3 for lung adenocarcinoma (LUAD), kidney renal clear cell carcinoma (KIRC), pancreatic adenocarcinoma (PAAD), head and neck squamous cell carcinoma (HNSC), and glioblastoma multiforme (GBM) were obtained from the Clinical Proteomic Tumor Analysis Consortium (CTPAC) module in the UALCAN database (Fig. 6A) (16).

2.3 Human protein atlas analysis

Human Protein Map (HPA) is a valuable and publicly available tool for protein localization and expression in human tissues and cells by mapping the entire human proteome with immunohistochemistry and immunocytochemistry (17). In this study, we screened the immunohistochemical staining of PFKFB3 in sections of normal tissues, including colon, kidney, stomach, breast, prostate, testicle, thyroid, cervix, and lung, and the corresponding tumor tissues, including colon adenocarcinoma (COAD), kidney renal papillary cell carcinoma (KIRP), stomach adenocarcinoma (STAD), breast invasive carcinoma (BRCA), prostate adenocarcinoma (PRAD), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), cervical squamous cell carcinoma (CESC), LUAD, and lung squamous cell carcinoma (LUSC) (Fig. 2, antibody: CAB020795).

2.4 Survival prognosis analysis

In “survival map” and “survival analysis” modules of GEPIA2, the Kaplan-Meier plotter was used to analyze the association between PFKFB3 expression and overall survival (OS), disease-free survival (DFS) among various cancers, including ACC, KIRC, KIRP, STAD, uveal melanoma (UVM), COAD, and SARC, according to TCGA database (18, 19) (Fig. 4A-B).

As shown in Fig. 4C, a high-quality prognostic dataset for TCGA was obtained from a TCGA prognostic study published in Cell (20). Samples with 0 expression level, follow-up time less than 30 days, and cancer species with less than 10 samples were excluded. Finally, the expression data of 44 cancer species and the OS, disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) data of the corresponding samples were obtained, and the final results after deleting the data without statistical difference are shown in Fig. 4C.

2.5 Correlation analysis of immune infiltration and PFKFB3 expression

We extracted the PFKFB3 expression profiles of each tumor separately according to a pan-cancer dataset (PANCAN, N = 19131, G = 60499), mapped the expression profiles to GeneSymbol, and further calculated the stromal, immune, and ESTIMATE scores of each patient in each tumor according to gene expression using the R software package ESTIMATE (21) (version 1.0.13, https://bioinformatics.mdanderson.org/public-software/estimate/) (Fig. 5A). In addition, the timer method (22) of the R software package IOBR (23) was further used to re-evaluate B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cell infiltration scores of each patient in each tumor according to PFKFB3 expression (Fig. 5B).

2.6 Correlation analysis of tumor stemness or RNA modification and PFKFB3 expression

A uniformly standardized pan-cancer dataset was downloaded from the University of California Santa Cruz (UCSC) database, TCGA pan-cancer (PANCAN, N = 10535, G = 60499), in which the expression data of the ENSG00000170525 (PFKFB3) gene were extracted for each sample. The samples were then screened from primary blood-derived cancer-peripheral blood and primary tumors. Samples with zero expression levels and cancer species with less than three samples in a single cancer species were filtered. Finally, the expression data of 37 cancer species were obtained, and the DNA surfactants tumor stemness scores were calculated from methylation signatures for each tumor obtained from previous studies (24). We integrated the stemness index of these samples with gene expression data (Fig. 6B).

The expression data of PFKFB3 and 44 marker genes of three types of RNA modifications (m¹A (10), m⁵C (13), and m⁶A (21)) in each sample were extracted according to the pan-cancer dataset (PANCAN, N = 19131, G = 60499). In addition, we filtered the samples with 0 expression levels and all normal samples and subsequently performed log2 (x + 0.001) transformation for each expression value. Finally, we calculated the Pearson correlation between PFKFB3 and the marker genes for RNA modification (Fig. 6C).

2.7 Interaction of PFKFB3 with functionally similar genes in normal samples

GeneMANIA is a user-friendly website that can provide a small set of genes that are most likely to share functions with the given gene (PFKFB3) based on their interactions (25) (Fig. 7A). The enrichment theory of Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was employed to encode this set, and further performed functional enrichment studies, including biological processes, molecular functions, and cellular components (26), and identified the specific functional pathways (27) (Fig. 7B-C).

2.8 Similar genes detection in various cancers

GEPIA2 was conducted to detect the top 20 genes with the most similar expression patterns to PFKFB3 among the 33 cancer types and tissues in the correlation analysis module (Fig. 8A). Subsequently, the correlation between PFKFB3 and each gene in the tumors was analyzed (Fig. 8C).

2.9 Mutation analysis of PFKFB3 in various cancers

cBioPortal, a platform that covers all tumor gene data in TCGA database, can provide multidimensional visual data. The mutation type and frequency of PFKFB3 gene in 32 cancers, a total of 39,532 samples were analyzed in “CancerTypesSummary” and “OncoPrint” module. "CancerTypesSummary" presents the mutation rate of PFKFB3 in various tumors as a bar graph (Fig. 9A). “OncoPrint” shows the mutation, copy number, and expression of PFKFB3 in all samples in a heat map (Fig. 9B).

3.1 Differential expression of PFKFB3 in normal tissues and various cancers

In order to explore the normal expression of PFKFB3 in various organs of the human body, the analysis results from the Human Protein Atlas database showed that the expression of PFKFB3 was higher in human lung, female reproductive system, kidney, and pancreas tissues than in other tissues (Fig. 1A), indicating its crucial role in maintaining the normal physiological function of these organs. To explore the changes in PFKFB3 expression in various tumor tissues and its possible pathological role, the TIMER2.0 database was conducted to analyze the differential expression of PFKFB3 between TCGA cancers and normal controls. Except for some cancers for which normal tissue data were not available, significant differences in PFKFB3 expression were detected between normal tissues and the other 15 cancer types. Results showed that PFKFB3 expression was high in some types of cancer tissues, including cholangial carcinoma (CHOL), COAD, HNSC, KIRC, PAAD, STAD, and THCA, and decreased in bladder urothelial carcinoma (BLCA), BRCA, diffuse large B-cell lymphoma (DLBC), kidney chromophobe (KICH), KIRP, liver hepatocellular carcinoma (LIHC), LUAD, LUSC, PRAD, and thymoma (THYM) cancers (Fig. 1B left, all p < 0.05).

Analysis of the GEPIA2 database revealed that PFKFB3 expression was higher in CHOL and PAAD cases, but lower in BRCA, diffuse large B-cell lymphoma (DLBC), KICH, KIRP, and thymoma (THYM) tumors. However, there was no statistically significant difference between the other tumors and the corresponding normal tissues (Fig. 1B, right and C).

To confirm the alteration of PFKFB3 expression at the protein level, the immunohistochemistry (IHC) analysis retrieved from the HPA was performed and was consistent with the gene transcript levels shown in Fig. 1. That is, PFKFB3 staining intensity was higher in COAD, TGCT, THCA, and CESC tumor samples but lower in KIRP, BRCA, PRAD, LUAD, and LUSC tumors than in the corresponding normal tissues (Fig. 2).

3.2 PFKFB3 expression is associated with the staging of tumors

To explore the relationship between PFKFB3 expression and pathological stages in different cancers, results from the CPTAC (Fig. 3A) and GEPIA2 (Fig. 3B) databases revealed a significant positive correlation between PFKFB3 expression and pathological stages of COAD, STAD, THCA, CESC, TGCT, and PAAD tumors, whereas there was a negative correlation between PFKFB3 and pathological stages of KIRP and ovarian serous cystadenocarcinoma (OV) (Fig. 3A, * p < 0.05). These results indicated that PFKFB3 played an important role in the progression of various tumors.

3.3 The impact of PFKFB3 expression on the survival rate

To explore whether the expression of PFKFB3 is correlated with the survival rate of patients with different tumors. The GEO and TCGA databases were used to investigate the association between PFKFB3 expression and survival prognosis in various cancer cases. The results in Fig. 4A show that high expression of PFKFB3 in ACC, KIRP, and STAD tumors and lower expression of PFKFB3 in KIRC tumors are closely related to unfavorable OS prognosis of patients. DFS represents the interval between surgery and the first diagnosis of any type of relapse or death (28). Results from Fig. 4B indicated that high expression of PFKFB3 was correlated with poor DFS time in ACC, COAD, SARC, and UVM cancers, while lower expression of PFKFB3 was correlated with poor DFS in KIRC patients. Collectively, these results revealed that the different expression patterns of PFKFB3 were significantly related to the prognosis and survival of patients in a cancer-dependent manner.

In order to further clarify the correlation between PFKFB3 expression and various survival outcomes, the Cox analysis was performed using the R packages “survival” and “forestplot” to illustrate the pan-cancer relationship between PFKFB3 association and survival. The results in Fig. 4C demonstrate that PFKFB3 expression was significantly correlated with OS in patients with KIPA, LIHC, KIRP, CESC, and ACC cancers (all p < 0.05), and was also correlated with DFS in patients with KIRP, CESC, ACC, and KIPAN (all p < 0.05). In addition, PFKFB3 expression was closely correlated with DFI in ACC patients (all p < 0.05) and correlated with PFI in ACC, UVM, KIPAN, SARC, and COAD patients (all p < or = 0.05). These results implied that PFKFB3 is significantly associated with the survival and prognosis of patients with a variety of tumors, indicating that PFKFB3 is expected to be a prognostic marker and potential therapeutic target of tumors.

3.5 PFKFB3 promoted immune infiltration among various cancers

The poor prognosis and survival rate of tumors are closely related to their migration and invasion ability. Therefore, we performed correlation analysis between PFKFB3 expression and tumor invasion and migration ability or immune infiltrating cell types. It has been reported that high expression of PFKFB3 is associated with tumor invasion and metastasis (7, 29–32). However, most of these studies were limited to laboratory studies, and the tumor types were relatively limited. In this study, we determined the immune invasion scores for 10,179 tumor samples out of 44 tumor types using biological information analysis, and then calculated the Pearson's correlation coefficient between PFKFB3 and immune infiltration scores in each tumor using the CorR. test function of the R software package Psych (version 2.1.6) to determine significantly correlated immune infiltration scores. A significant positive correlation was found between the expression of PFKFB3 and immune infiltration in 31 cancer species (Fig, 5A), for instance, glioblastoma and low-grade glioma (GBMLGG) (N = 656, p < 0.001), lower-grade glioma (N = 504, p < 0.001), BRCA (N = 1077, p < 0.001), LUAD (N = 500, p < 0.001), esophageal carcinoma (ESCA) (N = 181, p < 0.05), STES (N = 569, p < 0.05), SARC (N = 258, p < 0.01), KIRP (N = 285, p < 0.001), KIPAN (N = 878, p < 0.001), COAD (N = 282, p < 0.001), rectum adenocarcinoma (READ) (N = 373, p < 0.001), PRAD (N = 495, p < 0.001), STAD (N = 388, p < 0.001), HNSC (N = 517, p < 0.001), KIRC (N = 528, p < 0.001), LUSC (N = 491, p < 0.001), THYM (N = 118, p < 0.001), LIHC (N = 363, p < 0.001), skin cutaneous melanoma (SKCM) (N = 452, p < 0.05), BLCA (N = 405, p < 0.001), TCGA THCA (N = 503, p < 0.001), TARGET - neuroblastoma (N = 153, p < 0.001), mesothelioma (MESO) (N = 85, p < 0.001), TCGA - READ (N = 91, p < 0.001), OV (N = 416, p < 0.01), UVM (N = 79, p < 0.05), PAAD (N = 177, p < 0.001), acute myeloid leukemia (LAML) (N = 214, p < 0.001), PCPG (N = 177, p < 0.001), DLBC (N = 46, p < 0.05), KICH (N = 65, p < 0.001). In summary, the present results confirmed that high expression of PFKFB3 significantly promoted the invasion of multiple tumors, except LAML and SKCM. Therefore, controlling the high expression of PFKFB3 is expected to be an effective strategy for inhibiting tumor invasion.

According to the method described in section 2.5, we obtained six types of immune cell (B cell, CD4⁺ T cell, CD8⁺ T cell, Neutrophil, Macrophage and Dendritic cell) infiltration scores of 9,405 tumor samples out of 38 tumor types. Pearson's correlation coefficient between the PFKFB3 gene and immune cell infiltration scores for each tumor was calculated using the CorR. test function of the R software package Psych (version 2.1.6). As shown in Fig. 5B showed that the expression of PFKFB3 was significantly correlated with the infiltration of various immune cells in 38 cancer species, which might be a key factor leading to the poor prognosis of tumor survival.

3.6 Increased phosphoPFKFB3 was detected in certain cancers

Studies have shown that the phosphorylation of PFKFB3 is potentially associated with the progression of cancer and angiogenesis (4, 33–35) and remains a feature of various cancers (36). Hence, we compared the phosphorylation levels of PFKFB3 protein between primary tumor tissues and their normal controls using the CPTAC dataset (Fig. 6A). PFKFB3 phosphorylation was significantly increased in four types of cancers (LUAD, KIRC, PAAD, and HNSC) compared to the corresponding normal samples. The phosphor-PFKFB3 in GBM cells was also higher than that in the normal group, but the difference was not statistically significant.

3.7 Correlation between PFKFB3 expression and tumor stemness or RNA modification

Cancer progression is characterized by the progressive loss of the differentiated phenotype and acquisition of progenitor and stem cell-like features (24). The study of the correlation between PFKFB3 expression and the stemness of tumor cells remains an important step in the functional exploration of PFKFB3. Thus, we calculated Pearson correlations between PFKFB3 expression levels and tumor stemness in each tumor. The results illustrated that there was a significant correlation between the stemness of 14 tumors and PFKFB3 expression, including a significant positive correlation in 2 tumors, i.e., THYM (N = 119, p < 0.001), and THCA (N = 499, p < 0.001), and a significant inverse association in 12 tumors, including GBMLGG (N = 558, p < 0.05), LUAD (N = 451, p < 0.01), READ (N = 358, p < 0.01), BRCA (N = 774, p < 0.05), ESCA (N = 179, p < 0.05), STAD (N = 369, p < 0.05), LIHC (N = 366, p < 0.001), PAAD (N = 156, p < 0.001), TGCT (N = 147, p < 0.01), PCPG (N = 176, p < 0.05), BLCA (N = 403, p < 0.001), and CHOL (N = 36, p < 0.05) (Fig. 6B).

Specific chemical modifications of various biomolecules occur during tumorigenesis, and these modifications are effective in regulating molecular functions. Currently, many enzymes responsible for regulating protein and DNA modifications are targets for cancer therapy. However, RNA modifications (including m¹A, m⁵C, m⁶A) are now becoming a new frontier in this field (37). Increasing evidence suggests that RNA modification pathways are improperly regulated in human cancers, suggesting that it is an ideal target for cancer therapy (38). The key enzymes or binding proteins involved in RNA modification, named as writers, erasers, and readers, catalyze, eliminate, or recognize these modifications after translation (39). Therefore, in this study, we also explored the correlation between PFKFB3 expression in various tumors and the types of modifying enzymes in the process of RNA modification to understand the pathological functions of PFKFB3 and its association with tumor progression. The results shown in Fig. 6C revealed that the high expression of enzymes involved in RNA modification was significantly associated with PFKFB3 expression in almost 40 types of cancers.

3.8 PFKFB3 involved multiple protein interactions and molecular functions

The GeneMANIA database was used to create a protein-protein interaction (PPI) network for PFKFB3 (Fig. 7A). GO and KEGG analyses were performed to further investigate the function of PFKFB3 and its closely associated genes. The ADP metabolic process, pyruvate biosynthetic process, ATP production from ADP, and glycolytic process were the most abundant biological processes (BP) involved in the gene set according to GO analysis. Intramolecular transferase activity, intramolecular transferase activity, phosphotransferases, sugar-phosphatase activity, and carbohydrate phosphatase activity were connected to the enriched molecular functions (Fig. 7B). Furthermore, the glucagon signaling pathway, central carbon metabolism in cancer, and glycolysis/gluconeogenesis were the main enriched pathways according to the KEGG analysis (Fig. 7C). Therefore, the dysregulation of PFKFB3 and its related genes in the tumor environment may significantly alter various cellular metabolic processes, suggesting that PFKFB3 remains a vital factor involved in Warburg effect in tumors, and further promoting the progression of tumors.

3.9 Functional enrichment directed PFKFB3 into the pathways of kinase regulator activity

To explore the genes correlated with PFKFB3 expression in most tumors, the top 20 genes with the most similar expression pattern to PFKFB3 in a variety of tumors were detected based on GEPIA (Fig. 8A). These results suggest that they may act synergistically to promote the occurrence and progression of tumors. KEGG analysis showed that most of these genes were enriched in kinase regulator activity pathways (Fig. 8B). In addition, the expression of PFKFB3 in all tumors was positively correlated with each gene (Fig. 8C).

3.10 High mutation of PFKFB3 was detected in cancers

Mutations in PFKFB3 in various cancers were analyzed using the cBioPortal platform. A total of 39,532 patients from the PanCancer Studies database were analyzed. Amplification of PFKFB3 accounted for the largest proportion of all mutation types, of which bladder squamous cell carcinoma, ovarian epithelial tumor, breast invasive ductal carcinoma, and tubular STAD had the highest occurrence rates of 20%, 20%, 15.08%, and 12.15%, respectively (Fig. 9).

The increased glucose metabolism in tumor cells was mainly due to enhanced glycolysis and pentose phosphate pathways, thus meeting the bioenergy and biosynthetic requirements for rapid cell proliferation, namely the Warburg effect (40). The PFKFB family is a key enzyme in the synthesis of F-2,6-BP, and PFKFB3 is the most active isoenzyme in increasing the glycolytic and oncogenic properties of tumor cells (41).

Our study showed that PFKFB3 was differentially expressed in a cancer-dependent manner, that is, it was highly expressed in seven types of cancers, while it was less expressed in 10 types of cancers, which was partially confirmed by IHC. The results for PAAD (42–44), COAD (45, 46), STAD (47), HNSC (14, 48, 49), and THCA (50) were consistent with those of a previous study. The differential expression of PFKFB3 in KIRC, KICH, and KIRP was also consistent with a previous study by Li et al. (51). However, as in breast invasive carcinoma, our results showed that PFKFB3 expression was contradictory to the published studies, which demonstrated increased expression (7, 52, 53). This discrepancy might be because the samples analyzed in our study were derived from tumor metastases rather than in situ. The same discrepancy was also found in LIHC, LUAD, and PRAD; our findings challenge those of previous research (54–56). In addition, there are few studies on PFKFB3 in CHOL, KICH, KIRP, DLBC, LUSC, BLCA, and THYM, and our results may provide guidance for investigators to explore the role of PFKFB3 in these tumors.

Kaplan-Meier survival analysis in our study showed that high PFKFB3 expression was correlated with poor prognosis in ACC, KIRC, KIRP, KICH, STAD, COAD, SARC, UVM, CESC, and KIPAN. Similarly, PFKFB3 overexpression was previously reported to be correlated with shorter survival times in patients with pancreatic, gastric, and breast cancers (7, 44, 50, 57, 58). Previous studies have demonstrated the function of PFKFB3 in cancer stemness maintenance during the development of LIHC, malignant pleural mesothelioma, small cell lung carcinoma, ovarian cancer, and colorectal cancer (8, 59–62). In our study, we also clarified the correlation between the stemness of 14 tumors and PFKFB3 expression, including a significant positive correlation in two tumors (THYM and THCA) and a significant inverse association in 12 tumors (GBMLGG, LUAD, READ, BRCA, ESCA, STAD, LIHC, PAAD, TGCT, PCPG, BLCA, CHOL). In addition, we discovered that PFKFB3 expression is closely associated with tumor stage in COAD, KIRP, STAD, TGCT, THCA, CESC, OV, and PAAD cancers. These studies clearly demonstrate the great potential of PFKFB3 as a biomarker for predicting the prognosis of various cancers.

A previous study reported that PFKFB3 can induce cell invasion in several cancers (7, 29, 63). Our study demonstrated that the upregulation of PFKFB3 expression was significantly associated with cancer cell invasion, intravasation, and metastasis in up to 31 types of cancers, as well as the infiltration of 6 types of immune cells, illustrating that PFKFB3 is a promising predictor of tumor invasion and metastasis and may guide immunotherapy in the era of precision medicine.

Furthermore, our results explored the relationship between PFKFB3 phosphorylation, RNA modification, and cancer. Elevated phosphoPFKFB3 was found in several cancers, such as LUAD, KIRC, PAAD, and HNSC, demonstrating the potential role of phosphoPFKFB3 in cancer progression (4). The correlation between PFKFB3 expression and RNA modification also seems to be involved in the progression of almost 40 tumor types, which merits further study for the specific role of PFKFB3 in RNA modification.

The results of PPI network, GO, and KEGG analysis of PFKFB3 interacting molecules showed that they were mainly enriched in the ATP/ADP metabolic process, pyruvate biosynthetic process, and glycolytic and glucagon signaling pathways. These results suggest that metabolic dysregulation in tumors may be closely related to dysregulation of these genes. Moreover, the high frequency of PFKFB3 mutations in various tumors implies that PFKFB3 mutations may serve as a biomarker for some tumors, such as bladder squamous cell carcinoma, ovarian epithelial tumor, breast invasive ductal carcinoma, and tubular STAD.

In summary, this study revealed that dysregulation, mutation, or phosphorylation of PFKFB3 was significantly correlated with the staging and stemness of cancers, patient survival and prognosis, immune infiltration, and RNA modifications. The differential expression of PFKFB3 may serve as a potential biomarker and therapeutic target for the prevention or treatment of various cancers.

ACC	adrenocortical carcinoma
BLCA	bladder urothelial carcinoma
BRCA	breast invasive carcinoma
CESC	cervical squamous cell carcinoma
CHOL	cholangial carcinoma
COAD	colon adenocarcinoma
CTPAC	Clinical Proteomic Tumor Analysis Consortium
DFS	disease-free survival
DLBC	diffuse large B-cell lymphoma
DSS	disease-specific survival
ESCA	esophageal carcinoma
GBM	glioblastoma multiforme
GBMLGG	glioblastoma and low-grade glioma
GEO	Gene Expression Omnibus
GEPIA2	Gene Expression Profiling Interactive Analysis 2
GO	gene oncology
GTE	genotype-tissue expression
HNSC	head and neck squamous cell carcinoma
IHC	immunohistochemistry
KEGG	Kyoto Encyclopedia of Genes and Genomes
KICH	kidney chromophobe
KIRC	kidney renal clear cell carcinoma
KIRP	kidney renal papillary cell carcinoma
LAML	acute myeloid leukemia
LIHC	liver hepatocellular carcinoma
LUAD	lung adenocarcinoma
LUSC	lung squamous cell carcinoma
MESO	mesothelioma
OS	overall survival
OV	ovarian serous cystadenocarcinoma
PAAD	pancreatic adenocarcinoma
PFKFB3	6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3
PPI	protein-protein interaction
PRAD	prostate adenocarcinoma
RCC	renal cell carcinoma
READ	rectum adenocarcinoma
SARC	sarcoma
SKCM	skin cutaneous melanoma
STAD	stomach adenocarcinoma
TCGA	the Cancer Genome Atlas
TGCT	testicular germ cell tumors
THCA	thyroid carcinoma
THYM	thymoma
UCSC	the University of California Santa Cruz
UVM	uveal melanoma

Ethics approval and consent to participate: Not Applicable

Consent for publication: Not applicable, as the TCGA and GEO databases sharing their data on open access.

Availability of data and materials: The authors thank the contributors of the TCGA and GEO databases, and the authors for sharing their data on open access.

Competing interests: The authors declare no competing financial interests.

Funding: No funding support.

Authors' contributions: DL and YL: conceived and designed the study, performed data acquisition, bioinformatics analysis, and drafting and critical revision of the manuscript. CZ, JZ, and GTX contributed to the critical review of the manuscript. All authors contributed to the article and approved the submitted version

Acknowledgements: The authors thank the contributors of the TCGA and GEO databases, and the authors for sharing their data on open access.

Availability of data

Database	Website
TIMER2.0	http://timer.cistrome.org/
GEPIA2	http://gepia2.cancer-pku.cn
UALCAN database	http://ualcan.path.uab.edu/
UCSC database	https://xenabrowser.net/
GeneMANIA	http://www.genemania.org
cBioPortal	http://www.cbioportal.org/
HPA	https://www.Proteinatlas.org/
Kaplan-Meier plotter	http://kmplot.com/analysis/
TCGA	https://tcga-data.nci.nih.gov/
GEO	http://www.ncbi.nlm.nih.gov/geo/

Shi L, Pan H, Liu Z, Xie J, Han W. Roles of PFKFB3 in cancer. Signal transduction and targeted therapy 2017;2:17044 doi 10.1038/sigtrans.2017.44.
Icard P, Fournel L, Wu Z, Alifano M, Lincet H. Interconnection between Metabolism and Cell Cycle in Cancer. Trends in biochemical sciences 2019;44(6):490-501 doi 10.1016/j.tibs.2018.12.007.
Alvarez R, Mandal D, Chittiboina P. Canonical and Non-Canonical Roles of PFKFB3 in Brain Tumors. Cells 2021;10(11) doi 10.3390/cells10112913.
Bando H, Atsumi T, Nishio T, Niwa H, Mishima S, Shimizu C, et al. Phosphorylation of the 6-phosphofructo-2-kinase/fructose 2,6-bisphosphatase/PFKFB3 family of glycolytic regulators in human cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 2005;11(16):5784-92 doi 10.1158/1078-0432.Ccr-05-0149.
Herrero-Mendez A, Almeida A, Fernández E, Maestre C, Moncada S, Bolaños JP. The bioenergetic and antioxidant status of neurons is controlled by continuous degradation of a key glycolytic enzyme by APC/C–Cdh1. Nature Cell Biology 2009;11(6):747-52 doi 10.1038/ncb1881.
Chesney J, Telang S, Yalcin A, Clem A, Wallis N, Bucala R. Targeted disruption of inducible 6-phosphofructo-2-kinase results in embryonic lethality. Biochemical and biophysical research communications 2005;331(1):139-46 doi 10.1016/j.bbrc.2005.02.193.
Peng F, Li Q, Sun JY, Luo Y, Chen M, Bao Y. PFKFB3 is involved in breast cancer proliferation, migration, invasion and angiogenesis. International journal of oncology 2018;52(3):945-54 doi 10.3892/ijo.2018.4257.
Thirusangu P, Ray U, Sarkar Bhattacharya S, Oien DB, Jin L, Staub J, et al. PFKFB3 regulates cancer stemness through the hippo pathway in small cell lung carcinoma. Oncogene 2022;41(33):4003-17 doi 10.1038/s41388-022-02391-x.
De Bock K, Georgiadou M, Schoors S, Kuchnio A, Wong BW, Cantelmo AR, et al. Role of PFKFB3-driven glycolysis in vessel sprouting. Cell 2013;154(3):651-63 doi 10.1016/j.cell.2013.06.037.
Chen DP, Ning WR, Jiang ZZ, Peng ZP, Zhu LY, Zhuang SM, et al. Glycolytic activation of peritumoral monocytes fosters immune privilege via the PFKFB3-PD-L1 axis in human hepatocellular carcinoma. Journal of hepatology 2019;71(2):333-43 doi 10.1016/j.jhep.2019.04.007.
Peng ZP, Jiang ZZ, Guo HF, Zhou MM, Huang YF, Ning WR, et al. Glycolytic activation of monocytes regulates the accumulation and function of neutrophils in human hepatocellular carcinoma. Journal of hepatology 2020;73(4):906-17 doi 10.1016/j.jhep.2020.05.004.
Chen L, Zhao J, Tang Q, Li H, Zhang C, Yu R, et al. PFKFB3 Control of Cancer Growth by Responding to Circadian Clock Outputs. Sci Rep 2016;6:24324 doi 10.1038/srep24324.
Zhu W, Ye L, Zhang J, Yu P, Wang H, Ye Z, et al. PFK15, a Small Molecule Inhibitor of PFKFB3, Induces Cell Cycle Arrest, Apoptosis and Inhibits Invasion in Gastric Cancer. PloS one 2016;11(9):e0163768 doi 10.1371/journal.pone.0163768.
Li HM, Yang JG, Liu ZJ, Wang WM, Yu ZL, Ren JG, et al. Blockage of glycolysis by targeting PFKFB3 suppresses tumor growth and metastasis in head and neck squamous cell carcinoma. Journal of experimental & clinical cancer research : CR 2017;36(1):7 doi 10.1186/s13046-016-0481-1.
Yang J, Shi X, Yang M, Luo J, Gao Q, Wang X, et al. Glycolysis reprogramming in cancer-associated fibroblasts promotes the growth of oral cancer through the lncRNA H19/miR-675-5p/PFKFB3 signaling pathway. International journal of oral science 2021;13(1):12 doi 10.1038/s41368-021-00115-7.
Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK, et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 2017;19(8):649-58 doi 10.1016/j.neo.2017.05.002.
Thul PJ, Lindskog C. The human protein atlas: A spatial map of the human proteome. Protein science : a publication of the Protein Society 2018;27(1):233-44 doi 10.1002/pro.3307.
Menyhárt O, Nagy Á, Győrffy B. Determining consistent prognostic biomarkers of overall survival and vascular invasion in hepatocellular carcinoma. Royal Society Open Science 2018;5(12):181006 doi doi:10.1098/rsos.181006.
Györffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q, et al. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Research and Treatment 2010;123(3):725-31 doi 10.1007/s10549-009-0674-9.
Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell 2018;173(2):400-16.e11 doi 10.1016/j.cell.2018.02.052.
Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 2013;4:2612 doi 10.1038/ncomms3612.
Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer research 2017;77(21):e108-e10 doi 10.1158/0008-5472.Can-17-0307.
Zeng D, Ye Z, Shen R, Yu G, Wu J, Xiong Y, et al. IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures. Frontiers in immunology 2021;12:687975 doi 10.3389/fimmu.2021.687975.
Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 2018;173(2):338-54.e15 doi 10.1016/j.cell.2018.03.034.
Zuberi K, Franz M, Rodriguez H, Montojo J, Lopes CT, Bader GD, et al. GeneMANIA prediction server 2013 update. Nucleic acids research 2013;41(Web Server issue):W115-22 doi 10.1093/nar/gkt533.
Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics (Oxford, England) 2010;26(12):1572-3 doi 10.1093/bioinformatics/btq170.
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics : a journal of integrative biology 2012;16(5):284-7 doi 10.1089/omi.2011.0118.
Zuo S, Wei M, Zhang H, Chen A, Wu J, Wei J, et al. A robust six-gene prognostic signature for prediction of both disease-free and overall survival in non-small cell lung cancer. Journal of translational medicine 2019;17(1):152 doi 10.1186/s12967-019-1899-y.
Cantelmo AR, Conradi LC, Brajic A, Goveia J, Kalucka J, Pircher A, et al. Inhibition of the Glycolytic Activator PFKFB3 in Endothelium Induces Tumor Vessel Normalization, Impairs Metastasis, and Improves Chemotherapy. Cancer cell 2016;30(6):968-85 doi 10.1016/j.ccell.2016.10.006.
Lin S, Li Y, Wang D, Huang C, Marino D, Bollt O, et al. Fascin promotes lung cancer growth and metastasis by enhancing glycolysis and PFKFB3 expression. Cancer letters 2021;518:230-42 doi 10.1016/j.canlet.2021.07.025.
Wang B, Li D, Cherkasova V, Gerasymchuk M, Narendran A, Kovalchuk I, et al. Cannabinol Inhibits Cellular Proliferation, Invasion, and Angiogenesis of Neuroblastoma via Novel miR-34a/tRiMetF31/PFKFB3 Axis. Cancers 2022;14(8) doi 10.3390/cancers14081908.
Yalcin A, Solakoglu TH, Ozcan SC, Guzel S, Peker S, Celikler S, et al. 6-phosphofructo-2-kinase/fructose 2,6-bisphosphatase-3 is required for transforming growth factor β1-enhanced invasion of Panc1 cells in vitro. Biochemical and biophysical research communications 2017;484(3):687-93 doi 10.1016/j.bbrc.2017.01.178.
Lu C, Qiao P, Sun Y, Ren C, Yu Z. Positive regulation of PFKFB3 by PIM2 promotes glycolysis and paclitaxel resistance in breast cancer. Clinical and translational medicine 2021;11(4):e400 doi 10.1002/ctm2.400.
Galindo CM, Oliveira Ganzella FA, Klassen G, Souza Ramos EA, Acco A. Nuances of PFKFB3 Signaling in Breast Cancer. Clinical breast cancer 2022;22(4):e604-e14 doi 10.1016/j.clbc.2022.01.002.
Xing Z, Zhang Y, Liang K, Yan L, Xiang Y, Li C, et al. Expression of Long Noncoding RNA YIYA Promotes Glycolysis in Breast Cancer. Cancer research 2018;78(16):4524-32 doi 10.1158/0008-5472.Can-17-0385.
Hawkins LJ, Wang X, Xue X, Wang H, Storey KB. Phosphoproteomic Analysis of X enopus laevis Reveals Expression and Phosphorylation of Hypoxia-Inducible PFKFB3 during Dehydration. iScience 2020;23(10):101598 doi 10.1016/j.isci.2020.101598.
Barbieri I, Kouzarides T. Role of RNA modifications in cancer. Nature reviews Cancer 2020;20(6):303-22 doi 10.1038/s41568-020-0253-2.
Haruehanroengra P, Zheng YY, Zhou Y, Huang Y, Sheng J. RNA modifications and cancer. RNA biology 2020;17(11):1560-75 doi 10.1080/15476286.2020.1722449.
Zaccara S, Ries RJ, Jaffrey SR. Reading, writing and erasing mRNA methylation. Nat Rev Mol Cell Biol 2019;20(10):608-24 doi 10.1038/s41580-019-0168-5.
Koppenol WH, Bounds PL, Dang CV. Otto Warburg's contributions to current concepts of cancer metabolism. Nature reviews Cancer 2011;11(5):325-37 doi 10.1038/nrc3038.
Yi M, Ban Y, Tan Y, Xiong W, Li G, Xiang B. 6-Phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 and 4: A pair of valves for fine-tuning of glucose metabolism in human cancer. Molecular metabolism 2019;20:1-13 doi 10.1016/j.molmet.2018.11.013.
Ozcan SC, Mutlu A, Altunok TH, Gurpinar Y, Sarioglu A, Guler S, et al. Simultaneous inhibition of PFKFB3 and GLS1 selectively kills KRAS-transformed pancreatic cells. Biochemical and biophysical research communications 2021;571:118-24 doi 10.1016/j.bbrc.2021.07.070.
Richardson DA, Sritangos P, James AD, Sultan A, Bruce JIE. Metabolic regulation of calcium pumps in pancreatic cancer: role of phosphofructokinase-fructose-bisphosphatase-3 (PFKFB3). Cancer & metabolism 2020;8:2 doi 10.1186/s40170-020-0210-2.
Wang F, Qi XM, Wertz R, Mortensen M, Hagen C, Evans J, et al. p38γ MAPK Is Essential for Aerobic Glycolysis and Pancreatic Tumorigenesis. Cancer research 2020;80(16):3251-64 doi 10.1158/0008-5472.Can-19-3281.
Dianat-Moghadam H, Khalili M, Keshavarz M, Azizi M, Hamishehkar H, Rahbarghazi R, et al. Modulation of LXR signaling altered the dynamic activity of human colon adenocarcinoma cancer stem cells in vitro. Cancer cell international 2021;21(1):100 doi 10.1186/s12935-021-01803-4.
Ma H, Zhang J, Zhou L, Wen S, Tang HY, Jiang B, et al. c-Src Promotes Tumorigenesis and Tumor Progression by Activating PFKFB3. Cell reports 2020;30(12):4235-49.e6 doi 10.1016/j.celrep.2020.03.005.
He X, Cheng X, Ding J, Xiong M, Chen B, Cao G. Hyperglycemia induces miR-26-5p down-regulation to overexpress PFKFB3 and accelerate epithelial-mesenchymal transition in gastric cancer. Bioengineered 2022;13(2):2902-17 doi 10.1080/21655979.2022.2026730.
Chen L, Cao Y, Wu B, Cao Y. MicroRNA-3666 Suppresses Cell Growth in Head and Neck Squamous Cell Carcinoma Through Inhibition of PFKFB3-Mediated Warburg Effect. OncoTargets and therapy 2020;13:9029-41 doi 10.2147/ott.S251992.
Yang JG, Wang WM, Xia HF, Yu ZL, Li HM, Ren JG, et al. Lymphotoxin-α promotes tumor angiogenesis in HNSCC by modulating glycolysis in a PFKFB3-dependent manner. International journal of cancer 2019;145(5):1358-70 doi 10.1002/ijc.32221.
Atsumi T, Chesney J, Metz C, Leng L, Donnelly S, Makita Z, et al. High expression of inducible 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (iPFK-2; PFKFB3) in human cancers. Cancer research 2002;62(20):5881-7.
Li J, Zhang S, Liao D, Zhang Q, Chen C, Yang X, et al. Overexpression of PFKFB3 promotes cell glycolysis and proliferation in renal cell carcinoma. BMC Cancer 2022;22(1):83 doi 10.1186/s12885-022-09183-2.
O'Neal J, Clem A, Reynolds L, Dougherty S, Imbert-Fernandez Y, Telang S, et al. Inhibition of 6-phosphofructo-2-kinase (PFKFB3) suppresses glucose metabolism and the growth of HER2+ breast cancer. Breast Cancer Res Treat 2016;160(1):29-40 doi 10.1007/s10549-016-3968-8.
Wang B, Li D, Ilnytskyy Y, Kovalchuk I, Kovalchuk O. A miR-34a-guided, tRNA(i)(Met)-derived, piR_019752-like fragment (tRiMetF31) suppresses migration and angiogenesis of breast cancer cells via targeting PFKFB3. Cell death discovery 2022;8(1):355 doi 10.1038/s41420-022-01054-w.
Long Q, Zou X, Song Y, Duan Z, Liu L. PFKFB3/HIF-1α feedback loop modulates sorafenib resistance in hepatocellular carcinoma cells. Biochemical and biophysical research communications 2019;513(3):642-50 doi 10.1016/j.bbrc.2019.03.109.
Li X, Liu J, Qian L, Ke H, Yao C, Tian W, et al. Expression of PFKFB3 and Ki67 in lung adenocarcinomas and targeting PFKFB3 as a therapeutic strategy. Mol Cell Biochem 2018;445(1-2):123-34 doi 10.1007/s11010-017-3258-8.
Wang J, Li X, Xiao Z, Wang Y, Han Y, Li J, et al. MicroRNA-488 inhibits proliferation and glycolysis in human prostate cancer cells by regulating PFKFB3. FEBS open bio 2019;9(10):1798-807 doi 10.1002/2211-5463.12718.
Minchenko OH, Tsuchihara K, Minchenko DO, Bikfalvi A, Esumi H. Mechanisms of regulation of PFKFB expression in pancreatic and gastric cancer cells. World journal of gastroenterology 2014;20(38):13705-17 doi 10.3748/wjg.v20.i38.13705.
Han J, Meng Q, Xi Q, Wang H, Wu G. PFKFB3 was overexpressed in gastric cancer patients and promoted the proliferation and migration of gastric cancer cells. Cancer biomarkers : section A of Disease markers 2017;18(3):249-56 doi 10.3233/cbm-160143.
Gu Y, Ji F, Liu N, Zhao Y, Wei X, Hu S, et al. Loss of miR-192-5p initiates a hyperglycolysis and stemness positive feedback in hepatocellular carcinoma. Journal of experimental & clinical cancer research : CR 2020;39(1):268 doi 10.1186/s13046-020-01785-7.
Sarkar Bhattacharya S, Thirusangu P, Jin L, Staub J, Shridhar V, Molina JR. PFKFB3 works on the FAK-STAT3-SOX2 axis to regulate the stemness in MPM. British journal of cancer 2022 doi 10.1038/s41416-022-01867-7.
Jiang YX, Siu MKY, Wang JJ, Leung THY, Chan DW, Cheung ANY, et al. PFKFB3 Regulates Chemoresistance, Metastasis and Stemness via IAP Proteins and the NF-κB Signaling Pathway in Ovarian Cancer. Frontiers in oncology 2022;12:748403 doi 10.3389/fonc.2022.748403.
Yan S, Li Q, Li S, Ai Z, Yuan D. The role of PFKFB3 in maintaining colorectal cancer cell proliferation and stemness. Molecular biology reports 2022 doi 10.1007/s11033-022-07513-y.
Lei L, Hong LL, Ling ZN, Zhong Y, Hu XY, Li P, et al. A Potential Oncogenic Role for PFKFB3 Overexpression in Gastric Cancer Progression. Clinical and translational gastroenterology 2021;12(7):e00377 doi 10.14309/ctg.0000000000000377.

No competing interests reported.

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Systematic pan-cancer analysis identifies 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) as a biomarker of tumor invasion and metastasis, immunity, and prognosis

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Data collection:

2.2 Differential gene expression analysis

2.3 Human protein atlas analysis

2.4 Survival prognosis analysis

2.5 Correlation analysis of immune infiltration and PFKFB3 expression

2.6 Correlation analysis of tumor stemness or RNA modification and PFKFB3 expression

2.7 Interaction of PFKFB3 with functionally similar genes in normal samples

2.8 Similar genes detection in various cancers

2.9 Mutation analysis of PFKFB3 in various cancers

3. Results

3.1 Differential expression of PFKFB3 in normal tissues and various cancers

3.2 PFKFB3 expression is associated with the staging of tumors

3.3 The impact of PFKFB3 expression on the survival rate

3.5 PFKFB3 promoted immune infiltration among various cancers

3.6 Increased phosphoPFKFB3 was detected in certain cancers

3.7 Correlation between PFKFB3 expression and tumor stemness or RNA modification

3.8 PFKFB3 involved multiple protein interactions and molecular functions

3.9 Functional enrichment directed PFKFB3 into the pathways of kinase regulator activity

3.10 High mutation of PFKFB3 was detected in cancers

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

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