Bibliometric analysis of phosphoglycerate kinase 1 expression in breast cancer and its distinct upregulation in triple-negative breast cancer

Abstract

BACKGROUND

Phosphoglycerate kinase 1 (PGK1) has been identified as a possible biomarker for breast cancer (BC) and may play a role in the development and advancement of triple-negative BC (TNBC).

AIM

To explore the PGK1 and BC research status and PGK1 expression and mechanism differences among TNBC, non-TNBC, and normal breast tissue.

METHODS

PGK1 and BC related literature was downloaded from Web of Science Core Collection Core Collection. Publication counts, key-word frequency, cooperation networks, and theme trends were analyzed. Normal breast, TNBC, and non-TNBC mRNA data were gathered, and differentially expressed genes obtained. Area under the summary receiver operating characteristic curves, sensitivity and specificity of PGK1 expression were determined. Kaplan Meier revealed PGK1’s prognostic implication. PGK1 co-expressed genes were explored, and Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Disease Ontology applied. Protein-protein interaction networks were constructed. Hub genes identified.

RESULTS

PGK1 and BC related publications have surged since 2020, with China leading the way. The most frequent keyword was “Expression”. Collaborative networks were found among co-citations, countries, institutions, and authors. PGK1 expression and BC progression were research hotspots, and PGK1 expression and BC survival were research frontiers. In 16 TNBC vs non-cancerous breast and 15 TNBC vs non-TNBC datasets, PGK1 mRNA levels were higher in 1159 TNBC than 1205 non-cancerous breast cases [standardized mean differences (SMD): 0.85, 95% confidence interval (95%CI): 0.54-1.16, I² = 86%, P < 0.001]. PGK1 expression was higher in 1520 TNBC than 7072 non-TNBC cases (SMD: 0.25, 95%CI: 0.03-0.47, I² = 91%, P = 0.02). Recurrence free survival was lower in PGK1-high-expression than PGK1-low-expression group (hazard ratio: 1.282, P = 0.023). PGK1 co-expressed genes were concentrated in ATP metabolic process, HIF-1 signaling, and glycolysis/gluconeogenesis pathways.

CONCLUSION

PGK1 expression is a research hotspot and frontier direction in the BC field. PGK1 may play a strong role in promoting cancer in TNBC by mediating metabolism and HIF-1 signaling pathways.

Key Words: Phosphoglycerate kinase 1, Breast cancer, Triple-negative breast cancer, Bibliometric analysis, Computational pathology

Core Tip: The expression of phosphoglycerate kinase 1 (PGK1) in breast cancer (BC) was shown to be both a research hotspot and frontier of BC research. PGK1 mRNA levels were significantly higher in triple-negative BC (TNBC) than in non-cancerous breast tissue. Within the population with BC, PGK1 mRNA expression levels were distinctly upregulated in TNBC compared with non-TNBC. Recurrence-free survival was markedly lower in the PGK1-high-expression than the PGK1-low-expression group. PGK1 has a significant role in promoting TNBC through metabolism and HIF-1 signaling pathways.

INTRODUCTION

Breast cancer (BC) has become the most frequent malignancy to affect females. Based on the 2024 statistics from the American Cancer Society, BC accounted for 32.0% of the estimated new cases of female cancer, and the incidence of female BC has been said to exhibit a 0.6% annual growth rate[1]. Obtaining the best prognosis for BC patients is mainly dependent on early BC detection and intervention[2-8]. Thus, it is necessary that we explore more accurate BC biomarkers to improve methods for early, accurate identification and treatment to reduce mortality from BC[9-18].

BC is classified according to the presence or absence of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2). BC that does not express these receptors is classified as triple-negative BC (TNBC)[19-21], and this subtype accounts for approximately 12%-20% of all BC cases[22]. Compared with non-TNBC, TNBC has its own clinical characteristics: It is more common in younger women (< 50 years old), and it has higher degree of invasiveness, higher recurrence rate, shorter recurrence time, lower survival rate, and more BRCA1/2 gene mutations[23-27]. TNBC patients do not benefit from endocrine therapy, unlike ER/PR-positive cases, or from targeted therapy, as used for HER-2-positive cases[23-29]. The mechanisms of TNBC occurrence and development have been investigated by some researchers. Studies have found gene mutations, DNA repair defects, and non-coding RNA to be involved in the carcinogenic, proliferative, and invasive processes of TNBC. The Notch, Wnt/β-catenin, and Hedgehog signaling pathways have also been reported to be important contributors to incidences of TNBC[30-32]. Nevertheless, there are different opinions on the key mechanisms of TNBC[33,34], and many molecular events believed to be related to TNBC need more scientific attention.

Phosphoglycerate kinase 1 (PGK1) is a core enzymes of the glycolysis pathway[35], being involved in a reversible reaction that produces ATP during glycolysis[36], and plays a pivotal part in balancing energy production, redox, and biosynthesis[37]. The phenomenon of PGK1 overexpression has been observed in a variety of malignant tumors, e.g., hepatocellular carcinoma[38], non-small cell lung cancer[39], and pancreatic ductal adenocarcinoma[40]. The presence of higher PGK1 levels in BC tissue had been linked to a worse prognosis, and this trend is mirrored in TNBC[35,41-46]. This suggests that PGK1 could serve as a promising biomarker for BC and may play a role in the initiation and progression of TNBC.

In this research, we concentrated on employing bibliometric analysis to gain insights into the current status and future directions of PGK1 and BC studies, with a focus on comparing the expression levels and underlying mechanisms of PGK1 in BC vs normal breast tissue, as well as in TNBC vs non-TNBC, through computational pathology and various methods of high-throughput data mining.

MATERIALS AND METHODS

PGK1 and BC bibliometric analysis

Bibliometric analysis data sources: The Web of Science Core Collection (WOS) was used to search the scientific literature related to PGK1 and BC with the following search formula: (“PGK1” OR “phosphoglycerate kinase 1” OR “HEL-S-68p” OR “MIG10” OR “PGKA”) AND (“breast” OR “mammary” OR “nipple”) AND (“cancer” OR “tumor” OR “carcinoma” OR “neoplasm” OR “neoplasia” OR “malignancy” OR “malignant”). WOS was searched until September 3, 2022. The inclusion and exclusion criteria were as follows: (1) Including BC and PGK1 related studies, and excluding studies unrelated to PGK1; (2) Including English literature in WOS, and excluding literature in other languages (such as German, Spanish, etc.); (3) Including original research and review articles, and excluding comments, letters, and conference abstracts; and (4) Excluding retracted articles. The total number of papers irrelevant to BC and PGK1 was 39, as shown in Figure 1.

Figure 1 PRISMA flow diagram of the data screening procedure used in the current study. BC: Breast cancer; PGK1: Phosphoglycerate kinase 1.

Data processing methods: The Bibliometrix program in R language was employed to retrieve document data, draw the annual scientific publication curve and geographic distribution map of the corresponding authors; and making a word cloud, thematic plot, and trending topics. VOSviewer (version 1.6.18) was used to analyze the key word co-occurrence plot of the included BC- and PGK1-related literature. Using the leading Eigen clustering algorithm, cooperative networks of co-cited references, cooperative countries, university institutions, and authors were constructed.

Computational pathology associated with PGK1 in TNBC and non-TNBC

Transcriptome data for PGK1 mRNA levels in TNBC: The mRNA expression data for PGK1 in TNBC tissue were downloaded from the GEO, TCGA/GTEx, Metabric, ArrayExpress, and SRA databases. The first search term used was “breast cancer.” The pathological parameters of ER/PR/HER-2-negative patients were collected, including pathological diagnosis, molecular subtypes, and relevant follow-up data such as overall survival (OS), distant-metastasis-free survival (DMFS), and relapse-free survival (RFS). The search term was then changed to “triple-negative breast cancer” to collect relevant datasets relating to TNBC. The pathological parameters of each patient were also collected. The inclusion principles for transcriptome data of TNBC were as follows: (1) Primary TNBC tissue; and (2) Sample size ≥ 6. The following data sets were removed: (1) Animal and cell line data; (2) Relapse and metastatic TNBC; and (3) Duplicated samples. The molecular diagnosis of TNBC was provided by Pam50 molecular typing of the data sets. Data sets that provided ER/PR/HER-2 status without molecular typing were included if all were negative, otherwise they were excluded. A PRISMA flow diagram of data set filtering is presented in Figure 2.

Figure 2 PRISMA flow diagram of the screening procedure used for triple-negative breast cancer data sets including phosphoglycerate kinase 1 expression. BC: Breast cancer; PGK1: Phosphoglycerate kinase 1; TNBC: Triple-negative breast cancer; SMD: Standardized mean differences.

Data integration: Expression data from data sets obtained using the same platform were combined after removing batch effects using the limma-voom and sva packages. Global BC data sets were used to pinpoint upregulated genes in TNBC tissue (i.e., TNBC tissue vs non-cancerous breast tissue, and TNBC vs non-TN BC tissue). The criteria were [standardized mean differences (SMD)] > 0 and P < 0.05.

Protein levels of PGK1 in The Human Protein Atlas: The Human Protein Atlas (THPA) (https://www.proteinatlas.org/) is one of the largest human protein maps in the world and includes immunohistochemical results from tissue chips. This research initially examined variations in the location and intensity of PGK1 protein expression between normal breast cells and BC cells with the assistance of the THPA database.

Prognostic analysis of PGK1 in TNBC patients: TNBC patients were grouped using median PGK1 expression. TNBC samples were assigned to a PGK1-overexpression group and PGK1-underexpression group, according to the ideal cutoff point, using survminer v0.4.9 and ggplot2 v3.3.6 packages. Kaplan-Meier analysis was used to filter for candidate prognostic factors in TNBC patients. The hazard ratios (HRs) for PGK1 expression in the different TNBC cohorts were integrated using the meta v4.18.2 package.

Prospective mechanisms of PGK1’s effect in TNBC: Genes co-expressed with PGK1 were identified in TNBC tissue samples. The criteria for the co-expressed genes were as follows: (1) Pearson r ≥ 0.3; and (2) P < 0.05. The intersections of overexpressed genes in TNBC vs non-TNBC and genes co-expressed with PGK1 were taken for further signaling pathway exploration. Analyses of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) were accomplished with Clusterprofiler v4.4.2 to investigate the potential function of PGK1 in TNBC. STRING v11.5 was used to construct protein-protein interaction (PPI) networks. Cytoscape v3.9.0 was used to identify hub genes.

Statistical analysis

A Wilcoxon test was applied to evaluate the differential expression of PGK1 in the BC subgroups (i.e., BC vs non-cancerous breast tissue, TNBC vs non-cancerous breast tissue, and TNBC vs non-TNBC). Violin plots and receiver operating characteristic curves (ROCs) were drawn. Statistical analysis results with P < 0.05 were regarded as being significant.

RESULTS

PGK1 and BC bibliometric analysis results

Analysis of the published literature: Although the number of studies on PGK1 in BC was small, the annual number of PGK1 and BC publications has shown an upward trend since 2005 (Figure 3A). The numbers of articles published in 2020 and 2021 (8 and 9, respectively) were significantly increased from previous years, indicating that the interest of researchers in the link between PGK1 and BC had gradually increased.

Figure 3 Global scientific research on phosphoglycerate kinase 1 and breast cancer. A: Annual scientific production. Abscissa is the year, and ordinate is number of articles; B: Geographic distribution of corresponding authors. The more articles, the redder the area on the map. Horizontal and vertical coordinates represent longitude and latitude, respectively.

Furthermore, national and regional analysis of the literature suggested that the authors of PGK1-related BC studies were from 15 countries, with 20 articles from China, 5 from the United States, and 2 from Japan, as shown in Figure 3B. A total of 33 journals have published literature on this topic. The four journals with the highest number of articles appear in Table 1.

Table 1 Journals with the most published articles.

Journal	Number
Journal of Proteome Research	3
Scientific Reports	3
Frontiers in Cell and Developmental Biology	2
Journal of Translational Medicine	2

Key word analysis: The key words with the highest frequency, as defined by the author and added to WOS, appear in Table 2. Figure 4A shows a word cloud, and Figure 4B shows a key word co-occurrence plot. The term “expression” emerged as the most prevalent key word and served as a pivotal hub in the key word co-occurrence relationship, underscoring its significance in studies linking PGK1 and BC. Other key words with a high-frequency of co-occurrence relationships included “progression”, “gene”, and “survival”, showing these have been the focus of BC research. Key words closely related to PGK1 co-occurrence included “survival”, “progression”, “invasion”, and “glycolysis”.

Figure 4 Key words. A: Word cloud of key words. Word frequency was counted according to the Web of Science key words for each document. The word cloud plot is randomly colored. A larger font size indicates a higher frequency; B: Key word co-occurrence plot for breast cancer and phosphoglycerate kinase 1 research. Web of Science key words were clustered using VOSviewer algorithm. Different colors represent different clusters. Font size positively correlates with total co-occurrent numbers. Edges indicate co-occurrence relationships.

Table 2 Key words with the highest frequency.

Key word	Frequency
Expression	13
Progression	8
Gene	7
Survival	7
Breast cancer	6

Analysis of cooperation network: To complete the cooperative network analysis, first, a co-citation network (Figure 5A) was visualized. Three co-citation clusters were formed with Grandjean G, Ahmad SS, and Warburg O as the core authors, among which Sun S had the highest number of co-citations and was at the center of the co-citation network. Second, data on collaborations between countries were calculated, as shown in Figure 5B. The country that published the most papers was China, while working collaboratively with the United States. A network of cooperation was formed by Canada, India, Japan, Malaysia, and Singapore. France, Tunisia, and Qatar were also involved in cooperations.

Figure 5 Cooperation network analysis for research on breast cancer and phosphoglycerate kinase 1. A: Co-citation network; B: Collaborations among countries; C: University collaborations; D: Author collaborations. The co-cited documents and cooperating countries, universities, and authors were clustered using a “leading Eigen” algorithm. Different colors represent different clusters. Node size is positively correlated with the degree of connectivity of each node in the network. Edges indicate a co-citation or collaborative relationship.

Regarding university collaborations (Figure 5C), a number of Chinese and American academic institutions, including Shanghai Jiao Tong University, Capital Medical University, Washington State University, Nanjing Medical University, and the University of Texas MD Anderson Cancer Center, had established tight-knit collaborative networks. Another partnership cluster consisted of Laboratory Immunooncol Molecular, Institut Supérieur de Biotechnologie de Monastir, Qatar Foundation, etc. However, as a whole, university research institutions did not form close cooperative relationships.

Author collaborations also led to the formation of different cooperation clusters (Figure 5D). Jiang Y had the largest node area, indicating that he had the highest degree of connectivity in the author cooperation network.

Citation analysis: The most relevant authors and the H-indexes of authors are shown in Tables 3 and 4, respectively. The authors with the highest number of publications and H-indexes were Jiang Y and Zhang X. In the most cited documents (Table 5), Zhang et al[44] showed that PGK1 was overexpressed in tissues and cell lines with receptor tyrosine kinase ErbB2 (HER-2/NEU)-positivity, particularly HER-2/NEU1-positive BC in which PGK1 exhibited even higher levels of expression. Its expression was related to the state of the HER-2/NEU signal pathway. Shashni et al[47] believed that the generation of ATP in the cytoplasm and mitochondria decreased with the downregulation of PGK1, which initiated cell apoptosis and inhibited tumor metabolism. Qian et al[48] reported that PGK1 was overexpressed in an invasive ductal carcinoma of the breast. The most cited sources can provide research directions for scientific and technological efforts in the BC- and PGK1-related fields (Table 6)[48].

Table 3 Most relevant authors in breast cancer and phosphoglycerate kinase 1 literature.

Authors	Number of articles	Articles fractionalized
Zhang X	5	0.84
Jiang Y	5	0.47
Wang J	4	0.73
Li Y	4	0.59
Aryal UK	3	0.62
Mittal L	3	0.62
Sundararajan R	3	0.62
Lu Z	3	0.28
Liang Y	2	0.50
Ye T	2	0.50

Table 4 Top authors by H-index in breast cancer and phosphoglycerate kinase 1 literature.

Authors	H-index	G-index	M-index	Total citations	Number of publications	Publication year
Jiang Y	3	4	0.375	256	4	2015
Zhang X	3	4	0.375	79	4	2015
Wang J	3	4	0.250	35	4	2011
Aryal UK	3	3	0.750	42	3	2019
Mittal L	3	3	0.750	42	3	2019
Sundararajan R	3	3	0.750	42	3	2019
Li Y	2	3	0.500	53	3	2019
Camarillo IG	2	2	0.667	27	2	2020
Liang Y	2	2	0.667	15	2	2020
Ye T	2	2	0.667	15	2	2020

Table 5 Most cited documents in breast cancer and phosphoglycerate kinase 1 literature.

Ref.	Journal	DOI	Local citations	Global citations
Zhang et al[44], 2005	Mol Cell Proteomics	10.1074/mcp.M400221-MCP200	9	244
Shashni et al[47], 2013	J Drug Target	10.3109/1061186X.2012.736998	4	33
Qian et al[48], 2017	Mol Cell	10.1016/j.molcel.2017.01.027	3	133
Kabbage et al[53], 2008	J Biomed Biotechnol	10.1155/2008/564127	2	25
Fu et al[35], 2018	Life Sci	10.1016/j.lfs.2020.117863	2	11

Table 6 Most cited sources of breast cancer and phosphoglycerate kinase 1 research.

Sources	Number of articles
Cancer Research	96
Journal of Biological Chemistry	65
Proceedings of the National Academy of Sciences of the United States of America	38
Clinical Cancer Research	36
Cell	35

Thematic analysis: In the thematic plot, the horizontal axis shows a higher degree of importance from left to right, and the vertical axis displays a better degree of development from bottom to top. PGK1 and BC research topics that have attracted the most attention are “expression”, “gene”, “apoptosis”, “survival”, “invasion”, and “protein” as shown in Figure 6A.

Figure 6 Thematic plot of research on breast cancer and phosphoglycerate kinase 1 and trends in topics related to breast cancer and phosphoglycerate kinase 1 research. A: Using Web of Science key words, various research themes were detected via co-occurrence analysis and walktrap clustering analysis. Different colors represent different themes. Density represents the connection strength of basic knowledge units within a single topic. A larger topic density value indicates greater topic maturity. Centrality represents the strength of the connection between a topic and other topics. A higher centrality value of a topic suggests a strong interconnection with other topics, placing the topic at the heart of the research landscape. According to the density and centripetal degree values, the rectangular coordinate system was divided into four quadrants. The first quadrant contains core themes of high maturity; the second quadrant has isolated themes of high maturity, and the third quadrant contains new themes or themes that are about to disappear. The fourth quadrant encompasses basic themes of low maturity that may become research hotspots or future research trends; B: Topic trends were detected using co-word analysis. For each topic, the quartiles of publication year were calculated. The horizontal axis represents the first and third quantile years. Each node refers to the median publication year of each term. The larger the node, the more frequent the term.

The trends among topics demonstrated once again that the key words with the highest attention since 2005 have been “expression”, “progress”, “survival”, and “gene”, etc. According to this trend, new hot topics in PGK1 and BC in the last two years have been “survival”, “protein”, “invasion”, and “biomarkers”, etc. (Figure 6B).

Computational pathology results relating to PGK1 expression in TNBC and non-TNBC

mRNA expression levels of PGK1 in TNBC tissue: In a preliminary investigation, 31 high-throughput data sets from 7543 BC tissue samples were compared with 1685 non-cancerous breast tissue samples, and our analysis revealed the definitive overexpression of PGK1 in BC, with an SMD of 0.76 and area under the curve (AUC) of 0.8525 for the summary ROC (sROC) (Figures 7 and 8). Tables 7 and 8 showed the included data sets for TNBC vs non-cancerous breast tissue, and TNBC vs non-TNBC tissue, respectively. Upon analyzing 16 data sets comprising 1159 cases of TNBC and 1205 cases of non-cancerous breast tissue, a clear upregulation of PGK1 mRNA expression in TNBC was observed compared to non-cancerous tissue (Figure 9A), with an SMD of 0.85 and 95% confidence interval (95%CI) of 0.54-1.16, as shown in Figure 10A. More importantly, we compared PGK1 expression across 1520 cases of TNBC and 7072 cases of non-TNBC for the first time and found that TNBC exhibited higher PGK1 mRNA expression than non-TNBC tissue in 9 out of the 15 datasets analyzed (Figure 9B). As Figure 10B shows, the comparative difference in expression was obvious (SMD: 0.25, 95%CI: 0.03-0.47).

Figure 7 Expression levels of phosphoglycerate kinase 1 in breast cancer tissue vs normal breast tissue. A: mRNA expression level; B: Standardized mean differences; C: Summary receiver operating characteristic curves. ^aP < 0.05; ^bP < 0.01; ^cP < 0.001; ^dP < 0.0001. BC: Breast cancer; sROC: Summary receiver operating characteristic curve; AUC: Area under the curve; Sen: Sensitivity; Spe: Specificity; SMD: Standardized mean difference; 95%CI: 95% confidence interval.

Figure 8 Receiver operating characteristic curves. A-C: Receiver operating characteristic curves of phosphoglycerate kinase 1 expression in breast cancer tissue vs normal breast tissue. AUC: Area under the curve.

Figure 9 mRNA expression level of phosphoglycerate kinase 1 in triple-negative breast cancer. A: Triple-negative breast cancer (TNBC) tissue vs normal breast tissue; B: TNBC tissue vs non-TNBC tissue. TNBC: Triple-negative breast cancer. ^aP < 0.05; ^bP < 0.01; ^cP < 0.001; ^dP < 0.0001.

Figure 10  Comprehensive expression levels of phosphoglycerate kinase 1 in triple-negative breast cancer. A and C: Triple-negative breast cancer (TNBC) tissue vs normal breast tissue; B and D: TNBC tissue vs non-TNBC tissue. SMD: Standardized mean difference; 95%CI: 95% confidence interval; sROC: Summary receiver operating characteristic curve; AUC: Area under the curve; Sen: Sensitivity; Spe: Specificity.

Table 7 Included data sets for triple-negative breast cancer tissue vs non-cancerous breast tissue.

ID	Series
GPL1390	GSE10885-GPL1390, GSE10886, GSE10893-GPL1390		GSE1992-GPL1390, GSE2607-GPL1390, GSE6128
GPL13607	GSE59246		GSE70951-GPL13607
GPL17077	GSE57297		GSE80754
GPL17586	GSE115144	GSE73540	GSE76250	GSE134359
GPL570	GSE20711	GSE50567	GSE22544	GSE146558
	GSE45827	GSE61304	GSE25407	GSE103865
	GSE65194	GSE42568	GSE26910	GSE153796
	GSE29431	GSE5764	GSE54002	GSE135565
	GSE7904	GSE10780	GSE71053	GSE7307
	GSE31448	GSE10810	GSE147472	GSE3744
	GSE29044	GSE21422	GSE140494
GPL6244	GSE36295	GSE61724		GSE118432
	GSE86374	GSE37751		GSE81838
GPL6848	GSE26304		GSE18672
GPL8269	GSE22384-GPL8269		GSE41119
GPL887	GSE10885-GPL887		GSE24124
	GSE2607-GPL887		GSE9309
GPL96	GSE15852	GSE5364	GSE158309	GSE6883-GPL96
GSE29174
GSE41970-GPL16299
GSE50428
GSE64790
GSE92252
TCGA_TNBC

TNBC: Triple-negative breast cancer.

Table 8 Included data sets for triple-negative breast cancer tissue vs non- triple-negative breast cancer tissue.

ID	Series
GPL1390	GSE10885-GPL1390
GPL13607	GSE59246
GPL17077	GSE57297
GPL17586	GSE115144
GPL570	GSE20711	GSE29044	GSE10810	GSE54002	GSE103865
	GSE45827	GSE50567	GSE21422	GSE71053	GSE153796
	GSE65194	GSE61304	GSE22544	GSE147472	GSE135565
	GSE29431	GSE42568	GSE25407	GSE140494	GSE7307
	GSE7904	GSE5764	GSE26910	GSE146558	GSE3744
	GSE31448	GSE10780
GPL6244	GSE36295
GPL6848	GSE26304
GPL8269	GSE22384-GPL8269
GPL887	GSE10885-GPL887
GPL96	GSE15852
GSE148425
GSE29174
GSE50428
METABRIC_mRNA_Zscores
TCGA TNBC-nonTNBC

TNBC: Triple-negative breast cancer.

The overexpression of PGK1 mRNA had an excellent ability to distinguish TNBC from non-cancerous breast tissue (AUC of sROC = 0.8578, sensitivity = 0.7637, and specificity = 0.8240, Figure 11A and Figure 10C). In Figure 12, the 13 ROC charts of TNBC vs non-cancerous breast tissue indicated that six data sets yielded an AUC of more than 0.7. Figure 11B showed that PGK1’s ability to discern TNBC from non-TNBC tissue was moderate (AUC of sROC = 0.688, sensitivity = 0.7571, and specificity = 0.4754; Figure 10D, Figures 13-15).

Figure 11  Receiver operating characteristic curves for phosphoglycerate kinase 1 expression in triple-negative breast cancer. A: Triple-negative breast cancer (TNBC) tissue vs normal breast tissue; B: TNBC tissue vs non-TNBC tissue. AUC: Area under the curve.

Figure 12  Receiver operating characteristic curves. A-M: Receiver operating characteristic curves for phosphoglycerate kinase 1 expression in triple-negative breast cancer vs normal breast tissue. AUC: Area under the curve.

Figure 13  Receiver operating characteristic curves. A-L: Receiver operating characteristic curves for phosphoglycerate kinase 1 expression in triple-negative breast cancer vs non-triple-negative breast cancers. AUC: Area under the curve.

Figure 14  Funnel plots and forest plots for phosphoglycerate kinase 1 expression in breast cancer. A-C: Publication bias detection based on breast cancer (BC) vs normal breast tissue, triple-negative BC (TNBC) vs normal breast tissue, and TNBC vs non-TNBC tissue; D-F: Sensitivity analysis based on BC vs normal breast tissue, TNBC vs normal breast tissue, and TNBC vs non-TNBC tissue.

Figure 15  Forest plots of positive likelihood ratio (LRpos) and negative likelihood ratio (LRneg) for phosphoglycerate kinase 1 expression based on different groups. A and B: Breast cancer (BC) vs normal breast tissue; C and D: Triple-negative BC (TNBC) vs normal breast tissue; E and F: TNBC vs non-TNBC tissue.

Protein expression level of PGK1 in BC tissue: The THPA database showed that, in glandular epithelial cells of non-cancerous breast tissue and tumor cells, PGK1 was expressed in nuclear and cytoplasmic membranes. BC tumor cells showed diffuse but strong expression of PGK1 (Figure 16). However, owing to the restricted number of cases available in the public database, it was not feasible to conduct statistical analyses. In the future, we will increase the number of cases included and continue to verify the clinical significance of PGK1 protein expression.

Figure 16  Immunohistochemistry staining from the Human Protein Atlas showing low or no expression of phosphoglycerate kinase 1 in normal breast tissue cells, and high expression in breast cancer cells. A: HPA045385, female, age 45, breast (T-04000), normal tissue, NOS (M-00100), Patient ID: 3544 (low or no expression). (1) Adipocytes. Staining: Not detected; Intensity: Weak; Quantity: < 25%; Location: cytoplasmic/membranous; (2) Glandular cells. Staining: Low; Intensity: Weak; Quantity: > 75%; Location: cytoplasmic/membranous; and (3) Myoepithelial cells. Staining: Low; Intensity: Weak; Quantity: > 75%; Location: Cytoplasmic/membranous; B: HPA073644, female, age 43, breast (T-04000), skin (T-01000), normal tissue, NOS (M-00100), Patient ID: 2104 (low or no expression). Adipocytes, glandular cells, and myoepithelial cells. Staining: Not detected; Intensity: Negative; Quantity: None; C: CAB010065, female, age 59, breast (T-04000), lobular carcinoma (M-85203), Patient ID: 2805 (high expression). (1) Tumor cells. Staining: High; Intensity: Strong; Quantity: 75%-25%; Location: Cytoplasmic/membranous nuclear; D: HPA045385, female, age 83, breast (T-04000), duct carcinoma (M-85003), Patient ID: 2160 (high expression). (1) Tumor cells. Staining: High. Intensity: Strong. Quantity: 75%-25%. Location: Cytoplasmic/membranous nuclear. These immunohistochemical protein expression figures were from the Human Protein Atlas (THPA) database [71].

Prognostic value of PGK1 in TNBC patients: Compared with the PGK1-low-expression group, the PGK1-high-expression group had a substantially shorter RFS time (HR: 1.282, P = 0.023). Similar trends were observed for OS and DMFS, indicating that higher expression of PGK1 is detrimental to the prognosis of TNBC, as shown in Figure 17.

Figure 17  Kaplan-Meier curves for association between phosphoglycerate kinase 1 expression of triple-negative breast cancer patients. A: Overall survival; B: Recurrence-free survival; C: Distant metastasis free survival. OS: Overall survival; RFS: Recurrence-free survival; DMFS: Distant metastasis free survival; HR: Hazard ratio.

Potential functional mechanisms of PGK1 in TNBC: GO enrichment analysis demonstrated that genes co-expressed with PGK1 participate in biological processes that include “generation of precursor metabolites and energy”, “ATP metabolic process”, and “ATP metabolic process” (Figure 18A). Simultaneously, the co-expressed genes were notably enriched in the following KEGG pathways: “Carbon metabolism”, “Proteasome”, “HIF-1 signaling pathway”, “Central carbon metabolism in cancer”, “Cell cycle”, “Glycolysis/Gluconeogenesis”, and “Pentose phosphate pathway”, shown in Figure 18B. DO enrichment analysis showed that BC was the disease most correlated with PGK1 (Figure 18C). The major pathways relevant to the genes co-expressed with PGK1 suggest that PGK1 is closely related to metabolism and HIF-1 signaling.

Figure 18  Pathway and disease enrichment analysis annotation. A: Gene Ontology enrichment; B: Kyoto Encyclopedia of Genes and Genomes enrichment. Columnar length represents the number of gene enrichments; the longer the column, the more gene enrichment. Color indicates the size of the P value; the darker the color, the smaller the P value; C: Disease Ontology enrichment. The area of each circle represents the number of genes; the larger the circle, the more genes are involved. The color of each circle represents the P value; the darker the color, the smaller the P value. GO: Gene Ontology enrichment; KEGG: Kyoto Encyclopedia of Genes and Genomes.

The PPI network revealed 10 co-expressed genes closely related to PGK1, and TPI1 was a key gene among these 10 genes (Figure 19). The intersections of highly expressed genes between TNBC vs non-TNBC and genes co-expressed with PGK1 in TNBC (occurrence frequency ≥ 4) were shown in Figure 20.

Figure 19  Protein-protein interaction network analysis of phosphoglycerate kinase 1 related genes.

Figure 20  Intersection of triple-negative breast cancer vs non-triple-negative breast cancer gene overexpression and positively phosphoglycerate kinase 1 correlated gene co-expression in triple-negative breast cancer (occurrence ≥ 4). UP: Upregulated gene; CEG: Co-expressed gene.

DISCUSSION

There are few research documents on PGK1 and TNBC. Therefore, in this study, we concentrated on exploring the scientific knowledge gathered on BC and PGK1 in the literature in WOS with bibliometric methods to reveal the foci of recent research and suggest future research directions.

This investigation found that the total volume of PGK1 and BC related literature had been relatively small since 2005, but the number of annual publications had increased significantly from 2020, reflecting a growing interest among researchers in this area. More relevant articles came out of China than any other country. The Chinese researchers Jiang Y and Zhang X ranked first and second in terms of the number of publications and H-index. China had been an international leader in PGK1 research in BC. Different aggregations were formed among co-citations, countries, universities, and authors in the cooperation network. Although several close regional cooperative relationships had formed, some countries and institutions had not joined a cooperation cluster, and cooperation among cluster groups was also less interconnection. A lack of comprehensive collaboration might hinder communication and progress across the entire research domain. Key word analysis demonstrated that researchers were most concerned about the expression of PGK1 in BC and its link with BC progression. In the top three most cited papers, Zhang et al[44] and Qian et al[48] reported PGK1 overexpression separately in HER-2/NEU1-positive BC and invasive breast ductal carcinoma, and Shashni et al[47] believed that ATP generation and cancer metabolism could be affected by PGK1 downregulation. The journals with the most articles were the Journal of Proteome Research and Scientific Reports, and Cancer Research was the most cited journal. Overall, hot topics in PGK1 and BC were “expression” and “progression”. BC survival, invasion, and PGK1 proteins were several new areas of attention in this field that have emerged in the past two years.

This research gathered 31 high-throughput datasets on BC tissue (7543 cases) and non-cancerous breast tissue (1685 cases) and confirmed that PGK1 exhibited a markedly higher expression state in BC (SMD: 0.76; AUC of sROC = 0.8525), which was consistent with the results reported in previous items of literature. Li et al[42] compared 112 non-cancerous tissues and 1096 BC tissues and found that BC had stronger PGK1 expression than non-cancerous breast tissue. Similarly, Shao et al[49] found a rise in PGK1 mRNA levels in 145 BC tissues, which that contrasted with those of 69 non-cancerous tissues, and PGK1 mRNA was inversely correlated with the OS of BC. He et al[50] also compared 1066 BC and 112 non-cancerous samples and reached a conclusion in line with the above reports. Moreover, the expression level of PGK1 protein was shown to be increased in BC tissues compared with non-cancerous tissues.

The expression of PGK1 also depends on the type of BC[51]. Our study, for the first time, revealed that PGK1 expression was increased in TNBC and surpassed the levels found in both normal breast tissue and non-TNBC. Furthermore, genes co-expressed with PGK1 had been shown to be enriched in “generation of precursor metabolites and energy”, “ATP metabolic process”, and “response to hypoxia”. In KEGG pathways analysis, they were enriched in the pathways of “proteasome”, “HIF-1 signaling”, and “glycolysis/gluconeogenesis”.

It was discovered that the mRNA expression levels of 1159 TNBC tissue cases were noticeably higher than those of 1205 non-cancerous breast tissue cases in 16 high-throughput data sets, with an SMD of 0.85 and integrated AUC of 0.8578. This indicated that the increase in PGK1 mRNA might influence the occurrence of BC, including TNBC.

More crucially, this study also found that PGK1 might play different roles in promoting TNBC and non-TNBC. When compared with non-TNBC, TNBC samples showed much higher expression of PGK1, with the SMD of 15 data sets (including 1520 cases of TNBC and 7072 cases of non-TNBC) being 0.25, and the integrated AUC being 0.688. Previously, PGK1 was reported to be associated with the status of HER-2/ER. PGK1 expression in HER-2-positive patients (37 out of 88 cases) and ER-positive patients (70 out of 167 cases) was upregulated compared with that in the HER-2-negative group (74 out of 254 cases) and ER-negative group (66 out of 234 cases)[43]. One study also mentioned that, compared with the HER-2-positive group (19 cases), the TNBC group’s (15 cases) mRNA and protein levels of PGK1 were down-regulated[52]. These findings suggested that PGK1 expression was down-regulated in BC with ER/HER-2 negative status and TNBC. Through an analysis of large data sets combining global gene chip and RNA-seq data from multi-center studies (1520 cases of TNBC and 7072 cases of non-TNBC), it was finally concluded that, though PGK1 in TNBC had both high and low expression, PGK1 showed an overall trend of increased expression in TNBC.

The prognostic analysis showed that increased PGK1 expression was closely associated with a shorter RFS in TNBC cases, and a similar pattern was observed for OS, indicating that high PGK1 expression was detrimental to the survival prognosis of TNBC patients. Therefore, we concluded that PGK1 was overexpressed in TNBC compared to normal breast tissue and non-TNBC, and that its upregulation correlated with more adverse prognoses in patients with TNBC, implying a potential role for PGK1 in tumor progression.

In this study, GO and KEGG enrichment analyses demonstrated that genes co-expressed with PGK1 were related to glycolysis, hypoxia, and HIF-1 pathways, which was reflected in previous findings in the literature. PGK1 had been shown to not only be a crucial glycolytic enzyme in ATP generation during glycolysis but also to act as a protein kinase to suppress pyruvate metabolism in the mitochondria and enhance the glycolysis of tumor cells[53].

Obviously, the metabolism of BC cells, including TNBC cells, undergoes changes. Under aerobic conditions, normal cells obtain energy by oxidative phosphorylation (OXPHOS) in the mitochondria. In hypoxia, cells acquire energy via the metabolic pathway of glycolysis rather than mitochondrial processes that consume oxygen[54]. Despite the availability of sufficient oxygen, malignant tumor cells continue to fulfill their metabolic demands through the glycolytic mode rather than OXPHOS, and this phenomenon is referred to aerobic glycolysis or the Warburg effect[55,56]. The generation of tumor stromal blood vessels lags behind growth rate of tumor cells, and oxygen delivery is unable to keep up with the rapid growth and proliferation of tumor cells, putting tumor cells in a continuous hypoxic microenvironment, with interrupted mitochondrial OXPHOS and a shift in energy metabolism to aerobic glycolysis[57]. Tumor cells that utilize glycolysis would then have a survival advantage in a rapidly proliferating hypoxic environments[58]. In studies, TNBC tissue displays higher glucose uptake and lactic acid (glycolytic product) secretion, and more dependence on glycolysis than other BC subtypes[59]. PGK1, as the initial rate-limiting enzyme of the cellular glycolytic pathway to ATP synthesis, directly supplies energy for cellular activities, while the other reaction product, 3-PG, is oxidized by one-carbon metabolism. This is probably the reason why PGK1 is more significant than other glycolysis enzymes in contributing to the Warburg effect in tumors[56,60,61]. Several glycolytic enzyme genes, including PGK1, were reported to be upregulated by HIF-1 induction under a hypoxic microenvironment, providing a molecular basis for the Warburg effect in tumor cells[57,62]. In this way, processes, including cell growth, metastasis, drug resistance, and immune evasion, are regulated via the Warburg effect. The high levels of PGK1 in cancerous tissues could potentiate the Warburg effect, thereby influencing the progression and prognosis of both BC and TNBC.

The HIF-1/PGK1 pathway is one of the most prominent pathways in cancer cells[63]. PGK1 has been identified as a target gene of HIF-1[64] and is capable of establishing a positive-feedback loop with HIF-1[45]. HIF-1 can detect hypoxia within the tumor microenvironment, increase the expression of glucose transporters, activate genes involved in cancer glycolysis metabolism, and drive glucose toward glycolysis in cancerous cells, the latter of which is a pivotal regulatory step in the metabolic shift of tumor cells from oxidative phosphorylation to glycolysis[65-67]. Under hypoxia stress, protein receptor activation, or carcinogenic gene mutation expression, about 12% of cytoplasmic PGK1 is translocated by the accumulated HIF-1 to mitochondria, where PGK1 contributes to protein kinase activity. Pyruvate is diverted from the mitochondria into the cytoplasm to produce lactic acid, inhibiting the mitochondrial tricarboxylic acid cycle and enhancing glycolysis to support the energy and metabolite requirements of cancerous cells[64,68]. Additionally, HIF-1α expression is correlated with an ER-negative status[69]. The expression of HIF-1 was shown to be upregulated in TNBC and associated with a poorer prognosis, and it also affected the level of PGK1[66]. Regarding TNBC treatments, Sun et al[70] found that PGK1 expression inhibition increased the sensitivity of TNBC to paclitaxel treatment. Therefore, as PGK1 is a latent biomarker for different therapy options for TNBC, inhibiting its expression might represent a new therapeutic strategy.

CONCLUSION

Research into PGK1, a potential biomarker in BC, has made great advancements recently. PGK1 expression levels in TNBC are subject to regulation by upstream HIF-1 and are evidently upregulated compared to those in non-TNBC and non-cancerous breast tissue. PGK1 has been shown to enhance glycolysis in TNBC. Therefore, we believe that PGK1 provides a valuable marker for predicting the disease progression of TNBC. How to inhibit the occurrence and progress of TNBC through PGK1 needs further in-depth studies. There were some limitations to our study. Our bibliometric analysis was accomplished with a single data base, and specific BC histological classifications were not assigned to the cases in the non-TNBC group.

ACKNOWLEDGEMENTS

The authors would like to thank “Guangxi Zhuang Autonomous Region Clinical Medicine Research Center for Molecular Pathology and Intelligent Pathology Precision Diagnosis” for the technical support, and the public data from the Human Protein Atlas.