Next Article in Journal
(-)-Syringaresinol Exerts an Antidepressant-like Activity in Mice by Noncompetitive Inhibition of the Serotonin Transporter
Previous Article in Journal
Evaluation of Hippocampal Microanatomy and Neuro-Biomarkers Following Administration of Silver Nanoparticles Conjugated with Tenofovir Disoproxil Fumarate in Experimental Diabetic Rats
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Pharmaceuticals
Volume 17
Issue 12
10.3390/ph17121636
pharmaceuticals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Identification of a Potential PGK1 Inhibitor with the Suppression of Breast Cancer Cells Using Virtual Screening and Molecular Docking

by
Xianghui Chen
1,2,
Zanwen Zuo
3,
Xianbin Li
4,
Qizhang Li
3,* and
Lei Zhang
1,2,3,5,*
1
School of Medicine, Shanghai University, Shanghai 200444, China
2
Department of Pharmaceutical Botany, School of Pharmacy, Naval Medical University, Shanghai 200433, China
3
Innovative Drug Research Center, College of Life Sciences, Huaibei Normal University, Huaibei 235000, China
4
School of Computer and Big Data Science, Jiujiang University, Jiujiang 332000, China
5
Key Laboratory of Plant Secondary Metabolism and Regulation of Zhejiang Province, College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China
*
Authors to whom correspondence should be addressed.
Pharmaceuticals 2024, 17(12), 1636; https://doi.org/10.3390/ph17121636
Submission received: 14 November 2024 / Revised: 28 November 2024 / Accepted: 2 December 2024 / Published: 5 December 2024
(This article belongs to the Section Pharmacology)
Browse Figures
Versions Notes

Abstract

:
Background/Objectives: Breast cancer is the second most common malignancy worldwide and poses a significant threat to women’s health. However, the prognostic biomarkers and therapeutic targets of breast cancer are unclear. A prognostic model can help in identifying biomarkers and targets for breast cancer. In this study, a novel prognostic model was developed to optimize treatment, improve clinical prognosis, and screen potential phosphoglycerate kinase 1 (PGK1) inhibitors for breast cancer treatment. Methods: Using data from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified in normal individuals and breast cancer patients. The biological functions of the DEGs were examined using bioinformatics analysis. A novel prognostic model was then constructed using the DEGs through LASSO and multivariate Cox regression analyses. The relationship between the prognostic model, survival, and immunity was also evaluated. In addition, virtual screening was conducted based on the risk genes to identify novel small molecule inhibitors of PGK1 from Chemdiv and Targetmol libraries. The effects of the potential inhibitors were confirmed through cell experiments. Results: A total of 230 up- and 325 down-regulated DEGs were identified in HER2, LumA, LumB, and TN breast cancer subtypes. A new prognostic model was constructed using ten risk genes. The analysis from The Cancer Genome Atlas (TCGA) indicated that the prognosis was poorer in the high-risk group compared to the low-risk group. The accuracy of the model was confirmed using the ROC curve. Furthermore, functional enrichment analyses indicated that the DEGs between low- and high-risk groups were linked to the immune response. The risk score was also correlated with tumor immune infiltrates. Moreover, four compounds with the highest score and the lowest affinity energy were identified. Notably, D231-0058 showed better inhibitory activity against breast cancer cells. Conclusions: Ten genes (ACSS2, C2CD2, CXCL9, KRT15, MRPL13, NR3C2, PGK1, PIGR, RBP4, and SORBS1) were identified as prognostic signatures for breast cancer. Additionally, results showed that D231-0058 (2-((((4-(2-methyl-1H-indol-3-yl)-1,3-thiazol-2-yl)carbamoyl)methyl)sulfanyl)acetic acid) may be a novel candidate for treating breast cancer.

1. Introduction

Breast cancer is the most prevalent cancer among women [1]. Breast cancer exhibits high heterogeneity and is classified into four subtypes (luminal A, luminal B, HER-2 enriched (HER2+), and triple-negative (TN) breast cancer) [2]. As a result, the clinical management of breast cancer subtypes, including diagnosis, treatment, and prognosis, is different. Luminal breast cancer (luminal A and B) comprises approximately 70% of breast cancer cases. The HER2+ subtype is sensitive to targeted therapies. TN breast cancer accounts for about 10–15% of breast cancer cases and is the most aggressive subtype. Nonetheless, early detection, diagnosis, and treatment can improve cure rates and reduce mortality rates related to breast cancer. Additionally, understanding breast cancer can improve therapeutic outcomes for patients. Also, accurate and reliable novel molecular biomarkers are essential for predicting the prognosis of breast cancer patients.
The advancement of sequencing technology has allowed for the collection and storage of extensive gene expression data from different molecular subtypes of breast cancer. This information is available in various databases, including the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). The microarray data in the database reflects the general conditions of expression information targeting several genes. Microarray analysis can be used to acquire the expression information and explore differentially expressed genes (DEGs) during the occurrence and development of diseases based on bioinformatics techniques [3]. Furthermore, the role of DEGs in the development of various cancers, such as gastric cancer [4], craniopharyngioma [5], bladder cancer [6], etc., has been studied using bioinformatics techniques. After DEGs screening, some essential genes are identified as potential biomarkers that provide physicians with additional clinical insights. Some key genes can be identified as potential biomarkers through DEGs screening. Several potential biomarkers for triple-negative [7,8], HER2+ [9,10], and luminal [11] breast cancer have been detected via DEGs screening. Moreover, several key DEGs have been identified between different breast cancer subtypes [12,13]. Nevertheless, the characteristic signature genes targeting the four basic breast cancer subtypes have not been detected. Therefore, a prognostic model can be used to determine the overall risk of death for breast cancer patients, thus optimizing treatment and improving clinical prognosis. Although surgery, chemoradiotherapy, and targeted therapy can improve the long-term survival of some breast cancer patients, many patients have poor long-term efficacy and are prone to relapse, mainly due to drug resistance [14]. Therefore, effective prognostic markers and new practical therapeutics are needed based on these markers to enhance breast cancer treatment.
In the present study, a robust risk signature commonly found in four molecular subtypes of breast cancer was developed via integrated bioinformatics analysis. The key DEGs in each subtype were identified via the RRA method. Ten risk genes were screened using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and multivariate Cox regression analysis. Based on these genes, a prognostic risk model was then developed. Kaplan–Meier (KM) survival analysis showed that the prognostic model had robust predictive performance. The accuracy of the model was confirmed via receiver operating characteristic (ROC) curve analysis. A nomogram was constructed to facilitate the use of the risk-prognosis model. Notably, phosphoglycerate kinase 1 (PGK1) is believed be an effective therapeutic target [15]. Therefore, novel PGK1 inhibitors were detected via molecular docking and virtual screening.

2. Results

2.1. DEGs Identification and Functional Enrichment Analysis

Five gene expression datasets containing normal samples of HER2, LumA, LumB, and TN subtype tumor samples were included after conducting a search in the GEO database. DEGs between the breast tumor samples and the corresponding normal samples were then identified (Table S1). RRA analysis was conducted to identify differentially expressed genes from different datasets (p < 0.05 and |logFC| > 0.5). A total of 1801, 1428, 1421, and 1887 DEGs were found in HER2, LumA, LumB, and TN groups, respectively. The top 20 up-regulated and down-regulated genes consistently expressed in the different profiles are shown in Figure 1A–D. The Venn plots demonstrated that 230 up- and 325 down-regulated DEGs were common across the four subtypes (Figure 1E). Functional enrichment analysis using gene ontology (GO) terms and pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) was conducted for these genes. GO enrichment analysis identified 511 terms related to the DEGs, including 453 biological process (BP) terms, 26 cellular component (CC) terms, and 31 molecular function (MF) terms (Tabel S3). The top 10 BP, CC, and MF terms are shown in Figure 1F (endothelium development; cell–substrate adhesion; and endothelial cell differentiation in BP; collagen-containing extracellular matrix; lipid droplet; and platelet alpha granule in CC; and glycosaminoglycan binding; sulfur compound binding; and heparin-binding in MF). KEGG pathway analysis revealed nine pathways related to the DEGs (Tabel S4), including extracellular matrix (ECM)-receptor interaction, peroxisome proliferator-activated receptor (PPAR), leukocyte transendothelial migration, regulation of lipolysis in adipocytes, cytoskeleton organization in muscle cells, focal adhesion, proteoglycans in cancer, the PI3K-Akt signaling pathway, and malaria signaling pathways (Figure 1G).

2.2. Independent Prognostic Model Analysis

The RNA-seq dataset and clinical information from TCGA were combined to obtain more information. Patients who survived for less than 30 days and lacked the required data were excluded. The patients were divided into a training set (to identify possible prognostic genes) and a test set (for validation). Univariate Cox regression analysis revealed that 33 DEGs, comprising 14 high- and 19 low-risk genes, were significantly associated with survival (p < 0.05) (Figure 2A). LASSO regression analysis was performed, and a 15-gene signature was created (Figure 2B). Multivariate Cox regression identified ten genes, including four high- and six low-risk genes (ACSS2 (acyl-CoA synthetase short-chain family member 2), C2CD2 (C2 calcium-dependent domain containing 2), CXCL9 (C-X-C motif chemokine ligand 9), KRT15 (keratin 15), MRPL13 (mitochondrial ribosomal protein L13), NR3C2 (nuclear receptor subfamily 3 group c member 2), PGK1 (phosphoglycerate kinase 1), PIGR (polymeric immunoglobulin receptor), RBP4 (retinol-binding protein 4), and SORBS1 (sorbin and SH3 domain-containing 1)) (Table 1, Figure 2C). The heatmap of gene expressions from TCGA is shown in Figure 2D. The gene levels from GEO datasets were expressed as boxplots in Figure S1. Risk scores were calculated using expression levels and coefficients of 10 genes: Risk score = (−0.3552 × ACSS2 expression) + (0.7737 × C2CD2 expression) + (−0.2091 × CXCL9 expression) + (−0.1919 × KRT15 expression) + (0.3990 × MRPL13 expression) + (−0.4672 × NR3C2 expression) + (0.6295 × PGK1 expression) + (−0.2308 × PIGR expression) + (0.2766 × RBP4 expression) + (−0.4613 × SORBS1 expression). The patients in the training set were categorized into high- and low-risk groups according to the median prognostic risk score (0.9669) to assess the prognostic value of the risk signature. The KM survival curve demonstrated that the survival rate was lower in the high-risk group compared to the low-risk group (p = 9.65 × 10−10; Figure 2E), indicating that the risk score effectively predicted the prognosis of breast cancer patients.
The prognostic ability of the model was evaluated using the area under the curve (AUC) of the ROC. The model had high sensitivity and specificity (AUC value, 0.873) in the training set (Figure 2F). The model was validated using the test and entire datasets, and similar results were obtained (Figure 2G–J and Figure S2). Univariate Cox regression analysis related to clinical characteristics revealed that age, tumor status, T status, N status, and risk score were significantly associated with overall survival (OS) in the complete dataset (Figure 2K). Furthermore, multivariate Cox analysis showed that age, tumor status, and risk score had excellent independent prognostic value (Figure 2L).
The accuracy of the model and predicting the 1-, 3-, and 5-year overall survival was assessed via the ROC curve. The AUC of the risk score model was 0.859, outperforming clinical characteristics (Figure 2M), demonstrating the robust predictive capability of the model. Furthermore, the AUC values of the model in predicting 1-, 3-, and 5-year survival were 0.853, 0.711, and 0.675, respectively, indicating a good predictive ability of the prognostic model (Figure 2N). The model performed better when the area under the curve was larger. The findings indicate that the developed prognostic risk model can be used to assess breast cancer prognosis.

2.3. Correlation Between Risk Score and Clinical Characteristics of Breast Cancer Patients

The clinicopathological characteristics, including survival state, age, and T stage, were compared between the two risk groups. Survival state (p < 0.001), age (p < 0.01), and T stage (p < 0.05) were significantly different between the high- and low-risk groups (Figure 3A). Meanwhile, gene expression patterns were determined to assess the correlation between the genes in the model and risk scores. The results showed that PGK1, MRPL13, and C2CD2 were overexpressed in the high-risk group, while ACSS2, CXCL9, KRT15, NR3C2, PIGR, RBP4, and SORBS1 were highly expressed in the low-risk group. Furthermore, survival analysis of the entire TCGA cohort demonstrated that the low-risk group had a significantly better prognosis than the high-risk group across all clinically stratified subgroups, including age, clinical stage, N stage, and T stage (Figure 3B).

2.4. Nomogram Construction

Nomogram is widely used to quantify the results of Cox regression equations [16]. Herein, a nomogram was developed to predict the 3-year, 5-year, and 10-year overall survival status of breast cancer patients based on independent prognostic analysis (Figure 4A). Age, clinical stage, T and N stages, and risk score were treated as variables. The total score was calculated by adding the score of each factor which has a specific score (according to the regression coefficient). A higher score value indicates a higher mortality risk and poorer patient survival. The prediction model was calibrated via calibration curves (Figure 4B). The calibration lines were close to the optimal calibration lines, indicating that the model had good predictive accuracy in specific years.

2.5. Functional Enrichment Analyses

GO and KEGG analyses were conducted to further confirm the biological mechanisms underlying the different prognoses observed between the low- and high-risk groups. A total of 1953 DEGs were found with adjusted p < 0.05 and |logFC| > 0.5 between low- and high-risk groups (Figure 5A). GO functional enrichment showed that these DEGs were related to the regulation of T cell activation, humoral immune response, and lymphocyte differentiation for biological process (BP); external side of the plasma membrane, collagen-containing extracellular matrix, and intermediate filament for cellular component (CC); and cytokine activity, structural constituent of chromatin, and cytokine receptor binding for molecular function (MF) (Figure 5B). Furthermore, KEGG showed that the DEGs were related to pathways involving hematopoietic cell lineage, primary immunodeficiency, systemic lupus erythematosus, Th1 and Th2 cell differentiation, and intestinal immune network for IgA production (Figure 5C). Additionally, the GSEA results suggested that these DEGs were mainly related to chemical carcinogenesis, including DNA adducts (normalized enrichment score (NES) = 0.63, adjusted p < 0.01), cell cycle (NES = 0.44, p < 0.05), T cell receptor signaling pathway (NES = −0.56, p < 0.01), and Th1 and Th2 cell differentiation (NES = −0.54, p < 0.01) (Figure 5D). These results suggest that these DEGs are associated with the immune response.

2.6. Tumor Immune Infiltration Correlation Analyses

The relationship between prognostic risk and the infiltration of immune cells was also assessed. Compared with the low-risk group, the infiltration scores of most immune cells activated dendritic cells (aDCs), B cells, CD8+ T cells, dendritic cells (DCs), interdigitating dendritic cells (iDCs), mast cells, neutrophils, natural killer (NK) cells, plasmacytoid dendritic cells (pDCs), T helper cells, T follicular helper (Tfh), T helper (Th) 1 cells, Th2 cells, tumor-infiltrating lymphocytes (TIL), and regulatory T cells (Treg) were significantly decreased in the high-risk group (p < 0.05; Figure 6A). In addition, levels of APC_co-stimulation, CCR, check-points, cytolytic activity, HLA, inflammation-promoting factors, MHC_class_I, parainflammation, T_cell_co-inhibition, T_cell_co-stimulation, and Type_II_IFN_Response were higher in the low-risk group than in the high-risk group (p < 0.05; Figure 6B). Further results showed that ICB expression increased in the low-risk subgroup compared to the high-risk group (Figure 6C). These findings indicate that immune activation and immune status are significantly different between the two groups. Specifically, the low-risk group exhibited a stronger immune microenvironment and a better prognosis than the high-risk group. Therefore, patients in the high-risk group may have weakened immunity, which may result in a lower responsiveness to immunotherapy.

2.7. Identification of Novel Inhibitors of PGK1

The therapeutic potential for targeting the risk genes was investigated. Studies have shown that PGK1 is a promising therapeutic target for breast cancer treatment [17]. Herein, compounds that can bind to PGK1 were detected through virtual screening. A total of 1,620,219 compounds were screened from Chemdiv and Targetmol libraries via High Throughput Virtual Screening (HTVS) mode of Glide. The top 10% of compounds were then screened phase in SP mode. Next, 1267 compounds were identified in the XP mode. The rescoring of the relative binding affinity of ligand and protein was performed by MMGBSA (molecular mechanics generalized born surface area). The four best compounds with the highest score and the lowest affinity energy (<−16 kcal/mol) were selected based on the Quantitative Estimate of Drug-likeness (QED) value, pan-assay interference compounds, and docking scores (Table 2). D715-2871 formed two hydrogen bonds (bond lengths: 1.8 Å and 1.9 Å) with three amino acids (Thr376 and Ala215), four salt bridges, and a coordinate bond (Figure 7A). Y040-8304 formed three hydrogen bonds with three amino acids (Ala215, Thr376, and Asp375), four salt bridges, and two coordinate bonds (Figure 7B). D715-0344 formed six hydrogen bonds with five amino acids (Ala215, Lys220, Asp375, Thr376, and Asn337), two salt bridges, and a coordinate bond (Figure 7C). D231-0058 formed five non-covalent bonds with PGK1, including two hydrogen bonds, two salt bridges, and one coordinate bond (Figure 7D).

2.8. D231-0058 Inhibits the Growth of Breast Cancer Cells

These compounds were used to treat breast cancer T-47D and MCF-7 cells for validating the antitumor effects of the selected compounds. D715-2871 and Y040-8304 showed weaker inhibition activities, 100 μg/mL D715-0344 showed a better inhibition activity for MCF-7 cells, and D231-0058 exhibited significant inhibitory activity against tumor cells T-47D and MCF-7 (Figure 8A). The half maximal inhibitory concentration (IC50) of D231-0058 was determined via CCK8 assays. The IC50 values of T-47D cells at 24 and 48 h were similar. The IC50 values in MCF-7 cells were 28.66 μg/mL for 24 h and 18.88 μg/mL for 48 h, respectively (Figure 8B). Cell morphology changes were also observed (Figure 8C).

3. Discussion

Breast cancer is the most common malignancy among women worldwide, with a high mortality rate. Surgery, chemotherapy, radiation therapy, endocrine therapy, targeted therapy, and immunotherapy are the major treatment methods for breast cancer. Early detection can reduce breast cancer-related mortality [1]. Bioinformatics analyses are widely used for basic tumor research, clinical treatment, and prognostic prediction. Developing polygenic models that target genes for prognosis is promising for the diagnosis, treatment, and preventing cancers. Many prognostic models have been developed for various cancer types, including breast cancer [18,19]. Breast cancer is a highly heterogeneous neoplasm, and thus has different diagnosis, treatment, and prognosis methods. Many models have been constructed for breast cancer subtypes such as HER2+ [20], triple-negative [21], and ER+/HER2− [22]. In the present study, a prognostic model was developed using DEGs.
A single gene expression dataset has limited sample information and thus data from multiple datasets are necessary. Robust rank aggregation (RRA) analysis is widely used to integrate various gene expression datasets and identify critical genes [23]. RRA is used to compare different rankings of genes in an unbiased manner. Zhong et al. identified 194 high-ranking DEGs in triple-negative breast cancer (TNBC) compared with non-TNBC using four GEO datasets of TNBC patients [7]. Sun et al. identified 494 key DEGs in four gene expression profiles from GEO via the RRA method [24]. Herein, DEGs were identified from different subtypes through integrated analysis using the RRA method and Venn analysis for enrichment analysis. KEGG signaling pathway analysis showed that these genes were significantly enriched in ECM–receptor interaction, PPAR signaling pathway, and leukocyte transendothelial migration. The ECM–receptor interaction pathway is found to be a critical signaling pathway involved in promoting breast cancer cell metastasis [25]. The PPAR pathway and leukocyte transendothelial migration are activated in breast cancer [26,27].
LASSO and COX regressions are widely used to establish prognostic models [28,29]. Herein, a ten-gene risk signature, ACSS2, C2CD2, CXCL9, KRT15, MRPL13, NR3C2, PGK1, PIGR, RBP4, and SORBS1, was developed. ACSS2 catalyzes acetyl-CoA synthesis and provides a nucleocytosolic source for acetyl-CoA, thereby playing a key role in epigenetic regulation and metabolic homeostasis [30]. ACSS2 also plays a crucial role in cell growth in various cancers, such as breast cancer [31]. Therefore, ACSS2 is a promising target for inhibitor development [32]. C2CD2 is a prognostic biomarker for breast cancer in bioinformatics [33]. CXCL9, a chemokine, promotes lymphocytic infiltration in solid tumors [34]. CXCL9 overexpression results in T cell accumulation and improved survival in ovarian cancer, acting as a predictive biomarker [35]. Additionally, CXCL9 is a potential clinical biomarker in ER-negative [36] and triple-negative [37] breast cancer. KRT15 is a type I cytoskeletal protein [38]. KRT15 has been detected in the bulges of hair follicles. KRT15 is involved in the growth and differentiation of hair follicles as well as the growth of the lacrimal gland [39,40]. KRT15 has different functions in cancers. KRT15 can promote the migration and invasion of colorectal cancer cells [41]. However, a low expression level of KRT15 is significantly linked to a poorer prognosis in breast cancer [42]. MRPL13, belonging to the MRP family, is involved in mitochondrial protein translation and is essential for mitochondrial bioenergetic and metabolic processes [43]. Many reports have shown that MRPL13 is involved in tumorigenesis [44,45]. MRPL13 promotes cell proliferation, migration, and epithelial–mesenchymal transition (EMT) in breast cancer by activating the PI3K/AKT/mTOR signaling pathway [46]. Also, MRPL13 is a prognostic biomarker [47,48]. NR3C2 is involved in regulating electrolyte balance and blood pressure [49]. In addition, NR3C2 can suppress cell development [50] and is a prognostic target in breast cancer [51,52]. PGK1 is a crucial component involved in the glycolysis process [53]. PGK1 catalyzes 1,3-bisphosphoglycerate to ADP, producing 3-phosphoglycerate and ATP. In addition, studies have demonstrated that PGK1 participates in tumor development. Elevated succinate concentration enhances PGK1 succinylation, inhibiting the degradation of PGK1 and increasing lactate generation [54]. Reduced succinate and lactate levels can also inhibit tumor growth of glioblastoma. PGK1 down-regulation inhibits cell proliferation, invasion, and metastasis in breast cancer in vitro and in vivo [55]. PGK1 is an effective therapeutic target in the diagnosis, prognosis, and treatment of cancers, including breast cancer [15]. PIGR is widely expressed in epithelial cells and functions as a transporter for the transcytosis of polymeric immunoglobulins [56]. Furthermore, PIGR is a biomarker in many types of cancer [57]. Several studies have indicated that PIGR has prognostic value in breast cancer [58,59]. Nevertheless, the functions of PIGR in breast cancer should be further investigated. RBP4 is involved in transporting retinol (vitamin A) and other retinoid derivatives in circulation and is mainly secreted by the liver and adipose tissue [60]. RBP4 is also identified as a biomarker for diagnosing and prognosing hepatocellular carcinoma [61], gastric cancer [62], and breast cancer [63]. The SORBS1 gene, encoding Cbl-associated protein (CAP), plays an important role in insulin signaling [64] and regulates cell proliferation and invasion [65]. SORBS1 reduces cell proliferation, invasion, and migration in breast cancer through the regulation of miR-142-5p [66]. SORBS1 also blocks epithelial–mesenchymal transition by regulating PI3K/AKT signaling [67]. In addition, SORBS1 is an independent prognostic marker for breast cancer [68].
DEGs in breast cancers are majorly enriched in immune-related terms, suggesting that many immune-related signaling pathways are abnormally regulated in breast cancers [69]. Tumor immunotherapy has developed rapidly in recent years. Breast cancer is a non-immunological disease, and immunotherapy has an unsatisfactory effect in the early stages [70,71], which is a big challenge. Therefore, combinatorial strategies are useful in such cases. For example, progranulin (PGRN) is involved in many physiological processes [72] and is also related to poor prognosis [73]. Fang et al. found that PGRN can promote M2 polarization and PD-L1 expression of macrophages by activating the STAT3 pathway, thereby promoting the immune escape of breast tumors through PD-1/PD-L1 interaction [74]. This suggests that the combination of PGRN targeting and PD-L1/PD-1 inhibitors can improve the efficacy of clinical immunotherapy for breast cancer. Therefore, potential targets should be identified to improve breast cancer treatment.
Molecular docking technology is crucial in computer-aided drug design. This technology allows the screening of specific molecules with good drug-forming properties from bulk compound libraries. PGK1 is up-regulated in breast cancer and is a promising therapeutic target [75]. However, only a few PGK1 inhibitors have been reported. PGK1 generated ATP in the glycolytic pathway by converting 1,3-diphosphoglycerate (1,3-BPG) and ADP into 3-phosphoglycerate (3-PG) [76]. Terazosin can occupy the ADP-binding pocket and inhibit PGK1, which is a competitive inhibitor [77]. In one study, two novel small molecules (CHR-6494 and Z57346765) targeting the ADP-binding pocket were identified via virtual screening [78]. Liao et al. identified an ATP-competitive inhibitor of PGK1, DC-PGKI (ethyl 6,7-dichloro-3-(4-((4-(piperazin-1-yl)phenyl)carbamoyl) piperazin-1-yl) quinoxaline-2-carboxylate), through a high-throughput screening platform (IC50 value; 0.16 μM) in lung cancer cells [79]. This strategy has also been used in this study (Figure S3). The ADP-binding pocket is crucial for the activity of the metabolic enzyme PGK1. Herein, small-molecule compounds were screened from Chemdiv and Targetmol libraries via virtual screening, and finally, the top four compounds with the highest score and the lowest affinity energy were selected. Interestingly, the top three (D715-2871, Y040-8304, and D715-0344) have the same backbone structure (belonging to a class of compounds distinct from the side chains). The compounds have a 7-hydroxy-coumarin structure. D715-2871 has two methyl groups at C3 and C4 of coumarin, Y040-8304 has a butyl group at C4 and a methyl group at C8, and D715-0344 has an ethyl group at C4. The compounds also have identical 7-OH substituents, a propionamide bonded to an N atom with succinic acid. The carboxyl groups of succinic acid are reactive groups. D231-0058 has a completely different structure from the other three, except for the reactive group. In addition, some non-ATP-competitive inhibitors of PGK1 have been developed. GQQ-792, a thiodiketopiperazine derivative, binds to cysteine residues near the ATP-binding pocket [80]. Ilicicolin H is also a non-ATP-competitive inhibitor and binds to non-ATP binding sites [81]. The binding energy is used to evaluate the docking results where the smaller binding energy indicates a more stable conformation [82]. Z57346765 is a commercial PGK1 inhibitor with a binding energy below −9.515 kcal/mol. In this study, the compounds had lower binding energies, with only D231-0058 exhibiting better inhibitory effects against breast cancer cells. Therefore, the PGK1 inhibitor is a promising candidate for the treatment of breast cancer.

4. Materials and Methods

4.1. Compounds and Cells

Breast cancer cell lines T-47D and MCF-7 were obtained from the China Center for Type Culture Collection in Wuhan, China. They were cultivated in RPMI1640, supplemented with antibiotics (100 U/mL penicillin and 100 mg/mL streptomycin) and 10% fetal bovine serum. The cells were incubated at 37 °C in a 5% CO2 incubator. Compounds Y042-8649, D715-0537, Y043-8503, and D715-0389 were obtained from Tsbiochem (Shanghai, China).

4.2. Data Source and Preprocessing

Five microarray datasets (Table S2) were downloaded from the National Center for Biotechnology Information (NCBI) GEO database (https://www.ncbi.nlm.nih.gov/geo/, accessed on 24 March 2016). A total of 357 breast tumor samples and 51 normal breast samples were included in this analysis. Based on the breast cancer subtypes, these samples included 117 HER2, 58 LumA, 60 LumB, and 132 TN tumor samples. In addition, other gene expression profile datasets, GSE32641, GSE36295, GSE42568, GSE53752, and GSE139038, were downloaded to validate the mRNA expression level. The RNA-seq data and corresponding clinical information were obtained from TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga, accessed on 1 April 2022). A total of 1097 clinical information records were obtained, while 1109 breast cancer samples and 113 normal samples were collected to construct the model.

4.3. Differential Expression Analysis and Data Preprocessing

The matrix files were retrieved and analyzed using R (version 3.6.3). The R package limma (version 3.42.0) was adopted to identify DEGs between breast tumor and normal breast samples. The R package RobustRankAggreg (version 1.1) was used for the integrated analysis. The |logFC| > 0.5 and p < 0.05 were statistically significant.

4.4. Functional Enrichment Analysis

GO functional enrichment and KEGG enrichment analyses were performed using the R packages GO.db (version 3.10.0) and org.Hs.eg.db (version 3.10.0).

4.5. Construction of the Prognostic Model

Based on the BC tumor samples and clinical information in the TCGA dataset, the DEGs were subjected to examine the correlation between mRNA expression levels and OS with p < 0.05 by univariate Cox regression analysis using R packages survival (version 3.1-12). The LASSO algorithm was used to screen candidate genes. Breast tumor samples were randomly divided into the training set and the test set in a 1:1 ratio. The training set was utilized to build a prognostic model, while the test set and the entire set were employed to validate this model. Multivariate Cox regression analysis was used to screen independent prognostic DEGs with p < 0.05 as the threshold of significant correlation. Based on the expression levels of these genes and coefficients (coef) in the multivariate Cox proportional hazards regression analysis, a prognostic risk score formula was defined: Risk score = ∑ i = 0 n g e n e   e x p r e s s i o n   l e v e l × c o e f . All patients were categorized into low- and high-risk groups based on the median risk score. R package survival (version 3.1-12) was used to construct the KM curve, which was utilized to evaluate the prognostic value of the risk score. The log-rank p-value and the hazard ratio (HR), along with their 95% confidence intervals, were also presented. ROC curve analysis generated from R package survivalROC (version 1.0.3) was used to compare the sensitivity and specificity. The AUC was calculated by the ROC curve. Traditional clinical risk factors such as age, gender, tumor status, T status, N status, and risk score were presented by univariate and multivariate Cox analysis. The nomogram was created using multivariate Cox analysis with the R package rms (version 6.1-1).

4.6. Molecular Docking

The crystal structure of human PGK1 was obtained from the RCSB Protein Data Bank (PDB, 2X13) and has a resolution of 1.74 Å. The hydrogen atoms and charges were added into the protein structure using the Protein Preparation Wizard in the Schrödinger suite, which was then used for computational screening techniques. Chemdiv and Targetmol libraries (including more than 1620 thousand compounds) were selected to identify potential PGK1 inhibitors. For the virtual screening process, the High Throughput Virtual Screening (HTVS) protocol within the Glide module was initially employed, followed by Standard Precision (SP) docking of the top 10% of ligands from the HTVS based on their docking scores. Finally, Extra Precision (XP) docking was performed on the top 10% of the results obtained from SP docking. The QED was utilized to assess the drug-likeness of small molecules, retaining those with a QED score greater than 0.5. Potential pan-assay interference compounds (PAINS) were eliminated to minimize the likelihood of false positive results [83]. The MMGBSA was also used for the calculation of binding free energy between ligands and receptor molecules. PyMol software (version 2.5.5) was utilized for the docking analysis and visualization.

4.7. Cell Experiments

Cell viability was determined by CCK-8 assay (Yeasen, Shanghai, China), and was performed as previously described [84].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ph17121636/s1, Figure S1: RNA levels of ten risk genes in GEO datasets; Figure S2: (A–C) The risk curve of every sample arranged by risk score in the training (A), test (B), and entire (C) sets. (D–F) The scatter plot of survival overview of patients with breast cancer in the training (D), test (E), and entire (F) sets. The red dot represents death, and the green dot represents survival; Figure S3: The alignments of ADP (carbon skeletal structure colored with yellow) with D715-2871 (A), Y040-8304 (B), D715-0344 (C), and D231-0058 (D) in the pocket of PGK1 (2X13). Table S1. The information of HER2+, Luminal A, Luminal B and triple-negative subtype tumor samples in the GSE datasets. Table S2. The information of GSE datasets used in this study. Table S3. Results of GO enrichment analysis. Table S4. Results of KEGG enrichment analysis.

Author Contributions

Conceptualization, L.Z. and Q.L.; methodology, X.C., Z.Z. and Q.L.; validation, X.C., Z.Z. and X.L.; formal analysis, X.C., Z.Z., X.L. and Q.L.; investigation, X.C. and Q.L.; resources, X.C.; data curation, X.C., Z.Z. and X.L.; writing—original draft preparation, all authors; writing—review and editing, Q.L. and L.Z.; visualization, X.C. and Z.Z.; supervision, Q.L. and L.Z.; project administration, X.C.; funding acquisition, Q.L. and L.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by National Natural Science Foundation of China, grant numbers 82225047 and 32170274; the National Key Research and Development Program of China, grant number 2022YFC3501700; the Postgraduate Innovation Fund of Interdiscipline and New Medicine from School of Medicine of Shanghai University; the Collaborative Grant-in-Aid of the HBUT National “111” Center for Cellular Regulation and Molecular Pharmaceutics, grant number XBTK-2021006; the Open Project Funding of the Key Laboratory of Fermentation Engineering (Ministry of Education), grant number 202105FE02; the Natural Science Research Project of Anhui Educational Committee, grant number 2024AH051675; and the Excellent Scientific Research and Innovation Team of University in Anhui Province, grant number 2022AH010029.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original data presented in the study are openly available in TCGA at https://www.cancer.gov/ccg/research/genome-sequencing/tcga (accessed on 1 April 2022) and GEO (GSE32641, GSE36295, GSE42568, GSE53752, and GSE139038).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin. 2022, 72, 7–33. [Google Scholar] [CrossRef]
  2. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012, 490, 61–70. [Google Scholar] [CrossRef]
  3. Prasad, A.; Hasan, S.M.A.; Grouchy, S.; Gartia, M.R. DNA microarray analysis using a smartphone to detect the BRCA-1 gene. Analyst 2018, 144, 197–205. [Google Scholar] [CrossRef]
  4. Yang, J. Identification of novel biomarkers, MUC5AC, MUC1, KRT7, GAPDH, CD44 for gastric cancer. Med. Oncol. 2020, 37, 34. [Google Scholar] [CrossRef]
  5. Zou, Y.F.; Meng, L.B.; Wang, Q.Q.; He, Z.K.; Hu, C.H.; Shan, M.J.; Wang, D.Y.; Yu, X. Identification and functional enrichment analysis of potential diagnostic and therapeutic targets in adamantinomatous craniopharyngioma. J. Comput. Biol. 2020, 27, 55–68. [Google Scholar] [CrossRef]
  6. Xu, Y.; Wu, G.; Li, J.; Li, J.; Ruan, N.; Ma, L.; Han, X.; Wei, Y.; Li, L.; Zhang, H.; et al. Screening and identification of key biomarkers for bladder cancer: A study based on TCGA and GEO data. Biomed. Res. Int. 2020, 2020, 8283401. [Google Scholar] [CrossRef]
  7. Zhong, G.S.; Lou, W.Y.; Shen, Q.Y.; Yu, K.; Zheng, Y.J. Identification of key genes as potential biomarkers for triple-negative breast cancer using integrating genomics analysis. Mol. Med. Rep. 2020, 21, 557–566. [Google Scholar] [CrossRef]
  8. Li, M.; Guo, Y.; Feng, Y.M.; Zhang, N. Identification of triple-negative breast cancer genes and a novel high-risk breast cancer prediction model development based on PPI data and support vector machines. Front. Genet. 2019, 10, 180. [Google Scholar] [CrossRef] [PubMed]
  9. Lu, X.; Gao, C.; Liu, C.; Zhuang, J.; Su, P.; Li, H.; Wang, X.; Sun, C. Identification of the key pathways and genes involved in HER2-positive breast cancer with brain metastasis. Pathol. Res. Pract. 2019, 215, 152475. [Google Scholar] [CrossRef] [PubMed]
  10. Lin, Y.; Fu, F.; Lv, J.; Wang, M.; Li, Y.; Zhang, J.; Wang, C. Identification of potential key genes for HER-2 positive breast cancer based on bioinformatics analysis. Medicine 2020, 99, e18445. [Google Scholar] [CrossRef] [PubMed]
  11. Wang, P.; Liu, J.; Song, Y.; Liu, Q.; Wang, C.; Qian, C.; Zhang, S.; Zhu, W.; Yang, X.; Wan, F.; et al. Screening of immunosuppressive factors for biomarkers of breast cancer malignancy phenotypes and subtype-specific targeted therapy. PeerJ 2019, 7, e7197. [Google Scholar] [CrossRef] [PubMed]
  12. Xiao, B.; Hang, J.; Lei, T.; He, Y.; Kuang, Z.; Wang, L.; Chen, L.; He, J.; Zhang, W.; Liao, Y.; et al. Identification of key genes relevant to the prognosis of ER-positive and ER-negative breast cancer based on a prognostic prediction system. Mol. Biol. Rep. 2019, 46, 2111–2119. [Google Scholar] [CrossRef] [PubMed]
  13. Gan, S.Y.; Dai, H.X.; Li, R.J.; Liu, W.; Ye, R.F.; Ha, Y.P.; Di, X.Q.; Hu, W.H.; Zhang, Z.; Sun, Y.Q. Identification of key differentially expressed genes between ER-positive/HER2-negative breast cancer and ER-negative/HER2-negative breast cancer using integrated bioinformatics analysis. Gland Surg. 2020, 9, 661–675. [Google Scholar] [CrossRef] [PubMed]
  14. Negri, M.; Gentile, A.; de Angelis, C.; Monto, T.; Patalano, R.; Colao, A.; Pivonello, R.; Pivonello, C. Vitamin D-induced molecular mechanisms to potentiate cancer therapy and to reverse drug-resistance in cancer cells. Nutrients 2020, 12, 1798. [Google Scholar] [CrossRef]
  15. Qiu, A.; Wen, X.; Zou, Q.; Yin, L.; Zhu, S.; Sheng, Y.; He, Y.; Liu, Q.; Luo, D.; Guo, Z. Phosphoglycerate kinase 1: An effective therapeutic target in cancer. Front. Biosci. (Landmark Ed.) 2024, 29, 92. [Google Scholar] [CrossRef]
  16. Zhang, Y.; Yu, C. Development and validation of a Surveillance, Epidemiology, and End Results (SEER)-based prognostic nomogram for predicting survival in elderly patients with gastric cancer after surgery. J. Gastrointest. Oncol. 2021, 12, 278–296. [Google Scholar] [CrossRef]
  17. Sun, S.; Wu, H.; Wu, X.; You, Z.; Jiang, Y.; Liang, X.; Chen, Z.; Zhang, Y.; Wei, W.; Jiang, Y.; et al. Silencing of PGK1 promotes sensitivity to paclitaxel treatment by upregulating XAF1-mediated apoptosis in triple-negative breast cancer. Front. Oncol. 2021, 11, 535230. [Google Scholar] [CrossRef]
  18. Mao, J.; Liu, L.L.; Shen, Q.; Cen, M. Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes. J. Cell. Mol. Med. 2024, 28, e18549. [Google Scholar] [CrossRef]
  19. Wang, L.; Li, J.; Mei, N.; Chen, H.; Niu, L.; He, J.; Wang, R. Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer. Sci. Rep. 2024, 14, 16586. [Google Scholar] [CrossRef]
  20. Miglietta, F.; Ragazzi, M.; Fernandes, B.; Griguolo, G.; Massa, D.; Girardi, F.; Bottosso, M.; Bisagni, A.; Zarrilli, G.; Porra, F.; et al. A prognostic model based on residual cancer burden and tumor-infiltrating lymphocytes on residual disease after neoadjuvant therapy in HER2+ breast cancer. Clin. Cancer Res. 2023, 29, 3429–3437. [Google Scholar] [CrossRef]
  21. Li, N.N.; Qiu, X.T.; Xue, J.S.; Yi, L.M.; Chen, M.L.; Huang, Z.J. Predicting the prognosis and immunotherapeutic response of triple-negative breast cancer by constructing a prognostic model based on CD8+ T cell-related immune genes. Biomed. Environ. Sci. 2024, 37, 581–593. [Google Scholar] [PubMed]
  22. Song, X.; Wang, P.; Feng, R.; Chetry, M.; Li, E.; Wu, X.; Liu, Z.; Liao, S.; Lin, J. Prognostic model of ER-positive, HER2-negative breast cancer predicted by clinically relevant indicators. Clin. Transl. Oncol. 2024, 26, 389–397. [Google Scholar] [CrossRef] [PubMed]
  23. Vosa, U.; Kolde, R.; Vilo, J.; Metspalu, A.; Annilo, T. Comprehensive meta-analysis of microRNA expression using a robust rank aggregation approach. Methods Mol. Biol. 2014, 1182, 361–373. [Google Scholar]
  24. Sun, G.; Li, Y.; Peng, Y.; Lu, D.; Zhang, F.; Cui, X.; Zhang, Q.; Li, Z. Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis. J. Cell. Physiol. 2019, 234, 15215–15224. [Google Scholar] [CrossRef]
  25. Bao, Y.L.; Wang, L.; Shi, L.; Yun, F.; Liu, X.; Chen, Y.X.; Chen, C.; Ren, Y.N.; Jia, Y.F. Transcriptome profiling revealed multiple genes and ECM-receptor interaction pathways that may be associated with breast cancer. Cell. Mol. Biol. Lett. 2019, 24, 38. [Google Scholar] [CrossRef]
  26. Budczies, J.; Pfitzner, B.M.; Györffy, B.; Winzer, K.J.; Radke, C.; Dietel, M.; Fiehn, O.; Denkert, C. Glutamate enrichment as new diagnostic opportunity in breast cancer. Int. J. Cancer 2015, 136, 1619–1628. [Google Scholar] [CrossRef]
  27. Ye, H.; Shen, X.; Li, Y.; Zou, W.; Hassan, S.S.U.; Feng, Y.; Wang, X.; Tian, J.; Shao, X.; Tao, Y.; et al. Proteomic and metabolomic characterization of bone, liver, and lung metastases in plasma of breast cancer patients. Proteom. Clin. Appl. 2024, 3, e2300136. [Google Scholar] [CrossRef] [PubMed]
  28. Liu, L.; Wang, Q.; Zhou, J.Y.; Zhang, B. Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer. J. Ovarian Res. 2023, 16, 88. [Google Scholar] [CrossRef]
  29. Zhao, H.Y.; Yin, X.Z.; Wang, L.X.; Liu, K.L.; Liu, W.Y.; Bo, L.; Wang, L. Identifying tumour microenvironment-related signature that correlates with prognosis and immunotherapy response in breast cancer. Sci. Data 2023, 10, 119. [Google Scholar] [CrossRef]
  30. Zeaiter, N.; Belot, L.; Cunin, V.; Nahed, R.A.; Tokarska-Schlattner, M.; Le Gouellec, A.; Petosa, C.; Khochbin, S.; Schlattner, U. Acetyl-CoA synthetase (ACSS2) does not generate butyryl- and crotonyl-CoA. Mol. Metab. 2024, 81, 101903. [Google Scholar] [CrossRef]
  31. Miller, K.D.; O’Connor, S.; Pniewski, K.A.; Kannan, T.; Acosta, R.; Mirji, G.; Papp, S.; Hulse, M.; Mukha, D.; Hlavaty, S.I.; et al. Acetate acts as a metabolic immunomodulator by bolstering T-cell effector function and potentiating antitumor immunity in breast cancer. Nat. Cancer 2023, 4, 1491–1507. [Google Scholar] [CrossRef] [PubMed]
  32. Esquea, E.M.; Ciraku, L.; Young, R.G.; Merzy, J.; Talarico, A.N.; Ahmed, N.N.; Karuppiah, M.; Ramesh, A.; Chatoff, A.; Crispim, C.V.; et al. Selective and brain-penetrant ACSS2 inhibitors target breast cancer brain metastatic cells. Front. Pharmacol. 2024, 15, 1394685. [Google Scholar] [CrossRef] [PubMed]
  33. Zhang, Z.Q.; Li, J.; He, T.S.; Ding, J.Q. Bioinformatics identified 17 immune genes as prognostic biomarkers for breast cancer: Application study based on artificial intelligence algorithms. Front. Oncol. 2020, 10, 330. [Google Scholar] [CrossRef]
  34. Ozga, A.J.; Chow, M.T.; Luster, A.D. Chemokines and the immune response to cancer. Immunity 2021, 54, 859–874. [Google Scholar] [CrossRef] [PubMed]
  35. Seitz, S.; Dreyer, T.F.; Stange, C.; Steiger, K.; Brauer, R.; Scheutz, L.; Multhoff, G.; Weichert, W.; Kiechle, M.; Magdolen, V.; et al. CXCL9 inhibits tumour growth and drives anti-PD-L1 therapy in ovarian cancer. Br. J. Cancer 2022, 126, 1470–1480. [Google Scholar] [CrossRef]
  36. Liang, Y.K.; Deng, Z.K.; Chen, M.T.; Qiu, S.Q.; Xiao, Y.S.; Qi, Y.Z.; Xie, Q.; Wang, Z.H.; Jia, S.C.; Zeng, D.; et al. CXCL9 is a potential biomarker of immune infiltration associated with favorable prognosis in ER-negative breast cancer. Front. Oncol. 2021, 11, 710286. [Google Scholar] [CrossRef]
  37. Wu, L.; Sun, S.; Qu, F.; Sun, M.; Liu, X.; Sun, Q.; Cheng, L.; Zheng, Y.; Su, G. CXCL9 influences the tumor immune microenvironment by stimulating JAK/STAT pathway in triple-negative breast cancer. Cancer Immunol. Immunother. 2023, 72, 1479–1492. [Google Scholar] [CrossRef]
  38. Moll, R.; Divo, M.; Langbein, L. The human keratins: Biology and pathology. Histochem. Cell Biol. 2008, 129, 705–733. [Google Scholar] [CrossRef]
  39. Zhan, Q.; Signoretti, S.; Whitaker-Menezes, D.; Friedman, T.M.; Korngold, R.; Murphy, G.F. Cytokeratin15-positive basal epithelial cells targeted in graft-versus-host disease express a constitutive antiapoptotic phenotype. J. Investig. Dermatol. 2007, 127, 106–115. [Google Scholar] [CrossRef]
  40. Miyashita, H.; Niwano, H.; Yoshida, S.; Hatou, S.; Inagaki, E.; Tsubota, K.; Shimmura, S. Long-term homeostasis and wound healing in an in vitro epithelial stem cell niche model. Sci. Rep. 2017, 7, 43557. [Google Scholar] [CrossRef]
  41. Chen, W.D.; Miao, C.L. KRT15 promotes colorectal cancer cell migration and invasion through β-catenin/MMP-7 signaling pathway. Med. Oncol. 2022, 39, 68. [Google Scholar] [CrossRef] [PubMed]
  42. Zhong, P.C.; Shu, R.; Wu, H.W.; Liu, Z.W.; Shen, X.L.; Hu, Y.J. Low KRT15 expression is associated with poor prognosis in patients with breast invasive carcinoma. Exp. Ther. Med. 2021, 21, 305. [Google Scholar] [CrossRef]
  43. Kummer, E.; Ban, N. Mechanisms and regulation of protein synthesis in mitochondria. Nat. Rev. Mol. Cell Biol. 2021, 22, 307–325. [Google Scholar] [CrossRef]
  44. Jing, C.Q.; Fu, R.; Wang, C.; Li, X.R.; Zhang, W. MRPL13 act as a novel therapeutic target and could promote cell proliferation in non-small cell lung cancer. Cancer Manag. Res. 2021, 13, 5535–5545. [Google Scholar] [CrossRef]
  45. Zhong, X.G.; He, Z.J.; Fan, Y.; Yin, L.; Hong, Z.P.; Tong, Y.; Bi, Q.; Zhu, S.B. Multi-omics analysis of MRPL-13 as a tumor-promoting marker from pan-cancer to lung adenocarcinoma. Aging 2023, 15, 10640–10680. [Google Scholar] [CrossRef]
  46. Cai, M.M.; Li, H.N.; Chen, R.F.; Zhou, X. MRPL13 promotes tumor cell proliferation, migration and EMT process in breast cancer through the PI3K-AKT-mTOR pathway. Cancer Manag. Res. 2021, 13, 2009–2024. [Google Scholar] [CrossRef]
  47. Tao, Z.; Suo, H.D.; Zhang, L.; Jin, Z.N.; Wang, Z.; Wang, D.Y.; Wu, M.; Peng, N.X.; Zhao, Y.J.; Chen, B. MRPL13 is a prognostic cancer biomarker and correlates with immune infiltrates in breast cancer. Onco Targets Ther. 2020, 13, 12255–12268. [Google Scholar] [CrossRef]
  48. Wang, Y.; Wang, D.Y.; Bu, K.N.; Gao, J.D.; Zhang, B.L. Prognosis prediction and risk stratification of breast cancer patients based on a mitochondria-related gene signature. Sci. Rep. 2024, 14, 2859. [Google Scholar] [CrossRef] [PubMed]
  49. Horisberger, J.D.; Rossier, B.C. Aldosterone regulation of gene transcription leading to control of ion transport. Hypertension 1992, 19, 221–227. [Google Scholar] [CrossRef] [PubMed]
  50. Peng, Y.; Xi, X.; Li, J.T.; Ni, J.; Yang, H.B.; Wen, C.Y.; Wen, M.L. miR-301b and NR3C2 co-regulate cells malignant properties and have the potential to be independent prognostic factors in breast cancer. J. Biochem. Mol. Toxicol. 2021, 35, e22650. [Google Scholar] [CrossRef]
  51. Liu, J.; Liu, Z.; Zhou, Y.; Zeng, M.; Pan, S.; Liu, H.; Liu, Q.; Zhu, H. Identification of a novel transcription factor prognostic index for breast cancer. Front. Oncol. 2021, 11, 666505. [Google Scholar] [CrossRef] [PubMed]
  52. Gadwal, A.; Purohit, P.; Khokhar, M.; Vishnoi, J.R.; Pareek, P.; Choudhary, R.; Elhence, P.; Banerjee, M.; Sharma, P. In silico analysis of differentially expressed-aberrantly methylated genes in breast cancer for prognostic and therapeutic targets. Clin. Exp. Med. 2023, 23, 3847–3866. [Google Scholar] [CrossRef] [PubMed]
  53. Fu, Q.; Yu, Z. Phosphoglycerate kinase 1 (PGK1) in cancer: A promising target for diagnosis and therapy. Life Sci. 2020, 256, 117863. [Google Scholar] [CrossRef] [PubMed]
  54. Yang, S.; Zhan, Q.; Su, D.; Cui, X.; Zhao, J.; Wang, Q.; Hong, B.; Ju, J.; Cheng, C.; Yang, E.; et al. HIF1alpha/ATF3 partake in PGK1 K191/K192 succinylation by modulating P4HA1/succinate signaling in glioblastoma. Neuro-Oncol. 2024, 26, 1405–1420. [Google Scholar] [CrossRef] [PubMed]
  55. Chu, Z.; Huo, N.; Zhu, X.; Liu, H.X.; Cong, R.; Ma, L.Y.; Kang, X.F.; Xue, C.Y.; Li, J.T.; Li, Q.H.; et al. FOXO3A-induced LINC00926 suppresses breast tumor growth and metastasis through inhibition of PGK1-mediated Warburg effect. Mol. Ther. 2021, 29, 2737–2753. [Google Scholar] [CrossRef]
  56. Asano, M.; Komiyama, K. Polymeric immunoglobulin receptor. J. Oral Sci. 2011, 53, 147–156. [Google Scholar] [CrossRef]
  57. Chae, J.; Choi, J.; Chung, J. Polymeric immunoglobulin receptor (pIgR) in cancer. J. Cancer Res. Clin. Oncol. 2023, 149, 17683–17690. [Google Scholar] [CrossRef]
  58. Xiao, B.; Chen, L.; Ke, Y.; Hang, J.; Cao, L.; Zhang, R.; Zhang, W.; Liao, Y.; Gao, Y.; Chen, J.; et al. Identification of methylation sites and signature genes with prognostic value for luminal breast cancer. BMC Cancer 2018, 18, 405. [Google Scholar] [CrossRef]
  59. Chen, Q.T.; Zhou, Q. Identification of exosome-related gene signature as a promising diagnostic and therapeutic tool for breast cancer. Heliyon 2024, 10, e29551. [Google Scholar] [CrossRef]
  60. Yang, Q.; Graham, T.E.; Mody, N.; Preitner, F.; Peroni, O.D.; Zabolotny, J.M.; Kotani, K.; Quadro, L.; Kahn, B.B. Serum retinol binding protein 4 contributes to insulin resistance in obesity and type 2 diabetes. Nature 2005, 436, 356–362. [Google Scholar] [CrossRef]
  61. Wan, F.J.; Zhu, Y.J.; Wu, F.X.; Huang, X.J.; Chen, Y.; Zhou, Y.; Li, H.T.; Liang, L.F.; Qin, L.R.; Wang, Q.; et al. Retinol-binding protein 4 as a promising serum biomarker for the diagnosis and prognosis of hepatocellular Carcinoma. Transl. Oncol. 2024, 45, 101979. [Google Scholar] [CrossRef] [PubMed]
  62. Yu, Y.T.; Zhang, C.K.; Sun, Q.N.; Baral, S.; Ding, J.Y.; Zhao, F.Y.; Yao, Q.; Gao, S.Y.; Liu, B.; Wang, D.R. Retinol binding protein 4 serves as a potential tumor biomarker and promotes malignant behavior in gastric cancer. Cancer Manag. Res. 2024, 16, 891–908. [Google Scholar] [CrossRef] [PubMed]
  63. Wu, M.; Li, Q.; Wang, H. Identification of novel biomarkers associated with the prognosis and potential pathogenesis of breast cancer via integrated bioinformatics analysis. Technol. Cancer Res. Treat. 2021, 20, 1533033821992081. [Google Scholar] [CrossRef]
  64. Lin, W.H.; Huang, C.J.; Liu, M.W.; Chang, H.M.; Chen, Y.J.; Tai, T.Y.; Chuang, L.M. Cloning, mapping, and characterization of the human sorbin and SH3 domain containing 1 (SORBS1) gene: A protein associated with c-Abl during insulin signaling in the hepatoma cell line Hep3B. Genomics 2001, 74, 12–20. [Google Scholar] [CrossRef]
  65. Cho, W.C.; Jang, J.E.; Kim, K.H.; Yoo, B.C.; Ku, J.L. SORBS1 serves a metastatic role via suppression of AHNAK in colorectal cancer cell lines. Int. J. Oncol. 2020, 56, 1140–1151. [Google Scholar] [CrossRef] [PubMed]
  66. Yu, W.X.; Li, D.W.; Zhang, Y.D.; Li, C.A.; Zhang, C.Z.; Wang, L.B. MiR-142-5p acts as a significant regulator through promoting proliferation, invasion, and migration in breast cancer modulated by targeting SORBS1. Technol. Cancer Res. Treat. 2019, 18, 1533033819892264. [Google Scholar] [CrossRef] [PubMed]
  67. Feng, K.; Di, Y.; Han, M.; Yan, W.; Wang, Y. SORBS1 inhibits epithelial to mesenchymal transition (EMT) of breast cancer cells by regulating PI3K/AKT signaling and macrophage phenotypic polarization. Aging 2024, 16, 4789–4810. [Google Scholar] [CrossRef]
  68. Wang, N.; Gu, Y.; Li, L.; Chi, J.; Liu, X.; Xiong, Y.; Zhong, C. Development and validation of a prognostic classifier based on lipid metabolism-related genes for breast cancer. J. Inflamm. Res. 2022, 15, 3477–3499. [Google Scholar] [CrossRef]
  69. Noh, M.G.; Kim, S.S.; Kim, Y.J.; Jung, T.Y.; Jung, S.; Rhee, J.H.; Lee, J.H.; Lee, J.S.; Cho, J.H.; Moon, K.S.; et al. Evolution of the tumor microenvironment toward immune-suppressive seclusion during brain metastasis of breast cancer: Implications for targeted therapy. Cancers 2021, 13, 4895. [Google Scholar] [CrossRef]
  70. Esteva, F.J.; Hubbard-Lucey, V.M.; Tang, J.; Pusztai, L. Immunotherapy and targeted therapy combinations in metastatic breast cancer. Lancet Oncol. 2019, 20, e175–e186. [Google Scholar] [CrossRef]
  71. Lv, J.; Ren, J.; Zheng, J.; Zhang, F.; Han, M. Prognosis of tumor microenvironment in luminal B-type breast cancer. Dis. Markers 2022, 2022, 5621441. [Google Scholar] [CrossRef] [PubMed]
  72. Galimberti, D.; Fenoglio, C.; Scarpini, E. Progranulin as a therapeutic target for dementia. Expert Opin. Ther. Targets 2018, 22, 579–585. [Google Scholar] [CrossRef] [PubMed]
  73. Liu, L.L.; Xiang, M.; Zhou, J.Q.; Ren, Z.J.; Shi, W.J.; Du, X.H.; Fu, X.Y.; Li, P.P.; Wang, H.Y. Progranulin inhibits autophagy to facilitate intracellular colonization of Helicobacter pylori through the PGRN/mTOR/DCN axis in gastric epithelial cells. Front. Cell. Infect. Microbiol. 2024, 14, 1425367. [Google Scholar] [CrossRef]
  74. Fang, W.; Zhou, T.; Shi, H.; Yao, M.; Zhang, D.; Qian, H.; Zeng, Q.; Wang, Y.; Jin, F.; Chai, C.; et al. Progranulin induces immune escape in breast cancer via up-regulating PD-L1 expression on tumor-associated macrophages (TAMs) and promoting CD8(+) T cell exclusion. J. Exp. Clin. Cancer Res. 2021, 40, 4. [Google Scholar] [CrossRef] [PubMed]
  75. Shashni, B.; Sakharkar, K.R.; Nagasaki, Y.; Sakharkar, M.K. Glycolytic enzymes PGK1 and PKM2 as novel transcriptional targets of PPARγ in breast cancer pathophysiology. J. Drug Target. 2013, 21, 161–174. [Google Scholar] [CrossRef]
  76. Li, X.J.; Zheng, Y.H.; Lu, Z.M. PGK1 is a new member of the protein kinome. Cell Cycle 2016, 15, 1803–1804. [Google Scholar] [CrossRef]
  77. Chen, X.P.; Zhao, C.Y.; Li, X.L.; Wang, T.; Li, Y.Z.; Cao, C.; Ding, Y.H.; Dong, M.Q.; Finci, L.; Wang, J.H.; et al. Terazosin activates Pgk1 and Hsp90 to promote stress resistance. Nat. Chem. Biol. 2015, 11, 19–25. [Google Scholar] [CrossRef]
  78. He, Y.; Luo, Y.; Huang, L.; Zhang, D.; Hou, H.; Liang, Y.; Deng, S.; Zhang, P.; Liang, S. Novel inhibitors targeting the PGK1 metabolic enzyme in glycolysis exhibit effective antitumor activity against kidney renal clear cell carcinoma in vitro and in vivo. Eur. J. Med. Chem. 2024, 267, 116209. [Google Scholar] [CrossRef]
  79. Liao, L.; Dang, W.; Lin, T.; Yu, J.; Liu, T.; Li, W.; Xiao, S.; Feng, L.; Huang, J.; Fu, R.; et al. A potent PGK1 antagonist reveals PGK1 regulates the production of IL-1beta and IL-6. Acta Pharm. Sin. B 2022, 12, 4180–4192. [Google Scholar] [CrossRef]
  80. Wang, Y.; Sun, L.; Yu, G.; Qi, X.; Zhang, A.; Lu, Z.; Li, D.; Li, J. Identification of a novel non-ATP-competitive protein kinase inhibitor of PGK1 from marine nature products. Biochem. Pharmacol. 2021, 183, 114343. [Google Scholar] [CrossRef]
  81. Li, M.; Zhang, A.; Qi, X.; Yu, R.; Li, J. A novel inhibitor of PGK1 suppresses the aerobic glycolysis and proliferation of hepatocellular carcinoma. Biomed. Pharmacother. 2023, 158, 114115. [Google Scholar] [CrossRef] [PubMed]
  82. Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc. 2016, 11, 905–919. [Google Scholar] [CrossRef] [PubMed]
  83. Baell, J.; Walters, M.A. Chemistry: Chemical con artists foil drug discovery. Nature 2014, 513, 481–483. [Google Scholar] [CrossRef] [PubMed]
  84. Liu, Y.; Yang, J.; Guo, Z.; Li, Q.; Zhang, L.; Zhao, L.; Zhou, X. Immunomodulatory Effect of Cordyceps militaris Polysaccharide on RAW 264.7 Macrophages by Regulating MAPK Signaling Pathways. Molecules 2024, 29, 3408. [Google Scholar] [CrossRef]
Pharmaceuticals 17 01636 g001
Figure 1. Identification and functional enrichment analysis of DEGs. (A–D) Top 20 up-regulated and down-regulated genes in HER2 (A), LumA (B), LumB (C), and TN (D) subtype tumor samples from GSE29431, GSE38959, GSE45827, GSE65194, and GSE115275 datasets. The red color represents up-regulated genes, while green indicates down-regulated genes. The numbers shown in the figure represent the log fold change (logFC) of genes in each dataset. The cutoff criteria are p < 0.05 and |logFC| > 0.5. (E) The Venn diagram of DEGs of HER2, LumA, LumB, and TN subtype tumor samples from GSE29431, GSE38959, GSE45827, GSE65194, and GSE115275 datasets. (F) The bar plot of GO functional enrichment analysis. The top 10 terms of biological process, cellular component, and molecular function are shown. (G) The bar plot illustrates the results of KEGG functional enrichment analysis.
Figure 1. Identification and functional enrichment analysis of DEGs. (A–D) Top 20 up-regulated and down-regulated genes in HER2 (A), LumA (B), LumB (C), and TN (D) subtype tumor samples from GSE29431, GSE38959, GSE45827, GSE65194, and GSE115275 datasets. The red color represents up-regulated genes, while green indicates down-regulated genes. The numbers shown in the figure represent the log fold change (logFC) of genes in each dataset. The cutoff criteria are p < 0.05 and |logFC| > 0.5. (E) The Venn diagram of DEGs of HER2, LumA, LumB, and TN subtype tumor samples from GSE29431, GSE38959, GSE45827, GSE65194, and GSE115275 datasets. (F) The bar plot of GO functional enrichment analysis. The top 10 terms of biological process, cellular component, and molecular function are shown. (G) The bar plot illustrates the results of KEGG functional enrichment analysis.
Pharmaceuticals 17 01636 g001
Pharmaceuticals 17 01636 g002
Figure 2. Analysis of the prognostic model in BC. (A) Forest plot of the signature risk model. (B) Lasso model for screening the key genes. (C) Multivariate Cox analysis confirming hub genes for risk model. (D) The expression levels of ten hub genes in breast cancer tissues compared to normal tissues. (E,G,I) Kaplan–Meier analysis of survival differences between high-risk and low-risk groups in training (E), test (G), and entire (I) sets. (F,H,J) Receiver operating characteristic (ROC) curve analysis on the ten model gene signatures in the training (F), test (H), and entire (J) sets. AUC, the area under the curve. These curves are performed by R package survival ROC. (K) Univariate Cox analysis of risk score and clinicopathological features in the entire set. (L) Multivariate Cox analysis of clinicopathological features and risk score in the entire set. (M) The ROC curve of the risk score and clinical characteristics. (N) The ROC curve and AUC values for the predictive signature at 1-year, 3-year, and 5-year survival rates.
Figure 2. Analysis of the prognostic model in BC. (A) Forest plot of the signature risk model. (B) Lasso model for screening the key genes. (C) Multivariate Cox analysis confirming hub genes for risk model. (D) The expression levels of ten hub genes in breast cancer tissues compared to normal tissues. (E,G,I) Kaplan–Meier analysis of survival differences between high-risk and low-risk groups in training (E), test (G), and entire (I) sets. (F,H,J) Receiver operating characteristic (ROC) curve analysis on the ten model gene signatures in the training (F), test (H), and entire (J) sets. AUC, the area under the curve. These curves are performed by R package survival ROC. (K) Univariate Cox analysis of risk score and clinicopathological features in the entire set. (L) Multivariate Cox analysis of clinicopathological features and risk score in the entire set. (M) The ROC curve of the risk score and clinical characteristics. (N) The ROC curve and AUC values for the predictive signature at 1-year, 3-year, and 5-year survival rates.
Pharmaceuticals 17 01636 g002
Pharmaceuticals 17 01636 g003
Figure 3. Analysis of the relationships between risk score and clinical characteristics of breast cancer in the TCGA cohort. (A) Heat map of ten model genes and clinical characteristics in the high- and low-risk groups. *, p < 0.05; **, p < 0.01; and ***, p < 0.001. (B) Analysis of overall survival in TCGA-BC patients based on clinical stratification, focusing on high- and low-risk groups by age, clinical stage, N stage, and T stage.
Figure 3. Analysis of the relationships between risk score and clinical characteristics of breast cancer in the TCGA cohort. (A) Heat map of ten model genes and clinical characteristics in the high- and low-risk groups. *, p < 0.05; **, p < 0.01; and ***, p < 0.001. (B) Analysis of overall survival in TCGA-BC patients based on clinical stratification, focusing on high- and low-risk groups by age, clinical stage, N stage, and T stage.
Pharmaceuticals 17 01636 g003
Pharmaceuticals 17 01636 g004
Figure 4. The nomogram in predicting overall survival of breast cancer. (A) The nomogram predicts 1-, 3-, and 5-year overall survival. (B) Calibration maps were utilized to predict survival rates at 1, 3, and 5 years.
Figure 4. The nomogram in predicting overall survival of breast cancer. (A) The nomogram predicts 1-, 3-, and 5-year overall survival. (B) Calibration maps were utilized to predict survival rates at 1, 3, and 5 years.
Pharmaceuticals 17 01636 g004
Pharmaceuticals 17 01636 g005
Figure 5. Analysis of functional enrichment across different risk groups. (A) Volcano chart of differentially expressed genes; (B) GO analysis explored the potential function in terms of biological process (BP), cellular component (CC), and molecular function (MF); (C) KEGG analysis showed the potential pathway enrichment; (D) GSEA analysis demonstrated the potential activated and suppressed pathway enrichment in the high-risk group compared with the low-risk group.
Figure 5. Analysis of functional enrichment across different risk groups. (A) Volcano chart of differentially expressed genes; (B) GO analysis explored the potential function in terms of biological process (BP), cellular component (CC), and molecular function (MF); (C) KEGG analysis showed the potential pathway enrichment; (D) GSEA analysis demonstrated the potential activated and suppressed pathway enrichment in the high-risk group compared with the low-risk group.
Pharmaceuticals 17 01636 g005
Pharmaceuticals 17 01636 g006
Figure 6. Immune features analysis in risk groups. (A,B) ssGSEA (single-sample gene set enrichment analysis) scores for immune cells (A) and immune function (B) in TCGA cohort. (C) The expression of immune checkpoint-related genes and the correlation between risk scores. aDCs, activated dendritic cells; APC, antigen-presenting cell; CCR, chemokine receptor; HLA, human leukocyte antigen; iDCs, immature dendritic cells; IFN, interferon; MHC, major histocompatibility complex; NK, natural killer; pDCs, plasmacytoid dendritic cells; Tfh, T follicular helper; Th, T helper cell; TIL, tumor-infiltrating lymphocyte; Treg, T regulatory cell. * p < 0.05; ** p < 0.01; *** p < 0.001; ns, non-significant.
Figure 6. Immune features analysis in risk groups. (A,B) ssGSEA (single-sample gene set enrichment analysis) scores for immune cells (A) and immune function (B) in TCGA cohort. (C) The expression of immune checkpoint-related genes and the correlation between risk scores. aDCs, activated dendritic cells; APC, antigen-presenting cell; CCR, chemokine receptor; HLA, human leukocyte antigen; iDCs, immature dendritic cells; IFN, interferon; MHC, major histocompatibility complex; NK, natural killer; pDCs, plasmacytoid dendritic cells; Tfh, T follicular helper; Th, T helper cell; TIL, tumor-infiltrating lymphocyte; Treg, T regulatory cell. * p < 0.05; ** p < 0.01; *** p < 0.001; ns, non-significant.
Pharmaceuticals 17 01636 g006
Pharmaceuticals 17 01636 g007
Figure 7. Three-dimensional interaction between PGK1 (2X13) and D715-2871 (A), Y040-8304 (B), D715-0344 (C), and D231-0058 (D). Yellow dotted lines represent hydrogen bonds, pinkish-red dotted lines represent salt bridges, and green balls depict magnesium ions.
Figure 7. Three-dimensional interaction between PGK1 (2X13) and D715-2871 (A), Y040-8304 (B), D715-0344 (C), and D231-0058 (D). Yellow dotted lines represent hydrogen bonds, pinkish-red dotted lines represent salt bridges, and green balls depict magnesium ions.
Pharmaceuticals 17 01636 g007
Pharmaceuticals 17 01636 g008
Figure 8. Inhibitory activity of D715-2871, Y040-8304, D715-0344, and D231-0058 against breast cancer cells T-47D and MCF-7. (A) CCK8 assay for cell viability. Cancer cells were treated with D715-2871, Y040-8304, D715-0344, or D231-0058 (0, 0.1, 1, 10, and 100 μg/mL) for 24 h. Data were presented as mean ± SD (n = 6). (B) CCK8 assay for cell viability. Cancer cells were treated with D231-0058 (0, 1, 3, 10, 30, and 100 μg/mL) for 24 and 48 h. Data were presented as mean ± SD (n = 6). (C) Microscopic observation of the cells treated with D231-0058 (0, 1, 3, 10, 30, and 100 μg/mL) for 24 h.
Figure 8. Inhibitory activity of D715-2871, Y040-8304, D715-0344, and D231-0058 against breast cancer cells T-47D and MCF-7. (A) CCK8 assay for cell viability. Cancer cells were treated with D715-2871, Y040-8304, D715-0344, or D231-0058 (0, 0.1, 1, 10, and 100 μg/mL) for 24 h. Data were presented as mean ± SD (n = 6). (B) CCK8 assay for cell viability. Cancer cells were treated with D231-0058 (0, 1, 3, 10, 30, and 100 μg/mL) for 24 and 48 h. Data were presented as mean ± SD (n = 6). (C) Microscopic observation of the cells treated with D231-0058 (0, 1, 3, 10, 30, and 100 μg/mL) for 24 h.
Pharmaceuticals 17 01636 g008
Table 1. Multivariate Cox analysis.
Table 1. Multivariate Cox analysis.
IDCoefHRHR.95LHR.95Hp-Value
ACSS2−0.35520.70100.43791.12220.1389
C2CD20.77372.16771.35533.46720.0012
CXCL9−0.20910.81130.71760.91730.0008
KRT15−0.19190.82540.71220.95660.0108
MRPL130.39901.49031.00682.20610.0462
NR3C2−0.46720.62680.37451.04910.0755
PGK10.62951.87671.20222.92970.0056
PIGR−0.23080.79390.66190.95220.0129
RBP40.27661.31871.05481.64860.0152
SORBS1−0.46130.63040.43390.91610.0155
HR, hazard ratio; Coef, coefficient.
Table 2. Four compounds matched the selected condition.
Table 2. Four compounds matched the selected condition.
CAS No.StructureNameFormulaDocking ScoredGBind (kcal/mol)
D715-2871/Pharmaceuticals 17 01636 i0012-{2-[(3,4-dimethyl-2-oxo-2H-chromen-7-yl)oxy]acetamido}butanedioic acidC17H17NO8−16.709−53.47
Y040-8304957001-29-5Pharmaceuticals 17 01636 i0022-{2-[(4-butyl-8-methyl-2-oxo-2H-chromen-7-yl)oxy]acetamido}butanedioic acidC20H23NO8−16.417−89.62
D715-0344/Pharmaceuticals 17 01636 i0032-{2-[(4-ethyl-2-oxo-2H-chromen-7-yl)oxy]acetamido}butanedioic acidC17H17NO8−16.314−87.54
D231-0058775298-43-6Pharmaceuticals 17 01636 i0042-[({[4-(2-methyl-1H-indol-3-yl)-1,3-thiazol-2-yl]carbamoyl}methyl)sulfanyl]acetic acidC16H15N3O3S2−16.232−77.19
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chen, X.; Zuo, Z.; Li, X.; Li, Q.; Zhang, L. Identification of a Potential PGK1 Inhibitor with the Suppression of Breast Cancer Cells Using Virtual Screening and Molecular Docking. Pharmaceuticals 2024, 17, 1636. https://doi.org/10.3390/ph17121636

AMA Style

Chen X, Zuo Z, Li X, Li Q, Zhang L. Identification of a Potential PGK1 Inhibitor with the Suppression of Breast Cancer Cells Using Virtual Screening and Molecular Docking. Pharmaceuticals. 2024; 17(12):1636. https://doi.org/10.3390/ph17121636

Chicago/Turabian Style

Chen, Xianghui, Zanwen Zuo, Xianbin Li, Qizhang Li, and Lei Zhang. 2024. "Identification of a Potential PGK1 Inhibitor with the Suppression of Breast Cancer Cells Using Virtual Screening and Molecular Docking" Pharmaceuticals 17, no. 12: 1636. https://doi.org/10.3390/ph17121636

APA Style

Chen, X., Zuo, Z., Li, X., Li, Q., & Zhang, L. (2024). Identification of a Potential PGK1 Inhibitor with the Suppression of Breast Cancer Cells Using Virtual Screening and Molecular Docking. Pharmaceuticals, 17(12), 1636. https://doi.org/10.3390/ph17121636

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop