MDA-MB-231 BC cells were acquired from the American Type Culture Collection (ATCC) and cultured in Dulbecco's Modified Eagle's Medium/Ham's Nutrient Mixture F12 (DMEM-F12; MilliporeSigma). The medium was supplemented with 10% fetal bovine serum (FBS; Biowest) and the following antibiotics: penicillin 100 U/ml and streptomycin 100 µg/ml (Thermo Fisher Scientific, Inc.). The cells were authenticated by the 17 short tandem repeat profiling (ATCC Cell Line Authentication Service) in August 2020. The RNA extraction and microarray were processed in 2019, and the corresponding microarray series GSE136277 was made public in August 2019. Most of the experiments were concluded in 2020. Cultures were routinely tested for mycoplasma contamination using a PCR-based detection kit (cat. no. ab289834; Abcam). For propagation, cells were seeded and cultured in monolayers in 75-cm² tissue culture flasks. For some experiments, cells cultured in 6.0-cm-diameter culture plates to 60-80% confluence were cultured for 48 h in the following conditions: i) Normal glucose (NG), 5.6 mmol/l; ii) 30 mmol/l (HG); iii) HG + 60 µmol/l cisplatin (MilliporeSigma); and iv) NG + 60 µmol/l cisplatin. The cells were detached with 0.25% trypsin-EDTA solution (Biowest).

Supplementation with ~5.6 mmol/l D-glucose approximates normal blood sugar levels, whereas concentrations ~10 mmol/l and above mimic pre-diabetic and diabetic conditions, respectively. In most studies, HG conditions use 25-30 mmol/l glucose. In previous studies, it was found that 30 mmol/l glucose produced the most evident and reproducible effects on MDA-MB-231 cells (4,6). Furthermore, interstitial glucose can be similar to blood glucose, considering that interstitial glucose concentrations in skeletal muscle of patients with T2DM during a glucose tolerance test were similar to blood glucose levels (10).

The MTT method is a quantitative colorimetric assay that measures the metabolic activity of cells, which is proportional to changes in the cell populations. This assay was used to determine the changes in the cell population and the median lethal concentration (LC₅₀) of cisplatin under standard culture (17.5 mmol/l glucose), NG and HG conditions, and using an osmotic control of glucose 5.6 mmol/l plus mannitol 19.4 mmol/l (Mannitol). A total of 5,000 cells per well were seeded in 96-well plates and exposed to different concentrations of cisplatin (15-90 µmol/l). The culture medium was replaced with 200 µl serum-free DMEM and 50 µl of MTT solution (5 mg/ml) after 48 h of incubation at 37˚C. Then, the cells were incubated for an additional 4 h, the medium was removed, and 100 µl of dimethyl sulfoxide was added to dissolve the formazan crystals. The plate was read at 570 nm wavelength using a microplate reader iMark (Bio-Rad Laboratories, Inc.).

The cells were washed with PBS and lysed in RIPA Buffer (cat. no. 89900; Thermo Fisher Scientific, Inc.), containing a protease inhibitor cocktail (cat. no. I3786; MilliporeSigma) to prevent protein degradation. Proteins were quantified by the Bradford assay, and the cell lysates were stored at -70˚C. Equal amounts of protein (100 µg per well) were mixed with Laemmli sample buffer and separated by 10% SDS-polyacrylamide gel electrophoresis. The transfer was realized in a PVDF membrane (0.8 mm/cm², 2 h), using Tris buffer 25 mmol/l, glycine 192 mmol/l and methanol 20%. Membranes were then blocked with 5% BSA in TBS for 1 h at room temperature to prevent non-specific binding. Subsequently, membranes were incubated overnight at 4˚C with primary anti-caspase-3 antibody (1:500; cat. no. sc-7272; Santa Cruz Biotechnology, Inc.) in TBS-T (0.1% Tween-20) containing 1% BSA. Antibody binding was detected using an HRP-conjugated secondary antibody (1:5,000; cat. no. 7076S; Cell Signaling Technology, Inc.), incubated for 1 h at room temperature, followed by visualization with an enhanced chemiluminescence kit (Clarity Western ECL substrate, Bio-Rad Laboratories, Inc.), according to the manufacturer's instructions. The images were obtained using the C-Digit Blot Scanner (LI-COR Biosciences). The bands were quantified by densitometry analysis using the Image Studio 4.0 software (LI-COR Biosciences).

Total RNA was extracted using TRIzol (Invitrogen; Thermo Fisher Scientific, Inc.), according to the manufacturer's instructions. RNA quality was verified by 2% agarose gel electrophoresis, followed by ethidium bromide staining and visualization under UV light and quantified using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Inc.).

A total of 10 µg of total RNA obtained from cells cultured in NG and HG media was used for cDNA synthesis and labeling with the Superscript II kit (Invitrogen; Thermo Fisher Scientific, Inc.), incorporating dUTP-Cy5 for HG samples and dUTP-Cy3 for NG (control) samples. The absorbance was measured at 655 nm for Cy5 and 555 nm for Cy3. Fluorophore-labeled cDNAs were hybridized in similar quantities to the Human 10K 50-mer oligonucleotide collection (MWG Biotech Oligo Bio Sets).

Microarray images were acquired and quantified using a ScanArray 4000 scanner with QuantArray software (Packard BioChips; PerkinElmer, Inc.), and data were analyzed using Array-Pro Analyzer software (Media Cybernetics, Inc.). Background correction, normalization, intensity filtering, replicate analysis, and the selection of differentially expressed genes were performed using genArise, a free program developed by the Computing Unit of the Cellular Physiology Institute, National Autonomous University of Mexico (http://www.ifc.unam.mx/genarise/). The data generated in the present study were deposited in Gene Expression Omnibus (GEO) platform under accession number GSE136277 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136277). Differentially expressed genes were identified using the following statistical cutoffs: Z-score ≥2 or ≤-2 for biological significance, and a false discovery rate (FDR) ≤0.05 to account for multiple testing. These thresholds were applied from the outset of the analysis to ensure the robustness of the candidate genes included in subsequent evaluations.

The microarray analysis was validated by RT-qPCR for frizzled 3 (FZD3) and tetraspanin 1 (TSPAN1). Total RNA (1 µg) was reverse-transcribed using the SuperScript™ III First-Strand Synthesis System (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol (50˚C for 60 min, followed by enzyme inactivation at 70˚C for 15 min). Alternatively, when individual components were used, reverse transcription was performed in a 20 µl reaction mixture containing 200 U M-MLV reverse transcriptase, 1X first-strand buffer, 0.5 mM dNTP mix (Thermo Fisher Scientific, Inc.), 25 µg/ml oligo(dT) primers (Invitrogen; Thermo Fisher Scientific, Inc.), and 40 U RNase inhibitor, incubated at 37˚C for 60 min with enzyme inactivation at 95˚C for 15 min. The resulting cDNA was amplified using the Maxima SYBR Green/ROX qPCR Master Mix (Fermentas; Thermo Fisher Scientific, Inc.) on a StepOnePlus™ Real-Time PCR System (Applied Biosystems; Thermo Fisher Scientific, Inc.).

The resulting cDNA was amplified using the Maxima SYBR Green/ROX qPCR Master Mix (Fermentas; Thermo Fisher Scientific, Inc.) in a StepOnePlus^™ Real-Time PCR System (Applied Biosystems; Thermo Fisher Scientific, Inc.) with the following thermocycling conditions: Initial activation at 95˚C for 15 min; 40 cycles of 95˚C for 15 sec and annealing at the primer-specific melting temperature for 1 min; followed by a final extension at 72˚C for 10 min. Expression of the selected genes was normalized to hypoxanthine-guanine phosphoribosyltransferase (HPRT). The primer sequences used were as follows: FZD3 forward, 5'-CATGGAGATGTTTGGTGTTCCTT-3' and reverse, 5'-AAGTCGAGGATATGGCTCATCAC-3'; TSPAN1 forward, 5'-TGGGCTGCTATGGTGCTA-3' and reverse, 5'-TGCAGGTTTCATTGGCTGT-3'; and HPRT forward, 5'-CATTATGCTGAGGATTTGGAAAGG-3' and reverse, 5'-CTTGAGCACACAGAGGGCTACA-3'. Relative expression levels were calculated using the 2^-ΔΔCq method (11).

HPRT was used as a housekeeping gene because its expression has been considered moderately stable in BC cell lines, including MDA-MB-231 cells (12). Under hyperglycemic conditions, Liu et al (13) identified the HPRT gene as one of the more reliable reference genes in retinal pigment epithelial cells, and even HPRT expression was more stable than ACTB and GAPDH genes. Additionally, no changes in the expression of this gene under HG were found in our microarray study.

IHC expression of TSPAN1 and FZD3 in BC tissue. To investigate the protein expression patterns of TSPAN1 and FZD3, representative IHC images of ductal breast carcinoma were analyzed from 50 samples available in the Human Protein Atlas database (https://www.proteinatlas.org/), corresponding to women aged 30-37 years. Adjacent normal breast tissue and adenoma samples were used as controls.

The analysis of genes differentially regulated by HG and their associated functions, including GO categories (cellular components, molecular functions and biological processes), as well as biological pathways, was performed using Gene Set Enrichment Analysis (GSEA). For this purpose, the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt; http://www.webgestalt.org/) and WikiPathways Cancer were employed. Gene sets were considered significant at an FDR ≤0.05. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (https://www.genome.jp/kegg/pathway.html) was used to determine the interactions of differentially expressed genes within metabolic and signaling pathways.

All the overexpressed and downregulated genes associated with cancer were uploaded into the program STRING (https://string-db.org/), which predicts the protein-protein interaction (PPI) network. The confidence score for displaying interaction was 0.9. The formatted PPI networks were uploaded and visualized with the Cytoscape software (version 3.10.1) (14). Due to the complexity of this network, the CytoHubba plugin was used to select the 50 nodes with the highest degree score, and their interaction network was constructed (15). Furthermore, the overexpressed genes with at least 2-fold change and their involvement in chemoresistance, according to scientific studies (16-19), were selected, and their possible participation in HG-dependent chemoresistance was considered.

Overall survival (OS) analysis was performed for the key genes YBX3 (also known as CSDA), amphiregulin (AREG), amyloid precursor protein (APP) and N-cadherin (CDH2) using the KM Plotter database (20); (https://kmplot.com) to assess the prognostic value of the genes associated with cisplatin resistance in BC. A cohort of 153 patients with TNBC who were split into high and low expression groups was selected, considering the median expression of the gene. OS plots were created using data from BC samples negative for estrogen, progesterone and HER2 receptors, as detected by IHC and microarray. The hazard ratio (HR) with 95% confidence intervals (CIs) and log-rank P-values were calculated.

To evaluate whether the selected genes are coexpressed in a cohort of patients with BC and the potential molecular and regulatory interactions between proteins codified by them, the coexpression of AREG, FZD3, APP and CDH2 with YBX3 was performed using R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl) on TCGA Breast Invasive Carcinoma (BRCA) datasets (1,097 samples) with R2 internal identifier ps_avgpres_tcgabrca1097_tcgars. Pearson correlation coefficients and associated P-values were calculated, with scatter plots and linear regression used for visualization.

All experiments were performed in at least three independent replicates. Data are expressed as the mean ± SD. One-way ANOVA followed by Tukey's multiple comparison test was used for MTT, western blotting and RT-qPCR analyses, and performed using GraphPad Prism 8.02 software (Dotmatics). P≤0.05 was considered to indicate a statistically significant difference. Microarray and GSEA analyses were considered significant with fold-change ≥1.5 and FDR≤0.05, respectively. Co-expression analyses of AREG, FZD3, APP and CDH2 with YBX3 were performed using the R2 Genomics platform https://hgserver1.amc.nl/cgi-bin/r2/main.cgi, and survival analyses were conducted using the KM Plotter, with HRs, 95% CIs and log-rank P-values. P≤0.05 was considered to indicate a statistically significant difference.

To investigate whether HG induces drug resistance, the LC₅₀ of cisplatin for MDA-MB-231 cells was determined under standard culture conditions (glucose 17.5 mmol/l), NG, NG with mannitol (osmotic control) and HG. The osmotic control group showed no difference compared with the NG group. Cell viability progressively decreased as cisplatin concentration increased from 15 to 90 µmol/l in a concentration-dependent manner, with LC₅₀ values of 60, 15 and 75 µmol/l for standard, NG and HG conditions, respectively. These findings indicated that cells cultured under HG and standard conditions were more resistant to cisplatin compared with those cultured under NG. Thus, in the present study, cisplatin resistance was directly proportional to glucose concentration (Fig. 1A and B).

Apoptosis induced by cisplatin under normoglycemic and hyperglycemic conditions was evaluated in MDA-MB-231 cells by western blot analysis of caspase-3 activation. The caspase 3 antibody recognizes two band, one of higher molecular weight (MW) corresponding with the procapase 3, and other of lower MW corresponding to the cleaved/active caspase 3 (Fig. 1C). Both NG and HG groups exhibited similar levels of total caspase 3 independently of the cisplatin treatment (Fig. 1D). Whereas, cisplatin induced a significant increase in the proportion of active caspase 3 in both NG (P≤0.001) and HG (P≤0.001) conditions (Fig. 1E). Notably, the NG and NG/cisplatin groups displayed higher levels of active caspase-3 than the corresponding HG and HG/cisplatin groups (Fig. 1E). These results indicated that HG attenuated cisplatin-induced apoptosis, thereby promoting cisplatin chemoresistance.

Considering an expression change equal to or greater than 1.5-fold, 1,099 genes were differentially expressed. Among these, 457 were overexpressed, while 642 were downregulated under the HG condition. The top 15 overexpressed and downregulated genes in the HG microenvironment are presented in Table I. All differentially expressed genes are listed in Tables SI and SII.

Microarray data were subjected to functional enrichment analysis using WEB-Gestalt and the WikiPathways database. In the cellular component analysis, an activation of genes that encode protein constituents of the ribosomes, plasma membrane, cell junctions and endoplasmic reticulum was found (Fig. 2A). This was also reflected in the molecular function analysis, where changes in structural constituents of ribosomes, sodium symporter activity and ion transmembrane transport were identified. In addition, changes in hydrolase activity, calcium ion binding, kinase binding and protein-containing complex binding were observed (Fig. 2B). According to the enrichment analysis, the following signaling pathways were enriched in a hyperglycemic microenvironment: Wnt/β-catenin, angiogenesis, TP53 network, NF-κB survival signaling, apoptosis, TGF-beta receptor signaling and ATM-dependent DNA damage response (Fig. 2C).

The expression of TSPAN1 and FZD3 was evaluated by RT-qPCR to validate the microarray analysis. Both genes are associated with an aggressive BC phenotype and showed some of the highest Z-scores (3.81 for TSPAN1 and 2.85 for FZD3). In particular, FZD3, a protein of the Wnt/β-catenin pathway, exhibited the highest Z-score. RT-qPCR analysis confirmed that FZD3 expression under HG conditions was 3.81-fold higher compared with NG (Fig. 3). Similarly, TSPAN1 expression under HG conditions was 2.26-fold higher than under NG conditions, consistent with the microarray results (Fig. 3). These findings are also supported by data from the Human Protein Atlas, which shows higher expression of TSPAN1 and FZD3 proteins in BC tissues compared with healthy mammary tissue (Fig. 3).

According to the aforementioned analysis with the KEGG pathways program, it was found that a high number of genes of transmembrane transporters and ribosomal proteins were differentially expressed in HG conditions (Table II). Additionally, several genes associated with other metabolic processes were found, including: i) Synthesis of DNA, RNA and proteins; and ii) purine, pyrimidine and lipid metabolism. Noteworthy, the upregulation of some genes of electron chain transport or ECT (COX41, COX61, COX7B and NDUFB3) indicated an increase in the electron flux through this chain. However, subunits ATP5O and ATP5D of the ATP synthase were downregulated, suggesting the uncoupling between the ECT and ATP synthesis.

On the other hand, it was found that HG induces increased expression of some genes that promote cell survival (TNFSF7 and TRAF1) (21) or prevent apoptosis (XIAP and BCL2L2) (22,23). Additionally, HG leads to low expression of proapoptotic factors (BIK, PDCD8, PDCD5, BNIP1, DAPK1 and CCAR2) (24-26). These data indicated that HG prevents BC cell death and promotes cell survival under stress conditions, such as the presence of chemotherapeutic agents, such as cisplatin (Fig. 1).

The KEGG pathways program indicates that Wnt-β-catenin, Jak/STAT and Ras-MAPK are involved in the action of HG, together with processes such as cytokine-cytokine receptor interaction, regulation of actin cytoskeleton and focal adhesion (Table II). These results and the PPI analysis suggested that the Wnt-β-catenin signaling has a significant role in the action of HG.

In the PPI network of overexpressed genes constructed by Cytoscape, a total of 303 nodes and 834 edges were arranged (data not shown); further analysis by cytoHubba based on this network revealed a network with 50 principal nodes (Fig. 4). Between the highest degree score hubs, genes for ribosomal proteins were found, such as RPL13a, RPL23a, RPS3a1, RPS13, RPS8, RPS21, RPL6, RPL15, RPS7, MRPS7 and RPL9 with a high degree of interaction shown in red (Fig. 3). Interacting with these nodes genes coding for transcription and protein synthesis (PABPC1, EIF4G2, EIF5B, EEF1G and HNRNPA1), potassium channels (KCNG2, KCNG1 and KCNS1) and metabolism (COX4I1, COX6A1 and NDUFB3) were found. Other important hubs were associated with the Wnt/canonical pathway, such as WNT-1, DVL2 and LEF1. Other proteins related to cancer progression were also represented (APP, AREG and CDH2). It is noteworthy that ribosomal proteins were overrepresented and had the highest degree scores (red and orange in Fig. 4). Because of this, nodes of important proteins had lower degree scores (orange and yellow in Fig. 4). The PPI network according to the enrichment analysis indicates the relevance of Wnt/β-catenin (Fig. 2C).

Considering the hub genes with the highest number of interactions obtained from the CytoHubba analysis and their prognostic evaluation using the KM-plot platform, YBX3 or CSDA (HR=2.04; 95% CI, 1.01-4.01; P=0.041), AREG (HR=2.57; 95% CI, 1.26-5.24; P=0.0073), APP (HR=1.73; 95% CI, 0.87-3.44; P=0.11) and CDH2 (HR=1.74; 95% CI, 0.88-3.46; P=0.11) were associated with poor prognosis in diabetic patients with BC (Fig. 5). These data demonstrate that high expression of YBX3 and AREG is significantly associated with reduced survival probability, whereas APP and CDH2 did not reach statistical significance. Nevertheless, elevated YBX3 and AREG levels may serve as potential biomarkers for identifying diabetic patients with BC with a worse prognosis.

The coexpression analysis was performed in a cohort of 1,097 patients diagnosed with breast invasive carcinoma, aged 26 to 90 years, with heterogeneous clinical characteristics (Fig. 6), including estrogen receptor status (ER⁺ 71.7%, ER^- 22%), progesterone receptor status (PR⁺ 63.7%, PR^- 31.4%), HER2 status (HER2⁺ 14%, HER2^- 51.3%) and metastatic condition (M0 82.6%, MX 14.8% and M1 2%). The analysis revealed that APP and AREG expression were significantly correlated with YBX3 (R=0.373 and R=0.119, respectively), indicating that higher YBX3 expression is associated with increased APP and AREG expression. A positive but weak correlation was observed between YBX3 and FZD3 (R=0.139). Importantly, CDH2 and YBX3 also exhibited a weak but statistically significant correlation (R=0.131; P=1.29x10^-5).

Through the evaluation of hub genes with a greater number of interactions (Fig. 4), the survival of TNBC patients using the KM plotter (Fig. 5) and coexpression analysis (Fig. 6), the authors propose YBX3 as a central regulator positively correlated with AREG, APP and FZD3, suggesting that these genes may participate in a signaling network that promotes proliferation, invasion and therapy resistance under hyperglycemic conditions. In particular, YBX3 and AREG showed significant prognostic value, as their high expression was associated with reduced survival in patients with BC, highlighting their potential as biomarkers of poor outcome (Fig. 5).

By contrast, APP and CDH2 did not reach statistical significance in survival analyses, but coexpression studies demonstrated that both genes are positively correlated with YBX3 (Fig. 6). This finding suggests that APP and CDH2 may not act as independent prognostic markers, but rather as cooperative elements within YBX3-driven regulatory networks, thereby contributing indirectly to tumor progression and aggressiveness. Importantly, recent studies have shown that YBX3 is overexpressed in multiple cancer types and contributes to chemoresistance by regulating transcriptional and translational programs involved in EMT, proliferation, and survival pathways (19,41,42). Moreover, YBX3 regulates the expression of growth factors and ligands such as AREG, creating a positive feedback loop that amplifies growth factor signaling, enhances EMT, and promotes chemoresistance (43).

AREG is the primary EGFR ligand in MDA-MB-231 cells, and its binding to EGFR promotes BC growth and metastasis (44). APP overexpression drives EMT, proliferation and migration/invasion in BC cells, and it has been proposed as a risk factor for lymph node metastasis and poor prognosis in non-luminal BCs, including TNBC (45). CDH2 is a mesenchymal marker induced by EMT that facilitates migration and invasion, and its high expression correlates with poor prognosis in invasive micropapillary BC (46,47).

Taken together, these findings suggest that while AREG and CDH2 emerge as potential prognostic biomarkers, APP and CDH2 may act primarily as modulators of YBX3-driven transcriptional networks that support tumor progression rather than serving as independent prognostic indicators. Nevertheless, despite these associations, the current evidence remains insufficient to establish these hub genes as reliable biomarkers, underscoring the need for further validation in larger patient cohorts and in-depth functional studies.

It must be considered that an increase in the mRNA level of a gene is not always followed by an increase in the codified protein, and the protein generally realizes a function in metabolism, secretion, or gene expression regulation. This situation limits the conclusions of the estimations of the OS based on mRNA values, as our data were derived from the KM plot analysis.

Therapeutic implications and resistance bypass strategies. The present results provide a foundation for therapeutic strategies aimed at overcoming hyperglycemia-induced chemoresistance in TNBC. Previous studies have shown that inhibitors or interfering RNAs targeting YBX3 increase chemosensitivity, highlighting a promising approach for TNBC (19,42). Other potential therapeutic targets include metabolic reprogramming and transporter activity, considering the SLC6A6, SLC39A7 and RPS13 genes (16,17,29), which are overexpressed in HG and aforementioned. Pharmacological inhibition of these transporters, or modulation of glucose and lipid metabolism, could sensitize TNBC cells to chemotherapy. Combined strategies that target metabolic and oncogenic signaling pathways may prove particularly effective. The inhibition of the Wnt/β-catenin pathway or ATM-mediated DNA repair, in combination with chemotherapy, could synergistically reduce tumors. These findings support a framework for personalized combinatorial therapies in diabetic patients with BC, integrating metabolic modulators, inhibitors of YBX3-regulated networks, and conventional chemotherapeutics to overcome resistance and improve clinical outcomes.

Limitations of the present study. The present study has potential limitations. It was based on the analysis of the changes induced by HG in an invasive BC cell line, using an in vitro experimental and bioinformatics approach. Although the MDA-MB-231 cell line has been widely used as a model of invasive BC, it does not represent the genetic heterogeneity of invasive breast tumors. Therefore, the current findings need to be validated in other experimental models and clinical cohorts. However, the present study provides important insights and allows us to better understand the behavior of this type of cancer under HG conditions, as well as to identify potential signaling pathways and genes associated with chemoresistance and prognosis in patients with T2DM.

Although the use of HPRT as a housekeeping gene is considered unreliable for comparing the gene expression between cancerous and non-cancerous tissues (48), this gene has been recommended in hyperglycemic conditions (13), and its expression is moderately stable in BC cell lines (12), although in hyperglycemic conditions, it has been validated in other types of cells. Therefore, the use of an additional reference gene is recommended to further consolidate the overexpression of TSPAN and FZD3 genes.

The present findings and bibliographic references suggest that TSPAN1 and FZD3 play an important role in the induction of chemoresistant and invasive phenotypes by HG (18,32). However, gene knockdown experiments are required to better understand their roles.

The clinical implications were partially validated using public patient datasets and informatics platforms; however, these platforms are generally based on non-diabetic patients, and the estimation of the effect of selected overexpressed genes on OS was therefore performed in non-diabetic conditions. These results should be interpreted with caution, highlighting the need for further validation in diabetic patient cohorts.

Additionally, both diabetes and cancer are complex processes involving multiple genetic and environmental contexts; therefore, further studies are required to validate the clinical implications of our findings. These include validation at both protein (western blotting and IHC) and mRNA (RT-qPCR) levels of overexpressed genes (FZD3 and TSPAN1), as well as of potential genes associated with chemoresistance and prognosis (AREG and YBX3) in breast tumor tissues of diabetic patients.

In addition, changes in gene expression and the induction of cisplatin chemoresistance by diabetes in a mouse model of syngeneic BC are currently studied, and patient-derived organoids can be used to analyze cisplatin action under HG conditions in human tissues. Finally, the correlation of the selected genes and clinical outcomes should be studied in other patient cohorts, preferably in diabetic patients with BC.

In conclusion, these data indicate that alterations in gene expression induced by hyperglycemia lead to changes in the metabolism of cancer cells, affecting a variety of processes that promote a more aggressive phenotype through the activation of Wnt/β-catenin, NF-κB survival, angiogenesis and PI3K/AKT signaling, contributing to enhanced proliferation, migration, invasion and inhibition of apoptosis. Some potential biomarkers of cisplatin resistance under hyperglycemia could be AREG and YBX3.