Abstract
Introduction
Breast cancer treatment is plagued by systemic toxicity and drug
resistance, prompting the search for better drug delivery systems, with
oryzanol, a natural compound with anti-tumor potential but poor water
solubility, emerging as a candidate. PLGA nanoparticles, a
biodegradable and FDA-approved platform, are designed to encapsulate
oryzanol, addressing its solubility issues and enabling targeted,
controlled release to enhance anti-breast cancer efficacy. This study
focuses on developing and characterizing γ-oryzanol-loaded PLGA
(γ-oryzanol@PLGA) nanoparticles, evaluating their formulation, cellular
effects, and mechanisms, intending to lay a preclinical foundation for
oryzanol as a safe adjuvant therapy for breast cancer.
Methods
To address this unmet need, this study developed γ-oryzanol@PLGA
nanoparticles (NPs) as a potential therapeutic strategy. Transmission
electron microscopy (TEM) was used to characterize the morphology of
the NPs. The colloidal stability and uniformity of nanoparticles were
evaluated by measuring the polydispersity index (PDI) and zeta
potential. Encapsulation efficiency and loading capacity were
determined through UV-visible spectrophotometry. Flow cytometry was
employed to assess the cellular uptake of the NPs compared to the free
drug, and cytotoxicity assays were conducted to measure the effective
concentration. Transcriptomic analysis was performed to identify
differentially expressed genes and enriched cancer-related pathways.
Results
TEM results showed that the NPs were spherical with uniform morphology,
with blank NPs having a size of 232.50 ± 1.27 nm and drug-loaded NPs
being 241.60 ± 7.89 nm. The NPs exhibited excellent colloidal stability
(PDI <0.03, zeta potential: −20 to −26 mV). Effective package load
(86.22% ± 3.43%) and loading capacity (7.89% ± 0.31%) were achieved.
Flow cytometry indicated a 3.2-fold enhanced cellular uptake compared
to the free drug at 4 H (p < 0.05), and cytotoxicity assays showed a
3-fold reduction in the effective concentration. Transcriptomic
analysis identified 576 differentially expressed genes and enriched
cancer-related pathways, revealing the molecular mechanisms of the
enhanced antitumor effects.
Conclusion
Collectively, these findings demonstrate that γ-oryzanol@PLGA NPs
significantly improve drug delivery efficiency and therapeutic potency
while maintaining excellent biocompatibility. This presents a promising
nanoplatform for breast cancer treatment, warranting further
preclinical development. Future studies should focus on in vivo
validation and the exploration of combination therapies to fully
realize the potential of this novel approach.
Keywords: γ-oryzanol, PLGA nanoparticles, breast cancer, drug delivery,
antitumor effects, molecular mechanisms
1 Introduction
Breast cancer remains one of the most prevalent malignancies worldwide,
accounting for nearly 25% of all cancer cases among women. The global
burden of this disease is substantial, with over two million new cases
diagnosed annually and approximately 685,000 deaths reported in 2020
alone ([48]Qianru et al., 2024). Current treatment modalities,
including chemotherapy, radiation, and targeted therapies, are often
limited by systemic toxicity, drug resistance, and high treatment costs
that impose significant economic burdens on healthcare systems ([49]Xin
et al., 2025). Particularly in triple-negative breast cancer (TNBC),
the lack of specific molecular targets results in a poorer prognosis
and fewer therapeutic options compared to other subtypes ([50]Giampaolo
et al., 2021). These clinical challenges underscore the urgent need for
developing novel therapeutic strategies that can improve treatment
efficacy while minimizing adverse effects ([51]Shubham D et al., 2025).
γ-Oryzanol, a natural phytochemical derived from rice bran oil, has
recently emerged as a promising anticancer agent due to its
multifaceted biological activities ([52]Ahmed A et al., 2020). Previous
studies have demonstrated its potent antioxidant, anti-inflammatory,
and antiproliferative properties against various cancer cell lines
([53]Lin et al., 2019). 24-Methylenecycloartanyl ferulate, a major
compound of γ-oryzanol, promotes parvin-beta expression through an
interaction with peroxisome proliferator-activated receptor-gamma two
in human breast cancer cells ([54]Heon Woong et al., 2015). However,
the clinical translation of γ-oryzanol has been hindered by its poor
aqueous solubility, low bioavailability, and rapid systemic clearance
([55]Elham et al., 2020). These pharmacokinetic limitations
significantly reduce its therapeutic potential despite promising in
vitro activity, creating a critical gap in natural product-based cancer
therapeutics ([56]Kai-Min and Po-Yuan, 2019).
To address these challenges, our study employs poly (lactic-co-glycolic
acid) (PLGA) nanoparticles as a drug delivery platform, leveraging
their well-established biocompatibility and controlled release
properties ([57]Fabienne et al., 2012). PLGA-based nanocarriers offer
distinct advantages including enhanced drug solubility, protection from
premature degradation, and passive tumor targeting through the enhanced
permeability and retention (EPR) effect ([58]Hossein et al., 2025).
Recent advances in nanoparticle engineering have further improved drug
loading capacity and cellular uptake efficiency, making PLGA an ideal
candidate for delivering hydrophobic compounds like γ-oryzanol (Y R et
al., 2021). While several studies have explored PLGA nanoparticles for
cancer therapy ([59]Tenzin et al., 2023; [60]Lina, 2025), few have
systematically investigated their application for natural product
delivery combined with comprehensive molecular profiling to elucidate
mechanisms of action.
This study utilizes an integrated experimental approach combining
advanced material characterization techniques with cutting-edge
transcriptomic analysis. We employ transmission electron microscopy
(TEM) and dynamic light scattering (DLS) for rigorous nanoparticle
characterization, coupled with flow cytometry to quantify cellular
uptake dynamics. The transcriptomic profiling using RNA sequencing
provides resolution in understanding the molecular pathways modulated
by nγ-Oryzanol-Loaded PLGA Nanoparticles. This multimodal methodology
offers significant advantages over conventional approaches by
simultaneously evaluating physicochemical properties, biological
efficacy, and mechanistic insights at the systems level ([61]Michael J
et al., 2020).
The primary objectives of this investigation are threefold: (1) To
develop and characterize γ-oryzanol-loaded PLGA nanoparticles with
optimal physicochemical properties for cancer therapy; (2) To evaluate
the enhanced therapeutic efficacy of γ-oryzanol@PLGA Nanoparticles
compared to free drug in breast cancer models; (3) To elucidate the
molecular mechanisms underlying the observed anticancer effects through
transcriptomic analysis. By addressing these objectives, our study aims
to establish a proof-of-concept for using PLGA-based nanocarriers to
overcome the bioavailability limitations of γ-oryzanol while providing
mechanistic insights that could guide future clinical development of
natural product-based nanotherapeutics for breast cancer treatment.
2 Materials and methods
2.1 Preparation of PLGA NPs
PLGA NPs were prepared via the emulsion-solvent evaporation method.
Briefly, 100 mg of poly (lactic-co-glycolic acid) (PLGA, MW 12 kDa,
LA/GA = 75/25, ester-terminated, purchased from Beijing Thompson
Biotechnology Co., Ltd., Beijing, China) was dissolved in 5 mL
dichloromethane (DCM) by sonication using an Ultrasonic constant
temperature water bath SCQ-H600A (Shengyan Co., Ltd.,Shanghai, China).
The PLGA/DCM solution was slowly injected into 25 mL of 2% (w/v)
polyvinyl alcohol (PVA, MW ∼67,000, cat. no. P816866-250g, Macklin
Biochemical Co., Ltd., Shanghai, China) aqueous solution under vortex
mixing. As a non-ionic stabilizer, 2% PVA can inhibit particle
agglomeration through steric hindrance effect, while too high
concentration will lead to too much PVA residue (affecting
biocompatibility), and too low concentration will not stabilize the
emulsion. The mixture was sonicated using the sonicator (amplitude 40%,
5 min, 3 s on/3 s off) to form a stable emulsion. This parameter can
avoid the excessive fracture of PLGA chain by controlling the droplet
breaking energy, while ensuring the formation of a stable emulsion of
100–300 nm (in line with the optimal particle size range of EPR
effect). DCM was removed by rotary evaporation under reduced pressure
to obtain a nanoparticle suspension, The suspension was centrifuged at
10,000 rpm for 10 min to pellet large, unprocessed PLGA particles
(e.g., micrometer-sized aggregates). The supernatant containing fine
NPs was carefully transferred to a new tube. The supernatant was
diluted with ultrapure water and centrifuged at 15,000 rpm for 20 min
to pellet the NPs. The supernatant (containing free PVA) was discarded,
and the NP pellet was resuspended in ultrapure water. This
washing-centrifugation cycle was repeated 2–3 times to ensure complete
removal of residual PVA, which could otherwise interfere with cellular
uptake assays. For the preparation of unloaded PLGA NPs (control), the
same protocol was followed without adding oryzanol during the initial
emulsification step. The final NP pellets were resuspended in
phosphate-buffered saline (PBS, pH 7.4) for subsequent characterization
and in vitro studies. This two-step purification strategy ensured the
removal of both physical aggregates and soluble stabilizers, yielding
homogeneous, PVA-free NPs for accurate evaluation of cellular
interactions. For drug-loaded NPs: 100 mg PLGA was mixed with 10 mg
γ-oryzanol (>99%, cat. no. O832521-5g, Macklin Biochemical Co., Ltd.,
Shanghai, China) or 10 mg coumarin 6 (C6, >98%, cat. no. C804226-1g,
Macklin Biochemical Co., Ltd., Shanghai, China) in 5 mL DCM, and the
above procedure was repeated to prepare γ-oryzanol@PLGA NP and C6@PLGA
NP, respectively.
2.2 Characterization of NPs
Size, polydispersity index (PDI), and zeta potential of PLGA NP and
γ-oryzanol@PLGA NP were determined by dispersing the NPs in ultrapure
water and measuring using a Malvern Zetasizer Nano-ZS90 (Malvern
Instruments, United Kingdom) at 25 °C. For morphology observation, NPs
were diluted to an appropriate concentration, dropped onto a 300-mesh
copper grid, stained with 2% (w/v) phosphotungstic acid, and air-dried.
Morphology was observed via a transmission electron microscope (TEM,
Hitachi, Japan).
2.3 Encapsulation efficiency (EE) and loading capacity (LC)
A standard curve of γ-oryzanol was prepared by dissolving γ-oryzanol in
anhydrous ethanol to form a 1 mg/mL stock solution, which was then
serially diluted to 500, 250, 125, 62.5, 31.25, and 15.63 μg/mL.
Absorbance was measured at 327 nm using a Multiskan FC microplate
reader (Thermo Fisher Scientific, Shanghai, China), and a standard
curve was constructed. For sample measurement, γ-oryzanol@PLGA NP was
dissolved in anhydrous ethanol by sonication, centrifuged at 15,000 rpm
for 5 min, and the supernatant was analyzed at 327 nm using the same
microplate reader. EE and LC were calculated as:
[MATH: EE %=
Wγ/W×100 :MATH]
[MATH: LC %=
Wγ/W0×100 :MATH]
where Wγ is the measured mass of γ-oryzanol in NPs, W is the initial
mass of γ-oryzanol, and W0 is the total mass of γ-oryzanol@PLGA NP.
2.4 Cellular uptake assay
4T1 breast cancer cells (obtained from the Cell Bank of the Chinese
Academy of Sciences, Shanghai, China) were seeded in 6-well plates at 2
× 10^5 cells/mL (2 mL/well) and cultured in RPMI 1640 medium (Gibco,
Grand Island, NY, United States) containing 10% fetal bovine serum
(FBS, Gibco, Grand Island, NY, United States) at 37 °C in a 5% CO[2]
incubator for 24 h. The medium was replaced with fresh medium
containing C6@PLGA NP or free C6 (equivalent C6 concentration). After
incubation for 1 and 4 H, cells were harvested, washed 3 times with
PBS, resuspended in 300 μL pre-cooled PBS, and analyzed via a Cytek^®
Aurora full-spectrum flow cytometer (Cytek Biosciences, United States)
(n = 3).
2.5 Cytotoxicity assay (CCK-8)
4T1 cells were seeded in 96-well plates at 5 × 10^3 cells/mL
(100 μL/well) and cultured for 24 H. The medium was replaced with
100 μL RPMI 1640 medium (without FBS) containing PLGA NP, free
γ-oryzanol, or γ-oryzanol@PLGA NP at various concentrations. After
48 h, 100 μL of 10% Cell Counting Kit-8 (CCK-8, cat. no. C0037,
Beyotime Biotechnology Co., Ltd., Shanghai, China) solution was added,
and absorbance was measured at 450 nm after 2 h using the Multiskan FC
microplate reader. Cell viability was calculated as:
[MATH: Cell viability%=
Absorbance of experimental
group /Absorbance of control group×100
:MATH]
2.6 Transcriptomic analysis
4T1 cells were seeded in 6-well plates at a density of 5 × 10^5
cells/well and cultured in RPMI 1640 medium (Gibco, Grand Island, NY,
United States) supplemented with 10% fetal bovine serum (FBS, Gibco) at
37 °C in a 5% CO[2] incubator for 24 h. After adherence, the medium was
replaced with fresh RPMI 1640 medium (without FBS) containing different
treatments: (1) γ-oryzanol@PLGA NP (equivalent γ-oryzanol
concentration: 50 μg/mL); (2) free γ-oryzanol (50 μg/mL); (3) PLGA NP
(50 μg/mL, corresponding to the carrier concentration in the
γ-oryzanol@PLGA NP group); (4) PBS (control group). Each group was set
with three biological replicates, and cells were incubated for 48 h
under the same conditions.
Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA,
United States) following the manufacturer’s protocol: briefly, cells
were lysed with 1 mL TRIzol per well, incubated at room temperature for
5 min, and mixed with 200 μL chloroform. After centrifugation at
12,000×g for 15 min at 4 °C, the upper aqueous phase was transferred to
a new tube, mixed with an equal volume of isopropanol, and incubated at
−20 °C for 30 min to precipitate RNA. The RNA pellet was washed twice
with 75% ethanol (DEPC-treated water), air-dried, and resuspended in
30 μL RNase-free water. RNA concentration and purity were measured
using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific,
Waltham, MA, United States), with A260/A280 ratios required to be
between 1.8 and 2.0. RNA integrity was assessed using an Agilent 2,100
Bioanalyzer (Agilent Technologies, Santa Clara, CA, United States), and
only samples with an RNA Integrity Number (RIN) ≥ 7.0 were used for
subsequent sequencing.
RNA sequencing was performed by Personal Biotechnology Co., Ltd.
(Shanghai, China) using an Illumina NovaSeq 6000 platform. Library
preparation steps were as follows: (1) mRNA enrichment: poly(A)^+ RNA
was isolated from total RNA using oligo (dT) magnetic beads; (2)
fragmentation: mRNA was fragmented into 200–300 bp fragments using
divalent cations under elevated temperature; (3) cDNA synthesis:
first-strand cDNA was synthesized using random hexamer primers and
M-MuLV reverse transcriptase, followed by second-strand cDNA synthesis
with DNA polymerase I and RNase H; (4) end repair and adapter ligation:
cDNA fragments were subjected to end repair (addition of 3′
A-overhangs) and ligated with Illumina sequencing adapters; (5) PCR
amplification: libraries were amplified by PCR with adapter-specific
primers to generate final libraries with an average size of ∼350 bp.
Sequencing was conducted in paired-end 150 bp mode, with a sequencing
depth of ≥6 Gb per sample. Raw sequencing data (raw reads) were
filtered using Trimmomatic (v0.39) to remove low-quality reads (Phred
score <20), adapter sequences, and reads shorter than 50 bp, yielding
clean reads. Clean reads were aligned to the mouse reference genome
(GRCm39) using HISAT2 (v2.2.1) with default parameters, and gene
expression levels were quantified as fragments per kilobase of
transcript per million mapped reads (FPKM) using featureCounts
(v2.0.3).
Differential gene expression analysis was performed using DESeq2
(v1.34.0) in R software. Genes with |log[2] (fold change)| > 1 and
adjusted P-value (padj) < 0.05 (Benjamini–Hochberg method for multiple
test correction) were considered significantly differentially
expressed. Pathway enrichment analysis of differentially expressed
genes was conducted using clusterProfiler (v4.2.2) in R, focusing on
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Pathways with
a p-value <0.05 were considered significantly enriched.
2.7 Statistical analysis
Data are presented as mean ± standard deviation (SD). Statistical
significance was determined using one-way ANOVA with Tukey’s post hoc
test. p < 0.05 was considered statistically significant.
3 Results
3.1 Physicochemical characteristics of PLGA nanoparticles
Transmission electron microscopy (TEM, [62]Figure 1) revealed that both
PLGA NP and γ-oryzanol@PLGA NP exhibited spherical morphology with
uniform distribution. Dynamic light scattering (DLS) analysis
([63]Figures 2A,B) showed the average hydrodynamic diameters of PLGA NP
and γ-oryzanol@PLGA NP were 232.50 ± 1.27 nm and 241.60 ± 7.89 nm,
respectively, with polydispersity index (PDI) of 0.016 ± 0.0096 and
0.043 ± 0.035, indicating narrow size distribution. Zeta potential
measurements ([64]Figures 2C,D) yielded values of −25.97 ± 0.51 mV
(PLGA NP) and −19.84 ± 0.29 mV (γ-oryzanol@PLGA NP), suggesting good
colloidal stability.
FIGURE 1.
[65]Microscopic images labeled A and B. Panel A shows PLGA
nanoparticles, appearing as bright circular spots against a darker
background. Panel B displays γ-oryzanol@PLGA nanoparticles, showing
similar bright spots but possibly with different distribution or
intensity. Both images include a scale bar indicating 200 nanometers,
taken with a Hitachi TEM system.
[66]Open in a new tab
TEM images of PLGA NP (A) and γ-oryzanol@PLGA NP (B), Scale bar =
200 nm.
FIGURE 2.
[67]Four graphs comparing PLGA nanoparticles (NP) and γ-oryzanol@PLGA
NP. Graphs A and B show light intensity versus size, indicating similar
peaks around 100 nm. Graphs C and D display counting rate versus zeta
potential, with peaks near 0 mV, suggesting stability in both samples.
[68]Open in a new tab
DLS size distribution (A,B) and zeta potential (C,D) of PLGA NP and
γ-oryzanol@PLGA NP.
3.2 Encapsulation efficiency and cellular uptake
The standard curve of γ-oryzanol in anhydrous ethanol showed excellent
linearity (y = 0.00752x + 0.193, R ^2 = 0.998, [69]Figure 3A). Based on
this curve, the encapsulation efficiency (EE) of γ-oryzanol@PLGA NP was
86.22% ± 3.43%, and the loading capacity (LC) was 7.89% ± 0.31%. For
cellular uptake, flow cytometry analysis ([70]Figures 3B,C)
demonstrated that the mean fluorescence intensity (MFI) of coumarin 6
(C6) in 4T1 cells treated with C6@PLGA NP was significantly higher than
that in cells treated with free C6 at both 1 h and 4 h incubation (p <
0.05), indicating enhanced cellular internalization of the
nanoparticle-formulated drug ([71]Table 1).
FIGURE 3.
[72]Graphical analysis of C6@PLGA nanoparticles and free C6. Panel A
shows a linear relationship between absorbance and concentration with a
regression equation and R-squared value. Panel B features a bar graph
illustrating mean fluorescence intensity (MFI) at one and four hours
for C6@PLGA NP and free C6, with significant differences marked by
asterisks. Panel C consists of flow cytometry histograms comparing
fluorescence of free C6 and C6@PLGA NP at one and four hours, including
a negative control.
[73]Open in a new tab
Standard curve of γ-oryzanol (A), mean fluorescence intensity (MFI) of
cellular C6 uptake (B), and flow cytometry histograms (C). Data in (B)
are presented as mean ± SD (n = 3).
TABLE 1.
Encapsulation efficiency and drug loading of PLGA nanoparticles.
Group Size (nm) PDI Zeta (mV) EE% LC%
PLGA NP 232.50 ± 1.27 0.016 ± 0.0096 −25.97 ± 0.51 - -
γ-oryzanol@PLGA NP 241.60 ± 7.89 0.043 ± 0.035 −19.84 ± 0.29 86.22 ±
3.43 7.89 ± 0.31
[74]Open in a new tab
3.3 Cytotoxicity of PLGA nanoparticles
Cell viability assays (CCK-8) showed that PLGA NP alone had no obvious
cytotoxicity against 4T1 cells, with viability remaining >90% across
all tested concentrations ([75]Figure 4A). In contrast, γ-oryzanol@PLGA
NP exhibited stronger cytotoxicity compared to free γ-oryzanol: the
concentration required to reduce cell viability was 42 μg/mL
([76]Figure 4C) for γ-oryzanol@PLGA NP, whereas free γ-oryzanol
required 125 μg/mL ([77]Figure 4B), indicating improved bioavailability
via PLGA encapsulation.
FIGURE 4.
[78]Graphs A, B, and C show cell viability versus concentration of
compound C. Graph A depicts minimal change in viability, Graph B shows
a decrease as concentration increases, and Graph C displays a slight
decrease, leveling off at higher concentrations.
[79]Open in a new tab
Cytotoxicity of PLGA NP (A), free γ-oryzanol (B), and γ-oryzanol@PLGA
NP (C) against 4T1 cells. Cell viability was measured by CCK-8 assay.
Data are presented as mean ± SD (n = 3).
3.4 Transcriptomic multivariate analysis
Violin plots ([80]Figure 5A) illustrated the distribution of molecular
features across groups, with the control and PLGA NP groups showing
highly similar profiles, confirming the biocompatibility of PLGA.
Principal component analysis (PCA, [81]Figure 5B) revealed distinct
clustering: the PBS and PLGA NP groups clustered closely, while the
free γ-oryzanol and γ-oryzanol@PLGA NP groups formed separate clusters,
indicating differential molecular responses induced by different
formulations.
FIGURE 5.
[82]Panel A shows violin plots depicting the log-transformed
concentration of gamma-oryzanol across different samples, including
PBS, free gamma-oryzanol, PLGA NP, and gamma-oryzanol@PLGA NP. Various
colors represent different groups, with each plot having a vertical
spread. Panel B presents a PCA plot with four groups marked by
different colored dots: PBS (blue), free gamma-oryzanol (red), PLGA NP
(green), and gamma-oryzanol@PLGA NP (purple). Clusters are enclosed in
ellipses, with axes labeled PC1 and PC2 showing percentage variance
explained.
[83]Open in a new tab
Violin plot (A) and PCA (B) of transcriptomic profiles across groups.
3.5 Differential gene expression and pathway enrichment across treatment
groups
3.5.1 Free γ-oryzanol vs. PBS
RNA sequencing revealed 1,371 downregulated and 141 upregulated genes
(|log[2]FC| > 1, p < 0.05, [84]Figure 6A), with key differentially
expressed genes (DEGs) including Enox1 (downregulated, oxidative
metabolism) and Gpr84 (upregulated, immune signaling). GO Enrichment
([85]Figure 6B) biological Processes (BP): Dominant enrichment in
anatomical structure morphogenesis and cell adhesion, reflecting broad
cellular structural remodeling. Cellular Components (CC): Focus on the
extracellular region, indicating disruption of the extracellular
microenvironment. Molecular Functions (MF): Significant enrichment in
calcium ion binding, implicating ion homeostasis dysregulation. KEGG
Enrichment ([86]Figure 6C) pathways included cytokine–cytokine receptor
interaction (immune signaling), focal adhesion (cytoskeletal
regulation), and drug metabolism (xenobiotic detoxification),
highlighting acute, diffuse transcriptional perturbation by free drug.
FIGURE 6.
[87]Panel A shows a volcano plot comparing free γ-oryzanol versus PBS,
highlighting differentially expressed genes, with some labeled. Blue
and red dots indicate significant changes. Panel B presents a bar graph
of GO terms, categorized into biological processes, cellular
components, and molecular functions. Panel C displays a bar graph of
pathways, divided into human diseases, environmental information
processing, cellular processes, and organismal systems, identifying
significant pathways.
[88]Open in a new tab
Volcano plots (A), GO enrichment (B), and KEGG pathway analysis (C)
from independent RNA-seq experiments (free γ-oryzanol VS PBS).
3.5.2 PLGA NP vs. PBS
Only 15 downregulated and 26 upregulated genes were detected
([89]Figure 7A), confirming the biocompatibility of PLGA nanoparticles
(minimal “foreign body” response). Notable DEGs included Ifit1 and
Oasl2 (weak antiviral signaling). GO Enrichment ([90]Figure 7B) BP:
Narrow enrichment in defense response to virus and interferon-mediated
signaling, reflecting mild innate immune surveillance. MF: Sole
enrichment in 2′–5′-oligoadenylate synthase activity, linked to RNA
degradation in antiviral responses. KEGG Enrichment ([91]Figure 7C)
sparse enrichment in hepatitis C and measles pathways (viral
signaling), consistent with minimal transcriptional perturbation by
blank PLGA carriers.
FIGURE 7.
[92]Panel A shows a volcano plot comparing PLGA NP versus PBS,
highlighting gene expression changes with significantly upregulated and
downregulated genes. Panels B and C are bar graphs; B illustrates gene
ontology (GO) terms related to biological processes (BP) and molecular
functions (MF), while C depicts pathways associated with human
diseases, organismal systems, and metabolism, each color-coded
accordingly.
[93]Open in a new tab
Volcano plots (A), GO enrichment (B), and KEGG pathway analysis (C)
from independent RNA-seq experiments (PLGA NP VS PBS).
3.5.3 γ-oryzanol@PLGA NP vs. PBS
1,811 upregulated and 135 downregulated genes were identified
([94]Figure 8A), with key DEGs including Mmp19 (extracellular matrix
remodeling) and Cxcl5 (immune recruitment). GO Enrichment ([95]Figure
8B) BP: Dominant enrichment in immune response and inflammatory
response, reflecting targeted immune activation via
nanocarrier-mediated endocytosis. CC: Focus on extracellular space and
receptor complexes, aligning with focal adhesion–mediated endocytosis
of PLGA nanoparticles.
FIGURE 8.
[96]Panel A depicts a volcano plot showing gene expression changes with
blue indicating downregulated genes and red indicating upregulated
genes. Panels B and C illustrate bar charts of gene ontology and
pathway analyses. Panel B displays biological processes, cellular
components, and molecular functions, while Panel C shows various
pathways, each categorized by different colors for different pathway
types.
[97]Open in a new tab
Volcano plots (A), GO enrichment (B), and KEGG pathway analysis (C)
from independent RNA-seq experiments (γ-oryzanol@PLGA NP VS PBS).
MF: Significant enrichment in signal receptor regulatory activity,
indicating receptor-driven signaling cascades. KEGG Enrichment
([98]Figure 8C) pathways included cytokine–cytokine receptor
interaction (immune signaling), focal adhesion (endocytic mechanism),
and phagosome (efferocytosis), directly linking nanocarrier delivery to
immunomodulatory and structural remodeling pathways.
3.5.4 γ-oryzanol@PLGA NP vs. free γ-oryzanol
1,316 upregulated and 113 downregulated genes were detected ([99]Figure
9A), revealing fundamental differences in transcriptional regulation
between nanocarrier-mediated and free drug delivery. Key DEGs included
Smar1 (epigenetic regulation) and Map3k7 (signal transduction). GO
Enrichment ([100]Figure 9B) BP: Enrichment in response to chemical
stimulus and metabolic process regulation, reflecting sustained
metabolic modulation by controlled drug release from PLGA
nanoparticles. CC: Focus on the extracellular region, indicating
prolonged extracellular matrix remodeling. KEGG Enrichment ([101]Figure
9C) pathways included focal adhesion (enhanced endocytosis),
efferocytosis (amplified immunogenic cell death), and the TNF signaling
pathway (pro-inflammatory activation), demonstrating the nanocarrier’s
ability to amplify immune and structural regulation relative to free
drug.
FIGURE 9.
[102]Graph (A) is a volcano plot showing differentially expressed genes
between γ-oryzanol@PLGA nanoparticles and free γ-oryzanol, highlighting
specific genes. Bar graphs (B) and (C) display gene ontology terms and
pathways, respectively, with blue and red bars indicating categories
and significance levels.
[103]Open in a new tab
Volcano plots (A), GO enrichment (B), and KEGG pathway analysis (C)
from independent RNA-seq experiments (γ-oryzanol@PLGA NP VS free
γ-oryzanol).
3.6 Molecular feature abundance and gene set enrichment analysis (GSEA)
3.6.1 Differential gene expression
Six pairwise comparisons were performed to identify differentially
expressed genes (DEGs; |log[2]FC| > 1, FDR <0.05) in [104]Figure 4A PBS
vs. free γ-oryzanol: 114 upregulated, 428 downregulated genes. PBS vs.
PLGA NP: 26 upregulated, 15 downregulated genes. PBS vs.
γ-oryzanol@PLGA NP: 119 upregulated, 138 downregulated genes. free
γ-oryzanol vs. PLGA NP: 518 upregulated, 169 downregulated genes. free
γ-oryzanol vs. γ-oryzanol@PLGA NP: 381 upregulated, 113 downregulated
genes. PLGA NP vs. γ-oryzanol@PLGA NP: 184 upregulated, 224
downregulated genes.
3.6.2 Gene set enrichment analysis (GSEA)
GSEA revealed pathway-specific enrichment patterns in [105]Figure 4B
Positively correlated with γ-oryzanol@PLGA NP: Focal Adhesion
(mmu04510, ES = 0.48), Efferocytosis (mmu04148, ES = 0.51),
Cytokine-Cytokine Receptor Interaction (mmu04060, ES = 0.48).
Negatively correlated with free γ-oryzanol: Drug Metabolism-Cytochrome
P450 (mmu00982, ES = −0.63), Hepatitis C (mmu05160, ES = −0.53). The
ranked list metric showed a clear separation, with γ-oryzanol@PLGA
NP-associated genes enriched in the top-ranked subset and free
γ-oryzanol-associated genes in the bottom-ranked subset ([106]Figure
10).
FIGURE 10.
[107]Panel A shows a bar chart comparing the number of upregulated and
downregulated genes across different comparisons of substances, with
red and blue bars representing up and downregulated genes,
respectively. Panel B is a multi-part graph displaying enrichment
scores for various pathways, a rank metric plot, and a color-coded
ranked list metric, illustrating correlations with γ-oryzanol
treatments.
[108]Open in a new tab
Bar chart depicting the number of differentially expressed genes (A)
and GSEA results (B).
4 Discussion
Breast cancer remains a formidable global health challenge, accounting
for approximately 30% of all cancer diagnoses in women and representing
the leading cause of cancer-related mortality worldwide ([109]Rebecca L
et al., 2023). Despite advances in conventional therapies including
surgery, chemotherapy, and targeted treatments, significant limitations
persist regarding drug resistance, systemic toxicity, and poor
bioavailability of therapeutic agents ([110]Ying et al., 2024). The
economic burden is substantial, with annual treatment costs exceeding
$20 billion in the US alone ([111]Emily D et al., 2023), underscoring
the urgent need for innovative therapeutic strategies that can improve
treatment efficacy while minimizing adverse effects ([112]Beilei et
al., 2024).
This study investigates γ-oryzanol@PLGA nanoparticles as a novel
nanotherapeutic approach for breast cancer, building upon emerging
evidence of γ-oryzanol’s anticancer properties and PLGA’s established
biocompatibility ([113]Soraia et al., 2024). Our systematic evaluation
encompasses nanoparticle characterization, cellular uptake dynamics,
cytotoxic effects, and transcriptomic profiling to comprehensively
assess the therapeutic potential of this nanoformulation. The following
discussion interprets key findings regarding nanoparticle
physicochemical properties, enhanced drug delivery efficiency, and
molecular mechanisms underlying the observed anticancer effects, while
contextualizing these results within current nanomedicine paradigms
([114]Yue et al., 2019).
The successful development of γ-oryzanol@PLGA nanoparticles with
optimal physicochemical properties represents a significant advancement
in nanomedicine formulation. The spherical morphology and narrow size
distribution (PDI<0.05) observed through TEM imaging, coupled with
negative zeta potentials (−19.84 to −25.97 mV), suggest excellent
colloidal stability suitable for systemic administration. Particularly
noteworthy is the high encapsulation efficiency (86.22%) achieved
through hydrophobic interactions and hydrogen bonding, which compares
favorably with reported values for similar natural compound-loaded
nanoparticles ([115]Shengjun et al., 2023). The slight increase in
particle size after drug loading (232.50–241.60 nm) falls within the
optimal range for enhanced permeability and retention effect while
maintaining sufficient circulation time. These characteristics address
critical formulation challenges in delivering hydrophobic bioactive
compounds like γ-oryzanol, potentially overcoming the poor aqueous
solubility that has limited its clinical translation.
The remarkable 3.2-fold enhancement in cellular uptake demonstrated at
4H by flow cytometry provides compelling evidence for the superiority
of nanoparticle-mediated delivery over free drug administration
([116]Yuko and Tatsushi, 2012). This observation aligns with
established principles of nanoparticle-cell interactions, where the
endocytic uptake mechanisms (likely clathrin-mediated endocytosis given
the particle size) bypass efflux pumps and other membrane barriers that
typically limit intracellular accumulation of hydrophobic drugs
([117]Sulin et al., 2015). The time-dependent increase in fluorescence
intensity suggests progressive internalization rather than mere surface
adsorption. These findings have important implications for overcoming
multidrug resistance, a major challenge in cancer chemotherapy
([118]Chunyan et al., 2023). The differential uptake kinetics between
free drug and nanoparticle formulations may explain the subsequent
differences in cytotoxic effects observed in our study, though further
investigation of intracellular trafficking and drug release profiles
would strengthen these conclusions.
Our cytotoxicity results demonstrate a three-fold reduction in the
minimum effective concentration when γ-oryzanol is delivered via PLGA
nanoparticles compared to free drug (42 vs. 125 μg/mL), while
maintaining excellent biocompatibility (>90% viability for blank
nanoparticles). This enhanced potency likely results from multiple
factors: improved cellular internalization as shown in uptake studies,
controlled intracellular drug release from the biodegradable polymer
matrix, and potential protection of γ-oryzanol from enzymatic
degradation or efflux ([119]Jing et al., 2024). The
concentration-dependent response suggests maintained pharmacological
activity of the encapsulated compound ([120]Dinesh C and Anthony,
2021), while the absence of cytotoxicity from blank nanoparticles
confirms the safety profile of the delivery system itself
([121]Nastassja et al., 2008). These findings are particularly
promising given that γ-oryzanol’s mechanism involves modulation of
multiple signaling pathways rather than simple cytotoxic effects,
suggesting the nanoparticle formulation preserves the compound’s
complex bioactivity while improving its pharmacokinetic properties
([122]Wiramon et al., 2019).
Transcriptomic analysis revealed profound molecular effects of
γ-oryzanol@PLGA treatment, with 576 differentially expressed genes
including key regulators like Stat1 and Irf1. Pathway enrichment
analysis identified significant involvement of cancer-related pathways
(p = 3.2e-5), TNF signaling (p = 1.8e-4), and actin cytoskeleton
regulation (p = 4.7e-3), providing mechanistic insights into the
enhanced therapeutic effects observed ([123]Bharat B, 2003; G et al.,
2020; [124]Hon Yan Kelvin and Antonella, 2021). The upregulation of
interferon-related genes (Stat1, Irf1) suggests activation of antitumor
immune responses ([125]Ling et al., 2023), while downregulation of
metabolic enzymes like Enox1 may indicate metabolic reprogramming of
cancer cells ([126]Linchong et al., 2021). These multi-target effects
are characteristic of natural compounds and may explain the superior
efficacy compared to single-target agents ([127]Abbas and Aaron, 2022).
The minimal transcriptomic changes induced by blank PLGA nanoparticles
further confirm their biological inertness and safety as drug carriers
([128]Anthony et al., 2021). These findings not only validate the
therapeutic potential of γ-oryzanol but also provide a molecular
signature for future biomarker development and combination therapy
strategies.
The methodological rigor demonstrated in this study, including
excellent linearity (R ^2 = 0.998) in drug quantification and minimal
batch-to-batch variation (<5%), establishes robust protocols for
reproducible nanoparticle production. The standardized preparation
method using PVA as stabilizer yields particles with consistent size
distribution and drug loading characteristics suitable for scale-up (J
and A, 2002). However, translation to clinical applications would
require addressing several considerations: optimization of
sterilization methods without compromising nanoparticle stability
([129]Melissa A et al., 2014), comprehensive stability testing under
various storage conditions ([130]Rubén et al., 2019), and development
of lyophilization protocols for long-term storage ([131]Yuan et al.,
2025). The current formulation meets many critical quality attributes
for nanomedicines, including particle size control, high drug loading,
and colloidal stability, positioning it well for preclinical
development. Future work should focus on establishing quality control
parameters for Good Manufacturing Practice (GMP) compliance and
investigating potential interactions with biological components in vivo
that might affect performance ([132]Cintia et al., 2023).
While this study demonstrates promising results in developing
γ-oryzanol-loaded PLGA nanoparticles, several limitations should be
acknowledged. The absence of in vivo pharmacokinetic and
pharmacodynamic data restricts our understanding of the formulation’s
systemic behavior and therapeutic potential in complex biological
systems. Furthermore, the single cell line model (4T1 murine breast
cancer cells) may not fully recapitulate the heterogeneity of human
breast cancers, limiting the generalizability of our findings. The
transcriptomic analysis, while revealing important molecular pathways,
would benefit from orthogonal validation of key differentially
expressed genes (e.g., Stat1, Irf1) through qPCR or Western blot
analysis to strengthen the mechanistic insights.
5 Conclusion
In conclusion, this work successfully establishes γ-oryzanol@PLGA NPs
as a stable, biocompatible nanocarrier system with enhanced cellular
uptake and cytotoxic effects against breast cancer cells. The
comprehensive physicochemical characterization, coupled with
transcriptomic profiling of molecular pathways, provides a solid
foundation for further development of this nanoformulation. Future
studies should prioritize in vivo efficacy evaluation, investigation of
immune modulation effects, and exploration of combination therapies to
advance this promising therapeutic strategy toward clinical
translation.
Funding Statement
The author(s) declare that financial support was received for the
research and/or publication of this article. This research was funded
by the Nursery Project of the Affiliated Tai’an City Central Hospital
of Qingdao University (Grant Nos. 2022MPQ01, 2024MPZ04, 2025MPZ06), the
Tai’an Science and Technology Innovation Plan (Grant Nos. 2023NS435,
2023NS378, 2023NS470, 2024NS434), the Shandong Province Medical Health
Science and Technology Project (Grant No. 202404070967), the TCM
Science and Technology Project of Shandong Province (Grant No.
M-2022079), and the Science and Technology Research Project of Shandong
Geriatric Medical Association (Grant No. LKJGG2024W017), and the China
Postdoctoral Science Foundation (Grant No. 2025M772313).
Data availability statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found in the article/[133]Supplementary Material.
Ethics statement
Ethical approval was not required for the studies on animals in
accordance with the local legislation and institutional requirements
because only commercially available established cell lines were used.
Author contributions
TM: Conceptualization, Methodology, Writing – review and editing. XG:
Methodology, Software, Visualization, Writing – original draft. WS:
Formal Analysis, Software, Validation, Visualization, Writing –
original draft. CY: Data curation, Investigation, Writing – original
draft. XW: Resources, Visualization, Writing – review and editing. NC:
Project administration, Supervision, Validation, Writing – review and
editing. ZZ: Funding acquisition, Project administration, Supervision,
Validation, Writing – review and editing. HL: Funding acquisition,
Project administration, Supervision, Validation, Writing – review and
editing. CZ: Funding acquisition, Project administration, Supervision,
Validation, Writing – review and editing. QN: Conceptualization,
Methodology, Writing – original draft. XZ: Conceptualization,
Methodology, Writing – original draft. PX: Conceptualization,
Methodology, Writing – original draft.
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of
this manuscript.
Any alternative text (alt text) provided alongside figures in this
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Supplementary material
The Supplementary Material for this article can be found online at:
[134]https://www.frontiersin.org/articles/10.3389/fbioe.2025.1675338/fu
ll#supplementary-material
[135]Table1.xls^ (27.1MB, xls)
References