Transforming growth factor-β1 (TGF-β1), a
pleiotropic cytokine, has a dynamic biphasic effect on cancer
development and progression. The mechanism underlying its early
tumour suppression and late tumour promotion has become a key
scientific issue in the field of tumour therapy (1). Under physiological conditions,
TGF-β1 maintains epithelial homeostasis through the suppressor of
mother against decapentaplegic (SMAD)-dependent pathway. However,
during tumour progression, this pathway is often inactivated
because of SMAD mutations, receptor expression downregulation or
negative regulator overexpression, resulting in the loss of its
tumour-suppressive function (2,3).
With tumour progression, TGF-β1 signalling induces tumour-promoting
reprogramming, which activates epithelial-mesenchymal transition
(EMT), non-SMAD pathways (such as phosphatidylinositol PI3K/AKT and
MAPK), and the tumour stem cell maintenance network, inhibiting the
binding ability of alkB homolog 5 to the forkhead box A1 (FOXA1)
coding sequence region and thereby driving metastatic phenotype
formation and chemotherapy resistance (2-4).
More notably, TGF-β1 signalling establishes a
'vicious cycle' that promotes metastasis by reshaping the
three-dimensional regulatory network of the tumour
microenvironment, including activating cancer-associated
fibroblasts (CAFs), promoting angiogenesis and constructing an
immunosuppressive microenvironment such as expanding T-regulatory
(Treg) cells and inhibiting cytotoxic T lymphocyte (CTL) function
(1,5,6).
Recent studies have particularly emphasised that TGF-β1 forms a
coinhibitory loop with immune checkpoints such as programmed cell
death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1),
providing a theoretical basis for combined blocking strategies
(6-8). Although TGF-β1-targeting therapeutic
strategies (such as antibodies and kinase inhibitors) face
challenges, including heterogeneous efficacy and toxicity in
clinical translation, it is anticipated that by accurately
distinguishing tumour development stages, analysing signal network
heterogeneity and developing combined immunotherapy regimens, the
current bottleneck can be overcome (5,9,10).
This review systematically summarises the multidimensional
mechanisms of action of TGF-β1 related to cancer progression,
aiming to provide a molecular basis for the development of adaptive
treatment strategies, which has important clinical significance for
improving the survival prognosis in patients with advanced
cancer.
As a pleiotropic cytokine, the physiological
functions of TGF-β1 include maintaining epithelial-mesenchymal
balance, cell cycle arrest, apoptosis induction and DNA damage
repair. In healthy tissues, TGF-β1 activates cyclin-dependent
kinase inhibitor 2B (p15INK4b, CDKN2B) and CDKN1A (p21CIP1)
expression through the SMAD2/3-dependent pathway (11), inducing G1-phase arrest and
inhibiting excessive cell proliferation. In addition, TGF-β1
maintains the epithelial phenotype and inhibits the EMT process by
regulating E-cadherin expression (12).
As tumours progress, inflammatory factors and gene
mutations in the tumour microenvironment can lead to a shift in
TGF-β1 signalling towards a tumour-promoting phenotype (15), as shown in Fig. 1 and Table I. In the early stages of tumour
progression, the inactivation of TGF-β1 signalling mainly occurs
through three mechanisms, including SMAD4 gene mutations or
deletions (16,17), TGF-β receptor 2 (TGFβR2) promoter
methylation (18-20) and signalling pathway bias.
These key bias nodes include: i) The Ras homolog
gene family member A/Rho-associated coiled-coil containing protein
kinase (RhoA/ROCK) pathway-it involves inhibiting cofilin through
LIM domain kinase 1 phosphorylation, promoting actin remodelling
and increasing the efficiency of pseudopodia formation by a factor
of two (21); ii) Notch
crosstalk-TGF-β1 upregulates and activates the Notch1 intracellular
domain, synergistically enhancing matrix metallopeptidase 9 (MMP9)
expression with Smad3 and increasing vascular density in breast
cancer models (22); and iii)
PD-L1 bidirectional regulation-PD-L1 can increase the transcription
and expression of TGF-β1 in tumour cells and knockdown of PD-L1
expression can significantly inhibit TGF-β1-induced EMT,
proliferation and migration (23).
With the accumulation of genomic instability and
microenvironmental alterations, tumour cells escape their
growth-inhibiting effects through acquired mutations (such as SMAD4
deletion) or epigenetic modifications (such as downregulation of
the expression of TGF-β receptor II) to promote invasion and
metastasis (24-27). The critical point of this
functional transition is usually associated with the activation of
the EMT program. TGF-β1 drives tumour cells to acquire migration
and invasion characteristics by regulating the expression of core
EMT-related transcription factors (such as Snail family
transcriptional repressor, Twist family bHLH transcription factor
and zinc finger E-box binding homeobox 1) (Table II). The EMT-promoting effect of
TGF-β1 has been confirmed in various cancer models, and its
mechanism involves FOXA1 and non-coding RNA regulation (2,28).
Changes in the expression of key EMT regulators are
significantly associated with clinical metastasis risk. In a
pancreatic cancer model, peptidyl-prolyl cis-trans isomerase
NIMA-interacting 1 (Pin1) protein promotes fibroblast growth factor
pathway activation by stabilising the conformation of TGFβR1,
leading to cytoskeleton remodelling and enhanced motility.
Knockdown of Pin1 expression weakens tumour cell migration
(29). Platelet-derived TGF-β1
induces autophagy in hepatocellular carcinoma (HCC) cells through
the AMP kinase/mammalian target of rapamycin (mTOR) axis,
increasing the number of autophagosomes in an HCC orthotopic model
and significantly increasing both invasion and metastasis rates
(30). This synergistic effect of
multiple pathways explains why inhibition of a single SMAD pathway
often fails to completely block the metastatic effect of
TGF-β1.
The metastatic mechanism of EMT involves multiple
biological processes. Studies have shown that TGF-β1 significantly
increases the proportion of tumour stem cells by regulating the
Wnt/β-catenin pathway (31). In
addition, TGF-β1 promotes collagen deposition and changes in amino
acid metabolism through prolyl 4-hydroxylase subunit α3-mediated
proline hydroxylation, increasing the cell migration speed by a
factor of two (32). TGF-β1 can
also remodel the microenvironment. TGF-β1 induces autophagy in CAFs
and decreases the microenvironment pH through upregulation of
monocarboxylate transporter 4 expression, thus promoting
angiogenesis (33).
TGF-β1 maintains tumour stem cell characteristics
through a dual mechanism. First, TGF-β1 directly activates SRY-box
transcription factor 4 (SOX4) expression, and SOX4 overexpression
in acute myeloid leukemia-resistant cell lines significantly
increases chemotherapeutic drug tolerance (34). Second, by upregulating TGF-β1
autocrine secretion through C-terminal tensin-like protein, a
positive feedback loop is formed in bladder cancer tissues, leading
to a significant increase in the proportion of paclitaxel-resistant
cells (35). A study on
pancreatic cancer has shown that deficient expression of trefoil
factor family 1 promotes EMT, significantly increasing the
proportion of CD133+ cells (a tumour stem cell marker) and
significantly decreasing the proportion of gemcitabine-sensitive
cells (36).
This reprogramming of the stem cell microenvironment
makes it difficult for traditional chemotherapy to eradicate tumour
cells, suggesting that combining TGF-β pathway inhibitors may
improve treatment efficacy. In patients with head and neck squamous
cell carcinoma, SMAD4-mutant cells respond strongly to
TGF-β1-induced stem cell marker expression (37), providing a molecular subtyping
basis for precision treatment.
CAFs are the main effector cells of TGF-β1-mediated
matrix remodelling. TGF-β1 drives CAF activation through
SMAD-dependent and SMAD-independent pathways, thereby stimulating
collagen crosslinking and fibronectin deposition to form a
metastatic, sclerotic matrix. In a gallbladder cancer model, TGF-β1
upregulates FOXA1 expression through m6A modification, increasing
the expression of α-smooth muscle actin (α-SMA) and fibronectin in
CAFs (2). In types such as colon
cancer, TGF-β1-induced CAFs can increase the collagen fibre
diameter through Janus kinase (JAK)/signal transducer and activator
of transcription 3 (STAT3) signalling, resulting in the formation
of a dense physical barrier. Treatment that neutralises TGF-β1 can
promote the recruitment of antigen-specific CD8+ T cells into
tumours (38,39). This fibrotic microenvironment
activates latent TGF-β1 through integrin αvβ6, forming a positive
feedback loop that maintains the concentration of active TGF-β1 at
a high level (40). In a liver
cancer model, the stabilising effect of TGFβ receptor-associated
protein 1 (TGFβRAP1) on TGFβR1 enhances the contractility of CAFs,
an effect that can be reversed by small interfering
(si)RNA-mediated knockout of TGFβRAP1 (3). During the transformation of ductal
carcinoma in situ to invasive carcinoma of the breast,
TGF-β1 induces the loss of cadherin in myoepithelial cells via EMT
and upregulates the expression of MMP-9 and IL-6 (41,42), significantly increasing the
disruption of basement membrane integrity.
Microenvironmental factors play important regulatory
roles in the remodelling of TGF-β1 signalling. Under hypoxic
conditions, the hypoxia-inducible factor 1α-microRNA (miR)145 axis
promotes tumour angiogenesis by inhibiting
TGFβ1-SMAD2/3-thrombospondin-1 signalling (43).
In mouse models, platelet-released TGF-β1 increases
microvascular density and enhances vascular permeability (44). TGF-β1-induced
endothelial-mesenchymal transition significantly enhances tumour
angiogenesis (45), and a breast
cancer mouse model showed increased microvascular density in the
TGF-β1-treated group, accompanied by activation of the Notch
pathway (22).
Lymphangiogenesis depends mainly on the upregulation
of vascular endothelial growth factor C (VEGF-C) expression. As
observed in a tube formation assay and a tumour xenograft mouse
model, TGF-β1 induced high expression of VEGF-C by activating the
Smad2/3 pathway, thereby significantly promoting lymphangiogenesis
in a model of gastric cancer (46). Notably, transglutaminase 1 has
been shown to participate in vascular basement membrane
remodelling, and its inhibitors can significantly reduce the
perivascular cell coverage of tumours, thereby enhancing the
penetration of chemotherapeutic drugs (47).
TGF-β1 promotes the expansion and functional
activation of Tregs through the SMAD2/3-dependent pathway. In a
multiple myeloma model, Treg-derived TGF-β1 inhibited cyclic
GMP-AMP synthase/stimulator of interferon genes signalling and
promoted major histocompatibility complex class I (MHC I) molecule
loss and PD-L1 expression upregulation (48). In a type 1 diabetes study,
TGF-β1-regulated T-cell immunoreceptor with Ig and ITIM domains+
Tregs significantly inhibited the cytotoxicity of CD226+CD8+ T
cells (49). In patients with
colorectal cancer, the expression level of TGFBR2 in
tumour-infiltrating Tregs was several times greater than that in
peripheral blood, and its inhibitory activity was positively
correlated with the concentration of TGF-β1 (50).
TGF-β1 can limit the ability of CD4+ (but not CD8+)
anti-TGF-β chimeric antigen receptor (CAR) T-cells to partially
inhibit tumour growth through the secretion of granzyme B and
interferon (IFN)-γ. CD4+ T28zT2 T cells treated with TGF-β1
survived continuously in peripheral blood and tumours, maintained
mitochondrial morphology and function, and did not induce in
vivo toxicity. These findings indicate that reprogramming the
TGF-β signalling pathway using T28zT2 (anti-TGF-β CAR T cells) in
CD4+ T cells is a promising strategy for eliminating solid tumours
(51). This bidirectional
regulation is achieved through a Smad3-dependent mechanism: TGF-β1
promotes Foxp3 expression by phosphorylating Smad3, while Smad3
knockout completely reverses the amplification effect of Tregs
(52).
MDSCs are considered major tumour promoters and
markers of cancer immune evasion mechanisms (53). TGF-β inhibits the activation and
antitumour function of T lymphocytes, dendritic cells and natural
killer (NK) cells while promoting the production of regulatory T
cells and MDSCs, thereby inhibiting the antitumour activity of T
cells (54). In patients with
renal cell carcinoma, cellular homolog of Sloan-Kettering viral
oncogene Ski (c-Ski) protein degradation is induced by TGF-β. The
downregulation of TGF-β expression remodels the immunosuppressive
tumour microenvironment by limiting tumour-infiltrating MDSCs,
while c-Ski protein degradation is also inhibited (55). The long noncoding RNA
Exo-AGAP2-AS1 from glioma cells regulates TGF-β1 expression in
MDSCs through binding to miR-486-3p; thus, Exo-AGAP2-AS1
upregulation promotes glioblastoma multiforme cell growth and
metastasis through MDSCs (56).
TGF-β1 activates the TGF-β1/2-Smad2/3 pathway by
upregulating the expression of FAT atypical cadherin 1 (FAT1),
promoting the recruitment of MDSCs to tumour foci and increasing
the proportion of MDSCs. In glioma tumours, FAT1 expression is
positively correlated with the expression of surrogate markers of
MDSCs, including PD-L1, PD-L2 and IL-10 (57), suggesting that FAT1 may play a
role in MDSC-mediated immunosuppression.
TGF-β1 exhibits dose-dependent inhibition of NK
cells. In patients with metastatic melanoma, the use of anti-TGF-β1
antibodies restored the NK cell killing efficiency to baseline
levels (58). These findings
suggest that the relationship between high levels of TGF-β1 and NK
cell inhibition in cancer patients is a major mechanism underlying
tumour immune escape. This inhibitory mechanism involves the
downregulation of the expression of the natural killer group 2D
(NKG2D) ligands MHC class I polypeptide-related sequence A/B and
PD-L1 via Smad4 by TGF-β1, thereby blocking NK cell activation
signals (59).
The inhibition of NK cell function involves multiple
mechanisms. For example, TGF-β1 induces receptor expression
downregulation. After NK cells from patients with gastric cancer
were treated with TGF-β1, the expression levels of NKp30, NKp46,
NKG2D and DNAX accessory molecule-1 were significantly reduced
(60). Second, during metabolic
reprogramming, TGF-β1 significantly reduced mitochondrial oxidative
phosphorylation efficiency through mTOR complex 1 inhibition
(61,62). In response to cytokine
dysregulation, IL-15-driven proliferation signalling is blocked by
TGF-β1, and the proportion of cells undergoing cell cycle arrest in
the G1 phase increases (63).
TGF-β1 forms a positive feedback loop with the
PD-1/PD-L1 pathway. In chronic inflammation models, TGF-β1
upregulates PD-1 expression in macrophages via synergistic
SMAD3/STAT3 signalling, and PD-1+ macrophages can secrete TGF-β1,
resulting in the formation of an autocrine loop (64). In triple-negative breast cancer
cells, TGF-βRII deficiency leads to a doubling of PD-L1 expression,
while the proportion of lymphocyte activation gene 3+ T cells
increases, resulting in resistance to immune checkpoint inhibitor
(ICI) therapy (18,65).
Selective TGF-β1 inhibitors, such as compound 67,
can block the EMT process and significantly reduce the number of
metastatic lesions in an A549 lung cancer model (66). Tumour-activating prodrugs
targeting TGFβR1, such as the LY2157299 precursor (galunisertib),
are designed to selectively release the active drug within the
tumour microenvironment, thereby minimising systemic exposure and
reducing off-target toxicity. LY2157299 has been shown to decrease
the incidence of cardiovascular events (67,68) by limiting systemic TGF-β pathway
inhibition, which can otherwise lead to adverse effects. This
targeted approach enhances the therapeutic window by balancing
efficacy and safety.
In combination immunotherapy, TGF-β1 antibodies and
PD-1 inhibitors can synergistically remodel the immune
microenvironment. In patients with advanced colorectal cancer, the
combination therapy group showed a significant increase in CD8+ T
cell infiltration and a significant improvement in the objective
response rate (69-71). These advances provide a precise
intervention tool for understanding the dual role of TGF-β1.
BTN1A1 is a novel immune checkpoint mutually
exclusive with PD-L1, and its inhibitory effect on T cell
activation has been demonstrated both in vitro and in
vivo (65). Increased
expression of BTN1A1, a member of the butyrophilin subfamily, has
been detected in BRAF-mutant tumours (72). Disruption of intact BTN1A1 protein
expression leads to a large accumulation of Xanthine dehydrogenase
in the cytoplasm, inducing the activation of acute-phase response
genes and phosphorylated leukemia inhibitory factor genes
downstream of STAT3 (73). Novel
strategies have shown that simultaneous blockade of TGF-β1 and
BTN1A1 can increase CD8+ T-cell infiltration in the tumour
microenvironment by a factor of two, which is superior to the
effect of monotherapy (65).
These findings provide new insight into overcoming
ICB resistance. In PD-1 resistance models, TREX1 inhibitors
combined with anti-TGF-β1 antibodies can increase tumour regression
rates (74,75).
TGF-β1, a key regulator of the tumour
microenvironment, faces significant challenges related to targeted
therapy. Although systemic inhibition of TGF-β1 can enhance
antitumour immunity and limit the expansion, survival and function
of CD4+ T cells (51) (Table IV), dose adjustments are often
required because of cardiovascular toxicity and delayed wound
healing events (58,76). A recent study showed that
perfluorooctanoic acid induces pulmonary toxicity through the
TGF-β1/Smad pathway (77).
Therefore, an early phase Ib clinical trial of the use of
TGF-β-targeted drugs such as PF-03446962 in combination with
regorafenib for the treatment of metastatic colorectal cancer was
terminated because of serious adverse events (78) (Table IV). PF-03446962 is a monoclonal
antibody targeting activin receptor like kinase 1. However, a
recent study showed that the combination of PF-03446962 and
nivolumab has promising anti-cancer activity and safety in patients
with advanced HCC (79).
The clinical response rate to TGF-β1 monotherapy is
generally low, and its limitations also include the requirement
that the kinase inhibitor galunisertib achieves a 50% inhibitory
concentration of TGF-βRI in µmol (80).
However, the local delivery system of exosome-based
TGF-β1 siRNA significantly improved the degree of fibrosis in the
lung tissues of a fibrotic mouse model. siTGF-β1 effectively
delivers specific siRNAs to the lungs, leading to TGF-β1 mRNA
silencing and EMT pathway inhibition, thereby protecting lung
tissue from fibrotic damage (81). Recent research has indicated that
immunoliposomes can deliver siTGF-β1 to tumour cells (82), thereby improving a series of
effects, such as poor stability, a short half-life and high
toxicity of naked siRNA. In an A549 tumour-bearing nude mouse
model, transfection with siTGF-β1 significantly reduced tumour
growth and tumour volume (83).
These findings suggest that targeting siTGF-β1 with immunoliposomes
may be a novel strategy for cancer treatment.
In terms of selectively blocking downstream
pathways, the TAK1 inhibitor takinib, while preserving Smad2/3
signalling, specifically inhibited the noncanonical NF-κB pathway,
increasing the tumour growth inhibition rate in a melanoma model,
but wound healing impairment was not observed. Compared with
traditional inhibitors, this precise intervention significantly
improves the therapeutic index, particularly when combined with
ICIs (84-86). Consequently, future research is
expected to overcome the historical challenges associated with
TGF-β-targeted therapy.
The TGF-β1 and PD-1 pathways exhibit bidirectional
regulation. In colorectal and liver cancer models, TGF-β1 forms a
complex at the PD-1 promoter via SMAD3/STAT3 cosignalling,
significantly upregulating PD-1 expression in macrophages. PD-1+ T
cells, in turn, increase the secretion of active TGF-β1 (64,87), creating a positive feedback loop
of immunosuppression. Combined blockade can break this cycle. A
study showed that the combined blockade of TGF-β and PD-1 activates
GBM-infiltrating CD8+ T cells, which are characterised by
upregulated TGFβRI expression (88). This finding is consistent with
previous results from a phase 1b clinical trial in patients with
relapsed/refractory multiple myeloma (89).
Mechanistic studies have shown that combination
therapy enhances antitumour immunity through a triple effect
(90,91), significantly reducing the
proportion of Foxp3+ Tregs in tumours and decreasing
immunosuppressive capacity (92,93). This combined effect can also
remodel dendritic cell (DC) function, improve the
antigen-presenting capacity of DCs and promote the expression of
IFN-γ and granzyme b on CD8+ T cells and NK cells (94). Recent studies have shown that the
intrinsic tumour cell function of PD-L1 drives immunosuppression
and tumour progression through the PD-L1/JAK/STAT3/IL-6/MDSC axis
(95) and that inhibiting the
PD-L1 and TGF-β pathways has a synergistic effect on restoring CD4+
T-cell activity in vitro (96), thereby remodelling MDSC
polarisation.
The bidirectional regulatory nature of TGF-β1
signalling necessitates that intervention strategies must consider
disease stage specificity. In early-stage tumours, TGF-β1 maintains
genomic stability by activating p16INK4a (CDKN2A) and p21 wild-type
p53-activated fragment 1/CDK-interacting protein 1 (p21WAF1/CIP1)
(99,100), and targeted inhibition at this
stage may lead to cumulative DNA damage. A study using a
preclinical model has shown that in desmoglein-2 variant-induced
arrhythmic cardiomyopathy, overactivation of the activating
transcription factor 4/TGF-β1 pathway significantly promotes
collagen deposition only in the later stages of fibrosis, whereas
early inhibition of this pathway weakens the repair capacity of
cardiomyocytes (101).
A pancreatic ductal adenocarcinoma liver metastasis
model revealed that AVID200/BMS-986416-mediated depletion of TGF-β
can reduce the formation of metastatic lesions when it is applied
in the early stages of tumour development, but if treatment is
initiated in the late stages, it accelerates CAF-mediated immune
escape (reduced CD8+ T-cell infiltration) (103).
Single-cell transcriptome analysis revealed that
TGF-β1 is highly expressed in the tumour core (CD44+ stem cell-like
cells) in primary head and neck squamous cell carcinoma lesions
(103), whereas it is enriched
in the peripheral matrix (α-SMA + CAFs) in metastatic lesions
(104). Recent advances in
radiomics, such as the use of minimum redundancy maximum relevance
and recursive feature elimination algorithms, have enabled the
non-invasive prediction of TGF-β1 spatial distribution within
tumours. For instance, a CT radiomics model achieved an area under
the curve (AUC) of 0.849 in predicting TGF-β1 expression patterns
(105), highlighting its
potential as a tool for precision oncology. This approach leverages
quantitative imaging features to map TGF-β1 heterogeneity, which
may inform targeted therapeutic strategies. In a liver metastasis
model of colorectal cancer, Smad2/3 proteins are predominantly
phosphorylated in cells from primary lesions, whereas metastatic
lesions exhibit activation of the noncanonical
TGF-β/cysteine-and-glycine-rich protein 2 axis. This difference may
lead to a significant difference in the response rate to targeted
therapy (106,107).
Spatial heterogeneity is also reflected in the
immune microenvironment. TGF-β1 reduces CD8+ T-cell response
efficiency by inhibiting IL-4 secretion from follicular helper T
cells (108). Given the
significant differences in TGF-β1 activity across different regions
within the same tumour, this spatial heterogeneity directly leads
to stratification of immunotherapy response rates (109,110). In breast cancer tissues, regions
with high TGF-β1 activity have significantly lower CD8+ T-cell
infiltration density and significantly higher PD-1 expression
levels than regions with low TGF-β1 activity (111,112). By integrating single-cell
transcriptomics and spatial transcriptomics data, researchers have
mapped the quantitative correlation between TGF-β1 signalling
gradients and the degree of immunosuppression, providing new
evidence for precise intervention (113).
Researchers have developed spatial epigenomic
techniques for quantifying histone modifications, such as histone
H3 lysine 27 trimethylation (H3K27me3), H3K4me3 and H3K27ac, on the
basis of spatial transcriptomics (114). This remarkable approach can
reveal key spatial regulatory elements controlling identity (i.e.,
spatial enhancer prediction) and advance epigenetic research into
the spatial era.
Deterministic barcoding in tissue for spatial omics
sequencing supports the simultaneous recording of spatially
barcoded mRNA and analysis of proteins of interest (a set of 22
proteins) (115). The
development of spatial multiomics has also provided integrated,
spatial transcriptomics- and antibody-based proteomics analysis
without the need for complex infrastructure (116). This platform was tested in mouse
cerebral cortex samples and a significant correlation was observed
between specific mRNA and protein expression.
Patient-derived organoids (PDOs) combined with
single-cell multiomics can enable the dynamic optimisation of
treatment regimens (117). In a
study using a colorectal cancer model, gene or drug targeting of
TGF-βR1 enhanced NK cell-mediated PDO killing or activation
(118). AVID200 is a TGF-β
ligand-trapping agent and is reported to have a binding affinity
1,000 times greater than that of other trapping agents (102,119). Although studies on the effects
of AVID200 on solid tumours are limited, in a phase I study
(NCT03834662) of 19 patients with advanced or metastatic solid
tumours, AVID200 monotherapy was well tolerated and effectively
modulated TGF-β1/3 (120,121).
Compared with whole-cell RNA sequencing, single-cell
RNA sequencing can capture intratumoural heterogeneity.
Furthermore, cellular dynamic events can be studied using
pseudotime trajectory analysis of single-cell RNA sequencing data
(122). Currently, the Samsung
Medical Center colorectal cancer single-cell RNA sequencing dataset
can be used to identify important tumour epithelial cell subtypes
(122). Through pseudotime
trajectory analysis, researchers reported that TGFB1 and IL-1B may
be effective ligands and transcription factors that regulate
pseudotime-related gene expression. By constructing a LASSO Cox
regression for 20 pseudotime genes, the 3-year survival rate of
patients with colorectal cancer can be predicted with an AUC
>0.7 (122).
Current integrated technology pathways include
diagnostic, therapeutic and monitoring stages. For example,
epithelial cell adhesion molecule-based immunoisolation can be used
to capture circulating tumour cells (CTCs) that undergo EMT after
TGF-β1-induced stimulation (123,124). Spatial transcriptomics of a
gastric cancer brain metastasis model revealed atypical vascular
strategies supporting immune signatures. Tumours with high EMT and
transcriptomic gene expression signatures consisting of catenin β1,
secreted protein acidic and cysteine rich, ventral intermediate,
SMAD3, SMAD4, TGFB1, TGFB2 and TGFB3 are more prone to blood vessel
co-option than angiogenesis (125).
The role of DNA methylation driver genes (MDGs) in
pancreatic cancer has been assessed by integrating epigenomic,
transcriptomic and clinicopathological data. High signature
risk-scores (126), a
quantitative metrics derived from mathematical or bioinformatics
models based on MDG signature, were found to be associated with
poor histological grade and advanced TNM stage. The negative
regulatory genes of the cancer-immunity cycle demonstrated that the
immunosuppressive factors TGF-β1 and CD274 (PD-L1) were positively
correlated with risk scores. These factors are negatively
correlated with CD8+ lymphocytes but positively correlated with
MDSCs (126).
TGF-β1 exhibits a dual role in cancer progression,
acting as both a tumour suppressor and promoter through
stage-dependent mechanisms. Its signalling pathways drive tumour
invasion, metastasis and therapy resistance via SMAD and non-SMAD
pathways, EMT and immune evasion. Current TGF-β1-targeted therapies
face challenges, but combination immunotherapy holds promise.
Future precision strategies must integrate temporal, spatial,
cellular and technological dimensions to selectively modulate
TGF-β1's complex functions. Advancements in single-cell sequencing
and organoid models will be critical for guiding personalised
therapeutic interventions.
Not applicable.
CH wrote and revised the manuscript, including
conception and design of the study and literature search/selection.
Data authentication is not applicable. The author has read and
approved the final manuscript.
Not applicable.
Not applicable.
The author declares that he has no competing
interests.
Not Applicable.
This work was supported by the Natural Science Foundation of
Shenzhen Municipality (grant no. JCYJ20240813115601003).
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