Cancer cell signaling networks are highly complex
and dynamically regulated systems that play key roles in tumor
initiation, its progression, metastasis, therapeutic response and
drug resistance (1). Rather than
functioning as isolated pathways, these networks operate through
extensive crosstalk and feedback among multiple signaling cascades,
thereby regulating key malignant behaviors such as proliferation,
apoptosis, invasion, metabolic reprogramming and immune evasion
(2). Advances in multi-omics and
systems biology have offered deeper insights into the organization
and regulatory principles of these networks (3,4). Such
insights have improved the current understanding of tumor
heterogeneity and evolutionary dynamics, while also identifying new
therapeutic targets and more individualized treatment strategies
(5,6). Although several oncogenic pathways
overlap across cancers, their functional roles and clinical
relevance remain highly context dependent.
In this review, the core architecture of cancer
signaling networks, the major oncogenic pathways embedded within
them and the dynamic properties that shape their biological and
clinical effects were summarized. Particular emphasis was placed on
pathway crosstalk, feedback regulation and adaptive reprogramming,
and it was discussed how these dynamic features influence tumor
phenotypes, therapeutic resistance and clinical outcomes. Rather
than providing a static summary of individual signaling pathways,
this review adopts a network-level perspective that integrates
structural organization, dynamic regulation and translational
implications within a unified conceptual framework. Because several
oncogenic signaling pathways display context-dependent outputs for
different tumor types, this review revolves around common network
principles while incorporating representative tumor-specific
examples. By linking pathway architecture with adaptive network
behavior, the present study aimed to elucidate how signaling
interactions (not as isolated pathways) drive tumor progression and
mediate therapeutic responses.
Previous reviews have summarized major
cancer-related signaling pathways and their crosstalk, particularly
focusing on canonical pathways such as MAPK, PI3K/Akt/mTOR and
Wnt/β-catenin signaling (7,8). A recent review has also discussed how
signaling networks influence cancer metabolism and therapeutic
response (2). The present review
aimed to take a broader perspective by emphasizing how these
pathways interact within a dynamic network. This perspective may
provide a more integrated understanding of signaling networks in
tumor progression and therapeutic resistance. Compared with
previous reviews focusing mainly on individual signaling pathways
and selected crosstalk mechanisms, the present review emphasizes
the hierarchical organization of cancer signaling networks,
adaptive responses under therapeutic pressure,
microenvironment-mediated resistance and their translational
implications for network-based therapeutic strategies. Fig. 1 presents a simplified conceptual
framework of cancer signaling networks, showing the hierarchical
flow of signaling from membrane receptors through intracellular
transducers and downstream effectors to drive diverse tumor
phenotypes. It also highlights major dynamic interactions among
oncogenic pathways, including crosstalk, feedback regulation and
adaptive reprogramming.
Membrane receptors serve as the initiating nodes of
cancer signaling networks by sensing extracellular and
microenvironmental cues and transmitting signals intracellularly to
drive tumor cell proliferation, migration, survival and other
malignant behaviors. Major receptor classes include receptor
tyrosine kinases [e.g., EGFR, human EGFR 2 (HER2)], G
protein-coupled receptors, integrins, immune checkpoint receptors
[e.g., programmed cell death 1 (PD-1)/programmed death ligand 1
(PD-L1)] and cytokine receptors (e.g., TNF and Wnt receptors). Upon
ligand binding, these receptors activate downstream cascades,
particularly the MAPK, PI3K/Akt and JAK/STAT pathways, thereby
promoting tumorigenesis and disease progression (9–12).
Aberrant EGFR and HER2 activation is frequently observed across
multiple cancers (13,14), while increased PD-1/PD-L1 signaling
contributes to tumor immune evasion (15). As central regulatory nodes, membrane
receptors are primary targets in precision oncology, with targeted
inhibitors and monoclonal antibodies demonstrating substantial
clinical benefit across diverse malignancies (16).
Signal transducers act as critical intermediates
between membrane receptors and downstream effectors; they
integrate, amplify and diversify signaling inputs to dictate
cellular responses to extracellular stimuli (17). These molecules fall into four major
categories.
G proteins are categorized as heterotrimeric G
proteins, which couple to G protein-coupled receptors, and small
GTPases, including members of the Ras and Rho families (18). Heterotrimeric G proteins regulate
intracellular second messengers such as cAMP, inositol
1,4,5-trisphosphate and diacylglycerol (19), thereby modulating cellular
metabolism, ion channel activity and gene expression (20–23).
Small GTPases are central nodes in cancer signaling: Ras proteins
act as key signaling hubs, where oncogenic mutations often trigger
constitutive MAPK/ERK and PI3K/Akt activation, promoting
proliferation, survival and metastasis (24,25).
By contrast, Rho family proteins primarily regulate cytoskeletal
dynamics and cell motility, contributing to cancer invasion and
metastatic potential (26).
Kinase cascades, particularly the MAPK and
PI3K/Akt/mTOR pathways, amplify and diversify signaling through
sequential phosphorylation events (27,28).
These pathways regulate key biological processes, with MAPK being
primarily involved in proliferation, differentiation and stress
responses (29,30), and PI3K/Akt in cell survival,
metabolism and therapeutic resistance (31–33).
Their multi-layered structure and extensive branching not only
confer flexibility in signal processing but also foster tumor
heterogeneity and treatment resistance when persistently
activated.
Signal integration proteins organize signaling
pathways in space and time, including scaffold proteins such as
receptor for activated C kinase 1, A-kinase anchoring proteins and
IQ motif-containing GTPase-activating protein 1, as well as adaptor
proteins such as growth factor receptor-bound protein 2 (Grb2), Src
homology 2 domain-containing transforming protein and
Grb2-associated binder 1/2 (34–36).
By assembling signaling complexes, these molecules enhance signal
transduction efficiency and facilitate pathway crosstalk. Certain
proteins, such as β-arrestin and Grb2 (Table I), function as both scaffolds and
adaptors to coordinate signaling (37,38).
Dysregulated proteins disrupt signaling homeostasis, triggering
malignancy. Representative scaffold and adaptor proteins involved
in signaling integration, together with their associated pathways
and tumor-related functions, are summarized in Table I (39–48).
Regulatory enzymes and negative regulators
critically balance signaling. These include phosphatases such as
phosphatase and tensin homolog (PTEN), as well as ubiquitin ligases
and SUMOylation enzymes (49,50).
These molecules limit persistent pathway activation and preserve
network homeostasis. Loss or inactivation of PTEN leads to
uncontrolled amplification of oncogenic signaling and represents a
key event in cancer progression (51,52).
Downstream effectors are the final mediators that
execute the biological consequences of signaling activation. They
include transcription factors, cell cycle and apoptosis regulators,
metabolic enzymes, cytoskeletal proteins and immune modulators.
They collectively control processes such as proliferation,
apoptosis, differentiation, metabolism, migration and immune
responses (7). Specifically,
transcription factors including NF-κB, c-Myc and β-catenin regulate
tumor-associated gene expression (53,54),
while proteins such as Bcl-2 family members, caspases and p53
control apoptosis and cell cycle progression (55–57).
Metabolic regulators such as mTOR and HIF-1α support cellular
adaptation (58), while Rho GTPases
and Fascin mediate cytoskeletal remodeling and invasion (59,60).
Additionally, PD-L1 plays a pivotal role in tumor immune evasion
(61). Dysregulated effectors
generate malignant phenotypes, drive therapeutic resistance and
often yield poor clinical outcomes (62,63).
These downstream consequences arise not from isolated signaling
events but from the coordinated activation of oncogenic pathways
operating within interconnected networks.
Although the major oncogenic pathways described
below are broadly involved in cancer biology, their activation
patterns, biological functions and clinical relevance vary across
tumor types. In certain cancers, specific pathways act as dominant
drivers of tumor growth and therapeutic response, while in others,
they coordinate with additional signaling modules. Importantly,
these pathways are embedded within dynamic networks where crosstalk
and feedback regulation shape their functional output. Interactions
with parallel pathways and microenvironmental cues further modulate
these signals, underscoring the necessity of a network-level
perspective to understand tumor behavior and therapeutic response.
Such a framework accounts for adaptive reprogramming that
ultimately dictates clinical effects.
The PI3K/Akt/mTOR pathway centrally regulates cancer
cell proliferation, survival, metabolic reprogramming and
therapeutic resistance. Activated downstream of receptor tyrosine
kinases and G protein-coupled receptors, sequential activation of
PI3K, Akt and mTOR promotes cell growth while suppressing apoptosis
(64–68). Aberrant activation of this pathway
is frequent in cancer and is often driven by PTEN loss or
activating mutations in phosphatidylinositol-4,5-bisphosphate
3-kinase catalytic subunit α (PIK3CA), resulting in constitutive
output that supports tumor progression and resistance to therapy
(69–71). Its clinical relevance is
particularly notable in breast cancer, endometrial cancer and
glioblastoma, where PI3K alterations are prevalent. Given its
central role, the PI3K/Akt/mTOR pathway has become a major target
in precision oncology and its mechanistic inhibition may relieve
negative feedback on upstream receptor tyrosine kinases, thereby
triggering compensatory activation of parallel signaling cascades,
particularly MAPK/ERK (72). This
adaptive reprogramming sustains tumor survival, limits the
durability of single-pathway inhibition and drives therapeutic
resistance (73).
The MAPK/ERK cascade, organized around the
Ras-Raf-MEK-ERK kinase axis, regulates multiple biological
processes, including proliferation, differentiation, apoptosis and
migration (74,75). Oncogenic alterations, particularly
mutations in Ras or BRAF, frequently trigger constitutive MAPK/ERK
signaling, thereby driving tumor growth, metastasis and therapeutic
resistance; accordingly, the clinical importance of this pathway is
especially evident in melanoma and colorectal cancer, where Ras or
BRAF mutations often function as primary oncogenic drivers
(76–81).
Notably, the MAPK/ERK and PI3K/Akt/mTOR pathways are
intimately interconnected. Their extensive crosstalk coordinates
control of proliferation, survival and stress responses. In
numerous tumor contexts, inhibiting one pathway can induce
compensatory activation of the other, thereby maintaining
downstream signaling output and reducing therapeutic efficacy
(82). This reciprocal regulation
is a key mechanism of treatment resistance, providing a strong
rationale for pharmacological combination therapies targeting both
axes. Often, this compensatory response is mediated by the relief
of ERK-dependent negative feedback on upstream nodes, which
restores signaling after targeted inhibition (83).
The Wnt/β-catenin pathway is a vital regulator of
cancer cell stemness, self-renewal and metastatic potential. Upon
signaling, β-catenin accumulates in the cytoplasm and translocates
to the nucleus to drive the expression of genes involved in
proliferation and epithelial-mesenchymal transition (EMT) (84–88).
Aberrant Wnt activation has been implicated in colorectal cancer
and other tumor contexts, where it contributes to tumor initiation,
progression and the maintenance of stem-like and aggressive
phenotypes (89,90). Beyond its tumor-intrinsic effects,
the Wnt/β-catenin axis modulates the tumor microenvironment and
immune responses, further supporting tumor progression (91,92).
Mechanistically, stabilized nuclear β-catenin promotes the
transcription of EMT-related genes, such as Snail and c-Myc,
enhancing tumor invasion, metastasis and therapeutic resistance
(93). These features position the
pathway as a key candidate for combination-based therapeutic
strategies.
In addition to the major pathways described, several
other signaling cascades regulate cancer progression, including the
JAK/STAT, Notch, Hippo/Yes-associated protein (YAP), NF-κB and
Hedgehog pathways. These regulate diverse processes, such as
inflammation, immune modulation, cell fate determination and
stemness, in a context-dependent manner. For instance, JAK/STAT and
NF-κB signaling link inflammation, immune responses to therapeutic
resistance (94–98), whereas Notch and Hippo/YAP primarily
influence cell fate decisions and cellular plasticity (99,100).
Aberrant Hedgehog signaling further promotes tumor proliferation
and metastasis, particularly in cancers reactivating developmental
programs (101–103). Importantly, these pathways are
integrated through extensive crosstalk and feedback regulation,
forming a dynamic network with context-dependent outputs. Such
interactions, mediated by shared signaling intermediates and
transcriptional programs, coordinate regulation of inflammation,
immune evasion and tumor cell plasticity (104).
Cancer signaling pathways operate within adaptive
network systems rather than as isolated modules. These networks are
characterized by crosstalk, feedback regulation and adaptive
reprogramming that yield context-dependent signaling outputs
(105). Such dynamic properties
drive phenotypic plasticity, allowing cancer cells to respond to
environmental and therapeutic stresses. Consequently, it is
essential to understand how these signaling networks drive tumor
phenotypes, adapt to therapeutic pressure and shape clinical
outcomes.
Signaling events are tightly regulated
spatiotemporally, with their activation, propagation and
termination restricted within specific cellular compartments and
temporal windows. For example, receptor tyrosine kinases frequently
localize to membrane microdomains or lipid rafts for activation
(106), while the subcellular
localization and nucleo-cytoplasmic shuttling of proteins such as
NF-κB, β-catenin and STAT critically influence downstream gene
expression and cellular phenotypes. Furthermore, signaling pathways
may display transient or sustained activation patterns, as
exemplified by the MAPK/ERK cascade (107–110), triggering distinct cellular
outcomes ranging from proliferation to differentiation or
apoptosis.
A defining feature of cancer signaling networks is
their capacity for dynamic adaptation under stress. In response to
environmental changes or therapeutic pressure, cancer cells can
reprogram signaling activity to sustain survival. Inhibiting
dominant pathways, such as PI3K/Akt, often triggers compensatory
activation of alternative cascades, including MAPK, Wnt or JAK/STAT
(111–113). This adaptive reprogramming may
arise from altered protein expression, pathway switching or
regulatory mutations (114),
representing a central mechanism for drug resistance, metastasis
and tumor heterogeneity (115,116). In addition, resistance emerges
from pathway reactivation, activation of parallel signaling routes
or microenvironment-mediated survival signals (117).
Cancer signaling networks possess substantial
topological complexity, incorporating branched pathways, positive
and negative feedback loops, feedforward regulation and redundant
signaling routes. Key nodes such as Ras, Akt and β-catenin serve as
central hubs that integrate and distribute signals across multiple
axes (8,119). While this complex architecture
enhances cellular adaptability under stress, it also enables
compensatory signaling and therapeutic escape (120).
Certain signaling circuits exhibit oscillatory or
bistable behaviors in response to external stimuli, as seen in the
p53-MDM2 and NF-κB-IκB regulatory loops (121,122). These dynamic patterns trigger
switch-like responses or periodic signaling activity, enabling
flexible transitions between cellular states. Such behaviors drive
tumor heterogeneity, cellular adaptability and the development of
therapeutic resistance.
Signaling activation is closely linked to epigenetic
regulation, including chromatin remodeling, histone modification
and DNA methylation (123,124). These processes can encode prior
signaling events, generating a form of epigenetic ‘memory’ that
stabilizes tumor phenotypes even after the initial stimulus
dissipates (125).
Simultaneously, cancer signaling networks
continuously engage with the tumor microenvironment (104). Immune cells, fibroblasts and other
stromal components can modulate pathway activity, while tumor cells
reciprocally remodel their surroundings. These bidirectional
interactions further promote tumor progression, immune evasion and
adaptive responses to therapy (126). Tumor microenvironment-mediated
resistance is also an important cause of treatment failure. In
addition to tumor cells themselves, fibroblasts, immune cells,
endothelial cells, extracellular matrix components, hypoxia and
soluble cytokines can all influence pathway activity during therapy
(117,127). For instance, cancer-associated
fibroblasts may release hepatocyte growth factor, which activates
MET signaling and downstream PI3K/Akt and MAPK pathways, thereby
reducing the efficacy of EGFR tyrosine kinase inhibitors (128). Cytokines such as IL-6 and TGF-β
can also activate JAK/STAT3, SMAD or EMT-related signaling,
promoting tumor cell survival, immune escape and drug resistance
(95,97,129).
Furthermore, extracellular matrix remodeling may strengthen
integrin-FAK/Src signaling (130),
while hypoxia can induce HIF-1α-dependent programs related to
angiogenesis, metabolic adaptation and treatment resistance
(131). These findings suggest
that effective treatment may need to target both tumor-intrinsic
oncogenic pathways and protective signals from the surrounding
microenvironment.
The biological relevance of cancer signaling
networks lies in their coordinated ability to shape malignant
phenotypes and remodel the tumor microenvironment. Dysregulation of
these networks drives key features of cancer, including
uncontrolled proliferation, resistance to apoptosis, invasion,
metastasis and metabolic reprogramming (132,133). Beyond these tumor-intrinsic
effects, these networks reconfigure surrounding stromal and immune
components through cytokines, exosomes and other mediators
(134).
For example, persistent PI3K/Akt/mTOR and MAPK/ERK
activation promotes tumor growth and therapeutic resistance
(135–137), while Wnt/β-catenin signaling
enhances stemness and metastatic potential (138,139). These alterations can upregulate
immunosuppressive molecules, notably PD-L1 (140), and reprogram tumor-associated
stromal cells to reinforce immune evasion, angiogenesis and tumor
aggressiveness (141). Notably,
although many signaling principles are shared across cancers, the
phenotypic and therapeutic consequences of dysregulation are highly
context-dependent, shaped by lineage, genomic background and the
microenvironment. These differences ultimately regulate the
efficacy, durability and resistance patterns of targeted
therapies.
From a clinical perspective, the significance of
cancer signaling networks lies in their coordinated behavior,
rather than in isolated oncogenic drivers. Traditional approaches
targeting single signaling nodes such as EGFR, PI3K or BRAF have
yielded substantial clinical benefits in selected patient
populations (142,143). Representative examples include
EGFR tyrosine kinase inhibitors for EGFR-mutant non-small cell lung
cancer, BRAF/MEK inhibitors for BRAF-mutant melanoma and colorectal
cancer, PI3K pathway inhibitors for biomarker-selected tumors and
immune checkpoint inhibitors for tumors with specific
immune-related biomarkers. In the case of PI3K pathway inhibitors,
patients are usually selected according to molecular alterations in
the PI3K/Akt/mTOR axis, such as activating PIK3CA mutations, PTEN
loss, or other evidence of pathway activation (144). For example, PI3K inhibitors have
been used in hormone receptor-positive, HER2-negative,
PIK3CA-mutated advanced breast cancer (145). For immune checkpoint therapy,
‘high immune regulatory signaling’ mainly refers to clinically used
biomarkers, including increased PD-L1 expression, microsatellite
instability-high status, mismatch repair deficiency, high tumor
mutational burden or an inflamed tumor microenvironment (146). However, these biomarkers are not
perfect predictors, and their clinical value may differ among tumor
types and treatment settings (147).
However, the clinical benefit of single-target
inhibition is often limited by the adaptive nature of cancer
signaling networks (148). In many
tumor contexts, suppression of a dominant node fails to fully
extinguish oncogenic output, as parallel pathways are reactivated
or newly engaged. A representative example is the reciprocal
crosstalk between the PI3K/Akt/mTOR and MAPK/ERK axes, where
inhibition of one axis can relieve negative feedback or trigger
bypass signaling through the other, sustaining proliferation and
survival (149,150). Furthermore, intratumoral
heterogeneity and microenvironment-mediated survival signals may
promote treatment resistance by allowing resistant cell states to
persist or emerge under therapeutic pressure. These observations
underscore that cancer signaling networks function as integrated
systems rather than isolated pathways.
Accordingly, a network-based therapeutic framework
has emerged, guiding strategies by pathway interactions and dynamic
network behavior. Key approaches include targeting dominant
oncogenic drivers such as EGFR, PI3K or BRAF; co-inhibition of
parallel pathways to prevent compensatory signaling, notably
combined targeting of PI3K and MAPK; vertical inhibition within
signaling cascades to ensure sustained pathway suppression; and
integration of targeted therapy with immunotherapy or conventional
treatments to modulate both tumor-intrinsic and microenvironmental
signaling (151). From a
translational and pharmacological perspective, these strategies aim
not only to suppress primary oncogenic drivers, but to preempt or
overcome the pathway reactivation, bypass signaling and adaptive
reprogramming that frequently emerge during treatment.
Despite these advances, major challenges remain,
including intratumoral heterogeneity, acquired resistance and
adaptive network responses (152).
Addressing these challenges will require therapeutic strategies
accounting for the dynamic and context-dependent signaling
networks. A deeper understanding of network-level regulation and
its interaction with the tumor microenvironment is warranted for
advancing precision oncology with improved long-term clinical
outcomes. Mechanistically, resistance to targeted therapies often
arises from feedback reactivation of inhibited pathways or
compensatory activation of parallel signaling networks,
underscoring the need for rational combination therapies based on
network-level interactions (153).
Despite substantial progress in targeting cancer
signaling networks, certain challenges remain. Tumor heterogeneity
is a primary challenge, as pathway activity vary across patients,
tumor regions and microenvironments, complicating both network
analysis and therapeutic targeting (154,155). In addition, integrating
multi-omics data and monitoring dynamic signaling changes in real
time remain technically crucial (156), limiting a comprehensive
understanding of tumor adaptability and resistance mechanisms.
These limitations also hinder the identification of clinically
actionable vulnerabilities and the rational design of durable
combination therapies.
Emerging technologies are beginning to address these
limitations. Advances in single-cell omics, spatial transcriptomics
and computational modeling have greatly improved the signaling
network resolutions (157–159). These approaches enable more
precise characterization of dynamic network rewiring and
intercellular interactions, and support targeted discovery and
development of more personalized therapeutic strategies.
Looking forward, network-level intervention and
multi-target combination strategies are likely to become
increasingly important for overcoming therapeutic resistance and
improving patient outcomes. A deeper understanding of dynamic
signaling networks, with continued integration of multi-omics
profiling, real-time monitoring and computational modeling, will be
essential for advancing precision oncology.
Research on cancer signaling networks has
substantially advanced the current understanding of tumor biology
and facilitated the development of precision therapeutic
strategies. Rather than functioning as isolated pathways, these
networks operate as highly interconnected and dynamic systems that
shape tumor behavior, therapeutic response and drug resistance. In
this review, it was emphasized that the biological and clinical
significance of cancer signaling lies not only in individual
oncogenic pathways, but also in the network-level interactions that
link signaling architecture with adaptive reprogramming, phenotypic
plasticity and therapeutic adaptation. A key challenge moving
forward is to elucidate how crosstalk, compensatory activation and
adaptive reprogramming collectively drive tumor phenotypes across
diverse biological and clinical contexts. Addressing this challenge
will require integrated approaches that combine multi-omics
analysis, systems biology and real-time monitoring of signaling
dynamics. Ultimately, deciphering context-dependent network
behavior is essential to improve treatment outcomes and advance
precision oncology.
Not applicable.
Funding: No funding was received.
Not applicable.
HL and SS wrote the original draft. LW and QL
contributed to conceptualization, literature search and selection,
interpretation of the literature, and critical revision of the
manuscript. LW provided project administration. Data authentication
is not applicable. All authors have read and approved the final
manuscript.
Not applicable.
Not applicable.
The authors declare that they have no competing
interests.
AI-assisted tools were used only for minor language
polishing. Specifically, ChatGPT (OpenAI, GPT-5; http://chatgpt.com/) was used to improve the
readability and language of the manuscript. The authors reviewed
and edited the final manuscript and take full responsibility for
its content. All contents of this review were conceptualized,
analyzed and critically reviewed by the authors.
|
1
|
Zhou H, Tan L, Liu B and Guan XY: Cancer
stem cells: Recent insights and therapies. Biochem Pharmacol.
209:1154412023. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Manning BD and Dibble CC: Growth signaling
networks orchestrate cancer metabolic networks. Cold Spring Harb
Perspect Med. 14:a0415432024. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Rossi C, Cicalini I, Cufaro MC, Consalvo
A, Upadhyaya P, Sala G, Antonucci I, Del Boccio P, Stuppia L and De
Laurenzi V: Breast cancer in the era of integrating ‘Omics’
approaches. Oncogenesis. 11:172022. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Joo JI, Park HJ and Cho KH: Normalizing
input-output relationships of cancer networks for reversion
therapy. Adv Sci (Weinh). 10:e22073222023. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Cortés-Hernández LE, Eslami-S Z, Pantel K
and Alix-Panabières C: Molecular and functional characterization of
circulating tumor cells: From discovery to clinical application.
Clin Chem. 66:97–104. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Zendehdel H, Esgandari M, Panahinia P,
Fazeli R, Etezadi A and Rahimi S: The neutrophil-NET axis in
ovarian cancer: Drivers of tumor microenvironment remodeling and
therapeutic resistance. Int Immunopharmacol. 168:1158262026.
View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Hoxhaj G and Manning BD: The PI3K-AKT
network at the interface of oncogenic signalling and cancer
metabolism. Nat Rev Cancer. 20:74–88. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Fu D, Hu Z, Xu X, Dai X and Liu Z: Key
signal transduction pathways and crosstalk in cancer: Biological
and therapeutic opportunities. Transl Oncol. 26:1015102022.
View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Farooqi AA, Shepetov AM, Rakhmetova V,
Ruslan Z, Almabayeva A, Saussakova S, Baigonova K, Baimaganbetova
K, Sundetgali K and Kapanova G: Interplay between JAK/STAT pathway
and non-coding RNAs in different cancers. Noncoding RNA Res.
9:1009–1022. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Raji L, Tetteh A and Amin ARMR: Role of
c-Src in carcinogenesis and drug resistance. Cancers (Basel).
16:322023. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Xu Q, Fan G and Shao S: Role of TNFRSF12A
in cell proliferation, apoptosis, and proinflammatory cytokine
expression by regulating the MAPK and NF-κB pathways in thyroid
cancer cells. Cytokine. 186:1568412025. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Villarroel A, Del Valle-Pérez B, Fuertes
G, Curto J, Ontiveros N, Garcia de Herreros A and Duñach M: Src and
Fyn define a new signaling cascade activated by canonical and
non-canonical Wnt ligands and required for gene transcription and
cell invasion. Cell Mol Life Sci. 77:919–935. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Buccinnà B, Ramondetti C and Piccinini M:
AMPK activation attenuates HER3 upregulation and
neuregulin-mediated rescue of cell proliferation in
HER2-overexpressing breast cancer cell lines exposed to lapatinib.
Biochem Pharmacol. 204:1152282022. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Tsutsumi H, Iwama E, Ibusuki R, Shimauchi
A, Ota K, Yoneshima Y, Inoue H, Tanaka K, Nakanishi Y and Okamoto
I: Mutant forms of EGFR promote HER2 trafficking through efficient
formation of HER2-EGFR heterodimers. Lung Cancer. 175:101–111.
2023. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Ahn M, Mun JG, Han Y and Seo JH: Cancer
cell-derived extracellular vesicles: A potential target for
overcoming tumor immunotherapy resistance and immune evasion
strategies. Front Immunol. 16:16012662025. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Naim MJ and Samad A: A review on
EGFR-tyrosine kinase inhibitors and their resistance mechanisms.
Curr Pharm Des. March 11–2025.(Epub ahead of print). PubMed/NCBI
|
|
17
|
Ullo MF and Case LB: How cells sense and
integrate information from different sources. WIREs Mech Dis.
15:e16042023. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Bannoura SF, Khan HY, Uddin MH, Mohammad
RM, Pasche BC and Azmi AS: Targeting guanine nucleotide exchange
factors for novel cancer drug discovery. Expert Opin Drug Discov.
19:949–959. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Ray K, Ujvari B, Ramana V and Donald J:
Cross-talk between EGFR and IL-6 drives oncogenic signaling and
offers therapeutic opportunities in cancer. Cytokine Growth Factor
Rev. 41:18–27. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Pavlova NN and Thompson CB: Oncogenic
control of metabolism. Cold Spring Harb Perspect Med.
14:a0415312024. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Huff TC, Camarena V, Sant DW, Wilkes Z,
Van Booven D, Aron AT, Muir RK, Renslo AR, Chang CJ, Monje PV and
Wang G: Oscillatory cAMP signaling rapidly alters H3K4 methylation.
Life Sci Alliance. 3:e2019005292019. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Rimessi A, Pedriali G, Vezzani B, Tarocco
A, Marchi S, Wieckowski MR, Giorgi C and Pinton P: Interorganellar
calcium signaling in the regulation of cell metabolism: A cancer
perspective. Semin Cell Dev Biol. 98:167–180. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Kankanamge D, Tennakoon M, Weerasinghe A,
Cedeno-Rosario L, Chadee DN and Karunarathne A: G protein αq exerts
expression level-dependent distinct signaling paradigms. Cell
Signal. 58:34–43. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Wang Y, Tong Y, Tso PH and Wong YH:
Regulator of G protein signaling 19 suppresses Ras-induced
neoplastic transformation and tumorigenesis. Cancer Lett.
339:33–41. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Karapetis CS, Jonker D, Daneshmand M,
Hanson JE, O'Callaghan CJ, Marginean C, Zalcberg JR, Simes J, Moore
MJ, Tebbutt NC, et al: PIK3CA, BRAF, and PTEN status and benefit
from cetuximab in the treatment of advanced colorectal
cancer-results from NCIC CTG/AGITG CO.17. Clin Cancer Res.
20:744–753. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Lou Y, Jiang Y, Liang Z, Liu B, Li T and
Zhang D: Role of RhoC in cancer cell migration. Cancer Cell Int.
21:5272021. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Bendell JC, Javle M, Bekaii-Saab TS, Finn
RS, Wainberg ZA, Laheru DA, Weekes CD, Tan BR, Khan GN, Zalupski
MM, et al: A phase 1 dose-escalation and expansion study of
binimetinib (MEK162), a potent and selective oral MEK1/2 inhibitor.
Br J Cancer. 116:575–583. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Wu H, Wei M, Li Y, Ma Q and Zhang H:
Research progress on the regulation mechanism of key signal
pathways affecting the prognosis of glioma. Front Mol Neurosci.
15:9105432022. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Szabó Z, Hornyák L, Miskei M and
Székvölgyi L: Two targets, one hit: New anticancer therapeutics to
prevent tumorigenesis without cardiotoxicity. Front Pharmacol.
11:5699552021. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Kadasah SF: Targeting the MAPK pathway in
cancer. Int J Mol Sci. 27:2142025. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Daver N, Boumber Y, Kantarjian H, Ravandi
F, Cortes J, Rytting ME, Kawedia JD, Basnett J, Culotta KS, Zeng Z,
et al: A phase I/II study of the mTOR inhibitor everolimus in
combination with HyperCVAD chemotherapy in patients with
relapsed/refractory acute lymphoblastic leukemia. Clin Cancer Res.
21:2704–2714. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Shanware NP, Bray K and Abraham RT: The
PI3K, metabolic, and autophagy networks: Interactive partners in
cellular health and disease. Annu Rev Pharmacol Toxicol. 53:89–106.
2013. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Kim E, Kim JY, Smith MA, Haura EB and
Anderson ARA: Cell signaling heterogeneity is modulated by both
cell-intrinsic and -extrinsic mechanisms: An integrated approach to
understanding targeted therapy. PLoS Biol. 16:e20029302018.
View Article : Google Scholar : PubMed/NCBI
|
|
34
|
DeFea KA: Beta-arrestins as regulators of
signal termination and transduction: How do they determine what to
scaffold? Cell Signal. 23:621–629. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Ma TL, Zhou Y, Zhang CY, Gao ZA and Duan
JX: The role and mechanism of β-arrestin2 in signal transduction.
Life Sci. 275:1193642021. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Satpathy S, Wagner SA, Beli P, Gupta R,
Kristiansen TA, Malinova D, Francavilla C, Tolar P, Bishop GA,
Hostager BS and Choudhary C: Systems-wide analysis of BCR
signalosomes and downstream phosphorylation and ubiquitylation. Mol
Syst Biol. 11:8102015. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Song Q, Ji Q and Li Q: The role and
mechanism of β-arrestins in cancer invasion and metastasis
(review). Int J Mol Med. 41:631–639. 2018.PubMed/NCBI
|
|
38
|
Iwata T, Sedukhina AS, Kubota M, Oonuma S,
Maeda I, Yoshiike M, Usuba W, Minagawa K, Hames E, Meguro R, et al:
A new bioinformatics approach identifies overexpression of GRB2 as
a poor prognostic biomarker for prostate cancer. Sci Rep.
11:56962021. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Yang JC, Du YY, Zuo WQ, Yao JY, Ma K,
Liang Y, Zhao MG and Li ZM: RACK1: A multifunctional scaffold
protein involved in signal transduction, ribosomal regulation, and
cancer pathogenesis. Cell Signal. 144:1125192026. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Kolosov P, Biziaev N and Alkalaeva E: A
duality of function: An integrative model of RACK1 as a switch
between translational and signaling hubs. Int J Mol Sci.
26:117332025. View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Lau HR, Smith HS, Alural B, Martin CE, New
LA, Tilak M, Banerjee SL, Robeson HN, Bisson N, Gingras AC, et al:
ShcD adaptor protein drives invasion of triple negative breast
cancer cells by aberrant activation of EGFR signaling. Mol Oncol.
19:2833–2859. 2025. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Thines L, Roushar FJ, Hedman AC and Sacks
DB: The IQGAP scaffolds: Critical nodes bridging receptor
activation to cellular signaling. J Cell Biol. 222:e2022050622023.
View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Franke FC, Slusarenko BO, Engleitner T,
Johannes W, Laschinger M, Rad R, Nitsche U and Janssen KP: Novel
role for CRK adaptor proteins as essential components of SRC/FAK
signaling for epithelial-mesenchymal transition and colorectal
cancer aggressiveness. Int J Cancer. 147:1715–1731. 2020.
View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Bucko PJ and Scott JD: Drugs that regulate
local cell signaling: AKAP targeting as a therapeutic option. Annu
Rev Pharmacol Toxicol. 61:361–379. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Pérez-Baena MJ, Cordero-Pérez FJ,
Pérez-Losada J and Holgado-Madruga M: The role of GAB1 in cancer.
Cancers (Basel). 15:41792023. View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Bywaters BC and Rivera GM: Nck adaptors at
a glance. J Cell Sci. 134:jcs2589652021. View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Zhang Y, Yan M, Yu Y, Wang J, Jiao Y,
Zheng M and Zhang S: 14-3-3ε: A protein with complex physiology
function but promising therapeutic potential in cancer. Cell Commun
Signal. 22:722024. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Farhadi P and Park T: The p130Cas-Crk/CrkL
axis: A therapeutic target for invasive cancers unveiled by
collaboration among p130Cas, Crk, and CrkL. Int J Mol Sci.
26:40172025. View Article : Google Scholar : PubMed/NCBI
|
|
49
|
Dave B, Migliaccio I, Gutierrez MC, Wu MF,
Chamness GC, Wong H, Narasanna A, Chakrabarty A, Hilsenbeck SG,
Huang J, et al: Loss of phosphatase and tensin homolog or
phosphoinositol-3 kinase activation and response to trastuzumab or
lapatinib in human epidermal growth factor receptor
2-overexpressing locally advanced breast cancers. J Clin Oncol.
29:166–173. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
50
|
Yang J and Yin Y: PTEN in chromatin
remodeling. Cold Spring Harb Perspect Med. 10:a0361602020.
View Article : Google Scholar : PubMed/NCBI
|
|
51
|
Luongo F, Colonna F, Calapà F, Vitale S,
Fiori ME and De Maria R: PTEN tumor-suppressor: The dam of stemness
in cancer. Cancers (Basel). 11:10762019. View Article : Google Scholar : PubMed/NCBI
|
|
52
|
Guo Y, He J, Zhang H, Chen R, Li L, Liu X,
Huang C, Qiang Z, Zhou Z, Wang Y, et al: Linear ubiquitination of
PTEN impairs its function to promote prostate cancer progression.
Oncogene. 41:4877–4892. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
53
|
Li F, Zhang J, Arfuso F, Chinnathambi A,
Zayed ME, Alharbi SA, Kumar AP, Ahn KS and Sethi G: NF-κB in cancer
therapy. Arch Toxicol. 89:711–731. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
54
|
Shafi O and Siddiqui G: Tracing the
origins of glioblastoma by investigating the role of gliogenic and
related neurogenic genes/signaling pathways in GBM development: A
systematic review. World J Surg Oncol. 20:1462022. View Article : Google Scholar : PubMed/NCBI
|
|
55
|
Du Y, Li C, Zhao Z, Liu Y, Zhang C and Yan
J: Efficacy and safety of venetoclax combined with hypomethylating
agents for relapse of acute myeloid leukemia and myelodysplastic
syndrome post allogeneic hematopoietic stem cell transplantation: A
systematic review and meta-analysis. BMC Cancer. 23:7642023.
View Article : Google Scholar : PubMed/NCBI
|
|
56
|
Shabana SM, Gad NS, Othman AI, Mohamed AF
and El-Missiry MA: β-caryophyllene oxide induces apoptosis and
inhibits proliferation of A549 lung cancer cells. Med Oncol.
40:1892023. View Article : Google Scholar : PubMed/NCBI
|
|
57
|
Marimuthu P and Singaravelu K: Deciphering
the crucial residues involved in heterodimerization of Bak peptide
and anti-apoptotic proteins for apoptosis. J Biomol Struct Dyn.
36:1637–1648. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
58
|
Tong Y, Gao WQ and Liu Y: Metabolic
heterogeneity in cancer: An overview and therapeutic implications.
Biochim Biophys Acta Rev Cancer. 1874:1884212020. View Article : Google Scholar : PubMed/NCBI
|
|
59
|
Dang Y, Jiang N, Wang H, Chen X, Gao Y,
Zhang X, Qin G, Li Y and Chen R: Proto-oncogene serine/threonine
kinase PIM3 promotes cell migration via modulating Rho GTPase
signaling. J Proteome Res. 19:1298–1309. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
60
|
de Moraes RP, Pimenta R, Mori FNC, Dos
Santos GA, Viana NI, Guimarães VR, de Camargo JA, Leite KRM, Srougi
M, Nahas WC and Reis ST: Tissue expression of MMP-9, TIMP-1, RECK,
and miR338-3p in prostate gland: Can it predict cancer? Mol Biol
Res Commun. 10:149–156. 2021.PubMed/NCBI
|
|
61
|
Jiang X, Wang J, Deng X, Xiong F, Ge J,
Xiang B, Wu X, Ma J, Zhou M, Li X, et al: Role of the tumor
microenvironment in PD-L1/PD-1-mediated tumor immune escape. Mol
Cancer. 18:102019. View Article : Google Scholar : PubMed/NCBI
|
|
62
|
MacNeil IA, Khan SA, Sen A, Soltani SM,
Burns DJ, Sullivan BF and Laing LG: Functional signaling test
identifies HER2 negative breast cancer patients who may benefit
from c-Met and pan-HER combination therapy. Cell Commun Signal.
20:42022. View Article : Google Scholar : PubMed/NCBI
|
|
63
|
Da C, Pu J, Liu Z, Wei J, Qu Y, Wu Y, Shi
B, Yang J, He N and Hou P: HACE1-mediated NRF2 activation causes
enhanced malignant phenotypes and decreased radiosensitivity of
glioma cells. Signal Transduct Target Ther. 6:3992021. View Article : Google Scholar : PubMed/NCBI
|
|
64
|
van der Ploeg P, Uittenboogaard A, Thijs
AMJ, Westgeest HM, Boere IA, Lambrechts S, van de Stolpe A, Bekkers
RLM and Piek JMJ: The effectiveness of monotherapy with
PI3K/AKT/mTOR pathway inhibitors in ovarian cancer: A
meta-analysis. Gynecol Oncol. 163:433–444. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
65
|
Tian LY, Smit DJ and Jücker M: The role of
PI3K/AKT/mTOR signaling in hepatocellular carcinoma metabolism. Int
J Mol Sci. 24:26522023. View Article : Google Scholar : PubMed/NCBI
|
|
66
|
Courtney KD, Corcoran RB and Engelman JA:
The PI3K pathway as drug target in human cancer. J Clin Oncol.
28:1075–1083. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
67
|
Moghbeli M: PI3K/AKT pathway as a pivotal
regulator of epithelial-mesenchymal transition in lung tumor cells.
Cancer Cell Int. 24:1652024. View Article : Google Scholar : PubMed/NCBI
|
|
68
|
Xu W, Yang Z and Lu N: A new role for the
PI3K/Akt signaling pathway in the epithelial-mesenchymal
transition. Cell Adh Migr. 9:317–324. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
69
|
Fumarola C, Bonelli MA, Petronini PG and
Alfieri RR: Targeting PI3K/AKT/mTOR pathway in non small cell lung
cancer. Biochem Pharmacol. 90:197–207. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
70
|
Liu YY, Huang WL, Wang ST, Hsu HP, Kao TC,
Chung WP and Young KC: CD36 inhibition enhances the
anti-proliferative effects of PI3K inhibitors in PTEN-loss
anti-HER2 resistant breast cancer cells. Cancer Metab. 13:62025.
View Article : Google Scholar : PubMed/NCBI
|
|
71
|
Yu J, Wu X, Song J, Zhao Y, Li H, Luo M
and Liu X: Loss of MHC-I antigen presentation correlated with
immune checkpoint blockade tolerance in MAPK inhibitor-resistant
melanoma. Front Pharmacol. 13:9282262022. View Article : Google Scholar : PubMed/NCBI
|
|
72
|
Browne IM and Okines AFC: Resistance to
targeted inhibitors of the PI3K/AKT/mTOR pathway in advanced
oestrogen-receptor-positive breast cancer. Cancers (Basel).
16:22592024. View Article : Google Scholar : PubMed/NCBI
|
|
73
|
Chen Y and Zhou X: Research progress of
mTOR inhibitors. Eur J Med Chem. 208:1128202020. View Article : Google Scholar : PubMed/NCBI
|
|
74
|
Guo YJ, Pan WW, Liu SB, Shen ZF, Xu Y and
Hu LL: ERK/MAPK signalling pathway and tumorigenesis. Exp Ther Med.
19:1997–2007. 2020.PubMed/NCBI
|
|
75
|
Yang M and Huang CZ: Mitogen-activated
protein kinase signaling pathway and invasion and metastasis of
gastric cancer. World J Gastroenterol. 21:11673–11679. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
76
|
Lu H, Liu S, Zhang G, Wu B, Zhu Y,
Frederick DT, Hu Y, Zhong W, Randell S, Sadek N, et al: PAK
signalling drives acquired drug resistance to MAPK inhibitors in
BRAF-mutant melanomas. Nature. 550:133–136. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
77
|
Corcoran RB, André T, Atreya CE, Schellens
JHM, Yoshino T, Bendell JC, Hollebecque A, McRee AJ, Siena S,
Middleton G, et al: Combined BRAF, EGFR, and MEK inhibition in
patients with BRAFV600E-mutant colorectal cancer. Cancer
Discov. 8:428–443. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
78
|
Subbiah V, Kreitman RJ, Wainberg ZA,
Gazzah A, Lassen U, Stein A, Wen PY, Dietrich S, de Jonge MJA, Blay
JY, et al: Dabrafenib plus trametinib in BRAFV600E-mutated rare
cancers: The phase 2 ROAR trial. Nat Med. 29:1103–1112. 2023.
View Article : Google Scholar : PubMed/NCBI
|
|
79
|
Kciuk M, Gielecińska A, Budzinska A,
Mojzych M and Kontek R: Metastasis and MAPK pathways. Int J Mol
Sci. 23:38472022. View Article : Google Scholar : PubMed/NCBI
|
|
80
|
Wright CJ and McCormack PL: Trametinib:
First global approval. Drugs. 73:1245–1254. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
81
|
Thota R, Johnson DB and Sosman JA:
Trametinib in the treatment of melanoma. Expert Opin Biol Ther.
15:735–747. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
82
|
Boulos JC, Yousof Idres MR and Efferth T:
Investigation of cancer drug resistance mechanisms by
phosphoproteomics. Pharmacol Res. 160:1050912020. View Article : Google Scholar : PubMed/NCBI
|
|
83
|
Zhao Y, Murciano-Goroff YR, Xue JY, Ang A,
Lucas J, Mai TT, Da Cruz Paula AF, Saiki AY, Mohn D, Achanta P, et
al: Diverse alterations associated with resistance to KRAS(G12C)
inhibition. Nature. 599:679–683. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
84
|
Yang K, Wang X, Zhang H, Wang Z, Nan G, Li
Y, Zhang F, Mohammed MK, Haydon RC, Luu HH, et al: The evolving
roles of canonical WNT signaling in stem cells and tumorigenesis:
Implications in targeted cancer therapies. Lab Invest. 96:116–136.
2016. View Article : Google Scholar : PubMed/NCBI
|
|
85
|
Trejo-Solis C, Escamilla-Ramirez A,
Jimenez-Farfan D, Castillo-Rodriguez RA, Flores-Najera A and
Cruz-Salgado A: Crosstalk of the Wnt/β-catenin signaling pathway in
the induction of apoptosis on cancer cells. Pharmaceuticals
(Basel). 14:8712021. View Article : Google Scholar : PubMed/NCBI
|
|
86
|
Hiremath IS, Goel A, Warrier S, Kumar AP,
Sethi G and Garg M: The multidimensional role of the Wnt/β-catenin
signaling pathway in human malignancies. J Cell Physiol.
237:199–238. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
87
|
Wu Y, Ginther C, Kim J, Mosher N, Chung S,
Slamon D and Vadgama JV: Expression of Wnt3 activates Wnt/β-catenin
pathway and promotes EMT-like phenotype in trastuzumab-resistant
HER2-overexpressing breast cancer cells. Mol Cancer Res.
10:1597–1606. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
88
|
Zhang Z, Westover D, Tang Z, Liu Y, Sun J,
Sun Y, Zhang R, Wang X, Zhou S, Hesilaiti N, et al: Wnt/β-catenin
signaling in the development and therapeutic resistance of
non-small cell lung cancer. J Transl Med. 22:5652024. View Article : Google Scholar : PubMed/NCBI
|
|
89
|
Qu HL, Hasen GW, Hou YY and Zhang CX:
THBS2 promotes cell migration and invasion in colorectal cancer via
modulating Wnt/β-catenin signaling pathway. Kaohsiung J Med Sci.
38:469–478. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
90
|
Aoki T, Nishida N and Kudo M: Current
perspectives on the immunosuppressive niche and role of fibrosis in
hepatocellular carcinoma and the development of antitumor immunity.
J Histochem Cytochem. 70:53–81. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
91
|
Pai SG, Carneiro BA, Mota JM, Costa R,
Leite CA, Barroso-Sousa R, Kaplan JB, Chae YK and Giles FJ:
Wnt/beta-catenin pathway: Modulating anticancer immune response. J
Hematol Oncol. 10:1012017. View Article : Google Scholar : PubMed/NCBI
|
|
92
|
Tang L, Xu H, Wu T, Wu W, Lu Y, Gu J, Wang
X, Zhou M, Chen Q, Sun X and Cai H: Advances in tumor
microenvironment and underlying molecular mechanisms of bladder
cancer: A systematic review. Discov Oncol. 15:1112024. View Article : Google Scholar : PubMed/NCBI
|
|
93
|
Seo J, Ha J, Kang E and Cho S: The role of
epithelial-mesenchymal transition-regulating transcription factors
in anti-cancer drug resistance. Arch Pharm Res. 44:281–292. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
94
|
Ma Q, Hao S, Hong W, Tergaonkar V, Sethi
G, Tian Y and Duan C: Versatile function of NF-ĸB in inflammation
and cancer. Exp Hematol Oncol. 13:682024. View Article : Google Scholar : PubMed/NCBI
|
|
95
|
Yu H, Lee H, Herrmann A, Buettner R and
Jove R: Revisiting STAT3 signalling in cancer: New and unexpected
biological functions. Nat Rev Cancer. 14:736–746. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
96
|
Lalle G, Twardowski J and Grinberg-Bleyer
Y: NF-κB in cancer immunity: Friend or foe? Cells. 10:3552021.
View Article : Google Scholar : PubMed/NCBI
|
|
97
|
Sabaawy HE, Ryan BM, Khiabanian H and Pine
SR: JAK/STAT of all trades: Linking inflammation with cancer
development, tumor progression and therapy resistance.
Carcinogenesis. 42:1411–1419. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
98
|
Rinkenbaugh AL and Baldwin AS: The NF-κB
pathway and cancer stem cells. Cells. 5:162016. View Article : Google Scholar : PubMed/NCBI
|
|
99
|
Richter S, Bedard PL, Chen EX, Clarke BA,
Tran B, Hotte SJ, Stathis A, Hirte HW, Razak ARA, Reedijk M, et al:
A phase I study of the oral gamma secretase inhibitor R04929097 in
combination with gemcitabine in patients with advanced solid tumors
(PHL-078/CTEP 8575). Invest New Drugs. 32:243–249. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
100
|
Yuan Y, Zhong W, Ma G, Zhang B and Tian H:
Yes-associated protein regulates the growth of human non-small cell
lung cancer in response to matrix stiffness. Mol Med Rep.
11:4267–4272. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
101
|
Giammona A, Crivaro E and Stecca B:
Emerging roles of hedgehog signaling in cancer immunity. Int J Mol
Sci. 24:13212023. View Article : Google Scholar : PubMed/NCBI
|
|
102
|
Ding J, Li HY, Zhang L, Zhou Y and Wu J:
Hedgehog signaling, a critical pathway governing the development
and progression of hepatocellular carcinoma. Cells. 10:1232021.
View Article : Google Scholar : PubMed/NCBI
|
|
103
|
Wang Y, Wang H, Yan Z, Li G, Hu G, Zhang
H, Huang D, Wang Y, Zhang X, Yan Y, et al: The critical role of
dysregulated Hh-FOXM1-TPX2 signaling in human hepatocellular
carcinoma cell proliferation. Cell Commun Signal. 18:1162020.
View Article : Google Scholar : PubMed/NCBI
|
|
104
|
Zhang X, Ma H, Gao Y, Liang Y, Du Y, Hao S
and Ni T: The tumor microenvironment: Signal transduction.
Biomolecules. 14:4382024. View Article : Google Scholar : PubMed/NCBI
|
|
105
|
Wang X, Jiang W, Du Y, Zhu D, Zhang J,
Fang C, Yan F and Chen ZS: Targeting feedback activation of
signaling transduction pathways to overcome drug resistance in
cancer. Drug Resist Updat. 65:1008842022. View Article : Google Scholar : PubMed/NCBI
|
|
106
|
Delos Santos RC, Garay C and Antonescu CN:
Charming neighborhoods on the cell surface: Plasma membrane
microdomains regulate receptor tyrosine kinase signaling. Cell
Signal. 27:1963–1976. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
107
|
Ullah R, Yin Q, Snell AH and Wan L:
RAF-MEK-ERK pathway in cancer evolution and treatment. Semin Cancer
Biol. 85:123–154. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
108
|
Kang Y, Lv R, Feng Z and Zhu J:
Tumor-associated macrophages improve hypoxia-induced endoplasmic
reticulum stress response in colorectal cancer cells by regulating
TGF-β1/SOX4. Cell Signal. 99:1104302022. View Article : Google Scholar : PubMed/NCBI
|
|
109
|
Jiang Z, Zhang G, Huang L, Yuan Y, Wu C
and Li Y: Transmissible endoplasmic reticulum stress: A novel
perspective on tumor immunity. Front Cell Dev Biol. 8:8462020.
View Article : Google Scholar : PubMed/NCBI
|
|
110
|
Lebrun H, Turpin A and Zerbib P:
Therapeutic implications of B-RAF mutations in colorectal cancer. J
Visc Surg. 158:487–496. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
111
|
Radovich M, Solzak JP, Wang CJ, Hancock
BA, Badve S, Althouse SK, Bray SM, Storniolo AMV, Ballinger TJ,
Schneider BP and Miller KD: Initial phase I safety study of
gedatolisib plus cofetuzumab pelidotin for patients with metastatic
triple-negative breast cancer. Clin Cancer Res. 28:3235–3241. 2022.
View Article : Google Scholar : PubMed/NCBI
|
|
112
|
De Luca A, Maiello MR, D'Alessio A,
Pergameno M and Normanno N: The RAS/RAF/MEK/ERK and the PI3K/AKT
signalling pathways: Role in cancer pathogenesis and implications
for therapeutic approaches. Expert Opin Ther Targets. 16 (Suppl
2):S17–S27. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
113
|
Shapiro GI, LoRusso P, Cho DC, Musib L,
Yan Y, Wongchenko M, Chang I, Patel P, Chan IT, Sanabria-Bohorquez
S, et al: A phase Ib open-label dose escalation study of the
safety, pharmacokinetics, and pharmacodynamics of cobimetinib
(GDC-0973) and ipatasertib (GDC-0068) in patients with locally
advanced or metastatic solid tumors. Invest New Drugs. 39:163–174.
2021. View Article : Google Scholar : PubMed/NCBI
|
|
114
|
Perez-Stable C, de Las Pozas A,
Wangpaichitr M, Sha W, Wang H, Cai R and Schally AV: Growth
hormone-releasing hormone (GHRH) antagonist peptides combined with
PI3K isoform inhibitors enhance cell death in prostate cancer.
Cancers (Basel). 17:16432025. View Article : Google Scholar : PubMed/NCBI
|
|
115
|
Henderson V, Smith B, Burton LJ, Randle D,
Morris M and Odero-Marah VA: Snail promotes cell migration through
PI3K/AKT-dependent Rac1 activation as well as PI3K/AKT-independent
pathways during prostate cancer progression. Cell Adh Migr.
9:255–264. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
116
|
Palombo R, Passacantilli I, Terracciano F,
Capone A, Matteocci A, Tournier S, Alberdi A, Chiurchiù V, Volpe E
and Paronetto MP: Inhibition of the PI3K/AKT/mTOR signaling
promotes an M1 macrophage switch by repressing the ATF3-CXCL8 axis
in Ewing sarcoma. Cancer Lett. 555:2160422023. View Article : Google Scholar : PubMed/NCBI
|
|
117
|
Shee K, Yang W, Hinds JW, Hampsch RA, Varn
FS, Traphagen NA, Patel K, Cheng C, Jenkins NP, Kettenbach AN, et
al: Therapeutically targeting tumor microenvironment-mediated drug
resistance in estrogen receptor-positive breast cancer. J Exp Med.
215:895–910. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
118
|
Saleem H, Kulsoom Abdul U, Küçükosmanoglu
A, Houweling M, Cornelissen FMG, Heiland DH, Hegi ME, Kouwenhoven
MCM, Bailey D, Würdinger T and Westerman BA: The TICking clock of
EGFR therapy resistance in glioblastoma: Target Independence or
target compensation. Drug Resist Updat. 43:29–37. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
119
|
Georgescu MM, Islam MZ, Li Y, Traylor J
and Nanda A: Novel targetable FGFR2 and FGFR3 alterations in
glioblastoma associate with aggressive phenotype and distinct gene
expression programs. Acta Neuropathol Commun. 9:692021. View Article : Google Scholar : PubMed/NCBI
|
|
120
|
Yang Y, Mou Y, Wan LX, Zhu S, Wang G, Gao
H and Liu B: Rethinking therapeutic strategies of dual-target
drugs: An update on pharmacological small-molecule compounds in
cancer. Med Res Rev. 44:2600–2623. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
121
|
Nelson DE, Ihekwaba AEC, Elliott M,
Johnson JR, Gibney CA, Foreman BE, Nelson G, See V, Horton CA,
Spiller DG, et al: Oscillations in NF-kappaB signaling control the
dynamics of gene expression. Science. 306:704–708. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
122
|
Konopleva M, Martinelli G, Daver N,
Papayannidis C, Wei A, Higgins B, Ott M, Mascarenhas J and Andreeff
M: MDM2 inhibition: An important step forward in cancer therapy.
Leukemia. 34:2858–2874. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
123
|
Xu Y, Tsai CW, Chang WS, Han Y, Huang M,
Pettaway CA, Bau DT and Gu J: Epigenome-wide association study of
prostate cancer in African Americans identifies DNA methylation
biomarkers for aggressive disease. Biomolecules. 11:18262021.
View Article : Google Scholar : PubMed/NCBI
|
|
124
|
Creixell P, Schoof EM, Erler JT and
Linding R: Navigating cancer network attractors for tumor-specific
therapy. Nat Biotechnol. 30:842–848. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
125
|
Fenercioglu AK, Uzun H and Unal DO: The
convergent immunopathogenesis of cigarette smoke exposure: From
oxidative stress to epigenetic reprogramming in chronic disease.
Int J Mol Sci. 27:1872025. View Article : Google Scholar : PubMed/NCBI
|
|
126
|
Grout JA, Sirven P, Leader AM, Maskey S,
Hector E, Puisieux I, Steffan F, Cheng E, Tung N, Maurin M, et al:
Spatial positioning and matrix programs of cancer-associated
fibroblasts promote T-cell exclusion in human lung tumors. Cancer
Discov. 12:2606–2625. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
127
|
Dzobo K, Senthebane DA and Dandara C: The
tumor microenvironment in tumorigenesis and therapy resistance
revisited. Cancers (Basel). 15:3762023. View Article : Google Scholar : PubMed/NCBI
|
|
128
|
Dai S, Liu Y, Liu Z, Li R, Luo F, Li Y,
Dai L and Peng X: Cancer-associated fibroblasts mediate resistance
to anti-EGFR therapies in cancer. Pharmacol Res. 206:1073042024.
View Article : Google Scholar : PubMed/NCBI
|
|
129
|
Liu S, Ren J and Ten Dijke P: Targeting
TGFβ signal transduction for cancer therapy. Signal Transduct
Target Ther. 6:82021. View Article : Google Scholar : PubMed/NCBI
|
|
130
|
Huang J, Zhang L, Wan D, Zhou L, Zheng S,
Lin S and Qiao Y: Extracellular matrix and its therapeutic
potential for cancer treatment. Signal Transduct Target Ther.
6:1532021. View Article : Google Scholar : PubMed/NCBI
|
|
131
|
Sleeboom JJF, van Tienderen GS,
Schenke-Layland K, van der Laan LJW, Khalil AA and Verstegen MMA:
The extracellular matrix as hallmark of cancer and metastasis: From
biomechanics to therapeutic targets. Sci Transl Med.
16:eadg38402024. View Article : Google Scholar : PubMed/NCBI
|
|
132
|
Straub CS: Targeting IAPs as an approach
to anti-cancer therapy. Curr Top Med Chem. 11:291–316. 2011.
View Article : Google Scholar : PubMed/NCBI
|
|
133
|
Tanaka M and Siemann DW: Gas6/Axl
signaling pathway in the tumor immune microenvironment. Cancers
(Basel). 12:18502020. View Article : Google Scholar : PubMed/NCBI
|
|
134
|
Yang Y, Li CW, Chan LC, Wei Y, Hsu JM, Xia
W, Cha JH, Hou J, Hsu JL, Sun L, et al: Exosomal PD-L1 harbors
active defense function to suppress T cell killing of breast cancer
cells and promote tumor growth. Cell Res. 28:862–864. 2018.
View Article : Google Scholar : PubMed/NCBI
|
|
135
|
Avan A, Narayan R, Giovannetti E and
Peters GJ: Role of Akt signaling in resistance to DNA-targeted
therapy. World J Clin Oncol. 7:352–369. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
136
|
Gao Z, Long Y, Wu Y, Pu Y and Xue F:
LncRNA LINC02253 activates KRT18/MAPK/ERK pathway by mediating
N6-methyladenosine modification of KRT18 mRNA in gastric cancer.
Carcinogenesis. 43:419–429. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
137
|
Wang Z, Martin D, Vitale-Cross L, Feng X,
Molinolo AA, Allevato MM, Wu VH, Gilardi M, Xu H, Chen Q and
Gutkind JS: Abstract 5884: 4EBP1 is a tumor suppressor gene
unleashed by mTOR inhibition in head and neck squamous cell
carcinoma. Cancer Res. 78 (13 Suppl):S58842018. View Article : Google Scholar
|
|
138
|
Li P, Zhang Z and Sun P: DOT1L promotes
expression of CD44 through the Wnt/β-catenin signaling pathway in
early gastric carcinoma. J Cancer. 15:2276–2291. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
139
|
Ren J, Yang Y, Peng T and Xu D: Predictive
value of β-catenin in bladder cancer: A systematic review and
meta-analysis. Biosci Rep. 40:BSR202021272020. View Article : Google Scholar : PubMed/NCBI
|
|
140
|
Wang B, Cheng D, Ma D, Chen R, Li D, Zhao
W, Fang C and Ji M: Mutual regulation of PD-L1 immunosuppression
between tumor-associated macrophages and tumor cells: A critical
role for exosomes. Cell Commun Signal. 22:212024. View Article : Google Scholar : PubMed/NCBI
|
|
141
|
Kounatidis D, Vallianou NG, Karampela I,
Grivakou E and Dalamaga M: The intricate role of adipokines in
cancer-related signaling and the tumor microenvironment: Insights
for future research. Semin Cancer Biol. 113:130–150. 2025.
View Article : Google Scholar : PubMed/NCBI
|
|
142
|
Hendriks LE, Kerr KM, Menis J, Mok TS,
Nestle U, Passaro A, Peters S, Planchard D, Smit EF, Solomon BJ, et
al: Oncogene-addicted metastatic non-small-cell lung cancer: ESMO
clinical practice guideline for diagnosis, treatment and follow-up.
Ann Oncol. 34:339–357. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
143
|
Zecchin D, Moore C, Michailidis F,
Horswell S, Rana S, Howell M and Downward J: Combined targeting of
G protein-coupled receptor and EGF receptor signaling overcomes
resistance to PI3K pathway inhibitors in PTEN-null triple negative
breast cancer. EMBO Mol Med. 12:e119872020. View Article : Google Scholar : PubMed/NCBI
|
|
144
|
Hossain MT and Hossain MA: Targeting PI3K
in cancer treatment: A comprehensive review with insights from
clinical outcomes. Eur J Pharmacol. 996:1774322025. View Article : Google Scholar : PubMed/NCBI
|
|
145
|
André F, Ciruelos E, Rubovszky G, Campone
M, Loibl S, Rugo HS, Iwata H, Conte P, Mayer IA, Kaufman B, et al:
Alpelisib for PIK3CA-mutated, hormone receptor-positive advanced
breast cancer. N Engl J Med. 380:1929–1940. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
146
|
Disis ML, Adams SF, Bajpai J, Butler MO,
Curiel T, Dodt SA, Doherty L, Emens LA, Friedman CF, Gatti-Mays M,
et al: Society for immunotherapy of cancer (SITC) clinical practice
guideline on immunotherapy for the treatment of gynecologic cancer.
J Immunother Cancer. 11:e0066242023. View Article : Google Scholar : PubMed/NCBI
|
|
147
|
Havel JJ, Chowell D and Chan TA: The
evolving landscape of biomarkers for checkpoint inhibitor
immunotherapy. Nat Rev Cancer. 19:133–150. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
148
|
Das A, Bhattacharya B and Roy S:
Decrypting a path based approach for identifying the interplay
between PI3K and GSK3 signaling cascade from the perspective of
cancer. Genes Dis. 9:868–888. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
149
|
Kocher F, Amann A, Zimmer K, Geisler S,
Fuchs D, Pichler R, Wolf D, Kurz K, Seeber A and Pircher A: High
indoleamine-2,3-dioxygenase 1 (IDO) activity is linked to primary
resistance to immunotherapy in non-small cell lung cancer (NSCLC).
Transl Lung Cancer Res. 10:304–313. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
150
|
Sun Y, Meyers BA, Czako B, Leonard P,
Mseeh F, Harris AL, Wu Q, Johnson S, Parker CA, Cross JB, et al:
Allosteric SHP2 inhibitor, IACS-13909, overcomes EGFR-dependent and
EGFR-independent resistance mechanisms toward osimertinib. Cancer
Res. 80:4840–4853. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
151
|
Li W, Zheng C, Xu X, Xia Y, Zhang K, Huang
A, Zhang X, Zheng Y, Chen G and Zhang S: Combined therapy of
dabrafenib and an anti-HER2 antibody-drug conjugate for advanced
BRAF-mutant melanoma. Cell Mol Biol Lett. 29:502024. View Article : Google Scholar : PubMed/NCBI
|
|
152
|
Kwok HH, Li H, Yang J, Deng J, Lee NCM, Au
TW, Sit AK, Hsin MK, Ma SKY, Cheung LWK, et al: Single-cell
transcriptomic analysis uncovers intratumoral heterogeneity and
drug-tolerant persister in ALK-rearranged lung adenocarcinoma.
Cancer Commun (Lond). 43:951–955. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
153
|
Chandarlapaty S: Negative feedback and
adaptive resistance to the targeted therapy of cancer. Cancer
Discov. 2:311–319. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
154
|
Wu R, Guo W, Qiu X, Wang S, Sui C, Lian Q,
Wu J, Shan Y, Yang Z, Yang S, et al: Comprehensive analysis of
spatial architecture in primary liver cancer. Sci Adv.
7:eabg37502021. View Article : Google Scholar : PubMed/NCBI
|
|
155
|
Mei Y, Xiao W, Hu H, Lu G, Chen L, Sun Z,
Lü M, Ma W, Jiang T, Gao Y, et al: Single-cell analyses reveal
suppressive tumor microenvironment of human colorectal cancer. Clin
Transl Med. 11:e4222021. View Article : Google Scholar : PubMed/NCBI
|
|
156
|
Sun YF, Wu L, Liu SP, Jiang MM, Hu B, Zhou
KQ, Guo W, Xu Y, Zhong Y, Zhou XR, et al: Dissecting spatial
heterogeneity and the immune-evasion mechanism of CTCs by
single-cell RNA-seq in hepatocellular carcinoma. Nat Commun.
12:40912021. View Article : Google Scholar : PubMed/NCBI
|
|
157
|
Sinjab A, Han G, Treekitkarnmongkol W,
Hara K, Brennan PM, Dang M, Hao D, Wang R, Dai E, Dejima H, et al:
Resolving the spatial and cellular architecture of lung
adenocarcinoma by multiregion single-cell sequencing. Cancer
Discov. 11:2506–2523. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
158
|
Yan H, Shi J, Dai Y, Li X, Wu Y, Zhang J,
Gu Z, Zhang C and Leng J: Technique integration of single-cell RNA
sequencing with spatially resolved transcriptomics in the tumor
microenvironment. Cancer Cell Int. 22:1552022. View Article : Google Scholar : PubMed/NCBI
|
|
159
|
Hastings JF, O'Donnell YEI, Fey D and
Croucher DR: Applications of personalised signalling network models
in precision oncology. Pharmacol Ther. 212:1075552020. View Article : Google Scholar : PubMed/NCBI
|
|
160
|
Jiang Z, Zhang H, Gao Y and Sun Y:
Multi-omics strategies for biomarker discovery and application in
personalized oncology. Mol Biomed. 6:1152025. View Article : Google Scholar : PubMed/NCBI
|
|
161
|
Park S, Ock CY, Kim H, Pereira S, Park S,
Ma M, Choi S, Kim S, Shin S, Aum BJ, et al: Artificial
intelligence-powered spatial analysis of tumor-infiltrating
lymphocytes as complementary biomarker for immune checkpoint
inhibition in non-small-cell lung cancer. J Clin Oncol.
40:1916–1928. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
162
|
Yang X, Wang Y, Byrne R, Schneider G and
Yang S: Concepts of Artificial Intelligence for Computer-Assisted
Drug Discovery. Chem Rev. 119:10520–10594. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
163
|
Esteva A, Feng J, van der Wal D, Huang SC,
Simko JP, DeVries S, Chen E, Schaeffer EM, Morgan TM, Sun Y, et al:
Prostate cancer therapy personalization via multi-modal deep
learning on randomized phase III clinical trials. NPJ Digit Med.
5:712022. View Article : Google Scholar : PubMed/NCBI
|
