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<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">OR</journal-id>
<journal-title-group>
<journal-title>Oncology Reports</journal-title>
</journal-title-group>
<issn pub-type="ppub">1021-335X</issn>
<issn pub-type="epub">1791-2431</issn>
<publisher>
<publisher-name>D.A. Spandidos</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3892/or.2026.9131</article-id>
<article-id pub-id-type="publisher-id">OR-56-1-09131</article-id>
<article-categories>
<subj-group>
<subject>Review</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Cancer signaling networks in tumor progression and drug resistance: Crosstalk, adaptive reprogramming and therapeutic targeting (Review)</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author"><name><surname>Li</surname><given-names>Hongyan</given-names></name>
<xref rid="af1-or-56-1-09131" ref-type="aff">1</xref>
<xref rid="fn1-or-56-1-09131" ref-type="author-notes">&#x002A;</xref></contrib>
<contrib contrib-type="author"><name><surname>Song</surname><given-names>Shiyuan</given-names></name>
<xref rid="af2-or-56-1-09131" ref-type="aff">2</xref>
<xref rid="fn1-or-56-1-09131" ref-type="author-notes">&#x002A;</xref></contrib>
<contrib contrib-type="author"><name><surname>Wang</surname><given-names>Linlin</given-names></name>
<xref rid="af2-or-56-1-09131" ref-type="aff">2</xref>
<xref rid="c1-or-56-1-09131" ref-type="corresp"/></contrib>
<contrib contrib-type="author"><name><surname>Liu</surname><given-names>Qiang</given-names></name>
<xref rid="af1-or-56-1-09131" ref-type="aff">1</xref>
<xref rid="c2-or-56-1-09131" ref-type="corresp"/></contrib>
</contrib-group>
<aff id="af1-or-56-1-09131"><label>1</label>Oncology Department of Integrated Traditional Chinese and Western Medicine, Shenyang Tenth People&#x0027;s Hospital, Shenyang, Liaoning 110044, P.R. China</aff>
<aff id="af2-or-56-1-09131"><label>2</label>Department of Thoracic Surgery, Shenyang Tenth People&#x0027;s Hospital, Shenyang, Liaoning 110044, P.R. China</aff>
<author-notes>
<corresp id="c1-or-56-1-09131"><italic>Correspondence to</italic>: Dr Linlin Wang, Department of Thoracic Surgery, Shenyang Tenth People&#x0027;s Hospital, 11 Beihai Street, Dadong, Shenyang, Liaoning 110044, P.R. China, E-mail: <email>lsywll@126.com</email></corresp>
<corresp id="c2-or-56-1-09131">Dr Qiang Liu, Oncology Department of Integrated Traditional Chinese and Western Medicine, Shenyang Tenth People&#x0027;s Hospital, 11, Beihai Street, Dadong, Shenyang, Liaoning 110044, P.R. China, E-mail: <email>liuqiang8866@126.com</email></corresp>
<fn id="fn1-or-56-1-09131"><label>&#x002A;</label><p>Contributed equally</p></fn></author-notes>
<pub-date pub-type="collection"><month>07</month><year>2026</year></pub-date>
<pub-date pub-type="epub"><day>08</day><month>05</month><year>2026</year></pub-date>
<volume>56</volume>
<issue>1</issue>
<elocation-id>126</elocation-id>
<history>
<date date-type="received"><day>17</day><month>09</month><year>2025</year></date>
<date date-type="accepted"><day>28</day><month>04</month><year>2026</year></date>
</history>
<permissions>
<copyright-statement>Copyright: &#x00A9; 2026 Li et al.</copyright-statement>
<copyright-year>2026</copyright-year>
<license license-type="open-access">
<license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivs License</ext-link>, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.</license-p></license>
</permissions>
<abstract>
<p>Cancer signaling networks, highly dynamic with interconnected systems, regulate tumor initiation, its progression, therapeutic response and drug resistance. Rather than functioning as isolated pathways, these networks integrate extracellular and intracellular signals through coordinated interactions among membrane receptors, intracellular transducers and downstream effectors. Increasing evidence suggests that pathway crosstalk, feedback regulation and adaptive reprogramming are central to tumor phenotypic plasticity, microenvironmental adaptation and resistance to therapy. In this review, the core architecture of cancer signaling networks and the major oncogenic pathways embedded within them were summarized with a particular focus on PI3K/Akt/mTOR, MAPK/ERK and Wnt/&#x03B2;-catenin signaling. The dynamic network properties that shape cancer behavior, including compensatory activation, context-dependent signaling outputs and interactions with the tumor microenvironment, were further discussed. These features provide insights into why single-pathway inhibition often produces only a limited and transient clinical benefit. Importantly, a network-level understanding of cancer signaling has major translational implications. Therapeutic resistance frequently arises through pathway reactivation, bypass signaling and adaptive reprogramming, necessitating rational combination strategies and multi-target interventions. Advances in multi-omics, single-cell and spatial technologies and computational modeling are crucial for characterizing signaling network dynamics and identifying clinically relevant vulnerabilities.</p>
</abstract>
<kwd-group>
<kwd>cancer signaling networks</kwd>
<kwd>drug resistance</kwd>
<kwd>pathway crosstalk</kwd>
<kwd>adaptive reprogramming</kwd>
<kwd>precision oncology</kwd>
</kwd-group>
<funding-group>
<funding-statement><bold>Funding:</bold> No funding was received.</funding-statement>
</funding-group>
</article-meta>
</front>
<body>
<sec sec-type="intro">
<label>1.</label>
<title>Introduction</title>
<p>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 (<xref rid="b1-or-56-1-09131" ref-type="bibr">1</xref>). 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 (<xref rid="b2-or-56-1-09131" ref-type="bibr">2</xref>). Advances in multi-omics and systems biology have offered deeper insights into the organization and regulatory principles of these networks (<xref rid="b3-or-56-1-09131" ref-type="bibr">3</xref>,<xref rid="b4-or-56-1-09131" ref-type="bibr">4</xref>). Such insights have improved the current understanding of tumor heterogeneity and evolutionary dynamics, while also identifying new therapeutic targets and more individualized treatment strategies (<xref rid="b5-or-56-1-09131" ref-type="bibr">5</xref>,<xref rid="b6-or-56-1-09131" ref-type="bibr">6</xref>). Although several oncogenic pathways overlap across cancers, their functional roles and clinical relevance remain highly context dependent.</p>
<p>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.</p>
<p>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/&#x03B2;-catenin signaling (<xref rid="b7-or-56-1-09131" ref-type="bibr">7</xref>,<xref rid="b8-or-56-1-09131" ref-type="bibr">8</xref>). A recent review has also discussed how signaling networks influence cancer metabolism and therapeutic response (<xref rid="b2-or-56-1-09131" ref-type="bibr">2</xref>). 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. <xref rid="f1-or-56-1-09131" ref-type="fig">Fig. 1</xref> 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.</p>
</sec>
<sec>
<label>2.</label>
<title>Core architecture of cancer signaling networks</title>
<sec>
<title/>
<sec>
<title>Membrane receptors</title>
<p>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 (<xref rid="b9-or-56-1-09131" ref-type="bibr">9</xref>&#x2013;<xref rid="b12-or-56-1-09131" ref-type="bibr">12</xref>). Aberrant EGFR and HER2 activation is frequently observed across multiple cancers (<xref rid="b13-or-56-1-09131" ref-type="bibr">13</xref>,<xref rid="b14-or-56-1-09131" ref-type="bibr">14</xref>), while increased PD-1/PD-L1 signaling contributes to tumor immune evasion (<xref rid="b15-or-56-1-09131" ref-type="bibr">15</xref>). 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 (<xref rid="b16-or-56-1-09131" ref-type="bibr">16</xref>).</p>
</sec>
<sec>
<title>Signal transducers</title>
<p>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 (<xref rid="b17-or-56-1-09131" ref-type="bibr">17</xref>). These molecules fall into four major categories.</p>
</sec>
<sec>
<title>G proteins and their family members</title>
<p>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 (<xref rid="b18-or-56-1-09131" ref-type="bibr">18</xref>). Heterotrimeric G proteins regulate intracellular second messengers such as cAMP, inositol 1,4,5-trisphosphate and diacylglycerol (<xref rid="b19-or-56-1-09131" ref-type="bibr">19</xref>), thereby modulating cellular metabolism, ion channel activity and gene expression (<xref rid="b20-or-56-1-09131" ref-type="bibr">20</xref>&#x2013;<xref rid="b23-or-56-1-09131" ref-type="bibr">23</xref>). 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 (<xref rid="b24-or-56-1-09131" ref-type="bibr">24</xref>,<xref rid="b25-or-56-1-09131" ref-type="bibr">25</xref>). By contrast, Rho family proteins primarily regulate cytoskeletal dynamics and cell motility, contributing to cancer invasion and metastatic potential (<xref rid="b26-or-56-1-09131" ref-type="bibr">26</xref>).</p>
</sec>
<sec>
<title>Kinase cascades</title>
<p>Kinase cascades, particularly the MAPK and PI3K/Akt/mTOR pathways, amplify and diversify signaling through sequential phosphorylation events (<xref rid="b27-or-56-1-09131" ref-type="bibr">27</xref>,<xref rid="b28-or-56-1-09131" ref-type="bibr">28</xref>). These pathways regulate key biological processes, with MAPK being primarily involved in proliferation, differentiation and stress responses (<xref rid="b29-or-56-1-09131" ref-type="bibr">29</xref>,<xref rid="b30-or-56-1-09131" ref-type="bibr">30</xref>), and PI3K/Akt in cell survival, metabolism and therapeutic resistance (<xref rid="b31-or-56-1-09131" ref-type="bibr">31</xref>&#x2013;<xref rid="b33-or-56-1-09131" ref-type="bibr">33</xref>). 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.</p>
</sec>
<sec>
<title>Signal integration proteins</title>
<p>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 (<xref rid="b34-or-56-1-09131" ref-type="bibr">34</xref>&#x2013;<xref rid="b36-or-56-1-09131" ref-type="bibr">36</xref>). By assembling signaling complexes, these molecules enhance signal transduction efficiency and facilitate pathway crosstalk. Certain proteins, such as &#x03B2;-arrestin and Grb2 (<xref rid="tI-or-56-1-09131" ref-type="table">Table I</xref>), function as both scaffolds and adaptors to coordinate signaling (<xref rid="b37-or-56-1-09131" ref-type="bibr">37</xref>,<xref rid="b38-or-56-1-09131" ref-type="bibr">38</xref>). 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 <xref rid="tI-or-56-1-09131" ref-type="table">Table I</xref> (<xref rid="b39-or-56-1-09131" ref-type="bibr">39</xref>&#x2013;<xref rid="b48-or-56-1-09131" ref-type="bibr">48</xref>).</p>
</sec>
<sec>
<title>Regulatory enzymes and negative regulators</title>
<p>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 (<xref rid="b49-or-56-1-09131" ref-type="bibr">49</xref>,<xref rid="b50-or-56-1-09131" ref-type="bibr">50</xref>). 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 (<xref rid="b51-or-56-1-09131" ref-type="bibr">51</xref>,<xref rid="b52-or-56-1-09131" ref-type="bibr">52</xref>).</p>
</sec>
<sec>
<title>Downstream effectors</title>
<p>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 (<xref rid="b7-or-56-1-09131" ref-type="bibr">7</xref>). Specifically, transcription factors including NF-&#x03BA;B, c-Myc and &#x03B2;-catenin regulate tumor-associated gene expression (<xref rid="b53-or-56-1-09131" ref-type="bibr">53</xref>,<xref rid="b54-or-56-1-09131" ref-type="bibr">54</xref>), while proteins such as Bcl-2 family members, caspases and p53 control apoptosis and cell cycle progression (<xref rid="b55-or-56-1-09131" ref-type="bibr">55</xref>&#x2013;<xref rid="b57-or-56-1-09131" ref-type="bibr">57</xref>). Metabolic regulators such as mTOR and HIF-1&#x03B1; support cellular adaptation (<xref rid="b58-or-56-1-09131" ref-type="bibr">58</xref>), while Rho GTPases and Fascin mediate cytoskeletal remodeling and invasion (<xref rid="b59-or-56-1-09131" ref-type="bibr">59</xref>,<xref rid="b60-or-56-1-09131" ref-type="bibr">60</xref>). Additionally, PD-L1 plays a pivotal role in tumor immune evasion (<xref rid="b61-or-56-1-09131" ref-type="bibr">61</xref>). Dysregulated effectors generate malignant phenotypes, drive therapeutic resistance and often yield poor clinical outcomes (<xref rid="b62-or-56-1-09131" ref-type="bibr">62</xref>,<xref rid="b63-or-56-1-09131" ref-type="bibr">63</xref>). These downstream consequences arise not from isolated signaling events but from the coordinated activation of oncogenic pathways operating within interconnected networks.</p>
</sec>
</sec>
</sec>
<sec>
<label>3.</label>
<title>Core oncogenic signaling pathways</title>
<p>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.</p>
<sec>
<title/>
<sec>
<title>PI3K/Akt/mTOR pathway</title>
<p>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 (<xref rid="b64-or-56-1-09131" ref-type="bibr">64</xref>&#x2013;<xref rid="b68-or-56-1-09131" ref-type="bibr">68</xref>). 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 &#x03B1; (PIK3CA), resulting in constitutive output that supports tumor progression and resistance to therapy (<xref rid="b69-or-56-1-09131" ref-type="bibr">69</xref>&#x2013;<xref rid="b71-or-56-1-09131" ref-type="bibr">71</xref>). 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 (<xref rid="b72-or-56-1-09131" ref-type="bibr">72</xref>). This adaptive reprogramming sustains tumor survival, limits the durability of single-pathway inhibition and drives therapeutic resistance (<xref rid="b73-or-56-1-09131" ref-type="bibr">73</xref>).</p>
</sec>
<sec>
<title>MAPK/ERK pathway</title>
<p>The MAPK/ERK cascade, organized around the Ras-Raf-MEK-ERK kinase axis, regulates multiple biological processes, including proliferation, differentiation, apoptosis and migration (<xref rid="b74-or-56-1-09131" ref-type="bibr">74</xref>,<xref rid="b75-or-56-1-09131" ref-type="bibr">75</xref>). 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 (<xref rid="b76-or-56-1-09131" ref-type="bibr">76</xref>&#x2013;<xref rid="b81-or-56-1-09131" ref-type="bibr">81</xref>).</p>
<p>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 (<xref rid="b82-or-56-1-09131" ref-type="bibr">82</xref>). 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 (<xref rid="b83-or-56-1-09131" ref-type="bibr">83</xref>).</p>
</sec>
<sec>
<title>Wnt/&#x03B2;-catenin pathway</title>
<p>The Wnt/&#x03B2;-catenin pathway is a vital regulator of cancer cell stemness, self-renewal and metastatic potential. Upon signaling, &#x03B2;-catenin accumulates in the cytoplasm and translocates to the nucleus to drive the expression of genes involved in proliferation and epithelial-mesenchymal transition (EMT) (<xref rid="b84-or-56-1-09131" ref-type="bibr">84</xref>&#x2013;<xref rid="b88-or-56-1-09131" ref-type="bibr">88</xref>). 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 (<xref rid="b89-or-56-1-09131" ref-type="bibr">89</xref>,<xref rid="b90-or-56-1-09131" ref-type="bibr">90</xref>). Beyond its tumor-intrinsic effects, the Wnt/&#x03B2;-catenin axis modulates the tumor microenvironment and immune responses, further supporting tumor progression (<xref rid="b91-or-56-1-09131" ref-type="bibr">91</xref>,<xref rid="b92-or-56-1-09131" ref-type="bibr">92</xref>). Mechanistically, stabilized nuclear &#x03B2;-catenin promotes the transcription of EMT-related genes, such as Snail and c-Myc, enhancing tumor invasion, metastasis and therapeutic resistance (<xref rid="b93-or-56-1-09131" ref-type="bibr">93</xref>). These features position the pathway as a key candidate for combination-based therapeutic strategies.</p>
</sec>
<sec>
<title>Other crucial signaling pathways</title>
<p>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-&#x03BA;B 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-&#x03BA;B signaling link inflammation, immune responses to therapeutic resistance (<xref rid="b94-or-56-1-09131" ref-type="bibr">94</xref>&#x2013;<xref rid="b98-or-56-1-09131" ref-type="bibr">98</xref>), whereas Notch and Hippo/YAP primarily influence cell fate decisions and cellular plasticity (<xref rid="b99-or-56-1-09131" ref-type="bibr">99</xref>,<xref rid="b100-or-56-1-09131" ref-type="bibr">100</xref>). Aberrant Hedgehog signaling further promotes tumor proliferation and metastasis, particularly in cancers reactivating developmental programs (<xref rid="b101-or-56-1-09131" ref-type="bibr">101</xref>&#x2013;<xref rid="b103-or-56-1-09131" ref-type="bibr">103</xref>). 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 (<xref rid="b104-or-56-1-09131" ref-type="bibr">104</xref>).</p>
</sec>
</sec>
</sec>
<sec>
<label>4.</label>
<title>Dynamic network properties: Crosstalk, feedback and adaptive reprogramming</title>
<p>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 (<xref rid="b105-or-56-1-09131" ref-type="bibr">105</xref>). 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.</p>
<sec>
<title/>
<sec>
<title>Spatiotemporal specificity</title>
<p>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 (<xref rid="b106-or-56-1-09131" ref-type="bibr">106</xref>), while the subcellular localization and nucleo-cytoplasmic shuttling of proteins such as NF-&#x03BA;B, &#x03B2;-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 (<xref rid="b107-or-56-1-09131" ref-type="bibr">107</xref>&#x2013;<xref rid="b110-or-56-1-09131" ref-type="bibr">110</xref>), triggering distinct cellular outcomes ranging from proliferation to differentiation or apoptosis.</p>
</sec>
<sec>
<title>Dynamic plasticity and network reprogramming</title>
<p>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 (<xref rid="b111-or-56-1-09131" ref-type="bibr">111</xref>&#x2013;<xref rid="b113-or-56-1-09131" ref-type="bibr">113</xref>). This adaptive reprogramming may arise from altered protein expression, pathway switching or regulatory mutations (<xref rid="b114-or-56-1-09131" ref-type="bibr">114</xref>), representing a central mechanism for drug resistance, metastasis and tumor heterogeneity (<xref rid="b115-or-56-1-09131" ref-type="bibr">115</xref>,<xref rid="b116-or-56-1-09131" ref-type="bibr">116</xref>). In addition, resistance emerges from pathway reactivation, activation of parallel signaling routes or microenvironment-mediated survival signals (<xref rid="b117-or-56-1-09131" ref-type="bibr">117</xref>).</p>
<p><xref rid="f1-or-56-1-09131" ref-type="fig">Fig. 1</xref> outlines the signaling hierarchy and directionality from membrane receptors to downstream effectors, while <xref rid="f2-or-56-1-09131" ref-type="fig">Fig. 2</xref> illustrates the mechanistic basis of pathway crosstalk, therapeutic inhibition and adaptive responses under treatment pressure. Specifically, inhibiting dominant hubs such as PI3K/Akt/mTOR can induce compensatory activation of parallel routes, notably MAPK/ERK and Wnt/&#x03B2;-catenin, maintaining downstream signaling and promoting therapeutic resistance. These adaptive responses often stem from pathway reactivation, bypass signaling or microenvironment-derived cues such as cytokines and growth factors, which collectively shield signaling activity from therapeutic inhibition (<xref rid="b118-or-56-1-09131" ref-type="bibr">118</xref>).</p>
</sec>
<sec>
<title>Topological complexity and redundancy</title>
<p>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 &#x03B2;-catenin serve as central hubs that integrate and distribute signals across multiple axes (<xref rid="b8-or-56-1-09131" ref-type="bibr">8</xref>,<xref rid="b119-or-56-1-09131" ref-type="bibr">119</xref>). While this complex architecture enhances cellular adaptability under stress, it also enables compensatory signaling and therapeutic escape (<xref rid="b120-or-56-1-09131" ref-type="bibr">120</xref>).</p>
</sec>
<sec>
<title>Oscillation and bistable behaviors</title>
<p>Certain signaling circuits exhibit oscillatory or bistable behaviors in response to external stimuli, as seen in the p53-MDM2 and NF-&#x03BA;B-I&#x03BA;B regulatory loops (<xref rid="b121-or-56-1-09131" ref-type="bibr">121</xref>,<xref rid="b122-or-56-1-09131" ref-type="bibr">122</xref>). 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.</p>
</sec>
<sec>
<title>Epigenetic memory and microenvironmental adaptation</title>
<p>Signaling activation is closely linked to epigenetic regulation, including chromatin remodeling, histone modification and DNA methylation (<xref rid="b123-or-56-1-09131" ref-type="bibr">123</xref>,<xref rid="b124-or-56-1-09131" ref-type="bibr">124</xref>). These processes can encode prior signaling events, generating a form of epigenetic &#x2018;memory&#x2019; that stabilizes tumor phenotypes even after the initial stimulus dissipates (<xref rid="b125-or-56-1-09131" ref-type="bibr">125</xref>).</p>
<p>Simultaneously, cancer signaling networks continuously engage with the tumor microenvironment (<xref rid="b104-or-56-1-09131" ref-type="bibr">104</xref>). 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 (<xref rid="b126-or-56-1-09131" ref-type="bibr">126</xref>). 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 (<xref rid="b117-or-56-1-09131" ref-type="bibr">117</xref>,<xref rid="b127-or-56-1-09131" ref-type="bibr">127</xref>). 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 (<xref rid="b128-or-56-1-09131" ref-type="bibr">128</xref>). Cytokines such as IL-6 and TGF-&#x03B2; can also activate JAK/STAT3, SMAD or EMT-related signaling, promoting tumor cell survival, immune escape and drug resistance (<xref rid="b95-or-56-1-09131" ref-type="bibr">95</xref>,<xref rid="b97-or-56-1-09131" ref-type="bibr">97</xref>,<xref rid="b129-or-56-1-09131" ref-type="bibr">129</xref>). Furthermore, extracellular matrix remodeling may strengthen integrin-FAK/Src signaling (<xref rid="b130-or-56-1-09131" ref-type="bibr">130</xref>), while hypoxia can induce HIF-1&#x03B1;-dependent programs related to angiogenesis, metabolic adaptation and treatment resistance (<xref rid="b131-or-56-1-09131" ref-type="bibr">131</xref>). These findings suggest that effective treatment may need to target both tumor-intrinsic oncogenic pathways and protective signals from the surrounding microenvironment.</p>
</sec>
</sec>
</sec>
<sec>
<label>5.</label>
<title>Signaling networks, tumor phenotypes and therapeutic implications</title>
<sec>
<title/>
<sec>
<title>Tumor phenotypes and microenvironmental remodeling</title>
<p>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 (<xref rid="b132-or-56-1-09131" ref-type="bibr">132</xref>,<xref rid="b133-or-56-1-09131" ref-type="bibr">133</xref>). Beyond these tumor-intrinsic effects, these networks reconfigure surrounding stromal and immune components through cytokines, exosomes and other mediators (<xref rid="b134-or-56-1-09131" ref-type="bibr">134</xref>).</p>
<p>For example, persistent PI3K/Akt/mTOR and MAPK/ERK activation promotes tumor growth and therapeutic resistance (<xref rid="b135-or-56-1-09131" ref-type="bibr">135</xref>&#x2013;<xref rid="b137-or-56-1-09131" ref-type="bibr">137</xref>), while Wnt/&#x03B2;-catenin signaling enhances stemness and metastatic potential (<xref rid="b138-or-56-1-09131" ref-type="bibr">138</xref>,<xref rid="b139-or-56-1-09131" ref-type="bibr">139</xref>). These alterations can upregulate immunosuppressive molecules, notably PD-L1 (<xref rid="b140-or-56-1-09131" ref-type="bibr">140</xref>), and reprogram tumor-associated stromal cells to reinforce immune evasion, angiogenesis and tumor aggressiveness (<xref rid="b141-or-56-1-09131" ref-type="bibr">141</xref>). 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.</p>
</sec>
<sec>
<title>Therapeutic targeting and clinical implications</title>
<p>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 (<xref rid="b142-or-56-1-09131" ref-type="bibr">142</xref>,<xref rid="b143-or-56-1-09131" ref-type="bibr">143</xref>). 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 (<xref rid="b144-or-56-1-09131" ref-type="bibr">144</xref>). For example, PI3K inhibitors have been used in hormone receptor-positive, HER2-negative, PIK3CA-mutated advanced breast cancer (<xref rid="b145-or-56-1-09131" ref-type="bibr">145</xref>). For immune checkpoint therapy, &#x2018;high immune regulatory signaling&#x2019; 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 (<xref rid="b146-or-56-1-09131" ref-type="bibr">146</xref>). However, these biomarkers are not perfect predictors, and their clinical value may differ among tumor types and treatment settings (<xref rid="b147-or-56-1-09131" ref-type="bibr">147</xref>).</p>
<p>However, the clinical benefit of single-target inhibition is often limited by the adaptive nature of cancer signaling networks (<xref rid="b148-or-56-1-09131" ref-type="bibr">148</xref>). 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 (<xref rid="b149-or-56-1-09131" ref-type="bibr">149</xref>,<xref rid="b150-or-56-1-09131" ref-type="bibr">150</xref>). 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.</p>
<p>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 (<xref rid="b151-or-56-1-09131" ref-type="bibr">151</xref>). 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.</p>
<p>Despite these advances, major challenges remain, including intratumoral heterogeneity, acquired resistance and adaptive network responses (<xref rid="b152-or-56-1-09131" ref-type="bibr">152</xref>). 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 (<xref rid="b153-or-56-1-09131" ref-type="bibr">153</xref>).</p>
</sec>
</sec>
</sec>
<sec>
<label>6.</label>
<title>Current challenges and future perspectives</title>
<p>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 (<xref rid="b154-or-56-1-09131" ref-type="bibr">154</xref>,<xref rid="b155-or-56-1-09131" ref-type="bibr">155</xref>). In addition, integrating multi-omics data and monitoring dynamic signaling changes in real time remain technically crucial (<xref rid="b156-or-56-1-09131" ref-type="bibr">156</xref>), 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.</p>
<p>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 (<xref rid="b157-or-56-1-09131" ref-type="bibr">157</xref>&#x2013;<xref rid="b159-or-56-1-09131" ref-type="bibr">159</xref>). These approaches enable more precise characterization of dynamic network rewiring and intercellular interactions, and support targeted discovery and development of more personalized therapeutic strategies.</p>
<p>Furthermore, artificial intelligence (AI) seems promising in this field (<xref rid="b160-or-56-1-09131" ref-type="bibr">160</xref>). Machine learning and network-based algorithms can integrate multi-omics and spatial data to reconstruct signaling interactions, identify key regulatory hubs and predict context-specific responses (<xref rid="b161-or-56-1-09131" ref-type="bibr">161</xref>). In drug development, AI-assisted approaches may prioritize therapeutic targets, predict synergistic drug combinations and identify resistance-associated adaptations at an earlier stage (<xref rid="b162-or-56-1-09131" ref-type="bibr">162</xref>). In addition, AI-driven patient stratification based on signaling signatures may improve biomarker-guided treatment selection (<xref rid="b163-or-56-1-09131" ref-type="bibr">163</xref>). However, important challenges remain, including data heterogeneity, limited interpretability and insufficient clinical generalizability.</p>
<p>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.</p>
</sec>
<sec sec-type="conclusion">
<label>7.</label>
<title>Conclusion</title>
<p>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.</p>
</sec>
</body>
<back>
<ack>
<title>Acknowledgements</title>
<p>Not applicable.</p>
</ack>
<sec sec-type="data-availability">
<title>Availability of data and materials</title>
<p>Not applicable.</p>
</sec>
<sec>
<title>Authors&#x0027; contributions</title>
<p>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.</p>
</sec>
<sec>
<title>Ethics approval and consent to participate</title>
<p>Not applicable.</p>
</sec>
<sec>
<title>Patient consent for publication</title>
<p>Not applicable.</p>
</sec>
<sec sec-type="COI-statement">
<title>Competing interests</title>
<p>The authors declare that they have no competing interests.</p>
</sec>
<sec>
<title>Use of artificial intelligence tools</title>
<p>AI-assisted tools were used only for minor language polishing. Specifically, ChatGPT (OpenAI, GPT-5; <uri xlink:href="https://chatgpt.com/">http://chatgpt.com/</uri>) 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.</p>
</sec>
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<floats-group>
<fig id="f1-or-56-1-09131" position="float">
<label>Figure 1.</label>
<caption><p>Conceptual framework of cancer signaling networks driving tumor phenotypes and therapeutic resistance. Schematic representation of cancer signaling networks, illustrating the hierarchical flow of signal transduction from membrane receptors through intracellular transducers to core oncogenic pathways, including PI3K/Akt/mTOR, MAPK/ERK and Wnt/&#x03B2;-catenin. These pathways regulate key cellular processes such as proliferation, apoptosis, metabolism and drug resistance. The framework highlights directional signal flow and pathway integration, illustrating how signaling networks collectively shape tumor phenotypes, including tumor initiation, growth, immune evasion and therapeutic resistance. Dynamic features such as crosstalk, feedback regulation and adaptive reprogramming are shown alongside a signal integration layer of scaffold and adaptor proteins that ensure signaling specificity, efficiency and pathway coordination within the network. EGFR, epidermal growth factor receptor; HER2, human EGFR 2; TNF, tumor necrosis factor; GPCR, G protein-coupled receptor; PD-L1, programmed death ligand 1; RACK1, receptor for activated C kinase 1; AKAPs, A-kinase anchoring proteins; Shc, Src homology 2 domain-containing transforming protein; Grb2, growth factor receptor-bound protein 2; Ras, rat sarcoma viral oncogene homolog; Rho, Ras homolog family member; NF-&#x03BA;B, nuclear factor-&#x03BA;B; RTK, receptor tyrosine kinase; IP3, inositol 1,4,5-trisphosphate; DAG, diacylglycerol; cAMP, cyclic adenosine monophosphate; PI3K, phosphoinositide 3-kinase; Akt, protein kinase B; Gab1/2, Grb2-associated binder 1/2; Crk, CT10 regulator of kinase; IQGAP1, IQ motif-containing GTPase-activating protein 1; ATP, adenosine triphosphate; JAK, Janus kinase; STAT, signal transducer and activator of transcription; mTOR, mechanistic target of rapamycin; MAPK, mitogen-activated protein kinase; ERK, extracellular signal-regulated kinase; PTEN, phosphatase and tensin homolog; SUMO, small ubiquitin-like modifier; CD8, cluster of differentiation 8; NK, natural killer; HIF-1&#x03B1;, hypoxia-inducible factor-1&#x03B1;; CD4, cluster of differentiation 4; p53, tumor protein p53; Treg, regulatory T cell; MDSC, myeloid-derived suppressor cell.</p></caption>
<alt-text>Conceptual framework of cancer signaling networks driving tumor phenotypes and therapeutic resistance. Schematic representation of cancer signaling networks, illustrating the hierarchical...</alt-text>
<graphic xlink:href="or-56-01-09131-g00.tif"/>
</fig>
<fig id="f2-or-56-1-09131" position="float">
<label>Figure 2.</label>
<caption><p>Network-based signaling crosstalk and therapeutic resistance mechanisms in cancer. Schematic illustrating how inhibition of a dominant oncogenic pathway (e.g., PI3K/Akt/mTOR) triggers compensatory activation of parallel signaling pathways, such as MAPK/ERK and Wnt/&#x03B2;-catenin, through network crosstalk. These adaptive responses help sustain downstream signaling outputs to promote tumor cell survival and drive therapeutic resistance. The figure underscores the interconnected nature of cancer signaling networks, suggesting that effective treatment strategies may require multi-target or network-level interventions rather than single-pathway inhibition alone. GPCR, G protein-coupled receptor.</p></caption>
<alt-text>Network-based signaling crosstalk and therapeutic resistance mechanisms in cancer. Schematic illustrating how inhibition of a dominant oncogenic pathway (e.g., PI3K/Akt/mTOR) triggers...</alt-text>
<graphic xlink:href="or-56-01-09131-g01.tif"/>
</fig>
<table-wrap id="tI-or-56-1-09131" position="float">
<label>Table I.</label>
<caption><p>Representative scaffold and adaptor proteins in cancer signaling networks and their roles in signaling integration and tumor phenotypes.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="bottom">Protein</th>
<th align="center" valign="bottom">Functional category</th>
<th align="center" valign="bottom">Representative cancer types</th>
<th align="center" valign="bottom">Associated signaling pathways</th>
<th align="center" valign="bottom">Molecular function in signaling integration</th>
<th align="center" valign="bottom">Tumor-related functional role</th>
<th align="center" valign="bottom">Representative (Refs.)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">RACK1</td>
<td align="left" valign="top">Scaffold protein</td>
<td align="left" valign="top">Breast, gastric and hepatocellular cancers</td>
<td align="left" valign="top">PKC/Src/FAK, MAPK, PI3K/Akt</td>
<td align="left" valign="top">Scaffolds adhesion-and growth factor-related signaling complexes and facilitates signal convergence</td>
<td align="left" valign="top">Context-dependent regulation of proliferation, migration and invasion</td>
<td align="center" valign="top">(<xref rid="b39-or-56-1-09131" ref-type="bibr">39</xref>,<xref rid="b40-or-56-1-09131" ref-type="bibr">40</xref>)</td>
</tr>
<tr>
<td align="left" valign="top">&#x03B2;-arrestin</td>
<td align="left" valign="top">Scaffold/adaptor protein</td>
<td align="left" valign="top">Breast cancer, ovarian cancer, lung cancer</td>
<td align="left" valign="top">GPCR, MAPK/ERK, PI3K/Akt, NF-&#x03BA;B</td>
<td align="left" valign="top">Couples receptor desensitization to oncogenic signaling</td>
<td align="left" valign="top">Regulates proliferation, survival, migration and drug resistance</td>
<td align="center" valign="top">(<xref rid="b34-or-56-1-09131" ref-type="bibr">34</xref>,<xref rid="b35-or-56-1-09131" ref-type="bibr">35</xref>,<xref rid="b37-or-56-1-09131" ref-type="bibr">37</xref>)</td>
</tr>
<tr>
<td align="left" valign="top">Grb2</td>
<td align="left" valign="top">Adaptor protein</td>
<td align="left" valign="top">Breast cancer, lung cancer, leukemia</td>
<td align="left" valign="top">RTK/Ras/MAPK, PI3K/Akt</td>
<td align="left" valign="top">Links activated RTKs to Ras and PI3K cascades</td>
<td align="left" valign="top">Promotes mitogenic signaling, proliferation and survival</td>
<td align="center" valign="top">(<xref rid="b36-or-56-1-09131" ref-type="bibr">36</xref>,<xref rid="b38-or-56-1-09131" ref-type="bibr">38</xref>)</td>
</tr>
<tr>
<td align="left" valign="top">Shc</td>
<td align="left" valign="top">Adaptor protein</td>
<td align="left" valign="top">Breast cancer, prostate cancer, lung cancer</td>
<td align="left" valign="top">RTK/Ras/MAPK, PI3K/Akt</td>
<td align="left" valign="top">Recruits Grb2/SOS and amplifies receptor-derived signaling</td>
<td align="left" valign="top">Promotes proliferation, survival, and tumor progression</td>
<td align="center" valign="top">(<xref rid="b41-or-56-1-09131" ref-type="bibr">41</xref>)</td>
</tr>
<tr>
<td align="left" valign="top">IQGAP1</td>
<td align="left" valign="top">Scaffold protein</td>
<td align="left" valign="top">Colorectal, gastric and hepatocellular cancers</td>
<td align="left" valign="top">Rac1/Cdc42, MAPK, Wnt/&#x03B2;-catenin</td>
<td align="left" valign="top">Coordinates cytoskeletal remodeling and signaling complex assembly</td>
<td align="left" valign="top">Promotes migration, invasion and metastasis</td>
<td align="center" valign="top">(<xref rid="b42-or-56-1-09131" ref-type="bibr">42</xref>)</td>
</tr>
<tr>
<td align="left" valign="top">Crk</td>
<td align="left" valign="top">Adaptor protein</td>
<td align="left" valign="top">Lung cancer, glioma, breast cancer</td>
<td align="left" valign="top">Integrin/Src/FAK, small GTPase pathways</td>
<td align="left" valign="top">Couples adhesion and growth factor signaling to motility pathways</td>
<td align="left" valign="top">Promotes migration, invasion and metastasis</td>
<td align="center" valign="top">(<xref rid="b43-or-56-1-09131" ref-type="bibr">43</xref>)</td>
</tr>
<tr>
<td align="left" valign="top">AKAPs</td>
<td align="left" valign="top">Scaffold protein</td>
<td align="left" valign="top">Breast cancer, ovarian cancer, melanoma</td>
<td align="left" valign="top">cAMP/PKA, MAPK</td>
<td align="left" valign="top">Spatially organizes kinase complexes and restricts signaling to specific subcellular compartments</td>
<td align="left" valign="top">Regulates proliferation, survival and stress adaptation</td>
<td align="center" valign="top">(<xref rid="b44-or-56-1-09131" ref-type="bibr">44</xref>)</td>
</tr>
<tr>
<td align="left" valign="top">Gab1/Gab2</td>
<td align="left" valign="top">Adaptor protein</td>
<td align="left" valign="top">Breast cancer, leukemia, gastric cancer</td>
<td align="left" valign="top">RTK, PI3K/Akt, MAPK</td>
<td align="left" valign="top">Functions as a docking platform that amplifies receptor signaling and recruits multiple downstream effectors</td>
<td align="left" valign="top">Promotes proliferation, survival and oncogenic signaling</td>
<td align="center" valign="top">(<xref rid="b45-or-56-1-09131" ref-type="bibr">45</xref>)</td>
</tr>
<tr>
<td align="left" valign="top">Nck</td>
<td align="left" valign="top">Adaptor protein</td>
<td align="left" valign="top">Breast cancer, pancreatic cancer, melanoma</td>
<td align="left" valign="top">RTK, actin remodeling pathways</td>
<td align="left" valign="top">Links activated receptors to cytoskeletal regulators and motility pathways</td>
<td align="left" valign="top">Promotes invasion and migratory behavior</td>
<td align="center" valign="top">(<xref rid="b46-or-56-1-09131" ref-type="bibr">46</xref>)</td>
</tr>
<tr>
<td align="left" valign="top">14-3-3 proteins</td>
<td align="left" valign="top">Regulatory adaptor protein</td>
<td align="left" valign="top">Breast, lung, liver and other solid tumors</td>
<td align="left" valign="top">Cell cycle, apoptosis, PI3K/Akt, MAPK</td>
<td align="left" valign="top">Bind phosphorylated signaling proteins and modulate their localization, stability, and signaling output</td>
<td align="left" valign="top">Regulates cell survival, cell cycle progression and therapeutic response</td>
<td align="center" valign="top">(<xref rid="b47-or-56-1-09131" ref-type="bibr">47</xref>)</td>
</tr>
<tr>
<td align="left" valign="top">p130Cas</td>
<td align="left" valign="top">Scaffold/adaptor protein</td>
<td align="left" valign="top">Breast cancer, lung cancer, ovarian cancer</td>
<td align="left" valign="top">Integrin/Src/FAK, Rac1</td>
<td align="left" valign="top">Integrates adhesion-dependent signaling and links it to motility and invasion-associated pathways</td>
<td align="left" valign="top">Promotes migration, invasion and metastasis</td>
<td align="center" valign="top">(<xref rid="b48-or-56-1-09131" ref-type="bibr">48</xref>)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="tfn1-or-56-1-09131"><p>The cancer types listed are representative examples and are not intended to be exhaustive, as many of these proteins function across diverse malignancies. RTK, receptor tyrosine kinase; GPCR, G protein-coupled receptor; FAK, focal adhesion kinase; PKA, protein kinase A; Rac1, Ras-related C3 botulinum toxin substrate 1; Cdc42, cell division cycle 42.</p></fn>
</table-wrap-foot>
</table-wrap>
</floats-group>
</article>
