Targeting MAPK Pathways in Skin, Thyroid, and Pancreatic Cancer: A Perspective on Synthetic Inhibitors and Natural Modulators

ABSTRACT

Cancer frequently arises from the impaired functioning of the Mitogen‐activated protein kinase (MAPK) signaling system, driven by mutation or overexpression of key signaling components. RAF, MEK, ERK, and KRAS inhibitors have significantly improved clinical outcomes, but efficacy is still restricted due to pathway reactivation, adaptive resistance, and signaling cross‐talk. These limitations have led to the search for novel therapeutic approaches and molecular targets in the MAPK network. Melanoma, thyroid carcinoma, and pancreatic adenocarcinoma were selected because MAPK pathway alterations contribute to their pathogenesis and influence treatment response. This review examines the biological importance of MAPK signaling in these cancers and discusses MAPK‐targeted therapies, mechanisms of resistance, and combination treatment options. It also addresses the expanding evidence on natural compounds that modulate MAPK‐ related signaling networks and reviews recent transcriptomic findings that have enhanced the identification of clinically relevant molecular targets. Additionally, MAP4K4 has been linked to tumor progression and metastasis, and poor clinical outcomes, indicating its potential as a therapeutic target for future clinical studies. The findings presented in this review suggest that combining transcriptomic evidence with molecular and pharmacological analyses could aid target prioritization and accelerate the development of targeted strategies for MAPK‐driven malignancies.

Aberrant MAPK signaling drives tumor progression and resistance in multiple cancers. This review summarizes the MAPK dysfunction, targeted therapies, and resistance mechanisms in skin, thyroid, and pancreatic cancer, while highlighting the growing role of transcriptomics and computational approaches in next‐generation MAPK‐targeted drug discovery.

Abbreviations

AI

artificial intelligence

ATC

anaplastic thyroid carcinoma

BRAF

v‐raf murine sarcoma viral oncogene homolog B1

CAMK

calcium/calmodulin‐dependent kinase

CDK

cyclin‐dependent Kinases

CK1

casein kinase 1

DEGs

differentially Expressed Genes

DRP1

dynamin‐related protein 1

EGFR

epidermal growth factor receptor

ePKs

eukaryotic protein kinase

ERK

extracellular signal‐regulated kinase

FGFR

fibroblast growth factor receptor

GEO

gene expression omnibus

GSK

glycogen synthase kinase

IARC

International Agency for Research on Cancer

JAK/STAT

janus kinase/signal transducer and activator of transcription

JNK

c‐jun N‐terminal kinase

KRAS

kirsten rat sarcoma virus oncogene

MAP2K

mitogen‐activated protein kinase kinase

MAP3K

mitogen‐activated protein kinase kinase kinase

MAP4K4

mitogen‐activated protein kinase kinase kinase kinase

MAPK

mitogen‐activated protein kinase

ML

machine learning

NF1

neurofibromin 1

NLK

nemo‐like kinases

PAAD

pancreatic adenocarcinoma

PCNA

proliferating cell nuclear antigen

PDAC

pancreatic ductal adenocarcinoma

PDGFR

platelet‐derived growth factor receptor

PI3K

phosphoinositide 3‐kinase

PPI

protein–protein interaction

PRM

parallel reaction monitoring

PTC

papillary thyroid carcinoma

RAF

rapidly accelerated fibrosarcoma

RAI

radioactive iodine

RAS

rat sarcoma

RGC

receptor guanylate cyclase

RNA‐seq

RNA sequencing

SAPK

stress‐activated protein kinase

scRNA‐seq

single‐cell RNA sequencing

SILAC

stable isotope labeling by amino acids in cell culture

SKCM

skin cutaneous melanoma

TCGA

the cancer genome atlas

THCA

thyroid carcinoma

TK

tyrosine kinase

TKL

tyrosine kinase‐like

USP18

ubiquitin‐specific peptidase 18

VEGFR

vascular endothelial growth factor receptor

ZIPK

zipper‐interacting protein kinase

1. Overview of Protein Kinases

Protein kinases serve as pivotal regulators of cellular signaling, mediating a wide array of biological processes, including cell growth, differentiation, development, metabolism, and apoptosis, through reversible phosphorylation of targeted proteins [1]. The human genome encodes approximately 500 protein kinases, representing a relatively small yet functionally significant proportion of the proteome. Approximately one‐third of human proteins undergo phosphorylation by protein kinases, thereby regulating their activity, localization, and biological function. These enzymes aid in the transfer of phosphate groups from ATP to specific serine, threonine, or tyrosine amino acid residues, modulating protein function and signaling outcomes. They share a similar structural architecture: the glycine‐rich P‐loop, the DFG, and the HRD catalytic motif, which bind to Mg²⁺ and ATP [2]. Since many disease‐associated mutations arise proximal to these motifs, minor structural changes in any protein can have a significant effect on signaling.

Kinases play a significant role in intracellular signaling in immune cells because they bind to the cytoplasmic domains of surface receptors on T and B lymphocytes, where ligand binding activates downstream signaling cascades. Eukaryotic protein kinases (ePKs) are defined as one of the most widespread and heterogeneous family of proteins, which are systematically categorized into eight groups and superfamilies based on sequence homology and specificity of substrates: RGC (receptor guanylate cyclases), CK1 (casein kinase 1), STE (yeast sterile 7/11/20 homologs), CAMK (calcium/calmodulin‐dependent kinases), CGMC (containing CDKs, MAPKs, GSKs, and CLKs), TK (tyrosine kinases), TKL (tyrosine kinase‐like), and AGC (Kinases A, G, and C). These are among the gene families with the highest rates of mutations in cancer, occurring nearly four times more frequently than would be expected for an equivalent set of randomly selected genes [3].

2. Review Methodology

The article was prepared as a narrative review with a structured literature search to provide an overview of MAPK signaling, kinase‐targeted therapies, natural compounds, transcriptomic studies, and computational approaches in skin, thyroid, and pancreatic cancers. The literature search was primarily conducted using PubMed and Google Scholar. Articles published between 2020 and 2026 were primarily considered to include the latest developments in the field. Earlier publications were included where required to describe the biological basis of MAPK signaling in cancer.

“MAPK signaling”, “ERK”, “JNK”, “p38 MAPK”, “skin cancer”, “thyroid cancer”, “pancreatic cancer”, “kinase inhibitors”, “phytochemicals”, “transcriptomics”, “molecular docking”, “molecular dynamics”, and “MAP4K4” were the keyword combinations used in the searches. Both original research articles and relevant review articles published in English were included. Studies that reported clinical results, computational studies, and experimental validation were included. Articles with insufficient experimental evidence, duplicate reports, or incomplete methodological information were excluded. The selected literature was critically assessed and organized to present current knowledge targeting MAPK signaling in cancer.

3. MAPK Signaling Cascades: Structural Components and Functional Specificity

Mitogen‐activated protein kinases (MAPKs) are vital indicators of cellular signaling that mediate the relationship between external signals and the regulation of crucial intracellular processes. They ensure that cells respond appropriately to environmental changes through mechanisms that govern proliferation, differentiation, apoptosis, and response to stress [4]. The MAPK family is a group of protein kinases that regulate the activation or inhibition of their respective targets through phosphorylation. MAPKs are themselves activated by dual phosphorylation of conserved threonine (Thr) and tyrosine (Tyr) residues within their activation loop, which enables downstream signal transduction. They are classified into three major subfamilies, namely extracellular signal‐regulated kinases (ERK), c‐Jun N‐terminal kinases (JNK), and p38 MAPK, or stress‐activated protein kinases (SAPKs). Each subfamily is involved in distinctive cellular processes. The ERK signaling pathway is mainly activated by growth factors and mitogens, playing a crucial role in cell proliferation, differentiation, and survival [5]. In contrast, the JNK pathway is primarily activated by cellular stress signals, including ultraviolet radiation, inflammatory cytokines, and oxidative stress, and plays a crucial role in apoptosis, inflammation, and stress adaptation. The p38 pathway is stimulated by environmental stress and pro‐inflammatory cytokines and is critical for mediating inflammation, cellular differentiation, and adaptation to stress.

ERK, JNK, and p38 are the traditional MAPKs found in mammals. Among the eight known ERK isoforms, ERK1 and ERK2 are primarily activated through the classical MAPK/ERK signaling cascade via MEK1/2. However, ERK3/4, ERK7/8, and Nemo‐like kinases (NLK) are classified as atypical MAPKs due to their distinct activation motifs and structural and regulatory features [6]. P38α (MAPK14 or SAPK2a), p38β (the MAPK11, SAPK2b), p38γ (the MAPK12, SAPK3, ERK6), and p38δ (MAP13, SAPK4) are the isoforms of p38 MAPK. Classical MAPK signaling operates through a three‐tiered kinase cascade comprising MAPK kinase kinases (MAPKKKs or MAP3Ks), MAPK kinases (MAP2Ks), and terminal MAPKs. The MAP3Ks at the topmost levels of conventional MAPK pathways act on MAP2K, with MAPK as the ultimate effector. In the RAS‐RAF‐MEK‐ERK signaling cascade, RAS (Rat sarcoma) functions as the upstream activator, while RAF (Rapidly accelerated fibrosarcoma) serves as the MAP3K, MEK as the MAPKK, and ERK as the terminal MAPK [7]. Activated RAF phosphorylates MEK1/2, which subsequently activate ERK1/2 through dual phosphorylation of threonine and tyrosine residues within the conserved TEY motif. Similar hierarchical activation mechanisms regulate the JNK and p38 pathways, where MKK4 and MKK7 activate JNK, while MKK3 and MKK6 predominantly activate p38 MAPKs in response to inflammatory cytokines and environmental stress signals. The dual‐specificity kinases MEK1 and MEK2, which have molecular weights of 44 and 45 kDa, respectively, are the primary targets of RAF kinases [8].

Ras is among the earliest identified small GTP‐binding proteins, switching between an active GTP‐bound state and an inactive GDP‐bound state. Upon activation, Ras interacts with and recruits Raf kinases to the cell membrane, initiating a phosphorylation cascade that sequentially activates MEK and ERK, ultimately transmitting signals that regulate various cellular functions (Figure 1) [9]. Atypical MAPKs have conserved elements with classical MAPKs, containing certain activation loop motifs, such as TQE (NLK), SEG (ERK3/4), and TEY (ERK7/8). The N‐ and C‐terminal regions, which surround the kinase domains of ERK3 and ERK4, have different functional properties. The conserved C34 domain in the C‐terminal region is linked with the FHIEDE motif in ERK3 and the FRIEDE motif in ERK4. These sequences allow interactions with downstream substrates and the E3 ubiquitin ligase FBXW7, which maintains ERK3 stability via ubiquitination and proteasomal degradation [10]. ERK7 and ERK8 (also known as MAPK15) possess several proline‐rich PXXXP motifs within their C‐terminal regions, which are implicated in chromatin binding and interactions with proliferating cell nuclear antigen (PCNA) [11]. In contrast, the N‐terminal portion of NLK contains glutamate, histidine, and alanine residues (AHQ‐rich domain). This allows regulating the interaction of NLK with Zipper‐interacting protein kinase (ZIPK), a regulatory interaction that modulates the ability of NLK to suppress the Wnt/β‐catenin signaling pathway [12].

FIGURE 1.

MAPK multi‐tiered signaling cascade. The two main elements that trigger the ERK signaling pathway are growth factors and cytokines. RAS triggers RAF in response to extracellular stimuli, which in turn phosphorylates MEK1/2 and activates ERK1/2. The cellular stress signals, particularly oxidative stress, cytokines, and UV radiation, trigger the JNK pathway. JNK isoforms (JNK1, JNK2, and JNK3) are activated as a result of these stimuli, activating MEKK1, TAO1, ZAK, and ASK1, which phosphorylate MKK4/7. The p38 MAPK pathway is triggered by stress signals and inflammatory cytokines, with MKK3/6 activating p38α, β, γ, and δ through upstream kinases such as MEKK, MLK, DLK, and TAK1. After activation, these MAPKs are transported to the nucleus and alter transcription factors that are involved in vital cellular reactions such as differentiation, inflammation, apoptosis, and proliferation. P‐Phosphorylation; TF‐ Transcription factors.

4. Prevalence of MAPK Pathway Mutations in Major Cancer Types

Globally, cancer represents one of the leading public health concerns because of its widespread occurrence and high death rates, which exert a substantial burden on both communities and healthcare systems. The global cancer incidence has surged from 14.1 million in 2012 to 20.2 million in 2022, as reported by the International Agency for Research on Cancer (IARC) [13]. Bray et al. reported that the most widespread cancers identified were colorectal cancer (9.4%), stomach cancer (5.6%), lung cancer (11.4%), breast cancer (11.7%), and prostate cancer (7.3%) [14]. Lung cancer (18%) caused most deaths among women, followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and breast cancer (6.9%). The MAPK/ERK signaling cascade is frequently altered by oncogenic mutations, positioning it as a key therapeutic target in various cancers. Its activation and downstream interactions are tightly controlled under normal conditions but become disrupted in malignant cells, contributing to uncontrolled proliferation and survival.

Mutations in different components of the MAPK signaling pathway, including upstream membrane‐bound receptors such as the epidermal growth factor receptor (EGFR), intermediate signal transducers like the RAS family of GTPases, and downstream kinases such as BRAF (v‐raf murine sarcoma viral oncogene homolog B1), contribute to the activation of this cascade, leading to uncontrolled cell proliferation [15]. The stimulation of growth factor receptors in the cell membrane triggers two distinct but related pathways: the MAPK/ERK pathway and the phosphoinositide 3‐kinase (PI3K) signal, which activates protein kinase B (AKT) and its downstream substrates. Overexpression or abnormal activation of RTKs, along with their direct downstream targets, including phosphatidylinositol 3‐kinase, proto‐oncogene tyrosine‐protein kinase Src, and RAS, results in the upregulation of MAPK/ERK signaling. Glycine 12 or 13 (G12/13) mutations that interfere with GTPase‐activating protein (GAP) interactions and glutamine 61 (Q61) mutations that decrease the intrinsic GTPase activity of Ras are the two main categories of oncogenic Ras mutations. Kirsten Rat sarcoma viral oncogene (KRAS) is primarily mutated in various cancers (85%), followed by NRAS (12%) and HRAS (3%) [16].

Analysis of mutation profiles from cBioPortal (https://www.cbioportal.org/) revealed that the MAPK pathway alterations vary considerably across cancer types [17]. The alteration frequency was assessed across TCGA PanCancer Atlas datasets using a pathway‐specific gene panel representing the major members of the MAPK signaling cascade (RAS, RAF, MAP2K, MAP3K, MAP4K, and other MAPK families (Supporting Information S1). Genomic alterations, including somatic mutations, copy number alterations, and structural variants, were considered in the analysis. Based on the alteration frequency, skin cutaneous melanoma (SKCM) (∼90%), thyroid carcinoma (THCA) (∼73%), and pancreatic adenocarcinoma (PAAD) (∼70%) were selected for detailed discussion, as they consistently exhibited among the highest levels of MAPK pathway alterations among the cancer types analyzed. These cancers also represent distinct mechanisms of MAPK pathway activation, providing representative models for examining the biological significance of MAPK dysregulation and therapeutic targeting.

4.1. Skin Cutaneous Melanoma (SKCM)

The most lethal type of skin cancer, melanoma, is caused by the malignant alterations of melanocytes and is strongly driven by aberrant MAPK signaling. Mutations in Neurofibromin 1 (NF1), KIT, BRAF, KRAS, and NRAS are key components of this pathway that lead to frequent hyperactivation. BRAF mutations are the most prevalent, with the V600E substitution (valine → glutamic acid at codon 600) accounting for approximately 50% of cases [18]. As a consequence of this mutation, downstream MEK‐ERK signaling is continuously activated, resulting in the constitutive activation of the kinase. Other substitutions at codon 600, such as V600K (∼12%), V600D (∼5%), and V600R (∼1%), are less common but still contribute to MAPK pathway activation [19]. The deregulated MAPK signaling and sustained ERK activity in melanoma result in overactive pathway signaling, promoting uncontrolled cell growth, new blood vessel development, survival, and invasion. The KRAS Q61 mutation is also the most common alteration in melanoma, that decrease the intrinsic GTPase activity of KRAS [20]. NF1 loss‐of‐function mutations can also be a possible cause of hyperactivation of the MAPK pathway. In melanomas carrying NF1 alterations, the ability of the protein to inactivate RAS is impaired, causing prolonged RAS activity, enhanced RAF‐MEK‐ERK signaling, and cell proliferation [21]. The genetic variation of NF1 mutant melanomas is higher than that of BRAF‐mutant tumors, which can potentially be one of the reasons why they have a lower response to targeted inhibitors [22].

4.2. Thyroid Carcinoma (THCA)

Thyroid cancer is the most common endocrine malignancy, having various genetic alterations contributing to its development. The overexpression or mutation of upstream activators triggers MAPK signaling pathways. Papillary thyroid carcinoma (PTC), the predominant subtype, is characterized by aberrant activation of the MAPK pathway. The most common of these mutations is the BRAF V600E mutation, which occurs in about 50% of PTC cases [23]. The resulting mutation produces an oncogenic form of the BRAF protein, which has higher kinase activity and promotes tumor growth. Moreover, mutations in RAS isoforms (HRAS, NRAS, KRAS) are also observed in papillary thyroid carcinoma, which has a general frequency of about 10%–15% [24]. In addition to genetic alterations, hormonal signaling can further modulate MAPK activity. For example, Estrogen worsens the development of thyroid cancer by activating cytoplasmic MAPK signaling. Thyroid cancer cells are highly responsive to estrogen, which not only binds to nuclear estrogen receptors but also controls MAPK activity. In malignant and benign thyroid cells, Estradiol (E2) induces significant phosphorylation of MAP kinase isozymes, including ERK1/2 [25].

4.3. Pancreatic Adenocarcinoma (PAAD)

Pancreatic cancer is characterized by one of the poorest five‐year survival outcomes and remains highly resistant to therapy. It is the fourth leading cause of cancer‐related deaths worldwide, and it is expected to take the second position by 2030. The KRAS gene mutation is a feature of pancreatic cancer. The KRAS oncogene plays a central role in tumor initiation, making the signaling network an important feature for therapeutic targeting. The most frequent mutations in this pathway occur in KRAS, which is involved in up to 96% of pancreatic ductal adenocarcinomas (PDACs) [26]. In pancreatic cancer, the most common mutations are detected at codon 12 with a single amino acid missense mutation when glycine (G) is substituted by aspartate (G12D, 40%), cysteine (G12C, ∼1%), Arginine (G12R, ∼15%), and Valine (G12V, 29%). These mutations result in increased affinity for KRAS‐GTP, induce conformational alterations that prevent GAP‐mediated hydrolysis, and confer resistance to intrinsic GTPase activity [27]. Activation of the RAF‐MEK‐ERK pathway in PAAD is closely linked with uncontrolled cell growth and metastatic progression.

5. Therapeutic Targeting of the MAPK Pathway in Cancer

5.1. Clinical Outcomes of MAPK‐Targeted Therapies in Major Cancer Types

The high prevalence of MAPK pathway alterations in SKCM, THCA, and PAAD has established this signaling network as one of the most important therapeutic targets in cancer. Studies of kinase inhibitors have identified several components of the MAPK pathway, including RAF and MEK, that can be selectively targeted, leading to significant clinical benefits in selected patient populations. The development of these agents marked a shift from conventional cytotoxic therapies toward mechanism‐based precision medicine approaches [28]. However, therapeutic responses vary considerably across tumor types and molecular subgroups, reflecting differences in pathway dependency, tumor heterogeneity, and compensatory signaling mechanisms. Therefore, understanding the successes and limitations of these strategies is important for the development of new‐generation inhibitors and combinations of these agents.

Synthetic inhibition of the MAPK pathway has been pursued at multiple levels of the signaling cascade (Figure 2). The first clinically successful ones were agents that targeted the mutant BRAF [29]. Later, the durability of response was enhanced with the improvement of more complete suppression of the downstream signaling with the development of MEK inhibitors. Recently, ERK inhibitors have been identified as a new approach to overcome resistance to MAPK pathway activation, and mutant‐selective KRAS inhibitors have opened up new therapeutic possibilities in KRAS‐dependent malignancies [30]. These agents act at various points along the pathway, but each is effective in certain types of tumors.

FIGURE 2.

Therapeutic targets within the MAPK signaling pathway and clinically approved or investigational inhibitors evaluated in SKCM, THCA, and PAAD. Inhibitors are positioned according to their primary sites of action within the pathway. Arrows indicate pathway activation, whereas blunt‐ended lines indicate pharmacological inhibition. RTK‐Receptor tyrosine kinase; GDP‐ Guanosine diphosphate; GTP‐Guanosine Triphosphate; P‐Phosphorylation.

The identification of activating BRAF mutations, especially the BRAF V600E, made it clear that constitutive RAF‐MEK‐ERK signaling is a major oncogenic event and strongly supported targeted intervention. Selective BRAF inhibitors were proven to be clinically effective in patients with BRAF‐mutated melanoma, with a rapid and substantial tumor response, supporting the therapeutic potential of MAPK pathway inhibition [31]. However, the benefits of BRAF inhibitor monotherapy were often limited by the development of acquired resistance and disease progression. To improve treatment durability, therapeutic strategies evolved toward the simultaneous inhibition of multiple nodes within the MAPK cascade. The combined BRAF and MEK inhibition improved pathway suppression while reducing paradoxical MAPK activation associated with selective BRAF inhibition. As a result, dabrafenib–trametinib, vemurafenib–cobimetinib, and encorafenib–binimetinib became standard treatments for patients with mutant melanoma [32]. Long‐term follow‐up from clinical trials further demonstrated durable clinical benefit in a subset of patients, with five‐year survival rates exceeding 30% in several studies [33]. In spite of all these advances, there is still a significant proportion of patients for whom long‐term disease control is difficult. BRAF and MEK inhibition have been shown to delay disease progression and extend patient survival, but many develop resistance and experience tumor recurrence [34].

In thyroid carcinoma, a new therapeutic paradigm has developed, with the introduction of MAPK inhibitors that offer advantages beyond direct inhibition of tumor growth. Abnormal MAPK activation, specifically via BRAF V600E mutations, is a major factor in thyroid tumorigenesis, as well as conferring a dedifferentiative signature on thyroid tumors [35]. Increased MAPK signaling suppresses the expression of genes required for iodine metabolism, including the sodium–iodide symporter (NIS), thereby reducing the effectiveness of radioactive iodine (RAI) therapy [36]. This phenomenon is clinically significant because loss of RAI avidity is a major cause of treatment failure in advanced thyroid cancer.

The observation that MAPK activation contributes to dedifferentiation led to the development of redifferentiation strategies aimed at restoring iodine uptake. Both preclinical and clinical studies have shown that blocking the MAPK pathway with either BRAF or MEK inhibitors can upregulate NIS expression and restore RAI sensitivity in specific patients [37]. This has led to increased therapeutic choices for patients with thyroid cancers harboring a BRAF V600E mutation, especially in advanced and refractory contexts. In addition to suppressing oncogenic signaling in melanoma, targeting the MAPK pathway in thyroid cancer also aims to re‐sensitize effective treatment strategies, such as those involving surgery or radioactive iodine, and also broadens the significance of pathway inhibition.

The clinical application of MAPK inhibitors in PAAD has been more challenging than in melanoma and thyroid carcinoma. Early efforts targeted downstream components of the pathway, such as RAF, MEK, and ERK; the clinical benefits seen have been generally modest, but there have been promising pre‐clinical results [38]. Limited efficacy, dose‐limiting toxicities, and rapid development of adaptive resistance have collectively restricted the success of these approaches. One of the major obstacles to durable pathway inhibition in pancreatic cancer is the extensive signaling plasticity exhibited by KRAS‐driven tumors. The clinical success of KRAS‐targeted agents has been most evident for KRAS G12C mutations [39]. The relatively low frequency of this alteration in pancreatic cancer limits its overall clinical impact. Besides KRAS G12C inhibitors, multiple next‐generation KRAS‐ targeted inhibitors targeting KRAS G12D or pan‐KRAS signaling are in clinical and preclinical trials.

Despite the clinical benefits of MAPK‐targeted therapies, treatment‐related toxicities remain a concern. The specificity of adverse events varies among inhibitors and combinations of inhibitors. BRAF inhibitor monotherapy has been associated with rash, arthralgia, and cutaneous squamous cell carcinoma due to paradoxical activation of MAPK in BRAF wild‐type cells [40]. MEK inhibitor combination therapy is more tolerable in some cases by reducing paradoxical activation of MAPK. Thyroid cancer patients receiving combination therapy with BRAF‐MEK inhibitor combinations have experienced pyrexia, fatigue, chills, diarrhea, and dose changes [41].

These findings show that the therapeutic value of MAPK‐targeted inhibition is highly context‐dependent. The differences observed among SKCM, THCA, and PAAD underscore the complexity of MAPK signaling and highlight the need to better understand the factors that limit long‐term therapeutic efficacy. The targeted agents currently studied in SKCM, THCA, and PAAD, as well as their molecular targets, clinical status, and experimental potency, are summarized in Tables 1, 2, and 3.

TABLE 1.

Approved and investigational MAPK‐targeted agents for skin cutaneous melanoma.

S. no	Agent	Target	Clinical status	Primary mechanism	Cellular potency (nm)	Model system (cell lines)	Refs.
1.	Vemurafenib	BRAF V600E	FDA‐ approved	Selective inhibition of mutant BRAF, suppressing RAF‐MEK‐ERK signaling	173	A375	[42, 43]
2.	Dabrafenib a	BRAF V600E	FDA‐ approved	Selective inhibition of mutant BRAF kinase activity	17	A375	[44]
3.	Encorafenib a	BRAF V600E	FDA‐ approved	Inhibits mutant BRAF and downstream MAPK signaling	4	A375	[44]
4.	Trametinib	MEK1/2	FDA‐ approved	Blocks downstream MEK signaling	1–2.5	Human melanoma cell lines	[45]
5.	Cobimetinib	MEK1/2	FDA‐ approved	MEK inhibition, suppresses ERK activation	4.2–40	A375, ED013	[46]
6.	Binimetinib	MEK1/2	FDA‐ approved	Reversible inhibitor of MEK1/2	30–250	A375, SKMEL2	[47]
7.	Naporafenib	Pan‐RAF	Clinical trial	RAF inhibition, often evaluated in combinations	∼240	A375	[48]
8.	Tunlametinib	MEK1/2	Clinical trial	Potent selective MEK inhibitor	0.86–3.46	A375, Colo‐829	[49]
9.	Pimasertib	MEK1/2	Clinical trial	MEK pathway suppression	10—50	NRAS‐mutant melanoma cell lines	[50]
10.	Ulixertinib	ERK1/2	Clinical trial	Direct ERK inhibitor to overcome upstream resistance	∼180	A375	[51]

^aCellular potency values are reported as EC₅₀.

TABLE 2.

Approved and investigational MAPK‐targeted agents for thyroid carcinoma.

S. no	Agent	Target	Clinical status	Primary mechanism	Cellular potency (nm)	Model system (cell lines)	Refs.
1.	Dabrafenib	BRAF V600E	FDA‐ approved	Selective inhibition of mutant BRAF	25–100	K1, NIM1	[52]
2.	Trametinib	MEK1/2	FDA‐ approved	MEK inhibition and suppression of ERK signaling	0.82–2.04	Cal62, BHT101, BCPAP	[53]
3.	Vemurafenib	BRAF V600E	Clinical trial	Mutant BRAF inhibition	1400–5800	BCPAP, FRO	[54]
4.	Selumetinib a	MEK1/2	Clinical trial	Restores iodine uptake through MAPK suppression	110–44 700	8505C, TPC1, BCPAP, C643	[55]
5.	Sorafenib	RAF multikinase	FDA‐ approved	Multikinase inhibition including RAF signaling	1850–4200	BCPAP, TPC1,	[56]
6.	Lenvatinib b	VEGFR/FGFR/RET and related kinases	FDA‐ approved	Suppresses tumor growth and angiogenesis	3800–26 000	K1, RO82‐W‐1, FTC‐133	[57]
7.	Vandetanib b	RET/VEGFR/EGFR	FDA‐ approved	Inhibits upstream RTKs involved in MAPK activation	100–150	TT, MZ‐CRC‐1	[58]

^aCellular potency values for Selumetinib are reported as EC₅₀ values in the original study.

^bLenvatinib and vandetanib indirectly suppress MAPK signaling through inhibition of upstream signaling pathways.

TABLE 3.

Approved and investigational MAPK‐targeted agents for Pancreatic adenocarcinoma.

S. no	Agent	Target	Clinical status	Primary mechanism	Cellular potency (nm)	Model system (cell lines)	Refs.
1.	Trametinib	MEK1/2	Clinical trial	Suppresses downstream MAPK signaling	150–340	PANC‐1, MIA PaCa‐2	[59]
2.	Selumetinib	MEK1/2	Clinical trial	Blocks MEK‐mediated signaling	1010–15 400	AsPC‐1, PSN‐1, PANC‐1	[60]
3.	Binimetinib	MEK1/2	Clinical trial	Reversible MEK1/2 inhibition	92—316	MIA PaCa‐2, AsPC‐1, CAPAN‐2	[61]
4.	Refametinib	MEK1/2	Clinical trial	MEK inhibition; evaluated in combination with chemotherapy	53.3–1411.1	MIA PaCa‐2, BxPC‐3, PANC‐1	[62]
5.	Sorafenib	RAF and multiple kinases	Clinical trial	Inhibits RAF signaling and angiogenic pathways	5000–8000	MIA PaCa‐2, PANC‐1	[63]
6.	Sotorasib	KRAS G12C	Clinical trial	Locks KRAS G12C in inactive GDP‐bound state	∼9	MIA PaCa‐2	[64]
7.	Adagrasib	KRAS G12C	Clinical trial	Selective inhibition of KRAS G12C	∼5	MIA PaCa‐2	[65]
8.	Divarasib	KRAS G12C	Clinical trial	Potent KRAS G12C inhibition	∼0.19	MIA PaCa‐2	[66]
9.	MRTX1133	KRAS G12D	Clinical trial	Selective KRAS G12D inhibition	5–42 nm	AsPc‐1, HPAC, SW1990	[67]

5.2. Mechanisms of Resistance to MAPK‐Targeted Inhibitors

Targeting the MAPK pathway has yielded clinical success in many malignancies, but long‐term success is still elusive in many patients. Both intrinsic and acquired resistance can limit treatment efficacy and eventually lead to disease progression. A characteristic of resistance is the ability of tumor cells to restore MAPK signaling or activate alternative survival pathways despite continued therapeutic inhibition.

Resistance to BRAF/MEK inhibitors is often caused by the reactivation of the MAPK pathway in melanoma [68]. It has been demonstrated that melanoma cells can adaptively reactivate MAPK signaling, leading to decreased efficacy of pathway inhibition over time. Additionally, activation of alternative signaling pathways such as the PI3K/AKT pathways and molecular changes such as receptor tyrosine kinases and NF1 have been linked to therapeutic escape [69]. This indicates that while MAPK inhibitors are now having significantly better clinical effects on melanoma, pathway inhibition is still challenging.

Thyroid carcinoma resistance mechanisms are also linked to pathway reactivation and signaling crosstalk. Although BRAF and MEK inhibitors can suppress MAPK signaling and improve radioactive iodine responsiveness in selected patients, sustained responses are not always achieved. Activation of receptor tyrosine kinases, including EGFR, RET, FGFR, VEGFR, and PDGFR, can provide alternative signaling routes that reduce the effectiveness of MAPK inhibition [70]. The MAPK and PI3K/AKT pathways further contribute to tumor survival and disease progression. In addition, activation of JAK2/STAT3 signaling has been reported as a potential mechanism of resistance to MAPK‐targeted therapies [71]. Signaling through these pathways can lead to suppression of differentiation‐associated genes, including those involved in iodine uptake, which may limit the long‐term benefits of redifferentiation therapy.

The therapeutic targeting of MAPK signaling in pancreatic cancer has been especially challenging due to the high sensitivity of these tumors to mutant KRAS signaling. Inhibiting one of the components of the pathway usually leads to rapid activation of receptor tyrosine kinases and a return of downstream signaling [72]. Moreover, the tumor microenvironment promotes tumor survival and therapeutic resistance. All these factors explain the limited clinical activity seen with many MAPK inhibitors in pancreatic cancer. A better understanding of these processes is essential for the development of more therapeutic strategies capable of achieving durable pathway suppression and improved clinical outcomes.

5.3. Strategies to Overcome Resistance and Improve Therapeutic Response

The emergence of acquired resistance has led to the use of combination treatments to provide durable pathway suppression and reduced compensatory signaling. The combination of BRAF and MEK inhibitors is currently the standard treatment for melanoma, as it delays reactivation of the MAPK pathway and improves clinical efficacy compared with single‐agent treatment [73]. Combinations such as dabrafenib‐trametinib, vemurafenib‐cobimetinib, and encorafenib‐binimetinib have shown better response durability and reduced paradoxical MAPK activation [74].

In thyroid carcinoma, combination approaches have shown potential, particularly in tumors with the BRAF V600E mutation. In addition to direct pathway suppression, combined inhibition can be used to promote redifferentiation and reinstate sensitivity to radioactive therapy in certain patients [75]. The studies have broadened the opportunities for the treatment of advanced thyroid cancer using MAPK‐targeted approaches. The emergence of KRAS mutant‐selective inhibitors in PAAD has renewed interest in MAPK‐directed therapies. However, feedback activation of parallel signaling pathways often reduces the durability of single‐agent responses [76]. This has led to a growing interest in combination strategies against upstream regulators of the pathway. Inhibition of SHP2 has been shown to increase the activity of MEK inhibitors and inhibit pathway reactivation in various experimental models [77]. Similarly, inhibitors of SOS1 are being explored in combination with either KRAS or MEK inhibitors, in order to suppress compensatory feedback signaling in the RAS‐MAPK network [78]. These strategies aim to enhance pathway suppression while limiting the development of resistance. The combination strategies targeting different components of the MAPK pathway across three major cancer types are given in Table 4.

TABLE 4.

Combination MAPK‐targeted therapies in SKCM, THCA, and PAAD.

Sr. no	Cancer type	Combination	Target	Clinical status	Mechanism of action	Refs.
1.	SKCM	Dabrafenib + Trametinib	BRAF + MEK	FDA‐approved	Dual MAPK blockade reduces resistance and paradoxical activation	[79]
		Vemurafenib + Cobimetinib	BRAF + MEK	FDA‐approved	Vertical inhibition of the pathway	[80]
		Encorafenib + Binimetinib	BRAF + MEK	FDA‐approved	Sustained pathway suppression	[81]
		Lifirafenib + Mirdametinib	Pan‐RAF + MEK	Clinical trial	Simultaneous RAF and MEK inhibition	[82]
		Belvarafenib + Cobimetinib	Pan‐RAF + MEK	Clinical trial	Targets RAF dimers and downstream MEK signaling	[83]
2.	THCA	Dabrafenib + Trametinib	BRAF + MEK	FDA‐ approved	Dual MAPK inhibition and redifferentiation effects	[84]
3.	PAAD	Lifirafenib + Mirdametinib	Pan‐RAF + MEK	Clinical trial	Dual MAPK pathway inhibition	[38]

5.4. Natural Modulators Targeting the MAPK Pathway

The clinical efficacy of targeting the MAPK pathway has significantly contributed to the management of a number of cancers, including melanoma and other cancers that are driven by aberrantly active MAPKs. However, acquired resistance, pathway reactivation, dose‐limiting toxicity, and activation of compensatory signaling pathways have all limited the long‐term efficacy of these therapies. Such limitations have gained interest in naturally occurring bioactive compounds as complementary or alternative therapeutic approaches. In anticancer treatment, phytochemicals and their structural derivatives have become vital components that act on several cellular activities that play a role in tumor initiation and progression [85]. Numerous natural compounds have demonstrated preferential toxicity to cancer cells, and their structural complexity and bioactivities have enabled the development of unique anticancer agents.

The evidence now suggests that most of the phytochemical studies in the MAPK‐driven cancers function as pathway modulators rather than highly selective kinase inhibitors. While synthetic agents already in clinical use that directly inhibit mutant BRAF, MEK, or KRAS usually inhibit one signaling network at a time, natural compounds tend to influence many networks. Modulation of the MAPK pathway in SKCM, THCA, and PAAD is often found to be correlated with the activation of other pathways, such as PI3K/AKT, NF‐κB, the STAT3 pathway, and the apoptotic pathway, indicating the high degree of cross‐talk between these pathways [86].

The best‐studied model for investigating MAPK‐targeted phytochemicals is melanoma since the mutations of BRAF and NRAS are the leading drivers of tumor progression through the activation of the MAPK cascade [87]. Thus, many natural products have been tested in melanoma models to suppress malignant phenotypes by modulation of MAPK signaling. Shikonin induces apoptosis primarily by activating the stress‐responsive JNK and p38 signaling pathways, while fisetin inhibits MEK/ERK activation and blocks invasion of melanoma cells by targeting both NF‐κB and MEK/ERK signaling [88]. Similarly, melittin has been shown to block MAPK and PI3K/AKT/mTOR pathways, and taxifolin was found to block the USP18/Rac1/JNK/β‐catenin signaling axis, thereby blocking melanoma proliferation and migration. The results illustrate that specific phytochemicals act on MAPK signaling in different ways and with different biological effects, though they all show decreased proliferation, migration, and increased apoptosis. Despite these positive findings, this evidence has been largely preclinical and has only been partly validated in animal models.

In contrast to melanoma, there are relatively few natural compounds that have been examined in thyroid carcinoma, but the studies reveal that it is more important to disrupt interconnected signaling networks than to selectively inhibit the MAPK pathway. One of the best‐characterized examples is Erianin, which has been shown to simultaneously deplete both the MAPK/ERK and PI3K/AKT pathways, thereby inhibiting proliferation, inducing apoptosis and pyroptosis in ATC [89]. Furthermore, its synergistic effect with anlotinib shows the potential of natural compounds as combination agents instead of single‐agent therapy. Likewise, triptolide also inhibits the growth of thyroid cancer cells by regulating c‐JUN and NF‐κB signaling, and has a direct effect on stress‐activated MAPK pathways [90]. Interestingly, the effects of the phytochemicals on MAPK signaling are not similar. Lysicamine, for instance, reduced AKT phosphorylation very effectively but has a weaker effect on ERK activation, indicating that the contribution of the MAPK pathway varies among different natural compounds [91]. These observations show that the effective treatment of thyroid carcinoma generally requires targeting of multiple oncogenic pathways, rather than inhibition of MAPK signaling alone.

The KRAS‐mutated PAAD is a much more complex therapeutic environment, as persistent MAPK signaling is also linked to several compensatory survival pathways, all of which reduce the effectiveness of MAPK‐targeted therapy [92]. Therefore, recent studies have been primarily directed toward natural compounds that improve sensitivity to MAPK activation or disrupt alternative signaling pathways implicated in resistance. A remarkable example is tetrandrine, which stabilizes the death receptors DR4 and DR5 and, in combination with MAPK inhibitors, induces more apoptosis in pancreatic cancer models with a KRAS mutation [93]. In contrast, securinine's anti‐tumor activity is caused by activation of p38, resulting in CHOP‐induced endoplasmic reticulum stress and cell death [94]. Also, paeoniflorin has been shown to inhibit inflammatory signaling by decreasing the phosphorylation of p38 and the production of inflammatory cytokines, while sesquiterpene lactones like alantolactone and isoalantolactone lead to mitochondrial apoptosis with increased p38 activation [95].

Overall, the studies suggest that phytochemicals are effective at inhibiting cancer in various ways, but they all seem to work through a pathway that involves the MAPK pathway. Some compounds inhibit ERK or MEK activity directly, while others activate stress‐responsive p38 or JNK signaling pathways to induce apoptosis. In many cases, modulation of MAPK signaling occurs in parallel with changes in PI3K/AKT, NF‐κB, STAT3, β‐catenin, or mitochondrial apoptotic pathways, indicating that there is a significant amount of signaling cross‐talk that underlies their biological effects. In conclusion, natural compounds are intriguing multi‐target modulators of MAPK signaling; however, additional translational research is needed to determine their selectivity and efficacy. A comparative summary of natural compounds, their molecular targets, mechanisms of action, and current level of experimental evidence is provided in Table 5.

TABLE 5.

Phytochemicals reported to modulate MAPK signaling in major cancer types, together with their MAPK‐related targets, mechanism of action, and available experimental evidence.

Sr. no	Natural compound	Compound class	Cancer type (s)	MAPK‐related targets	Mechanism of action	Experimental evidence	Refs.
1.	Shikonin	Naphtho‐ quinone	SKCM	ERK1/2, JNK, p38	Activates p38 and JNK, leading to apoptosis and suppression of tumor growth	In vitro, in vivo	[96]
2.	Fisetin	Flavonoid	SKCM	MEK1/2, ERK1/2	Suppresses MEK/ERK phosphorylation	In vitro, 3D skin model	[97]
3.	Taxifolin	Flavonoid	SKCM	JNK	Suppresses the USP18/ Rac1/JNK/ β‐catenin signaling axis	In vitro	[98]
4.	Plumbagin	Naphtho‐ quinone	SKCM	MAPK1, MAPK14, BRAF	Regulates MAPK‐related genes involved in invasion and metastasis	In vitro	[99]
5.	Resveratrol	Stilbene polyphenol	PTC	ERK1/2	Suppresses ERK1/2‐ mediated MAPK signaling and inhibits proliferation.	In vitro	[100]
6.	Erianin	Bibenzyl	ATC, PAAD	MEK1/2, ERK1/2	Suppresses MAPK/ERK signaling, directly targets MEK1/2, inhibits proliferation, and induces G2/M arrest	In vitro, In vivo	[89]
7.	Triptolide	Diterpenoid epoxide	THCA	c‐JUN	Suppresses c‐JUN‐ mediated MAPK signaling together with NF‐κB signaling	In vitro	[90]
8.	Kirenol	Diterpenoid	THCA	ERK	Induces apoptosis and suppresses proliferation by modulating MAPK/ERK signaling	In vitro	[101]
9.	Curcumin	Polyphenol	THCA	MAPK	Modulates MAPK signaling, inducing autophagy and selective cytotoxicity	In vitro	[102]
10.	Tetrandrine	Bisbenzy‐ lisoquinoline alkaloid	PAAD	MAPK	Synergizes with MAPK inhibitors by stabilizing DR4/DR5‐mediated apoptotic signaling	In vitro, in vivo	[93]
11.	Paeoniflorin	Monoterpene glycoside	PAAD	p38	Reduces p38 phosphorylation and inflammatory cytokine production	Network pharmacology, In vitro	[95]
12.	Securinine	Alkaloid	PAAD	p38	Activates the p38 MAPK/CHOP axis to induce ER stress, cell‐cycle arrest, and apoptosis	In vitro, in vivo	[94]
13.	Alanto‐ lactone	Sesqui‐ terpene lactone	PAAD	p38	Promotes mitochondrial apoptosis through ROS generation and activation of p38	In vitro	[103]
14.	Isolanto‐ lactone	Sesqui‐ terpene lactone	PAAD	p38	Induces ROS‐ dependent activation of p38 MAPK	In vitro	[103]
15.	Silibinin	Flavonolignan	SKCM	MEK, MAPK	Suppresses MEK/MAPK signaling	In vitro	[104]
16.	Apigenin	Flavonoid	SKCM	ERK, JNK, p38	Modulates phosphorylation of ERK, JNK, and p38, resulting in reduced proliferation	In vitro	[105]
17.	Gallic acid	Phenolic acid	PAAD	p38	Induces p38 phosphorylation and alters downstream MAPK signaling associated with apoptosis	In vitro	[106]
18.	Isoliquiri‐ tigenin	Chalcone flavonoid	PAAD	p38	Targets p38 MAPK, disrupts autophagic flux, and enhances apoptosis and chemosensitivity	In vitro	[107]
19.	Betulinic acid	Pentacyclic triterpenoid	SKCM	p38, JNK	Activates stress‐associated p38/JNK signaling, inducing apoptosis	In vitro, in vivo	[108]
20.	Delphinidin	Anthocyanidin	SKCM	ERK, p38	Suppresses ERK and p38 phosphorylation	In vitro	[109]
21.	Rhoifolin	Flavonoid glycoside	PAAD	MAPK	Suppresses oncogenic MAPK signaling and enhances apoptotic responses	In vitro	[110]
22.	Oridonin	Diterpenoid	PAAD	p38	Activates p38 MAPK signaling, leading to apoptosis	In vitro	[111]
23.	Pentagal‐ loylglucose (PGG)	Hydrolysable tannin (Polyphenol)	SKCM	ERK, JNK, p38	Suppresses phosphorylation of ERK, JNK, and p38, inhibiting MAPK‐mediated CREB/MITF signaling	In vitro	[112]
24.	Luteolin	Flavonoid	SKCM	ERK	Attenuates UV‐induced oxidative stress and inflammation by modulating MAPK signaling	In vitro	[113]
25.	Genistein	Isoflavone	PTC	MAPK/ERK	Suppresses ERK phosphorylation, induces cell‐cycle arrest and apoptosis in BRAF‐mutant thyroid cancer cells	In vitro	[114]
26.	Quercetin	Flavonol	SKCM, THCA, PAAD	RAF, MEK, ERK1/2	Modulates Raf/MEK/ERK signaling, suppressing proliferation, EMT, and metastasis while promoting apoptosis	In vitro, in vivo	[115]
27.	Berberine	Isoquinoline alkaloid	SKCM, THCA, PAAD	ERK, p38, JNK	Suppresses MAPK signaling through ERK activation and modulation of p38/JNK	In vitro, in vivo	[116]
28.	Myricetin	Flavonol	SKCM, THCA, PAAD	MEK1, ERK, p38	Suppresses MEK/ERK and p38 activation	In vitro, in vivo	[117]
29.	Punicalagin	Ellagitannin	THCA	ERK, p38	Modulates MAPK signaling and induces autophagy	In vitro	[118]
30.	Kaempferol	Flavonol	SKCM, PAAD	ERK1/2	Suppresses ERK phosphorylation, reducing proliferation, migration, and inducing apoptosis	In vitro, in vivo	[119]

6. Transcriptomic and Computational Approaches for MAPK‐Target Discovery

6.1. Transcriptomic Profiling for MAPK Target Prioritization

Transcriptomic profiling has emerged as an essential tool to explore the molecular mechanisms driving cancer progression, and it has greatly broadened the knowledge of MAPK pathway deregulation beyond the simple study of mutations. Genomic profiling identifies genetic alterations in a gene involved in the MAPK pathway, but it may not reveal if the gene is transcriptionally active or if the changes in the gene occur during tumor evolution [120]. The combination of high‐throughput transcriptomic platforms, such as RNA‐ sequencing (RNA‐seq) and microarray analysis, has transformed the study of MAPK signaling from basic expression monitoring to a deeper exploration of systemic regulatory networks. Large‐scale bioinformatic analysis of public repositories like The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) has enabled differential gene expression, identification of dysregulated signaling pathways and clinically relevant molecular signatures, and thus has been used to obtain valuable data for the discovery of biomarkers and prioritization of therapeutic targets. Accordingly, transcriptomic analysis has become an important complement to genome studies, providing a dynamic view of the regulation of the MAPK pathway during the development of cancer and treatment.

Mutations in genes, including BRAF, RAS, or upstream receptor tyrosine kinases, commonly initiate MAPK signaling; however, the mutation status alone is not a reliable indicator of pathway activation or therapeutic response. It has been shown in multiple studies that tumors with the same driver mutations can have very different transcriptional profiles due to differences in tumor type, stage of disease, epigenetic control of MAPK signaling, interactions with the tumor microenvironment, and cross‐talk with other parallel signaling pathways [121]. Similarly, elevated levels of expression of MAPK‐related genes do not necessarily indicate that these kinases have elevated activity, as activation of ERK, JNK, and p38 is largely dependent on post‐translational modifications [122]. Therefore, transcriptomic data should be studied in the biological context and not as direct evidence of pathway activation.

In addition to confirming the presence of canonical MAPK mutations, transcriptomic studies have identified novel MAPK‐ related genes, cancer‐specific networks of regulatory genes, and adaptive signaling networks that could play a role in disease progression. For instance, transcriptomic studies in papillary thyroid cancer revealed that MAPK4 was significantly dysregulated and correlated with metastatic progression of the disease, thus making MAPK4 a new therapeutic target [123]. Similarly, expression profiling of medullary thyroid carcinoma revealed a distinct MAPK‐associated transcriptional signature in RET‐mutant and wild‐type tumors, such that similar histological features can drive different signaling programs in the tumor [124]. These analyses point to a significant benefit of the transcriptomics approach of prioritizing relevant targets that should be further functionally tested and computationally explored. However, differential gene expression as a single criterion is not enough to conclude about the oncogenic function, since many genes that have been identified by transcriptomics are not well investigated at the protein and functional levels.

Although bulk RNA sequencing continues to be the predominant method for discovering differentially expressed genes (DEGs), next‐generation sequencing‐based approaches now allow more detailed characterization of tumor‐specific expression profiles. More recently, single‐cell RNA‐sequencing (scRNA‐seq) has demonstrated significant heterogeneity in MAPK pathway activity between individual tumor cells that cannot be detected using bulk‐omic data [125]. The emerging cellular populations in these studies are associated with drug resistance and immune interactions, particularly in melanoma and pancreatic cancer [126]. Despite the application of single‐cell transcriptomics in thyroid cancer being limited, integration with bulk datasets may lead to improved identification of clinically relevant MAPK‐related biomarkers and potential therapeutic targets [127].

Beyond target discovery, transcriptomic profiling has greatly contributed to understanding the adaptive signaling in MAPK‐driven cancers. In melanoma, resistance to BRAF inhibitors occurs through mutations in other driver genes, with studies reporting alternative splicing events, isoform switching, and activation of compensatory signaling pathways that re‐establish proliferative signals despite effective MAPK inhibition [128]. Integrated signaling networks control the progression of PAAD, as demonstrated by similar transcriptome‐based analyses, which identified coordinated regulatory networks between MAPK signaling and long non‐coding RNA, aquaporins, metabolic regulators, and inflammatory pathways [129]. Therefore, transcriptomics is a fundamental tool for many computational methods, such as pathway enrichment, protein interaction network analysis, molecular docking, and also structure‐based drug discovery, in which the targets prioritized by expression analysis can be systematically studied.

6.2. In Silico Strategies for MAPK‐Targeted Drug Discovery

The identification of DEGs does not automatically make them the perfect therapeutic targets. To identify the functionally relevant regulators from secondary transcriptional changes, genes identified in the transcriptomic analysis need to be further prioritized [130]. Computational techniques like molecular docking, pharmacophore modeling, and virtual screening have gained popularity in view of the increasing complexity of cancer‐related targets and the need for more productive drug‐development pipelines [131]. Although a considerable number of drugs are tested experimentally or clinically, conventional screening methods were primarily responsible for their original identification. These in silico techniques speed up early‐stage discovery procedures by enabling more accurate candidate molecule identification. Molecular docking is a structure‐based computational approach that stimulates the interaction between a ligand and its target protein to predict binding orientation and estimate binding affinity. In the case of MAPK proteins, substrate specificity is determined by the ATP‐binding pocket as well as conserved docking domains. It is crucial to consider MAPK docking domains that facilitate substrate recognition and binding, as this complements computational docking studies. Among these, the D domain, also referred to as the docking site or DEJL motif, is defined by groups of positively charged amino acids followed by a hydrophobic region (e.g., Lys/Arg‐Xaa_2‐6‐Φ‐X‐Φ, where Φ represents a hydrophobic residue [132]. The DEF domain (docking site for ERK, FXFP) is another brief motif that is frequently found downstream of the phosphorylation site, which is important for ERK1/2‐mediated substrate binding [133]. Last, in ERK, JNK, and p38, the CD domain represents an external region that is a conserved area outside the kinase catalytic core and interacts with D domains via hydrophobic and electrostatic contacts.

Computer‐assisted studies of phytochemicals have progressively shifted their focus from evaluation of individual compounds against single kinases to analysis of potential interactions with targets of the MAPK pathway. Melanoma is the most extensively explored for computational discovery of MAPK‐targeted phytochemicals, mainly because the constitutive activation of the MAPK pathway by BRAF, MEK, and ERK offers well‐defined therapeutic targets for structure‐based drug design. The majority of docking studies focused on the binding affinity of individual phytochemicals against these kinases (Table 6). For instance, Theaflagallin and hesperidin from Camellia sinensis had favorable binding against BRAF V600E, MEK, and ERK, while dalbergin had strong affinity toward ERK1, ERK2, and MAPK14, indicating that structurally diverse phytochemicals could converge onto the same signaling pathway via different molecular interactions [134].

TABLE 6.

Molecular docking studies of compounds targeting MAPK‐related proteins.

Sr. no	Compound	Cancer	MAPK‐related target	PDB Id	Docking score (kcal/mol)	Molecular dynamics	Study type	Refs.
1.	Theaflagallin	SKCM	BRAF V600E	4MNF	−10.8	200 ns	In silico	[134]
2.	Dalbergin	SKCM	ERK1	4QTB	−9.7	100 ns	In silico	[141]
3.	Octadecenoic acid	SKCM	MAPK1	4MNE	−8.16	100 ns	In vitro	[142]
4.	Nuciferine	SKCM	BRAF V600E	3OG7	−9.2	—	In silico	[143]
5.	Hesperidin	SKCM	ERK2	6GDQ	−10.336	—	In silico	[144]
6.	Quercetin	PTC	JUN	—	− 5.36	—	In vitro	[145]
7.	Xanthohumol	THCA	MEK1	4ARK	−10.70	12 ns	In silico	[146]
8.	Nootkatone	PAAD	MAPK3	—	−8.03	100 ns	In silico	[137]
9.	Bruceine A	PAAD	p38α	5UOJ	98.16 a	—	In vitro, in vivo	[147]
10.	Paeoniflorin	PAAD	p38	—	−8	—	In vitro	[95]

Note: Not reported in the original study.

^aLibDock score.

In contrast to melanoma, computational studies of MAPK signaling are relatively limited for thyroid and pancreatic cancers and are mostly focused on disease‐specific molecular differences. Most studies of thyroid cancer have been directed toward BRAF, KRAS, and downstream MEK/ERK signaling, due to frequent mutations of the MAPK pathway in PTC. A representative example is quercetin, whose predicted modulation of the MAPK‐based signaling pathway was evaluated in PTC, and naldemedine, whose interaction with KRAS G12D was screened [100, 135]. Conversely, computational studies in pancreatic cancer have focused mainly on KRAS and ERK, with compounds like borneol and nootkatone for their ability to interfere with MAPK‐driven signaling [136, 137]. Target selection in thyroid and pancreatic cancer is still focused on a few dominant oncogenic drivers, while MAPK‐related targets are routinely assessed within a single computational framework in melanoma. This difference probably reflects the comparatively limited availability of computational studies rather than reduced biological relevance of MAPK signaling, highlighting an opportunity to explore a broader spectrum of MAPK family members in these malignancies.

While high binding affinity remains a primary criterion for prioritizing MAPK inhibitors, the ligand ranking by docking score may not capture the stability of protein–ligand interaction. Docking is typically done using simplified scoring functions and is usually treated as a static binding event, which may not necessarily represent the dynamic nature of a kinase protein or correlate with experimental binding affinity. Therefore, molecular dynamics is important to investigate the stability of ligand–protein complexes throughout the simulation [138]. Post‐docking binding free‐energy calculations, such as MM/PBSA and MM/GBSA, further enhance the ability of docking to rank ligands by increasing the reliability of the estimation of interaction strength compared to the docking score alone [139]. Though these advances have been made, computational predictions are still hypothesis‐generating and not a proof of biological activity. Experimental validation remains a crucial step to prove target engagement, inhibitory potency, and therapeutic relevance of the compounds before they are considered for development.

The combination of complementary computational approaches has significantly enhanced the early phase of MAPK target identification for drug discovery. The majority of studies have targeted kinases like BRAF, MEK, and ERK, whereas other members of the MAPK family remain under‐explored. In recent years, the use of ADMET prediction has been growing as an additional tool to support structural analysis in computational drug discovery studies, as it provides an early evaluation of drug‐like properties [140]. These predictions are indicative and should be read in conjunction with experimental pharmacokinetic and toxicological data.

7. MAP4K4 as an Emerging Cancer Target in MAPK Signaling

While a large number of studies have been performed elucidating the role of the RAF‐MEK‐ERK pathway in MAPK signaling, emerging transcriptomic, proteomic, and functional data show that upstream regulators also play a role in tumor progression. Among these, MAP4K4 has been identified as a possible therapeutic target related to cell proliferation, migration, invasion, and inflammatory signaling in several malignancies. Therefore, MAP4K4 is discussed here as an example of a MAPK‐related target with growing biological relevance.

Mitogen‐activated protein kinase kinase kinase (MAP4K4) was first discovered in Saccharomyces cerevisiae [148]. In mammals, MAP4Ks comprise six paralogs—MAP4K1 through MAP4K6—each involved in the upstream regulation of MAPK signaling cascades. These include MAP4K4 (also called NIK‐NCK interacting kinase and HGK‐hepatocyte progenitor kinase‐like germinal center kinase‐like kinase), which encodes a 1288 amino acids containing an N‐terminal kinase catalytic domain, a C‐terminal citron homology (CNH) domain rich in leucine residues, and proline‐rich motifs for protein–protein interaction [149].

Mass spectrometry‐based proteomic approaches have recently been applied to study MAP4K4 in cancer. Differential expression of kinases was analyzed using SILAC (Stable Isotope Labeling by Amino acids in Cell culture) combined with parallel reaction monitoring (PRM), in bone metastasis‐derived prostate cancer cell lines (PC3 vs. PC3MLN4), and MAP4K4 was among the most significantly expressed kinases in this case study [150]. MAP4K4 has been phosphorylated on Thr‐87 in the activation loop and multiple residues in the linker region, as identified by mass spectrometry [151]. Growing evidence supports the involvement of MAP4K4 in the biology of melanoma, thyroid, and pancreatic cancers. In melanoma, it was demonstrated that MAP4K4 interacts with RAP2C and SLK to modulate the phosphorylation of DRP1, and silencing of MAP4K4 in 501mel and A375 cells resulted in cell growth inhibition and promotion of apoptosis, suggesting an involvement in the regulation of mitochondria and melanoma cell survival [152]. Large‐scale transcriptomic analysis of thyroid cancer found MAP4K4 to be part of tumor‐enriched biomarker signatures correlated with advanced tumor stage, metastasis, poor surgical outcome, and immune‐invasive phenotypes [153]. MAP4K4 has also been found to be alternatively spliced in PTC, indicating that its expression and post‐transcriptional regulation are both likely to play roles in the progression of the disease [154]. In pancreatic cancer, the therapeutic relevance of MAP4K4 is strongest. MAP4K4 overexpression has been associated with poor prognosis and promotes tumor growth through the MAP4K4‐MLK3 signaling axis. The selective inhibitor of MAP4K4, F389‐0746 (IC₅₀ = 120.7 nm), has been found to inhibit the growth of pancreatic tumors in both in vitro and in vivo models [155]. More recently, Queuine showed greater MAP4K4 inhibition compared to gemcitabine, with a lower IC₅₀, stable protein binding, and synergistic activity in combination with gemcitabine, further supporting MAP4K4 as a potential therapeutic target [156].

Three primary methods are often used by MAP4K4 to promote tumor growth: stimulation of cell proliferation pathways, cytoskeleton reorganization that promotes migration and invasion, and disruption of anti‐tumor immune responses [157]. To understand how MAP4K4 is integrated into broader signaling processes, we have constructed a protein–protein interaction (PPI) network using the STRING database (Homo sapiens), with a confidence score of 0.4, and visualized it in Cytoscape (v3.10.3). The network is centered on MAP4K4, and shows its direct associations with a wide array of signaling proteins with DISC1, MAPRE2, RAP2A, MAP2K4, MINK1, PPP2CA, CTTNBP2NL, SLMAP, SIKE1, PDCD10, STRN4, STRN3, STRN, STRIP1, PPP2CB, TNIK, MAP3K1, MOB4, NCK1, FGFR1OP2, and STK24, totaling 21 interactions (Figure 3). The overall network comprises 67 nodes and 370 edges, where MAP4K4 has a degree of 21, a closeness centrality of 0.59, a shortest path length of 1.68, and a clustering coefficient of 0.51. This reflects a moderate level of interconnectivity among its neighbors. These interactions are linked to proliferation, cytoskeletal organization, and immune signaling. The evidence available indicates that MAP4K4 is an emerging upstream kinase with growing translational importance in cancer. Further integration of transcriptomic analysis with computational drug discovery and biological validation could support MAP4K4 as a clinically actionable target.

FIGURE 3.

Protein–protein interaction network of MAP4K4 constructed using Cytoscape (version 3.10.3). A circular layout was applied to display MAP4K4 first‐neighbor interactors.

8. Future Perspectives

At present, many kinase inhibitors intended for novel possible targets have advanced to clinical stages and appear promising for approval in the future. Machine learning algorithms can now be used as a powerful resource to analyze large‐scale gene expression data. Through the utilization of machine learning, researchers can find intricate patterns and associations within data. For cancer and other diseases, machine learning strategies are proving effective in repurposing existing kinase inhibitors and clinically approved drugs [158].

Artificial intelligence (AI) and machine learning (ML) are significant in computer screening, drug repurposing, molecular design, and the prediction of protein–protein interactions. By combining and evaluating various omics data, including transcriptomic data from each patient's tumor, AI‐based models are being created to predict the optimal treatment combinations for each patient [159]. Several technical parameters, ranging from the sequencing platform and library preparation to the details of sample processing, can affect the reliability of RNA‐seq‐based machine learning classifiers.

The recent developments in single‐cell RNA sequencing and spatial transcriptomics have taken knowledge of tumor heterogeneity to a new level by integrating transcriptomic data at single‐cell resolution with the spatial structure of tumor tissues [160]. These methods provide valuable information on cell–cell interactions and tumor architecture.

In parallel, advances in computer‐aided drug design allow rapid exploration and refinement of ligand–kinase interactions using physics‐based and machine learning‐assisted scoring functions. This method is useful for decreasing the number of compounds that should be tested in vitro and helps to concentrate resources on the compounds that are most likely to become drugs [161].

9. Conclusion

A major characteristic of the emergence and growth of diverse cancers is the MAPK pathway disruption. These pathways, such as ERK, JNK, and p38, are not only crucial for cell regulation but also provide several prospects for developing cancer therapies. The MAPK/ERK signaling pathway has both oncogenic and tumor suppressive properties, as per the tissue‐specific tumor microenvironment. In this review, we have summarized the role of the MAPK signaling pathway in the progression of SKCM, THCA, and PAAD, where MAPK mutations are prevalent.

Natural phytochemicals, when compared to synthetic inhibitors, have enormous potential to control MAPK activity and overcome oncogenic signaling due to their multidimensional structural properties, lower toxicity, and ability to target multiple pathways. Additionally, rational computational drug design is an accelerated method to identify promising drug candidates through a combination of various techniques such as molecular docking, virtual screening, and pharmacophore modeling. These methods assist in clarifying the associations of the phytochemicals and pathway targets and focus compounds for experimental validation. MAP4K4 plays a role in tumorigenic events and, therefore, it would be an effective target in cancer treatment. In conclusion, transcriptomic studies of the MAPK signaling pathway provide a framework for understanding the diverse cellular functions associated with cancer.

Funding

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File: adbi70156‐sup‐0001‐SuppMat.docx.

Biographies

Divya Wasnik is a Ph.D. scholar at the School of Bioengineering Sciences and Research, MIT ADT University, Pune, India. She holds a Master's degree in Biotechnology, and her research interests focus on Bioinformatics, RNA‐seq data analysis, and computational drug discovery.

K. Venkateswara Swamy is a professor at the School of Bioengineering Sciences and Research, MIT‐ADT University, Pune, India. He obtained his Ph.D. in Biochemistry from Sri Krishnadevaraya University, Andhra Pradesh. He authored about 85 international research publications in reputed journals. He is also a recipient of various awards, like the Young Scientist Award, Research Award, and Yuvaratna Sansodhan Award by various agencies. With over 15 years of teaching and research experience, his expertise spans biochemistry, bioinformatics, computational biology, molecular docking and dynamics, and systems biology. He also received research grants from the Department of Science and Technology (DST) and the Indian Council of Medical Research (ICMR), India.

Renu Vyas obtained her Ph.D. from CSIR National Chemical Laboratory, Pune, and pursued her post‐doctoral studies at the University of Tennessee, Knoxville, USA.  A recipient of several national and international fellowships and grants, Dr. Vyas possesses multidisciplinary research experience in the field of organic synthesis, bioinformatics, drug design, and machine learning.  She has authored fifty international research publications, fifteen patents, and co‐authored a book titled ‘Practical Chemoinformatics and also edited a book, Advances in Bioengineering, by Springer. She is currently working as Head of the MIT ADTU School of Bioengineering Sciences & Research and Dean, Faculty of Technology, MIT ADT University, Pune.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.