Signaling Complex Research Articles

Polyamines Interaction with Gaseous Signaling Molecules for Resilience Against Drought and Heat Stress in Plants.

Plants face a range of environmental stresses, such as heat and drought, that significantly reduce their growth, development, and yield. Plants have developed complex signaling networks to regulate physiological processes and improve their ability to withstand stress. The key regulators of plant stress responses include polyamines (PAs) and gaseous signaling molecules (GSM), such as hydrogen sulfide (H2S), nitric oxide (NO), methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), and ethylene (ET). The functions of PAs and GSM in stress perception, signal transduction, and stress-responsive pathways have been explored. However, there is a lack of detailed, updated information on the interaction of PAs and GSM in the adaptation of drought and heat stress. This review explores the interaction between PAs and GSM for the adaptation to drought and heat stress. It explores their synergistic effects in mitigating the negative impacts of drought and heat stress on plant growth, development, and productivity. Moreover, a comprehensive analysis of physiological, biochemical, and molecular approaches demonstrates that their interaction activates key stress-responsive pathways, enhances antioxidant systems, and modulates gene expression. These combined effects contribute to improved drought and heat tolerance in plants. The information presented in the review provides valuable insights into plant stress resilience strategies and suggests potential measures for developing climate-resilient crops to address the increasing environmental challenges.

Classifying Food Items During an Eating Occasion: A Machine Learning Approach with Slope Dynamics for Windowed Kinetic Data.

Wearable devices equipped with a range of sensors have emerged as promising tools for monitoring and improving individuals' health and lifestyle. Contribute to the investigation and development of effective and reliable methods for dietary monitoring based on raw kinetic data generated by wearable devices. This study uses resources from the NOTION study. A total of 20 healthy subjects (9 women and 11 men, aged 20-31 years) were equipped with two commercial smartwatches during four eating occasions under semi-naturalistic conditions. All meals were video-recorded, and acceleration data were extracted and analyzed. Food recognition on these features was performed using random forest (RF) models with 5-fold cross-validation. The performance of the classifiers was expressed in out-of-bag sensitivity and specificity. Acceleration along the x-axis and power show the highest and lowest rates of median variable importance, respectively. Increasing the window size from 1 to 5 s leads to a gain in performance for almost all food items. The RF classifier reaches the highest performance in identifying meatballs (89.4% sensitivity and 81.6% specificity) and the lowest in identifying sandwiches (74.6% sensitivity and 72.5% specificity). Monitoring food items using simple wristband-mounted wearable devices is feasible and accurate for some foods while unsatisfactory for others. Machine learning tools are necessary to deal with the complexity of signals gathered by the devices, and research is ongoing to improve accuracy further and work on large-scale and real-time implementation and testing.

Complexity and recurrence of body surface electrocardiograms correlate with estimated reentrant atrial activity using electrocardiographic imaging in atrial fibrillation patients

BackgroundComplexity and signal recurrence metrics obtained from body surface potential mapping (BSPM) allow quantifying atrial fibrillation (AF) substrate complexity. This study aims to correlate electrocardiographic imaging (ECGI) detected reentrant patterns with BSPM-calculated signal complexity and recurrence metrics.MethodsBSPM signals were recorded from 28 AF patients (17 male, 11 women, 62.69 ± 8.09 y.o.), followed by ECGI calculation. Signal complexity and recurrence metrics were computed on BSPM and ECGI signals. Rotors per second and rotor duration were computed on ECGI signals for each atrium and the whole atrial surface. Correlation between BSPM metrics and ECGI reentrant patterns for the entire atrial surface and for left atrium (LA) and right atrium (RA) were analyzed.ResultsAtrial complexity and recurrence metrics strongly correlated when computed on BSPM and ECGI. Higher sample entropy and relative harmonic energy (RHE) correlated with rotors of short duration. The highest dominant frequency of the ECGI signals did not correlate with the reentrant activity of the ECGI. Higher short- and long-term recurrence of BSPM signals correlated with longer duration rotors, particularly for long-term recurrence (rLA=0.74 vs. rRA=0.42). Only ECGI-based reentrant parameters showed higher LA complexity compared to RA (p < 0.05).ConclusionsBSPM metrics strongly correlate with metrics measured on ECGI signals. BSPM metrics indicate a more elevated atrial electro-structural remodeling aligned with more short-duration rotors from ECGI computations. Although BSPM delivers qualitative AF reentry data, ECGI remains essential for identifying regional substrate complexity.

Design and Application of Uniaxially Sensitive Stress Sensor.

Current stress sensors for microsystems face integration challenges and complex signal decoding. This paper proposes a real-time uniaxially sensitive stress sensor. It is obtained by simple combinations of bar resistors using their sensitivity differences in different axes. With the aid of a Wheatstone bridge, the sensor can measure the uniaxial stress magnitude by simple calibration of the stress against the output voltage and detect the bidirectional stress magnitude and direction in a micro-zone by simple rotation. The theoretical sensitivity obtained from simulation is 0.087 mV/V·MPa when the X-bridge is stressed in the X-direction under 1 V of excitation, and the test sensitivity of the X-bridge prepared in this paper is 0.1 mV/V·MPa. The design is structurally and procedurally simple, exhibits better temperature stability, and reduces interface requirements, making it suitable for the health monitoring of multi-chip microsystem chips.

The SIRT5-JIP4 interaction promotes osteoclastogenesis by modulating RANKL-induced signaling transduction

Receptor activator of nuclear factor kappa-B ligand (RANKL) initiates a complex signaling cascade that is crucial for inducing osteoclast differentiation and activation. RANKL-induced signaling has been analyzed in detail, and the involvement of TNF receptor-associated factor 6 (TRAF6), calmodulin-dependent protein kinase (CaMK), NF-κB, mitogen-activated protein kinase (MAPK), activator protein-1 (AP-1), and molecules that contain an immunoreceptor tyrosine-based activation motif (ITAM) has been reported. However, the precise molecular steps that regulate RANKL signaling remain largely unknown. Here, we revealed the indispensable role of a class III histone deacetylase (SIRT5) in the processes of RANKL-induced osteoclast differentiation and activation. SIRT5 expression in osteoclasts was increased during osteoclastogenesis upon stimulation with RANKL. The RANKL-induced signaling activation was suppressed in SIRT5-deficient osteoclasts but enhanced by SIRT5 overexpression. Mice with global or conditional monocytic lineage knockout of SIRT5 had increased bone mass and reduced osteoclast numbers. In the cytoplasm, SIRT5 interacted with the scaffold protein JNK-interacting protein 4 (JIP4) to finely regulate MAPK signaling, which was critical for osteoclast differentiation and activation. Pharmacological inhibition of the catalytic activity of SIRT5 effectively reversed bone loss in ovariectomized mice. Taken together, the results of this study reveal that the SIRT5-JIP4 axis is a novel positive regulator that finely regulates RANKL-induced osteoclast differentiation and suggest that targeting this axis is a therapeutic strategy for preventing osteoporotic bone loss.Graphical

Ultrafast multicellular calcium imaging of calcium spikes in mouse beta cells in tissue slices.

The crucial steps in beta cell stimulus-secretion coupling upon stimulation with glucose are oscillatory changes in metabolism, membrane potential, intracellular calcium concentration, and exocytosis. The changes in membrane potential consist of bursts of spikes, with silent phases between them being dominated by membrane repolarization and absence of spikes. Assessing intra- and intercellular coupling at the multicellular level is possible with ever-increasing detail, but our current ability to simultaneously resolve spikes from many beta cells remains limited to double-impalement electrophysiological recordings. Since multicellular calcium imaging of spikes would enable a better understanding of coupling between changes in membrane potential and calcium concentration in beta cell collectives, we set out to design an appropriate methodological approach. Combining the acute tissue slice method with ultrafast calcium imaging, we were able to resolve and quantify individual spikes within bursts at a temporal resolution of >150 Hz over prolonged periods, as well as describe their glucose-dependent properties. In addition, by simultaneous patch-clamp recordings we were able to show that calcium spikes closely follow membrane potential changes. Both bursts and spikes coordinate across islets in the form of intercellular waves, with bursts typically displaying global and spikes more local patterns. This method and the associated findings provide additional insight into the complex signaling within beta cell networks. Once extended to tissue from diabetic animals and human donors, this approach could help us better understand the mechanistic basis of diabetes and find new molecular targets.

Probing Biomolecular Interactions with Paramagnetic Nuclear Magnetic Resonance Spectroscopy.

Recent advances in computational methods like AlphaFold have transformed structural biology, enabling accurate modeling of protein complexes and driving applications in drug discovery and protein engineering. However, predicting the structure of systems involving weak, transient, or dynamic interactions, or of complexes with disordered regions, remains challenging. Nuclear Magnetic Resonance (NMR) spectroscopy offers atomic-level insights into biomolecular complexes, even in weakly interacting and dynamic systems. Paramagnetic NMR, in particular, provides long-range structural restraints, easily exceeding distances over 25 Å, making it ideal for studying large protein complexes. Advances in chemical tools for introducing paramagnetic tags into proteins, combined with progress in electron paramagnetic resonance (EPR) spectroscopy, have enhanced the method's utility. This perspective article discusses paramagnetic NMR approaches for analyzing biomolecular complexes in solution and in the solid state, emphasizing quantities like pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements. Additionally, dynamic nuclear polarization offers a promising method to amplify NMR signals of large complexes, even in complex environments. The integration of AlphaFold protein structure prediction with paramagnetic NMR holds great potential for advancing our understanding of biomolecular interactions.

Expression study of Wnt/β-catenin signaling pathway associated lncRNAs in schizophrenia

Schizophrenia is one of the most debilitating mental illnesses affecting any age group. The mechanism and etiology of schizophrenia are extremely complex and multiple signaling pathways recruit genes implicated in the etiology of this disease. While the role of Wnt/β-catenin signaling in this disorder has been verified, the impact of long noncoding RNAs (lncRNAs) associated with this pathway has not been studied in schizophrenia. The objective of this study was to examine the expression levels of Wnt/β-catenin-related lncRNAs, namely CCAT2, SNHG5, PTCSC3, and DANCR, as well as the CTNNB1 gene encoding beta-catenin protein in two groups of schizophrenia patients (drug-naïve and medicated) compared with healthy individuals. This study included 50 medicated patients in the remission phase of the disease, 25 drug-naive patients in the acute phase, and 50 control subjects. There was no significant difference in CTNNB1 gene expression in the medicated patients compared to controls (P value = 0.9754). However, the expression of this gene was significantly decreased in drug-naïve first-episode patients compared with controls (P value < 0.001). In contrast, expression of DANCR, PTCSC3, SNHG5, and CCAT2 genes was significantly higher in medicated (P values < 0.001, < 0.001, = 0.01, < 0.001, respectively) and drug-naive first-episode patients (P value < 0.001) compared to control subjects. ROC curve analysis revealed that DANCR, PTCSC3, SNHG5, and CCAT2 genes had diagnostic power with specificity and sensitivity of 80% and above in separation between study subgroups. In brief, our data demonstrated dysregulation of Wnt/β pathway related genes and lncRNAs in the peripheral blood of patients with schizophrenia and their potential as biomarkers for this disorder.

Molecular Insights on Signaling Cascades in Breast Cancer: A Comprehensive Review.

The complex signaling network within the breast tumor microenvironment is crucial for its growth, metastasis, angiogenesis, therapy escape, stem cell maintenance, and immunomodulation. An array of secretory factors and their receptors activate downstream signaling cascades regulating breast cancer progression and metastasis. Among various signaling pathways, the EGFR, ER, Notch, and Hedgehog signaling pathways have recently been identified as crucial in terms of breast cancer proliferation, survival, differentiation, maintenance of CSCs, and therapy failure. These receptors mediate various downstream signaling pathways such as MAPK, including MEK/ERK signaling pathways that promote common pro-oncogenic signaling, whereas dysregulation of PI3K/Akt, Wnt/β-catenin, and JAK/STAT activates key oncogenic events such as drug resistance, CSC enrichment, and metabolic reprogramming. Additionally, these cascades orchestrate an intricate interplay between stromal cells, immune cells, and tumor cells. Metabolic reprogramming and adaptations contribute to aggressive breast cancer and are unresponsive to therapy. Herein, recent insights into the novel signaling pathways operating within the breast TME that aid in their advancement are emphasized and current developments in practices targeting the breast TME to enhance treatment efficacy are reviewed.

FvPHR1 Improves the Quality of Woodland Strawberry Fruit by Up-Regulating the Expression of FvPHT1;7 and FvSWEET9.

Phosphorus (P) is vital for plant growth, and continuous P fertiliser application is necessary to increase yield and quality, but it can cause environmental pollution. Plants maintain a steady phosphate (Pi) supply through complex signalling pathways. Phosphate starvation response 1 (PHR1), a key regulator of Pi starvation signals in plants, enables plants to maintain a sufficient Pi level. However, the role of PHR1 in fruit quality remains unclear. In this study, we determined the function of PHR1 in Fragaria vesca (FvPHR1) by overexpressing the FvPHR1 gene. We identified and validated two downstream genes of FvPHR1 by investigating plant phenotypes and analysing RNA-Seq data. FvPHR1 directly enhanced the expression of phosphate transporter 1;7 (FvPHT1;7), increasing Pi uptake and improving photosynthesis efficiency. Additionally, FvPHR1 upregulated the expression of sugar will eventually be exported transporter 9 (FvSWEET9), which encodes a sugar transporter that facilitates sugar transport from leaves to fruit. FvPHR1 can enhance photosynthetic products in a source via the phosphate signalling pathway and facilitate sugar transport to a sink through FvSWEET9. FvPHR1 plays a complicated role in improving fruit quality, providing a molecular foundation for developing strawberry cultivars with highly efficient Pi utilisation processes and high sugar content.

Cryo-EM structure of an activated GPR4–Gs signaling complex

G protein-coupled receptor 4 (GPR4) belongs to the subfamily of proton-sensing GPCRs (psGPCRs), which detect pH changes in extracellular environment and regulate diverse physiological responses. GPR4 was found to be overactivated in acidic tumor microenvironment as well as inflammation sites, with a triad of acidic residues within the transmembrane domain identified as crucial for proton sensing. However, the 3D structure remains unknown, and the roles of other conserved residues within psGPCRs are not well understood. Here we report cryo-electron microscopy (cryo-EM) structures of active zebrafish GPR4 at both pH 6.5 and 8.5, each highlighting a distribution of histidine and acidic residues at the extracellular region. Cell-based assays show that these ionizable residues moderately influence the proton-sensing capacity of zebrafish GPR4, compared to the more significant effects of the triad residues. Furthermore, we reveal a cluster of aromatic residues within the orthosteric pocket that may propagate the signaling to the intercellular region via repacking the aromatic patch at the central region. This study provides a framework for future signaling and functional investigation of psGPCRs.

A weighted frequency domain energy operator spectral method based on soft thresholding fast iterative filtering for rolling bearing incipient fault feature extraction

The incipient features in the vibration signal are a typical signature of rolling bearing faults, and their extraction is vital for early-stage bearing fault diagnosis, yet quite challenging given the complexity of the noisy vibration signal. Therefore, a weighted frequency domain energy operator (FDEO) spectral method based on soft thresholding fast iterative filtering (FIF) is proposed in this work. Firstly, a soft thresholding FIF technique is proposed to improve the equivalent filter properties of FIF such as the continuity and the transition band lengths. Secondly, an integrated sparsity measure named frequency domain average Shannon kurtosis entropy that incorporates randomness and impulse characteristics is constructed to estimate the incipient fault feature information of the noisy signal. Finally, the weighted FDEO spectrum is designed to complete the bearing fault feature identification, it suppresses the chaotic signal component in the frequency domain. The proposed incipient fault extraction framework is evaluated by simulation and experiment, and its performance in an intense noise background is verified through comparisons with other signal decomposition methods.

The level of cognitive functioning in school-aged children is predicted by resting EEG Directed Phase Lag Index

Quantifying cognitive potential relies on psychometric measures that do not directly reflect cortical activity. While the relationship between cognitive ability and resting state EEG signal dynamics has been extensively studied in children with below-average cognitive performances, there remains a paucity of research focusing on individuals with normal to above-average cognitive functioning. This study aimed to elucidate the resting EEG dynamics in children aged four to 12 years across normal to above-average cognitive potential. Our findings indicate that signal complexity, as measured by Multiscale Entropy (MSE), was not significantly predictive of the level of cognitive functioning. However, utilizing Directed Phase Lag Index (DPLI) as an effective connectivity measure, we observed consistent patterns of information flow between anterior and posterior regions. Fronto-parietal as well as local connectivity patterns were seen across most of the cognitive functions. Moreover, specific connectivity patterns were obtained for each intellectual quotient index (namely verbal comprehension, visuospatial, fluid reasoning, and processing speed indexes as well as full-scale intellectual quotient). These results underscore the presence of long-range connections and support fronto-parietal theories of cognitive abilities within the resting state brain dynamics of children.

Coastal carbon sentinels: A decade of forest change along the eastern shore of the US signals complex climate change dynamics

Increased frequency and intensity of storms, sea level rise, and warming temperatures are affecting forests along the eastern coast of the United States. However, we lack a clear understanding of how the structure of coastal forests is being altered by climate change drivers. Here, we used data from the Forest Inventory and Analyses program of the US Forest Service to examine structure and biomass change in forests along the mid-Atlantic, Southeastern, and Gulf coasts of the US. We selected plots that have been resampled at low (5 m) and mid (30–50 m) elevations in coastal areas of states from Texas to New Jersey, allowing us to determine change in live trees, standing dead wood, and downed dead wood biomass (and carbon) stocks across a decade at the county level. Forest area increased by 1.9% and 0.3% in low and mid elevation counties, respectively. Live tree biomass density increased by 13% and 16% in low mid elevation counties, respectively. Standing dead biomass decreased by 9.2% and 2.8% in low and mid elevation counties, respectively. Downed dead wood increased by 22% in low elevation counties and decreased 50% in mid elevation counties. Annualized growth and harvest were both higher (16% and 58% respectively) in mid elevation than low elevation counties, while annualized mortality was 25% higher in low elevation counties. Annualized growth in low elevation counties was negatively correlated to sea level rise rates, and positively correlated to number of storms, illustrating tradeoffs associated with different climate change drivers. Overall, our results illustrate the vulnerability of US coastal low and mid elevation forests to climate change and sea level rise, with indications that the complexity and rate of change in associated ecosystem functions (growth, mortality, and carbon storage) within the greater social environment (agricultural abandonment) may increase.

Histochemical indications for a chemically complex signal produced by the cervical gill slit gland of the pygmy sperm whale (Kogia breviceps).

The pygmy sperm whale (Kogia breviceps) possesses an exocrine gland associated with its false gill slit pigmentation pattern. The cervical gill slit gland is a compound tubuloalveolar gland that produces a holocrine secretion and displays maturational changes in size and secretory histology. While the morphology of the cervical gill slit gland has been described in detail, to date, the chemical composition of its secretion remains uncharacterized. This study used histochemical staining techniques and quantitative lipid analysis to identify and characterize the constituents expressed in the secretory cells and secretion of the cervical gill slit gland. Results demonstrate that the secretion, like those of terrestrial artiodactyls that function in chemical communication, includes a complex mixture of carbohydrates, proteins, and lipids. Differences in staining intensity across germinal and secretory epithelial layers demonstrate differential expression, or maturation, of mucins and proteins. Additionally, a highly unusual and primary constituent of the secretion is uric acid. Uric acid was identified within the secretion using histochemical stains and polarized light imaging, and chemically verified using scanning electron microscopy with energy dispersive spectrometry. While uric acid is not a common constituent of mammalian exocrine glands, urate-based compounds are abundant in the secretions of marine organisms used in chemical communication. Thus, uric acid may contribute to the chemical message produced by K. breviceps in its marine environment. We hypothesize that the chemical signals produced by the gill slit gland may be shared at close-range by conspecifics, and that the mode of sensory reception is likely gustation.

Proximity Labeling: Precise Proteomics Technology for Mapping Receptor Protein Neighborhoods at the Cancer Cell Surface.

Cell surface receptors are pivotal to cancer cell transformation, disease progression, metastasis, early detection, targeted therapy, drug responses, and clinical outcomes. Since they coordinate complex signaling communication networks in the tumor microenvironment, mapping the physical interaction partners of cell surface receptors in vivo is vital for understanding their roles, functional states, and suitability as therapeutic targets. Yet traditional methods like immunoprecipitation and affinity purification-mass spectrometry often fail to detect key but weak or transient receptor-protein interactions. Proximity labeling, a cutting-edge proteomics technology, addresses these technical challenges by enabling precise mapping of protein neighborhoods around a receptor target on the cell surface of cancer cells. This technique has been successfully applied in vitro and in vivo for proteomic mapping across various model systems. This review explores the fundamental principles, technologies, advantages, limitations, and applications of proximity labeling in cancer biology, focusing on mapping receptor microenvironments. By advancing mechanistic insights into cancer cell receptor signaling mechanisms, proximity labeling is poised to transform cancer research, improve targeted therapies, and illuminate avenues to overcome drug resistance.

On Oscillations in the External Electrical Potential of Sea Urchins.

Sea urchins display complex bioelectric activity patterns, even with their decentralized nervous system. Electrophysiological recordings showed distinct spiking patterns. The baseline potential was about 8.80 mV. It had transient spikes with amplitudes up to 21.05 mV. We observed many types of depolarization events. They included burst-like activity and prolonged state fluctuations lasting several seconds. Frequency domain analysis showed a power-law behavior. It had a scaling exponent of 6.21 ± 0.06, indicating critical dynamics. The analysis showed potential variations between 3.69 and 21.05 mV. The oscillation periods ranged from 4 to 3102 s. The varied timing of bioelectric signals suggests that these organisms can process information. This challenges traditional views of neural computation in simpler animals. These findings provide quantitative insights into the complex signaling mechanisms of the sea urchin's distributed nervous system.

Complexity of intrinsic brain activity in relapsing-remitting multiple sclerosis patients: patterns, association with structural damage, and clinical disability.

Functional plasticity has been demonstrated in multiple sclerosis (MS) studies. However, the intrinsic brain activity complexity alterations remain unclear. Here, using a coarse-graining time-series procedure algorithm, we obtained multiscale entropy (MSE) from a retrospective multi-centre dataset (208 relapsing-remitting MS patients and 228 healthy controls). By linear mixed model analysis, we demonstrated (1) increased entropy at scale 1 and decreased entropy at scale 6, indicating that regional brain activity shifted towards randomness in the stable MS subgroups (n = 159), and (2) decreased entropy across scales 1-6, trending towards regularity in the acute MS subgroups (n = 49). The main results of the correlation analysis included the following: (1) Decreased entropy was associated with lesion volume and brain volume specifically on longer time scales (scale 3-5), and (2) increased entropy of scale 3 was associated with clinical disability scores. These findings reflect the critical role of structural disruption in the brain activity complexity of BOLD signals in MS patients.

Role of MAPK and JNK Signaling Pathways in Skin Cancer Progression and Therapeutic Approach

Mitogen-activated protein kinase (MAPK) and c-Jun N-terminal kinase (JNK) pathways exhibit complex signal transduction pathways that modulate multiple processes and the deregulated form of MAPK is involved in many types of cancers including skin cancer. These signaling pathways regulate cellular processes such as differentiation, inflammation, cell proliferation, and survival. Moreover, MAPKs are crucial signaling pathways in regulating various cellular processes, including cell proliferation, differentiation, survival, and apoptosis. Their dysregulation is commonly observed in cancer, making them attractive targets for therapeutic intervention. The critical mechanisms and pathways of MAPKs and JNKs in skin cancer including basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma are summarized in this review. In addition to providing new insights into MAPK and JNK pathways in cancer progression and proliferation, we also offered the significance of the mechanism of JNK in drug resistance and the association of KLF4 in influencing JNKs for future endeavors to be targeted as therapeutic approaches as understanding the molecular aspects of these signaling pathways are crucial for novel therapeutic breakthroughs.

Endocrine treatment mechanisms in triple-positive breast cancer: from targeted therapies to advances in precision medicine.

Triple-positive breast cancer (TPBC), defined by the co-expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), poses unique therapeutic challenges due to complex signaling interactions and resulting treatment resistance. This review summarizes key findings on the molecular mechanisms and cross-talk among ER, PR, and HER2 pathways, which drive tumor proliferation and resistance to conventional therapies. Current strategies in TPBC treatment, including endocrine and HER2-targeted therapies, are explored alongside emerging approaches such as immunotherapy and CRISPR/Cas9 gene editing. Additionally, we discuss the tumor microenvironment (TME) and its role in treatment resistance, highlighting promising avenues for intervention through combination therapies and predictive biomarkers. By addressing these interdependent pathways and optimizing therapeutic strategies, precision medicine holds significant potential for improving TPBC patient outcomes and advancing individualized cancer care.