The methodological architecture of the study deserves closer examination, because the reliability of any shared molecular signature depends heavily on how the underlying data were assembled and interrogated. The investigators drew on publicly available gene expression datasets deposited in the Gene Expression Omnibus, applying standardized preprocessing pipelines to harmonize samples across studies that were originally generated by different laboratories, on different microarray or sequencing platforms, and with different clinical phenotyping conventions. Cross-dataset integration of this kind is a well-established strategy in computational genomics, but it introduces well-known challenges: batch effects, differences in tissue composition, and variable proportions of contaminating cell types can all masquerade as genuine biological signals. The fact that the authors identified six concordantly upregulated and eleven concordantly downregulated differentially expressed genes shared between IPAH and malignant pleural mesothelioma suggests that their harmonization procedures were sufficiently rigorous to survive the noise inherent in meta-analytic transcriptomics, yet the modest number of overlapping genes is itself informative. It implies that while the two diseases do share a transcriptional core, the majority of their differential expression programs remain disease-specific, consistent with the idea that IPAH is not simply a malignancy in disguise but a vascular disorder that has co-opted selected cancer-like programs.

The enrichment patterns of the shared genes add biological texture to this picture. The concordantly upregulated genes clustered around cell cycle regulation, a finding that resonates with decades of histopathological observation in pulmonary arterial hypertension. Pulmonary arterial smooth muscle cells and endothelial cells within remodeled vessels of IPAH patients are known to proliferate in an apparently uncontrolled fashion, forming the concentric laminar lesions and plexiform structures that pathologists have long recognized. That these proliferating cells share a transcriptional signature of cell cycle activation with mesothelioma tumor cells provides molecular corroboration for the cancer hypothesis first articulated by Voelkel and colleagues in the late 1990s. The downregulated arm of the shared signature, enriched for neutrophil chemotaxis, is equally telling. It points to a perturbation of innate immune trafficking, suggesting that IPAH vasculature may fail to recruit or retain neutrophils in the manner expected of inflamed tissue, or alternatively that the peripheral blood compartment sampled in these datasets reflects a redistribution of these cells away from circulation and into the remodeled pulmonary vasculature itself.

The convergence of three independent machine learning algorithms on CXCR2, S100A9, and LCN2 strengthens the case that these genes are not artifacts of any single feature-selection approach. Least absolute shrinkage and selection operator regression, random forest classification, and support vector machine recursive feature elimination each operate on fundamentally different mathematical principles, and genes that survive all three filters are less likely to be statistical flukes. CXCR2 is a chemokine receptor whose ligands include interleukin-8 and related CXC chemokines, and it sits at the interface of inflammation and cell migration. Its concordant downregulation, together with the observed decrease in neutrophil infiltration in IPAH samples, suggests a coordinated suppression of neutrophil-directed chemotactic signaling. S100A9, a calcium-binding protein released abundantly by activated neutrophils and monocytes, functions as a danger-associated molecular pattern and has been implicated in sterile inflammation across multiple cardiovascular diseases. LCN2, also known as lipocalin-2 or neutrophil gelatinase-associated lipocalin, participates in iron sequestration and metabolic reprogramming, and its correlation with metabolic pathways in this study aligns with the growing recognition that Warburg-like aerobic glycolysis characterizes proliferating pulmonary vascular cells in IPAH just as it characterizes many tumors.

The immune infiltration analysis performed by single-sample gene set enrichment adds a cellular dimension to the transcriptomic findings. The reported increase in Th17 cells alongside decreased neutrophils in IPAH paints a picture of adaptive immune skewing concurrent with innate immune depletion from the sampled compartment. Th17 cells, characterized by their production of interleukin-17, are known to promote fibrosis and vascular remodeling in several disease contexts, and their expansion in IPAH is consistent with the inflammatory phenotype that has been described in advanced pulmonary vascular disease. The correlation between CXCR2 and S100A9 expression and neutrophil infiltration provides internal consistency: if these genes are expressed by or regulate neutrophil biology, their abundance should track with neutrophil representation in the sample, and the observed correlations satisfy that expectation. The separate correlation of LCN2 with metabolic reprogramming rather than with immune cell fractions suggests that this gene may serve a dual role, bridging inflammatory signaling and the metabolic rewiring that underpins the proliferative phenotype of remodeled pulmonary vasculature.

The drug-repurposing component of the study, while preliminary, illustrates how computational target identification can be coupled to structure-based screening. Navarixin, a clinically investigated CXCR2 antagonist, emerged as the strongest binder to CXCR2 in molecular docking, with a predicted binding free energy of minus 10.1 kilocalories per mole. Molecular dynamics simulations subsequently confirmed that the docked pose remained stable over the simulated trajectory, with the ligand maintaining its contacts within the receptor binding pocket. These in silico results are encouraging but must be interpreted with appropriate caution. Docking scores are approximations that depend on the quality of the protein structure, the force field parameters, and the sampling algorithm, and a favorable docking score does not guarantee pharmacological activity, selectivity, or clinical efficacy. Nevertheless, the identification of an existing CXCR2 antagonist with predicted high affinity provides a concrete, testable hypothesis: if CXCR2 signaling contributes to the immune dysregulation observed in IPAH, then pharmacological blockade of this receptor might modulate disease-relevant pathways in ways that current vasodilator therapies cannot.

The qRT-PCR validation in human peripheral blood samples adds an experimental anchor to the computational pipeline. Confirming significant downregulation of all three candidate biomarker genes in an independent cohort of IPAH patients demonstrates that the transcriptomic signal detected in silico is reproducible in a real-world clinical population, at least for the genes and the sample type tested. Peripheral blood, however, is a surrogate compartment, and the expression levels measured there may not directly reflect what is happening within the pulmonary vasculature itself. Future studies that examine these genes in lung tissue, pulmonary artery specimens, or isolated vascular cells from IPAH patients would provide a more direct test of whether the shared signature is operative at the site of disease.

The statistical rigor applied to the classification model is noteworthy and sets a standard for computational biomarker studies. Achieving an area under the receiver operating characteristic curve of 0.909 in the training set and 0.875 in an external validation set is promising, but the authors went further by employing leave-one-out cross-validation, which yielded an AUC of 0.879, and bootstrap optimism correction, which produced an optimism-adjusted AUC of 0.896. These additional metrics guard against the overfitting that plagues many machine learning studies in biomedicine, where apparent performance in the training data inflates expectations that are not borne out in independent samples. The narrow gap between the training and corrected performance figures suggests that the three-gene signature is reasonably robust, though the limited size of the external validation cohort remains a constraint that only larger, multicentre studies can overcome.

The ROTS sensitivity analysis, which confirmed that all 1710 differentially expressed genes in the mesothelioma dataset were reproducible and directionally concordant at a false discovery rate threshold below 0.05 despite an imbalanced case-to-control ratio, addresses a common criticism of transcriptomic meta-analyses. Imbalanced group sizes can inflate false positive rates and produce unstable rankings, and demonstrating that the full differential expression profile withstands a rank-based reproducibility test strengthens confidence that the downstream analyses were built on a solid foundation.

Looking forward, several avenues of investigation follow naturally from these findings. Functional experiments using pulmonary arterial smooth muscle cells or endothelial cells, in which CXCR2, S100A9, or LCN2 are knocked down or overexpressed, could establish whether these genes are causal drivers of the proliferative and inflammatory phenotype or merely passive markers of it. Animal models of pulmonary hypertension, such as the Sugen-hypoxia rat or the monocrotaline mouse, could be used to test whether pharmacological CXCR2 blockade alters the course of vascular remodeling. Proteomic studies could determine whether the transcriptomic findings translate to measurable changes in circulating protein levels, which would be a prerequisite for developing a clinically deployable biomarker panel. And longitudinal studies tracking these gene expression signatures over the natural history of IPAH could reveal whether they carry prognostic information or respond to therapeutic intervention. Until such work is completed, the present study should be understood as a well-executed hypothesis-generating exercise that has identified a plausible molecular bridge between two seemingly disparate diseases and has laid the groundwork for the experimental and clinical studies needed to determine whether that bridge can bear the weight of therapeutic translation.

Subject of Research: Shared transcriptomic biomarkers between idiopathic pulmonary arterial hypertension and malignant pleural mesothelioma identified by machine learning and molecular docking

Article Title: Machine learning, molecular docking, and experimental validation identify shared molecular signatures between idiopathic pulmonary arterial hypertension and malignant pleural mesothelioma: a retrospective study

Article References: Zhang, N., Wang, C., Wang, R., Guo, X., Yu, J., Wang, J., Luo, Q., & Wu, Y. (2026). Machine learning, molecular docking, and experimental validation identify shared molecular signatures between idiopathic pulmonary arterial hypertension and malignant pleural mesothelioma: a retrospective study. Frontiers in Genetics, 17, Article 1894027. https://doi.org/10.3389/fgene.2026.1894027

Image Credits: AI Generated

DOI: 10.3389/fgene.2026.1894027

Keywords: idiopathic pulmonary arterial hypertension, malignant pleural mesothelioma, machine learning, molecular docking, biomarkers, transcriptomics, CXCR2, S100A9, LCN2, immune infiltration, molecular dynamics, drug repurposing

The study, published in Frontiers in Bioinformatics by Yumeng Liu and Gang Luo of the Department of Pediatrics at Liaoning Maternal and Child Health Hospital, took aim at a long-standing gap in sepsis medicine. Childhood sepsis cannot be regarded as a mere miniature version of adult sepsis. Children undergo continuous development of their immune systems, and the inflammatory response, alterations in organ function, and clinical diagnostic criteria following infection all exhibit age-related characteristics. Consequently, candidate markers identified in adult cohorts cannot be directly applied to the diagnostic context observed in children. By screening and validating molecular markers specifically within child cohorts, the researchers sought diagnostic insights more aligned with the clinical and biological characteristics of pediatric sepsis.

The scientific premise of the work rests on oxidative stress, a state that arises when reactive oxygen species, or ROS, are produced in amounts greater than the body can neutralize with antioxidants. This imbalance drives mitochondrial dysfunction, which has been identified as a central pathophysiological mechanism underlying organ failure in sepsis, both in experimental models and in clinical patients. In a landmark study of 28 severe sepsis patients during the first 24 hours of admission, Brealey and colleagues observed that mitochondrial dysfunction, along with antioxidant depletion and impaired energy metabolism, were key drivers of multiple organ failure. Recent clinical studies in pediatric populations have demonstrated that affected children exhibit significant alterations in oxidative stress markers and antioxidant capacity, indicating that disruption of the oxidative-antioxidant balance may play a critical role in the pathophysiology of pediatric sepsis. Excessive ROS generation also triggers the MAPK and PI3K/Akt signaling pathways, elevates NF-κB levels, and consequently induces the expression of pro-inflammatory genes, and elevated NF-κB in sepsis patients serves as a stronger predictor of disease severity than conventional inflammatory markers.

To hunt down the molecular fingerprints of this oxidative storm, the researchers mined five open-access transcriptomic datasets from the Gene Expression Omnibus database, all comprising peripheral whole-blood samples from children with sepsis and healthy controls, all generated on the same GPL570 microarray platform to ensure comparability. Two datasets, GSE26440 and GSE26378, were merged to form the training cohort, while three independent datasets, GSE13904, GSE9692, and GSE8121, were combined into a validation cohort. The team corrected batch effects using the empirical Bayes ComBat method, verified the integration with principal component analysis, and then applied differential expression analysis with the limma package, using thresholds of |log2 fold change| greater than 0.585 and an adjusted p-value below 0.05. This rigorous pipeline yielded 396 significant differentially expressed genes, including 290 upregulated and 106 downregulated genes, and after intersecting these with a curated list of 817 oxidative stress-associated genes from prior literature, 36 differentially expressed oxidative stress-related genes emerged as the study’s raw material.

Functional enrichment analysis revealed what these 36 genes were doing. Gene Ontology analysis highlighted their roles in regulating inflammatory responses and reactive oxygen species metabolism, while Kyoto Encyclopedia of Genes and Genomes pathway analysis identified nine pathways implicated in sepsis progression, including neutrophil extracellular trap formation, the Toll-like receptor and NOD-like receptor signaling pathways that orchestrate innate immunity, and the regulation of inflammatory mediators affecting TRP channels. Neutrophil extracellular traps, or NETs, the DNA-based web-like structures that neutrophils deploy to capture and kill pathogens, were flagged as particularly critical in sepsis pathogenesis. NET formation relies on ROS generated by NADPH oxidase, and in sepsis an excessive immune response drives uncontrolled NET production that aggravates lung injury. Gene set enrichment analysis further showed that the oxidative stress phenotypic score was significantly higher in the sepsis cohort than in controls, with upregulation of ROS metabolic processes and sepsis-associated pathways in the high-score group.

The methodological heart of the study was a two-stage computational gauntlet. First, weighted gene co-expression network analysis, or WGCNA, was applied to the top 15 percent most variable genes in the training cohort. At a soft-thresholding power of beta equal to 8, the network closely approximated a scale-free topology, and the dynamic tree-cut algorithm resolved 14 distinct co-expression modules. One module, dubbed turquoise, showed a striking positive correlation with sepsis, with a Spearman coefficient of 0.65 and a p-value of 4.57 times 10 to the power of minus 29, marking its member genes as collectively pivotal in disease progression. Second, six machine learning algorithms, Bayesian analysis, bagged trees, LASSO regression, random forest, learning vector quantization, and Boruta, were each set loose on the oxidative stress-related differentially expressed genes. Only genes identified by all six methods, a threshold of six out of six, were retained as robust candidates. This consensus rule ensured that the screening results were not dependent on any single algorithm, and it ultimately distilled the field down to seven candidate diagnostic genes: DDAH2, GADD45A, HP, MAPK14, PGD, RETN, and S100A9.

From these seven candidates, the researchers constructed a protein-protein interaction network using the STRING database and excluded DDAH2 for low connectivity, leaving six hub genes: GADD45A, HP, MAPK14, PGD, RETN, and S100A9. Each of these genes encodes a protein with a plausible mechanistic link to sepsis biology. MAPK14, better known as p38 alpha, is a central kinase in inflammatory signaling cascades. S100A9 is an alarmin released abundantly by activated neutrophils and monocytes. HP is haptoglobin, an acute-phase protein that binds free hemoglobin. PGD encodes an enzyme in the pentose phosphate pathway central to cellular antioxidant defense. GADD45A participates in DNA repair and stress responses, and RETN, or resistin, is an inflammatory adipokine. Expression levels of all six hub genes were significantly elevated in septic children compared with controls, and receiver operating characteristic analysis revealed area under the curve, or AUC, values of 0.951, 0.982, 0.964, 0.944, 0.962, and 0.982 in the training data, and between 0.908 and 0.977 in the independent validation cohort, a level of diagnostic performance that few single biomarkers in clinical use today can match.

The team did not stop at individual genes. Seven machine learning classifiers, including random forest, support vector machine, LogitBoost, k-nearest neighbors, Naive Bayes, AdaBoost, and CancerClass, were trained on the pooled training cohort with five-fold cross-validation repeated ten times, and then evaluated on the untouched validation cohort. The k-nearest neighbors model, which classifies samples by their similarity to neighboring expression profiles, achieved the highest AUC in validation and was selected as the final predictive model. A nomogram built from the six hub genes pushed diagnostic accuracy even further, reaching an AUC of 0.994. The researchers also performed immune cell deconvolution using CIBERSORT, estimating the relative proportions of 22 immune cell types in peripheral blood. The analysis revealed marked alterations in the sepsis cohort: M0 macrophages, M2 macrophages, monocytes, activated mast cells, and neutrophils were significantly elevated, while follicular helper T cells, CD8 T cells, CD4 memory T cells, resting dendritic cells, and activated NK cells were markedly reduced. Most hub genes were positively correlated with neutrophils and negatively linked with resting dendritic cells, suggesting that oxidative stress-related genes may modulate the abundance and activity of distinct immune cell subsets in the immune microenvironment.

Perhaps the most clinically tantalizing result came from the drug-discovery arm of the study. The researchers queried the Drug-Gene Interaction database and the Connectivity Map to identify small molecules predicted to downregulate the hub genes, then performed blind molecular docking using the CB-Dock2 platform. The MAPK14 protein demonstrated strong binding affinities for doxycycline and ralimetinib mesylate, with AutoDock Vina scores of minus 8.4 and minus 8.1 kilocalories per mole respectively, while the S100A9 protein exhibited significant binding affinity for tasquinimod at minus 7.0 kilocalories per mole. Doxycycline, an antibiotic already in widespread clinical use, engaged MAPK14 through multiple ionic bonds, hydrophobic forces, and hydrogen bonds, while ralimetinib mesylate, a p38 MAPK inhibitor, and tasquinimod, an experimental immunomodulator, engaged their targets through complementary interaction networks. These findings position MAPK14 and S100A9 as promising therapeutic targets and open up new treatment options that could be explored for repurposing in pediatric sepsis.

The authors are careful to frame their findings as a foundation rather than a finished clinical tool. The analysis is based entirely on publicly available retrospective datasets, and heterogeneity across cohorts in sample sources, platforms, and clinical annotations may still influence performance estimates. The immune cell deconvolution relied on a single algorithm and reflects peripheral blood composition rather than tissue-level infiltration, and the molecular docking results suggest potential binding but do not demonstrate therapeutic efficacy in patients. The ceRNA network of 102 nodes and 160 edges, which implicates 89 long non-coding RNAs and 10 microRNAs in the post-transcriptional regulation of GADD45A, MAPK14, and PGD, remains exploratory pending experimental validation. Still, the study’s significance is hard to overstate. Compared with conventional biomarkers such as procalcitonin, C-reactive protein, and lactate, this six-gene oxidative stress signature may more sensitively reflect early molecular alterations associated with immune dysregulation, providing complementary diagnostic information precisely when conventional indicators are inconclusive. If prospective, multicenter validation confirms these results, the era of catching pediatric sepsis in its first critical hours, before organ failure takes hold, may move one decisive step closer to reality.

Subject of Research: Oxidative stress-related biomarkers and machine learning diagnostic models for pediatric sepsis

Article Title: Identification and validation of biomarkers related to oxidative stress in pediatric sepsis utilizing WGCNA and multiple machine learning algorithms

Article References: Liu, Y., & Luo, G. (2026). Identification and validation of biomarkers related to oxidative stress in pediatric sepsis utilizing WGCNA and multiple machine learning algorithms. Frontiers in Bioinformatics, 6, Article 1745381. https://doi.org/10.3389/fbinf.2026.1745381

Image Credits: AI Generated

DOI: 10.3389/fbinf.2026.1745381

Keywords: pediatric sepsis, oxidative stress, biomarkers, machine learning, WGCNA, GADD45A, MAPK14, S100A9, molecular docking, immune cell deconvolution, diagnostic model, reactive oxygen species

The research team, led by Qianqian Yin and Yaru Yang of Xuzhou Central Hospital affiliated with Southeast University, together with colleagues including corresponding authors Xiaoyan Liu and Jianhua Zheng, recruited twenty patients diagnosed with PMOS and ten control women. Follicular fluid, the nutrient-rich liquid surrounding the oocyte inside each ovarian follicle, was collected during procedures related to in vitro fertilization and embryo transfer. Because this fluid is in direct contact with the developing egg and the surrounding cumulus cells, it offers a uniquely intimate window into the microenvironment in which fertilization competence is established. Rather than studying blood, where systemic changes can be diluted and confounded, the researchers chose to interrogate the immediate biochemical neighborhood of the egg itself.

The technological centerpiece of the study was four-dimensional label-free quantitative proteomics, an approach that has rapidly become one of the most powerful tools in modern biology. Traditional proteomic methods separate peptides along a single dimension of mass spectrometric analysis, but four-dimensional platforms add ion mobility separation and retention time alignment, dramatically increasing the depth, resolution, and accuracy of protein identification. Label-free quantification, meanwhile, avoids the chemical labeling steps that can introduce bias and expense, instead relying on the raw intensity of detected peptide signals to estimate relative protein abundance across samples. This combination allowed the team to comprehensively profile the whole proteome of follicular fluid and to detect subtle but meaningful differences between patient groups that earlier methods might have missed.

When the data were analyzed with strict statistical thresholds requiring a fold change greater than 1.5 and a p-value below 0.05, thirty-three proteins emerged as differentially expressed between the PMOS and control groups. Twenty-four of these proteins were elevated in the patients, while nine were reduced. That modest number may sound unremarkable, but each of these molecules represents a potential thread in the tangled web of PMOS pathogenesis, and the patterns revealed by subsequent bioinformatic analysis were strikingly coherent rather than random.

Functional enrichment analysis using the Gene Ontology framework showed that the altered proteins clustered around several biological themes. The first was immunoglobulin production and the humoral immune response, indicating that antibody-mediated immune activity is heightened within the follicular environment of PMOS patients. The second was leukocyte migration, suggesting that immune cells are being actively recruited into the ovarian follicle. The third and perhaps most surprising theme was cellular energy metabolism, with proteins involved in glycolysis and pyruvate metabolism prominently represented. These findings imply that the follicle in PMOS is not merely a passive site of hormonal dysfunction but an active immunometabolic battlefield where energy production and immune defense are simultaneously rewired.

Pathway-level analysis using the Kyoto Encyclopedia of Genes and Genomes reinforced this picture. The differentially expressed proteins were predominantly enriched in glycolysis and gluconeogenesis, pyruvate metabolism, the hypoxia-inducible factor-1 signaling pathway, and extracellular matrix-receptor interactions. The involvement of HIF-1 signaling is particularly intriguing. This pathway is the cell’s master response to low oxygen, and its activation can drive cells to shift their energy production from efficient mitochondrial respiration toward rapid but wasteful glycolysis, a phenomenon famously exploited by cancer cells. Its enrichment in PMOS follicular fluid raises the possibility that the ovarian follicle in these patients exists in a state of relative hypoxia or metabolic stress, forcing the egg and its supporting cells to operate under conditions that may compromise developmental potential.

Protein-protein interaction network analysis added another layer of insight, suggesting a synergistic relationship between metabolic reprogramming and immune dysregulation in PMOS. In other words, the altered energy metabolism and the disturbed immune signaling do not appear to be independent phenomena but are likely intertwined, each feeding into and amplifying the other. This kind of systems-level view is exactly what proteomics is designed to deliver, and it points toward a conceptual model in which PMOS arises from a self-reinforcing loop of inflammatory and metabolic disturbance within the ovary, rather than from a single defective gene or hormone.

To ensure that the mass spectrometry findings were not artifacts, the team turned to enzyme-linked immunosorbent assay, a targeted and highly specific method for measuring individual proteins. Three candidates were selected for validation: L-lactate dehydrogenase A chain, known as LDHA, a key enzyme that drives the final step of glycolysis and converts pyruvate into lactate; protein S100-A9, or S100A9, an inflammatory mediator released by neutrophils and monocytes that acts as a danger signal to the immune system; and serum amyloid A1, or SAA1, an acute-phase protein classically associated with inflammation and tissue remodeling. All three were confirmed to be significantly higher in the follicular fluid of PMOS patients than in controls, lending robust analytical credibility to the discovery set and highlighting a concrete biochemical axis linking glycolytic flux, inflammation, and acute-phase response within the follicle.

The implications of this work are twofold. Clinically, the identified proteins could serve as diagnostic biomarkers, offering a way to identify or monitor PMOS through a simple assessment of follicular fluid, and potentially as therapeutic targets whose modulation might restore a healthier follicular environment. Scientifically, the study reframes PMOS as a disorder of immunometabolic reprogramming and extracellular matrix remodeling, providing a new perspective on pathogenesis that goes beyond the traditional focus on androgen excess and insulin resistance. The authors are careful to note the limitations of their work: the sample size was modest, with twenty patients and ten controls, and the findings now require validation in larger cohorts, ideally integrated with detailed clinical phenotypes and functional experiments that can test whether the candidate proteins play causal roles or are merely markers of the disease process.

Nevertheless, the study stands as a compelling example of how cutting-edge proteomic technology can illuminate diseases that have long resisted conventional investigation. By capturing a snapshot of the protein milieu surrounding the human egg, Yin, Yang, and their colleagues have transformed PMOS from a vaguely defined clinical syndrome into a condition with a measurable molecular fingerprint. If subsequent research confirms and extends these findings, the glycolytic enzymes, inflammatory proteins, and matrix components identified in follicular fluid could become the foundation for new diagnostic tests and, ultimately, new treatments aimed at protecting the delicate metabolic environment in which every human life begins.

Subject of Research: Proteomic profiling of follicular fluid in polyendocrine metabolic ovarian syndrome

Article Title: Identification of differentially expressed proteins in follicular fluid from patients with polyendocrine metabolic ovarian syndrome using 4D label-free proteomics

Article References: Yin, Q., Yang, Y., Yan, X., Cao, Y., Liu, X., & Zheng, J. (2026). Identification of differentially expressed proteins in follicular fluid from patients with polyendocrine metabolic ovarian syndrome using 4D label-free proteomics. Journal of Ovarian Research. https://doi.org/10.1186/s13048-026-02271-y

Image Credits: AI Generated

DOI: 10.1186/s13048-026-02271-y

Keywords: polyendocrine metabolic ovarian syndrome, follicular fluid, proteomics, 4D label-free quantification, glycolysis, HIF-1 signaling, immune dysregulation, LDHA, S100A9, SAA1, biomarkers, fertility