Objectives: The objective of the study was to investigate the mRNA expression of critical gene patterns, including IL-6, CCL2, IL-10, MAPK1, MAPK8, MMP9, COL1A1, COL3A1, and TGFB1, and their associations with adverse postoperative outcomes in reconstructive and plastic surgery patients, depending on age. Methods: A total of 95 women participated in this prospective longitudinal cohort study and underwent reconstructive/plastic surgery. The mean age was 35.48 ± 6.61 years (range: 19–57). mRNA expression of IL-6, CCL2, IL-10, MAPK1, MAPK8, MMP9, COL1A1, COL3A1, and TGFB1 genes was evaluated in peripheral blood leukocytes using a PCR-based method with reverse transcription of cDNA. Results: The risk of postoperative complications significantly increased with elevated expression levels of IL-6 and COL3A1 (7.5-fold, p = 0.007), CCL2 (6.2-fold, p = 0.012), and MAPK1 (25.5-fold, p < 0.001). Increased expression of MAPK8, IL-10, and MMP9 was associated with a 13.2-fold higher risk (p < 0.001). The strongest association was observed for COL1A1 overexpression, which increased complication risk by 58.33-fold (p < 0.001). Risk stratification using the Molecular Complication Risk Index (MCRI), incorporating weighted gene contributions, identified an unfavorable molecular profile predominantly among women aged ≥ 40 years. Receiver operating characteristic analysis confirmed the model’s discriminative ability (AUC = 0.78; 95% CI 0.68–0.87), with an optimal cut-off value of MCRI ≥ 8.5 (sensitivity 76%, specificity 71%, p < 0.05). Conclusions: The transcriptional activity of IL-6, CCL2, IL-10, MAPK1, MAPK8, MMP9, COL1A1, COL3A1, and TGFB1 is associated with postoperative wound healing risk. Women aged over 40 years are at the highest risk of complications. Implementation of the MCRI model may enable early identification of high-risk patients, support targeted preventive strategies, and improve personalized surgical planning.

Postoperative wound healing complications present a major challenge in plastic and reconstructive surgery, prolonging recovery and impairing outcomes. Early risk identification is difficult due to complex interactions among clinical, laboratory, and molecular factors. This study developed and evaluated machine-learning (ML) models to predict wound healing outcomes and identify key complication predictors. Utilizing a dataset of 95 women and 76 variables (including hematological, biochemical, coagulation, and gene expression profiles), we evaluated several ML approaches, including Decision Tree, Extra Trees, Gaussian/Bernoulli Naive Bayes, Logistic Regression, and Support Vector Machine. Model performance was assessed via k-fold cross-validation, ROC analysis, and SHAP feature importance. Molecular markers (COL1A1, MMP9, MAPK1, MAPK8, IL10, and CCL2) emerged as the strongest predictors, whereas conventional clinical variables showed limited value. The models achieved high discriminative performance, with validation ROC–AUC values ranging from 0.903 to 0.913. Extra Trees and Gaussian Naive Bayes demonstrated the highest sensitivity for detecting complications (Recall = 0.820 ± 0.238 and 0.807 ± 0.246, respectively). These findings highlight the value of integrating molecular-genetic biomarkers with ML for personalized risk stratification and preventive care in reconstructive surgery.

Despite advances in surgical techniques and perioperative care, postoperative complications — including impaired wound healing, pathological scarring, infection, and flap or graft failure — remain a significant clinical problem, adversely affecting patient outcomes and satisfaction while increasing healthcare costs. The Aim of this study is to evaluate age-dependent expression of the IL6, CCL2, MAPK1, MAPK8, IL10, MMP9, COL1A1 and COL3A1 in a cohort of women undergoing reconstructive and plastic surgery cohort, and to assess the molecular and genetic pathways involved in wound healing. Materials and Methods. Ninety-five women participated in the prospective longitudinal cohort study and underwent reconstructive / plastic surgery. The mean age 35.48 ± 6.61 years (range: 19–57 years). All participants provided written informed consent prior to enrollment and subsequently underwent comprehensive demographic, clinical, instrumental, and laboratory assessments. The study was conducted in accordance with the principles of the Council of European Convention on Human Rights and Biomedicine. Ethical approval was obtained from the Biomedical Ethics Commission of Bukovinian State Medical University. Participants were stratified by age: 40 yr undergoing plastic and reconstructive breast surgery exhibit a molecular profile associated with a high risk of postoperative complications. Women 30–40 yr demonstrate a more balanced gene expression profile, consistent with a more favorable reparative tissue response and wound healing.

Plastic and reconstructive surgery is essential for restoring structural integrity, functional capacity, and aesthetic appearance. But, biological mechanisms underlying possible postoperative complications and wound healing are underestimated. The aim of the study: to investigate the correlations between the postoperative complications with some clinical- laboratory and molecular- genetic markers of wound healing after plastic and reconstructive surgical interventions. Material and Methods. A total of 95 women were enrolled in this prospective longitudinal cohort study and underwent reconstructive/plastic surgery. The mean age was 35.48±6.61 years (range 19-57). All participants received comprehensive clinical, demographic, laboratory, and instrumental assessments. The study complied with GCP standards and the Council of Europe Convention on Human Rights and Biomedicine. Gene transcription levels of IL6, CCL2, IL10, MMP9, COL1A1, COL3A1, MAPK1, and MAPK8 in peripheral blood were validated using a pathway- specific PCR array. Linkage between variables were assessed using Spearman’s or Pearson’s correlation coefficients. Results. Among the analyzed markers, MMP9, IL-10, COL1A1, and IL-6 demonstrated the strongest associations with postoperative complications, highlighting the central role of dysregulated matrix turnover and persistent inflammation (r=0.65-0.99; p≤0.04-0.001). Elevated MMP9 expression likely reflects excessive ECM degradation, while increased COL1A1 expression indicates enhanced fibrotic activity. The coexistence of these processes suggests an imbalance between collagen synthesis and degradation, a mechanism known to contribute to pathological healing, including fibrosis, capsular contracture, and impaired tissue integrity. Notably, very high correlations between IL-6 and IL-10 (r≈0.95) and between MAPK1 and IL-6 (r≈0.99) point to a synchronized activation of inflammatory and intracellular signaling pathways. The strongest mechanistic axis leading to complications appears to be: IL-6 / MAPK → COL1A1 / COL3A1 → MMP9 → postoperative complications. Conclusions. Strong correlations observed between IL-6, IL-10, MAPK1/MAPK8, and collagen- related genes (COL1A1, COL3A1) indicate coordinated regulation of immune responses and fibrogenesis rather than independent pathway activation in wound healing after plastic and reconstructive surgery.

Background. Perioperative care and postoperative complications, such as impaired wound healing, infection, flap or graft failure, pathological scarring, remain a significant clinical challenge, negatively affecting patient outcomes and satisfaction. The aim of the study is to evaluate the IL6, CCL2, MAPK1, MAPK8, IL10, MMP9, COL1A1 and COL3A1 genes’ transcriptional activity, functional networks, and major metabolic and immunogenetic pathways involved in the wound healing in plastic and reconstructive surgery. Materials and methods. Ninety-five women participated in the prospective longitudinal cohort study and underwent reconstructive/plastic surgery. Their mean age was 35.48 ± 6.61 years (from 19 to 57). All women underwent a set of clinical, demographic, laboratory and instrumental examinations. The study was conducted in accordance with the principles of the GCP, Council of European Convention on Human Rights and Biomedicine. Transcriptional activity of IL6, CCL2, MAPK1, MAPK8, IL10, MMP9, COL1A1 and COL3A1 genes in the peripheral blood was validated by pathway-specific PCR array. Functional linkage of the genes and protein-protein interaction were constructed using GeneMANIA software and the STRING Interaction Network program. Results. Two hundred and sixty-seven gene-gene interactions of the IL6, CCL2, MAPK1, MAPK8, IL10, MMP9, COL1A1, COL3A1 genes were established with the 20 most involved genes, which realize their activity mainly through the following functional linkage: co-expression (50.06 %) and co-localization (31.81 %) dominate, significantly less — common protein domains (8.63 %) and predicted (5.30 %) and physical interactions (2.49 %), additional signaling pathways (1.71 %). The central place in the gene-gene interaction network belongs to IL6, which acts as a key mediator of the pro-inflammatory response and connects immune (CCL2, IL10), signaling (MAPK1, MAPK8), and matrix (MMP9, COL1A1, COL3A1) components. IL10 gene shows the strongest expression (almost 3 times) in interaction with MAPK8 gene (М: 2.81 (р = 0.003)), the latter activates inflammation, remodeling, and with excess expression — fibrosis. Conclusions. The IL6, CCL2, MAPK1, MAPK8, IL10, MMP9, COL1A1 and COL3A1 genes’ transcriptional activity influence major metabolic and immunogenetic pathways involved in the wound healing in plastic and reconstructive surgery.

Postoperative wound healing complications present a major challenge in plastic and reconstructive surgery, prolonging recovery and impairing outcomes. Early risk identification is difficult due to complex interactions among clinical, laboratory, and molecular factors. This study developed and evaluated machine-learning (ML) models to predict wound healing outcomes and identify key complication predictors. Utilizing a dataset of 95 women and 76 variables (including hematological, biochemical, coagulation, and gene expression profiles), we evaluated several ML approaches, including Decision Tree, Extra Trees, Gaussian/Bernoulli Naive Bayes, Logistic Regression, and Support Vector Machine. Model performance was assessed via k-fold cross-validation, ROC analysis, and SHAP feature importance. Molecular markers (COL1A1, MMP9, MAPK1, MAPK8, IL10, and CCL2) emerged as the strongest predictors, whereas conventional clinical variables showed limited value. The models achieved high discriminative performance, with validation ROC–AUC values ranging from 0.903 to 0.913. Extra Trees and Gaussian Naive Bayes demonstrated the highest sensitivity for detecting complications (Recall = 0.820 ± 0.238 and 0.807 ± 0.246, respectively). These findings highlight the value of integrating molecular-genetic biomarkers with ML for personalized risk stratification and preventive care in reconstructive surgery.