Opioid Use Disorder is a chronic condition characterized by compulsive opioid use despite negative consequences, resulting in severe health risks such as overdose and contraction of infectious diseases. High dropout rates in opioid agonist therapy highlight the need for more effective relapse prevention strategies. Animal and clinical studies indicate that opioids influence gut microbiota, which in turn plays a critical role in addiction development and alters behavioral responses to opioids. This study provides a comprehensive review of the literature on the effects of opioids on the gut microbiome and explores the potential of microbiome manipulation as a therapeutic target in opioid addiction.

Background: Ovarian cancer (OC) is the third most common and second most lethal onco-gynecological disease in the world, with high-grade serous ovarian cancer (HGSOC) making up the majority of OC cases worldwide. The current serological biomarkers used for OC diagnosis are lacking sensitivity and specificity, thus new biomarkers are greatly needed. Recently, the chromatin remodeling complex gene ARID1A, Notch and Wnt pathway gene expression, as well as HOX-related gene promoter methylation have been linked with promoting OC. Methods: In this pilot study, 10 gene expression biomarkers and 4 promoter methylation biomarkers were examined as potential diagnostic and prognostic indicators of OC in 65 fresh-frozen gynecologic tumor tissues. Results: Out of 10 genes analyzed, the expression of eight biomarkers was significantly reduced in OC cases compared to benign, and HOX-related gene promoter methylation significantly increased in OC tumors. Out of 14 biomarkers, CTNNB1 showed the best single biomarker separation of HGSOC from benign cases (AUC = 0.97), while a combination of the seven Notch pathway-related gene expressions (NOTCH1, NOTCH2, NOTCH3, NOTCH4, DLL1, JAG2, and HES1) demonstrated the best separation of HGSOC from the benign cases (AUC = 1). Conclusions: The combination of multiple gene expression or gene promoter methylation biomarkers shows great promise for the development of an effective biomarker-based diagnostic approach for OC.

Background: Lung cancer is the second most common malignancy and stands as a leading cause of cancer-related deaths worldwide. Currently, one of the main treatment options for lung cancer is radiotherapy, but this treatment is associated with complications, such as an increased risk of cardiac-related morbidity and mortality. However, currently available methods for predicting radiation-induced heart disease (RIHD) remain suboptimal. Methods: In this pilot study, using the RT-qPCR method, we analyzed the expression levels of six miRNAs (miRNA-1-3p, miRNA-21-5p, miRNA-24-3p, miRNA-29a-3p, miRNA-34a-5p, and miRNA-222-3p). Results: Fourteen pairs of locally advanced non-small-cell lung cancer patients’ plasma samples, taken before and after radiotherapy, were examined. It was observed that miRNA-1-3p, miRNA-21-5p, miRNA-24-3p, miRNA-29a-3p, and miRNA-222-3p were downregulated, while miRNA-34a-5p was upregulated in lung cancer patients’ plasma after treatment. Additionally, after definitive radiotherapy, patients with an increased NT-proBNP value displayed a statistically significant difference in miRNA-222-3p levels compared to the normal range of this indicator. The panel of the combined four miRNAs for assessing the risk of cardiac comorbidities demonstrated an AUC of 0.79, sensitivity of 71.43%, and specificity of 100%, with further improved values upon integration with clinical biomarker NT-proBNP. Conclusions: This pilot study shows that the identification of changes in miRNA expression levels in lung cancer patients’ plasma before and after radiotherapy could be used for the early diagnosis of RIHD.

Background/Objectives: Early and accurate detection of postoperative complications (POCs) remains a major challenge in colorectal cancer (CRC) surgery, underscoring the need for reliable molecular biomarkers. This study evaluated whether plasma miR-21 and miR-181a can predict POCs following left-sided CRC resection. Methods: This longitudinal observational sub-study was conducted within a randomized controlled trial. Adult patients undergoing elective left-sided CRC resection were included. Plasma miR-21 and miR-181a levels were measured preoperatively and on postoperative day (POD) 6 using RT-qPCR. POCs were assessed according to Clavien–Dindo classification. Of 40 enrolled patients, 38 were included in the final analysis (15 with and 23 without postoperative complications). Discriminative performance was assessed using receiver operating characteristic analysis and correlations with inflammatory markers were evaluated. Results: No significant differences in plasma miR-21 or miR-181a levels were observed between groups at baseline or POD6 (all p > 0.05). Both biomarkers showed limited discriminative ability (AUC = 0.61 and 0.54, respectively), while a combined model of miR-181a + TNF-α improved performance (AUC = 0.76, 95% CI: 0.57, 0.94, p = 0.01). At baseline, miR-21 correlated strongly with miR-181a (ρ = 0.81, p < 0.001) and moderately inversely with TNF-α (ρ = −0.35, p = 0.043). Conclusions: MiR-21 or miR-181a measured at baseline or POD6 show limited predictive value for POCs after CRC surgery. Further studies would benefit from larger sample sizes and optimized sampling strategies that reflect possible early dynamic changes in these biomarkers.

Ovarian (OC) and endometrial (EC) cancers are among the most clinically challenging gynecologic malignancies. The lack of reliable non-invasive biomarkers limits early detection of these malignancies. Notch and Wnt pathway members are frequently deregulated in EC and OC tissues and may serve as biomarkers. Liquid biopsy samples, including uterine lavage (UL), plasma and urine may serve as less-invasive alternatives to tissue biopsies. In this study, CTNNB1, NOTCH2, DLL1, HES1 expression was determined by RT-qPCR in 103 uterine lavage, 15 urine sediment, 15 plasma and 65 tissue samples from patients with gynecologic conditions. NOTCH2, DLL1 and CTNNB1 expressions were decreased in UL from EC patients compared to non-cancer samples (p ≤ 0.003), while HES1 expression was increased in high-grade serous OC (HGSOC) (p = 0.014). UL HES1 expression distinguished HGSOC from non-cancer cases (AUC = 0.688), while UL NOTCH2 separated EC and non-cancer cases (AUC = 0.865). HES1 showed concordance between tumor and UL, whereas HES1, CTNNB1, and NOTCH2 differed between plasma, urine sediment and tumor samples. The study provides the first comparison of these biomarkers between multiple types of liquid biopsy sample types, supporting UL as a promising biosample type for gynecologic cancer biomarker development.