The distinguishing of uterine leiomyosarcomas (ULMS) and uterine leiomyomas (ULM) before the operation and histopathological evaluation of tissue is one of the current challenges for clinicians and researchers. Recently, a few new and innovative methods have been developed. However, researchers are trying to create different scales analyzing available parameters and to combine them with imaging methods with the aim of ULMs and ULM preoperative differentiation ULMs and ULM. Moreover, it has been observed that the technology, meaning machine learning models and artificial intelligence (AI), is entering the world of medicine, including gynecology. Therefore, we can predict the diagnosis not only through symptoms, laboratory tests or imaging methods, but also, we can base it on AI. What is the best option to differentiate ULM and ULMS preoperatively? In our review, we focus on the possible methods to diagnose uterine lesions effectively, including clinical signs and symptoms, laboratory tests, imaging methods, molecular aspects, available scales, and AI. In addition, considering costs and availability, we list the most promising methods to be implemented and investigated on a larger scale.

Background Previous studies have shown clinical relevance of programmed death-ligand 1 (PD-L1) and soluble PD-L1 (sPD-L1) in human cancers. However, still contradictory results exist. Our aim was evaluation of PD-L1-expressing monocytic myeloid-derived suppressor cells (M-MDSCs), monocytes/macrophages (MO/MA), tumour cells (TC) and immune/inflammatory cells (IC) as well as investigation of the sPD-L1 in ovarian cancer (OC) patients. Methods The group of 74 pretreatment women were enrollment to the study. The expression of PD-L1 on M-MDSCS and MO/MA was assessed by flow cytometry. The profile of sPD-L1 was examined with ELISA. The expression of PD-L1 in mononuclear cells (MCs) was analyzed using real time PCR. PD-L1 immunohistochemical analysis was prepared on TC and IC. An in silico validation of prognostic significance of PD-L1 mRNA expression was performed based microarray datasets. Results OC patients had significantly higher frequency of MO/MA versus M-MDSC in the blood, ascites and tumour (each p < 0.0001). In contrast, PD-L1 expression was higher on M-MDSCs versus MO/MA in the blood and ascites (each p < 0.0001), but not in the tumour (p > 0.05). Significantly higher accumulation of blood-circulating M-MDSC, MO/MA, PD-L1+M-MDSC, PD-L1+MO/MA and sPD-L1 was observed in patients versus control (p < 0.001, p < 0.05, p < 0.001, p < 0.001 and p < 0.0001, respectively). Accumulation of these factors was clinicopathologic-independent (p > 0.05). The expression of PD-L1 was significantly higher on IC versus TC (p < 0.0001) and was clinicopathologic-independent (p > 0.05) except higher level of PD-L1+TC in the endometrioid versus mucinous tumours. Interestingly, blood-circulating sPD-L1 positively correlated with PD-L1+M-MDSCs (p = 0.03) and PD-L1+MO/MA (p = 0.02) in the blood but not with these cells in the ascites and tumours nor with PD-L1+TC/IC (each p > 0.05). PD-L1 and sPD-L1 were not predictors of overall survival (OS; each p > 0.05). Further validation revealed no association between PD-L1 mRNA expression and OS in large independent OC patient cohort (n = 655, p > 0.05). Conclusions Although PD-L1 may not be a prognostic factor for OC, our study demonstrated impaired immunity manifested by up-regulation of PD-L1/sPD-L1. Furthermore, there was a positive association between PD-L1+ myeloid cells and sPD-L1 in the blood, suggesting that sPD-L1 may be a noninvasive surrogate marker for PD-L1+myeloid cells immunomonitoring in OC. Overall, these data should be under consideration during future clinical studies/trials.

Cancer is a disease that induces many local and systemic changes in immunity. The difficult nature of ovarian cancer stems from the lack of characteristic symptoms that contributes to a delayed diagnosis and treatment. Despite the enormous progress in immunotherapy, its efficacy remains limited. The heterogeneity of tumors, lack of diagnostic biomarkers, and complex immune landscape are the main challenges in the treatment of ovarian cancer. Integrative approaches that combine the tumor microenvironment – local immunity – together with periphery – systemic immunity – are urgently needed to improve the understanding of the disease and the efficacy of treatment. In fact, multiparametric analyses are poised to improve our understanding of ovarian tumor immunology. We outline an integrative approach including local and systemic immunity in ovarian cancer. Understanding the nature of both localized and systemic immune responses will be crucial to boosting the efficacy of immunotherapies in ovarian cancer patients.

An amendment to this paper has been published and can be accessed via the original article.

Background: Ovarian cancer (OC) is the most lethal malignancy of the female reproductive tract. Consequently, a better understanding of the malignant features in OC is pertinent. Mortalin (mtHsp70/GRP75/PBP74/HSPA9/HSPA9B) promotes cancer development, progression, metastasis, and recurrence. Yet, there is no parallel evaluation and clinical relevance of mortalin in the peripheral and local tumor ecosystem in OC patients. Methods: A cohort of 92 pretreatment women was recruited, including 50 OC patients, 14 patients with benign ovarian tumors, and 28 healthy women. Blood plasma and ascites fluid-soluble mortalin concentrations were measured by ELISA. Mortalin protein levels in tissues and OC cells were analyzed using proteomic datasets. The gene expression profile of mortalin in ovarian tissues was evaluated through the analysis of RNAseq data. Kaplan–Meier analysis was used to demonstrate the prognostic relevance of mortalin. Results: First, we found upregulation of local mortalin in two different ecosystems, i.e., ascites and tumor tissues in human OC compared to control groups. Second, abundance expression of local tumor mortalin is associated with cancer-driven signaling pathways and worse clinical outcome. Third, high mortalin level in tumor tissues, but not in the blood plasma or ascites fluid, predicts worse patient prognosis. Conclusions: Our findings demonstrate a previously unknown mortalin profile in peripheral and local tumor ecosystem and its clinical relevance in OC. These novel findings may serve clinicians and investigators in the development of biomarker-based targeted therapeutics and immunotherapies.