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Abstract DNA damage response (DDR) deficiency has been one of the emerging targets in treating breast cancer in recent years. On the one hand, DDR coordinates cell cycle and signal transduction, whose dysfunction may lead to cell apoptosis, genomic instability, and tumor development. Conversely, DDR deficiency is an intrinsic feature of tumors that underlies their response to treatments that inflict DNA damage. In this review, we systematically explore various mechanisms of DDR, the rationale and research advances in DDR-targeted drugs in breast cancer, and discuss the challenges in its clinical applications. Notably, poly (ADP-ribose) polymerase (PARP) inhibitors have demonstrated favorable efficacy and safety in breast cancer with high homogenous recombination deficiency (HRD) status in a series of clinical trials. Moreover, several studies on novel DDR-related molecules are actively exploring to target tumors that become resistant to PARP inhibition. Before further clinical application of new regimens or drugs, novel and standardized biomarkers are needed to develop for accurately characterizing the benefit population and predicting efficacy. Despite the promising efficacy of DDR-related treatments, challenges of off-target toxicity and drug resistance need to be addressed. Strategies to overcome drug resistance await further exploration on DDR mechanisms, and combined targeted drugs or immunotherapy will hopefully provide more precise or combined strategies and expand potential responsive populations.

Abstract Background Early detection is critical for improving the survival of breast cancer (BC) patients. Exhaled breath testing as a non-invasive technique might help to improve BC detection. However, the breath test accuracy for BC diagnosis is unclear. Methods This multi-center cohort study consecutively recruited 5047 women from four areas of China who underwent BC screening. Breath samples were collected through standardized breath collection procedures. Volatile organic compound (VOC) markers were identified from a high-throughput breathomics analysis by the high-pressure photon ionization–time-of-flight mass spectrometry (HPPI-TOFMS). Diagnostic models were constructed using the random forest algorithm in the discovery cohort and tested in three external validation cohorts. Results A total of 465 (9.21%) participants were identified with BC. Ten optimal VOC markers were identified to distinguish the breath samples of BC patients from those of non-cancer women. A diagnostic model (BreathBC) consisting of 10 optimal VOC markers showed an area under the curve (AUC) of 0.87 in external validation cohorts. BreathBC-Plus, which combined 10 VOC markers with risk factors, achieved better performance (AUC = 0.94 in the external validation cohorts), superior to that of mammography and ultrasound. Overall, the BreathBC-Plus detection rates were 96.97% for ductal carcinoma in situ, 85.06%, 90.00%, 88.24%, and 100% for stages I, II, III, and IV BC, respectively, with a specificity of 87.70% in the external validation cohorts. Conclusions This is the largest study on breath tests to date. Considering the easy-to-perform procedure and high accuracy, these findings exemplify the potential applicability of breath tests in BC screening.

Abstract The rapid advancement of tumor immunotherapies poses challenges for the tools used in cancer immunology research, highlighting the need for highly effective biomarkers and reproducible experimental models. Current immunotherapy biomarkers encompass surface protein markers such as PD‐L1, genetic features such as microsatellite instability, tumor‐infiltrating lymphocytes, and biomarkers in liquid biopsy such as circulating tumor DNAs. Experimental models, ranging from 3D in vitro cultures (spheroids, submerged models, air–liquid interface models, organ‐on‐a‐chips) to advanced 3D bioprinting techniques, have emerged as valuable platforms for cancer immunology investigations and immunotherapy biomarker research. By preserving native immune components or coculturing with exogenous immune cells, these models replicate the tumor microenvironment in vitro. Animal models like syngeneic models, genetically engineered models, and patient‐derived xenografts provide opportunities to study in vivo tumor‐immune interactions. Humanized animal models further enable the simulation of the human‐specific tumor microenvironment. Here, we provide a comprehensive overview of the advantages, limitations, and prospects of different biomarkers and experimental models, specifically focusing on the role of biomarkers in predicting immunotherapy outcomes and the ability of experimental models to replicate the tumor microenvironment. By integrating cutting‐edge biomarkers and experimental models, this review serves as a valuable resource for accessing the forefront of cancer immunology investigation.

Abstract The necessity of increasing the efficiency of organ preservation has encouraged researchers to explore the mechanisms underlying diabetes-related myocardial injuries. This study intended to evaluate the protective effects of oxymatrine (OMT) in myocardial injury caused by type 2 diabetes mellitus. A model of diabetic rats was established to simulate type 2 diabetes mellitus using an intraperitoneal injection of a single dose of 65 mg/kg streptozotocin with a high-fat and high-cholesterol diet, and diabetic rats were subsequently treated with OMT (60, 120 mg/kg) by gavage for 8 weeks. Thereafter, diabetic rats demonstrated notable decreases in left ventricular systolic pressure (LVSP), ±dp/dtmax, and in the activities of glutathione peroxidase, superoxide dismutase, and catalase. Moreover, we found notable increases in left ventricular end-diastolic pressure, fasting blood glucose, and malondialdehyde, as well as changes in cell apoptosis and decreased expression levels of Nrf2, HO-1, tyrosine protein kinase JAK (JAK), and signal transducer and transcription activator (STAT). Treatment with OMT alleviated all of the measured parameters. Collectively, these findings suggest that activation of the Nrf2/HO-1 and inhibition of the JAK/STAT signaling are involved in mediating the cardioprotective effects of OMT and also highlight the benefits of OMT in ameliorating myocardial injury in diabetic rats.

Abstract Background Large uncertainty in modeling land carbon (C) uptake heavily impedes the accurate prediction of the global C budget. Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models (ESMs). Here we present a Matrix-based Ensemble Model Inter-comparison Platform (MEMIP) under a unified model traceability framework to evaluate multiple soil organic carbon (SOC) models. Using the MEMIP, we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter (SOM) models. By comparing the model outputs from the C-only and CN modes, the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled. Results Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation (1900–2000). The SOC difference between the multi-layer models was remarkably higher than between the single-layer models. Traceability analysis indicated that over 80% of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes, while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation. Conclusions The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction, especially between models with similar process representation. Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences. We stressed the importance of analyzing ensemble outputs from the fundamental model structures, and holding a holistic view in understanding the ensemble uncertainty.

This study provides the first socio-ecological overview of the Seriola dumerili fishery occurring in the Balearic Islands. This pelagic top-predator is among the five most valued fish resources of the Balearic community. Despite its ecological importance and potential vulnerability to aggregation fishing, few studies address the ecology of this large Carangidae species. Shining a light on its ecology is vital to ensure adequate species conservation and the sustainable and effective management of the fishery. Historical catches from 1950–1999, alongside detailed landing data for the last 21 years, were analysed to identify potential patterns in ecological and socio-economic factors. Significant inter-annual variability among the years was found in historical catches of greater amberjack, while catches and mean prices of the different size categories revealed significant results between seasons and months, respectively. Additionally, the purse seine fleet accounted for the highest percentage of S. dumerili catches. CPUE did not appear to change greatly between months and years after the annual 8-month fishing ban imposed in 2011 and therefore a re-evaluation of the closure was intended. Overall, this study suggests seasonality influences the S. dumerili fishery in the Balearic Islands, within which ecological influences show a higher regulating power than socio-economic factors.

Abstract Background MYO18B has been identified as a novel tumor suppressor gene in several cancers. However, its specific roles in the progression of hepatocellular carcinoma (HCC) has not been well defined. Methods We firstly identified the expression and prognostic values of MYO18B in HCC using TCGA cohort and our clinical data. Then, MYO18B knockdown by RNA inference was implemented to investigate the effects of MYO18B on HCC cells. Quantitative RT-PCR and Western blot were used to determine gene and protein expression levels. CCK-8 and colony formation assays were performed to examine cell proliferation capacity. Wound healing and transwell assays were used to evaluate the migration and invasion of HepG2 cells. Results MYO18B was overexpressed and correlated with poor prognosis in HCC. MYO18B expression was an independent risk factor for overall survival. Knockdown of MYO18B significantly inhibited the proliferation, migration and invasion of HepG2 cells. Meanwhile, MYO18B knockdown could effectively suppress the phosphorylation of PI3K, AKT, mTOR and P70S6K, suggesting that MYO18B might promote HCC progression by targeting PI3K/AKT/mTOR signaling pathway. Conclusions MYO18B promoted tumor growth and migration via the activation of PI3K/AKT/mTOR signaling pathway. MYO18B might be a promising target for clinical intervention of HCC.