Congenital anomalies of the kidney and urinary tract (CAKUT) are the leading cause of pediatric kidney failure, but predicting individual progression remains challenging. This multicenter study developed and validated POCC, a machine learning model for predicting kidney failure risk at 1, 3, and 5 years post-diagnosis in CAKUT patients. Two versions were created using data from 2249 children. The general model achieved internal AUCs of 0.93-0.99 and external AUCs of 0.89-0.98 and 0.81-0.90 in two independent validations at pediatric and general hospitals, respectively. The specialized model, integrating congenital-hereditary features, achieved internal AUCs of 0.93-0.99 and external AUCs of 0.91-0.96 in pediatric hospitals. Deployed online, POCC demonstrated 90.7% accuracy in real-world validation. As the first tool for multi-timepoint risk prediction across diverse CAKUT subphenotypes per patient, POCC has strong potential to support personalized management.

The need for a cost-effective, rapid, and increasingly accessible alternative to the 21-gene assay prompted this study, which developed a novel MRI-based intratumoral heterogeneity score (ITHscore) to quantify tumor heterogeneity and integrated it with radiomic and clinical features to predict the 21-gene recurrence score (RS). This retrospective study included ER+/HER2− breast cancer patients who underwent 21-gene assay and preoperative MRI at our institution (April 2017–March 2019). Patients were randomly split into training (70

Radiotherapy is frequently combined with chemotherapy, targeted therapy and immunotherapy to improve the efficacy of cancer treatment. Nevertheless, some radioresistant cancer cells seem to possess higher drug resistance, leading to failure of cancer treatment. Thus, it is crucial to explore the mechanisms of drug resistance in cancer RT. This review outlines molecular, cellular, and microenvironmental adaptations in cancer RT. These adaptations confer enhanced chemotherapy resistance in cancer cells surviving from ionizing radiation (IR). In addition, RT also activates oncogene signaling pathways, induces epigenetic remodeling and alters post-translational modifications, collectively driving resistance to targeted therapy. RT alters tumor intrinsic properties, promotes immunosuppressive effects and remodels TME, thus inducing immunotherapy resistance. Various emerging strategies including phytochemicals, small molecules, synthetic compounds, macromolecules, nanoparticles, photodynamic therapy (PDT), photothermal therapy (PTT), MicroRNA (miRNA) therapy, PROTACs, adoptive cell therapies, and engineered Salmonella, have been developed to overcome radioresistance and drug resistance in cancer RT. We highlight the importance of evaluating therapeutic effects and side effects of these emerging strategies. Through summarizing mechanisms and emerging strategies for drug resistance in cancer RT, this review aims to clarify obstacles in combined cancer treatment and guide future direction of research on cancer therapy.

The application of 21-gene assays in clinical practice is jeopardized by their cost and availability. This study aimed to predict the recurrence score (RS) of a 21-gene assay using MRI peritumoral radiomics in ER+/HER2- breast cancers. 154 and 39 patients with ER+/HER2- breast cancer from two centers were enrolled, who underwent 21-gene test and preoperative MRI. Patients from Center 1 were divided into training (n = 108) and internal validation (n = 46) cohorts, and patients from Center 2 were enrolled in the external validation cohort. Radiomics features were extracted from the tumoral, peritumoral and dilation volumes of interest with peritumoral ranges of 1 mm, 3 mm, 5 mm, 7 mm, and 9 mm. After feature selection, RS-prediction models were constructed using support vector machine method to distinguish high (RS ≥ 26) from low RS (RS < 26). As the thickness of the peritumor tissue increased, the AUC of models increased and then decreased, with the 3-mm model performing the best. Among all RS-prediction models, the 3 mm peritumoral model based on T2WI (T2-p3) achieved larger AUCs (0.70 and 0.69 in the internal and external validation cohorts, separately). The peritumoral-fusion model integrating intratumoral radiomic and imaging-clinicopathological features with the T2-p3 model, obtained greater AUCs (0.82 and 0.75 in the internal and external validation cohorts, separately). MRI peritumoral radiomic data exhibits the potential to serve as a biomarker of recurrence risk in patients with ER+/HER2- breast cancer.

Supplementary Figure 7. Molecular structure of SLC3A2/PD-L1 BsADC. VcMMAE, mc-vc-PAB-MMAE. Drug payload, MMAE, monomethyl auristatin E; Linker, mc-vc-PAB, maleimidocaproyl-valine-citrulline-p-aminobenzoyloxycarbonyl.

This corrects the article published in European Journal of Histochemistry 2024;68:4140 doi: 10.4081/ejh.2024.4140 IF: 2.1 Q4 B4 IF: 2.1 Q4 B4

Circadian rhythms in gut microbiota composition are crucial for metabolic function and disease progression, yet the diurnal oscillation patterns of gut microbiota in atherosclerotic cardiovascular disease (ASCVD) and their role in disease progression remain unknown. Here, we investigated gut bacterial dynamics in Apoe-/- mice over 24 hours and elucidated dynamic changes in fecal microbiota composition and function among C57BL/6 and Apoe-/- mice with standard chow diet or high-fat/high-cholesterol diet under ad libitum conditions. Compared with C57BL/6 mice, Apoe-/- mice exhibited significant differences in fecal microbial composition. Rhythmicity analysis revealed that the temporal dynamics of fecal microbiota composition and function in Apoe-/- mice differed significantly from those in C57BL/6 mice, particularly in B. coccoides-dominated oscillatory modules. Functional annotation showed that rhythmic B. coccoides strains inhibited ASCVD progression by enhancing intestinal and endothelial barrier functions. These findings demonstrate that diurnal oscillations in gut microbiota are closely associated with ASCVD progression and provide new insights for microbiota-targeted precision therapies.

Supplementary Figure 3. SLC3A2/PD-L1 BsAb showed Neutralizing activities and T cells activation. A, SLC3A2/PD-L1 BsAb blocked the interaction between PD-L1-His and biotinylated PD-1-His protein by ELISA. IC50, half-maximal inhibitory concentration. B, ADCC activities of SLC3A2/PD-L1 BsAb to A375 using human PBMCs as effector cells. Supernatants were harvested to measure the LDH release by ELISA. C, SLC3A2/PD-L1 BsAb induced IFN-γ secretion in mixed leukocyte reaction (MLR). PD-L1 mAb was used as a positive control. SLC3A2 mAb was used as a negative control. D, SLC3A2/PD-L1 BsAb reversed PD-L1-mediated inhibition of T-cell proliferation. T cell numbers were quantified with CCK-8. *P < 0.05, **P < 0.01 and ***P < 0.001 by unpaired t-test and two-way ANOVA with Tukey’s multiple comparison test. Data = mean ± SEM of three independent experiments.

Supplementary Table 2. The sequence of 19G4 antibody.

BACKGROUDS: Immunoblockade therapy based on PD-1 checkpoint has shown remarkable progress in various tumors, but its effectiveness in glioma patients is still lacking. Thus, it is in urgent need to uncover an ideal signature for glioma. METHODS: Five cohorts comprised 1973 patients from The Cancer Genome Atlas Program (TCGA), Chinese Glioma Genome Atlas (CGGA), and the Gene Expression Omnibus (GEO) database were included in the present study. By performing consensus clustering, limma and survival analysis, 42 prognostic genes were screened. Subsequently, a consensus immune cell infiltration-related signature (IRS) was developed using a 10-fold cross-validation framework with 101 combinations of 10 machine-learning algorithms, and the predictive performance of IRS was comprehensively analyzed. Ultimately, we evaluated the response of distinct risk subgroups to screen candidate drugs designed to address specific risk factors in the backgrounds of personalized medicine. RESULTS: Three molecular subtypes with distinct immune status and survival outcome were identified through consensus clustering analysis. By performing machine learning analysis on the common differentially expressed genes (DEGs), a consensus IRS was developed by the combined StepCox[both] + SuperPC algorithm. The IRS demonstrated high accuracy and robustness performance across multiple cohorts including TCGA, CGGA-693, CGGA325, GSE16011 and GSE43378. Moreover, the IRS could independently predict the survival outcome regardless of the impact of other clinical variables. Compared to the low IRS group, patients in the high IRS group are more sensitive to the chemotherapy drugs, while low IRS group patients may receive benefits from immunotherapy. Additionally, several candidate drugs were screened from multiple databases for poor survival outcome patients. Notably, a novel biomarker, C1QB, from the IRS, was highly expressed in glioma tissue and promotes progression in U87 cells by enhancing proliferative and migratory capacities while inhibiting apoptosis. CONCLUSIONS: The IRS could accurately predict glioma patients survival and may contribute to the development of personalized therapy. Moreover, as a key gene in IRS, over expression of C1QB could significantly enhance glioma cell viability.

Supplementary Figure 6. SLC3A2/PD-L1 BsADC inhibited tumor growth efficacy in SNU-899 xenograft models (A), NCI-H460 xenograft models (B), and A375 xenograft models (C), with or without human PBMCs engraftment in vivo. D, H&E staining of important organ sections from MC38 mice at the endpoint treatment. Scale bar, 20 μm.

Supplementary Figure 4. LC-MS analysis and average DAR value of antibody-drug conjugates. A, LC-MS analysis of SLC3A2/PD-L1 BsADC. LC-MS, liquid chromatography-MS. The average DAR of SLC3A2/PD-L1 BsADC was 0.82. B, LC-MS analysis of SLC3A2 ADC. The average DAR of SLC3A2/PD-L1 BsADC was 1.63. C, LC-MS analysis of PD-L1 ADC. The average DAR of PD-L1 ADC was 0.93.

Yes-associated protein 1 (YAP1) has attracted attention for its potential in the treatment of various types of malignancies. The Hippo-YAP1 axis is inhibited in bladder cancer (BC), which is a major driver of BC progression and oncogenesis. Hippo pathway activity is controlled by the phosphorylation cascade in the MST1/2-LATS1/2-YAP1 axis, in addition to other modifications such as ubiquitination of the Hippo pathway proteins through the co-regulation of E3 ligases and deubiquitinases. In this study, we identified USP20 as a Hippo/YAP1 pathway-related deubiquitinase using combined siRNA screening and a deubiquitinase overexpression assay. Further analysis revealed that USP20 directly regulated the expression of YAP1 and its downstream target genes connective tissue growth factor and cysteine-rich angiogenic inducer 61. A tissue microarray assay confirmed that USP20 expression was elevated in tumor tissues and correlated with YAP1 expression. Analysis of the underlying mechanisms revealed that USP20 directly interacted with the YAP1 protein and promoted its stability through inhibition of K48-linked poly-ubiquitination. Our findings revealed that USP20 serves as a deubiquitinase and regulates the Hippo-YAP1 pathway in BC.

Supplementary Figure 5. Flow cytometry analysis for apoptosis. A, Flow cytometric analysis for cell apoptosis in SNU-899 cells. B, Flow cytometric analysis for cell apoptosis in H460 cells. C, Flow cytometric analysis for cel

Accurate cancer diagnosis and tissue origin identification are crucial for precision oncology. We explored the potential of tumor-specific RNA transcripts (Tumor-SRTs) and tissue-specific RNA transcripts (Tissue-SRTs) as dual biomarkers using machine learning. Tumor-SRTs effectively distinguished malignant from normal tissues across training, test, and validation sets. Classifiers trained on 25 Tissue-SRTs exhibited high performance, validated externally with varying predictive accuracy across tissue types. We developed SRT-based Cancer Diagnostics (SRT-CD), an intelligent diagnostic system, to diagnose primary and metastatic cancers and determine tissue origin of Cancers of Unknown Primary. SRT-CD achieved top-1/top-3 accuracies of 80%/98.1% for primary and 76.9%/92.3% for metastatic cancers in external validation. This study establishes SRT-CD as a robust tool for clinical cancer diagnosis and tissue origin identification, and guiding personalized treatment strategies. ### Competing Interest Statement The authors have declared no competing interest.

The gut microbiota has been demonstrated to be correlated with the clinical phenotypes of diseases, including cancers. However, there are few studies on clinical subtyping based on the gut microbiota, especially in breast cancer (BC) patients. Here, using machine learning methods, we analysed the gut microbiota of BC, colorectal cancer (CRC), and gastric cancer (GC) patients to identify their shared metabolic pathways and the importance of these pathways in cancer development. Based on the gut microbiota-related metabolic pathways, human gene expression profile and patient prognosis, we established a novel BC subtyping system and identified a subtype called “challenging BC”. Tumours with this subtype have more genetic mutations and a more complex immune environment than those of other subtypes. A score index was proposed for in-depth analysis and showed a significant negative correlation with patient prognosis. Notably, activation of the TPK1-FOXP3-mediated Hedgehog signalling pathway and TPK1-ITGAE-mediated mTOR signalling pathway was linked to poor prognosis in “challenging BC” patients with high scores, as validated in a patient-derived xenograft (PDX) model. Furthermore, our subtyping system and score index are effective predictors of the response to current neoadjuvant therapy regimens, with the score index significantly negatively correlated with both treatment efficacy and the number of immune cells. Therefore, our findings provide valuable insights into predicting molecular characteristics and treatment responses in “challenging BC” patients.

Esophageal cancer (EC) poses a significant health concern, particularly among the elderly, warranting effective treatment strategies. While immunotherapy holds promise in activating the immune response against tumors, its specific impact and associated reactions in EC patients remain uncertain. Precise prognosis prediction becomes crucial for guiding appropriate interventions. This study, based on data from the First Affiliated Hospital of Xiamen University (January 2017 to May 2021), focuses on 113 EC patients undergoing immunotherapy. The primary objectives are to elucidate the effectiveness of immunotherapy in EC treatment and to introduce a stacking ensemble learning method for predicting the survival of EC patients who have undergone immunotherapy, in the context of small sample sizes, addressing the imperative of supporting clinical decision-making for healthcare professionals. Our method incorporates five sub-learners and one meta-learner. Leveraging optimal features from the training dataset, this approach achieved compelling accuracy (89.13%) and AUC (88.83%) in predicting three-year survival status, surpassing conventional techniques. The model proves efficient in guiding clinical decisions, especially in scenarios with small-size follow-up data.

Retinopathy is a common complication of diabetes mellitus and the leading cause of visual impairment. Danggui Buxue decoction (RRP) has been used as a traditional drug for the treatment of diabetic nephropathy for many years. The aim of this study was to investigate the effects of RRP on hypoxia-induced retinal Müller cell injury. A model of retinal Müller cell damage was created using high glucose levels (25 mmol/L) and/or exposure to low oxygen conditions (1% O2). RRP was given to rats by continuous gavage for 7 days to obtain drug-containing serum. After sterilization, the serum was added to the culture medium at a ratio of 10%. Cell viability, apoptosis, and cell proliferation were assessed using the CCK-8 kit, Annexin V-FITC/propidium iodide apoptosis kit, and EdU kit. The mRNA levels of angiogenesis factors (ANGPTL4, VEGF) and inflammatory factors (IL-1B, ICAM-1) were detected by RT-qPCR. Western blot analysis was employed to assess the levels of proteins related to the ATF4/CHOP pathway. Following hypoxia for 48 h and 72 h, there was a significant decrease in cell viability and proliferation, as well as a notable increase in apoptosis compared to the control group (21% O2). However, high glucose stimulation had no significant effect, and high glucose combined with hypoxia had no further damage to cells. After 48 h of exposure to low oxygen levels, the mRNA expression levels of ANGPTL4, VEGF, IL-1B, and ICAM-1 in retinal Müller cells were significantly higher than in the control group (21% O2). RRP treatment significantly alleviated the increase of cell apoptosis and the upregulation of IL-1B and-1 in retinal Müller cells induced by hypoxia. RRP has the potential to reduce the suppression of the ATF4/CHOP pathway in hypoxia-induced retinal Müller cells, and it significantly alleviates cell apoptosis through regulating inflammatory factors and the ATF4/CHOP pathway.

Bladder cancer (BCa) exhibits the escalating incidence and mortality due to the untimely and inaccurate early diagnosis. Urinary exosome metabolites, carrying critical tumor cell information and directly related to bladder, emerge as promising non-invasive diagnostic biomarkers of BCa. Herein, the magnetic 3D ordered macroporous zeolitic imidazolate framework-8 (magMZIF-8) is synthesized and used for efficient urinary exosome isolation. Notably, beyond retaining the single crystals and micropores of conventional ZIF-8, MZIF-8 is further enhanced with highly oriented and ordered macropores (150 nm) and the large specific surface area (973 m2·g–1), which could enable the high purity and yield separation of exosomes via leveraging the combination of size exclusion, affinity, and electrostatic interactions between magMZIF-8 and the surfaces of exosome. Furthermore, the magnetic and hydrophilic properties of magMZIF-8 will further simplify the process and enhance the efficiency of separation. After conditional optimization, a 50 mL of urine is sufficient for exosome metabolomics analysis, and the time for isolating exosomes from 42 urine samples was 2 hours only. Incorporating machine learning algorithms with LC-MS/MS analysis of the metabolic patterns obtained from isolated exosomes, early-stage BCa patients were differentiated from healthy controls, with area under the curve (AUC) value of 0.844–0.9970 in the training set and 0.875-1.00 in the test set, signifying its potential as a reliable diagnostic tool. This study offers a promising approach for the non-invasive and efficient diagnosis of BCa on a large scale via exosome metabolomics.