CD276 enhances sunitinib resistance in clear cell renal cell carcinoma by promoting DNA damage repair and activation of FAK-MAPK signaling pathway.

OBJECTIVE: This study aimed to explore the effect of CD276 expression on the sunitinib sensitivity of clear cell renal cell carcinoma (ccRCC) cell and animal models and the potential mechanisms involved. METHODS: CD276 expression levels of ccRCC and normal samples were analyzed via online databases and real-time quantitative PCR (RT-qPCR). CD276 was knocked down in ccRCC cell models (sunitinib-resistant 786-O/R cells and sunitinib-sensitive 786-O cells) using shRNA transfection, and the cells were exposed to a sunitinib (2 µM) environment. Cells proliferation was then analyzed using MTT assay and colony formation experiment. Alkaline comet assay, immunofluorescent staining, and western blot experiments were conducted to assess the DNA damage repair ability of the cells. Western blot was also used to observe the activation of FAK-MAPK pathway within the cells. Finally, a nude mouse xenograft model was established and the nude mice were orally administered sunitinib (40 mg/kg/d) to evaluate the in vivo effects of CD276 knockdown on the therapeutic efficacy of sunitinib against ccRCC. RESULTS: CD276 was significantly upregulated in both ccRCC clinical tissue samples and cell models. In vitro experiments showed that knocking down CD276 reduced the survival rate, IC50 value, and colony-forming ability of ccRCC cells. Knocking down CD276 increased the comet tail moment (TM) values and γH2AX foci number, and reduced BRCA1 and RAD51 protein levels. Knocking down CD276 also decreased the levels of p-FAK, p-MEK, and p-ERK proteins. CONCLUSION: Knocking down CD276 effectively improved the sensitivity of ccRCC cell and animal models to sunitinib treatment.

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.

BACKGROUND: Early prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients. METHODS: The DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways. RESULTS: A 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635-0.741 and AUC = 0.650, 95%CI: 0.595-0.705) and tested (AUC = 0.686, 95%CI: 0.594-0.778 and AUC = 0.626, 95%CI: 0.529-0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722-0.816 and test: 0.762, 95%CI: 0.679-0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665-0.767 and test AUC = 0.695, 95%CI: 0.656-0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system. CONCLUSION: Dynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

Mucosal-associated invariant T cells promote ductular reaction through amphiregulin in biliary atresia.

BACKGROUND: Biliary atresia (BA) is a neonatal fibro-inflammatory cholangiopathy with ductular reaction as a key pathogenic feature predicting poor survival. Mucosal-associated invariant T (MAIT) cells are enriched in human liver and display multiple roles in liver diseases. We aimed to investigate the function of MAIT cells in BA. METHODS: First, we analyzed correlations between liver MAIT cell and clinical parameters (survival, alanine transaminase, bilirubin, histological inflammation and fibrosis) in two public cohorts of patients with BA (US and China). Kaplan-Meier survival analysis and spearman correlation analysis were employed for survival data and other clinical parameters, respectively. Next, we obtained liver samples or peripheral blood from BA and control patients for bulk RNA sequencing, flow cytometry analysis, immunostaning and functional experiments of MAIT cells. Finally, we established two in vitro co-culture systems, one is the rhesus rotavirus (RRV) infected co-culture system to model immune dysfunction of human BA which was validated by single cell RNA sequencing and the other is a multicellular system composed of biliary organoids, LX-2 and MAIT cells to evaluate the role of MAIT cells on ductular reaction. FINDINGS: Liver MAIT cells in BA were positively associated with low survival and ductular reaction. Moreover, liver MAIT cells were activated, exhibited a wound healing signature and highly expressed growth factor Amphiregulin (AREG) in a T cell receptor (TCR)-dependent manner. Antagonism of AREG abrogated the proliferative effect of BA MAIT cells on both cholangiocytes and biliary organoids. A RRV infected co-culture system, recapitulated immune dysfunction of human BA, disclosed that RRV-primed MAIT cells promoted cholangiocyte proliferation via AREG, and further induced inflammation and fibrosis in the multicellular system. INTERPRETATION: MAIT cells exhibit a wound healing signature depending on TCR signaling and promote ductular reaction via AREG, which is associated with advanced fibrosis and predictive of low survival in BA. FUNDING: This work was funded by National Natural Science Foundation of China grant (82001589 and 92168108), National Key R&D Program of China (2023YFA1801600) and by Basic and Applied Basic Research Foundation of Guangdong (2020A1515110921).

Pro-cancer role of CD276 as a novel biomarker for clear cell renal cell carcinoma.

OBJECTIVE: Renal cell carcinoma (RCC) is a common malignant tumor with a high incidence in males and the elderly, and clear cell RCC (ccRCC) is the most common RCC subtype. ccRCC is highly metastatic with a poor prognosis. Therefore, it is crucial to obtain a detailed understanding of the molecular mechanism of ccRCC and to identify suitable biomarkers to realize early diagnosis and improve prognosis. METHODS: We analyzed data from the Cancer Genome Atlas, investigated the overall differential expression of CD276 in ccRCC, and evaluated the influence of CD276 on patient survival and prognosis. We also performed gene set enrichment analysis (GSEA) and pathway enrichment analysis and investigated cell infiltration and drug responsiveness to further assess the regulatory effect of CD276 on ccRCC. Furthermore, we verified CD276 expression in RCC cell lines and control cell lines. RESULTS: The CD276 expression level in ccRCC samples was higher than that in corresponding samples adjacent to the tumors. Moreover, high CD276 expression levels were positively correlated with poor prognosis in patients with RCC. GSEA revealed that CD276 was significantly involved in immune-related pathways, and the level of CD276 expression was confirmed as associated with immune cell infiltration to some extent. Notably, some drugs were predicted to act on CD276, and this was confirmed by molecular docking. Furthermore, high levels of CD276 expression in RCC cell lines were verified. CONCLUSION: CD276 expression was significantly increased in ccRCC tissues and cells and positively correlated with patient prognosis. CD276 is a potential prognostic biomarker of ccRCC. Overall, this study provides a potential therapeutic strategy for ccRCC.

Radiomics signature of osteoarthritis: Current status and perspective.

Osteoarthritis (OA) is one of the fast-growing disability-related diseases worldwide, which has significantly affected the quality of patients' lives and brings about substantial socioeconomic burdens in medical expenditure. There is currently no cure for OA once the bone damage is established. Unfortunately, the existing radiological examination is limited to grading the disease's severity and is insufficient to precisely diagnose OA, detect early OA or predict OA progression. Therefore, there is a pressing need to develop novel approaches in medical image analysis to detect subtle changes for identifying early OA development and rapid progressors. Recently, radiomics has emerged as a unique approach to extracting high-dimensional imaging features that quantitatively characterise visible or hidden information from routine medical images. Radiomics data mining via machine learning has empowered precise diagnoses and prognoses of disease, mainly in oncology. Mounting evidence has shown its great potential in aiding the diagnosis and contributing to the study of musculoskeletal diseases. This paper will summarise the current development of radiomics at the crossroads between engineering and medicine and discuss the application and perspectives of radiomics analysis for OA diagnosis and prognosis. The translational potential of this article: Radiomics is a novel approach used in oncology, and it may also play an essential role in the diagnosis and prognosis of OA. By transforming medical images from qualitative interpretation to quantitative data, radiomics could be the solution for precise early OA detection, progression tracking, and treatment efficacy prediction. Since the application of radiomics in OA is still in the early stages and primarily focuses on fundamental studies, this review may inspire more explorations and bring more promising diagnoses, prognoses, and management results of OA.

Control of two insect pests by expression of a mismatch corrected double-stranded RNA in plants.

RNA interference (RNAi) has emerged as an efficient technology for pest control by silencing the essential genes of targeted insects. Owing to its nucleotide sequence-guided working mechanism, RNAi has a high degree of species-specificity without impacts on non-target organisms. However, as plants are inevitably under threat by two or more insect pests in nature, the species-specific mode of RNAi-based technology restricts its wide application for pest control. In this study, we artificially designed an intermediate dsRNA (iACT) targeting two ß-Actin (ACT) genes of sap-sucking pests Bemisia tabaci and Myzus persicae by mutual correction of their mismatches. When expressing hairpin iACT (hpiACT) from tobacco nuclear genome, transgenic plants are well protected from both B. tabaci and M. persicae, either individually or simultaneously, as evidenced by reduced fecundity and suppressed ACT gene expression, whereas expression of hpRNA targeting BtACT or MpACT in transgenic tobacco plants could only confer specific resistance to either B. tabaci or M. persicae, respectively. In sum, our data provide a novel proof-of-concept that two different insect species could be simultaneously controlled by artificial synthesis of dsRNA with sequence optimization, which expands the range of transgenic RNAi methods for crop protection.

An optimized water table depth detected for mitigating global warming potential of greenhouse gas emissions in wetland of Qinghai-Tibetan Plateau.

Climate change and human activities have intensified variations of water table depth (WTD) in wetlands around the world, which may strongly affect greenhouse gas emissions. Here, we analyzed how emissions of CO2, CH4, and N2O from the Zoige wetland on the Qinghai-Tibetan Plateau (QTP) vary with the WTD. Our data indicate that the wetland shows net positive global warming potential (11.72 tCO2-e ha-1 yr-1), and its emissions of greenhouse gases are driven primarily by WTD. Our analysis suggests that an optimal WTD exists, which at our study site was approximately 18 cm, for mitigating increases in global warming potential from the wetland. Our study provides insights into how climate change and human acitivies affect greenhouse gas emissions from alpine wetlands, and they suggest that water table management may be effective at mitigating future increases in emissions.

Deep learning-based bronchial tree-guided semi-automatic segmentation of pulmonary segments in computed tomography images.

Background: Pulmonary segments are valuable because they can provide more precise localization and intricate details of lung cancer than lung lobes. With advances in precision therapy, there is an increasing demand for the identification and visualization of pulmonary segments in computed tomography (CT) images to aid in the precise treatment of lung cancer. This study aimed to integrate multiple deep-learning models to accurately segment pulmonary segments in CT images using a bronchial tree (BT)-based approach. Methods: The proposed segmentation method for pulmonary segments using the BT-based approach comprised the following five essential steps: (I) segmentation of the lung using a U-Net (R231) (public access) model; (II) segmentation of the lobes using a V-Net (self-developed) model; (III) segmentation of the airway using a combination of a differential geometric approach method and a BronchiNet (public access) model; (IV) labeling of the BT branches based on anatomical position; and (V) segmentation of the pulmonary segments based on the distance of each voxel to the labeled BT branches. This five-step process was applied to 14 high-resolution breath-hold CT images and compared against manual segmentations for evaluation. Results: For the lung segmentation, the lung mask had a mean dice similarity coefficient (DSC) of 0.98±0.03. For the lobe segmentation, the V-Net model had a mean DSC of 0.94±0.06. For the airway segmentation, the average total length of the segmented airway trees per image scan was 1,902.8±502.1 mm, and the average number of the maximum airway tree generations was 8.5±1.3. For the segmentation of the pulmonary segments, the proposed method had a DSC of 0.73±0.11 and a mean surface distance of 6.1±2.9 mm. Conclusions: This study demonstrated the feasibility of combining multiple deep-learning models for the auxiliary segmentation of pulmonary segments on CT images using a BT-based approach. The results highlighted the potential of the BT-based method for the semi-automatic segmentation of the pulmonary segment.

Resistance to both aphids and nematodes in tobacco plants expressing a Bacillus thuringiensis crystal protein.

BACKGROUND: Bacillus thuringiensis (Bt) and its crystal toxin or δ-endotoxins (Cry) offer great potential for the efficient control of crop pests. A vast number of pests can potentially infect the same host plant, either simultaneously or sequentially. However, no effective Bt-Cry protein has been reported to control both aphids and plant parasitic nematodes due to its highly specific activity. RESULTS: Our study indicated that the Cry5Ba2 protein was toxic to the green peach aphid Myzus persicae, which had a median lethal concentration (LC50) of 9.7 ng µL-1 and fiducial limits of 3.1-34.6 ng µL-1. Immunohistochemical localization of Cry5Ba2 revealed that it could bind to the apical tip of microvilli in midgut regions. Moreover, transgenic tobacco plants expressing Cry5Ba2 exhibited significant resistance to Myzus persicae, as evidenced by reduced insect survival and impaired fecundity, and also intoxicated the Meloidogyne incognita as indicated by a decrease in galls and progeny reproduction. CONCLUSION: In sum, we identified a new aphicidal Bt toxin resource that could simultaneously control both aboveground and belowground pests, thus extending the application range of Bt-based strategy for crop protection. © 2024 Society of Chemical Industry.

Novel drimane-type sesquiterpenoids and nucleosides from the Helicoma septoconstrictum suppress the growth of ovarian cancer cells.

Four new drimane-type sesquiterpenoids and two new nucleoside derivatives (1-6), were isolated from the fungus Helicoma septoconstrictum. Their structures were determined based on the combination of the analysis of their HR-ESI-MS, NMR, ECD calculations data and acid hydrolysis. All the isolated compounds were detected for their bio-activities against MDA-MB-231, A549/DDP, A2780 and HepG2 cell lines. Helicoside C (4) exhibited superior cytotoxicity against the A2780 cell line with IC50 7.5 ± 1.5 µM. The analysis of reactive oxygen species (ROS) revealed that Helicoside C induced an increase in intracellular ROS. Furthermore, the flow cytometry and mitochondrial membrane potential (MMP) analyses unveiled that Helicoside C mediated mitochondrial-dependent apoptosis in A2780 cells. The western blotting test showed that Helicoside C could suppress the STAT3's phosphorylation. These findings offered crucial support for development of H. septoconstrictum and highlighted the potential application of drimane-type sesquiterpenoids in pharmaceuticals.

Relationship between bullous pemphigoid and malignancy: A Mendelian randomization study.

Bullous pemphigoid (BP) is the most common autoimmune blistering disease, which primarily affects the elderly. However, the relationship between BP and malignancy remains controversial in traditional observational studies. The aim of this study, which included only European populations, was to assess the potential causative link between BP and 13 types of malignant tumors in a two-sample Mendelian randomization (MR) study. BP was not associated with an increased risk of developing 13 types of malignant tumors. This study did not find a causal relationship between BP and malignant tumors. However, further research is warranted to examine the generalizability of this conclusion in non-European populations.

Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy.

OBJECTIVE: This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes of interest (VOIs). METHODS: We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised (analysis of variance (ANOVA) and Pearson correlation test) and supervised (Student T test) approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1 score. RESULTS: For multi-omics using RD features, the model performance in EG model shows: AUC, 0.817 ± 0.031; 95% CI 0.805, 0.825; p < 0.001, which is better than single VOI (ESO or GTV). CONCLUSION: Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction.

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

Marker Ki-67 is a potential biomarker for the diagnosis and prognosis of prostate cancer based on two cohorts.

BACKGROUND: Prostate cancer (PCa) is a widespread malignancy, predominantly affecting elderly males, and current methods for diagnosis and treatment of this disease continue to fall short. The marker Ki-67 (MKI67) has been previously demonstrated to correlate with the proliferation and metastasis of various cancer cells, including those of PCa. Hence, verifying the association between MKI67 and the diagnosis and prognosis of PCa, using bioinformatics databases and clinical data analysis, carries significant clinical implications. AIM: To explore the diagnostic and prognostic efficacy of antigens identified by MKI67 expression in PCa. METHODS: For cohort 1, the efficacy of MKI67 diagnosis was evaluated using data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. For cohort 2, the diagnostic and prognostic power of MKI67 expression was further validated using data from 271 patients with clinical PCa. RESULTS: In cohort 1, MKI67 expression was correlated with prostate-specific antigen (PSA), Gleason Score, T stage, and N stage. The receiver operating characteristic (ROC) curve showed a strong diagnostic ability, and the Kaplan-Meier method demonstrated that MKI67 expression was negatively associated with the progression-free interval (PFI). The time-ROC curve displayed a weak prognostic capability for MKI67 expression in PCa. In cohort 2, MKI67 expression was significantly related to the Gleason Score, T stage, and N stage; however, it was negatively associated with the PFI. The time-ROC curve revealed the stronger prognostic capability of MKI67 in patients with PCa. Multivariate COX regression analysis was performed to select risk factors, including PSA level, N stage, and MKI67 expression. A nomogram was established to predict the 3-year PFI. CONCLUSION: MKI67 expression was positively associated with the Gleason Score, T stage, and N stage and showed a strong diagnostic and prognostic ability in PCa.

AaLaeA targets AaFla1 to mediate the production of antitumor compound in Alternaria alstroemeria.

Endophytic fungi are an important source of novel antitumor substances. Previously, we isolated an endophytic fungus, Alternaria alstroemeria, from the medicinal plant Artemisia artemisia, whose crude extracts strongly inhibited A549 tumor cells. We obtained a transformant, namely AaLaeAOE26 , which completely loses its antitumor activity due to overexpression of the global regulator AaLaeA. Re-sequencing analysis of the genome revealed that the insertion site was in the noncoding region and did not destroy any other genes. Metabolomics analysis revealed that the level of secondary antitumor metabolic substances was significantly lower in AaLaeAOE26 compared with the wild strain, in particular flavonoids were more downregulated according to the metabolomics analysis. A further comparative transcriptome analysis revealed that a gene encoding FAD-binding domain protein (Fla1) was significantly downregulated. On the other hand, overexpression of AaFla1 led to significant enhancement of antitumor activity against A549 with a sevenfold higher inhibition ratio than the wild strain. At the same time, we also found a significant increase in the accumulation of antitumor metabolites including quercetin, gitogenin, rhodioloside, liensinine, ginsenoside Rg2 and cinobufagin. Our data suggest that the global regulator AaLaeA negatively affects the production of antitumor compounds via controlling the transcription of AaFla1 in endophytic A. alstroemeria.

Astrocyte PERK and IRE1 Signaling Contributes to Morphine Tolerance and Hyperalgesia through Upregulation of Lipocalin-2 and NLRP3 Inflammasome in the Rodent Spinal Cord.

BACKGROUND: Endoplasmic reticulum stress plays a crucial role in the pathogenesis of neuroinflammation and chronic pain. This study hypothesized that PRKR-like endoplasmic reticulum kinase (PERK) and inositol-requiring enzyme type 1 (IRE1) regulate lipocalin-2 (LCN2) and Nod-like receptor family pyrin domain containing 3 (NLRP3) expression in astrocytes, thereby contributing to morphine tolerance and hyperalgesia. METHODS: The study was performed in Sprague-Dawley rats and C57/Bl6 mice of both sexes. The expression of LCN2 and NLRP3 was assessed by Western blotting. The tail-flick, von Frey, and Hargreaves tests were used to evaluate nociceptive behaviors. Chromatin immunoprecipitation was conducted to analyze the binding of activating transcription factor 4 (ATF4) to the promoters of LCN2 and TXNIP. Whole-cell patch-clamp recordings were used to evaluate neuronal excitability. RESULTS: Pharmacologic inhibition of PERK and IRE1 attenuated the development of morphine tolerance and hyperalgesia in male (tail latency on day 7, 8.0 ± 1.13 s in the morphine + GSK2656157 [10 µg] group vs. 5.8 ± 0.65 s in the morphine group; P = 0.04; n = 6 rats/group) and female (tail latency on day 7, 6.0 ± 0.84 s in the morphine + GSK2656157 [10 µg] group vs. 3.1 ± 1.09 s in the morphine group; P = 0.0005; n = 6 rats/group) rats. Activation of PERK and IRE1 upregulated expression of LCN2 and NLRP3 in vivo and in vitro. Chromatin immunoprecipitation analysis showed that ATF4 directly bound to the promoters of the LCN2 and TXNIP. Lipocalin-2 induced neuronal hyperexcitability in the spinal cord and dorsal root ganglia via melanocortin-4 receptor. CONCLUSIONS: Astrocyte endoplasmic reticulum stress sensors PERK and IRE1 facilitated morphine tolerance and hyperalgesia through upregulation of LCN2 and NLRP3 in the spinal cord.

A comprehensive predictive model for radiation-induced brain injury in risk stratification and personalized radiotherapy of nasopharyngeal carcinoma.

BACKGROUND AND PURPOSE: Radiation-induced brain injury (RBI) is a severe radiotoxicity for nasopharyngeal carcinoma (NPC) patients, greatly affecting their long-term life quality and survival. We aim to establish a comprehensive predictive model including clinical factors and newly developed genetic variants to improve the precision of RBI risk stratification. MATERIALS AND METHODS: By performing a large registry-based retrospective study with magnetic resonance imaging follow-up on RBI development, we conducted a genome-wide association study and developed a polygenic risk score (PRS) for RBI in 1189 NPC patients who underwent intensity-modulated radiotherapy. We proposed a tolerance dose scheme for temporal lobe radiation based on the risk predicted by PRS. Additionally, we established a nomogram by combining PRS and clinical factors for RBI risk prediction. RESULTS: The 38-SNP PRS could effectively identify high-risk individuals of RBI (P = 1.42 × 10-34). Based on genetic risk calculation, the recommended tolerance doses of temporal lobes should be 57.6 Gy for individuals in the top 10 % PRS subgroup and 68.1 Gy for individuals in the bottom 50 % PRS. Notably, individuals with high genetic risk (PRS > P50) and receiving high radiation dose in the temporal lobes (D0.5CC > 65 Gy) had an approximate 50-fold risk over individuals with low PRS and receiving low radiation dose (HR = 50.09, 95 %CI = 24.27-103.35), showing an additive joint effect (Pinteraction < 0.001). By combining PRS with clinical factors including age, tumor stage, and radiation dose of temporal lobes, the predictive accuracy was significantly improved with C-index increased from 0.78 to 0.85 (P = 1.63 × 10-2). CONCLUSIONS: The PRS, together with clinical factors, could improve RBI risk stratification and implies personalized radiotherapy.

Multi-omics fusion with soft labeling for enhanced prediction of distant metastasis in nasopharyngeal carcinoma patients after radiotherapy.

Omics fusion has emerged as a crucial preprocessing approach in medical image processing, significantly assisting several studies. One of the challenges encountered in integrating omics data is the unpredictability arising from disparities in data sources and medical imaging equipment. Due to these differences, the distribution of omics futures exhibits spatial heterogeneity, diminishing their capacity to enhance subsequent tasks. To overcome this challenge and facilitate the integration of their joint application to specific medical objectives, this study aims to develop a fusion methodology for nasopharyngeal carcinoma (NPC) distant metastasis prediction to mitigate the disparities inherent in omics data. The multi-kernel late-fusion method can reduce the impact of these differences by mapping the features using the most suiTable single-kernel function and then combining them in a high-dimensional space that can effectively represent the data. The proposed approach in this study employs a distinctive framework incorporating a label-softening technique alongside a multi-kernel-based Radial basis function (RBF) neural network to address these limitations. An efficient representation of the data may be achieved by utilizing the multi-kernel to map the inherent features and then merging them in a space with many dimensions. However, the inflexibility of label fitting poses a constraint on using multi-kernel late-fusion methods in complex NPC datasets, hence affecting the efficacy of general classifiers in dealing with high-dimensional characteristics. The label softening increases the disparity between the two cohorts, providing a more flexible structure for allocating labels. The proposed model is evaluated on multi-omics datasets, and the results demonstrate its strength and effectiveness in predicting distant metastasis of NPC patients.

A heat-inducible expression system for external control of gene expression in plastids.

Inducible expression systems can overcome the trade-off between high-level transgene expression and its pleiotropic effects on plant growth. In addition, they can facilitate the expression of biochemical pathways that produce toxic metabolites. Although a few inducible expression systems for the control of transgene expression in plastids have been developed, they all depend on chemical inducers and/or nuclear transgenes. Here we report a temperature-inducible expression system for plastids that is based on the bacteriophage λ leftward and rightward promoters (pL/pR) and the temperature-sensitive repressor cI857. We show that the expression of green fluorescent protein (GFP) in plastids can be efficiently repressed by cI857 under normal growth conditions, and becomes induced over time upon exposure to elevated temperatures in a light-dependent process. We further demonstrate that by introducing into plastids an expression system based on the bacteriophage T7 RNA polymerase, the temperature-dependent accumulation of GFP increased further and was ~24 times higher than expression driven by the pL/pR promoter alone, reaching ~0.48% of the total soluble protein. In conclusion, our heat-inducible expression system provides a new tool for the external control of plastid (trans) gene expression that is cost-effective and does not depend on chemical inducers.

Circulating Regulatory T Cells: A Novel Marker Associated with Liver Metastasis and the Treatment Response of Transarterial Embolization in Gastroenteropancreatic Neuroendocrine Tumors.

INTRODUCTION: To investigate the role of circulating regulatory T cells (Tregs) as a novel marker associated with liver metastases and treatment response to transarterial embolization (TAE) in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs). METHODS: Circulating Tregs, defined as the CD4+CD25+CD127low/- population, were examined by flow cytometry in peripheral blood mononuclear cells (PBMCs) from patients with GEP-NETs. Clinicopathological parameters, radiologic response, and hepatic progression-free survival (hPFS) data were collected. RESULTS: The association between circulating Tregs and clinicopathological parameters was analyzed in 139 GEP-NET patients. Higher Treg levels were significantly associated with more progressive clinical features, including a higher WHO grade, more advanced TNM stage, and the presence of liver metastases. A Treg level ≥ 8.015% distinguished between patients with and without liver metastases. Among a cohort of 51 GEP-NET patients who were subjected to TAE for reducing liver metastasis burden, patients with higher Treg levels depicted unfavorable responses and significantly reduced hPFS after TAE treatment. We also revealed that patients with Treghigh (≥8.975%) displayed significantly shorter median hPFS than patients with Treglow (< 8.975%). Additionally, after adjusting for other confounding clinical parameters, the association between Tregs and treatment response as well as hPFS remained significant, suggesting that Tregs may have a strong and independent prognostic impact in GEP-NETs. CONCLUSIONS: Our data suggest that circulating Tregs are a novel immunological marker associated with liver metastases and treatment response to TAE in patients with GEP-NETs.