The biological applications of near-infrared optical nanomaterials in atherosclerosis.

PURPOSE OF REVIEW: Atherosclerosis, a highly pathogenic and lethal disease, is difficult to locate accurately via conventional imaging because of its scattered and deep lesions. However, second near-infrared (NIR-II) nanomaterials show great application potential in the tracing of atherosclerotic plaques due to their excellent penetration and angiographic capabilities. RECENT FINDINGS: With the development of nanotechnology, among many nanomaterials available for the visual diagnosis and treatment of cardiovascular diseases, optical nanomaterials provide strong support for various biomedical applications because of their advantages, such as noninvasive, nondestructive and molecular component imaging. Among optical nanomaterials of different wavelengths, NIR-II-range (900 ~ 1700 nm) nanomaterials have been gradually applied in the visual diagnosis and treatment of atherosclerosis and other vascular diseases because of their deep biological tissue penetration and limited background interference. This review explored in detail the prospects and challenges of the biological imaging and clinical application of NIR-II nanomaterials in treating atherosclerosis.

MRI Radiomics Combined with Clinicopathological Factors for Predicting 3-Year Overall Survival of Hepatocellular Carcinoma After Hepatectomy.

Background: A limited number of studies have examined the use of radiomics to predict 3-year overall survival (OS) after hepatectomy in patients with hepatocellular carcinoma (HCC). This study develops 3-year OS prediction models for HCC patients after liver resection using MRI radiomics and clinicopathological factors. Materials and Methods: A retrospective analysis of 141 patients who underwent surgical resection of HCC was performed. Patients were randomized into two set: the training set (n=98) and the validation set (n=43) including the survival groups (n=111) and non-survival groups (n=30) based on 3-year survival after hepatectomy. Furthermore, x2 or Fisher's exact test, univariate and multivariate logistic regression analyses were conducted to determine independent clinicopathological risk factors associated with 3-year OS. 1688 quantitative imaging features were extracted from preoperative T2-weighted imaging (T2WI) and contrast-enhanced magnetic resonance imaging (CE-MRI) of arterial phase (AP), portal venous phases (PVP)and delay period (DP). The features were selected using the variance threshold method, the select K best method and the least absolute shrinkage and selection operator (LASSO) algorithm. By using Bernoulli Naive Bayes (BernoulliNB) and Multinomial Naive Bayes (MultinomialNB) classifiers, we constructed models based on the independent clinicopathological factors and Rad-scores. To determine the best model, receiver operating characteristics (ROC) and Delong's test were used. Moreover, calibration curves were used to determine the calibration ability of the model, while decision curve analysis (DCA) was implemented to evaluate its clinical benefit. Results: The fusion model showed excellent prediction precision with AUC of 0.910 and 0.846 in training and validation set and revealed significant diagnostic accuracy and value in the calibration curve and DCA analysis. Conclusion: Nomograms based on MRI radiomics and clinicopathological factors have significant predictive value for 3-year OS after hepatectomy and can be used for risk classification.

Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.

BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

Acidic/hypoxia dual-alleviated nanoregulators for enhanced treatment of tumor chemo-immunotherapy.

Chemotherapy plays a crucial role in triple-negative breast cancer (TNBC) treatment as it not only directly kills cancer cells but also induces immunogenic cell death. However, the chemotherapeutic efficacy was strongly restricted by the acidic and hypoxic tumor environment. Herein, we have successfully formulated PLGA-based nanoparticles concurrently loaded with doxorubicin (DOX), hemoglobin (Hb) and CaCO3 by a CaCO3-assisted emulsion method, aiming at the effective treatment of TNBC. We found that the obtained nanomedicine (DHCaNPs) exhibited effective drug encapsulation and pH-responsive drug release behavior. Moreover, DHCaNPs demonstrated robust capabilities in neutralizing protons and oxygen transport. Consequently, DHCaNPs could not only serve as oxygen nanoshuttles to attenuate tumor hypoxia but also neutralize the acidic tumor microenvironment (TME) by depleting lactic acid, thereby effectively overcoming the resistance to chemotherapy. Furthermore, DHCaNPs demonstrated a notable ability to enhance antitumor immune responses by increasing the frequency of tumor-infiltrating effector lymphocytes and reducing the frequency of various immune-suppressive cells, therefore exhibiting a superior efficacy in suppressing tumor growth and metastasis when combined with anti-PD-L1 (αPD-L1) immunotherapy. In summary, this study highlights that DHCaNPs could effectively attenuate the acidic and hypoxic TME, offering a promising strategy to figure out an enhanced chemo-immunotherapy to benefit TNBC patients.

Computed tomography perfusion deficit volume predicts the functional outcome of endovascular therapy for basilar artery occlusion.

OBJECTIVES: To investigate the relationship between baseline computed tomography perfusion deficit volumes and functional outcomes in patients with basilar artery occlusion (BAO) undergoing endovascular therapy. METHODS: This was a single-center study in which the data of 64 patients with BAO who underwent endovascular therapy were retrospectively analyzed. All the patients underwent multi-model computed tomography on admission. The posterior-circulation Acute Stroke Prognosis Early Computed Tomography Score was applied to assess the ischemic changes. Perfusion deficit volumes were obtained using Syngo.via software. The primary outcome of the analysis was a good functional outcome (90-day modified Rankin Scale score ≤ 3). Logistic regression and receiver operating characteristic curves were used to explore predictors of functional outcome. RESULTS: A total of 64 patients (median age, 68 years; 72 % male) were recruited, of whom 26 (41 %) patients achieved good functional outcomes, while 38 (59 %) had poor functional outcomes. Tmax > 10 s, Tmax > 6 s, and rCBF < 30 % volume were independent predictors of good functional outcomes (odds ratio range, 1.0-1.2; 95 % confidence interval [CI], 1.0-1.4]) and performed well in the receiver operating characteristic curve analyses, exhibiting positive prognostic value; the areas under the curve values were 0.85 (95 % CI, 0.75-0.94), 0.81 (95 % CI, 0.70-0.90), and 0.78 (95 % CI, 0.67-0.89). CONCLUSION: Computed tomography perfusion deficit volume represents a valuable tool in predicting high risk of disability and mortality in patients with BAO after endovascular treatment.

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.

OBJECTIVES: Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer. METHODS: A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves. RESULTS: Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively. CONCLUSIONS: The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients. ADVANCES IN KNOWLEDGE: This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

Development and Validation of a Dual-Energy CT-Based Model for Predicting the Number of Central Lymph Node Metastases in Clinically Node-Negative Papillary Thyroid Carcinoma.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a dual-energy CT (DECT)-based model for preoperative prediction of the number of central lymph node metastases (CLNMs) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) patients. MATERIALS AND METHODS: Between January 2016 and January 2021, 490 patients who underwent lobectomy or thyroidectomy, CLN dissection, and preoperative DECT examinations were enrolled and randomly allocated into the training (N = 345) and validation cohorts (N = 145). The patients' clinical characteristics and quantitative DECT parameters obtained on primary tumors were collected. Independent predictors of> 5 CLNMs were identified and integrated to construct a DECT-based prediction model, for which the area under the curve (AUC), calibration, and clinical usefulness were assessed. Risk group stratification was performed to distinguish patients with different recurrence risks. RESULTS: More than 5 CLNMs were found in 75 (15.3%) cN0 PTC patients. Age, tumor size, normalized iodine concentration (NIC), normalized effective atomic number (nZeff) and the slope of the spectral Hounsfield unit curve (λHu) in the arterial phase were independently associated with> 5 CLNMs. The DECT-based nomogram that incorporated predictors demonstrated favorable performance in both cohorts (AUC: 0.842 and 0.848) and significantly outperformed the clinical model (AUC: 0.688 and 0.694). The nomogram showed good calibration and added clinical benefit for predicting> 5 CLNMs. The KaplanMeier curves for recurrence-free survival showed that the high- and low-risk groups stratified by the nomogram were significantly different. CONCLUSION: The nomogram based on DECT parameters and clinical factors could facilitate preoperative prediction of the number of CLNMs in cN0 PTC patients.

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study.

Background: For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. Methods: In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. Findings: For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. Interpretation: The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. Funding: National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.

Clinical characteristics and outcome correlates of Chinese patients with takotsubo syndrome: Results from the first Chinese takotsubo syndrome registry.

OBJECTIVE: To investigate clinical features and outcomes of Chinese patients with Takotsubo syndrome (TTS). METHODS: We established the first Chinese Registry of Takotsubo Syndrome (ChiTTS Registry) and analyzed demographic, clinical, therapeutical, and outcome data to characterize clinical and outcome features of Chinese TTS patients. RESULTS: In 112 enrolled patients in the ChiTTS registry from 02/01/2016 to 12/28/2021, the mean age was 59.4 ± 18.7 years old, and 27.7% were men. A total of 41.1% patients experienced respiratory and circulatory complications during hospitalization, and 17.3% patients developed cardiogenic shock. Physical triggers, dyspnea, tachycardia, and younger age (< 70 years old) predicted in-hospital complications. The MACCE rate during follow up was 13.9% per patient per year and the rate of all-cause death was 12.8% per patient per year. TTS patients with in-hospital complications developed more long-term MACCE (24.6% vs. 6.6% per patient-year, P < 0.001) and higher all-cause mortality (21.9% vs. 6.6% per patient-year, P = 0.001) than those without. The Kaplan-Meier survival analysis showed that more MACCE occurred in TTS patients with tachycardia during 3-year follow-up (HR 4.18; 95% CI 1.80-9.74; log-rank test P < 0.001). Among all medications at discharge, only beta-blocker was associated with reduced long-term MACCE (HR: 0.35; 95% CI: 0.12-0.996; P = 0.049). CONCLUSION: We investigated clinical and outcome features of patients in the first Chinese TTS Registry. Tachycardiac TTS patients developed more inpatient and long-term adverse cardiovascular events.

Precise prediction of the sensitivity of platinum chemotherapy in SCLC: Establishing and verifying the feasibility of a CT-based radiomics nomogram.

Objectives: To develop and validate a CT-based radiomics nomogram that can provide individualized pretreatment prediction of the response to platinum treatment in small cell lung cancer (SCLC). Materials: A total of 134 SCLC patients who were treated with platinum as a first-line therapy were eligible for this study, including 51 patients with platinum resistance (PR) and 83 patients with platinum sensitivity (PS). The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were applied for feature selection and model construction. The selected texture features were calculated to obtain the radiomics score (Rad-score), and the predictive nomogram model was composed of the Rad-score and the clinical features selected by multivariate analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to assess the performance of the nomogram. Results: The Rad-score was calculated using 10 radiomic features, and the resulting radiomics signature demonstrated good discrimination in both the training set (area under the curve [AUC], 0.727; 95% confidence interval [CI], 0.627-0.809) and the validation set (AUC, 0.723; 95% CI, 0.562-0.799). To improve diagnostic effectiveness, the Rad-score created a novel prediction nomogram by combining CA125 and CA72-4. The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.900; 95% CI, 0.844-0.947) and the validation set (AUC, 0.838; 95% CI, 0.534-0.735). The radiomics nomogram proved to be clinically beneficial based on decision curve analysis. Conclusion: We developed and validated a radiomics nomogram model for predicting the response to platinum in SCLC patients. The outcomes of this model can provide useful suggestions for the development of tailored and customized second-line chemotherapy regimens.

Prediction of placenta accreta spectrum with nomogram combining radiomic and clinical factors: A novel developed and validated integrative model.

OBJECTIVE: To develop and validate a clinicoradiomic nomogram based on sagittal T2WI images to predict placenta accreta spectrum (PAS). METHODS: Between October 2016 and April 2022, women suspected of PAS by ultrasound were enrolled. After taking into account exclusion criteria, 132 women were retrospectively included in the study. The variance threshold SelectKBest and the least absolute shrinkage and selection operator were applied to select radiomic features, which was further used to calculate the Rad-score. Multivariable logistic regression was used to screen clinical factor. RESULTS: Based on 13 radiomic features, five radiomic models were constructed. A clinical factor of intraplacental T2-hypointense bands was obtained by multivariate logistic regression. The area under the curve (AUC) value of the stochastic gradient descent (SGD) radiomic model was 0.82 in the training cohort and 0.78 in the test cohort. After adding clinical factors to the SGD radiomic model, the AUC value of the clinicoradiomic model was significantly increased from 0.82 and 0.78 to 0.84 in both the training and test cohorts. The nomogram of the clinicoradiomic model was constructed, which had good performance verified by calibration and a decision curve. CONCLUSION: The presented nomogram could be useful for predicting PAS.

Proteomic analysis of DZIP3 interactome and its role in proliferation and metastasis in gastric cancer cells.

Gastric cancer is a serious malignant tumor in the world, accounting for the third cause of cancer death worldwide. The pathogenesis of gastric cancer is very complex, in which epigenetic inheritance plays an important role. In our study, we found that DZIP3 was significantly up-regulated in gastric cancer tissues as compared to adjacent normal tissue, which suggested it may be play a crucial part in gastric cancer. To clarify the mechanism of it, we further analyzed the interacting proteome and transcriptome of DZIP3. An association between DZIP3 and some epigenetic regulators, such as CUL4B complex, was verified. We also present the first proteomic characterization of the protein-protein interaction (PPI) network of DZIP3. Then, the transcriptome analysis of DZIP3 demonstrated that knockdown DZIP3 increased a cohort of genes, including SETD7 and ZBTB4, which have essential role in tumors. We also revealed that DZIP3 promotes proliferation and metastasis of gastric cancer cells. And the higher expression of DZIP3 is positively associated with the poor prognosis of several cancers. In summary, our study revealed a mechanistic role of DZIP3 in promoting proliferation and metastasis in gastric cancer, supporting the pursuit of DZIP3 as a potential target for gastric cancer therapy.

Cadmium chloride exposure impairs the growth and behavior of Drosophila via ferroptosis.

Cadmium (Cd) is a widely distributed toxic heavy metal that enters the environment via anthropogenic mobilization and accumulates in plants and animals, causing metabolic abnormalities even mortality. Although the toxic effects and stress damage of cadmium have been investigated extensively over the past few decades, research on its ability to trigger ferroptosis, growth retardation, and behavioral abnormalities is insufficient. As a result, the effects of CdCl2 exposure on growth and development, activity and sleep, and ferroptosis in this study were examined in fruit fly (Drosophila melanogaster). When exposed to 0.5 mM CdCl2, the entire growth period from larvae to adults was prolonged, and the rates of pupation and eclosion were decreased. Additionally, CdCl2 exposure resulted in a decrease in body weight and individual size of fruit fly and high lethality rate. Moreover, CdCl2 exposure altered fruit fly behavior, including decreased activity and increased sleep duration, particularly in females. Ferrostatin-1 (Fer-1) is a potent selective ferroptosis inhibitor that effectively slows lipid hydroperoxide accumulation to rescue body size reduction and restore activity and sleep in CdCl2-exposed female flies. CdCl2 exposure could induce ferroptosis in fruit fly mechanistically, as evidenced by inhibition of Nrf2 signaling pathway, accumulation of lipid peroxidation, impairment of GPX4 antioxidant system, and upregulation of iron metabolism. Our findings suggest that Cd exposure triggers ferroptosis, which leads to growth retardation and behavioral disorders in fruit fly.

Detection of mild cognitive impairment in type 2 diabetes mellitus based on machine learning using privileged information.

Type 2 diabetes mellitus (T2DM) patients may develop into mild cognitive impairment (MCI) or even dementia. However, there is lack of reliable machine learning model for detection MCI in T2DM patients based on machine learning method. In addition, the brain network changes associated with MCI have not been studied. The aim of this study is to develop a machine learning based algorithm to help detect MCI in T2DM. There are 164 participants were included in this study. They were divided into T2DM-MCI (n = 56), T2DM-nonMCI (n = 49), and normal controls (n = 59) according to the neuropsychological evaluation. Functional connectivity of each participant was constructed based on resting-state magnetic resonance imaging (rs-fMRI). Feature selection was used to reduce the feature dimension. Then the selected features were set into the cascaded multi-column random vector functional link network (RVFL) classifier model using privileged information. Finally, the optimal model was trained and the classification performance was obtained using the testing data. The results show that the proposed algorithm has outstanding performance compared with classic methods. The classification accuracy of 73.18 % (T2DM-MCI vs NC) and 79.42 % (T2DM-MCI vs T2DM-nonMCI) were achieved. The functional connectivity related to T2DM-MCI mainly distribute in the frontal lobe, temporal lobe, and central region (motor cortex), which could be used as neuroimaging biomarkers to recognize MCI in T2DM patients. This study provides a machine learning model for diagnosis of MCI in T2DM patients and has potential clinical significance for timely intervention and treatment to delay the development of MCI.

Integrated application of transcriptome and metabolomics reveals potential therapeutic targets for the polarization of atherosclerotic macrophages.

The polarization of macrophages often leads to severe calcification and necrosis in aged atherosclerotic plaques, which eventually leads to poor prognosis of ischaemic cardiovascular and cerebrovascular diseases. More reliable diagnostic methods are urgently needed to discover therapeutic targets of macrophage polarization in aged atherosclerotic plaques. Metabolomics of aged plaques (n = 20) and macrophage polarization transcriptomes (n = 30) were integrated to identify metabolic therapeutic targets of macrophage polarization associated with aged plaque. Finally, metabolic inhibitors were used to verify the reliability of the target genes. Integrated multiomics analysis revealed that 6 metabolic pathways (including 21 genes) regulate macrophage polarization in aged atherosclerosis. Targeted treatment of macrophage polarization with metabolic inhibitors can effectively reduce the adverse risk of aged atherosclerosis. The combination of transcriptomics and metabolomics approaches can identify effective therapeutic targets for macrophage polarization in arteriosclerosis.

xCT contributes to colorectal cancer tumorigenesis through upregulation of the MELK oncogene and activation of the AKT/mTOR cascade.

Colorectal cancer (CRC) is one of the most commonly diagnosed and deadly malignant tumors globally, and its occurrence and progression are closely related to the poor histological features and complex molecular characteristics among patients. It is urgent to identify specific biomarkers for effective treatment of CRC. In this study, we performed comprehensive experiments to validate the role of xCT expression in CRC tumorigenesis and stemness and confirmed xCT knockdown significantly suppressed the proliferation, migration, and stemness of CRC cells in vitro and effectively inhibited CRC tumorigenesis and metastasis in vivo. In addition, bioinformatic analysis and luciferase assays were used to identify E2F1 as a critical upstream transcription factor of SLC7A11 (the gene encoding for xCT) that facilitated CRC progression and cell stemness. Subsequent RNA sequencing, western blotting, rescue assay, and immunofluorescence assays revealed MELK directly co-expressed with xCT in CRC cells, and its upregulation significantly attenuated E2F1/xCT-mediated tumorigenesis and stemness in CRC. Further molecular mechanism exploration confirmed that xCT knockdown may exert an antitumor effect by controlling the activation of MELK-mediated Akt/mTOR signaling. Erastin, a specific inhibitor of xCT, was also proven to effectively inhibit CRC tumorigenesis and cell stemness. Altogether, our study showed that E2F1/xCT is a promising therapeutic target of CRC that promotes tumorigenesis and cell stemness. Erastin is also an effective antitumoral agent for CRC.

DNA-Functionalized Liposomes In Vivo Fusion for NIR-II/MRI Guided Pretargeted Ferroptosis Therapy of Metastatic Breast Cancer.

In clinic, metastasis is still the main reason for death for cancer patients. Therefore, it is necessary to track cancer metastases accurately, kill cancer cells effectively, and then improve the prognosis of patients with advanced cancer. Therefore, we designed a liposome-based pretargeted system modified with single-stranded DNA and targeting peptide injected in sequence and then assembled in vivo for multimodality imaging-guided pretargeted synergistic therapy of metastatic breast cancer. The pretargeted system is composed of the first liposome, loaded with near-infrared fluorescence imaging (NIR-II) probe downconversion nanoprobes (DCNP) and magnetic resonance imaging (MRI) contrast agent SPIO (L1/C-Lipo/DS), for primary/metastatic tumor MRI/NIR-II dual-modal imaging, and the second liposome, loaded with glucose oxidase (GOx) and doxorubicin (DOX) (L2/C-Lipo/GD), as the therapeutic component. The SPIO in L1/C-Lipo/DS accumulated in the tumor tissue will provide a necessary iron ion for the therapeutic liposome (L2/C-Lipo/GD) to exert the pretargeted ferroptosis therapy to cancer cells. We demonstrate that the DNA-mediated pretargeting strategy can realize the multimodality imaging-guided synergistically enhanced antitumor effect between the two liposomes. This pretargeted and synergistic in vivo assembly nanomedicine strategy for diagnosis and treatment holds clinical translation potential for cancer management.