Identification of ribosomal protein S21 as a potential prognostic and immunotherapy biomarker for hepatocellular carcinoma.

BACKGROUND: Recent studies show that ribosomal protein S21 (RPS21) plays a role in the development and progression of various malignancies. However, the biological value of RPS21 in hepatocellular carcinoma (HCC) and its association with immunotherapy remain unknown. METHODS: Here, we examined the differential expression of RPS21 between HCC and normal liver tissues, using the TCGA, ICGC and GEO databases, followed by verification by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) in LO2, SMMC7721, HepG2, and MHCC-97H cell lines. Kaplan-Meier and Cox regression analyses were applied to investigate how RPS21 expression influenced overall survival, and a nomogram was established to predict prognosis among HCC patients. We further analyzed how RPS21 expression was related to tumor immune microenvironment, immunotherapy efficiency, and genomic alterations, and investigated potential underlying mechanisms. RESULTS: RPS21 upregulation was observed in HCC tissues and cell lines, compared to normal controls. Survival analysis revealed that RPS21 overexpression was significantly associated with poor clinical outcomes (all p < 0.05). Functional enrichment analyses indicated that differentially expressed genes relative to RPS21 expression were mainly involved in tumor response, proliferation, and metabolism. Additionally, RPS21 expression was positively correlated with the infiltration of activated CD4+ T cells and tumor mutational burden (all p < 0.05). Moreover, RPS21 was co-expressed with immune-related genes and immune checkpoint genes. Analyses of drug sensitivity predict that HCC patients with low RPS21 expression were more sensitive to targeted immunotherapy. CONCLUSIONS: The present results suggested that RPS21 might be a promising prognostic marker and a potential immunotherapy target for patients with HCC.

Processing speed dysfunction is associated with functional corticostriatal circuit alterations in childhood epilepsy with centrotemporal spikes: a PET and fMRI study.

PURPOSE: Epilepsy with centrotemporal spikes (ECTS) is the most common epilepsy syndrome in children and usually presents with cognitive dysfunctions. However, little is known about the processing speed dysfunction and the associated neuroimaging mechanism in ECTS. This study aims to investigate the brain functional abnormality of processing speed dysfunction in ECTS patients by using the 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: This prospective study recruited twenty-eight ECTS patients who underwent the 18F-FDG PET, rs-fMRI, and neuropsychological examinations. Twenty children with extracranial tumors were included as PET controls, and 20 healthy children were recruited as MRI controls. The PET image analysis investigated glucose metabolism by determining standardized uptake value ratio (SUVR). The MRI image analysis explored abnormal functional connectivity (FC) within the cortical-striatal circuit through network-based statistical (NBS) analysis. Correlation analysis was performed to explore the relationship between SUVR, FC, and processing speed index (PSI). RESULTS: Compared with healthy controls, ECTS patients showed normal intelligence quotient but significantly decreased PSI (P = 0.04). PET analysis showed significantly decreased SUVRs within bilateral caudate, putamen, pallidum, left NAc, right rostral middle frontal gyrus, and frontal pole of ECTS patients (P < 0.05). Rs-fMRI analysis showed absolute values of 20 FCs were significantly decreased in ECTS patients compared with MRI controls, which connected 16 distinct ROIs. The average SUVR of right caudate and the average of 20 FCs were positively correlated with PSI in ECTS patients (P = 0.034 and P = 0.005, respectively). CONCLUSION: This study indicated that ECTS patients presented significantly reduced PSI, which is closely associated with decreased SUVR and FC of cortical-striatal circuit. Caudate played an important role in processing speed dysfunction. CLINICAL TRIAL REGISTRATION: NCT04954729; registered on July 8, 2021, public site, https://clinicaltrials.gov/ct2/show/NCT04954729.

CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network.

BACKGROUND: At present, numerous challenges exist in the diagnosis of pancreatic SCNs and MCNs. After the emergence of artificial intelligence (AI), many radiomics research methods have been applied to the identification of pancreatic SCNs and MCNs. PURPOSE: A deep neural network (DNN) model termed Multi-channel-Multiclassifier-Random Forest-ResNet (MMRF-ResNet) was constructed to provide an objective CT imaging basis for differential diagnosis between pancreatic serous cystic neoplasms (SCNs) and mucinous cystic neoplasms (MCNs). MATERIALS AND METHODS: This study is a retrospective analysis of pancreatic unenhanced and enhanced CT images in 63 patients with pancreatic SCNs and 47 patients with MCNs (3 of which were mucinous cystadenocarcinoma) confirmed by pathology from December 2010 to August 2016. Different image segmented methods (single-channel manual outline ROI image and multi-channel image), feature extraction methods (wavelet, LBP, HOG, GLCM, Gabor, ResNet, and AlexNet) and classifiers (KNN, Softmax, Bayes, random forest classifier, and Majority Voting rule method) are used to classify the nature of the lesion in each CT image (SCNs/MCNs). Then, the comparisons of classification results were made based on sensitivity, specificity, precision, accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), with pathological results serving as the gold standard. RESULTS: Multi-channel-ResNet (AUC 0.98) was superior to Manual-ResNet (AUC 0.91).CT image characteristics of lesions extracted by ResNet are more representative than wavelet, LBP, HOG, GLCM, Gabor, and AlexNet. Compared to the use of three classifiers alone and Majority Voting rule method, the use of the MMRF-ResNet model exhibits a better evaluation effect (AUC 0.96) for the classification of the pancreatic SCNs and MCNs. CONCLUSION: The CT image classification model MMRF-ResNet is an effective method to distinguish between pancreatic SCNs and MCNs.

Magnetic and Folate Functionalization Enables Rapid Isolation and Enhanced Tumor-Targeting of Cell-Derived Microvesicles.

Cell-derived microvesicles (MVs), which are biogenic nanosized membrane-bound vesicles that convey bioactive molecules between cells, have recently received attention for use as natural therapeutic platforms. However, the medical applications of MV-based delivery platforms are limited by the lack of effective methods for the efficient isolation of MVs and the convenient tuning of their targeting properties. Herein, we report the development of magnetic and folate (FA)-modified MVs based on a donor cell-assisted membrane modification strategy. MVs inherit the membrane properties of their donor cells, which allows them to be modified with the biotin and FA on their own membrane. By conjugating with streptavidin-modified iron oxide nanoparticles (SA-IONPs), the MVs can be conveniently, efficiently, and rapidly isolated from the supernatant of their donor cells using magnetic activated sorting. Moreover, the conjugated magnetic nanoparticles and FA confer magnetic and ligand targeting activities on the MVs. Then, the MVs were transformed into antitumor delivery platforms by directly loading doxorubicin via electroporation. The modified MVs exhibited significantly enhanced antitumor efficacy both in vitro and in vivo. Taken together, this study provides an efficient and convenient strategy for the simultaneous isolation of cell-derived MVs and transformation into targeted drug delivery nanovectors, thus facilitating the development of natural therapeutic nanoplatforms.