Genome-wide hypermethylation is closely associated with abnormal expression of genes involved in neural development in induced pluripotent stem cells derived from a Down syndrome mouse model.

Mental retardation is the main clinical manifestation of Down syndrome (DS), and neural abnormalities occur during the early embryonic period and continue throughout life. Tc1, a model mouse for DS, carries the majority part of the human chromosome 21 and has multiple neuropathy phenotypes similar to patients with DS. To explore the mechanism of early neural abnormalities of Tc1 mouse, induced pluripotent stem (iPS) cells from Tc1 mice were obtained, and genome-wide gene expression and methylation analysis were performed for Tc1 and wild-type iPS cells. Our results showed hypermethylation profiles for Tc1 iPS cells, and the abnormal genes were shown to be related to neurodevelopment and distributed on multiple chromosomes. In addition, important genes involved in neurogenesis and neurodevelopment were shown to be downregulated in Tc1 iPS cells. In short, our study indicated that genome-wide hypermethylation leads to the disordered expression of genes associated with neurodevelopment in Tc1 mice during early development. Overall, our work provided a useful reference for the study of the molecular mechanism of nervous system abnormalities in DS.

A radiomics signature to identify malignant and benign liver tumors on plain CT images.

BACKGROUND: In regular examinations, it may be difficult to visually identify benign and malignant liver tumors based on plain computed tomography (CT) images. RCAD (radiomics-based computer-aided diagnosis) has proven to be helpful and provide interpretability in clinical use. OBJECTIVE: This work aims to develop a CT-based radiomics signature and investigate its correlation with malignant/benign liver tumors. METHODS: We retrospectively analyzed 168 patients of hepatocellular carcinoma (malignant) and 117 patients of hepatic hemangioma (benign). Texture features were extracted from plain CT images and used as candidate features. A radiomics signature was developed from the candidate features. We performed logistic regression analysis and used a multiple-regression coefficient (termed as R) to assess the correlation between the developed radiomics signature and malignant/benign liver tumors. Finally, we built a logistic regression model to classify benign and malignant liver tumors. RESULTS: Thirteen features were chosen from 1223 candidate features to constitute the radiomics signature. The logistic regression analysis achieved an R = 0.6745, which was much larger than Rα = 0.3703 (the critical value of R at significant level α = 0.001). The logistic regression model achieved an average AUC of 0.87. CONCLUSIONS: The developed radiomics signature was statistically significantly correlated with malignant/benign liver tumors (p < 0.001). It has potential to help enhance physicians' diagnostic abilities and play an important role in RCADs.

Long non-coding RNA LINC01296 is a potential prognostic biomarker in patients with colorectal cancer.

Colorectal cancer (CRC), one of the most malignant cancers, is currently the fourth leading cause of cancer deaths worldwide. Recent studies indicated that long non-coding RNAs (lncRNAs) could be robust molecular prognostic biomarkers that can refine the conventional tumor-node-metastasis staging system to predict the outcomes of CRC patients. In this study, the lncRNA expression profiles were analyzed in five datasets (GSE24549, GSE24550, GSE35834, GSE50421, and GSE31737) by probe set reannotation and an lncRNA classification pipeline. Twenty-five lncRNAs were differentially expressed between CRC tissue and tumor-adjacent normal tissue samples. In these 25 lncRNAs, patients with higher expression of LINC01296, LINC00152, and FIRRE showed significantly better overall survival than those with lower expression (P < 0.05), suggesting that these lncRNAs might be associated with prognosis. Multivariate analysis indicated that LINC01296 overexpression was an independent predictor for patients' prognosis in the test datasets (GSE24549, GSE24550) (P = 0.001) and an independent validation series (GSE39582) (P = 0.027). Our results suggest that LINC01296 could be a novel prognosis biomarker for the diagnosis of CRC.

Single-cell RNA sequencing of neural stem cells derived from human trisomic iPSCs reveals the abnormalities during neural differentiation of Down syndrome.

Introduction: Down syndrome (DS) is the most common genetic condition that causes intellectual disability in humans. The molecular mechanisms behind the DS phenotype remain unclear. Therefore, in this study, we present new findings on its molecular mechanisms through single-cell RNA sequencing. Methods: Induced pluripotent stem cells (iPSCs) from the patients with DS and the normal control (NC) patients were differentiated into iPSCs-derived neural stem cells (NSCs). Single-cell RNA sequencing was performed to achieve a comprehensive single-cell level differentiation roadmap for DS-iPSCs. Biological experiments were also performed to validate the findings. Results and Discussion: The results demonstrated that iPSCs can differentiate into NSCs in both DS and NC samples. Furthermore, 19,422 cells were obtained from iPSC samples (8,500 cells for DS and 10,922 cells for the NC) and 16,506 cells from NSC samples (7,182 cells for DS and 9,324 cells for the NC), which had differentiated from the iPSCs. A cluster of DS-iPSCs, named DS-iPSCs-not differentiated (DSi-PSCs-ND), which had abnormal expression patterns compared with NC-iPSCs, were demonstrated to be unable to differentiate into DS-NSCs. Further analysis of the differentially expressed genes revealed that inhibitor of differentiation family (ID family) members, which exhibited abnormal expression patterns throughout the differentiation process from DS-iPSCs to DS-NSCs, may potentially have contributed to the neural differentiation of DS-iPSCs. Moreover, abnormal differentiation fate was observed in DS-NSCs, which resulted in the increased differentiation of glial cells, such as astrocytes, but decreased differentiation into neuronal cells. Furthermore, functional analysis demonstrated that DS-NSCs and DS-NPCs had disorders in axon and visual system development. The present study provided a new insight into the pathogenesis of DS.

Differential Diagnosis of DCIS and Fibroadenoma Based on Ultrasound Images: a Difference-Based Self-Supervised Approach.

Differentiation of ductal carcinoma in situ (DCIS, a precancerous lesion of the breast) from fibroadenoma (FA) using ultrasonography is significant for the early prevention of malignant breast tumors. Radiomics-based artificial intelligence (AI) can provide additional diagnostic information but usually requires extensive labeling efforts by clinicians with specialized knowledge. This study aims to investigate the feasibility of differentially diagnosing DCIS and FA using ultrasound radiomics-based AI techniques and further explore a novel approach that can reduce labeling efforts without sacrificing diagnostic performance. We included 461 DCIS and 651 FA patients, of whom 139 DCIS and 181 FA patients constituted a prospective test cohort. First, various feature engineering-based machine learning (FEML) and deep learning (DL) approaches were developed. Then, we designed a difference-based self-supervised (DSS) learning approach that only required FA samples to participate in training. The DSS approach consists of three steps: (1) pretraining a Bootstrap Your Own Latent (BYOL) model using FA images, (2) reconstructing images using the encoder and decoder of the pretrained model, and (3) distinguishing DCIS from FA based on the differences between the original and reconstructed images. The experimental results showed that the trained FEML and DL models achieved the highest AUC of 0.7935 (95% confidence interval, 0.7900-0.7969) on the prospective test cohort, indicating that the developed models are effective for assisting in differentiating DCIS from FA based on ultrasound images. Furthermore, the DSS model achieved an AUC of 0.8172 (95% confidence interval, 0.8124-0.8219), indicating that our model outperforms the conventional radiomics-based AI models and is more competitive.

Development and Validation of a Novel Radiomics-Based Nomogram With Machine Learning to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors.

Backgroud: Tumor grade is the determinant of the biological aggressiveness of pancreatic neuroendocrine tumors (PNETs) and the best current tool to help establish individualized therapeutic strategies. A noninvasive way to accurately predict the histology grade of PNETs preoperatively is urgently needed and extremely limited. Methods: The models training and the construction of the radiomic signature were carried out separately in three-phase (plain, arterial, and venous) CT. Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) were applied for feature preselection and radiomic signature construction. SVM-linear models were trained by incorporating the radiomic signature with clinical characteristics. An optimal model was then chosen to build a nomogram. Results: A total of 139 PNETs (including 83 in the training set and 56 in the independent validation set) were included in the present study. We build a model based on an eight-feature radiomic signature (group 1) to stratify PNET patients into grades 1 and 2/3 groups with an AUC of 0.911 (95% confidence intervals (CI), 0.908-0.914) and 0.837 (95% CI, 0.827-0.847) in the training and validation cohorts, respectively. The nomogram combining the radiomic signature of plain-phase CT with T stage and dilated main pancreatic duct (MPD)/bile duct (BD) (group 2) showed the best performance (training set: AUC = 0.919, 95% CI = 0.916-0.922; validation set: AUC = 0.875, 95% CI = 0.867-0.883). Conclusions: Our developed nomogram that integrates radiomic signature with clinical characteristics could be useful in predicting grades 1 and 2/3 PNETs preoperatively with powerful capability.

A Novel Multiresolution-Statistical Texture Analysis Architecture: Radiomics-Aided Diagnosis of PDAC Based on Plain CT Images.

Early screening of PDAC (pancreatic ductal adenocarcinoma) based on plain CT (computed tomography) images is of great significance. Therefore, this work conducted a radiomics-aided diagnosis analysis of PDAC based on plain CT images. We explored a novel MSTA (multiresolution-statistical texture analysis) architecture to extract texture features and built machine learning models to classify PDACs and HPs (healthy pancreases). We also performed significance tests of differences to analyze the relationships between histopathological characteristics and texture features. The MSTA architecture originates from the analysis of histopathological characteristics and combines multiresolution analysis and statistical analysis to extract texture features. The MSTA architecture achieved better experimental results than the traditional architecture that scales the coefficient matrices of the multiresolution analysis. In the validation of the classifications, the MSTA architecture achieved an accuracy of 81.19% and an AUC (area under the ROC (receiver operating characteristic) curve) of 0.88 (95% confidence interval: 0.84-0.92). In the test of the classifications, it achieved an accuracy of 77.66% and an AUC of 0.79 (95% confidence interval: 0.71-0.87). Moreover, the significance tests of differences showed that the extracted texture features may be relevant to the histopathological characteristics. The MSTA architecture is beneficial for the radiomics-aided diagnosis of PDAC based on plain CT images. Its texture features can potentially enhance radiologists' imaging interpretation abilities.

Identification of Pancreaticoduodenectomy Resection for Pancreatic Head Adenocarcinoma: A Preliminary Study of Radiomics.

BACKGROUND: In a pathological examination of pancreaticoduodenectomy for pancreatic head adenocarcinoma, a resection margin without cancer cells in 1 mm is recognized as R0; a resection margin with cancer cells in 1 mm is recognized as R1. The preoperative identification of R0 and R1 is of great significance for surgical decision and prognosis. We conducted a preliminary radiomics study based on preoperative CT (computer tomography) images to evaluate a resection margin which was R0 or R1. METHODS: We retrospectively analyzed 258 preoperative CT images of 86 patients (34 cases of R0 and 52 cases of R1) who were diagnosed as pancreatic head adenocarcinoma and underwent pancreaticoduodenectomy. The radiomics study consists of five stages: (i) delineate and segment regions of interest (ROIs); (ii) by solving discrete Laplacian equations with Dirichlet boundary conditions, fit the ROIs to rectangular regions; (iii) enhance the textures of the fitted ROIs combining wavelet transform and fractional differential; (iv) extract texture features from the enhanced ROIs combining wavelet transform and statistical analysis methods; and (v) reduce features using principal component analysis (PCA) and classify the resection margins using the support vector machine (SVM), and then investigate the associations between texture features and histopathological characteristics using the Mann-Whitney U-test. To reduce overfitting, the SVM classifier embedded a linear kernel and adopted the leave-one-out cross-validation. RESULTS: It achieved an AUC (area under receiver operating characteristic curve) of 0.8614 and an accuracy of 84.88%. Setting p ≤ 0.01 in the Mann-Whitney U-test, two features of the run-length matrix, which are derived from diagonal sub-bands in wavelet decomposition, showed statistically significant differences between R0 and R1. CONCLUSIONS: It indicates that the radiomics study is rewarding for the aided diagnosis of R0 and R1. Texture features can potentially enhance physicians' diagnostic ability.

Dysfunctions of mitochondria in close association with strong perturbation of long noncoding RNAs expression in down syndrome.

Trisomy 21 is the most common chromosomal disorder and underlies Down syndrome. Epigenetics, such as DNA methylation and post-translational histone modifications, plays a vital role in Down syndrome. However, the functions of epigenetics-related long noncoding RNAs (lncRNAs), found to have an impact on neural diseases such as Alzheimer's disease, remain unknown in Down syndrome. In this study, we analyzed the RNA sequencing data from Down syndrome-induced pluripotent stem cells (iPSCs) and normal iPSCs. A large number of lncRNAs were identified differentially expressed in Down syndrome-iPSCs. Notably, stronger perturbation was shown in the expression of lncRNAs compared to protein coding genes (Kolmogorov-Smirnov test, P<0.05), suggesting that lncRNAs play more important roles in Down syndrome. Through gene set enrichment analysis and bi-clustering, we also found that most of the differential expressed lncRNAs were closely associated with mitochondrial functions (e.g. mitochondrion organization, P=3.21×10-17; mitochondrial ATP synthesis coupled electron transport, P=1.73×10-19 and mitochondrial membrane organization, P=4.04×10-8). PCR-array and qRT-PCR results revealed that almost all genes related to mitochondria were down-regulated in Down syndrome-iPSCs, implying that mitochondria were dysfunctional in Down syndrome (e.g. ATP5B, Fold Change=-8.2317; COX6A1, Fold Change=-12.7788 and SLC25A17, Fold Change=-22.1296). All in all, our study indicated that a stronger perturbation of lncRNAs expression may lead to the dysfunction of mitochondria in Down syndrome.

Identification and functional analysis of long non-coding RNAs in human and mouse early embryos based on single-cell transcriptome data.

Epigenetics regulations have an important role in fertilization and proper embryonic development, and several human diseases are associated with epigenetic modification disorders, such as Rett syndrome, Beckwith-Wiedemann syndrome and Angelman syndrome. However, the dynamics and functions of long non-coding RNAs (lncRNAs), one type of epigenetic regulators, in human pre-implantation development have not yet been demonstrated. In this study, a comprehensive analysis of human and mouse early-stage embryonic lncRNAs was performed based on public single-cell RNA sequencing data. Expression profile analysis revealed that lncRNAs are expressed in a developmental stage-specific manner during human early-stage embryonic development, whereas a more temporal-specific expression pattern was identified in mouse embryos. Weighted gene co-expression network analysis suggested that lncRNAs involved in human early-stage embryonic development are associated with several important functions and processes, such as oocyte maturation, zygotic genome activation and mitochondrial functions. We also found that the network of lncRNAs involved in zygotic genome activation was highly preservative between human and mouse embryos, whereas in other stages no strong correlation between human and mouse embryo was observed. This study provides insight into the molecular mechanism underlying lncRNA involvement in human pre-implantation embryonic development.