Radiomics model based on dual-energy CT can determine the source of thrombus in strokes with middle cerebral artery occlusion.

PURPOSE: To develop thrombus radiomics models based on dual-energy CT (DECT) for predicting etiologic cause of stroke. METHODS: We retrospectively enrolled patients with occlusion of the middle cerebral artery who underwent computed tomography (NCCT) and DECT angiography (DECTA). 70 keV virtual monoenergetic images (simulate conventional 120kVp CTA images) and iodine overlay maps (IOM) were reconstructed for analysis. Five logistic regression radiomics models for predicting cardioembolism (CE) were built based on the features extracted from NCCT, CTA and IOM images. From these, the best one was selected to integrate with clinical information for further construction of the combined model. The performance of the different models was evaluated and compared using ROC curve analysis, clinical decision curves (DCA), calibration curves and Delong test. RESULTS: Among all the radiomic models, model NCCT+IOM performed the best, with AUC = 0.95 significantly higher than model NCCT, model CTA, model IOM and model NCCT+CTA in the training set (AUC = 0.88, 0.78, 0.90,0.87, respectively, P < 0.05), and AUC = 0.92 in the testing set, significantly higher than model CTA (AUC = 0.71, P < 0.05). Smoking and NIHSS score were independent predictors of CE (P < 0.05). The combined model performed similarly to the model NCCT+IOM, with no statistically significant difference in AUC either in the training or test sets. (0.96 vs. 0.95; 0.94 vs. 0.92, both P > 0.05). CONCLUSION: Radiomics models constructed based on NCCT and IOM images can effectively determine the source of thrombus in stroke without relying on clinical information.

Identification of restrictive molecules involved in oncolytic virotherapy using genome-wide CRISPR screening.

Oncolytic viruses (OVs) offer a novel approach to treat solid tumors; however, their efficacy is frequently suboptimal due to various limiting factors. To address this challenge, we engineered an OV containing targets for neuron-specific microRNA-124 and Granulocyte-macrophage colony-stimulating factor (GM-CSF), significantly enhancing its neuronal safety while minimally compromising its replication capacity. Moreover, we identified PARP1 as an HSV-1 replication restriction factor using genome-wide CRISPR screening. In models of glioblastoma (GBM) and triple-negative breast cancer (TNBC), we showed that the combination of OV and a PARP inhibitor (PARPi) exhibited superior efficacy compared to either monotherapy. Additionally, single-cell RNA sequencing (scRNA-seq) revealed that this combination therapy sensitized TNBC to immune checkpoint blockade, and the incorporation of an immune checkpoint inhibitor (ICI) further increased the survival rate of tumor-bearing mice. The combination of PARPi and ICI synergistically enhanced the ability of OV to establish durable tumor-specific immune responses. Our study effectively overcomes the inherent limitations of OV therapy, providing valuable insights for the clinical treatment of TNBC, GBM, and other malignancies.

Yinhuang granule alleviates carbon tetrachloride-induced liver fibrosis in mice and its mechanism.

BACKGROUND: Liver fibrosis is a formidable global medical challenge, with no effective clinical treatment currently available. Yinhuang granule (YHG) is a proprietary Chinese medicine comprising Scutellariae Radix and Lonicerae Japonicae Flos. It is frequently used for upper respiratory tract infections, pharyngitis, as well as acute and chronic tonsillitis. AIM: To investigate the potential of YHG in alleviating carbon tetrachloride (CCl4)-induced liver fibrosis in mice. METHODS: To induce a hepatic fibrosis model in mice, this study involved intraperitoneal injections of 2 mL/kg of CCl4 twice a week for 4 wk. Meanwhile, liver fibrosis mice in the low dose of YHG (0.4 g/kg) and high dose of YHG (0.8 g/kg) groups were orally administered YHG once a day for 4 wk. Serum alanine/aspartate aminotransferase (ALT/AST) activity and liver hydroxyproline content were detected. Sirius red and Masson's trichrome staining assay were conducted. Real-time polymerase chain reaction, western-blot and enzyme-linked immunosorbent assay were conducted. Liver glutathione content, superoxide dismutase activity level, reactive oxygen species and protein carbonylation amount were detected. RESULTS: The administration of YHG ameliorated hepatocellular injury in CCl4-treated mice, as reflected by decreased serum ALT/AST activity and improved liver histological evaluation. YHG also attenuated liver fibrosis, evident through reduced liver hydroxyproline content, improvements in Sirius red and Masson's trichrome staining, and lowered serum hyaluronic acid levels. Furthermore, YHG hindered the activation of hepatic stellate cells (HSCs) and ameliorated oxidative stress injury and inflammation in liver from CCl4-treated mice. YHG prompted the nuclear accumulation of nuclear factor erythroid 2-related factor 2 (Nrf2) and upregulated the expression of Nrf2-dependent downstream antioxidant genes. In addition, YHG promoted mitochondrial biogenesis in liver from CCl4-treated mice, as demonstrated by increased liver adenosine triphosphate content, mitochondrial DNA levels, and the expression of peroxisome proliferator-activated receptor gamma coactivator 1 alpha and nuclear respiratory factor 1. CONCLUSION: YHG effectively attenuates CCl4-induced liver fibrosis in mice by inhibiting the activation of HSCs, reducing inflammation, alleviating liver oxidative stress damage through Nrf2 activation, and promoting liver mitochondrial biogenesis.

[Tetramethylpyrazine regulates angiogenesis of endothelial cells in cerebral ischemic stroke injury via SIRT1/VEGFA signaling pathway].

This study aims to investigate whether tetramethylpyrazine(TMP) can stimulate angiogenesis in cerebral microvascular endothelial cells and alleviate cerebral ischemic stroke(CIS) and to explore the underlying mechanisms. In the animal study, adult Sprague-Dawley rats(n=15) were assigned into sham surgery(sham), middle cerebral artery occlusion/reperfusion(MCAO/R), and MCAO/R+TMP(intraperitoneal injection of 20 mg·kg~(-1)) groups. The neurological function was evaluated by the Z-Longa method. The cerebral infarction volume was detected by TTC staining. Enzyme-linked immunosorbent assay(ELISA) was employed to detect the expression of vascular endothelial growth factor(VEGF), angiopoietin(Ang), and platelet-derived growth factor(PDGF). Immunofluorescence staining was employed to detect Ki67 and the expression of vascular endothelial growth factor A(VEGFA) and slient information regulator 1(SIRT1). Western blot was employed to determine the expression levels of VEGFA, SIRT1, angiopoietin-2(Ang-2), and platelet-derived growth factor B(PDGFB). In the cell study, mouse brain-derived endothelial cells(Bend.3) were cultured, and the optimal concentration of TMP was determined. Then, VEGF, Ang, and PDGF were detected by ELISA after the addition of cabozantinib. Western blot was employed to measure the expression of VEGFA, Ang-2, and PDGFB. Immunofluorescence staining was used to detect CD31, CD34, and Ki67, and the proliferation, migration, and tube formation ability of Bend.3 cells were observed in vitro. Western blot and immunofluorescence staining were performed to measure the expression of SIRT1 and VEGFA after addition of the SIRT1-specific inhibitor selisistat(EX-527). The results showed that compared with the sham group, the MCAO/R group had severe neurological function damage, increased infarction volume, up-regulated expression of VEGF, VEGFA, Ang, Ang-2, PDGF, and PDGFB, and down-regulated expression of Ki67 and SIRT1(P<0.01). Compared with the MCAO/R group, the MCAO/R+TMP group presented alleviated neurological function damage, reduced infarction volume, and activated expression of VEGF, VEGFA, Ang, Ang-2, PDGF, PDGFB, Ki67, and SIRT1(P<0.01). The cell experiments showed that compared with the normal group, Bend.3 cells were activated by oxygen glucose deprivation/reoxygenation(OGD/R) treatment(P<0.05, P<0.01). Compared with the OGD/R group, the OGD/R+TMP group upregulated the expression levels of VEGF, VEGFA, Ang, Ang-2, PDGF, PDGFB, SIRT1, Ki67, CD31, and CD34, enhanced the angiogenic ability of Bend.3 cells without being inhibited by BMS or EX-527(P<0.05, P<0.01, P<0.001). The results suggest that TMP can activate the SIRT1/VEGFA signaling pathway to stimulate angiogenesis and alleviate CIS injury.

Associating Peritoneal Metastasis With T2-Weighted MRI Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study.

BACKGROUND: The preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision-making. PURPOSE: To investigate the performance of T2 -weighted (T2W) MRI-based deep learning (DL) and radiomics methods for PM evaluation in EOC patients. STUDY TYPE: Retrospective. POPULATION: Four hundred seventy-nine patients from five centers, including one training set (N = 297 [mean, 54.87 years]), one internal validation set (N = 75 [mean, 56.67 years]), and two external validation sets (N = 53 [mean, 55.58 years] and N = 54 [mean, 58.22 years]). FIELD STRENGTH/SEQUENCE: 1.5 or 3 T/fat-suppression T2W fast or turbo spin-echo sequence. ASSESSMENT: ResNet-50 was used as the architecture of DL. The largest orthogonal slices of the tumor area, radiomics features, and clinical characteristics were used to construct the DL, radiomics, and clinical models, respectively. The three models were combined using decision-level fusion to create an ensemble model. Diagnostic performances of radiologists and radiology residents with and without model assistance were evaluated. STATISTICAL TESTS: Receiver operating characteristic analysis was used to assess the performances of models. The McNemar test was used to compare sensitivity and specificity. A two-tailed P < 0.05 was considered significant. RESULTS: The ensemble model had the best AUCs, outperforming the DL model (0.844 vs. 0.743, internal validation set; 0.859 vs. 0.737, external validation set I) and clinical model (0.872 vs. 0.730, external validation set II). After model assistance, all readers had significantly improved sensitivity, especially for those with less experience (junior radiologist1, from 0.639 to 0.820; junior radiologist2, from 0.689 to 0.803; resident1, from 0.623 to 0.803; resident2, from 0.541 to 0.738). One resident also had significantly improved specificity (from 0.633 to 0.789). DATA CONCLUSIONS: T2W MRI-based DL and radiomics approaches have the potential to preoperatively predict PM in EOC patients and assist in clinical decision-making. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Apparent diffusion coefficient measurements of bone marrow infiltration patterns in multiple myeloma for the assessment of tumor burden - a feasibility study.

BACKGROUND: The purpose of our study was to explore and compare the tumor burden of different bone marrow infiltration patterns and evaluate the feasibility of apparent diffusion coefficient (ADC) value to identify patterns in multiple myeloma (MM). PATIENTS AND METHODS: Ninety-three patients with newly diagnosed multiple myeloma and 23 controls had undergone routine magnetic resonance imaging (MRI) and diffusion-weighted MRI (DWI) from January 2019 to November 2020. Five bone marrow (BM) infiltration patterns were allocated according to routine MRI. The laboratory data and ADC values of patterns were analyzed and compared. ROC analysis was used to establish the best diagnostic ADC threshold value for identifying these patterns and distinguishing normal pattern from controls. Besides, the correlation between the ADC values of diffuse pattern and the plasma cells ratio was assessed. RESULTS: The values of hemoglobin, beta-2 microglobulin (ß2-MG), plasma cell, M protein, the percentages of stage, high-risk fluorescence in situ hybridization, and ADC values showed significant difference among patterns. ADCmean at a specific value (368.5×10-6 mm2/s) yielded a maximum specificity (95.5%) and sensitivity (92.0%) in diagnosing MM. A specific value (335.5×10-6mm2/s) yielded a maximum specificity (84.7%) and sensitivity (88.0%) in discriminating visually normal pattern in MM from controls. There was a moderate positive correlation between the plasma cells ratio and ADCs of diffuse infiltration patterns (r = 0.648, P < 0.001). CONCLUSIONS: The bone marrow infiltration patterns in MM patients can indicate the tumor burden and ADC value has the ability to discriminate these patterns objectively.

Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based deep learning radiomics nomogram (DLRN) to differentiate between type I and type II epithelial ovarian cancer (EOC). MATERIALS AND METHODS: This multicenter study incorporated 437 patients from five centers, divided into training (n = 271), internal validation (n = 68), and external validation (n = 98) sets. The deep learning (DL) model was constructed using the largest orthogonal slices of the tumor area. The extracted radiomics features were employed in building the radiomics model. The clinical model was developed based on clinical characteristics. A DLRN was built by integrating the DL signature, radiomics signature, and independent clinical predictors. Model performances were evaluated through receiver operating characteristic (ROC) analysis, Brier score, calibration curve, and decision curve analysis (DCA). The areas under the ROC curve (AUCs) were compared using the DeLong test. A two-tailed P < 0.05 was considered significantly different. RESULTS: The DLRN exhibited satisfactory discrimination between type I and type II EOC with the AUC of 0.888 (95% confidence interval [CI] 0.810, 0.966) and 0.866 (95% CI 0.786, 0.946) in the internal and external validation sets, respectively. These AUCs significantly exceeded those of the clinical model (P = 0.013 and 0.043, in the internal and external validation sets, respectively). The DLRN demonstrated optimal classification accuracy and clinical application value, according to Brier scores, calibration curves, and DCA. CONCLUSION: A T2-weighted MRI-based DLRN showed promising potential in differentiating between type I and type II EOC, which could offer assistance in clinical decision-making.

Functional Interrogation of Ca2+ Signals in Human Cancer Cells In Vitro and Ex Vivo by Fluorescent Microscopy and Molecular Tools.

Genetically encoded calcium indicators (GECIs) and high-resolution confocal microscopy enable dynamic visualization of calcium signals in cells and tissues. Two-dimensional and 3D biocompatible materials mimic the mechanical microenvironments of tumor and healthy tissues in a programmable manner. Cancer xenograft models and ex vivo functional imaging of tumor slices reveal physiologically relevant functions of calcium dynamics in tumors at different progression stages. Integration of these powerful techniques allows us to quantify, diagnose, model, and understand cancer pathobiology. Here, we describe detailed materials and methods used to establish this integrated interrogation platform, from generating transduced cancer cell lines that stably express CaViar (GCaMP5G + QuasAr2) to in vitro and ex vivo calcium imaging of the cells in 2D/3D hydrogels and tumor tissues. These tools open the possibility for detailed explorations of mechano-electro-chemical network dynamics in living systems.

ExosomePurity: tumour purity deconvolution in serum exosomes based on miRNA signatures.

Exosomes cargo tumour-characterized biomolecules secreted from cancer cells and play a pivotal role in tumorigenesis and cancer progression, thus providing their potential for non-invasive cancer monitoring. Since cancer cell-derived exosomes are often mixed with those from healthy cells in liquid biopsy of tumour patients, accurately measuring the purity of tumour cell-derived exosomes is not only critical for the early detection but also essential for unbiased identification of diagnosis biomarkers. Here, we propose 'ExosomePurity', a tumour purity deconvolution model to estimate tumour purity in serum exosomes of cancer patients based on microribonucleic acid (miRNA)-Seq data. We first identify the differently expressed miRNAs as signature to distinguish cancer cell- from healthy cell-derived exosomes. Then, the deconvolution model was developed to estimate the proportions of cancer exosomes and normal exosomes in serum. The purity predicted by the model shows high correlation with actual purity in simulated data and actual data. Moreover, the model is robust under the different levels of noise background. The tumour purity was also used to correct differential expressed gene analysis. ExosomePurity empowers the research community to study non-invasive early diagnosis and to track cancer progression in cancers more efficiently. It is implemented in R and is freely available from GitHub (https://github.com/WangHYLab/ExosomePurity).

Targeting mTOR Complex 2 in Castration-Resistant Prostate Cancer with Acquired Docetaxel Resistance.

Purpose: Mammalian Target of rapamycin (mTOR) plays a central role in regulating cell growth, proliferation, and cell cycle. The key component of mTORC2 is highly expressed in docetaxel-resistant prostate cells. However, the underlying molecular effects on prostate cells remain unclear. Methods: A docetaxel-resistant human prostate cell line (PC-3/DTX) was constructed to investigate the role of mTORC2 in docetaxel resistance. The lentivirus was transfected into cells to knock down the expression of Rictor, and cell viability was measured by Cell Counting Kit 8 (CCK-8). Flow cytometry was used to analyze the cell cycle, and the changes in related signal cascades were assessed by immunohistochemistry (IHC) staining and Western blot. Results: Docetaxel showed the lowest IC50 (50% inhibitory concentration) in PC-3/DTX cells with sh-RNA. Decreased Rictor expression resulted in a larger proportion of arrested cells in the G0/G1 phase in PC-3/DTX cells. The IC50 values of the AZD8055 group were lower than in the Rapamycin group when treated with docetaxel again. Furthermore, a larger proportion of PC-3/DTX cells were arrested in the G0/G1 phase in the AZD8055 group compared to the Rapamycin group. The IHC results of the prostate cancer tissues from a CRPC patient revealed the over expression of Rictor only, while Raptor expression was unaffected. Conclusion: We investigated the role of mTORC2 signaling on the acquired docetaxel -resistant PC-3 cells to identify potential methods for clinical treatment. MTORC2 expression is essential for docetaxel drug resistance of PC-3 cells. The mTORC1/2 inhibitor AZD8055 caused more significant disruption of mTORC2 kinase activity than the mTORC1 inhibitor Rapamycin, which lead to decreased docetaxel-mediated resistance. Therefore, reversing docetaxel resistance, may become a therapeutic option in the treatment of mCRPC patients.

T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study.

BACKGROUND: Preoperative differentiation between benign and borderline epithelial ovarian tumors (EOTs) is challenging and can significantly impact clinical decision making. The purpose was to investigate whether radiomics based on T2-weighted MRI can discriminate between benign and borderline EOTs preoperatively. METHODS: A total of 417 patients (309, 78, and 30 samples in the training and internal and external validation sets) with pathologically proven benign and borderline EOTs were included in this multicenter study. In total, 1130 radiomics features were extracted from manually delineated tumor volumes of interest on images. The following three different models were constructed and evaluated: radiomics features only (radiomics model); clinical and radiological characteristics only (clinic-radiological model); and a combination of them all (combined model). The diagnostic performances of models were assessed using receiver operating characteristic (ROC) analysis, and area under the ROC curves (AUCs) were compared using the DeLong test. RESULTS: The best machine learning algorithm to distinguish borderline from benign EOTs was the logistic regression. The combined model achieved the best performance in discriminating between benign and borderline EOTs, with an AUC of 0.86 ± 0.07. The radiomics model showed a moderate AUC of 0.82 ± 0.07, outperforming the clinic-radiological model (AUC of 0.79 ± 0.06). In the external validation set, the combined model performed significantly better than the clinic-radiological model (AUCs of 0.86 vs. 0.63, p = 0.021 [DeLong test]). CONCLUSIONS: Radiomics, based on T2-weighted MRI, can provide critical diagnostic information for discriminating between benign and borderline EOTs, thus having the potential to aid personalized treatment options.

Towards an integrative understanding of cancer mechanobiology: calcium, YAP, and microRNA under biophysical forces.

An increasing number of studies have demonstrated the significant roles of the interplay between microenvironmental mechanics in tissues and biochemical-genetic activities in resident tumor cells at different stages of tumor progression. Mediated by molecular mechano-sensors or -transducers, biomechanical cues in tissue microenvironments are transmitted into the tumor cells and regulate biochemical responses and gene expression through mechanotransduction processes. However, the molecular interplay between the mechanotransduction processes and intracellular biochemical signaling pathways remains elusive. This paper reviews the recent advances in understanding the crosstalk between biomechanical cues and three critical biochemical effectors during tumor progression: calcium ions (Ca2+), yes-associated protein (YAP), and microRNAs (miRNAs). We address the molecular mechanisms underpinning the interplay between the mechanotransduction pathways and each of the three effectors. Furthermore, we discuss the functional interactions among the three effectors in the context of soft matter and mechanobiology. We conclude by proposing future directions on studying the tumor mechanobiology that can employ Ca2+, YAP, and miRNAs as novel strategies for cancer mechanotheraputics. This framework has the potential to bring insights into the development of novel next-generation cancer therapies to suppress and treat tumors.

Enhancing Cancer Driver Gene Prediction by Protein-Protein Interaction Network.

With the advances in gene sequencing technologies, millions of somatic mutations have been reported in the past decades, but mining cancer driver genes with oncogenic mutations from these data remains a critical and challenging area of research. In this study, we proposed a network-based classification method for identifying cancer driver genes with merging the multi-biological information. In this method, we construct a cancer specific genetic network from the human protein-protein interactome (PPI) to mine the network structure attributes, and combine biological information such as mutation frequency and differential expression of genes to achieve accurate prediction of cancer driver genes. Across seven different cancer types, the proposed algorithm always achieves high prediction accuracy, which is superior to the existing advanced methods. In the analysis of the predicted results, about 40 percent of the top 10 candidate genes overlap with the Cancer Gene Census database. Interestingly, the feature comparison indicates that the network based features are still more important than the biological features, including the mutation frequency and genetic differential expression. Further analyses also show that the integration of network structure attributes and biological information is valuable for predicting new cancer driver genes.

A multi-epitope vaccine GILE against Echinococcus Multilocularis infection in mice.

Introduction: The objective of this study is to construct a multi-epitope vaccine GILE containing B-cell and T-cell epitopes against Echinococcus Multilocularis (E. multilocularis) infection based on the dominant epitopes of E. multilocularis EMY162, LAP, and GLUT1. Methods: The structure and hydrophobicity of GILE were predicted by SWISSMODEL, pyMOL, SOPMA and VMD, and its sequence was optimized by Optimum™ Codon. The GILE gene was inserted into pCzn1 and transformed into Escherichia coli Arctic express competent cells. IPTG was added to induce the expression of recombinant proteins. High-purity GILE recombinant protein was obtained by Ni-NTA Resin. BALB/c mice were immunized with GILE mixed with Freund's adjuvant, and the antibody levels and dynamic changes in the serum were detected by ELISA. Lymphocyte proliferation was detected by MTS. The levels of IFN-g and IL-4 were detected by ELISpot and flow cytometry (FCM). T cells were detected by FCM. The growth of hepatic cysts was evaluated by Ultrasound and their weights were measured to evaluate the immune protective effect of GILE. Results: The SWISS-MODEL analysis showed that the optimal model was EMY162 95-104-LAP464-479-LAP495-510-LAP396-410-LAP504-518-EMY162112-126. The SOPMA results showed that there were Alpha helix (14.88%), Extended strand (26.25%), Beta turn (3.73%) and Random coil (45.82%) in the secondary structure of GILE. The restriction enzyme digestion and sequencing results suggested that the plasmid pCzn1-GILE was successfully constructed. The SDSPAGE results indicated that the recombinant protein was 44.68 KD. The ELISA results indicated that mice immunized with GILE showed higher levels of serum antibodies compared to the PBS group. The FCM and ELISpot results indicated that mice immunized with GILE secreted more IFN-g and IL-4. Immunization with GILE also led to a significant decrease in the maximum diameter and weight of cysts and stimulated the production of CD4+ and CD8+ T Cell. Discussion: A multi-epitope vaccine GILE with good immunogenicity and antigenicity has been successfully constructed in this study, which may provide important theoretical and experimental bases for the prevention and treatment of E. multilocularis infection.

Therapeutic Response-Based Reclassification of Multiple Tumor Subtypes Reveals Intrinsic Molecular Concordance of Therapy Across Histologically Disparate Cancers.

Cancers that are histologically defined as the same type of cancer often need a distinct therapy based on underlying heterogeneity; likewise, histologically disparate cancers can require similar treatment approaches due to intrinsic similarities. A comprehensive analysis integrated with drug response data and molecular alterations, particularly to reveal therapeutic concordance mechanisms across histologically disparate tumor subtypes, has not yet been fully exploited. In this study, we integrated pharmacological, genomic, and transcriptomic profiling data provided from the Cancer Genome Project (CGP) in a systematic in silico investigation of the pharmacological subtypes of cancers and the intrinsic concordance of molecular mechanisms leading to similar therapeutic responses across histologically disparate tumor subtypes. We further developed a novel approach to redefine cell-to-cell similarity and drug-to-drug similarity from the therapeutic concordance, providing a new point of view to study cancer heterogeneity. This study demonstrates how pharmacological and omics data can be used to systematically classify cancers in terms of response to various compounds and provides us with a purely therapy-oriented perspective to view tumor classifications independent of histology subtypes. The knowledge of pharmacological subtypes of 367 drugs are available via our website (http://www.hywanglab.cn/dtdb/), providing the resources for precision medicine in the perspective of therapeutic response-based re-classification of tumor.

The Relevance of Regenerating Gene 1a Polymorphisms to Radiation Sensitivity and Survival of Nasopharyngeal Carcinoma Receiving Radiotherapy in a Southern Chinese Population.

OBJECTIVE: Gene polymorphism is closely related to tumor development, therapeutic response and prognosis. The relationship between regenerating gene 1A (Reg1A) polymorphism and nasopharyngeal carcinoma (NPC) is unclear. This retrospective study aimed to analyze the association between Reg1a polymorphisms and metastasis, radiation sensitivity and survivals in patients with NPC. METHODS: A total of 308 patients who had received radiotherapy at the Affiliated Xinhua Hospital, Hainan Medical College, between January 2010 and December 2018 with NPC, were enrolled for assessment of Reg1a polymorphisms through direct DNA sequencing. RESULTS: In the polymorphism of gene REG1A, patients with rs10165462 20CC genotype had later T stages (OR = 4.051, 95% CI: 1.775-9.244, P = 0.001), whereas carriers with rs12072 2922CC genotype had earlier T stages (OR = 1.891, 95% CI: 1.018-3.514, P = 0.044) after adjustments for age and gender, respectively. Among rs10165462 20 C/T polymorphism, 20TT wild-type was associated with better radiation response (P = 0.0019), and multivariate analysis showed that it was the only genotype of polymorphism that was significantly associated with better radiation response (OR = 0.265, 95% CI: 0.096-0.727, P = 0.01). Patients with the 20TT wild-type had a better five-year overall survival (60.9%) rate and five-year progression-free survival (60.8%) than those with the 20CC genotype (41.8% and 39.4%, P = 0.01 and P = 0.004, respectively). Patients with variant alleles (CC + CT) had significantly poorer OS (45.2%) and PFS (41.8%) compared with wild-type (TT) carriers (60.9% and 60.8%; P = 0.037 and P = 0.015, respectively). As for rs12072, patients with variant alleles (TT + TC) had significantly adverse OS and PFS compared with wild-type (CC) carriers (62.5% vs 44.8% and 62.5% vs 42.9%; P = 0.024 and P = 0.027, respectively). Cox regression showed that rs10165462 20CT was the only prognostic factor for OS (HR = 1.642, 95% CI 1.038-2.598, P = 0.034) and PFS (HR = 1.705, 95% CI 1.080-2.692, P = 0.022). CONCLUSION: Reg1a polymorphisms may be a predictor of radiation response, local invasion, OS and PFS in patients with NPC who undergo radiotherapy treatment.

Controllable Preparation of Core-Shell Composites and Their Templated Hollow Carbons Based on a Well-Orchestrated Molecular Bridge-Linked Organic-Inorganic Hybrid Interface.

Controlling the interfacial effect is facing challenges because of the weak interactions between the inorganic and the organic materials. We found that the silane coupling agents with -NH2 groups (e.g., KH550) play a key role as a molecular bridge that links an inorganic silica template with an organic precursor (i.e., pyrrole) in the process of constructing a spherical silica core-polypyrrole shell structure. The molecular bridge is also suitable for inorganic core templates with cube or rod shapes for the construction of different core-shell structures. These template core-polymeric shell structures can be transformed into well-defined hollow carbons after carbonization and template removal. The outer diameter, hollow-core size, and carbon shell thickness of hollow carbon materials (e.g., hollow carbon spheres) could be facilely controlled by changing the template size or the pyrrole amount. We believe that our work will provide a guideline for the preparation of well-orchestrated carbon-based composites and their templated hollow carbons.

Differentiating Between Multiple Myeloma and Metastasis Subtypes of Lumbar Vertebra Lesions Using Machine Learning-Based Radiomics.

OBJECTIVE: To determine whether machine learning based on conventional magnetic resonance imaging (MRI) sequences have the potential for the differential diagnosis of multiple myeloma (MM), and different tumor metastasis lesions of the lumbar vertebra. METHODS: We retrospectively enrolled 107 patients newly diagnosed with MM and different metastasis of the lumbar vertebra. In total 60 MM lesions and 118 metastasis lesions were selected for training classifiers (70%) and subsequent validation (30%). Following segmentation, 282 texture features were extracted from both T1WI and T2WI images. Following regression analysis using the least absolute shrinkage and selection operator (LASSO) algorithm, the following machine learning models were selected: Support-Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Artificial Neural Networks (ANN), and Naïve Bayes (NB) using 10-fold cross validation, and the performances were evaluated using a confusion matrix. Matthews correlation coefficient (MCC), sensitivity, specificity, and accuracy of the models were also calculated. RESULTS: To differentiate MM and metastasis, 13 features in the T1WI images and 9 features in the T2WI images were obtained. Among the 10 classifiers, the ANN classifier from the T2WI images achieved the best performance (MCC = 0.605) with accuracy, sensitivity, and specificity of 0.815, 0.879, and 0.790, respectively, in the validation cohort. To differentiate MM and metastasis subtypes, eight features in the T1WI images and seven features in the T2WI images were obtained. Among the 10 classifiers, the ANN classifier from the T2WI images achieved the best performance (MCC = 0.560, 0.412, 0.449), respectively, with accuracy = 0.648; sensitivity 0.714, 0.821, 0.897 and specificity 0.775, 0.600, 0.640 for the MM, lung, and other metastases, respectively, in the validation cohort. CONCLUSIONS: Machine learning-based classifiers showed a satisfactory performance in differentiating MM lesions from those of tumor metastasis. While their value for distinguishing myeloma from different metastasis subtypes was moderate.

Unbiased RNA-Seq-driven identification and validation of reference genes for quantitative RT-PCR analyses of pooled cancer exosomes.

BACKGROUND: Exosomes are extracellular vesicles (EVs) derived from endocytic compartments of eukaryotic cells which contain various biomolecules like mRNAs or miRNAs. Exosomes influence the biologic behaviour and progression of malignancies and are promising candidates as non-invasive diagnostic biomarkers or as targets for therapeutic interventions. Usually, quantitative real-time polymerase chain reaction (qRT-PCR) is used to assess gene expression in cancer exosomes, however, the ideal reference genes for normalization yet remain to be identified. RESULTS: In this study, we performed an unbiased analysis of high-throughput mRNA and miRNA-sequencing data from exosomes of patients with various cancer types and identify candidate reference genes and miRNAs in cancer exosomes. The expression stability of these candidate reference genes was evaluated by the coefficient of variation "CV" and the average expression stability value "M". We subsequently validated these candidate reference genes in exosomes from an independent cohort of ovarian cancer patients and healthy control individuals by qRT-PCR. CONCLUSIONS: Our study identifies OAZ1 and hsa-miR-6835-3p as the most reliable individual reference genes for mRNA and miRNA quantification, respectively. For superior accuracy, we recommend the use of a combination of reference genes - OAZ1/SERF2/MPP1 for mRNA and hsa-miR-6835-3p/hsa-miR-4468-3p for miRNA analyses.