Ensemble learning prediction framework for EGFR amplification status of glioma based on terahertz spectral features.

Epidermal growth factor receptor (EGFR) plays a pivotal role in the initiation and progression of gliomas. In particular, in glioblastoma, EGFR amplification emerges as a catalyst for invasion, proliferation, and resistance to radiotherapy and chemotherapy. Current approaches are not capable of providing rapid diagnostic results of molecular pathology. In this study, we propose a terahertz spectroscopic approach for predicting the EGFR amplification status of gliomas for the first time. A machine learning model was constructed using the terahertz response of the measured glioma tissues, including the absorption coefficient, refractive index, and dielectric loss tangent. The novelty of our model is the integration of three classical base classifiers, i.e., support vector machine, random forest, and extreme gradient boosting. The ensemble learning method combines the advantages of various base classifiers, this model has more generalization ability. The effectiveness of the proposed method was validated by applying an individual test set. The optimal performance of the integrated algorithm was verified with an area under the curve (AUC) maximum of 85.8 %. This signifies a significant stride toward more effective and rapid diagnostic tools for guiding postoperative therapy in gliomas.

MicroRNAs: A novel signature in the metastasis of esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a malignant epithelial tumor, characterized by squamous cell differentiation, it is the sixth leading cause of cancer-related deaths globally. The increased mortality rate of ESCC patients is predominantly due to the advanced stage of the disease when discovered, coupled with higher risk of metastasis, which is an exceedingly malignant characteristic of cancer, frequently leading to a high mortality rate. Unfortunately, there is currently no specific and effective marker to predict and treat metastasis in ESCC. MicroRNAs (miRNAs) are a class of small non-coding RNA molecules, approximately 22 nucleotides in length. miRNAs are vital in modulating gene expression and serve pivotal regulatory roles in the occurrence, progression, and prognosis of cancer. Here, we have examined the literature to highlight the intimate correlations between miRNAs and ESCC metastasis, and show that ESCC metastasis is predominantly regulated or regulated by genetic and epigenetic factors. This review proposes a potential role for miRNAs as diagnostic and therapeutic biomarkers for metastasis in ESCC metastasis, with the ultimate aim of reducing the mortality rate among patients with ESCC.

circRNADisease v2.0: an updated resource for high-quality experimentally supported circRNA-disease associations.

circRNADisease v2.0 is an enhanced and reliable database that offers experimentally verified relationships between circular RNAs (circRNAs) and various diseases. It is accessible at http://cgga.org.cn/circRNADisease/ or http://cgga.org.cn:9091/circRNADisease/. The database currently includes 6998 circRNA-disease entries across multiple species, representing a remarkable 19.77-fold increase compared to the previous version. This expansion consists of a substantial rise in the number of circRNAs (from 330 to 4246), types of diseases (from 48 to 330) and covered species (from human only to 12 species). Furthermore, a new section has been introduced in the database, which collects information on circRNA-associated factors (genes, proteins and microRNAs), molecular mechanisms (molecular pathways), biological functions (proliferation, migration, invasion, etc.), tumor and/or cell line and/or patient-derived xenograft (PDX) details, and prognostic evidence in diseases. In addition, we identified 7 159 865 relationships between mutations and circRNAs among 30 TCGA cancer types. Due to notable enhancements and extensive data expansions, the circRNADisease 2.0 database has become an invaluable asset for both clinical practice and fundamental research. It enables researchers to develop a more comprehensive understanding of how circRNAs impact complex diseases.

Investigation of water quality and aquatic ecological succession of a newly constructed river replenished by reclaimed water in Beijing.

The potential to create new ecosystems in rivers is possible through the use of reclaimed water as a replenishment source, although the long-term effects of this method are unknown. In this study, the water quality and aquatic ecological evolution of a newly constructed river replenished by reclaimed water in Beijing (the Jing River) were investigated, and the conventional water quality, phytoplankton indicators, and submerged plant growth conditions from October 2018 to December 2020 were analyzed. Spearman's correlation and redundancy analysis between possible influential environmental factors and algal indicators were conducted. The results show that the major water quality indicators could meet the water quality standards for landscape water. There were seven phyla present, including 322 species of phytoplankton. The phytoplankton density increased, followed by a decreasing trend. Phytoplankton densities at each monitoring site reached 10 × 106 to 25 × 106 cells/L in 2019 before decreasing in 2020, then ranging from 8 × 106 to 20 × 106 cells/L. Phytoplankton growth was influenced by changing water quality and ecosystems. Consequently, the submerged plant coverage rate gradually increased from 2018 (0%) to 2020 (26.27%-37.06%), as did biodiversity. Through the implementation of ecological restoration measures in the Jing River, the reclaimed water environment evolved into a more natural water environment, which could provide some reference for similar areas to use reclaimed water as a water replenishment source.

Prediction of IDH mutation status of glioma based on terahertz spectral data.

Gliomas are the most common type of primary tumor in the central nervous system in adults. Isocitrate dehydrogenase (IDH) mutation status is an important molecular biomarker for adult diffuse gliomas. In this study, we were aiming to predict IDH mutation status based on terahertz time-domain spectroscopy technology. Ninety-two frozen sections of glioma tissue from nine patients were included, and terahertz spectroscopy data were obtained. Through Least Absolute Shrinkage and Selection Operator (LASSO), Principal component analysis (PCA), and Random forest (RF) algorithms, a predictive model for predicting IDH mutation status in gliomas was established based on the terahertz spectroscopy dataset with an AUC of 0.844. These results indicate that gliomas with different IDH mutation status have different terahertz spectral features, and the use of terahertz spectroscopy can establish a predictive model of IDH mutation status, providing a new way for glioma research.

Temporal and spatial stability of the EM/PM molecular subtypes in adult diffuse glioma.

Detailed characterizations of genomic alterations have not identified subtype-specific vulnerabilities in adult gliomas. Mapping gliomas into developmental programs may uncover new vulnerabilities that are not strictly related to genomic alterations. After identifying conserved gene modules co-expressed with EGFR or PDGFRA (EM or PM), we recently proposed an EM/PM classification scheme for adult gliomas in a histological subtype- and grade-independent manner. By using cohorts of bulk samples, paired primary and recurrent samples, multi-region samples from the same glioma, single-cell RNA-seq samples, and clinical samples, we here demonstrate the temporal and spatial stability of the EM and PM subtypes. The EM and PM subtypes, which progress in a subtype-specific mode, are robustly maintained in paired longitudinal samples. Elevated activities of cell proliferation, genomic instability and microenvironment, rather than subtype switching, mark recurrent gliomas. Within individual gliomas, the EM/PM subtype was preserved across regions and single cells. Malignant cells in the EM and PM gliomas were correlated to neural stem cell and oligodendrocyte progenitor cell compartment, respectively. Thus, while genetic makeup may change during progression and/or within different tumor areas, adult gliomas evolve within a neurodevelopmental framework of the EM and PM molecular subtypes. The dysregulated developmental pathways embedded in these molecular subtypes may contain subtype-specific vulnerabilities.

Substances released during the decomposition of Vallisneria natans and Thalia dealbata.

Two types of aquatic plants commonly used for the ecological restoration of rivers and lakes, Vallisneria natans (Lour.) Hara and Thalia dealbata Fraser ex Roscoe, were selected and grouped by plant parts (root, stem and foliage), and decomposing release experiments were conducted. The influence of the released substances on the water quality was analyzed, as well as the amount of nutrients released by each part of these two plants. The calculated maximum chemical oxygen demand releases from the foliage of V. natans and the foliage of T. dealbata were approximately 5.4 g/kg and 22.65 g/kg, respectively. Through three-dimensional fluorescence spectrum and parallel factor analyses, the different material compositions of the decomposing liquids from the plants were determined, and the main dissolved organic components of the decomposing liquid of V. natans were amino-acid-like and microbially derived humics, and those T. dealbata were soluble microbial by-product-like substances. The carbon-to-nitrogen ratio and humification index of each experimental group were compared. The experimental results showed that different parts of V. natans and T. dealbata had different rates of nutrient release. The dissolved organic matter in the decomposed solution can be utilized by microorganisms, which have the potential to become additional carbon sources. This study provides a new method for the treatment of aquatic plant litter. Different plant species can be used in combination according to their characteristics to ensure that better results are achieved during water treatment processes that use plant decomposing liquids as additional carbon sources.

WES data from 286 diffuse gliomas under the 2021 WHO Classification of Tumors of the Central Nervous System.

Diffuse gliomas (DGs) are the most common and lethal primary neoplasms in the central nervous system. The latest 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) was published in 2021, immensely changing the approach to diagnosis and decision making. As a part of the Chinese Glioma Genome Atlas (CGGA) project, our aim was to provide genomic profiling of gliomas in a Chinese cohort. Two hundred eighty six gliomas with different grades were collected over the last decade. Using the Illumina HiSeq platform, over 75.8 million high-quality 150 bp paired-end reads were generated per sample, yielding a total of 43.4 billion reads. We also collected each patient's clinical and pathological information and used it to annotate their genetic data. All patients were diagnosed and classified by neuro-pathologist under the 2021 WHO classification. This dataset provides an important reference for researchers and will significantly advance our understanding of gliomas.

Identification of tumor-associated antigens and immune subtypes of lower-grade glioma and glioblastoma for mRNA vaccine development.

BACKGROUND: mRNA became a promising therapeutic approach in many diseases. This study aimed to identify the tumor antigens specifically expressed in tumor cells for lower-grade glioma (LGG) and glioblastoma (GBM) patients. METHODS: In this work, the mRNA microarray expression profile and clinical data were obtained from 301 samples in the Chinese Glioma Genome Atlas (CGGA) database, the mRNA sequencing data and clinical data of 701 samples were downloaded from The Cancer Genome Atlas (TCGA) database. Genetic alterations profiles were extracted from CGGA and cBioPortal datasets. R language and GraphPad Prism software were applied for the statistical analysis and graph work. RESULTS: PTBP1 and SLC39A1, which were overexpressed and indicated poor prognosis in LGG patients, were selected as tumor-specific antigens for LGG patients. Meanwhile, MMP9 and SLC16A3, the negative prognostic factors overexpressed in GBM, were identified as tumor-specific antigens for GBM patients. Besides, three immune subtypes (LGG1-LGG3) and eight WGCNA modules were identified in LGG patients. Meanwhile, two immune subtypes (GBM1-GBM2) and 10 WGCNA modules were selected in GBM. The immune characteristics and potential functions between different subtypes were diversity. LGG2 and GBM1 immune subtype were associated with longer overall survival than other subtypes. CONCLUSION: In this study, PTBP1 and SLC39A1 are promising antigens for mRNA vaccines development in LGG, and MMP9 and SLC16A3 were potential antigens in GBM. Our analyses indicated that mRNA vaccine immunotherapy was more suitable for LGG2 and GBM1 subtypes. This study was helpful for the development of glioma immunotherapies.

A novel chemokine-based signature for prediction of prognosis and therapeutic response in glioma.

AIMS: Gliomas are the primary malignant brain tumor and characterized as the striking cellular heterogeneity and intricate tumor microenvironment (TME), where chemokines regulate immune cell trafficking by shaping local networks. This study aimed to construct a chemokine-based gene signature to evaluate the prognosis and therapeutic response in glioma. METHODS: In this study, 1024 patients (699 from TCGA and 325 from CGGA database) with clinicopathological information and mRNA sequencing data were enrolled. A chemokine gene signature was constructed by combining LASSO and SVM-RFE algorithm. GO, KEGG, and GSVA analyses were performed for function annotations of the chemokine signature. Candidate mRNAs were subsequently verified through qRT-PCR in an independent cohort including 28 glioma samples. Then, through immunohistochemical staining (IHC), we detected the expression of immunosuppressive markers and explore the role of this gene signature in immunotherapy for glioma. Lastly, the Genomics of Drug Sensitivity in Cancer (GDSC) were leveraged to predict the potential drug related to the gene signature in glioma. RESULTS: A constructed chemokine gene signature was significantly associated with poorer survival, especially in glioblastoma, IDH wildtype. It also played an independent prognostic factor in both datasets. Moreover, biological function annotations of the predictive signature indicated the gene signature was positively associated with immune-relevant pathways, and the immunosuppressive protein expressions (PD-L1, IBA1, TMEM119, CD68, CSF1R, and TGFB1) were enriched in the high-risk group. In an immunotherapy of glioblastoma cohort, we confirmed the chemokine signature showed a good predictor for patients' response. Lastly, we predicted twelve potential agents for glioma patients with higher riskscore. CONCLUSION: In all, our results highlighted a potential 4-chemokine signature for predicting prognosis in glioma and reflected the intricate immune landscape in glioma. It also threw light on integrating tailored risk stratification with precision therapy for glioblastoma.

Perineural Invasion Is a Significant Indicator of High Malignant Degree and Poor Prognosis in Esophageal Cancer: A Systematic Review and Meta-Analysis.

Background: Perineural invasion (PNI) is a malignant metastatic mode of tumors and has been reported in many tumors including esophageal cancer (EC). However, the role of PNI in EC has been reported differently. This systematic review and meta-analysis aims to focus on the role of PNI in EC. Methods: Eight databases of CNKI, VIP, Wanfang, Scopus, Wiley, ISI, PubMed, and EBSCO are used for literature search. The association of PNI with gender, pathological stages of T and N (pT and pN), lymphovascular invasion (LVI), lymph node metastasis, 5-year overall survival (OS), and 5-year disease-free survival (DFS) was examined in the meta-analysis by Revman5.0 Software. The pooled OR/HR and 95% CI were used to assess the risk and prognostic value. Results: Sixty-nine published studies were screened for analysis of PNI in EC. The incidence of PNI in esophageal squamous carcinoma (ESCC) and esophageal adenocarcinoma (EAC) was different, but not statistically significant (p > 0.05). The PNI-positive patients had a significantly higher risk of pT stage (OR = 3.85, 95% CI = 2.45-6.05, p < 0.00001), pN stage (OR = 1.86, 95% CI = 1.52-2.28, p < 0.00001), LVI (OR = 2.44, 95% CI = 1.55-3.85, p = 0.0001), and lymph node metastasis (OR = 2.87, 95% CI = 1.56-5.29, p = 0.0007). Furthermore, the cumulative analysis revealed a significant correlation between PNI and poor OS (HR = 1.37, 95% CI = 1.24-1.51, p < 0.0001), as well as poor DFS (HR = 1.55, 95% CI = 1.38-1.74, p < 0.0001). Conclusion: PNI occurrence is significantly related to tumor stage, LVI, lymph node metastasis, OS, and DFS. These results indicate that PNI can serve as an indicator of high malignant degree and poor prognosis in EC.

Image-Based Differentiation of Intracranial Metastasis From Glioblastoma Using Automated Machine Learning.

Purpose: The majority of solitary brain metastases appear similar to glioblastomas (GBMs) on magnetic resonance imaging (MRI). This study aimed to develop and validate an MRI-based model to differentiate intracranial metastases from GBMs using automated machine learning. Materials and Methods: Radiomics features from 354 patients with brain metastases and 354 with GBMs were used to build prediction algorithms based on T2-weighted images, contrast-enhanced (CE) T1-weighted images, or both. The data of these subjects were subjected to a nested 10-fold split in the training and testing groups to build the best algorithms using the tree-based pipeline optimization tool (TPOT). The algorithms were independently validated using data from 124 institutional patients with solitary brain metastases and 103 patients with GBMs from the cancer genome atlas. Results: Three groups of models were developed. The average areas under the receiver operating characteristic curve (AUCs) were 0.856 for CE T1-weighted images, 0.976 for T2-weighted images, and 0.988 for a combination in the testing groups, and the AUCs of the groups of models in the independent validation were 0.687, 0.831, and 0.867, respectively. A total of 149 radiomics features were considered as the most valuable features for the differential diagnosis of GBMs and metastases. Conclusion: The models established by TPOT can distinguish glioblastoma from solitary brain metastases well, and its non-invasiveness, convenience, and robustness make it potentially useful for clinical applications.

A Novel TNFSF-Based Signature Predicts the Prognosis and Immunosuppressive Status of Lower-Grade Glioma.

Purpose: Tumour necrosis factor (TNF) superfamilies play important roles in cell proliferation, migration, differentiation, and apoptosis. We believe that TNF has a huge potential and might cast new insight into antitumour therapies. Therefore, we established this signature based on TNF superfamilies. Results: A six-gene signature derived from the TNF superfamilies was established. The Riskscore correlated significantly with the expression of immune checkpoint genes and infiltrating M2 macrophages in the tumour specimen. This signature was also associated with mutations in genes that regulate tumour cell proliferation. Univariate and multivariate regression analyses further confirmed the Riskscore, TNFRSF11b, and TNFRSF12a as independent risk factors in The Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets. Conclusion: Our signature could accurately predict the prognosis of lower-grade gliomas (LGG). In addition, this six-gene signature could predict the immunosuppressive status of LGG and provide evidence that TNF superfamilies had correlations with some critical mutations that could be effectively targeted now.

Corner-opening and corner-capping of mono-substituted T8 POSS: product distribution and isomerization.

We applied chromatographic and spectroscopic techniques to revisit the product distribution of the corner-opening and corner-capping reactions of monosubstituted T8 POSS. The monosubstituted Si is more likely to be removed than the remaining seven Si atoms during the corner-opening. After the corner-capping, the yield of monosubstituted T8 POSS is much higher than the yields of the ortho- and meta-isomers of disubstituted T8 POSS, and the para-isomer is negligible.

Effectiveness of online expressive writing in reducing psychological distress among the asymptomatic COVID-19 patients in Fangcang Hospitals: A quasi-experiment study.

Objective: This study aimed to assess the applicability and effectiveness of an online format of expressive writing (EW) in reducing psychological distress among the asymptomatic COVID-19 patients in Fangcang Hospitals with a quasi-experiment. Method: Altogether 244 patients were assigned to the EW group(n=122) and the control group(n=122). Besides the routine psychological intervention (broadcast relaxation training at a fixed time) in Fangcang hospitals, The EW group was engaged in 8-day theme-based adaption EW intervention, whereas the control group received no interventions. All the participants were tested with the Brief Profile of Mood States (BPOMS) and Inpatient Mental Health Preliminary Screening Scale(IMHPS) before and after the intervention. After the intervention, the writing quality and intervention satisfaction of the EW group were evaluated by a self-designed writing quality questionnaire and EW satisfaction questionnaire. Results: The results indicated that the EW significantly improved in the BPOMS test, whereas the control group showed no significant change. The IMHPS score in the control group was statistically deteriorated than that before intervention, whereas the EW group showed no significant change. The writing quality was highly correlated with the score change of BPMOS. The overall satisfaction of patients with EW was 81.13%. Conclusion: EW can reduce psychological distress among the asymptomatic COVID-19 patients in Fangcang Hospitals. The higher the quality of writing, the greater the improvement of mood states. As a new form of psychological intervention in Fangcang hospitals with high patient satisfaction, EW has a value of popularization and application.

Molecular Characterization and Clinical Relevance of ANXA1 in Gliomas via 1,018 Chinese Cohort Patients.

Annexin A1 (ANXA1) is a calcium-dependent phospholipid-binding protein and has been implicated in multiple functions essential in cancer, including cell proliferation, apoptosis, chemosensitivity, metastasis, and invasion. However, the biological role and clinical behavior of ANXA1 in glioma remain unclear. In this study, RNA-seq (n = 1018 cases) and whole-exome sequencing (WES) (n = 286 cases) data on a Chinese cohort, RNA-seq data with different histological regions of glioblastoma blocks (n = 270 cases), and scRNA-seq data (n = 7630 cells) were used. We used the R software to perform statistical calculations and graph rendering. We found that ANXA1 is closely related to the malignant progression in gliomas. Meanwhile, ANXA1 is significantly associated with clinical behavior. Furthermore, the mutational profile revealed that glioma subtypes classified by ANXA1 expression showed distinct genetic features. Functional analyses suggest that ANXA1 correlates with the immune-related function and cancer hallmark. At a single-cell level, we found that ANXA1 is highly expressed in M2 macrophages and tumor cells of the mesenchymal subtype. Importantly, our result suggested that ANXA1 expression is significant with the patient's survival outcome. Our study revealed that ANXA1 was closely related to immune response. ANXA1 plays a key factor in M2 macrophages and MES tumor cells. Patients with lower ANXA1 expression levels tended to experience improved survival. ANXA1 may become a valuable factor for the diagnosis and treatment of gliomas in clinical practice.

Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas.

PURPOSE: The present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas. METHODS: This retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant IDH, 16 patients with astrocytomas with wild-type IDH, and 65 patients with oligodendrogliomas with mutant IDH and 1p/19q codeletion). Radiomic features were extracted from magnetic resonance images, including T1-weighted, T2-weighted, and contrast T1-weighted images. Elastic net and support vector machines with radial basis function kernel were applied in nested 10-fold cross-validation loops to predict the 1p/19q status. Receiver operating characteristic analysis and precision-recall analysis were used to evaluate the model performance. Student's t-tests were then used to compare the posterior probabilities of 1p/19q co-deletion prediction in the group with different 1p/19q status. RESULTS: Six valuable radiomic features, along with age, were selected with the nested 10-fold cross-validation loops. Five features showed significant difference in patients with different 1p/19q status. The area under curve and accuracy of the predictive model were 0.8079 (95% confidence interval, 0.733-0.8755) and 0.758 (0.6879-0.8217), respectively, and the F1-score of the precision-recall curve achieved 0.6667 (0.5201-0.7705). The posterior probabilities in the 1p/19q co-deletion group were significantly different from the non-deletion group. CONCLUSION: Combined radiomics analysis and machine learning showed potential clinical utility in the preoperative prediction of 1p/19q status, which can aid in making customized neurosurgery plans and glioma management strategies before postoperative pathology.

Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach.

The detection of mutations in telomerase reverse transcriptase promoter (pTERT) is important since preoperative diagnosis of pTERT status helps with evaluating prognosis and determining the surgical strategy. Here, we aimed to establish a radiomics-based machine-learning algorithm and evaluated its performance with regard to the prediction of mutations in pTERT in patients with World Health Organization (WHO) grade II gliomas. In total, 164 patients with WHO grade II gliomas were enrolled in this retrospective study. We extracted a total of 1,293 radiomics features from multi-parametric magnetic resonance imaging scans. Elastic net (used for feature selection) and support vector machine with linear kernel were applied in nested 10-fold cross-validation loops. The predictive model was evaluated by receiver operating characteristic and precision-recall analyses. We performed an unpaired t-test to compare the posterior predictive probabilities among patients with differing pTERT statuses. We selected 12 valuable radiomics features using nested 10-fold cross-validation loops. The area under the curve (AUC) was 0.8446 (95% confidence interval [CI], 0.7735-0.9065) with an optimal summed value of sensitivity of 0.9355 (95% CI, 0.8802-0.9788) and specificity of 0.6197 (95% CI, 0.5071-0.7371). The overall accuracy was 0.7988 (95% CI, 0.7378-0.8598). The F1-score was 0.8406 (95% CI, 0.7684-0.902) with an optimal precision of 0.7632 (95% CI, 0.6818-0.8364) and recall of 0.9355 (95% CI, 0.8802-0.9788). Posterior probabilities of pTERT mutations were significantly different between patients with wild-type and mutant TERT promoters. Our findings suggest that a radiomics analysis with a machine-learning algorithm can be useful for predicting pTERT status in patients with WHO grade II glioma and may aid in glioma management.

Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas.

OBJECTIVE: To predict vascular endothelial growth factor (VEGF) expression in patients with diffuse gliomas using radiomic analysis. MATERIALS AND METHODS: Preoperative magnetic resonance images were retrospectively obtained from 239 patients with diffuse gliomas (World Health Organization grades II-IV). The patients were randomly assigned to a training group (n = 160) or a validation group (n = 79) at a 2:1 ratio. For each patient, a total of 431 radiomic features were extracted. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature selection. A machine-learning model for predicting VEGF status was then developed using the selected features and a support vector machine classifier. The predictive performance of the model was evaluated in both groups using receiver operating characteristic curve analysis, and correlations between selected features were assessed. RESULTS: Nine radiomic features were selected to generate a VEGF-associated radiomic signature of diffuse gliomas based on the mRMR algorithm. This radiomic signature consisted of two first-order statistics or related wavelet features (Entropy and Minimum) and seven textural features or related wavelet features (including Cluster Tendency and Long Run Low Gray Level Emphasis). The predictive efficiencies measured by the area under the curve were 74.1% in the training group and 70.2% in the validation group. The overall correlations between the 9 radiomic features were low in both groups. CONCLUSIONS: Radiomic analysis facilitated efficient prediction of VEGF status in diffuse gliomas, suggesting that using tumor-derived radiomic features for predicting genomic information is feasible.

Genome-Wide CRISPR-Cas9 Screening Identifies NF-κB/E2F6 Responsible for EGFRvIII-Associated Temozolomide Resistance in Glioblastoma.

Amplification of epidermal growth factor receptor (EGFR) and active mutant EGFRvIII occurs frequently in glioblastoma (GBM) and contributes to chemo/radio-resistance in various cancers, especially in GBM. Elucidating the underlying molecular mechanism of temozolomide (TMZ) resistance in GBM could benefit cancer patients. A genome-wide screening under a clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 library is conducted to identify the genes that confer resistance to TMZ in EGFRvIII-expressing GBM cells. Deep sgRNA sequencing reveals 191 candidate genes that are responsible for TMZ resistance in EGFRvIII-expressing GBM cells. Notably, E2F6 is proven to drive a TMZ resistance, and E2F6 expression is controlled by the EGFRvIII/AKT/NF-κB pathway. Furthermore, E2F6 is shown as a promising therapeutic target for TMZ resistance in orthotopic GBM cell line xenografts and GBM patient-derived xenografts models. After integrating clinical data with paired primary-recurrent RNA sequencing data from 134 GBM patients who received TMZ treatment after surgery, it has been revealed that the E2F6 expression level is a predictive marker for TMZ response. Therefore, the inhibition of E2F6 is a promising strategy to conquer TMZ resistance in GBM.