Non-apoptotic regulated cell death mediates reprogramming of the tumour immune microenvironment by macrophages.

Tumour immune microenvironment (TIME) plays an indispensable role in tumour progression, and tumour-associated macrophages (TAMs) are the most abundant immune cells in TIME. Non-apoptotic regulated cell death (RCD) can avoid the influence of tumour apoptosis resistance on anti-tumour immune response. Specifically, autophagy, ferroptosis, pyroptosis and necroptosis mediate the crosstalk between TAMs and tumour cells in TIME, thus reprogram TIME and affect the progress of tumour. In addition, although some achievements have been made in immune checkpoint inhibitors (ICIs), there is still defect that ICIs are only effective for some people because non-apoptotic RCD can bypass the apoptosis resistance of tumour. As a result, ICIs combined with targeting non-apoptotic RCD may be a promising solution. In this paper, the basic molecular mechanism of non-apoptotic RCD, the way in which non-apoptotic RCD mediates crosstalk between TAMs and tumour cells to reprogram TIME, and the latest research progress in targeting non-apoptotic RCD and ICIs are reviewed.

USP5 promotes tumorigenesis by activating Hedgehog/Gli1 signaling pathway in osteosarcoma.

Changes in protein ubiquitination have been linked to cancer. Deubiquitinating enzymes (DUBs) counteract E3 ligase activities and have emerged as promising targets for cancer treatment. Ubiquitin-specific peptidase 5 (USP5) is a member of the DUBs family and has been implicated in promoting tumorigenesis in numerous cancers. However, the clinical significance and biological function of USP5 in osteosarcoma (OS) remains unclear. Here, we found elevated USP5 expression in OS tissues compared with normal bone tissues. Furthermore, we observed significant associations of elevated USP5 levels with increased mortality and more malignant phenotypes in OS patients. Moreover, our results revealed that USP5 could facilitate metastasis and cell progression in OS by activating the hedgehog (Hh) signaling pathway using cultured cells and animal tumor models. Mechanistically, USP5 appeared to stabilize and deubiquitinate Gli1, a key mediator of the Hh signaling pathway. Additionally, the oncogenic effect of USP5 in OS was dependent on Gli1 stability. Our findings support the model where USP5 contributes to OS pathogenesis by activating the Hh/Gli1 signaling pathway, making USP5 a potential diagnostic and therapeutic target for OS.

PSMB2 plays an oncogenic role in glioma and correlates to the immune microenvironment.

There has been an upward trend in the incidence of glioma, with high recurrence and high mortality. The beta subunits of the 20S proteasome are encoded by the proteasome beta (PSMB) genes and may affect the proteasome's function in glioma, assembly and inhibitor binding. This study attempted to reveal the function of the proliferation and invasion of glioma cells, which is affected by proteasome 20S subunit beta 2 (PSMB2). We subjected the data downloaded from the TCGA database to ROC, survival, and enrichment analyses. After establishing the stable PSMB2 knockdown glioma cell line. We detect the changes in the proliferation, invasion and migration of glioma cells by plate colony formation assay, transwell assay, wound healing assay and flow cytometry and PSMB2 expression was verified by quantitative PCR and Western blotting to identify the mRNA and protein levels. PSMB2 expression was higher in glioma tissues, and its expression positively correlated with poor prognosis and high tumor grade and after PSMB2 knockdown, the proliferation, invasion and migration of glioma cells were weakened.

Deep learning-based quantification of total bleeding volume and its association with complications, disability, and death in patients with aneurysmal subarachnoid hemorrhage.

OBJECTIVE: The relationships between immediate bleeding severity, postoperative complications, and long-term functional outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) remain uncertain. Here, the authors apply their recently developed automated deep learning technique to quantify total bleeding volume (TBV) in patients with aSAH and investigate associations between quantitative TBV and secondary complications, adverse long-term functional outcomes, and death. METHODS: Electronic health record data were extracted for adult patients admitted to a single institution within 72 hours of aSAH onset between 2018 and 2021. An automatic deep learning model was used to fully segment and quantify TBV on admission noncontrast head CT images. Patients were subgrouped by TBV quartile, and multivariable logistic regression, restricted cubic splines, and subgroup analysis were used to explore the relationships between TBV and each clinical outcome. RESULTS: A total of 819 patients were included in the study. Sixty-six (8.1%) patients developed hydrocephalus, while 43 (5.3%) experienced rebleeding, 141 (17.2%) had delayed cerebral ischemia, 88 (10.7%) died in the 12 months after discharge, and 208 (25.7%) had a modified Rankin Scale score ≥ 3 12 months after discharge. On multivariable analysis, patients in the highest TBV quartile (> 37.94 ml) had an increased risk of hydrocephalus (adjusted OR [aOR] 4.38, 95% CI 1.61-11.87; p = 0.004), rebleeding (aOR 3.26, 95% CI 1.03-10.33; p = 0.045), death (aOR 6.92, 95% CI 1.89-25.37; p = 0.004), and 12-month disability (aOR 3.30, 95% CI 1.62-6.72; p = 0.001) compared with the lowest TBV quantile (< 8.34 ml). The risks of hydrocephalus (nonlinear, p = 0.025), rebleeding, death, and disability (linear, p > 0.05) were positively associated with TBV by restricted cubic splines. In subgroup analysis, TBV had a stronger effect on 12-month outcome in female than male patients (p for interaction = 0.0499) and on rebleeding prevalence in patients with endovascular coiling than those with surgical clipping (p for interaction = 0.008). CONCLUSIONS: Elevated TBV is associated with a greater risk of hydrocephalus, rebleeding, death, and poor prognosis.

Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage.

OBJECTIVE: Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP). METHODS: A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis. RESULTS: Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions. CONCLUSIONS: This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.

Carbon quantum dots of ginsenoside Rb1 for application in a mouse model of intracerebral Hemorrhage.

After intracerebral hemorrhage (ICH) occurs, the overproduction of reactive oxygen species (ROS) and iron ion overload are the leading causes of secondary damage. Removing excess iron ions and ROS in the meningeal system can effectively alleviate the secondary damage after ICH. This study synthesized ginsenoside Rb1 carbon quantum dots (RBCQDs) using ginsenoside Rb1 and ethylenediamine via a hydrothermal method. RBCQDs exhibit potent capabilities in scavenging ABTS + free radicals and iron ions in solution. After intrathecal injection, the distribution of RBCQDs is predominantly localized in the subarachnoid space. RBCQDs can eliminate ROS and chelate iron ions within the meningeal system. Treatment with RBCQDs significantly improves blood flow in the meningeal system, effectively protecting dying neurons, improving neurological function, and providing a new therapeutic approach for the clinical treatment of ICH.

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis.

BACKGROUND: Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking. MATERIALS AND METHODS: PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively. RESULTS: A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P=0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P=0.001). CONCLUSION: This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.

Comprehensive analyses of m1A regulator-mediated modification patterns determining prognosis in lower-grade glioma (running title: m1A in LGG).

N1-methyladenosine (m1A) modification is a crucial post-transcriptional regulatory mechanism of messenger RNA (mRNA) in living organisms. Few studies have focused on analysis of m1A regulators in lower-grade gliomas (LGG). We employed the Nonnegative Matrix Factorization (NMF) technique on The Cancer Genome Atlas (TCGA) dataset to categorize LGG patients into 2 groups. These groups exhibited substantial disparities in terms of both overall survival (OS) and levels of infiltrating immune cells. We collected the significantly differentially expressed immune-related genes between the 2 clusters, and performed LASSO regression analysis to obtain m1AScores, and established an m1A-related immune-related gene signature (m1A-RIGS). Next, we categorized all patients with LGG into high- and low-risk subgroups, predictive significance of m1AScore was confirmed by conducting univariate/multivariate Cox regression analyses. Additionally, we confirmed variations in immune-related cells and ssGSEA and among the high-/low-risk subcategories in the TCGA dataset. Finally, our study characterized the effects of MSR1 and BIRC5 on LGG cells utilizing Edu assay and flow cytometry to explore the effects of modulation of these genes on glioma. The results of this study suggested that m1A-RIGS may be an excellent prognostic indicator for patients with LGG, and could also promote development of novel immune-based treatment strategies for LGG. Additionally, BIRC5 and MSR1 may be potential therapeutic targets for LGG.

Multiomics and blood-based biomarkers of moyamoya disease: protocol of Moyamoya Omics Atlas (MOYAOMICS).

BACKGROUND: Moyamoya disease (MMD) is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels. The etiology of MMD remains enigmatic, making diagnosis and management challenging. The MOYAOMICS project was initiated to investigate the molecular underpinnings of MMD and explore potential diagnostic and therapeutic strategies. METHODS: The MOYAOMICS project employs a multidisciplinary approach, integrating various omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, to comprehensively examine the molecular signatures associated with MMD pathogenesis. Additionally, we will investigate the potential influence of gut microbiota and brain-gut peptides on MMD development, assessing their suitability as targets for therapeutic strategies and dietary interventions. Radiomics, a specialized field in medical imaging, is utilized to analyze neuroimaging data for early detection and characterization of MMD-related brain changes. Deep learning algorithms are employed to differentiate MMD from other conditions, automating the diagnostic process. We also employ single-cellomics and mass cytometry to precisely study cellular heterogeneity in peripheral blood samples from MMD patients. CONCLUSIONS: The MOYAOMICS project represents a significant step toward comprehending MMD's molecular underpinnings. This multidisciplinary approach has the potential to revolutionize early diagnosis, patient stratification, and the development of targeted therapies for MMD. The identification of blood-based biomarkers and the integration of multiple omics data are critical for improving the clinical management of MMD and enhancing patient outcomes for this complex disease.

Identifying the prognosis implication, immunotherapy response prediction value, and potential targeted compound inhibitors of integrin subunit α3 (ITGA3) in human cancers.

The integrin subunit α3 (ITGA3) is a member of the integrin alpha chain protein family, which could promote progression, metastasis, and invasion in some cancers. Still, its function in the tumor microenvironment (TME), cancer prognosis, and immunotherapy remains unclear. A multifaceted analysis of ITGA3 in pan-cancer utilizing various databases and online web tools revealed ITGA3 was aberrantly expressed in tumor tissues and upregulated in most cancers, which may be related to ITGA3 genomic alterations and methylation modification. In addition, ITGA3 was significantly correlated with the poor or better prognosis of cancer patients, immune-related pathways in hallmark, immune infiltration, and immune checkpoints, revealing a biological function of ITGA3 in the tumor progression, tumor microenvironment, and tumor immunity. We also found that ITGA3 could predict the response to tumor immunotherapy based on cytokine-treated samples and immunotherapy cohorts. ITGA3 may participate in shaping and regulating the tumor microenvironment to affect the tumor immune response, which was a promising immunotherapy response predictive biomarker and potential therapeutic target to work synergistically with cancer immunotherapy to boost the response and efficacy. Finally, potential targeted compound inhibitors and sensitive drugs were screened using databases ConnectivityMap (CMap) and CellMiner, and AutoDock Tools was used for molecular docking.

Aberrant RBMX expression is relevant for cancer prognosis and immunotherapy response.

Cancer accounts for the highest rates of morbidity and mortality worldwide. RNA binding motif protein X-linked (RBMX) is a nuclear RNA-binding protein, associated with certain types of cancer by participating in the integration of sister chromatids and a combination of ribonucleoprotein complexes. However, the specific role of RBMX in cancer immunity remains unknown. This study presents the aberrant expression levels, single-cell distributions, effective prognostic roles, immune cell infiltration associations, and immunotherapy responses of RBMX as a biomarker in various types of cancer. Moreover, it validates the aberrant expression of RBMX in clinical cancer samples. Furthermore, we also evaluated the relationships between RBMX expression and myeloid-derived suppressor cells in clinical samples by immunofluorescent staining. The results showed that knockdown of RBMX can impair the proliferation, migration, and invasion of liver cancer cells. Finally, we indicated that RBMX may play an immunoregulatory role in cancer progression, affecting the therapeutic effects of immune checkpoint inhibitors in patients with cancer.

Predictive role of E2F6 in cancer prognosis and responses of immunotherapy.

BACKGROUND: E2F6 is a member of the E2F transcription factor family. Numerous studies have demonstrated that E2F6 is critical to cancer development and progression, but its role in cancer immunotherapy remains unclear. METHODS: Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases were used to obtain RNA-seq data for cancer and normal tissues, and we utilized the cBioPortal to analyze E2F6 genomic alterations in pan-cancer. The protein localization of E2F6 was obtained using the Human Protein Atlas (HPA), and the upregulation of E2F6 expression in clinical glioblastoma multiforme (GBM) tissues was detected by Western blot analysis. The ComPPI website was used to analyze the protein interaction information of E2F6. To evaluate the role of E2F6 in pan-cancer prognosis, we used univariate Cox regression and Kaplan-Meier methods, and gene set enrichment analysis (GSEA) was utilized to identify markers associated with E2F6 expression in tumors. TIMER 2.0 was used to study E2F6-related immune cell infiltration in tumor tissues, and the correlation of E2F6 with immunotherapy biomarkers was investigated using Spearman correlation analysis. The role of E2F6 in the cell cycle was analyzed by flow cytometry, and the Cell Counting Kit-8 (CCK-8) and colony formation assays were utilized to determine the proliferative ability of cells. RESULTS: In most tumor types, E2F6 was highly expressed and was a good predictor of prognosis. E2F6 was significantly related to markers of immune activation, tumor immune cell infiltration, and immune regulators. Furthermore, E2F6 knockdown significantly attenuated the proliferative ability of glioma cells. Finally, E2F6 effectively predicted anti-programmed cell death 1 (PD1) treatment response. CONCLUSION: E2F6 is an effective biomarker that predicts the prognosis of cancer patients treated with anti-immune checkpoint therapy.

Multifunctional Carbon Quantum Dots: Iron Clearance and Antioxidation for Neuroprotection in Intracerebral Hemorrhage Mice.

Iron overload and oxidative stress are pivotal in the pathogenesis of brain injury secondary to intracerebral hemorrhage (ICH). There is a compelling need for agents that can chelate iron and scavenge free radicals, particularly those that demonstrate substantial brain penetration, to mitigate ICH-related damage. In this study, we have engineered an amine-functionalized aspirin-derived carbon quantum dot (NACQD) with a nominal diameter of 6-13 nm. The NACQD possesses robust iron-binding and antioxidative capacities. Through intrathecal administration, NACQD therapy substantially reduced iron deposition and oxidative stress in brain tissue, alleviated meningeal inflammatory responses, and improved the recovery of neurological function in a murine ICH model. As a proof of concept, the intrathecal injection of NACQD is a promising therapeutic strategy to ameliorate the ICH injury.

The HNF4A-CHPF pathway promotes proliferation and invasion through interactions with MAD1L1 in glioma.

Chondroitin polymerizing factor (CHPF) is an important glycosyltransferases that participates in the biosynthesis of chondroitin sulfate (CS). Our previous study showed that silencing CHPF expression inhibited glioma cell proliferation in vitro, but the molecular mechanisms by which CHPF contributes to development of glioma have not been characterized. In this study, we found that CHPF was up-regulated in glioma tissues and was positively correlated with malignant clinical pathological characteristics of patients with glioma. Silencing CHPF expression inhibited proliferation, colony formation, migration, and cell cycle of glioma cells. Moreover, silencing CHPF suppressed glioma malignance in vivo. Immunoprecipitation, co-immunoprecipitation, GST pulldown, and liquid chromatography-mass spectrometry (LC-MS/MS) assays were used to verify the interaction between CHPF and Mitotic arrest deficient 1-like 1 (MAD1L1). In addition, Chromatin Immunoprecipitation (ChIP)-PCR analysis showed that HNF4A bound to the CHPF promoter region, which indicated that the transcription factor hepatocyte nuclear factor 4A (HNF4A) could regulate the expression of CHPF in glioma cells.

Corrigendum to "Endovascular treatment of acute basilar artery occlusion caused by vertebral artery stump syndrome: A clinical analysis of 37 cases" [Heliyon 9 (4X), (March 2023) Article e14956].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e14956.].

Deep learning-assisted identification and quantification of aneurysmal subarachnoid hemorrhage in non-contrast CT scans: Development and external validation of Hybrid 2D/3D UNet.

Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993-0.999) and segmented (Dice scores 0.550-0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815-0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.

The role of lysosomal peptidases in glioma immune escape: underlying mechanisms and therapeutic strategies.

Glioblastoma is the most common primary malignant tumor of the central nervous system, which has the characteristics of strong invasion, frequent recurrence, and rapid progression. These characteristics are inseparable from the evasion of glioma cells from immune killing, which makes immune escape a great obstacle to the treatment of glioma, and studies have confirmed that glioma patients with immune escape tend to have poor prognosis. The lysosomal peptidase lysosome family plays an important role in the immune escape process of glioma, which mainly includes aspartic acid cathepsin, serine cathepsin, asparagine endopeptidases, and cysteine cathepsins. Among them, the cysteine cathepsin family plays a prominent role in the immune escape of glioma. Numerous studies have confirmed that glioma immune escape mediated by lysosomal peptidases has something to do with autophagy, cell signaling pathways, immune cells, cytokines, and other mechanisms, especially lysosome organization. The relationship between protease and autophagy is more complicated, and the current research is neither complete nor in-depth. Therefore, this article reviews how lysosomal peptidases mediate the immune escape of glioma through the above mechanisms and explores the possibility of lysosomal peptidases as a target of glioma immunotherapy.

Randomized, prospective, multicenter trial assessing the numen coil embolization system in the endovascular treatment of small intracranial aneurysms: outcomes from the CATCH Trial.

BACKGROUND AND PURPOSE: The CATCH (Coil Application Trial in China) trial was designed to assess the safety and efficacy of the Numen Coil Embolization System in the treatment of intracranial aneurysms in comparison with the Axium coil (ev3/Medtronic). Although the endovascular treatment of small (< 5 mm) intracranial aneurysms has been reported with favorable long-term clinical and angiographic outcomes, randomized trials are still lacking. Data for aneurysms smaller than 5 mm were extracted from the CATCH trial. MATERIALS AND METHODS: A randomized, prospective, multicenter trial was conducted at ten centers throughout China. Enrolled subjects with small intracranial aneurysms were randomly assigned to receive treatment with the Numen Coil or the Axium coil. The primary outcome was successful aneurysm occlusion at the 6-month follow-up. In contrast, the secondary outcomes included complete aneurysm occlusion, recurrence rate, clinical deterioration, and safety data at the 6-month and 12-month follow-ups. RESULTS: A total of 124 patients were enrolled in the study. Overall, 58 patients were assigned to the Numen group, and 66 were assigned to the Axium group. At the 6-month follow-up, the successful aneurysm occlusion rate was 93.1% (54/58) in the MicroPort NeuroTech group and 97.0% (64/66) in the Axium group, with a common odds ratio of 0.208 (95% confidence interval, 0.023-1.914; P = 0.184). Complications were comparable between the groups. CONCLUSIONS: Compared with the Aixum coil, the Numen coil is safe and effective in treating small intracranial aneurysms. TRIAL REGISTRATION: (13/12/2016, NCT02990156).

Combination of multi-modal MRI radiomics and liquid biopsy technique for preoperatively non-invasive diagnosis of glioma based on deep learning: protocol for a double-center, ambispective, diagnostical observational study.

Background: 2021 World Health Organization (WHO) Central Nervous System (CNS) tumor classification increasingly emphasizes the important role of molecular markers in glioma diagnoses. Preoperatively non-invasive "integrated diagnosis" will bring great benefits to the treatment and prognosis of these patients with special tumor locations that cannot receive craniotomy or needle biopsy. Magnetic resonance imaging (MRI) radiomics and liquid biopsy (LB) have great potential for non-invasive diagnosis of molecular markers and grading since they are both easy to perform. This study aims to build a novel multi-task deep learning (DL) radiomic model to achieve preoperative non-invasive "integrated diagnosis" of glioma based on the 2021 WHO-CNS classification and explore whether the DL model with LB parameters can improve the performance of glioma diagnosis. Methods: This is a double-center, ambispective, diagnostical observational study. One public database named the 2019 Brain Tumor Segmentation challenge dataset (BraTS) and two original datasets, including the Second Affiliated Hospital of Nanchang University, and Renmin Hospital of Wuhan University, will be used to develop the multi-task DL radiomic model. As one of the LB techniques, circulating tumor cell (CTC) parameters will be additionally applied in the DL radiomic model for assisting the "integrated diagnosis" of glioma. The segmentation model will be evaluated with the Dice index, and the performance of the DL model for WHO grading and all molecular subtype will be evaluated with the indicators of accuracy, precision, and recall. Discussion: Simply relying on radiomics features to find the correlation with the molecular subtypes of gliomas can no longer meet the need for "precisely integrated prediction." CTC features are a promising biomarker that may provide new directions in the exploration of "precision integrated prediction" based on the radiomics, and this is the first original study that combination of radiomics and LB technology for glioma diagnosis. We firmly believe that this innovative work will surely lay a good foundation for the "precisely integrated prediction" of glioma and point out further directions for future research. Clinical trail registration: This study was registered on ClinicalTrails.gov on 09/10/2022 with Identifier NCT05536024.