Predicting the complexity and mortality of polytrauma patients with machine learning models.

We aim to develop machine learning (ML) models for predicting the complexity and mortality of polytrauma patients using clinical features, including physician diagnoses and physiological data. We conducted a retrospective analysis of a cohort comprising 756 polytrauma patients admitted to the intensive care unit (ICU) at Pizhou People's Hospital Trauma Center, Jiangsu, China between 2020 and 2022. Clinical parameters encompassed demographics, vital signs, laboratory values, clinical scores and physician diagnoses. The two primary outcomes considered were mortality and complexity. We developed ML models to predict polytrauma mortality or complexity using four ML algorithms, including Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and eXtreme Gradient Boosting (XGBoost). We assessed the models' performance and compared the optimal ML model against three existing trauma evaluation scores, including Injury Severity Score (ISS), Trauma Index (TI) and Glasgow Coma Scale (GCS). In addition, we identified several important clinical predictors that made contributions to the prognostic models. The XGBoost-based polytrauma mortality prediction model demonstrated a predictive ability with an accuracy of 90% and an F-score of 88%, outperforming SVM, RF and ANN models. In comparison to conventional scoring systems, the XGBoost model had substantial improvements in predicting the mortality of polytrauma patients. External validation yielded strong stability and generalization with an accuracy of up to 91% and an AUC of 82%. To predict polytrauma complexity, the XGBoost model maintained its performance over other models and scoring systems with good calibration and discrimination abilities. Feature importance analysis highlighted several clinical predictors of polytrauma complexity and mortality, such as Intracranial hematoma (ICH). Leveraging ML algorithms in polytrauma care can enhance the prognostic estimation of polytrauma patients. This approach may have potential value in the management of polytrauma patients.

mTOR signaling promotes rapid m6A mRNA methylation to regulate NK-cell activation and effector functions.

Natural killer (NK) cells can be rapidly activated in response to cytokines during host defense against malignant cells or viral infection. However, it remains unclear what mechanisms precisely and rapidly regulate the expression of the numerous genes involved in activating NK cells. In this study, we discovered that NK-cell N6-methyladenosine (m6A) methylation levels were rapidly upregulated upon short-term NK-cell activation and were repressed in the tumor microenvironment. Deficiency of methyltransferase-like 3 (METTL3) or METTL14 moderately influenced NK-cell homeostasis, while double knockout of METTL3/14 significantly impacted NK-cell homeostasis, maturation, and antitumor immunity. This suggests a cooperative role of METTL3 and METTL14 in regulating NK-cell development and effector functions. Using methylated RNA immunoprecipitation sequencing (MeRIP-seq), we demonstrated that genes involved in NK-cell effector functions, such as Prf1 and Gzmb, were directly modified by m6A methylation. Furthermore, inhibiting mTOR complex 1 (mTORC1) activation prevented m6A methylation levels from increasing when NK cells were activated, and this could be restored by S-adenosylmethionine (SAM) supplementation. Collectively, we have unraveled crucial roles for rapid m6A mRNA methylation downstream of the mTORC1-SAM signal axis in regulating NK-cell activation and effector functions.

Transcriptomic Analysis of the Rat Dorsal Root Ganglion After Fracture.

In fractures, pain signals are transmitted from the dorsal root ganglion (DRG) to the brain, and the DRG generates efferent signals to the injured bone to participate in the injury response. However, little is known about how this process occurs. We analyzed DRG transcriptome at 3, 7, 14, and 28 days after fracture. We identified the key pathways through KEGG and GO enrichment analysis. We then used IPA analysis to obtain upstream regulators and disease pathways. Finally, we compared the sequencing results with those of nerve injury to identify the unique transcriptome changes in DRG after fracture. We found that the first 14 days after fracture were the main repair response period, the 3rd day was the peak of repair activity, the 14th day was dominated by the stimulus response, and on the 28th day, the repair response had reached a plateau. ECM-receptor interaction, protein digestion and absorption, and the PI3K-Akt signaling pathway were most significantly enriched, which may be involved in repair regeneration, injury response, and pain transmission. Compared with the nerve injury model, DRG after fracture produced specific alterations related to bone repair, and the bone density function was the most widely activated bone-related function. Our results obtained some important genes and pathways in DRG after fracture, and we also summarized the main features of transcriptome function at each time point through functional annotation clustering of GO pathway, which gave us a deeper understanding of the role played by DRG in fracture.

Direction of Arrival Estimation and Highlighting Characteristics of Testing Wideband Echoes from Multiple Autonomous Underwater Vehicles.

Multiple autonomous underwater vehicles (AUVs) have gradually become the trend in underwater operations. Identifying and detecting these new underwater multi-targets is difficult when studying underwater moving targets. A 28-element transducer is used to test the echo of multiple AUVs with different layouts in a lake. The characteristics of the wideband echo signals are studied. Under the condition that the direction of arrival (DOA) is not known, an autofocus coherent signal subspace (ACCSM) method is proposed. The focusing matrix is constructed based on the received data. The spatial spectrum of the array signal of multiple AUVs at different attitudes is calculated. The algorithm estimates the DOA of the echo signals to overcome the shortcomings of traditional wideband DOA estimation and improve its accuracy. The results show that the highlights are not only related to the number of AUVs, but are also modified by scale and attitude. The contribution of the microstructure of the target in the overall echo cannot be ignored. Different parts of the target affect the number of highlights, thus resulting in varying numbers of highlights at different attitude angle intervals. The results have significant implications for underwater multi-target recognition.

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics.

BACKGROUND: There is a paucity of research investigating the application of machine learning techniques for distinguishing between lipid-poor adrenal adenoma (LPA) and subclinical pheochromocytoma (sPHEO) based on radiomic features extracted from non-contrast and dynamic contrast-enhanced computed tomography (CT) scans of the abdomen. METHODS: We conducted a retrospective analysis of multiphase spiral CT scans, including non-contrast, arterial, venous, and delayed phases, as well as thin- and thick-thickness images from 134 patients with surgically and pathologically confirmed. A total of 52 patients with LPA and 44 patients with sPHEO were randomly assigned to training/testing sets in a 7:3 ratio. Additionally, a validation set was comprised of 22 LPA cases and 16 sPHEO cases from two other hospitals. We used 3D Slicer and PyRadiomics to segment tumors and extract radiomic features, respectively. We then applied T-test and least absolute shrinkage and selection operator (LASSO) to select features. Six binary classifiers, including K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP), were employed to differentiate LPA from sPHEO. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were compared using DeLong's method. RESULTS: All six classifiers showed good diagnostic performance for each phase and slice thickness, as well as for the entire CT data, with AUC values ranging from 0.706 to 1. Non-contrast CT densities of LPA were significantly lower than those of sPHEO (P < 0.001). However, using the optimal threshold for non-contrast CT density, sensitivity was only 0.743, specificity 0.744, and AUC 0.828. Delayed phase CT density yielded a sensitivity of 0.971, specificity of 0.641, and AUC of 0.814. In radiomics, AUC values for the testing set using non-contrast CT images were: KNN 0.919, LR 0.979, DT 0.835, RF 0.967, SVM 0.979, and MLP 0.981. In the validation set, AUC values were: KNN 0.891, LR 0.974, DT 0.891, RF 0.964, SVM 0.949, and MLP 0.979. CONCLUSIONS: The machine learning model based on CT radiomics can accurately differentiate LPA from sPHEO, even using non-contrast CT data alone, making contrast-enhanced CT unnecessary for diagnosing LPA and sPHEO.

A novel oncolytic virus-based biomarker participates in prognosis and tumor immune infiltration of glioma.

Background: Glioma is the most common central nervous malignancy. Due to its poor survival outcomes, it is essential to identify novel individualized therapy. Oncolytic virus (OV) treatment is a key therapy regulating tumor microenvironment in malignant glioma. Herein, we aim to identify the key genes after OV infection and its role in glioma. Methods: Performing an RNA-seq analysis, the differentially expressed genes (DEGs) between EV-A71-infection and mock group were screened with GFold values. DAVID online analysis was performed to identify the functional classification. Overall survival (OS) or disease-free survival (DFS) was evaluated to analyze the relation between PTBP1 expression levels and prognosis of glioma patients. Additionally, the ssGSEA and TIMER algorithms were applied for evaluating immune cell infiltration in glioma. Results: Following EV-A71 infection in glioma cells, PTBP1, one of the downregulated DEGs, was found to be associated with multiple categories of GO and KEGG enrichment analysis. We observed elevated expression levels of PTBP1 across various tumor grades of glioma in comparison to normal brain samples. High PTBP1 expression had a notable impact on the OS of patients with low-grade glioma (LGG). Furthermore, we observed an obvious association between PTBP1 levels and immune cell infiltration in LGG. Notably, PTBP1 was regarded as an essential prognostic biomarker in immune cells of LGG. Conclusion: Our research uncovered a critical role of PTBP1 in outcomes and immune cell infiltration of glioma patients, particularly in those with LGG.

The effect of rhythm training on the motor coordination abilities of 8-12-year-old freestyle swimmers.

This study aims to evaluate the effect of rhythm training in the early coordination development and to find out its role in the transfer of specific performance in young swimmers. Eighty young swimmers aged eight to twelve were divided randomly into a control group (CON, n = 40, M/F=17/23) or an experimental group (EXP, n = 40, M/F=19/21). A total of 72 swimmers completed all the training programs and tests included in this study (CON, n = 35, M/F=15/20; EXP, n = 37, M/F=16/21). The training programme lasted 12 weeks. During the study period, all groups participated in the same swimming training program with the control group participating six times a week, and the experimental group participating four times a week with an additional two rhythm trainings each week. The height, weight, walking backwards ability, jumping sideways ability, and 25 m freestyle were measured for each participant and an index of coordination was analyzed from recordings of the participant's 25 m freestyle. The results indicated that participation in rhythm training resulted in improved general motor and swimming-specific coordination among the swimmers, but the results varied by number of years of sport-specific training experience. The swimmers with more training experience improved more in swimming-specific coordination but less in general motor coordination. This study strengthens the evidence for the effectiveness of early rhythm training in swimmers, indicating that it is feasible to design programs to address general and sport-specific coordination in young athletes.

MicroRNA-142a-3p regulates neurogenic skeletal muscle atrophy by targeting Mef2a.

Peripheral nerve injury can lead to progressive muscle atrophy and poor motor function recovery, which is a difficult point of treatment, and the mechanism needs to be further explored. In previous studies, we found that miR-142a-3p was significantly upregulated and persistently highly expressed in denervated mouse skeletal muscle. Here, we show that overexpression of miR-142a-3p inhibited the growth and differentiation of C2C12 myoblast, while knockdown of miR-142a-3p had a promoting effect. In vitro, knockdown of miR-142a-3p in denervated mouse skeletal muscle effectively increased proliferating muscle satellite cells and ameliorated muscle atrophy. Mechanistically, the myoregulator Mef2a was proved to be an important downstream target of miR-142a-3p, and miR-142a-3p regulates skeletal muscle differentiation and regeneration by inhibiting the expression of Mef2a. The co-knockdown of Mef2a and miR-142a-3p effectively alleviated or offset the biological effects of miR-142a-3p knockdown. In conclusion, our data revealed that miR-142a-3p regulates neurogenic skeletal muscle atrophy by targeting Mef2a.

Screening epitope peptides based on a phage-displayed random peptide and peptide microarrays to contribute to improving the diagnostic efficiency of systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) is one of the most common autoimmune diseases in China. At present, there are hundreds of autoantibodies in SLE patients; however, only a dozen of the autoantibodies can be routinely detected, and the available diagnostic antibodies are not sufficient for diagnosis or differential diagnosis of SLE patients with atypical clinical manifestations or other autoimmune diseases. Therefore, it is necessary to find new diagnostic markers to improve the diagnostic effect of SLE. METHODS: The displayed random peptide library and peptide microarray were combined to identify SLE-related epitope peptides. A case-control design was used. The IgG antibodies in the sera from SLE patients, healthy controls, and other autoimmune disease controls underwent a reaction with the phage-display random peptide library, respectively. Selected epitope peptides were used to construct a peptide chip. A total of 644 serum samples (including 296 SLE patients, 168 disease controls, and 180 healthy controls) were used for further screening and verification. Peptides with an area under the curve (AUC) > 0.650 were further verified by ELISA. Finally, 500 serum samples (including 200 SLE patients, 150 disease controls, and 150 healthy controls) were used to verify and evaluate the diagnostic and differential diagnostic efficacy of the selected peptides. RESULTS: After the previous screening, five epitope peptides (SLE_P19, SLE_P20, SLE_P27, SLE_P28, and SLE_P29) may have potential as SLE diagnostic markers. Additionally, SLE_P27 was superior to the other four peptides in the diagnosis and differential diagnosis of SLE and rheumatoid arthritis (RA). The AUC of SLE_P27 was 0.938, the sensitivity was 76.00%, the specificity was 92.70%, the positive likelihood ratio was 10.411, the negative likelihood ratio was 0.259, and the accuracy was 84.40%. The diagnostic efficacy of SLE can be increased by combining the five selected peptides with the anti-double stranded DNA antibody (anti-dsDNA) and anti-Smith antibodies (anti-Sm). CONCLUSIONS: In this study, we identified five peptides that may serve as potential biomarkers for SLE diagnosis using the strategy of combining the displayed random peptide library with the peptide microarray. The combination of selected peptides and existing autoantibodies can significantly improve the diagnostic efficiency. These specific peptides are expected to be new diagnostic markers for SLE.

High-expression of Galactosidase alpha is correlated with poor prognosis and immune infiltration in low-grade glioma.

Background: Galactosidase alpha (GLA), a member of galactosidase (GAL) family, contributes to cancer diagnosis and targeted therapy. Up to now, neither prognosis nor immune infiltration has been demonstrated in cases with low-grade glioma (LGG). In LGG, we investigated the association between GLA expression and immune infiltration levels. Methods: GLA expression levels in pan-cancer were evaluated utilizing the Oncomine database. In addition, GLA level was screened via analyzing the gene expression omnibus (GEO) data and the Cancer Genome Atlas (TCGA) data, and evaluated in LGG tissues and adjacent tissues by using qPCR. TIMER database was utilized for evaluating the correlation between GLA level and LGG immune infiltrates. A correlation was found between GLA levels and LGG immune infiltrates utilizing the TIMER database. Moreover, we then assessed the TIMER data to explore clinical outcome in multiple immune cells and the correction between GLA expression and immune markers. Results: The mRNA levels of GLA were upregulated in LGG tissues. GLA expression was associated with a poor outcome of patients with LGG. Additionally, the infiltration levels of several immune cells were obviously enriched in LGG with a higher GLA level. Moreover, LGG prognosis was worsened with high GLA levels in immune cells. Conclusions: These results suggested that GLA levels in LGG might be more predictive of immune infiltration, with potential value for assessment of tumor development.

Diversification of ranunculaceous petals in shape supports a generalized model for plant lateral organ morphogenesis and evolution.

Peltate organs, such as the prey-capturing traps of carnivorous plants and nectary-bearing petals of ranunculaceous species, are widespread in nature and have intrigued and perplexed scientists for centuries. Shifts in the expression domains of adaxial/abaxial genes have been shown to control leaf peltation in some carnivorous plants, yet the mechanisms underlying the generation of other peltate organs remain unclear. Here, we show that formation of various peltate ranunculaceous petals was also caused by shifts in the expression domains of adaxial/abaxial genes, followed by differentiated regional growth sculpting the margins and/or other parts of the organs. By inducing parameters to specify the time, position, and degree of the shifts and growth, we further propose a generalized modeling system, through which various unifacial, bifacial, and peltate organs can be simulated. These results demonstrate the existence of a hierarchical morphospace system and pave the way to understand the mechanisms underlying plant organ diversification.

Mechanisms underlying the formation of complex color patterns on Nigella orientalis (Ranunculaceae) petals.

Complex color patterns on petals are widespread in flowering plants, yet the mechanisms underlying their formation remain largely unclear. Here, by conducting detailed morphological, anatomical, biochemical, optical, transcriptomic, and functional studies, we investigated the cellular bases, chromogenic substances, reflectance spectra, developmental processes, and underlying mechanisms of complex color pattern formation on Nigella orientalis petals. We found that the complexity of the N. orientalis petals in color pattern is reflected at multiple levels, with the amount and arrangement of different pigmented cells being the key. We also found that biosynthesis of the chromogenic substances of different colors is sequential, so that one color/pattern is superimposed on another. Expression and functional studies further revealed that a pair of R2R3-MYB genes function cooperatively to specify the formation of the eyebrow-like horizontal stripe and the Mohawk haircut-like splatters. Specifically, while NiorMYB113-1 functions to draw a large splatter region, NiorMYB113-2 functions to suppress the production of anthocyanins from the region where a gap will form, thereby forming the highly specialized pattern. Our results provide a detailed portrait for the spatiotemporal dynamics of the coloration of N. orientalis petals and help better understand the mechanisms underlying complex color pattern formation in plants.

Identification of drug targets and prognosis projection for uterine carcinosarcoma based on alternative splicing events.

BACKGROUND: Uterine carcinosarcoma (UCS) is an invasive variant of endometrial cancer. The complicated heterogeneity and low frequency of UCS suggest the relevant research is lack. There is an urgent need to further explore the pathogenic mechanism and identify new biomarkers of UCS from different angels to improve its diagnosis and prognosis. OBJECTIVE: This study is to explore the importance of alternative splicing (AS) events in UCS, construct AS-based prognosis model and excavate key splicing factors (SFs). METHOD: UCS related gene transcriptome data and AS events data were collected from The Cancer Genome Atlas (TCGA) and TCGA SpliceSeq database. The AS events related to survival were determined by Cox regression analysis, Least absolute shrinkage and selection operator (Lasso) regression analysis and optimal subset analysis. The corresponding risk score was calculated and its efficiency on prognosis was evaluated by Kaplan-Meier (K-M) survival estimate and validated by the receiver operating characteristic (ROC) curve. The prognosis model was constructed with risk score and clinic characters as independent variables to predict patients' survival. On the other hand, Kendall test was applied to inspect the correlation between the SFs and the prognosis-related AS events and a AS-SF network was constructed. Finally, the key SFs were screened through network nodes analysis and survival analysis. RESULT: Seven AS events the most related to survival were detected and the risk score was obtained. K-M survival estimate and ROC curve validation suggested the risk score was effective. Then Cox model was constructed based on the risk score and a nomogram model was obtained which provided the highest prediction accuracy of 95%. Through the AS-SF network analysis, 16 SFs were screened, among which four survival-related SFs were eventually obtained. CONCLUSION: The prognosis model could predict the survival rate of UCS patients by their clinical characters and AS-based risk score. And four newly discovered SFs could reveal the molecular mechanism of UCS and act as the potential drug targets and prognosis biomarkers.

A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome.

After sequencing, it is common to screen ncRNA according to expression differences. But this may lose a lot of valuable information and there is currently no indicator to characterize the regulatory function and participation degree of ncRNA on transcriptome. Based on existing pathway enrichment methods, we developed a new algorithm to calculating the participation degree of ncRNA in transcriptome (PDNT). Here we analyzed multiple data sets, and differentially expressed genes (DEGs) were used for pathway enrichment analysis. The PDNT algorithm was used to calculate the Contribution value (C value) of each ncRNA based on its target genes and the pathways they participates in. The results showed that compared with ncRNAs screened by log2 fold change (FC) and p-value, those screened by C value regulated more DEGs in IPA canonical pathways, and their target DEGs were more concentrated in the core region of the protein-protein interaction (PPI) network. The ranking of disease critical ncRNAs increased integrally after sorting with C value. Collectively, we found that the PDNT algorithm provides a measure from another view compared with the log2FC and p-value and it may provide more clues to effectively evaluate ncRNA.

MiR-142a-3p: A novel ACh receptor transcriptional regulator in association with peripheral nerve injury.

Long-term denervation leads to the disintegration of nicotinic acetylcholine receptor (nAChR) located at the endplate structure, which translates to deficits in functional activation despite nerve repair. Because of a lack of effective measures to protect AChR expression, we explored the effect of alterations in muscular miR-142a-3p on nAChR. In this study, we constructed a model of miR-142a-3p knockdown by transfecting a miR-142a-3p inhibitor short hairpin RNA (shRNA) into C2C12 myotubes, and we injected this miR-142a-3p inhibitor shRNA into the tibialis anterior (TA) muscle in uninjured mice and in denervated mice by transecting the sciatic nerve. Our results showed that miR-142a-3p knockdown led to an increased number and area of AChR clusters in myotubes in vitro and larger neuromuscular endplates in adult mice. Furthermore, miR-142a-3p knockdown delayed the disintegration of motor endplates after denervation. Last, upon miR-142a-3p knockdown in uninjured and denervated mice, we observed an increase in the mRNA levels of five AChR subunits as well as mRNAs of genes implicated in AChR transcription and AChR clustering. Together, these results suggest that miR-142a-3p may be a potential target for therapeutic intervention to prevent motor endplate degradation following peripheral nerve injury.

A systematic study of traditional Chinese medicine treating hepatitis B virus-related hepatocellular carcinoma based on target-driven reverse network pharmacology.

Hepatocellular carcinoma (HCC) is a serious global health problem, and hepatitis B virus (HBV) infection remains the leading cause of HCC. It is standard care to administer antiviral treatment for HBV-related HCC patients with concurrent anti-cancer therapy. However, a drug with repressive effects on both HBV infection and HCC has not been discovered yet. In addition, drug resistance and side effects have made existing therapeutic regimens suboptimal. Traditional Chinese medicine (TCM) has multi-ingredient and multi-target advantages in dealing with multifactorial HBV infection and HCC. TCM has long been served as a valuable source and inspiration for discovering new drugs. In present study, a target-driven reverse network pharmacology was applied for the first time to systematically study the therapeutic potential of TCM in treating HBV-related HCC. Firstly, 47 shared targets between HBV and HCC were screened as HBV-related HCC targets. Next, starting from 47 targets, the relevant chemical components and herbs were matched. A network containing 47 targets, 913 chemical components and 469 herbs was established. Then, the validated results showed that almost 80% of the herbs listed in chronic hepatitis B guidelines and primary liver cancer guidelines were included in the 469 herbs. Furthermore, functional analysis was conducted to understand the biological processes and pathways regulated by these 47 targets. The docking results indicated that the top 50 chemical components bound well to targets. Finally, the frequency statistical analysis results showed the 469 herbs against HBV-related HCC were mainly warm in property, bitter in taste, and distributed to the liver meridians. Taken together, a small library of 913 chemical components and 469 herbs against HBV-related HCC were obtained with a target-driven approach, thus paving the way for the development of therapeutic modalities to treat HBV-related HCC.

Quantum Correlation Swapping between Two Werner States Undergoing Local and Nonlocal Unitary Operations.

In this paper, quantum correlation (QC) swapping between two Werner-like states, which are transformed from Werner states undergoing local and nonlocal unitary operations, are studied. Bell states measures are performed in the middle node to realize the QC swapping and correspondingly final correlated sates are obtained. Two different QC quantifiers, i.e., measurement-induced disturbance (MID) and ameliorated MID, are employed to characterize and quantify all the concerned QCs in the swapping process. All QCs in the concerned states are evaluated analytically and numerically. Correspondingly, their characteristics and properties are exposed in detail. It is exposed that, through the QC swapping process, one can obtain the long-distance QC indeed. Moreover, the similarities of monotony features of MID and AMID between the initial states and final states are exposed and analyzed.

Visual three-dimensional spatial distribution of motor neurons innervating superficial limb muscles in mice.

The coordination of motor function in the spinal cord depends on selective connections between distinct classes of motor neurons and their target muscles. However, knowledge regarding the anatomical connections between the superficial limb skeletal muscles and the motor neurons that innervate them is limited. In this study, with a combination of the multiple retrograde tracing method with 3DISCO clearing, we explored the spatial distribution of different motor neuron pools targeting specific superficial muscles of the forelimbs or hindlimbs in mouse spinal cords, which were dominated by the radial, median, ulnar, or sciatic nerve. This study reveals the precise interrelationship among different motor neuron pools innervating limb muscles under the same space and time. The data will help to further understand the neural loop and muscular motor coordination.

ABCD3 is a prognostic biomarker for glioma and associated with immune infiltration: A study based on oncolysis of gliomas.

Background: Gliomas are the most lethal primary brain tumors and are still a major therapeutic challenge. Oncolytic virus therapy is a novel and effective means for glioma. However, little is known about gene expression changes during this process and their biological functions on glioma clinical characteristics and immunity. Methods: The RNA-seq data after oncolytic virus EV-A71 infection on glioma cells were analyzed to screen significantly downregulated genes. Once ABCD3 was selected, The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Genotype-Tissue Expression (GTEx), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA) data were used to analyze the relationship between ABCD3 expression and clinical characteristics in glioma. We also evaluated the influence of ABCD3 on the survival of glioma patients. CIBERSORT and Tumor Immune Estimation Resource (TIMER) were also used to investigate the correlation between ABCD3 and cancer immune infiltrates. Gene set enrichment analysis (GSEA) was performed to functionally annotate the potential functions or signaling pathways related to ABCD3 expression. Results: ABCD3 was among the top 5 downregulated genes in glioma cells after oncolytic virus EV-A71 infection and was significantly enriched in several GO categories. Both the mRNA and protein expression levels of ABCD3 were upregulated in glioma samples and associated with the prognosis and grades of glioma patients. The Kaplan-Meier (K-M) curve analysis revealed that patients with high ABCD3 expression had shorter disease-specific survival (DSS) and overall survival (OS) than those with low ABCD3 expression. Moreover, ABCD3 expression could affect the immune infiltration levels and diverse immune marker sets in glioma. A positive correlation was found between ABCD3 and macrophages and active dendritic cells in the microenvironment of both the GBM and LGG. Gene sets including the plk1 pathway, tyrobp causal network, ir-damage and cellular response, and interleukin-10 signaling showed significant differential enrichment in the high ABCD3 expression phenotype. Conclusion: Our results suggested that ABCD3 could be a potential biomarker for glioma prognosis and immunotherapy response and also further enriched the theoretical and molecular mechanisms of oncolytic virus treatment for malignant gliomas.