Integrative analyses and validation of ferroptosis-related genes and mechanisms associated with cerebrovascular and cardiovascular ischemic diseases.

BACKGROUND: There has been a gradual increase in the occurrence of cardiovascular and cerebrovascular ischemic diseases, particularly as comorbidities. Yet, the mechanisms underlying these diseases remain unclear. Ferroptosis has emerged as a potential contributor to cardio-cerebral ischemic processes. Therefore, this study investigated the shared biological mechanisms between the two processes, as well as the role of ferroptosis genes in cardio-cerebral ischemic damage, by constructing co-expression modules for myocardial ischemia (MI) and ischemic stroke (IS) and a network of protein-protein interactions, mRNA-miRNA, mRNA-transcription factors (TFs), mRNA-RNA-binding proteins (RBPs), and mRNA-drug interactions. RESULTS: The study identified seven key genes, specifically ACSL1, TLR4, ADIPOR1, G0S2, PDK4, HP, PTGS2, and subjected them to functional enrichment analysis during ischemia. The predicted miRNAs were found to interact with 35 hub genes, and interactions were observed between 11 hub genes and 30 TF transcription factors. Additionally, 10 RBPs corresponding to 16 hub genes and 163 molecular compounds corresponding to 30 hub genes were identified. This study also clarified the levels of immune infiltration between MI and IS and different subtypes. Finally, we identified four hub genes, including TLR4, by using a diagnostic model constructed by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis; ADIPOR1, G0S2, and HP were shown to have diagnostic value for the co-pathogenesis of MI and cerebral ischemia by both validation test data and RT-qPCR assay. CONCLUSIONS: To the best our knowledge, this study is the first to utilize multiple algorithms to comprehensively analyze the biological processes of MI and IS from various perspectives. The four hub genes, TLR4, ADIPOR1, G0S2, and HP, have proven valuable in offering insights for the investigation of shared injury pathways in cardio-cerebral injuries. Therefore, these genes may serve as diagnostic markers for cardio-cerebral ischemic diseases.

Investigating the causal impact of gut microbiota on glioblastoma: a bidirectional Mendelian randomization study.

BACKGROUND: Currently, the influence of microbiota on the occurrence, progression, and treatment of cancer is a topic of considerable research interest. Therefore, based on the theory of the gut-brain axis proved by previous studies, our objective was to uncover the causal relationship between glioblastoma and the gut microbiome using Mendelian randomization analysis. METHODS: We conducted a bidirectional Mendelian randomization study using summary statistics of gut microbiota derived from the MiBioGen consortium, the largest database of gut microbiota. Summary statistics for glioblastoma were obtained from IEU OpenGWAS project, which included 91 cases and 218,701 controls. We assessed the presence of heterogeneity and horizontal pleiotropy in the analyzed data. We primarily employed the inverse variance weighting method to investigate the causal relationship between gut microbiota and glioblastoma after excluding cases of horizontal pleiotropy. Four other analysis methods were employed as supplementary. Excluding abnormal results based on leave-one-out sensitivity analysis. Finally, reverse Mendelian randomization analysis was performed. RESULTS: Four genus-level taxa and one family-level taxa exhibited causal associations with glioblastoma. And these results of reverse Mendelian randomization analysis shown glioblastoma exhibited causal associations with three genus-level taxa and one family-level taxa. However, the Prevotella7(Forward, P=0.006, OR=0.34, 95%CI:0.158-0.732; Reverse, P=0.004, OR=0.972, 95%CI:0.953-0.991) shown the causal associations with glioblastoma in the bidirectional Mendelian randomization. CONCLUSIONS: In this bidirectional Mendelian randomization study, we identified five gut microbiota species with causal associations to glioblastoma. However, additional randomized controlled trials are required to clarify the impact of gut microbiota on glioblastoma and to reveal its precise mechanisms.

HQG-Net: Unpaired Medical Image Enhancement With High-Quality Guidance.

Unpaired medical image enhancement (UMIE) aims to transform a low-quality (LQ) medical image into a high-quality (HQ) one without relying on paired images for training. While most existing approaches are based on Pix2Pix/CycleGAN and are effective to some extent, they fail to explicitly use HQ information to guide the enhancement process, which can lead to undesired artifacts and structural distortions. In this article, we propose a novel UMIE approach that avoids the above limitation of existing methods by directly encoding HQ cues into the LQ enhancement process in a variational fashion and thus model the UMIE task under the joint distribution between the LQ and HQ domains. Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module. We train the enhancement network adversarially with a discriminator to ensure the generated HQ image falls into the HQ domain. We further propose a content-aware loss to guide the enhancement process with wavelet-based pixel-level and multiencoder-based feature-level constraints. Additionally, as a key motivation for performing image enhancement is to make the enhanced images serve better for downstream tasks, we propose a bi-level learning scheme to optimize the UMIE task and downstream tasks cooperatively, helping generate HQ images both visually appealing and favorable for downstream tasks. Experiments on three medical datasets verify that our method outperforms existing techniques in terms of both enhancement quality and downstream task performance. The code and the newly collected datasets are publicly available at https://github.com/ChunmingHe/HQG-Net.

Bioinformatics and experimental analyses of glutamate receptor and its targets genes in myocardial and cerebral ischemia.

BACKGROUND: There is a mutual hemodynamic and pathophysiological basis between the heart and brain. Glutamate (GLU) signaling plays an important role in the process of myocardial ischemia (MI) and ischemic stroke (IS). To further explore the common protective mechanism after cardiac and cerebral ischemic injuries, the relationship between GLU receptor-related genes and MI and IS were analyzed. RESULTS: A total of 25 crosstalk genes were identified, which were mainly enriched in the Toll-like receptor signaling pathway, Th17 cell differentiation, and other signaling pathways. Protein-protein interaction analysis suggested that the top six genes with the most interactions with shared genes were IL6, TLR4, IL1B, SRC, TLR2, and CCL2. Immune infiltration analysis suggested that immune cells such as myeloid-derived suppressor cells and monocytes were highly expressed in the MI and IS data. Memory B cells and Th17 cells were expressed at low levels in the MI and IS data; molecular interaction network construction suggested that genes such as JUN, FOS, and PPARA were shared genes and transcription factors; FCGR2A was a shared gene of MI and IS as well as an immune gene. Least absolute shrinkage and selection operator logistic regression analysis identified nine hub genes: IL1B, FOS, JUN, FCGR2A, IL6, AKT1, DRD4, GLUD2, and SRC. Receiver operating characteristic analysis revealed that the area under the curve of these hub genes was > 65% in MI and IS for all seven genes except IL6 and DRD4. Furthermore, clinical blood samples and cellular models showed that the expression of relevant hub genes was consistent with the bioinformatics analysis. CONCLUSIONS: In this study, we found that the GLU receptor-related genes IL1B, FOS, JUN, FCGR2A, and SRC were expressed in MI and IS with the same trend, which can be used to predict the occurrence of cardiac and cerebral ischemic diseases and provide reliable biomarkers to further explore the co-protective mechanism after cardiac and cerebral ischemic injury.

DM-Fusion: Deep Model-Driven Network for Heterogeneous Image Fusion.

Heterogeneous image fusion (HIF) is an enhancement technique for highlighting the discriminative information and textural detail from heterogeneous source images. Although various deep neural network-based HIF methods have been proposed, the most widely used single data-driven manner of the convolutional neural network always fails to give a guaranteed theoretical architecture and optimal convergence for the HIF problem. In this article, a deep model-driven neural network is designed for this HIF problem, which adaptively integrates the merits of model-based techniques for interpretability and deep learning-based methods for generalizability. Unlike the general network architecture as a black box, the proposed objective function is tailored to several domain knowledge network modules to model the compact and explainable deep model-driven HIF network termed DM-fusion. The proposed deep model-driven neural network shows the feasibility and effectiveness of three parts, the specific HIF model, an iterative parameter learning scheme, and data-driven network architecture. Furthermore, the task-driven loss function strategy is proposed to achieve feature enhancement and preservation. Numerous experiments on four fusion tasks and downstream applications illustrate the advancement of DM-fusion compared with the state-of-the-art (SOTA) methods both in fusion quality and efficiency. The source code will be available soon.

Integrative pan-cancer analysis and clinical characterization of the N7-methylguanosine (m7G) RNA modification regulators in human cancers.

Background: RNA modification is one of the epigenetic mechanisms that regulates post-transcriptional gene expression, and abnormal RNA modifications have been reported to play important roles in tumorigenesis. N7-methylguanosine (m7G) is an essential modification at the 5' cap of human mRNA. However, a systematic and pan-cancer analysis of the clinical relevance of m7G related regulatory genes is still lacking. Methods: We used univariate Cox model and Kaplan-Meier analysis to generate the forest plot of OS, PFI, DSS and identified the correlation between the altered expression of m7G regulators and patient survival in 33 cancer types from the TCGA and GTEx databases. Then, the "estimate" R-package, ssGSEA and CIBERSORT were used to depict the pan-cancer immune landscape. Through Spearman's correlation test, we analyzed the correlation between m7G regulators and the tumor microenvironment (TME), immune subtype, and drug sensitivity of the tumors, which was further validated in NSCLC. We also assessed the changes in the expression of m7G related regulatory genes in NSCLC with regards to the genetic and transcriptional aspects and evaluated the correlation of METTL1 and WDR4 expression with TMB, MSI and immunotherapy in pan-cancer. Results: High expression of most of the m7G regulators was significantly associated with worse prognosis. Correlation analyses revealed that the expression of majority of the m7G regulators was correlated with tumor immune infiltration and tumor stem cell scores. Drug sensitivity analysis showed that the expression of CYFP1,2 was closely related to drug sensitivity for various anticancer agents (p < 0.001). Analysis of the pan-cancer immune subtype revealed significant differences in the expression of m7G regulators between different immune subtypes (p < 0.001). Additionally, the types and proportions of mutations in METTL1 and WDR4 and their relevance to immunotherapy were further described. Conclusion: Our study is the first to evaluate the correlation between the altered expression of m7G regulators and patient survival, the degree of immune infiltration, TME and drug sensitivity in pan-cancer datasets.

Multi-modal sequence learning for Alzheimer's disease progression prediction with incomplete variable-length longitudinal data.

Alzheimer's disease (AD) is a neurodegenerative disorder with a long prodromal phase. Predicting AD progression will clinically help improve diagnosis and empower sufferers in taking proactive care. However, most existing methods only target individuals with a fixed number of historical visits, and only predict the cognitive scores once at a fixed time horizon in the future, which cannot meet practical requirements. In this study, we consider a flexible yet more challenging scenario in which individuals may suffer from the (arbitrary) modality-missing issue, as well as the number of individuals' historical visits and the length of target score trajectories being not prespecified. To address this problem, a multi-modal sequence learning framework, highlighted by deep latent representation collaborated sequence learning strategy, is proposed to flexibly handle the incomplete variable-length longitudinal multi-modal data. Specifically, the proposed framework first employs a deep multi-modality fusion module that automatically captures complementary information for each individual with incomplete multi-modality data. A comprehensive representation is thus learned and fed into a sequence learning module to model AD progression. In addition, both the multi-modality fusion module and sequence learning module are collaboratively trained to further promote the performance of AD progression prediction. Experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset validate the superiority of our method.

Adding predictive and diagnostic values of pulmonary ground-glass nodules on lung cancer via novel non-invasive tests.

Pulmonary ground-glass nodules (GGNs) are highly associated with lung cancer. Extensive studies using thin-section high-resolution CT images have been conducted to analyze characteristics of different types of GGNs in order to evaluate and determine the predictive and diagnostic values of GGNs on lung cancer. Accurate prediction of their malignancy and invasiveness is critical for developing individualized therapies and follow-up strategies for a better clinical outcome. Through reviewing the recent 5-year research on the association between pulmonary GGNs and lung cancer, we focused on the radiologic and pathological characteristics of different types of GGNs, pointed out the risk factors associated with malignancy, discussed recent genetic analysis and biomarker studies (including autoantibodies, cell-free miRNAs, cell-free DNA, and DNA methylation) for developing novel diagnostic tools. Based on current progress in this research area, we summarized a process from screening, diagnosis to follow-up of GGNs.

Identification of immune-associated lncRNAs as a prognostic marker for lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) accounts for the largest proportion of lung cancer patients and has the highest morbidity and mortality worldwide. Accumulating evidence shows that immune-associated long non-coding RNAs (lncRNAs) play a role in LUAD, although their predictive value for immunotherapy treatment and cancer-related death remains poorly investigated. METHODS: Gene expression profiles and clinical data were obtained from The Cancer Genome Atlas. We constructed a risk model by univariate and multivariate Cox regression and least absolute shrinkage and selection operator regression analysis and subsequently divided each sample into low- or high-risk category. Survival and receiver operating characteristic (ROC) analyses were applied to assess the prognostic value of the model. Additionally, immune and somatic mutation status were analysed between the two risk groups. Finally, the model was applied to pancreatic ductal adenocarcinoma (PDAC) samples to explore the applicability of the model in other cancers. RESULTS: We obtained data from 499 LUAD patients and randomised the samples into a training set (N=351) and validation set (N=148) at a 7:3 ratio. We detected 7 immune-associated lncRNAs (AP000695.2, AC026355.2, LINC01843, ITGB1-DT, LINC01150, AL590226.1 and AC091185.1) that were applicable for establishing a risk signature. Survival analysis revealed that patients categorised in the high-risk group had shorter overall survival (OS) than those in the low-risk group. ROC analyses showed excellent AUC values in all data sets (>0.65 at 1, 3, and 5 years). Notably, ESTIMATE algorithm and analysis of PCA, (ss)GSEA, and somatic mutations revealed that the high-risk group had a stronger immunosuppressive status and a higher tumour mutation burden (TMB). Moreover, patients in the low-risk group responded better to immunotherapy due to higher levels of immune-checkpoint receptor genes and TLS-related genes. Our model using the 7 immune-associated lncRNAs showed similar applicability for PDAC patients. CONCLUSIONS: We constructed a model for risk signatures based on 7 immune-associated lncRNAs and showed its prognostic value for identifying immune and somatic mutation characteristics in LUAD patients, which may assist clinical treatment plans and elucidate molecular mechanisms of LUAD immunity.

T-cell exhaustion interrelates with immune cytolytic activity to shape the inflamed tumor microenvironment.

Direct quantification of exhausted T cells in human cancer is lacking, and its predictive value for checkpoint-based treatment remains poorly investigated. We sought to systematically characterize the pan-cancer landscape and molecular hallmarks of T-cell dysfunction for the purpose of precision immunotherapy. Here, we defined a transcriptional signature for T-cell exhaustion through analyzing differential gene expression between PD-1-high and PD-1-negative CD8+ T lymphocytes from primary non-small cell lung cancer (NSCLC), followed by positive correlation tests with PDCD1 in TCGA lung carcinomas. A 78-gene signature for exhausted CD8+ T cells (GET) was identified and validated to reflect dysfunctional immune state spanning different species and disease models. We discovered that GET estimation significantly correlated with intratumoral immune cytolytic activity (CYT) and T-cell-inflamed gene expression profile (GEP) across 30 solid tumor types. Miscellaneous tumor-intrinsic and -extrinsic properties, in particular leukocyte proportions, genomic abnormalities, specific mutational signatures, and signaling pathways, were notably associated with GET levels. Furthermore, higher GET expression predicted an increased likelihood of clinical response to immune checkpoint inhibitors. These findings highlight the interrelation between T-cell exhaustion and immune cytolytic activity at the pan-cancer scale. The resulting inflamed tumor microenvironment may further crosstalk with other molecular and clinicopathological factors, which should be properly considered during immunotherapy biomarker development. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Complex migration of antibiotic resistance in natural aquatic environments.

Antibiotic resistance is a worsening global concern, and the environmental behaviors and migration patterns of antibiotic resistance genes (ARGs) have attracted considerable interest. Understanding the long-range transport of ARG pollution is crucial. In this study, we characterized the dynamics of ARG changes after their release into aquatic environments and demonstrated the importance of traditional chemical contaminants in the transmission mechanisms of ARGs. We hypothesized that the main route of ARG proliferation switches from active transmission to passive transmission. This antibiotic-dominated switch is motivated and affected by non-corresponding contaminants. The effect of anthropogenic activities gradually weakens from inland aquatic environments to ocean environments; however, the effect of changes in environmental conditions is enhanced along this gradient. The insights discussed in this study will help to improve the understanding of the distribution and migration of ARG pollution in various aquatic environments, and provide a modern perspective to reveal the effect of corresponding contaminants and non-corresponding contaminants in the process of antibiotic resistance proliferation.

Identification of microRNAs associated with glioma diagnosis and prognosis.

The sensitivity and specificity of microRNAs (miRNAs) for diagnosing glioma are controversial. We therefore performed a meta-analysis to systematically identify glioma-associated miRNAs. We initially screened five miRNA microarray datasets to evaluate the differential expression of miRNAs between glioma and normal tissues. We next compared the expression of the miRNAs in different organs and tissues to assess the sensitivity and specificity of the differentially expressed miRNAs in the diagnosis of glioma. Finally, pathway analysis was performed using GeneGO. We identified 27 candidate miRNAs associated with glioma initiation, progression, and patient prognosis. Sensitivity and specificity analysis indicated miR-15a, miR-16, miR-21, miR-23a, and miR-9 were up-regulated, while miR-124 was down-regulated in glioma. Ten signaling pathways showed the strongest association with glioma development and progression: the p53 pathway feedback loops 2, Interleukin signaling pathway, Toll receptor signaling pathway, Parkinson's disease, Notch signaling pathway, Cadherin signaling pathway, Apoptosis signaling pathway, VEGF signaling pathway, Alzheimer disease-amyloid secretase pathway, and the FGF signaling pathway. Our results indicate that the integration of miRNA, gene, and protein expression data can yield valuable biomarkers for glioma diagnosis and treatment. Indeed, six of the miRNAs identified in this study may be useful diagnostic and prognostic biomarkers in glioma.