Regulation of IFN-γ-mediated PD-L1 expression by MYC in colorectal cancer with wild-type KRAS and TP53 and its clinical implications

Introduction: In the tumor microenvironment, interferon gamma (IFN-γ) secreted by tumor infiltrating lymphocytes can upregulate programmed cell death 1 ligand 1 (PD-L1) expression in many cancers. The present study evaluated the expression of PD-L1 in selected colorectal cancer cell lines with IFN-γ treatment and explored the correlation between programmed cell death 1 ligand 1 expression and KRAS/TP53 mutation status.Methods: The selected colorectal cancer cell lines had known KRAS mutations or TP53 mutations. TCGA data analysis were used to investigate the correlation between overall survival of patient with anti-PD-1/PD-L1 immunotherapy and KRAS/TP53 mutation status. Besides, the correlation between PD-L1 expression and KRAS/TP53 mutation status were also investigated by using TCGA data analysis. In vitro experiments were used to explore the mechanism underlying KRAS- and TP53-related PD-L1 expression.Results: Firstly, TCGA data analysis for gene expression and overall survival and an in vitro study revealed that the wild-type KRAS/TP53 cell lines exhibited hyperresponsiveness to interferon gamma exposure and correlated with better survival in patients receiving anti-PD-1/PD-L1 treatment. Secondly, experimental data revealed that interferon gamma induced the upregulation of programmed cell death 1 ligand 1 mainly through regulating MYC in wild-type KRAS and TP53 colorectal cancers.Discussion: Our findings revealed that the response to anti-PD-1/PD-L1 cancer immunotherapy frequently happened in wild-type KRAS and TP53 colorectal cancers, which were also found to show higher programmed cell death 1 ligand 1 expression. Our results indicate that the wild-type KRAS/TP53 colorectal cancer cell lines may respond better to interferon gamma treatment, which causes increased programmed cell death 1 ligand 1 expression and may be a mechanism underlying the better responses to anti-PD-1/PD-L1 therapies in wild-type KRAS and wild-type TP53 colorectal cancer. Furthermore, the experimental results suggest that interferon gamma regulated programmed cell death 1 ligand 1 expression through the regulation of MYC, which may further affect the response to PD-1/PD-L1 cancer immunotherapy. These results suggest a novel potential treatment strategy for enhancing the efficacy of PD-1/PD-L1 blockade immunotherapy in most colorectal cancer patients

Paclitaxel Induces the Apoptosis of Prostate Cancer Cells via ROS-Mediated HIF-1α Expression

Prostate cancer (PCa) is the most common malignancy to endanger the health of male genitourinary system. Clinically, paclitaxel (PTX) (C47H51NO14), a diterpene alkaloid, is commonly used as an effective natural antineoplastic drug during the treatment of PCa. However, the mechanism and pathway involved in the function of PTX are poorly understood. In the current study, we employed the CCK-8 assay, revealing that PTX can inhibit the survival and induce the apoptosis of PC3M cells (a human prostate cancer cell line) in a concentration-dependent manner. Reactive oxygen species (ROS), as a metabolic intermediate produced by the mitochondrial respiratory chain, are highly accumulated under the PTX treatment, which results in a sharp decrease of the mitochondrial membrane potential in PC3M cells. Additionally, the migration and invasion of PC3M cells are weakened due to PTX treatment. Further analysis reveals that N-acetylcysteine (NAC), which functions as an antioxidant, not only rescues the decreased mitochondrial membrane potential induced by the abnormal ROS level, but also restores the migration and invasion of PC3M cells. In a subsequent exploration of the detailed mechanism, we found that hypoxia-inducible factor (HIF)-1α works as a downstream gene that can respond to the increased ROS in PC3M cells. Under PTX treatment, the expression levels of HIF-1α mRNA and protein are significantly increased, which stimulate the activation of JNK/caspase-3 signaling and promote the apoptosis of PC3M cells. In summary, we demonstrate that PTX regulates the expression of HIF-1α through increased ROS accumulation, thereby promoting the activation of JNK/caspase-3 pathway to induce the apoptosis of PCa cells. This study provides new insights into the mechanism of antineoplastic action of taxanes and unveils the clinical benefit of the ROS-HIF-1α signaling pathway, which may offer a potential therapeutic target to prevent the development of PCa

CoSinGAN: Learning COVID-19 Infection Segmentation from a Single Radiological Image

Computed tomography (CT) images are currently being adopted as the visual evidence for COVID-19 diagnosis in clinical practice. Automated detection of COVID-19 infection from CT images based on deep models is important for faster examination. Unfortunately, collecting large-scale training data systematically in the early stage is difficult. To address this problem, we explore the feasibility of learning deep models for lung and COVID-19 infection segmentation from a single radiological image by resorting to synthesizing diverse radiological images. Specifically, we propose a novel conditional generative model, called CoSinGAN, which can be learned from a single radiological image with a given condition, i.e., the annotation mask of the lungs and infected regions. Our CoSinGAN is able to capture the conditional distribution of the single radiological image, and further synthesize high-resolution (512 × 512) and diverse radiological images that match the input conditions precisely. We evaluate the efficacy of CoSinGAN in learning lung and infection segmentation from very few radiological images by performing 5-fold cross validation on COVID-19-CT-Seg dataset (20 CT cases) and an independent testing on the MosMed dataset (50 CT cases). Both 2D U-Net and 3D U-Net, learned from four CT slices by using our CoSinGAN, have achieved notable infection segmentation performance, surpassing the COVID-19-CT-Seg-Benchmark, i.e., the counterparts trained on an average of 704 CT slices, by a large margin. Such results strongly confirm that our method has the potential to learn COVID-19 infection segmentation from few radiological images in the early stage of COVID-19 pandemic

Drr4covid: Learning Automated COVID-19 Infection Segmentation From Digitally Reconstructed Radiographs

Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination, where infection segmentation is an essential step for assessment and quantification. However, due to the heterogeneity of X-ray imaging and the difficulty of annotating infected regions precisely, learning automated infection segmentation on CXRs remains a challenging task. We propose a novel approach, called DRR4Covid, to learn COVID-19 infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid consists of an infection-aware DRR generator, a segmentation network, and a domain adaptation module. Given a labeled Computed Tomography scan, the infection-aware DRR generator can produce infection-aware DRRs with pixel-level annotations of infected regions for training the segmentation network. The domain adaptation module is designed to enable the segmentation network trained on DRRs to generalize to CXRs. The statistical analyses made on experiment results have indicated that our infection-aware DRRs are significantly better than standard DRRs in learning COVID-19 infection segmentation (p <; 0.05) and the domain adaptation module can improve the infection segmentation performance on CXRs significantly (p <; 0.05). Without using any annotations of CXRs, our network has achieved a classification score of (Accuracy: 0.949, AUC: 0.987, F1-score: 0.947) and a segmentation score of (Accuracy: 0.956, AUC: 0.980, F1-score: 0.955) on a test set with 558 normal cases and 558 positive cases. Besides, by adjusting the strength of radiological signs of COVID-19 infection in infection-aware DRRs, we estimate the detection limit of X-ray imaging in detecting COVID-19 infection. The estimated detection limit, measured by the percent volume of the lung that is infected by COVID-19, is 19.43% ± 16.29%, and the estimated lower bound of infected voxel contribution rate for significant radiological signs of COVID-19 infection is 20.0%. Our codes are made publicly available at https://github.com/PengyiZhang/DRR4Covid

Improved shift-invariant sparse coding for noise attenuation of magnetotelluric data

Magnetotelluric (MT) method is widely used for revealing deep electrical structure. However, natural MT signals are susceptible to cultural noises. In particular, the existing data-processing methods usually fail to work when MT data are contaminated by persistent or coherent noises. To improve the quality of MT data collected with strong ambient noises, we propose a novel time-series editing method based on the improved shift-invariant sparse coding (ISISC), a data-driven machine learning algorithm. First, a redundant dictionary is learned autonomously from the raw MT data. Second, cultural noises are reconstructed using the learned dictionary and the orthogonal matching pursuit (OMP) algorithm. Finally, the de-noised MT data are obtained by subtracting the reconstructed cultural noises from the raw MT data. The synthetic data, field experimental data and measured data are tested to verify the effectiveness of the newly proposed method. The results show that our new scheme can effectively remove strong cultural noises and has better adaptability and efficiency than the predefined dictionary-based methods. The method can be used as an alternative when a remote reference station is not available.ISSN:1343-8832ISSN:1880-598

The Roles of microRNAs in Regulating the Expression of PD-1/PD-L1 Immune Checkpoint

Engagement of programmed death-ligand 1 (PD-L1) with its receptor programmed death 1 (PD-1) on T cells has been speculated to play a major role in suppressing the immune system, which helps tumor cells evade anti-tumor immunity. With the development of whole genome sequencing technologies, microRNAs have gained more attention as an important new layer of molecular regulation. Recent studies have revealed that altered expression of microRNAs play a pivotal role in immune checkpoint and various cellular processes in cancer. In this review, we focused on the latest progress about microRNAs research which involves the regulation of PD-1/PD-L1 immune checkpoint

Global trends and hotspots evolution in soil microplastic pollution research: A bibliometric analysis based on the Web of Science

Soil microplastic (MP) pollution is a growing global environmental issue leading to a series of ecosystem problems and functional losses. However, understanding the advances and trends in soil MP pollution research proved to be difficult based on individual studies. Herein, CiteSpace visualization analysis software was used to sort the literature obtained on soil MP research from the Web of Science (WoS) TM core collection database, and bibliometric analysis of publication volume, country/region, keywords and other information was conducted. Results showed that soil microplastics (MP) research had much lower publication volume than hydrological system MP research, and was still in its developing stage. However, the number of research hotspots in soil MP research increased continuously since 2018. Countries such as China, the United States, the Netherlands and Australia have published more papers in this field than other countries, and collaboration among research institutions is strong. The source, pollution status, analysis methods and toxic effects of MPs in soils are current research hotspots. In the future, the focus of MP research in terrestrial ecosystems should shift from ecotoxicology to the impact of MPs on ecosystems and earth system feedback. The bibliometric analysis results of this study can serve as a reference for in-depth studies of the pollution status of MPs in soils. For MP soil pollution research, cross-background, cross-institution, cross-country cooperation and cross-disciplinary roles should be encouraged to accelerate the growth and diversification of the field

AStruct: detection of allele-specific RNA secondary structure in structuromic probing data

Abstract Background Uncovering functional genetic variants from an allele-specific perspective is of paramount importance in advancing our understanding of gene regulation and genetic diseases. Recently, various allele-specific events, such as allele-specific gene expression, allele-specific methylation, and allele-specific binding, have been explored on a genome-wide scale due to the development of high-throughput sequencing methods. RNA secondary structure, which plays a crucial role in multiple RNA-associated processes like RNA modification, translation and splicing, has emerged as an essential focus of relevant research. However, tools to identify genetic variants associated with allele-specific RNA secondary structures are still lacking. Results Here, we develop a computational tool called ‘AStruct’ that enables us to detect allele-specific RNA secondary structure (ASRS) from RT-stop based structuromic probing data. AStruct shows robust performance in both simulated datasets and public icSHAPE datasets. We reveal that single nucleotide polymorphisms (SNPs) with higher AStruct scores are enriched in coding regions and tend to be functional. These SNPs are highly conservative, have the potential to disrupt sites involved in m6A modification or protein binding, and are frequently associated with disease. Conclusions AStruct is a tool dedicated to invoke allele-specific RNA secondary structure events at heterozygous SNPs in RT-stop based structuromic probing data. It utilizes allelic variants, base pairing and RT-stop information under different cell conditions to detect dynamic and functional ASRS. Compared to sequence-based tools, AStruct considers dynamic cell conditions and outperforms in detecting functional variants. AStruct is implemented in JAVA and is freely accessible at: https://github.com/canceromics/AStruct

The different immunoregulatory functions on dendritic cells between mesenchymal stem cells derived from bone marrow of patients with low-risk or high-risk myelodysplastic syndromes.

Myelodysplastic syndrome (MDS) is a group of progressive,clonal, neoplastic bone marrow disorders characterized by hematopoietic stem cell dysregulation and abnormalities in the immune system. Mesenchymal stem cells (MSC) appear to modulate the immune system at the very first step of the immune response through the inhibition of dendritic cells (DCs) differentiation and maturation. However, it is still unclear whether the effects of MSC on the development of DCs will be altered with disease state. In addition, it is not clear whether there are differences in the effects between low-risk and high-risk MDS-MSC on DCs development. In this study, our data confirm that MDS-MSC mediate a potent inhibition of DCs differentiation. Additionally, MDS-MSC greatly alter DCs functions, including endocytosis, IL-12 secretion, their ability to inhibit T cell proliferation. Moreover, our results show that there are major differences in DCs development and function between low-risk and high-risk MDS-MSC. Compared to high-risk MDS-MSC, low-risk MDS-MSC is characterized by a poor ability to inhibit DCs differentiation and maturation; and correspondingly, less dysfunctional DC endocytosis, mildly decreased IL-12 secretion, and a reduction in DC-mediated inhibition of T cell proliferation. Finally, our results demonstrate that MDS-MSC derived TGF-β1 is largely responsible for the inhibitory effects. These results elucidate the different immunoregulatory role of MSC in low-risk and high-risk MDS on DCs development, which may be important for understanding the pathogenesis of MDS and the development of novel immune therapies for the treatment of MDS