Bacterial transport mediated by micro-nanobubbles in porous media.

Determining the role of micro-nanobubbles (MNBs) in controlling the risk posed by pathogens to soil and groundwater during reclaimed water irrigation requires clarification of the mechanism of how MNBs block pathogenic bacteria. In this study, real-time bioluminescence imaging was used to investigate the effects of MNBs on the transport and spatiotemporal distribution of bioluminescent Escherichia coli 652T7 strain in porous media. The presence of MNBs significantly increased the retention of bacteria in the porous media, decreasing the maximum relative effluent concentration (C/C0) by 78 % from 0.97 (without MNBs) to 0.21 (with MNBs). The results suggested that MNBs provided additional sites at the air-water interface (AWI) for bacterial attachment and acted as physical obstacles to reduce bacterial passage. These effects varied with environmental conditions such as solution ionic strength and pore water velocity. The results indicated that MNBs enhanced electrostatic attachment of bacteria at the AWI and their mechanical straining in pores. This study suggests that adding MNBs in pathogen-containing water is an effective measure for increasing filtration efficiency and reducing the risk of pathogenic contamination during agricultural irrigation.

Annual atrazine residue estimation in Chinese agricultural soils by integrated modeling of machine learning and mechanism-based models.

This study presents a comprehensive approach to estimating annual atrazine residues in China's agricultural soils, integrating machine learning algorithms and mechanism-based models. First, machine learning was used to predict essential parameters influencing atrazine's adsorption, degradation, and dispersivity of solute transport. The results demonstrated that soil organic matter was the most important input variable for predicting adsorption and degradation; clay content was the primary variable for predicting dispersivity. The SHapley Additive exPlanations (SHAP) contribution of various soil properties on target variables were also analyzed to reveal whether each input variable has a positive, negative, or complex effect. Subsequently, these parameters inform the construction of a detailed model across 23,692 subregions of China, with a 20 km × 20 km resolution. The model considered regional variations and soil layer heterogeneity, including rainfall, soil depth-specific properties, and parameters for adsorption, degradation, and dispersivity. Utilizing the convection-dispersion equations and the Phydrus, the model simulated atrazine's transport and degradation patterns across diverse soil environments after applying 250 mL of atrazine (40%) per Chinese mu. The outcomes provided a spatially explicit distribution of atrazine residues, specifying that the arid areas have the highest residual risk, followed by the Northeast, Southwest, and Southeast. Atrazine levels may exceed national drinking water standards at 50 cm depth in Inner Mongolia, the Qinghai-Tibet Plateau, and the Jungar Basin. This study's integrative approach may also offer valuable insights and tools for evaluating residues of various pesticides and herbicides in agricultural soils.

Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients.

Background: Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined. Methods: We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD. Results: Out of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion. Conclusion: Our research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis, mirrors the tumor's local immune condition, and supports clinical decision-making in tumor therapies.

The relationship between brain metastasis and HER2 expression status in gastric cancer

Background Brain metastasis (BM) in gastric cancer (GC) is underestimated, and human epidermal growth factor receptor 2 (HER2) overexpression is a durable poor prognostic factor. We explored the relationship between the two and made a survival analysis. Methods HER2 expression and BM status were collected from GC patients who were diagnosed between December 2009 and May 2021. We collected GC patients diagnosed between 2010 and 2016 from the SEER database. The primary endpoint was survival from the diagnosis of BM. Multivariable logistic regression was used to determine potential risk factors of BM at diagnosis in SEER database. Survival analysis was performed using the Kaplan–Meier method. Result There were 513 HER2-positive GC patients, including 16 (3.1%) with BM. Among 38 brain metastasis GC patients we collected, 16 (42.1%) patients were HER2 positive. We collected 34,199 GC patients from the SEER database and there were 260 (0.76%) patients with BM at diagnosis. GC patients that are male, white, of younger age, with primary lesions located in the proximal stomach or with distant lymph nodes, liver, bone, or lung metastasis are more likely to develop BM. The median overall survival time from diagnosis of BM was 12.73 months, and the survival time from brain metastasis of HER2-positive patients was numerically shorter, though the difference was not significant (5.30 months vs.16.13 months, P = 0.28.) Conclusion The incidence of BM in patients with HER2-positive gastric cancer is 4.08 times higher than that in general patients. The median overall survival time from BM is shorter for HER2-positive patients (AU)

Emerging role of RNA modification and long noncoding RNA interaction in cancer.

RNA modification, especially N6-methyladenosine, 5-methylcytosine, and N7-methylguanosine methylation, participates in the occurrence and progression of cancer through multiple pathways. The function and expression of these epigenetic regulators have gradually become a hot topic in cancer research. Mutation and regulation of noncoding RNA, especially lncRNA, play a major role in cancer. Generally, lncRNAs exert tumor-suppressive or oncogenic functions and its dysregulation can promote tumor occurrence and metastasis. In this review, we summarize N6-methyladenosine, 5-methylcytosine, and N7-methylguanosine modifications in lncRNAs. Furthermore, we discuss the relationship between epigenetic RNA modification and lncRNA interaction and cancer progression in various cancers. Therefore, this review gives a comprehensive understanding of the mechanisms by which RNA modification affects the progression of various cancers by regulating lncRNAs, which may shed new light on cancer research and provide new insights into cancer therapy.

The relationship between brain metastasis and HER2 expression status in gastric cancer.

BACKGROUND: Brain metastasis (BM) in gastric cancer (GC) is underestimated, and human epidermal growth factor receptor 2 (HER2) overexpression is a durable poor prognostic factor. We explored the relationship between the two and made a survival analysis. METHODS: HER2 expression and BM status were collected from GC patients who were diagnosed between December 2009 and May 2021. We collected GC patients diagnosed between 2010 and 2016 from the SEER database. The primary endpoint was survival from the diagnosis of BM. Multivariable logistic regression was used to determine potential risk factors of BM at diagnosis in SEER database. Survival analysis was performed using the Kaplan-Meier method. RESULT: There were 513 HER2-positive GC patients, including 16 (3.1%) with BM. Among 38 brain metastasis GC patients we collected, 16 (42.1%) patients were HER2 positive. We collected 34,199 GC patients from the SEER database and there were 260 (0.76%) patients with BM at diagnosis. GC patients that are male, white, of younger age, with primary lesions located in the proximal stomach or with distant lymph nodes, liver, bone, or lung metastasis are more likely to develop BM. The median overall survival time from diagnosis of BM was 12.73 months, and the survival time from brain metastasis of HER2-positive patients was numerically shorter, though the difference was not significant (5.30 months vs.16.13 months, P = 0.28.) CONCLUSION: The incidence of BM in patients with HER2-positive gastric cancer is 4.08 times higher than that in general patients. The median overall survival time from BM is shorter for HER2-positive patients.

Dynamic single-cell mapping unveils Epstein‒Barr virus-imprinted T-cell exhaustion and on-treatment response.

Epstein‒Barr virus (EBV)-associated gastric cancer (GC) manifests an intriguing immunotherapy response. However, the cellular basis for EBV-imprinted tumour immunity and on-treatment response remains undefined. This study aimed to finely characterize the dynamic tumour immune contexture of human EBV (+) GC treated with immunochemotherapy by longitudinal scRNA-seq and paired scTCR/BCR-seq. EBV (+) GC exhibits an inflamed-immune phenotype with increased T-cell and B-cell infiltration. Immunochemotherapy triggers clonal revival and reinvigoration of effector T cells which step to determine treatment response. Typically, an antigen-specific ISG-15+CD8+ T-cell population is highly enriched in EBV (+) GC patients, which represents a transitory exhaustion state. Importantly, baseline intratumoural ISG-15+CD8+ T cells predict immunotherapy responsiveness among GC patients. Re-emerged clonotypes of pre-existing ISG-15+CD8+ T cells could be found after treatment, which gives rise to a CXCL13-expressing effector population in responsive EBV (+) tumours. However, LAG-3 retention may render the ISG-15+CD8+ T cells into a terminal exhaustion state in non-responsive EBV (+) tumours. In accordance, anti-LAG-3 therapy could effectively reduce tumour burden in refractory EBV (+) GC patients. Our results delineate a distinct implication of EBV-imprinted on-treatment T-cell immunity in GC, which could be leveraged to optimize the rational design of precision immunotherapy.

A novel nomogram integrated with PDL1 and CEA to predict the prognosis of patients with gastric cancer

Introduction This study aimed to develop a prognostic nomogram for patients with gastric cancer (GC) based on the levels of programmed death 1 ligand 1 (PDL1) and carcinoembryonic antigen (CEA). Methods The nomogram was developed using data from a primary cohort of 247 patients who had been clinicopathologically diagnosed with GC, as well as a validation cohort of 63 patients. Furthermore, the nomogram divided the patients into three different risk groups for overall survival (OS)—the low-risk, middle-risk, and high-risk groups. Univariate and multivariate Cox hazard analyses were used to determine all of the factors included in the model. Decision curve analysis and receiver operating characteristic (ROC) curves were used to assess the accuracy of the nomogram. Results The Kaplan–Meier survival analysis revealed that metastasis stage, clinical stage, and CEA and PDL1 levels were predictors for progress-free survival (PFS) and OS of patients with GC. Metastasis stage, clinical stage, and CEA and PDL1 levels were found to be independent risk factors for the PFS and OS of patients with GC in a multivariate analysis, and the nomogram was based on these factors. The concordance index of the nomogram was 0.763 [95% confidence interval (CI) 0.740–0.787]. The area under the concentration–time curve of the nomogram model was 0.81 (95% CI 0.780–0.900). According to the decision curve analysis and ROC curves, the nomogram model had a higher overall net efficiency in forecasting OS than clinical stage, CEA and PDL1 levels. Conclusion In conclusion, we proposed a novel nomogram that integrated PDL1 and CEA, and the proposed nomogram provided more accurate and useful prognostic predictions for patients with GC (AU)

Precise microdissection of gastric mixed adeno-neuroendocrine carcinoma dissects its genomic landscape and evolutionary clonal origins.

Gastric mixed adenoneuroendocrine carcinoma (MANEC) is a clinically aggressive and heterogeneous tumor composed of adenocarcinoma (ACA) and neuroendocrine carcinoma (NEC). The genomic properties and evolutionary clonal origins of MANEC remain unclear. We conduct whole-exome and multiregional sequencing on 101 samples from 33 patients to elucidate their evolutionary paths. We identify four significantly mutated genes, TP53, RB1, APC, and CTNNB1. MANEC resembles chromosomal instability stomach adenocarcinoma in that whole-genome doubling in MANEC is predominant and occurs earlier than most copy-number losses. All tumors are of monoclonal origin, and NEC components show more aggressive genomic properties than their ACA counterparts. The phylogenetic trees show two tumor divergence patterns, including sequential and parallel divergence. Furthermore, ACA-to-NEC rather than NEC-to-ACA transition is confirmed by immunohistochemistry on 6 biomarkers in ACA- and NEC-dominant regions. These results provide insights into the clonal origin and tumor differentiation of MANEC.

Predicting bacterial transport through saturated porous media using an automated machine learning model.

Escherichia coli, as an indicator of fecal contamination, can move from manure-amended soil to groundwater under rainfall or irrigation events. Predicting its vertical transport in the subsurface is essential for the development of engineering solutions to reduce the risk of microbiological contamination. In this study, we collected 377 datasets from 61 published papers addressing E. coli transport through saturated porous media and trained six types of machine learning algorithms to predict bacterial transport. Eight variables, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content were used as input variables while the first-order attachment coefficient and spatial removal rate were set as target variables. The eight input variables have low correlations with the target variables, namely, they cannot predict target variables independently. However, using the predictive models, input variables can effectively predict the target variables. For scenarios with higher bacterial retention, such as smaller median grain size, the predictive models showed better performance. Among six types of machine learning algorithms, Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. In most predictive models, pore water velocity, ionic strength, median grain size, and column length showed higher importance than other input variables. This study provided a valuable tool to evaluate the transport risk of E.coli in the subsurface under saturated water flow conditions. It also proved the feasibility of data-driven methods that could be used for predicting other contaminants' transport in the environment.

Immunomodulatory Mechanism and Potential Application of Dental Pulp-Derived Stem Cells in Immune-Mediated Diseases.

Dental pulp stem cells (DPSCs) are mesenchymal stem cells (MSCs) derived from dental pulp tissue, which have high self-renewal ability and multi-lineage differentiation potential. With the discovery of the immunoregulatory ability of stem cells, DPSCs have attracted much attention because they have similar or even better immunomodulatory effects than MSCs from other sources. DPSCs and their exosomes can exert an immunomodulatory ability by acting on target immune cells to regulate cytokines. DPSCs can also migrate to the lesion site to differentiate into target cells to repair the injured tissue, and play an important role in tissue regeneration. The aim of this review is to summarize the molecular mechanism and target cells of the immunomodulatory effects of DPSCs, and the latest advances in preclinical research in the treatment of various immune-mediated diseases, providing new reflections for their clinical application. DPSCs may be a promising source of stem cells for the treatment of immune-mediated diseases.

A novel nomogram integrated with PDL1 and CEA to predict the prognosis of patients with gastric cancer.

INTRODUCTION: This study aimed to develop a prognostic nomogram for patients with gastric cancer (GC) based on the levels of programmed death 1 ligand 1 (PDL1) and carcinoembryonic antigen (CEA). METHODS: The nomogram was developed using data from a primary cohort of 247 patients who had been clinicopathologically diagnosed with GC, as well as a validation cohort of 63 patients. Furthermore, the nomogram divided the patients into three different risk groups for overall survival (OS)-the low-risk, middle-risk, and high-risk groups. Univariate and multivariate Cox hazard analyses were used to determine all of the factors included in the model. Decision curve analysis and receiver operating characteristic (ROC) curves were used to assess the accuracy of the nomogram. RESULTS: The Kaplan-Meier survival analysis revealed that metastasis stage, clinical stage, and CEA and PDL1 levels were predictors for progress-free survival (PFS) and OS of patients with GC. Metastasis stage, clinical stage, and CEA and PDL1 levels were found to be independent risk factors for the PFS and OS of patients with GC in a multivariate analysis, and the nomogram was based on these factors. The concordance index of the nomogram was 0.763 [95% confidence interval (CI) 0.740-0.787]. The area under the concentration-time curve of the nomogram model was 0.81 (95% CI 0.780-0.900). According to the decision curve analysis and ROC curves, the nomogram model had a higher overall net efficiency in forecasting OS than clinical stage, CEA and PDL1 levels. CONCLUSION: In conclusion, we proposed a novel nomogram that integrated PDL1 and CEA, and the proposed nomogram provided more accurate and useful prognostic predictions for patients with GC.

Effects of nitrate and Fe/As molar ratio on direct iron(III)-arsenite precipitation in high-sulfate-chloride wastewaters.

The addition of an arsenite-chloride solution into an arsenite-sulfate solution is extremely beneficial for the removal of As(III) via Fe(III) salt precipitation at pH 2.3. However, the applicability of this method to complicated high-As(III) metallurgical wastewaters still requires further verification. This work investigated the effects of nitrate and Fe/As molar ratio on As(III) immobilization using Fe(III) in three acid radical media including sulfate, chloride, and nitrate at pH 2.3. Our results indicated that 72.1‒93.5% of As(III) was precipitated, which was 5‒10% less than those obtained in the nitrate-free systems. The Fe/As molar ratio of 4 was the optimal condition with an average of 93% As(III) removal based on a broad sulfate/chloride molar ratio range (1:1‒16). However, a maximum of 96% As(III) removal was observed under the Fe/As molar ratio of 1.5 and the sulfate/chloride condition of 1:16. The negative correlation between complexation and precipitation was attributed to the enhanced initial complexation by the synergistic effect of the mononitratoiron complex and FeH2AsO32+. The variation of Fe/As molar ratios resulted in the diverse solid species, thus further affecting the As(III) removal efficiency. Despite producing tooeleite as a major As(III) host phase, ferrihydrite and poorly crystalline ferric arsenite hydroxysulfate formed simultaneously at the Fe/As molar ratio of 4 participated in As(III) immobilization compared with the solid products at Fe/As molar ratios ≤ 2.

Vemurafenib inhibits immune escape biomarker BCL2A1 by targeting PI3K/AKT signaling pathway to suppress breast cancer.

Objectives: To investigate the role of immune escape encoding genes on the prognosis of BC, and to predict the novel targeting agents. Methods: Human immune genes and immune escape encoding genes were obtained from the IMMPORT database and the previous study. Sample information and clinical data on BC were obtained from the TCGA and GTEX databases. Obtaining differentially expressed protein data from cBioportal database. To construct a risk score model by lasso analysis, and nomogram was used to predict score core. GSCA, TIMER and CELLMINER databases were used for immune and drug susceptibility correlation analyses. Cell experiments were verified by MTT, Western blotting, and RT-qPCR. Results: We found prognostic models consisting of eleven immune escape related protein-coding genes with ROC curves that performed well in the ontology data (AUC for TCGA is 0.672) and the external data (AUC for GSE20685 is 0.663 and for GES42568 is 0.706). Five core prognostic models are related to survival (EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4) were summarized, and a nomogram was constructed to validate a C-index of 0.695, which was superior to other prognostic models. Relevant drugs targeting core genes were identified based on drug sensitivity analysis, and found that Vemurafenib downregulates the PI3K-AKT pathway and BCL2A1 protein in BC, as confirmed by external data and cellular assays. Conclusions: Briefly, our work establishes and validates an 11-immune escape risk model, and five core prognostic factors that are mined deeply from this model, and elucidates in detail that Vemurafenib suppresses breast cancer by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1, confirms the validity of the prognostic model, and provides corresponding targeted agents to guide individualized treatment of BC patients.

Facial muscle movements in patients with Parkinson's disease undergoing phonation tests.

Purpose: Parkinson's disease (PD) is a serious neurodegenerative disease affecting the elderly. In general, the locomotion deficit, which seriously affects the daily life of patients with PD, usually occurs at a later stage. The mask face symptom meanwhile progressively worsens. However, facial muscle disorders and changes involved in the freezing mask are unclear. Method: In this study, we recruited 35 patients with PD and 26 age- and sex-balanced controls to undergo phonation tests, while the built-in camera on the laptop recorded their facial expressions during the whole pronunciation process. Furthermore, FaceReader (version 7.0; Noldus Information Technology, Wageningen, Netherlands) was used to analyze changes in PD facial landmark movement and region movement. Results: The two-tailed Student's t-test showed that the changes in facial landmark movement among 49 landmarks were significantly lower in patients with PD than in the control group (P < 0.05). The data on facial region movement revealed that the eyes and upper lip of patients with PD differed significantly from those in the control group. Conclusion: Patients with PD had defects in facial landmark movement and regional movement when producing a single syllable, double syllable, and multiple syllables, which may be related to reduced facial expressions in patients with PD.

Comparison of response evaluation criteria in solid tumors and tumor regression grade in evaluating the effect of preoperative systemic therapy of gastric cancer.

BACKGROUND: Both Response Evaluation Criteria in Solid Tumors (RECIST) and tumor regression grade (TRG) play key roles in evaluating tumor response. We analyzed the consistency of TRG and RECIST 1.1 for gastric cancer (GC) patients and compared their prognostic values. METHODS: Patients with GC who received preoperative chemotherapy or chemoimmunotherapy and had records of TRG from December 2013 to October 2021 were enrolled retrospectively. TRG 0-1 and 2-3 are considered as corresponding to complete response (CR)/partial response (PR) and stable disease (SD)/progress disease (PD) in RECIST 1.1, respectively. The primary endpoints were disease-free survival (DFS) and overall survival (OS). The consistency of RECIST and TRG was examined by kappa statistics. Survival analysis was performed using the Kaplan Meier method. RESULT: One hundred fifty seven GC patients were enrolled, including 125 with preoperative chemotherapy and 32 with chemoimmunotherapy. Among them, 56 patients had measurable lesions. Only 19.6% (11/56) of the patients had consistent results between RECIST 1.1 and TRG. TRG was correlated with both OS and DFS (P = 0.02 and 0.03, respectively) while response according to RECIST1.1 was not (P = 0.86 and 0.23, respectively). The median DFS had not reached in the TRG 0-1 group and was 16.13 months in TRG 2-3 group. TRG 2-3 was associated with young age and peritoneal or liver metastasis. Besides, preoperative chemoimmunotherapy had a significantly higher pCR rate than chemotherapy alone (34.4% vs 8.0%, P < 0.001). CONCLUSION: TRG was in poor agreement with RECIST 1.1. TRG was better than RECIST 1.1 in predicting DFS and OS for GC patients who received preoperative therapy.

Interactive removal of bacterial and viral particles during transport through low-cost filtering materials.

Pathogen filtration is critically important for water sanitation. However, it is a big challenge to balance removal efficiency and filtering material cost. In this study, we quantified the removal processes of a bacterial strain Escherichia coli 652T7 and a model bacteriophage MS2 (ATCC 15597-B1) during their transport through columns containing iron filings (IF), calcined magnesite (CM), natural ore limestone (OL) or corn stalk biochar (BC) under saturated flow conditions. Experimental results showed that 99.98, 79.55, 63.79, and 62.59% of injected E. coli 652T7 and 98.78, 92.26, 68.79, and 69.82% of injected MS2 were removed by IF, CM, OL, and BC, respectively. The differences in removal percentage were attributed to the disparities of the microorganisms and filtering materials in surface function groups, surface charges, and surface morphology. Transport modeling with advection-dispersion equation (ADE) and interaction energy calculation with extended Derjaguin, Landau, Verwey, and Overbeek (XDLVO) model indicated that E. coli 652T7 and MS2 were mostly removed via irreversible attachment. In IF columns, E. coli 652T7 promoted the transport of MS2 but not vice versa. In CM columns, MS2 facilitated the transport of E. coli 652T7 and vice versa at a less extent. Such changes were a combined result of attachment site competition, steric effect, and mechanical straining. We found that the sum of the removal percentages of the two microorganisms in their respective transport experiments were similar to those calculated from their co-transport experiments. This result suggests that the removals were mainly limited by the attachment sites in the filtering materials.

Novel prognostic model predicts overall survival in colon cancer based on RNA splicing regulation gene expression.

Colon cancer is the third most common cancer and the second leading cause of cancer-related death worldwide. Dysregulated RNA splicing factors have been reported to be associated with tumorigenesis and development in colon cancer. In this study, we interrogated clinical and RNA expression data of colon cancer patients from The Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) database. Genes regulating RNA splicing correlated with survival in colon cancer were identified and a risk score model was constructed using Cox regression analyses. In the risk model, RNA splicing factor peroxisome proliferator-activated receptor-γ coactivator-1α (PPARGC1) is correlated with a good survival outcome, whereas Cdc2-like kinase 1(CLK1), CLK2, and A-kinase anchor protein 8-like (AKAP8L) with a bad survival outcome. The risk model has a good performance for clinical prognostic prediction both in the TCGA cohort and the other two validation cohorts. In the tumor microenvironment (TME) analysis, the immune score was higher in the low-risk group, and TME-related pathway gene expression was also higher in low-risk group. We further verified the mRNA and protein expression levels of these four genes in the adjacent nontumor, tumor, and liver metastasis tissues of colon cancer patients, which were consistent with bioinformatics analysis. In addition, knockdown of AKAP8L can suppress the proliferation and migration of colon cancer cells. Animal studies have also shown that AKAP8L knockdown can inhibit tumor growth in colon cancer in vivo. We established a prognostic risk model for colon cancer based on genes related to RNA splicing regulation and uncovered the role of AKAP8L in promoting colon cancer progression.

PD-L1 regulates cell proliferation and apoptosis in acute myeloid leukemia by activating PI3K-AKT signaling pathway.

As immune checkpoint inhibitors (ICIs) continue to advance, more evidence has emerged that anti-PD-1/PD-L1 immunotherapy is an effective treatment against cancers. Known as the programmed death ligand-1 (PD-L1), this co-inhibitory ligand contributes to T cell exhaustion by interacting with programmed death-1 (PD-1) receptor. However, cancer-intrinsic signaling pathways of the PD-L1 molecule are not well elucidated. Therefore, the present study aimed to evaluate the regulatory network of PD-L1 and lay the basis of successful use of anti-PD-L1 immunotherapy in acute myeloid leukemia (AML). Data for AML patients were extracted from TCGA and GTEx databases. The downstream signaling pathways of PD-L1 were identified via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The key PD-L1 related genes were selected by weighted gene co-expression network analysis (WGCNA), MCC algorithm and Molecular Complex Detection (MCODE). The CCK-8 assay was used to assess cell proliferation. Flow cytometry was used to determine cell apoptosis and cell cycle. Western blotting was used to identify the expression of the PI3K-AKT signaling pathway. PD-L1 was shown to be elevated in AML patients when compared with the control group, and high PD-L1 expression was associated with poor overall survival rate. The ECM-receptor interaction, as well as the PI3K-AKT signaling pathway, were important PD-L1 downstream pathways. All three analyses found eight genes (ITGA2B, ITGB3, COL6A5, COL6A6, PF4, NMU, AGTR1, F2RL3) to be significantly associated with PD-L1. Knockdown of PD-L1 inhibited AML cell proliferation, induced cell apoptosis and G2/M cell cycle arrest. Importantly, PD-L1 knockdown reduced the expression of PI3K and p-AKT, but PD-L1 overexpression increased their expression. The current study elucidates the main regulatory network and downstream targets of PD-L1 in AML, assisting in the understanding of the underlying mechanism of anti-PD-1/PD-L1 immunotherapy and paving the way for clinical application of ICIs in AML.

Hepatitis B virus X protein mediated epigenetic alterations in the pathogenesis of hepatocellular carcinoma.

Chronic hepatitis B virus (HBV) infection is a worldwide health problem. Hepatitis B virus X protein (HBx), a pleiotropic regulatory protein encoded by HBV, is necessary for the transcription of HBV covalently closed circular DNA (cccDNA) minichromosomes, and affects the epigenetic regulation of host cells. The epigenetic reprogramming of HBx on host cell genome is strongly involved in HBV-related HCC carcinogenesis. Here, we review the latest findings of the epigenetic regulation induced by HBx protein in hepatocellular carcinoma (HCC), including DNA methylation, histone modification and non-coding RNA expression. The influence of HBx on the epigenetic regulation of cccDNA is also summarized. In addition, preliminary studies of targeted drugs for epigenetic changes induced by HBx are also discussed. The exploration of epigenetic markers as potential targets will help to develop new prevention and/or treatment methods for HBx-related HCC.