Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer.

Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.

Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5.

Sepsis is a disease with a very high mortality rate, mainly involving an immune-dysregulated response due to bacterial infection. Most studies are currently limited to the whole blood transcriptome level; however, at the single cell level, there is still a great deal unknown about specific cell subsets and disease markers. We obtained 29 peripheral blood single-cell sequencing data, including 66,283 cells from 10 confirmed samples of sepsis infection and 19 healthy samples. Cells related to the sepsis phenotype were identified and characterized by the "scissor" method. The regulatory relationships of sepsis-related phenotype cells in the cellular communication network were clarified using the "cell chat" method. The least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) were used to identify sepsis signature genes of diagnostic value. External validation was performed using multiple datasets from the GEO database (GSE28750, GSE185263, GSE57065) and 40 clinical samples. Bayesian algorithm was used to calculate the regulatory network of LILRA5 co-expressed genes. The stability of atenolol-targeting LILRA5 was determined by molecular docking techniques. Ultimately, action trajectory and survival analyses demonstrate the effectiveness of atenolol-targeted LILRA5 in treating patients with sepsis. We successfully identified 1215 healthy phenotypic cells and 462 sepsis phenotypic cells. We focused on 447 monocytes of the sepsis phenotype. Among the cellular communications, there were a large number of differences between these cells and other immune cells showing a significant inflammatory phenotype compared to the healthy phenotypic cells. Together, the three machine learning algorithms identified the LILRA5 marker gene in sepsis patients, and validation results from multiple external datasets as well as real-world clinical samples demonstrated the robust diagnostic performance of LILRA5. The AUC values of LILRA5 in the external datasets GSE28750, GSE185263, and GSE57065 could reach 0.875, 0.940, and 0.980, in that order. Bayesian networks identified a large number of unknown regulatory relationships for LILRA5 co-expression. Molecular docking results demonstrated the possibility of atenolol targeting LILRA5 for the treatment of sepsis. Behavioral trajectory analysis and survival analysis demonstrate that atenolol has a desirable therapeutic effect. LILRA5 is a marker gene in sepsis patients, and atenolol can stably target LILRA5.

Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis.

BACKGROUND: Although significant advances have been made in intensive care medicine and antibacterial treatment, sepsis is still a common disease with high mortality. The condition of sepsis patients changes rapidly, and each hour of delay in the administration of appropriate antibiotic treatment can lead to a 4-7% increase in fatality. Therefore, early diagnosis and intervention may help improve the prognosis of patients with sepsis. METHODS: We obtained single-cell sequencing data from 12 patients. This included 14,622 cells from four patients with bacterial infectious sepsis and eight patients with sepsis admitted to the ICU for other various reasons. Monocyte differentiation trajectories were analyzed using the "monocle" software, and differentiation-related genes were identified. Based on the expression of differentiation-related genes, 99 machine-learning combinations of prognostic signatures were obtained, and risk scores were calculated for all patients. The "scissor" software was used to associate high-risk and low-risk patients with individual cells. The "cellchat" software was used to demonstrate the regulatory relationships between high-risk and low-risk cells in a cellular communication network. The diagnostic value and prognostic predictive value of Enah/Vasp-like (EVL) were determined. Clinical validation of the results was performed with 40 samples. The "CBNplot" software based on Bayesian network inference was used to construct EVL regulatory networks. RESULTS: We systematically analyzed three cell states during monocyte differentiation. The differential analysis identified 166 monocyte differentiation-related genes. Among the 99 machine-learning combinations of prognostic signatures constructed, the Lasso + CoxBoost signature with 17 genes showed the best prognostic prediction performance. The highest percentage of high-risk cells was found in state one. Cell communication analysis demonstrated regulatory networks between high-risk and low-risk cell subpopulations and other immune cells. We then determined the diagnostic and prognostic value of EVL stabilization in multiple external datasets. Experiments with clinical samples demonstrated the accuracy of this analysis. Finally, Bayesian network inference revealed potential network mechanisms of EVL regulation. CONCLUSIONS: Monocyte differentiation-related prognostic signatures based on the Lasso + CoxBoost combination were able to accurately predict the prognostic status of patients with sepsis. In addition, low EVL expression was associated with poor prognosis in sepsis.

Etoposide-induced protein 2.4 ameliorates high glucose-induced epithelial-mesenchymal transition by activating adenosine monophosphate-activated protein kinase pathway in renal tubular cells.

Epithelial-mesenchymal transition (EMT), known as the transition of tubular epithelial cells into fibroblasts, is one of the potential mechanisms of renal fibrosis, which promotes the development of diabetic kidney disease (DKD). Etoposide-induced protein 2.4 (EI24) is known as an endoplasmic reticulum (ER)-localized Bcl-2-binding transmembrane protein with various functions that can affect autophagy, apoptosis and differentiation. However, whether EI24 is involved in EMT of renal tubular epithelial cells and the exact mechanism is still not known. In this study, we first reported that EI24 expression was significantly downregulated in the kidneys of diabetic mice and in high glucose-stimulated HK2 cells. Knockdown of EI24 led to EMT of HK2 cells, as indicated by decreased E-cadherin and increased α-smooth muscle actin (α-SMA). Meanwhile, overexpression of EI24 ameliorated high glucose-induced EMT of HK2 cells via activation of the adenosine monophosphate-activated protein kinase (AMPK) pathway. Then, DNA methyltransferase (DNMT) inhibitor 5-Aza-2'-deoxycytidine (5-Aza) treatment enhanced EI24 expression and alleviated EMT in high glucose-treated HK2 cells and the kidneys of diabetic mice. Furthermore, DNMT1 and DNMT3a upregulation were found to be involved in the decrease of EI24 in high glucose-stimulated HK2 cells. Silencing of DNMT1 and DNMT3a effectively reversed high glucose-induced downregulation of EI24 and aggravation of EMT. Our findings demonstrate that the DNA methyltransferase-regulated EI24 affects EMT of renal tubular cells via AMPK signaling pathway. It is suggested that EI24 may be a potential therapeutic target for diabetic renal injury.

METTL14-regulated PI3K/Akt signaling pathway via PTEN affects HDAC5-mediated epithelial-mesenchymal transition of renal tubular cells in diabetic kidney disease.

Histone deacetylase 5 (HDAC5) belongs to class II HDAC subfamily and is reported to be increased in the kidneys of diabetic patients and animals. However, little is known about its function and the exact mechanism in diabetic kidney disease (DKD). Here, we found that HDAC5 was located in renal glomeruli and tubular cells, and significantly upregulated in diabetic mice and UUO mice, especially in renal tubular cells and interstitium. Knockdown of HDAC5 ameliorated high glucose-induced epithelial-mesenchymal transition (EMT) of HK2 cells, indicated in the increased E-cadherin and decreased α-SMA, via the downregulation of TGF-ß1. Furthermore, HDAC5 expression was regulated by PI3K/Akt signaling pathway and inhibition of PI3K/Akt pathway by LY294002 treatment or Akt phosphorylation mutation reduced HDAC5 and TGF-ß1 expression in vitro high glucose-cultured HK2 cells. Again, high glucose stimulation downregulated total m6A RNA methylation level of HK2 cells. Then, m6A demethylase inhibitor MA2 treatment decreased Akt phosphorylation, HDAC5, and TGF-ß1 expression in high glucose-cultured HK2 cells. In addition, m6A modification-associated methylase METTL3 and METTL14 were decreased by high glucose at the levels of mRNA and protein. METTL14 not METTL3 overexpression led to PI3K/Akt pathway inactivation in high glucose-treated HK2 cells by enhancing PTEN, followed by HDAC5 and TGF-ß1 expression downregulation. Finally, in vivo HDACs inhibitor TSA treatment alleviated extracellular matrix accumulation in kidneys of diabetic mice, accompanied with HDAC5, TGF-ß1, and α-SMA expression downregulation. These above data suggest that METTL14-regulated PI3K/Akt signaling pathway via PTEN affected HDAC5-mediated EMT of renal tubular cells in diabetic kidney disease.

Trichostatin A increases BDNF protein expression by improving XBP-1s/ATF6/GRP78 axis in Schwann cells of diabetic peripheral neuropathy.

Diabetic peripheral neuropathy (DPN) is the common complication of diabetes mellitus. Histone deacetylase (HDAC) inhibitor trichostatin A (TSA) is reported to ameliorate the peripheral nerves degeneration of DPN. However, the exact mechanism is still not well elucidated. Here, we first revealed that TSA promoted nerve conduction and brain derived neurotrophic factor (BDNF) expression in the sciatic nerves of diabetic mice. In line, TSA also reversed high glucose-reduced mature BDNF expression in vitro cultured rat Schwann cells (RSC96). Then unexpectedly, the downstream targets of TSA HDAC1 and HDAC5 were not involved in TSA-improved BDNF expression. Furthermore, unfolded protein response (UPR) chaperone GRP78 was revealed to be downregulated with high glucose stimulation in RSC96 cells, which was avoided with TSA treatment. Also, GRP78 upregulation mediated TSA-improved mature BDNF expression in high glucose-cultured RSC96 cells by binding with BDNF. As well, TSA treatment enhanced the binding of GRP78 with BDNF in RSC96 cells. Again, UPR-associated transcription factors XBP-1s and ATF6 were involved in TSA-increased GRP78 expression in high glucose-stimulated RSC96 cells. Finally, conditioned medium from high glucose-cultured RSC96 cells delayed neuron SH-SY5Y differentiation and that from TSA-treated high glucose-cultured RSC96 cells promoted SH-SY5Y cell differentiation. Taken together, our findings suggested that TSA increased BDNF expression to ameliorate DPN by improving XBP-1s/ATF6/GRP78 axis in Schwann cells.

EBV-Positive Gastric Cancer: Current Knowledge and Future Perspectives.

Gastric cancer is the fifth most common malignant tumor and second leading cause of cancer-related deaths worldwide. With the improved understanding of gastric cancer, a subset of gastric cancer patients infected with Epstein-Barr virus (EBV) has been identified. EBV-positive gastric cancer is a type of tumor with unique genomic aberrations, significant clinicopathological features, and a good prognosis. After EBV infects the human body, it first enters an incubation period in which the virus integrates its DNA into the host and expresses the latent protein and then affects DNA methylation through miRNA under the action of the latent protein, which leads to the occurrence of EBV-positive gastric cancer. With recent developments in immunotherapy, better treatment of EBV-positive gastric cancer patients appears achievable. Moreover, studies show that treatment with immunotherapy has a high effective rate in patients with EBV-positive gastric cancer. This review summarizes the research status of EBV-positive gastric cancer in recent years and indicates areas for improvement of clinical practice.

IFN-γ/mTORC1 decreased Rab11 in Schwann cells of diabetic peripheral neuropathy, inhibiting cell proliferation via GLUT1 downregulation.

Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus. Rab11 is conserved gene-regulating vesicle traffic and reported to be involved in the pathogenesis of diabetes mellitus by affecting insulin sensitivity. We aimed to investigate the role of Rab11 in the pathogenesis of DPN. In this study, Rab11 expression decreased in the sciatic nerves of diabetic mice with impaired conduction function versus those of normal mice. In vitro experiment revealed interferon-γ (IFN-γ), not high glucose and interleukin 1ß was the main factor to lead to Rab11 downregulation in RSC96 cells. Again, both Rab11 knockdown and IFN-γ treatment caused cell viability inhibition and the decrease in BrdU-positive cells. In contrast, overexpression of Rab11 reversed IFN-γ-reduced cell proliferation. Furthermore, mTORC1 not mTORC2 was proven to be suppressed by IFN-γ treatment in RSC96 cells, indicated in decreased phospho-p70S6K. Inhibition of the mTORC1 pathway resulted in Rab11 expression downregulation in RSC96 cells. Activation of the mTORC1 pathway effectively prevented IFN-γ-reduced Rab11 expression in RSC96 cells. Also, glucose transporter 1 (GLUT1) was found to be downregulated in RSC96 cells with Rab11 silence and overexpression of GLUT1 reversed Rab11 blocking-caused proliferation inhibition. Taken together, our findings suggest that IFN-γ decreases Rab11 expression via the inhibition of the mTORC1 signaling pathway, causing reduced cell proliferation in Schwann cells of DPN by GLUT1 downregulation.

STAT3 phosphorylation mediates high glucose-impaired cell autophagy in an HDAC1-dependent and -independent manner in Schwann cells of diabetic peripheral neuropathy.

Schwann cells are the main supportive cells of the peripheral nerves. Schwann cells suffer inhibition of autophagy under hyperglycemia treatment in diabetic peripheral neuropathy (DPN). However, the exact mechanism is still not fully elucidated. We first observed the decrease of autophagy markers (LC3-II/LC3-I, P62) in the sciatic nerves of diabetic mice vs. normal mice, accompanied with the loss of myelinated nerve fibers and abnormal myelin sheath. In line with this, LC3-II/LC3-I and P62 were also significantly reduced in high glucose-treated rat Schwann cell 96 (RSC96) cells compared with normal glucose-treated cells. Furthermore, we found that trichostatin A [an inhibitor of histone deacetylase (HDAC)] evidently improved LC3-II/LC3-I in high glucose-treated RSC96 cells, without an effect on P62 expression. Again, HDAC1 and HDAC5 were revealed to be increased in RSC96 cells stimulated with high glucose. Inhibition of HDAC1 but not HDAC5 by small hairpin RNA vector enhanced LC3-II/LC3-I in high glucose-cultured RSC96 cells. In addition, LC3-II conversion regulators [autophagy-related protein (Atg)3, Atg5, and Atg7] were detected in high glucose-treated and HDAC1-knockdown RSC96 cells, and Atg3 was proven to be the key target of HDAC1. The presuppression of Atg3 offset the improvement of LC3-II/LC3-I resulting from HDAC1 inhibition in high glucose-treated RSC96 cells. The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway was activated in RSC96 cells treated with high glucose, which was indicated by increased STAT3 phosphorylation. Blocking STAT3 phosphorylation by chemical inhibitor AG490 induced HDAC1 down-regulation followed by increases in Atg3 and LC3-II/LC3-I. Interestingly, we also found that AG490 treatment enhanced P62 expression in high glucose-stimulated RSC96 cells. Taken together, our findings demonstrate that hyperglycemia inhibits LC3-II/LC3-I in an HDAC1-Atg3-dependent manner and decreases P62 expression in an HDAC-independent manner via the JAK-STAT3 signaling pathway in the Schwann cells of DPN.-Du, W., Wang, N., Li, F. Jia, K., An, J., Liu, Y., Wang, Y., Zhu, L., Zhao, S. Hao, J. STAT3 phosphorylation mediates high glucose-impaired cell autophagy in an HDAC1-dependent and -independent manner in Schwann cells of diabetic peripheral neuropathy.