MiR-378 Inhibits Angiotensin II-Induced Cardiomyocyte Hypertrophy by Targeting AKT2.

Cardiomyocyte hypertrophy plays a crucial role in heart failure development, potentially leading to sudden cardiac arrest and death. Previous studies suggest that micro-ribonucleic acids (miRNAs) show promise for the early diagnosis and treatment of cardiomyocyte hypertrophy.To investigate the miR-378 expression in the cardiomyocyte hypertrophy model, reverse transcription-polymerase chain reaction (RT-qPCR), Western blot, and immunofluorescence tests were conducted in angiotensin II (Ang II)-induced H9c2 cells and Ang II-induced mouse model of cardiomyocyte hypertrophy. The functional interaction between miR-378 and AKT2 was studied by dual-luciferase reporter, RNA pull-down, Western blot, and RT-qPCR assays.The results of RT-qPCR analysis showed the downregulated expression of miR-378 in both the cell and animal models of cardiomyocyte hypertrophy. It was observed that the introduction of the miR-378 mimic inhibited the hypertrophy of cardiomyocytes induced by Ang II. Furthermore, the co-transfection of AKT2 expression vector partially mitigated the negative impact of miR-378 overexpression on Ang II-induced cardiomyocytes. Molecular investigations provided evidence that miR-378 negatively regulated AKT2 expression by interacting with the 3' untranslated region (UTR) of AKT2 mRNA.Decreased miR-378 expression and AKT2 activation are linked to Ang II-induced cardiomyocyte hypertrophy. Targeting miR-378/AKT2 axis offers therapeutic opportunity to alleviate cardiomyocyte hypertrophy.

Application of G-quadruplex targets in gastrointestinal cancers: Advancements, challenges and prospects.

Genomic instability and inflammation are considered to be two enabling characteristics that support cancer development and progression. G-quadruplex structure is a key element that contributes to genomic instability and inflammation. G-quadruplexes were once regarded as simply an obstacle that can block the transcription of oncogenes. A ligand targeting G-quadruplexes was found to have anticancer activity, making G-quadruplexes potential anticancer targets. However, further investigation has revealed that G-quadruplexes are widely distributed throughout the human genome and have many functions, such as regulating DNA replication, DNA repair, transcription, translation, epigenetics, and inflammatory response. G-quadruplexes play double regulatory roles in transcription and translation. In this review, we focus on G-quadruplexes as novel targets for the treatment of gastrointestinal cancers. We summarize the application basis of G-quadruplexes in gastrointestinal cancers, including their distribution sites, structural characteristics, and physiological functions. We describe the current status of applications for the treatment of esophageal cancer, pancreatic cancer, hepatocellular carcinoma, gastric cancer, colorectal cancer, and gastrointestinal stromal tumors, as well as the associated challenges. Finally, we review the prospective clinical applications of G-quadruplex targets, providing references for targeted treatment strategies in gastrointestinal cancers.

Development and validation of a decision tree classification model for the essential hypertension based on serum protein biomarkers.

Background: Essential hypertension (EH) is a key risk factor for cardiovascular disease. However, the etiology of EH is complex and unknown. So far, there is no good protein biomarker for screening EH. The purpose of this study was to discover potential biomarkers for EH by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and establish a decision-tree classification model. Methods: A total of 108 patients with clinically confirmed EH and 105 HC were enrolled in the present study from September 2020 to April 2021 and were randomly divided into the training group and the blind-test group. The serum protein expression profiles were performed using MALDI-TOF MS combined with magnetic beads with weak cation exchange (MB-WCX). The training group, which comprised 54 EH patients and 53 HC, was used to screen the statistically differential protein peaks by SPSS 19.0 and construct a decision-tree classification model by C5.0 algorithms of SPSS Modeler 18.0. All protein peak intensities of samples in the blind-test group, which comprised 54 EH patients and 52 HC, were used to verify the diagnostic capabilities of the model by classification model. Results: EH patients had higher age, systolic and diastolic blood pressures than HC group. The intensities of 60 protein peaks differed significantly between the EH patients and HC. An optimal decision-tree classification model of EH was successfully established with mass-to-charge ratios of 1,326.7, 1,785.3, 4,228.0, and 8,963.8 as differential protein peaks by the software analysis. The decision-tree classification model was able to distinguish between EH patients and HC and had a sensitivity of 94.44%, a specificity of 94.33%, an accuracy of 94.39%, and an area under the receiver operating characteristic (ROC) curve of 0.96. The blind-test results indicated a sensitivity of 87.04%, a specificity of 88.46%, an accuracy of 87.74%, and an area under the ROC curve of 0.928. Conclusions: MALDI-TOF MS combined with MB-WCX can be used to screen for serum differential protein expression profiles in EH patients. The decision-tree classification model based on mass-to-charge ratios of 1,326.7, 1,785.3, 4,228.0, and 8,963.8 could provide a new and reliable method for screening and identifying EH with high sensitivity and specificity.

c-MYC and HIF1α promoter G-quadruplexes dependent metabolic regulation mechanism of berberine in colon cancer.

Background: G-quadruplexes are molecular switches regulating gene transcription. c-MYC and hypoxia-inducible factor 1-alpha (HIF1α) play important roles in cell proliferation, apoptosis, and metabolic regulation in colon cancer. Whether berberine can regulate metabolism by interacting with c-MYC and HIF1α G-quadruplexes in colon cancer needs to be explored. Methods: The binding mode of berberine with c-MYC and HIF1α G-quadruplexes were explored by ultraviolet and visible absorption spectroscopy and fluorescence spectroscopy. Circular dichroism (CD) spectroscopy was performed to evaluate the effects of berberine on the stability of c-MYC and HIF1α G-quadruplexes. After different concentrations of berberine acting on HCT116 cells for 24 h, cell proliferation and apoptosis were detected by MTT assay and flow cytometry; quantitative real-time polymerase chain reaction and western blot were performed to detect mRNA and protein expression of c-MYC and HIF1α; transcriptome sequencing was used to analyze the metabolic pathways. For the effects of berberine on colon cancer mouse model with dose of 50 mg·kg-1 for 14 days, tumor growth were monitored, hematoxylin and eosin staining and immunofluorescence staining were performed to analyze histopathology and protein expression of c-MYC and HIF1α, central carbon metabolism was detected in tumor tissues. Results: The binding ability of berberine with c-MYC G-quadruplex was different to that of berberine with HIF1α G-quadruplex. Both binding modes involved π-π stacking. The stoichiometric ratios were 1:1, 1:3, and 3:1 for berberine with c-MYC G-quadruplex and only 1:1 for berberine with HIF1α G-quadruplex. Temperature had a greater effect on the binding of berberine to c-MYC G-quadruplex. Berberine could improve the thermal stability of both c-MYC and HIF1α G-quadruplexes. Berberine inhibited the gene transcription and protein expression of c-MYC and HIF1α in colon cancer HCT116 cells. In vivo, berberine delayed tumor progression and inhibited the protein expression of c-MYC and HIF1α. Twelve differential metabolites such as decreased adenosine triphosphate were obtained, indicating that berberine could regulate the metabolic pathways of the tricarboxylic acid (TCA) cycle and glycolysis/gluconeogenesis, among others. Conclusions: Berberine may inhibit colon cancer by regulating the TCA cycle and glycolysis/gluconeogenesis based on the interaction with c-MYC and HIF1α G-quadruplexes.

Identification and validation of xenobiotic metabolism-associated prognostic signature based on five genes to evaluate immune microenvironment in colon cancer.

BACKGROUND: Xenobiotic metabolism plays an important role in the progression of colon cancer; however, little is known about its related biomarkers. This study sought to construct a prognostic model related to xenobiotic metabolism in colon cancer, and further reveal the characteristics of tumor immune microenvironment based on the prognostic model. METHODS: Transcriptome data of 41 normal colon tissues and 473 colon tumor tissues and the clinical features of 452 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Data on xenobiotic metabolism genes (XMGs) were obtained from the hallmark xenobiotic metabolism set of the Molecular Signatures Database (MSigDB) and articles. Additionally, data on differential XMGs in colon cancer were acquired for a functional enrichment analysis by R software. An XMG prognostic model was constructed by a Cox regression analysis, and evaluated using Kaplan-Meier survival curves, risk curves, receiver operating characteristic (ROC) curves, and an independent prognostic analysis in a training cohort and validation cohort. Moreover, tumor immune infiltration and negative regulatory immune genes of cancer-immunity cycle (CIC), including immune checkpoints and immune cytokines, were further analyzed between low- and high-risk groups in both the training and validation cohorts. Differences with P value <0.05 were interpreted as statistically significant. RESULTS: A total of 126 differential XMGs were distinguished in the colon cancer data set, which were mainly enriched in the metabolism pathways of drugs and nutrients. There were 5 optimized genes (i.e., CYP2W1, GSTM1, TGFB2, MPP2, and ACOX1) used to construct the prognosis model, which effectively predicted prognosis and had good ROC curves. Between low- and high-risk groups, there were significant differences in abundance for T cells CD4 memory resting and T cells regulatory (Tregs), and expression of PDCD1, LAG3, NOS3, TGFB1, and ICAM1 in the training cohort and validation cohort. CONCLUSIONS: The XMGs in the prognostic model have a good prediction effect on the prognosis of colon cancer patients. The T cells CD4 memory resting, and Tregs, immune checkpoints PDCD1 and LAG3, and CIC negative regulatory immune cytokines NOS3, TGFB1, and ICAM1 are closely associated with xenobiotic metabolism.

Identification of gene biomarkers and immune cell infiltration characteristics in rectal cancer.

BACKGROUND: Compared with colon cancer, the increase of morbidity is more significant for rectal cancer. The current study set out to identify novel and critical biomarkers or features that may be used as promising targets for early diagnosis and treatment monitoring of rectal cancer. METHODS: Microarray datasets of rectal cancer with a minimum sample size of 30 and RNA-sequencing datasets of rectal adenocarcinoma (READ) were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The method of robust rank aggregation was utilized to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was structured using the STRING platform, and hub genes were identified using the Cytoscape plugin cytoHubba and an UpSet diagram. R software was employed to perform functional enrichment analysis. Receiver operating characteristic (ROC) curves based on the GEO data and Kaplan-Meier curves based on the TCGA data were drawn to assess the diagnostic and prognostic values of the hub genes. Immune cell infiltration analysis was conducted with CIBERSORT, and the diagnostic value and correlations between prognostic genes and infiltrated immune cells were analyzed by principal component analysis (PCA), ROC curves, and correlation scatter plots. RESULTS: A total of 137 robust DEGs were obtained by integrating datasets in GEO. Twenty-four hub genes, including CHGA, TTR, SAA1, SPP1, MMP1, TGFBI, COL1A1, and PCK1, were identified as a diagnostic gene biomarker group for rectal cancer, and SAA1, SPP1, and SI were identified as potential novel prognostic biomarkers. Functionally, the hub genes were mainly involved in the rectal cancer related interleukin (IL)-17 and proximal tubule bicarbonate reclamation pathways. Twelve sensitive infiltrated immune cells were identified, and were correlated with prognostic genes. CONCLUSIONS: The integrated gene biomarker group combined with immune cell infiltration can effectively indicate rectal cancer.

Screening of serum protein biomarkers in hemorrhagic cerebral infarction by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology.

BACKGROUND: Serum samples of patients with hemorrhagic cerebral infarction (HCI), cerebral infarction (CI), and healthy controls (HCs) were used to screen statistically different protein peaks as potential biomarkers and to establish a decision tree classification model. METHODS: The serum samples from clinically confirmed patients with HCI and CI from November 2018 to October 2019 were collected, along with those of HCs who visited our hospital during the same period. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) with CM10 ProteinChip was used to analyze the differences in serum protein expression profiles of 30 patients with HCI, 32 patients with CI, and 31 HCs in the training group, and a decision tree classification model was established. At the same time, the blind test group (18 patients with HCI, 21 patients with CI, and 17 HCs) was tested by a blind method. RESULTS: Model 1 was successfully established by software analysis with a mass-to-charge ratio of 3,495.2, 8,941.0, and 15,890.4 as a differential protein peak. The sensitivity, specificity, and accuracy of model 1 in distinguishing HCI from HCs were 86.8%, 87.1%, and 86.9%, respectively. After verification of model 1 by the blind test group, the results showed that the sensitivity, specificity, and accuracy were 88.9%, 94.1%, and 91.4%, respectively. The sensitivity, specificity, and accuracy of model 2 with a mass-to-charge ratio of 2,941.3 as a differential protein peak were 86.7%, 75.0%, and 80.6%, respectively. After verification of model 2 by the blind test group, the results showed that the sensitivity, specificity, and accuracy were 83.3%, 90.4%, and 87.2%, respectively. CONCLUSIONS: Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and CM10 ProteinChip can be used to screen serum protein markers in patients with HCI. Mass-to-charge ratio of 3,495.2, 8,941.0, 15,890.4, and 2,941.3 may be potential protein biomarkers of HCI and used to distinguish HCI patients from HCs and CI.

The effect of hemodialysis on serum magnesium concentration in hemodialysis patients.

BACKGROUND: In recent years, the concentration of magnesium in dialysis patients has drawn an increasing amount of attention. Both hemodialysis and peritoneal dialysis may affect magnesium metabolism, but studies in this area are very limited. This study aimed to investigate the serum magnesium concentrations in hemodialysis patients and examine the factors related to the serum magnesium concentration, and to explore the effect of hemodialysis with conventional hemodialysis solution (magnesium ion concentration 0.5 mmol/L) on blood magnesium concentration. METHODS: In this single-center, retrospective study, linear regression models were established to explore the factors related to serum magnesium concentration in hemodialysis patients. Serum magnesium concentration was also compared before and after hemodialysis treatment. RESULTS: The data of 148 hemodialysis patients were collected and analyzed. The mean value of pre-hemodialysis total serum magnesium concentration was 1.11±0.14 mmol/L. The prevalence of hypermagnesemia was 73.65%, and 2 patients had hypomagnesemia (1.35%). Data analysis indicated that total platelet count, serum phosphorus level, serum creatinine, plasma albumin, and total serum cholesterol level were significantly related to serum magnesium concentration. After hemodialysis treatment, the serum magnesium concentration was significantly lower when conventional hemodialysis was used. After hemodialysis, the average decrease in the serum magnesium concentration was 0.14 mmol/L. CONCLUSIONS: Most patients who received hemodialysis had mild hypermagnesemia when routine dialysate was supplied. Serum magnesium concentration is related to patients' nutritional status. Hemodialysis treatment can decrease the total serum magnesium concentration.