OBJECTIVE: To delineate the clinical and genetic features of a Chinese girl harboring a rare de novo variant of SYNGAP1 associated with Mental retardation, autosomal dominant 5 (MRD5), and to conduct a comprehensive genotype-phenotype correlation analysis within the Chinese population through an extensive literature review. METHODS: A 5-year-old girl presenting with seizures without an obvious cause was enrolled in September 2020. Genomic DNA was extracted from the patient and her parents. Whole exome sequencing (WES) was performed on the proband to identify suspected pathogenic variants based on her clinical phenotype. Sanger sequencing was used for validation, followed by bioinformatic analysis of the variant. Additionally, data from 54 previously reported Chinese cases with SYNGAP1 variants were integrated to summarize the distribution of variant types and clinical characteristics. Ethical approval was obtained from the Ethics Committee of Kunming Children's Hospital (Ethics No.: 2021-03-055-K01). RESULTS: WES identified a heterozygous nonsense variant, SYNGAP1 c.725G>A (p.Trp242*), in the proband. Sanger sequencing confirmed it was a de novo variant. According to the ACMG guidelines, this variant was classified as pathogenic (PVS1+PS2). Based on the clinical manifestations, the patient was diagnosed with MRD5. Bioinformatic analysis suggested that this variant introduces a premature stop codon at tryptophan 242, disrupting the PH domain and leading to the loss of the C2, Ras-GAP, and C-terminal domains. The pooled analysis of Chinese cases revealed that nonsense (38.2%) and frameshift (36.4%) variants were the predominant types. Intellectual disability/developmental delay was present in 100.0% of patients, epilepsy in 83.6%, and autism spectrum disorder in 41.3%. The incidence of epilepsy differed significantly among variant types (P = 0.045). Exons 8 and 15 were identified as mutation hotspots. CONCLUSION This study has identified a SYNGAP1 c.725G>A variant in the Chinese population and confirmed it as a potential cause of MRD5, which expanded the mutational spectrum of this disorder.
Humans ; Female ; Child, Preschool ; Intellectual Disability/genetics* ; ras GTPase-Activating Proteins/genetics* ; Phenotype ; Exome Sequencing ; Genetic Association Studies

Obesity and its related metabolic diseases have become a major global public health threat, and its rising incidence significantly increases the risk of cardiovascular and cerebrovascular diseases, diabetes and other complications. Pancreatic lipase is a key enzyme that converts food-borne lipids into triglycerides and fatty acids, and the effective inhibition of its activity has become an important strategy for the treatment of obesity. This paper discusses the screening methods of pancreatic lipase inhibitors, and summarizes and reviews the basic principles, advantages and disadvantages and application status of traditional screening methods, modern new screening methods and virtual screening methods. In view of the problems faced by the screening methods of pancreatic lipase inhibitors, future research urgently needs to move towards a collaborative innovation path of multi-technology integration, intelligent screening and complex systematization of traditional Chinese medicine, so as to open up new research paradigms.

OBJECTIVES: To evaluate the correlation of mean arterial oxygen tension (PaO₂) during the first 24 h following intensive care unit (ICU) admission with mortality in critically ill patients with acute kidney injury (AKI) and determine the optimal PaO₂ threshold for devising oxygen therapy strategies for these patients. METHODS: We collected the clinical data of ICU patients with AKI from the MIMIC-IV database. Based on the optimal first 24-h PaO₂ threshold determined by receiver operating characteristic (ROC) curve analysis and the Youden index maximization principle, we classified the patients into hyperoxia group (with PaO₂ ≥137.029 mmHg) and hypoxemia group (PaO₂<137.029 mm Hg). Multivariable logistic regression and propensity score matching were used to evaluate the correlation of first 24-h PaO₂ levels with in-hospital mortality of the patients. RESULTS: Among the 18 335 patients, 46.7% were in the hyperoxia group, who had an overall mortality rate of 16.9%. The optimal PaO₂ threshold (137.029 mm Hg) had a sensitivity of 78.3%, a specificity of 63.7%, and an AUC of 0.76 (95% CI: 0.74=0.78). Hyperoxia within the first 24 h after ICU admission was associated with a significantly lower in-hospital mortality (OR=0.78) and 90-day mortality (OR=0.77), particularly in stage 1 AKI patients. A non-linear relationship was identified between PaO₂ and mortality of the patients (P<0.001). Kaplan-Meier survival curves indicated a significantly increased 90-day survival rate in the patients in hyperoxia group (P<0.001), who also had shorter durations of mechanical ventilation, less vasopressor use, and shorter lengths of hospital/ICU stay. CONCLUSIONS Maintenance of a PaO₂ level ≥137.029 mmHg within 24 h after ICU admission may improve clinical outcomes of critically ill AKI patients, which underscores the importance of targeted oxygen delivery in ICU care.
Humans ; Acute Kidney Injury/blood* ; Male ; Female ; Middle Aged ; Intensive Care Units ; Aged ; Oxygen/blood* ; Hospital Mortality ; Partial Pressure ; Adult ; Databases, Factual

Objective To investigate the clinical relevance and diagnostic or prognostic value of ST3β-galactoside α-2, 3-sialyltransferase 1 (ST3GAL1) in glioma and to confirm its role in promoting malignant phenotypes. Methods Using data from The Cancer Genome Atlas (TCGA) database, we analyzed the correlation between ST3GAL1 expression levels in glioma and clinical parameters to evaluate its diagnostic and prognostic value. The impact of ST3GAL1 on malignant phenotypes of glioma cells-including proliferation, cell cycle progression, apoptosis, and invasion was further validated through ST3GAL1 knockdown experiments. Results The expression level of ST3GAL1 was significantly higher in glioma tissues compared to healthy brain tissues and showed a strong correlation with clinical characteristics of glioma patients. Survival analysis and receiver operating characteristic (ROC) curve demonstrated that ST3GAL1 could serve as a potential diagnostic and prognostic biomarker for glioma. Knockdown of ST3GAL1 suppressed proliferation, invasion, and migration capabilities of glioma cell lines, and induced G1-phase cell cycle arrest. Conclusion ST3GAL1 promotes malignant phenotypes in glioma and plays a critical role in its malignant progression, suggesting its potential as a biomarker for glioma diagnosis and prognosis.
Humans ; Sialyltransferases/metabolism* ; Glioma/diagnosis* ; Cell Proliferation/genetics* ; Cell Line, Tumor ; Brain Neoplasms/enzymology* ; beta-Galactoside alpha-2,3-Sialyltransferase ; Disease Progression ; Prognosis ; Cell Movement/genetics* ; Apoptosis/genetics* ; Male ; Female ; Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/metabolism* ; Middle Aged

Osteosarcoma is one of the most common primary malignant bone tumors, which has reached a "bottleneck" in the current clinical treatment of patients with osteosarcoma due to its poor prognosis and lack of therapeutic modalities. Recently, more and more researches have found that ferroptosis, as a novel cell death modality, may play an important role in osteosarcoma treatment. In recent years, with more in-depth studies on the mechanisms and molecular pathways related to ferroptosis, its specific therapeutic strategies in osteosarcoma have also been validated, which is expected to provide new ideas for the clinical treatment of osteosarcoma patients.

Through bioinformatics predictions, we identified that GTF2I and FAT1 were downregulated in thyroid carcinoma (TC). Further, Pearson's correlation coefficient revealed a positive correlation between GTF2I expression and FAT1 expression. Therefore, we selected them for this present study, where the effects of bone marrow mesenchymal stem cell-derived EVs (BMSDs-EVs) enriched with GTF2I were evaluated on the epithelial-to-mesenchymal transition (EMT) and stemness maintenance in TC. The under-expression of GTF2I and FAT1 was validated in TC cell lines. Ectopically expressed GTF2I and FAT1 were found to augment malignant phenotypes of TC cells, EMT, and stemness maintenance. Mechanistic studies revealed that GTF2I bound to the promoter region of FAT1 and consequently upregulated its expression. MSC-EVs could shuttle GTF2I into TPC-1 cells, where GTF2I inhibited TC malignant phenotypes, EMT, and stemness maintenance by increasing the expression of FAT1 and facilitating the FAT1-mediated CDK4/FOXM1 downregulation. In vivo experiments confirmed that silencing of GTF2I accelerated tumor growth in nude mice. Taken together, our work suggests that GTF2I transferred by MSC-EVs confer antioncogenic effects through the FAT1/CDK4/FOXM1 axis and may be used as a promising biomarker for TC treatment.
Mice ; Animals ; Cell Line, Tumor ; Cell Proliferation ; Mice, Nude ; Epithelial-Mesenchymal Transition ; Thyroid Neoplasms/pathology* ; Extracellular Vesicles/pathology* ; Mesenchymal Stem Cells ; Transcription Factors, TFIII/metabolism* ; Neoplastic Stem Cells/pathology*

Objective:To investigate the application value of CT and MRI imageomics based on machine learning method in the diagnosis of pancreatic cancer.Methods:The clinical data of 62 patients with surgically resected and pathologically confirmed pancreatic cancer, who underwent enhanced CT scan, MRI plain or enhanced scan in Shanghai General Hospital between January 2014 and December 2021 were collected. According to the chronological order of surgery, 49 patients from January 2014 to December 2020 were enrolled in the training set and 13 patients from January 2021 to December 2021 were enrolled in the validation set. 3D-slicer 4.8.1 software was used to draw the region of interest in each layer of CT and MRI images for cancerous and paracancerous tissue segment. Image features were extracted by Python and the optimal feature set from the training set data was obtained by using Lasso regression model. The machine learning decision tree model was constructed. The receiver operating characteristic curve(ROC) curve was drawn, and the area under the curve (AUC) was calculated to evaluate the value of these three kinds of imageomics models in the diagnosis of pancreatic cancer.Results:The 1 767 CT features and 1 674 MRI features were obtained from enhanced CT scan, MRI plain scan and enhanced MRI scan, respectively. For the differential diagnosis model of cancerous tissue and paracancerous tissue, the enhanced CT scan data model obtained the optimal feature set involving 6 features, the MRI plain scan model obtained the optimal feature set involving 16 features, and the enhanced MRI scan model obtained the optimal feature set involving 15 features. The diagnostic model based on enhanced CT scan had an AUC of 0.98 in the training set and 1 in the verification group. The AUC of the MRI plain scan and enhanced MRI scan models in both the training set and the validation set was 1. The specificity and sensitivity of machine learning decision tree model based on the three kinds of imageomics models in the diagnosis of cancerous tissue and paracancerous tissue were 100%. For the differential diagnosis model of splenic artery wrapping, the enhanced CT scan model didn′t obtain the optimal features and had no diagnostic efficacy. The MRI plain scan model and enhanced MRI scan model obtained the optimal feature set involving 5 and 4 features, respectively. The AUC of the MRI plain scan model in the training set and the validation set were 0.862 and 0.750, respectively, with diagnostic sensitivity of 93.8% and 50.0%, and specificity of 78.6% and 100%, respectively. The AUC of the enhanced MRI scan model in the training set and the validation set were 0.950 and 0.861, respectively, with diagnostic sensitivity of 90.0% and 93.6%, and specificity of 100% and 78.6%, respectively.Conclusions:Based on the radiomics of CT enhanced, MRI plain scan and enhanced MRI scan, the machine learning diagnostic model has an accuracy of more than 90% in differentiating pancreatic cancer from paracancerous tissue. For the differentiation of splenic artery wrapping in pancreatic cancer, the diagnostic model based on enhanced MRI scan haS the best diagnostic efficiency.

Objective:To screen the differentially expressed genes (DEG) related to inflammatory response associated with the prognosis of colon cancer based on the bioinformatics approach, and to construct and validate a prognostic model for colon cancer.Methods:RNA sequencing and clinical data of 472 colon cancer patients and normal colon tissues of 41 healthy people were retrieved from the Cancer Genome Atlas (TCGA) database. Gene expression related to prognosis of colon cancer and clinical data were retrieved from the International Cancer Genome Consortium (ICGC) database. The retrieval time was all from the establishment of library to November 2022. A total of 200 genes associated with inflammatory response obtained from the Gene Set Enrichment Analysis (GSEA) database were compared with the RNA sequencing gene dataset of colon cancer and normal colon tissues obtained from the TCGA database, and then DEG associated with inflammatory response were obtained. The prognosis-related DEG in the TCGA database were analyzed by using Cox proportional risk model, and the inflammatory response-related DEG were intersected with the prognosis-related DEG to obtain the prognosis-related inflammatory response-related DEG. The prognostic model of colon cancer was constructed by using LASSO Cox regression. Risk scores were calculated, and colon cancer patients in the TCGA database were divided into two groups of low risk (< the median value) and high risk (≥the median value) according to the median value of risk scores. Principal component analysis (PCA) was performed on patients in both groups, and survival analysis was performed by using Kaplan-Meier method. The efficacy of risk score in predicting the overall survival (OS) of colon cancer patients in the TCGA database was analyzed based on the R software timeROC program package. Clinical data from the ICGC database were applied to externally validate the constructed prognostic model, and patients with colon cancer in the ICGC database were classified into high and low risk groups based on the median risk score of patients with colon cancer in the TCGA database. By using R software, single-sample gene set enrichment analysis (ssGESA), immunophenotyping difference analysis, immune microenvironment correlation analysis, and immune checkpoint gene difference analysis of immune cells and immune function were performed for prognosis-related inflammation response-related DEG in the TCGA database.Results:A total of 60 inflammatory response-related DEG and 12 prognosis-related DEG were obtained; and 6 prognosis-related inflammatory response-related DEG (CCL24, GP1BA, SLC4A4, SRI, SPHK1, TIMP1) were obtained by taking the intersection set. LASSO Cox regression analysis showed that a prognostic model for colon cancer was constructed based on 6 prognosis-related inflammatory response-related DEG, and the risk score was calculated as = -0.113×CCL24+0.568×GP1BA+ (-0.375)×SLC4A4+(-0.051)×SRI+0.287×SPHK1+0.345×TIMP1. PCA results showed that patients with colon cancer could be better classified into 2 clusters. The OS in the high-risk group was worse than that in the low-risk group in the TCGA database ( P < 0.001); the area of the curve (AUC) of the prognostic risk score for predicting the OS rates of 1-year, 3-year, 5-year was 0.701, 0.685, and 0.675, respectively. The OS of the low-risk group was better than that of the high-risk group in the ICGC database; AUC of the prognostic risk score for predicting the OS rates of 1-year, 2-year, 3-year was 0.760, 0.788, and 0.743, respectively. ssGSEA analysis showed that the level of immune cell infiltration in the high-risk group in the TCGA database was high, especially the scores of activated dendritic cells, macrophages, neutrophils, plasmacytoid dendritic cells, T helper cells, and follicular helper T cells in the high-risk group were higher than those in the low-risk group, while the score of helper T cells 2 (Th2) in the high-risk group was lower compared with that in the low-risk group (all P < 0.05); in terms of immune function, the high-risk group had higher scores of antigen-presenting cell (APC) co-inhibition, APC co-stimulation, immune checkpoint, human leukocyte antigen (HLA), promotion of inflammation, parainflammation, T-cell stimulation, type Ⅰ interferon (IFN) response, and type ⅡIFN response scores compared with those in the low-risk group (all P < 0.05). The results of immunophenotyping analysis showed that IFN-γ-dominant type (C2) had the highest inflammatory response score, and the differences were statistically significant when compared with trauma healing type (C1) and inflammatory response type (C3), respectively (all P < 0.05). Immune microenvironment stromal cells and immune cells were all positively correlated with prognostic risk scores ( r values were 0.35 and 0.21, respectively, both P < 0.01). The results of immune checkpoint difference analysis showed there was a statistically significant difference in programmed-death receptor ligand 1 (PD-L1) expression level between high-risk group and low-risk group ( P = 0.002), and PD-L1 expression level was positively correlated with prognostic risk score ( r = 0.23, P < 0.01). Conclusions:Inflammatory response-related genes may play an important role in tumor immunity of colon cancer and can be used in the prognostic analysis and immunotherapy of colon cancer patients.

Objective:To explore the characteristics of pyroptosis-related genes in colon cancer cells screened by bioinformatics, and to verify the constructed prognostic model of colon cancer based on differentially expressed pyroptosis-related genes.Methods:Genetic data of RNA sequencing and clinical data of colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Fifty-two genes associated with pyroptosis were identified by searching the literature and compared with the RNA sequencing gene dataset of colon cancer and normal colon tissues obtained from TCGA database to obtain differentially expressed pyroptosis-related genes in clinical samples. The protein interaction network of differentially expressed pyroptosis-related genes was analyzed by using STRING website and R software. Based on the differential expression of pyroptosis-related genes in clinical samples of TCGA database, colon cancer patients in TCGA database were divided into pyroptosis and non-pyroptosis groups, and genes with significant differential expression between the two groups were screened at P < 0.05 according to gene expression; based on these differentially expressed genes, LASSO Cox regression was used to construct a prognostic model of colon cancer associated with pyroptosis. Patients collected from TCGA database were divided into high risk (≥ median value) and low risk (< median value) groups according to the median value of risk scores calculated by the model, and the overall survival of the two groups was analyzed by Kaplan-Meier survival function. The time ROC package of R software was used to analyze the efficacy of applying risk scores to predict the different survival time of colon cancer patients in TCGA database. Multivariate Cox regression was used to analyze the effects of clinicopathological factors and risk scores calculated by the model on the survival of patients in TCGA database. R software was used to analyze and obtain the differential genes between high and low risk groups of colon cancer patients in TCGA database. R software was used to conduct Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and single sample gene set enrichment analysis of immune cells and immune function (ssGESA) for differentially expressed pyroptosis-related genes. Results:Thirty-eight differentially expressed pyroptosis-related genes between colon cancer tissues and normal tissues of clinical samples were obtained based on data of TCGA database. A prognostic model consisting of 13 pyroptosis-related genes was established by applying LASSO Cox regression, the risk score = 0.118×MID2+0.354×IL20RB+0.083×HOXC11+0.011×TMEM88+0.021×SYNGR3+0.246×UPK3B+0.030×EGFL7+0.109×TMPRSS11E+0.138×IFITM10+0.161×RNF207+0.097×LINGO1+0.202×HEYL+0.025×ROBO3. Survival analysis showed that TCGA database had worse overall survival in the high-risk group than in the low-risk group ( P < 0.001). Receiver operating characteristic (ROC) curve analysis showed that the area under the curve of the prognostic model risk score in predicting the survival of colon cancer patients in TCGA database at 1, 3 and 5 years was all > 0.7. Multivariate Cox regression analysis showed that risk score was an independent influencing factor for survival of colon cancer patients in TCGA database (high risk vs. low risk HR = 3.988, 95% CI 2.865-5.551, P < 0.001). GO and KEGG enrichment analysis showed that the differentially expressed genes between high and low risk groups (SULF1, FBLN2, COL1A1, DES, SFRP2, FNDC1, MYH11, APOE, C3, SPP1, COL1A2, COL10A1, THBS2, AEBP1, CNN1, IGHG1, and SFRP4) were upregulated in the high risk group, which were mainly associated with cellular matrix structural components and extracellular matrix (ECM) receptor interactions. ssGSEA analysis showed that the level of immune cell infiltration was higher in high risk group, especially B cells, macrophages, mast cells, helper T cells, and tumor-infiltrating lymphocytes were higher than those in low risk group; for immune function, chemokine receptors, immune checkpoints, human leukocyte antigens, parainflammation, T cell suppression, T-cell stimulation, and type Ⅱ interferon response in high risk group were higher than those in low risk group. Conclusions:The constructed prognostic model of colon cancer based on pyroptosis-related genes is valuable for predicting the prognosis of colon cancer patients. Pyroptosis-related genes may play an important role in tumor immunity of colon cancer and can be used for prognostic analysis of colon cancer patients.

Objective:To explore the value of prognostic model based on ferroptosis-related long non-coding RNA (lncRNA) in predicting the prognosis of patients with colon cancer.Methods:Ferroptosis-related genes were downloaded from FerrDb database, and the RNA sequencing gene data and clinical data of colon cancer patients from the establishment of the database to November 2021 were downloaded from the Cancer Genome Atlas (TCGA) database. Through R3.6.3 software, the colon cancer gene expression data obtained from TCGA database and ferroptosis-related genes obtained from FerreDb database were analyzed to obtain differentially expressed ferroptosis-related genes in colon cancer and normal tissues. The expression correlation between ferroptosis-related genes and lncRNA in colon cancer was calculated by using R3.6.3 software to determine ferroptosis-related lncRNA in colon cancer. The survival-related differentially expressed ferroptosis-related lncRNA was screened and included in the multivariate Cox proportional hazards model to construct a colon cancer prognosis model; and the risk score of colon cancer patients was calculated by the prognostic model according to the lncRNA expression. According to the median risk score, the clinical cases collected from TCGA database were divided into high-risk group and low-risk group with 223 cases in each group. Kaplan-Meier survival analysis was performed for the two groups. The receiver operating characteristic (ROC) curve was used to analyze the effect of prognostic model risk score and clinical characteristics on predicting the survival of all patients. GSEA 4.1.0 software was used for gene set enrichment analysis (GSEA) of lncRNA in high-risk and low-risk groups, and ggpubr package of R3.6.3 software was used for single sample GSEA (ssGSEA) of immune cells and immune function of differentially expressed lncRNA between high-risk and low-risk groups.Results:According to the intersection of ferroptosis-related genes and differentially expressed genes obtained from databases, 65 differentially expressed ferroptosis-related genes were obtained, and 24 lncRNA related to the prognosis of colon cancer were analyzed, and then prognostic model was constructed based on lncRNA. Kaplan-Meier survival analysis showed that the survival of low-risk group was better than that of high-risk group ( P < 0.001); ROC curve analysis showed that the area under the curve (AUC) of 1-, 2-, 3-year survival predicted by the prognostic model risk score was more than 0.75, and the AUC of 1-year survival predicted by the risk score for all patients was greater than age, gender, the National Comprehensive Cancer Network (NCCN), T staging, N staging and M staging. GSEA showed that differentially expressed lncRNA in high-risk and low-risk groups concentrated in tumor and immune-related pathways; ssGSEA showed that there were differences in T cells, macrophages, mast cells, neutrophils, immune stimulation, human leukocyte antigen, type Ⅰ and type Ⅱ interferon response between high-risk group and low-risk group (all P < 0.05), and the expression levels of CD200 and TNFRSF14 at the immune checkpoint were significantly different (both P < 0.01). Conclusions:Ferroptosis-related lncRNA may play an important role in tumor immunity of colon cancer, and it can be used for the prognosis analysis of patients with colon cancer.