Research trends between diabetes mellitus and bariatric surgery researches: Bibliometric analysis and visualization from 1998 to 2023.

This study conducted a bibliometric analysis using the Web of Science Core Collection (WOSCC) to explore the relationship between diabetes mellitus and bariatric surgery (BS) from January 1985 to August 2023. No publications were found between 1985 and 1998. However, from 1998 to 2023, a total of 9,496 English articles were identified, accumulating 291,289 citations (241,563 excluding self-citations) and achieving an H-Index of 197. Leading contributors to the field were the United States, China, and Italy. Noteworthy authors in this area of research included Philip R. Schauer, Wei-Jei Lee, and Carel W. le Roux. The major journals that featured this research were 'Obesity Surgery,' 'Diabetes Care,' and 'Surgery for Obesity and Related Diseases.' The most highly cited article focused on lifestyle, diabetes, and cardiovascular risks 10 years after BS, emphasizing the significant attention given to the nutritional, cardiac, and general internal medicine impacts of diabetes and BS. The increase in research output during the review period indicates a growing interest in the relationship between diabetes and BS, providing a valuable reference for future studies in this evolving field.

Novel diagnostic biomarkers of oxidative stress, ferroptosis, immune infiltration characteristics and experimental validation in ischemic stroke.

Ischemic stroke (IS) is a prominent type of cerebrovascular disease leading to death and disability in an aging society and is closely related to oxidative stress. Gene expression profiling (GSE222551) was derived from Gene Expression Omnibus (GEO), and 1934 oxidative stress (OS) genes were obtained from the GeneCards database. Subsequently, we identified 149 differentially expressed genes related to OS (DEOSGs). Finally, PTGS2, FOS, and RYR1 were identified as diagnostic markers of IS. Moreover, GSE16561 was used to validate the DEOSGs. Two diagnostic genes (PTGS2 and FOS) were significantly highly expressed, while RYR1 was significantly lowly expressed in the IS group. Remarkably, immune infiltration characteristics of these three genes were analyzed, and we found that PTGS2, FOS, and RYR1 were mainly correlated with Mast cells activated, Neutrophils, and Plasma cells, respectively. Next, we intersected three DEOSGs with the ferroptosis gene set, the findings revealed that only PTGS2 was a differentially expressed gene of ferroptosis. High PTGS2 expression levels in the infarcted cortex of middle cerebral artery occlusion (MCAO) rats were confirmed by immunofluorescence (IF), western blotting (WB), and Immunohistochemistry (IHC). Inhibition of PTGS2 clearly improved the neurological outcome of rats by decreasing infarct volume, neurological problems, and modified neurological severity scores following IS compared with the controls. The protective effect of silencing PTGS2 may be related to anti-oxidative stress and ferroptosis. In conclusion, this work may provide a new perspective for the research of IS, and further research based on PTGS2 may be a breakthrough.

Assessment of transient elastography in diagnosing MAFLD and the early effects of sleeve gastrectomy on MAFLD among the Chinese population.

BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) has become a prevalent chronic liver disease among patients with obesity. Bariatric surgery, such as sleeve gastrectomy (SG), shows promise in improving the unfavorable condition of MAFLD. Transient elastography (TE) can be utilized to assess the extent of steatosis and liver fibrosis, providing a noninvasive method for preoperative prediction and postoperative evaluation of MAFLD. This study aims to investigate the effectiveness of TE in diagnosing MAFLD by evaluating liver steatosis and tissue stiffness, as well as assessing the early impact of SG in the treatment of obesity-associated MAFLD. METHODS: In this study, the authors collected preoperative and 6-month postoperative data from patients with obesity who were diagnosed with MAFLD by intraoperative liver biopsy. The patients underwent SG at our hospital between August 2021 and April 2023. The authors estimated the diagnostic accuracy for the steatosis and fibrosis categories using the area under the receiver operating characteristic curve (AUROC). The authors also evaluated the influence of disease prevalence on the positive predictive value and negative predictive value. MAFLD diagnosis was based on the liver steatosis activity and fibrosis scoring system. The authors used univariate and multivariate logistic regression analyses to identify factors contributing to severe MAFLD. To visualize the results, the authors created a nomogram and enhanced it with bootstrap resampling for internal validation. Additionally, the authors plotted receiver operating characteristic and calibration curves. The authors compared preoperative and postoperative data, including general information, laboratory tests, and TE results, to assess the early impact of SG in the treatment of obesity-associated MAFLD. RESULTS: Based on the results of liver biopsy, the AUROC for controlled attenuation parameter (CAP) in identifying steatosis was found to be 0.843 (95% CI: 0.729-0.957) for S≥S1, 0.863 (95% CI: 0.786-0.940) for S≥S2, and 0.872 (95% CI: 0.810-0.934) for S=S3. The Youden limits for S≥S1, S≥S2, and S≥S3 were determined to be 271 dB/m, 292 dB/m, and 301 dB/m, respectively. Similarly, the AUROC for liver stiffness measurement (LSM)/E in detecting liver fibrosis was 0.927 (95% CI: 0.869-0.984) for F≥F2, 0.919 (95% CI: 0.824-0.979) for F≥F3, and 0.949 (95% CI: 0.861-0.982) for F=F4, with Youden cutoff values of 7.5 kPa, 8.3 kPa, and 10.4 kPa, respectively. Patients with A≥3 and/or F≥3 were classified as having severe MAFLD. Multivariate logistic regression analysis identified CAP, E, LDL, and AST as the best diagnostic factors for severe MAFLD, and a nomogram was constructed based on these factors. The AUROC of the nomogram for the assessment of severe MAFLD was 0.824 (95% CI: 0.761-0.887), which was further validated by 1000 bootstrap resamplings with a bootstrap model area under curve of 0.823. Finally, after a 6-month follow-up period, the steatosis grade and fibrosis stage of the patients were graded based on the optimal cutoff values for CAP and LSM. Significant reductions in BMI, waist circumference, HbA1c, fasting glycemia, triglycerides, high density lipoprotein (HDL), glutamic pyruvic transaminase (ALT), glutamic oxaloacetic transaminase (AST), gamma glutamyl transpeptidase (GGT), CAP, LSM, steatosis grade, and fibrosis stage were observed compared to the preoperative values. CONCLUSION: In this prospective study, the authors investigated the use of CAP and LSM as alternatives to liver biopsy for evaluating hepatic steatosis and fibrosis in patients with obesity combined with MAFLD. Furthermore, the authors examined the impact of SG on metabolic indicators and the progression of fatty liver disease during the early postoperative period, and observed significant improvements in both aspects.

Construction and validation of a nomogram for predicting diabetes remission at 3 months after bariatric surgery in patients with obesity combined with type 2 diabetes mellitus.

AIM: Bariatric metabolic surgery (BMS) is a proven treatment option for patients with both obesity and type 2 diabetes mellitus (T2DM). However, there is a lack of comprehensive reporting on the short-term remission rates of diabetes, and the existing data are inadequate. Hence, this study aimed to investigate the factors that may contribute to diabetes remission (DR) in patients with obesity and T2DM, 3 months after undergoing BMS. Furthermore, our objective was to develop a risk-predicting model using a nomogram. METHODS: In total, 389 patients with obesity and T2DM, who had complete preoperative information and underwent either laparoscopic sleeve gastrectomy or laparoscopic gastric bypass surgery between January 2014 and May 2023, were screened in the Chinese Obesity and Metabolic Surgery Database. The patients were randomly divided into a training set (n = 272) and a validation set (n = 117) in a 7:3 ratio. Potential factors for DR were analysed through univariate and multivariate logistic regression analyses and then modelled using a nomogram. The model's performance was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Calibration plots were used to assess prediction accuracy and decision curve analyses were conducted to evaluate the clinical usefulness of the model. RESULTS: Glycated haemoglobin, triglycerides, duration of diabetes, insulin requirement and hypercholesterolaemia were identified as independent factors influencing DR. We have incorporated these five indicators into a nomogram, which has shown good efficacy in both the training cohort (AUC = 0.930) and validation cohort (AUC = 0.838). The calibration plots indicated that the model fits well in both the training and the validation cohorts, and decision curve analyses showed that the model had good clinical applicability. CONCLUSION: The prediction model developed in this study holds predictive value for short-term DR following BMS in patients with obesity and T2DM.

Novel diagnostic biomarkers of oxidative stress, immune- infiltration characteristics and experimental validation of SERPINE1 in colon cancer.

BACKGROUND: Colon cancer (CC) is a prevalent malignant tumor that affects the colon in the gastrointestinal tract. Its aggressive nature, strong invasiveness, and rapid progression make it a significant health concern. In addition, oxidative stress can lead to the production of reactive oxygen species (ROS) that surpass the body's antioxidant defense capacity, causing damage to proteins, lipids, and DNA, potentially promoting tumor development. However, the relationship between CC and oxidative stress requires further investigation. METHODS: We collected gene expression data and clinical data from 473 CC patients from The Cancer Genome Atlas (TCGA) dataset. Additionally, we obtained 433 oxidative stress genes from Genecards ( https://www.genecards.org/ ). Using univariate, multivariate, and LASSO Cox regression analyses, we developed predictive models for oxidative stress-related genes in CC patients. To validate the models, we utilized data from the Gene Expression Omnibus (GEO) database. We assessed the accuracy of the models through various techniques, including the creation of a nomogram, receiver operating characteristic curve (ROC) analysis, and principal component analysis (PCA). The Cytoscape program was utilized to identify hub genes among differentially expressed genes (DEGs) in tumor patients using the TCGA dataset. Subsequently, we conducted survival analysis, clinical relevance analysis, and immune cell relevance analysis for the intersected genes obtained by combining the hub genes with the genes from the predictive models. Moreover, we investigated the mRNA expression and potential functions of these intersected genes using a range of experimental approaches. RESULTS: In both the TCGA and GSE17538 datasets, patients classified as high-risk had significantly shorter overall survival compared to those in the low-risk group (TCGA: p < 0.001; GSE17538: p = 0.010). As a result, we decided to further investigate the role of SERPINE1. Our survival analysis revealed that patients with high expression of SERPINE1 had a significantly lower probability of survival compared to those with low expression (p < 0.05). Additionally, our clinical correlation analysis showed a significant relationship between SERPINE1 expression and T, N, and M stages, as well as tumor grade. Furthermore, our immune infiltration correlation analysis demonstrated notable differences in multiple immune cells between the high- and low-expression groups of SERPINE1. To validate our findings, we conducted experimental tests and observed that knocking down SERPINE1 in colon cancer cells resulted in significant reductions in cell viability and proliferation. Interestingly, we also noticed an increase in oxidative stress parameters, such as ROS and MDA levels, while the levels of reduced GSH decreased upon SERPINE1 knockdown. These findings suggest that the antineoplastic effect of silencing SERPINE1 may be associated with the induction of oxidative stress. CONCLUSION: In conclusion, this study introduces a new approach for the early diagnosis and treatment of CC, and further exploration of SERPINE1 could potentially lead to a significant advancement.

Uncovering the pathogenesis of obesity complicated with papillary thyroid carcinoma via bioinformatics and experimental validation.

This study aimed to investigate the common molecular mechanism between obesity and papillary thyroid cancer (PTC), the most common pathological type of thyroid cancer. In this study, we obtained gene expression datasets for obesity (GSE151839) and PTC (GSE33630) from the Gene Expression Omnibus (GEO). We used the Perl program and R software to identify differentially expressed genes (DEGs) and common genes, perform GO function and KEGG pathway enrichment analysis, construct a protein-protein interaction (PPI) network, identify hub genes, and perform transcription factors (TFs) analysis. After undergoing validation in external datasets and in vitro experiments, common targets for both diseases were ultimately identified. A total of 23 genes that were differentially expressed (DEGs) between obesity and papillary thyroid carcinoma (PTC) were identified in our study. Among these DEGs, 17 genes were up-regulated while 6 genes were down-regulated. Then the top ten key genes were identified from the PPI network using cytoHubba and MCODE plug-in. Further evidence from external datasets revealed that MMP9, MNDA, TNC, and CHIT1 were identified as hub genes for both diseases. The study utilized Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST) to perform an enrichment analysis of TFs. This analysis identified ELF4 and STAT3 as common TFs for both diseases. Additionally, in vitro experiments were conducted to further analyze the clinical significance and biological functions of these TFs. The identification and investigation of hub genes and their corresponding TFs that regulate abnormalities in obesity and PTC can enhance our comprehension of the underlying connection between these two diseases, thus leading to the development of novel diagnostic approaches.

Identification and validation of immune-related hub genes based on machine learning in prostate cancer and AOX1 is an oxidative stress-related biomarker.

To investigate potential diagnostic and prognostic biomarkers associated with prostate cancer (PCa), we obtained gene expression data from six datasets in the Gene Expression Omnibus (GEO) database. The datasets included 127 PCa cases and 52 normal controls. We filtered for differentially expressed genes (DEGs) and identified candidate PCa biomarkers using a least absolute shrinkage and selector operation (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. A difference analysis was conducted on these genes in the test group. The discriminating ability of the train group was determined using the area under the receiver operating characteristic curve (AUC) value, with hub genes defined as those having an AUC greater than 85%. The expression levels and diagnostic utility of the biomarkers in PCa were further confirmed in the GSE69223 and GSE71016 datasets. Finally, the invasion of cells per sample was assessed using the CIBERSORT algorithm and the ESTIMATE technique. The possible prostate cancer (PCa) diagnostic biomarkers AOX1, APOC1, ARMCX1, FLRT3, GSTM2, and HPN were identified and validated using the GSE69223 and GSE71016 datasets. Among these biomarkers, AOX1 was found to be associated with oxidative stress and could potentially serve as a prognostic biomarker. Experimental validations showed that AOX1 expression was low in PCa cell lines. Overexpression of AOX1 significantly reduced the proliferation and migration of PCa cells, suggesting that the anti-tumor effect of AOX1 may be attributed to its impact on oxidative stress. Our study employed a comprehensive approach to identify PCa biomarkers and investigate the role of cell infiltration in PCa.

Tetrandrine citrate suppresses lung adenocarcinoma growth via SLC7A11/GPX4-mediated ferroptosis.

Ferroptosis is a mode of programmed cell death that plays a crucial role in tumor biology processes. Although tetrandrine citrate (TetC) has been demonstrated to exert anti-tumor effects, it is still unclear whether TetC inhibits lung adenocarcinoma (LUAD) progression by inducing ferroptosis. The study showcased the inhibitory effect of TetC on the viability and progression of tumor cells, including intracellular iron overload, accumulation of reactive oxygen species (ROS), over-expression of malondial-dehyde (MDA), and depletion of glutathione (GSH). Notably, TetC-induced cell death was clearly reversed by three different ferroptosis-related inhibitors. TetC also induced changes in the mitochondrial morphology of LUAD cells, similar to those observed in typical ferroptosis. Further analysis through Western blot (WB) and Immunofluorescence (IF) assays identified that TetC inhibited the expression and fluorescence intensity of both solute carrier family 7 (SLC7A11) and glutathione peroxidase-4 (GPX4). More importantly, over-expression of SLC7A11 could rescue the TetC-induced ferroptosis. Finally, in our vivo experiment, we discovered that TetC significantly slowed the growth rate of subcutaneous transplanted A549 cells, ultimately proving to be biosafe. In conclusion, our study first identified the mechanism by which TetC-induced ferroptosis in LUAD via SLC7A11/GPX4 signaling.

Anti-cancer, Anti-collagenase and Anti-elastase Potentials of Some Natural Derivatives: In vitro and in silico Studies.

The anti-cancer activities of the compounds were evaluated against KYSE-150, KYSE-30, and KYSE-270 cell lines and also on investigated esophageal line HET 1 A as a standard. Modified inhibitory impact on enzymes of collagenase and elastase were used Thring and Moon methods, respectively. Among both compounds, both of them recorded impact on cancer cells being neutral against the control, both had IC50 lower than 100 µM and acted as a potential anticancer drug. The chemical activities of Skullcapflavone I and Skullcapflavone II against elastase and collagenase were investigated utilizing the molecular modeling study. IC50 values of Skullcapflavone I and Skullcapflavone II on collagenase enzyme were obtained 106.74 and 92.04 µM and for elastase enzyme were 186.70 and 123.52 µM, respectively. Anticancer effects of these compounds on KYSE 150, KYSE 30, and KYSE 270 esophageal cancer cell lines studied in this work. For Skullcapflavone I, IC50 values for these cell lines were obtained 14.25, 19.03, 25.10 µM, respectively. Also, for Skullcapflavone II were recorded 20.42, 34.17, 22.40 µM, respectively. The chemical activities of Skullcapflavone I and Skullcapflavone II against some of the expressed surface receptor proteins (CD44, EGFR, and PPARγ) in the mentioned cell lines were assessed using the molecular docking calculations. The calculations showed the possible interactions and their characteristics at an atomic level.

Identification and Verification of Immune-Related Genes Prognostic Signature Based on ssGSEA for Adrenocortical Carcinoma (ACC).

PURPOSE: Adrenocortical carcinoma (ACC) is an endocrine malignant tumor with poor prognosis. The study aimed to construct ACC immune-related gene prognostic signature and verify the efficacy of prognostic signature. METHODS: ACC RNA-seq data and clinical information are downloaded from TCGA databases and GEO databases. We used single sample gene set enrichment analysis (ssGSEA) to assess immune cell infiltration in ACC patients and ACC patients were divided into high- and low-immune cell infiltration clusters. The validity of ssGSEA grouping was verified using the ESTIMATE algorithm. A total of 275 differentially expressed immune-related genes (IRGs) were obtained from the intersection of IRGs and differentially expressed genes (DEGs) in high and low immune cell infiltration clusters. LASSO analysis was used to identify 13 IRGs that regulate the prognosis of ACC patients through immune infiltration. Kaplan-Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that these 13 immune-related gene signatures were innovative and significant prognostic factors, which were independent of clinical features. Finally, ACC prognostic nomogram was constructed, ROC curve and calibration curve were drawn to evaluate the accuracy of the prognostic nomogram. RESULTS: LASSO regression analysis was used to screen out ACC survival-related genes. Univariate and multivariate Cox proportional risk regression models were used to analyze and construct the ACC prognosis nomogram. The AUC for predicting 1-, 3- and 5-year overall survival rate of ACC patients was 0.799, 0.966 and 0.969, suggesting good prediction accuracy. The calibration curve shows that the predicted results of the prognostic nomogram are in good agreement with the actual situation. CONCLUSION: ssGSEA technique plays an important role in the construction of ACC prognostic model. Based on IRGs associated with survival independently predicted ACC prognosis, we identified thirteen immune-related genes as prognostic signature for ACC.