Background: The purpose of this research was to determine the index that contributes the most to assessing the effectiveness of weight loss 1 year following bariatric surgery and to implement it as the clinical outcome to develop and confirm a nomogram to predict whether bariatric surgery would be effective. Methods: Patient information was extracted from the Chinese Obesity and Metabolic Surgery Database for this retrospective study. The most contributing weight loss effectiveness evaluation index was created using canonical correlation analysis (CCA), and the predictors were screened using logistic regression analysis. A nomogram for estimating the likelihood of effectiveness of weight loss was constructed, and its performance was further verified. Results: Information was obtained for 540 patients, including 30 variables. According to the CCA, ≥25 percentage total weight loss was found to be the most correlated with patient information and contribute the most as a weight loss effectiveness evaluation index. Logistic regression analysis and nomogram scores identified age, surgical strategy, abdominal circumference, weight loss history, and hyperlipidemia as predictors of effectiveness in weight loss. The prediction model’s discrimination, accuracy, and clinical benefit were demonstrated by the consistency index, calibration curve, and decision curve analysis. Conclusions: The authors determined a 25 percentage total weight loss as an index for weight loss effectiveness assessment by CCA and next established and validated a nomogram, which demonstrated promising performance in predicting the probability of effectiveness of weight loss in bariatric surgery. The nomogram might be a valuable tool in clinical practice.

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.

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.

Background: MicroRNAs (miRNAs) exert an essential contribution to obesity and type 2 diabetes mellitus (T2DM). This study aimed to investigate the differences of miRNAs in the presence and absence of T2DM in patients with obesity, as well as before and after bariatric surgery in T2DM patients with obesity. Characterization of the common changes in both was further analyzed.Methods: We enrolled 15 patients with obesity but without T2DM and 15 patients with both obesity and T2DM. Their preoperative clinical data and serum samples were collected, as well as 1 month after bariatric surgery. The serum samples were analyzed by miRNA sequencing, and the miRNAs profiles and target genes characteristics were compared.Results: Patients with T2DM had 16 up-regulated and 32 down-regulated miRNAs compared to patients without T2DM. Improvement in metabolic metrics after bariatric surgery of T2DM patients with obesity was correlated with changes in miRNAs, as evidenced by the upregulation of 20 miRNAs and the downregulation of 30 miRNAs. Analysis of the two miRNAs profiles identified seven intersecting miRNAs that showed opposite changes. The target genes of these seven miRNAs were substantially enriched in terms or pathways associated with T2DM.Conclusion: We determined the expression profiles of miRNAs in the obese population, with and without diabetes, before and after bariatric surgery. The miRNAs that intersected in the two comparisons were discovered. Both the miRNAs discovered and their target genes were closely associated with T2DM, demonstrating that they might be potential targets for the regulation of T2DM.

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.