Molecular characterization, clinical value, and cancer-immune interactions of genes related to disulfidptosis and ferroptosis in colorectal cancer.

BACKGROUND: This research strived to construct a new signature utilizing disulfidptosis-related ferroptosis (SRF) genes to anticipate response to immunotherapy, prognosis, and drug sensitivity in individuals with colorectal cancer (CRC). METHODS: The data for RNA sequencing as well as corresponding clinical information of individuals with CRC, were extracted from The Cancer Genome Atlas (TCGA) dataset. SRF were constructed with the help of the random forest (RF), least absolute shrinkage and selection operator (LASSO), and stepwise regression algorithms. To validate the SRF model, we applied it to an external cohort, GSE38832. Prognosis, immunotherapy response, drug sensitivity, molecular functions of genes, and somatic mutations of genes were compared across the high- and low-risk groups (categories). Following this, all statistical analyses were conducted with the aid of the R (version 4.23) software and various packages of the Cytoscape (version 3.8.0) tool. RESULTS: SRF was developed based on five genes (ATG7, USP7, MMD, PLIN4, and THDC2). Both univariate and multivariate Cox regression analyses established SRF as an independent, prognosis-related risk factor. Individuals from the high-risk category had a more unfavorable prognosis, elevated tumor mutational burden (TMB), and significant immunosuppressive status. Hence, they might have better outcomes post-immunotherapy and might benefit from the administration of pazopanib, lapatinib, and sunitinib. CONCLUSION: In conclusion, SRF can act as a new biomarker for prognosis assessment. Moreover, it is also a good predictor of drug sensitivity and immunotherapy response in CRC but should undergo optimization before implementation in clinical settings.

Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder.

Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder's adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub ( https://github.com/tjcadd2020/xMarkerFinder ), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server ( https://www.biosino.org/xmarkerfinder ).

Establishment and evaluation of a prognostic model for patients with unresectable gastric cancer liver metastases.

BACKGROUND: Liver metastases (LM) is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer (GC). The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis, thereby enhancing the ability to evaluate patient outcomes. AIM: To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis, thereby enhancing patient outcome assessment. METHODS: Retrospective analysis was conducted on clinical data pertaining to GCLM (type III), admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018. The dataset was divided into a development cohort and validation cohort in a ratio of 2:1. In the development cohort, we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients. Subsequently, we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis, calibration curves, and clinical decision curves. A nomogram was created to visually represent the prediction model, which was then externally validated using the validation cohort. RESULTS: A total of 372 patients were included in this study, comprising 248 individuals in the development cohort and 124 individuals in the validation cohort. Based on Cox analysis results, our final prediction model incorporated five independent risk factors including albumin levels, primary tumor size, presence of extrahepatic metastases, surgical treatment status, and chemotherapy administration. The 1-, 3-, and 5-years Area Under the Curve values in the development cohort are 0.753, 0.859, and 0.909, respectively; whereas in the validation cohort, they are observed to be 0.772, 0.848, and 0.923. Furthermore, the calibration curves demonstrated excellent consistency between observed values and actual values. Finally, the decision curve analysis curve indicated substantial net clinical benefit. CONCLUSION: Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model, demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.

Identification of risk factors and development of a predictive model for chronic kidney disease in patients with obesity: a four-year cohort study.

OBJECTIVE: The sneaky onset and dismal prognosis of chronic kidney disease (CKD) make it an important public health issue. Obesity-related kidney illness has garnered more attention in recent times. Establishing and validating a risk prediction model for chronic renal illness in overweight or obese adults was the goal of this investigation. METHODS: Data from the China Health and Retirement Longitudinal Study were used for analysis. The definition of CKD was reduced renal function (eGFR < 60 mL/min/1.73 m²), while overweight and obesity were characterized through a body mass index exceeding 24 kg/m². The dataset was divided into derivation and validation cohorts using a 7:3 ratio. With respect to the derivation cohort, we constructed a prediction model using LASSO analysis and multivariate logistic regression. The model's performance was evaluated using Hosmer-Lemeshow tests, calibration curves, decision curve analysis, and receiver operating characteristic (ROC) curves. The validation cohort's model was subjected to additional assessment. RESULTS: The study was based on survey data from 2011 to 2015 and comprised 3246 individuals who were overweight or obese, with 2274 being part of the derivation cohort and 972 being part of the validation cohort. The research constructed a prediction model that included age, sex, fasting blood glucose, glycated hemoglobin, triglyceride, hypertension, and BMI. The validation cohort's area under the ROC curve was 0.812 (95% CI = 0.763, 0.859) while the derivation cohort's was 0.789 (95% CI = 0.754, 0.831). Hosmer-Lemeshow tests were utilized to evaluate the model's accuracy in the validation and derivation cohorts (P = 0.681 and 0.547, respectively). The calibration curve showed a high level of consistency between the actual observations and the projected outcomes. According to decision curve analysis, the model offered significant net advantages. CONCLUSIONS: The forecasting model established in this research has predictive value for CKD in patients with overweight or obesity. These findings could help doctors conduct early detection and intervention in clinical practice and further improve patient prognosis.

Multi-kingdom microbial signatures in excess body weight colorectal cancer based on global metagenomic analysis.

Excess body weight (EBW) increases the risk of colorectal cancer (CRC) and is linked to lower colonoscopy compliance. Here, we extensively analyzed 981 metagenome samples from multiple cohorts to pinpoint the specific microbial signatures and their potential capability distinguishing EBW patients with CRC. The gut microbiome displayed considerable variations between EBW and lean CRC. We identify 44 and 37 distinct multi-kingdom microbial species differentiating CRC and controls in EBW and lean populations, respectively. Unique bacterial-fungal associations are also observed between EBW-CRC and lean-CRC. Our analysis revealed specific microbial functions in EBW-CRC, including D-Arginine and D-ornithine metabolism, and lipopolysaccharide biosynthesis. The best-performing classifier for EBW-CRC, comprising 12 bacterial and three fungal species, achieved an AUROC of 0.90, which was robustly validated across three independent cohorts (AUROC = 0.96, 0.94, and 0.80). Pathogenic microbial species, Anaerobutyricum hallii, Clostridioides difficile and Fusobacterium nucleatum, are EBW-CRC specific signatures. This work unearths the specific multi-kingdom microbial signatures for EBW-CRC and lean CRC, which may contribute to precision diagnosis and treatment of CRC.

NAFLDkb: A Knowledge Base and Platform for Drug Development against Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with a broad spectrum of histologic manifestations. The rapidly growing prevalence and the complex pathologic mechanisms of NAFLD pose great challenges for treatment development. Despite tremendous efforts devoted to drug development, there are no FDA-approved medicines yet. Here, we present NAFLDkb, a specialized knowledge base and platform for computer-aided drug design against NAFLD. With multiperspective information curated from diverse source materials and public databases, NAFLDkb presents the associations of drug-related entities as individual knowledge graphs. Practical drug discovery tools that facilitate the utilization and expansion of NAFLDkb have also been implemented in the web interface, including chemical structure search, drug-likeness screening, knowledge-based repositioning, and research article annotation. Moreover, case studies of a knowledge graph repositioning model and a generative neural network model are presented herein, where three repositioning drug candidates and 137 novel lead-like compounds were newly established as NAFLD pharmacotherapy options reusing data records and machine learning tools in NAFLDkb, suggesting its clinical reliability and great potential in identifying novel drug-disease associations of NAFLD and generating new insights to accelerate NAFLD drug development. NAFLDkb is freely accessible at https://www.biosino.org/nafldkb and will be updated periodically with the latest findings.

Host-microbiota interactions contributing to the heterogeneous tumor microenvironment in colorectal cancer.

Colorectal cancer (CRC) exhibits pronounced heterogeneity and is categorized into four widely accepted consensus molecular subtypes (CMSs) with unique tumor microenvironments (TMEs). However, the intricate landscape of the microbiota and host-microbiota interactions within these TMEs remains elusive. Using RNA-sequencing data from The Cancer Genome Atlas, we analyzed the host transcriptomes and intratumoral microbiome profiles of CRC samples. Distinct host genes and microbial genera were identified among the CMSs. Immune microenvironments were evaluated using CIBERSORTx and ESTIMATE, and microbial coabundance patterns were assessed with FastSpar. Through LASSO penalized regression, we explored host-microbiota associations for each CMS. Our analysis revealed distinct host gene signatures within the CMSs, which encompassed ferroptosis-related genes and specific immune microenvironments. Moreover, we identified 293, 153, 66, and 109 intratumoral microbial genera with differential abundance, and host-microbiota associations contributed to distinct TMEs, characterized by 829, 1,270, 634, and 1,882 robust gene-microbe associations for each CMS in CMS1-CMS4, respectively. CMS1 featured inflammation-related HSF1 activation and gene interactions within the endothelin pathway and Flammeovirga. Integrin-related genes displayed positive correlations with Sutterella in CMS2, whereas CMS3 spotlighted microbial associations with biosynthetic and metabolic pathways. In CMS4, genes involved in collagen biosynthesis showed positive associations with Sutterella, contributing to disruptions in homeostasis. Notably, immune-rich subtypes exhibited pronounced ferroptosis dysregulation, potentially linked to tissue microbial colonization. This comprehensive investigation delineates the diverse landscapes of the TME within each CMS, incorporating host genes, intratumoral microbiota, and their complex interactions. These findings shed light on previously uncharted mechanisms underpinning CRC heterogeneity and suggest potential therapeutic targets.NEW & NOTEWORTHY This study determined the following: 1) providing a comprehensive landscape of consensus molecular subtype (CMS)-specific tumor microenvironments (TMEs); 2) constructing CMS-specific networks, including host genes, intratumoral microbiota, and enriched pathways, analyzing their associations to uncover unique patterns that demonstrate the intricate interplay within the TME; and 3) revealing a connection between immune-rich subtypes and ferroptosis activation, suggesting a potential regulatory role of the microbiota in ferroptosis dysregulation of the colorectal cancer TME.

Identification of the shared gene signature and biological mechanism between type 2 diabetes and colorectal cancer.

Background: The correlation of type 2 diabetes mellitus (T2DM) with colorectal cancer (CRC) has garnered considerable attention in the scientific community. Despite this, the molecular mechanisms underlying the interaction between these two diseases are yet to be elucidated. Hence, the present investigation aims to explore the shared gene signatures, immune profiles, and drug sensitivity patterns that exist between CRC and T2DM. Methods: RNA sequences and characteristics of patients with CRC and T2DM were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. These were investigated using weighted gene co-expression network analysis (WGCNA) to determine the co-expression networks linked to the conditions. Genes shared between CRC and T2DM were analyzed by univariate regression, followed by risk prognosis assessment using the LASSO regression model. Various parameters were assessed through different software such as the ESTIMATE, CIBERSORT, AND SSGSEA utilized for tumor immune infiltration assessment in the high- and low-risk groups. Additionally, pRRophetic was utilized to assess the sensitivity to chemotherapeutic agents in both groups. This was followed by diagnostic modeling using logistic modeling and clinical prediction modeling using the nomogram. Results: WGCNA recognized four and five modules that displayed a high correlation with T2DM and CRC, respectively. In total, 868 genes were shared between CRC and T2DM, with 14 key shared genes being identified in the follow-up analysis. The overall survival (OS) of patients in the low-risk group was better than that of patients in the high-risk group. In contrast, the high-risk group exhibited higher expression levels of immune checkpoints The Cox regression analyses established that the risk-score model possessed independent prognostic value in predicting OS. To facilitate the prediction of OS and cause-specific survival, the nomogram was established utilizing the Cox regression model. Conclusion: The T2DM + CRC risk-score model enabled independent prediction of OS in individuals with CRC. Moreover, these findings revealed novel genes that hold promise as therapeutic targets or biomarkers in clinical settings.

Management of esophagogastric fistula caused by adjustable gastric band erosion: A case report and literature review.

Laparoscopic adjustable gastric banding (LAGB) is commonly used in the treatment of morbid obesity. However, with clinical application and long-term follow-up, the shortcomings of this procedure were also exposed, bringing about surgery-related complications include dysphagia, intragastric band migration, slippage, and gastric band erosion. Lower esophageal and gastric fistula is a rare but dangerous complication after LAGB. We describe a case of esophagogastric fistula occurring twelve years after a laparoscopic band procedure and its successful management in a multidisciplinary and staged manner, followed by a short review of the literature.

Molecular subtypes identified by multiomics analysis based on cuproptosis-related genes precisely predict response to immunotherapy and chemotherapy in colorectal cancer.

Cuproptosis is a newly reported type of programmed cell death that is involved in the progression of various diseases. Some studies have reported its potential significance in multiple tumors. Colorectal cancer (CRC) is one of the malignant tumors with high incidence and mortality. The purpose of this study was to further explore the importance of cuproptosis in the CRC development and treatment. We analyzed the expression, alterations, and promoter methylation of cuproptosis-related genes (CRGs) in patients with CRC. Three machine learning methods was used to determine cuproptosis-related feature genes and a diagnostic model was built based on them. Using the unsupervised clustering, patients with CRC were classified into distinct clusters. Then, the LASSO method was used to establish a cuproptosis risk model. We analyzed the association of risk scores with outcomes, immune microenvironment, response to immunotherapy, and sensitivity to chemotherapeutic drugs. The results showed that the expression of CRGs was dysregulated in CRC. The diagnostic model based on cuproptosis-related feature genes showed great clinical value. The patients in two clusters displayed different prognosis and microenvironment. Furthermore, the risk score was correlated with clinical characteristics, immune infiltration and response to immunotherapy and chemotherapy. Above all, the present findings revealed the involvement of cuproptosis in CRC development and provided a diagnostic tool to evaluate CRC occurrence risk. The immune infiltration and drug sensitivity analysis results helped to predict the response of patients in different subtypes of CRC to immunotherapy and chemotherapy.

Gold Nanocluster-Based Fluorescent Microneedle Platform toward Visual Detection of ATP.

Adenosine triphosphate (ATP) participates in the regulation of most biological processes, and the ATP level is closely associated with many diseases. However, it still remains challenging to achieve on-site monitoring of ATP in an equipment-free and efficient way. Microneedles, a minimally invasive technology that can extract biomarkers from liquid biopsies, have recently emerged as useful tools for early diagnosis of a broad range of diseases. In this work, we developed hydrogel microneedles that are loaded with ATP-specific dual-emitting gold nanoclusters (RhE-AuNCs) for fast sampling and on-needle detection of ATP. These RhE-AuNCs were photo-crosslinked to the hydrogel matrix to form a fluorescent microneedle patch. Based on the ATP-induced Förster resonance energy transfer in RhE-AuNCs, a highly selective, sensitive, and reliable ATP sensor was developed. Moreover, simultaneous capture and visual detection of ATP was achieved by the AuNC-loaded microneedle sensing platform, which exhibits promising sensing performance. This work provides a new approach to design a point-of-care ATP sensing platform, which also holds great potential for the further development of microneedle-based analytical devices.

Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer.

Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.

Microbial genes outperform species and SNVs as diagnostic markers for Crohn's disease on multicohort fecal metagenomes empowered by artificial intelligence.

Dysbiosis of gut microbial community is associated with the pathogenesis of CD and may serve as a promising noninvasive diagnostic tool. We aimed to compare the performances of the microbial markers of different biological levels by conducting a multidimensional analysis on the microbial metagenomes of CD. We collected fecal metagenomic datasets generated from eight cohorts that altogether include 870 CD patients and 548 healthy controls. Microbial alterations in CD patients were assessed at multidimensional levels including species, gene, and SNV level, and then diagnostic models were constructed using artificial intelligence algorithm. A total of 227 species, 1047 microbial genes, and 21,877 microbial SNVs were identified that differed between CD and controls. The species, gene, and SNV models achieved an average AUC of 0.97, 0.95, and 0.77, respectively. Notably, the gene model exhibited superior diagnostic capability, achieving an average AUC of 0.89 and 0.91 for internal and external validations, respectively. Moreover, the gene model was specific for CD against other microbiome-related diseases. Furthermore, we found that phosphotransferase system (PTS) contributed substantially to the diagnostic capability of the gene model. The outstanding performance of PTS was mainly explained by genes celB and manY, which demonstrated high predictabilities for CD with metagenomic datasets and was validated in an independent cohort by qRT-PCR analysis. Our global metagenomic analysis unravels the multidimensional alterations of the microbial communities in CD and identifies microbial genes as robust diagnostic biomarkers across geographically and culturally distinct cohorts.

The risk factors of postoperative nausea and vomiting in patients undergoing laparoscopic sleeve gastrectomy and laparoscopic distal gastrectomy: a propensity score matching analysis.

Postoperative nausea and vomiting (PONV) is a common side effect after laparoscopic surgery. The aim of the study is to investigate the variables that could predict PONV in patients who underwent laparoscopic gastrectomy. We divided patients who underwent laparoscopic gastrectomy into PONV and No-PONV groups. Propensity score matching (PSM) was applied to adjust confounding factors for further validation, and ordinal logistic regression analysis was used to identify predictors for PONV. In the ordinal logistic regression analysis, the preoperative neutrophil-to-lymphocyte ratio (NLR) (odds ratio [OR]: 3.19, 95% confidence interval [CI]: 1.38-7.38; p < 0.01) was identified as an independent risk factor for the presence of PONV and a predictor of the severity of PONV (OR: 3.44, 95% CI: 1.67-5.20; p < 0.01) in 94 PSM patients. Besides, NLR was positively correlated with the PONV score (r = 0.534, p < 0.001). In the receiver-operating characteristic (ROC) curve analysis, an NLR with an optimal cutoff value of 1.59 predicted severe PONV with a sensitivity of 72% and specificity of 81%. The NLR was an independent risk factor for the presence of PONV, and a high NLR tends to be positively associated with the severity of PONV after laparoscopic gastrectomy.

Bromodomain-containing protein 9 activates proliferation and epithelial-mesenchymal transition of colorectal cancer via the estrogen pathway in vivo and in vitro.

Background: Bromodomain-containing protein 9 (BRD9) has been reported to be upregulated in multiple malignancies and facilitate cancer progression. However, there is a paucity of data relating to its expression and biological role in colorectal cancer (CRC). Therefore, this current study examined the prognostic role of BRD9 in CRC and the underlying mechanisms involved. Methods: Real-time polymerase chain reaction (PCR) and Western blotting were used to examine the expression of BRD9 in paired fresh CRC and para-tumor tissues from colectomy patients (n=31). Immunohistochemistry (IHC) was performed to assess BRD9 expression in 524 paraffin-embedded archived CRC samples. The clinical variables are including age, sex, carcinoembryonic antigen (CEA), location of tumor, T stage, N stage, and TNM classification. The effect of BRD9 on the prognosis of CRC patients was explored by Kaplan-Meier and Cox regression analyses. Cell counting kit 8 (CCK-8), clone formation assay, transwell assay, and flow cytometry were used to determine CRC cell proliferation, migration, invasion, and apoptosis, respectively. Xenograft models in nude mice were established to investigate the role of the BRD9 in vivo. Results: BRD9 mRNA and protein expression levels were significantly upregulated in CRC cells compared to normal colorectal epithelial cells (P<0.001). IHC analysis of 524 paraffin-embedded archived CRC tissues showed that high BRD9 expression was significantly associated with TNM classifications, CEA, and lymphatic invasion (P<0.01). Univariate and multivariate analyses indicated that BRD9 [hazard ratio (HR): 3.04, 95% confidence interval (CI): 1.78-5.20; P<0.01] expression and sex (HR: 6.39, 95% CI: 3.94-10.37; P<0.01) were independent prognostic factors for overall survival in the entire cohort. Overexpressing BRD9 promoted CRC cell proliferation, while silencing BRD9 inhibited the proliferation of CRC cells. Furthermore, we showed that BRD9 silencing significantly inhibited epithelial-mesenchymal transition (EMT) via the estrogen pathway. Finally, we demonstrated that silencing BRD9 significantly inhibited the proliferation and tumorigenicity of SW480 and HCT116 cells in vitro and in vivo in nude mice (P<0.05). Conclusions: This study demonstrated that BRD9 high could be an independent prognostic risk factor for CRC. Furthermore, the BRD9/estrogen pathway may contribute to the proliferation of CRC cells and EMT, suggesting that BRD9 may be a novel molecular target in the therapeutic treatment of CRC.

Body roundness index is related to the low estimated glomerular filtration rate in Chinese population: A cross-sectional study.

Background: Kidney disease is related to visceral obesity. As a new indicator of obesity, body roundness index (BRI) has not been fully revealed with kidney disease. This study's objective is to assess the relationship between estimated glomerular filtration rate (eGFR) and BRI among the Chinese population. Methods: This study enrolled 36,784 members over the age of 40, they were from 7 centers in China by using a random sampling method. BRI was computed using height and waist circumference, eGFR ≤ 90 mL/min/1.73 m2 was considered to indicate low eGFR. To lessen bias, propensity score matching was employed, multiple logistic regression models were utilized to examine the connection between low eGFR and BRI. Results: The age, diabetes and coronary heart disease rates, fasting blood glucose, and triglycerides were all greater in participants with low eGFR. The BRI quartile was still positively connected with low eGFR after controlling for confounding variables, according to multivariate logistic regression analysis. (OR [95%CI] Q2:1.052 [1.021-1.091], OR [95%CI] Q3:1.189 [1.062-1.284], OR [95%CI] Q4:1.283 [1.181-1.394], P trend < 0.001). Stratified research revealed that the elders, women, habitual smokers, and those with a history of diabetes or hypertension experienced the connection between BRI level and low eGFR. According to ROC, BRI was able to detect low eGFR more accurately. Conclusion: Low eGFR in the Chinese community is positively connected with BRI, which has the potential to be used as an effective indicator for screening kidney disease to identify high-risk groups and take appropriate measures to prevent subsequent complications.

The association between the body roundness index and the risk of colorectal cancer: a cross-sectional study.

BACKGROUND: Colorectal cancer (CRC), has a link between obesity, especially visceral fat. The body roundness index (BRI) can more accurately assess body fat and visceral fat levels. It is, however, unknown whether BRI is associated with CRC risk. METHODS: 53,766 participants were enrolled from the National Health and Nutrition Examination Survey (NHANES). Analysing the corelation between BRI and CRC risk was performed using logistic regression. Stratified analyses revealed the association based on the population type. Receiver operating characteristic curve (ROC) was performed for predicting CRC risk using different anthropometric indices. RESULTS: The risk of CRC mounting apparently with elevated BRI for participants with CRC compared to normal participants (P-trend < 0.001). The association persisted even after adjusting for all covariates (P-trend = 0.017). In stratified analyses, CRC risk increased with increasing BRI, especially among those who were inactive (OR (95% CI): Q3 3.761 (2.139, 6.610), P < 0.05, Q4 5.972 (3.347, 8.470), P < 0.01), overweight (OR (95% CI): Q3 2.573 (1.012, 7.431), P < 0.05, Q4 3.318 (1.221, 9.020), P < 0.05) or obese (OR (95% CI): Q3 3.889 (1.829, 8.266), P < 0.001, Q4 4.920 (2.349, 10.308), P < 0.001). ROC curve showed that BRI had a better ability in forecasting the risk of CRC than other anthropometric indices such as body weight etc. (all P < 0.05). CONCLUSIONS: CRC risk and BRI have a positive and significant relationship, particularly in inactive participants with BMI ≥ 25 kg/m2. It is hoped that these results will raise awareness of the importance of reducing visceral fat deposition.

Murine neonatal dermal fibroblast acquires a lymphoid tissue organizer cell-like activity upon synergistic activation of TNF-α receptor and LTß receptor.

Tertiary lymphoid organs (TLOs) are ectopic aggregates of immune cells. As accumulating studies demonstrate TLOs as a predictor of better prognosis in certain cancers, targeting TLO formation, which is tightly regulated by the lymphoid tissue organizer cells (LTOs), has become intriguing in cancer treatment. However, the clinical outcome of these attempts is limited, because the approaches for activating tumor adjacent LTO is lack and little is known about what type of self-cell can be used as LTO to initiate TLO formation. Here we demonstrate that co-stimulation with membrane-bound ligand LTα1ß2 and soluble TNF-α could induced an LTO-like activity in murine neonatal dermal fibroblast, featured by high expression of cell migration-associated chemokines and adhesion molecules that resemble typical LTO gene signature. Furthermore, the LTO-phenotypic dermal fibroblast could enhance the attachment and survival of T and B cell and proliferation of T cell. These findings suggest dermal fibroblast as a promising target for TLO induction to improve cancer immunotherapy.

Comprehensive analysis of the prognostic and immunotherapeutic implications of STAT family members in human colorectal cancer.

Background: Colorectal cancer (CRC) is the third most prevalent cancer worldwide and the second leading cause of cancer mortality. Signal transducer and activator of transcription (STAT) proteins are a group of transcription factors implicated in cell signal transduction and gene transcription in several cancer types. However, the level of expression, genetic alterations, and biological function of different STATs, as well as their prognostic and immunotherapeutic value in CRC remain unclear. Methods: The mRNA and protein expression levels, genetic alterations, prognostic value, gene-gene and protein-protein interaction networks, and biological function of STATs in CRC were studied using the GEPIA, HPA, cBioPortal, PrognoScan, Kaplan-Meier plotter, GeneMANIA, STRING, and Metascape databases. The expression of STATs in CRC was confirmed using immunohistochemistry (IHC). Finally, the relationship between STAT expression and immune infiltration as well as immunotherapy-associated indicators was also investigated. Results: The expression levels of STAT2/5A/5B are downregulated in CRC, and the STAT1/3/4/5B expressions were significantly associated with the tumor stage of patients with CRC. The abnormal expression of STAT2/4/5B in patients with CRC is related to the prognosis of patients with CRC. The STATs and their neighboring proteins are primarily associated with lymphocyte activation, cytokine-mediated signaling pathways, positive regulation of immune response, regulation of cytokine production, and growth hormone receptor signaling pathways in cancer. The expression of STATs was significantly associated with immune infiltration and immunotherapy response-associated indicators. Conclusion: This study may help further understand the molecular mechanism of CRC and provide new prognostic biomarkers and immunotherapy targets in patients with CRC.