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Abstract Background Gut bacteria are related to colorectal cancer (CRC) and its clinicopathologic characteristics. Objective To develop gut bacterial subtypes and explore potential microbial targets for CRC. Methods Stool samples from 914 volunteers (376 CRCs, 363 advanced adenomas, and 175 normal controls) were included for 16S rRNA sequencing. Unsupervised learning was used to generate gut microbial subtypes. Gut bacterial community composition and clustering effects were plotted. Differences of gut bacterial abundance were analyzed. Then, the association of CRC‐associated bacteria with subtypes and the association of gut bacteria with clinical information were assessed. The CatBoost models based on gut differential bacteria were constructed to identify the diseases including CRC and advanced adenoma (AA). Results Four gut microbial subtypes (A, B, C, D) were finally obtained via unsupervised learning. The characteristic bacteria of each subtype were Escherichia‐Shigella in subtype A, Streptococcus in subtype B, Blautia in subtype C, and Bacteroides in subtype D. Clinical information (e.g., free fatty acids and total cholesterol) and CRC pathological information (e.g., tumor depth) varied among gut microbial subtypes. Bacilli, Lactobacillales, etc., were positively correlated with subtype B. Positive correlation of Blautia, Lachnospiraceae, etc., with subtype C and negative correlation of Coriobacteriia, Coriobacteriales, etc., with subtype D were found. Finally, the predictive ability of CatBoost models for CRC identification was improved based on gut microbial subtypes. Conclusion Gut microbial subtypes provide characteristic gut bacteria and are expected to contribute to the diagnosis of CRC.

Abstract Background Emergence of novel immuno‐therapeutics has shown promising improvement in the clinical outcome of colorectal cancer (CRC). Objective To identify robust immune checkpoints based on expression and immune infiltration profiles of clinical CRC samples. Methods One dataset from The Cancer Genome Atlas database and two from Gene Expression Omnibus were independently employed for the analysis. Genes associated with overall survival were identified, and distribution of each immune checkpoint with respect to different clinical features was determined to explore key immune checkpoints. Multiple staining methods were used to verify the correlation between key immune checkpoint ICOS and clinical pathological features. Differentially expressed mRNA and long non‐coding RNA (lncRNA) were then detected for gene set enrichment analysis and gene set variation analysis to investigate the differentially enriched biological processes between low‐ and high‐expression groups. Significant immune‐related mRNAs and lncRNA were subjected to competing endogenous RNA (ceRNA) network analysis. Correlation of inducible T‐cell costimulator (ICOS) and top 10 genes in ceRNA network were further considered for validation. Results ICOS was identified from 14 immune checkpoints as the most highly correlated gene with survival and clinical features in CRC. The expression of ICOS protein in the poorly differentiated group was lower than that in the moderately differentiated group, and the expression in different pathological stages was significant. In addition, the expressions of ICOS were negatively correlated with Ki67. A conspicuous number of immune‐related pathways were enriched in differentially expressed genes in the ICOS high‐ and low‐expression groups. Integration with immune infiltration data revealed a multitude of differentially expressed immune‐related genes enriched for ceRNA network. Furthermore, expression of top 10 genes investigated from ceRNA network showed high correlation with ICOS. Conclusion ICOS might serve as a robust immune checkpoint for prognosis with several genes being potential targets of ICOS‐directed immunotherapy in CRC.

Abstract Background Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC). Methods A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CRC samples were divided into three age groups. Ggplot2 and Microbiota Process packages were used for visual description of species composition and PCA in healthy individuals and CRC samples. LEfSe analysis was performed for species relative abundance data in healthy/CRC groups according to age. Spearman correlation coefficient of age‐differentiated bacteria in healthy individuals and CRC samples was calculated separately. Finally, the age prediction model and CRC risk prediction model were constructed based on the age‐differentiated bacteria. Results The structure and composition of the gut microbiota were significantly different among the three groups. For example, the abundance of Bacteroides vulgatus in the old group was lower than that in the other two groups, the abundance of Bacteroides fragilis increased with aging. In addition, seven species of bacteria whose abundance increases with aging were screened out. Furthermore, the abundance of pathogenic bacteria (Escherichia_coli, Butyricimonas_virosa, Ruminococcus_bicirculans, Bacteroides_fragilis and Streptococcus_vestibularis) increased with aging in CRCs. The abundance of probiotics (Eubacterium_eligens) decreased with aging in CRCs. The age prediction model for healthy individuals based on the 80 age‐related differential bacteria and model of CRC patients based on the 58 age‐related differential bacteria performed well, with AUC of 0.79 and 0.71, respectively. The AUC of CRC risk prediction model based on 45 disease differential bacteria was 0.83. After removing the intersection between the disease‐differentiated bacteria and the age‐differentiated bacteria from the healthy samples, the AUC of CRC risk prediction model based on remaining 31 bacteria was 0.8. CRC risk prediction models for each of the three age groups showed no significant difference in accuracy (young: AUC=0.82, middle: AUC=0.83, old: AUC=0.85). Conclusion Age as a factor affecting microbial composition should be considered in the application of gut microbiota to predict the risk of CRC.

The aim of the present study was to assess the apparent diffusion coefficient (ADC) in diffusion-weighted imaging (DWI), thyroid radioactive iodine uptake (RAIU), thyroid scintigraphy and thyrotropin receptor antibody (TRAb) levels in the differential diagnosis between Graves' disease (GD) and painless thyroiditis (PT). A total of 102 patients with GD and 37 patients with PT were enrolled in the study. DWI was obtained with a 3.0-T magnetic resonance scanner, and ADC values were calculated. RAIU and thyroid scintigraphy were performed. Tissue samples were obtained from patients with GD (6 cases) following thyroidectomy, and from patients with PT (2 cases) following biopsy. Receiver operating characteristic (ROC) curves were drawn, optimal cut-off values were selected, and the sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were assessed. It was found that the ADC, TRAb and RAIU were significantly higher in GD than in PT (P<0.05). ROC curves showed areas under the curves for RAIU, ADC and TRAb that were >0.900. RAIU was the reference method. Sensitivity, specificity, accuracy, PPV and NPV were 96.078, 91.892, 95.000, 97.059 and 89.474% for ADC, and 88.235, 75.676, 84.892, 90.909 and 70.000% for TRAb, after the optimal thresholds of 1.837×10-3 mm²/sec and 1.350 IU/ml were determined respectively. Histopathology showed that tissue cellularity in PT was much higher than in GD due to massive lymphocytic infiltration. The results of the present study indicate that RAIU, ADC and TRAb are of diagnostic value for differentiating between GD and PT. DWI has great potential for thyroid pathophysiological imaging because it reflects differences in tissue cellularity between GD and PT. [ABSTRACT FROM AUTHOR]

Background: Gut microbes and age are both factors that influence the development of disease. The community structure of gut microbes is affected by age., Objective: To plot time-dependent gut microbe profiles in individuals over 45 years old and explore the correlation between age and gut microbes., Methods: Fecal samples were collected from 510 healthy individuals over 45 years old. Shannon index, Simpson index, Ace index, etc. were used to analyze the diversity of gut microbes. The beta diversity analysis, including non-metric multidimensional scaling (NMDS), was used to analyze community distribution. Linear discriminant analysis (LDA) and random forest (RF) algorithm were used to analyze the differences of gut microbes. Trend analysis was used to plot the abundances of characteristic gut microbes in different ages., Results: The individuals aged 45-49 had the highest richness of gut bacteria. Fifteen characteristic gut microbes, including Siphoviridae and Bifidobacterium breve , were screened by RF algorithm. The abundance of Ligiactobacillus and Microviridae were higher in individuals older than 65 years. Moreover, the abundance of Blautia&#95;A massiliensis , Lubbockvirus and Enterocloster clostridioformis decreased with age and the abundance of Klebsiella variicola and Prevotell a increased with age. The functional genes, such as human diseases and aging, were significantly different among different aged individuals., Conclusions: The individuals in different ages have characteristic gut microbes. The changes in community structure of gut microbes may be related to age-induced diseases.