Third-generation PacBio sequencing to explore gut bacteria and gender in colorectal cancer.

BACKGROUND: Gut bacteria have an important influence on colorectal cancer (CRC). The differences of gut bacteria between genders have been the hot spots. OBJECTIVE: To analyze the relationship between gut bacteria and gender differences in patients with CRC. METHODS: A total of 212 patients with CRC and 212 healthy volunteers were recruited. The subjects' fecal samples were obtained, and the fecal microorganisms were analyzed by the third-generation sequencing PacBio. The composition of gut bacteria was analyzed. Linear discriminant analysis Effect Size (LEfSe) was used to analyze the differences in gut bacteria. Pearson coefficient was used to calculate the correlation between differential bacteria. CRC risk prediction models were used to rank the importance of effective differential bacteria. RESULTS: Escherichia flexneri and Phocaeicola vulgatus were the most frequent bacteria in both male and female CRC patients. Bacteroides, Verrucomicrobia and Akkermansiaceae were highly enriched in male CRC group, while Bacteroidetes, Phocaeicola and Tissierellales were highly enriched in female CRC group. Peptostreptococcus anaerobius and Phocaeicola vulgatus were important CRC related bacteria in males and females, respectively. Peptostreptococcus anaerobius was the most important characteristic bacterium of males (AUC = 0.951), and the sensitivity and specificity of the discovery set were 78.74 % and 93.98 %, respectively. Blautia stercoris was the most important characteristic bacterium of females (AUC = 0.966), and the sensitivity and specificity of the discovery set were 90.63 % and 90.63 %, respectively. CONCLUSION: Gut bacteria varied in different genders. Therefore, gender should be considered when gut bacteria are applied in the diagnose and prevention of CRC.

CFViSA: A comprehensive and free platform for visualization and statistics in omics-data.

INTRODUCTION: The rapid growth of omics technologies has led to the use of bioinformatics as a powerful tool for unravelling scientific puzzles. However, the obstacles of bioinformatics are compounded by the complexity of data processing and the distinct nature of omics data types, particularly in terms of visualization and statistics. OBJECTIVES: We developed a comprehensive and free platform, CFViSA, to facilitate effortless visualization and statistical analysis of omics data by the scientific community. METHODS: CFViSA was constructed using the Scala programming language and utilizes the AKKA toolkit for the web server and MySQL for the database server. The visualization and statistical analysis were performed with the R program. RESULTS: CFViSA integrates two omics data analysis pipelines (microbiome and transcriptome analysis) and an extensive array of 79 analysis tools spanning simple sequence processing, visualization, and statistics available for various omics data, including microbiome and transcriptome data. CFViSA starts from an analysis interface, paralleling a demonstration full course to help users understand operating principles and scientifically set the analysis parameters. Once analysis is conducted, users can enter the task history interface for figure adjustments, and then a complete series of results, including statistics, feature tables and figures. All the graphic layouts were printed with necessary statistics and a traceback function recording the options for analysis and visualization; these statistics were excluded from the five competing methods. CONCLUSION: CFViSA is a user-friendly bioinformatics cloud platform with detailed guidelines for integrating functions in multi-omics analysis with real-time visualization adjustment and complete series of results provision. CFViSA is available at http://www.cloud.biomicroclass.com/en/CFViSA/.

Advances in single-cell sequencing technology in microbiome research.

With the rapid development of histological techniques and the widespread application of single-cell sequencing in eukaryotes, researchers desire to explore individual microbial genotypes and functional expression, which deepens our understanding of microorganisms. In this review, the history of the development of microbial detection technologies was revealed and the difficulties in the application of single-cell sequencing in microorganisms were dissected as well. Moreover, the characteristics of the currently emerging microbial single-cell sequencing (Microbe-seq) technology were summarized, and the prospects of the application of Microbe-seq in microorganisms were distilled based on the current development status. Despite its mature development, the Microbe-seq technology was still in the optimization stage. A retrospective study was conducted, aiming to promote the widespread application of single-cell sequencing in microorganisms and facilitate further improvement in the technology.

Classification of Colorectal Cancer Subtypes Based on Endoplasmic Reticulum Stress.

INTRODUCTION: Endoplasmic reticulum stress (ERS) is associated with the occurrence and development of colorectal cancer (CRC). METHODS: One thousand nine CRC samples and 3 ERS gene sets from GEO database were used to screen and validate genes related to stage and prognosis of CRC. Twenty thousand five hundred thirty samples from the TCGA database validated the ERS genes related to prognosis. PPI network construction and coexpression analysis were used to investigate the correlation of genes. ConsensusClusterPlus analysis was used to classify CRC subtypes. Cox regression and the LASSO algorithm were used to screen ERS genes related to prognosis. HE staining, immunohistochemical staining, and RT-qPCR of 50 owner-central samples were used to verify the genes. The ERscore model was constructed based on the ERS genes related to prognosis. The nomogram model was used to verify that different subtypes of CRC patients have different prognosis. RESULTS: Fifty ERS differentially expressed genes related to CRC stage and 8 ERS model genes related to prognosis were screened. Three subtypes of CRC were classified based on the former 50 genes. The clinical characteristics were significantly different among the subtypes. The ERscore model was constructed based on the latter 8 genes, and its accuracy was verified by clinical samples. Finally, the nomogram was constructed based on ERscore, age, and CRC stage, and the accuracy of the nomogram prediction was verified. CONCLUSION: ERS-related genes can be used as classification criteria for CRC, and the related clinical characteristics of different CRC subtypes are different.

Identification of Molecular Targets of Bile Acids Acting on Colorectal Cancer and Their Correlation with Immunity.

BACKGROUND: Bile acids (BAs) are closely related to the occurrence and development of colorectal cancer (CRC), but the specific mechanism is still unclear. AIMS: To identify potential targets related to BAs in CRC and analyze the correlation with immunity. METHODS: The expression of BAs and CRC-related genes in TCGA was studied and screened using KEGG. GSE71187 was used for external validation of differentially expressed genes. Immunofluorescence, immunohistochemistry, and enzymatic cycling assays were used to detect the expression levels of the differentially expressed genes ki67 and BAs. Weighted gene coexpression network analysis (WGCNA) was used to identify genes associated with differential gene expression and immunity. The Cibersort algorithm was used to detect the infiltration of 22 kinds of immune cells in cancer tissues. The PPI network and ceRNA network were constructed to reveal the possible molecular mechanisms behind tumorigenesis. RESULTS: The BA-related gene UGT2A3 is positively correlated with good prognoses in CRC. The expression level of UGT2A3 was negatively related to the BA level and positively related to the Ki67 proliferation index. The expression level of UGT2A3 was higher in the moderately differentiation and advanced stage (stage IV) of CRC. In addition, the expression level of UGT2A3 is correlated with CD8+ T cells. A PPI network related to UGT2A3 and T-cell immune-related genes was constructed. A ceRNA network containing 32 miRNA‒mRNA and 40 miRNA‒lncRNA regulatory pairs was constructed. CONCLUSION: UGT2A3 is a potential molecular target of bile acids in the regulation of CRC and is related to T-cell immunity.

GPX3 is a key cholesterol-related gene associated with prognosis and tumor-infiltrating T cells in colorectal cancer.

High cholesterol is an important factor inducing colorectal cancer (CRC). The study aims to determine the key genes and regulatory mechanism associated with tumor-infiltrating T cells underlying cholesterol-induced CRC. Gene expression data and clinical data from CRCS in The Cancer Genome Atlas (TCGA) were selected for differential expression and survival analysis. A total of 5,815 DEGs and 21 cholesterol-associated KEGG pathways were identified. Subsequently, 128 CRCs and 127 patients without obvious intestinal lesions were recruited to analyze the relationship between GPX3 expression, cholesterol levels, and pathologic condition. The results showed that the expression of cholesterol-related gene GPX3 was negatively associated with cholesterol level, but positively correlated with Ki-67 proliferation index in CRC. The expression of GPX3 was higher in CRC patients who were in poorly differentiated and advanced stage. In addition, a mice model of high-cholesterol diet intervention was constructed to detect the levels of cholesterol and GPX3 in the peripheral blood of mice, and it was found that the expression level of GPX3 in high-cholesterol mice was lower than that in normal diet mice. CD8+ T cells were isolated from the spleen of mice and the T cell surface receptors were detected. It was found that the expression of CD69 in CD8+ T cells of mice interfered with the high-cholesterol diet, while the expression of PD1, TIM-3, and CTLA-4 was increased. CD8+ T cells were co-cultured with MC38 cells to detect the proliferation rate of CRC cells. The results showed that the tumor cell proliferation ratio in the high cholesterol group was higher than that in the control group. Furthermore, GPX3 downstream genes associated with m6A modification and tumor-infiltrating T cells were screened, and a T cell immune-related ceRNA network was constructed. In total, 53 GPX3 downstream genes associated with m6A modification and tumor-infiltrating T cells were identified. A PPI network that contained 45 nodes and 85 interaction pairs was constructed. The ceRNA network, including 39 miRNA-target and 43 lncRNA-miRNA regulatory pairs, was constructed. In conclusion, GPX3 is a potential target for cholesterol regulation of T cell immunity in CRC.

Advances in immunotyping of colorectal cancer.

Immunotherapy has transformed treatment for various types of malignancy. However, the benefit of immunotherapy is limited to a minority of patients with mismatch-repair-deficient (dMMR) and microsatellite instability-high (MSI-H) (dMMR-MSI-H) colorectal cancer (CRC). Understanding the complexity and heterogeneity of the tumor immune microenvironment (TIME) and identifying immune-related CRC subtypes will improve antitumor immunotherapy. Here, we review the current status of immunotherapy and typing schemes for CRC. Immune subtypes have been identified based on TIME and prognostic gene signatures that can both partially explain clinical responses to immune checkpoint inhibitors and the prognosis of patients with CRC. Identifying immune subtypes will improve understanding of complex CRC tumor heterogeneity and refine current immunotherapeutic strategies.

Single-cell transcriptome analysis reveals T population heterogeneity and functions in tumor microenvironment of colorectal cancer metastases.

Cell mediated immune escape, a microenvironment factor, induces tumorigenesis and metastasis. The purpose of this study was to display the characteristics of T cell populations in immune microenvironments for colorectal cancer (CRC) metastasis. Unsupervised cluster analysis was conducted to identify functionally distinct T cell clusters from 3,003 cells in peripheral blood and 4,656 cells in tissues. Subsequently, a total of 8 and 4 distinct T cell population clusters were identified from tumor tissue and peripheral blood, respectively. High levels of CD8+TEX, CD4+TRM, TH1-like T cells, CD8+TEM, tumor-Treg from tissues, and CD4+TN from peripheral blood are essential components of immune microenvironment for the prediction of CRC metastasis. Moreover, exhausted T cells are characterized by higher expression of multiple inhibitory receptors, including PDCD1 and LAG3. Some genes such as PFKFB3, GNLY, circDCUN1D4, TXNIP and NR4A2 in T cells of cluster were statistically different between CRC metastasis and non-metastasis. The ligand-receptor interactions identified between different cluster cells and metastases-related DEGs identified from each cluster revealed that the communications of cells, alterations of functions, and numbers of T subsets may contribute to the metastasis of CRC. The mutation frequency of KiAA1551, ATP8B4 and LNPEP in T cells from tissues and SOR1 from peripheral blood were higher in metastatic CRC than that in non-metastatic CRC. In conclusion, the discovery of differential genes in T cells may provide potential targets for immunotherapy of CRC metastasis and relevant insights into the clinical prediction and prognosis of CRC metastasis.

Aging characteristics of colorectal cancer based on gut microbiota.

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.

Gut bacteria and sex differences in colorectal cancer.

Introduction. Differences in gut bacteria that are associated with the occurrence and development of colorectal cancer (CRC) exist between sexes, and males have a higher morbidity of CRC.Gap Statement. Clinical data for the relationship between gut bacteria and sexes in patients with CRC are not available and are needed to support individualized screening and treatment programmes.Aim. To analyse the relationship between gut bacteria and sexes in patients with CRC.Methodology. A total of 6 077 samples recruited by Fudan University's Academy of Brain Artificial Intelligence Science and Technology were included, and the gut bacteria composition mainly shows the top 30 genera. Linear discriminant analysis Effect Size (LEfSe) was used to analyse the differences in gut bacteria. Pearson correlation coefficients were calculated to demonstrate the relationship of discrepant bacteria. CRC risk prediction models were used to rank the importance of valid discrepant bacteria.Results. Bacteroides, Eubacterium and Faecalibacterium were the top three bacteria in males with CRC, while Bacteroides, Subdoligranulum and Eubacterium were the top three bacteria in females with CRC. The abundance of gut bacteria (Escherichia, Eubacteriales, Clostridia, etc.) was higher in males with CRC compared with that in females with CRC. In addition, Dorea and Bacteroides were important CRC-related bacteria (P<0.001). Finally, the importance of discrepant bacteria was ranked based on CRC risk prediction models. Blautia, Barnesiella and Anaerostipes were the top three important discrepant bacteria between males with CRC and females with CRC. The value of AUC was 1.0, the sensitivity was 92.0 %, the specificity was 68.4 %, and the accuracy was 83.3 % in the discovery set.Conclusion. Gut bacteria were correlated with sexes and CRC. It is necessary to consider gender when gut bacteria are used to treat and predict CRC.

Analysis of bacterial diversity and community structure in gastric juice of patients with advanced gastric cancer.

BACKGROUND: The occurrence and development of gastric cancer are related to microorganisms, which can be used as potential biomarkers of gastric cancer. OBJECTIVE: To screen the microbiological markers of gastric cancer from the microorganisms of gastric juice. METHODS: Gastric juice samples were collected from 61 healthy people and 78 patients with gastric cancer (48 cases of early gastric cancer and 30 cases of advanced gastric cancer). The bacterial 16 S rRNA V1-V4 region of gastric juice samples was sequenced. The Shannon index, Simpson index, Ace index and Chao index were used to analyze the diversity of gastric juice samples. The RDP classifier Bayesian algorithm was used to analyze the community structure of 97% OTU representative sequences with similar levels. Linear discriminant analysis and ST-test were used to analyze the differences. Six machine learning algorithms, including the logistic regression algorithm, random forest algorithm, neural network algorithm, support vector machine algorithm, Catboost algorithm and gradient lifting tree algorithm, were used to construct risk prediction models for gastric cancer and advanced gastric cancer. RESULTS: The microbiota diversity and the abundance of bacteria was different in the healthy group, early gastric cancer and advanced gastric cancer (P < 0.05). The top five abundant bacteria among the three groups were Streptococcus, Rhodococcus, Prevotella, Pseudomonas and Helicobacter. Bacterial flora such as Streptococcus, Rhodococcus and Ochrobactrum were significantly different between the healthy group and the gastric cancer group. The accuracy of the random forest prediction model is the highest (82.73% correct). The bacteria with the highest predictive value included Streptococcus, Lactobacillus and Ochrobactrum. The abundance of bacteria such as Fusobacterium, Capnocytophaga, Atopobium, Corynebacterium was high in the advanced gastric cancer group. CONCLUSION: Gastric juice bacteria can be used as potential biomarkers to predict the occurrence and development of gastric cancer.

Progress in Research on Colorectal Cancer-Related Microorganisms and Metabolites.

Intestinal flora is an important component in the human body, which have been reported to be involved in the occurrence and development of colorectal cancer (CRC). Indeed, changes in the intestinal flora in CRC patients compared to those in control subjects have been reported. Several bacterial species have been shown to exhibit the pro-inflammatory and pro-carcinogenic properties, which could consequently have an impact on colorectal carcinogenesis. In this review, we summarize the current knowledge on the potential links between the intestinal microbiota and CRC. We illustrated the mechanisms by which intestinal flora imbalance affects CRC, mainly focusing on inflammation, microbial metabolites, and specific bacteria species. In addition, we discuss how a diet exhibits a strong impact on microbial composition and provides risks for developing CRC. Finally, we describe the potential future directions that are based on intestinal microbiota manipulation for CRC diagnosis and treatment.

Metastatic Castration-Resistant Prostate Cancer with Neuroendocrine Transformation and BRCA 1 Germ-Line Mutation: A Case Report and Literature Review.

A 63-year-old man with a significantly high prostate-specific antigen level was diagnosed via pathology to have advanced prostate adenocarcinoma due to multiple lung metastases. He was then treated with androgen deprivation therapy (ADT) comprising bicalutamide and goserelin. Only after 6 months of stable disease, the cancer progressed and the drug was changed to abiraterone; however, no significant therapeutic effect was observed and the disease was considered as castration-resistant prostate cancer. The histopathologic analysis of the biopsied metastatic lymph node confirmed small-cell neuroendocrine carcinoma, and genetic testing revealed BRCA1 germ-line mutation. The oral PARP inhibitor olaparib was used and achieved a partial tumor response over a period of 2.5 months. Meanwhile, palliative radiotherapy was performed for pain control in the sacrococcygeal region with complete symptom relief. The combination chemotherapy strategy of etoposide and cisplatin was used after the failure of olaparib and achieved pain alleviation in the left leg. The patient received one cycle of this chemotherapy strategy and eventually died of a rapid tumor progression, respiratory failure, and heart failure on April 27, 2019.

Survival prediction model for patients with mycosis fungoides/Sezary syndrome.

Aim: A nomogram was constructed to forecast the overall survival (OS) of patients with mycosis fungoides/Sezary syndrome. Patients & methods: The clinicopathological information of patients was obtained from the Surveillance, Epidemiology and End Results (SEER) database. A model was established based on the independent prognostic factors. Predictive ability of the model was evaluated with the concordance index and calibration curves. Risk stratification was conducted for patients with similar tumor node metastasis (TNM) stages. Results: The model included 1997 eligible patients and seven prognostic factors for OS. The concordance index of the nomogram was 0.84 in the training and external validation cohorts, which indicated good predictive ability of the model and reliability of the results. The high agreement between the model predictions and actual observations was identified by calibration curves, which demonstrated the prediction accuracy of the model. Risk stratification displayed significant differences for patients with similar TNM stages, which suggested that the OS of patients with similar TNM stages could be further distinguished. Conclusion: We established a reliable nomogram to predict the OS of patients with mycosis fungoides/Sezary syndrome, which highlighted the advantages of nomograms over the conventional TNM staging system and promoted the application of individualized therapeutic strategies.

Survival prediction models for patients with anal carcinoma receiving definitive chemoradiation: A population-based study.

The present study aimed to develop two nomograms in order to predict cancer-specific survival (CSS) and overall survival (OS) of patients with anal carcinoma receiving definitive chemoradiotherapy. Data from studies including patients with anal carcinoma, who were determined to be positive histologically and diagnosed between 2004 and 2010, were obtained from the Surveillance, Epidemiology, and End Results database. Significant prognostic factors for CSS and OS of patients were screened to develop nomograms through univariate and multivariate analyses. Nomograms were validated using internal and external data. The predictive abilities of the generated models were evaluated by concordance index (C-index) and calibration curves. Risk stratification was performed for patients with the same TNM stage. A total of 1,473 patients and six independent prognostic factors for CSS and OS, namely age, sex, ethnicity, marital status at diagnosis, T stage and N stage, were included in the nomogram calculations. Calibration curves demonstrated that nomogram prediction was in high accordance with actual observation. The C-indices of nomograms were greater than those of models based on the sixth edition of the American Joint Committee on Cancer TNM staging system for CSS prediction (training cohort, 0.72 vs. 0.70; validation cohort, 0.68 vs. 0.62) and OS (training cohort, 0.70 vs. 0.66; validation cohort, 0.68 vs. 0.62). Survival curves demonstrated significant survival differences among the different risk groups. Nomograms were more accurate than the conventional TNM staging system in prognosis prediction. In addition, survival performances of patients with the same TNM stage could be further distinguished by risk stratification, which provided individualized prediction for patients. These survival prediction methods may aid clinicians in patient counseling and in selecting more individualized therapeutic strategies.

Single-Cell Analysis Reveals Characterization of Infiltrating T Cells in Moderately Differentiated Colorectal Cancer.

Objective: This study aimed to characterize the tumor-infiltrating T cells in moderately differentiated colorectal cancer. Methods: Using single-cell RNA sequencing data of isolated 1632 T cells from tumor tissue and 1252 T cells from the peripheral blood of CRC patients, unsupervised clustering analysis was performed to identify functionally distinct T cell populations, followed by correlations and ligand-receptor interactions across cell types. Finally, differential analysis of the tumor-infiltrating T cells between colon cancer and rectal cancer were carried out. Results: A total of eight distinct T cell populations were identified from tumor tissue. Tumor-Treg showed a strong correlation with Th17 cells. CD8+TRM was positively correlated with CD8+IEL. Seven distinct T cell populations were identified from peripheral blood. There was a strong correlation between CD4+TN and CD4+blood-TCM. Colon cancer and rectal cancer showed differences in the composition of tumor-infiltrating T cell populations. Tumor-infiltrating CD8+IEL cells were found in rectal cancer but not in colon cancer, while CD8+ TN cells were found in the peripheral blood of colon cancer but not in that of rectal cancer. A larger number of tumor-infiltrating CD8+ Tex (88.94%) cells were found in the colon cancer than in the rectal cancer (11.06%). The T cells of the colon and rectal cancers showed changes in gene expression pattern. Conclusions: We characterized the T cell populations in the CRC tumor tissue and peripheral blood.