Gut microbes on the risk of advanced adenomas.

BACKGROUND: More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. OBJECTIVE: To analyze the characteristic microbes in AA. METHODS: Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and ß diversity. α diversity included box diagram, and ß diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. RESULTS: The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. CONCLUSION: Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing.

Investigating the influence of the gut microbiome on cholelithiasis: unveiling insights through sequencing and predictive modeling.

BACKGROUND: Cholelithiasis is one of the most common disorders of hepatobiliary system. Gut bacteria may be involved in the process of gallstone formation and are, therefore considered as potential targets for cholelithiasis prediction. OBJECTIVE: To reveal the correlation between cholelithiasis and gut bacteria. METHODS: Stool samples were collected from 100 cholelithiasis and 250 healthy individuals from Huzhou Central Hospital; The 16S rRNA of gut bacteria in the stool samples was sequenced using the third-generation Pacbio sequencing platform; Mothur v.1.21.1 was used to analyze the diversity of gut bacteria; Wilcoxon rank-sum test and linear discriminant analysis of effect sizes (LEfSe) were used to analyze differences in gut bacteria between patients suffering from cholelithiasis and healthy individuals; Chord diagram and Plot-related heat maps were used to analyze the correlation between cholelithiasis and gut bacteria; six machine algorithms were used to construct models to predict cholelithiasis. RESULTS: There were differences in the abundance of gut bacteria between cholelithiasis and healthy individuals, but there were no differences in their community diversity. Increased abundance of Costridia, Escherichia flexneri, and Klebsiella pneumonae were found in cholelithiasis, while Bacteroidia, Phocaeicola, and Phocaeicola vulgatus were more abundant in healthy individuals. The top four bacteria that were most closely associated with cholelithiasis were Escherichia flexneri, Escherichia dysenteriae, Streptococcus salivarius, and Phocaeicola vulgatus. The cholelithiasis model based on CatBoost algorithm had the best prediction effect (sensitivity: 90.48%, specificity: 88.32%, and AUC: 0.962). CONCLUSION: The identification of characteristic gut bacteria may provide new predictive targets for gallstone screening. As being screened by the predictive model, people at high risk of cholelithiasis can determine the need for further testing, thus enabling early warning of cholelithiasis.

Cholesterol induction in CD8+ T cell exhaustion in colorectal cancer via the regulation of endoplasmic reticulum-mitochondria contact sites.

BACKGROUND: Hypercholesterolemia is one of the risk factors for colorectal cancer (CRC). Cholesterol can participate in the regulation of human T cell function and affect the occurrence and development of CRC. OBJECTIVE: To elucidate the pathogenesis of CRC immune escape mediated by CD8+ T cell exhaustion induced by cholesterol. METHODS: CRC samples (n = 217) and healthy individuals (n = 98) were recruited to analyze the relationship between peripheral blood cholesterol levels and the clinical features of CRC. An animal model of CRC with hypercholesterolemia was established. Intraperitoneal intervention with endoplasmic reticulum stress (ERS) inhibitors in hypercholesterolemic CRC mice was performed. CD69, PD1, TIM-3, and CTLA-4 on CD8+ T cells of spleens from C57BL/6 J mice were detected by flow cytometry. CD8+ T cells were cocultured with MC38 cells (mouse colon cancer cell line). The proliferation, apoptosis, migration and invasive ability of MC38 cells were detected by CCK-8 assay, Annexin-V APC/7-AAD double staining, scratch assay and transwell assay, respectively. Transmission electron microscopy was used to observe the ER structure of CD8+ T cells. Western blotting was used to detect the expression of ERS and mitophagy-related proteins. Mitochondrial function and energy metabolism were measured. Immunoprecipitation was used to detect the interaction of endoplasmic reticulum-mitochondria contact site (ERMC) proteins. Immunofluorescence colocalization was used to detect the expression and intracellular localization of ERMC-related molecules. RESULTS: Peripheral blood cholesterol-related indices, including Tc, low density lipoproteins (LDL) and Apo(a), were all increased, and high density lipoprotein (HDL) was decreased in CRCs. The proliferation, migration and invasion abilities of MC38 cells were enhanced, and the proportion of tumor cell apoptosis was decreased in the high cholesterol group. The expression of IL-2 and TNF-α was decreased, while IFN-γ was increased in the high cholesterol group. It indicated high cholesterol could induce exhaustion of CD8+ T cells, leading to CRC immune escape. Hypercholesterolemia damaged the ER structure of CD8+ T cells and increased the expression of ER stress molecules (CHOP and GRP78), lead to CD8+ T cell exhaustion. The expression of mitophagy-related proteins (BNIP3, PINK and Parkin) in exhausted CD8+ T cells increased at high cholesterol levels, causing mitochondrial energy disturbance. High cholesterol enhanced the colocalization of Fis1/Bap31, MFN2/cox4/HSP90B1, VAPB/PTPIP51, VDAC1/IPR3/GRP75 in ERMCs, indicated that high cholesterol promoted the intermolecular interaction between ER and mitochondrial membranes in CD8+ T cells. CONCLUSION: High cholesterol regulated the ERS-ERMC-mitophagy axis to induce the exhaustion of CD8+ T cells in CRC.

Correlation of gut microbiota with leukopenia after chemotherapy in patients with colorectal cancer.

BACKGROUND: The most common toxic side effect after chemotherapy, one of the main treatments for colorectal cancer (CRC), is myelosuppression. OBJECTIVE: To analyze the correlation between gut microbiota and leukopenia after chemotherapy in CRC patients. METHODS: Stool samples were collected from 56 healthy individuals and 55 CRC patients. According to the leukocytes levels in peripheral blood, the CRC patients were divided into hypoleukocytes group (n = 13) and normal leukocytes group (n = 42). Shannon index, Simpson index, Ace index, Chao index and Coverage index were used to analyze the diversity of gut microbiota. LDA and Student's t-test(St test) were used for analysis of differences. Six machine learning algorithms, including logistic regression (LR) algorithm, random forest (RF) algorithm, neural network (NN) algorithm, support vector machine (SVM) algorithm, catboost algorithm and gradient boosting tree algorithm, were used to construct the prediction model of gut microbiota with leukopenia after chemotherapy for CRC. RESULTS: Compared with healthy group, the microbiota alpha diversity of CRC patients was significantly decreased (p < 0.05). After analyzing the gut microbiota differences of the two groups, 15 differential bacteria, such as Bacteroides, Faecalibacterium and Streptococcus, were screened. RF prediction model had the highest accuracy, and the gut microbiota with the highest predictive value were Peptostreptococcus, Faecalibacterium, and norank_f__Ruminococcaceae, respectively. Compared with normal leukocytes group, the microbiota alpha diversity of hypoleukocytes group was significantly decreased (p < 0.05). The proportion of Escherichia-Shigella was significantly decreased in the hypoleukocytes group. After analyzing the gut microbiota differences of the two groups, 9 differential bacteria, such as Escherichia-Shigella, Fusicatenibacter and Cetobacterium, were screened. RF prediction model had the highest accuracy, and the gut microbiota with the highest predictive value were Fusicatenibacte, Cetobacterium, and Paraeggerthella. CONCLUSION: Gut microbiota is related to leukopenia after chemotherapy. The gut microbiota may provide a novel method for predicting myelosuppression after chemotherapy in CRC patients.

Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria.

BACKGROUND: The mortality of colorectal cancer is high, the malignant degree of poorly differentiated colorectal cancer is high, and the prognosis is poor. OBJECTIVE: To screen the characteristic intestinal microbiota of poorly differentiated intestinal cancer. METHODS: Fecal samples were collected from 124 patients with moderately differentiated CRC and 123 patients with poorly differentiated CRC, and the bacterial 16S rRNA V1-V4 region of the fecal samples was sequenced. Alpha diversity analysis was performed on fecal samples to assess the diversity and abundance of flora. The RDP classifier Bayesian algorithm was used to analyze the community structure. Linear discriminant analysis and Student's t test were used to screen the differences in flora. The PICRUSt1 method was used to predict the bacterial function, and six machine learning models, including logistic regression, random forest, neural network, support vector machine, CatBoost and gradient boosting decision tree, were used to construct a prediction model for the poor differentiation of colorectal cancer. RESULTS: There was no significant difference in fecal flora alpha diversity between moderately and poorly differentiated colorectal cancer (P > 0.05). The bacteria that accounted for a large proportion of patients with poorly differentiated and moderately differentiated colorectal cancer were Blautia, Escherichia-Shigella, Streptococcus, Lactobacillus, and Bacteroides. At the genus level, there were nine bacteria with high abundance in the poorly differentiated group, including Bifidobacterium, norank_f__Oscillospiraceae, Eisenbergiella, etc. There were six bacteria with high abundance in the moderately differentiated group, including Megamonas, Erysipelotrichaceae_UCG-003, Actinomyces, etc. The RF model had the highest prediction accuracy (100.00% correct). The bacteria that had the greatest variable importance in the model were Pseudoramibacter, Megamonas and Bifidobacterium. CONCLUSION: The degree of pathological differentiation of colorectal cancer was related to gut flora, and poorly differentiated colorectal cancer had some different bacterial flora, and intestinal bacteria can be used as biomarkers for predicting poorly differentiated CRC.

Screening and analysis of RNAs associated with activated memory CD4 and CD8 T cells in liver cancer.

BACKGROUND: Liver cancer is one of the most common malignant tumors in the world. T cell-mediated antitumor immune response is the basis of liver cancer immunotherapy. OBJECTIVE: To screen and analyze the RNAs associated with activated memory CD4 T cells and CD8 T cells in liver cancer. METHODS: ESTIMATE was used to calculate the stromal and immune scores of tumor samples, which were downloaded from The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) in high and low stromal and immune scores were screened, followed by functional enrichment of overlapped DEGs. We then conducted a survival analysis to identify immune-related prognostic indicators and constructed protein-protein interaction (PPI) networks and ceRNA networks. Finally, chemical small-molecule-target interaction pairs associated with liver cancer were screened. RESULTS: A total of 55,955 stromal-related DEGs and 1811 immune-related DEGs were obtained. The 1238 overlapped DEGs were enriched in 1457 biological process terms and 74 KEGG pathways. In addition, a total of 120 activated memory CD4 T cell-related genes and 309 CD8 T cell-related genes were identified. The survival analysis revealed that upregulated expression of T cell-related genes including EOMES, CST7, and CD5L indicated the favorable prognosis of liver cancer. EOMES was regulated by has-miR-23b-3p and has-miR-23b-3p was regulated by lncRNA AC104820.2 in the ceRNA network of activated memory CD4 T cell-related genes. In addition, EOMES was regulated by has-miR-23a-3p and has-miR-23a-3p was regulated by lncRNA AC000476.1 in the ceRNA network of CD8 T cells. CONCLUSION: T cell-related RNAs EOMES, CST7, CD5L, has-miR-23b-3p, and has-miR-23a-3p may be associated with the prognosis of liver cancer. And the molecular characteristics of these T cell-related genes were plotted.

Inhibitory effect of sodium butyrate on colorectal cancer cells and construction of the related molecular network.

BACKGROUND: Sodium butyrate (NaB) is produced through the fermentation of dietary fiber that is not absorbed and digested by the small intestine. PURPOSE: Here, we aimed to investigate the effects of NaB on the proliferation, invasion, and metastasis of CRC cells and their potential underlying molecular mechanism(s). METHODS: The cell counting kit-8 (CCK-8) assay and EdU assay were used to detect cell proliferation ability, flow cytometry was used to investigate the induction of apoptosis and cell cycle progression, and the scratch-wound healing and transwell assays were used to evaluate cell migration and invasion, respectively. The human CRC genome information for tissues and CRC cells treated with NaB obtained from the NCBI GEO database was reannotated and used for differential RNA analysis. Functional and pathway enrichment analyses were performed for differentially expressed lncRNAs and mRNAs. A protein-protein interaction (PPI) network for the hub genes was constructed using the Cytoscape software. Targeted miRNAs were predicted based on the lnCeDB database, and a ceRNA network was constructed using the Cytoscape software. The Kaplan-Meier method was used to analyze patient prognosis using the clinical information and exon-seq data for CRC obtained from the Broad Institute's GDAC Firehose platform. RESULTS: NaB decreased the proliferation ability of CRC cells in a dose- and time-dependent manner. The number of apoptotic CRC cells increased with the increase in NaB concentrations, and NaB induced a G1 phase block in CRC cells. Moreover, NaB suppressed the migratory and invasive capabilities of CRC cells. There were 666 differentially expressed mRNAs and 30 differentially expressed lncRNAs involved in the CRC inhibition by NaB. The PPI network and ceRNA network were constructed based on the differentially expressed mRNAs and lncRNAs. Three differentially expressed mRNAs, including HMGA2, LOXL2, and ST7, were significantly correlated with the prognosis of CRC. CONCLUSION: NaB induces the apoptosis and inhibition of CRC cell proliferation, invasion, and metastasis by modulating complex molecular networks. RNA prediction and molecular network construction need to be the focus of further research in this direction.

Effects of postoperative adjuvant chemotherapy and palliative chemotherapy on the gut microbiome in colorectal cancer.

BACKGROUND: The gut microbiome changes are related to the colorectal cancer (CRC). Chemotherapy is one of the main treatment methods for CRC. PURPOSE: To explore the effect of chemotherapy on the gut bacteria and fungi in CRC. METHODS: Total of 11 advanced CRC patients treated with the FOLFIRI regimen, 15 postoperative CRC patients treated with the XELOX regimen, and corresponding CRC patients without surgery and chemotherapy were recruited. The 16S ribosomal RNA and ITS sequences were sequenced, and bioinformatics analysis was executed to screen for the distinctive gut microbiome. RESULTS: The abundances of Veillonella, Humicola, Tremellomycetes and Malassezia were increased in postoperative CRC patients treated with the XELOX regimen. The abundances of Faecalibacterium, Clostridiales, phascolarctobacterium, Humicola and Rhodotorula were decreased, and the abundances of Candida, Magnusiomyces, Tremellomycetes, Dipodascaceae, Saccharomycetales, Malassezia and Lentinula were increased in advanced CRC patients treated with the FOLFIRI regimen. The abundances of Humicola, Rhodotorula, and Magnusiomyces were decreased, and the abundances of Candida, Tremellomycetes, Dipodascaceae, Saccharomycetales, Malassezia and Lentinula were increased in advanced CRC patients treated with the FOLFIRI regimen combined with cetuximab compared with those treated with the FOLFIRI regimen alone. CONCLUSIONS: The community structure of gut bacteria and fungi changes in chemotherapy on CRCs.

Analysis of T lymphocyte-related biomarkers in pancreatic cancer.

BACKGROUND: Pancreatic cancer remains one of the most lethal cancers. OBJECTIVE: This study aimed to analyze T cell-related biomarkers and their molecular network in pancreatic cancer. METHODS: RNAseq sequencing data and clinical data of pancreatic cancer were obtained from TCGA database. The STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm was used to screen the DEGs related to the tumor immune cells. The pearson correlation analysis were used to analyze the relationships between DEGs and T cells. Additionally, the T cell-related DEGs were subjected to protein-protein interaction, competing endogenous RNA (ceRNA), and chemical small molecule-target network construction. Furthermore, the prognosis-associated DEGs were screened. RESULTS: A total of 412 stromal score-associated and 312 immune score-associated DEGs were obtained. From these DEGs, 50 CD4+ T cell-related genes and 13 CD8+ T cell-related genes were selected. The PPI networks associated with immune cell-related genes were constructed and found that CD22, SELL, and OLR1 had higher degrees in the PPI network. The number of ceRNA regulatory relation pairs obtained from CD4+ T cells and CD8+ T cells were 59 and 48, respectively. Additionally, both CD4+ T cell- and CD8+ T cell-related genes predicted 29 small molecules. CXCL9 and GIMAP7 were screened out from CD4+ T cell-related genes, which were related with the survival of pancreatic cancer. CONCLUSION: We mapped T cell-related gene profile in pancreatic cancer and constructed their potential regulatory network.

Analysis of prognosis, genome, microbiome, and microbial metabolome in different sites of colorectal cancer.

BACKGROUND: The colorectum includes ascending colon, transverse colon, descending colon, sigmoid colon, and rectum. Different sites of colorectal cancer (CRC) are different in many aspects, including clinical symptoms, biological behaviour, and prognosis. PURPOSE: This study aimed to analyse prognosis, genes, bacteria, fungi, and microbial metabolome in different sites of CRC. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database and STAT were used to statistically describe and analyse the prognosis in different sites of CRC. RNA sequences of CRC from Broad Institute's GDAC Firehose were re-annotated and reanalysed based on different sites using weighted gene co-expression network analysis (WGCNA). The Kaplan-Meier method was used to analyse the prognosis and Cytoscape was used to construct a drug-target network based on DGIdb databases. Bacterial 16S V3-V4 and fungal ITS V3-V4 ribosomal RNA genes of stool samples were sequenced. Gas chromatography/mass spectrometry (GS/MS) was performed to detect the microbial metabolites in stool samples. Bioinformatics analysis was performed to compare distinct gut microorganisms and microbial metabolites between rectal and sigmoid cancers. RESULTS: The prognosis in CRC with different sites is significantly different. The closer to the anus predicted longer survival time. The difference between genes and co-expression pairs in CRC with different sites were constructed. The relative abundance of 112 mRNAs and 26 lncRNAs correlated with the sites of CRC were listed. Nine differentially expressed genes at different sites of CRC were correlated with prognosis. A drug-gene interaction network contained 227 drug-gene pairs were built. The relative abundance of gut bacteria and gut fungus, and the content of microbe-related metabolites were statistically different between rectal and sigmoid cancers. CONCLUSIONS: There are many differences in prognosis, genome, drug targets, gut microbiome, and microbial metabolome in different colorectal cancer sites. These findings may improve our understanding of the role of the CRC sites in personalized and precision medicine.

Relationship between intestinal microorganisms and T lymphocytes in colorectal cancer.

Colorectal cancer (CRC) is a common type of malignant cancer worldwide. Recent studies have identified the gut microbiota as the origin of CRC, and T lymphocyte-mediated immune functions have been shown to play an important role in this disease. By summarizing previous literature, we found that Fusobacterium nucleatum may protect CRC from immune cell attack by inhibiting T cells and influencing the production of many chemokines and cytokines. Some bacterial metabolites and probiotics have been shown to participate in the regulation of CRC through T cell-mediated molecular pathways. To visualize the relevant data, an association network of intestinal microorganisms and T lymphocytes associated with CRC was constructed. This work may provide direction for - and insight into - further research on the relationship between intestinal microorganisms and T lymphocytes in CRC.

Advances in bacteria-based drug delivery systems for anti-tumor therapy.

In recent years, bacteria have gained considerable attention as a promising drug carrier that is critical in improving the effectiveness and reducing the side effects of anti-tumor drugs. Drug carriers can be utilised in various forms, including magnetotactic bacteria, bacterial biohybrids, minicells, bacterial ghosts and bacterial spores. Additionally, functionalised and engineered bacteria obtained through gene engineering and surface modification could provide enhanced capabilities for drug delivery. This review summarises the current studies on bacteria-based drug delivery systems for anti-tumor therapy and discusses the prospects and challenges of bacteria as drug carriers. Furthermore, our findings aim to provide new directions and guidance for the research on bacteria-based drug systems.

Trace elements in pancreatic cancer.

BACKGROUND: Pancreatic cancer (PCA) is an extremely aggressive malignant cancer with an increasing incidence and a low five-year survival rate. The main reason for this high mortality is that most patients are diagnosed with PCA at an advanced stage, missing early treatment options and opportunities. As important nutrients of the human body, trace elements play an important role in maintaining normal physiological functions. Moreover, trace elements are closely related to many diseases, including PCA. REVIEW: This review systematically summarizes the latest research progress on selenium, copper, arsenic, and manganese in PCA, elucidates their application in PCA, and provides a new reference for the prevention, diagnosis and treatment of PCA. CONCLUSION: Trace elements such as selenium, copper, arsenic and manganese are playing an important role in the risk, pathogenesis, diagnosis and treatment of PCA. Meanwhile, they have a certain inhibitory effect on PCA, the mechanism mainly includes: promoting ferroptosis, inducing apoptosis, inhibiting metastasis, and inhibiting excessive proliferation.

Identification of enterotype and its predictive value for patients with colorectal cancer.

BACKGROUND: Gut microbiota dysbiosis involved in the pathogenesis of colorectal cancer (CRC). The characteristics of enterotypes in CRC development have not been determined. OBJECTIVE: To characterize the gut microbiota of healthy, adenoma, and CRC subjects based on enterotype. METHODS: The 16 S rRNA sequencing data from 315 newly sequenced individuals and three previously published datasets were collected, providing total data for 367 healthy, 320 adenomas, and 415 CRC subjects. Enterotypes were analyzed for all samples, and differences in microbiota composition across subjects with different disease states in each enterotype were determined. The predictive values of a random forest classifier based on enterotype in distinguishing healthy, adenoma, and CRC subjects were evaluated and validated. RESULTS: Subjects were classified into one of three enterotypes, namely, Bacteroide- (BA_E), Blautia- (BL_E), and Streptococcus- (S_E) dominated clusters. The taxonomic profiles of these three enterotypes differed among the healthy, adenoma, and CRC cohorts. BA_E group was enriched with Bacteroides and Blautia; BL_E group was enriched by Blautia and Coprococcus; S_E was enriched by Streptococcus and Ruminococcus. Relative abundances of these genera varying among the three human cohorts. In training and validation sets, the S_E cluster showed better performance in distinguishing among CRC patients, adenoma patients, and healthy controls, as well as between CRC and non-CRC individuals, than the other two clusters. CONCLUSION: This study provides the first evidence to indicate that changes in the microbial composition of enterotypes are associated with disease status, thereby highlighting the diagnostic potential of enterotypes in the treatment of adenoma and CRC.

Advances in reprogramming of energy metabolism in tumor T cells.

Cancer is a leading cause of human death worldwide, and the modulation of the metabolic properties of T cells employed in cancer immunotherapy holds great promise for combating cancer. As a crucial factor, energy metabolism influences the activation, proliferation, and function of T cells, and thus metabolic reprogramming of T cells is a unique research perspective in cancer immunology. Special conditions within the tumor microenvironment and high-energy demands lead to alterations in the energy metabolism of T cells. In-depth research on the reprogramming of energy metabolism in T cells can reveal the mechanisms underlying tumor immune tolerance and provide important clues for the development of new tumor immunotherapy strategies as well. Therefore, the study of T cell energy metabolism has important clinical significance and potential applications. In the study, the current achievements in the reprogramming of T cell energy metabolism were reviewed. Then, the influencing factors associated with T cell energy metabolism were introduced. In addition, T cell energy metabolism in cancer immunotherapy was summarized, which highlighted its potential significance in enhancing T cell function and therapeutic outcomes. In summary, energy exhaustion of T cells leads to functional exhaustion, thus resulting in immune evasion by cancer cells. A better understanding of reprogramming of T cell energy metabolism may enable immunotherapy to combat cancer and holds promise for optimizing and enhancing existing therapeutic approaches.

Age and aging process alter the gut microbes.

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_A massiliensis, Lubbockvirus and Enterocloster clostridioformis decreased with age and the abundance of Klebsiella variicola and Prevotella 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.

Synergistic effect of sodium butyrate and oxaliplatin on colorectal cancer.

BACKGROUND: Oxaliplatin (OXA) is a chemotherapy agent commonly used in the treatment of colorectal cancer (CRC). Sodium butyrate (NaB) has an antitumor effect. METHODS: In total, 30 patients in stage III who completed 8 cycles of chemotherapy regimens were recruited for this study. The patients were divided into good and bad groups based on the chemotherapy efficacy. Gas chromatography-mass spectrometry (GC/MS) was used to detect microbial metabolites in stool samples from CRC patients. Cell counting kit-8 (CCK-8), Annexin-V APC/7-AAD double staining, Transwell assays, scratch-wound assays, and EdU assays were used to detect cell proliferation, apoptosis, invasion and migration, respectively. Fluoroelectron microscopy was used to observe the cell structures. To verify the inhibitory effect of NaB and OXA at animal level, a subcutaneous transplanted tumor model was established. Finally, 16S sequencing technology was used to detect intestinal bacteria. GC-MS was used to detect metabolites in mouse stools. RESULTS: NaB was a differential metabolite that affected the efficacy of OXA. NAB and oxaliplatin can synergically inhibit cell proliferation, migration and invasion, and induce cell apoptosis. Animal experiments confirmed the inhibitory effect of oxaliplatin and sodium butyrate on tumor in mice. In addition, the intestinal microbe detection and microbial metabolite detection in fecal samples from mice showed significant differences between butyrate-producing bacteria and NaB. CONCLUSION: NaB and OXA can synergistically inhibit the proliferation, invasion and metastasis of CRC cells and promote the apoptosis of CRC cells. NaB, as an OXA synergist, has the potential to become a new clinical adjuvant in CRC chemotherapy.

Gut microbes involvement in gastrointestinal cancers through redox regulation.

Gastrointestinal (GI) cancers are among the most common and lethal cancers worldwide. GI microbes play an important role in the occurrence and development of GI cancers. The common mechanisms by which GI microbes may lead to the occurrence and development of cancer include the instability of the microbial internal environment, secretion of cancer-related metabolites, and destabilization of the GI mucosal barrier. In recent years, many studies have found that the relationship between GI microbes and the development of cancer is closely associated with the GI redox level. Redox instability associated with GI microbes may induce oxidative stress, DNA damage, cumulative gene mutation, protein dysfunction and abnormal lipid metabolism in GI cells. Redox-related metabolites of GI microbes, such as short-chain fatty acids, hydrogen sulfide and nitric oxide, which are involved in cancer, may also influence GI redox levels. This paper reviews the redox reactions of GI cells regulated by microorganisms and their metabolites, as well as redox reactions in the cancer-related GI microbes themselves. This study provides a new perspective for the prevention and treatment of GI cancers.

Colorectal cancer and gut viruses: a visualized analysis based on CiteSpace knowledge graph.

Background: Gut microbiome is a complex community of microbes present in the human gut and plays an important role in the occurrence and progression of colorectal cancer (CRC). However, the relationship between virus and CRC has not been fully understood. Objective: To explore the hot spots and research trends in the field of CRC and virus. Methods: By using the bibliometric analysis tool CiteSpace and based on the articles of the Web of Science Core Collection (WoSCC) database, the country, institution, highly cited literature, keywords and so on were visually analyzed. Results: A total of 356 research articles on CRC from 2001 to 2023 were thoroughly analyzed. The USA and China have made the largest contribution in the field of virus and CRC. The Helmholtz Association published the most papers. There were relatively few cooperations among institutions from different countries. The results of keyword cluster analysis proved that the literature on the relationship between human cytomegalovirus (CMV) and CRC was the most widely studied aspect in this field. "Gut microbiota," "inflammatory bowel disease," "hepatitis b virus," and "human papillomavirus infection" are the current research hotspots; "oncolytic virus," "apoptosis," and "gut microbiome" are the recent research frontiers and should be paid closer attention. Conclusion: By using CiteSpace bibliometric software, the visual analysis reflected the research trends and hot topics of virus and CRC. In addition, the prevalence and mechanism of specific virus on CRC were also reviewed, which provides valuable references for future CRC research.

Application and progress of highcontent imaging in molecular biology.

Humans have adopted many different methods to explore matter imaging, among which high content imaging (HCI) could conduct automated imaging analysis of cells while maintaining its structural and functional integrity. Meanwhile, as one of the most important research tools for diagnosing human diseases, HCI is widely used in the frontier of medical research, and its future application has attracted researchers' great interests. Here, the meaning of HCI was briefly explained, the history of optical imaging and the birth of HCI were described, and the experimental methods of HCI were described. Furthermore, the directions of the application of HCI were highlighted in five aspects: protein localization changes, gene identification, chemical and genetic analysis, microbiology, and drug discovery. Most importantly, some challenges and future directions of HCI were discussed, and the application and optimization of HCI were expected to be further explored.