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.

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.

ObjectiveThis study aimed to characterize the tumor-infiltrating T cells in moderately differentiated colorectal cancer.MethodsUsing 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.ResultsA 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.ConclusionsWe characterized the T cell populations in the CRC tumor tissue and peripheral blood.

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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.