Colorectal cancer (CRC) is the third most diagnosed cancer worldwide due to its high difficulty in early diagnosis, high mortality rate and short life span. Recent publications have demonstrated the involvement of the commensal gut microbiota in the initiation, progression and chemoresistance of CRC. However, this microbial community has not been explored within CRC patients after anti-cancer treatments. To this end, we performed next generation sequencing-based metagenomic analysis to determine the composition of the microbiota in CRC patients after anti-cancer treatments. The microbial 16S rRNA genes were analyzed from a total of 69 fecal samples from four clinical groups, including healthy individuals, CRC patients, and CRC patients treated with surgery or chemotherapy. The findings suggested that surgery greatly reduced the bacterial diversity of the microbiota in CRC patients. Moreover, Fusobacterium nucleatum were shown to confer chemoresistance during CRC therapy, and certain bacterial strains or genera, such as the genus Sutterella and species Veillonella dispar, were specifically associated with CRC patients who were treated with chemotherapeutic cocktails, suggesting their potential relationships with chemoresistance. These candidate bacterial genera or strains may have the ability to enhance the dosage response to conventional chemotherapeutic cocktails or reduce the side effects of these cocktails. A combination of common CRC risk factors, such as age, gender and BMI, identified in this study improved our understanding of the microbial community and its compositional variation during anti-cancer treatments. However, the underlying mechanisms of these microbial candidates remain to be investigated in animal models. Taken together, the findings of this study indicate that fecal microbiome-based approaches may provide additional methods for monitoring and optimizing anti-cancer treatments.

Background: Cholangiocarcinoma (CCA) is the second most common fatal primary hepatobiliary malignant carcinoma, characterized by early invasion and extremely poor outcomes. It is therefore necessary to identify a novel biomarker to better diagnose CAA and predict its prognosis. Recently, emerging evidence has revealed that some lncRNAs play an important role in the tumorigenesis and progression of CAA. In order to support this search for novel diagnostic and prognostic biomarkers for CAA, we conducted a meta-analysis to analyze the published association between lncRNA expression and its clinical value in CAA. Methods: Eligible studies were pooled and analyzed according to our inclusion and exclusion criteria after a comprehensive literature search. Stata 14.0 software was used to analyze the data from relevant studies and to construct a forest plot. Different effect sizes were selected for the meta-analysis. Results: In total, 24 publications were included in this meta-analysis. After review of their full-text, 16 articles studied the association between lncRNAs and clinicopathological characteristics, 2 discussing diagnosis and 16 discussing prognosis. Our results showed that overexpression of CCAT1 was significantly correlated with tumor stage (I + II vs III + IV) (OR, 4.99; 95% CI 2.77–8.99; P<0.001) and lymph node metastasis in CCA (OR, 4.75; 95% CI 2.65–8.52; P<0.001). Furthermore, elevated CCAT lncRNA family expression predicted a shorter overall survival (HR, 2.09; 95% CI 1.17–3.00; P<0.001), especially CCAT2. Upregulation of CCAT2 was also obviously associated with tumor stage in CCA (OR, 5.29; 95% CI 2.64–10.58; P=0.001). Conclusion: This is the first meta-analysis to assess the relationship between expression of lncRNAs and the clinical values of patients with CCA. lncRNAs can function as potential molecular biomarkers of the clinicopathology and prognosis of CCA.

Background: It is widely acknowledged that the molecular biological characteristics of diffuse-type gastric cancer are different from intestinal-type gastric cancer. Notwithstanding that significant progress in high-throughput sequencing technology has been made, there is a paucity of effective prognostic biomarkers for diffuse gastric cancer for clinical practice. Methods: We downloaded four GEO datasets (GSE22377, GSE38749, GSE47007 and GSE62254) to establish and validate a prognostic two-gene signature for diffuse gastric cancer. The TGCA-STAD dataset was used for external validation. The optimal gene signature was established by using Cox regression analysis. Receiver operating characteristic (ROC) methodology was used to find the best prognostic model. Gene set enrichment analysis was used to analyze the possible signaling pathways of the two genes (MEF2C and TRIM15). Results: A total of four differently expressed genes (DEGs) (two upregulated and two downregulated) were identified. After a comprehensive analysis, two DEGs (MEF2C and TRIM15) were utilized to construct a prognostic model. A prognostic prediction model was constructed according to T stage, N stage, M stage and the expression of MEF2C and TRIM15. The area under the time-dependent receiver operator characteristic was used to evaluate the performance of the prognosis model in the GSE62254 dataset. Conclusions: We demonstrated that MEF2C and TRIM15 might be key genes. We also established a prognostic nomogram based on the two-gene signature that yielded a good performance for predicting overall survival in diffuse-type gastric cancer.

Gastric cancer (GC) is one of the most common cancers globally. There are currently few effective chemotherapeutic drugs available for GC patients. The inhibitors of phosphatidylinositol kinase containing an FYVE finger structure (PIKfyve) have shown significant anticancer effects in several types of cancers, but their effectiveness in GC remains unknown. In this study, we investigate the effect of APY0201, an inhibitor of PIKfyve, on GC tumor growth and its mechanism of action. It was found that APY0201 inhibited GC cell proliferation in in vitro GC cell model, organoid model, and in vivo xenograft tumor model. Through analyzing cell autophagy, we found that APY0201 might block autophagic flux by impairing lysosome degradation function of GC cells, inducing the accumulation of autophagosomes, thus causing the inhibition of GC cell proliferation. We also found that APY0201 induced G1/S phase arrest in GC cells. Importantly, APY0201 was also effective in inducing repression of autophagy and cell cycle arrest in the mouse tumor xenograft. Our results suggest that APY0201 could be a new promising therapeutic option for GC.