Objective. At present, the relationship between autophagosomes and the prognosis of various cancers has become a subject of active investigation. A series of studies have demonstrated the correlation between autophagy microtubule‐associated protein light chain 3 (LC‐3), Beclin‐1, and colorectal cancer (CRC). Since autophagy has dual regulatory roles in tumors, the results of this correlation are also uncertain. Hence, we summarized the relationship between Beclin‐1, LC‐3, and CRC using systematic reviews and meta‐analysis to clarify their prognostic significance in it. Methods. PubMed, EMBASE, Cochrane Library, and Web of Science databases were searched online up to April 1, 2019. The quality of the involving studies was assessed against the Newcastle‐Ottawa Scale (NOS). Pooled hazard ratio (HR) and 95% confidence interval (CI) in a fixed or random effects model were used to assess the strength of correlation between Beclin‐1, LC‐3, and CRC. Results. A total of 9 articles were collected, involving 2,297 patients. Most literatures scored more than 6 points, suggesting that the quality of our including research was acceptable. Our finding suggested that the expression of Beclin‐1 was not associated with overall survival (HR = 0.68, 95% CI (0.31–1.52), P = 0.351). Nonetheless, LC‐3 expression exerted significant impact on OS (HR = 0.51, 95% CI (0.35–0.74), P < 0.05). Subgroup analysis exhibited that Beclin‐1 expression was associated with OS at TNM stage III (HR = 0.04, 95% CI = 0.02–0.08, P < 0.05), surgical treatment (HR = 1.53, 95% CI (1.15–2.02), P = 0.003), and comprehensive treatment (HR = 0.27 95% CI (0.08–0.92), P = 0.036), respectively. Similarly, the results showed the increased LC‐3 expression in CRC was related to OS in multivariate analyses (HR = 0.44, 95% CI (0.34–0.57), P < 0.05), stages (HR = 0.51, 95% CI (0.35–0.74), P < 0.05), and comprehensive treatment (HR = 0.44, 95% CI (0.34–0.57), P < 0.05). Conclusions. Autophagy‐related proteins of LC‐3 might be an important marker of CRC progression. However, since the number of the original studies was limited, more well‐designed, large‐scale, high‐quality studies are warranted to provide more convincing and reliable information.

Aim: To characterize the epidemiology of appendiceal mucinous adenocarcinoma. Methods: Prognostic factors were evaluated with univariate and multivariate analyses. The results were used to generate...

Background. Severe asthma (SA), a heterogeneous inflammatory disease characterized by immune cell infiltration, is particularly difficult to treat and manage. The airway epithelium is an important tissue in regulating innate and adaptive immunity, and targeting airway epithelial cell may contribute to improving the efficacy of asthma therapy. Methods. Bioinformatics methods were utilized to identify the hub genes and signaling pathways involved in SA. Experiments were performed to determine whether these hub genes and signaling pathways were affected by the differences in immune cell infiltration. Results. The weighted gene coexpression network analysis identified 14 coexpression modules, among which the blue and salmon modules exhibited the strongest associations with SA. The blue module was mainly enriched in actomyosin structure organization and was associated with regulating stem cell pluripotency signaling pathways. The salmon module was mainly involved in cornification, skin development, and glycosphingolipid biosynthesis-lacto and neolacto series. The protein-protein interaction network and module analysis identified 11 hub genes in the key modules. The CIBERSORTx algorithm revealed statistically significant differences in CD8+ T cells ( ), T follicular helper cells ( ), resting mast cells ( ), and neutrophils ( ) between patients with SA and mild-moderate asthma patients. Pearson’s correlation analysis identified 11 genes that were significantly associated with a variety of immune cells. We further predicted the utility of some potential drugs and validated our results in external datasets. Conclusion. Our results may help provide a better understanding of the relationship between the airway epithelial transcriptome and clinical data of SA. And this study will help to guide the development of SA-targeted molecular therapy.