Aim The aim was to explore the diagnostic value of computed tomographic colonography (CTC) compared with conventional colonoscopy in individuals at high risk for colorectal cancer. Method PubMed, Embase, the Cochrane Library and the Web of Science were searched by two independent reviewers for potentially eligible studies published up to 31 October 2018 that were based on a per-patient analysis. STATA, META-DISC and REVMAN were used to perform this meta-analysis. A random-effect model was used, and a subgroup analysis was conducted to explore the sources of heterogeneity. Results A total of 14 full-text articles, involving 3578 patients, were included in this meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and the area under the summary receiver operating characteristic curve of CTC for detecting polyps ≥ 6 mm were 0.87 (95% CI 0.83-0.90), 0.90 (95% CI 0.86-0.93), 9.08 (95% CI 6.28-13.13), 0.14 (95% CI 0.11-0.18) and 0.94 (95% CI 0.92-0.96), respectively. For polyps ≥ 10 mm, the pooled sensitivity, specificity, positive likelihood ratio and negative likelihood ratio of CTC were 0.91 (95% CI 0.86-0.94), 0.98 (95% CI 0.95-0.99), 40.36 (95% CI 19.16-85.03), 0.90 (95% CI 0.06-0.14) and 0.98 (95% CI 0.96-0.99), respectively. Conclusion In this meta-analysis, CTC had high diagnostic accuracy for detecting polyps ≥ 6 mm and ≥ 10 mm in patients at high risk of developing colorectal cancer and it had a higher sensitivity and specificity for detecting polyps ≥ 10 mm than polyps ≥ 6 mm. However, the results should be used cautiously due to the significant heterogeneity.

The hypoxic tumor microenvironment and long noncoding RNAs (lncRNAs) are pivotal in cancer progression and correlate with the survival outcome of patients. However, the role of hypoxia-related lncRNAs (HRLs) in colorectal cancer (CRC) development remains largely unknown. Herein, we developed a hypoxia-related lncRNA signature to predict patients’ survival and immune infiltration. The RNA-sequencing data of 500 CRC patients were obtained from The Cancer Genome Atlas (TCGA) dataset, and HRLs were selected using Pearson’s analysis. Next, the Cox regression analysis was applied to construct a risk signature consisting of 9 HRLs. This signature could predict the overall survival (OS) of CRC patients with high accuracy in training, validation, and entire cohort. This signature was an independent risk factor and exerted predictive ability in different subgroups. Functional analysis revealed different molecular features between high- and low-risk groups. A series of drugs including cisplatin showed different sensitivities between the two groups. The expression pattern of immune checkpoints was also distinct between the two clusters in this model. Furthermore, the high-risk group had higher immune, stromal, and ESTIMATE score and a more repressive immune microenvironment than the low-risk group. Moreover, MYOSLID, one of the lncRNAs in this signature, could significantly regulate the proliferation, invasion, and metastasis of CRC.