Background/Aims: Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and the second leading cause of cancer-related deaths worldwide. Thus, methods for early diagnosis of CRC are urgently needed. We aimed to identify potential long non-coding RNAs (lncRNAs) in circulatory exosomes that may serve as biomarkers for the detection of early-stage CRC. Methods: Exosomes from the plasma of CRC patients (n = 50) and healthy individuals (n = 50) were isolated by ultracentrifugation, followed by extraction of total exosomal RNAs using TRIzol reagent. Microarray analysis was used for exosomal lncRNA profiling in the two groups, and real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to determine the expression level of lncRNAs in all patients and healthy subjects. Results: The expression of six lncRNAs (LNCV6_116109, LNCV6_98390, LNCV6_38772, LNCV_108266, LNCV6_84003, and LNCV6_98602) was found to be significantly up-regulated in CRC patients compared with that in healthy individuals by qRT-PCR. The receiver operating characteristic curve was used to verify their diagnostic accuracy. The values of the area under the curve for these lncRNAs were 0.770 (LNCV6_116109), 0.7500 (LNCV6_98390), 0.6500 (LNCV6_38772), 0.6900 (LNCV_108266), 0.7500 (LNCV6_84003), and 0.7200 (LNCV6_98602). Conclusion: Our study suggested that the expression of these six exosomal lncRNAs (LNCV6_116109, LNCV6_98390, LNCV6_38772, LNCV_108266, LNCV6_84003, and LNCV6_98602) was significantly up-regulated in the plasma of CRC patients, and that they may serve as potential non-invasive biomarkers for early diagnosis of CRC.

Sorafenib demonstrated good efficacy and acceptable tolerability in treating an advanced HCC patient population, with or without prior treatment. The presence of ascites or distant metastasis predicted poorer OS, and the presence of adverse effects predicted improved OS.

Adjuvant interferon (IFN) therapy following curative treatment for hepatocellular carcinoma (HCC) has been extensively investigated; however, the clinical benefits with different hepatitis backgrounds remain unclear. Medline, Embase, PubMed and the Cochrane Library databases were searched to identify randomized trials and cohort studies that enrolled HCC patients who received curative surgery or ablation therapy followed by IFN and control subjects; the studies were required to include data on early or late recurrence and mortality rates of HCC. Hepatitis B virus (HBV) associated with HCC (HBV-HCC) and hepatitis C virus (HCV) associated with HCC (HCV-HCC) were separately analyzed and recurrence, mortality and clinicopathological factors were compared. A total of 14 studies (9 randomized trials and 5 cohort studies, including 1,385 patients in total) were eligible for meta-analysis. IFN was found to decrease mortality and early recurrence rates, but exerted no effect on late recurrence rate. The effect of IFN differed between HBV-HCC and HCV-HCC cases. In HCV-HCC, IFN significantly reduced mortality as well as recurrence rates. However, in HBV-HCC patients, IFN reduced mortality rather than recurrence rates, although it also reduced the recurrence rate in certain subgroups. In conclusion, the effect of adjuvant IFN on postoperative recurrence differed between HBV-HCC and HCV-HCC cases; therefore, different strategies with adjuvant IFN should be used to treat HCC with different hepatitis backgrounds.

Colonoscopy is commonly used to screen for colorectal cancer (CRC). We develop a deep learning model called CRCNet for optical diagnosis of CRC by training on 464,105 images from 12,179 patients and test its performance on 2263 patients from three independent datasets. At the patient-level, CRCNet achieves an area under the precision-recall curve (AUPRC) of 0.882 (95% CI: 0.828–0.931), 0.874 (0.820–0.926) and 0.867 (0.795–0.923). CRCNet exceeds average endoscopists performance on recall rate across two test sets (91.3% versus 83.8%; two-sided t-test, p < 0.001 and 96.5% versus 90.3%; p = 0.006) and precision for one test set (93.7% versus 83.8%; p = 0.02), while obtains comparable recall rate on one test set and precision on the other two. At the image-level, CRCNet achieves an AUPRC of 0.990 (0.987–0.993), 0.991 (0.987–0.995), and 0.997 (0.995–0.999). Our study warrants further investigation of CRCNet by prospective clinical trials.