Long non-coding RNAs (lncRNAs) have recently emerged as vital players in tumor biology with potential value in cancer diagnosis, prognosis, and therapeutics. The lncRNA HULC (highly up-regulated in liver cancer) is increased in many malignancies, yet its serum expression profile and clinical value in gastric cancer (GC) patients remain unclear. Quantitative real-time polymerase chain reaction (RT-qPCR) for large-scale analysis of the serum expression of HULC in GC patients reliably detected circulating HULC and revealed that it is upregulated in GC patients. A high serum HULC level correlated with tumor size, lymph node metastasis, distant metastasis, tumor-node-metastasis stage, and H. pylori infection. The area under the ROC curve for HULC was up to 0.888, which was higher than that for CEA (0.694) and CA72-4 (0.514). Follow-up detection and Kaplan-Meier curve analysis revealed HULC is a good predictor of GC prognosis. Our present study indicates that circulating HULC may represent a novel serum tumor marker for early diagnosis and monitoring progression and prognosis of GC.

Background Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

The expression levels of serum PCAT-1 in MM patients were significantly higher than that in healthy controls, suggesting that it may be useful in the auxiliary diagnosis of MM.

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BackgroundIntracavitary electrocardiogram (IC ECG) guidance emerges as a new technique for peripherally inserted central catheters (PICCs) placement and demonstrates many potential advantages in recent observational studies.AimsTo determine whether IC ECG-guided PICCs provide more accurate positioning of catheter tips compared to conventional anatomical landmarks in patients with cancer undergoing chemotherapy.MethodsIn this multicenter, open-label, randomized controlled study (ClinicalTrials.gov number, NCT02409589), a total of 1,007 adult patients were assigned to receive either IC ECG guidance (n = 500) or anatomical landmark guidance (n = 507) for PICC positioning. The confirmative catheter tip positioning x-ray data were centrally interpreted by independent radiologists. All reported analyses in the overall population were performed on an intention-to-treat basis. Analyses of pre-specified subgroups and a selected large subpopulation were conducted to explore consistency and accuracy.ResultsIn the IC ECG-guided group, the first-attempt success rate was 89.2% (95% confidence interval [CI], 86.5% to 91.9%), which was significantly higher than 77.4% (95% CI, 73.7% to 81.0%) in the anatomical landmark group (P < 0.0001). This trend of superiority of IC ECG guidance was consistently noted in almost all prespecified patient subgroups and two selected large subpopulations, even when using optimal target rates for measurement. In contrast, the superiority nearly disappeared when PICCs were used via the left instead of right arms (interaction P-value = 0.021). No catheter-related adverse events were reported during the PICC intra-procedures in either group.ConclusionsOur findings indicated that the IC ECG-guided method had a more favorable positioning accuracy versus traditional anatomical landmarks for PICC placement in adult patients with cancer undergoing chemotherapy. Furthermore, there were no significant safety concerns reported for catheterization using the two techniques.