Alpha-1-acid glycoprotein as potential biomarker for alpha-fetoprotein-low hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>The outcome of patients with hepatocellular carcinoma (HCC) remains poor because of late diagnosis. We determined the performances of α -1-acid glycoprotein (AAG) and des-γ-carboxy prothrombin (DCP) for the diagnosis of HCC, especially for α-fetoprotein (AFP)-low HCC.</p> <p>Methods</p> <p>Of the 220 patients included in this retrospective study, 124 had HCC, and 61 (49%) of these were AFP-low HCC (AFP ≤ 20 ng/mL). The remaining 96 patients, including 49 with chronic hepatitis B or C and 47 with cirrhosis, were considered as control. Plasma AAG was analyzed using high performance liquid chromatography (HPLC) and confirmed using Western blot technique.</p> <p>Results</p> <p>When all patients with HCC were evaluated, the area under receiver operating characteristic (ROC) curves for AAG (0.94, 95% CI: 0.91-0.97) and DCP (0.92, 95% CI: 0.88-0.95) were similar (<it>P </it>= 0.40). AAG had better area under ROC curve (0.96, 95% CI: 0.94-0.99) than DCP (0.87, 95% CI: 0.81-0.93) for AFP-low HCC (<it>P </it>< 0.05). At the specificity 95%, the sensitivity of AAG was higher in AFP-low HCC than in AFP-high HCC (82% and 62%, respectively). In contrast, higher sensitivity was obtained from DCP in discriminating HCC patients with low AFP than that in high AFP (57% and 90%, respectively).</p> <p>Conclusion</p> <p>Our cross-sectional study showed that AAG was better performance in diagnosing HCC patients with low AFP, while DCP did better in those with high AFP.</p

Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets

Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92-0.96; p &lt; 0.001; n = 106 early-stage validation cohort, accuracy, 81%; AUC, 0.89; 95% CI, 0.83-0.95; p &lt; 0.001), independent of age of the individuals, smoking habits, whole-blood storage time, and various inflammatory conditions. PSO enabled selection of gene panels to diagnose cancer from TEPs, suggesting that swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources