Chao Cui, Shuai Gao, Jingfei Shi, Kai Wang. Gut and Liver -0001;0:. https://doi.org/10.5009/gnl240539

To investigate the potential of promoter methylation of two tumor suppressor genes (TSGs) as biomarkers for hepatocellular carcinoma (HCC). A total of 189 subjects were included in this retrospective cohort, which contained 121 HCC patients without any history of curative treatment, 37 patients with chronic hepatitis B (CHB), and 31 normal controls (NCs). DNA samples were extracted from 400 μL of serum of each subject and then modified using bisulfite treatment. Methylation of the promoters of the TSGs (metallothionein 1M, MT1M; and metallothionein 1G, MT1G) was determined using methylation-specific polymerase chain reaction. The diagnostic value of combined MT1M and MT1G promoter methylation was evaluated using the area under the receiver operating characteristic curves. Our results indicated that the methylation status of serum MT1M (48.8%, 59/121) and MT1G (70.2%, 85/121) promoters in the HCC group was significantly higher than that in the CHB group (MT1M 5.4%, 2/37, P < 0.001; MT1G 16.2%, 6/37, P < 0.001) and NC group (MT1M 6.5%, 2/31, P < 0.001; MT1G 12.9%, 4/27, P < 0.001). Aberrant serum MT1M promoter methylation gave higher specificity to discriminate HCC from CHB (94.6%) and NCs (93.5%), whereas combined methylation of serum MT1M and MT1G promoters showed higher diagnostic sensitivity (90.9%), suggesting that they are potential markers for noninvasive detection of HCC. Furthermore, MT1M promoter methylation was positively correlated with tumor size (rs = 0.321, P < 0.001), and HCC patients with both MT1M and MT1G promoter methylation tended to show a higher incidence of vascular invasion or metastasis (P = 0.018). MT1M and MT1G promoter methylation may be used as serum biomarkers for noninvasive detection of HCC.

Background: G-protein-coupled bile acid receptor Gpbar1 (TGR5) is a newly identified liver tumor suppressor in carcinogenesis. This present study was therefore to determine the potential value of serum TGR5 promoter methylation in identifying hepatocellular carcinoma (HCC) from chronic hepatitis B (CHB) patients.

Small nucleolar RNA host gene 3 (SNHG3), a long noncoding RNA (lncRNA), acts as an oncogene in hepatocellular carcinoma (HCC), whereas microRNA (miR)-326 plays an inhibitory role in some types of human cancers, including melanoma, osteosarcoma, and gastric cancer. In the present study, by analyzing 47 tissue specimens of human HCC, we found that the relative expression levels of SNHG3 were significantly higher in HCC tissues than those in the adjacent noncancerous tissues, whereas the relative expression levels of miR-326 were significantly lower in HCC tissues. Furthermore, the relative mRNA levels of Sma and Mad Related Family 3 (SMAD3) and zinc finger E-box binding homeobox 1 (ZEB1) were significantly higher in HCC tissues compared with the adjacent noncancerous tissues. In human HCC cell lines, SNHG3 overexpression promoted the proliferation, migration, and epithelial-mesenchymal transition and inhibited apoptosis, whereas knockdown of SNHG3 expression exerted the opposite effects. Importantly, miR-326 or miR-326 inhibitor restored the aforementioned effects of SNHG3 overexpression or SNHG3 knockdown. We thus found that the miR-326-response element is present in SNHG3 and the 3'-untranslated region of SMAD3 mRNA. In fact, SNHG3 overexpression increased the expression levels of SMAD3 and ZEB1, while miR-326 decreased the expression levels of SMAD3. These results suggest that SNHG3 may function as a competing endogenous RNA (ceRNA) for miR-326, which in turn enhances SMAD3 and ZEB1 expression. In conclusion, we propose that SNHG3 promotes HCC progression via the miR-326/SMAD3/ZEB1 signaling pathway. The findings may provide novel targets for the diagnosis and treatment of HCC.

A novel hyperspectral full-waveform light detection and ranging (LiDAR) system was proposed in this study. First, the optical set-up of this system was introduced. Second, the spectral reflectance of two kinds of leaves was measured using this system as well as a common spectrometer. A workflow was proposed to show how to calculate spectral reflectance from the LiDAR data. Three vegetation indices (VIs) – simple ratio index, normalized difference vegetation index and photochemical reflectance index – were calculated using the reflectance derived from the data collected by these two instruments. Finally, the LiDAR-derived VIs were used to test for the estimation of leaf biochemical contents. Results show that: (1) LiDAR reflectance is different from that measured by spectrometer, but the LiDAR and spectrometer spectral shapes are clearly similar; (2) leaf biochemical contents can be successfully estimated by LiDAR-derived VIs with a root mean square error of 0.46–0.55% for nitrogen content, 1.42–2.98 mg g⁻¹ for chlorophyll content and 0.35–0.56 mg g⁻¹ for carotenoid content. These results demonstrate the possibilities for estimating leaf biochemical contents using the new hyperspectral full-waveform LiDAR data.