Basement membrane-related lncRNA signature for the prognosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the main type of primary liver cancer. This study aimed to develop a basement membrane (BM) related lncRNAs risk signature to evaluate the prognosis of HCC patients. We screened differentially expressed BM-related lncRNAs (DE-BMRlncRNAs) for risk evaluation, and identified six DE-BMRlncRNAs (AC072054.1, NUP50-DT, AC026412.3, AC109322.2, POLH-AS1 and LINC00595) for prognostic risk signature. HCC patients were divided to high or low risk according to median risk score. Our prognostic model predicted that patients with higher risk score had worse prognosis. We also created a nomogram to assist clinical decision-making according to risk score and clinicopathological features. Meanwhile, we confirmed the expression of six lncRNAs in HCC tissue and cells. POLH-AS1 knockdown inhibited the migration and invasion of HCC cells. In conclusion, we established a predictive model based on BMRlncRNAs to predict the prognosis of HCC. Our findings offer a rationale to further explore BM-related biomarkers for HCC.

Amplification-Free Analysis of Bladder Cancer MicroRNAs on Wrinkled Silica Nanoparticles with DNA-Functionalized Quantum Dots.

Bladder cancer (BC) occurrence and progression are accompanied by alterations in microRNAs (miRNAs) expression levels. Simultaneous detection of multiple miRNAs contributes to the accuracy and reliability of the BC diagnosis. In this work, wrinkled silica nanoparticles (WSNs) were applied as the microreactor for multiplex miRNAs analysis without enzymes or nucleic acid amplification. Conjugated on the surface of WSNs, the S9.6 antibody was adopted as the universal module for binding DNA/miRNA duplexes, regardless of their sequence. Furthermore, single-stranded DNA (ssDNA) was labeled with quantum dots (QDs) for identifying a given miRNA to form QDs-ssDNA/miRNA, which enabled the specific capture of the corresponding QDs on the wrinkled surface of WSNs. Based on the detection of fluorescence signals that were ultimately focused on WSNs, target miRNAs could be sensitively identified to a femtomolar level (5 fM) with a wide dynamic range of up to 6 orders of magnitude. The proposed strategy achieved high specificity to obviously distinguish single-base mutation sequences and possessed multiplex assay capability. Moreover, the assay exhibited excellent practicability in the multiplex detection of miRNAs in clinical serum specimens.