Long non-coding RNA BZRAP1-AS1 functions in malignancy and prognosis for non-small-cell lung cancer

Long noncoding RNA POU6F2-AS2 contributes to the aggressiveness of nonsmall-cell lung cancer via microRNA-125b-5p-mediated E2F3 upregulation

The role of the majority of long noncoding RNAs (lncRNAs) in the progression of nonsmall-cell lung cancer (NSCLC) remains elusive, despite their potential value, thus warranting in-depth studies. For example, detailed functions of the lncRNA POU6F2 antisense RNA 2 (POU6F2-AS2) in NSCLC are unknown. Herein, we investigated the expression status of POU6F2-AS2 in NSCLC. Furthermore, we systematically delineated the biological roles of POU6F2-AS2 in NSCLC alongside its downstream molecular events. We measured the expression levels of POU6F2-AS2 using quantitative real-time polymerase chain reaction and performed a series of functional experiments to address its regulatory effects in NSCLC cells. Using bioinformatic platforms, RNA immunoprecipitation, luciferase reporter assays, and rescue experiments, we investigated the potential mechanisms of POU6F2-AS2 in NSCLC. Subsequently, we confirmed the remarkable overexpression of POU6F2-AS2 in NSCLC using The Cancer Genome Atlas database and our own cohort. Functionally, inhibiting POU6F2-AS2 decreased NSCLC cell proliferation, colony formation, and motility, whereas POU6F2-AS2 overexpression exhibited contrasting effects. Mechanistically, POU6F2-AS2 acts as an endogenous decoy for microRNA-125b-5p (miR-125b-5p) in NSCLC that causes the overexpression of the E2F transcription factor 3 (E2F3). Moreover, suppressing miR-125b-5p or increasing E2F3 expression levels sufficiently recovered the anticarcinostatic activities in NSCLC induced by POU6F2-AS2 silencing. Thus, POU6F2-AS2 aggravates the oncogenicity of NSCLC by targeting the miR-125b-5p/E2F3 axis. Our findings suggest that POU6F2-AS2 is a novel therapeutic target for NSCLC.

A prognostic model based on tumor microenvironment-related lncRNAs predicts therapy response in pancreatic cancer

Pancreatic cancer is an aggressive malignant tumor with high mortality and a low survival rate. The immune and stromal cells that infiltrate in the tumor microenvironment (TME) significantly impact immunotherapy and drug responses. Therefore, we identify the TME-related lncRNAs to develop a prognostic model for predicting the therapy efficacy in pancreatic cancer patients. Firstly, we identified differentially expressed genes (DEGs) for weighted gene co-expression network analysis (WGCNA) to identify the TME-related module eigengenes. According to the module eigengenes, the TME-related prognostic lncRNAs were screened through the univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses to construct a prognostic risk score (RS) model. Next, the predictive power of this model was evaluated by the time-dependent receiver operating characteristic (ROC) curve and Kaplan-Meier analyses. In addition, functional enrichment, immune cell infiltration, and somatic mutation analyses were performed. Finally, tumor immune dysfunction and exclusion (TIDE) score and drug sensitivity analyses were applied to predict therapy response. In this study, 11 TME-related prognostic lncRNAs were identified to develop the prognostic RS model. According to the RS, the low-risk patients had a better prognosis, lower rates of somatic mutation, lower TIDE scores, and higher sensitivity to gemcitabine and paclitaxel compared to high-risk patients. The findings above suggested that low-risk patients may benefit more from immunotherapy, and high-risk patients may benefit more from chemotherapy. Within this study, we established a prognostic RS model based on 11 TME-related lncRNAs, which may help improve clinical decision-making.