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Circular RNAs (CircRNAs) are a group of noncoding RNAs that have essential function in the development and progression of various cancers. The expression pattern and function of circRNA in lung cancer is not fully understood. In the present study, we aimed to investigate the expression profiles and underlying mechanism of circRNA circ_POLA2 in lung cancer cell stemness. Circ_POLA2 was highly expressed in lung cancer tissues and predicted a poor prognosis in lung cancer patients. Knockdown of circ_POLA2 inhibited the stemness of lung cancer cells, which is evident by the decreased sphere-formation ability, ALDH1 activity, and stemness marker expression, but had no effects on cell viability. Mechanistically, circ_POLA2 functioned as a ceRNA by sponging miR-326. Furthermore, miR-326 negatively regulated G protein subunit beta 1 (GNB1) expression by targeting its 3'-UTR (untranslated region). Intriguingly, we found that GNB1 was overexpressed and associated with poor prognosis in lung cancer patients. Overexpression of GNB1 could antagonize the inhibitory effect of circ_POLA2 knockdown on lung cancer cell stemness. In conclusion, circ_POLA2 promotes lung cancer cell stemness and progression via regulating the miR-326/GNB1 axis, which might serve as a novel therapeutic target for lung cancer patients.

Cutaneous malignant melanoma (hereafter called melanoma) is one of the most aggressive cancers with increasing incidence and mortality rates worldwide. In this study, we performed a systematic investigation of the tumor microenvironmental and genetic factors associated with melanoma to identify prognostic biomarkers for melanoma. We calculated the immune and stromal scores of melanoma patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and found that they were closely associated with patients' prognosis. Then the differentially expressed genes were obtained based on the immune and stromal scores, and prognostic immune-related genes further identified. Functional analysis and the protein-protein interaction network further revealed that these genes enriched in many immune-related biological processes. In addition, the abundance of six infiltrating immune cells was analyzed using prognostic immune-related genes by TIMER algorithm. The unsupervised clustering analysis using immune-cell proportions revealed eight clusters with distinct survival patterns, suggesting that dendritic cells were most abundant in the microenvironment and CD8+ T cells and neutrophils were significantly related to patients' prognosis. Finally, we validated these genes in three independent cohorts from the Gene Expression Omnibus database. In conclusion, this study comprehensively analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for melanoma.