With the discovery of new chemotherapeutic drugs, chemotherapy becomes increasingly valuable. However, the resistance of tumor cells to chemotherapeutic agents significantly limits the effectiveness and causes chemotherapy failure. MicroRNAs have been shown to regulate drug resistance in many types of cancer. In the present study, we measured the chemosensitivity of five bladder cancer (BCa) cell lines to seven commonly used chemotherapeutic drugs by Vita‑Blue assay. We then identified the most sensitive (5637) and most tolerant cell lines (H‑bc) and conducted a multi‑group test. This test included expression group analyses of coding and non‑coding genes (miR‑omic and RNA‑seq). Based on our analyses, we selected miR‑22‑3p as a target. We then determined its own target gene [neuroepithelial cell transforming 1 (NET1)] by bioinformatic analysis and confirmed this finding by TaqMan‑quantitative reverse transcription polymerase chain reaction (qRT‑PCR), western blot analysis and luciferase reporter assay. The effect of miR‑22‑3p on BCa multi‑chemoresistance was also determined by transfecting cells with the miR‑22‑3p‑mimic or miR‑22‑3p‑antagomiR. We assessed the involvement of NET1 in BCa chemoresistance by siRNA‑mediated NET1 inhibition or pINDUCER21‑enhanced green fluorescent protein‑NET1‑mediated overexpression. Plate colony formation and apoptosis assays were conducted to observe the effects of miR‑22‑3p and NET1 on BCa chemoresistance. In conclusion, our results suggest that miR‑22‑3p promotes BCa chemoresistance by targeting NET1 and may serve as a new prognostic biomarker for BCa patients.
Long noncoding RNA (lncRNA) nuclear enriched abundant transcript 1 (NEAT1) is nuclear-located and transcribed from chromatin 11. To date, little is known about the cellular functions and regulatory mechanisms of NEAT1 in prostate cancer (PCa). In this stud y, whole-genome RNA sequencing data were downloaded from TCGA and GEO databases. Biological information was used to analyze the different expressions of NEAT1. In situ hybridization (ISH) was performed to detect the expression of NEAT1 in PCa and paracarcinomatous clinical samples. Then, NEAT1 was knocked down in PC3 cells through lentiviral infection with a plasmid construct. Bioinformatics and integrative analytical approaches were utilized to identify the relationships of NEAT1 with specific cancer-related gene sets.Cell proliferation assay and colony formation assay were performed to evaluate the cell proliferative ability. Glycolysis stress test, metabolism assay, and infiltrating T-cell function analysis were implemented to assess the changes in metabolism and immune microenvironment of PCa. We found that the expression of NEAT1 was higher in PCa than in non-neoplastic tissues. The cell proliferative capability of PCa cells was significantly reduced in the NEAT1 knockdown group. PCR array and bioinformatics analysis revealed that the enrichment of acidic substance-related gene sets was associated with NEAT1 expression. NEAT1 depletion inhibited PCa cell aerobic glycolysis accompanied by the reduction of lactate levels in the medium. Further, we found that lactate dehydrogenase A LDHA expression was positively regulated by NEAT1. At last, co-culture systems indicated that NEAT1 or LDHA knockdown promoted the secretion of CD8+ T-lymphocyte factors, including TNF--antitumor effects.
ObjectivesThe purpose of this study is to evaluate the diagnostic accuracy of the clinical variables of patients with prostate cancer (PCa) and to provide a strategy to reduce unnecessary biopsies.Patients and methodsA Chinese cohort that consists of 833 consecutive patients who underwent prostate biopsies from January 2018 to April 2022 was collected in this retrospective study. Diagnostic ability for total PCa and clinically significant PCa (csPCa) was evaluated by prostate imaging–reporting and data system (PI-RADS) score and other clinical variables. Univariate and multivariable logistic regression analyses were performed to figure out the independent predictors. Diagnostic accuracy was estimated by plotting receiver operating characteristic curves.ResultsThe results of univariate and multivariable analyses demonstrated that the PI-RADS score (P < 0.001, OR: 5.724, 95% CI: 4.517–7.253)/(P < 0.001, OR: 5.199, 95% CI: 4.039–6.488) and prostate-specific antigen density (PSAD) (P < 0.001, OR: 2.756, 95% CI: 1.560–4.870)/(P < 0.001, OR: 4.726, 95% CI: 2.661–8.396) were the independent clinical factors for predicting total PCa/csPCa. The combination of the PI-RADS score and PSAD presented the best diagnostic performance for the detection of PCa and csPCa. For the diagnostic criterion of “PI-RADS score ≥ 3 or PSAD ≥ 0.3”, the sensitivity and negative predictive values were 94.0% and 93.1% for the diagnosis of total PCa and 99.2% and 99.3% for the diagnosis of csPCa, respectively. For the diagnostic criterion “PI-RADS score >3 and PSAD ≥ 0.3”, the specificity and positive predictive values were 96.8% and 92.6% for the diagnosis of total PCa and 93.5% and 82.4% for the diagnosis of csPCa, respectively.ConclusionsThe combination of the PI-RADS score and PSAD can implement the extraordinary diagnostic performance of PCa. Many patients may safely execute active surveillance or take systematic treatment without prostate biopsies by stratification according to the PI-RADS score and the value of PSAD.
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