Luteolin is a falvonoid compound derived from Lonicera japonica Thunb. Numerous reports have demonstrated that luteolin has anticancer effects on many kinds of tumors. This study investigated the effects of luteolin on prostate cancer (PCa), assessing the PC3 and LNCaP cells. The cell viability and apoptosis were assessed by performing Cell Counting Kit-8 assay and Annexin V–fluorescein isothiocyanate/propidium iodide double staining. Luteolin was found to inhibit androgen-sensitive and androgen-independent PCa cell lines’ growth and induced apoptosis. To uncover the exact mechanisms and molecular targets, microRNA (miR) array analysis was performed. miR-301 was found to be markedly downregulated. Then, the expression of miR-301 was retrospectively analyzed in the primary PCa tissues by quantitative reverse transcription polymerase chain reaction and in situ hybridization methods. According to the quantitative reverse transcription polymerase chain reaction results of miR-301, the 54 PCa patients were divided into two groups: high and low miR-301 groups. The division indicator is a relative expression ≥5. Compared to the low-expression group, high miR-301 expression was associated with a significantly shorter overall survival (P=0.029). The proapoptotic gene, DEDD2, was predicted to be the direct target of miR-301. It was clarified in accordance with bioinformatics and luciferase activity analyses. The overexpression of miR-301 by plasmid decreased the luteolin effect. Taken together, these results suggest that luteolin inhibits PCa cell proliferation through miR-301, the poor predictive factor of PCa.

Pleiotrophin promotes chemoresistance to doxorubicin in osteosarcoma by upregulating P-glycoprotein Pleiotrophin promotes chemoresistance to doxorubicin in osteosarcoma by upregulating P-glycoprotein

Background: The prognosis is very poor for lung cancer patients with bone metastasis. Unfortunately, a suitable method has yet to become available for the early diagnosis of bone metastasis in lung cancer patients. The present work describes an attempt to develop a novel model for the early identification of lung cancer patients with bone metastasis risk. Methods: As the test group, 205 primary lung cancer patients were recruited, of which 127 patients had bone metastasis; the other 78 patients without bone metastasis were set as the negative control. Additionally, 106 healthy volunteers were enrolled as the normal control. Serum levels of several cytokines in the bone microenvironment (CaN, OPG, PTHrP, and IL-6) and bone turnover markers (tP1NP, β-CTx) were detected in all samples by ECLIA or ELISA assay. Receiver operating characteristic (ROC) curves and multivariate analyses were performed to evaluate diagnostic abilities and to assess the attributable risk of bone metastasis for each of these indicators; the diagnostic model was established via logistic regression analysis. The prospective validation group consisted of 44 patients with stage IV primary lung cancer on whom a follow-up of at least 2 years was conducted, during which serum bone biochemical marker concentrations were monitored. Results: The serological molecular model for the diagnosis of bone metastasis was logit (p). ROC analysis showed that when logit (p) > 0.452, the area under curve of the model was 0.939 (sensitivity: 85.8%, specificity: 89.7%). Model validation demonstrated accuracy with a high degree of consistency (specificity: 85.7%, specificity: 87.5%, Kappa: 0.770). The average predictive time for bone metastasis occurrence of the model was 9.46 months earlier than that of the bone scan diagnosis. Serum OPG, PTHrP, tP1NP, β-CTx, and the diagnostic model logit (p) were all positively correlated with bone metastasis progression (P < 0.05). Conclusions: This diagnostic model has the potential to be a simple, non-invasive, and sensitive tool for diagnosing the occurrence and monitoring the progression of bone metastasis in patients with lung cancer.

Osteosarcoma (OS) originates in the skeletal system and has a rising global incidence. Long Non-coding RNAs (lncRNAs) are key regulators of human cancers development and progression. However, their roles in the development of OS are not well understood. This research aimed to investigate the effect of a long non-coding RNA (lncRNA), MRUL, on OS and revealed its potential molecular mechanisms. The bioinformatics analysis demonstrated that lncRNA MRUL was involved in regulating nucleic acid-templated transcription, cellular macromolecule biosynthetic process, immune response, and inflammatory response. In this work, the expression of lncRNA MRUL was detected by quantitative real-time polymerase chain reaction (qRT-RCR) in both cancer tissues and cell lines. We found that lncRNA MRUL was up-regulated in cancer tissues and cell lines. Functional experiments showed that knockdown of lncRNA MRUL inhibited OS cell proliferation, and metastasis. At the same time, we found that lncRNA MRUL interacted with miR-125a-5p to suppress FUT4 expression. Moreover, inhibition of miR-125a-5p abrogated the biological roles of lncRNA MRUL knockdown on OS cell proliferation, migration, and invasion. In conclusion, these results demonstrated that OS-upregulated lncRNA MRUL promoted cell proliferation, and metastasis via negatively regulating miR-125a-5p, and imply that lncRNA MRUL may be a potential biomarker for OS.

Osteosarcoma is one of the most common malignant bone tumors with high chemoresistance and poor prognosis, exhibiting abnormal gene regulation and epigenetic events. Methotrexate (MTX) is often used as a primary agent in neoadjuvant chemotherapy for osteosarcoma; However, the high dosage of methotrexate and strong drug resistance limit its therapeutic efficacy and application prospects. Studies have shown that abnormal expression and dysfunction of some coding or non-coding RNAs (e.g., DNA methylation and microRNA) affect key features of osteosarcoma progression, such as proliferation, migration, invasion, and drug resistance. Comprehensive multi-omics analysis is critical to understand its chemoresistant and pathogenic mechanisms. Currently, the network analysis-based non-negative matrix factorization (netNMF) method is widely used for multi-omics data fusion analysis. However, the effects of data noise and inflexible settings of regularization parameters affect its performance, while integrating and processing different types of genetic data is also a challenge. In this study, we introduced a novel adaptive total variation netNMF (ATV-netNMF) method to identify feature modules and characteristic genes by integrating methylation and gene expression data, which can adaptively choose an anisotropic smoothing scheme to denoise or preserve feature details based on the gradient information of the data by introducing an adaptive total variation constraint in netNMF. By comparing with other similar methods, the results showed that the proposed method could extract multi-omics fusion features more effectively. Furthermore, by combining the mRNA and miRNA data of methotrexate (MTX) resistance with the extracted feature genes, four genes, Carboxypeptidase E (CPE), LIM, SH3 protein 1 (LASP1), Pyruvate Dehydrogenase Kinase 1 (PDK1) and Serine beta-lactamase-like protein (LACTB) were finally identified. The results showed that the gene signature could reliably predict the prognostic status and immune status of osteosarcoma patients.