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Prostate cancer is still a significant global health burden in the coming decade. Novel biomarkers for detection and prognosis are needed to improve the survival of distant and advanced stage prostate cancer patients. The tumor microenvironment is an important driving factor for tumor biological functions. To investigate RNA prognostic biomarkers for prostate cancer in the tumor microenvironment, we obtained relevant data from The Cancer Genome Atlas (TCGA) database. We used the bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) to construct tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Then, the Cox regression model was performed to screen RNAs associated with prostate cancer survival. The differentially expressed gene profile in tumor stroma was significantly enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, we constructed three ceRNA networks with 152 RNAs associated with the prostate cancer tumor microenvironment. Cox regression analysis screened 31 RNAs as the potential prognostic biomarkers for prostate cancer. The most interesting 8 prognostic biomarkers for prostate cancer included lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B. In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer. Our findings might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BCa) is the most commonly diagnosed cancer and the leading cause of cancer death among females around the world. Recent studies have indicated that long non-coding RNAs (lncRNAs) can serve as an independent biomarker for diagnosis and prognosis in many types of cancer, including pancreatic adenocarcinoma, gastric cancer, liver cancer, and lung cancer. Previous studies have shown that many lncRNAs are associated with the occurrence and development of BCa. However, few studies have combined multiple lncRNAs to predict the prognosis of early-stage BCa patients. Methods: Systematic and comprehensive analysis of data from The Cancer Genome Atlas (TCGA) was conducted to identify lncRNA signatures with prognostic value in BCa. Additionally, the relative expression levels of the 8 lncRNA of several BCa cell lines were detected by quantitative real-time PCR (qPCR) and the results were substituted into a risk score formula. Finally, migration assays were used to verify the result from prognostic analysis according to the risk scores among cell lines with different risk scores. Results: Our study included 808 BCa patients with complete clinical data. A panel of 8 lncRNAs was identified using Wilcox tests as different between normal and tumor tissue of the BCa patients. This panel was used to analyze the survival of BCa patients. Patients with low risk scores had greater overall survival (OS) than those with high risk scores. Multivariate Cox regression analyses demonstrated that the lncRNA signature was an independent prognostic factor. Gene Set Enrichment Analysis (GSEA) suggested that the lncRNAs might be involved in several molecular signaling pathways implicated in BCa such as the DNA replication pathway, the cell cycle pathway, and the pentose phosphate pathway. Validation experiments in breast cancer cells to test cell migration by using wound-healing assays supported the results of the model. Conclusion: Our study demonstrated that a panel of 8 lncRNAs has the potential to be used as an independent prognostic biomarker of BCa. [ABSTRACT FROM AUTHOR]

Keywords: alternative splicing; splicing network; splicing modules; cancer splicing; prognosis 3 This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics Alternative splicing, splicing network, splicing modules, cancer splicing, prognosis. [Extracted from the article]

Abstract: Serum samples from non-Hodgkin lymphoma (NHL) patients who had not undergone chemotherapy, lymphnoditis patients, and healthy adults were analyzed using surface-enhanced laser desorption–ionization time-of-flight mass spectrometry (SELDI-TOF MS) to detect the differentially expressed serum proteins. Models were developed to distinguish between the healthy adult group and the NHL group, with a sensitivity of 69% and specificity of 90%, and between the lymphnoditis group and the NHL group with a sensitivity of 74% and specificity of 84%. A protein with the m/z of M10 197.91 u was expressed at a significantly higher level in the NHL group, compared to the other groups. Furthermore, differences were also significant among different stages of NHL and among samples with different International Prognosis Index (IPI) scores or lactase dehydrogenase (LDH) levels. The three identified proteins may offer a new serological approach for early diagnosis, differential diagnosis, and pathogenic investigation of NHL. And the protein with the m/z of M10 197.91 u may be a new serological biomarker for monitoring treatment response and evaluating the prognosis of patients with NHL. [Copyright &y& Elsevier]