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Increasing evidence has demonstrated that enhanced PHF20 expression plays a crucial role in cancer development and progression. However, little is known about the prognostic value of PHF20 expression in breast cancer (BRCA). In this study, we attempted to explore the expression pattern of PHF20 and its associations with prognosis and immune status in BRCA. The gene expression data and clinical information of 1109 BRCA samples were downloaded from TCGA database. The expression level of PHF20 protein was determined using UALCAN and HPA database. Kaplan–Meier method and CIBERSORT algorithm were used to analyze the associations of PHF20 expression with overall survival (OS) and immune microenvironment, respectively. Besides, GSEA analysis was conducted to explore potential biological functions and molecular mechanisms of PHF20 in BRCA. Moreover, starBase database was applied to construct a ceRNA nework. PHF20 was highly expressed in BRCA samples both at the transcriptional and protein level, and was strongly correlated with the OS and immune status. Univariable and multivariate Cox regression analyses identified PHF20 as an independent prognostic factor. Additionally, GSEA analysis showed that high PHF20 expression was closely associated with TGF-β signaling pathway, Wnt signaling pathway, and adherens junction. Furthermore, three ceRNA networks (AC037198.1/hsa-miR-223-3p/PHF20, CBR3-AS1/hsa-miR-223-3p/PHF20, and ZNF561-AS1/hsa-miR-223-3p/PHF20) were identified by starBase analysis. Functional experiments validated that PHF20 knockdown inhibited the cell viability and progression in BRCA cells. PHF20 overexpression was significantly associated with poor prognosis and immune status in BRCA, and could act as a potential novel prognostic biomarker for BRCA. [ABSTRACT FROM AUTHOR]

Background. Non-small cell lung cancer (NSCLC) is the most prevalent malignant tumor of the lung cancer, for which the molecular mechanisms remain unknown. In this study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC by bioinformatics analysis. Methods. From the Gene Expression Omnibus database, GSE118370 and GSE10072 microarray datasets were obtained. Identifying the differentially expressed genes (DEGs) between lung adenocarcinoma and normal samples was done. By using bioinformatics tools, a protein-protein interaction (PPI) network was constructed, modules were analyzed, and enrichment analyses were performed. The expression and prognostic values of 14 hub genes were validated by the GEPIA database, and the correlation between hub genes and survival in lung adenocarcinoma was assessed by UALCAN, cBioPortal , String and Cytoscape, and Timer tools. Results. We found three genes (PIK3R1, SPP1, and PECAM1) that have a clear correlation with OS in the lung adenocarcinoma patient. It has been found that lung adenocarcinoma exhibits high expression of SPP1 and that this has been associated with poor prognosis, while low expression of PECAM1 and PIK3R1 is associated with poor prognosis P < 0.05 . We also found that the expression of SPP1 was associated with miR-146a-5p, while the high expression of miR-146a-5p was related to good prognosis P < 0.05 . On the contrary, the lower miR-21-5p on upstream of PIK3R1 is associated with a higher surviving rate in cancer patients P < 0.05 . Finally, we found that the immune checkpoint genes CD274(PD-L1) and PDCD1LG2(PD-1) were also related to SPP1 in lung adenocarcinoma. Conclusions. The results indicated that SPP1 is a cancer promoter (oncogene), while PECAM1 and PIK3R1 are cancer suppressor genes. These genes take part in the regulation of biological activities in lung adenocarcinoma, which provides a basis for improving detection and immunotherapeutic targets for lung adenocarcinoma. [ABSTRACT FROM AUTHOR]

Triple‐negative breast cancer (TNBC) is a type of breast cancer that has a higher risk of distant recurrence and metastasis, leading to a relatively aggressive biological behaviour and poor outcome. So far, the clinical management of TNBC is challenging because of its heterogeneity and paucity of specific targeted therapy. Recently, various studies have identified a lot of differently expressed long non‐coding RNAs (lncRNAs) in TNBC. Those lncRNAs have been reported to play important roles in the multistep process of TNBC tumorigenesis. Here, we review the biological characteristics of lncRNAs, and present the current state of knowledge concerning the expression, function and regulation of lncRNAs in TNBC. Accumulating studies explored the potential lncRNAs‐based therapeutics in TNBC, including the techniques of genetic modification using antisense oligonucleotides, locked nucleic acid and RNA nanotechnology. In current review, we also discuss the future prospects of studies about lncRNAs in TNBC and development of lncRNA‐based strategies for clinical TNBC patients. [ABSTRACT FROM AUTHOR]

Background: Biomarkers play critical roles in early detection, diagnosis and monitoring of therapeutic outcome and recurrence of cancer. Previous biomarker research on ovarian cancer (OC) has mostly focused on the discovery and validation of diagnostic biomarkers. The primary purpose of this study is to identify serum biomarkers for prognosis and therapeutic outcomes of ovarian cancer. Experimental Design: Forty serum proteins were analyzed in 70 serum samples from healthy controls (HC) and 101 serum samples from serous OC patients at three different disease phases: post diagnosis (PD), remission (RM) and recurrence (RC). The utility of serum proteins as OC biomarkers was evaluated using a variety of statistical methods including survival analysis. Results: Ten serum proteins (PDGF-AB/BB, PDGF-AA, CRP, sFas, CA125, SAA, sTNFRII, sIL-6R, IGFBP6 and MDC) have individually good area-under-the-curve (AUC) values (AUC = 0.69–0.86) and more than 10 three-marker combinations have excellent AUC values (0.91–0.93) in distinguishing active cancer samples (PD & RC) from HC. The mean serum protein levels for RM samples are usually intermediate between HC and OC patients with active cancer (PD & RC). Most importantly, five proteins (sICAM1, RANTES, sgp130, sTNFR-II and sVCAM1) measured at remission can classify, individually and in combination, serous OC patients into two subsets with significantly different overall survival (best HR = 17, p<10−3). Conclusion: We identified five serum proteins which, when measured at remission, can accurately predict the overall survival of serous OC patients, suggesting that they may be useful for monitoring the therapeutic outcomes for ovarian cancer. [ABSTRACT FROM AUTHOR]

BACKGROUND: MicroRNAs (miRNAs) are recognized as potential biomarkers for detection of breast cancer. Many experiments have been done to explore the aberrant expression of circulating miRNAs in breast cancer patients. However, an overwhelming number of miRNAs are identified and some expression characteristics are inconsistent between studies. OBJECTIVE: To distinguish the candidates for breast cancer detection from spurious miRNAs.METHODS: One solution is to take the intersections between studies. As repeated efforts can improve reliability and reduce error, valuable candidate miRNAs in this study are defined as those validated and consistently reported by multiple studies. Data validated by real-time PCR (RT-PCR) were collected, and the most frequently reported miRNAs with consistent regulation were identified by using a vote-counting strategy based on the number of relevant studies, total sample size and fold change. RESULTS: The top four miRNAs (miR-21, -155, -222and -10b) are consistently regulated in comparisons of the pre-operative patients and the control group. And blood sample type was found to affect the regulation characteristics of miRNAs. CONCLUSION: MiR-21, -155, -222 and -10b are reliable candidate biomarkers for detection of breast cancer. [ABSTRACT FROM AUTHOR]

It has been demonstrated that there are abundant stable microRNAs (miRNAs) in plasma/serum, which can be detected and are potentially disease specific. However, the lack of suitable endogenous controls for serum miRNA detection is the restriction for the widely usage of this kind of biomarkers and for the between-laboratory comparison of the findings. We first systematically screened for endogenous control miRNAs (ECMs) by testing 10 pooling samples (using both Solexa sequencing and TaqMan low density array) and 50 individual samples (using quantitative reverse transcription–PCR) of different cancer traits and healthy controls. Then we assessed serum miRNAs used as potential biomarkers for breast cancer risk prediction based on a two-stage case–control analysis, including 48 breast cancer patients and 48 controls for the discovery stage and 76 breast cancer patients and 76 controls for validation. We identified two candidate ECMs (miRNA-191 and miRNA-484). Normalized by the two ECMs, we found four miRNAs (miR-16, miR-25, miR-222 and miR-324-3p) that were consistently differentially expressed between breast cancer cases and controls. The area under the receiver operating characteristic curve is 0.954 for the four-miRNA signature in the discovery stage (sensitivity = 0.917 and specificity = 0.896) and 0.928 in the validation stage (sensitivity = 0.921 and specificity = 0.934). In conclusion, the four-miRNA signature from serum may serve as a non-invasive prediction biomarker for breast cancer. Furthermore, we proposed the combination of miRNA-484 and miRNA-191 as endogenous control for serum miRNA detection, at least for most common cancers. [ABSTRACT FROM AUTHOR]