Long non-coding RNAs (lncRNAs) have been recently observed in various human cancers. However, the role of lncRNAs in pancreatic duct adenocarcinoma (PDAC) remains unclarified. The aim of this study was to detect the expression of lncRNA MALAT1 in PDAC formalin-fixed, paraffin embedded (FFPE) tissues and to investigate the clinical significance of the MALAT1 level. The expression of MALAT1 was examined in 45 PDAC and 25 adjacent non-cancerous FFPE tissues, as well as in five PDAC cell lines and a normal pancreatic epithelium cell line HPDE6c-7, using qRT-PCR. The relationship between MALAT1 level and clinicopathological parameters of PDAC was analyzed with the Kaplan-Meier method and Cox proportional hazards model. The relative level of MALAT1 was significantly higher in PDAC compared to the adjacent normal pancreatic tissues (p=0.009). When comparing the MALAT1 level in the cultured cell lines, remarkably higher expression of MALAT1 was found in aspc-1 PDAC cells compared with the immortal pancreatic duct epithelial cell line HPDE6c-7 (q=7.573, p<0.05). Furthermore, MALAT1 expression level showed significant correlation with tumor size (r=0.35, p=0.018), tumor stage (r=0.439, p=0.003) and depth of invasion (r=0.334, p=0.025). Kaplan-Meier analysis revealed that patients with higher MALAT1 expression had a poorer disease free survival (p=0.043). Additionally, multivariate analysis indicated that overexpression of MALAT1, as well as the tumor location and nerve invasion, was an independent predictor of disease-specific survival of PDAC. MALAT1 might be considered as a potential prognostic indicator and may be a target for diagnosis and gene therapy for PDAC.

Comprehensive investigation of a novel differentially expressed lncRNA expression profile signature to assess the survival of patients with colorectal adenocarcinoma

Growing evidence indicates that microRNAs (miRNAs) play critical roles in the initiation and progression of breast carcinoma (BC) and are promising diagnostic biomarkers. In the present study, we aimed to identify a multi-marker miRNA pool with high diagnostic performance for BC. We collected miRNA expression profiles of BC samples and normal breast tissues from The Cancer Genome Atlas (TCGA) and screened differentially expressed miRNAs by conducting two‑sample t-tests and by calculating log2 fold-change (log2FC) ratios. Statistical significance was established at p1. Then, we generated receiver operating characteristic (ROC) curves, calculated the area under the curve (AUC) with a 95% confidence interval (95% CI), and calculated the diagnostic sensitivity and specificity using MedCalc software. Additionally, we predicted the targets of candidate miRNAs using 10 online databases: TarBase, miRTarBase, TargetScan, TargetMiner, microRNA.org, RNA22, PicTar-vert, miRDB, PITA and PolymiRTS. Target genes that were predicted by at least four algorithms were chosen, and cooperative targets of multiple miRNAs were further selected for GO and KEGG pathway analyses through the DAVID online tool. Eventually, a total of 66 differentially expressed miRNAs were identified after miRNA expression profiles were analyzed in BC and normal breast samples. Of these, we selected nine dysregulated miRNAs as candidate diagnostic markers: seven upregulated miRNAs (hsa-miR-21, hsa-miR-96, hsa-miR-183, hsa-miR‑182, hsa-miR-141, hsa-miR-200a and hsa-miR-429) and two downregulated miRNAs (hsa-miR-139 and hsa-miR‑145). The ROC curve for the combination of these nine differently expressed miRNAs showed extremely high diagnostic accuracy, with an AUC of 0.995 (95% CI, 0.988‑0.999) and diagnostic sensitivity and specificity of 98.7 and 98.9%, respectively. In conclusion, the combination of these nine miRNAs significantly improved the accuracy of breast cancer diagnosis.

Functional genomics studies over the past decades have unveiled that the human genome transcribes a large amount of long noncoding RNAs (lncRNAs) which have been implicated in regulation of diverse cellular functions. Dysregulation of lncRNAs are often associated with human diseases including cancer. The metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is among the most abundant and highly conserved lncRNAs. Emerging evidence has indicated that MALAT1 may have complex and extensive functions in the development and progression of cancer. In this review, we first update on the role of MALAT1 in tumorigenesis and then discuss possible molecular mechanisms that underline the MALAT1-mediated gene regulation, leading to cancer invasion and metastasis.

Identification of a RNA-Seq based prognostic signature with five lncRNAs for lung squamous cell carcinoma

Long noncoding RNAs (lncRNAs) are related to different biological processes in non-small cell lung cancer (NSCLC). However, the possible molecular mechanisms underlying the effects of the long noncoding RNA HOXA11-AS (HOXA11 antisense RNA) in NSCLC are unknown. HOXA11-AS was knocked down in the NSCLC A549 cell line and a high throughput microarray assay was applied to detect changes in the gene profiles of the A549 cells. Bioinformatics analyses (gene ontology (GO), pathway, Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses) were performed to investigate the potential pathways and networks of the differentially expressed genes. The molecular signatures database (MSigDB) was used to display the expression profiles of these differentially expressed genes. Furthermore, the relationships between the HOXA11-AS, de-regulated genes and clinical NSCLC parameters were verified by using NSCLC patient information from The Cancer Genome Atlas (TCGA) database. In addition, the relationship between HOXA11-AS expression and clinical diagnostic value was analyzed by receiver operating characteristic (ROC) curve. Among the differentially expressed genes, 277 and 80 genes were upregulated and downregulated in NSCLC, respectively (fold change ≥2.0, P < 0.05 and false discovery rate (FDR) < 0.05). According to the degree of the fold change, six upregulated and three downregulated genes were selected for further investigation. Only four genes (RSPO3, ADAMTS8, DMBT1, and DOCK8) were reported to be related with the development or progression of NSCLC based on a PubMed search. Among all possible pathways, three pathways (the PI3K-Akt, TGF-beta and Hippo signaling pathways) were the most likely to be involved in NSCLC development and progression. Furthermore, we found that HOXA11-AS was highly expressed in both lung adenocarcinoma and squamous cell carcinoma based on TCGA database. The ROC curve showed that the area under curve (AUC) of HOXA11-AS was 0.727 (95% CI 0.663–0.790) for lung adenocarcinoma and 0.933 (95% CI 0.906–0.960) for squamous cell carcinoma patients. Additionally, the original data from TCGA verified that ADAMTS8, DMBT1 and DOCK8 were downregulated in both lung adenocarcinoma and squamous cell carcinoma, whereas RSPO3 expression was upregulated in lung adenocarcinoma and downregulated in lung squamous cell carcinoma. For the other five genes (STMN2, SPINK6, TUSC3, LOC100128054, and C8orf22), we found that STMN2, TUSC3 and C8orf22 were upregulated in squamous cell carcinoma and that STMN2 and USC3 were upregulated in lung adenocarcinoma. Furthermore, we compared the correlation between HOXA11-AS and de-regulated genes in NSCLC based on TCGA. The results showed that the HOXA11-AS expression was negatively correlated with DOCK8 in squamous cell carcinoma (r = −0.124, P = 0.048) and lung adenocarcinoma (r = −0.176, P = 0.005). In addition, RSPO3, ADAMTS8 and DOCK8 were related to overall survival and disease-free survival (all P < 0.05) of lung adenocarcinoma patients in TCGA. Our results showed that the gene profiles were significantly changed after HOXA11-AS knock-down in NSCLC cells. We speculated that HOXA11-AS may play an important role in NSCLC development and progression by regulating the expression of various pathways and genes, especially DOCK8 and TGF-beta pathway. However, the exact mechanism should be verified by functional experiments.