Identification and functional characterization of long non-coding RNAs in human gastric cancer.

Abnormal regulation of long non-coding RNAs (lncRNAs) appears to be a primary feature of numerous types of human cancer. However, the association between the dysregulation of lncRNAs and functional alterations in gastric cancer (GC) remains unclear. In previous studies, we applied microarray and bioinformatics analyses to screen for key lncRNAs from the tumor tissues and matched adjacent non-tumor tissues of 10 patients with GC. There were seven key lncRNAs demonstrated to be significantly different between carcinoma tissues and adjacent non-tumor tissues. In the present study, the expression of these seven selected lncRNAs were validated in 82 patients with GC to further investigate the association between lncRNAs and GC clinical characterization. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) results demonstrated that RP5-919F19, MCPH1 antisense RNA 1 (CTD-2541M15) and urothelial carcinoma-associated 1 (UCA1) exhibited consistent upregulation in cancer compared with adjacent non-tumor tissues, whereas AP000459, LOC101928316, tumor suppressor candidate 8 (LINC01071) and maternally expressed 3 (MEG3) showed consistent downregulation. The results from the microarray and RT-qPCR experiments achieved 100% agreement. A correlation analysis indicated that RP5-919F19, LOC101928316 and MEG3 were significantly associated with tumor differentiation degree, RP5-919F19, UCA1 and MEG3 were significantly associated with lymph node metastasis, and RP5-919F19, CTD-2541M15 and UCA1 were significantly associated with tumor-node-metastasis stage (P<0.05). In addition, it was identified that the differential expression of LINC01071 and LOC101928316 significantly correlated with the age and gender of the GC patients, respectively (P<0.05). The results suggest that the lncRNAs RP5-919F19, LOC101928316, CTD-2541M15, UCA1 and MEG3 are closely associated with the invasion and metastasis of GC, which reveals these indicators as potential specificity biomarkers for the diagnosis, prognosis and classification of GC. Thus, these lncRNAs merit further study as novel candidate biomarkers for the clinical diagnosis of GC and as potential targets for therapy.

Dysregulated lncRNA-UCA1 contributes to the progression of gastric cancer through regulation of the PI3K-Akt-mTOR signaling pathway.

The long non-coding RNA (lncRNA) urothelial carcinoma-associated 1 (UCA1) has been recently shown to be dysregulated during disease occurrence and to play an important role in the progression of several cancers. However, the biological role and potential regulation mechanism of UCA1 in the carcinogenesis of gastric cancer remain unclear. In the present study, we found that UCA1 was aberrantly upregulated in gastric cancer tissues and gastric cancer cell lines, and was associated with TNM stage and metastasis. UCA1 silencing significantly inhibited gastric cancer BGC-823 cell proliferation and increased its apoptosis. We also found that UCA1 played an important role in the migration and invasion of gastric cancer cells in vitro and in vivo. The molecular mechanism of UCA1 suggested that UCA1 regulates the PI3K-Akt-mTOR signaling proteins and their downstream mediators, to alter gastric cancer progression in vitro and in vivo. Collectively, the results showed a pivotal role of UCA1 in the tumorigenesis of gastric cancer. In addition, the study characterized a novel lncRNA-mRNA regulatory network, which may lead to a better understanding of the pathogenesis of gastric cancer and assist in lncRNA-directed diagnosis and therapy for this malignancy.

Differential expression profiles of microRNAs as potential biomarkers for the early diagnosis of lung cancer.

Lung cancer is one of the most lethal malignancies worldwide. To reduce the high morbidity and mortality of the disease, sensitive and specific biomarkers for early detection are urgently needed. Tumor-specific microRNAs (miRNAs) seem to be potential biomarkers for the early diagnosis and treatment of cancer. In this study, the microarray of miRNAs and mRNAs on the same samples was performed and the intersection taken with The Cancer Genome Atlas (TCGA) lung cancer miRNA/RNAseq dataset. Then, miRNA-mRNA regulatory network was constructed to identify miRNA candidates associated with lung cancer through integrating gene expression and miRNA-target prediction. Furthermore, the expression levels of miRNA candidates were validated by stem-loop real-time reverse transcription PCR (qRT-PCR) in larger lung cancer population. The relationship between signature miRNAs and the risk of lung cancer were assessed by conditional logistic regression analysis. Diagnostic value of these miRNAs was determined by areas under receiver operating characteristic curves (ROC). The Affymetrix microarray analysis identified a total of 116 miRNAs and 502 mRNAs that could distinguish lung tumor tissues from adjacent non-tumor tissues, of which 70 miRNAs and 136 mRNAs were upregulated, while 46 miRNAs and 366 mRNAs were downregulated, respectively. In combination with TCGA analysis, we identified 32 miRNAs and 377 mRNAs related to lung cancer. Then, 28 key miRNAs related to 61 inter-section mRNAs were identified by miRNA-mRNA network analysis. The miRNA function analysis was indicative of that 18 upregulated and 10 downregulated miRNAs involved in signaling pathways related to Environmental Information Processing and Human Diseases. Population result showed that the expression of 7 miRNAs (miR-205-5p, miR-3917, miR-30a-3p, miR-30a-5p, miR-30c-2-3p, miR-30d-5p and miR-27a-5p) was consistent with the analysis result of microarray and TCGA. In addition, upregulation of miR-205-5p, miR-3917 and downregulation of miR-30a-3p, miR-30a-5p, miR-30c-2-3p, miR-30d-5p, miR-27a-5p increased the risk of lung cancer by conditional logistic regression analysis. The diagnostic accuracy of miR-205-5p, miR-3917, miR-27a-5p, miR-30a-3p, miR-30a-5p, miR-30c-2-3p, miR-30d-5p showed that their corresponding AUCs were 0.728, 0.661, 0.637, 0.758, 0.772, 0.734, 0.776, respectively. Therefore, there are a set of signature miRNAs which may be promising biomarkers for the early screening of high-risk populations and early diagnosis of lung cancer.

Integrated analysis of long non-coding RNA­associated ceRNA network reveals potential lncRNA biomarkers in human lung adenocarcinoma.

Accumulating evidence has highlighted the important roles of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in tumor biology. However, the roles of cancer specific lncRNAs in lncRNA-related ceRNA network of lung adenocarcinoma (LUAD) are still unclear. In the present study, the 465 RNA sequencing profiles in LUAD patients were obtained from the cancer genome atlas (TCGA) database, which provides large sample RNA sequencing data free of charge, and 41 cancer specific lncRNAs, 25 miRNAs and 1053 mRNAs (fold change >2, p<0.05) were identified. Then, the lncRNA-miRNA-mRNA ceRNA network of LUAD was constructed with 29 key lncRNAs, 24 miRNAs and 72 mRNAs. Subsequently, we selected these 29 key lncRNAs to analyze their correlation with clinical features, and 21 of them were aberrantly expressed with tumor pathological stage, TNM staging system, lymph node metastasis and patient outcome assessment, respectively. Furthermore, there were 5 lncRNAs (BCRP3, LINC00472, CHIAP2, BMS1P20 and UNQ6494) positively correlated with overall survival (OS, log-rank p<0.05). Finally, 7 cancer specific lncRNAs were randomly selected to verify the expression in 53 newly diagnosed LUAD patients using qRT-PCR. The expression results between TCGA and qRT-PCR were 100% in agreement. The correlation between AFAP1-AS1 and LINC00472 and clinical features were also confirmed. Thus, our results showed the lncRNA expression profiles and we constructed an lncRNA-miRNA-mRNA ceRNA network in LUAD. The present study provides novel insight for better understanding of lncRNA-related ceRNA network in LUAD and facilitates the identification of potential biomarkers for diagnosis and prognosis.

Integrated analysis of long non-coding RNA competing interactions reveals the potential role in progression of human gastric cancer.

Abnormal expression of long non-coding RNAs (lncRNAs) have been shown to play an important role in tumor biology. The Cancer Genome Atlas (TCGA) platform is a large sample sequencing database of lncRNAs, and further analysis of the associations between these data and patients' clinical related information can provide new approaches to find the functions of lncRNA. In the present study, 361 RNA sequencing profiles of gastric cancer (GC) patients were selected from TCGA. Then, we constructed the lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network of GC. There were 25 GC specific lncRNAs (fold change >2, p<0.05) identified, 19 of them were included in ceRNA network. Subsequently, we selected these 19 key lncRNAs and analyzed the correlations with clinical features and overall survival, 14 of them were discriminatively expressed with tumor size, tumor grade, TNM stage and lymphatic metastasis (p<0.05). In addition, eight lncRNAs (RPLP0P2, FOXD2-AS1, H19, TINCR, SLC26A4-AS1, SMIM10L2A, SMIM10L2B and SNORD116-4) were found to be significantly associated with overall survival (log-rank p<0.05). Finally, two key lncRNAs HOTAIR and UCA1 were selected for validation of their expression levels in 82 newly diagnosed GC patients by qRT-PCR. Results showed that the fold changes between TCGA and qRT-PCR were 100% in agreement. In addition, we also found that HOTAIR was significantly correlated with tumor size and lymphatic metastasis (p<0.05), and UCA1 was significantly correlated with tumor size, TNM stage and lymphatic metastasis (p<0.05). The clinical relevance of the two lncRNAs and the bioinformatics analysis results were almost the same. Overall, our study showed the GC specific lncRNAs expression patterns and a ceRNA network in GC. Clinical features related to GC specific lncRNAs also suggested these lncRNAs are worthwhile for further study as novel candidate biomarkers for the clinical diagnosis of GC and potential indicators for prognosis.

Differential expression profiles of long non-coding RNAs reveal potential biomarkers for identification of human gastric cancer.

Gastric cancer (GC) is one of the most lethal malignancies worldwide. To reduce its high mortality, sensitive and specific biomarkers for early detection are urgently needed. Recent studies have reported that tumor-specific long non-coding RNAs (lncRNAs) seem to be potential biomarkers for the early diagnosis and treatment of cancer. In the present study, lncRNA and mRNA expression profiling of GC specimens and their paired adjacent non-cancerous tissues was performed. Differentially expressed lncRNAs and mRNAs were identified through microarray analysis. The function of differential mRNA was determined by gene ontology and pathway analysis and the functions of lncRNAs were studied by constructing a co-expression network to find the relationships with corresponding mRNAs. We connected the co-expression network, mRNA functions, and the results of the microarray profile differential expression and selected 14 significantly differentially expressed key lncRNAs and 21 key mRNAs. Quantitative RT-PCR (qRT-PCR) was conducted to verify these key RNAs in 50 newly diagnosed GC patients. The data showed that RP5-919F19, CTD-2541M15 and UCA1 was significantly higher expressed. AP000459, LOC101928316, RP11-167N4 and LINC01071 expression was significantly lower in 30 advanced GC tumor tissues than adjacent non-tumor tissues P<0.05. Then, we further validated the above significant differential expression candidate lncRNAs in 20 early stage GC patients. Results showed that CTD-2541M15 and UCA1 were significantly higher expressed, AP000459, LINC01071 and MEG3 expression was significantly lower in 20 early stage GC patient tumor tissues than adjacent non-tumor tissues (P<0.05). In addition, expression of these lncRNAs shows gradual upward trend from early stage GC to advanced GC. Furthermore, conditional logistic regression analysis revealed the aberrant expression of CTD-2541M15, UCA1 and MEG3 closely linked with GC. There is a set of differentially expressed lncRNAs in GC which may be associated with the progression and development of GC. The differential expression profiles of lncRNAs in GC may be promising biomarkers for the early detection and early screening of high­risk populations.