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Abstract Long noncoding RNA (lncRNA) has played the important function in regulation of various biological processes and in diagnostic value has been widely appreciated. In the present study, we have found that LOC101928316 was significantly downregulated in gastric cancer (GC) tissues specimen, GC cell lines, and associated with the GC patients tumor, node, and metastasis (TNM) stage and degree of differentiation (P ＜ 0.05). LOC101928316 overexpression can significantly inhibit SGC‐7901 cell migration, invasion, and proliferation (P＜0.05). LOC101928316 molecular mechanism investigates suggested that LOC101928316 can regulate PI3K‐Akt‐mTOR signaling pathway and change the GC development progression in vivo and in vitro. In vivo experiment also revealed that LOC101928316‐Overexpression can inhibit the tumorigenicity of GC cells in tumor‐burdened experimental nude mice (P ＜ 0.05). LOC101928316 may function as anti‐oncogene and also plays an important role in GC tumorigenesis. Collectively, our data provided the key role of LOC101928316 in the tumorigenesis of GC. In addition, the present study elucidates LOC101928316 potential regulatory network, which may help us to lead a better knowing of the pathogenesis of GC and probe the lncRNA as a novel biomarker to diagnosis and therapy for this malignant tumor.

Abstract Recent scientific evidence has suggested that long noncoding RNAs (lncRNAs) play an important part in tumorigenesis as an important member of competing endogenous RNAs (ceRNAs). Hundreds of RNA sequence data and relevant clinic information are freely accessible in The Cancer Genome Atlas (TCGA) datasets. However, the role of cancer‐related lncRNAs in papillary thyroid cancer (PTC) is not fully understood yet. In this study, we identified 461 RNA sequencing data from TCGA. Subsequently, 45 lncRNAs, 21 miRNAs, and 78 mRNAs were chosen to construct a ceRNA network of PTC. Then, we analyzed the correlation between these 45 PTC‐specific lncRNAs and clinic features and patient outcome. Thirty‐seven of these lncRNAs were found to be closely related to age, race, gender, lymph node metastasis, TNM staging system, and patient outcome. Additionally, three of them were linked to PTC patient overall survival. Eventually, we selected eight lncRNAs randomly and performed quantificational real‐time polymerase chain reaction (qRT‐PCR) in 28 newly diagnosed patients with PTC to verify the reliability of the above results. The results of qRT‐PCR are totally in agreement with the bioinformatics analysis. Additionally, it was found that HAND2‐AS1 was negatively related to tumor size (P

Abstract Accumulating evidence implies that long noncoding RNAs (lncRNAs) play a crucial role in predicting survival for Hepatocellular carcinoma (HCC) patients. This study aims to capture the current research hotspots of HCC, based on the analysis of publications related to HCC research from 2013 to 2017, and to identify a novel lncRNA signature for HCC prognosis through the data mining in The Cancer Genome Atlas (TCGA). “Prognosis” and “biomarker” were located in the core of the HCC research hotspot. Moreover, long noncoding RNA was the top one research frontier in HCC research. The associations between survival outcome and the expression of lncRNAs were evaluated by the univariate and multivariate Cox proportional hazards regression analyses. Four lncRNAs (LINC00261, TRELM3P, GBP1P1, and CDKN2B‐AS1) were identified as significantly correlated with overall survival (OS). These four lncRNAs were gathered as a single prognostic signature. There was a significant positive correlation between HCC patients with low‐risk scores and overall survival (HR = 1.802, 95%CI [1.224‐2.652], P = .003). Further analysis suggested that the prognostic value of this four‐lncRNA signature was independent in clinical features. The enrichment analysis of prognostic lncRNA‐related gene was performed to find out the related pathways. Our study indicates that this novel lncRNA expression signature may be a useful biomarker of the prognosis for HCC patients, based on bioinformatics analysis.

Abstract Cutaneous melanoma (CM) is the most malignant tumor of skin cancers because of its rapid development and high mortality rate. Long noncoding RNAs (lncRNAs), which play essential roles in the tumorigenesis and metastasis of CM and interplay with microRNAs (miRNAs) and mRNAs, are hopefully considered to be efficient biomarkers to detect deterioration during the progression of CM to improve the prognosis. Bioinformatics analysis was fully applied to predict the vital lncRNAs and the associated miRNAs and mRNAs, which eventually constructed the competing endogenous RNA (ceRNA) network to explain the RNA expression patterns in the progression of CM. Further statistical analysis emphasized the importance of these key genes, which were statistically significantly related to one or few clinical features from the ceRNA network. The results showed the lncRNAs MGC12926 and LINC00937 were verified to be strongly connected with the prognosis of CM patients.