Competing endogenous RNA network analysis reveals potential long non-coding RNAs as predictive biomarkers of gastric cancer

LncRNA MORT (ZNF667-AS1) in Cancer-Is There a Possible Role in Gynecological Malignancies?

Gynecological cancers (GCs) are currently among the major threats to female health. Moreover, there are different histologic subtypes of these cancers, which are defined as ‘rare’ due to an annual incidence of <6 per 100,000 women. The majority of these tend to be associated with a poor prognosis. Long non-coding RNAs (lncRNAs) play a critical role in the normal development of organisms as well as in tumorigenesis. LncRNAs can be classified into tumor suppressor genes or oncogenes, depending on their function within the cellular context and the signaling pathways in which they are involved. These regulatory RNAs are potential therapeutic targets for cancer due to their tissue and tumor specificity. However, there still needs to be a deeper understanding of the mechanisms by which lncRNAs are involved in the regulation of numerous biological functions in humans, both in normal health and disease. The lncRNA Mortal Obligate RNA Transcript (MORT; alias ZNF667-AS1) has been identified as a tumor-related lncRNA. ZNF667-AS1 gene, located in the human chromosome region 19q13.43, has been shown to be silenced by DNA hypermethylation in several cancers. In this review, we report on the biological functions of ZNF667-AS1 from recent studies and describe the regulatory functions of ZNF667-AS1 in human disease, including cancer. Furthermore, we discuss the emerging insights into the potential role of ZNF667-AS1 as a biomarker and novel therapeutic target in cancer, including GCs (ovarian, cervical, and endometrial cancers).

Long noncoding RNA LINC01291 promotes the aggressive properties of melanoma by functioning as a competing endogenous RNA for microRNA-625-5p and subsequently increasing IGF-1R expression

Studies have confirmed the relationship between dysregulated long noncoding RNAs and melanoma pathogenesis. However, the regulatory functions of long intergenic non-protein coding RNA 1291 (LINC01291) in melanoma remain unknown. Therefore, we evaluated LINC01291 expression in melanoma and explored its roles in regulating tumor behaviors. Further, the molecular events via which LINC01291 affects melanoma cells were investigated. LINC01291 expression in melanoma cells was analyzed using The Cancer Genome Atlas database and quantitative real-time polymerase chain reaction. Functional assays, including the Cell Counting Kit-8 assay, colony formation assay, flow cytometry, cell migration and invasion assays, and tumor xenograft models, were used to examine LINC01291’s role in melanoma cells. Additionally, bioinformatics analysis, RNA immunoprecipitation, luciferase reporter assay, and western blotting were conducted to determine the tumor-promoting mechanism of LINC01291. LINC01291 was upregulated in melanoma tissues and cell lines. Following LINC01291 knockdown, cell proliferation, colony formation, migration, and invasion were diminished, whereas apoptosis was enhanced and the cell cycle was arrested at G0/G1. In addition, loss of LINC01291 decreased the chemoresistance of melanoma cells to cisplatin. Furthermore, LINC01291 interference inhibited melanoma tumor growth in vivo. Mechanistically, LINC01291 functions as a competing endogenous RNA by sponging microRNA-625-5p (miR-625-5p) in melanoma cells and maintaining insulin-like growth factor 1 receptor (IGF-1R) expression. Rescue experiments revealed that the roles induced by LINC01291 depletion in melanoma cells could be reversed by suppressing miR-625-5p or overexpressing IGF-1R. Our study identified the LINC01291/miR-625-5p/IGF-1R competing endogenous RNA pathway in melanoma cells, which may represent a novel diagnostic biomarker and an effective therapeutic target for melanoma.

Long Noncoding RNA WEE2-AS1 Plays an Oncogenic Role in Glioblastoma by Functioning as a Molecular Sponge for MicroRNA-520f-3p

The long noncoding RNA WEE2 antisense RNA 1 (WEE2-AS1) plays an oncogenic role in hepatocellular carcinoma and triple negative breast cancer progression. In this study, we investigated the expression and roles of WEE2-AS1 in glioblastoma (GBM). Furthermore, the molecular mechanisms behind the oncogenic actions of WEE2-AS1 in GBM cells were explored in detail. WEE2-AS1 expression was detected using quantitative real-time polymerase chain reaction. The roles of WEE2-AS1 in GBM cells were evaluated by the cell counting kit-8 assay, flow cytometric analysis, Transwell cell migration and invasion assays, and tumor xenograft experiments. WEE2-AS1 expression was evidently enhanced in GBM tissues and cell lines compared with their normal counterparts. An increased level of WEE2-AS1 was correlated with the average tumor diameter, Karnofsky Performance Scale score, and shorter overall survival among GBM patients. Functionally, depleted WEE2-AS1 attenuated GBM cell proliferation, migration, and invasion in vitro, promoted cell apoptosis, and impaired tumor growth in vivo. Mechanistically, WEE2-AS1 functioned as a molecular sponge for microRNA-520f-3p (miR-520f-3p) and consequently increased specificity protein 1 (SP1) expression in GBM cells. A series of recovery experiments revealed that the inhibition of miR-520f-3p and upregulation of SP1 could partially abrogate the influences of WEE2-AS1 downregulation on GBM cells. In conclusion, WEE2-AS1 can adsorb miR-520f-3p to increase endogenous SP1 expression, thereby facilitating the malignancy of GBM. Therefore, targeting the WEE2-AS1–miR-520f-3p–SP1 pathway might be a promising therapy for the management of GBM in the future.

A potential prognostic prediction model of colon adenocarcinoma with recurrence based on prognostic lncRNA signatures

BACKGROUND

Colon adenocarcinoma (COAD) is one of the common gastrointestinal malignant diseases, with high mortality rate and poor prognosis due to delayed diagnosis. This study aimed to construct a prognostic prediction model for patients with colon adenocarcinoma (COAD) recurrence.

METHODS

Differently expressed RNAs (DERs) between recurrence and non-recurrence COAD samples were identified based on expression profile data from the NCBI Gene Expression Omnibus (GEO) repository and The Cancer Genome Atlas (TCGA) database. Then, recurrent COAD discriminating classifier was established using SMV-RFE algorithm, and receiver operating characteristic curve was used to assess the predictive power of classifier. Furthermore, the prognostic prediction model was constructed based on univariate and multivariate Cox regression analysis, and Kaplan-Meier survival curve analysis was used to estimate this model. Furthermore, the co-expression network of DElncRNAs and DEmRNAs was constructed followed by GO and KEGG pathway enrichment analysis.

RESULTS

A total of 54 optimized signature DElncRNAs were screened and SMV classifier was constructed, which presented a high accuracy to distinguish recurrence and non-recurrence COAD samples. Furthermore, six independent prognostic lncRNAs signatures (LINC00852, ZNF667-AS1, FOXP1-IT1, LINC01560, TAF1A-AS1, and LINC00174) in COAD patients with recurrence were screened, and the prognostic prediction model for recurrent COAD was constructed, which possessed a relative satisfying predicted ability both in the training dataset and validation dataset. Furthermore, the DEmRNAs in the co-expression network were mainly enriched in glycan biosynthesis, cardiac muscle contraction, and colorectal cancer.

CONCLUSIONS

Our study revealed that six lncRNA signatures acted as an independent prognostic biomarker for patients with COAD recurrence.

Gastric cancer risk-scoring system based on analysis of a competing endogenous RNA network

Background

Long noncoding RNAs (lncRNAs) can play vital roles in tumor initiation, progression, invasion, and metastasis. However, the functional role of the lncRNA-based competing endogenous RNA (ceRNA) networks in gastric cancer (GC) is still unclear. We aimed to identify novel lncRNAs and their association with GC prognosis.

Methods

The lncRNA, miRNA, and mRNA expression profiles of GC patients data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using the edge-R package. Then, the relationship among lncRNAs-miRNAs-mRNAs was integrated into a constructed ceRNA network with Cytoscape software. Using Cox regression analysis, a risk score system based on DEGs associated with patient prognosis in GC was established. Finally, a nomogram was founded to predict the prognosis of GC patients.

Results

A total of 971 differentially expressed lncRNAs (DElncRNAs), 144 differentially expressed miRNAs (DEmiRNAs) and 2,789 differentially expressed mRNAs (DEmRNAs) were identified and found to be associated with GC risk. Using the bioinformatics method, a ceRNA network involving 62 DElncRNAs, 21 DEmiRNAs and 59 DEmRNAs was constructed. Based on the results of the Cox regression analysis, a risk-scoring system involving 3 lncRNAs (i.e., ADAMTS9-AS1, C15orf54, and AL391152.1) was set up for the survival analysis of GC patients. The area under the receiver operating characteristic (ROC) curve for the risk-scoring system was 0.674, with a C-index of 0.64 [95% confidence interval (CI): 0.59–0.69, P=2.806485e−08]. Univariate and multivariate Cox regression analyses demonstrated that the risk-scoring system was an independent prognostic factor for GC. The risk-scoring system is positively associated with advanced tumor grade. The expression of these 3 lncRNAs were validated in GEPIA database. A nomogram based on these 3 lncRNAs was created to predict the prognosis of GC patients.

Conclusions

Our study established a novel lncRNA-expression-based ceRNA network and an ADAMTS9-AS1-C15orf54-AL391152.1-based risk-scoring system, which can be used to predict the prognosis of GC patients.

Integrative Analysis of the Doxorubicin-Associated LncRNA-mRNA Network Identifies Chemoresistance-Associated lnc-TRDMT1-5 as a Biomarker of Breast Cancer Progression

Increasing evidence has revealed close relationships between long non-coding RNAs (lncRNAs) and chemoresistance in multiple types of tumors; however, functional lncRNAs in breast cancer (BC) have not been completely identified. In this study, we aimed to identify novel lncRNAs that might play critical roles in doxorubicn resistance, which could reveal potential biomarkers of BC. Using a BC dataset (GSE81971), we identified 452 lncRNAs that were upregulated and 659 that were downregulated; furthermore, there were 1896 differentially expressed mRNAs, of which 1137 were upregulated and 758 were downregulated in MCF-7/ADR cells compared with the expression in MCF-7 cells. We constructed an lncRNA–mRNA network by integrating probe reannotation and regulatory interactions. To elucidate the key lncRNAs in BC, we further analyzed dysregulated lncRNA–mRNA crosstalk, and six candidate lncRNAs (lnc-TRDMT1-5, ZNF667-AS1, lnc-MPPE1-13, DSCAM-AS1:5, DSCAM-AS1:2, and lnc-CFI-3) were identified. Notably, the expression level of lnc-TRDMT1-5 was significantly upregulated in resistant cells compared with sensitive cells, and its levels were increased in BC tissues compared with adjacent tissues. Levels were positively associated with estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) expression levels. High expression of lnc-TRDMT1-5 predicted poor prognosis in ER-positve and HER2-positive BC patients, especially in patients with chemoresistance. Bioinformatic and functional analysis revealed that lnc-TRDMT1-5 was involved in many crucial pathways in cancer, such as the PI3K/AKT and Wnt signaling pathways. Subcellular localization predicted that lnc-TRDMT1-5 was located in the cytoplasm, and the lncRNA–miRNA–mRNA network showed that lnc-TRDMT1-5 might serve as a regulator in BC. Here, our results demonstrated a dysregulated lncRNA–mRNA network that might provide new treatment strategies for chemoresistant BC, and the results identified a new lncRNA, lnc-TRDMT1-5, with oncogenic and prognostic functions in human BC.