Long Noncoding RNA CCDC26 Promotes Thyroid Cancer Malignant Progression via miR-422a/EZH2/Sirt6 Axis

LINC00115 aggravates thyroid cancer progression by targeting miR-489-3p, which downregulates EVA1A to regulate the Hippo signaling pathway

LINC00115 has been documented to regulate many different cancers; however, its function in thyroid cancer (THCA) remains unexplored. Therefore, we examined the effects of LINC00115 on THCA and the associated molecular mechanisms. In THCA cell lines and tumor samples, the expression levels of LINC00115, miR-489-3p, and EVA1A were analyzed by qRT-PCR along with respective controls. Cell viability, migration, and apoptosis were analyzed by employing CCK-8, transwell, and western blotting assays, respectively. Xenograft experiments were done to assess in vivo tumor growth. The interaction among LINC00115, miR-489-3p, and EVA1A was tested using RNA-binding protein immunoprecipitation and luciferase assays. Key proteins of the Hippo signaling pathway were ascertained by western blotting. The outcomes elucidated that LINC00115 was overexpressed in THCA cell lines and tumor tissues. LIN00115 knockdown reduced in vitro proliferation and migration but facilitated apoptosis in THCA cells and inhibited in vivo tumor growth. The target of LINC00115 was miR-489-3p, which binds to EVA1A in THCA. Functional assays revealed that miR-489-3p inhibition boosted THCA cell proliferation and migration, but hindered apoptosis. However, EVA1A knockdown resulted in the opposite effects via the Hippo signaling pathway. Additionally, miR-489-3p inhibition partially negated the effects of LINC00115 knockdown in THCA cells, and EVA1A knockdown remarkably impeded the effects of miR-489-3p inhibition in THCA cells. Thus, LINC00115 knockdown suppressed THCA carcinogenesis via targeting miR-489-3p, which regulates EVA1A expression and affects the Hippo signaling pathway.

LINC00891 Promotes Tumorigenesis and Metastasis of Thyroid Cancer by Regulating SMAD2/3 via EZH2

BACKGROUND

Thyroid cancer (TC), the most common endocrine malignant tumor, is increasingly causing a huge threat to our health nowadays.

METHODS

To explore the tumorigenesis mechanism of thyroid cancer, we identified that long intergenic non-coding RNA-00891 (LINC00891) was upregulated in TC using the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and local databases. LINC00891 expression was correlated with histological type and lymph node metastasis (LNM). The high expression of LINC00891 could serve as a diagnostic marker for TC and its LNM. In vitro experiments demonstrated that LINC00891 knockdown could inhibit cell proliferation, migration, invasion and prompt apoptosis and G1 arrest of TC cells. We also investigated the related mechanisms of LINC00891 promoting TC progression using RNA sequencing, Gene Set Enrichment Analysis, and Western blotting.

RESULTS

Our experiments demonstrated that LINC00891 promoted TC progression via the EZH2-SMAD2/3 signaling axis. In addition, overexpression of EZH2 could reverse the suppressive epithelial-to-mesenchymal transition (EMT) caused by LINC00891 knockdown.

CONCLUSION

In conclusion, the LINC00891/EZH2/SMAD2/3 regulatory axis participated in tumorigenesis and metastasis of thyroid cancer, which may provide a novel target for treatment.

Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM

Introduction: Lung cancer is one of the most frequent neoplasms worldwide with approximately 2.2 million new cases and 1.8 million deaths each year. The expression levels of programmed death ligand-1 (PDL1) demonstrate a complex association with lung cancer. Neuroblastoma is a high-risk malignant tumor and is mainly involved in childhood patients. Identification of new biomarkers for these two diseases can significantly promote their diagnosis and therapy. However, in vivo experiments to discover potential biomarkers are costly and laborious. Consequently, artificial intelligence technologies, especially machine learning methods, provide a powerful avenue to find new biomarkers for various diseases. Methods: We developed a machine learning-based method named LDAenDL to detect potential long noncoding RNA (lncRNA) biomarkers for lung cancer and neuroblastoma using an ensemble of a deep neural network and LightGBM. LDAenDL first computes the Gaussian kernel similarity and functional similarity of lncRNAs and the Gaussian kernel similarity and semantic similarity of diseases to obtain their similar networks. Next, LDAenDL combines a graph convolutional network, graph attention network, and convolutional neural network to learn the biological features of the lncRNAs and diseases based on their similarity networks. Third, these features are concatenated and fed to an ensemble model composed of a deep neural network and LightGBM to find new lncRNA-disease associations (LDAs). Finally, the proposed LDAenDL method is applied to identify possible lncRNA biomarkers associated with lung cancer and neuroblastoma. Results: The experimental results show that LDAenDL computed the best AUCs of 0.8701, 107 0.8953, and 0.9110 under cross-validation on lncRNAs, diseases, and lncRNA-disease pairs on Dataset 1, respectively, and 0.9490, 0.9157, and 0.9708 on Dataset 2, respectively. Furthermore, AUPRs of 0.8903, 0.9061, and 0.9166 under three cross-validations were obtained on Dataset 1, and 0.9582, 0.9122, and 0.9743 on Dataset 2. The results demonstrate that LDAenDL significantly outperformed the other four classical LDA prediction methods (i.e., SDLDA, LDNFSGB, IPCAF, and LDASR). Case studies demonstrate that CCDC26 and IFNG-AS1 may be new biomarkers of lung cancer, SNHG3 may associate with PDL1 for lung cancer, and HOTAIR and BDNF-AS may be potential biomarkers of neuroblastoma. Conclusion: We hope that the proposed LDAenDL method can help the development of targeted therapies for these two diseases.

Anesthetic‑specific lncRNA and mRNA profile changes in blood during colorectal cancer resection: A prospective, matched‑case pilot study

Prometastatic and antitumor effects of different anesthetics have been previously analyzed in several studies with conflicting results. Thus, the underlying perioperative molecular mechanisms mediated by anesthetics potentially affecting tumor phenotype and metastasis remain unclear. It was hypothesized that anesthetic‑specific long non‑coding RNA (lncRNA) expression changes are induced in the blood circulation and play a crucial role in tumor outcome. In the present study, high‑throughput sequencing and quantitative PCR were performed in order to identify lncRNA and mRNA expression changes affected by two therapeutic regimes, total intravenous anesthesia (TIVA) and volatile anesthetic gas (VAG) in patients undergoing colorectal cancer (CRC) resection. Total blood RNA was isolated prior to and following resection and characterized using RNA sequencing. mRNA‑lncRNA interactions and their roles in cancer‑related signaling of differentially expressed lncRNAs were identified using bioinformatics analyses. The comparison of these two time points revealed 35 differentially expressed lncRNAs in the TIVA‑group, and 25 in the VAG‑group, whereas eight were shared by both groups. Two lncRNAs in the TIVA‑group, and 23 in the VAG‑group of in silico identified target‑mRNAs were confirmed as differentially regulated in the NGS dataset of the present study. Pathway analysis was performed and cancer relevant canonical pathways for TIVA were identified. Target‑mRNA analysis of VAG revealed a markedly worsened immunological response against cancer. In this proof‑of‑concept study, anesthesic‑specific expression changes in lncRNA and mRNA profiles in blood were successfully identified. Moreover, the data of the present study provide the first evidence that anesthesia‑induced lncRNA pattern changes may contribute further in the observed differences in CRC outcome following tumor resection.

lncRNA polymorphism affects the prognosis of gastric cancer

Background

Previous studies have found that lncRNA polymorphisms are associated with the prognosis of gastric cancer (GC), but the specific roles of many lncRNA polymorphism sites in gastric cancer are still unclear. Our study aims to deeply explore the relationship between genetic polymorphism of lncRNA and the prognosis of GC.

Methods

The genotypes of candidate SNP locus were detected by Sequenom Mass ARRAY SNP. We deeply analyzed the association of lncRNA polymorphisms with GC prognosis by univariate and multivariate Cox regression, stratified analysis, conjoint analysis, and log-rank test.

Results

We found that mutations at rs2579878 and rs10036719 loci reduced the risk of poor prognosis of GC. Stratified analysis showed that rs2795025, rs10036719, and rs12516079 polymorphisms were all associated with tumor prognosis. In addition, conjoint analyses showed that the interaction between these two polymorphic sites (rs2795025 and rs12516079) could increase the risk of poor prognosis. Multivariate analysis also found that the AG/AA genotype of rs10036719 and AG genotype of rs12516079 were independent prognostic factors. Moreover, the high expression of both CCDC26 and LINC02122 were shown to be associated with the poor survival status of GC patients.

Conclusions

We find that the genetic polymorphism of lncRNA plays a role in the development of GC and is closely related to the survival time of patients. It could serve as a predictor of the prognosis of GC.

LINC01232 serves as a novel biomarker and promotes tumour progression by sponging miR-204-5p and upregulating RAB22A in clear cell renal cell carcinoma

BACKGROUND

Long non-coding RNAs (lncRNAs) are involved in the progression of various cancers, including clear cell renal cell carcinoma (ccRCC). This study aimed to investigate the expression and prognostic value of long intergenic non-protein coding RNA (LINC) 01232 in ccRCC and preliminary explore the molecular mechanism underlying the role of LINC01232 in ccRCC progression.

METHODS

Tumour tissues and adjacent normal tissues of 122 patients with ccRCC were collected in this study. The levels of LINC01232, microRNA (miR)-204-5p and RAB22A were measured by quantitative real-time PCR. The proliferation, migration and invasion of ccRCC cells were detected by cell counting kit-8 assay and Transwell assay, respectively. The interaction among LINC01232, miR-204-5p and RAB22A was confirmed by bioinformatics analysis, dual-luciferase reporter assay and Pearson correlation analysis. The association of LINC01232 and miR-204-5p with ccRCC patient survival was verified by the Kaplan-Meier method and log-rank test. The prognostic value of LINC01232 in ccRCC was confirmed by Cox regression analysis.

RESULTS

LINC01232 expression was increased in ccRCC tumour tissues and ccRCC cells and independently predicted the prognosis of ccRCC patients. In addition, LINC01232 silencing inhibited ccRCC cell proliferation, migration and invasion. Moreover, LINC01232 served as a sponge for miR-204-5p, and miR-204-5p reduction reversed the inhibitory effect of LINC01232 silencing on ccRCC cell function. Furthermore, LINC01232 could sponge miR-204-5p, causing the elevation of RAB22A in ccRCC, thereby promoting ccRCC cell function.

CONCLUSION

LINC01232 may be an independent prognostic biomarker in ccRCC and plays an oncogenic role in ccRCC progression by sponging miR-204-5p and upregulating RAB22A.