Screening of important lncRNAs associated with the prognosis of lung adenocarcinoma, based on integrated bioinformatics analysis

Prognostic Value of lncRNA DRAIC and miR-3940-3p in Lung Adenocarcinoma and Their Effect on Lung Adenocarcinoma Cell Progression

Purpose

Lung adenocarcinoma (LUAD) is a most common malignant tumor, even worse for diseases with relatively poor prognosis. Non-coding RNAs have the potential to be biomarkers for the prognosis of various cancers. LncRNA DRAIC and miR-3940-3p have been screened as dysregulated RNAs in LUAD. The clinical significance and biological function of lncRNA DRAIC and miR-3940-3p in LUAD were assessed in this study.

Patients and Methods

A total of 122 cases of LUAD patients with complete clinical information were enrolled. The expression levels of lncRNA DRAIC and miR-3940-3p were determined via RT-qPCR in LUAD tissues and cells. The relationship between lncRNA DRAIC or miR-3940-3p expression and the clinicopathological features of patients was analyzed based on the Pearson Chi-square test. For the prognostic value, the Kaplan–Meier plot and multi-variate Cox proportional regression analysis were introduced. Finally, the effect of lnc DRAIC and miR-3940-3p on the LUAD cellular function was investigated by CCK-8 and Transwell assay.

Results

lnc DRAIC was upregulated in LUAD tissues and cells, but miR-3940-3p was downregulated. Both of them showed significant associations with and TNM stage, lymph node metastasis, and a poor prognosis. Lnc-DRAIC and miR-3940-3p have the potential as independent prognostic factors for LUAD. Furthermore, the inhibition of lnc DRAIC can inhibit cell proliferation, migration, and invasion of LUAD partly as a ceRNA of miR-3940-3p.

Conclusion

lncRNA DRAIC/miR-3940-3p axis may be involved in the progression of LUAD and can be developed to promising prognostic factors, which may provide new insights into the treatment of LUAD.

Up-regulation of FOXN3-AS1 in invasive ductal carcinoma of breast cancer patients

Oncogenic and tumor-suppressive roles of long non-coding RNA make them an appropriate target for expression analysis in cancer studies. In this study, we selected two lncRNAs (EMX2OS and FOXN3-AS1) that are resided near the GWAS-identified SNPs for breast cancer (rs2901157 and rs141061110). These transcripts have been identified in different cancer types as either oncogenes or tumor suppressors. In the present investigation, we aimed to quantify the expression level of EMX2OS and FOXN3-AS1 in 44 breast cancer samples and normal adjacent tissues (ANCTs). The FOXN3-AS1 expression level was significantly increased in breast cancer samples compared with ANCTs (P value = 0.02), Also its amounts could distinguish two sets of samples with an accuracy of 70% (P value = 0.009). We have found an association between FOXN3-AS1 expression and tumor size (P value = 0.02). On the other hand, no significant differences were found in the EMX2OS expression level between two sets of samples (P value = 0.44); however, EMX2OS expression level has a significant association with the age of the patients (P value = 0.03). According to our result, FOXN3-AS1 can be demonstrated as a probable diagnostic marker in breast cancer so we suggest further functional studies to find the precise role of these lncRNAs in breast cancer progression.

Long Non-coding RNA RUNDC3A-AS1 Promotes Lung Metastasis of Thyroid Cancer via Targeting the miR-182-5p/ADAM9

Long non-coding RNAs (lncRNAs) have been identified as influential indicators in variety of malignancies. Among which, LncRNA RUNDC3A-AS1 is reported to upregulate in thyroid cancer. However, the expression pattern and the pathological function of lncRNA RUNDC3A-AS1 in thyroid cancer is unclear. In this study, we examined the expression levels of lncRNA RUNDC3A-AS1 in the thyroid cancer tissues and cell lines via RT-qPCR analysis. The effects of RUNDC3A-AS1 on thyroid cancer cell metastasis were detected by transwell chamber assay, scratch assay in vitro and lung metastasis model in vivo. The results indicated that RUNDC3A-AS1 was highly expressed in the thyroid cancer tissues and cell lines. Functionally, knockdown of RUNDC3A-AS1 could repress the migration and invasion of thyroid cancer cells in vitro, and inhibit thyroid cancer metastasis to lung in vivo. Mechanistically, RUNDC3A-AS1 served as an inhibitor of miR-182-5p in tumor tissues and cell lines. RUNDC3A-AS1 inhibited the expression of miR-182-5p to increase the expression level of ADAM9, thus further aggravating the malignancy of thyroid cancer. Therefore, the RUNDC3A-AS1/miR-182-5p/ADAM9 axis may be a potential therapeutic target for the treatment of thyroid cancer metastasis.

Identification of a Prognostic 3-Gene Risk Prediction Model for Thyroid Cancer

Objective: We aimed to screen the genes associated with thyroid cancer (THCA) prognosis, and construct a poly-gene risk prediction model for prognosis prediction and improvement. Methods: The HTSeq-Counts data of THCA were accessed from TCGA database, including 505 cancer samples and 57 normal tissue samples. "edgeR" package was utilized to perform differential analysis, and weighted gene co-expression network analysis (WGCNA) was applied to screen the differential co-expression genes associated with THCA tissue types. Univariant Cox regression analysis was further used for the selection of survival-related genes. Then, LASSO regression model was constructed to analyze the genes, and an optimal prognostic model was developed as well as evaluated by Kaplan-Meier and ROC curves. Results: Three thousand two hundred seven differentially expressed genes (DEGs) were obtained by differential analysis and 23 co-expression genes (|COR| > 0.5, P < 0.05) were gained after WGCNA analysis. In addition, eight genes significantly related to THCA survival were screened by univariant Cox regression analysis, and an optimal prognostic 3-gene risk prediction model was constructed after genes were analyzed by the LASSO regression model. Based on this model, patients were grouped into the high-risk group and low-risk group. Kaplan-Meier curve showed that patients in the low-risk group had much better survival than those in the high-risk group. Moreover, great accuracy of the 3-gene model was revealed by ROC curve and the remarkable correlation between the model and patients' prognosis was verified using the multivariant Cox regression analysis. Conclusion: The prognostic 3-gene model composed by GHR, GPR125, and ATP2C2 three genes can be used as an independent prognostic factor and has better prediction for the survival of THCA patients.