[Progress of Long Non-coding RNAs in Non-small Cell Lung Cancer]

Development and Validation of a Novel Stemness-Index-Related Long Noncoding RNA Signature for Breast Cancer Based on Weighted Gene Co-Expression Network Analysis

Background: Breast cancer (BC) is a major leading cause of woman deaths worldwide. Increasing evidence has revealed that stemness features are related to the prognosis and progression of tumors. Nevertheless, the roles of stemness-index-related long noncoding RNAs (lncRNAs) in BC remain unclear.

Methods: Differentially expressed stemness-index-related lncRNAs between BC and normal samples in The Cancer Genome Atlas database were screened based on weighted gene co-expression network analysis and differential analysis. Univariate Cox and least absolute shrinkage and selection operator regression analyses were performed to identify prognostic lncRNAs and construct a stemness-index-related lncRNA signature. Time-dependent receiver operating characteristic curves were plotted to evaluate the predictive capability of the stemness-index-related lncRNA signature. Moreover, correlation analysis and functional enrichment analyses were conducted to investigate the stemness-index-related lncRNA signature-related biological function. Finally, a quantitative real-time polymerase chain reaction was used to detect the expression levels of lncRNAs.

Results: A total of 73 differentially expressed stemness-index-related lncRNAs were identified. Next, FAM83H-AS1, HID1-AS1, HOXB-AS1, RP11-1070N10.3, RP11-1100L3.8, and RP11-696F12.1 were used to construct a stemness-index-related lncRNA signature, and receiver operating characteristic curves indicated that stemness-index-related lncRNA signature could predict the prognosis of BC well. Moreover, functional enrichment analysis suggested that differentially expressed genes between the high-risk group and low-risk group were mainly involved in immune-related biological processes and pathways. Furthermore, functional enrichment analysis of lncRNA-related protein-coding genes revealed that FAM83H-AS1, HID1-AS1, HOXB-AS1, RP11-1070N10.3, RP11-1100L3.8, and RP11-696F12.1 were associated with neuroactive ligand–receptor interaction, AMPK signaling pathway, PPAR signaling pathway, and cGMP-PKG signaling pathway. Finally, quantitative real-time polymerase chain reaction revealed that FAM83H-AS1, HID1-AS1, RP11-1100L3.8, and RP11-696F12.1 might be used as the potential diagnostic biomarkers of BC.

Conclusion: The stemness-index-related lncRNA signature based on FAM83H-AS1, HID1-AS1, HOXB-AS1, RP11-1070N10.3, RP11-1100L3.8, and RP11-696F12.1 could be used as an independent predictor for the survival of BC, and FAM83H-AS1, HID1-AS1, RP11-1100L3.8, and RP11-696F12.1 might be used as the diagnostic markers of BC.

Exploring the Effect of Differentially Expressed Long Non-coding RNAs Driven by Copy Number Variation on Competing Endogenous RNA Network by Mining Lung Adenocarcinoma Data

Lung cancer is the first cause of cancer death, and gene copy number variation (CNV) is a vital cause of lung cancer progression. Prognosis prediction of patients followed by medication guidance by detecting CNV of lung cancer is emerging as a promising precise treatment in the future. In this paper, the differences in CNV and gene expression between cancer tissue and normal tissue of lung adenocarcinoma (LUAD) from The Cancer Genome Atlas Lung Adenocarcinoma data set were firstly analyzed, and greater differences were observed. Furthermore, CNV-driven differentially expressed long non-coding RNAs (lncRNAs) were screened out, and then, a competing endogenous RNA (ceRNA) regulatory network related to the gene CNV was established, which involved 9 lncRNAs, seven microRNAs, and 178 downstream messenger RNAs (mRNAs). Pathway enrichment analyses sequentially performed revealed that the downstream mRNAs were mainly enriched in biological pathways related to cell division, DNA repair, and so on, indicating that these mRNAs mainly affected the replication and growth of tumor cells. Besides, the relationship between lncRNAs and drug effects was explored based on previous studies, and it was found that LINC00511 and LINC00942 in the CNV-associated ceRNA network could be used to determine tumor response to drug treatment. As examined, the drugs affected by these two lncRNAs mainly targeted metabolism, target of rapamycin signaling pathway, phosphatidylinositol-3-kinase signaling pathway, epidermal growth factor receptor signaling pathway, and cell cycle. In summary, the present research was devoted to analyzing CNV, lncRNA, mRNA, and microRNA of lung cancer, and nine lncRNAs that could affect the CNV-associated ceRNA network we constructed were identified, two of which are promising in determining tumor response to drug treatment.

[Expression of Serum LncRNA HOTAIR in Non-small Cell Lung Cancer and Its Clinical Significance]

BACKGROUND

The expression of long noncoding RNA HOX antisense RNA (HOTAIR) is abnormal in a variety of tumors. The aim of this study is to explore the serum levels and clinical significance of HOTAIR in patients with non-small cell lung cancer (NSCLC).

METHODS

The serum levels of HOTAIR were detected by real-time quantitative polymerase chain reaction (PCR) in 64 NSCLC patients and 64 normal controls. The relationships between the serum levels of HOTAIR and clinical pathological parameters were analyzed.

RESULTS

Compared with normal controls, the serum levels of HOTAIR in patients with NSCLC increased significantly (P<0.01). The serum levels of HOTAIR were correlated with tumor size, tumor-node-metastasis (TNM) stage and lymph node metastasis (P<0.05), but not with age, gender, smoking, differentiation and histology (P>0.05).

CONCLUSIONS

The serum levels of HOTAIR in patients with NSCLC are significantly higher, and HOTAIR may be involved in the pathogenesis of NSCLC.

Circulating long noncoding RNA GAS5 is a novel biomarker for the diagnosis of nonsmall cell lung cancer

The recently discovered long noncoding RNAs have the potential to regulate many biological processes, which are aberrantly expressed in many tumor types. Our previous study showed that the long noncoding RNA-growth arrest-specific transcript 5 (GAS5) was decreased in lung cancer tissue, which contributed to the proliferation and apoptosis of nonsmall cell lung cancer (NSCLC). GAS5 was also associated with the prognosis of lung cancer patients. These results suggest that GAS5 may represent a novel prognostic indicator and a target for gene therapy in NSCLC. However, the expression and diagnosis significance of GAS5 in the plasma of NSCLC patients was unknown. The plasma samples were more readily available than the tissue samples in clinical, so we designed the study to investigate the diagnosis value of GAS5 in blood samples. In our study, 90 patients with NSCLC and 33 healthy controls were included. Blood samples were collected before surgery and therapy. We extracted the free RNA in the plasma and analyzed the expression of GAS5 with quantitative reverse transcription PCR. Suitable statistics methods were used to compare the plasma GAS5 levels of preoperative and postoperative plasma samples between the NSCLC patients and healthy controls. Receiver-operating characteristic curve analysis was used to evaluate the diagnostic sensitivity and specificity of plasma GAS5 in NSCLC. The results showed that GAS5 was detectable and stable in the plasma of NSCLC patients. Furthermore, the plasma levels of GAS5 were significantly down-regulated in NSCLC patients compared with healthy controls (P = 0.000). Moreover, GAS5 levels increased markedly on the seventh day after surgery compared with preoperative GAS5 levels in NSCLC patients (P = 0.003). GAS5 expression levels could be used to distinguish NSCLC patients from control patients with an area under the curve of 0.832 (P < 0.0001; sensitivity, 82.2%; specificity, 72.7%). The combination of the GAS5 and carcinoembryonic antigen could produce an area of 0.909 under the receiver-operating characteristic curve in distinguishing NSCLC patients from control subjects (95% confidence interval 0.857-0.962, P = 0.000). We have demonstrated that GAS5 expression was decreased in NSCLC Plasma. Plasma samples were more accessible than tissue samples in clinical; therefore, GAS5 could be an ideal biomarker for the diagnosis of NSCLC.