A novel LncRNA-based prognostic score reveals TP53-dependent subtype of lung adenocarcinoma with poor survival

An Autophagy-Related Long Non-Coding RNA Prognostic Signature for Patients with Lung Squamous Carcinoma Based on Bioinformatics Analysis

Purpose

Lung cancer is the most common and deadly cancer type affecting humans. Although huge progress has been made on early diagnosis and precision treatment, the overall 5 year survival rate remains low. In this study, we constructed an autophagy-related long non-coding RNA (lncRNA) prognostic signature for guiding clinical practice.

Methods

From The Cancer Genome Atlas, we retrieved mRNA and lncRNA expression matrices of patients with lung squamous carcinoma. We then established a prognostic risk model using Lasso regression and multivariate Cox regression. The model generated a risk score to differentiate high- and low-risk groups. An ROC curve and nomogram were used to visualize the predictive ability of the current signatures. Finally, we used Gene Set Enrichment Analysis to determine gene ontology and pathway enrichment.

Results

After screening 1248 autophagy-related lncRNAs, we selected seven lncRNAs (LUCAT1, AC022150.2, AL035425.3, AC138976.2, AC106786.1, GPRC5D-AS1 and AP006545.2) for our signature. Univariate (hazard ratio [HR] = 2.147, 95% confidence interval [CI]: 1.681–2.743, P < 0.001) and multivariate (HR = 2.096, 95% CI: 1.652–2.658, P < 0.001) Cox regression analyses revealed that the risk score is an independent predictive factor for LUSC patients. Further, areas under the receiver operating characteristic curve were 0.622, 0.699, and 0.721, respectively, for the 1 year, 3 year, and 5 year risk scores—indicating a reliable model. Selected lncRNAs were primarily enriched in autophagy, metabolism, MAPK pathway, and JAK/STAT pathway. Further drug sensitivity analysis revealed that low-risk patients were more sensitive to Cisplatin, Docetaxel, Vinblastine, and Vinorelbine. Finally, a multi-omics analysis found that lncRNA-linked proteins IKBKB and SQSTM1 were expressed at low levels and significantly correlated in tumor samples, compared with normal tissue.

Conclusion

Our prognostic model successfully predicted patient prognosis in lung cancer.

A Long Intergenic Non-coding RNA, LINC01426, Promotes Cancer Progression via AZGP1 and Predicts Poor Prognosis in Patients with LUAD

Various long non-coding RNAs (lncRNAs) are closely associated with lung adenocarcinoma (LUAD), playing oncogenic or anti-oncogenic roles in tumorigenesis and progression. Herein, we report a novel lncRNA—long intergenic non-protein coding RNA 1426 (LINC01426)—that has not yet been characterized in LUAD. We note that LINC01426 expression was markedly upregulated in LUAD tissues, and that functional assays verified that LINC01426 knockdown markedly inhibited cell proliferation, migration, and invasion in vitro. Xenografts derived from A549 cells knocked down of LINC01426 had evidently lower tumor weights and smaller tumor volumes. Our study also found that LINC01426 bound to hsa-miR-30b-3p as a competitive endogenous RNA in LUAD. Moreover, LINC01426 affected LUAD wound healing by interacting and combining with AZGP1, and LINC01426 expression was significantly associated with tumor-node-metastasis (TNM) staging and prognosis in patients with LUAD. To summarize, our study elucidates the oncogenic roles of LINC01426 in LUAD tumorigenesis and progression. We think that LINC01426 can serve as a potential diagnostic biomarker and therapeutic target in patients with LUAD.

Long non-coding RNA SLC16A1-AS1: its multiple tumorigenesis features and regulatory role in cell cycle in oral squamous cell carcinoma

Altered expressions of long non-coding RNAs (lncRNAs) are potential cancer prognostic biomarkers that play a critical role in the development of tumorigenesis and metastasis of cancer. However, the relationship between the expression of lncRNAs in oral squamous cell carcinoma (OSCC) and the diagnosis, progression, and prognosis of OSCC has not been thoroughly elucidated. To identify the differentially expressed lncRNAs between OSCC tissue and normal tissue, RNA-Seq data were used. lncRNA SLC16A1-AS1 was significantly highly expressed in OSCC samples than that in normal samples. Systematic bioinformatics analysis revealed that SLC16A1-AS1 was associated with histological tumor grades and overall survival status, as well as copy number variation, somatic mutation, tumor mutation burden, tumor stemness, tumor microenvironment and infiltrating immune cells. According to three advanced bioinformatic algorithms prediction (WGCNA, GSEA and GSVA), SLC16A1-AS1 played an essential role in OSCC proliferation and its biological function was related to cell-cycle regulation. Loss-of-function experiments were performed to determine the biological functions of SLC16A1-AS in OSCC cells. Silencing SLC16A1-AS1 significantly reduced the cell proliferation rate and colony-forming ability in both CAL27 and SCC25 cell lines. Flow cytometry and western blot analysis revealed that SLC16A1-AS1 silencing induced G0/G1 cell cycle arrest and inhibited the expression of cyclin D1 in both CAL27 and SCC25 cells. In conclusion, our study comprehensively investigated the role of the lncRNA SLC16A1-AS1 in OSCC growth and proved that it may serve as a new diagnostic indicator and a new target for the treatment of OSCC.

A five-long non-coding RNA signature with the ability to predict overall survival of patients with lung adenocarcinoma

An increasing number of studies have indicated that the abnormal expression of certain long non-coding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with lung adenocarcinoma (LUAD). The aim of the present study was to establish an lncRNA signature to predict the survival of patients with LUAD. The gene expression profiles and associated clinical information of patients with LUAD were downloaded from The Cancer Genome Atlas database. The cohort was randomly sub-divided into training and verification cohorts. Univariate Cox regression analysis was performed on differentially expressed lncRNAs in the training cohort to select candidate lncRNAs closely associated with survival. Next, a risk score (RS) model consisting of 5 lncRNAs was established by multivariate Cox regression analysis on candidate lncRNAs. Using the median RS obtained from the training cohort as a cut-off point, patients were classified into high- and low-risk groups. Kaplan-Meier survival analysis revealed a significant difference in OS between high- and low-risk groups. The survival prediction ability of the 5-lncRNA signature was further tested in the verification and total cohorts. The results proved that the 5-lncRNA signature had good reliability and stability in survival prediction for patients with LUAD. The univariate Cox regression analysis for the 5-lncRNA signature in each cohort indicated that the 5-lncRNA signature was closely associated with survival. Multivariate Cox regression analysis and stratification analysis proved that the prognostic signature was an independent predictor of survival for patients with LUAD. In addition, functional enrichment analysis indicated that the 5 prognostic lncRNAs may be involved in the tumorigenesis of LUAD through cancer-associated pathways and biological processes. Taken together, the present study provided a 5-lncRNA signature that may serve as an independent survival predictor for patients with LUAD.