A Seven-Long Non-coding RNA Signature Improves Prognosis Prediction of Lung Adenocarcinoma: An Integrated Competing Endogenous RNA Network Analysis

Genetic differences between smokers and never-smokers with lung cancer

Smoking is a major risk factor for lung cancer, therefore lung cancer epidemiological trends reflect the past trends of cigarette smoking to a great extent. The geographic patterns in mortality closely follow those in incidence. Although lung cancer is strongly associated with cigarette smoking, only about 15% of smokers get lung cancer, and also some never-smokers develop this malignancy. Although less frequent, lung cancer in never smokers is the seventh leading cause of cancer deaths in both sexes worldwide. Lung cancer in smokers and never-smokers differs in many aspects: in histological types, environmental factors representing a risk, and in genes associated with this disease. In this review, we will focus on the genetic differences between lung cancer in smokers versus never-smokers: gene expression, germ-line polymorphisms, gene mutations, as well as ethnic and gender differences. Finally, treatment options for smokers and never-smokers will be briefly reviewed.

QTL-mapping in the obese Berlin Fat Mouse identifies additional candidate genes for obesity and fatty liver disease

The Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and the metabolic syndrome. This study aimed to identify genetic variants associated with liver weight, liver triglycerides, and body weight using the obese BFMI sub-line BFMI861-S1. BFMI861-S1 mice are insulin resistant and store ectopic fat in the liver. In generation 10, 58 males and 65 females of the advanced intercross line (AIL) BFMI861-S1xB6N were phenotyped under a standard diet over 20 weeks. QTL analysis was performed after genotyping with the MiniMUGA Genotyping Array. Whole-genome sequencing and gene expression data of the parental lines was used for the prioritization of positional candidate genes. Three QTLs associated with liver weight, body weight, and subcutaneous adipose tissue (scAT) weight were identified. A highly significant QTL on chromosome (Chr) 1 (157–168 Mb) showed an association with liver weight. A QTL for body weight at 20 weeks was found on Chr 3 (34.1–40 Mb) overlapping with a QTL for scAT weight. In a multiple QTL mapping approach, an additional QTL affecting body weight at 16 weeks was identified on Chr 6 (9.5–26.1 Mb). Considering sequence variants and expression differences, Sec16b and Astn1 were prioritized as top positional candidate genes for the liver weight QTL on Chr 1; Met and Ica1 for the body weight QTL on Chr 6. Interestingly, all top candidate genes have previously been linked with metabolic traits. This study shows once more the power of an advanced intercross line for fine mapping. QTL mapping combined with a detailed prioritization approach allowed us to identify additional and plausible candidate genes linked to metabolic traits in the BFMI861-S1xB6N AIL. By reidentifying known candidate genes in a different crossing population the causal link with specific traits is underlined and additional evidence is given for further investigations.

Overexpression of lncRNA A2M-AS1 inhibits cell growth and aggressiveness via regulating the miR-587/bone morphogenetic protein 3 axis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a malignant tumor that occurs in the lungs. Numerous reports have substantiated the participation of long non-coding RNAs (lncRNAs) in the tumorigenesis of LUAD. Previously, lncRNA alpha-2-macroglobulin antisense RNA 1 (A2M-AS1) was confirmed to be an important regulator in the biological processes of LUAD and dysregulation of A2M-AS1 was associated with non-small cell lung cancer (NSCLC) progression. However, the precise mechanism of A2M-AS1 in LUAD has not been elucidated. Therefore, our study was designed to investigate the detailed molecular mechanism of A2M-AS1 in LUAD. Herein, the expression of lncRNA A2M-AS1, microRNA (miRNA) miR-587, and bone morphogenetic protein 3 (BMP3) in LUAD cell lines and tissues were detected by real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting. The viability, proliferation, migration and invasion of LUAD cells were tested by cell counting kit-8 (CCK-8), colony formation and Transwell assays. In vivo tumor growth was investigated by xenograft animal experiment. Interactions among A2M-AS1, miR-587 and BMP3 were measured by RNA pulldown and luciferase reporter assays. In this study, A2M-AS1 was downregulated in LUAD tissues and cells and related to poor prognosis in LUAD patients. A2M-AS1 overexpression suppressed LUAD cell proliferation, migration and invasion in vitro and inhibited tumor growth in vivo. Mechanistically, A2M-AS1 directly bound with miR-587 to promote BMP3 expression in LUAD cells. Low expression of BMP3 was found in LUAD tissues and cells and was closely correlated with poor prognosis in LUAD patients. BMP3 deficiency reserved the inhibitory influence of A2M-AS1 overexpression on LUAD cell behaviors. Overall, A2M-AS1 inhibits cell growth and aggressiveness via regulating the miR-587/BMP3 axis in LUAD.

WT1-AS/IGF2BP2 Axis Is a Potential Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma According to ceRNA Network Comprehensive Analysis Combined with Experiments

Lung adenocarcinoma (LUAD) is one of the most common malignancies, and there is still a lack of effective biomarkers for early detection and prognostic prediction. Here, we comprehensively analyze the characteristics of. an RNA sequencing data set of LUAD samples. In total, 395 long non-coding RNAs (lncRNAs), 89 microRNAs (miRNAs), and 872 mRNAs associated with c-Myc were identified, which were differentially expressed between tumor and normal tissues. The most relevant pathway was found to be WT1-AS–miR-200a-3p–IGF2BP2 according to the rules of competitive endogenous RNA (ceRNA) regulation. WT1-AS and IGF2BP2 expression were positively correlated and increased in LUAD samples, while miR-200a-3p had relatively low expression. The high expression of WT1-AS and IGF2BP2 was associated with poor prognosis in LUAD patients, while low expression of miR-200a-3p predicted reduced survival (p < 0.05). The analysis of the multi-gene regulation model indicated that the WT1-AS (downregulation)–miR-200a-3p (upregulation)–IGF2BP2 (downregulation) pattern significantly improved the survival of LUAD patients. Finally, reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting were detected in LUAD cells, and the results are consistent with the bioinformatics analysis. In summary, the WT1-AS/IGF2BP2 axis is a potential prognostic biomarker in LUAD and is expected to become an effective target for diagnosis and treatment.

An 11-Gene Signature Risk-Prediction Model Based on Prognosis-Related miRNAs and Their Target Genes in Lung Adenocarcinoma

Aberrant expression of microRNAs may affect tumorigenesis and progression by regulating their target genes. This study aimed to construct a risk model for predicting the prognosis of patients with lung adenocarcinoma (LUAD) based on differentially expressed microRNA-regulated target genes. The miRNA sequencing data, RNA sequencing data, and patients’ LUAD clinical data were downloaded from the The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs and genes were screened out by combining differential analysis with LASSO regression analysis to further screen out miRNAs associated with patients’ prognosis, and target gene prediction was performed for these miRNAs using a target gene database. Overlapping gene screening was performed for target genes and differentially expressed genes. LASSO regression analysis and survival analysis were then used to identify key genes. Risk score equations for prognostic models were established using multifactorial COX regression analysis to construct survival prognostic models, and the accuracy of the models was evaluated using subject working characteristic curves. The groups were divided into high- and low-risk groups according to the median risk score, and the correlation with the clinicopathological characteristics of the patients was observed. A total of 123 up-regulated miRNAs and 22 down-regulated miRNAs were obtained in this study. Five prognosis-related miRNAs were screened using LASSO regression analysis and Kaplan-Meier method validation, and their target genes were screened with the overlap of differentially expressed genes before multifactorial COX analysis finally resulted in an 11-gene risk model for predicting patient prognosis. The area under the ROC curve proved that the model has high accuracy. The 11-gene risk-prediction model constructed in this study may be an effective predictor of prognosis.

A Novel Model Based on Genomic Instability-Associated Long Non-Coding RNAs for Predicting Prognosis and Response to Immunotherapy in Patients With Lung Adenocarcinoma

Background: Emerging scientific evidence has shown that long non-coding RNAs (lncRNAs) exert critical roles in genomic instability (GI), which is considered a hallmark of cancer. To date, the prognostic value of GI-associated lncRNAs (GI-lncRNAs) remains largely unexplored in lung adenocarcinoma (LUAC). The aims of this study were to identify GI-lncRNAs associated with the survival of LUAC patients, and to develop a novel GI-lncRNA-based prognostic model (GI-lncRNA model) for LUAC.

Methods: Clinicopathological data of LUAC patients, and their expression profiles of lncRNAs and somatic mutations were obtained from The Cancer Genome Atlas database. Pearson correlation analysis was conducted to identify the co-expressed mRNAs of GI-lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to determine the main biological function and molecular pathways of the differentially expressed GI-lncRNAs. Univariate and multivariate Cox proportional hazard regression analyses were performed to identify GI-lncRNAs significantly related to overall survival (OS) for construction of the GI-lncRNA model. Kaplan–Meier survival analysis and receiver operating characteristic curve analysis were performed to evaluate the predictive accuracy. The performance of the newly developed GI-lncRNA model was compared with the recently published lncRNA-based prognostic index models.

Results: A total of 19 GI-lncRNAs were found to be significantly associated with OS, of which 9 were identified by multivariate analysis to construct the GI-lncRNA model. Notably, the GI-lncRNA model showed a prognostic value independent of key clinical characteristics. Further performance evaluation indicated that the area under the curve (AUC) of the GI-lncRNA model was 0.771, which was greater than that of the TP53 mutation status and three existing lncRNA-based models in predicting the prognosis of patients with LUAC. In addition, the GI-lncRNA model was highly correlated with programed death ligand 1 (PD-L1) expression and tumor mutational burden in immunotherapy for LUAC.

Conclusion: The GI-lncRNA model was established and its performance was found to be superior to existing lncRNA-based models. As such, the GI-lncRNA model holds promise as a more accurate prognostic tool for the prediction of prognosis and response to immunotherapy in patients with LUAC.