Differentially Expressed Mutant Genes Reveal Potential Prognostic Markers For Lung Adenocarcinoma

2021 ◽  
Author(s):  
Yue Liu ◽  
Shizheng Qiu ◽  
Yang Hu ◽  
Yadong Wang
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Markers ◽  
Differentially Expressed ◽  
Mutant Genes

scholarly journals Screening prognostic markers for non-small cell lung cancer based on data mining and bioinformatics analysis

2020 ◽  
Author(s):  
Bin Han ◽  
Kaushik Chandra Aman ◽  
Dongqing Wei ◽  
Shulin Zhang ◽  
Minjie Meng
Keyword(s):  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Small Cell Lung Cancer ◽  
Differentially Expressed Genes ◽  
Cell Lung Cancer ◽  
Poor Prognosis ◽  
Prognostic Markers ◽  
Small Cell ◽  
Differentially Expressed ◽  
Small Cell Lung

Abstract Background At present, non-small cell lung cancer has a high morbidity and mortality, and the recurrence and metastasis situation is serious. It is impossible to accurately predict the prognosis of cancer patients clinically. Biomarker is a kind of biomolecule with wide application prospects, and its potential in cancer prognosis is gradually revealed, and it is expected to be applied clinically. Results We integrated four gene expression profiles (GSE19188, GSE19804, GSE101929 and GSE18842) from the GEO database and screened the commonly differentially expressed genes using the GEO2R online tool. We screened 952 commonly differentially expressed genes. Gene ontology analysis showed that CDEGs were mainly enriched in biological processes such as cell adherin, angiogenesis and positive regulation of angiogenesis, and KEGG pathways such as ECM-receptor interaction and cell adherin molecules (CAMs). Up-regulation of G2 and S phase-expressed protein 1(GTSE1) expression is associated with poor prognosis of lung adenocarcinoma(LADE) and lung squamous cell carcinoma(LUSC). Up-regulation of Neuromedin-U(NMU) expression, down-regulation of Proto-oncogene c-Fos(FOS) and Cyclin-dependent kinase inhibitor 1C(CDKN1C) is only associated with poor prognosis of LADE. Conclusions We believe that GTSE1, NMU, FOS, and CDKN1C have potential application value as prognostic markers for lung adenocarcinoma, and are of great significance for lung adeno carcinoma efficacy evaluation and relapse monitoring. At the same time, GTSE1 may also be used as a new target for cancer treatment New ways.

scholarly journals Construction and validation of a novel prognostic signature of microRNAs in lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10470
Author(s):  
Wanzhen Li ◽  
Shiqing Liu ◽  
Shihong Su ◽  
Yang Chen ◽  
Gengyun Sun
Keyword(s):  
Lung Adenocarcinoma ◽  
Signaling Pathway ◽  
Prognostic Value ◽  
Target Genes ◽  
Predictive Accuracy ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Tissue Samples ◽  
Gene Set ◽  
Differentially Expressed Mirnas

MicroRNA (miRNA, miR) has been reported to be highly implicated in a wide range of biological processes in lung cancer (LC), and identification of differentially expressed miRNAs between normal and LC samples has been widely used in the discovery of prognostic factors for overall survival (OS) and response to therapy. The present study was designed to develop and evaluate a miRNA-based signature with prognostic value for the OS of lung adenocarcinoma (LUAD), a common histologic subtype of LC. In brief, the miRNA expression profiles and clinicopathological factors of 499 LUAD patients were collected from The Cancer Genome Atlas (TCGA) database. Kaplan–Meier (K-M) survival analysis showed significant correlations between differentially expressed miRNAs and LUAD survival outcomes. Afterward, 1,000 resample LUAD training matrices based on the training set was applied to identify the potential prognostic miRNAs. The least absolute shrinkage and selection operator (LASSO) cox regression analysis was used to constructed a six-miRNA based prognostic signature for LUAD patients. Samples with different risk scores displayed distinct OS in K-M analysis, indicating considerable predictive accuracy of this signature in both training and validation sets. Furthermore, time-dependent receiver operating characteristic (ROC) analysis demonstrated the nomogram achieved higher predictive accuracy than any other clinical variables after incorporating the clinical information (age, sex, stage, and recurrence). In the stratification analysis, the prognostic value of this classifier in LUAD patients was validated to be independent of other clinicopathological variables, such as age, gender, tumor recurrence, and early stage. Gene set annotation analyses were also conducted through the Hallmark gene set and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, indicating target genes of the six miRNAs were positively related to various molecular pathways of cancer, such as hallmark UV response, Wnt signaling pathway and mTOR signaling pathway. In addition, fresh cancer tissue samples and matched adjacent tissue samples from 12 LUAD patients were collected to verify the expression of miR-582’s target genes in the model, further revealing the potential relationship between SOX9, RASA1, CEP55, MAP4K4 and LUAD tumorigenesis, and validating the predictive value of the model. Taken together, the present study identified a robust signature for the OS prediction of LUAD patients, which could potentially aid in the individualized selection of therapeutic approaches for LUAD patients.

scholarly journals Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8128 ◽  
Author(s):  
Cheng Yue ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Differentially Expressed ◽  
Lasso Regression

Background Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient’s outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

Construction of NEIL3 as a Prognostic Biomarker and Co-expressed Prognostic Signature in Lung Adenocarcinoma

2020 ◽  
Author(s):  
Cui Zhao ◽  
Jian Liu ◽  
Haomiao Zhou ◽  
Xin Qian ◽  
Hui Sun ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Tumor Size ◽  
T Regulatory Cells ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Advanced Stage ◽  
Regulatory Cells ◽  
Prognostic Signature

Abstract Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death. This study aimed to develop and validate reliable prognostic biomarkers and signature.Methods: Differentially expressed genes were identified based on three Gene Expression Omnibus (GEO) datasets. Based on three LUAD cohorts’ data included 1052 samples and extracted from our cohort, GEO, The Cancer Genome Atlas, we explored clinicopathological features and the expression of NEIL3 to determine its clinical effect in LUAD. Western blotting, Real-time quantitative PCR; (22 pairs of tumor and normal tissues), and immunohistochemical analyses (406-tumor tissues subjected to microarray) were conducted. TIMER and ImmuCellAI analyzed relationship between NEIL3 expression and the abundance of tumor-infiltrating immune cells in LUAD. The co-expressed-gene prognostic signature was established based on the Cox regression analysis.Results: This study identified 502 common differentially expressed genes and confirmed that NEIL3 was significantly overexpressed in LUAD samples(P<0.001). Increased NEIL3 expression was related to advanced stage, larger tumor size and poor overall survival (p < 0.001) in three LUAD cohorts. The proportions of natural T regulatory cells and induced T regulatory cells increased in the high NEIL3 group, whereas those of B cells, Th17 cells and dendritic cells decreased. Gene set enrichment analysis indicated that NEIL3 may activate cell cycle progression and P53 signaling pathway, leading to poor outcomes. We identified nine prognosis-associated hub genes among 370 genes co-expressed with NEIL3. A 10-gene prognostic signature including NEIL3 and nine key co-expressed genes was constructed. Higher risk-score was correlated with more advanced stage, larger tumor size and worse outcome (p<0.05). Finally, the signature was verified in test cohort (GSE50081) with superior diagnostic accuracy. Conclusions: This study suggested that NEIL3 has the potential to be an immune-related therapeutic target and an independent predictor for LUAD. We also developed a prognostic signature for LUAD with a precise diagnostic accuracy.

scholarly journals Efficiency Analysis of Competing Tests for Finding Differentially Expressed Genes in Lung Adenocarcinoma

2008 ◽  
Vol 6 ◽  
pp. CIN.S791 ◽  
Author(s):  
Rick Jordan ◽  
Satish Patel ◽  
Hai Hu ◽  
James Lyons-Weiler
Keyword(s):  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Efficiency Analysis ◽  
Differentially Expressed ◽  
Classification Error ◽  
Sample Classification ◽  
Expression Of Genes ◽  
Using Data ◽  
Intuitive Method ◽  
Transformation Methods

In this study, we introduce and use Efficiency Analysis to compare differences in the apparent internal and external consistency of competing normalization methods and tests for identifying differentially expressed genes. Using publicly available data, two lung adenocarcinoma datasets were analyzed using caGEDA ( http://www.bioinformatics2.pitt.edu/GE2/GEDA.html ) to measure the degree of differential expression of genes existing between two populations. The datasets were randomly split into at least two subsets, each analyzed for differentially expressed genes between the two sample groups, and the gene lists compared for overlapping genes. Efficiency Analysis is an intuitive method that compares the differences in the percentage of overlap of genes from two or more data subsets, found by the same test over a range of testing methods. Tests that yield consistent gene lists across independently analyzed splits are preferred to those that yield less consistent inferences. For example, a method that exhibits 50% overlap in the 100 top genes from two studies should be preferred to a method that exhibits 5% overlap in the top 100 genes. The same procedure was performed using all available normalization and transformation methods that are available through caGEDA. The ‘best’ test was then further evaluated using internal cross-validation to estimate generalizable sample classification errors using a Naïve Bayes classification algorithm. A novel test, termed D1 (a derivative of the J5 test) was found to be the most consistent, and to exhibit the lowest overall classification error, and highest sensitivity and specificity. The D1 test relaxes the assumption that few genes are differentially expressed. Efficiency Analysis can be misleading if the tests exhibit a bias in any particular dimension (e.g. expression intensity); we therefore explored intensity-scaled and segmented J5 tests using data in which all genes are scaled to share the same intensity distribution range. Efficiency Analysis correctly predicted the ‘best’ test and normalization method using the Beer dataset and also performed well with the Bhattacharjee dataset based on both efficiency and classification accuracy criteria.

scholarly journals Relevance of STK11 Mutations Regarding Immune Cell Infiltration, Drug Sensitivity, and Cellular Processes in Lung Adenocarcinoma

2020 ◽  
Vol 10 ◽  
Author(s):  
Zhenqing Li ◽  
Bo Ding ◽  
Jianxun Xu ◽  
Kai Mao ◽  
Pengfei Zhang ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Drug Sensitivity ◽  
Immune Cell ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Wild Type ◽  
Serine Threonine Kinase ◽  
Threonine Kinase ◽  
Cellular Processes

Serine/threonine kinase 11 (STK11) is one member of the serine/threonine kinase family, which is involved in regulating cell polarity, apoptosis, and DNA damage repair. In lung adenocarcinoma (LUAD), it can play as one tumor suppressor and always be mutated. In this study, we aimed to assess the relevance of STK11 mutations in LUAD, in which we also studied the correlation among immune cell infiltration, drug sensitivity, and cellular processes. By performing the bioinformatics analysis of the Cancer Genome Atlas (TCGA) about LUAD patients, we found that the mutation efficiency of STK11 mutations is about 19%. Additionally, the differentially expressed gene analysis showed that there were 746 differentially expressed genes (DEGs) between LUAD patients with and without STK11 mutations. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis showed that the DEGs were enriched in various tumorigenesis signaling pathways and metabolic processes. Among these DEGs, the top ranking 21 genes were found that they were more frequently mutated in the STK11 mutation group than in the wild-type group (p-value&lt;0.01). Finally, the LUAD patients with STK11 mutations suffered the worse immune cell infiltration levels than the LUAD patients with wild-type. The STK11 gene copy number was correlated with immune cell infiltration. Aiming to develop the therapeutic drugs, we performed Genomics of Drug Sensitivity in Cancer (GDSC) data to identify the potential therapeutic candidate and the results showed that Nutlin-3a(-) may be a sensitive drug for LUAD cases harboring STK11 mutations. The specific genes and pathways shown to be associated with LUAD cases involving STK11 mutations may serve as targets for individualized LUAD treatment.

scholarly journals Cigarette smoke alters the transcriptome of non-involved lung tissue in lung adenocarcinoma patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Giulia Pintarelli ◽  
Sara Noci ◽  
Davide Maspero ◽  
Angela Pettinicchio ◽  
Matteo Dugo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Cigarette Smoke ◽  
Lung Tissue ◽  
Gene Networks ◽  
Lung Parenchyma ◽  
Fold Change ◽  
Differentially Expressed ◽  
Significant Gene ◽  
Never Smokers

Abstract Alterations in the gene expression of organs in contact with the environment may signal exposure to toxins. To identify genes in lung tissue whose expression levels are altered by cigarette smoking, we compared the transcriptomes of lung tissue between 118 ever smokers and 58 never smokers. In all cases, the tissue studied was non-involved lung tissue obtained at lobectomy from patients with lung adenocarcinoma. Of the 17,097 genes analyzed, 357 were differentially expressed between ever smokers and never smokers (FDR < 0.05), including 290 genes that were up-regulated and 67 down-regulated in ever smokers. For 85 genes, the absolute value of the fold change was ≥2. The gene with the smallest FDR was MYO1A (FDR = 6.9 × 10−4) while the gene with the largest difference between groups was FGG (fold change = 31.60). Overall, 100 of the genes identified in this study (38.6%) had previously been found to associate with smoking in at least one of four previously reported datasets of non-involved lung tissue. Seven genes (KMO, CD1A, SPINK5, TREM2, CYBB, DNASE2B, FGG) were differentially expressed between ever and never smokers in all five datasets, with concordant higher expression in ever smokers. Smoking-induced up-regulation of six of these genes was also observed in a transcription dataset from lung tissue of non-cancer patients. Among the three most significant gene networks, two are involved in immunity and inflammation and one in cell death. Overall, this study shows that the lung parenchyma transcriptome of smokers has altered gene expression and that these alterations are reproducible in different series of smokers across countries. Moreover, this study identified a seven-gene panel that reflects lung tissue exposure to cigarette smoke.

scholarly journals A Pilot Proteogenomic Study with Data Integration Identifies MCT1 and GLUT1 as Prognostic Markers in Lung Adenocarcinoma

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0142162 ◽  
Author(s):  
Paul A. Stewart ◽  
Katja Parapatics ◽  
Eric A. Welsh ◽  
André C. Müller ◽  
Haoyun Cao ◽  
...  
Keyword(s):  
Data Integration ◽  
Lung Adenocarcinoma ◽  
Prognostic Markers
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