scholarly journals Screening of key genes responsible for Pennisetum setaceum ‘Rubrum’ leaf color using transcriptome sequencing

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242618
Author(s):  
Ting Zhu ◽  
Xia Wang ◽  
Zhimin Xu ◽  
Jie Xu ◽  
Rui Li ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Differentially Expressed Genes ◽  
Transcriptome Sequencing ◽  
Differentially Expressed ◽  
Chlorophyll Degradation ◽  
Anthocyanin Synthesis ◽  
Leaf Color ◽  
Chlorophyll Metabolism ◽  
Pennisetum Setaceum ◽  
Key Genes

Pennisetum setaceum ‘Rubrum’ is an ornamental grass plant that produces purple leaves in high-light environments and light purple or green leaves in low-light environments, the latter of which greatly reduces its aesthetic appeal. Therefore, we aimed to identify the key genes associated with leaf coloration and elucidate the molecular mechanisms involved in the color changes in P. setaceum ‘Rubrum’ leaves. We performed transcriptome sequencing of P. setaceum ‘Rubrum’ leaves before and after shading. A total of 19,043 differentially expressed genes were identified, and the numbers of upregulated and downregulated genes at T1 stage, when compared with their expression at the T0 stage, were 10,761 and 8,642, respectively. The possible pathways that determine P. setaceum ‘Rubrum’ leaf color included flavonoid biosynthesis, flavone and flavonol biosynthesis, and carotenoid biosynthesis. There were 31 differentially expressed genes related to chlorophyll metabolism, of which 21 were related to chlorophyll biosynthesis and 10 to chlorophyll degradation, as well as three transcription factors that may be involved in the regulation of chlorophyll degradation. There were 31 key enzyme genes involved in anthocyanin synthesis and accumulation in P. setaceum ‘Rubrum’ leaves, with four transcription factors that may be involved in the regulation of anthocyanin metabolism. The transcriptome data were verified and confirmed reliable by real-time fluorescence quantitative PCR analysis. These findings provide a genetic basis for improving leaf color in P. setaceum ‘Rubrum.’

scholarly journals De novo transcriptome sequencing and anthocyanin metabolite analysis reveals leaf color of Acer pseudosieboldianum in autumn

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yu-Fu Gao ◽  
Dong-Hui Zhao ◽  
Jia-Qi Zhang ◽  
Jia-Shuo Chen ◽  
Jia-Lin Li ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
De Novo ◽  
Regulatory Genes ◽  
Differentially Expressed ◽  
Anthocyanin Synthesis ◽  
Leaf Color ◽  
Metabolite Analysis ◽  
Color Formation ◽  
Content Change ◽  
Final Color

Abstract Background Leaf color is an important ornamental trait of colored-leaf plants. The change of leaf color is closely related to the synthesis and accumulation of anthocyanins in leaves. Acer pseudosieboldianum is a colored-leaf tree native to Northeastern China, however, there was less knowledge in Acer about anthocyanins biosynthesis and many steps of the pathway remain unknown to date. Results Anthocyanins metabolite and transcript profiling were conducted using HPLC and ESI-MS/MS system and high-throughput RNA sequencing respectively. The results demonstrated that five anthocyanins were detected in this experiment. It is worth mentioning that Peonidin O-hexoside and Cyanidin 3, 5-O-diglucoside were abundant, especially Cyanidin 3, 5-O-diglucoside displayed significant differences in content change at two periods, meaning it may be play an important role for the final color. Transcriptome identification showed that a total of 67.47 Gb of clean data were obtained from our sequencing results. Functional annotation of unigenes, including comparison with COG and GO databases, yielded 35,316 unigene annotations. 16,521 differentially expressed genes were identified from a statistical analysis of differentially gene expression. The genes related to leaf color formation including PAL, ANS, DFR, F3H were selected. Also, we screened out the regulatory genes such as MYB, bHLH and WD40. Combined with the detection of metabolites, the gene pathways related to anthocyanin synthesis were analyzed. Conclusions Cyanidin 3, 5-O-diglucoside played an important role for the final color. The genes related to leaf color formation including PAL, ANS, DFR, F3H and regulatory genes such as MYB, bHLH and WD40 were selected. This study enriched the available transcriptome information for A. pseudosieboldianum and identified a series of differentially expressed genes related to leaf color, which provides valuable information for further study on the genetic mechanism of leaf color expression in A. pseudosieboldianum.

scholarly journals Transcriptome Analysis of Differentially Expressed Genes in Wild Jujube Seedlings under Salt Stress

2020 ◽  
Vol 145 (3) ◽  
pp. 174-185 ◽  
Author(s):  
Xinyi Chang ◽  
Junli Sun ◽  
Lianling Liu ◽  
Wang He ◽  
Baolong Zhao
Keyword(s):  
Salt Stress ◽  
Differentially Expressed Genes ◽  
Transcriptome Sequencing ◽  
Carbon Fixation ◽  
Acid Metabolism ◽  
Chlorophyll Synthesis ◽  
Differentially Expressed ◽  
Gene Encoding ◽  
Chlorophyll Metabolism ◽  
Photosynthetic Antenna

Wild jujube (Ziziphus acidojujuba) and cultivated jujube (Ziziphus jujuba) belong to the family Rhamnaceae. Jujubes have marked drought- and salt-tolerant properties. After salt stress, wild jujube seedling growth was inhibited and photosynthetic efficiency was reduced. A bioinformatics approach was used to analyze the transcriptomics data from wild jujube seedlings grown under salt stress, and the genes differentially expressed under the salt stress were identified to provide a theoretical basis for the development and use of wild jujube plantations in salinized soil. The transcriptome sequencing from leaves of wild jujube seedlings was carried out using second-generation sequencing technology. The effects of salt stress on the differential expression of photosynthesis-related genes in wild jujube seedlings were analyzed. Transcriptome sequencing revealed a total of 5269 differentially expressed genes (DEGs), of which 2729 were up-regulated and 2540 were down-regulated. DEGs were mainly enriched with respect to photosynthesis, photosynthetic antenna proteins, glyoxylic acid and dicarboxylic acid metabolism, linolenic acid metabolism, cysteine and methionine metabolism, and porphyrin and chlorophyll metabolism. Among them, the photosynthesis pathway-related DEGs were most highly enriched. Further analysis of porphyrin and chlorophyll synthesis and photosynthesis-related pathways revealed that they were significantly enriched by 97 photosynthesis-related DEGs. The DEGs in the photosynthesis and photosynthetic antenna protein pathways were down-regulated, whereas the DEGs glutamyl-tRNA reductase (HEMA), ferrochelatase (HEMH), and pheophorbide a oxygenase (PAO) in the porphyrin and chlorophyll synthesis pathways were up-regulated, with the remainder being down-regulated. The nuclear gene encoding Rubisco, the key enzyme in the photosynthetic carbon fixation pathway, was also down-regulated. The results showed that the photosynthetic rate of wild jujube seedlings decreased following exposure to salinity stress, an effect that was related to the increased synthesis of 5-aminolevulinic acid and heme, and the up-regulation of expression of a gene encoding a chlorophyll-degrading enzyme, and was related to the down-regulation of gene expression in photosynthesis-related pathways such as light energy capture and carbon fixation. Selection of nine DEGs related to photosynthesis and chlorophyll biosynthesis by quantitative real-time-PCR confirmed that expression changes of these nine DEGs were consistent with the transcriptome sequencing results.

De novo transcriptome sequencing and anthocyanin metabolite analysis reveals leaf color of Acer pseudosieboldianum in autumn

2020 ◽  
Author(s):  
Yu-fu Gao ◽  
Dong-hui Zhao ◽  
Jia-qi Zhang ◽  
Jia-shuo Chen ◽  
Liping Rong
Keyword(s):  
Differentially Expressed Genes ◽  
De Novo ◽  
Regulatory Genes ◽  
Differentially Expressed ◽  
Anthocyanin Synthesis ◽  
Leaf Color ◽  
Metabolite Analysis ◽  
Color Formation ◽  
Content Change ◽  
Final Color

Abstract Background Leaf color is an important ornamental trait of colored-leaf plants. The change of leaf color is closely related to the synthesis and accumulation of anthocyanins in leaves. Acer pseudosieboldianum is a colored-leaf tree native to Northeastern China, however, there was less knowledge in Acer about anthocyanins biosynthesis and many steps of the pathway remain unknown to date. Results Anthocyanins metabolite and transcript profiling were conducted using HPLC and ESI-MS/MS system and high-throughput RNA sequencing respectively. The results demonstrated that five anthocyanins were detected in this experiment. It is worth mentioning that Peonidin O-hexoside and Cyanidin 3 5-O-diglucoside were abundant, especially Cyanidin 3 5-O-diglucoside displayed significant differences in content change at two periods, meaning it may be play an important role for the final color. Transcriptome identification showed that a total of 67.47 Gb of clean data were obtained from our sequencing results. Functional annotation of unigenes, including comparison with COG and GO databases, yielded 35,316 unigene annotations. 16,521 differentially expressed genes were identified from a statistical analysis of differentially gene expression. The genes related to leaf color formation including PAL, ANS, DFR, F3H were selected. Also, we screened out the regulatory genes such as MYB, bHLH and WD40. Combined with the detection of metabolites, the gene pathways related to anthocyanin synthesis were analyzed. Conclusion Cyanidin 3, 5-O-diglucoside played an important role for the final color. The genes related to leaf color formation including PAL, ANS, DFR, F3H and regulatory genes such as MYB, bHLH and WD40 were selected. This study enriched the available transcriptome information for A. pseudosieboldianum and identified a series of differentially expressed genes related to leaf color, which provides valuable information for further study on the genetic mechanism of leaf color expression in A. pseudosieboldianum.

scholarly journals Integrated analyses of rice dark response and leaf color control reveal links with porphyrin and chlorophyll metabolism

2020 ◽  
Author(s):  
Tianxingzi Wang ◽  
Yue Chen ◽  
Zheng Zhu ◽  
Yuqing Liu ◽  
Gaowei Yan ◽  
...  
Keyword(s):  
Stress Response ◽  
Differentially Expressed Genes ◽  
Chlorophyll Biosynthesis ◽  
Light Signal ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Leaf Color ◽  
Chlorophyll Metabolism ◽  
Color Control ◽  
Dark Stress

Abstract Background: Light is a key regulatory signal for rice growth and development. Under dark stress, rice shows leaf yellowing. Whole genome transcriptomic analysis will identify differentially expressed genes (DEG) in dark-treated rice seedlings and DEG-enriched metabolic pathways. Rice leaf color is an essential agronomic trait. Traditional genetic experiments have reported over a hundred of leaf color control (LCC) genes and some of them were also regulated by light signal. Thus, an integrated analysis for the two set of data will be helpful for illustration of the mechanism for both dark-response and leaf color control.Results: Transcriptome changes in response to dark treatment were surveyed by RNA-Seq analysis. About 13,115 differentially expressed genes (DEGs) were identified. One hundred and fifty rice LCC genes were collected. It was found that 102 LCC genes (68.0%) were also dark-response DEGs, which suggests an overlap between dark response and LCC networks. Fifty DEG overlapped LCC genes was associated with chloroplast development. KEGG analysis revealed enrichment of LCC genes in porphyrin and chlorophyll metabolism (PCM) (18/44, 40.9%). Of the 18 LCC genes in the PCM pathway, 15 were dark-response DEGs (83.3%). More interestingly, all of them are involved in a central PCM sub-pathway, chlorophyll biosynthesis.Conclusions: Integrated analysis for dark stress-response and leaf color control identified the correlation between the two processes and mutually supported evidences were obtained. It was found that PCM pathway, particularly chlorophyll biosynthesis process, plays important roles in rice LCC and dark stress-response. This study provides important clues for understanding the mechanisms of dark response and leaf color control and identifying additional LCC genes.

scholarly journals AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1037.2-1038
Author(s):  
X. Sun ◽  
S. X. Zhang ◽  
S. Song ◽  
T. Kong ◽  
C. Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Shanxi Province ◽  
Receptor Interaction ◽  
Term Therapy ◽  
Pathway Enrichment Analysis ◽  
Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

scholarly journals Key Genes and Prognostic Analysis in HER2+ Breast Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382098329
Author(s):  
Yujie Weng ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Zhongxian Li ◽  
Rong Jia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Differentially Expressed ◽  
Protein Kinase Activity ◽  
Protein Protein Interaction ◽  
Risk Of Death ◽  
Her2 Breast Cancer ◽  
Key Genes

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes ( CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

scholarly journals Bioinformatics-based Screening of Key Genes for Saponin Metabolism in Quinoa

2021 ◽  
Author(s):  
Chengang Guo ◽  
Zhimin wei ◽  
Wei Lyu ◽  
Yanlou Geng
Keyword(s):  
Differentially Expressed Genes ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Hierarchical Cluster ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Rna Seq ◽  
Protein Coding ◽  
Key Genes

Abstract Quinoa saponins have complex, diverse and evident physiologic activities. However, the key regulatory genes for quinoa saponin metabolism are not yet well studied. The purpose of this study was to explore genes closely related to quinoa saponin metabolism. In this study, the significantly differentially expressed genes in yellow quinoa were firstly screened based on RNA-seq technology. Then, the key genes for saponin metabolism were selected by gene set enrichment analysis (GSEA) and principal component analysis (PCA) statistical methods. Finally, the specificity of the key genes was verified by hierarchical clustering. The results of differential analysis showed that 1654 differentially expressed genes were achieved after pseudogenes deletion. Therein, there were 142 long non-coding genes and 1512 protein-coding genes. Based on GSEA analysis, 116 key candidate genes were found to be significantly correlated with quinoa saponin metabolism. Through PCA dimension reduction analysis, 57 key genes were finally obtained. Hierarchical cluster analysis further demonstrated that these key genes can clearly separate the four groups of samples. The present results could provide references for the breeding of sweet quinoa and would be helpful for the rational utilization of quinoa saponins.

scholarly journals Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8831 ◽  
Author(s):  
Xiaojiao Guan ◽  
Yao Yao ◽  
Guangyao Bao ◽  
Yue Wang ◽  
Aimeng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Esophageal Cancer ◽  
Esophageal Carcinoma ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Diagnostic Model ◽  
Link Type ◽  
Key Genes

Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs (RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22) in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.

Integrated Bioinformatics and Machine Learning Algorithms Analyses Highlight Related Pathways and Genes Associated with Alzheimer's Disease

2021 ◽  
Vol 17 ◽  
Author(s):  
Hui Zhang ◽  
Qidong Liu ◽  
Xiaoru Sun ◽  
Yaru Xu ◽  
Yiling Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Analysis ◽  
Predictive Model ◽  
Differentially Expressed Genes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Differentially Expressed ◽  
Diagnosis And Treatment ◽  
Ppi Network ◽  
Key Genes

Background: The pathophysiology of Alzheimer's disease (AD) is still not fully studied. Objective: This study aimed to explore the differently expressed key genes in AD and build a predictive model of diagnosis and treatment. Methods: Gene expression data of the entorhinal cortex of AD, asymptomatic AD, and control samples from the GEO database were analyzed to explore the relevant pathways and key genes in the progression of AD. Differentially expressed genes between AD and the other two groups in the module were selected to identify biological mechanisms in AD through KEGG and PPI network analysis in Metascape. Furthermore, genes with a high connectivity degree by PPI network analysis were selected to build a predictive model using different machine learning algorithms. Besides, model performance was tested with five-fold cross-validation to select the best fitting model. Results: A total of 20 co-expression gene clusters were identified after the network was constructed. Module 1 (in black) and module 2 (in royal blue) were most positively and negatively correlated with AD, respectively. Total 565 genes in module 1 and 215 genes in module 2, respectively, overlapped in two differentially expressed genes lists. They were enriched in the G protein-coupled receptor signaling pathway, immune-related processes, and so on. 11 genes were screened by using lasso logistic regression, and they were considered to play an important role in predicting AD samples. The model built by the support vector machine algorithm with 11 genes showed the best performance. Conclusion: This result shed light on the diagnosis and treatment of AD.

scholarly journals Identification of nine key genes by bioinformatics analysis for predicting poor prognosis in smoking-induced lung adenocarcinoma

2020 ◽  
Vol 9 (2) ◽  
pp. LMT30
Author(s):  
Chuanli Ren ◽  
Weixiu Sun ◽  
Xu Lian ◽  
Chongxu Han
Keyword(s):  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Kaplan Meier ◽  
Key Genes ◽  
Core Genes

Aim: To screen and identify key genes related to the development of smoking-induced lung adenocarcinoma (LUAD). Materials & methods: We obtained data from the GEO chip dataset GSE31210. The differentially expressed genes were screened by GEO2R. The protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape. Finally, core genes were screened. The overall survival time of patients with the core genes was analyzed by Kaplan–Meier method. Gene ontology and Kyoto encyclopedia of genes and genomes bioaccumulation was calculated by DAVID. Results: Functional enrichment analysis indicated that nine key genes were actively involved in the biological process of smoking-related LUAD. Conclusion: 23 core genes and nine key genes among them were correlated with adverse prognosis of LUAD induced by smoking.

Sign in / Sign up

Export Citation Format

Share Document