scholarly journals Deciphering N6-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jie Zhu ◽  
Min Wang ◽  
Daixing Hu
Keyword(s):  
Lung Adenocarcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Gene Expressions ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Selection Operator ◽  
Cox Analysis

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Among these, lung adenocarcinoma (LUAD) accounts for most cases. Due to the improvement of precision medicine based on molecular characterization, the treatment of LUAD underwent significant changes. With these changes, the prognosis of LUAD becomes diverse. N6-methyladenosine (m6A) is the most predominant modification in mRNAs, which has been a research hotspot in the field of oncology. Nevertheless, little has been studied to reveal the correlations between the m6A-related genes and prognosis in LUAD. Thus, we conducted a comprehensive analysis of m6A-related gene expressions in LUAD patients based on The Cancer Genome Atlas (TCGA) database by revealing their relationship with prognosis. Different expressions of the m6A-related genes in tumor tissues and non-tumor tissues were confirmed. Furthermore, their relationship with prognosis was studied via Consensus Clustering Analysis, Principal Components Analysis (PCA), and Least Absolute Shrinkage and Selection Operator (LASSO) Regression. Based on the above analyses, a m6A-based signature to predict the overall survival (OS) in LUAD was successfully established. Among the 479 cases, we found that most of the m6A-related genes were differentially expressed between tumor and non-tumor tissues. Six genes, HNRNPC, METTL3, YTHDC2, KIAA1429, ALKBH5, and YTHDF1 were screened to build a risk scoring signature, which is strongly related to the clinical features pathological stages (p<0.05), M stages (p<0.05), T stages (p < 0.05), gender (p=0.04), and survival outcome (p=0.02). Multivariate Cox analysis indicated that risk value could be used as an independent prognostic factor, revealing that the m6A-related genes signature has great predictive value. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database.

scholarly journals Construction and Validation of an Immune-Related Prognostic Model Based on TP53 Status in Colorectal Cancer

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1722 ◽  
Author(s):  
Xiaojuan Zhao ◽  
Jianzhong Liu ◽  
Shuzhen Liu ◽  
Fangfang Yang ◽  
Erfei Chen
Keyword(s):  
Colorectal Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Expression Omnibus ◽  
Tnm Staging ◽  
The Cancer Genome Atlas ◽  
Rna Transcripts ◽  
Cancer Genome Atlas ◽  
Selection Operator ◽  
Cox Analysis

Growing evidence has indicated that prognostic biomarkers have a pivotal role in tumor and immunity biological processes. TP53 mutation can cause a range of changes in immune response, progression, and prognosis of colorectal cancer (CRC). Thus, we aim to build an immunoscore prognostic model that may enhance the prognosis of CRC from an immunological perspective. We estimated the proportion of immune cells in the GSE39582 public dataset using the CIBERSORT (Cell type identification by estimating relative subset of known RNA transcripts) algorithm. Prognostic genes that were used to establish the immunoscore model were generated by the LASSO (Least absolute shrinkage and selection operator) Cox regression model. We established and validated the immunoscore model in GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) cohorts, respectively; significant differences of overall survival analysis were found between the low and high immunoscore groups or TP53 subgroups. In the multivariable Cox analysis, we observed that the immunoscore was an independent prognostic factor both in the GEO cohort (HR (Hazard ratio) 1.76, 95% CI (confidence intervals): 1.26–2.46) and the TCGA cohort (HR 1.95, 95% CI: 1.20–3.18). Furthermore, we established a nomogram for clinical application, and the results suggest that the nomogram is a better predictive model for prognosis than immunoscore or TNM staging.

scholarly journals Systematical identifications of prognostic meaningful lung adenocarcinoma subtypes and the underlying mutational and expressional characters

2019 ◽  
Author(s):  
Zhenyang Lv ◽  
Ting Lei
Keyword(s):  
Lung Adenocarcinoma ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Gene Expressions ◽  
Master Genes ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Key Pathways

Abstract Background Lung adenocarcinoma (LUAD) is one of the most common cancer types, threatening the human health around the world. However, the high heterogeneity and complexity of LUAD limit the benefits of targeted therapies. This study aimed to identify the key prognosis impacting genes and relevant subtypes for LUAD. Methods We recognized significant mutations and prognosis-relevant genes based on the omics data of 515 LUAD samples from The Cancer Genome Atlas. Mutation significance was estimated by MutSigCV. Prognosis analysis was based on the cox proportional hazards regression (Coxph) model. Specifically, the Coxph model was combined with a causal regulatory network to help reveal which genes play master roles among numerous prognosis impacting genes. Based on expressional profiles of the master genes, LUAD patients were clustered into different sub-types by a consensus clustering method and the importance of master genes were further evaluated by random forest. Results Significant mutations did not influence the prognosis directly. However, a collection of prognosis relevant genes were recognized, where 75 genes like GAPDH and GGA2 which are involved in mTOR signaling, lysosome or other key pathways are further identified as the master ones. Interestingly, the master gene expressions help separate LUAD patients into two sub-types displaying remarkable differences in expressional profiles, prognostic outcomes and genomic mutations in certain genes, like SMARCA4 and COL11A1. Meanwhile, the subtypes were re-discovered from two additional LUAD cohorts based on the top-10 important master genes. Conclusions This study can promote precision treatment of LUAD by providing a comprehensive description on the key prognosis-relevant genes and an alternative way to classify LUAD subtypes.

scholarly journals Development and validation of a 13-m6a related lncRNA prognostic signature for lung adenocarcinoma

2021 ◽  
Author(s):  
Pingfan Wu ◽  
Xiaowen Zhao ◽  
Ling Xue ◽  
Xiaojing Yang ◽  
Yuxiang Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Treatment Strategies ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
P53 Signaling ◽  
Selection Operator

Abstract Considerable evidence suggests that N6-methyladenosine (m6A) is involved in the regulation of long non-coding RNA (lncRNA), whichparticipates in the occurrence, development and prognosis of tumorscancerBut the relationship between m6A regulators-related lncRNA (mRlncRNA) and lung adenocarcinoma (LUAD) remains unclear. This study aims to determine a feature based on mRlncRNA for prognostic evaluation of LUAD patients. By integrating the gene expression data of LUAD and normal samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, the m6A gene and mRlncRNA with imbalanced expression were screened out. Then we used the least absolute shrinkage and selection operator (LASSO) to obtain the 13-lncRNA prognostic signature in the TCGA training cohort. Patients were divided into two risk groups based on the risk score of lncRNAs characteristics, and their overall survival (OS) was significantly different. The predictive power of this signature was verified in TCGA testing cohort and entire TCGA cohort. These landmark lncRNAs were involved in several biologiocal processes and pathways related to cell cycle, DNA replication, P53 signaling pathway and mismatch repair. Besides, the high-risk group was low-response to cisplatin, while high-response to mitomycin, docetaxel and immunotherapy. In conclusion, we identified a 13-mRlncRNA model associated with prognosis and treatment sensitivity in LUAD, which may provide clues about the influence of m6A on lncRNA in LUAD and promote the further improvement of LUAD individualized treatment strategies.

scholarly journals A Novel Ferroptosis-Related lncRNAs Signature Predicts Clinical Prognosis and is Associated with Immune Landscape in Pancreatic Cancer

2021 ◽  
Author(s):  
Haiqin Ping ◽  
Xingqing Jia ◽  
Hengning Ke
Keyword(s):  
Pancreatic Cancer ◽  
Immune Cell ◽  
Pearson Correlation ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Clinical Prognosis ◽  
Pathway Enrichment ◽  
Cancer Genome Atlas ◽  
Pearson Correlation Analysis ◽  
Selection Operator

Abstract Pancreatic cancer is one of the most lethal malignancies and currently therapies are severely lacking. In this study, we aimed to establish a novel ferroptosis-related lncRNAs signature to predict the prognosis of patients with pancreatic cancer and evaluate the predictive abilities of candidate lncRNAs. According to The Cancer Genome Atlas (TCGA) database, a total of 182 patients with pancreatic cancer were included in our study. Ferroptosis-related lncRNAs were screened by Pearson correlation analysis with 60 reported ferroptosis-related genes. Through univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate regression analyses, a novel signature based on five ferroptosis-related lncRNAs(ZNF236-DT, CASC8, PAN3-AS1, SH3PXD2A-AS1, LINP1) was constructed. Risk-related differentially expressed genes (DEGs) were subjected to enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.The results revealed that that immune cell infiltration, immune-related functions and checkpoints were factors to affect prognoisis of pancreatic cancer. In summary, we identified the prognostic ferroptosis-related lncRNAs in pancreatic cancer and these lncRNAs may serve as therapeutic targets for pancreatic cancer.

scholarly journals Immune implication of an autophagy-related prognostic signature in uveal melanoma

2021 ◽  
Author(s):  
Samuel Chuah ◽  
Valerie Chew
Keyword(s):  
Uveal Melanoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Lasso Regression ◽  
Cancer Hallmarks ◽  
Selection Operator ◽  
Stat3 Signalling

Uveal Melanoma (UM) is a rare cancer deriving from melanocytes within the uvea. It has a high rate of metastasis, especially to the liver, and a poor prognosis thereafter. Autophagy, an intracellular programmed digestive process, has been associated with the development and progression of cancers, with controversial pro- and anti-tumour roles. Although previous studies have been conducted on autophagy-related genes (ARGs) in various cancer types, its role in UM requires a deeper understanding for improved diagnosis and development of novel therapeutics. In the current study, Zheng et al. used univariate Cox regression followed by least absolute shrinkage and selection operator (Lasso) regression to identify a robust 9-ARG signature prognostic of survival in a total of 230 patients with UM. The authors used the Cancer Genome Atlas (TCGA) UM cohort as a training cohort (n=80) to identify the signature and validated it in another four independent cohorts of 150 UM patients from the Gene Expression Omnibus (GEO) repository (GSE22138, GSE27831, GSE44295 and GSE84976). This 9-ARG signature was also significantly associated with the enrichment of cancer hallmarks, including angiogenesis, IL6-KJAK-STAT3 signalling, reactive oxygen species pathway and oxidative phosphorylation. More importantly, this signature is associated with immune-related functional pathways and immune cell infiltration. Thus, this 9-ARG signature predicts prognosis and provides deeper insights into the immune mechanisms in UM, with potential implications for future immunotherapy.

Identification of Three Differentially Expressed miRNAs as Potential Biomarkers for Lung Adenocarcinoma Prognosis

2020 ◽  
Vol 23 (2) ◽  
pp. 148-156
Author(s):  
Wei Wang ◽  
Bin Liu ◽  
Xiaoran Duan ◽  
Xiaolei Feng ◽  
Tuanwei Wang ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Regression Model ◽  
Lupus Erythematosus ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Differentially Expressed Mirnas ◽  
Selection Operator

Objective: The aim of this study areto screen MicroRNAs (miRNAs) related to the prognosis of lung adenocarcinoma (LUAD) and to explore the possible molecular mechanisms. Methods: The data for a total of 535 patients with LUAD data were downloaded from The Cancer Genome Atlas (TCGA) database. The miRNAs for LUAD prognosis were screened by both Cox risk proportional regression model and Last Absolute Shrinkage and Selection Operator (LASSO) regression model. The performances of the models were verified by time-dependent Receiver Operating Characteristic (ROC) curve. The possible biological processes linked to the miRNAs’ target genes were analyzed by Gene Ontology (GO), Kyoto gene and genome encyclopedia (KEGG). Results: Among 127 differentially expressed miRNAs identified from the screening analysis, there are 111 up-regulated and 16 down-regulated miRNAs. Three of them, hsa-miR-1293, hsa-miR-490 and hsa-miR- 5571, were also significantly associated with the survival of the LUAD patients. The targets of the three miRNAs are significantly enriched in systemic lupus erythematosus pathways. Conclusion: Hsa-miR-1293, hsa-miR-490 and hsa-miR-5571 can be potentially used as novel biomarkers for the prognosis prediction of LUAD.

scholarly journals Cancer Essential Genes Stratified Lung Adenocarcinoma Patients with Distinct Survival Outcomes and Identified a Subgroup from the Terminal Respiratory Unit Type with Different Proliferative Signatures in Multiple Cohorts

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2128
Author(s):  
Kuo-Hao Ho ◽  
Tzu-Wen Huang ◽  
Ann-Jeng Liu ◽  
Chwen-Ming Shih ◽  
Ku-Chung Chen
Keyword(s):  
Lung Adenocarcinoma ◽  
Topoisomerase I ◽  
Essential Gene ◽  
Survival Rates ◽  
Essential Genes ◽  
The Cancer Genome Atlas ◽  
Survival Outcomes ◽  
Consensus Clustering ◽  
Sequencing Data ◽  
Cancer Genome Atlas

Background: Heterogeneous features of lung adenocarcinoma (LUAD) are used to stratify patients into terminal respiratory unit (TRU), proximal-proliferative (PP), and proximal-inflammatory (PI) subtypes. A more-accurate subtype classification would be helpful for future personalized medicine. However, these stratifications are based on genes with variant expression levels without considering their tumor-promoting roles. We attempted to identify cancer essential genes for LUAD stratification and their clinical and biological differences. Methods: Essential genes in LUAD were identified using genome-scale CRIPSR screening of RNA sequencing data from Project Achilles and The Cancer Genome Atlas (TCGA). Patients were stratified using consensus clustering. Survival outcomes, genomic alterations, signaling activities, and immune profiles within clusters were investigated using other independent cohorts. Findings: Thirty-six genes were identified as essential to LUAD, and there were used for stratification. Essential gene-classified clusters exhibited distinct survival rates and proliferation signatures across six cohorts. The cluster with the worst prognosis exhibited TP53 mutations, high E2F target activities, and high tumor mutation burdens, and harbored tumors vulnerable to topoisomerase I and poly(ADP ribose) polymerase inhibitors. TRU-type patients could be divided into clinically and molecularly different subgroups based on these essential genes. Conclusions: Our study showed that essential genes to LUAD not only defined patients with different survival rates, but also refined preexisting subtypes.

scholarly journals The Bioinformatics-Based Analysis Identifies 7 Immune-Related Genes as Prognostic Biomarkers for Colon Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jie Pan ◽  
Zongqi Weng ◽  
Chaorong Xue ◽  
Bingqiang Lin ◽  
Mengxin Lin
Keyword(s):  
Colon Cancer ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Prognostic Biomarkers ◽  
Protein Protein Interaction ◽  
Causative Factors ◽  
Cancer Genome Atlas ◽  
Selection Operator ◽  
Cox Analysis ◽  
Immune Related Genes

Colon cancer poses a great threat to human health. Currently, there is no effective treatment for colon cancer due to its complex causative factors. Immunotherapy has now become a new method for tumor treatment. In this study, 487 DEGs were screened from The Cancer Genome Atlas (TCGA) database and ImmPort database, and GeneOntology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed. Hierarchical clustering of all samples revealed a significant correlation between colon cancer and immunity. The weighted gene co-expression network analysis (WGCNA) algorithm was used to identify key gene modules associated with immunity in colon cancer, here, module grey60 showed the highest correlation. A protein-protein interaction (PPI) network was constructed using the STRING database to screen hub genes, and subsequently, 7 immune-related genes the most closely associated with colon cancer were identified by differential expression in cancer and paracancer. Finally, a risk prediction model was developed using least absolute shrinkage and selection operator (LASSO) COX analysis, and the accuracy of the model was validated by GSE14333. This study determined that IRF4 and TNFRSF17 were immune-related genes in colon cancer, providing immune-related prognostic biomarkers for colon cancer.

scholarly journals Knockdown of GTF2E2 inhibits the growth and progression of lung adenocarcinoma via RPS4X in vitro and in vivo

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guoshu Bi ◽  
Donglin Zhu ◽  
Yunyi Bian ◽  
Yiwei Huang ◽  
Cheng Zhan ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Diagnosis And Prognosis ◽  
Cancer Genome Atlas ◽  
Short Hairpin Rnas

Abstract Background Lung adenocarcinoma (LUAD) is one of the most common malignancies worldwide. However, the molecular mechanism of LUAD tumorigenesis and development remains unclear. The purpose of this study was to comprehensively illustrate the role of GTF2E2 in the growth and progression of LUAD. Methods and materials We obtained the mRNA expression data from The Cancer Genome Atlas, Gene Expression Omnibus database, and our institution. Systematic bioinformatical analyses were performed to investigate the expression and prognostic value of GTF2E2 in LUAD. The results were validated by immunohistochemistry and qPCR. The effect of knocking down GTF2E2 using two short hairpin RNAs was investigated by in vitro and in vivo assays. Subsequently, shotgun liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) analyses were applied to identified potential GTF2E2 interacting proteins, and the downstream molecular mechanisms of GTF2E2-signaling were further explored by a series of cellular functional assays. Results We found that GTF2E2 expression was significantly increased in LUAD tissue compared with adjacent normal tissue and was negatively associated with patients’ overall survival. Besides, we demonstrated that GTF2E2 knockdown inhibited LUAD cell proliferation, migration, invasion, and promote apoptosis in vitro, as well as attenuated tumor growth in vivo. Results from LC–MS/MS suggested that RPS4X might physically interact with GTF2E2 and mediated GTF2E2’s regulatory effect on LUAD development through the mTOR pathway. Conclusion Our findings indicate that GTF2E2 promotes LUAD development by activating RPS4X. Therefore, GTF2E2 might serve as a promising biomarker for the diagnosis and prognosis of LUAD patients, thus shedding light on the precise and personalized therapy for LUAD in the future.

scholarly journals Comprehensive Analysis of APA Events and Their Association With Tumor Microenvironment in Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuchu Zhang ◽  
Libing Shen ◽  
Qili Shi ◽  
Guofang Zhao ◽  
Fajiu Wang
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Lung Adenocarcinoma ◽  
Expression Patterns ◽  
Alternative Polyadenylation ◽  
Clinical Results ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Proportional Hazard ◽  
Cancer Genome Atlas

BackgroundAlternative polyadenylation (APA) is a pervasive posttranscriptional mechanism regulating gene expression. However, the specific dysregulation of APA events and its potential biological or clinical significance in lung adenocarcinoma (LUAD) remain unclear.MethodsHere, we collected RNA-Seq data from two independent datasets: GSE40419 (n = 146) and The Cancer Genome Atlas (TCGA) LUAD (n = 542). The DaPars algorithm was employed to characterize the APA profiles in tumor and normal samples. Spearman correlation was used to assess the effects of APA regulators on 3′ UTR changes in tumors. The Cox proportional hazard model was used to identify clinically relevant APA events and regulators. We stratified 512 patients with LUAD in the TCGA cohort through consensus clustering based on the expression of APA factors.FindingsWe identified remarkably consistent alternative 3′ UTR isoforms between the two cohorts, most of which were shortened in LUAD. Our analyses further suggested that aberrant usage of proximal polyA sites resulted in escape from miRNA binding, thus increasing gene expression. Notably, we found that the 3′ UTR lengths of the mRNA transcriptome were correlated with the expression levels of APA factors. We further identified that CPSF2 and CPEB3 may serve as key regulators in both datasets. Finally, four LUAD subtypes according to different APA factor expression patterns displayed distinct clinical results and oncogenic features related to tumor microenvironment including immune, metabolic, and hypoxic status.InterpretationOur analyses characterize the APA profiles among patients with LUAD and identify two key regulators for APA events in LUAD, CPSF2 and CPEB3, which could serve as the potential prognostic genes in LUAD.

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