Prognostic model of lung adenocarcinoma constructed by the CENPA complex genes is closely related to immune infiltration

2021 ◽  
pp. 153680
Author(s):  
Haomiao Zhou ◽  
Tingting Bian ◽  
Li Qian ◽  
Cui Zhao ◽  
Weiju Zhang ◽  
...  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yang Chen ◽  
Caiming Zhong ◽  
Shujun Bao ◽  
Zheng Fang ◽  
Hao Tang

There is a known link between DNA methylation and cancer immunity/immunotherapy; however, the effect of DNA methylation on immunotherapy in lung adenocarcinoma (LUAD) remains to be elucidated. In the current study, we aimed to screen key markers for prognostic analysis of LUAD based on DNA methylation regulatory factor clustering. We classified LUAD using the NMF clustering method, and as a result, we obtained 20 DNA methylation regulatory genes. These 20 regulatory genes were used to determine the pattern of DNA methylation regulation, and patients were grouped for further analysis. The risk score model was analyzed in the TCGA dataset and an external validation set, and the correlation between the risk score and DNA methylation regulatory gene expression was explored. We analyzed the correlation between the prognostic model and immune infiltration and checkpoints. Finally, we analyzed the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functions of the prognosis model and established the nomogram model and decision tree model. The survival analyses of ClusterA and ClusterB were significantly different. Survival analysis showed that patients with a high risk score had a poor prognosis. Survival models (tobacco, T, N, M, stage, sex, age, status, and risk score) were abnormally correlated with T cells and macrophages. The higher the risk score associated with smoking was and the higher the stage was, the more severe the LUAD and the more maladjusted the immune system were. Immune infiltration and abnormal expression of immune checkpoint genes in the prognostic model of LUAD were associated with the risk score. The prognostic models were mainly enriched in the cell cycle and DNA replication. Characterization of DNA methylation regulatory patterns is helpful to improve our understanding of the immune microenvironment in LUAD and to guide the development of a more personalized immunotherapy strategy in the future.


2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Qidong Cai ◽  
Boxue He ◽  
Pengfei Zhang ◽  
Zhenyu Zhao ◽  
Xiong Peng ◽  
...  

Abstract Background Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. Method RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). Results Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. Conclusion In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.


PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8128 ◽  
Author(s):  
Cheng Yue ◽  
Hongtao Ma ◽  
Yubai Zhou

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.


2020 ◽  
Vol 83 ◽  
pp. 106454 ◽  
Author(s):  
Yidan Sun ◽  
Ying Zhang ◽  
Shiqi Ren ◽  
Xiaojiang Li ◽  
Peiying Yang ◽  
...  

2021 ◽  
Author(s):  
Zhenyu Zhao ◽  
Boxue He ◽  
Qidong Cai ◽  
Pengfei Zhang ◽  
Xiong Peng ◽  
...  

Abstract Background: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. A recent study shows that immunotherapy is an effective method of LUAD treatment, and tumor mutation burden (TMB) was associated with the immune microenvironment and affected the immunotherapy. Exploration of the gene signature associated with tumor mutation burden and immune infiltrates in predicting prognosis in lung adenocarcinoma in this study, we explored the correlation of TMB with immune infiltration and prognosis in LUAD.Materials and Methods: In this study, we firstly got mutation data and LUAD RNA-Seq data of the LUAD from The Cancer Genome Atlas (TCGA), and according to the TMB we divided the patients into high/low-TMB levels groups. The gene ontology (GO) pathway enrichment analysis and KOBAS-Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were utilized to explore the molecular function of the differentially expressed genes (DEGs) between the two groups. The function enrichment analyses of DEGs were related to the immune pathways. Then, the ESTIMATE algorithm, CIBERSORT, and ssGSEA analysis were utilized to identify the relationship between TMB subgroups and immune infiltration. According to the results, Venn analysis was utilized to select the immune-related genes in DEGs. Univariate and Lasso Cox proportional hazards regression analyses were performed to construct the signature which positively associated with the immune infiltration and affected the survival. Finally, we verified the correlation between the signature and immune infiltration. Result: The exploration of the immune infiltration suggested that high-TMB subgroups positively associated with the high level of immune infiltration in LUAD patients. According to the TMB-related immune signature, the patients were divided into High/Low-risk groups, and the high-risk group was positively associated with poor prognostic. The results of the PCA analysis confirmed the validity of the signature. We also verified the effectiveness of the signature in GSE30219 and GSE72094 datasets. The ROC curves and C-index suggested the good clinical application of the TMB-related immune signature in LUAD prognosis. Another result suggested that the patients of the high-risk group were positively associated with higher TMB levels, PD-L1expression, and immune infiltration levels.Conclusion: In conclusion, our signature provides potential biomarkers for studying aspects of the TMB in LUAD such as TMB affected immune microenvironment and prognosis. This signature may provide some biomarkers which could improve the biomarkers of PD-L1 immunotherapy response and were inverted for the clinical application of the TMB in LUAD. LUAD male patients with higher TMB-levels and risk scores may benefit from immunotherapy. The high-risk patients along with higher PD-L1 expression of the signature may suitable for immunotherapy and improve their survival by detecting the TMB of LUAD.


2021 ◽  
Vol 8 ◽  
Author(s):  
Yi Wang ◽  
Yinhao Chen ◽  
Bingye Zhu ◽  
Limin Ma ◽  
Qianwei Xing

Background: This study was designed to establish a sensitive prognostic model based on apoptosis-related genes to predict overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).Methods: Obtaining the expression of apoptosis-related genes and associated clinical parameters from online datasets (The Cancer Genome Atlas, TCGA), their biological function analyses were performed through differently expressed genes. By means of LASSO, unadjusted and adjusted Cox regression analyses, this predictive signature was constructed and validated by internal and external databases (both TCGA and ArrayExpress).Results: A total of nine apoptosis-related genes (SLC27A2, TNFAIP2, IFI44, CSF2, IL4, MDK, DOCK8, WNT5A, APP) were ultimately screened as associated hub genes and utilized to construct a prognosis model. Then our constructed riskScore model significantly passed the validation in both the internal and external datasets of OS (all p &lt; 0.05) and verified their expressions by qRT-PCR. Moreover, we conducted the Receiver Operating Characteristic (ROC), finding the area under the ROC curves (AUCs) were all above 0.70 which indicated that riskScore was a stable independent prognostic factor (p &lt; 0.05). Furthermore, prognostic nomograms were established to figure out the relationship between 1-, 3- and 5-year OS and individual parameters for ccRCC patients. Additionally, survival analyses indicated that our riskScore worked well in predicting OS in subgroups of age, gender, grade, stage, T, M, N0, White (all p &lt; 0.05), except for African, Asian and N1 (p &gt; 0.05). We also explored its association with immune infiltration and applied cMap database to seek out highly correlated small molecule drugs.Conclusion: Our study successfully constructed a prognostic model containing nine hub apoptosis-related genes for ccRCC, helping clinicians predict patients’ OS and making the prognostic assessment more standardized. Future prospective studies are required to validate our findings.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21006-e21006
Author(s):  
Lihui Liu ◽  
Chao Wang ◽  
Sini Li ◽  
Pei Xue ◽  
Hua Bai ◽  
...  

e21006 Background: Recently, immune checkpoint inhibitors have led to a paradigm shift in treatment for patients with lung adenocarcinoma (LUAD), however, the identification of biomarkers to enable patient selection is urgently required. The endoplasmic reticulum oxidoreductin-1-like ( ERO1L) gene encodes an endoplasmic reticulum luminal localized glycoprotein known to associated with hypoxia. The role of ERO1L in the crafting of the tumor immune microenvironment (TIME) is yet to be elucidated. Methods: In this study, raw datasets (including RNA-seq, methylation, sgRNA-seq, phenotype, and survival data) were obtained from public databases. This data was analyzed and used to explore the biological landscape of ERO1L in immune infiltration. Expression data was used to characterize samples. Using gene signatures and cell quantification, stromal and immune infiltration was determined. These findings were used to predict sensitivity to immunotherapy. Results: We identified ERO1L to be an oncogene, the mRNA expression of which is significantly higher in LUAD compared with normal tissues. High expression levels of ERO1L were associated with poor prognoses in terms of overall survival (HR: 1.52, 95% CI: 1.27-1.82) and progression-free survival (HR: 1.93, 95% CI: 1.47-2.53). This overexpression was found to be a result of hypomethylation of the ERO1L promoter. Overexpression of ERO1L resulted in an immune-suppressive TIME via the recruitment of immune-suppressive cells including regulatory T cells (Spearman’s ρ = 0.199, p < 0.001) cancer associated fibroblasts (ρ = 0.286, p < 0.001), and myeloid-derived suppressor cells (ρ = 0.423, p < 0.001), and also indicated the polarization of M1-type to M2-type macrophage. On the contrary, overexpression of ERO1L was closely associated with deficiency of immune-active cells including B cells (ρ = -0.250, p < 0.001), CD8+ T cells (ρ = -0.299, p < 0.001), and NK cells (ρ = -0.258, p < 0.001). Using the Tumor Immune Dysfunction and Exclusion (TIDE) framework, it was identified that patients in the ERO1Lhigh group possessed a significantly lower response rate (31.0%) to immunotherapy compared with the ERO1Llow group (86.0%). Mechanistic analysis revealed that overexpression of ERO1L was associated with the upregulation of JAK-STAT (NES = 1.65, FDR q-value = 0.0) and NF-κB (NES = 2.03, FDR q-value = 0.0) signaling pathways, thus affecting chemokine and cytokine patterns in the TIME. Conclusions: Our study provides clear insight into the potential role of ERO1L in tumor immunology. Overexpression of ERO1L was indicative of a hypoxia-induced immune-suppressive TIME, which was shown to confer resistance to immunotherapy in patients with LUAD. ERO1L was shown to mediate cytokine and chemokine patterns in the TIME, which were resulted from activations of JAK-STAT and NF-κB signaling pathways.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e20543-e20543
Author(s):  
Benxu Tan ◽  
Yonghong Chen ◽  
Lei Xia ◽  
Xian Yu ◽  
Yusheng Huang ◽  
...  

e20543 Background: CDKN2A and CDKN2B both acted as tumor suppressor genes by regulating the cell cycle, which in humans were located at chromosome 9, band p21.3. The frequencies of homozygous deletion (HomDel) in CDKN2A and CDKN2B in lung adenocarcinoma (LUAD) were 12.5% and 12.1%, respectively. However, the genomic, immunogenomic features and impact on the prognosis of LUAD patients with CDKN2A/B HomDel were still unclear. Methods: The cohort of this study was from The Cancer Genome Atlas (TCGA). A total of 508 LUAD patients, including 99 CDKN2A/B HomDel (homdel) and 509 CDKN2A/B wild (wild). This study explored the difference of genomic and immunogenomic landscape between homdel and wild by analysis of whole-exome sequencing (WES) and RNA sequencing data. Results: The most frequently mutated genes were TP53, TTN, MUC16, and CSMD3. Their frequencies in homdel and wild are 46% and 48%, 43% and 46%, 35% and 41%, 33% and 38%, respectively. There was no significant difference of tumor mutational burden (TMB) between homdel and wild (median TMB, 133 in homdel vs 177 in wild; Wilcoxon test, p = 0.11), and clinical characteristics including age, gender, smoking history, and tumor stage were not significantly different between homdel and wild. Homdel had a shorter overall survival (OS) than wild (Log-rank test, p = 0.04, Hazard Ratio: 0.7, 95% CI: 0.49-1.02), but there was no significant difference in progression-free survival (PFS) (Log-rank test, p = 0.05, Hazard Ratio: 0.73, 95% CI: 0.51-1.04). We used single sample gene set enrichment analysis (ssGSEA) to calculate the enrichment score (ES) of 25 immune-related pathways such as antigen presentation and T cell-mediated immunity, and then used the consensus clustering algorithm (ConsensusClusterPlus) to cluster homdel and wild respectively, and both clustered into low and high immune infiltration groups. For the high immune infiltration and low immune infiltration in homdel and wild, high immune infiltration had a longer OS (Log-rank test, p = 0.009, Hazard Ratio: 2.19, 95% CI: 1.22-3.94) and PFS (Log-rank test, p = 0.044, Hazard Ratio: 1.8, 95% CI: 1.01-3.2) than low immune infiltration in homdel. However, there was no significant heterogeneity between high and immune infiltration in terms of PFS (Log-rank test, p = 0.28, Hazard Ratio: 1.21, 95% CI: 0.87-1.68) and OS (Log-rank test, p = 0.96, Hazard Ratio: 1.01, 95% CI: 0.71-1.44) in the wild group, the wild group had longer OS than homdel group with low immune infiltration (Log-rank test, p = 0.003, Hazard Ratio: 0.5, 95% CI: 0.29-0.88), while had the same OS with homdel with high immune infiltration, irrespective of immune infiltration. And so was PFS (Log-rank test, p = 0.005, Hazard Ratio: 0.48, 95% CI: 0.27-0.82). Conclusions: CDKN2A/B homdel was an unfavorable prognostic factor for LUAD, but which with high immune infiltration might improve patient survival time.


2021 ◽  
Author(s):  
Boxuan Liu ◽  
Yun Zhao ◽  
Shuanying Yang

Abstract Background: Lung adenocarcinoma is the most occurred pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis, precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.Methods: The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate high and low risk group and a ROC curve and Nomogram to visualize the predictive ability of current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.Results: A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1 and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate(HR=1.075, 95% CI: 1.046–1.104) and multivariate(HR =1.088, 95%CI = 1.057 − 1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-year, 5-year, was 0.735, 0.672 and 0.662 respectively. Finally, the lncRNAs included in our signature were primarily enriched in autophagy process, metabolism, p53 pathway and JAK/STAT pathway. Conclusions: Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis.


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