scholarly journals Comprehensive Analysis of Immune-Related Metabolic Genes in Lung Adenocarcinoma

2021 ◽  
Author(s):  
FangFang Li ◽  
Chun Huang ◽  
LingXiao Qiu ◽  
Ping Li ◽  
guojun zhang
Keyword(s):  
Lung Cancer ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Immune Cell ◽  
Comprehensive Analysis ◽  
Lasso Regression ◽  
Hub Genes ◽  
Cell Functions ◽  
Metabolic Genes

Abstract Purpose: The immunotherapy of lung adenocarcinoma has received more and more attention. Different immune cells can affect other metabolic genes and lifespan, and cell metabolism directly regulates immune cell functions. Therefore, it is crucial to explore the role of immune-related metabolic genes in lung adenocarcinoma. Methods: This study screened and studied immune-related metabolic genes from three aspects. First of all, we divide them into three categories based on different immune characteristics and research immunity and clinical pathology. Secondly, we used LASSO regression analysis to screen the immune-related metabolic genes and constructed the clinical prediction model for the screened genes. Finally, we selected the intersection of immune metabolism genes highly expressed in tumor sites and immune metabolism genes that are negatively related to survival and obtained potential genes. Results: We first identified immune-related metabolic genes and immune cells that may affect tumor progression in lung cancer. Then, through LASSO regression analysis, we screened out nine hub genes (TK1, TCN1, CAV1, ACMSD, HS3ST2, HS3ST5, AMN, ADRA2C, ACOXL) and constructed a prognostic model. Finally, through the screening of tumor-related immune metabolism genes, we obtained five hub genes (HMMR, PFKP, RRM2, TCN1 and TK1). Our qRT-PCR result also showed that RRM2 positively correlates with CDK2, CDK4, CDK6, CDK8.Conclusion: We conduct a comprehensive analysis of the immune infiltration of the tumor microenvironment of lung cancer, and finally determined RRM2 as a promising immune metabolism checkpoint for lung adenocarcinoma based on the high correlation of RRM2 with immune cells and CDK family.

scholarly journals Comprehensive Analysis of Immune-Related Metabolic Genes in Lung Adenocarcinoma

2021 ◽  
Author(s):  
Fangfang Li ◽  
Chun Huang ◽  
Llingxiao Qiu ◽  
Ping Li ◽  
Guojun Zhang
Keyword(s):  
Lung Cancer ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Immune Cell ◽  
Comprehensive Analysis ◽  
Lasso Regression ◽  
Cell Functions ◽  
Qrt Pcr ◽  
Metabolic Genes

Abstract Purpose The immunotherapy of lung adenocarcinoma has received more and more attention. Different immune cells can affect other metabolic genes and lifespan, and cell metabolism directly regulates immune cell functions. Therefore, it is crucial to explore the role of immune-related metabolic genes in lung adenocarcinoma. Methods In this study, we divided immune-related metabolic genes into three categories based on different immune characteristics and researched immune and clinical pathology. LASSO regression analysis was used to screen immune-related metabolic genes, and a clinical prediction model of the screened genes was constructed. Finally, we selected the intersection of immune metabolism genes that are highly expressed in the tumor site and immune metabolism genes that are negatively related to survival, and used qRT-PCR for experimental verification. Results We first screened out immune-related metabolic genes that may affect lung cancer tumor progression, and screened out 9 pivot genes (TK1, TCN1, CAV1, ACMSD, HS3ST2, HS3ST5, AMN, ADRA2C, ACOXL) through LASSO regression analysis and constructed Prognosis model. Finally, through the screening of tumor-related immune metabolism genes, we obtained five pivot genes (HMMR, PFKP, RRM2, TCN1 and TK1). Our qRT-PCR results also show that RRM2 is positively correlated with CDK2, CDK4, CDK6, and CDK8, revealing the close relationship between RRM2 and immune cell tumor infiltration. Conclusion We conducted a comprehensive analysis of the immune infiltration of the tumor microenvironment of lung cancer, and finally determined RRM2 as a promising immune metabolism checkpoint for lung adenocarcinoma based on the high correlation of RRM2 with immune cells and CDK family.

scholarly journals Comprehensive Analysis of Tumor-Infiltrating Immune Cells and Relevant Therapeutic Strategy in Esophageal Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Guangrong Lu ◽  
Liping Chen ◽  
Shengjie Wu ◽  
Yuao Feng ◽  
Tiesu Lin
Keyword(s):  
Esophageal Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Risk Model ◽  
Risk Group ◽  
Tumor Stage ◽  
Tumor Location ◽  
High Risk Group ◽  
Comprehensive Analysis ◽  
Lasso Regression

A growing body of evidence has indicated that behaviors of cancers are defined by not only intrinsic activities of tumor cells but also tumor-infiltrating immune cells (TIICs) in the tumor microenvironment. However, it still lacks a well-structured and comprehensive analysis of TIICs and its therapeutic value in esophageal cancer (EC). The proportions of 22 TIICs were evaluated between 150 normal tissues and 141 tumor tissues of EC by the CIBERSORT algorithm. Besides, correlation analyses between proportions of TIICs and clinicopathological characters, including age, gender, histologic grade, tumor location, histologic type, LRP1B mutation, TP53 mutation, tumor stage, lymph node stage, and TNM stage, were conducted. We constructed a risk score model to improve prognostic capacity with 5 TIICs by least absolute shrinkage and selection operator (lasso) regression analysis. The risk score=−1.86∗plasma+2.56∗T cell follicular helper−1.37∗monocytes−3.64∗activated dendritic cells−2.24∗resting mast cells (immune cells in the risk model mean the proportions of immune cell infiltration in EC). Patients in the high-risk group had significantly worse overall survival than these in the low-risk group (HR: 2.146, 95% CI: 1.243-3.705, p=0.0061). Finally, we identified Semustine and Sirolimus as two candidate compounds for the treatment of EC based on CMap analysis. In conclusion, the proportions of TIICs may be important to the progression, prognosis, and treatment of EC.

scholarly journals TRIM68, PIKFYVE, and DYNLL2: The Possible Novel Autophagy- and Immunity-Associated Gene Biomarkers for Osteosarcoma Prognosis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jie Jiang ◽  
Dachang Liu ◽  
Guoyong Xu ◽  
Tuo Liang ◽  
Chaojie Yu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Clinical Information ◽  
Lasso Regression ◽  
Osteosarcoma Cells ◽  
Screening Process ◽  
Cox Regression Analysis ◽  
Human Osteosarcoma Cells

IntroductionOsteosarcoma is among the most common orthopedic neoplasms, and currently, there are no adequate biomarkers to predict its prognosis. Therefore, the present study was aimed to identify the prognostic biomarkers for autophagy-and immune-related osteosarcoma using bioinformatics tools for guiding the clinical diagnosis and treatment of this disease.Materials and MethodsThe gene expression and clinical information data were downloaded from the Public database. The genes associated with autophagy were extracted, followed by the development of a logistic regression model for predicting the prognosis of osteosarcoma using univariate and multivariate COX regression analysis and LASSO regression analysis. The accuracy of the constructed model was verified through the ROC curves, calibration plots, and Nomogram plots. Next, immune cell typing was performed using CIBERSORT to analyze the expression of the immune cells in each sample. For the results obtained from the analysis, we used qRT-PCR validation in two strains of human osteosarcoma cells.ResultsThe screening process identified a total of three genes that fulfilled all the screening criteria. The survival curves of the constructed prognostic model revealed that patients with the high risk presented significantly lower survival than the patients with low risk. Finally, the immune cell component analysis revealed that all three genes were significantly associated with the immune cells. The expressions of TRIM68, PIKFYVE, and DYNLL2 were higher in the osteosarcoma cells compared to the control cells. Finally, we used human pathological tissue sections to validate the expression of the genes modeled in osteosarcoma and paracancerous tissue.ConclusionThe TRIM68, PIKFYVE, and DYNLL2 genes can be used as biomarkers for predicting the prognosis of osteosarcoma.

scholarly journals A Novel Framework to Predict Breast Cancer Prognosis Using Immune-Associated LncRNAs

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhijian Huang ◽  
Chen Xiao ◽  
Fushou Zhang ◽  
Zhifeng Zhou ◽  
Liang Yu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Comprehensive Analysis ◽  
Cancer Prognosis ◽  
Clinicopathological Factors ◽  
Cox Regression Analysis

Background: Breast cancer (BC) is one of the most frequently diagnosed malignancies among females. As a huge heterogeneity of malignant tumor, it is important to seek reliable molecular biomarkers to carry out the stratification for patients with BC. We surveyed immune- associated lncRNAs that may be used as potential therapeutic targets in BC.Methods: LncRNA expression data and clinical information of BC patients were downloaded from the TCGA database for a comprehensive analysis of candidate genes. A model consisting of immune-related lncRNAs enriched in BC cancerous tissues was established using the univariate Cox regression analysis and the iterative Lasso Cox regression analysis. The prognostic performance of this model was validated in two independent cohorts (GSE21653 and BC-KR), and compared with known prognostic biomarkers. A nomogram that integrated the immune-related lncRNA signature and clinicopathological factors was constructed to accurately assess the prognostic value of this signature. The correlation between the signature and immune cell infiltration in BC was also analyzed.Results: The Kaplan-Meier analysis showed that the OS of Patients in the low-risk group had significantly better survival than those in the high-risk group, Clinical subgroup analysis showed that the predictive ability was independent of clinicopathological factors. Univariate/multivariate Cox regression analysis showed immune lncRNA signature is an important prognostic factor and an independent prognostic marker. In addition, GSEA and GSVA analysis as well as comprehensive analysis of immune cells showed that the signature was significantly correlated with the infiltration of immune cells.Conclusion: We successfully constructed an immune-associated lncRNA signature that can accurately predict BC prognosis.

scholarly journals Identification of genes with therapeutic and prognostic values in lung adenocarcinoma microenvironment

2020 ◽  
Author(s):  
Xueping Jiang ◽  
Yanping Gao ◽  
Nannan Zhang ◽  
Cheng Yuan ◽  
Yuan Luo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Venn Diagram ◽  
Ppi Network ◽  
Survival Curves ◽  
Hub Genes ◽  
Expression Levels ◽  
Kegg Analysis

Abstract Background As the most diagnosed malignancy, lung cancer is also the primary cause of cancer death in the entire world. The therapy of lung adenocarcinoma (LUAD), which is the most prevalent subtype of lung cancer, draw researchers’ increasing attentions. This research aimed to investigate the tumor microenvironment (TME)-related hub genes which might be novel targets for treatment. Materials and methods LUAD-associated data packages, including RNA-Seq information and clinical data of 522 patients, were obtained from The Cancer Genome Atlas (TCGA). For better evaluation of stromal and immune cell components, immune scores, stromal scores and estimate scores were obtained with ESTIMATE algorithm based on gene expression levels in tumors. The R package heatmap and clustering analysis were used to explore interested genes. Differentially expressed genes (DEGs) were identified by Venn diagram. Protein-protein interaction (PPI) network was applied to explore intrinsic connections of DEGs. Kaplan-Meier (K-M) survival curves, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to investigate the prognostic values and intricate biological functions of DEGs. The relationships between 4 survival-related hub genes and 6 types of immune cells were examined using TIMER database. The LinkedOmics database was applied to look for kinase targets of hub genes. Results The immune/stromal/estimate scores were significantly correlated with clinical features, including the grades and sizes of LUAD, distant metastasis and outcomes. A total of 702 DEGs, 589 up-regulated and 113 down-regulated, were identified. GO and KEGG analysis showed that the DEGs had significant correlations with tumor immunology. PPI network suggested that the top 8 nodes were FPR2, C3AR1, MCHR1, CCR5, FPR1, CCL19, CCR2 and CXCL10. K-M survival curves indicated that FPR2, C3AR1, MCHR1 and CCR5, as hub genes, were significantly correlated with the overall survival (OS) of LUAD patients. The expression levels of C3AR1 and CCR5 were positively correlated with immune cell infiltration. LYN, LCK and SYK were the targeted kinases of these hub genes. Conclusion FPR2, C3AR1, MCHR1 and CCR5 were TME-related genes and potential biomarkers for the therapy and prognosis of LUAD.

scholarly journals Significance of Parkinson Family Genes in the Prognosis and Treatment Outcome Prediction for Lung Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Yanqi Li ◽  
Xiao Lu ◽  
Jiao Zhang ◽  
Quanxing Liu ◽  
Dong Zhou ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Treatment Outcome ◽  
Lung Adenocarcinoma ◽  
Immune Cell ◽  
Genetic Alterations ◽  
Treatment Choice ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Lasso Regression ◽  
Family Gene

Epidemiological investigations have shown that patients with Parkinson’s disease (PD) have a lower probability of developing lung cancer. Subsequent research revealed that PD and lung cancer share specific genetic alterations. Therefore, the utilisation of PD biomarkers and therapeutic targets may improve lung adenocarcinoma (LUAD) diagnosis and treatment. We aimed to identify a gene-based signature from 25 Parkinson family genes for LUAD prognosis and treatment choice. We analysed Parkinson family gene expression and protein levels in LUAD, utilising multiple databases. Least absolute shrinkage and selection operator (LASSO) regression was used to construct a prognostic model based on the TCGA-LUAD cohort. We validated the model in external GEO cohorts. Immune cell infiltration was compared between risk groups, and GEO data was used to explore the model’s predictive ability for LUAD treatment response. Nearly all Parkinson family genes exhibited significant differential expression between LUAD and normal tissues. LASSO regression confirmed that our seven Parkinson family gene-based signature had excellent prognostic performance for LUAD, as validated in three GEO cohorts. The high-risk group was clearly associated with low tumour immune cell infiltration, suggesting that immunotherapy may not be an optimal treatment choice. This is the first Parkinson family gene-based model for the prediction of LUAD prognosis and treatment outcome. The association of these genes with poor prognosis and low immune infiltration requires further investigation.

scholarly journals SFTPA1 is a potential prognostic biomarker correlated with immune cell infiltration and response to immunotherapy in lung adenocarcinoma

2021 ◽  
Author(s):  
Lu Yuan ◽  
Xixi Wu ◽  
Longshan Zhang ◽  
Mi Yang ◽  
Xiaoqing Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
T Cells ◽  
Lung Adenocarcinoma ◽  
Expression Profile ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Regulatory Pathways ◽  
Chronic Lung Diseases ◽  
Potential Biomarker

AbstractPulmonary surfactant protein A1 (SFTPA1) is a member of the C-type lectin subfamily that plays a critical role in maintaining lung tissue homeostasis and the innate immune response. SFTPA1 disruption can cause several acute or chronic lung diseases, including lung cancer. However, little research has been performed to associate SFTPA1 with immune cell infiltration and the response to immunotherapy in lung cancer. The findings of our study describe the SFTPA1 expression profile in multiple databases and was validated in BALB/c mice, human tumor tissues, and paired normal tissues using an immunohistochemistry assay. High SFTPA1 mRNA expression was associated with a favorable prognosis through a survival analysis in lung adenocarcinoma (LUAD) samples from TCGA. Further GeneOntology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that SFTPA1 was involved in the toll-like receptor signaling pathway. An immune infiltration analysis clarified that high SFTPA1 expression was associated with an increased number of M1 macrophages, CD8+ T cells, memory activated CD4+ T cells, regulatory T cells, as well as a reduced number of M2 macrophages. Our clinical data suggest that SFTPA1 may serve as a biomarker for predicting a favorable response to immunotherapy for patients with LUAD. Collectively, our study extends the expression profile and potential regulatory pathways of SFTPA1 and may provide a potential biomarker for establishing novel preventive and therapeutic strategies for lung adenocarcinoma.

scholarly journals The landscape of gene co-expression modules correlating with prognostic genetic abnormalities in AML

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chao Guo ◽  
Ya-yue Gao ◽  
Qian-qian Ju ◽  
Chun-xia Zhang ◽  
Ming Gong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Hox Genes ◽  
Expression Profiles ◽  
Penalized Regression ◽  
Diagnostic Utility ◽  
Lasso Regression ◽  
Hub Genes ◽  
Npm1 Mutation ◽  
Genetic Abnormalities

Abstract Background The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA. Methods We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and ‘WGCNA’ package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by ‘glmnet’ package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model. Results A total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the ‘lightyellow’ module for NPM1 mutation, the ‘saddlebrown’ module for RUNX1 mutation, the ‘lightgreen’ module for TP53 mutation). ORA revealed that the ‘lightyellow’ module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The ‘saddlebrown’ module was enriched in immune response process. And the ‘lightgreen’ module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743–0.79). Conclusions We established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

NLRP3 is a Novel Prognostic Biomarker and Correlates With Immune Infiltrates in Lung Adenocarcinoma and Skin Cutaneous Melanoma

2021 ◽  
Author(s):  
Chenxi Yuan ◽  
Qingwei Wang ◽  
Xueting Dai ◽  
Yipeng Song ◽  
Jinming Yu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Potential Biomarker ◽  
The Impact ◽  
Nlrp3 Expression

Abstract Background: Lung adenocarcinoma (LUAD) and skin cutaneous melanoma (SKCM) are common tumors around the world. However, the prognosis in advanced patients is poor. Because NLRP3 was not extensively studied in cancers, so that we aimed to identify the impact of NLRP3 on LUAD and SKCM through bioinformatics analyses. Methods: TCGA and TIMER database were utilized in this study. We compared the expression of NLRP3 in different cancers and evaluated its influence on survival of LUAD and SKCM patients. The correlations between clinical information and NLRP3 expression were analyzed using logistic regression. Clinicopathologic characteristics associated with overall survival in were analyzed by Cox regression. In addition, we explored the correlation between NLRP3 and immune infiltrates. GSEA and co-expressed gene with NLRP3 were also done in this study. Results: NLRP3 expressed disparately in tumor tissues and normal tissues. Cox regression analysis indicated that up-regulated NLRP3 was an independent prognostic factor for good prognosis in LUAD and SKCM. Logistic regression analysis showed increased NLRP3 expression was significantly correlated with favorable clinicopathologic parameters such as no lymph node invasion and no distant metastasis. Specifically, a positive correlation between increased NLRP3 expression and immune infiltrating level of various immune cells was observed. Conclusion: Together with all these findings, increased NLRP3 expression correlates with favorable prognosis and increased proportion of immune cells in LUAD and SKCM. These conclusions indicate that NLRP3 can serve as a potential biomarker for evaluating prognosis and immune infiltration level.

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