Screening and Identification of Four Prognostic Genes Related to Immune Infiltration and G-Protein Coupled Receptors Pathway in Lung Adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is a common malignant tumor with the highest morbidity and mortality worldwide. The degree of tumor immune infiltration and clinical prognosis depend on immune-related genes, but their interaction with the tumor immune microenvironment, the specific mechanism driving immune infiltration and their prognostic value are still not very clear. Therefore, the aim of this work was focused on the elucidation of these unclear aspects.MethodsTCGA LUAD samples were divided into three immune infiltration subtypes according to the single sample gene set enrichment analysis (ssGSEA), in which the associated gene modules and hub genes were screened by weighted correlation network analysis (WGCNA). Four key genes related to immune infiltration were found and screened by differential expression analysis, univariate prognostic analysis, and Lasso-COX regression, and their PPI network was constructed. Finally, a Nomogram model based on the four genes and tumor stages was constructed and confirmed in two GEO data sets.ResultsAmong the three subtypes—high, medium, and low immune infiltration subtype—the survival rate of the patients in the high one was higher than the rate in the other two subtypes. The four key genes related to LUAD immune infiltration subtypes were CD69, KLRB1, PLCB2, and P2RY13. The PPI network revealed that the downstream genes of the G-protein coupled receptors (GPCRs) pathway were activated by these four genes through the S1PR1. The risk score signature based on these four genes could distinguish high and low-risk LUAD patients with different prognosis. The Nomogram constructed by risk score and clinical tumor stage showed a good ability to predict the survival rate of LUAD patients. The universality and robustness of the Nomogram was confirmed by two GEO datasets.ConclusionsThe prognosis of LUAD patients could be predicted by the constructed risk score signature based on the four genes, making this score a potential independent biomarker. The screening, identification, and analysis of these four genes could contribute to the understanding of GPCRs and LUAD immune infiltration, thus guiding the formulation of more effective immunotherapeutic strategies.

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Expression of lncRNAs Associated with GPCRs and Clinical Prognosis in GBM

10.21203/rs.3.rs-143404/v1 ◽

2021 ◽

Author(s):

Hao Wang ◽

Yaohui Wang ◽

wei xiang

Keyword(s):

Prognostic Factors ◽

Regression Model ◽

Multiple Regression ◽

G Protein ◽

Risk Score ◽

Univariate Analysis ◽

Multiple Regression Model ◽

G Protein Coupled Receptors ◽

Clinical Prognosis ◽

G Protein Coupled

Abstract Background: Glioblastoma (GBM) is a primary malignant tumor of the central nervous system with a poor prognosis. Long non-coding RNAs (lncRNAs) play a variety of key regulatory roles in a variety of biological processes, and have an important influence on the occurrence and development of tumors by regulating the expression of target genes. However, their role in the prognosis of GBM is still lacking in accurate prognostic markers.This study aims to establish an effective lncRNAs model to evaluate the prognosis of GBM.Methods: We used data of mRNA, lncRNA and clinical follow-up from The Cancer Genome Atlas (TCGA) to conduct univariate analysis, clustering analysis, coding gene difference analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis for GBM patients. lncRNAs that are closely related to the survival and prognosis of GBM were found and a multiple regression model was constructed to calculate the risk score of the samples, so as to accurately predict the clinical prognosis of GBM patients.Results: Through multiple systematic analysis, we found 5 lncRNAs that are closely related to the survival and prognosis of GBM, and these 5 lncRNAs can be used as independent prognostic factors. Through GO enrichment analysis and KEGG pathway analysis, we found that there is a close relationship between GBM and G protein coupled receptors. Therefore, 93 mRNAs associated with G-protein-coupled receptors and 5 lncRNAs associated with independent prognostic factors were selected to calculate the correlation, respectively. In other words, a tumor-specific lncRNAs/mRNAs co-expression network was constructed through biological prediction based on correlation analysis. The results showed that lncRNAs and mRNAs interregulated very closely in GBM patients. In addition, we also constructed a multiple regression model with these 5 lncRNAs which was able to calculate the risk score of each sample to accurately predict the clinical prognosis of GBM patients.Conclusion: Our study found lncRNAs independently were related to the prognosis of GBM, and successfully constructed a multiple regression model related to lncRNAs, providing a new perspective for better evaluation of the role of lncRNAs in the clinical prognosis of GBM.

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GPR143 is a Prognostic Biomarker Associated with Immune Infiltration in Melanoma

10.21203/rs.3.rs-430494/v1 ◽

2021 ◽

Author(s):

Zixuan Xing ◽

Shaobo Wu ◽

Qijuan Zang ◽

Hao Lei ◽

Yi Wei ◽

...

Keyword(s):

G Protein ◽

Prognostic Biomarker ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Congenital Nystagmus ◽

Immune Infiltration ◽

Interactive Analysis ◽

Tcga Dataset ◽

Cox Analysis ◽

G Protein Coupled

Abstract Background: Skin cutaneous melanoma (SKCM) is considered one of the most aggressive and lethal cancers of the skin. G-protein coupled receptor 143 (GPR143), which has been reported to cause congenital nystagmus, belongs to the superfamily of G protein-coupled receptors. Methods and Results: We analyzed the expression of GPR143 and survival of SKCM patients in SKCM via Gene Expression Profiling Interactive Analysis (GEPIA). Then, logistic regression and multivariate cox analysis was used to analyze the influence of GPR143 expression on clinicopathological elements and survival. We explored the immune response of 22 TIICs in SKCM via CIBERSORT and used TIMER to assess the correlation of GPR143 expression and immune infiltration level. Finally, we used gene set enrichment analysis (GSEA) to assess the TCGA dataset. The outcomes suggest that GPR143 expression in tumor samples is remarkedly higher than in normal samples and high GPR143 expression is associated with poorer prognosis. The result of multivariate analysis indicated that increased GPR143 expression is an independent prognostic factor for prognosis. We found GPR143 expression level has prominent negative correlations with infiltrating levels of B cell, CD8+ T cells, etc. GSEA indicated that pigment metabolic process, pigment biosynthetic process and other pathways were identified as differentially enriched pathways in Gene Ontology (GO). Oxidative phosphorylation, Parkinson’s disease and other pathways were showed significantly differential enrichment in GPR143 high expression phenotype in Kyoto Encyclopedia of Genes and Genomes (KEGG).Conclusions: In conclusion, GPR143 may be a novel prognostic biomarker and associated with immune infiltrates in SKCM.

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