Identification of Signature Genes and Characterizations of Tumor Immune Microenvironment and Tumor Purity in Lung Adenocarcinoma Based on Machine Learning

Feng, Hao; Zhao, Ye; Yan, Weijian; Wei, Xiao-Ping; Jiang, Lin; Jiang, Peng; Wang, Cheng; Li, Bin

doi:10.3389/fmed.2022.843749

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…Combined with our results that GIMAP6 expression was significantly upregulated in DM, we speculated that GIMAP6 might contribute to regulating the development, activation, and apoptosis of CD4 + T cells. GIMAP6 has been recognized as a prognostic biomarker in head and neck squamous cell carcinoma, breast cancer, and female lung adenocarcinoma and as a predictor of response to immunotherapy in lung adenocarcinoma ( 64 – 66 ). We are looking forward to furthering studies exploring the relationship between GIMAP6 and CD4 + T cells in DM.…”

Section: Discussionmentioning

confidence: 99%

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

Huang

Zhang

et al. 2022

Front. Immunol.

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BackgroundDermatomyositis is an autoimmune disease characterized by damage to the skin and muscles. CD4+ T cells are of crucial importance in the occurrence and development of dermatomyositis (DM). However, there are few bioinformatics studies on potential pathogenic genes and immune cell infiltration of DM. Therefore, this study intended to explore CD4+ T-cell infiltration–associated key genes in DM and construct a new model to predict the level of CD4+ T-cell infiltration in DM.MethodsGSE46239, GSE142807, GSE1551, and GSE193276 datasets were downloaded. The WGCNA and CIBERSORT algorithms were performed to identify the most correlated gene module with CD4+ T cells. Matascape was used for GO enrichment and KEGG pathway analysis of the key gene module. LASSO regression analysis was used to identify the key genes and construct the prediction model. The correlation between the key genes and CD4+ T-cell infiltration was investigated. GSEA was performed to research the underlying signaling pathways of the key genes. The key gene-correlated transcription factors were identified through the RcisTarget and Gene-motif rankings databases. The miRcode and DIANA-LncBase databases were used to build the lncRNA-miRNA-mRNA network.ResultsIn the brown module, 5 key genes (chromosome 1 open reading frame 106 (C1orf106), component of oligomeric Golgi complex 8 (COG8), envoplakin (EVPL), GTPases of immunity-associated protein family member 6 (GIMAP6), and interferon-alpha inducible protein 6 (IFI6)) highly associated with CD4+ T-cell infiltration were identified. The prediction model was constructed and showed better predictive performance in the training set, and this satisfactory model performance was validated in another skin biopsy dataset and a muscle biopsy dataset. The expression levels of the key genes promoted the CD4+ T-cell infiltration. GSEA results revealed that the key genes were remarkably enriched in many immunity-associated pathways, such as JAK/STAT signaling pathway. The cisbp_M2205, transcription factor-binding site, was enriched in C1orf106, EVPL, and IF16. Finally, 3,835 lncRNAs and 52 miRNAs significantly correlated with key genes were used to build a ceRNA network.ConclusionThe C1orf106, COG8, EVPL, GIMAP6, and IFI6 genes are associated with CD4+ T-cell infiltration. The prediction model constructed based on the 5 key genes may better predict the level of CD4+ T-cell infiltration in damaged muscle and lesional skin of DM. These key genes could be recognized as potential biomarkers and immunotherapeutic targets of DM.

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Section: Discussionmentioning

confidence: 99%

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

Huang

Zhang

et al. 2022

Front. Immunol.

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“…Perhaps background levels of EPA and DHA or the response to these fatty acids will also vary within each subpopulation of subjects with diabetes. Future studies using machine-learning approaches, as demonstrated in other disciplines, in the context of experiments with EPA/DHA will assist in moving the field forward [41][42][43][44].…”

Section: Integration Of Differing Inputs Of Heterogeneity Will Requir...mentioning

confidence: 99%

Heterogeneity in the response to n-3 polyunsaturated fatty acids

Shaikh

Bazinet

2023

Current Opinion in Clinical Nutrition &Amp; Metabolic Care

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Purpose of reviewA central goal in the study of long chain n-3 polyunsaturated fatty acids (PUFA) is to translate findings from the basic sciences to the population level to improve human health and prevent chronic diseases. A tenet of this vision is to think in terms of precision medicine and nutrition, that is, stratification of individuals into differing groups that will have different needs across the lifespan for n-3 PUFAs. Therefore, there is a critical need to identify the sources of heterogeneity in the human population in the dietary response to n-3 PUFA intervention.Recent findingsWe briefly review key sources of heterogeneity in the response to intake of long chain n-3 PUFAs. These include background diet, host genome, composition of the gut microbiome, and sex. We also discuss the need to integrate data from newer rodent models (e.g. population-based approaches), multi -omics, and analyses of big data using machine learning and data-driven cluster analyses.SummaryAccounting for vast heterogeneity in the human population, particularly with the use of big data integrated with preclinical evidence, will drive the next generation of precision nutrition studies and randomized clinical trials with long-chain n-3 PUFAs.

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“…Marker genes for 29 immune cell subtypes were obtained from published literature (Table S1). The ESTIMATE score system consists of the immune and stromal scores [12]. ESTIMATE analyzes bulk tumor data and predicts tumor purity and immune as well as stromal cell infiltrations by single-sample gene set expression analysis (ssGSEA) [10].…”

Section: Evaluation Of Immune Cells and Acquisition Of Estimate Scoresmentioning

confidence: 99%

Identification of Prognostic Genes and Immune Landscape Signatures Based on Tumor Microenvironment in Lung Adenocarcinoma

et al. 2022

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Background. Lung adenocarcinoma is the most common lung cancer subtype and accounts for the highest proportion of cancer-related deaths. The tumor microenvironment influences prognostic outcomes in lung adenocarcinoma (LUAD). Materials and Methods. We used the ESTIMATE algorithm (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) to investigate the role of microenvironment-related genes and stromal cells in lung adenocarcinoma prognosis. This analysis was done on lung adenocarcinoma cases from The Cancer Genome Atlas (TCGA). The cases were divided into high and low groups on the basis of immune and stromal scores, respectively. Results. There were close correlations between immune scores with prognosis and disease stage. There were 367 differentially expressed genes. Combining the Gene Expression Omnibus (GEO) database, we found 14 prognosis-related genes. Results. Based on the enrichment levels of the immune cell types, we clustered LUAD into Immunity_H and Immunity_L subtypes. Most of these genes were upregulated in Immunity_H subtype. Finally, using the Human Protein Atlas (HPA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, most of the proteins corresponding to prognostic genes were verified to be differentially expressed between the tumor and normal groups. Conclusions. The key genes identified in this study are involved in molecular mechanisms of LUAD.

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Identification of Signature Genes and Characterizations of Tumor Immune Microenvironment and Tumor Purity in Lung Adenocarcinoma Based on Machine Learning

Cited by 10 publications

References 29 publications

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

Heterogeneity in the response to n-3 polyunsaturated fatty acids

Identification of Prognostic Genes and Immune Landscape Signatures Based on Tumor Microenvironment in Lung Adenocarcinoma

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