Dataset Information

Complement-related molecular classification and a gene signature for lung adenocarcinoma.

ABSTRACT:

Background

Lung adenocarcinoma (LUAD) is a major cause of cancer-related death worldwide, and the roles of complement-related genes in it have not been thoroughly investigated yet. In the study, we aimed to systemically examine the prognostic performance of complement-related genes, classify the patients into two different clusters and stratify the patients into different risk groups using a complement-related gene signature.

Methods

To achieve this, clustering analyses, Kaplan-Meier survival analyses, immune infiltration analyses were performed. LUAD patients from The Cancer Genome Atlas (TCGA) were classified into two subtypes (C1 and C2). A prognostic signature, consisting of four complement-related genes, was established using TCGA-LUAD cohort and validated in six Gene Expression Omnibus datasets and an independent cohort from our center.

Results

The prognosis of C2 patients is better than that of C1 patients and the prognosis of low risk patients is significantly better than high risk patients across the public datasets. In our cohort, the OS of patients in low risk group is better than that in high risk group but the difference is not significant. Patients with a lower risk score were characterized by a higher immune score, a higher level of BTLA, higher infiltration levels of T cells, B lineage, myeloid dendritic cells, neutrophils, endothelial cells, and a lower infiltration level of fibroblast.

Conclusions

In summary, our study has established a new classification method and developed a prognostic signature for LUAD, while future studies are needed for further exploration of the underlying mechanism.

SUBMITTER: Zhang L

PROVIDER: S-EPMC9942347 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Complement-related molecular classification and a gene signature for lung adenocarcinoma.

Zhang Lin L Yang Yannan Y Lin Weihao W Shao Fei F Gao Yibo Y He Jie J

Experimental hematology & oncology 20230221 1

<h4>Background</h4>Lung adenocarcinoma (LUAD) is a major cause of cancer-related death worldwide, and the roles of complement-related genes in it have not been thoroughly investigated yet. In the study, we aimed to systemically examine the prognostic performance of complement-related genes, classify the patients into two different clusters and stratify the patients into different risk groups using a complement-related gene signature.<h4>Methods</h4>To achieve this, clustering analyses, Kaplan-Me ...[more]

PMID: 36810204

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Project description:AimLung cancer is one of the most common cancers in China and has a high mortality rate. Most patients who are diagnosed have lost the opportunity to undergo surgery. Aberrant metabolism is closely associated with tumorigenesis. We aimed to identify an effective metabolism-related prediction model for assessing prognosis based on the cancer genome atlas (TCGA) and GSE116959 databases.MethodsTCGA and GSE116959 datasets from Gene Expression Omnibus were used to obtain lung adenocarcinoma (LUAD) data. Additionally, we captured metabolism-related genes (MRGs) from the GeneCards database. First, we extracted differentially expressed genes using R to analyze the LUAD data. We then selected the same differentially expressed genes, including 168 downregulated and 77 upregulated genes. Finally, 218 differentially expressed MRGs (DEMRGs) were included to perform functional enrichment analysis and construct a protein-protein interaction network with the help of Cytoscape and Search Tool for the Retrieval of Interacting Genes database. Cytoscape was used to visualize the intensive intervals in the network. Then univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses, which assisted in identifying the overall survival (OS)-related DEMRGs and building a 10-DEMRG prognosis model, were performed. The prognostic values, tumor immunity relevance, and molecular mechanism were further investigated. A nomogram incorporating signature, age, gender, and TNM stage was established.ResultsA 10-DEMRG model was established to forecast the OS of LUAD through Least Absolute Shrinkage and Selection Operator regression analysis. This prognostic signature stratified LUAD patients into low-risk and high-risk groups. The receiver operating characteristic curve and K-M analysis indicated good performance of the DEMRGs signature at predicting OS in the TCGA dataset. Univariate and multivariate Cox regression also revealed that the DEMRGs signature was an independent prognosis factor in LUAD. We noticed that the risk score was substantially related to the clinical parameters of LUAD patients, covering age and stage. Immune analysis results showed that risk score was associated with some immune cells and immune checkpoints. Nomogram also verified the clinical value of the DEMRGs signature.ConclusionIn this study, we constructed a DEMRGs signature and established a prognostic nomogram that is robust and reliable to predict OS in LUAD. Overall, the findings could help with therapeutic customization and personalized therapies.

Project description:BackgroundLung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes.MethodsFirst, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature's prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature's prognostic ability.ResultsA nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature's mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells' key roles in contributing to gene signature's prognostic ability.ConclusionsThis study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.

Project description:PurposeThe dysregulation of copper metabolism is closely related to the occurrence and progression of cancer. This study aims to investigate the prognostic value of copper metabolism-related genes (CMRGs) in lung adenocarcinoma (LUAD) and its characterization in the tumor microenvironment (TME).MethodsThe differentially expressed CMRGs were identified in The Cancer Genome Atlas (TCGA) of LUAD. The least absolute shrinkage and selection operator regression (LASSO) and multivariate Cox regression analysis were used to establish the copper metabolism-related gene signature (CMRGs), which was also validated in Gene Expression Omnibus (GEO) database (GSE72094). The expression of key genes was verified by quantitative real-time PCR (qRT-PCR). Then, the CMRGS was used to develop a nomogram to predict the 1-year, 3-year, and 5-year overall survival (OS). In addition, differences in tumor mutation burden (TMB), biological characteristics and immune cell infiltration between high-risk and low-risk groups were systematically analyzed. Immunophenoscore (IPS) and an anti-PD-L1 immunotherapy cohort (IMvigor210) were used to verify whether CMRGS can predict the response to immunotherapy in LUAD.Results34 differentially expressed CMRGs were identified in the TCGA dataset, 11 of which were associated with OS. The CMRGS composed of 3 key genes (LOXL2, SLC31A2 and SOD3) had showed good clinical value and stratification ability in the prognostic assessment of LUAD patients. The results of qRT-PCR confirmed the expression of key CMRGs in LUAD and normal tissues. Then, all LUAD patients were divided into low-risk and high-risk groups based on median risk score. Those in the low-risk group had a significantly longer OS than those in the high-risk group (P<0.0001). The area under curve (AUC) values of the nomogram at 1, 3, and 5 years were 0.734, 0.735, and 0.720, respectively. Calibration curves comparing predicted and actual OS were close to ideal model, indicating a good consistency between prediction and actual observation. Functional enrichment analysis showed that the low-risk group was enriched in a large number of immune pathways. The results of immune infiltration analysis also confirmed that there were a variety of immune cell infiltration in the low-risk group. In addition, multiple immune checkpoints were highly expressed in the low-risk group and may benefit better from immunotherapy.ConclusionCMRGS is a promising biomarker to assess the prognosis of LUAD patients and may be serve as a guidance on immunotherapy.

Project description:BackgroundCuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients.MethodsRNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB).ResultsWe have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.

Project description:BackgroundLung adenocarcinoma (LUAD) is the most common malignant tumor that seriously affects human health. Previous studies have indicated that abnormal levels of glycosylation promote progression and poor prognosis of lung cancer. Thus, the present study aimed to explore the prognostic signature related to glycosyltransferases (GTs) for LUAD.MethodsThe gene expression profiles were obtained from The Cancer Genome Atlas (TCGA) database, and GTs were obtained from the GlycomeDB database. Differentially expressed GTs-related genes (DGTs) were identified using edge package and Venn diagram. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and ingenuity pathway analysis (IPA) methods were used to investigate the biological processes of DGTs. Subsequently, Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct a prognostic model for LUAD. Kaplan-Meier (K-M) analysis was adopted to explore the overall survival (OS) of LUAD patients. The accuracy and specificity of the prognostic model were evaluated by receiver operating characteristic analysis (ROC). In addition, single-sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze the infiltrating immune cells in the tumor environment.ResultsA total of 48 DGTs were mainly enriched in the processes of glycosylation, glycoprotein biosynthetic process, glycosphingolipid biosynthesis-lacto and neolacto series, and cell-mediated immune response. Furthermore, B3GNT3, MFNG, GYLTL1B, ALG3, and GALNT13 were screened as prognostic genes to construct a risk model for LUAD, and the LUAD patients were divided into high- and low-risk groups. K-M curve suggested that patients with a high-risk score had shorter OS than those with a low-risk score. The ROC analysis demonstrated that the risk model efficiently diagnoses LUAD. Additionally, the proportion of infiltrating aDCs (p < 0.05) and Tgds (p < 0.01) was higher in the high-risk group than in the low-risk group. Spearman's correlation analysis manifested that the prognostic genes (MFNG and ALG3) were significantly correlated with infiltrating immune cells.ConclusionIn summary, this study established a novel GTs-related risk model for the prognosis of LUAD patients, providing new therapeutic targets for LUAD. However, the biological role of glycosylation-related genes in LUAD needs to be explored further.

Dataset Information

Complement-related molecular classification and a gene signature for lung adenocarcinoma.

Background

Methods

Results

Conclusions

Publications

Complement-related molecular classification and a gene signature for lung adenocarcinoma.

Similar Datasets

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