Single cell transcriptome revealed tumor associated antigen (TAA) profile in lung adenocarcinoma (LUAD).
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ABSTRACT:
SUBMITTER: Lv F
PROVIDER: S-EPMC8170805 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PSMD14 is a 19S-proteasome-associated deubiquitinating enzyme that facilitates protein degradation by the 20S proteasome core particle. Although accumulating evidence indicates that PSMD14 has emerged as a critical oncogenic factor by promoting tumor growth, the expression and function of PSMD14 in non-small cell lung cancer (NSCLC) remain largely unknown. In this study, we assessed PSMD14 expression and correlated it with clinical-pathological features and patient survival in NSCLC. We also determined the roles of PSMD14 in the regulation of lung adenocarcinoma (LUAD) cell growth. The results showed that PSMD14 expression was significantly upregulated in human NSCLC tissues compared with adjacent non-cancerous tissues. The PSMD14 level was associated with tumor size, lymph node invasion, and TNM stage in LUAD patients. Importantly, high PSMD14 expression was associated with poor overall survival (OS) and disease-free survival (DFS) in LUAD patients. Further, knockdown of PSMD14 significantly inhibited cell growth and caused G1 arrest and cellular senescence by increasing p21 stability in LUAD cells. PSMD14 knockdown also promoted cell apoptosis by increasing cleaved caspase-3 levels in H1299 cells. PSMD14 may serve as a potential prognostic marker and therapeutic target for LUAD patients.
| S-EPMC7086243 | biostudies-literature
Project description:In this study, we performed single-cell transcriptome data analysis of fifty primary and metastatic lung adenocarcinoma (LUAD) samples from the GSE123902 and GSE131907 datasets to determine the landscape of inter-patient and intra-tumoral heterogeneity. The gene expression profiles and copy number variations (CNV) showed significant heterogeneity in the primary and metastatic LUAD samples. We observed upregulation of pathways related to translational initiation, endoplasmic reticulum stress, exosomes, and unfolded protein response in the brain metastasis samples as compared to the primary tumor samples. Pathways related to exosomes, cell adhesion and metabolism were upregulated and the epithelial-to-mesenchymal-transition (EMT) pathway was downregulated in brain metastasis samples from chemotherapy-treated LUAD patients as compared to those from the untreated LUAD patients. Tumor cell subgroups in the brain metastasis samples showed differential expression of genes related to type II alveolar cells, chemoresistance, glycolysis and oxidative phosphorylation (metabolic reprogramming), and EMT. Thus, single-cell transcriptome analysis demonstrated intra-patient and intra-tumor heterogeneity in the regulation of pathways related to tumor progression, chemoresistance and metabolism in the primary and metastatic LUAD tissues. Moreover, our study demonstrates that single cell transcriptome analysis is a potentially useful tool for accurate diagnosis and personalized targeted treatment of LUAD patients.
| S-EPMC7695431 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.MethodsRaw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.ResultsA prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041-39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779-3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.ConclusionOur study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.
| S-EPMC8183047 | biostudies-literature
Project description:BackgroundHypoxia signaling plays a critical role in the development of lung adenocarcinoma (LUAD). We herein aimed to explore the prognostic value of hypoxia-related genes and construct the hypoxia-related prognostic signature for LUAD patients.MethodsA total of 26 hypoxia-related genes were collected. Five hundred thirteen and 246 LUAD samples were obtained from the Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. Univariate Cox regression and LASSO Cox regression analyses were conducted to screen the hypoxia-related genes associated with the prognosis of LUAD patients, which would be used for constructing prognosis predictive model for LUAD patients. Multivariate Cox regression analysis was done to determine the independent prognostic factors. The Nomogram model was constructed to predict the prognosis of LUAD patients.ResultsBased on 26 hypoxia-related genes, LUAD samples could be divided into 4 clusters with different prognoses. Among which, 6 genes were included to construct the Risk Score and the LUAD patients with higher Risk Score had worse prognosis. Besides, the Nomogram based on all the independent risk factors could relatively reliably predict the survival probability. And 9 types of immune cells' infiltration was significantly differential between high and low risk LUAD patients.ConclusionThe Risk Score model based on the 6 crucial hypoxia-related genes could relatively reliably predict the prognosis of LUAD patients.
| S-EPMC8677978 | biostudies-literature
Project description:Lung cancer is one of the most common malignant tumors in the world, with a high rate of malignancy and mortality. Seeking new biomarkers and potential drug targets is urgent for effective treatment of the disease. Deubiquitinase UCHL5/UCH37, as an important component of the 26S proteasome, plays critical roles in ubiquitinated substrate degradation. Although previous studies have shown that UCHL5 promotes tumorigenesis, its role in lung cancer remains largely unknown. In this study, we evaluated the expression and clinical significance of UCHL5 in non-small cell lung cancer (NSCLC). The results demonstrated that the UCHL5 expression level was significantly upregulated in NSCLC tissues compared with the adjacent noncancerous tissues. The level of UCHL5 was associated with tumor size, lymph node invasion, TNM stage and malignant tumor history in patients with lung adenocarcinoma (LUAD). Importantly, high UCHL5 expression predicted a poor overall survival (OS) and a poor disease-free survival (DFS) in patients with LUAD. Univariate regression analysis showed that tumor size, lymph node invasion, TNM stage and UCHL5 expression were associated with OS and DFS in patients with LUAD. The multivariate analysis indicated that the UCHL5 expression level was an independent prognostic factor for OS (HR=1.171, 95% CI=1.052-1.303) and DFS (HR=1.143, 95% CI=1.031-1.267) in these patients. UCHL5 knockdown in LUAD cells significantly inhibited cell proliferation and reduced the expression of key cell cycle proteins. These findings indicate that UCHL5 may serve as a potential prognostic marker and a new therapeutic target for patients with LUAD.
| S-EPMC7545677 | biostudies-literature
Project description:As an early type of lung adenocarcinoma, ground glass nodule (GGN) has been detected increasingly and now accounts for most lung cancer outpatients. GGN has a satisfactory prognosis and its characteristics are quite different from solid adenocarcinoma (SADC). We compared the GGN adenocarcinoma (GGN-ADC) with SADC using the single-cell RNA sequencing (scRNA-seq) to fully understand GGNs. The tumor samples of five patients with lung GGN-ADCs and five with SADCs underwent surgery were digested to a single-cell suspension and analyzed using 10× Genomic scRNA-seq techniques. We obtained 60,459 cells and then classified them as eight cell types, including cancer cells, endothelial cells, fibroblasts, T cells, B cells, Nature killer cells, mast cells, and myeloid cells. We provided a comprehensive description of the cancer cells and stromal cells. We found that the signaling pathways related to cell proliferation were downregulated in GGN-ADC cancer cells, and stromal cells had different effects in GGN-ADC and SADC based on the analyses of scRNA-seq results. In GGN-ADC, the signaling pathways of angiogenesis were downregulated, fibroblasts expressed low levels of some collagens, and immune cells were more activated. Furthermore, we used flow cytometry to isolate the cancer cells and T cells in 12 GGN-ADC samples and in an equal number of SADC samples, including CD4+ T and CD8+ T cells, and validated the expression of key molecules by quantitative real-time polymerase chain reaction analyses. Through comprehensive analyses of cell phenotypes in GGNs, we provide deep insights into lung carcinogenesis that will be beneficial in lung cancer prevention and therapy.
| S-EPMC7536439 | biostudies-literature
Project description:We analyzed 40,799 single cells from nine samples and found that in EGFR-mutant LUAD, tumor cells generated an immunosuppressive microenvironment by releasing cytokines to attract suppressive immune cells such as myeloid-derived suppressor cells (MDSCs) directly or indirectly by interacting with macrophages, dendritic cells, or other cell types. By contrast, EGFR wild-type LUAD had a greater ability to recruit immunocompetent T cells such as TRMs and Th1 cells, thus driving a proinflammatory microenvironment.
2022-02-09 | GSE171145 | GEO
Project description:Expression profiles of 18,175 unique genes and three major genetic changes, p53, EGFR and K-ras, were investigated in 149 patients with non-small cell lung cancer (NSCLC), including 90 patients with adenocarcinomas (AD) to determine their relationships with various clinicopathologic features and Gene Ontology (GO) terms. Keywords: Disease state analysis Expression profiles in 149 patients with NSCLC, 9 patients with SCLC and 5 for normal lung tissue.
| S-ECPF-GEOD-11969 | biostudies-other
Project description:BackgroundLung adenocarcinoma (LUAD) remains one of the world's most known aggressive malignancies with a high mortality rate. Molecular biological analysis and bioinformatics are of great importance as they have recently occupied a large area in the studies related to the identification of various biomarkers to predict survival for LUAD patients. In our study, we attempted to identify a new prognostic model by developing a new algorithm to calculate the allele frequency deviation (AFD), which in turn may assist in the early diagnosis and prediction of clinical outcomes in LUAD.MethodFirst, a new algorithm was developed to calculate AFD using the whole-exome sequencing (WES) dataset. Then, AFD was measured for 102 patients, and the predictive power of AFD was assessed using Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and area under the curve (AUC). Finally, multivariable cox regression analyses were conducted to evaluate the independence of AFD as an independent prognostic tool.ResultThe Kaplan-Meier analysis showed that AFD effectively segregated patients with LUAD into high-AFD-value and low-AFD-value risk groups (hazard ratio HR = 1.125, 95% confidence interval CI 1.001-1.26, p = 0.04) in the training group. Moreover, the overall survival (OS) of patients who belong to the high-AFD-value group was significantly shorter than that of patients who belong to the low-AFD-value group with 42.8% higher risk and 10% lower risk of death for both groups respectively (HR for death = 1.10; 95% CI 1.01-1.2, p = 0.03) in the training group. Similar results were obtained in the validation group (HR = 4.62, 95% CI 1.22-17.4, p = 0.02) with 41.6%, and 5.5% risk of death for patients who belong to the high and low-AFD-value groups respectively. Univariate and multivariable cox regression analyses demonstrated that AFD is an independent prognostic model for patients with LUAD. The AUC for 5-year survival were 0.712 and 0.86 in the training and validation groups, respectively.ConclusionAFD was identified as a new independent prognostic model that could provide a prognostic tool for physicians and contribute to treatment decisions.
| S-EPMC8390239 | biostudies-literature
Project description:BackgroundThe tumor immune microenvironment (TIME) played an important role in immunotherapy prognosis and treatment response. Immune cells constitute a large part of the tumor microenvironment and regulate tumor progression. Our research is dedicated to studying the infiltrating immune cell in lung adenocarcinoma (LUAD) and seeking potential targets.MethodsThe scRNA-seq data were collected from our FDZSH and two public datasets. The code for cell-type mapping algorithms was downloaded from the CIBERSORTx portal. The bioinformatics data of LUAD patients could be approached from The Cancer Genome Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analyses were performed to construct a risk model. TIMER2 and TIDE helped with the immune infiltration estimation, while PROGENy helped the cancer-related pathways' enrichment analysis. GSE31210 dataset and IMVigor ICB therapy cohort validated our findings as the external validation datasets.ResultsWe clustered the scRNA-seq dataset (integrating our FDZSH datasets and other public datasets) into 23 subpopulations. After curated cell annotation, we implemented Cibersort and WGCNA analysis to anchor the brown module and natural killer cell cluster1 due to the most relationship with tumor trait. The overlap of the brown module gene, natural killer cell signature, and DEGs of tumor and adjacent normal samples was screened by LASSO Cox regression. The obtained 5-gene risk model showed an excellent prognostic performance in the validation dataset. Furthermore, there was a correlation between risk score and tumor-infiltrating immune cells and tumor genomics abnormity. Patients with higher risk scores had a significantly lower immunotherapy response rate.ConclusionOur observations implied that immune cells played a pivotal role in TIME and established a 5-gene signature (including IDH2, ADRB2, SFTPC, CCDC69, and CCND2) on the basement of nature killer markers targeted by WGCNA analysis. The significance of clinical outcome and immunotherapy response prediction was validated robustly.
| S-EPMC9270162 | biostudies-literature