scholarly journals Four hub genes regulate tumor infiltration by immune cells, antitumor immunity in the tumor microenvironment, and survival outcomes in lung squamous cell carcinoma patients

Aging ◽  
2021 ◽  
Author(s):  
Tuo Zhang ◽  
Haitang Yang ◽  
Beibei Sun ◽  
Feng Yao
Keyword(s):  
Squamous Cell Carcinoma ◽  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Immune Cells ◽  
Antitumor Immunity ◽  
Lung Squamous Cell Carcinoma ◽  
Survival Outcomes ◽  
Tumor Infiltration ◽  
Hub Genes

Mine TCGA Database for Tumor Microenvironment-Related Genes of Prognostic value in lung squamous cell carcinoma

2020 ◽  
Author(s):  
Xiao Chen ◽  
Rui Li ◽  
Yun-Hong Yin ◽  
Xiao Liu ◽  
Xi-Jia Zhou ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Immune Cells ◽  
Lung Squamous Cell Carcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes

Abstract Background: Tumor microenvironment (TME) plays a significant role in the development of cancer. However, the roles of TME in lung squamous cell carcinoma (LUSC) are not well studied. In our study, we aimed to identify differentially expressed tumor microenvironment-related genes as biomarker for predicting the prognosis of LUSC.Methods: We combined The Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissue using Expression data (ESTIMATE) datasets to identified differentially expressed genes in lung squamous cell carcinoma microenvironment. Then, functional enrichment analysis and protein-protein interaction (PPI) network were conducted. The top six genes in the PPI network were regarded as tumor microenvironment-related hub genes. Finally, the relationship between hub genes and tumor-infiltrating immune cells was deciphered using TIMER.Results: Our study revealed that immune and stromal scores are associated with specific clinicopathologic variables in LUSC. These variables include gender, age, distant metastasis and prognosis. In addition, a total of 874 upregulated and 72 downregulated genes were identified. Functional enrichment analysis demonstrated a correlation between DEGs and the tumor microenvironment, tumor immune cells differentiation and activation. C3AR1, CSF1R, CCL2, CCR1, TYROBP, CD14were selected as the hub genes. A positive correlation was obtained between the expression of hub genes and the abundance of six immune cells.Conclusions: The results of the present study showed that ESTIMATE algorithm-based stromal and immune scores may be a reference indicator of cancer prognosis. We identified five TME-related genes, which could be used to predict the prognosis of LUSC patients.

scholarly journals Identification of novel immunomodulators in lung squamous cell carcinoma based on transcriptomic data

2021 ◽  
Vol 19 (2) ◽  
pp. 1843-1860
Author(s):  
Xin Lin ◽  
◽  
Xingyuan Li ◽  
Binqiang Ma ◽  
Lihua Hang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Immune Cells ◽  
Survival Data ◽  
Prognostic Model ◽  
Patient Survival ◽  
Lung Squamous Cell Carcinoma ◽  
Training Data

<abstract> <p>Cells in the tumor microenvironment are well known for their role in cancer development and prognosis. The processes of genetic changes and possible remodeling in the tumor microenvironment of lung squamous cell carcinoma, on the other hand, are mainly unclear. In this investigation, 1164 immunological differentially expressed genes (DEGs) were shown to have predictive significance. A prognostic model with high prediction accuracy was constructed using these genes and survival data. There were 1020 upregulated genes and 144 downregulated genes found, with 57 genes found to be important in the development of LUSC. We used least absolute shrinkage and selection operator (LASSO) regression analysis to determine the risk profiles of 9 genes based on the expression values of 57 prognosis-related genes. The AUCs of the developed prognostic model for predicting patient survival at 1, 3, and 5 years were 0.66, 0.61, and 0.63, respectively, based on the training data. For immune-correlation analysis in this survival model, we chose IGLC7, which was seen to predict patient survival with high accuracy. The effects on immune cells and synergistic effects with other immunomodulators were then investigated. We discovered that IGLC7 is involved in immune response and inflammatory activity using gene ontology analysis and genomic sequence variance analysis (GSVA), with a potential effect, especially on B cells and T cells. In conclusion, IGLC7 expression levels are related to the malignancy of LUSC based on the constructed prognostic model and can thus be a therapeutic target for patients with LUSC. Furthermore, IGLC7 may work in concert with other immune checkpoint members to regulate the immune microenvironment of LUSC. These discoveries might lead to a fresh understanding of the complicated interactions between cancer cells and the tumor microenvironment, particularly the population of immune cells, and a novel approach to future immunotherapeutic treatments for patients with LUSC.</p> </abstract>

scholarly journals Ki-67 labeling index affects tumor infiltration patterns of lung squamous cell carcinoma

2015 ◽  
Vol 12 (5) ◽  
pp. 7303-7309 ◽  
Author(s):  
DAISUKE MASUDA ◽  
RYOTA MASUDA ◽  
TOMOHIKO MATSUZAKI ◽  
NAOKO IMAMURA ◽  
NAOHIRO ARUGA ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Labeling Index ◽  
Tumor Infiltration ◽  
Ki 67

scholarly journals P3.01-033 Changes in the Tumor Microenvironment during Lymphatic Metastasis of Lung Squamous Cell Carcinoma

2017 ◽  
Vol 12 (1) ◽  
pp. S1140
Author(s):  
Shinnosuke Ikemura ◽  
Nao Aramaki ◽  
Satoshi Fujii ◽  
Keisuke Kirita ◽  
Shigeki Umemura ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Lymphatic Metastasis ◽  
Lung Squamous Cell Carcinoma

scholarly journals Identification of Biomarkers Related to CD8+ T Cell Infiltration With Gene Co-expression Network in Lung Squamous Cell Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Min Tang ◽  
Yukun Li ◽  
Xianyu Luo ◽  
Jiao Xiao ◽  
Juan Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Squamous Cell Carcinoma ◽  
T Cell ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Cell Infiltration ◽  
Hub Genes ◽  
T Cell Infiltration ◽  
Potential Biomarker

Lung squamous cell carcinoma (LSCC) is one of the most common types of lung cancer in adults worldwide. With the development of modern medicine, cancer treatment that harnesses the power of the immune system might be particularly effective for treating LSCC. In this research, LSCC expression data, which quantify the cellular composition of immune cells, were analyzed by weighted gene coexpression network analysis (WGCNA) and a deconvolution algorithm based on the Gene Expression Omnibus (GEO) database, and the results indicated a close relationship between LSCC and CD8+ T cells. Six hub genes (SYT3, METTL8, HSPB3, GFM1, ERLIN2, and CLCN2) were verified by gene–gene network and protein–protein interaction (PPI) network analyses. We found that the six hub genes were increased in cancer tissues and were closely correlated with cancer development and progression. After immune correlation analysis, METTL8 was selected as a prognostic biomarker. Finally, we found that the METTL8 levels were increased in multiple lung cancer cell lines and LSCC tissues. METTL8 inhibition could clearly induce G1 cell cycle arrest and suppress proliferation. Therefore, METTL8, which is related to CD8+ T cell infiltration, might be identified as a potential biomarker and gene therapy target in LSCC.

scholarly journals Identification of Novel Biomarkers Related to Lung Squamous Cell Carcinoma Using Integrated Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Haiyan Wang ◽  
Lizhi Huang ◽  
Li Chen ◽  
Jing Ji ◽  
Yuanyuan Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Early Stage ◽  
Prognostic Significance ◽  
Lung Squamous Cell Carcinoma ◽  
Ppi Network ◽  
Hub Genes ◽  
Clinicopathologic Characteristics

Background. Lung squamous cell carcinoma (LUSC) is one of the most common types of lung carcinoma and has specific clinicopathologic characteristics. In this study, we screened novel molecular biomarkers relevant to the prognosis of LUSC to explore new diagnostic and treatment approaches for this disease. Methods. We downloaded GSE73402 from the Gene Expression Omnibus (GEO) database. GSE73402 contains 62 samples, which could be classified as four subtypes according to their pathology and stages. Via weighted gene coexpression network analysis (WGCNA), the main module was identified and was further analyzed using differentially expressed genes (DEGs) analysis. Then, by protein-protein interaction (PPI) network and Gene Expression Profiling Interactive Analysis (GEPIA), hub genes were screened for potential biomarkers of LUSC. Results. Via WGCNA, the yellow module containing 349 genes was identified, and it is strongly related to the subtype of CIS (carcinoma in situ). DEGs analysis detected 180 genes that expressed differentially between the subtype of CIS and subtype of early-stage carcinoma (Stage I and Stage II). A PPI network of DEGs was constructed, and the top 20 genes with the highest correlations were selected for GEPIA database to explore their effect on LUSC survival prognosis. Finally, ITGA5, TUBB3, SCNN1B, and SERPINE1 were screened as hub genes in LUSC. Conclusions. ITGA5, TUBB3, SCNN1B, and SERPINE1 may have great diagnostic and prognostic significance for LUSC and have great potential to be new treatment targets for LUSC.

scholarly journals Identification of key genes associated with progression and prognosis for lung squamous cell carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9086 ◽  
Author(s):  
Xiaohan Ma ◽  
Huijun Ren ◽  
Ruoyu Peng ◽  
Yi Li ◽  
Liang Ming
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Characteristic Curve ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Clinical Prognosis

Background Lung squamous cell carcinoma (LUSC) is a major subtype of lung cancer with limited therapeutic options and poor clinical prognosis. Methods Three datasets (GSE19188, GSE33532 and GSE33479) were obtained from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between LUSC and normal tissues were identified by GEO2R, and functional analysis was employed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. Protein–protein interaction (PPI) and hub genes were identified via the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Hub genes were further validated in The Cancer Genome Atlas (TCGA) database. Subsequently, survival analysis was performed using the Kapla–Meier curve and Cox progression analysis. Based on univariate and multivariate Cox progression analysis, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic value of the model. Results A total of 116 up-regulated genes and 84 down-regulated genes were identified. These DEGs were mainly enriched in the two pathways: cell cycle and p53 signaling way. According to the degree of protein nodes in the PPI network, 10 hub genes were identified. The mRNA expression levels of the 10 hub genes in LUSC were also significantly up-regulated in the TCGA database. Furthermore, a novel seven-gene signature (FLRT3, PPP2R2C, MMP3, MMP12, CAPN8, FILIP1 and SPP1) from the DEGs was constructed and acted as a significant and independent prognostic signature for LUSC. Conclusions The 10 hub genes might be tightly correlated with LUSC progression. The seven-gene signature might be an independent biomarker with a significant predictive value in LUSC overall survival.

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