Identification of genes with therapeutic and prognostic values in lung adenocarcinoma microenvironment

Abstract Background As the most diagnosed malignancy, lung cancer is also the primary cause of cancer death in the entire world. The therapy of lung adenocarcinoma (LUAD), which is the most prevalent subtype of lung cancer, draw researchers’ increasing attentions. This research aimed to investigate the tumor microenvironment (TME)-related hub genes which might be novel targets for treatment. Materials and methods LUAD-associated data packages, including RNA-Seq information and clinical data of 522 patients, were obtained from The Cancer Genome Atlas (TCGA). For better evaluation of stromal and immune cell components, immune scores, stromal scores and estimate scores were obtained with ESTIMATE algorithm based on gene expression levels in tumors. The R package heatmap and clustering analysis were used to explore interested genes. Differentially expressed genes (DEGs) were identified by Venn diagram. Protein-protein interaction (PPI) network was applied to explore intrinsic connections of DEGs. Kaplan-Meier (K-M) survival curves, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to investigate the prognostic values and intricate biological functions of DEGs. The relationships between 4 survival-related hub genes and 6 types of immune cells were examined using TIMER database. The LinkedOmics database was applied to look for kinase targets of hub genes. Results The immune/stromal/estimate scores were significantly correlated with clinical features, including the grades and sizes of LUAD, distant metastasis and outcomes. A total of 702 DEGs, 589 up-regulated and 113 down-regulated, were identified. GO and KEGG analysis showed that the DEGs had significant correlations with tumor immunology. PPI network suggested that the top 8 nodes were FPR2, C3AR1, MCHR1, CCR5, FPR1, CCL19, CCR2 and CXCL10. K-M survival curves indicated that FPR2, C3AR1, MCHR1 and CCR5, as hub genes, were significantly correlated with the overall survival (OS) of LUAD patients. The expression levels of C3AR1 and CCR5 were positively correlated with immune cell infiltration. LYN, LCK and SYK were the targeted kinases of these hub genes. Conclusion FPR2, C3AR1, MCHR1 and CCR5 were TME-related genes and potential biomarkers for the therapy and prognosis of LUAD.

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Bioinformatics study on genes related to a high-risk postoperative recurrence of lung adenocarcinoma

Science Progress ◽

10.1177/00368504211018053 ◽

2021 ◽

Vol 104 (3) ◽

pp. 003685042110180

Author(s):

Xiao Lin ◽

Meng Zhou ◽

Zehong Xu ◽

Yusheng Chen ◽

Fan Lin

Keyword(s):

Cell Cycle ◽

Gene Ontology ◽

High Risk ◽

Lung Adenocarcinoma ◽

Candidate Genes ◽

Postoperative Recurrence ◽

High Expression ◽

Ppi Network ◽

Hub Genes ◽

Expression Levels

In this study, we aimed to screen out genes associated with a high risk of postoperative recurrence of lung adenocarcinoma and investigate the possible mechanisms of the involvement of these genes in the recurrence of lung adenocarcinoma. We identify Hub genes and verify the expression levels and prognostic roles of these genes. Datasets of GSE40791, GSE31210, and GSE30219 were obtained from the Gene Expression Omnibus database. Enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the screened candidate genes using the DAVID database. Then, we performed protein–protein interaction (PPI) network analysis through the database STRING. Hub genes were screened out using Cytoscape software, and their expression levels were determined by the GEPIA database. Finally, we assessed the relationships of Hub genes expression levels and the time of survival. Forty-five candidate genes related to a high-risk of lung adenocarcinoma recurrence were screened out. Gene ontology analysis showed that these genes were enriched in the mitotic spindle assembly checkpoint, mitotic sister chromosome segregation, G2/M-phase transition of the mitotic cell cycle, and ATP binding, etc. KEGG analysis showed that these genes were involved predominantly in the cell cycle, p53 signaling pathway, and oocyte meiosis. We screened out the top ten Hub genes related to high expression of lung adenocarcinoma from the PPI network. The high expression levels of eight genes (TOP2A, HMMR, MELK, MAD2L1, BUB1B, BUB1, RRM2, and CCNA2) were related to short recurrence-free survival and they can be used as biomarkers for high risk of lung adenocarcinoma recurrence. This study screened out eight genes associated with a high risk of lung adenocarcinoma recurrence, which might provide novel insights into researching the recurrence mechanisms of lung adenocarcinoma as well as into the selection of targets in the treatment of the disease.

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ITGB1-DT/ARNTL2 axis may be a novel biomarker in lung adenocarcinoma: a bioinformatics analysis and experimental validation

Cancer Cell International ◽

10.1186/s12935-021-02380-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Bai-Quan Qiu ◽

Xia-Hui Lin ◽

Song-Qing Lai ◽

Feng Lu ◽

Kun Lin ◽

...

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Malignant Tumors ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Hub Genes ◽

Immune Infiltration ◽

Prognostic Effect ◽

Cancer Genome Atlas

Abstract Background Lung cancer is one of the most lethal malignant tumors that endangers human health. Lung adenocarcinoma (LUAD) has increased dramatically in recent decades, accounting for nearly 40% of all lung cancer cases. Increasing evidence points to the importance of the competitive endogenous RNA (ceRNA) intrinsic mechanism in various human cancers. However, behavioral characteristics of the ceRNA network in lung adenocarcinoma need further study. Methods Groups based on SLC2A1 expression were used in this study to identify associated ceRNA networks and potential prognostic markers in lung adenocarcinoma. The Cancer Genome Atlas (TCGA) database was used to obtain the patients' lncRNA, miRNA, and mRNA expression profiles, as well as clinical data. Informatics techniques were used to investigate the effect of hub genes on prognosis. The Cox regression analyses were performed to evaluate the prognostic effect of hub genes. The methylation, GSEA, and immune infiltration analyses were utilized to explore the potential mechanisms of the hub gene. The CCK-8, transwell, and colony formation assays were performed to detect the proliferation and invasion of lung cancer cells. Results We eventually identified the ITGB1-DT/ARNTL2 axis as an independent fact may promote lung adenocarcinoma progression. Furthermore, methylation analysis revealed that hypo-methylation may cause the dysregulated ITGB1-DT/ARNTL2 axis, and immune infiltration analysis revealed that the ITGB1-DT/ARNTL2 axis may affect the immune microenvironment and the progression of lung adenocarcinoma. The CCK-8, transwell, and colonu formation assays suggested that ITGB1-DT/ARNTL2 promotes the progression of lung adenocarcinoma. And hsa-miR-30b-3p reversed the ITGB1/ARNTL2-mediated oncogenic processes. Conclusion Our study identified the ITGB1-DT/ARNTL2 axis as a novel prognostic biomarker affects the prognosis of lung adenocarcinoma.

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Multi-Omic Analyses of the m5C Regulator ALYREF Reveal Its Essential Roles in Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.633415 ◽

2021 ◽

Vol 11 ◽

Author(s):

Chen Xue ◽

Yalei Zhao ◽

Ganglei Li ◽

Lanjuan Li

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Immune Cell ◽

The Cancer Genome Atlas ◽

Cancer Tissue ◽

Hub Genes ◽

Specific Expression ◽

Tissue Samples ◽

Expression Levels ◽

Advanced Tumor

The ALYREF protein acts as a crucial epigenetic regulator in several cancers. However, the specific expression levels and functional roles of ALYREF in cancers are largely unknown, including for hepatocellular carcinoma (HCC). In a pan-cancer tissue analysis that included HCC, we assessed the expression of ALYREF compared to normal tissues using The Cancer Genome Atlas database. Associations between ALYREF gene expression and the clinical characteristics of HCC patient samples were assessed using the UALCAN database. Kaplan-Meier plots were performed to assess HCC patient prognosis, and the TIMER database was used to explore associations between ALYREF expression and immune-cell infiltrations. The same methods were used to assess eIF4A3 expression in HCC patient samples. In addition, ALYREF- and elF4A3-related differentially expressed genes (DEGs) were determined using LinkedOmics, associated protein functionalities were predicted for positively associated DEGs, and both the TargetScan and miRDB databases were used to predict potential upstream miRNAs for control of ALYREF and eIF4A3 expression. We found that ALYREF gene expression was dysregulated in several cancers and was significantly elevated in HCC patient tissue samples and HCC cell lines. The overexpression of ALYREF was significantly related to both advanced tumor-node-metastasis stages and poor HCC prognosis. Furthermore, we found that eIF4A3 expression was significantly correlated with ALYREF expression, and that upregulated eIF4A3 was significantly associated with poor HCC patient outcomes. In the protein-protein interaction network, we identified eight hub genes based on the positively associated DEGs in common between ALYREF and eIF4A3, and the high expression levels of these hub genes were positively associated with patient clinical outcomes. In addition, we identified miR-4666a-5p and miR-6124 as potential regulators of ALYREF and eIF4A3 expression. These findings suggest that increased ALYREF expression may function as a novel biomarker for both HCC diagnosis and prognosis predictions.

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Identification of five Hub Genes Associated with Development and Prognosis of Lung Adenocarcinoma by Co-Expression Analysis

Current Bioinformatics ◽

10.2174/1574893616666210624141520 ◽

2021 ◽

Vol 16 ◽

Author(s):

Sangsang Chen ◽

Xuqing Zhu ◽

Jing Zheng ◽

Tingting Xu ◽

Yinmin Xu ◽

...

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Hub Genes ◽

Network Analyses ◽

Study Gene Expression

Objective: Non-small cell lung cancer (NSCLC) is one of the most common types of lung cancer, while lung adenocarcinoma (LUAD) is the most common subtype of NSCLC. In this study, we aimed to identify potential markers that are associated with the prognosis and development of LUAD. Methods and results: In this study, gene expression profiles from 594 LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) database, and 2,503 differentially expressed genes (DEGs) were obtained. Secondly, weighted gene co-expression network analysis (WGCNA) was used to construct a co-expression network for these DEGs, and 16 modules were obtained. Among these, the genes related to the most significant module (turquoise) were found to be closely associated with the stage of LUAD. Then, functional annotation revealed that the genes in the turquoise module were mainly enriched in the pathways involved in the cell cycle and meiosis. Seven candidate hub genes were further screened by using WGCNA and protein-protein interaction network analyses. Expression data of the 7 candidate hub genes in different pathological stages in TCGA-LUAD were taken as the training set, while those in the GSE41271 dataset were used as the validation set. As a result, 5 hub genes (KIF11, KIF23, KIF4A, NUSPA1, RRM2) closely related to the pathological stage of LUAD were screened. Finally, survival and tissue expression analyses were performed on the five hub genes. The results suggested that the five hub genes were closely related to the occurrence and prognosis of LUAD. Conclusion: The study identified five hub genes that could be used as important predictors for the prognosis and development of LUAD.

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Identification of immune-infiltrating cell-related biomarkers in hepatocellular carcinoma based on gene co-expression network analysis

Diagnostic Pathology ◽

10.1186/s13000-021-01118-y ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Yinghui Hou ◽

Guizhi Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Network Analysis ◽

Early Diagnosis ◽

The Cancer Genome Atlas ◽

Ppi Network ◽

Sequencing Data ◽

Hub Genes ◽

Survival Prognosis ◽

Expression Levels ◽

Cancer Tissues

Abstract Background Hepatocellular carcinoma (HCC) is often caused by chronic liver infection or inflammation. Searching for potential immunotherapy targets will aid the early diagnosis and treatment of HCC. Methods Firstly, detailed HCC data were downloaded from The Cancer Genome Atlas database. GDCRNATools was used for the comprehensive analysis of RNA sequencing data. Subsequently, the CIBERSORT package was used to estimate infiltration scores of 22 types of immune cells in complex samples. Furthermore, hub genes were identified via weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis. In addition, multiple databases were used to validate the expression of hub gene in the tumor tissue. Finally, prognostic, diagnostic and immunohistochemical analysis of key hub genes was performed. Results In the present study, 9 hub genes were identified using WGCNA and PPI network analysis. Furthermore, the expression levels of 9 genes were positively correlated with the infiltration levels of CD8-positive T (CD8+ T) cells. In multiple dataset validations, the expression levels of CCL5, CXCR6, CD3E, and LCK were decreased in cancer tissues. In addition, survival analysis revealed that patients with LCK low expression had a poor survival prognosis (P < 0.05). Immunohistochemistry results demonstrated that CCL5, CD3E and LCK were expressed at low levels in HCC cancer tissues. Conclusion The identification of CCL5, CXCR6, CD3E and LCK may be helpful in the development of early diagnosis and therapy of HCC. LCK may be a potential prognostic biomarker for immunotherapy for HCC.

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Identification of Hub Genes and Pathways With Immune Cell Infiltration in Cholangiocarcinoma by Weighted Gene Co-expression Network Analysis

10.21203/rs.3.rs-135007/v1 ◽

2020 ◽

Author(s):

Ye Yan ◽

Lihao Zhao ◽

Huafang Su ◽

Gang Li ◽

Sujing Jiang

Keyword(s):

T Cell ◽

Network Analysis ◽

Protein Interactions ◽

Immune Cell ◽

The Cancer Genome Atlas ◽

Prognostic Biomarkers ◽

Cd4 T Cell ◽

Cell Infiltration ◽

Hub Genes ◽

Kegg Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most frequent tumor in biliary tract and the second most common primary tumor of the liver. However, the molecular biomarkers in tumorigenesis of CCA remain unclear. Therefore, we aim to explore the underlying mechanisms of progression and screen for novel prognostic biomarkers and therapeutic targets.Method: The genes expression sequencing of normal and CCA samples were selected from the Gene Expression Omnibus database (GEO) and the Cancer Genome Atlas (TCGA). The weighted gene co-expression network analysis (WGCNA) was used to build the co-expression network. Gene ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied for the selected genes. The protein-protein interactions of these modules are visualized using cytoscape. Furthermore, the significance of these genes was confirmed by survival analysis. The tumor immune estimation resource (TIMER) was presented to investigate assess the relationship between the hub genes and the immune cells infiltration.Results: Ten hub genes with CCA development were identified in this study containing CDC20, CCNA2, TOP2A, AURKA, CCNB2, UBE2C, NUSAP1, PRC1, PTTG1 and MCM4. Key genes of CCNB2 and PTTG1 might be potential prognostic biomarkers for CCA. GO analysis indicated the enrichment terms of nuclear division, collagen-containing extracellular matrix and cell adhesion molecule binding. KEGG analysis demonstrated that the cell cycle pathway was the significantly altered pathway. There was a negative correlation between TOP2A, AURKA, CCNB2, PRC1 expression and the infiltration of CD4+T cell, while MCM4 expression was positively associated with the infiltration of neutrophil cells. No significant association between CDC20 levels and CD4+T cell, CD8+T cell, B cell, neutrophil, macrophage, or dendritic cell infiltration in CCA, the same as CCNA2, UBE2C, NUSAP1, PTTG1 respectively.Conclusion: These candidate genes may involve in the development of CCA. Our results offer novel insights into the etiology, prognosis, and treatment of CCA.

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Identification of Key Genes and Pathways in Gefitinib-Resistant Lung Adenocarcinoma using Bioinformatics Analysis

Evolutionary Bioinformatics ◽

10.1177/11769343211023767 ◽

2021 ◽

Vol 17 ◽

pp. 117693432110237

Author(s):

Kailin Mao ◽

Fang Lin ◽

Yingai Zhang ◽

Hailong Zhou

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Signaling Pathways ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Underlying Mechanisms ◽

Gefitinib Resistance

Gefitinib resistance is a serious threat in the treatment of patients with non-small cell lung cancer (NSCLC). Elucidating the underlying mechanisms and developing effective therapies to overcome gefitinib resistance is urgently needed. The differentially expressed genes (DEGs) were screened from the gene expression profile GSE122005 between gefitinib-sensitive and resistant samples. GO and KEGG analyses were performed with DAVID. The protein-protein interaction (PPI) network was established to visualize DEGs and screen hub genes. The functional roles of CCL20 in lung adenocarcinoma (LUAD) were examined using gene set enrichment analysis (GSEA). Functional analysis revealed that the DEGs were mainly concentrated in inflammatory, cell chemotaxis, and PI3K signal regulation. Ten hub genes were identified based on the PPI network. The survival analysis of the hub genes showed that CCL20 had a significant effect on the prognosis of LUAD patients. GSEA analysis showed that CCL20 high expression group was mainly enriched in cytokine-related signaling pathways. In conclusion, our analysis suggests that changes in inflammation and cytokine-related signaling pathways are closely related to gefitinib resistance in patients with lung cancer. The CCL20 gene may promote the formation of gefitinib resistance, which may serve as a new biomarker for predicting gefitinib resistance in patients with lung cancer.

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Comprehensive Analysis of Immune-Related Metabolic Genes in Lung Adenocarcinoma

10.21203/rs.3.rs-753680/v1 ◽

2021 ◽

Author(s):

FangFang Li ◽

Chun Huang ◽

LingXiao Qiu ◽

Ping Li ◽

guojun zhang

Keyword(s):

Lung Cancer ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Immune Cells ◽

Immune Cell ◽

Comprehensive Analysis ◽

Lasso Regression ◽

Hub Genes ◽

Cell Functions ◽

Metabolic Genes

Abstract Purpose: The immunotherapy of lung adenocarcinoma has received more and more attention. Different immune cells can affect other metabolic genes and lifespan, and cell metabolism directly regulates immune cell functions. Therefore, it is crucial to explore the role of immune-related metabolic genes in lung adenocarcinoma. Methods: This study screened and studied immune-related metabolic genes from three aspects. First of all, we divide them into three categories based on different immune characteristics and research immunity and clinical pathology. Secondly, we used LASSO regression analysis to screen the immune-related metabolic genes and constructed the clinical prediction model for the screened genes. Finally, we selected the intersection of immune metabolism genes highly expressed in tumor sites and immune metabolism genes that are negatively related to survival and obtained potential genes. Results: We first identified immune-related metabolic genes and immune cells that may affect tumor progression in lung cancer. Then, through LASSO regression analysis, we screened out nine hub genes (TK1, TCN1, CAV1, ACMSD, HS3ST2, HS3ST5, AMN, ADRA2C, ACOXL) and constructed a prognostic model. Finally, through the screening of tumor-related immune metabolism genes, we obtained five hub genes (HMMR, PFKP, RRM2, TCN1 and TK1). Our qRT-PCR result also showed that RRM2 positively correlates with CDK2, CDK4, CDK6, CDK8.Conclusion: We conduct a comprehensive analysis of the immune infiltration of the tumor microenvironment of lung cancer, and finally determined RRM2 as a promising immune metabolism checkpoint for lung adenocarcinoma based on the high correlation of RRM2 with immune cells and CDK family.

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Identification of hub genes in colon cancer via bioinformatics analysis

Journal of International Medical Research ◽

10.1177/0300060520953234 ◽

2020 ◽

Vol 48 (9) ◽

pp. 030006052095323

Author(s):

Jun Liu ◽

Gui-Li Sun ◽

Shang-Ling Pan ◽

Meng-Bin Qin ◽

Rong Ouyang ◽

...

Keyword(s):

Colon Cancer ◽

Bioinformatics Analysis ◽

Expression Profiles ◽

Organic Anion ◽

The Cancer Genome Atlas ◽

Prognostic Indicators ◽

Venn Diagram ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Objectives This study aimed to investigate hub genes and their prognostic value in colon cancer via bioinformatics analysis. Methods Differentially expressed genes (DEGs) of expression profiles (GSE33113, GSE20916, and GSE37364) obtained from Gene Expression Omnibus (GEO) were identified using the GEO2R tool and Venn diagram software. Function and pathway enrichment analyses were performed, and a protein–protein interaction (PPI) network was constructed. Hub genes were verified based on The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) databases. Results We identified 207 DEGs, 62 upregulated and 145 downregulated genes, enriched in Gene Ontology terms “organic anion transport,” “extracellular matrix,” and “receptor ligand activity”, and in the Kyoto Encyclopedia of Genes and Genomes pathway “cytokine-cytokine receptor interaction.” The PPI network was constructed and nine hub genes were selected by survival analysis and expression validation. We verified these genes in the TCGA database and selected three potential predictors ( ZG16, TIMP1, and BGN) that met the independent predictive criteria. TIMP1 and BGN were upregulated in patients with a high cancer risk, whereas ZG16 was downregulated. The immunostaining results from HPA supported these findings. Conclusion This study indicates that these hub genes may be promising prognostic indicators or therapeutic targets for colon cancer.

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SFTPA1 is a potential prognostic biomarker correlated with immune cell infiltration and response to immunotherapy in lung adenocarcinoma

Cancer Immunology Immunotherapy ◽

10.1007/s00262-021-02995-4 ◽

2021 ◽

Author(s):

Lu Yuan ◽

Xixi Wu ◽

Longshan Zhang ◽

Mi Yang ◽

Xiaoqing Wang ◽

...

Keyword(s):

Lung Cancer ◽

T Cells ◽

Lung Adenocarcinoma ◽

Expression Profile ◽

Immune Cell ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Regulatory Pathways ◽

Chronic Lung Diseases ◽

Potential Biomarker

AbstractPulmonary surfactant protein A1 (SFTPA1) is a member of the C-type lectin subfamily that plays a critical role in maintaining lung tissue homeostasis and the innate immune response. SFTPA1 disruption can cause several acute or chronic lung diseases, including lung cancer. However, little research has been performed to associate SFTPA1 with immune cell infiltration and the response to immunotherapy in lung cancer. The findings of our study describe the SFTPA1 expression profile in multiple databases and was validated in BALB/c mice, human tumor tissues, and paired normal tissues using an immunohistochemistry assay. High SFTPA1 mRNA expression was associated with a favorable prognosis through a survival analysis in lung adenocarcinoma (LUAD) samples from TCGA. Further GeneOntology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that SFTPA1 was involved in the toll-like receptor signaling pathway. An immune infiltration analysis clarified that high SFTPA1 expression was associated with an increased number of M1 macrophages, CD8+ T cells, memory activated CD4+ T cells, regulatory T cells, as well as a reduced number of M2 macrophages. Our clinical data suggest that SFTPA1 may serve as a biomarker for predicting a favorable response to immunotherapy for patients with LUAD. Collectively, our study extends the expression profile and potential regulatory pathways of SFTPA1 and may provide a potential biomarker for establishing novel preventive and therapeutic strategies for lung adenocarcinoma.

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