scholarly journals DOCK2 is a Biomarker for Predicting the Prognosis of Lung Adenocarcinoma and Associated with Immune Infiltration

2021 ◽  
Author(s):  
Jie He ◽  
Tongtong Zhang ◽  
Jian Sun ◽  
Guangnan Liu
Keyword(s):  
Immune Response ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Clinical Sample ◽  
Clinical Samples ◽  
Data Set ◽  
Immune Infiltration ◽  
Low Expression

Abstract Background: dedicator of cytokinesis 2 is an atypical guanine exchange factor, which is particularly expressed in hematopoietic cells and modulates the activation along with the migration of immune cells by activating Ras--related C3 botulinum toxin substrate (Rac). Nevertheless, the role of DOCK2 in lung adenocarcinoma (LUAD) remains unclear.Methods: Herein, we performed bioinformatics analysis of lung adenocarcinoma data abstracted from TCGA (The Cancer Genome Altas) and GEO (Gene Expression Omnibus) data resources, and combined with web tools consisting of LinkedOmics, TIMER, and TISIDB. Finally, combined with clinical lung adenocarcinoma samples, we verified the expression of DOCK2 in tissue and its effect on the prognosis of lung adenocarcinoma.Results: In the TCGA lung adenocarcinoma data set, the expression of DOCK2 was down-regulated in tumor tissues and verified in multiple independent cohorts. In addition, the low expression of DOCK2 indicates a poor overall survival(OS) in both TCGA and other GEO data sets and in our clinical samples. COX regression data illustrated that the low expression of DOCK2 was an independent predictor for OS. Functional network analysis shows that DOCK2 participates in immune response through interleukin production, neuroinflammatory response, acquired immune response, leukocyte migration and activation of lymph node cells, and is related to multiple immune-related pathways. Besides, the expression of DOCK2 was remarkably related with many kinds of tumor infiltrating immune cells.Conclusion: combined with bioinformatics analysis and clinical sample verification, our study shows that DOCK2 can independently estimate the prognosis of lung adenocarcinoma and is related to immune infiltration. As a promising prognostic indicator and potential target of immunotherapy, the potential effect of DOCK2 on lung adenocarcinoma and its molecular mechanism are worthy of further discussion.

scholarly journals EMILIN2 Correlated With Its Methylation and Immune Infiltration Could Be an Independent Prognostic Biomarker in LGG

2021 ◽  
Author(s):  
Li-chong Wang ◽  
Zhe Zhang ◽  
Zi-long Tan ◽  
Qiao-li Lv ◽  
Shu-hui Chen ◽  
...  
Keyword(s):  
Immune Cells ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Low Grade ◽  
Central Nervous System Neoplasms ◽  
Immune Infiltration ◽  
Nervous System Neoplasms ◽  
Slow Growing

Abstract Low-grade gliomas (LGGs) are slow-growing brain cancer in central nervous system neoplasms. EMILIN2 is an extracellular matrix (ECM) protein which could influence the progress of some tumour which is unclear in LGG. In our study, the methylation, expression, prognosis and immune value of EMILIN2 were analysed in LGG through bioinformatics analysis. we first analysed the LGG data from TCGA and discovered that the EMILIN2 expression, negatively correlated to the EMILIN2 methylation could predict a poor prognosis and associated with different clinical parameters. Moreover, univariate and multivariate Cox regression were performed in CGGA showed that the EMILIN2 could be an independent prognostic biomarker in LGG. Finally, EMILIN2 expression showed a correlation with gene makers in some immune cells which identified the significance of EMILIN2 in immune infiltration. In conclusion, EMILIN2 could act as an independent prognostic biomarker which might be associated with the malignancy and development of gliomas and play a crucial role in glioma in immune infiltration.

scholarly journals A Novel Immune-Related Prognostic Model for Response to Immunotherapy and Survival in Patients With Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Yujia Zheng ◽  
He Tian ◽  
Zheng Zhou ◽  
Chu Xiao ◽  
Hengchang Liu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cell Death ◽  
Programmed Cell Death ◽  
Lung Adenocarcinoma ◽  
Regression Model ◽  
Immune Cells ◽  
Cox Regression ◽  
Consensus Clustering ◽  
Cox Regression Model ◽  
Immune Infiltration

Lung adenocarcinoma is one of the most malignant diseases worldwide. The immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and programmed cell death-ligand 1 (PD-L1) have changed the paradigm of lung cancer treatment; however, there are still patients who are resistant. Further exploration of the immune infiltration status of lung adenocarcinoma (LUAD) is necessary for better clinical management. In our study, the CIBERSORT method was used to calculate the infiltration status of 22 immune cells in LUAD patients from The Cancer Genome Atlas (TCGA). We clustered LUAD based on immune infiltration status by consensus clustering. The differentially expressed genes (DEGs) between cold and hot tumor group were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Last, we constructed a Cox regression model. We found that the infiltration of M0 macrophage cells and follicular helper T cells predicted an unfavorable overall survival of patients. Consensus clustering of 22 immune cells identified 5 clusters with different patterns of immune cells infiltration, stromal cells infiltration, and tumor purity. Based on the immune scores, we classified these five clusters into hot and cold tumors, which are different in transcription profiles. Hot tumors are enriched in cytokine–cytokine receptor interaction, while cold tumors are enriched in metabolic pathways. Based on the hub genes and prognostic-related genes, we developed a Cox regression model to predict the overall survival of patients with LUAD and validated in other three datasets. In conclusion, we developed an immune-related signature that can predict the prognosis of patients, which might facilitate the clinical application of immunotherapy in LUAD.

scholarly journals Identification of key genes associated with lung adenocarcinoma by bioinformatics analysis

2021 ◽  
Vol 104 (1) ◽  
pp. 003685042199727
Author(s):  
Xinyu Wang ◽  
Jiaojiao Yang ◽  
Xueren Gao
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Gene Expression Omnibus ◽  
Poor Overall Survival ◽  
Key Genes ◽  
Low Expression

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, comprising around 40% of all lung cancer. Until now, the pathogenesis of LUAD has not been fully elucidated. In the current study, we comprehensively analyzed the dysregulated genes in lung adenocarcinoma by mining public datasets. Two sets of gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. The dysregulated genes were identified by using the GEO2R online tool, and analyzed by R packages, Cytoscape software, STRING, and GPEIA online tools. A total of 275 common dysregulated genes were identified in two independent datasets, including 54 common up-regulated and 221 common down-regulated genes in LUAD. Gene Ontology (GO) enrichment analysis showed that these dysregulated genes were significantly enriched in 258 biological processes (BPs), 27 cellular components (CCs), and 21 molecular functions (MFs). Furthermore, protein-protein interaction (PPI) network analysis showed that PECAM1, ENG, KLF4, CDH5, and VWF were key genes. Survival analysis indicated that the low expression of ENG was associated with poor overall survival (OS) of LUAD patients. The low expression of PECAM1 was associated with poor OS and recurrence-free survival of LUAD patients. The cox regression model developed based on age, tumor stage, ENG, PECAM1 could effectively predict 5-year survival of LUAD patients. This study revealed some key genes, BPs, CCs, and MFs involved in LUAD, which would provide new insights into understanding the pathogenesis of LUAD. In addition, ENG and PECAM1 might serve as promising prognostic markers in LUAD.

NLRP3 is a Novel Prognostic Biomarker and Correlates With Immune Infiltrates in Lung Adenocarcinoma and Skin Cutaneous Melanoma

2021 ◽  
Author(s):  
Chenxi Yuan ◽  
Qingwei Wang ◽  
Xueting Dai ◽  
Yipeng Song ◽  
Jinming Yu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Potential Biomarker ◽  
The Impact ◽  
Nlrp3 Expression

Abstract Background: Lung adenocarcinoma (LUAD) and skin cutaneous melanoma (SKCM) are common tumors around the world. However, the prognosis in advanced patients is poor. Because NLRP3 was not extensively studied in cancers, so that we aimed to identify the impact of NLRP3 on LUAD and SKCM through bioinformatics analyses. Methods: TCGA and TIMER database were utilized in this study. We compared the expression of NLRP3 in different cancers and evaluated its influence on survival of LUAD and SKCM patients. The correlations between clinical information and NLRP3 expression were analyzed using logistic regression. Clinicopathologic characteristics associated with overall survival in were analyzed by Cox regression. In addition, we explored the correlation between NLRP3 and immune infiltrates. GSEA and co-expressed gene with NLRP3 were also done in this study. Results: NLRP3 expressed disparately in tumor tissues and normal tissues. Cox regression analysis indicated that up-regulated NLRP3 was an independent prognostic factor for good prognosis in LUAD and SKCM. Logistic regression analysis showed increased NLRP3 expression was significantly correlated with favorable clinicopathologic parameters such as no lymph node invasion and no distant metastasis. Specifically, a positive correlation between increased NLRP3 expression and immune infiltrating level of various immune cells was observed. Conclusion: Together with all these findings, increased NLRP3 expression correlates with favorable prognosis and increased proportion of immune cells in LUAD and SKCM. These conclusions indicate that NLRP3 can serve as a potential biomarker for evaluating prognosis and immune infiltration level.

scholarly journals Prognostic and Predictive Value of BGN in Colon Cancer Outcomes and Response to Immunotherapy

2022 ◽  
Vol 11 ◽  
Author(s):  
Zi-Xuan He ◽  
Sheng-Bing Zhao ◽  
Xue Fang ◽  
Ji-Fu E ◽  
Hong-Yu Fu ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Immune Response ◽  
Cancer Patients ◽  
Immune Cells ◽  
Matrix Protein ◽  
Cox Regression ◽  
Extracellular Matrix Protein ◽  
Great Promise ◽  
Clinical Prognosis ◽  
Expression Levels

BackgroundColon cancer is one of the most frequent malignancies and causes high mortality worldwide. Exploring the tumor-immune interactions in the tumor microenvironment and identifying new prognostic and therapeutic biomarkers will assist in decoding the novel mechanism of tumor immunotherapy. BGN is a typical extracellular matrix protein that was previously validated as a signaling molecule regulating multiple processes of tumorigenesis. However, its role in tumor immunity requires further investigation.MethodsThe differentially expressed genes in three GEO datasets were analyzed, and BGN was identified as the target gene by intersection analysis of PPIs. The relevance between clinical outcomes and BGN expression levels was evaluated using data from the GEO database, TCGA and tissue microarray of colon cancer samples. Univariable and multivariable Cox regression models were conducted for identifying the risk factors correlated with clinical prognosis of colon cancer patients. Next, the association between BGN expression levels and the infiltration of immune cells as well as the process of the immune response was analyzed. Finally, we predicted the immunotherapeutic response rates in the subgroups of low and high BGN expression by TIS score, ImmuCellAI and TIDE algorithms.ResultsBGN expression demonstrated a statistically significant upregulation in colon cancer tissues than in normal tissues. Elevated BGN was associated with shorter overall survival as well as unfavorable clinicopathological features, including tumor size, serosa invasion and length of hospitalization. Mechanistically, pathway enrichment and functional analysis demonstrated that BGN was positively correlated with immune and stromal scores in the TME and primarily involved in the regulation of immune response. Further investigation revealed that BGN was strongly expressed in the immunosuppressive phenotype and tightly associated with the infiltration of multiple immune cells in colon cancer, especially M2 macrophages and induced Tregs. Finally, we demonstrated that high BGN expression presented a better immunotherapeutic response in colon cancer patients.ConclusionBGN is an encouraging predictor of diagnosis, prognosis and immunotherapeutic response in patients with colon cancer. Assessment of BGN expression represents a novel approach with great promise for identifying patients who may potentially benefit from immunotherapy.

scholarly journals Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Guomin Wu ◽  
Qihao Wang ◽  
Ting Zhu ◽  
Linhai Fu ◽  
Zhupeng Li ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Clinical Features ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

Low expression of RGL4 is associated with a poor prognosis and immune infiltration in lung adenocarcinoma patients

2020 ◽  
Vol 83 ◽  
pp. 106454 ◽  
Author(s):  
Yidan Sun ◽  
Ying Zhang ◽  
Shiqi Ren ◽  
Xiaojiang Li ◽  
Peiying Yang ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Poor Prognosis ◽  
Immune Infiltration ◽  
Low Expression

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

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.

scholarly journals Exploration of Various Roles of Hypoxia Genes in Osteosarcoma

2021 ◽  
Author(s):  
Jimin Ma ◽  
Yakun Zhu ◽  
Ziming Guo ◽  
Xuefei Yang ◽  
Haitao Fan
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
High Expression ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Survival Difference ◽  
High Expression Group ◽  
Low Expression

Abstract Background: Osteosarcoma is a primary malignant tumor that often metastasizes in orthopedic diseases. Although multi-drug chemotherapy and surgical treatment have significantly improved the survival and prognosis of patients with osteosarcoma, the survival rate is still very low due to frequent metastases in patients with osteosarcoma. In-depth exploration of the relationship between various influencing factors of osteosarcoma is very important for screening promising therapeutic targets. Methods: This study used multivariate COX regression analysis to select the hypoxia genes SLC2A1 and FBP1 in patients with osteosarcoma, and used the expression of these two genes to divide the patients with osteosarcoma into high-risk and low-risk groups. Then, we first constructed a prognostic model based on the patient's risk value, and compared the survival difference between the high expression group and the low expression group. Second, in the high expression group and the low expression group, compare the differences in tumor invasion and inflammatory gene expression between the two groups of immune cells. Finally, the ferroptosis-related genes with differences between the high expression group and the low expression group were screened, and the correlation between these genes was analyzed. Results: In the high-risk group, immune cells with higher tumor invasiveness, macrophages M0 and immune cells with lower invasiveness included: mast cell resting, regulatory T cells (Tregs) and monocytes. Finally, among genes related to ferroptosis, we found AKR1C2, AKR1C1 and ALOX15 that may be related to hypoxia. These ferroptosis-related genes were discovered for the first time in osteosarcoma. Among them, the hypoxia gene FBP1 is positively correlated with the ferroptosis genes AKR1C1 and ALOX15, and the hypoxia gene SLC2A1 is negatively correlated with the ferroptosis genes AKR1C2, AKR1C1 and ALOX15. Conclusion: This study constructed a prognostic model based on hypoxia-related genes SLC2A1 and FBP1 in patients with osteosarcoma, and explored their correlation with immune cells, inflammatory markers and ferroptosis-related genes. This indicates that SLC2A1 and FBP1 are promising targets for osteosarcoma research.

scholarly journals Immunoautophagy-Related Long Noncoding RNA (IAR-lncRNA) Signature Predicts Survival in Hepatocellular Carcinoma

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1301
Author(s):  
Yulu Wang ◽  
Fangfang Ge ◽  
Amit Sharma ◽  
Oliver Rudan ◽  
Maria F. Setiawan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Immune Cells ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Clinical Samples ◽  
Special Role ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Background: The dysregulation of autophagy and immunological processes has been linked to various pathophysiological conditions, including cancer. Most notably, their particular involvement in hepatocellular carcinoma (HCC) is becoming increasingly evident. This has led to the possibility of developing a prognostic signature based on immuno-autophagy-related (IAR) genes. Given that long non-coding RNAs (lncRNAs) also play a special role in HCC, a combined signature utilizing IAR genes and HCC-associated long noncoding RNAs (as IARlncRNA) may potentially help in the clinical scenario. Method: We used Pearson correlation analysis, Kaplan–Meier survival curves, univariate and multivariate Cox regression, and ROC curves to generate and validate a prognostic immuno-autophagy-related long non-coding RNA (IARlncRNA) signature. The Chi-squared test was utilized to investigate the correlation between the obtained signature and the clinical characteristics. CIBERSORT algorithms and the Wilcoxon rank sum test were applied to investigate the correlation between signature and infiltrating immune cells. GO and KEGG analyses were performed to derived signature-dependent pathways. Results: Herein, we build an IAR-lncRNA signature (as first in the literature) and demonstrate its prognostic ability in hepatocellular carcinoma. Primarily, we identified three IARlncRNAs (MIR210HG, AC099850.3 and CYTOR) as unfavorable prognostic determinants. The obtained signature predicted the high-risk HCC group with shorter overall survival, and was further associated with clinical characteristics such as tumor grade (t = 10.918, p = 0.001). Additionally, several infiltrating immune cells showed varied fractions between the low-risk group and the high-risk HCC groups in association with the obtained signature. In addition, pathways analysis described by the signature clearly distinguishes both risk groups in HCC. Conclusions: The immuno-autophagy-related long non-coding RNA (IARlncRNA) signature we established exhibits a prognostic ability in hepatocellular carcinoma. To our knowledge, this is the first attempt in the literature to combine three determinants (immune, autophagy and LnRNAs), thus requiring molecular validation of this obtained signature in clinical samples.

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