scholarly journals The Immune Cell Infiltration Patterns and Characterization Score in Bladder Cancer to Identify Prognostic.

2022 ◽  
Author(s):  
Yongsheng Zhang ◽  
Yunlong Wang ◽  
Jichuang Wang ◽  
Kaixiang Zhang
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
T Cells ◽  
Gene Expression Data ◽  
Cd8 T Cells ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Expression Data ◽  
Common Gene

Abstract Bladder cancer (BLCA) is among the most frequent types of cancer. Patients with BLCA have a significant recurrence rate and a poor post-surgery survival rate. Recent research has found a link between tumor immune cell infiltration (ICI) and the prognosis of BLCA patients. However, the ICI picture of BLCA remains unclear. Common gene expression data was obtained by combining the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) expression databases. Two computational algorithms were proposed to unravel the ICI landscape of BLCA patients. The R package "limma" was applied to find differentially expressed genes (DEGs). Principal-component analysis (PCA) was used to calculate the ICI score. A total of 569 common gene expression data were retrieved from TCGA and GEO cohorts. CD8+ T cells were found to have a substantial positive connection with activated memory CD4+ T cells and immune score. On the contrary, CD8+ T cells were found to have a substantial negative connection with Macrophages M0. Thirty-eight DEGs were selected. Two ICI patterns were defined by unsupervised clustering method. Patients of BLCA were separated into two groups. The high ICI score group exhibits better outcome than the low one (p < 0.001). Finally, the group with a high tumor mutation burden (TMB) as well as a high ICI score had the best outcome. (p <0.001). Combining TMB and ICI score resulted in a more accurate survival prediction, suggesting that ICI score could be used as a prognostic marker for BLCA patients.

scholarly journals Identification of new biomarkers and pathways associated with treatment and prognosis in melanoma patients

2020 ◽  
Author(s):  
Biao Huang ◽  
Wei Han ◽  
Zu-Feng Sheng ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Prognostic Value ◽  
Immune Cell ◽  
Gene Interaction ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Strong Positive Correlation ◽  
Hub Genes

Abstract Background Skin cutaneous melanoma (SKCM) is known as the most malignancy and treatment-resistant in human tumor, causing about 72% of deaths in skin carcinoma. However, the potential mechanism and new effective targets remain to be further elucidated. Available datasets such as Gene Expression Omnibus (GEO) can be utilized to search for novel therapeutic targets and prognostic biomarkers. Methods Three data sets were downloaded from GEO database . The differentially expressed genes (DEGs) were identified via Venn software. Protein‐protein interaction network of DEGs was developed and the module hub genes analysis was constructed by Cytoscape. Subsequently, multiple online tools and Kaplan-Meier survival curves were analyzed to detect underlying signaling pathways, gene expression, drug-gene interaction and prognostic value of hub genes. In addition, we explored the correlation between hub genes and immune cell infiltration. At last, the related miRNA, lncRNA networks were constructed by R software. Results A total of 308 DEGs and 12 hub genes were identified. Function and pathway enrichment results demonstrated a correlation between DEGs and the tumor microenvironment, immune response and melanoma tumorigenesis. Subsequently, we focused on assessing potential value of 12 hub genes. Seven hub genes ( CCL4, CCL5, NMU, GAL, CXCL9, CXCL10, CXCL13 ) were identified with significant overall survival for prognosis. What’s more, five of these seven hub genes were found to be related to clinical stages (P values<0.05). In addition, the most important pathways of hub genes include interleukin-10 signaling, peptide ligand-binding receptors, which play important roles in tumor microenvironment for immune activation or immunosuppressive by regulating the infiltration of immune cells. Our results revealed a strong positive correlation between gene expression (CCL4, CCL5, CXCL9, CXCL10 and CXCL13) and immune cell infiltration (B-cell, CD8+ T cells, CD4+ T cells, macrophages, Neutrophils, Dendritic cells). Interestingly, 8 of 12 hub genes (CXCL10, CCL4, CCL5, IL6, CXCL2, PTGER3, GAL, NPY1R) were also found in the predicted drug-gene interaction. The related miRNA, lncRNA for diagnosis and prognosis were found in networks. Conclusion In conclusion, CCL4, CCL5, NMU, GAL, CXCL9, CXCL10, CXCL13 were of high prognostic value and may be potential targets for the diagnosis and therapy of patients with melanoma.

scholarly journals Comprehensive analysis of immune cell enrichment in the tumor microenvironment of head and neck squamous cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ikko Mito ◽  
Hideyuki Takahashi ◽  
Reika Kawabata-Iwakawa ◽  
Shota Ida ◽  
Hiroe Tada ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Head And Neck ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Cell Types ◽  
Expression Data ◽  
Immune Microenvironment ◽  
Cancer Tissues

AbstractHead and neck squamous carcinoma (HNSCC) is highly infiltrated by immune cells, including tumor-infiltrating lymphocytes and myeloid lineage cells. In the tumor microenvironment, tumor cells orchestrate a highly immunosuppressive microenvironment by secreting immunosuppressive mediators, expressing immune checkpoint ligands, and downregulating human leukocyte antigen expression. In the present study, we aimed to comprehensively profile the immune microenvironment of HNSCC using gene expression data obtained from public database. We calculated enrichment scores of 33 immune cell types based on gene expression data of HNSCC tissues and adjacent non-cancer tissues. Based on these scores, we performed non-supervised clustering and identified three immune signatures—cold, lymphocyte, and myeloid/dendritic cell (DC)—based on the clustering results. We then compared the clinical and biological features of the three signatures. Among HNSCC and non-cancer tissues, human papillomavirus (HPV)-positive HNSCCs exhibited the highest scores in various immune cell types, including CD4+ T cells, CD8+ T cells, B cells, plasma cells, basophils, and their subpopulations. Among the three immune signatures, the proportions of HPV-positive tumors, oropharyngeal cancers, early T tumors, and N factor positive cases were significantly higher in the lymphocyte signature than in other signatures. Among the three signatures, the lymphocyte signature showed the longest overall survival (OS), especially in HPV-positive patients, whereas the myeloid/DC signature demonstrated the shortest OS in these patients. Gene set enrichment analysis revealed the upregulation of several pathways related to inflammatory and proinflammatory responses in the lymphocyte signature. The expression of PRF1, IFNG, GZMB, CXCL9, CXCL10, PDCD1, LAG3, CTLA4, HAVCR2, and TIGIT was the highest in the lymphocyte signature. Meanwhile, the expression of PD-1 ligand genes CD274 and PDCD1LG2 was highest in the myeloid/DC signature. Herein, our findings revealed the transcriptomic landscape of the immune microenvironment that closely reflects the clinical and biological significance of HNSCC, indicating that molecular profiling of the immune microenvironment can be employed to develop novel biomarkers and precision immunotherapies for HNSCC.

scholarly journals Identification of the Key Serum Biomarkers to Diagnose and Predict Metastasis of Osteosarcoma Based on the Analysis of Immune Cell Infiltration Characteristics

2021 ◽  
Author(s):  
Zhihao Chen ◽  
Liubing Li ◽  
Ziyuan Li ◽  
Xi Wang ◽  
Mingxiao Han ◽  
...  
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Immune Cell ◽  
Serum Biomarkers ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Diagnostic Model ◽  
Immune Cell Infiltration ◽  
Differentially Expressed Mirnas

Abstract Background: The potential functions of circular RNAs (circRNAs) and micro RNAs (miRNAs) in osteosarcoma (OS) have not been fully elucidated. Especially, the behavior and mechanism of immune responses in OS development and progression have not been fully demonstrated. It was reported that circRNAs and miRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of many cancers. This study aimed to identify novel key serum biomarkers to diagnose and predict metastasis of OS based on the analysis of immune cell infiltration characteristics.Methods: The differentially-expressed circRNAs (DEcircRNAs), differentially-expressed miRNAs (DEmiRNAs),and differentially-expressed mRNAs (DEmRNAs) of human OS were investigated based on the microarray data downloaded from Gene Expression Omnibus (GEO) datasets. Then, we analyzed immune characteristics pattern of tumor-infiltrating immune cells in OS. On this basis, we identified statistically-significant transcription factors and performed pathway enrichment analysis. Subsequently, we constructed protein-protein interaction (PPI) and competitive endogenous RNA (ceRNA) networks. Moreover, the biological characteristic of targets in ceRNA networks was proposed. Finally, the expression and diagnostic capability of these potential biomarkers from ceRNA network were confirmed by RT-qPCR in patients’ serum.Results: Seven differentially-expressed circRNAs (DEcircRNAs), 166 differentially-expressed miRNAs (DEmiRNAs) and 175 differentially-expressed mRNAs (DEmRNAs) were identified in total. The highest level of infiltration in OS patients were M0 macrophages, M2 macrophages and CD8+ T cells. Further, M0 macrophages and CD8+ T cells were showed the largest negative correlation coefficients. These significant immune characteristics pattern of tumor-infiltrating immune cells were revealed by the principal component analysis in OS. Moreover, we found 185 statistically-significant transcription factors in which the main significant molecules show the potential in immunotherapy of OS. Hsa-circ-0010220, hsa-miR-326, hsa-miR-338-3p, and FAM98A from ceRNA networks associated with immune cell infiltration were confirmed as the potential novel biomarkers for OS diagnosis, of which FAM98A could distinguish and predict metastasis. Most importantly, a novel diagnostic model consisting of the four promising biomarkers (hsa-circ-0010220, hsa-miR-326, hsa-miR-338-3p, and FAM98A) was highlighted with 0.928 AUC value.Conclusions: In summary, the potenial serum biomarkers to diagnose and predict metastasis of OS based on the analysis of immune cell infiltration characteristics were found, and a novel diagnostic model consisting of four promising serum biomarkers was proposed firstly. These results provided a new perspective for the immunotherapy of OS.

scholarly journals POS0736 IDENTIFICATION OF MOLECULAR PHENOTYPES AND IMMUNE CELL INFILTRATION IN SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS ACCORDING TO LONGITUDINAL GENE EXPRESSION

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 619.1-619
Author(s):  
S. Song ◽  
S. X. Zhang ◽  
J. Qiao ◽  
R. Zhao ◽  
J. Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systemic Lupus Erythematosus ◽  
T Cells ◽  
Lupus Erythematosus ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Systemic Lupus ◽  
Signature Genes

Background:Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with highly heterogeneous clinical presentation characterized by disease unpredictable flares and multi-systemic involvement1 2. This clinical heterogeneity calls for design a molecular stratification to improve clinical trial design and formulate personalization treatment therapies.Objectives:This research was conducted to develop a reliable method to stratify SLE patients combined gene expression information and disease status.Methods:The mRNA expression profile of GSE138458 (contained 307 patients and 23 controls) and GSE49454 (contained 111 patients and 16 controls) were downloaded from the publicly GEO databases. After background adjustment, batch correction, and other pre-procession, obtaining a big gene matrix to identify the differentially expressed genes (DEGs) in SLE compared with healthy controls, which were screened by P value < 0.01. SLE subtypes were identified by non-negative matrix factorization (NMF) based on DEGs. Acquired signature genes in different SLE subtypes were conducted to process pathway enrichment analysis in Metascape. SLEDAI score and immune cell infiltration was also performed between subtypes by software package R (version 4.0.3).Results:Total 1202 DEGs were imputed to NMF unsupervised machine learning method. Patients with SLE were stratified into two subsets based on 184 signature genes derived from obtained DEGs(Fig.1A, 1B). GO and KEGG enrichment analysis showed that signature genes were mainly involved in negative regulation of innate immune response, toll-like receptor signaling pathway, regulation of immune effector process and so on(Fig.1C). Patients in Sub1 group had severe disease activity measures compared with those in Sub2(Fig.1D). SLEDAI scores from GSE49454 dataset were also higher in Sub1 compare with Sub2(Fig.1E). Further, immune cell infiltration results revealed an insufficient of regulatory T cell, CD8 T cells and naive CD4 T cells in Sub1 and neutrophils cells in Sub2(P<0.05)(Fig.1F).Conclusion:Our findings indicate that patients with SLE could be stratified into 2 subtypes which had different lymphocyte status and closely related to disease activity. This phenotyping may help us understand the etiology of the disease, inform patient in the design of clinical trials and guide treatment decision.References:[1]Dorner T, Furie R. Novel paradigms in systemic lupus erythematosus. Lancet 2019;393(10188):2344-58. doi: 10.1016/S0140-6736(19)30546-X [published Online First: 2019/06/11].[2]Fanouriakis A, Tziolos N, Bertsias G, et al. Update οn the diagnosis and management of systemic lupus erythematosus. Annals of the rheumatic diseases 2021;80(1):14-25. doi: 10.1136/annrheumdis-2020-218272 [published Online First: 2020/10/15].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

scholarly journals Prognostic value of immune cell infiltration in bladder cancer: A gene expression‑based study

2020 ◽  
Vol 20 (2) ◽  
pp. 1677-1684 ◽  
Author(s):  
Yao Wang ◽  
Hong‑Jun Ba ◽  
Zi‑Chuan Liu ◽  
Xu‑Bin Deng ◽  
Min Zhou
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Prognostic Value ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration

scholarly journals Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

2020 ◽  
Author(s):  
Naiqiang Zhu ◽  
Jingyi Hou
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Blue Module

Abstract Background: Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas.Method: The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results: Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level.Conclusion: Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

scholarly journals Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

2020 ◽  
Author(s):  
Naiqiang Zhu ◽  
Jingyi Hou
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Blue Module

Abstract Background: Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas.Method: The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “clusterProfiler” package in R for annotation and visualization. Results: Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level.Conclusion: Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

scholarly journals Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

2020 ◽  
Author(s):  
Naiqiang Zhu ◽  
Jingyi Hou
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Blue Module

Abstract Background Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we aimed to characterize infiltrating immune cells and genes associated with the immunologic response to sarcomas. Method The “CIBERSORT” algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the “ESTIMATE” algorithm was used to assess the “Estimate,” “Immune,” and “Stromal” scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Gens and Genomes (KEGG) analysis were applied using the “clusterProfiler” package in R for annotation and visualization. Results Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level. Conclusion Based on the immune cell infiltration and immune microenvironment, three key genes were identified, which suggest novel molecular mechanisms of sarcoma metastasis.

scholarly journals Identification of biomarkers and pathways associated with immune infiltration and prognosis in melanoma patients

2020 ◽  
Author(s):  
Biao Huang ◽  
Wei Han ◽  
Zu-Feng Sheng ◽  
Guo-Liang Shen
Keyword(s):  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cell ◽  
Primary Melanoma ◽  
Roc Curves ◽  
Cell Infiltration ◽  
Peptide Ligand ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Go Enrichment

Abstract Background Skin cutaneous melanoma (SKCM) is one of the most malignancy and aggressive cancers, causing about 72% of deaths in skin carcinoma. Although extensive study has explored the mechanism of recurrence and metastasis, the tumorigenesis of melanoma remains unclear. Exploring the tumorigenesis mechanism may help to identify prognostic biomarkers that could serve to guide cancer therapy . Methods We identified differentially expressed genes (DEGs) between patients with primary melanoma and normal skin in three data sets including GSE46517, GSE15605, GSE114445. Functional annotation including KEGG pathway and gene ontology (GO) enrichment analysis was performed by DAVID. Subsequently, protein ‐ protein interaction network of DEGs was developed and the most significant module was constructed as hub genes for further study. ClueGO was used to investigate significant pathways in hub genes. The Kaplan–Meier method was performed to assess prognostic genes. ROC curves were used to describe diagnosis value of hub genes. Then, genes expression and clinical characteristics were validated by TCGA profiles. In addition, we explored the correlation between prognostic genes and immune cell infiltration. Results A total of 308 DEGs and 12 hub genes were identified. GO enrichment results demonstrated a correlation between DEGs and the immune response, inflammatory response and transcriptional control. Hallmark pathways of hub genes including interleukin-10 signaling, chemokine receptors bind chemokine signaling ， peptide ligand-binding receptors. Survival analysis and ROC curves suggested that CCL4, CCL5, NMU, CXCL9, CXCL10, CXCL13 were independent prognostic factors (except GAL). Furthermore, prognostic genes were highly expressed in tumor tissue and related to pathological stages and Breslow depth. In addition, the expressions of CCL4, CCL5, CXCL9, CXCL10, CXCL13 were positively correlated with six immune cell infiltration (B-cell, CD8+ T cells, CD4+ T cells, macrophages, Neutrophils, Dendritic cells) and 28 types of TILs such as activated CD8 T cells, regulatory T cells, natural killer T cells while NMU and GAL were negatively correlated with it. Conclusion In summary, this study identified significant hub genes and pathways closely associated with immune infiltrations and prognosis in SKCM patients, which might help us evaluate underlying carcinogenesis progress from skin to melanoma.

Sign in / Sign up

Export Citation Format

Share Document