scholarly journals Tumor infiltrating immune cells (TIICs) as a biomarker for prognosis benefits in patients with osteosarcoma

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ying Chen ◽  
Bo Zhao ◽  
Xiaohu Wang
Keyword(s):  
Risk Score ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Cell Types ◽  
Difficult Problem ◽  
Model Base ◽  
Model Based ◽  
Kaplan Meier ◽  
Score Model

Abstract Background Osteosarcoma is a rare malignant bone tumor in adolescents and children. Poor prognosis has always been a difficult problem for patients with osteosarcoma. Recent studies have shown that tumor infiltrating immune cells (TIICs) are associated with the clinical outcome of osteosarcoma patients. The aim of our research was to construct a risk score model based on TIICs to predict the prognosis of patients with osteosarcoma. Methods CIBERSORTX algorithm was used to calculate the proportion of 22 TIIC types in osteosarcoma samples. Kaplan-Meier curves were drawn to investigate the prognostic value of 22 TIIC types. Forward stepwise approach was used to screen a minimal set of immune cell types. Multivariate Cox PHR analysis was performed to construct an immune risk score model. Results Osteosarcoma samples with CIBERSORTX output p value less than 0.05 were selected for research. Kaplan-Meier curves indicated that naive B cells (p = 0.047) and Monocytes (p = 0.03) in osteosarcoma are associated with poor prognosis. An immune risk score model was constructed base on eight immune cell types, and the ROC curve showed that the immune risk score model is reliable in predicting the prognosis of patients with osteosarcoma (AUC = 0.724). Besides, a nomogram model base on eight immune cell types was constructed to predict the survival rate of patients with osteosarcoma. Conclusions TIICs are closely related to the prognosis of osteosarcoma. The immune risk score model based on TIICs is reliable in predicting the prognosis of osteosarcoma.

scholarly journals Tumor infiltrating immune cells (TIICs) as a biomarker for prognosis benefits in patients with osteosarcoma

2020 ◽  
Author(s):  
Bo Zhao ◽  
Xiaohu Wang
Keyword(s):  
Risk Score ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Difficult Problem ◽  
P Value ◽  
Model Base ◽  
Model Based ◽  
Kaplan Meier ◽  
Forward Stepwise ◽  
Score Model

Abstract Background: Osteosarcoma is a rare malignant bone tumor in adolescents and children. Poor prognosis has always been a difficult problem for patients with osteosarcoma. Recent studies have shown that Tumor infiltrating immune cells (TIICs) are associated with the clinical outcome of osteosarcoma patients. The aim of our research was to construct a risk score model based on TIICs to predict the prognosis of patients with osteosarcoma. Methods: CIBERSORT algorithm was used to calculate the proportion of 22 TIICs in osteosarcoma samples. Kaplan-Meier curves were drawn to investigate the prognostic value of 22 TIICs. Forward stepwise approach was used to screen a minimal set of immune cells. Multivariate Cox PHR analysis was performed to construct an immune risk score model. Results: Osteosarcoma samples with CIBERSORT output p value less than 0.05 were selected for research. Kaplan-Meier curves indicated that naive B cells (p=0.047) and Monocytes (p=0.03) in osteosarcoma are associated with poor prognosis. An immune risk score model was constructed base on eight immune cells, and the ROC curve showed that the immune risk score model is reliable in predicting the prognosis of patients with osteosarcoma (AUC=0.724). Besides, a nomogram model base on eight immune cells was constructed to predict the survival rate of patients with osteosarcoma.Conclusions: TIICs are closely related to the prognosis of osteosarcoma. The immune risk score model based on TIICs is reliable in predicting the prognosis of osteosarcoma.

scholarly journals Upregulated CCNE1 Correlates with Poor Prognosis, Tumor Immune Infiltration and Escape in Breast Cancer

2021 ◽  
Author(s):  
Jiaxi Feng ◽  
Yanan Hu ◽  
Dan Liu ◽  
Shanshan Wang ◽  
Mengci Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Immune Cell ◽  
High Expression ◽  
Expression Level ◽  
Immune Cell Infiltration ◽  
Immune Infiltration ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Background Breast cancer (BC) is the most common malignant tumor in women and widely known for its poor prognosis. More and more research has discovered that cyclin E1 (CCNE1) plays an important role in progression of various types of cancer. But its specific mechanism in BC progression still needs further research to explore.Methods At first, we determined the expression and prognostic value of CCNE1 through The Cancer Genome Atlas (TCGA) database and The Genotype-Tissue Expression (GTEx) data. Then, we predicted the upstream non-coding RNAs of CCNE1 through StarBase, GEPIA, and Kaplan-Meier plotter database. We further studied the correlation of CCNE1 expression with BC immune cell infiltration, biomarkers of immune cells and immune checkpoints expression through TIMER and GEPIA databases.Results The results suggested that CCNE1 was significantly upregulated in BC and its high expression was correlated with poor prognosis in BC patients. Next, we identified long noncoding RNA (lncRNA) LINC00511 / microRNA-195-5p (miR-195-5p) / CCNE1 axis as the most potential pathway that could regulate CCNE1 expression in BC through StarBase, GEPIA, and Kaplan-Meier plotter database. Furthermore, our in-depth research discovered that CCNE1 expression level was significantly correlated with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression in BC. conclusions In summary, high expression level of CCNE1 was significantly correlated with poor prognosis, tumor immune infiltration and escape in BC.

scholarly journals Upregulated CCNE1 Correlates with Poor Prognosis, Tumor Immune Infiltration and Escape in Breast Cancer

2021 ◽  
Author(s):  
Jiaxi Feng ◽  
Yanan Hu ◽  
Dan Liu ◽  
Shanshan Wang ◽  
Mengci Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Immune Cell ◽  
High Expression ◽  
Expression Level ◽  
Immune Cell Infiltration ◽  
Immune Infiltration ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract BackgroundBreast cancer (BC) is the most common malignant tumor in women and widely known for its poor prognosis. More and more research has discovered that cyclin E1 (CCNE1) plays an important role in progression of various types of cancer. But its specific mechanism in BC progression still needs further research to explore.MethodsAt first, we determined the expression and prognostic value of CCNE1 through The Cancer Genome Atlas (TCGA) database and The Genotype-Tissue Expression (GTEx) data. Then, we predicted the upstream non-coding RNAs of CCNE1 through StarBase, GEPIA, and Kaplan-Meier plotter database. We further studied the correlation of CCNE1 expression with BC immune cell infiltration, biomarkers of immune cells and immune checkpoints expression through TIMER and GEPIA databases.ResultsThe results suggested that CCNE1 was significantly upregulated in BC and its high expression was correlated with poor prognosis in BC patients. Next, we identified long noncoding RNA (lncRNA) LINC00511 / microRNA-195-5p (miR-195-5p) / CCNE1 axis as the most potential pathway that could regulate CCNE1 expression in BC through StarBase, GEPIA, and Kaplan-Meier plotter database. Furthermore, our in-depth research discovered that CCNE1 expression level was significantly correlated with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression in BC.ConclusionIn summary, high expression level of CCNE1 was significantly correlated with poor prognosis, tumor immune infiltration and escape in BC.

ceRNA network development and tumor-infiltrating immune cell analysis in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Li Chen ◽  
Weijie Zou ◽  
Lei Zhang ◽  
Huijuan Shi ◽  
Zhi Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
High Morbidity ◽  
Clinical Prognosis ◽  
Cerna Network ◽  
Kaplan Meier

Abstract Background: Hepatocellular carcinoma is among the primary causes of cancer deaths globally. Despite efforts to understand liver cancer, its high morbidity and mortality remain high. Herein, we constructed two nomograms based on ceRNA networks and invading immune cells to describe the molecular mechanisms along with the clinical prognosis of HCC patients.Methods: RNA maps of tumors and normal samples were downloaded from TCGA. HTseq counts and fragments per megapons per thousand bases were read from 421 samples, including 371 tumor samples and 50 normal samples. We established a ceRNA network based on differential gene expression in normal versus tumor subjects. CIBERSORT was employed to differentiate 22 immune cell types according to tumor transcriptomes. Kaplan-Meier along with Cox proportional hazard analyses were employed to determine the prognosis-linked factors. Nomograms were constructed based on prognostic immune cells and ceRNAs. We employed ROC (Receiver operating characteristic) and calibration curve analyses to estimate these nomogram. Results: The difference analysis found 2028 mRNAs, 128 miRNAs, and 136 lncRNAs to be significantly differentially expressed in tumor samples relative to normal samples. We set up a ceRNA network containing 21 protein-coding mRNAs, 12 miRNAs, and 3 lncRNAs. In kaplan-Meier analysis, 21 of the 36 ceRNAs were considered significant. Of the 22 cell types, resting dendritic cell levels were markedly different in tumor samples versus normal controls. Calibration and ROC curve analysis of the ceRNA network, as well as immune-infiltration of tumor showed resultful accuracy (three-year survival AUC: 0.691, five-year survival AUC: 0.700; three-years survival AUC: 0.674, five-year survival AUC: 0.694). Our data suggest that Tregs, CD4 T-cells, mast cells, SNHG1, HMMR and hsa-miR-421 are associated with HCC based on ceRNA-immune cells co-expression patterns. Conclusion: On the basis of ceRNA network modeling and immune cell infiltration analysis, our study offers an effective bioinformatics strategy for studying HCC molecular mechanisms and prognosis.

scholarly journals Investigating the significance of tumor-infiltrating immune cells for the prognosis of lung squamous cell carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7918 ◽  
Author(s):  
Yueyan Zhu ◽  
Xiaoqin Zhang
Keyword(s):  
T Cells ◽  
Regression Analysis ◽  
Risk Score ◽  
Immune Cells ◽  
Cox Regression ◽  
Helper T Cells ◽  
Cox Regression Analysis ◽  
Follicular Helper ◽  
Kaplan Meier ◽  
Score Model

Objective Increasing evidence has indicated an association between immune cells infiltration in LSCC and clinical outcome. The aim of this research was tantamount to comprehensively investigate the effect of 22 tumor infiltrating immune cells (TIICs) on the prognosis of LSCC patients. Methods In our research, the CIBERSORT algorithm was utilized to calculate the proportion of 22 TIICs in 502 cases from the TCGA cohort. Cases with a CIBERSORT P-value of <0.05 were kept for further study. Using the CIBERSORT algorithm, we first investigated the difference of immune infiltration between normal tissue and LSCC in 22 subpopulations of immune cells. Kaplan-Meier analysis was used to analyze the effect of 22 TIICs on the prognosis of LSCC. An immune risk score model was constructed based on TIICs correlated with LSCC-related recurrence. Multivariate cox regression analysis was used to investigate whether the immune risk score was an independent factor for prognosis prediction of LSCC. Nomogram was under construction to comprehensively predict the survival rate of LSCC. Results The results of the different analysis showed that except of memory B cells, naive CD4+T cells, T cells and activated NK cells, the remaining immune cells all had differential infiltration in normal tissues and LSCC (p < 0.05). Kaplan-Meier analysis revealed two immune cells statistically related to LSCC-related recurrence, including activated mast cells and follicular helper T cells. Immune risk score model was constructed based on three immune cells including resting memory CD4+T cells, activated mast cells and follicular helper T cells retained by forward stepwise regression analysis. The Kaplan-Meier curve indicated that patients in the high-risk group linked to poor outcome (P = 8.277e−03). ROC curve indicated that the immune risk score model was reliable in predicting recurrence risk (AUC = 0.614). Multivariate cox regression analysis showed that the immune risk score model was just an independent factor for prognosis prediction of LSCC (HR = 2.99, 95% CI [1.65–5.40]; P = 0.0002). The nomogram model combined immune risk score and clinicopathologic parameter score to predict 3-year survival in patients with LSCC. Conclusions Collectively, tumor-infiltrating immune cells play a major role in the prognosis of LSCC.

scholarly journals Profiles and clinical significance of immune cell infiltration in pancreatic adenocarcinoma

2020 ◽  
Author(s):  
Jie Mei ◽  
Rui Xu ◽  
Dandan Xia ◽  
Xuejing Yang ◽  
Huiyu Wang ◽  
...  
Keyword(s):  
Clinical Significance ◽  
Immune Cells ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Cell Types ◽  
Biological Processes ◽  
Protein Protein Interaction ◽  
Kaplan Meier

AbstractBackgroundIt has been well defined that tumor-infiltrating immune cells (TIICs) play critical roles in pancreatic cancer (PAAD) progression. The aim of this research was to comprehensively explore the composition of TIICs in PAAD and their potential clinical significance.Methods178 samples from TCGA and 63 samples from GSE57495 dataset were enrolled into our study. ImmuCellAI was applied to calculate the infiltrating abundance of 24 immune cell types in PAAD and further survival analysis revealed the prognostic values of TIICs in PAAD. Moreover, Gene ontology (GO) enticement analysis of differentially expressed genes (DEGs) between low- and high-risk groups was performed as well.ResultsDifferent kinds of TIICs had distinct infiltrating features. Besides, Specific TIICs subsets had notable prognostic values in PAAD. We further established a 6-TIICs signature to assess the prognosis of PAAD patients. Kaplan-Meier and Cox regression analyses both suggested the significant prognostic value of the signature in PAAD. We next extracted 1,334 DEGs based on the risk model, and the hub modules in the protein-protein interaction (PPI) network of DEGs were involved in regulating immune-related biological processes.ConclusionsOverall, the current study illuminated the immune cells infiltrating landscape in PAAD and developed a TIICs-dependent prognostic signature, which could be used as an effective prognostic classifier for PAAD patients.

The Intersection of Metformin and Inflammation

2021 ◽  
Author(s):  
Leena P. Bharath ◽  
Barbara S. Nikolajczyk
Keyword(s):  
Mitochondrial Function ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Remarkable Effect ◽  
Tumor Onset ◽  
Age Related ◽  
Immune Mediated ◽  
Autoimmune Conditions ◽  
Near Term

The biguanide metformin is the most commonly used antidiabetic drug. Recent studies show that metformin not only improves chronic inflammation by improving metabolic parameters but also has a direct anti-inflammatory effect. In light of these findings, it is essential to identify the inflammatory pathways targeted by metformin to develop a comprehensive understanding of the mechanisms of action of this drug. Commonly accepted mechanisms of metformin action include AMPK activation and inhibition of mTOR pathways, which are evaluated in multiple diseases. Additionally, metformin's action on mitochondrial function and cellular homeostasis processes such as autophagy, is of particular interest because of the importance of these mechanisms in maintaining cellular health. Both dysregulated mitochondria and failure of the autophagy pathways, the latter of which impair clearance of dysfunctional, damaged, or excess organelles, affect cellular health drastically and can trigger the onset of metabolic and age-related diseases. Immune cells are the fundamental cell types that govern the health of an organism. Thus, dysregulation of autophagy or mitochondrial function in immune cells has a remarkable effect on susceptibility to infections, response to vaccination, tumor onset, and the development of inflammatory and autoimmune conditions. Here we summarize the latest research on metformin's regulation of immune cell mitochondrial function and autophagy as evidence that new clinical trials on metformin with primary outcomes related to the immune system should be considered to treat immune-mediated diseases over the near term.

scholarly journals Toll-Like Receptor-Based Strategies for Cancer Immunotherapy

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Saghar Pahlavanneshan ◽  
Ali Sayadmanesh ◽  
Hamidreza Ebrahimiyan ◽  
Mohsen Basiri
Keyword(s):  
Cancer Immunotherapy ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Genetically Engineered ◽  
Current Status ◽  
Tlr Signaling ◽  
Functional Roles ◽  
Immunotherapy Of Cancer ◽  
Signaling Components

Toll-like receptors (TLRs) are expressed and play multiple functional roles in a variety of immune cell types involved in tumor immunity. There are plenty of data on the pharmacological targeting of TLR signaling using agonist molecules that boost the antitumor immune response. A recent body of research has also demonstrated promising strategies for improving the cell-based immunotherapy methods by inducing TLR signaling. These strategies include systemic administration of TLR antagonist along with immune cell transfer and also genetic engineering of the immune cells using TLR signaling components to improve the function of genetically engineered immune cells such as chimeric antigen receptor-modified T cells. Here, we explore the current status of the cancer immunotherapy approaches based on manipulation of TLR signaling to provide a perspective of the underlying rationales and potential clinical applications. Altogether, reviewed publications suggest that TLRs make a potential target for the immunotherapy of cancer.

scholarly journals Neuroinflammation in Amyotrophic Lateral Sclerosis and Frontotemporal Dementia and the Interest of Induced Pluripotent Stem Cells to Study Immune Cells Interactions With Neurons

2021 ◽  
Vol 14 ◽  
Author(s):  
Elise Liu ◽  
Léa Karpf ◽  
Delphine Bohl
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Immune System ◽  
Frontotemporal Dementia ◽  
Immune Cells ◽  
Immune Cell ◽  
Current Knowledge ◽  
Cell Types ◽  
Induced Pluripotent ◽  
Different Cell Types ◽  
Lateral Sclerosis

Inflammation is a shared hallmark between amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). For long, studies were conducted on tissues of post-mortem patients and neuroinflammation was thought to be only bystander result of the disease with the immune system reacting to dying neurons. In the last two decades, thanks to improving technologies, the identification of causal genes and the development of new tools and models, the involvement of inflammation has emerged as a potential driver of the diseases and evolved as a new area of intense research. In this review, we present the current knowledge about neuroinflammation in ALS, ALS-FTD, and FTD patients and animal models and we discuss reasons of failures linked to therapeutic trials with immunomodulator drugs. Then we present the induced pluripotent stem cell (iPSC) technology and its interest as a new tool to have a better immunopathological comprehension of both diseases in a human context. The iPSC technology giving the unique opportunity to study cells across differentiation and maturation times, brings the hope to shed light on the different mechanisms linking neurodegeneration and activation of the immune system. Protocols available to differentiate iPSC into different immune cell types are presented. Finally, we discuss the interest in studying monocultures of iPS-derived immune cells, co-cultures with neurons and 3D cultures with different cell types, as more integrated cellular approaches. The hope is that the future work with human iPS-derived cells helps not only to identify disease-specific defects in the different cell types but also to decipher the synergistic effects between neurons and immune cells. These new cellular tools could help to find new therapeutic approaches for all patients with ALS, ALS-FTD, and FTD.

scholarly journals Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

2020 ◽  
Author(s):  
Jianfeng Zheng ◽  
Jinyi Tong ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Training Set ◽  
Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

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