Exploration of the immune cell landscape in brain cancer utilizing gene expression and copy number data

Brain cancer is a common cancer that affects more than 700,000 people in the US every year. We explore the dynamic changes in the abundance of immune cells based on RNA and DNA samples extracted from a large cohort of brain cancer patients. We used gene expression data and copy number data from a large brain cancer collections - the REMBRANDT project (REpository for Molecular BRAin Neoplasia DaTa) that includes 671 patients. We applied virtual flow cytometry tools CIBERSORT and xCell to estimate the abundance of the immune cells in the RNA of these samples. The immune cell landscape in this dataset is compared with that of the TCGA brain cancer collection, that includes 511 patients with Lower Grade Glioma (TCGA-LGG) and 156 patients with Glioblastoma (TCGA-GBM). We also discuss how well the results align with published literature, and how this computational analysis can help better understand how immune cells affect clinical outcome and survival in brain cancer patients.

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Drug Repositioning Based on the Reversal of Gene Expression Signatures Identifies TOP2A as a Therapeutic Target for Rectal Cancer

Cancers ◽

10.3390/cancers13215492 ◽

2021 ◽

Vol 13 (21) ◽

pp. 5492

Author(s):

Robson Francisco Carvalho ◽

Luisa Matos do Canto ◽

Sarah Santiloni Cury ◽

Torben Frøstrup Hansen ◽

Lars Henrik Jensen ◽

...

Keyword(s):

Gene Expression ◽

Rectal Cancer ◽

Cancer Patients ◽

Copy Number ◽

Topoisomerase Ii ◽

Drug Repositioning ◽

Gene Copy ◽

Common Disease ◽

Topoisomerase Ii Inhibitors ◽

Gene Expression Signatures

Rectal cancer is a common disease with high mortality rates and limited therapeutic options. Here we combined the gene expression signatures of rectal cancer patients with the reverse drug-induced gene-expression profiles to identify drug repositioning candidates for cancer therapy. Among the predicted repurposable drugs, topoisomerase II inhibitors (doxorubicin, teniposide, idarubicin, mitoxantrone, and epirubicin) presented a high potential to reverse rectal cancer gene expression signatures. We showed that these drugs effectively reduced the growth of colorectal cancer cell lines closely representing rectal cancer signatures. We also found a clear correlation between topoisomerase 2A (TOP2A) gene copy number or expression levels with the sensitivity to topoisomerase II inhibitors. Furthermore, CRISPR-Cas9 and shRNA screenings confirmed that loss-of-function of the TOP2A has the highest efficacy in reducing cellular proliferation. Finally, we observed significant TOP2A copy number gains and increased expression in independent cohorts of rectal cancer patients. These findings can be translated into clinical practice to evaluate TOP2A status for targeted and personalized therapies based on topoisomerase II inhibitors in rectal cancer patients.

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The Toolbox for Untangling Chromosome Architecture in Immune Cells

Frontiers in Immunology ◽

10.3389/fimmu.2021.670884 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shuai Liu ◽

Keji Zhao

Keyword(s):

Gene Expression ◽

Immune Cells ◽

Spatial Organization ◽

Chromosome Structure ◽

Immune Cell ◽

Genome Structure ◽

Chromosome Architecture ◽

The Past ◽

Important Player ◽

The Way

The code of life is not only encrypted in the sequence of DNA but also in the way it is organized into chromosomes. Chromosome architecture is gradually being recognized as an important player in regulating cell activities (e.g., controlling spatiotemporal gene expression). In the past decade, the toolbox for elucidating genome structure has been expanding, providing an opportunity to explore this under charted territory. In this review, we will introduce the recent advancements in approaches for mapping spatial organization of the genome, emphasizing applications of these techniques to immune cells, and trying to bridge chromosome structure with immune cell activities.

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ILDR1 Is a Prognostic Biomarker and Associated With Immune Inﬁltration in Gastric Cancer

10.21203/rs.3.rs-944596/v1 ◽

2021 ◽

Author(s):

Yanling Ma ◽

WenBo Qi ◽

BaoHong Gu ◽

XueMei Li ◽

ZhenYu Yin ◽

...

Keyword(s):

Gastric Cancer ◽

T Cells ◽

Dendritic Cells ◽

Cancer Patients ◽

Cd8 T Cells ◽

Immune Cells ◽

Copy Number ◽

Sequencing Data ◽

Number Variation ◽

Gastric Cancer Patients

Abstract Objective: To investigate the association between ILDR1 and prognosis and immune infiltration in gastric cancer. Methods: We analyzed the RNA sequencing data of 9736 tumor tissues and 8587 normal tissues in the TCGA and GTEx databases through the GEPIA2 platform. The expression of ILDR1 in gastric cancer and normal gastric mucosa tissues with GEPIA and TIMER. Clinical subgroup analysis was made through Kaplan-Meier analysis. Analyzed the correlation between ILDR1 and VEGFA expression in gastric cancer, through the gene sequencing data of gastric cancer in TCGA. Explored the relationship between ILDR1 methylation and the prognosis of gastric cancer patients through the MethSurv database. The correlation between ILDR1 and immune cells and the correlation of copy number variation were explored through the TIMER database. Results: ILDR1-high GC patients had a lower PFS and OS. High ILDR1 expression was significantly correlated with tumor grade. There was a negative correlation between the ILDR1 expression and the abundances of CD8+ T, Macrophages and DC and etc. The methylation level of ILDR1 is associated with a good prognosis of gastric cancer. ILDR1 copy number variation was correlated with immune cells, IDLR1 arm-loss was associated with the infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells, and arm-duplication was associated with the infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. Conclusion: The increased expression of ILDR1 is associated with poor prognosis in patients with gastric cancer. ILDR1 can be used as a novel predictive biomarker to provide a new therapeutic target for gastric cancer patients.

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GJA1 Expression and Its Prognostic Value in Cervical Cancer

BioMed Research International ◽

10.1155/2020/8827920 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Silu Meng ◽

Xinran Fan ◽

Jianwei Zhang ◽

Ran An ◽

Shuang Li

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Logistic Regression ◽

Gap Junction ◽

Signaling Pathway ◽

Immune Cells ◽

Immune Cell ◽

Normal Tissues ◽

Kaplan Meier ◽

Chi Squared

Gap Junction Protein Alpha 1 (GJA1) belongs to the gap junction family and has been widely studied in cancers. We evaluated the role of GJA1 in cervical cancer (CC) using public data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. The difference of GJA1 expression level between CC and normal tissues was analyzed by the Gene Expression Profiling Interactive Analysis (GEPIA), six GEO datasets, and the Human Protein Atlas (HPA). The relationship between clinicopathological features and GJA1 expression was analyzed by the chi-squared test and the logistic regression. Kaplan–Meier survival analysis and Cox proportional hazard regression analysis were used to assessing the effect of GJA1 expression on survival. Gene set enrichment analysis (GSEA) was used to screen the signaling pathways regulated by GJA1. Immune Cell Abundance Identifier (ImmuCellAI) was chosen to analyze the immune cells affected by GJA1. The expression of GJA1 in CC was significantly lower than that in normal tissues based on the GEPIA, GEO datasets, and HPA. Both the chi-squared test and the logistic regression showed that high-GJA1 expression was significantly correlated with keratinization, hormone use, tumor size, and FIGO stage. The Kaplan–Meier curves suggested that high-GJA1 expression could indicate poor prognosis ( p = 0.0058 ). Multivariate analysis showed that high-GJA1 expression was an independent predictor of poor overall survival (HR, 4.084; 95% CI, 1.354-12.320; p = 0.013 ). GSEA showed many cancer-related pathways, such as the p53 signaling pathway and the Wnt signaling pathway, were enriched in the high-GJA1-expression group. Immune cell abundance analysis revealed that the abundance of CD8 naive, DC, and neutrophil was significantly increased in the high-GJA1-expression group. In conclusion, GJA1 can be regarded as a potential prognostic marker of poor survival and therapeutic target in CC. Moreover, many cancer-related pathways may be the critical pathways regulated by GJA1. Furthermore, GJA1 can affect the abundance of immune cells.

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MMP1, MMP9, and COX2 Expressions in Promonocytes Are Induced by Breast Cancer Cells and Correlate with Collagen Degradation, Transformation-Like Morphological Changes in MCF-10A Acini, and Tumor Aggressiveness

BioMed Research International ◽

10.1155/2013/279505 ◽

2013 ◽

Vol 2013 ◽

pp. 1-15 ◽

Cited By ~ 15

Author(s):

G. K. Chimal-Ramírez ◽

N. A. Espinoza-Sánchez ◽

D. Utrera-Barillas ◽

L. Benítez-Bribiesca ◽

J. R. Velázquez ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Immune Cells ◽

Immune Cell ◽

Morphological Changes ◽

Tumor Stroma ◽

Collagen Degradation ◽

Tumor Aggressiveness ◽

Soluble Factors ◽

Immune Effector

Tumor-associated immune cells often lack immune effector activities, and instead they present protumoral functions. To understand how tumors promote this immunological switch, invasive and noninvasive breast cancer cell (BRC) lines were cocultured with a promonocytic cell line in a Matrigel-based 3D system. We hypothesized that if communication exists between tumor and immune cells, coculturing would result in augmented expression of genes associated with tumor malignancy. Upregulation of proteasesMMP1andMMP9and inflammatoryCOX2genes was found likely in response to soluble factors. Interestingly, changes were more apparent in promonocytes and correlated with the aggressiveness of the BRC line. Increased gene expression was confirmed by collagen degradation assays and immunocytochemistry of prostaglandin 2, a product of COX2 activity. Untransformed MCF-10A cells were then used as a sensor of soluble factors with transformation-like capabilities, finding that acini formed in the presence of supernatants of the highly aggressive BRC/promonocyte cocultures often exhibited total loss of the normal architecture. These data support that tumor cells can modify immune cell gene expression and tumor aggressiveness may importantly reside in this capacity. Modeling interactions in the tumor stroma will allow the identification of genes useful as cancer prognostic markers and therapy targets.

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Calculation of immune cell proportion from batch tumor gene expression profile based on support vector regression

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720020500304 ◽

2020 ◽

Vol 18 (05) ◽

pp. 2050030

Author(s):

Dongmei Ai ◽

Gang Liu ◽

Xiaoxin Li ◽

Yuduo Wang ◽

Man Guo

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Support Vector Regression ◽

Gene Expression Profile ◽

Immune Cells ◽

Immune Cell ◽

Relative Proportion ◽

Support Vector ◽

Cancer Tissues ◽

Cell Proportion

In addition to tumor cells, a large number of immune cells are found in the tumor microenvironment (TME) of cancer patients. Tumor-infiltrating immune cells play an important role in tumor progression and patient outcome. We improved the relative proportion estimation algorithm of immune cells based on RNA-seq gene expression profiling and solved the multiple linear regression model by support vector regression ([Formula: see text]-SVR). These steps resulted in increased robustness of the algorithm and more accurate calculation of the relative proportion of different immune cells in cancer tissues. This method was applied to the analysis of infiltrating immune cells based on 41 pairs of colorectal cancer tissues and normal solid tissues. Specifically, we compared the relative fractions of six types of immune cells in colorectal cancer tissues to those found in normal solid tissue samples. We found that tumor tissues contained a higher proportion of CD8 T cells and neutrophils, while B cells and monocytes were relatively low. Our pipeline for calculating immune cell proportion using gene expression profile data can be freely accessed from GitHub at https://github.com/gutmicrobes/EICS.git.

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Comparative analysis of gene expression and DNA copy number data for pancreatic and breast cancers using an orthogonal decomposition

Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004. ◽

10.1109/csb.2004.1332506 ◽

2004 ◽

Author(s):

J.A. Berger ◽

S. Hautaniemi ◽

S.K. Mitra

Keyword(s):

Gene Expression ◽

Comparative Analysis ◽

Copy Number ◽

Orthogonal Decomposition ◽

Copy Number Data ◽

Breast Cancers ◽

Dna Copy Number

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ImmuSort, a database on gene plasticity and electronic sorting for immune cells

Scientific Reports ◽

10.1038/srep10370 ◽

2015 ◽

Vol 5 (1) ◽

Cited By ~ 19

Author(s):

Pingzhang Wang ◽

Yehong Yang ◽

Wenling Han ◽

Dalong Ma

Keyword(s):

Gene Expression ◽

Immune Cells ◽

Immune Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Experimental Conditions ◽

Evaluation Scores ◽

Cell Groups ◽

Differential Gene ◽

Disease Associated Genes

Abstract Gene expression is highly dynamic and plastic. We present a new immunological database, ImmuSort. Unlike other gene expression databases, ImmuSort provides a convenient way to view global differential gene expression data across thousands of experimental conditions in immune cells. It enables electronic sorting, which is a bioinformatics process to retrieve cell states associated with specific experimental conditions that are mainly based on gene expression intensity. A comparison of gene expression profiles reveals other applications, such as the evaluation of immune cell biomarkers and cell subsets, identification of cell specific and/or disease-associated genes or transcripts, comparison of gene expression in different transcript variants and probe set quality evaluation. A plasticity score is introduced to measure gene plasticity. Average rank and marker evaluation scores are used to evaluate biomarkers. The current version includes 31 human and 17 mouse immune cell groups, comprising 10,422 and 3,929 microarrays derived from public databases, respectively. A total of 20,283 human and 20,963 mouse genes are available to query in the database. Examples show the distinct advantages of the database. The database URL is http://immusort.bjmu.edu.cn/.

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Novel Prognostic Biomarkers in Gastric Cancer: CGB5, MKNK2, and PAPPA2

Frontiers in Oncology ◽

10.3389/fonc.2021.683582 ◽

2021 ◽

Vol 11 ◽

Author(s):

Min Qin ◽

Zhihai Liang ◽

Heping Qin ◽

Yifang Huo ◽

Qing Wu ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Regression Analysis ◽

Immune Cells ◽

Prognostic Model ◽

Prognostic Markers ◽

Immune Cell ◽

Risk Group ◽

P Value ◽

Expression Data

IntroductionGastric cancer is one of the most common malignant tumors of the digestive tract. However, there are no adequate prognostic markers available for this disease. The present study used bioinformatics to identify prognostic markers for gastric cancer that would guide the clinical diagnosis and treatment of this disease.Materials and MethodsGene expression data and clinical information of gastric cancer patients along with the gene expression data of 30 healthy samples were downloaded from the TCGA database. The initial screening was performed using the WGCNA method combined with the analysis of differentially expressed genes, which was followed by univariate analysis, multivariate COX regression analysis, and Lasso regression analysis for screening the candidate genes and constructing a prognostic model for gastric cancer. Subsequently, immune cell typing was performed using CIBERSORT to analyze the expression of immune cells in each sample. Finally, we performed laboratory validation of the results of our analyses using immunohistochemical analysis.ResultsAfter five screenings, it was revealed that only three genes fulfilled all the screening requirements. The survival curves generated by the prognostic model revealed that the survival rate of the patients in the high-risk group was significantly lower compared to the patients in the low-risk group (P-value < 0.001). The immune cell component analysis revealed that the three genes were differentially associated with the corresponding immune cells (P-value < 0.05). The results of immunohistochemistry also support our analysis.ConclusionCGB5, MKNK2, and PAPPA2 may be used as novel prognostic biomarkers for gastric cancer.

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