TIMER2.0 for analysis of tumor-infiltrating immune cells

Abstract

Tumor progression and the efficacy of immunotherapy are strongly influenced by the composition and abundance of immune cells in the tumor microenvironment. Due to the limitations of direct measurement methods, computational algorithms are often used to infer immune cell composition from bulk tumor transcriptome profiles. These estimated tumor immune infiltrate populations have been associated with genomic and transcriptomic changes in the tumors, providing insight into tumor–immune interactions. However, such investigations on large-scale public data remain challenging. To lower the barriers for the analysis of complex tumor–immune interactions, we significantly improved our previous web platform TIMER. Instead of just using one algorithm, TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms. TIMER2.0 provides four modules for investigating the associations between immune infiltrates and genetic or clinical features, and four modules for exploring cancer-related associations in the TCGA cohorts. Each module can generate a functional heatmap table, enabling the user to easily identify significant associations in multiple cancer types simultaneously. Overall, the TIMER2.0 web server provides comprehensive analysis and visualization functions of tumor infiltrating immune cells.

Analysis of N6-methyladenosine (m6A) methyltransferase reveals METTL14 and ZC3H13 as tumor suppressor genes in breast cancer

“…The correlation of mRNA expression level of METTL14, ZC3H13, APC with the abundance of 7 types of in ltrating immune cells ( CD4+T cells, CD8+T cells, Treg cells, B cells, neutrophils, macrophages and Dendritic cells), and the association between immune in ltrates and somatic CNV of METTL14 and ZC3H13 in breast cancer patients were calculated using The Tumor IMmune Estimation Resource 2.0 (TIMER 2.0) algorithm database [26,27] (http://timer.cistrome.org/), noting that the abundance of Treg cells were analyzed by the CIBERSORT method. Tumor purity is an important factor affecting the analysis of immune in ltration in tumor samples by genomic methods.…”

CLDN6 and CLDN10 are Associated with Immune Infiltration of Ovarian Cancer: A Study of Claudin Family

“…In order to more accurately describe the relationship of gene expression and immune cell in ltration, several methods including TIMER, CIBERSORT, quanTIseq, xCell, MCP-counter and EPIC algorithms were used to assess the immune in ltration of tumor tissue [21] . TIMER2.0 provides a platform for analysis immune in ltrates across diverse cancer types based on available TCGA RNA-seq data [22,23] . The correlations between claudins (CLDN6 and CLDN10) expression and various immune cells in ltration of ovarian cancer were shown in Table 2.…”

Eight-Gene Metabolic Signature Related with Tumor-Associated Macrophages Predicting Overall Survival for Hepatocellular Carcinoma

“…The data of immune in ltration of the Cancer Genome Atlas (TCGA) tumors were obtained from the TIMER(Tumor Immune Estimation Resource) website(http://timer.cistrome.org/) [19]. The RNAsequencing of TCGA dataset came from the Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) (including 343 HCC cases) and corresponding clinical data were acquired from UCSC Xena website(https://xenabrowser.net/).…”

“…Two algorithm were carried out for estimation of immune in ltration in different risk groups: TIMER, which is a calculating method for estimating the abundance exhibited by 6 types of tumor-in ltrating immune cell (B cell, CD4 T and CD8 T cells, neutrophil, macrophage, as well as dendritic cell) [21]; CIBERSORT-ABS, which is a methodology on the basis of the gene expression pro le for evaluating the absolute abundance exhibited by 22 immune cell populations [19].…”