TIMEDB: tumor immune micro-environment cell composition database with automatic analysis and interactive visualization

Abstract

Deciphering the cell-type composition in the tumor immune microenvironment (TIME) can significantly increase the efficacy of cancer treatment and improve the prognosis of cancer. Such a task has benefited from microarrays and RNA sequencing technologies, which have been widely adopted in cancer studies, resulting in extensive expression profiles with clinical phenotypes across multiple cancers. Current state-of-the-art tools can infer cell-type composition from bulk expression profiles, providing the possibility of investigating the inter-heterogeneity and intra-heterogeneity of TIME across cancer types. Much can be gained from these tools in conjunction with a well-curated database of TIME cell-type composition data, accompanied by the corresponding clinical information. However, currently available databases fall short in data volume, multi-platform dataset integration, and tool integration. In this work, we introduce TIMEDB (https://timedb.deepomics.org), an online database for human tumor immune microenvironment cell-type composition estimated from bulk expression profiles. TIMEDB stores manually curated expression profiles, cell-type composition profiles, and the corresponding clinical information of a total of 39,706 samples from 546 datasets across 43 cancer types. TIMEDB comes readily equipped with online tools for automatic analysis and interactive visualization, and aims to serve the community as a convenient tool for investigating the human tumor microenvironment.

Silencing lncRNA‐DARS‐AS1 suppresses nonsmall cell lung cancer progression by stimulating miR‐302a‐3p to inhibit ACAT1 expression

“…Recent research findings have revealed that the rapid proliferation of tumor cells indices significant changes in energy metabolism, resulting in the emergence of a tumor microenvironment characterized by hypoxia, acidosis, oxidative stress, and other distinctive features. 55,56 Existing literature suggests that lncRNA DARS-AS1 in myeloma is directly upregulated by hypoxia inducible factor (HIF)-1. 17 Furthermore, the interaction between DARS-AS1 and…”

Quantitative Annotations of T-Cell Repertoire Specificity

“…TCRanno is an open-source Python package with source code freely available at https://github.com/deepomicslab/TCRanno. A simplified web implementation is available under the TIMEDB (62) platform: https://timedb.deepomics.org/submit/tcrAnalyses.…”

RIGATonI: An R software for Rapid Identification of Genomic Alterations in Tumors affecting lymphocyte Infiltration

“…Databases of cellular deconvolution results have emerged which include ensemble analyses across The Cancer Genome Atlas (TCGA) and other sources 9,10 . As an exploratory tool for researchers without strong bioinformatic resources, this is exceptionally useful.…”

“…Although there are many techniques to assess immunity in cancer using bulk RNA sequencing data, there are alternative approaches that have yet to be explored. Current approaches ( Figure 1A ) include gene signature analyses (ImSig 4 , Thorsson immune landscape of cancer 5 ), cellular deconvolution (MCP-counter 6 , QuantiseqR 7 , CIBERSORT 8 ), and ensemble results provided in databases (TIMER2.0 9 , TIMEDB 10 ). Gene set enrichment involves assessing the relative expression of gene sets related to immunity and then clustering cancers based on these results.…”

“…This technique is extremely valuable for small subsets of tumors and for researchers who have a good understanding of tumor immunity. Databases of cellular deconvolution results have emerged which include ensemble analyses across The Cancer Genome Atlas (TCGA) and other sources 9, 10 . As an exploratory tool for researchers without strong bioinformatic resources, this is exceptionally useful.…”