Cancer Stemness Online: A Resource for Investigating Cancer Stemness and Associations with Immune Response.

Cancer progression involves the gradual loss of a differentiated phenotype and the acquisition of progenitor and stem cell-like features, which are potential culprits of immunotherapy resistance. Although the state-of-the-art predictive computational methods have facilitated the prediction of cancer stemness, there remains a lack of efficient resources to accommodate various usage requirements. Here, we present the Cancer Stemness Online, an integrated resource for efficiently scoring cancer stemness potential at both bulk and single-cell levels.

A machine learning model identifies M3-like subtype in AML based on PML/RARα targets.

The typical genomic feature of acute myeloid leukemia (AML) M3 subtype is the fusion event of PML/RARα, and ATRA/ATO-based combination therapy is current standard treatment regimen for M3 subtype. Here, a machine-learning model based on expressions of PML/RARα targets was developed to identify M3 patients by analyzing 1228 AML patients. Our model exhibited high accuracy.

SORC: an integrated spatial omics resource in cancer.

The interactions between tumor cells and the microenvironment play pivotal roles in the initiation, progression and metastasis of cancer. The advent of spatial transcriptomics data offers an opportunity to unravel the intricate dynamics of cellular states and cell-cell interactions in cancer. Herein, we have developed an integrated spatial omics resource in cancer (SORC, http://bio-bigdata.