Unsupervised cell functional annotation for single-cell RNA-seq

An analysis of the prognostic role of reactive oxygen species-associated genes in breast cancer

BACKGROUND

This study aimed to type breast cancer in relation to reactive oxygen species (ROS), clinical indicators, single nucleotide variant (SNV) mutations, functional differences, immune infiltration, and predictive responses to immunotherapy or chemotherapy, and constructing a prognostic model.

METHODS

We used uniCox analysis, ConsensusClusterPlus, and the proportion of ambiguous clustering (PAC) to analyze The Cancer Genome Atlas (TCGA) data to determine optimal groupings and obtain differentially expressed ROS-related genes. Clinical indicators were then combined with the classification results and the Chi-square test was used to assess differences. We further examined SNV mutations, and functional differences using gene set enrichment analysis (GSEA) analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, immune cell infiltration, and response to immunotherapy and chemotherapy. A prognostic model for breast cancer was constructed using these differentially expressed genes, immunotherapy or chemotherapy responses, and survival curves. RT-qPCR was used to detect the differences in the expression of LCE3D, CA1, PIRT and SMR3A in breast cancer cell lines and normal breast epithelial cell line.

RESULTS

We identified two distinct tumor types with significant differences in ROS-related gene expression, clinical indicators, SNV mutations, functional pathways, and immune infiltration. The response to specific chemotherapy drugs and immunotherapy treatments also documented significant differences. The prognostic model constructed with 16 genes linked to survival could efficiently divide patients into high- and low-risk groups. The high-risk group showed a poorer prognosis, higher tumor purity, distinct immune microenvironment, and lower immunotherapy response. RT-qPCR results showed that LCE3D, CA1, PIRT and SMR3A are highly expressed in breast cancer.

CONCLUSION

Our methodical examination presented an enhanced insight into the molecular and immunological heterogeneity of breast cancer. It can contribute to the understanding of prognosis and offer valuable insights for personalized treatment strategies. Further, the prognostic model can potentially serve as a powerful tool for risk stratification and therapeutic decision-making in clinical settings.

UNIFAN: A Tool for Unsupervised Single-Cell Clustering and Annotation

UNIFAN is an unsupervised cell type annotation tool for single-cell RNA sequencing data (scRNA-seq). Given single-cell expression data as input, UNIFAN outputs cell clusters as well as annotations for each cluster. The clustering process utilizes information on pathways and biological processes and these are also used to annotate the resulting clusters. In this software article, we focus on how to install UNIFAN and on the main steps involved in using UNIFAN for cell type annotations.