Triple-negative breast cancer (TNBC) is a subtype of breast cancer with high intratumoral heterogeneity. Recent studies revealed that TNBC patients might comprise cells with distinct molecular subtypes. In addition, gene regulatory networks (GRNs) constructed based on single-cell RNA sequencing (scRNA-seq) data have demonstrated the significance for decoding the key regulators. We performed a comprehensive analysis of the GRNs for the intrinsic subtypes of TNBC patients using scRNA-seq. The copy number variations (CNVs) were inferred from scRNA-seq data and identified 545 malignant cells. The subtypes of the malignant cells were assigned based on the PAM50 model. The cell-cell communication analysis revealed that the macrophage plays a dominant role in the tumor microenvironment. Next, the GRN for each subtype was constructed through integrating gene co-expression and enrichment of transcription-binding motifs. Then, we identified the critical genes based on the centrality metrics of genes. Importantly, the critical gene ETV 6 was ubiquitously upregulated in all subtypes, but it exerted diverse roles in each subtype through regulating different target genes. In conclusion, the construction of GRNs based on scRNA-seq data could help us to dissect the intratumoral heterogeneity and identify the critical genes of TNBC.

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Objectives To identify novel genetic loci associated with systemic lupus erythematosus (SLE) and to evaluate potential genetic differences between ethnic Chinese and European populations in SLE susceptibility. Methods A new genome-wide association study (GWAS) was conducted from Jining, North China, on 1,506 individuals (512 SLE cases and 994 matched healthy controls). The association results were meta-analyzed with existing data on Chinese populations from Hong Kong, Guangzhou and Central China, as well as GWAS results from four cohorts of European ancestry. A total of 26 774 individuals (9,310 SLE cases and 17 464 controls) were included in this study. Results Meta-analysis on four Chinese cohorts identifies KLF2 as a novel locus associated with SLE (rs2362475; OR = 0.85, P = 2.00E-09). KLF2 is likely an Asian-specific locus as no evidence of association was detected in the four European cohorts (OR = 0.98, p = 0.58), with evidence of heterogeneity (p = 0.0019) between the two ancestral groups. Meta-analyses of results from both Chinese and Europeans identify STAB2 (rs10082873; OR = 0.89, P = 4.08E-08) and DOT1L (rs4807205; OR = 1.12, P = 8.17E-09) as trans-ancestral association loci, surpassing the genome-wide significance. Conclusions We identified three loci associated with SLE, with KLF2 a likely Chinese-specific locus, highlighting the importance of studying diverse populations in SLE genetics. We hypothesize that DOT1L and KLF2 are plausible SLE treatment targets, with inhibitors of DOT1L and inducers of KLF2 already available clinically.

Objective Systemic lupus erythematosus (SLE) is a prototypical autoimmune disease with differences in prevalence and severity among ancestral groups. This study was undertaken to identify novel genetic components, either shared by or distinct between Asian and European populations. Methods Both trans‐ancestral and ancestry‐specific meta‐analyses of genome‐wide association studies (GWAS) for SLE were performed, involving 30,604 participants of European, Chinese, or Thai origin. Using public epigenomic data and expression quantitative trait loci, fine‐mapping analyses were conducted to identify putative causal variants and genes for the newly identified loci. Performance of polygenic risk scores for the Thai cohort was evaluated by comparing different training data. Results A 1‐bp deletion upstream of IFNLR1 was found to be associated with SLE, with the risk allele correlated with increased expression of IFNLR1. This gene encodes interferon‐λ (IFNλ) receptor 1, providing evidence of a role of type III IFN signaling in SLE. An intronic variant in SLC29A3 was found to be associated with SLE in Asians only. The putative risk variant may modulate SLC29A3 expression in a monocyte‐specific manner. SLC29A3 encodes a lysosomal nucleoside transporter, and subsequent analyses suggested that reduced lysosomal function and phagocytosis might be the mechanism underlying this association. Ancestry‐shared loci in or near TAOK3, CHD9, CAMK1D, ATXN1, and TARBP1 and Asian‐specific loci close to PEX2, FCHSD2, and TMEM116 also reached the genome‐wide significant association with SLE. In addition, trans‐ancestral meta‐analysis was shown to be valuable in risk prediction for individuals without ancestry‐matched data. Conclusion In this study both shared and Asian‐specific loci for SLE were identified, and functional annotation provided evidence of the involvement of increased type III IFN signaling and reduced lysosomal function in SLE.

Background Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated. Methods To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes. Results Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy. Conclusions Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/. The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway.