Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology.

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Systematic biases in reference-based plasma cell-free DNA fragmentomic profiling.

Plasma cell-free DNA (cfDNA) fragmentation patterns are emerging directions in cancer liquid biopsy with high translational significance. Conventionally, the cfDNA sequencing reads are aligned to a reference genome to extract their fragmentomic features. In this study, through cfDNA fragmentomics profiling using different reference genomes on the same datasets in parallel, we report systematic biases in such conventional reference-based approaches. The biases in cfDNA fragmentomic features vary among races in a sample-dependent manner and therefore might adversely affect the performances of cancer diagnosis assays across multiple clinical centers. In addition, to circumvent the analytical biases, we develop Freefly, a reference-free approach for cfDNA fragmentomics profiling. Freefly runs ∼60-fold faster than the conventional reference-based approach while generating highly consistent results. Moreover, cfDNA fragmentomic features reported by Freefly can be directly used for cancer diagnosis. Hence, Freefly possesses translational merit toward the rapid and unbiased measurement of cfDNA fragmentomics.

Cell-free DNA end characteristics enable accurate and sensitive cancer diagnosis.

The fragmentation patterns of cell-free DNA (cfDNA) in plasma can potentially be utilized as diagnostic biomarkers in liquid biopsy. However, our knowledge of this biological process and the information encoded in fragmentation patterns remains preliminary. Here, we investigated the cfDNA fragmentomic characteristics against nucleosome positioning patterns in hematopoietic cells. cfDNA molecules with ends located within nucleosomes were relatively shorter with altered end motif patterns, demonstrating the feasibility of enriching tumor-derived cfDNA in patients with cancer through the selection of molecules possessing such ends. We then developed three cfDNA fragmentomic metrics after end selection, which showed significant alterations in patients with cancer and enabled cancer diagnosis. By incorporating machine learning, we further built high-performance diagnostic models, which achieved an overall area under the curve of 0.95 and 85.1% sensitivity at 95% specificity. Hence, our investigations explored the end characteristics of cfDNA fragmentomics and their merits in building accurate and sensitive cancer diagnostic models.

Genetic trade-offs between complex diseases and longevity.

Longevity was influenced by many complex diseases and traits. However, the relationships between human longevity and genetic risks of complex diseases were not broadly studied. Here, we constructed polygenic risk scores (PRSs) for 225 complex diseases/traits and evaluated their relationships with human longevity in a cohort with 2178 centenarians and 2299 middle-aged individuals. Lower genetic risks of stroke and hypotension were observed in centenarians, while higher genetic risks of schizophrenia (SCZ) and type 2 diabetes (T2D) were detected in long-lived individuals. We further stratified PRSs into cell-type groups and significance-level groups. The results showed that the immune component of SCZ genetic risk was positively linked to longevity, and the renal component of T2D genetic risk was the most deleterious. Additionally, SNPs with very small p-values (p ≤ 1x10-5 ) for SCZ and T2D were negatively correlated with longevity. While for the less significant SNPs (1x10-5  < p ≤ 0.05), their effects on disease and longevity were positively correlated. Overall, we identified genetically informed positive and negative factors for human longevity, gained more insights on the accumulation of disease risk alleles during evolution, and provided evidence for the theory of genetic trade-offs between complex diseases and longevity.

Glycosaminoglycan biosynthesis pathway in host genome is associated with Helicobacter pylori infection.

Helicobacter pylori is a causative pathogen of many gastric and extra-gastric diseases. It has infected about half of the global population. There were no genome-wide association studies (GWAS) for H. pylori infection conducted in Chinese population, who carried different and relatively homogenous strain of H. pylori. In this work, we performed SNP (single nucleotide polymorphism)-based, gene-based and pathway-based genome-wide association analyses to investigate the genetic basis of host susceptibility to H. pylori infection in 480 Chinese individuals. We also profiled the composition and function of the gut microbiota between H. pylori infection cases and controls. We found several genes and pathways associated with H. pylori infection (P < 0.05), replicated one previously reported SNP rs10004195 in TLR1 gene region (P = 0.02). We also found that glycosaminoglycan biosynthesis related pathway was associated with both onset and progression of H. pylori infection. In the gut microbiome association study, we identified 2 species, 3 genera and several pathways had differential abundance between H. pylori infected cases and controls. This paper is the first GWAS for H. pylori infection in Chinese population, and we combined the genetic and microbial data to comprehensively discuss the basis of host susceptibility to H. pylori infection.

Genome-wide association study and protein network analysis for understanding candidate genes involved in root development at the rapeseed seedling stage.

Root system is essential for plants to absorb water and nutrients. The root related traits are complex quantitative traits and regulated by genetic control. Here, we used two association mapping panels to perform a genome-wide association study (GWAS) on seven root related traits in Brassica napus at the seedling stage and obtained 27 SNP loci significantly associated with the phenotypes. We further conducted a genome-wide LD block analysis of the candidate peak regions and obtained 295 candidate genes with high association peaks across seven phenotypes in LD region. In addition, a protein interaction network using the candidate genes identified here was constructed, and 113 genes were associated. Seven genes, BnaA03g47330D, BnaC09g16810D, BnaA06g22840D, BnaA03g28390D, BnaA08g19920D, BnaA03g28930D and BnaA03g11440D were in a large cluster, and may play important roles in interacting with other related genes. Our data may provide resources for molecular breeding and functional analysis of root growth and development in rapeseed.

QTL Mapping of Seed Glucosinolate Content Responsible for Environment in Brassica napus.

Glucosinolates (GSLs) are a major class of secondary metabolites. The content of seed GSL is largely regulated by environments in rapeseed (Brassica napus). However, the genetic control of seed GSL content responsible for environment in B. napus has been poorly understood. In the current study, a doubled haploid (DH) population from a cross between winter and semi-winter lines of rapeseed was grown in two distinct eco-environments, Germany and China, to evaluate the eco-environment effect and dissect the quantitative trait loci (QTL) responsible for environment for seed GSL in rapeseed. The deviation value of GSL content between eco-environments (GSLE) was calculated for each line in the DH population and the QTLs for GSLE were detected. GSLE ranged from -46.90 to 36.13 µmol g-1 meal in the DH population, suggesting the prominent eco-environmental effects for seed GSL in rapeseed. Four QTLs for GSLE were identified on chromosomes A04, A06, and A09 explaining 4.70∼9.93% of the phenotypic variation. Comparison of QTLs of seed GSL content between different eco-environments found three QTLs for GSL on A02 from 37.6 to 45.4 cM, A04 from 0 to 17.2 cM, and A09 from 67.0 to 98.6 cM exhibited significant difference of QTL effect between the German and Chinese eco-environments (P < 0.01), indicating the environment sensibility of these loci on seed GSL content. Moreover, flowering time (FT), an important environment adaptation trait in plant, was also investigated in this study. Comparative QTL analysis among GSLE, GSL, and FT revealed that three regions on chromosomes A02, A04, and A09 not only exhibited significant differences in QTL effect between Germany and China, but also co-located with the QTL intervals of GSLE and FT. Our results revealed that most of the GSL loci can influence GSL accumulation under different eco-environments, whereas the three QTL intervals on A02, A04, and A09 might be sensitive to the eco-environments for seed GSL content.