Ji Zhang

Next-generation sequencing (NGS) enables genotyping of large-scale single nucleotide polymorphisms (SNPs), but interpretation of SNP-NGS mixtures remains challenging because most SNPs are biallelic, locus amplification efficiency affects allele read counts, sequencing noise occurs, and true alleles may drop out. In this proof-of-principle study, we developed a read-count-based fully continuous probabilistic genotyping (PG) framework for PCR-based hybridization-capture SNP-NGS mixtures. The framework incorporates allele read counts, sequencing noise, allele dropout, locus amplification efficiency (LAE), and a co-ancestry coefficient for population substructure. Noise and LAE parameters were calibrated using 35 single-source samples. We then examined single-source profiles and known-truth in silico mixtures, and assessed laboratory mixtures (15 two-person and 9 three-person). Performance was assessed using true-contributor likelihood ratios (LRs), non-contributor LRs, and genotype deconvolution. No-reference deconvolution of the laboratory mixtures was also compared with EuroForMix MPS. LAE correction improved agreement between allele read counts and relative DNA template from R² = 0.502 ± 0.103 to 0.800 ± 0.102. Most true-contributor tests yielded LRs greater than 1. LRs below 1 were observed mainly for low-template contributors in highly unbalanced mixtures. Non-contributor testing using 203 individuals from the 1000 Genomes CHS and CEU populations generated 4,872 computational H2-true tests, and no likelihood ratio greater than 1 was observed. These tests are not a casework false-positive rate. Genotype deconvolution was most reliable for highest-template contributors and for balanced to moderately unbalanced mixtures. These findings support read-count-based modelling as a calibration-dependent approach to PCR-based hybridization-capture SNP-NGS mixture interpretation and identify conditions under which the model is currently informative or limited.

Purpose Colorectal cancer (CRC) exhibits substantial sex-related differences in incidence, prognosis, and clinicopathological features. However, existing evidence on these disparities remains limited and inconclusive. This study aimed to investigate sex-specific patterns in tumor differentiation, metastasis, and mismatch repair (MMR) status, and to determine whether these associations persist after adjustment for clinicopathological covariates. Methods A retrospective cohort study was conducted in 2,912 patients who underwent surgery for CRC at West China Hospital between November 2008 and June 2023. Sex differences were assessed across subgroups stratified by tumor differentiation grade, metastatic status, and age at onset. Three multivariable Firth penalized logistic regression models were constructed to assess whether sex remained an independent predictor of metastasis, deficient MMR (dMMR), and poor differentiation (G3) after adjustment for age, body mass index (BMI), stage, tumor location, and relevant molecular markers. Results Significant sex-related differences were observed in tumor differentiation, metastasis, and MMR status. Female patients had higher rates of poorly differentiated (G3) tumors, dMMR, and metastasis. However, these differences were markedly heterogeneous and were most evident in moderately differentiated (G2) tumors, late-onset CRC, and non-metastatic disease, whereas no significant sex differences were identified in early-onset disease. In multivariable analysis, female sex remained an independent predictor of metastasis (adjusted OR 1.36, 95% CI 1.04–1.79, P = 0.024), but not of dMMR (adjusted OR 1.28, P = 0.108) or G3 differentiation (adjusted OR 1.15, P = 0.146), suggesting that the latter associations were largely explained by tumor location. Conclusion Sex-related differences in CRC are context-dependent and vary according to tumor differentiation, metastatic status, and age at diagnosis. The higher metastatic risk observed in female patients appears to be an independent association, whereas differences in dMMR and tumor grade mainly reflect the greater prevalence of right-sided tumors in women. These findings support the incorporation of sex as a biological variable in metastatic risk stratification, while highlighting the importance of accounting for anatomical confounding when interpreting molecular and histological sex differences.

DNA mixture interpretation remains one of the most technically demanding challenges in forensic genetics. While probabilistic genotyping (PG) systems have substantially advanced likelihood ratio (LR) evaluation, comparatively less attention has been devoted to the systematic reconstruction of contributor genotypes, particularly in no-suspect and database-search contexts. This review synthesizes recent developments in DNA mixture deconvolution through a four-strategy framework: (i) physical and biological separation, (ii) high-information genetic markers, (iii) continuous probabilistic algorithms, and (iv) integration with database searching infrastructures. Upstream approaches, including single-cell isolation and sequencing, reduce mixture complexity at the molecular level. Marker innovations such as microhaplotypes, MiniHaps and DIP-STRs increase per-locus information content and enhance resistance to degradation. Downstream probabilistic models—extended from STRs to SNPs and microhaplotypes—leverage quantitative signal data to infer contributor genotypes, with recent advances in Hamiltonian Monte Carlo, variational inference, and deep learning improving inferential stability and reconstruction accuracy. Importantly, genotype deconvolution and LR evaluation represent mathematically distinct objectives, requiring different validation metrics and potentially separate architectural optimization. The convergence of molecular innovation, algorithmic refinement, and LR-based database searching is progressively transforming mixture interpretation from a purely evidential assessment into an integrated investigative framework. Future progress will depend on standardized marker panels, deconvolution-specific performance metrics, and scalable LR-enabled database infrastructures.

Purpose: The poor prognosis of KRAS-mutant colorectal cancer is attributed to its immunosuppressive tumor microenvironment and the lack of effective targeted therapies. Transforming growth factor-β-activated kinase 1 (TAK1), serving as a critical upstream regulator of both the NF-κB and MAPK signaling pathways, promotes tumor progression through its aberrant activation. In this study, we aimed to investigate the anti-tumor and immunomodulatory effects of the TAK1 inhibitor NG25 in KRAS-mutant colorectal cancer, focusing on its impact on T cell differentiation, PD-L1 expression, and tumor immune microenvironment remodeling. Methods: The anti-tumor and immune-enhancing effects of NG25 were evaluated through an in vitro tumor cell-lymphocyte co-culture system, and the orthotopic colorectal cancer models in both immunodeficient Balb/c nude mice and immunocompetent Balb/c mice. Results: NG25 significantly suppressed the tumor progression in immunocompetent Balb/c mice, while concurrently increasing the spleen and thymus indices and promoting the proliferation of T and B lymphocytes. In tumor microenvironment, NG25 treatment could promote CD8⁺ T cell infiltration and increase the proportion of CD3⁺CD8⁺ T cell subsets. Mechanistic studies revealed that NG25 downregulates PD-L1 expression on both KRAS-mutant tumor cells and T cells through inhibiting TAK1/NF-κB axis. However, this regulatory effect was absent in KRAS wild-type tumor cells. Conclusion: NG25 blocks the NF-κB signaling pathway by targeting TAK1, remodels the immunosuppressive microenvironment of KRAS-mutated colorectal cancer, reduces PD-1 expression and enhances the anti-tumor effect of CD8⁺ T cells. This study provides a theoretical basis for TAK1-targeted therapy and offers a new strategy for immunotherapy of KRAS-mutated colorectal cancer.

The likelihood ratio (LR) is a recommended metric for assessing the strength of genetic information in relationship testing, one of the most important tasks in forensic science. LR calculation incorporate population frequencies, which is affected by population substructure. This study utilized population frequency data from 18 short tandem repeat (STR) loci across 13 Chinese populations, encompassing both majority and minority ethnic groups. Six kinship types were constructed for each population. To understand the impact of population substructure on kinship testing, LRs were calculated using various frequency data: population-specific allele frequencies, national allele frequencies, and national allele frequencies adjusted with overall national F_ST or population-specific F_ST. LRs were also compared using the cutoff and comparison methods. The study found that LRs calculated using national allele frequencies tend to be the largest, which could overestimate the degree of relatedness compared to population-specific allele frequencies. Fst correction decreased the LR values, resulting in more conservative outcomes and suggested more distant relationships. While the F_ST correction had a minimal effect on the majority and some minority populations across different kinships, it was insufficiently conservative for more isolated minority populations when the overall national F_ST was applied. In conclusion, for isolated subpopulations with F_ST values above the national average, utilizing population-specific allele frequencies and applying higher F_ST values (e.g. 0.03 or 0.05) leads to more accurate and conservative inferences of relatedness. In contrast, for other groups, national frequencies without F_ST correction appear sufficient for relationship testing.