Biomedical subjects

Xing Hua

Publications and source records attributed to Xing Hua.

5 recordsLinked to original sources

PubMed2026

Identification of immune cell type-specific susceptibility genes in multiple cancers using transcriptome-wide association studies.

BACKGROUND: Transcriptome-wide association studies (TWAS) integrate gene expression and genome-wide association studies (GWAS) to identify disease susceptibility genes. Because gene expression varies substantially across cell types within tissues, cell type-specific prediction models may enhance the power of TWAS. METHODS: We conducted cell type-specific TWAS leveraging single-cell RNA sequencing data from the OneK1K cohort (14 immune cell types, 1.27 million cells) and GWAS summary statistics for 7 cancers (>290 000 cases in total). To improve prediction accuracy, we developed a modeling framework that incorporates shared gene expression effects across cell types. RESULTS: At a false discovery rate of 5%, we identified 106 (Bonferroni 5%: 13) previously unreported loci for breast cancer, 51 (4) loci for prostate cancer, 11 (4) loci for lung cancer, 39 (5) loci for melanoma, 9 (1) loci for ovarian cancer, and 2 (1) loci for diffuse large B-cell lymphoma, with most genes exhibiting cell type specificity. Gene set analyses confirmed joint associations of unreported genes with breast and prostate cancer risk in UK Biobank data. Additional lung tissue single-cell RNA sequencing data with 113 individuals validated 18 of 32 (56.3%) statistically significant genes for lung cancer. Across cancers, 139 statistically significant genes were shared by at least 2 cancer types and were primarily enriched in specific immune cell types. CONCLUSION: Cell type-specific TWAS improve the identification of novel cancer susceptibility loci and provide insights into the immune landscape of cancer etiology.

Humans↗

PubMed2026

Fecal immunochemical tests from population-based colorectal cancer screening programs support prospective microbiome cohorts.

BACKGROUND: Large, prospective cohorts are needed to research the gut microbiome's role in colorectal cancer (CRC) risk. We evaluated the gut microbiome leveraging residual fecal immunochemical tests (FIT) from a CRC screening program in Turin, Italy, and conducted one of the largest population-based case-control studies across the adenoma-carcinoma sequence to date. METHODS: We extracted DNA from residual FIT stool, used whole-genome shotgun sequencing, and included those with CRC (N = 44), advanced adenomas (N = 269), early adenomas (N = 134), and FIT-negative controls (N = 478). Alpha diversity, beta diversity, and species, gene, and pathway relative abundances were estimated. Multivariable logistic regression models were used to estimate associations of these metrics with colorectal neoplasms. RESULTS: Alpha diversity was mostly inversely associated with colorectal neoplasms, particularly early adenomas (OR: 0.45, 95% CI: 0.25-0.80; P = 0.01). Presence of oral pathogens, including Parvimonas micra, was associated with higher odds of CRC. Furthermore, Escherichia coli and Bacteroides fragilis were strongly associated with higher odds of all colorectal neoplasms. Several genes and pathways were associated with colorectal neoplasms. CONCLUSIONS: Our findings align with smaller studies of the gut microbiome and colorectal neoplasms, supporting that CRC screening programs provide opportunities to prospectively study the gut microbiome's association with cancer risk in large populations.

Humans↗

PubMed2026

Methylation profiling of normal tissue adjacent to breast tumors reveals two distinct groups with divergent tumor microenvironment features.

We previously identified diverse genetic evolutionary patterns in whole-genome sequencing of paired normal tissue adjacent to tumor (NAT) and tumor tissues from Hong Kong breast cancer (HKBC) patients. Here, we investigated whether DNA methylation (DNAm) contributes to NAT heterogeneity and shapes the tumor microenvironment (TME). Genome-wide DNAm profiling was performed on paired NAT and tumor tissues from 188 HKBC patients using the Infinium 850 K array. RNA-seq data were available for 76 NATs and 177 tumors. Cellular composition was inferred using MethylCIBERSORT, CIBERSORTx, and EpiDISH, and histopathologic features were assessed on 115 H&E-stained sections. Unsupervised clustering identified two distinct NAT subtypes with divergent TME characteristics. Cluster 1 (N = 139) showed higher epithelial and fibroblast content and enrichment of estrogen response pathways. Cluster 2 (N = 49) exhibited an immune-metabolic phenotype characterized by increased fat and immune cells, stromal disruption, inflammatory pathway activation, and greater macrophage infiltration. Cluster 2 patients also demonstrated significantly younger epigenetic age estimated using multiple epigenetic clocks. These DNAm-defined NAT subtypes and associated TME features were validated in 97 NAT samples from TCGA breast cancer patients. Overall, our findings identify DNAm-driven NAT heterogeneity with distinct TME landscapes, providing new insights into field cancerization and tumor evolution in breast cancer.

Journal Article↗

PubMed2025

A prognostic signature for lung adenocarcinoma in people who have never smoked.

Knowledge of tumor cell dynamics can inform prognosis and treatment yet is largely lacking for lung adenocarcinoma in people who have never smoked (NS-LUAD). With RNA-seq data from 684 NS-LUAD and validation in an independent dataset, we identified three subtypes with distinct phenotypic traits and cell compositions. Additional genomic and histological data further characterized the subtypes. 'Steady', marked by low proliferation, high alveolar cell fraction, moderate-to-well differentiation, and fewer driver genes' alterations, is linked to prolonged survival and low immune evasion. 'Proliferative' shows high proliferation markers, TP53 mutations, and gene fusions. 'Chaotic', with high epithelial-to-mesenchymal transition markers, has the worst prognosis even within stage I tumors. Lacking known molecular or histological characteristics, this aggressive subtype is solely identified by transcriptomic data. A 60-gene signature recapitulates the overall classification and strongly predicts survival even within subgroups based on tumor stage or known genomic features, emphasizing its potential for improving NS-LUAD prognostication in clinical settings.

Journal Article↗

PubMed2025

Animal farming and the oral microbiome in the Agricultural Health Study.

BACKGROUND: Raising farm animals imparts various exposures that may shape the human microbiome. The oral microbiome has been increasingly implicated in disease development. Animal farming has also been associated with certain chronic diseases such as cancer; however, underlying biological mechanisms are unclear. We investigated associations between raising farm animals and the oral microbiome in the Agricultural Health Study. METHODS: This analysis included 1,245 participants (865 farmers and 380 spouses) who provided oral wash specimens and information on types and numbers of specific animals raised on their farms within 2 years before sample collection. The oral microbiome was measured by sequencing the V4 region of the 16S ribosomal RNA gene. We evaluated associations of farm animal exposures with alpha and beta diversity metrics (within- and between-sample diversity, respectively), as well as presence and relative abundance of specific bacterial genera. All analyses adjusted for potential confounders (e.g., age, sex, smoking, alcohol consumption). RESULTS: Overall, 63 % of participants raised farm animals, most commonly cattle (46 %) and hogs (20 %). Those who raised a large number of hogs (≥2,000 vs. no hogs) had higher alpha diversity. Conversely, raising sheep/goats and raising larger numbers of poultry were associated with lower alpha diversity. Beta diversity was not significantly different between participants with and without any farm animals. Participants raising any farm animals had higher relative abundance of Porphyromonas and lower relative abundances of Prevotella and Ruminococcaceae UCG-014. Several genera were more likely to be absent with specific animal exposures (e.g., Capnocytophaga for cattle and sheep/goats; Corynebacterium, Dialister, Stomatobaculum, and Solobacterium for sheep/goats and poultry). CONCLUSIONS: This was the largest study of farm animal exposures and the human microbiome to date. Findings suggest that raising specific farm animals may influence the oral microbiome, supporting the need to further investigate the potential role of animal farming in disease etiology.

Microbiota↗