BcHTT4 Inhibits Branching of Non-Heading Chinese Cabbage at the Vegetative Stage

Branching is speculated to contribute to the plant architecture and crop yield. As a quantitative trait, branching is regulated by multiple genes in non-heading Chinese cabbage (NHCC). Several related candidate genes have been discovered in previous studies on the branching of NHCC, but their specific functions and regulatory mechanisms still need to be verified and explored. In this study, we found that the expression of BcHTT4, the ortholog to HEAT-INDUCED TAS1 TARGET4 (HTT4) in Arabidopsis, was significantly different between ‘Suzhouqing’ (common type) and ‘Maertou’ (multiple shoot branching type) in NHCC, which was consistent with the previous transcriptome sequencing results. The silencing of BcHTT4 expression in non-heading Chinese cabbage promotes axillary bud growth at the vegetative stage. When BcHTT4 is overexpressed in Arabidopsis, branching will decrease. In further study, we found that BcHTT4 interacts with immunophilin BcFKBP13 in vivo and in vitro through yeast two-hybrid analysis and bimolecular fluorescence complementation (BiFC) assays. Moreover, quantitative real-time PCR analysis showed that when the expression of BcHTT4 was silenced in ‘Suzhouqing’, the expression of BcFKBP13 also decreased significantly. Our findings reveal that BcHTT4 is involved in the branching mechanism and interacts with immunophilin BcFKBP13 in NHCC

Transcriptomics-based liquid biopsy panel for early non-invasive identification of peritoneal recurrence and micrometastasis in locally advanced gastric cancer

Abstract Background This study aimed to develop a novel six-gene expression biomarker panel to enhance the early detection and risk stratification of peritoneal recurrence and micrometastasis in locally advanced gastric cancer (LAGC). Methods We used genome-wide transcriptome profiling and rigorous bioinformatics to identify a six-gene expression biomarker panel. This panel was validated across multiple clinical cohorts using both tissue and liquid biopsy samples to predict peritoneal recurrence and micrometastasis in patients with LAGC. Results Through genome-wide expression profiling, we identified six mRNAs and developed a risk prediction model using 196 samples from a surgical specimen training cohort. This model, incorporating a 6-mRNA panel with clinical features, demonstrated high predictive accuracy for peritoneal recurrence in gastric cancer patients, with an AUC of 0.966 (95% CI: 0.944–0.988). Transitioning from invasive surgical or endoscopic biopsy to noninvasive liquid biopsy, the model retained its predictive efficacy (AUC = 0.963; 95% CI: 0.926–1.000). Additionally, the 6-mRNA panel effectively differentiated patients with or without peritoneal metastasis in 95 peripheral blood specimens (AUC = 0.970; 95% CI: 0.936–1.000) and identified peritoneal micrometastases with a high efficiency (AUC = 0.941; 95% CI: 0.874–1.000). Conclusions Our study provides a novel gene expression biomarker panel that significantly enhances early detection of peritoneal recurrence and micrometastasis in patients with LAGC. The RSA model's predictive capability offers a promising tool for tailored treatment strategies, underscoring the importance of integrating molecular biomarkers with clinical parameters in precision oncology