Improving the performance of rice (Oryza sativa) under drought stress has the potential to significantly affect rice productivity. Here, we report that the ERF family transcription factor OsLG3 positively regulates drought tolerance in rice. In our previous work, we found that OsLG3 has a positive effect on rice grain length without affecting grain quality. In this study, we found that OsLG3 was more strongly expressed in upland rice than in lowland rice under drought stress conditions. By performing candidate gene association analysis, we found that natural variation in the promoter of OsLG3 is associated with tolerance to osmotic stress in germinating rice seeds. Overexpression of OsLG3 significantly improved the tolerance of rice plants to simulated drought, whereas suppression of OsLG3 resulted in greater susceptibility. Phylogenetic analysis indicated that the tolerant allele of OsLG3 may improve drought tolerance in cultivated japonica rice. Introgression lines and complementation transgenic lines containing the elite allele of OsLG3^IRAT109 showed increased drought tolerance, demonstrating that natural variation in OsLG3 contributes to drought tolerance in rice. Further investigation suggested that OsLG3 plays a positive role in drought stress tolerance in rice by inducing reactive oxygen species scavenging. Collectively, our findings reveal that natural variation in OsLG3 contributes to rice drought tolerance and that the elite allele of OsLG3 is a promising genetic resource for the development of drought-tolerant rice varieties.

Screening for gastric cancer (GC) is critical for improving patient survival, yet current clinical methods suffer from invasiveness and limited sensitivity. The aim of this study is to develop a machine learning model for the non-invasive diagnosis of GC using the whole-genome methylation profile of circulating free DNA (cfDNA) in plasma. Plasma cfDNA from 61 patients with GC and 29 healthy controls. First, whole‑genome bisulfite sequencing (WGBS) was performed on 29 paired GC and adjacent normal tissues to identify differentially methylated regions (DMRs). These tissue DMRs were then mapped to plasma cfDNA methylation data, yielding 32 stable DMRs that changed consistently in both tissues and plasma. Using LASSO and Random Forest (RF) algorithms, eight core diagnostic markers were selected. Four machine learning algorithms (Support Vector Machine, RF, K-Nearest Neighbor, Naïve Bayes) were used to build diagnostic models. WGBS revealed global hypomethylation and CpG island hypermethylation in GC, consistent with plasma cfDNA methylation patterns. The RF model built on the eight cfDNA methylation markers (annotated to genes including ATP8A2) performed best, achieving AUC values of 0.978 and 0.938 in the training and independent test sets, respectively, with an accuracy of 88.46%, sensitivity of 94.44%, and specificity of 75.00%. After adjusting for age, the model achieved an AUC of 0.924 on the test set, still demonstrating high discriminatory performance. Functional analysis implicated these markers in pathways such as phagocytosis and axon guidance, and showed significant correlation with immune cell infiltration. Notably, these core DMRs are not covered by the probes on the methylation arrays (450K and EPIC v2), highlighting the advantage of WGBS for novel biomarker discovery. In conclusion, tissue-plasma concordance analysis identified a panel of plasma cfDNA methylation biomarkers with strong diagnostic potential. The resulting RF model provides a promising non-invasive strategy for GC screening.