Panicum virgatum, a model plant of cellulosic ethanol conversion, not only has high large biomass and strong adaptability to soil, but also grows well in marginal soil and has the advantage of improving saline-alkali soil. GRAS transcription factor gene family play important roles in individual environment adaption, and these vital functions has been proved in several plants, however, the research of GRAS in the development of switchgrass (Panicum virgatum) were limited. A comprehensive study was investigated to explore the relationship between GRAS gene family and resistance. According to the phylogenetic analysis, a total of 144 GRAS genes were identified and renamed which were classified into eight subfamilies. Chromosome distribution, tandem and segmental repeats analysis indicated that gene duplication events contributed a lot to the expansion of GRAS genes in the switchgrass genome. Sixty-six GRAS genes in switchgrass were identified as having orthologous genes with rice through gene duplication analysis. Most of these GRAS genes contained zero or one intron, and closely related genes in evolution shared similar motif composition. Interaction networks were analyzed including DELLA and ten interaction proteins that were primarily involved in gibberellin acid mediated signaling. Notably, online analysis indicated that the promoter regions of the identified PvGRAS genes contained many cis-elements including light responsive elements, suggesting that PvGRAS might involve in light signal cross-talking. This work provides key insights into resistance and bioavailability in switchgrass and would be helpful to further study the function of GRAS and GRAS-mediated signal transduction pathways.

Background. Ischemic stroke (IS) is a serious disease with a high rate of death and disability, and a growing number of people are becoming victims. Existing drugs not only have limited therapeutic effects but also have obvious side effects. Most importantly, drug resistance due to long-term or improper use of drugs is detrimental to patients. Therefore, it is urgent to find some alternative or supplementary medicines to alleviate the current embarrassment. Powerful Tianma Eucommia Capsule (PTEC) is mainly used to treat IS in China for thousands of years; however, the molecular mechanism is not clear. Methods. Pharmacology ingredients and target genes were filtered and downloaded from websites. A pharmacology ingredient-target gene network was constructed to predict the molecular interactions between ingredients and target genes. Enrichment analysis was performed to explore the possible signal pathways. LeDock was used to simulate the interaction form between proteins and main active ingredients and to deduce key amino acid positions. Results. Two hundred eighty-nine target genes and seventy-four pharmacological ingredients were obtained from public databases. Several key ingredients (quercetin, kaempferol, and stigmasterol) and primary core target genes (PTGS1, NCOA2, and PRSS1) were detected through ingredient-target gene network analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis demonstrated that ingredients affect networks mainly in nuclear receptor activity and G protein-coupled amine receptor activity; besides, fluid shear stress and atherosclerosis, human cytomegalovirus infection, and hepatitis B signaling pathways might be the principal therapy ways. A series of presumed key amino acid sites (189ASP, 190SER, 192GLN, 57HIS, and 99TYE) were calculated in PRSS1. Six of the target genes were differentially expressed between male and female patients. Conclusions. Seven new putative target genes (ACHE, ADRA1A, AR, CHRM3, F7, GABRA1, and PRSS1) were observed in this work. Based on the result of GO and KEGG analysis, this work will be helpful to further demonstrate the molecular mechanism of PTEC treatment of IS.

Background. Diffuse large B‐cell lymphoma (DLBCL) is one of the largest lymphoma subcategories. Usually, 50%–70% of DLBCL patients can be cured by the standard treatment. But, at least one third have bad prognosis. Based on this situation, the research on DLBCL therapy strategy is still indispensable. Methods. A prognostic signature was built according to the public data and bioinformatics methods, the stability and reliability was assessed and validated. GSEA was performed to explore the difference in different groups. Consensus clustering and immune infiltration analysis were conducted comprehensively. Results. In this work, a signature based on multiple metabolism‐associated genes (MTGs) was established, containing 16 MTGs, to predict the prognosis of DLBCL patients. The accuracy and effectiveness of this signature have been verified by three external validation sets. According to the risk formula, DLBCL patients were divided into high‐ and low‐risk groups, and the survival rate of the low‐risk group was significantly higher than that of the high‐risk group. Furthermore, gene set enrichment analysis (GSEA) demonstrated that beta‐alanine metabolism and regulation of actin cytoskeleton signal pathways were enriched in the low‐risk group. The actual survival and nomogram‐predicted survival matched well both in the training cohort and verification cohorts. Conclusion. In general, our prognostic signature can provide reliable and valuable information for medical workers in predicting the prognosis of DLBCL. A preprint was made available by the research square in the following link: “https://www.researchsquare.com/article/rs-1468741/v2.”

Lung cancer remains a leading cause of global morbidity and mortality, imposing a significant healthcare burden worldwide. To comprehensively investigate potential risk factors for lung cancer and provide a theoretical basis for its prevention and treatment, we utilized publicly available genome-wide association study data of European ancestry. We treated various traits as exposures and lung cancer as the outcome. Causal relationships were assessed using 5 Mendelian randomization (MR) methods: inverse-variance weighted, constrained maximum likelihood and model averaging, debiased inverse-variance weighted, contamination mixture, and Mendelian randomization-robust adjusted profile score. We subsequently performed meta-analysis and median-based MR to synthesize the roles of different risk factors in lung cancer development. Following outlier removal via radial MR and MR analyses, we identified 88, 113, 248, and 232 exposure datasets showing causal associations with lung adenocarcinoma, small cell lung cancer, lung cancer overall, and squamous cell lung cancer, respectively. Further integration via meta-analysis and median MR revealed that higher educational attainment may reduce lung cancer risk by delaying age at first sexual intercourse, increasing age at first childbirth, lowering body mass index and adiposity, and reducing smoking frequency. Our comprehensive analysis suggests that education may play a protective role against lung carcinogenesis, potentially mediated through behavioral changes such as reduced smoking and modifications in physical health indicators including body mass index and adiposity.

To investigate the prognostic value of a model based on ferroptosis-related long non-coding RNAs (lncRNAs) for patients with diffuse large B-cell lymphoma (DLBCL). Ferroptosis-related lncRNA expression matrices and corresponding clinical information for DLBCL patients were downloaded from the GEO public database (datasets GSE10846 and GSE11318). A prognostic risk model was constructed using the GSE10846 dataset through the following steps: identification of ferroptosis-related genes, Pearson correlation analysis with lncRNAs, Kaplan-Meier survival analysis, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. The accuracy of the model was evaluated using Receiver Operating Characteristic (ROC) curves. The model was subsequently validated using the GSE11318 dataset. Further systematic assessments of the model's biological significance and clinical translational potential were conducted via immune infiltration analysis and drug sensitivity prediction. A prognostic model comprising 9 lncRNAs was established. Time-dependent ROC curve analysis showed that the Area Under the Curve (AUC) values for 3-year and 5-year survival predictions were both greater than 0.75. Consistent results were obtained in the validation set, indicating good model robustness. Immune microenvironment analysis revealed that the high-risk group exhibited an immunosuppressive "cold tumor" phenotype, characterized by a significant reduction in the infiltration of effector immune cells such as cytotoxic cells and CD8⁺ T cells (all P < 0.001). Drug sensitivity analysis indicated that the high-risk group was more sensitive to PI3K/mTOR inhibitors (e.g., Dactolisib) but showed resistance to certain histone deacetylase inhibitors. Enrichment analysis suggested that these lncRNAs might influence DLBCL progression by regulating pathways such as protein degradation and RNA processing. The prognostic model constructed based on ferroptosis-related lncRNAs demonstrates considerable reliability. It not only effectively predicts patient survival but also reflects tumor immune status and drug response characteristics, showing potential as an auxiliary tool for prognosis assessment and personalized treatment in DLBCL.