Leiming You

Rice ( Oryza sativa ) is a critically important food crop and a model cereal, with its momilactones among the first discovered phytoalexins. Notably, while momilactone biosynthesis is related to that of the gibberellin (GA) phytohormones, it was acquired via lateral gene transfer of an associated biosynthetic gene cluster ( mBGC ), but has been suggested to depend on paralog(s) of the ent ‐kaurene oxidase ( KO ) required for GA production. Rice contains a five‐gene tandem array of the KO family, with three acting in GA biosynthesis ( OsKO1–3 ), while the divergent KO‐like OsKOL4 has been shown to catalyze reactions consistent with a role in the production of phytoalexins, such as the phytocassanes and momilactones. Here, building on biochemical characterization of distinct activity for OsKOL5 , genetic evidence is provided indicating OsKOL4 is much more important for momilactone biosynthesis, although both play a role in the production of the phytocassanes. Indeed, knock‐out lines ( kol4 , kol5 , and kol4/5 ) are more susceptible, while overexpression increases resistance, to both the fungal blast pathogen Magnaporthe oryzae and bacterial blight pathogen Xanthomonas oryzae . Intriguingly, consistent with previously reported phytotoxicity of momilactone intermediates, a lesion mimic phenotype is observed with kol4 and kol4/5 but not kol5 lines. Concordantly, phylogenomic analysis suggests acquisition of mBGC was preceded by tandem duplication of KO to generate KOL4 , with subsequent duplication generating KOL5 . Thus, despite the deleterious effects of KOL4 loss, it can be speculated that linkage to the essential KOs may have alleviated such negative selection leading to mBGC recruitment, potentially relevant to plant BGC evolution more generally.

Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. The long non-coding RNA (lncRNA) has been found to have great potential as a prognostic biomarker or therapeutic target for cancer patients. However, the prognostic value and tumor immune infiltration of lncRNAs in HCC has yet to be fully elucidated. To identify prognostic biomarkers of lncRNA in HCC by integrated bioinformatics analysis and explore their functions and relationship with tumor immune infiltration. The prognostic risk assessment model for HCC was constructed by comprehensively using univariate/multivariate Cox regression analysis, Kaplan–Meier survival analysis, and the least absolute shrinkage and selection operator regression analysis. Subsequently, the accuracy, independence, and sensitivity of our model were evaluated, and a nomogram for individual prediction in the clinic was constructed. Tumor immune microenvironment (TIME), immune checkpoints, and human leukocyte antigen alleles were compared in high- and low-risk patients. Finally, the functions of our lncRNA signature were examined using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis. A 6-lncRNA panel of HCC consisting of RHPN1-AS1, LINC01224, CTD-2510F5.4, RP1-228H13.5, LINC01011, and RP11-324I22.4 was eventually identified, and show good performance in predicting the survivals of patients with HCC and distinguishing the immunomodulation of TIME of high- and low-risk patients. Functional analysis also suggested that this 6-lncRNA panel may play an essential role in promoting tumor progression and immune regulation of TIME. In this study, 6 potential lncRNAs were identified as the prognostic biomarkers in HCC, and the regulatory mechanisms involved in HCC were initially explored.

Stomach adenocarcinoma (STAD) is a type of cancer which often at itsadvanced stage apon diagnosis and mortality in clinical practice. Several factors influencethe prognosis of STAD, including the expression and regulation of immune cells in the tumor microenvironment. We here investigated the biomarkers related to the diagnosis and prognosis of gastric cancer, hoping to provide insights for the diagnosis and treatment of gastric cancer in the future. STAD and normal patient RNA sequencing data sets were accessed from the cancer genome atlas (TCGA database). Differential genes were determined and obtained by using the R package DESeq2. The stromal, immune, and ESTIMATE scores are calculated by the ESTIMATE algorithm, followed by the modular genes screening using the R package WGCNA. Subsequently, the intersection between the modular gene and the differential gene was taken and the STRING database was used for PPI network module analysis. The R packages clusterProfiler, enrichplot, and ggplot2 were used for GO and KEGG enrichment analysis. Cox regression analysis was used to screen survival-related genes, and finally, the R package Venn Diagram was used to take the intersection and obtain 7 hub genes. The time-dependent ROC curve and Kaplan–Meier survival curve were used to find the SERPINE1 gene, which plays a critical role in prognosis. Finally, the expression pattern, clinical characteristics, and regulatory mechanism of SERPINE1 were analyzed in STAD. We revealed that the expression of SERPINE1 was significantly increased in the samples from STAD compared with normal samples. Cox regression, time-dependent ROC, and Kaplan–Meier survival analyses demonstrated that SERPINE1 was significantly related to the adverse prognosis of STAD patients. The expression of SERPINE1 increased with the progression of T, N, and M classification of the tumor. In addition, the results of immune infiltration analysis indicated that the immune cells’ expression were higher in high SERPINE1 expression group than that in low SERPINE1 expression group, including CD4⁺ T cells, B cells, CD8⁺ T cells, macrophages, neutrophils and other immune cells. SERPINE1 was closely related to immune cells in the STAD immune microenvironment and had a synergistic effect with the immune checkpoints PD1 and PD-L1. In conclusion, we proved that SERPINE1 is a promising prognostic and diagnostic biomarker for STAD and a potential target for immunotherapy.