Development and Validation of a Machine Learning Model for Bone Metastasis in Prostate Cancer: Based on Inflammatory and Nutritional Indicators.

OBJECTIVE: To establish a predictive model for prostate cancer bone metastasis utilizing multiple machine learning algorithms. METHODS: Retrospective analysis of the clinical data of prostate cancer initially diagnosed in the Department of Urology of Gansu Provincial People's Hospital from June 2017 to June 2022. Logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) are used to jointly screen the model features. The filtered features are incorporated into algorithms including LR, random forest (RF), extreme gradient boosting (XGBoost), naive Bayes (NB), k-nearest neighbor (KNN), and decision tree (DT), to develop prostate cancer bone metastasis models. RESULTS: A total of 404 patients were finally screened. Gleason score, T stage, N stage, PSA, and ALP were used as features for modeling. The average AUC of the 5-fold cross-validation for each machine learning model in the training set is as follows: LR (AUC=0.9054), RF (AUC=0.9032), NB (AUC=0.8961), KNN (AUC=0.8704), DT (AUC=0.8526), XGBoost (AUC=0.8066). The AUC of each machine learning model in the test set is KNN (AUC=0.9390, 95%CI: 0.8760-1), RF (AUC=0.9290, 95%CI: 0.8718-0.9861), NB (AUC=0.9268, 95%CI: 0.8615-0.9920), LR (AUC=0.9212, 95%CI: 0.8506-0.9917), XGBoost (AUC=0.8292, 95%CI: 0.7442-0.9141), DT (AUC=0.8057, 95%CI: 0.7100-0.9014). A comprehensive evaluation showed that LR performed well in interpretability and clinical applications. CONCLUSION: A bone metastasis model of prostate cancer was established, and it was observed that indicators such as inflammation and nutrition had a weak correlation with bone metastasis.

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Prostate cancer is currently one of the most common malignancies that endanger the lives and health of elderly men. In recent years, immunotherapy, which exploits the activation of anti-cancer host immune cells to accomplish tumor-killing effects, has emerged as a new study avenue in the treatment of prostate cancer. As an important component of immunotherapy, cancer vaccines have a unique position in the precision treatment of malignant tumors. Monocyte cell vaccines, dendritic cell vaccines, viral vaccines, peptide vaccines, and DNA/mRNA vaccines are the most often used prostate cancer vaccines. Among them, Sipuleucel-T, as a monocyte cell-based cancer vaccine, is the only FDA-approved therapeutic vaccine for prostate cancer, and has a unique position and role in advancing the development of immunotherapy for prostate cancer. However, due to its own limitations, Sipuleucel-T has not been widely adopted. Meanwhile, owing to the complexity of immunotherapy and the specificity of prostate cancer, the remaining prostate cancer vaccines have not shown good clinical benefit in large randomized phase II and phase III trials, and further in-depth studies are still needed.

Soybean Mosaic Virus 6K1 Interactors Screening and GmPR4 and GmBI1 Function Characterization.

Host proteins are essential during virus infection, and viral factors must target numerous host factors to complete their infectious cycle. The mature 6K1 protein of potyviruses is required for viral replication in plants. However, the interaction between 6K1 and host factors is poorly understood. The present study aims to identify the host interacting proteins of 6K1. Here, the 6K1 of Soybean mosaic virus (SMV) was used as the bait to screen a soybean cDNA library to gain insights about the interaction between 6K1 and host proteins. One hundred and twenty-seven 6K1 interactors were preliminarily identified, and they were classified into six groups, including defense-related, transport-related, metabolism-related, DNA binding, unknown, and membrane-related proteins. Then, thirty-nine proteins were cloned and merged into a prey vector to verify the interaction with 6K1, and thirty-three of these proteins were confirmed to interact with 6K1 by yeast two-hybrid (Y2H) assay. Of the thirty-three proteins, soybean pathogenesis-related protein 4 (GmPR4) and Bax inhibitor 1 (GmBI1) were chosen for further study. Their interactions with 6K1 were also confirmed by bimolecular fluorescence complementation (BiFC) assay. Subcellular localization showed that GmPR4 was localized to the cytoplasm and endoplasmic reticulum (ER), and GmBI1 was located in the ER. Moreover, both GmPR4 and GmBI1 were induced by SMV infection, ethylene and ER stress. The transient overexpression of GmPR4 and GmBI1 reduced SMV accumulation in tobacco, suggesting their involvement in the resistance to SMV. These results would contribute to exploring the mode of action of 6K1 in viral replication and improve our knowledge of the role of PR4 and BI1 in SMV response.

RSC3 K of soybean cv. Kefeng No.1 confers resistance to soybean mosaic virus by interacting with the viral protein P3.

Soybean mosaic virus (SMV) is one of the most devastating viral pathogens of soybean (Glycine max (L.) Merr). In total, 22 Chinese SMV strains (SC1-SC22) have been classified based on the responses of 10 soybean cultivars to these pathogens. However, although several SMV-resistance loci in soybean have been identified, no gene conferring SMV resistance in the resistant soybean cultivar (cv.) Kefeng No.1 has been cloned and verified. Here, using F2 -derived F3 (F2:3 ) and recombinant inbred line (RIL) populations from a cross between Kefeng No.1 and susceptible soybean cv. Nannong 1138-2, we localized the gene in Kefeng No.1 that mediated resistance to SMV-SC3 strain to a 90-kb interval on chromosome 2. To study the functions of candidate genes in this interval, we performed Bean pod mottle virus (BPMV)-induced gene silencing (VIGS). We identified a recombinant gene (which we named RSC3 K) harboring an internal deletion of a genomic DNA fragment partially flanking the LOC100526921 and LOC100812666 reference genes as the SMV-SC3 resistance gene. By shuffling genes between infectious SMV DNA clones based on the avirulent isolate SC3 and virulent isolate 1129, we determined that the viral protein P3 is the avirulence determinant mediating SMV-SC3 resistance on Kefeng No.1. P3 interacts with RNase proteins encoded by RSC3 K, LOC100526921, and LOC100812666. The recombinant RSC3 K conveys much higher anti-SMV activity than LOC100526921 and LOC100812666, although those two genes also encode proteins that inhibit SMV accumulation, as revealed by gene silencing in a susceptible cultivar and by overexpression in Nicotiana benthamiana. These findings demonstrate that RSC3 K mediates the resistance of Kefeng No.1 to SMV-SC3 and that SMV resistance of soybean is determined by the antiviral activity of RNase proteins.

The E3 Ligase GmPUB21 Negatively Regulates Drought and Salinity Stress Response in Soybean.

E3-ubiquitin ligases are known to confer abiotic stress responses in plants. In the present study, GmPUB21, a novel U-box E3-ubiquitin ligase-encoding gene, was isolated from soybean and functionally characterized. The expression of GmPUB21, which possesses E3-ubiquitin ligase activity, was found to be significantly up-regulated by drought, salinity, and ABA treatments. The fusion protein GmPUB21-GFP was localized in the cytoplasm, nucleus, and plasma membrane. Transgenic lines of the Nicotiana benthamiana over-expressing GmPUB21 showed more sensitive to osmotic, salinity stress and ABA in seed germination and inhibited mannitol/NaCl-mediated stomatal closure. Moreover, higher reactive oxygen species accumulation was observed in GmPUB21 overexpressing plants after drought and salinity treatment than in wild-type (WT) plants. Contrarily, silencing of GmPUB21 in soybean plants significantly enhanced the tolerance to drought and salinity stresses. Collectively, our results revealed that GmPUB21 negatively regulates the drought and salinity tolerance by increasing the stomatal density and aperture via the ABA signaling pathway. These findings improved our understanding of the role of GmPUB21 under drought and salinity stresses in soybean.

Discovery and characterization of differentially expressed soybean miRNAs and their targets during soybean mosaic virus infection unveils novel insight into Soybean-SMV interaction.

BACKGROUND: Soybean mosaic virus (SMV) is one of the most devastating pathogens of soybean. MicroRNAs (miRNAs) are a class of non-coding RNAs (21-24 nucleotides) which are endogenously produced by the plant host as part of a general gene expression regulatory mechanisms, but also play roles in regulating plant defense against pathogens. However, miRNA-mediated plant response to SMV in soybean is not as well documented. RESULT: In this study, we analyzed 18 miRNA libraries, including three biological replicates from two soybean lines (Resistant and susceptible lines to SMV strain SC3 selected from the near-isogenic lines of Qihuang No. 1 × Nannong1138-2) after virus infection at three different time intervals (0 dpi, 7 dpi and 14 dpi). A total of 1,092 miRNAs, including 608 known miRNAs and 484 novel miRNAs were detected. Differential expression analyses identified the miRNAs profile changes during soybean-SMV interaction. Then, miRNAs potential target genes were predicted via data mining, and functional annotation was done by Gene Ontology (GO) analysis. The expression patterns of several miRNAs were validated by quantitative real-time PCR. We also validated the miRNA-target gene interaction by agrobacterium-mediated transient expression in Nicotiana benthamiana. CONCLUSION: We have identified a large number of miRNAs and their target genes and also functional annotations. We found that multiple miRNAs were differentially expressed in the two lines and targeted a series of NBS-LRR resistance genes. It is worth mentioning that many of these genes exist in the previous fine-mapping interval of the resistance gene locus. Our study provides additional information on soybean miRNAs and an insight into the role of miRNAs during SMV-infection in soybean.

A cell wall-localized NLR confers resistance to Soybean mosaic virus by recognizing viral-encoded cylindrical inclusion protein.

Soybean mosaic virus (SMV) causes severe yield losses and seed quality reduction in soybean (Glycine max) production worldwide. Rsc4 from cultivar Dabaima is a dominant genetic locus for SMV resistance, and its mapping interval contains three nucleotide-binding domain leucine-rich repeat-containing (NLR) candidates (Rsc4-1, Rsc4-2, and Rsc4-3). The NLR-type resistant proteins were considered as important intracellular pathogen sensors in the previous studies. In this study, based on transient expression assay in Nicotiana benthamiana leaves, we found that the longest transcript of Rsc4-3 is sufficient to confer resistance to SMV, and CRISPR/Cas9-mediated editing of Rsc4-3 in resistant cultivar Dabaima compromised the resistance. Interestingly, Rsc4-3 encodes a cell-wall-localized NLR-type resistant protein. We found that the internal polypeptide region responsible for apoplastic targeting of Rsc4-3 and the putative palmitoylation sites on the N terminus are essential for the resistance. Furthermore, we showed that viral-encoded cylindrical inclusion (CI) protein partially localizes to the cell wall and can interact with Rsc4-3. Virus-driven or transient expression of CI protein of avirulent SMV strains is enough to induce resistance response in the presence of Rsc4-3, suggesting that CI is the avirulent gene for Rsc4-3-mediated resistance. Taken together, our work identified a unique NLR that recognizes plant virus in the apoplast, and provided a simple and effective method for identifying resistant genes against SMV infection.

Molecular-networking-guided discovery of species-specific markers for discriminating five medicinal Paris herbs.

BACKGROUND: Paridis Rhizoma (PR) is a famous traditional herbal medicine. Apart from two officially recorded species, viz. Paris polyphylla Smith var. yunnanensis (Franch.) Hand. - Mazz. (PPY) and P. polyphylla Smith var. chinensis (Franch.) Hara (PPC), there are still many other species used as folk medicine. It is necessary to understand the metabolic differences among Paris species. PURPOSE: To establish a strategy that can discover species-specific steroidal saponin markers to distinguish closely-related Paris herbs for quality and safety control. METHODS: A new strategy of molecular-networking-guided discovery of species-specific markers was proposed. Firstly, the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was applied to obtain the MS and MS/MS data of all samples. Then, molecular networking (MN) was created using MS/MS data to prescreen the steroidal saponins for subsequent analysis. Next, the principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) models were established to discover potential markers. Finally, the verification, identification and distribution of chemical markers were performed. RESULTS: A total of 126 steroidal saponins were screened out from five species using MN. Five species were classified successfully by OPLS-DA model, and 18 species-specific markers were discovered combining the variable importance in the projection (VIP) value, P value (one-way ANOVA) and their relative abundance. These markers could predict the species of Paris herbs correctly. CONCLUSION: These results revealed that this new strategy could be an efficient way for chemical discrimination of medicinal herbs with close genetic relationship.