DNASE1L3 as a Novel Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma Based on Data Mining.

Previous researches have highlighted that low-expressing deoxyribonuclease1-like 3 (DNASE1L3) may play a role as a potential prognostic biomarker in several cancers. However, the diagnosis and prognosis roles of DNASE1L3 gene in lung adenocarcinoma (LUAD) remain largely unknown. This research aimed to explore the diagnosis value, prognostic value, and potential oncogenic roles of DNASE1L3 in LUAD. We performed bioinformatics analysis on LUAD datasets downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), and jointly analyzed with various online databases. We found that both the mRNA and protein levels of DNASE1L3 in patients with LUAD were noticeably lower than that in normal tissues. Low DNASE1L3 expression was significantly associated with higher pathological stages, T stages, and poor prognosis in LUAD cohorts. Multivariate analysis revealed that DNASE1L3 was an independent factor affecting overall survival (HR = 0.680, p = 0.027). Moreover, decreased DNASE1L3 showed strong diagnostic efficiency for LUAD. Results indicated that the mRNA level of DNASE1L3 was positively correlated with the infiltration of various immune cells, immune checkpoints in LUAD, especially with some m6A methylation regulators. In addition, enrichment function analysis revealed that the co-expressed genes may participate in the process of intercellular signal transduction and transmission. GSEA indicated that DNASE1L3 was positively related to G protein-coupled receptor ligand biding (NES = 1.738; P adjust = 0.044; FDR = 0.033) and G alpha (i) signaling events (NES = 1.635; P adjust = 0.044; FDR = 0.033). Our results demonstrated that decreased DNASE1L3 may serve as a novel diagnostic and prognostic biomarker associating with immune infiltrates in lung adenocarcinoma.

Multi-Omics of Pine Wood Nematode Pathogenicity Associated With Culturable Associated Microbiota Through an Artificial Assembly Approach.

Pinewood nematode (PWN), the causal agent of pine wilt disease (PWD), causes massive global losses of Pinus species each year. Bacteria and fungi existing in symbiosis with PWN are closely linked with the pathogenesis of PWD, but the relationship between PWN pathogenicity and the associated microbiota is still ambiguous. This study explored the relationship between microbes and the pathogenicity of PWN by establishing a PWN-associated microbe library, and used this library to generate five artificial PWN-microbe symbiont (APMS) assemblies with gnotobiotic PWNs. The fungal and bacterial communities of different APMSs (the microbiome) were explored by next-generation sequencing. Furthermore, different APMSs were used to inoculate the same Masson pine (Pinus massoniana) cultivar, and multi-omics (metabolome, phenomics, and transcriptome) data were obtained to represent the pathogenicity of different APMSs at 14 days post-inoculation (dpi). Significant positive correlations were observed between microbiome and transcriptome or metabolome data, but microbiome data were negatively correlated with the reactive oxygen species (ROS) level in the host. Five response genes, four fungal genera, four bacterial genera, and nineteen induced metabolites were positively correlated with the ROS level, while seven induced metabolites were negatively correlated. To further explore the function of PWN-associated microbes, single genera of functional microbes (Mb1-Mb8) were reloaded onto gnotobiotic PWNs and used to inoculate pine tree seedlings. Three of the genera (Cladophialophora, Ochroconis, and Flavobacterium) decreased the ROS level of the host pine trees, while only one genus (Penicillium) significantly increased the ROS level of the host pine tree seedlings. These results demonstrate a clear relationship between associated microbes and the pathogenicity of PWN, and expand the knowledge on the interaction between PWD-induced forest decline and the PWN-associated microbiome.