Background Long noncoding RNAs (lncRNAs) have been shown to play important roles in the regulation of plant growth and development. Recent transcriptomic analyses have revealed the gene expression profiling in wheat spike development, however, the possible regulatory roles of lncRNAs in wheat spike morphogenesis remain largely unclear. Results Here, we analyzed the genome-wide profiling of lncRNAs during wheat spike development at six stages, and identified a total of 8,889 expressed lncRNAs, among which 2,753 were differentially expressed lncRNAs (DE lncRNAs) at various developmental stages. Three hundred fifteen differentially expressed cis- and trans-regulatory lncRNA-mRNA pairs comprised of 205 lncRNAs and 279 genes were predicted, which were found to be mainly involved in the stress responses, transcriptional and enzymatic regulations. Moreover, the 145 DE lncRNAs were predicted as putative precursors or target mimics of miRNAs. Finally, we identified the important lncRNAs that participate in spike development by potentially targeting stress response genes, TF genes or miRNAs. Conclusions This study outlines an overall view of lncRNAs and their possible regulatory networks during wheat spike development, which also provides an alternative resource for genetic manipulation of wheat spike architecture and thus yield.

Background: Kidney renal clear cell carcinoma is the malignant tumor with the highest incidence and poor prognosis in renal cell carcinoma. In view of its limited diagnostic strategies and poor prognosis, bioinformatics analysis has been used to explore the possible mechanisms of renal clear cell carcinoma and effective prognostic-related biomarkers.Method: The sequencing information of 3 types of RNA (mRNA, lncRNA and miRNA) in 539 cases of kidney renal clear cell carcinoma tumor tissues and 72 cases of normal tissues is obtained from the TCGA database. Heat map and volcano map of differentially expressed genes were drawn through R language; The CeRNA network was visualized by Cytoscape software (version 3.7.2). Methods such as univariate Cox regression analysis, lasso regression screening, and multivariate Cox regression analysis were used to construct a prognostic model based on the CeRNA network. The CIBERSORT algorithm was used to analyze the degree of infiltration of 22 kinds of immune cells from each sample of kidney renal clear cell carcinoma. Construction of a prognostic model based on tumor-infiltrating immune cells, The R "corrplot" software package was used for co-expression analysis based on the CeRNA network and tumor-infiltrating immune cells model.Results: There are 3074 differentially expressed mRNAs (1055 upregulated and 2019 downregulated), and 359 differentially expressed lncRNAs (71 upregulated and 280 downregulated) and 132 differentially expressed miRNAs (70 upregulated and 62 downregulated) that have been identified through differential analysis. A complete mRNA-miRNA-lncRNA (SIX1-hsa-miR-200b-3p-MALAT1) network was obtained based on the CeRNA network-based prognostic model construction. 2 immune cells (Mast cells resting, T cells follicular helper) were identified by constructing a prognostic model based on tumor-infiltrating immune cells. There was a negative correlation between lncRNA MALAT1 and Mast cells resting (R= -0.27, P<0.001); while there was a positive correlation between lncRNA MALAT1 and T cells follicular helper (R=0.23, P<0.001).Conclusion: Based on CeRNA network and tumor-infiltrating immune cells, we explored the possible mechanism of kidney renal clear cell carcinoma and obtained effective biomarkers for predicting prognosis by Bioinformatics analysis in this study.