Quantitative trait loci (QTL) associated with resistance to Fusarium head blight (FHB), which is mainly caused by Fusarium graminearum Schwabe [telomorph: Gibberella zeae Schw. (Petch)], have been identified in wheat (Triticum aestivum L.) from different countries. Due to the differences of genetic backgrounds and analysis methods, the linked marker and significance levels of QTL are not consistent across studies. Such discrepancies make it difficult to select diagnostic flanking markers. Meta‐analysis has been used to estimate the confidence intervals (CIs) of QTL in plant and animal genomes. The objective of this study was to cluster 249 FHB resistance QTL identified in 46 unique lines from 45 studies based on the estimated QTL CI by meta‐analysis. A total of 209 QTL conditioning FHB resistance type I, II, III and IV were classified into 43 clusters on 21 chromosomes. Among them, 119 QTL were significant and 116 QTL explained more than 10% of phenotypic variation. There are 19 confirmed QTL located on chromosomes 3A, 5A, 7A, 1B, 3BS, 5B, 6B, and 2D. The results provide chromosome locations and linked markers for overlapping and unique QTL. Markers flanking QTL clusters can be used to pyramid diverse QTL more efficiently through marker‐assisted breeding.

Background and Objective: Despite striking advances in multimodality management, gastric cancer (GC) remains the third cause of cancer mortality globally and identifying novel diagnostic and prognostic biomarkers is urgently demanded. The study aimed to identify potential key genes associated with the pathogenesis and prognosis of GC.Methods: Differentially expressed genes between GC and normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Key genes related to the pathogenesis and prognosis of GC were identified by employing protein–protein interaction network and Cox proportional hazards model analyses.Results: We identified nine hub genes (TOP2A, COL1A1, COL1A2, NDC80, COL3A1, CDKN3, CEP55, TPX2, and TIMP1) which might be tightly correlated with the pathogenesis of GC. A prognostic gene signature consisted of CST2, AADAC, SERPINE1, COL8A1, SMPD3, ASPN, ITGBL1, MAP7D2, and PLEKHS1 was constructed with a good performance in predicting overall survivals.Conclusion: The findings of this study would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of GC.

Fusarium head blight (FHB), mainly caused by Fusarium graminearum Schwabe [telomorph: Gibberella zeae Schw. (Petch)], is an increasingly important disease of wheat (Triticum aestivum L.). Host-plant resistance provides the best hope for reducing economic losses associated with FHB, but new sources of resistance are limited. The moderately resistant winter wheat cultivar, Ernie, may provide a source of resistance that differs from Sumai 3 but these genes have not been mapped. Also hindering resistance breeding may be associations of resistance with agronomic traits such as late maturity that may be undesirable in some production environments. This research was conducted to identify QTL associated with type II FHB resistance (FHB severity, FHBS), and to determine if they are associated with days to anthesis (DTA), number of spikelets (NOS), and the presence/absence of awns. Two hundred and forty-three F(8) recombinant inbred lines from a cross between the resistant cultivar, Ernie and susceptible parent, MO 94-317 were phenotyped for type II FHB resistance using point inoculation in the greenhouse during 2002 and 2003. Genetic linkage maps were constructed using 94 simple sequence repeat (SSR) and 146 amplified fragment length polymorphic (AFLP) markers. Over years four QTL regions on chromosomes 2B, 3B, 4BL and 5A were consistently associated with FHB resistance. These QTL explained 43.3% of the phenotypic variation in FHBS. Major QTL conditioning DTA and NOS were identified on chromosome 2D. Neither the QTL associated with DTA and NOS nor the presence/absence of awns were associated with FHB resistance in Ernie. Our results suggest that the FHB resistance in Ernie appears to differ from that in Sumai 3, thus pyramiding the QTL in Ernie with those from Sumai 3 could result in enhanced levels of FHB resistance in wheat.

Background and Objective: Non-small cell lung cancer (NSCLC) accounts for 80–85% of all patients with lung cancer and 5-year relative overall survival (OS) rate is less than 20%, so that identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study attempted to identify potential key genes associated with the pathogenesis and prognosis of NSCLC.Methods: Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NSCLC samples and normal ones were analyzed using limma package, and RobustRankAggreg (RRA) package was used to conduct gene integration. Moreover, Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE) were utilized to establish protein–protein interaction (PPI) network of these DEGs. Furthermore, functional enrichment and pathway enrichment analyses for DEGs were performed by Funrich and OmicShare. While the expressions and prognostic values of top genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online dataset.Results: A total of 249 DEGs (113 upregulated and 136 downregulated) were identified after gene integration. Moreover, the PPI network was established with 166 nodes and 1784 protein pairs. Topoisomerase II alpha (TOP2A), a top gene and hub node with higher node degrees in module 1, was significantly enriched in mitotic cell cycle pathway. In addition, Interleukin-6 (IL-6) was enriched in amb2 integrin signaling pathway. The mitotic cell cycle was the most significant pathway in module 1 with the highest P-value. Besides, five hub genes with high degree of connectivity were selected, including TOP2A, CCNB1, CCNA2, UBE2C, and KIF20A, and they were all correlated with worse OS in NSCLC. Conclusion: The results showed that TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 may be potential key genes, while the mitotic cell cycle pathway may be a potential pathway contribute to progression in NSCLC. Further, it could be used as a new biomarker for diagnosis and to direct the synthesis medicine of NSCLC.