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Objective To construct and validate a prognosis prediction model and a risk stratification tool for more precise and individualized evaluation of prognosis for patients following resection of esophageal squamous cell carcinoma (ESCC), and provide real-world evidence for informing optimal decision-making about adjuvant therapy. Methods The comprehensive clinical data and follow-up data were collected from consecutive patients with ESCC in the Anyang Cancer Hospital (Anyang center) from May 31, 2011 to July 31, 2018, and in the Cancer Hospital of Shantou University Medical College (Shantou center) from August 1, 2009 to December 31, 2018. Patients from the Anyang center formed the training cohort, and a two-phase selection based on backward stepwise multivariable Cox proportional hazard regression and minimization of AIC was used to construct prediction model for overall survival (OS). Bootstrap with 1 000 resamples was used for internal validation, and cohort from the Shantou center was used for external validation. Furthermore, a risk stratification tool was constructed according to the tertiles of the total points derived from nomogram in the training cohort. Results A total of 4 171 eligible patients were included in the training cohort, and 1 895 patients were included in the validation cohort. The final model incorporated nine variables: age, sex, primary tumor location, T stage, N stage, number of lymph nodes harvested, tumor size, adjuvant treatment, and preoperative hemoglobin level. A significant interaction was observed between N stage and adjuvant treatment (P < 0.001), which means that N+ stage patients were likely to benefit from addition of adjuvant therapy as opposed to surgery alone, but adjuvant therapy did not improve OS for N0 stage patients. The C-index of the model was 0.728 (95% CI: 0.713-0.742) in the training cohort, 0.722 (95% CI: 0.711-0.739) after bootstrapping, and 0.679 (95% CI: 0.662-0.697) in the external validation cohort. Calibration plots demonstrated favorable agreement between model prediction and actual observation for 1-, 3- and 5-year OS. In both training and validation cohorts, this model outperformed the seventh edition of the AJCC TNM (tumor, lymph node, and metastasis) staging system in terms of the accuracy of prognostic prediction (P < 0.05). Moreover, within each TNM staging group, this model achieved ideal risk stratification. Conclusions The prediction model constructed in this study may provide individualized survival prediction for patients with resected ESCC in China. This study also demonstrated that the N stage may be a fundamental determinant in planning postoperative adjuvant therapy for ESCC patients.

Summary: Although forward‐genetics‐metabolomics methods such as mGWAS and mQTL have proven effective in providing myriad loci affecting metabolite contents, they are somehow constrained by their respective constitutional flaws such as the hidden population structure for GWAS and insufficient recombinant rate for QTL. Here, the combination of mGWAS and mQTL was performed, conveying an improved statistical power to investigate the flavonoid pathways in common wheat. A total of 941 and 289 loci were, respectively, generated from mGWAS and mQTL, within which 13 of them were co‐mapped using both approaches. Subsequently, the mGWAS or mQTL outputs alone and their combination were, respectively, utilized to delineate the metabolic routes. Using this approach, we identified two MYB transcription factor encoding genes and five structural genes, and the flavonoid pathway in wheat was accordingly updated. Moreover, we have discovered some rare‐activity‐exhibiting flavonoid glycosyl‐ and methyl‐transferases, which may possess unique biological significance, and harnessing these novel catalytic capabilities provides potentially new breeding directions. Collectively, we propose our survey illustrates that the forward‐genetics‐metabolomics approaches including multiple populations with high density markers could be more frequently applied for delineating metabolic pathways in common wheat, which will ultimately contribute to metabolomics‐assisted wheat crop improvement. [ABSTRACT FROM AUTHOR]

Summary: Wheat needs different durations of vernalization, which accelerates flowering by exposure to cold temperature, to ensure reproductive development at the optimum time, as that is critical for adaptability and high yield. TaVRN1 is the central flowering regulator in the vernalization pathway and encodes a MADS‐box transcription factor (TF) that usually works by forming hetero‐ or homo‐dimers. We previously identified that TaVRN1 bound to an MADS‐box TF TaSOC1 whose orthologues are flowering activators in other plants. The specific function of TaSOC1 and the biological implication of its interaction with TaVRN1 remained unknown. Here, we demonstrated that TaSOC1 was a flowering repressor in the vernalization and photoperiod pathways by overexpression and knockout assays. We confirmed the physical interaction between TaSOC1 and TaVRN1 in wheat protoplasts and in planta, and further validated their genetic interplay. A Flowering Promoting Factor 1‐like gene TaFPF1‐2B was identified as a common downstream target of TaSOC1 and TaVRN1 through transcriptome and chromatin immunoprecipitation analyses. TaSOC1 competed with TaVRT2, another MADS‐box flowering regulator, to bind to TaVRN1; their coding genes synergistically control TaFPF1‐2B expression and flowering initiation in response to photoperiod and low temperature. We identified major haplotypes of TaSOC1 and found that TaSOC1‐Hap1 conferred earlier flowering than TaSOC1‐Hap2 and had been subjected to positive selection in wheat breeding. We also revealed that wheat SOC1 family members were important domestication loci and expanded by tandem and segmental duplication events. These findings offer new insights into the regulatory mechanism underlying flowering control along with useful genetic resources for wheat improvement. [ABSTRACT FROM AUTHOR]

Achieving high yield and good quality in crops is essential for human food security and health. However, there is usually disharmony between yield and quality. Seed storage protein (SSP) and starch, the predominant components in cereal grains, determine yield and quality, and their coupled synthesis causes a yield–quality trade‐off. Therefore, dissection of the underlying regulatory mechanism facilitates simultaneous improvement of yield and quality. Here, we summarize current findings about the synergistic molecular machinery underpinning SSP and starch synthesis in the leading staple cereal crops, including maize, rice and wheat. We further evaluate the functional conservation and differentiation of key regulators and specify feasible research approaches to identify additional regulators and expand insights. We also present major strategies to leverage resultant information for simultaneous improvement of yield and quality by molecular breeding. Finally, future perspectives on major challenges are proposed. [ABSTRACT FROM AUTHOR]

Stem rust is one of the most destructive diseases of wheat worldwide. The recent emergence of wheat stem rust race Ug99 (TTKS based on the North American stem rust race nomenclature system) and related strains threaten global wheat production because they overcome widely used genes that had been effective for many years. Host resistance is likely to be more durable when several stem rust resistance genes are pyramided in a single wheat variety; however, little is known about the resistance genotypes of widely used wheat germplasm. In this study, a diverse collection of wheat germplasm was haplotyped for stem rust resistance genes Sr2, Sr22, Sr24, Sr25, Sr26, Sr36, Sr40, and 1A.1R using linked microsatellite or simple sequence repeat (SSR) and sequence tagged site (STS) markers. Haplotype analysis indicated that 83 out of 115 current wheat breeding lines from the International Maize and Wheat Improvement Center (CIMMYT) likely carry Sr2. Among those, five out of 94 CIMMYT spring lines tested had both Sr2 and Sr25 haplotypes. Five out of 22 Agriculture Research Service (ARS) lines likely have Sr2 and a few have Sr24, Sr36, and 1A.1R. Two out of 43 Chinese accessions have Sr2. No line was found to have the Sr26 and Sr40 haplotypes in this panel of accessions. DArT genotyping was used to identify new markers associated with the major stem resistance genes. Four DArT markers were significantly associated with Sr2 and one with Sr25. Principal component analysis grouped wheat lines from similar origins. Almost all CIMMYT spring wheats were clustered together as a large group and separated from the winter wheats. The results provide useful information for stem rust resistance breeding and pyramiding.