Comprehensive comparative analysis of prognostic value of serum systemic inflammation biomarkers for colorectal cancer: Results from a large multicenter collaboration

BackgroundThe incidence of colorectal cancer (CRC) is common and reliable biomarkers are lacking. We aimed to systematically and comprehensively compare the ability of various combinations of serum inflammatory signatures to predict the prognosis of CRC. Moreover, particular attention has been paid to the clinical feasibility of the newly developed inflammatory burden index (IBI) as a prognostic biomarker for CRC.MethodsThe discrimination capacity of the biomarkers was compared using receiver operating characteristic curves and Harrell’s C-index. Kaplan-Meier curves and log-rank tests were used to compare survival differences between the groups. Cox proportional hazard regression analysis was used to determine the independent prognostic factors. Logistic regression analysis was used to assess the relationship between IBI, short-term outcomes, and malnutrition.ResultsIBI had the optimal prediction accuracy among the systemic inflammation biomarkers for predicting the prognosis of CRC. Taking IBI as a reference, none of the remaining systemic inflammation biomarkers showed a gain. Patients with high IBI had significantly worse overall survival than those with low IBI (56.7% vs. 80.2%; log-rank P<0.001). Multivariate Cox regression analysis showed that continuous IBI was an independent risk factor for the prognosis of CRC patients (hazard ratio = 1.165, 95% confidence interval [CI] = 1.043–1.302, P<0.001). High IBI was an independent risk factor for short-term outcomes (odds ratio [OR] = 1.537, 95% CI = 1.258–1.878, P<0.001), malnutrition (OR = 2.996, 95% CI = 1.471–6.103, P=0.003), and recurrence (OR = 1.744, 95% CI = 1.176–2.587, p = 0.006) in CRC patients.ConclusionsIBI, as a reflection of systemic inflammation, is a feasible and promising biomarker for assessing the prognosis of CRC patients

The association of serum betaine concentrations with the risk of new-onset cancers: results from two independent nested case-control studies

Abstract Evidence from epidemiologic studies on the association of circulating betaine levels with the incident risk of cancer has been inconsistent. We aimed to investigate the prospective association of serum betaine concentrations with the risk of cancer. We performed two, nested, case-control studies utilizing data from the “H-type Hypertension Prevention and Control Public Service Project” (HHPCP) and the China Stroke Primary Prevention Trial (CSPPT), with 2782 participants (1391 cancer cases and 1391 matched controls) in the discovery cohort, and 228 participants (114 cancer cases and 114 matched controls) in the validation cohort. Odds ratios (OR) of the association between betaine and cancer were calculated using conditional logistic regression models. There was an association between serum betaine as a continuous variable and total cancer (OR = 1.03, 95%CI = 0.99–1.07, p = 0.097). Among cancer subtypes, a positive association was found between serum betaine and the risk of lung cancer, and an inverse association was found with other cancers. Interestingly, a U-shaped association was observed between serum betaine and digestive cancers, with a turning point of 5.01 mmol/L for betaine (betaine < 5.01 mmol/L, OR = 0.82, 95%CI = 0.59–1.14, p = 0.228; betaine ≥ 5.01 mmol/L, OR = 1.08, 95%CI = 1.01–1.17, p = 0.036). In the validation cohort, a significant association between serum betaine as a continuous variable and total cancer (OR = 1.48, 95%CI = 1.06–2.05, P = 0.020) was also found. High serum betaine was associated with increased risk of total cancer and lung cancer, and a U-shaped association was found with the risk of digestive cancers, with a turning point at about 5.01 mmol/L

Whole-Genome Sequencing Identifies a Rice Grain Shape Mutant, gs9-1.

BackgroundBreeding for genes controlling key agronomic traits is an important goal of rice genetic improvement. To gain insight into genes controlling grain morphology, we screened M3 plants derived from 1,000 whole-genome sequenced (WGS) M2 Kitaake mutants to identify lines with altered grain size.ResultsIn this study, we isolated a mutant, named fast-neutron (FN) 60-4, which exhibits a significant reduction in grain size. We crossed FN60-4 with the parental line Kitaake and analyzed the resulting backcross population. Segregation analysis of 113 lines from the BC2F2 population revealed that the mutant phenotype is controlled by a single semi-dominant locus. Mutant FN60-4 is reduced 20% in plant height and 8.8% in 1000-grain weight compared with Kitaake. FN60-4 also exhibits an 8% reduction in cell number and a 9% reduction in cell length along the vertical axis of the glume. We carried out whole-genome sequencing of DNA pools extracted from segregants with long grains or short grains, and revealed that one gene, LOC_Os09g02650, cosegregated with the grain size phenotype in the BC1F2 and BC2F2 populations. This mutant allele was named grain shape 9-1 (gs9-1). gs9-1 carries a 3-bp deletion that affects two amino acids. This locus is a new allele of the BC12/GDD1/MTD1 gene that encodes a kinesin-like protein involved in cell-cycle progression, cellulose microfibril deposition and gibberellic acid (GA) biosynthesis. The GA biosynthesis-related gene KO2 is down-regulated in gs9-1. The dwarf phenotype of gs9-1 can be rescued by adding exogenous GA3. In contrast to the phenotypes for the other alleles, the gs9-1 is less severe, consistent with the nature of the mutation, which does not disrupt the open reading frame as observed for the other alleles.ConclusionsIn this study, we isolated a mutant, which exhibits altered grain shape and identified the mutated gene, gs9-1. Our study reveals that gs9-1 is a semi-dominant gene that carries a two-amino acid mutation. gs9-1 is allelic to the BC12/GDD1/MTD1 gene involved in GA biosynthesis. These results demonstrate the efficiency and convenience of cloning genes from the whole-genome sequenced Kitaake mutant population to advance investigations into genes controlling key agronomic traits in rice

The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer

Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC

Cholesterol-modified prognostic nutritional index (CPNI) as an effective tool for assessing the nutrition status and predicting survival in patients with breast cancer

Abstract Background Malnutrition is associated with poor overall survival (OS) in breast cancer patients; however, the most predictive nutritional indicators for the prognosis of patients with breast cancer are not well-established. This study aimed to compare the predictive effects of common nutritional indicators on OS and to refine existing nutritional indicators, thereby identifying a more effective nutritional evaluation indicator for predicting the prognosis in breast cancer patients. Methods This prospective study analyzed data from 776 breast cancer patients enrolled in the “Investigation on Nutritional Status and its Clinical Outcome of Common Cancers” (INSCOC) project, which was conducted in 40 hospitals in China. We used the time-dependent receiver operating characteristic curve (ROC), Kaplan–Meier survival curve, and Cox regression analysis to evaluate the predictive effects of several nutritional assessments. These assessments included the patient-generated subjective nutrition assessment (PGSGA), the global leadership initiative on malnutrition (GLIM), the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI). Utilizing machine learning, these nutritional indicators were screened through single-factor analysis, and relatively important variables were selected to modify the PNI. The modified PNI, termed the cholesterol-modified prognostic nutritional index (CPNI), was evaluated for its predictive effect on the prognosis of patients. Results Among the nutritional assessments (including PGSGA, GLIM, CONUT, NRI, and PNI), PNI showed the highest predictive ability for patient prognosis (time-dependent ROC = 0.58). CPNI, which evolved from PNI, emerged as the superior nutritional index for OS in breast cancer patients, with the time-dependent ROC of 0.65. It also acted as an independent risk factor for mortality (p < 0.05). Moreover, the risk of malnutrition and mortality was observed to increase gradually among both premenopausal and postmenopausal age women, as well as among women categorized as non-overweight, overweight, and obese. Conclusions The CPNI proves to be an effective nutritional assessment tool for predicting the prognosis of patients with breast cancer

The Sequences of 1504 Mutants in the Model Rice Variety Kitaake Facilitate Rapid Functional Genomic Studies.

The availability of a whole-genome sequenced mutant population and the cataloging of mutations of each line at a single-nucleotide resolution facilitate functional genomic analysis. To this end, we generated and sequenced a fast-neutron-induced mutant population in the model rice cultivar Kitaake (Oryza sativa ssp japonica), which completes its life cycle in 9 weeks. We sequenced 1504 mutant lines at 45-fold coverage and identified 91,513 mutations affecting 32,307 genes, i.e., 58% of all rice genes. We detected an average of 61 mutations per line. Mutation types include single-base substitutions, deletions, insertions, inversions, translocations, and tandem duplications. We observed a high proportion of loss-of-function mutations. We identified an inversion affecting a single gene as the causative mutation for the short-grain phenotype in one mutant line. This result reveals the usefulness of the resource for efficient, cost-effective identification of genes conferring specific phenotypes. To facilitate public access to this genetic resource, we established an open access database called KitBase that provides access to sequence data and seed stocks. This population complements other available mutant collections and gene-editing technologies. This work demonstrates how inexpensive next-generation sequencing can be applied to generate a high-density catalog of mutations

The Sequences of 1504 Mutants in the Model Rice Variety Kitaake Facilitate Rapid Functional Genomic Studies.

The availability of a whole-genome sequenced mutant population and the cataloging of mutations of each line at a single-nucleotide resolution facilitate functional genomic analysis. To this end, we generated and sequenced a fast-neutron-induced mutant population in the model rice cultivar Kitaake (Oryza sativa ssp japonica), which completes its life cycle in 9 weeks. We sequenced 1504 mutant lines at 45-fold coverage and identified 91,513 mutations affecting 32,307 genes, i.e., 58% of all rice genes. We detected an average of 61 mutations per line. Mutation types include single-base substitutions, deletions, insertions, inversions, translocations, and tandem duplications. We observed a high proportion of loss-of-function mutations. We identified an inversion affecting a single gene as the causative mutation for the short-grain phenotype in one mutant line. This result reveals the usefulness of the resource for efficient, cost-effective identification of genes conferring specific phenotypes. To facilitate public access to this genetic resource, we established an open access database called KitBase that provides access to sequence data and seed stocks. This population complements other available mutant collections and gene-editing technologies. This work demonstrates how inexpensive next-generation sequencing can be applied to generate a high-density catalog of mutations

Overexpression of a rice BAHD acyltransferase gene in switchgrass (Panicum virgatum L.) enhances saccharification

Abstract Background Switchgrass (Panicum virgatum L.) is a promising bioenergy feedstock because it can be grown on marginal land and produces abundant biomass. Recalcitrance of the lignocellulosic components of the switchgrass cell wall to enzymatic degradation into simple sugars impedes efficient biofuel production. We previously demonstrated that overexpression of OsAT10, a BAHD acyltransferase gene, enhances saccharification efficiency in rice. Results Here we show that overexpression of the rice OsAT10 gene in switchgrass decreased the levels of cell wall-bound ferulic acid (FA) in green leaf tissues and to a lesser extent in senesced tissues, and significantly increased levels of cell wall-bound p-coumaric acid (p-CA) in green leaves but decreased its level in senesced tissues of the T0 plants under greenhouse conditions. The engineered switchgrass lines exhibit an approximate 40% increase in saccharification efficiency in green tissues and a 30% increase in senesced tissues. Conclusion Our study demonstrates that overexpression of OsAT10, a rice BAHD acyltransferase gene, enhances saccharification of lignocellulosic biomass in switchgrass