Background: Malnutrition is prevalent among patients with cancer. The Global Leadership Initiative on Malnutrition (GLIM) released new universal criteria for diagnosing malnutrition in 2019. The objectives of this study were to assess the prevalence of malnutrition in patients with cancer using the GLIM criteria, explore the correlation between the GLIM criteria, and clinical outcomes, and compare the GLIM criteria with subjective global assessment (SGA).Methods: This retrospective analysis was conducted on 2,388 patients with cancer enrolled in a multicenter study. Nutritional risk was screened using the Nutritional Risk Screening-2002, and the nutritional status was assessed using SGA and GLIM criteria. Chi-square analysis and Wilcoxon rank sum test, stratified by age 65 years, were used to evaluate the effect of GLIM-defined malnutrition on clinical outcomes. Logistic regression analysis was used to analyze the nutritional status and complications, and the interrater reliability was measured using a kappa test.Results: The prevalence of malnutrition defined by the GLIM criteria was 38.9% (929/2,388). GLIM-defined malnutrition was significantly associated with in-hospital mortality (P = 0.001) and length of hospital stays (P = 0.001). Multivariate logistic regression analysis showed GLIM-defined malnutrition significantly increased complications (odds ratio [OR] 1.716, 95% CI 1.227–2.400, P = 0.002). The GLIM criteria had a “moderate agreement” (kappa = 0.426) compared with the SGA.Conclusions: The prevalence of malnutrition in hospitalized patients with cancer is high, and malnourishment in patients with cancer is associated with poorer clinical outcomes. The use of the GLIM criteria in assessing the nutritional status of inpatients with cancer is recommended and can be used as the basis for nutritional interventions.

Objective: Summaries of the relationships between the microbiota and liver cirrhosis and their conclusions are not consistent. This study describes microbial differences in patients with liver cirrhosis by performing a meta-analysis. Methods: We searched PubMed, Embase, Web of Science, and the Cochrane Library and collected related articles published before March 10, 2024. Ratio of autochthonous to non-autochthonous taxa was calculated as the cirrhosis dysbiosis ratio (CDR). Using a random-effects model, the standard mean deviation (SMD) and 95% confidence interval (CI) were calculated. We subsequently performed subgroup, sensitivity, and publication bias analyses. cirrhosis dysbiosis ratio. Results: A total of 53 eligible papers including 5076 participants were included. The pooled estimates revealed a moderately significant reduction in gut microbiome richness in patients with liver cirrhosis compared with controls, including the Shannon, Chao1, observed species, ACE, and PD indices, but no significant difference was observed for the Simpson index. Over 80% of the studies reported significant differences in β diversity. Families Enterobacteriaceae and Pasteurellaceae, belonging to the phylum Proteobacteria, along with the family Streptococcaceae and the genera Haemophilus, Streptococcus, and Veillonella, were significantly associated with liver cirrhosis compared to the control group. In contrast, the healthy group exhibited a higher abundance of the class Clostridia, particularly the families Lachnospiraceae and Ruminococcaceae, which are known for their diversity and role as common gut commensals. Furthermore, the class Bacilli, predominantly represented by the genus Streptococcus, was markedly enriched in the cirrhosis group. Conclusions: The microbiota richness of liver cirrhosis patients was lower than that of healthy controls. Alterations in gut microbiota linked to liver cirrhosis were characterized by a decrease in Lachnospiraceae, Ruminococcaceae, and Clostridia and an enrichment of Enterobacteriaceae, Pasteurellaceae, Streptococcaceae, Bacilli, and Streptococcus.

Background & AIMS To compare the association of geriatric nutritional risk index (GNRI) and controlling nutritional status (CONUT) scores with malnutrition, and to study their association with clinical outcomes in older adult cancer patients. Methods This retrospective analysis was conducted on 854 older adult cancer patients collected from 34 hospitals in 18 cities in China between June and September 2014. Anthropometric and hematological examination results at admission were collected, and subjective global assessment was used. Clinical outcomes, such as complications, length of hospital stays, and hospital costs, were recorded. Receiver operating characteristic curves were used to evaluate the accuracy of the two nutritional assessment tools for malnutrition. The association between GNRI and CONUT score and clinical outcomes was analyzed using the chi-square test, t-test, or rank sum test. Results Among 854 patients with cancer, the prevalence of malnutrition was 42.7%. Compared with subjective global assessment, the GNRI had a significantly higher accuracy than the CONUT score in predicting malnutrition (area under the curve 0.704, 95% confidence interval, 0.658–0.750, P < 0.001). The GNRI was significantly associated with the occurrence of complications (χ2 = 4.985, P = 0.026), and low GNRI (≤98) was associated with a longer length of hospital stay (t = −2.179, P = 0.030). Conclusions The GNRI may be used to assess malnutrition in older adult cancer patients and can predict poor clinical outcomes in these patients.

The integration of HBV DNA into the human genome can disrupt its structure in hepatocellular carcinoma (HCC), but the complexity of HBV genomic integration remains elusive. Here we applied long-read sequencing to precisely elucidate the HBV integration pattern in the human hepatocellular genome. The DNA library was sequenced using the long-read sequencing on GridION and PacBio Sequel II, respectively. The DNA and mRNA were sequenced using next-generation sequencing on Illumina NextSeq. BLAST (Basic Local Alignment Search Tool) and local scripts were used to analyze HBV integration patterns. We established an analytical strategy based on the long-read sequences, and analyzed the complexity of HBV DNA integration into the hepatocellular genome. A total of 88 integrated breakpoints were identified. HBV DNA integration into human genomic DNA was mainly fragmented with different orientations, rarely with a complete genome. The same HBV integration breakpoints were identified among the three platforms. Most breakpoints were observed at P, X, and S genes in the HBV genome, and observed at introns, intergenic sequences, and exons in the human genome. Tumor tissue harbored a much higher integrated number than the adjacent tissue, and the distribution of HBV integrated into human chromosomes was more concentrated. HBV integration shows different patterns between cancer cells and adjacent normal cells. We for the first time obtained the entire HBV integration pattern through long-read sequencing and demonstrated the value of long-read sequencing in detecting the genomic integration structures of viruses in host cells.

Background: Older patients are at an increased risk of malnutrition due to many factors related to poor clinical outcomes. Objective: This study aims to develop an assisted diagnosis model using machine learning (ML) for identifying older patients with malnutrition and providing the focus of individualized treatment. Methods: We reanalyzed a multicenter, observational cohort study including 2660 older patients. Baseline malnutrition was defined using the global leadership initiative on malnutrition (GLIM) criteria, and the study population was randomly divided into a derivation group (2128/2660, 80%) and a validation group (532/2660, 20%). We applied 5 ML algorithms and further explored the relationship between features and the risk of malnutrition by using the Shapley additive explanations visualization method. Results: The proposed ML models were capable to identify older patients with malnutrition. In the external validation cohort, the top 3 models by the area under the receiver operating characteristic curve were light gradient boosting machine (92.1%), extreme gradient boosting (91.9%), and the random forest model (91.5%). Additionally, the analysis of the importance of features revealed that BMI, weight loss, and calf circumference were the strongest predictors to affect GLIM. A BMI of below 21 kg/m2 was associated with a higher risk of GLIM in older people. Conclusions: We developed ML models for assisting diagnosis of malnutrition based on the GLIM criteria. The cutoff values of laboratory tests generated by Shapley additive explanations could provide references for the identification of malnutrition. Trial Registration: Chinese Clinical Trial Registry ChiCTR-EPC-14005253; https://www.chictr.org.cn/showproj.aspx?proj=9542

DNA methylation is a widespread epigenetic signal in human genome. With Nanopore technology, differential methylation modifications including 5-methylcytosine (5mC) and 6-methyladenine (6mA) can be identified. 5mC is the most important modification in mammals, although 6mA may also function in growth and development as well as in pathogenesis. While the role of 5mC at CpG islands in promoter regions associated with transcriptional regulation has been well studied, but the relationship between 6mA and transcription is still unclear. Thus, we collected two pairs of tumor tissues and adjacent normal tissues from hepatocellular carcinoma (HCC) surgical samples for Nanopore sequencing and transcriptome sequencing. It was found that 2,373 genes had both 5mC and 6mA, along with up- and down-regulated methylation sites. These genes were regarded as unstable methylation genes. Compared with 6mA, 5mC had more inclined distribution of unstable methylation sites. Chi-square test showed that the levels of 5mC were consistent with both up- and down-regulated genes, but 6mA was not significant. Moreover, the top three unstable methylation genes, TBC1D3H, CSMD1, and ROBO2, were all related to cancer. Transcriptome and survival analyses revealed four potential tumor suppressor genes including KCNIP4, CACNA1C, PACRG, and ST6GALNAC3. In this study, we firstly proposed to combine 5mC and 6mA methylation sites to explore functional genes, and further research found top of these unstable methylation genes might be functional and some of them could serve as potential tumor suppressor genes. Our study provided a new solution for epigenetic regulation research and therapy of HCC.