Mitochondria-derived small RNAs as diagnostic biomarkers in lung cancer patients through a novel ratio-based expression analysis methodology.

Small non-coding RNAs are potential diagnostic biomarkers for lung cancer. Mitochondria-derived small RNA (mtRNA) is a novel regulatory small non-coding RNA that only recently has been identified and cataloged. Currently, there are no reports of studies of mtRNA in human lung cancer. Currently, normalization methods are unstable, and they often fail to identify differentially expressed small non-coding RNAs (sncRNAs). In order to identify reliable biomarkers for lung cancer screening, we used a ratio-based method using mtRNAs newly discovered in human peripheral blood mononuclear cells. In the discovery cohort (AUC = 0.981) and independent validation cohort (AUC = 0.916) the prediction model of eight mtRNA ratios distinguished lung cancer patients from controls. The prediction model will provide reliable biomarkers that will allow blood-based screening to become more feasible and will help make lung cancer diagnosis more accurate in clinical practice.

Comprehensive landscape of tRNA-derived fragments in lung cancer.

Transfer RNA (tRNA)-derived fragment (tRDF) is a novel small non-coding RNA that presents in different types of cancer. The comprehensive understanding of tRDFs in non-small cell lung cancer remains largely unknown. In this study, 1,550 patient samples of non-small cell lung cancer (NSCLC) were included, and 52 tRDFs with four subtypes were identified. Six tRDFs were picked as diagnostic signatures based on the tRDFs expression patterns, and area under the curve (AUC) in independent validations is up to 0.90. Two signatures were validated successfully in plasma samples, and six signatures confirmed the consistency of distinguished expression in NSCLC cell lines. Ten tRDFs along with independent risk scores can be used to predict survival outcomes by stages; 5a_tRF-Ile-AAT/GAT can be a prognosis biomarker for early stage. Association analysis of tRDFs-signatures-correlated mRNAs and microRNA (miRNA) were targeted to the cell cycle and oocyte meiosis signaling pathways. Five tRDFs were assessed to associate with PD-L1 immune checkpoint and correlated with the genes that target in PD-L1 checkpoint signaling pathway. Our study is the first to provide a comprehensive analysis of tRDFs in lung cancer, including four subtypes of tRDFs, investigating the diagnostic and prognostic values, and demonstrated their biological function and transcriptional role as well as potential immune therapeutic value.

Identification of Key Biomarkers in Systemic Lupus Erythematosus by a Multi-Cohort Analysis.

Systemic lupus erythematosus (SLE) is an autoimmune disease that affects multiple body systems with heterogeneous clinical manifestations. Since gene expression analyses have been accomplished on diverse types of samples to specify SLE-related genes, single-cohort transcriptomics have not produced reliable results. Using an integrated multi-cohort analysis framework, we analyzed whole blood cells from SLE patients from three transcriptomics cohorts (n=1222) and identified a five-gene signature that distinguished SLE patients from controls. We validated the diagnostic performance of this five-gene signature in six independent validation cohorts (n= 469), with an area under the receiver operating characteristic curve of 0.88 [95% CI 0.7 - 0.96]. This five-gene signature may be associated with the proportion of SLE immune cells, and generalizable across ages and sample types with real diagnostic value for clinical application.

Corrigendum to "Prognostic Factors for COVID-19 Hospitalized Patients with Preexisting Type 2 Diabetes".

[This corrects the article DOI: 10.1155/2022/9322332.].

Association of Septic Shock with Mortality in Hospitalized COVID-19 Patients in Wuhan, China.

Purpose: Septic shock is a severe complication of COVID-19 patients. We aim to identify risk factors associated with septic shock and mortality among COVID-19 patients. Methods: A total of 212 COVID-19 confirmed patients in Wuhan were included in this retrospective study. Clinical outcomes were designated as nonseptic shock and septic shock. Log-rank test was conducted to determine any association with clinical progression. A prediction model was established using random forest. Results: The mortality of septic shock and nonshock patients with COVID-19 was 96.7% (29/30) and 3.8% (7/182). Patients taking hypnotics had a much lower chance to develop septic shock (HR = 0.096, p=0.0014). By univariate logistic regression analysis, 40 risk factors were significantly associated with septic shock. Based on multiple regression analysis, eight risk factors were shown to be independent risk factors and these factors were then selected to build a model to predict septic shock with AUC = 0.956. These eight factors included disease severity (HR = 15, p < 0.001), age > 65 years (HR = 2.6, p=0.012), temperature > 39.1°C (HR = 2.9, p=0.047), white blood cell count > 10 × 109 (HR = 6.9, p < 0.001), neutrophil count > 75 × 109 (HR = 2.4, p=0.022), creatine kinase > 5 U/L (HR = 1.8, p=0.042), glucose > 6.1 mmol/L (HR = 7, p < 0.001), and lactate > 2 mmol/L (HR = 22, p < 0.001). Conclusions: We found 40 risk factors were significantly associated with septic shock. The model contained eight independent factors that can accurately predict septic shock. The administration of hypnotics could potentially reduce the incidence of septic shock in COVID-19 patients.

Development of a tRNA-derived small RNA diagnostic and prognostic signature in liver cancer.

Liver cancer presents divergent clinical behaviors. There remain opportunities for molecular markers to improve liver cancer diagnosis and prognosis, especially since tRNA-derived small RNAs (tsRNA) have rarely been studied. In this study, a random forests (RF) diagnostic model was built based upon tsRNA profiling of paired tumor and adjacent normal samples and validated by independent validation (IV). A LASSO model was used to developed a seven-tsRNA-based risk score signature for liver cancer prognosis. Model performance was evaluated by a receiver operating characteristic curve (ROC curve) and Precision-Recall curve (PR curve). The five-tsRNA-based RF diagnosis model had area under the receiver operating characteristic curve (AUROC) 88% and area under the precision-recall curve (AUPR) 87% in the discovery cohort and 87% and 86% in IV-AUROC and IV-AUPR, respectively. The seven-tsRNA-based prognostic model predicts the overall survival of liver cancer patients (Hazard Ratio 2.02, 95% CI 1.36-3.00, P < 0.001), independent of standard clinicopathological prognostic factors. Moreover, the model successfully categorizes patients into high-low risk groups. Diagnostic and prognostic modeling can be reliably utilized in the diagnosis of liver cancer and high-low risk classification of patients based upon tsRNA characterization.

Prognostic Factors for COVID-19 Hospitalized Patients with Preexisting Type 2 Diabetes.

BACKGROUND: Type 2 diabetes (T2D) as a worldwide chronic disease combined with the COVID-19 pandemic prompts the need for improving the management of hospitalized COVID-19 patients with preexisting T2D to reduce complications and the risk of death. This study aimed to identify clinical factors associated with COVID-19 outcomes specifically targeted at T2D patients and build an individualized risk prediction nomogram for risk stratification and early clinical intervention to reduce mortality. METHODS: In this retrospective study, the clinical characteristics of 382 confirmed COVID-19 patients, consisting of 108 with and 274 without preexisting T2D, from January 8 to March 7, 2020, in Tianyou Hospital in Wuhan, China, were collected and analyzed. Univariate and multivariate Cox regression models were performed to identify specific clinical factors associated with mortality of COVID-19 patients with T2D. An individualized risk prediction nomogram was developed and evaluated by discrimination and calibration. RESULTS: Nearly 15% (16/108) of hospitalized COVID-19 patients with T2D died. Twelve risk factors predictive of mortality were identified. Older age (HR = 1.076, 95% CI = 1.014-1.143, p=0.016), elevated glucose level (HR = 1.153, 95% CI = 1.038-1.28, p=0.0079), increased serum amyloid A (SAA) (HR = 1.007, 95% CI = 1.001-1.014, p=0.022), diabetes treatment with only oral diabetes medication (HR = 0.152, 95%CI = 0.032-0.73, p=0.0036), and oral medication plus insulin (HR = 0.095, 95%CI = 0.019-0.462, p=0.019) were independent prognostic factors. A nomogram based on these prognostic factors was built for early prediction of 7-day, 14-day, and 21-day survival of diabetes patients. High concordance index (C-index) was achieved, and the calibration curves showed the model had good prediction ability within three weeks of COVID-19 onset. CONCLUSIONS: By incorporating specific prognostic factors, this study provided a user-friendly graphical risk prediction tool for clinicians to quickly identify high-risk T2D patients hospitalized for COVID-19.

Analysis and Performance Assessment of the Whole Genome Bisulfite Sequencing Data Workflow: Currently Available Tools and a Practical Guide to Advance DNA Methylation Studies.

DNA methylation is associated with transcriptional repression, genomic imprinting, stem cell differentiation, embryonic development, and inflammation. Aberrant DNA methylation can indicate disease states, including cancer and neurological disorders. Therefore, the prevalence and location of 5-methylcytosine in the human genome is a topic of interest. Whole-genome bisulfite sequencing (WGBS) is a high-throughput method for analyzing DNA methylation. This technique involves library preparation, alignment, and quality control. Advancements in epigenetic technology have led to an increase in DNA methylation studies. This review compares the detailed experimental methodology of WGBS using accessible and up-to-date analysis tools. Practical codes for WGBS data processing are included as a general guide to assist progress in DNA methylation studies through a comprehensive case study.

Comprehensive Analysis of a tRNA-Derived Small RNA in Colorectal Cancer.

Colorectal cancer often presents as a highly variable disease with myriad forms that are at times difficult to detect in early screenings with sufficient accuracy, for which novel diagnostic methods are an attractive and valuable area of improvement. To improve colorectal cancer diagnosis and prognosis, new biomarkers that can be assembled into a diagnostic panel must be identified, and tRNA-derived small RNAs (tsRNAs) are a particularly interesting and increasingly visible new class of molecules to examine. In this study, small RNA-seq data were profiled for the expression of 104 human tsRNAs in tumor tissue and adjacent normal tissue samples, and a diagnostic model was built based on four differentially expressed tsRNAs: tRF-22-WB86Q3P92, tRF-22-WE8SPOX52, tRF-22-WE8S68L52, tRF-18-8R1546D2. Furthermore, the diagnostic model was validated by two independent validation datasets (AUC was 0.97 and 0.99), and a LASSO model was applied to develop a seven-tsRNA-based risk score model for colorectal cancer prognosis. Finally, a tsRNA-mRNA interaction network was established according to potential mRNA targets predicted by bioinformatic methods. In conclusion, the results suggest that abnormal expression of tsRNA in colorectal cancer may have a functional effect on tumor action and moreover, that some of the tsRNAs identified in this study with diagnostic and prognostic potential could be of clinical significance.

Age-related compositional changes and correlations of gut microbiome, serum metabolome, and immune factor in rats.

Aging is a complex physiological process associated with degenerative disorder of metabolism and immune function, which contributes to the occurrence of senile diseases. The gut microbiota affects systemic inflammation in aging processes probably through metabolism, but their relationship is still unclear. In this study, 16S-rRNA-sequencing technology, gas chromatography-time-of-flight mass spectrometry (GC-TOFMS)-based metabolic profiling, and immune factor analysis combined with advanced differential and association analysis were employed to investigate the correlation between the microbiome, metabolome, and immune factors in male Wistar rats across lifespan. Our findings showed significant changes in the ileum microbiome and serum metabolome compositions across aging process. A two-level strategy was applied to demonstrate that key metabolites associated with age such as 4-hydroxyproline, proline, and lysine were clustered together and positively correlated with beneficial microbes including Bifidobacterium, Lactobacillus, and Akkermansia. Function analysis explored association between serum metabolite class and specific gut bacteria's metabolism pathways. Further correlation analysis on all the alteration patterns provided an interaction network of main immune factors such as IL-10, IgA, IgM, and IgG with key gut bacteria and serum metabolites. This study offers new insights into the relationship between immune factors, serum metabolome, and the gut microbiome.

Risk Factors Associated With Clinical Outcomes in 323 Coronavirus Disease 2019 (COVID-19) Hospitalized Patients in Wuhan, China.

BACKGROUND: With evidence of sustained transmission in more than 190 countries, coronavirus disease 2019 (COVID-19) has been declared a global pandemic. Data are urgently needed about risk factors associated with clinical outcomes. METHODS: A retrospective review of 323 hospitalized patients with COVID-19 in Wuhan was conducted. Patients were classified into 3 disease severity groups (nonsevere, severe, and critical), based on initial clinical presentation. Clinical outcomes were designated as favorable and unfavorable, based on disease progression and response to treatments. Logistic regression models were performed to identify risk factors associated with clinical outcomes, and log-rank test was conducted for the association with clinical progression. RESULTS: Current standard treatments did not show significant improvement in patient outcomes. By univariate logistic regression analysis, 27 risk factors were significantly associated with clinical outcomes. Multivariate regression indicated age >65 years (P < .001), smoking (P = .001), critical disease status (P = .002), diabetes (P = .025), high hypersensitive troponin I (>0.04 pg/mL, P = .02), leukocytosis (>10 × 109/L, P < .001), and neutrophilia (>75 × 109/L, P < .001) predicted unfavorable clinical outcomes. In contrast, the administration of hypnotics was significantly associated with favorable outcomes (P < .001), which was confirmed by survival analysis. CONCLUSIONS: Hypnotics may be an effective ancillary treatment for COVID-19. We also found novel risk factors, such as higher hypersensitive troponin I, predicted poor clinical outcomes. Overall, our study provides useful data to guide early clinical decision making to reduce mortality and improve clinical outcomes of COVID-19.

Metabolome and gut microbiota variation with long-term intake of Panax ginseng extracts on rats.

Ginseng, a widely used functional food and food additive, has been proven to have promotion effects of health on the body. However, whether the long-term intake of Ginseng is beneficial or has side effects on an organism is still unclear. In this study, untargeted GC-TOFMS metabolomic analysis of serum, cecum and ileum intestinal contents was conducted to understand the effect of the long-term intake of Ginseng extracts. 16S rRNA microbial sequencing technology was applied to investigate the effect of Ginseng extracts on the structure of gut microbiota. Cytokines in spleen were detected to determine the effect of Ginseng extracts on the immune system. Compared to control groups, the metabolites in serum, cecum and ileum, such as amino acids, amines and other metabolites related to carbohydrate metabolism, significantly varied between the C and GS groups. Ginseng extracts affected the structure of gut microbiota with a decreased abundance of TM7, while the abundance of Proteobacteria, Methylobacteriaceae, Parasutterella, Sutterella increased in the GS group. The increased abundance of Bifidobacterium and Lactobacillus demonstrated that Ginseng extracts contribute to probiotic amplification. Highly correlated with Bifidobacterium and Lactobacillus, interleukin 4 (IL4), IL10 and immunoglobulin A (IgA) levels were significantly elevated after the long-term intake of Ginseng extracts. These results indicated that the long-term administration of Ginseng extracts positively affected the host-gut metabolism, immune system, the anti-inflammation process and the gut intestinal microbiota structure.

Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.

Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing values, missing not at random (MNAR), missing at random (MAR), and missing completely at random (MCAR). Our study comprehensively compared eight imputation methods (zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC)) for different types of missing values using four metabolomics datasets. Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate imputation accuracy. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes analysis were used to evaluate the overall sample distribution. Student's t-test followed by correlation analysis was conducted to evaluate the effects on univariate statistics. Our findings demonstrated that RF performed the best for MCAR/MAR and QRILC was the favored one for left-censored MNAR. Finally, we proposed a comprehensive strategy and developed a public-accessible web-tool for the application of missing value imputation in metabolomics ( https://metabolomics.cc.hawaii.edu/software/MetImp/ ).

Strategy for an Association Study of the Intestinal Microbiome and Brain Metabolome Across the Lifespan of Rats.

There is increased appreciation for the diverse roles of the microbiome-gut-brain axis on mammalian growth and health throughout the lifespan. Numerous studies have demonstrated that the gut microbiome and their metabolites are extensively involved in the communication between brain and gut. Association study of brain metabolome and gut microbiome is an active field offering large amounts of information on the interaction of microbiome, brain and gut but data size and complicated hierarchical relationships were found to be major obstacles to the formation of significant, reproducible conclusions. This study addressed a two-level strategy of brain metabolome and gut microbiome association analysis of male Wistar rats in the process of growth, employing several analytical platforms and various bioinformatics methods. Trajectory analysis showed that the age-related brain metabolome and gut microbiome had similarity in overall alteration patterns. Four high taxonomical level correlated pairs of "metabolite type-bacterial phylum", including "lipids-Spirochaetes", "free fatty acids (FFAs)-Firmicutes", "bile acids (BAs)-Firmicutes", and "Neurotransmitters-Bacteroidetes", were screened out based on unit- and multivariant correlation analysis and function analysis. Four groups of specific "metabolite-bacterium" association pairs from within the above high level key pairs were further identified. The key correlation pairs were validated by an independent animal study. This two-level strategy is effective in identifying principal correlations in big data sets obtained from the systematic multiomics study, furthering our understanding on the lifelong connection between brain and gut.

Heat-stress-induced metabolic changes and altered male reproductive function.

Heat stress can cause systemic physiological and biochemical alterations in living organisms. In reproductive systems, heat stress induces germ cell loss and poor quality semen. However, until now, little has been known about such a complex regulation process, particularly in the perspective of metabolism. In this study, serum, hypothalamus, and epididymis samples derived from male SD (Sprague-Dawley) rats being exposed to high environmental temperature (40 °C) 2 h per day for 7 consecutive days were analyzed using metabonomics strategies based on GC/TOFMS. Differentially expressed metabolites reveal that the energy metabolism, amino acid neurotransmitters, and monoamine neurotransmitters pathways are associated with heat stress, in accordance with changes of the three upstream neuroendocrine system pathways in the SNS (sympathetic adrenergic system), hypothalamic pituitary adrenal axis (HPA), and hypothalamic pituitary testis axis (HPT) axis. Many of these metabolites, especially in the epididymis, were found to be up-regulated, presumably due to a self-preserving action to resist the environmental hot irritation to maintain normal functioning of the male reproductive system.

[Urine metabonomic study on long-term use of total ginsenosides in rats].

Due to its effect of systems regulation and promotion on body, Ginseng is always referred to be long-term used as a dietary supplement. But it was still unclear about its target of the tonic effects and also the side-effects long-term use may bring. Urine metabolomic method is suitable for long-term studies of pharmaco-dynamics, pharmacology and toxicology of traditional Chinese medicine because of its characteristics of non-invasive and monitoring the whole-body metabolism. This study was designed to detect the dynamic variation of rat urine metabolome along with a long-term administration of total ginsenosides using GC-TOF based metabolomic technology. Our result showed that either short-term or chronic administration of ginsenosides did not impact the rat urine metabolome significantly (as the PCA subgroup was not successful). By comparison, the short-term (1-3 w) dose of ginsenosides had the biggest metabolic influence including TCA cycle, catecholamines and neurotransmitter amino acids. Medium-term (6-10 w) dose had a gradually lower effect and long-term (27 w) dose almost had no effect. Our study indicates that both short and long-term administration of ginsenosides showed almost no obvious side-effect on the experimental animals.

[Surveillance of soil-transmitted nematode infections in Menghai County, Yunnan Province from 2006 to 2009].

In the national monitoring point, Menghai County, Yunnan Province, the total prevalence rate of soil-transmitted nematode infections was 88.12%, of which the rates of Ascaris lumbricoides, Trichuris trichiura, hookworm and cestode infections were 69.70%, 59.79%, 36.79%, and 1.30% respectively from 2006 to 2009. The infection intensities were slight. The Ascaris lumbricoides infection rate in soil of vegetable garden was 44.5%. In conclusion, the infection rate of soil-transmitted nematodes is high in the monitoring point.