In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

The 20 years transition of clinical characteristics and metabolic risk factors in primary liver cancer patients from China.

Background: The clinical characteristics of primary liver cancer (PLC) patients are changing, maybe due to hepatitis viral vaccination and lifestyle changes, etc. The linkage between these changes and outcomes among these PLCs has not yet been fully elucidated. Methods: It was identified total of 1691 PLC cases diagnosed between 2000 ~ 2020. Cox proportional hazards models were utilized to determine the connections between the clinical presentations and their close risk factor(s) from PLC patients. Results: The average age of PLC patients increased gradually from 52.74 ± 0.5 years in 2000 ~ 2004 to 58.63 ± 0.44 years in 2017 ~ 2020, accompanied by an increased proportion of females from 11.11% to 22.46%, and non-viral hepatitis-related PLC was raised from 1.5% to 22.35%. 840 (49.67%) PLC patients with alpha-fetoprotein (AFP) < 20ng/mL (AFP-negative). The mortality was 285 (16.85%) or 532 (31.46%) PLC patients with alanine transaminase (ALT) between 40 ~ 60 IU/L or ALT > 60 IU/L. The PLC patients with pre-diabetes/diabetes or dyslipidemia also increased from 4.29% or 11.1% in 2000 ~ 2004 to 22.34% or 46.83% in 2017 ~ 2020. The survival period of the PLC patients with normoglycemia or normolipidemic was 2.18 or 3.14 folds longer than those patients with pre-diabetes/diabetes or hyperlipidemia (P<0.05). Conclusions: It was gradually increased that age, the proportion of females, non-viral hepatitis-related causes, AFP-negative, and abnormal glucose/lipids among PLC patients. Proper control of glucose/lipids or ALT may improve the prognosis of PLCs.

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer.

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction. Here, we devise an innovative weight engineering approach and ANN platform called artificial neural network encoder (ANNE) using an autoencoder and apply it to a breast cancer dataset to extract knowledge learned by the autoencoder model that explains clinical traits. Intriguingly, the extracted biological knowledge in the form of gene-gene associations from ANNE shows immune-related components such as chemokines, carbonic anhydrase, and iron metabolism that modulate immune-related processes and the tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological "knowledge" learned by an ANN model is indeed encoded as weights throughout its neuronal connections, and it is possible to extract learned knowledge via a novel weight engineering approach to uncover important biological insights.

Characteristics of Treated Asthmatics Experiencing Exacerbations in a US Database: A Retrospective Cohort Study.

RATIONALE: The National Heart, Lung, and Blood Institute (NHLBI) recommend a stepwise approach to asthma management, with the goals of maintaining asthma control and reducing exacerbations. Although asthma treatments reduce the frequency of exacerbations, they still occur. We aimed to characterize the treated United States of America (US) adult asthma population, including those experiencing exacerbations, in terms of socio-demographics, clinical characteristics, and healthcare resource utilization (HRU). PATIENTS AND METHODS: A retrospective cohort of asthma patients aged ≥18 years on 01 January 2014 with ≥1 ICD-9 asthma code (493.xx) enrolled in a US healthcare claims database during 2013-2014. Patients who had ≥2 asthma medication dispensings during 2013 (baseline), including ≥1 in the 90-day period before index date, were classified according to NHLBI step. Patients with chronic obstructive pulmonary disease, cystic fibrosis, or lung cancer diagnoses were excluded. Demographics, comorbidities, clinical characteristics, and HRU were described during baseline. Exacerbations and HRU were described during 2014 (follow-up period). RESULTS: In total, 72,156 patients were included; 10,590 (14.7%) had ≥1 exacerbation during follow-up. Approximately 44% of patients were classified as NHLBI Steps 1-2, 41% as Steps 3-4, and 11% as Steps 5-6. Exacerbation frequency increased with step (Steps 1, 2, and 3: 12-14%; Steps 4, 5, and 6: 16-26%). Compared with the overall population during baseline, patients with an exacerbation had similar demographics, but differences were observed for comorbid allergic rhinitis (46.4% vs 40.1%, respectively), blood eosinophil counts ≥300 cells/µL (45.5% vs 39.6%, respectively), and asthma-related healthcare encounters (62.9% vs 52.4%, respectively). Overall, asthma-related HRU during follow-up increased with NHLBI step. CONCLUSION: Exacerbations were observed among patients classified at each NHLBI step and were more frequent with increasing step. Exacerbations and asthma-related HRU highlight the continued unmet need in the treated US asthma population.

The Clinical Characteristics of Acute Pancreatitis in Gerontal Patients: A Retrospective Study.

BACKGROUND: The incidence of acute pancreatitis (AP) in ageing patients has increased in recent years, and results regarding the clinical outcomes of these patients are controversial. The aim of this study was to compare the clinical outcomes of AP in ageing patients over 60 years old. METHODS: Eighty patients aged ≥80 years (oldest group) were compared to 393 patients aged 60 to 79 years (older group). The clinical course and biochemical and radiological data were evaluated. The primary endpoints were mortality rate, intensive care unit (ICU) admission rate and in-hospital length of stay (LOS). The secondary endpoints were the incidence of operative treatment and complications of AP. RESULTS: Abdominal pain (61.3% vs 46.3%, P=0.013) was less common in the oldest group. Jaundice (17.5% vs 8.9%, P=0.021) and dyspnoea (26.3% vs 11.5%, P=0.001) were more obvious in the oldest group than in the older group. The mean BMI was lower in the oldest group than in the older group (21.07±3.18 vs 22.36±2.89, P = 0.001). Age over 80 years (P=0.011) and organ failure (P<0.05) were independent risk factors for mortality. More severe AP (P=0.001), abdominal pain (P=0.033) and organ failure (P<0.05) were associated with the ICU admission rate. Age over 80 years (P=0.001), more severe AP (P=0.001), female sex (P=0.018), jaundice (P=0.038), operative treatment (P<0.05) and organ failure (P<0.05) were risk factors for increased LOS. CONCLUSION: The oldest group had a higher death rate and longer LOS than the older group. More attention should be given to the clinical symptoms of this frail population. We propose that more comprehensive and goal-directed attendant diagnostic procedures should be performed to detect the disease early and to improve the outcomes of ageing patients.

Gene Signature and Identification of Clinical Trait-Related m6 A Regulators in Pancreatic Cancer.

Pancreatic cancer (PC) has a very poor prognosis and is usually diagnosed only at an advanced stage. The discovery of new biomarkers for PC will help in early diagnosis and a better prognosis for patients. Recently, N6-methyladenosine (m6A) RNA modifications and their regulators have been implicated in the development of many cancers. To investigate the functions and mechanisms of m6A modifications in the development of PC, 19 m6A regulators, including m6A-methyltransferases (ZC3H13, RBM15/15B, WTAP, KIAA1429, and METTL3/14), demethylases (FTO and ALKBH5), and binding proteins (YTHDF1/2/3, YTHDC1/2, IGF2BP1/2/3, HNRNPC, and HNRNPA2B1) were analyzed in 178 PC tissues from the cancer genome atlas (TCGA) database. The results were verified in PC cell lines Mia-PaCa-2, BXPC-3, and the control cell line HDE-CT. The m6A regulators-based sample clusters were significantly related to overall survival (OS). Further, lasso regression identified a six-m6A-regulator-signature prognostic model (KIAA1429, HNRNPC, METTL3, YTHDF1, IGF2BP2, and IGF2BP3). Model-based high-risk and low-risk groups were significantly correlated with OS and clinical traits (pathologic M, N, and clinical stages and vital status). The risk signature was verified as an independent prognostic marker for patients with PC. Finally, gene set enrichment analysis revealed m6A regulators (KIAA1429, HNRNPC, and IGF2BP2) were related to multiple biological behaviors in PC, including adipocytokine signaling, the well vs. poorly differentiated tumor pathway, tumor metastasis pathway, epithelial mesenchymal transition pathway, gemcitabine resistance pathway, and stemness pathway. In summary, the m6A regulatory factors which related to clinical characteristics can be involved in the malignant progression of PC, and the constructed risk markers may be a promising prognostic biomarker that can guide the individualized treatment of PC patients.

Identification of pathology-specific regulators of m6A RNA modification to optimize lung cancer management in the context of predictive, preventive, and personalized medicine.

RELEVANCE: Lung cancer is the most common malignant tumor with high morbidity (11.6% of the total diagnosed cancer cases) and mortality (18.4% of the total cancer deaths), and its 5-year survival rate is very low (20%). Clarification of any molecular events and the discovery of effective biomarkers will offer increasing promise for lung canner management. N6-methyladenosine (m6A) modification is one of the important RNA modifications that are closely associated with lung cancer, and are tightly regulated by m6A regulators. Elucidation of pathology-specific m6A regulators will directly contribute to lung cancer medical services in the context of predictive, preventive, and personalized medicine (PPPM). PURPOSE: To investigate pathology-specific regulators of m6A RNA modifications in lung cancer and further inspect the m6A regulator gene signature as useful tools for PPPM in lung cancers. METHODS: The gene expression data of 19 m6A regulators (m6A-methyltransferases-ZC3H13, KIAA1429, RBM15/15B, WTAP, and METTL3/14; demethylases-FTO and ALKBH5; and m6A-binding proteins-HNRNPC, YTHDF1/2/3, YTHDC1/2, IGF2BP1/2/3, and HNRNPA2B1) and clinical data of 1013 lung cancer patients [511 lung adenocarcinoma (LUAD) and 502 lung squamous carcinoma (LUSC)] and 109 controls (Con) were obtained from the TCGA database. Quantitative real-time PCR (qRT-PCR) was used to verify m6A regulators in lung cancer cell lines. Protein-protein interaction (PPI), gene co-expression, survival analysis, and heatmap were used to analyze these m6A regulators in this set of lung cancer clinical data. Lasso regression was used to optimize the pathology-specific m6A regulator gene signature. Gene set enrichment analysis (GSEA) was used to reveal the functional characteristics of m6A regulators. RESULTS: Those 19 m6A regulator profiling was significantly differentially expressed in lung cancer tissues relative to control tissues, which was also verified in lung cancer cell lines. Those m6A regulators interacted mutually, and those regulator-based sample clusters were correlated with clinical traits, including survival status, gender, tobacco smoking history, primary disease, and pathologic stage. Further, lasso regression based on the 19 m6A regulators optimized and identified a three-m6A-regulator signature (KIAA1429, METTL3, and IGF2BP1) as independent prognostic factor, which classified 1013 lung cancer patients into high-risk and low-risk groups according to median value (0.84) of the lasso regression risk scores. This three-m6A-regulator signature profiling was significantly related to lung cancer overall survival, cancer status, and the above-described clinical traits. Further, GSEA revealed that KIAA1429, METTL3, and IGF2BP1 were significantly related to multiple biological behaviors, including proliferation, apoptosis, metastasis, energy metabolism, drug resistance, and recurrence, and that KIAA1429 and IGF2BP1 had potential target genes, including E2F3, WTAP, CCND1, CDK4, EGR2, YBX1, and TLX, which were associated with cancers. CONCLUSION: This study provided the first view of the pathology-specific regulators of m6A RNA modification in lung cancers and identified the three-m6A-regulator signature (KIAA1429, METTL3, and IGF2BP1) as an independent prognostic model to classify lung cancers into high- and low-risk groups for patient stratification, prognostic assessment, and personalized treatment toward PPPM in lung cancers.

Genetic Regulation of Liver Metabolites and Transcripts Linking to Biochemical-Clinical Parameters.

Given the central metabolic role of the liver, hepatic metabolites and transcripts reflect the organismal physiological state. Biochemical-clinical plasma biomarkers, hepatic metabolites, transcripts, and single nucleotide polymorphism (SNP) genotypes of some 300 pigs were integrated by weighted correlation networks and genome-wide association analyses. Network-based approaches of transcriptomic and metabolomics data revealed linked of transcripts and metabolites of the pentose phosphate pathway (PPP). This finding was evidenced by using a NADP/NADPH assay and HDAC4 and G6PD transcript quantification with the latter coding for first limiting enzyme of this pathway and by RNAi knockdown experiments of HDAC4. Other transcripts including ARG2 and SLC22A7 showed link to amino acids and biomarkers. The amino acid metabolites were linked with transcripts of immune or acute phase response signaling, whereas the carbohydrate metabolites were highly enrich in cholesterol biosynthesis transcripts. Genome-wide association analyses revealed 180 metabolic quantitative trait loci (mQTL) (p < 10-4). Trans-4-hydroxy-L-proline (p = 6 × 10-9), being strongly correlated with plasma creatinine (CREA), showed strongest association with SNPs on chromosome 6 that had pleiotropic effects on PRODH2 expression as revealed by multivariate analysis. Consideration of shared marker association with biomarkers, metabolites, and transcripts revealed 144 SNPs associated with 44 metabolites and 69 transcripts that are correlated with each other, representing 176 mQTL and expression quantitative trait loci (eQTL). This is the first work to report genetic variants associated with liver metabolite and transcript levels as well as blood biochemical-clinical parameters in a healthy porcine model. The identified associations provide links between variation at the genome, transcriptome, and metabolome level molecules with clinically relevant phenotypes. This approach has the potential to detect novel biomarkers displaying individual variation and promoting predictive biology in medicine and animal breeding.

Characteristics and comparison between diabetes mellitus and non-diabetes mellitus among chronic kidney disease patients: A cross-sectional study of the Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE).

Although the prevalence of chronic kidney disease (CKD) and diabetes mellitus (DM) is increasing globally, information on Chinese CKD patients with DM is lacking. A total of 3499 pre-dialysis CKD patients from across China were enrolled in the Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE) between November 2011 and April 2016. We divided the C-STRIDE patients into CKD with DM and CKD without DM groups and compared their clinical, demographic, and laboratory data in this cross-sectional study. CKD patients with DM were older, had a higher male-to-female ratio, and had more complications than CKD patients without DM. Age, smoking, and 24-h urinary protein levels were associated with co-occurrence of CKD and DM. Less than 50% of patients in either group took antilipemic, cardiovascular, cerebrovascular, or anti-anemic drugs. In addition, only 18.38% of CKD patients with DM had undergone a renal biopsy, and diabetic nephropathy was confirmed in 35.4% of them. Our findings suggest that several types of medication and renal biopsies should be used more frequently in the treatment of Chinese CKD patients with DM.